Level 2 Research Articles

Bears in our midst: Familiarity with Level 2 driving automation and attending to surprise on-road events

This study presents an on-road paradigm to measure the effect of Level 2 (L2) system familiarity on attention strategies to salient, but non-hazardous, driving-related events when using the driving automation. A vehicle with an oversized pink teddy bear on the back overtook participants three times while they drove a 2019 Mercedes-Benz C300 equipped with a L2 system for 1 h. This L2 system requires drivers to keep their hands on the wheel while activated. The L2 system was turned on or off, depending on the assigned condition, and participants varied in their familiarity with L2 systems. Cameras recorded participant eye glance behavior. After the drive, participants were asked to recall the bear and the number of times their mind had wandered from the driving task during the drive. Results show that the driving automation support gave only participants familiar with L2 systems an advantage for greater bear recall. Unfamiliar participants were at a relative disadvantage when assisted by the L2 system, with generally poorer bear recall than unfamiliar participants who drove with the system off. Better bear recall corresponded with wider on-road gaze dispersion and more instances of mind wandering. Our findings support the effectiveness of this paradigm to measure driver attention when using a L2 system under real-world conditions and highlight the need to consider the role of driving automation familiarity in future research.

Evaluation of satellite and reanalysis estimates of surface and root-zone soil moisture in croplands of Jiangsu Province, China

High-quality and long-term surface soil moisture (SSM) and root-zone soil moisture (RZSM) data are critical for agricultural water management of Jiangsu province, which is a major agricultural province in China. However, few studies assessed the accuracy of SSM and RZSM datasets in croplands of Jiangsu province. The study addressed this gap by firstly using observations from ninety-one sites to assess thirteen satellite and model-based SSM products (Advanced Scatterometer (ASCAT), European Space Agency Climate Change Initiative (ESA CCI) Combined/Passive/Active, Soil Moisture and Ocean Salinity in version IC (SMOS-IC), Land Parameter Retrieval Model (LPRM) Advanced Microwave Scanning Radiometer 2 (AMSR2), Soil Moisture Active Passive (SMAP)-Multi-Temporal Dual-channel Algorithm (MTDCA)/Level 3 (L3)/Level 4 (L4)/SMAP-INRAE-BORDEAUX (IB)/Multi-channel Collaborative Algorithm (MCCA), the fifth generation of the land component of the European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5-Land), and the Noah land surface model driven by Global Land Data Assimilation System (GLDAS-Noah)), and four RZSM products (ERA5-Land, GLDAS-Noah, SMAP-L4 and ESA CCI (retrieved using ESA CCI Combined SSM coupled with an exponential filter)). We also inter-compared time-invariant and time-variant Triple Collocation Analysis (TCA)-based R with in situ-based R calculated using SSM anomalies. Various evaluation strategies were compared using different groups of available sites and temporal samplings. Our results showed that the model-based and combined SSM products (i.e., ERA5-Land, SMAP-L4, ESA CCI Combined/Passive/Active, GLDAS-Noah, ASCAT) performed better than the other SSM products and ERA5-Land, SMAP-L4 and ESA CCI RZSM generally performed better than the GLDAS-Noah RZSM product with higher R. Similar performance rankings were observed among time-invariant and time-variant TCA-R and in situ-based R, in which the TCA-R values for all SSM datasets were higher than the in situ-based R as the representativeness errors of the in situ measurements may bias in situ-based R. The accuracy of the ESA CCI, GLDAS-Noah and ERA5-Land SSM products was expected to be enhanced by considering the water effect and high uncertainties were observed for MTDCA and SMAP-MCCA SSM over dense vegetation periods and regions. Also, it is important to select appropriate evaluation strategies to conduct the SSM and RZSM evaluations according to the situation as the available sites and temporal samplings may bias the evaluation results.

A new Level 4 multi-sensor ice surface temperature product for the Greenland Ice Sheet

Abstract. The Greenland Ice Sheet (GIS) is subject to amplified impacts of climate change and its monitoring is essential for understanding and improving scenarios of future climate conditions. Surface temperature over the GIS is an important variable, regulating processes related to the exchange of energy and water between the surface and the atmosphere. Few local observation sites exist; thus spaceborne platforms carrying thermal infrared instruments offer an alternative for surface temperature observations and are the basis for deriving ice surface temperature (IST) products. In this study several satellite IST products for the GIS were compared, and the first multi-sensor, gap-free (Level 4, L4) product was developed and validated for 2012. High-resolution Level 2 (L2) products from the European Space Agency (ESA) Land Surface Temperature Climate Change Initiative (LST_cci) project and the Arctic and Antarctic Ice Surface Temperatures from Thermal Infrared Satellite Sensors (AASTI) dataset were assessed using observations from the PROMICE (Programme for Monitoring of the Greenland Ice Sheet) stations and IceBridge flight campaigns. AASTI showed overall better performance compared to LST_cci data, which had superior spatial coverage and availability. Both datasets were utilised to construct a daily, gap-free L4 IST product using the optimal interpolation (OI) method. The resulting product performed satisfactorily when compared to surface temperature observations from PROMICE and IceBridge. Combining the advantages of satellite datasets, the L4 product allowed for the analysis of IST over the GIS during 2012, when a significant melt event occurred. Mean summer (June–August) IST was −5.5 ± 4.5 ∘C, with an annual mean of −22.1 ± 5.4 ∘C. Mean IST during the melt season (May–August) ranged from −15 to −1 ∘C, while almost the entire GIS experienced at least between 1 and 5 melt days when temperatures were −1 ∘C or higher. Finally, this study assessed the potential for using the satellite L4 IST product to improve model simulations of the GIS surface mass balance (SMB). The L4 IST product was assimilated into an SMB model of snow and firn processes during 2012, when extreme melting occurred, to assess the impact of including a high-resolution IST product on the SMB model. Compared with independent observations from PROMICE and IceBridge, inclusion of the L4 IST dataset improved the SMB model simulated IST during the key onset of the melt season, where model biases are typically large and can impact the amount of simulated melt.

On-Road Assessment of Driver Mode Awareness of Assisted and Automated Driving

Increasingly, today’s vehicles offer Society of Automotive Engineers (SAE) Level 2 (L2) technology, and a limited number of SAE Level 3 (L3) automated driving systems (ADSs) are being developed. Since L3 ADSs can operate in L2 and manual driving, drivers’ correct mode awareness is essential. To investigate driver’s mode awareness after transitions between modes, an on-road experiment was conducted using a Wizard-of-Oz method to simulate Assisted Driving (L2-like) and Automated Driving (L3/L4-like). A total of 36 drivers completed the on-road experiment. Participants experienced periods of manual driving, assisted driving, automated driving, and transitions between these modes. After each transition, participants’ mode awareness was measured. It was hypothesized that drivers’ mode awareness would increase with system exposure over the planned automated driving periods. Results showed that drivers exhibited declarative mode awareness during both Assisted (ASD) and Automated Driving (AD) modes.

Driver Engagement Strategies for L3 ADS: A Review of the Literature

The completion of the driving task by Level 3 (L3) Automated Driving Systems (ADS) allows drivers to turn their attention away from the road and direct it towards other activities; however, the driver must remain receptive to requests to intervene (RTI) and be prepared to complete the driving task upon deactivation of the driving automation. This transfer of control is called a takeover. The objectives of this research activity were to understand the human factors underlying driver disengagement while using L3 ADS and to identify factors influencing takeover quality and performance when the ADS-equipped vehicle has exited its operational design domain. A structured, comprehensive literature review approach was used to synthesize findings in the following areas: common measures used to assess takeover performance, factors that affect takeovers, and strategies that improve driver takeover performance.

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

This paper introduces a method for estimating a series of sea state parameters from satellite-borne synthetic aperture radar (SAR). The method was realized in a near real time (NRT) application which allows for the processing of data from different satellites and modes. The algorithm estimates the total significant wave height Hs, dominant and secondary swell and windsea wave heights, first, and second moment wave periods, the mean wave period and the period of wind sea. The algorithm was applied to the Sentinel-1 (S1) C-band Interferometric Wide Swath Mode (IW), Extra Wide (EW) and Wave Mode (WV) Level-1 (L1) products and also extended to X-band TerraSAR-X (TS-X) StripMap (SM) products. The scenes are processed in raster and result in continuous sea state fields, with the exception of S1 WV, where averaged values for sea state parameters for along-orbit imagettes of 20 km × 20 km are presented.The developed empirical algorithm consists of two parts: a first CWAVE_EX (extended CWAVE) part, based on a linear regression approach, and a subsequent machine learning part using the support vector machine (SVM) technique. A series of new data preparation steps (i.e. filtering, denoising) and new features estimated from SAR images are also introduced. The algorithm was tuned and validated using two independent global wave model hindcasts, WaveWatch-3 and MFWAM as well as National Data Buoy Center (NDBC) measurements. The achieved root mean squared errors (RMSE) for CWAVE_EX for the total Hs are 0.60 m for low-resolution modes S1 IW (10 m pixel) and EW (40 m pixel) and 0.35 m for S1 WV and TS-X SM (pixel spacing ca.1–4 m) in comparison to model predictions. The RMSEs of the retrieved wave periods are in the range of 0.45–0.90 s for all of the satellites and models considered. Similarly, the dominant and secondary swell, and wind sea wave height RMSEs are in the range of 0.35–0.80 m. The SVM postprocessing improves the accuracy of the initial results of CWAVE_EX for Hs and reaches an RMSE of 0.25 m for S1 WV. Comparisons to 64 NDBC buoys, collocated at distances shorter than 50 km to S1 WV imagettes worldwide, result in an RMSE of 0.41 m. All results and the methods presented are novel in terms of the accuracy achieved, combining the classical approach with machine learning techniques, and performing an automatic NRT processing of multiparametric sea state fields from L1 data with automatic switching for different satellites and modes.The complete archive of S1 WV L1 Single Look Complex products from December 2014 until February 2021 was processed to create a sea state parameter database and validated using model hindcast and buoy measurements. The derived parameters are available to the public within the scope of the European Space Agency's Climate Change Initiative.

Determinants of Severity of Covid 19 Positive Cases in ICU: A Hospital Based Study

Introduction: A mutated variant of COVID 19, B. 1.617.2 was first identified in India, also known as the deltavariant. This delta variant was largely responsible for the second wave of pandemic in India. The second wave wasepidemiologically different from first wave with very high death rates. This study was done during the secondwave to estimate the determinants of severity of COVID 19 cases admitted in ICU. The Findings of the study maysupport in prevention and management of severe cases of COVID 19.Methodology: This was a hospital-based cros- sectional study conducted from April to June 2021, among 100COVID-19 positive patients admitted in ICU of a tertiary care center, Level-3(L3) facility of COVID-19 in NorthIndia. A telephonic conversation was done with patients/attendants for data collection after explaining to themthe purpose. Those who gave consent were interviewed. A pre-designed, pre-validated, and semi-structuredquestionnaire was used for data collection. Data analysis was done using R software and Chi square (χ2) test wasused as test of association.Results: Out of 100 patients admitted in ICU, 41% patients were under the age of 45 years mostly from urban setup. Of all 64% had no co-morbidities, 83% had no addiction, 66% were not on any medication. Majority of themwere either incompletely vaccinated or not vaccinated for Covid-19. 54% of ICU cases under study died. Therewas no significant statistical association between mortality and age, gender, occupation, comorbidities, addiction,medication, blood group, and residence. Lesser mortality of vegetarian patients was the only variable found to bestatistically significant (χ2 = 0.037 and p< 0.05).Conclusion: Delta variant responsible for the second wave of COVID 19 pandemic in India had no favorites;it caused severe infection and death despite the young age and good health, but it was found in our study thatvegetarians had higher chances of survival than non vegetarians. Hence everyone should be vaccinated and mustfollow the COVID appropriate behavior.

Abstract 2184: Landscape of genomic alterations of ultra-rare sarcomas (URS) from 1,079 patients in the AACR GENIE real world database: Clinical implications

Abstract Introduction: Sarcomas are heterogenous tumors of more than 150 different tumor types. Recently, 56 soft tissue (ST-URS) and 21 bone (B-URS) sarcomas were classified as URS based on incidence of less than 1 in a million by the Connective Tissue Oncology Society. There is only one histology-based treatment approved in URS. Methods: AACR GENIE database cBioPortal were accessed. Individual alterations annotated by OncoKB therapeutic evidence level, TCGA PanCancer pathway alterations, and demographic data were gathered. Detailed cancer type was reclassified into genomically distinct diseases as appropriate. Results: From 112, 222 patients, 3,424 soft tissue sarcomas and 832 bone sarcomas were identified. 1,079 of 4,251 (25.4%) sarcomas were URS including 950 ST-URS and 129 B-URS. Patients included were 54.1% (n=584) female and identified race as 66.1% (n=713) white, 8.5% (n=92) black, and 6.8% (n=73) Asian. 39/56 ST-URS and 11/21 B-URS subtypes had cases in database. The median number of alterations was 3.0 (SD 5.94, Range 0-81), 3.0 for B-URS and 4.0 for ST-URS. Oncogenic alterations were present in 63.6% of B-URS versus 74.0% of ST-URS (p=0.01). Genomic sequencing identified Level 1 (L1) (FDA-approved drug) in 4.9% (n=53) of URS; 5.6% (n=53) of ST-URS and 0 B-URS (p=0.006). Level 2 (L2) (Standard of care) alterations were seen in 1.6% (n=15) ST-URS and no B-URS. 19.5% (n=210) of all URS and 19.9% (n=189) of ST-URS versus 16.3% (n=21) of B-URS (p=0.3) had an FDA approved drug for use in a biomarker approved indication or approved drug in another indication (Level 1-3). Level 3A gene mutations included BRAF (n=12), NRAS (n=2), TSC2 (n=8). 100 level 3B gene alterations were observed; NRAS (n=21), PIK3CA (n=16), IDH1 (n=8), IDH2 (n=4), TSC2 (n=9), HRAS (n=8), TSC1 (n=5), ATM (n=7), BRCA1/2 (n=4). All L1 (n=36) fusions involved NTRK alterations and all L2 fusions (n=14) involved ALK fusion products in inflammatory myofibroblastic tumors. 17 L1 SMARCB1 copy number alterations (CNA) were seen in epithelioid sarcomas. 1 L3A TSC2 CNA was observed in PECOMA; 29 L3B CNAs were observed including SMARCB1 del (n=7), FGFR1 amp (N=6), BRCA2 del (n=4), and Met amp (n=2). 62.9% of cases had PanCancer pathways altered; TP53 (32.7%), RTK-RAS (33.5%), and cell cycle (33.2%) pathway alterations were most common. TP53 (40.3% versus 31.7%, p=0.05) and cell cycle (41.1% versus 32.1%, p=0.04) alterations were more common in B-URS, while RTK-RAS (34.5% versus 25.6%, p=0.04) alterations were more common in ST-URS. Conclusions: Genomic sequencing of URS reveals actionable alterations, their co-alterations, and key clinical characteristics using a large multi-institution publicly accessible registry of real-world patient data opening additional treatment options. Sequencing should be routine care in URS. Increased racial diversity in genetic samples and public databases is warranted. Citation Format: Justin Tyler Moyers, Danielle Brazel, Priyanka Kumar, Hung Doan, Jason Roszik, Jennifer B. Valerin, Roberto Carmagnani Pestana, Warren A. Chow, Vivek K. Subbiah. Landscape of genomic alterations of ultra-rare sarcomas (URS) from 1,079 patients in the AACR GENIE real world database: Clinical implications [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2184.

Abstract 1186: Hedgehog and beyond: Genomic alterations in basal cell carcinoma (BCC) - A survey of 73 patients in the AACR GENIE real-world database

Abstract Introduction: Basal cell carcinoma (BCC) is the most common skin cancer, accounting for up to 90% of all skin cancers. However, locally advanced and metastatic disease, requiring systemic treatment, is rare. While most BCC is driven by the Hedgehog (Hh) pathway involving SMO and PTCH1 alterations, there is variation in BCC response to SMO inhibitors. Nearly 30% of tumors develop resistance to or relapse after treatment. We surveyed the GENIE database for targetable alterations in patients with BCC. Methods: Patient data were accessed via the GENIE database interface, cBioPortal. Individual demographic data, alterations with annotations by OncoKB therapeutic evidence level, and TCGA PanCancer pathway alterations were collected. Results: A total of 73 patients with BCC were identified. The median age at sequencing was 66 years (Range 26-88). Most patients were male 60.3% (n=44) and 89% (n=65) were white. The median number of alterations was 35.00 (SD 67.54, Range 0-525). The median number of oncogenic alterations was 6.00 (SD 6.86, Range 0-52). Sample tissue was collected from metastases and the primary tumor site in 34.2% (n=25) of cases and in 54.8% (n= 40) of cases respectively. NGS identified 77.0% (n=57) of samples with an FDA approved drug for use in a biomarker approved indication or approved drug in another indication (Evidence Level 1-3). Level 4 (L4) (potential biomarker) mutations were present in 71.6% (n=53) of cases. The most common Level 3B gene mutations were PTCH1 (63.5%, n=47), ATM (5.4%, n=4), CHEK2 (4.1%, n=3), PIK3CA (4.1%, n=3), ERCC2 (2.7%, n=2), and HRAS (2.7%, n=2). Common Level 4 (L4) mutations included ARID1A 17.6%, n=13), CDKN2A (9.5%, n=7), MTOR (4.1%, n=3), and NF1 (2.7%, n=2). No L1-L4 fusions were identified. L1-L4 copy number alterations (CNA) were identified in two cases. The only L4 CNA was CDKN2A (2.7%, n=3). PanCancer pathways were altered in 93.2% (n=68) of cases. The median number of altered pathways was 6.00 (SD 2.70, Range 0-9). Frequently altered pathways included RTK-RAS (76.7%, n=56), TP53 (71.2%, n=52), Cell Cycle (69.9%, n=51), PI3K (67.1%, n=49), and NOTCH (65.8%, n=48). Additionally, alterations in DNA damage repair genes (ATM, BRCA1, RAD51, CHEK2, ARID1A) occurred in 16 (22.9%) cases, of which 11 (14.9%) patients had concurrent DDR and PTCH1 alterations. 13.5% of patients (n=10) had alterations other than PTCH1 with L3 evidence for treatment. Among 57 patients with potentially actionable L3B/L4 genomic alterations, 45.6% (n=26) had PTCH1 as their lone alteration, while 36.8% (n=21) had other alterations concurrent with PTCH1 and 13.5% (n=10) had alterations without PTCH1 or Hh alterations. Conclusion: Survey of the GENIE database found most patients had targetable alterations in BCC. Targets other than PTCH1 were common and may support the use of targeted therapies other than SMO inhibitors or in combination with SMO inhibitors in BCC. Citation Format: Priyanka R. Kumar, Danielle M. Brazel, Hung T. Doan, Justin T. Moyers. Hedgehog and beyond: Genomic alterations in basal cell carcinoma (BCC) - A survey of 73 patients in the AACR GENIE real-world database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1186.

Abstract 1185: Survey of genomic alterations in Merkel cell carcinoma (MCC) in the AACR Project Genie real-world database

Abstract Introduction: MCC is a rare and highly aggressive cutaneous neuroendocrine carcinoma with increasing incidence over the last few decades. Cytotoxic chemotherapy and checkpoint inhibitors have provided modest treatment options, however, there are no approved or standard of care targeted treatment options. Here we surveyed the presence of targetable alterations in the AACR Project Genie Database Methods: Individual patient data were accessed from the cBioPortal. Variables of interest include demographic data, alterations annotated by OncoKB therapeutic evidence level, and TCGA PanCancer pathway alterations. Results: A total of 295 patients with MCC were cataloged in the database. Patients included were 34.6% (n=102) female with a median age of 71. Patients identified race as 91.2% (n=269) white, 2.4% (n=7) African American, and 0.4% (n=2) Asian. The median number of alterations was 3.00 (SD 21.4, Range 0-178). Oncogenic alterations represented 17.4% (n=660 of 3799) of mutations. Sampled tissue originated from primary tumor in 54% (n=161) verses metastases in 42.4% (n=125). NGS identified 18.5% (n=55) with an FDA approved drug for use in a biomarker approved indication or approved drug in another indication (Level 1-3). An additional 13.9% (n=41) had a Level 4 (L4) mutation. The most common Level 3B gene mutations include PIK3CA (4.1%, n=12), and BRCA1/2 (4.1%, n=12), HRAS (1.7%, n=5), TSC1/2 (1.7%, n=5). The most common L4 mutations include PTEN (4.4%, n=13), ARID1A (3.7%, n=11), and CDKN2A (2.4%, n=7). Only three fusions were identified; L3B fusions ATM-CDK12 and TSC2 along with Level 4 PTEN. Copy number alterations were identified in a small subset of patients. L3B CNAs include ATM, CHEK1 (n=1), BARD1 (n=1), BRCA2 (n=1), RAD51B (n=1), RAD51D (n=1). Level 4 CNAs identified CDKN2A (n=4) and PTEN (n=1). In 60.7% cases, PanCancer pathways were altered and 28.5% had alterations in multiple pathways. The most commonly altered pathways were RTK-RAS (37.6%, n=111), TP53 (33.9%, n=100), Cell Cycle (33.9, n=100), PI3K (32.2%, n=95) and NOTCH (30.8%, n=91). Additionally, oncogenic DNA damage repair gene alterations (BRCA1/2, CHEK1/2, ATM, PALB2, BRIP1, RAD51) were present in 7.5% (n=22) of cases. Conclusions: Here we identified a subset of patients with targetable mutations in MCC. The majority of patients had oncogenic alterations in cancer pathways which may support the investigation of kinase inhibitors as single agent or in combination with immunotherapy or cytotoxic chemotherapy in Merkel Cell Carcinoma. Citation Format: Danielle Brazel, Priyanka Kumar, Hung Doan, Justin T. Moyers. Survey of genomic alterations in Merkel cell carcinoma (MCC) in the AACR Project Genie real-world database [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 1185.

Influence of membership in outgroups varying in competence and warmth on observers' Level-2 visual perspective taking.

Visual perspective taking (VPT), the ability to adopt another person's viewpoint, entails two distinct processes, Level-1 (L1)-VPT and Level-2 (L2-VPT), referring to the ability to perceive whether and how a target sees an object, respectively. Whereas previous efforts investigated the impact of targets' social characteristics on L1-VPT, the present work is the first to do so regarding L2-VPT. Specifically, we investigate the impact of targets' membership in outgroups varying in perceived competence and warmth, the two fundamental dimensions of social perception. Participants in four experiments engaged in a L2-VPT task. Avatars belonged to a low competence low warmth group (LCLW; e.g. the homeless) or to a high competence low warmth group (HCLW; e.g. bankers) in Experiments 1-3, and to a LCLW or high competence high warmth group (HCHW; e.g. female students) in Experiment 4. Participants answered as quickly as possible whether a cued number matched a number present in a scene from either their own or the avatar's perspective. We consistently found support for the presence of both egocentric and altercentric interference, but this was not modulated by group competence and warmth, suggesting that membership in outgroups varying in competence and warmth does not influence L2-VPT. We discuss the findings' implications in the light of recent views on VPT.

Ozone Monitoring Instrument (OMI) collection 4: establishing a 17-year-long series of detrended level-1b data

Abstract. The Ozone Monitoring Instrument (OMI) was launched on 15 July 2004, with an expected mission lifetime of 5 years. After more than 17 years in orbit the instrument is still functioning satisfactorily and in principle can continue doing so until the expected decommissioning of its platform Aura in 2025. In order to continue the datasets acquired by OMI and the Microwave Limb Sounder, the mission was extended up to at least 2023. Actions have been taken to ensure the proper functioning of the OMI operations, the data processing, and the calibration monitoring system until the eventual end of the mission. For the data processing a new level-0 (L0) to level-1b (L1b) data processor was built based on the recent developments for the TROPOspheric Monitoring Instrument (TROPOMI). With corrections for the degradation of the instrument now included, it is feasible to generate a new data collection to supersede the current collection-3 data products and reprocess the data of the entire mission up to now. This paper describes the differences between the collection-3 and collection-4 data. It will be shown that the collection-4 L1b data comprise a clear improvement with respect to the previous collections. By correcting for the gentle optical and electronic aging that has occurred over the past 17 years, OMI's ability to make trend-quality ozone measurements has further improved.

Beam test performance of a prototype module with Short Strip ASICs for the CMS HL-LHC tracker upgrade

The Short Strip ASIC (SSA) is one of the four front-end chips designed for the upgrade of the CMS Outer Tracker for the High Luminosity LHC. Together with the Macro-Pixel ASIC (MPA) it will instrument modules containing a strip and a macro-pixel sensor stacked on top of each other. The SSA provides both full readout of the strip hit information when triggered, and, together with the MPA, correlated clusters called stubs from the two sensors for use by the CMS Level-1 (L1) trigger system. Results from the first prototype module consisting of a sensor and two SSA chips are presented. The prototype module has been characterized at the Fermilab Test Beam Facility using a 120 GeV proton beam.

Development of the Questionnaire on Non-Driving Related Tasks (QNDRT) in automated driving: revealing age and gender differences

ABSTRACT Automated vehicles (AVs) enable driver-passengers to perform non-driving related tasks (NDRTs). However, it remains unclear if and how they would engage with NDRTs. Therefore, we developed a questionnaire on NDRTs, the QNDRT, for SAE Level 3 (L3) and 5 (L5) driving automation. It comprises 24 items with a general NDRT Work subscale querying general aspects of working while driving in an AV (8 items; L3: 4 items, L5: 4) and a specific NDRT Communication subscale assessing different communication modalities (16 items; L3: 8, L5: 8). Hence, we carried out a cross-sectional questionnaire study and queried 725 participants (351 female, 374 male) from 18 to 96 years. We applied factor analyses and extracted a stable unidimensional factor structure for both NDRT subscales with good psychometric properties (e.g. high and clear factor loadings; satisfactory communalities, item discrimination, and internal consistency). The initial findings revealed that significantly smaller values were assigned to both subscale factors by participants older than 55 years in contrast to younger ones and by women when compared to men. The QNDRT can be used in future (quasi-)experimental L3 and L5 studies and in population surveys to obtain more insight into working and communicating while driving in an AV.

First SMOS Sea Surface Salinity dedicated products over the Baltic Sea

Abstract. This paper presents the first Soil Moisture and Ocean Salinity (SMOS) Sea Surface Salinity (SSS) dedicated products over the Baltic Sea. The SSS retrieval from L-band brightness temperature (TB) measurements over this basin is really challenging due to important technical issues, such as the land–sea and ice–sea contamination, the high contamination by radio-frequency interference (RFI) sources, the low sensitivity of L-band TB at SSS changes in cold waters, and the poor characterization of dielectric constant models for the low SSS range in the basin. For these reasons, exploratory research in the algorithms used from the level 0 up to level 4 has been required to develop these dedicated products. This work has been performed in the framework of the European Space Agency regional initiative Baltic+ Salinity Dynamics. Two Baltic+ SSS products have been generated for the period 2011–2019 and are freely distributed: the Level 3 (L3) product (daily generated 9 d maps in a 0.25∘ grid; https://doi.org/10.20350/digitalCSIC/13859, González-Gambau et al., 2021a) and the Level 4 (L4) product (daily maps in a 0.05∘ grid; https://doi.org/10.20350/digitalCSIC/13860, González-Gambau et al., 2021b)​​​​​​​, which are computed by applying multifractal fusion to L3 SSS with SST maps. The accuracy of L3 SSS products is typically around 0.7–0.8 psu. The L4 product has an improved spatiotemporal resolution with respect to the L3 and the accuracy is typically around 0.4 psu. Regions with the highest errors and limited coverage are located in Arkona and Bornholm basins and Gulfs of Finland and Riga. The impact assessment of Baltic+ SSS products has shown that they can help in the understanding of salinity dynamics in the basin. They complement the temporally and spatially very sparse in situ measurements, covering data gaps in the region, and they can also be useful for the validation of numerical models, particularly in areas where in situ data are very sparse.

Profiling the Enthusiastic, Neutral, and Sceptical Users of Conditionally Automated Cars in 17 Countries: A Questionnaire Study

The L3Pilot project tested SAE Level 3 (L3) conditionally automated driving functions addressing driving and travel behavior, impacts on safety, efficiency, environment and socio-economics, and user acceptance. To investigate individual variance in acceptance of conditionally automated cars, an online survey was performed among 18,631 respondents from 17 countries evaluating differences in age, gender, knowledge about the functionality of conditionally automated cars, awareness, information consumption behavior, and expected benefits of conditionally automated cars. Respondents were divided into Enthusiasts, Neutrals, and Sceptics differing in a high, moderate, and low acceptance of conditionally automated cars, respectively. Enthusiasts, Neutrals, and Sceptics differed most with regard to the expected benefits in the productive use of travel time, comfort, and safety of conditionally automated cars. Enthusiasts were male, younger, more knowledgeable about conditionally automated cars, more aware of automated cars, and more likely to receive information about automated cars from different sources, expecting improvements in the productive use of travel time, comfort, and safety due to conditionally automated cars. All groups were most knowledgeable about the lane keeping behavior of conditionally automated cars and least knowledgeable about the operation of conditionally automated cars in dedicated operational design domains. The results indicate that the communication and marketing of automated cars should create a realistic image of the capabilities and limitations of conditionally automated cars where user education programs should be harmonized to calibrate expectations and educate the public.

Level 2 processor and auxiliary data for ESA Version 8 final full mission analysis of MIPAS measurements on ENVISAT

Abstract. High quality long-term data sets of altitude-resolved measurements of the atmospheric composition are important because they can be used both to study the evolution of the atmosphere and as a benchmark for future missions. For the final ESA reprocessing of MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) on ENVISAT (ENViromental SATellite) data, numerous improvements were implemented in the Level 2 (L2) processor Optimised Retrieval Model (ORM) version 8.22 (V8) and its auxiliary data. The implemented changes involve all aspects of the processing chain, from the modelling of the measurements with the handling of the horizontal inhomogeneities along the line of sight to the use of the optimal estimation technique to retrieve the minor species, from a more sensitive approach to detecting the spectra affected by clouds to a refined method for identifying low quality products. Improvements in the modelling of the measurements were also obtained with an update of the used spectroscopic data and of the databases providing the a priori knowledge of the atmosphere. The HITRAN_mipas_pf4.45 spectroscopic database was finalised with new spectroscopic data verified with MIPAS measurements themselves, while recently measured cross-sections were used for the heavy molecules. The Level 2 Initial Guess (IG2) data set, containing the climatology used by the MIPAS L2 processor to generate the initial guess and interfering species profiles when the retrieved profiles from previous scans are not available, was improved taking into account the diurnal variation of the profiles defined using climatologies from both measurements and models. Horizontal gradients were generated using the ECMWF ERA-Interim data closest in time and space to the MIPAS data. Further improvements in the L2 V8 products derived from the use of the L1b V8 products, which were upgraded to reduce the instrumental temporal drift and to handle the abrupt changes in the calibration gain. The improvements introduced into the ORM V8 L2 processor and its upgraded auxiliary data, together with the use of the L1b V8 products, lead to the generation of the MIPAS L2 V8 products, which are characterised by an increased accuracy, better temporal stability and a greater number of retrieved species.

Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China

The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04 m3/m3 at the 9-km scale, with an averaged ubRMSE value of 0.041 m3/m3 (0.050 m3/m3) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE < 0.04) and high correlation (R > 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China.

Impact of Age on Takeover Behavior in Automated Driving in Complex Traffic Situations: A Case Study of Beijing, China

Research on the influence of age on various automated driving conditions will contribute to an understanding of driving behavior characteristics and the development of specific automated driving systems. This study aims to analyze the relationship between age and takeover behavior in automated driving, where 16 test conditions were taken into consideration, including two driving tasks, two warning times and four driving scenarios. Forty-two drivers in Beijing, China in 2020 were recruited to participate in a static driving simulator with Level 3 (L3) conditional automation to obtain detailed test information of the recorded takeover time, mean speed and mean lateral offset. An ANOVA test was proposed to examine the significance among different age groups and conditions. The results confirmed that reaction time increased significantly with age and the driving stability of the older group was worse than the young and middle groups. It was also indicated that the older group could not adapt to complex tasks well when driving due to their limited cognitive driving ability. Additionally, the higher urgency of a scenario explained the variance in the takeover quality. According to the obtained influencing mechanisms, policy implications for the development of vehicle automation, considering the various driving behaviors of drivers, were put forward, so as to correctly identify the high-risk driving conditions in different age groups. For further research, on-road validation will be necessary in order to check for driving simulation-related effects.

Structure of High-Resolution SAR Winds Over the Venice Lagoon Area

The spatial characteristics at mesoscale <inline-formula> <tex-math notation="LaTeX">$\gamma $ </tex-math></inline-formula> of the strongest sea surface winds blowing on the Adriatic Sea and Venice Lagoon, Italy, have been investigated through 500 m <inline-formula> <tex-math notation="LaTeX">$\times500$ </tex-math></inline-formula> m wind fields extracted, without external information, from the C-band Sentinel-1 synthetic aperture radar (SAR) images interferometric wide (IW) swath ground range detected high resolution (GRDH) level 1 (L1) by a deep learning method based on a residual neural network (ResNet). The derived SAR wind directions have been used to retrieve the wind speed using the <monospace>C&#x005F;SARMOD2</monospace> geophysical model function (GMF). The resulting wind fields have been compared with European Centre for Medium-range Weather Forecasts (ECMWF) and <i>in situ</i> data: the wind direction bias <inline-formula> <tex-math notation="LaTeX">$|\beta _\theta |\le 1 {\textstyle ^\circ }$ </tex-math></inline-formula> indicates a very good agreement and the value of RMS difference against <i>in situ</i> winds of <inline-formula> <tex-math notation="LaTeX">$16 {\textstyle ^\circ }$ </tex-math></inline-formula> results smaller than those reported in the literature; the wind speed bias <inline-formula> <tex-math notation="LaTeX">$\beta _{w}=-1.5\,\, {\textstyle \text {ms}^{-1}}$ </tex-math></inline-formula> with respect to <i>in situ</i> data indicates an underestimation of SAR speed. However, the innovation brought by ResNet results is evident in the study of the spatial structure of the wind fields, showing unprecedented details. The spatial gradients of wind direction and speed obtained (<inline-formula> <tex-math notation="LaTeX">$\approx 6 {\textstyle ^\circ }/\text {km}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$0.5\,\, {\textstyle \text {ms}^{-1}}/\text {km}$ </tex-math></inline-formula>, respectively) are well consistent with those derived from previous experimental campaigns. The maps of the atmospheric wind stress curl derived from the ResNet wind fields exhibit the presence of contiguous cells with positive and negative values roughly elongated in the wind direction, not reproducible with such profusion of details by any other existing datasets. ResNet wind fields provide exhaustive coverage of the Venice lagoon, allowing to study their spatial structure: under northeasterly winds, the wind speed increases on average from northern to southern lagoon by 30&#x0025; in agreement with a case study reported in the literature. Local differences of ResNet wind direction with respect to ECMWF (<inline-formula> <tex-math notation="LaTeX">$\pm 30 {\textstyle ^\circ }$ </tex-math></inline-formula> maximum) generate local differences of wind speed as large as <inline-formula> <tex-math notation="LaTeX">$\pm 2\,\, {\textstyle \text {ms}^{-1}}$ </tex-math></inline-formula>.