L1 Data Research Articles

A fast and efficient method to estimate inland water levels using CYGNSS L1 data and DTMs: Application to Floods, lakes and reservoirs monitoring

A fast and efficient method to estimate inland water levels using CYGNSS L1 data and DTMs: Application to Floods, lakes and reservoirs monitoring

Suitability of different in-water algorithms for eutrophic and absorbing waters applied to Sentinel-2 MSI and Sentinel-3 OLCI data

Optically complex waters present significant challenges for remote sensing due to high concentrations of optically active substances (OASs) and their inherent optical properties (IOPs), as well as the adjacency effect. OASs and IOPs can be derived from atmospheric correction processors’ in-water algorithms applied to data from Sentinel-2 MultiSpectral Instrument (S2 MSI) and Sentinel-3 Ocean and Land Color Instrument (S3 OLCI). This study compared S3 OLCI Level-2 in-water products for Case-2 waters with alternative in-water algorithms derived from ACOLITE, POLYMER, C2RCC, and A4O. Fifty in-water algorithms were evaluated using an extensive match-up dataset from lakes and coastal areas, focusing particularly on small lakes with high colored dissolved organic matter absorption at 442 nm (up to 48 m-1). The Chl a band ratio introduced by Gons et al. (2022) applied to data processed by ACOLITE performed best for S3 OLCI Chl a retrieval (dispersion = 23%, bias = 10%). Gons et al. (2022) band ratio also showed consistent agreement between S3 OLCI and S2 MSI resampled data (intercept of 6.27 and slope of 0.83, close to the 1:1 line); however, lower Chl a values (<20 mg/m3) were overestimated by S2 MSI. When estimating errors associated with proximity to land, S2 MSI Chl a in-water algorithms had higher errors close to the shore (on average 315%) compared to S3 OLCI (on average 150%). Chl a retrieved with POLYMER had the lowest errors close to the shore for both S2 MSI and S3 OLCI data (on average 70%). Total suspended matter (TSM) retrieval with C2RCC performed well for S2 MSI (dispersion 24% and bias −12%). Total absorption was most accurately derived from C2RCC applied to S3 OLCI L1 data (dispersion < 43% and bias < −39%), and it was better estimated than its individual components: phytoplankton, mineral particles, and colored dissolved organic matter absorption. However, none of the colored dissolved organic matter absorption in-water algorithms performed well (dispersion > 59% and bias < −29%).

Enhancing the processing advantage: two psycholinguistic investigations of formulaic expressions in Chinese as a second language

Abstract The Holistic Hypothesis asserts that formulaic expressions (FEs) are processed more rapidly than non-formulaic expressions (non-FE items) by both native speakers (NSs) and L2 learners of English. This study utilized an acceptability judgment task and a self-paced reading task to investigate the online processing of FEs and non-FE items among bilingual speakers (L1 English, L2 Chinese) in both contextual and non-contextual conditions. Meanwhile, a familiarity rating task was employed to measure whether there is a familiarity effect in item processing. The results consistently provided support for the Holistic Hypothesis, indicating that learners of Chinese at each level exhibited faster processing of FEs compared to non-FE items, regardless of the presence or absence of context. However, the influence of item familiarity, rather than the proficiency effect, contributed to the improvement of L2 learners’ eventual processing abilities. Distinct patterns also emerged when comparing data from NSs and L2 learners of Chinese, highlighting L2 learners’ more pronounced processing advantage, characterized by faster response times (RTs) to FEs compared to non-FE items. Through an analysis of Chinese L2 data, this study sheds light on the interplay between the usage based approach and chunking within the cognitive approach to L2 learning.

Improving PRISMA hyperspectral spatial resolution and geolocation by using Sentinel-2: development and test of an operational procedure in urban and rural areas

Hyperspectral (HS) satellites like PRISMA (PRecursore IperSpettrale della Missione Applicativa) offer remarkable capabilities, yet they are constrained by a relatively coarse spatial resolution, curbing their efficacy in those applications that require pinpoint accuracy. Here we propose a fusion process, aimed at the enhancement of PRISMA HS spatial resolution by using the spatial and spectral information of Sentinel-2 multispectral (MS) data (HS-MS fusion process), validated against four airborne HS flights simultaneous to satellite overpasses on different land use distributions. Adopting the PRISMA panchromatic (PAN) image, the proposed solution was also compared with the results of a HS-PAN pansharpening process. A two-steps operational workflow is proposed, based on two state-of-the-art and open-source algorithms. The first step consisted of the geocoding of PRISMA L2 products using Senintel-2 as reference and was accomplished with the phase-based algorithm implemented in AROSICS (Automated and Robust Open-Source Image Co-registration Software). The geometric displacement in L2 data was found to be between 80 m and 250 m, irregularly spatially distributed throughout the same scene and among scenes, and it was corrected by means of thousands of regularly spatially distributed tie points. A second-order polynomial transformation function was integrated in the algorithm. The second step consisted of employing the HySure (HS Super resolution) fusion algorithm to perform both the HS-MS fusion and the HS-PAN pansharpening, returning a PRISMA HS improved dataset with a spatial resolution of 10 m and 5 m, respectively. Four different per-band accuracy metrics were used to evaluate the accuracy of both products against airborne data. Overall, HS-MS data achieved increased accuracy in all validation metrics, i.e. + 28 % (root mean square error, RMSE), +23 % (spectral angle mapper, SAM), +7% (peak signal-to-noise ratio, PSNR) and + 11 % (universal image quality index, UIQI), with respect of HS-PAN data. These outcomes showed that using the spectral information of Sentinel-2 both spectral and spatial patterns were reconstructed more consistently in three different urban and rural scenarios, avoiding the presence of blur and at-edge artefacts as opposed to HS-PAN pansharpening, therefore suggesting an optimal strategy for satellite HS data resolution enhancement.

Coding all clauses in L2 data: A call for consistency

Linguistic complexity is considered a hallmark of language proficiency (Norris & Ortega, 2009), and it is often a focus of second language (L2) research (Pallotti, 2015). Within the construct of complexity, syntactic complexity considers the combination of words and clauses. The term clause, however, is underspecified in the field, and L2 researchers only occasionally define how clause is operationalized in the coding. After a review of the importance of consistent clause coding, this commentary argues that coordinated verb phrases, nonfinite clauses, and verbal small clauses can reach the complexity of finite clauses by the production of another element, making these constructions commensurate with subordinate finite clauses. Since these constructions are structurally similar to accepted clauses (e.g., imperatives), their exclusion from the clausal complexity calculation threatens the validity and reliability of the coding. Because a narrow definition requires an arbitrary division along a continuum of complex verbal constructions, the field should adopt this broader definition of clause. Further, researchers should clarify their methodology choices and acknowledge coding challenges, which are common in L2 data, especially L2 speech data. The increased transparency in research methodology supports the interpretation of research results and facilitates comparisons across L2 studies.

Word searches and fluency during problem-solving tasks: a mixed-methods study of L1 Finnish and L2 English interaction

Word searches (WSs) have been widely studied in L1 and L2 interaction but have rarely been combined with fluency research. The present study bridges the gap between these research fields by examining WSs and their connections to fluency in L1 and L2 dialogue data. From a larger project, 50 participants’ dialogues (25 L1 Finnish, 25 L2 English) were analyzed for WSs. The mixed-methods analyses focused on potential differences and connections in WS use across the L1 and L2 dialogues and the connections between WSs and fluency among selected participants. The results demonstrated differences especially in the use of solitary WSs across L1 and L2 and some connections between L1 and L2 WSs among individual participants, despite group-level connections not being statistically significant. The qualitative analyses illustrated the potential fluency-enhancing functions of collaborative WSs. The study has implications for L2 speech fluency research and L2 teaching.

Uniqueness for entropy solution to a class of nonlinear degenerate weighted elliptic p(.)-Laplacian problem with L1-data

In the present paper, we prove the uniqueness of entropy solution to a class of nonlinear degenerate weighted elliptic p(·)-Laplacian problems. These particular problems are delineated by Dirichlet-type boundary conditions and L1 data. We use a priori estimates in the framework of weighted variable exponent Sobolev spaces theory.

Existence of entropy solutions to nonlinear degenerate weighted elliptic p(.)-Laplacian problem and L1-data

Our aim in this paper is to study the existence result of entropy solution for a specific type of nonlinear degenerate weighted elliptic p(.)-Laplacian problems with Dirichlet-type boundary condition and L1 data. In the framework of the theory of weighted Sobolev spaces with variable exponents, we use the regularization approach combined with a priori estimates.

Cross-Core Data Sharing for Energy-Efficient GPUs

Graphics Processing Units (GPUs) are the accelerator of choice in a variety of application domains because they can accelerate massively parallel workloads and can be easily programmed using general-purpose programming frameworks such as CUDA and OpenCL. Each Streaming Multiprocessor (SM) contains an L1 data cache (L1D) to exploit the locality in data accesses. L1D misses are costly for GPUs due to two reasons. First, L1D misses consume a lot of energy as they need to access the L2 cache (L2) via an on-chip network and the off-chip DRAM in case of L2 misses. Second, L1D misses impose performance overhead if the GPU does not have enough active warps to hide the long memory access latency. We observe that threads running on different SMs share 55% of the data they read from the memory. Unfortunately, as the L1Ds are in the non-coherent memory domain, each SM independently fetches data from the L2 or the off-chip memory into its L1D, even though the data may be currently available in the L1D of another SM. Our goal is to service L1D read misses via other SMs, as much as possible, to cut down costly accesses to the L2 or the off-chip DRAM. To this end, we propose a new data sharing mechanism, called Cross-Core Data Sharing (CCDS) . CCDS employs a predictor to estimate whether or not the required cache block exists in another SM. If the block is predicted to exist in another SM’s L1D, CCDS fetches the data from the L1D that contains the block. Our experiments on a suite of 26 workloads show that CCDS improves average energy and performance by 1.30 × and 1.20 ×, respectively, compared to the baseline GPU. Compared to the state-of-the-art data-sharing mechanism, CCDS improves average energy and performance by 1.37 × and 1.11 ×, respectively.

A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning

Downscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage.

An analysis of roadside particulate matter pollution and population exposure over the Pearl River Delta region of China under clear-sky condition using new ultra-high-resolution PM2.5 satellite-retrieval algorithms

Abstract Roadside air pollution is one of the serious air pollution problems in urban areas. Even though roadside air pollution has been reported to cause adverse human health impacts, the spatial distribution of roadside air pollution in a large urban agglomeration has yet to be fully assessed. This study aimed to analyse roadside fine particulate matter (PM2.5) pollution and the population exposure in 11 cities in the Pearl River Delta (PRD) region of China. We developed satellite-retrieval algorithms with dark target method, vector support machine model and random forest model to retrieve the spatial distribution of PM2.5 at an ultra-high-spatial-resolution (30 m) based on 30 m Landsat-8 L1 data. Our results show that the retrieved PM2.5 had a promising consistency with PM2.5 measurements at general and roadside stations (R 2 = 0.86; RMSE = 7.72 µg m−3). Moreover, on average, the roadside PM2.5 in Dongguan, Foshan, and Guangzhou was relatively higher (up to 107.60 µg m−3) whereas that in Hong Kong was relatively lower (up to 30.40 µg m−3). The roadside PM2.5 pollution typically occurred in roads for motorized vehicles i.e. motorway, trunk, primary and secondary road. Our results also show that roadside PM2.5 was up to 17% higher in holidays than in workdays in all the PRD cities except Hong Kong that showed roadside PM2.5 higher in workdays than in holidays. The population-weighted PM2.5 decreased with increasing distances from roads in every PRD city, and population-weighted PM2.5 was estimated to be up to 22% higher at roadsides than at distances of 1500 m away from roads. This study pinpointed the seriousness of roadside air pollution in the PRD region.

Towards a graded lexical inventory of multi-word combinations

Abstract Collocations have become increasingly important in our understanding of foreign language learning. When it comes to setting vocabulary learning goals, concerns about how to address collocations still arise today. This article explores the distribution of collocations in L1 and L2 Spanish production with the ultimate goal of informing the design of graded lexical inventories of multi-word combinations. To do so, we explore three defining properties of collocations in L1 and L2 production data, and across different levels of L2 proficiency: syntactic structure, semantic transparency, and the strength of association. Results indicate that there is an increase of collocational density and diversity, but that isolated features of collocations fail to predict L2 proficiency. Findings suggest the need to evaluate collocation use at a high level of granularity.

Mixed Gaussian-impulse noise removal using non-convex high-order TV penalty

To restore images with clear edge details and no staircase artifacts from degraded versions, this paper incorporates the ℓ2 plus ℓ0 data fidelity and non-convex high-order total variation regularizer to establish an optimization model for eliminating mixed Gaussian-impulse noise. Among them, the ℓ2 fidelity is adopted to suppress Gaussian noise, while the ℓ0-norm is more suitable for detecting and removing impulse noise. In addition, the non-convex regularization displays excellent performance in overcoming the staircase effect and preserving edge details. Computationally, by using the iteratively reweighted ℓ1 algorithm and variable splitting method, this work designs a modified alternating minimization method to solve the optimization problem we construct. In theory, the convergence proof of our resulting algorithm is also presented. Finally, in the experimental section, we conduct extensive numerical experiments on degraded images and compare with other existing techniques. From the intuitive effects and restoration accuracy, it follows that our newly proposed method is effective and competitive for image deblurring and mixed noise removal.

Strongly nonlinear parabolic problems with natural growth terms and L1 data in Musielak-Orlicz-Sobolev spaces

Strongly nonlinear parabolic problems with natural growth terms and L1 data in Musielak-Orlicz-Sobolev spaces

Affective and sensory–motor norms for idioms by L1 and L2 English speakers

AbstractIn the present study, we developed affective (valence and arousal) and sensory–motor (concreteness and imageability) norms for 210 English idioms rated by native English speakers (L1) and English second-language speakers (L2). Based on internal consistency analyses, the ratings were found to be highly reliable. Furthermore, we explored various relations within the collected measures (valence, arousal, concreteness, and imageability) and between these measures and some available psycholinguistic norms (familiarity, literal plausibility, and decomposability) for the same set of idioms. The primary findings were that (i) valence and arousal showed the typical U-shape relation, for both L1 and L2 data; (ii) idioms with more negative valence were rated as more arousing; (iii) the majority of idioms were rated as either positive or negative with only 4 being rated as neutral; (iv) familiarity correlated positively with valence and arousal; (v) concreteness and imageability showed a strong positive correlation; and (vi) the ratings of L1 and L2 speakers significantly differed for arousal and concreteness, but not for valence and imageability. We discuss our interpretation of these observations with reference to the literature on figurative language processing (both single words and idioms).

CacheIEE: Cache-Assisted Isolated Execution Environment on ARM Multi-Core Platforms

ARM TrustZone technology has been widely used to create Trusted Execution Environments (TEEs) for enhancing the security of applications. However, the increasing number of installed security-sensitive applications in the secure world will inevitably enlarge the trusted computing base (TCB) of TEE systems. To minimize the TCB of the secure world and increase application portability, Isolated Execution Environments (IEEs) are proposed to protect applications in enclaves created in the normal world. However, existing IEE systems cannot provide the same level of security as the TEE systems, particularly, on resolving the multi-vector attacks that include both physical memory disclosure attacks and software attacks. In this paper, we develop a new cache-assisted IEE system called CacheIEE that creates enclaves in the L1 data cache of the normal world to protect sensitive data against multi-vector attacks. First, by always storing the sensitive data in the L1 data cache, CacheIEE can effectively prevent physical memory disclosure attacks. Second, we protect the L1 data cache against untrusted rich OS running in other cores. To support more applications, CacheIEE can process large-size sensitive data in the L1 data cache with constrained capacity. We implement a system prototype of CacheIEE and verify its security and practicability.

Efficacy of clustering and switching strategies in verbal fluency tasks in a Finnish-English language attrition population

ABSTRACT Verbal Fluency (VF) task total scores are widely used in language attrition studies, but they do not provide insight into the processes underlying optimal performance. We analyse the efficacy of clustering (subcategories within a category) and switching (shifting between these subcategories) strategies in phonemic (PVF) and semantic (SVF) tasks. First, we focus on L1 Finnish and L2 English performance among attriters (N = 38). Our analyses suggest similar processes underlying performance in both languages. These processes seem to remain unaffected by immersion time in the L2 environment (LoR) and frequency of L1 use, highlighting the importance of including L2 data alongside comparisons to L1 monolingual populations to account for a broad bilingual effect in language attrition studies. Second, we compare attriters’ and monolinguals’ (N = 50) performance in L1. Our findings suggest that attriters rely on clustering in PVF more systematically than monolinguals, and they struggle to initiate a search for a new subcategory or return to a previous category (switching) after depleting a cluster in PVF and SVF tasks. Thus, our analysis demonstrates a difference in processing strategies between the groups that could potentially contribute to similar total scores in PVF and lower total scores in the attriter group in SVF.

Exploring request strategies in Austrian Italian learners: Pragmatic transfer insights

Instant-message requests produced by intermediate learners (N = 60) of Italian as a second language (L2) with Austrian German as their first language (L1) were quantitatively analyzed and compared with the same speech acts performed by L1 speakers of Italian (N = 60). A cross-cultural comparison of requests in L1 Austrian German and L1 Italian was conducted firstly to verify whether any divergent trends in the two groups could be attributed to negative pragmatic transfer from the learners' L1. Cross-linguistic data were gathered through a multiple-choice questionnaire, L1 and L2 data through a discourse completion task. The analysis revealed minimal differences between L1 speakers of Italian and Austrian German in perceiving the most appropriate way to structure the request in terms of directness level and use of face-saving strategies. A substantial alignment of learners’ realizations with those of L1 speakers was found in the overall structuring of the request, except that L1 speakers exhibited a stronger tendency to apologize. As for the use of modifiers, small differences were found in the total number as well as in the preference for some types over others. The findings suggest that any effects of transfer from L1 Austrian German to L2 Italian are generally positive.

Photometric correction of images obtained from Chandrayaan-2 Imaging Infra-Red Spectrometer (IIRS) data

India’s second mission to the Moon, Chandrayaan-2, carried one of the most advanced imaging spectrometers ever sent to image a planetary surface – the IIRS (Imaging Infra-Red Spectrometer) – a 250 band spectrometer developed at the Space Applications Centre (SAC), ISRO, India. In this study, IIRS data between 800 and 2000 nm have been used to derive photometrically corrected reflectance from calibrated radiance data supplied by ISRO’s online archive (Pradan) of Chandrayaan-2 data. Additionally, the photometric properties of three IIRS bands (950, 1500, and 1700 nm) have been studied in detail. Since the images obtained by IIRS are at different viewing geometry orientations, the purpose of performing a photometric correction on IIRS images is to re-project each pixel to a standard viewing geometry of incidence angle, i = 30°, emission angle, e = 0° and, phase angle, α = 30°. In order to correct the lunar limb darkening effect, a combination of the Lommel-Seeliger function and McEwen’s model was used. In addition, topographic data from the merged Digital Elevation Model of Lunar Reconnaissance Orbiter – Lunar Orbiter Laser Altimeter (LRO-LOLA) and SELENE Terrain Camera (TC) were utilized to remove effects from local topography. A photometric function was then applied on four images captured by IIRS which were included in the first public release of data in August 2021. Radiance and reflectance (obtained after implementing photometric correction on IIRS images) values were compared with L1 and L2 M3 (Moon Mineralogy Mapper) data from Chandrayaan-1. It was found that although IIRS images are much darker than M3, corrected IIRS reflectance images had a much smaller variation in terms of reflectance values with M3. Moreover, the associated spectral reddening and reflectance values are within range when compared with data from other lunar missions. In addition, the reflectance spectra derived from the corrected IIRS images have been plotted for each image. However, the spectra exhibit significantly deeper absorptions than their corresponding M3 data for the same pixel. Phase functions derived from three bands have a good correlation with the reflectance values and phase angles. Consequently, this function can be applied to all IIRS images as and when more data are released to the public.

Can L2 Pronunciation Be Evaluated without Reference to a Native Model? Pillai Scores for the Intrinsic Evaluation of L2 Vowels

In this article, we explore the possibility of evaluating L2 pronunciation, and, more specifically, L2 vowels, without referring to a native model, i.e., intrinsically. Instead of comparing L2 vowel productions to native speakers’ productions, we use Pillai scores to measure the overlap between target vowel categories in L2 English (/iː/ — /ɪ/, /ɑː/ — /æ/, /ɜː/ — /ʌ/, /uː/ — /ʊ/) for L1 French, L1 Spanish, and L1 Italian learners (n = 40); and in L2 French (/y/ — /u/, /ø/ — /o/, /ø/ — /e/, /ɛ˜/ — /e /, /ɑ˜/ — /a/, /ɔ˜/ — /o/) for L1 English, L1 Spanish, and L1 Italian learners (n = 48). We assume that a greater amount of overlap within a contrast indicates assimilated categories in a learner’s production, whereas a smaller amount of overlap indicates the establishment of phonological categories and distinct realisations for members of the contrast. Pillai scores were significant predictors of native ratings of comprehensibility and/or nativelikeness for many of the contrasts considered. Despite some limitations and caveats, we argue that Pillai scores and similar methods for the intrinsic evaluation of L2 pronunciation can be used, (i) to avoid direct comparisons of L2 users’ performance with native monolinguals, following recent trends in SLA research; (ii) when comparable L1 data are not available; (iii) within longitudinal studies to track the progressive development of new phonological categories.