Frame Length Research Articles

A priori parameter estimation of space SAR operating in the sliding spotlight scan mode and in the terrain observation mode by progressive scans

In the detailed mode of operation of the synthetic aperture radar (SAR), an improved level of geometric resolution of the terrain in a small frame is achieved. If, however, the frame length exceeds the size of the bottom trace in the azimuthal direction, then it is necessary to use a sliding spotlight operation mode (SSL), in which antenna pattern is scanned in the swath along the azimuth angle. To increase the width of the swath, ScanSAR mode is used. In the traditional implementation of this mode, image serration occurs. This effect can be eliminated by using terrain observation by progressive scans mode (TOPS)). In this mode, when shooting a frame, the antenna pattern runs quickly and alternately through all the specified partial swath bands. Despite the presence of a large number of publications in domestic and foreign literature devoted to the analysis of the main parameters of SAR operating in SSL and TOPS modes, there is a need to clarify calculation methods and increase the number of system parameters issued by them, which generally determine the basic appearance of the equipment. The purpose of the work is to clarify and detail the presentation of the methodology for a priori estimation of an increased number of parameters describing the main system characteristics of space SAR operating in SSL and TOPS. The paper develops and describes in detail a methodology with an exhaustive list of calculation ratios for a priori estimation of parameters describing the main system characteristics of space SAR operating in SSL and TOPS. Based on it, algorithms and software modules have been developed that allow you to perform calculations and display the results in a visual graphical form. Examples of computer calculations of 12 system parameters dependencies on the angle of sight of space radars operating in these two modes are given. The application of the developed methodology makes it possible to quickly perform an a priori assessment of a variety of basic system parameters of space radars operating in SSL and TOPS modes, which facilitates the selection of the most acceptable basic appearance of the equipment for practical implementation.

Unveiling Starlink LEO Satellite OFDM-Like Signal Structure Enabling Precise Positioning

This letter unveils the unknown structure of Starlink low Earth orbit (LEO) satellites' orthogonal frequency division multiplexing (OFDM)-like reference signals (RSs). The spectrum of Starlink's dowlink signals is presented, and the frame length is estimated. A blind receiver is proposed, which acquires via a sequential generalized likelihood ratio test multiple satellites, estimates their RSs and respective Doppler, and tracks their carrier and code phases. Experimental results are presented showing six tracked Starlink LEO satellites, three of which transmitted pure tones, while the other transmitted OFDM-like signals. The achieved horizontal positioning error with the six satellites was 6.5 m.

Imperceptible and Reversible Acoustic Watermarking Based on Modified Integer Discrete Cosine Transform Coefficient Expansion

This paper aims to explore an alternative reversible digital watermarking solution to guarantee the integrity of and detect tampering with data of probative importance. Since the payload for verification is embedded in the contents, algorithms for reversible embedding and extraction, imperceptibility, payload capacity, and computational time are issues to evaluate. Thus, we propose a reversible and imperceptible audio information-hiding algorithm based on modified integer discrete cosine transform (intDCT) coefficient expansion. In this work, the original signal is segmented into fixed-length frames, and then intDCT is applied to each frame to transform signals from the time domain into integer DCT coefficients. Expansion is applied to DCT coefficients at a higher frequency to reserve hiding capacity. Objective evaluation of speech quality is conducted using listening quality objective mean opinion (MOS-LQO) and the segmental signal-to-noise ratio (segSNR). The audio quality of different frame lengths and capacities is evaluated. Averages of 4.41 for MOS-LQO and 23.314 [dB] for segSNR for 112 ITU-T test signals were obtained with a capacity of 8000 bps, which assured imperceptibility with the sufficient capacity of the proposed method. This shows comparable audio quality to conventional work based on Linear Predictive Coding (LPC) regarding MOS-LQO. However, all segSNR scores of the proposed method have comparable or better performance in the time domain. Additionally, comparing histograms of the normalized maximum absolute value of stego data shows a lower possibility of overflow than the LPC method. A computational cost, including hiding and transforming, is an average of 4.884 s to process a 10 s audio clip. Blind tampering detection without the original data is achieved by the proposed embedding and extraction method.

Word separation in continuous sign language using isolated signs and post-processing

Continuous Sign Language Recognition (CSLR) is a long challenging task in Computer Vision due to the difficulties in detecting the explicit boundaries between the words in a sign sentence. To deal with this challenge, we propose a two-stage model. In the first stage, the predictor model, which includes a combination of CNN, SVD, and LSTM, is trained with the isolated signs. In the second stage, we apply a post-processing algorithm to the Softmax outputs obtained from the first part of the model in order to separate the isolated signs in the continuous signs. While the proposed model is trained on the isolated sign classes with similar frame numbers, it is evaluated on the continuous sign videos with a different frame length per each isolated sign class. Due to the lack of a large dataset, including both the sign sequences and the corresponding isolated signs, two public datasets in Isolated Sign Language Recognition (ISLR), RKS-PERSIANSIGN and ASLLVD, are used for evaluation. Results of the continuous sign videos confirm the efficiency of the proposed model to deal with isolated sign boundaries detection. The intuition behind the proposed post-processing methodology is to improve the recognition accuracy by removing the untrained and repetitive signs using the sliding window approach during the inference phase. This marks the first instance of such a mechanism within this domain. So, we present our methodology as a baseline for research community to enrich the methodology as well as evaluating on the other real data.

Cloning and Expression Analysis of Chemosensory Protein AzanCSP4 from Agrilus zanthoxylumi (Coleoptera: Buprestidae)

Abstract Chemosensory proteins (CSPs) are widely distributed in insect tissues and are involved in olfactory and non-olfactory functions. In this study, based on the transcriptome data of Agrilus zanthoxylumi Hou (Coleoptera: Buprestidae), the AzanCSP4 of was cloned by RT-PCR and bioinformatically analyzed, and RT-qPCR was conducted to analyze their expression levels of AzanCSP4 in different genders and tissues (head, thorax, abdomen, leg and wing). Sequence analysis showed that AzanCSP4 had an open reading frame (ORF) length of 366 bp, encoding 121 amino acids with an estimated molecular weight 13.96 kD. The encoded protein had no transmembrane domain, and the signal peptide was located in the position 1–15 at the N-terminal of the amino acid sequence. Sequence alignment revealed that AzanCSP4 had four conserved cysteines. Phylogenetic analysis revealed that the AzanCSP4 and AmalCSP6 from Agrilus mali Matsumura (Coleoptera: Buprestidae) were closely clustered into the same clade. RT-qPCR results showed that AzanCSP4 of A. zanthoxylumi was expressed in different tissues of both male and female adults, and the expression in the same tissue was greater in female adults than in male adults. The expression of AzanCSP4 in the head of female adults was significantly higher than that in other tissues of male and female adults. This study provides a theoretical basis for further research on the function of AzanCSP4, especially on the chemical communication mechanism in A. zanthoxylumi.

Metatarsal lengthening by distraction osteogenesis for brachymetatarsia in paediatric patients: is it safe and effective?

The primary aim is to assess the efficacy of the surgical callus distraction technique of the metatarsus in paediatric patients. Secondary objectives are to assess complications and treatment duration. We have also described the details of our surgical technique. A case series review of paediatric patients who had metatarsal lengthening at our unit between 2014 and 2022. Patient demographics, duration of time in frame, complications and metatarsal length achieved were recorded. The AOFAS Midfoot and the MOXFQ were taken pre-operatively and at final follow-up. Sixteen metatarsals in 8 patients (14 feet) underwent lengthening between 2014 and 2022 using the MiniRail OrthoFix 100 (Orthofix Medical Inc, Lewisville, TX, USA). The mean age was 13.3 (12-17) years. The average duration between surgery and implant removal was 5.2months. According to Paley's classification, there was one obstacle encountered in a patient who required a revision of their osteotomy and one problem in another patient who had an infected metatarsophalangeal joint stabilising k-wire treated with oral antibiotics. The Mean AOFAS Midfoot score improved from 53.10 to 86.40 (p < 0.0001) and the Mean MOXFQ improved from 32.5000 to 12.1250 (p < 0.05); these were statistically significant. Gradual metatarsal lengthening using the MiniRail external fixator is a safe and effective method to treat brachymetatarsia in paediatric patients. This preliminary report describes and supports metatarsal lengthening in appropriate patients. Holistic care in terms of a pre-operative assessment, psychological support and preparation for the extended rehabilitation period are vital.

Motion characteristics of a modularized floating solar farm in waves

Modularized floating solar farms exhibit the potential to replace conventional steel-frame ones, effectively remedying hydroelastic issues of a very large floating structure through discrete modules with mechanical connections. However, the response of the discrete modules under cyclic wave loading has not been fully understood. This paper assesses the motion characteristics and expansibility of modularized floaters in waves, based on computational results from fluid–structural interaction simulations. A crucial factor, denoted as the ratio of frame length to wavelength R=Ls/λ, is determined to predict the motions of a large floating solar system in head waves. Results indicate that the motion characteristics is predictable based on the R value. The empirical relationship between the R value and the motion of every unit in an array is analyzed. In particular, the results calculated from using the multiple-rigid-bodies method are also compared with those from using the single-large-hydroelastic-body method, and it was found that these two results are similar when R &amp;gt; 1. This similarity allows for predicting the multi-hinged bodies' behavior in waves through a simplified hydroelastic approach. Overall, this study reports insights that are useful for the design and optimization of modularized solar farms and can help address cyclic loading and motion concerns for long-term durability.

RFID tag recognition model for Internet of Things for training room management

With the rapid development of the Internet of Things and intelligent technology, the application of Radio Frequency Identification (RFID) technology in training room management is becoming increasingly widespread. An efficient and accurate RFID system can significantly improve the management efficiency and resource utilization of the training room, thereby improving teaching quality and reducing management costs. Although RFID technology has many advantages, there are still some problems in practical applications, such as label collision and recognition of unknown labels. These issues not only affect the performance of the system but may also cause interference with actual teaching and management. This study proposes a grouping-based bit arbitration query tree algorithm and anti-collision technology to solve label collisions and reduce label recognition time in the technology. A new unknown label recognition algorithm is also proposed to improve the recognition efficiency and accuracy of identifying new unknown labels. Related experiments have shown that the recognition accuracy of the algorithm designed this time is 95.86%. Compared with other algorithms, the number of idle time slots is the smallest. When the number of queries is 1000, the algorithm has 1842 queries, and the communication complexity is the best. When the number of unknown tags is 10,000, the actual accuracy rate is 95.642%. Compared with traditional recognition algorithms, the new unknown label recognition algorithm has a smaller frame length in the same label proportion and good recognition performance. On a theoretical level, the research content on RFID technology helps to improve and develop the basic theories of the Internet of Things and intelligent recognition technology and provides solutions and application technologies for equipment management and IoT applications in training rooms. On a practical level, the research results can provide specific guidance for the management of training rooms, help solve equipment management and safety maintenance problems in practical applications, and improve the management efficiency of training rooms.

The identification of minor impact collisions in a long video for detecting property damages caused by fleeing vehicles using three-dimensional convolutional neural network

Abstract A parked vehicle damaged by a hit-and-run can only be repaired at the expense of the owner, unless the fleeing vehicle is identified and the driver apprehended. Identifying the fleeing vehicle involves using a video investigation method that searches for perpetrators through CCTV footage of the crime scene. When the length of the recorded video is long, the investigation may require an extended amount of time from the investigator, resulting in an added burden on their daily work. Some commercial companies are using object recognition and tracking technology to detect hit-and-run incidents; however, detecting small movements of a vehicle during a minor collision still remains a challenge. Therefore, there is a need for a system that can detect small movement in a vehicle in a lengthy video. Automatic recognition and tracking require a sufficient amount of training dataset. However, such a dataset for hit-and-run incidents is not publicly available. One of the reasons behind this scarcity is that it may violate personal information protection acts. On the other hand, instead of using real accident videos, we could use actors to simulate such accident scenes. Although this may be feasible, creating such a dataset would require substantial costs. In this paper, we describe a new dataset for hit-and-run incidents. We collected 833 hit-and-run videos by recreating a parking lot using miniaturized cars. This dataset has been made publicly available through Kaggle. We used three-dimensional convolution neural network, which is frequently used in the field of action recognition, to detect small movements of vehicles during hit-and-run incidents. In addition, the proportion of the area that surrounds the target vehicle to the min-max box of the vehicle itself and the length of the input frame are varied to compare the accuracy. As a result, we were able to achieve better accuracy by using the lowest proportion and the shortest input frame.

Optimization of User Service Rate with Image Compression in Edge Computing-Based Vehicular Networks

The prevalence of intelligent transportation systems in alleviating traffic congestion and reducing the number of traffic accidents has risen in recent years owing to the rapid advancement of information and communication technology (ICT). Nevertheless, the increase in Internet of Vehicles (IoV) users has led to massive data transmission, resulting in significant delays and network instability during vehicle operation due to limited bandwidth resources. This poses serious security risks to the traffic system and endangers the safety of IoV users. To alleviate the computational load on the core network and provide more timely, effective, and secure data services to proximate users, this paper proposes the deployment of edge servers utilizing edge computing technologies. The massive image data of users are processed using an image compression algorithm, revealing a positive correlation between the compression quality factor and the image’s spatial occupancy. A performance analysis model for the ADHOC MAC (ADHOC Medium Access Control) protocol is established, elucidating a positive correlation between the frame length and the number of service users, and a negative correlation between the service user rate and the compression quality factor. The optimal service user rate, within the constraints of compression that does not compromise detection accuracy, is determined by using the target detection result as a criterion for effective compression. The simulation results demonstrate that the proposed scheme satisfies the object detection accuracy requirements in the IoV context. It enables the number of successfully connected users to approach the total user count, and increases the service rate by up to 34%, thereby enhancing driving safety, stability, and efficiency.

Convex optimization-based high-speed and security joint optimization scheme in optical access networks.

Data rate and security are essential performance metrics for passive optical networks (PON). However, existing optical access networks lack standardized metrics to evaluate rate and security performance uniformly. This paper introduces a high-speed and security joint optimization scheme for optical access networks using convex optimization. Evaluation metrics for data rate and security performance in PON are established. According to the evaluation metrics, the security optimization objective function Us, high-speed optimization objective function GMI, and high-speed security joint-optimization objective function Hs are established. An optimization problem is formulated to maximize weighted rate and security indicators, factoring in constraints such as maximum power, probability, amplifier capacity, normalized mutual information, and key and frame lengths. An alternating optimization method is applied to iteratively address sub-problems by exploiting successive convex approximations and differences of convex functions. This transforms non-convex sub-problems into convex optimizations. Experimental results highlight notable improvements in objective function values, confirming the effectiveness of the proposed high-speed security optimization algorithm for optical access networks.

Evolving precision: rRNA expansion segment 7S modulates translation velocity and accuracy in eukaryal ribosomes.

Ribosome-enhancedtranslational miscoding of the genetic code causes protein dysfunction and loss of cellular fitness. During evolution, open reading frame length increased, necessitating mechanisms for enhanced translation fidelity. Indeed, eukaryal ribosomes are more accurate than bacterial counterparts, despite their virtually identical, conserved active centers. During the evolution of eukaryotic organisms ribosome expansions at the rRNA and protein level occurred, which potentially increases the options for translation regulation and cotranslational events. Here we tested the hypothesis that ribosomal RNA expansions can modulate the core function of the ribosome, faithful protein synthesis. We demonstrate that a short expansion segment present in all eukaryotes' small subunit, ES7S, is crucial for accurate protein synthesis as its presence adjusts codon-specific velocities and guarantees high levels of cognate tRNA selection. Deletion of ES7S in yeast enhances mistranslation and causes protein destabilization and aggregation, dramatically reducing cellular fitness. Removal of ES7S did not alter ribosome architecture but altered the structural dynamics of inter-subunit bridges thus affecting A-tRNA selection. Exchanging the yeast ES7S sequence with the human ES7S increases accuracy whereas shortening causes the opposite effect. Our study demonstrates that ES7S provided eukaryal ribosomes with higher accuracy without perturbing the structurally conserved decoding center.

Perancangan Alat Press Hidrolik Kapasitas 20 Ton

A press tool is a tool to help shape, bend, cut products from sheet metal (plate) base material which is operated using a press tool. To increase effectiveness and productivity, currently many hydraulic systems are combined with other systems such as electrical/electronic, pneumatic and mechanical systems so that more optimal hydraulic system work can be obtained. This press applies pressing or loading movements using hydraulic power. In order for the pressing process to be even or precise, one cylinder is needed which moves the top plate (Punch). The working load on the single acting cylinder has a maximum capacity of 20 tons. The design of this press tool is designed by following the type and shape of the selected material sample. The design of this press tool aims to make the user's work easier to get more precise press results. The material used is profile steel. Simulation results on the press frame: frame length 400 mm, frame width 600 mm, frame height 850 mm, frame strength 16407 N/m2

Timing synchronization of LACO-OFDM under non-linear distortions using ELM

Layered asymmetrically-clipped optical orthogonal frequency division multiplexing (LACO-OFDM) is a modulation technique for visible light communication (VLC) that provides power and spectral efficiencies. The LACO-OFDM technique utilizes all the available subcarriers and will have a relatively higher peak-to-average power ratio (PAPR). Hence, the modulated samples are prone to distortions caused by the non-linear devices and diffused channels, resulting in reduced accuracy of delay offset estimation for timing synchronization. In this paper, we propose a method to improve the timing synchronization accuracy of LACO-OFDM modulated systems using extreme learning machines (ELM). The ELM is trained offline to identify the delay offset using the timing metric (TM) as a feature identification problem. Among all types of neural networks that are used as classifiers and non-linear estimators, the ELMs are the least complex and require significantly less computational capacity. The simulations are performed to evaluate the accuracy of delay offset estimation in the presence of unknown non-linear effects of high-powered light-emitting diodes (LEDs) and diffused channel characteristics. The results show that the probability of error (PoE) of delay offset estimation and bit error rate (BER) is significantly reduced for scenarios like different frame lengths, channel coefficients, and unknown LED parameters in contrast to conventional methods. The proposed approach attains a PoE of 10−1 at signal-to-noise ratios (SNR)s of 4 dB and 6 dB with various TM, surpassing conventional methods which only achieve this beyond 15 dB. Moreover, the PoE remains below 10−2 for SNRs exceeding 7 dB and 10 dB in the proposed method, while conventional methods require SNRs above 25 dB to achieve comparable results.

In-plane and out-of-plane vibration analysis of laminated composite frames with warping effects

This paper addresses the free vibration analysis of a general planar frame structure, consisting of an assembly of laminated composite beam members. A mathematical model is established for the beams by introducing a novel displacement field, which encompasses the influence of shear deformation, rotary inertia, material coupling, warping phenomenon, as well as in-plane and out-of-plane deformations. By incorporating these various factors, a comprehensive representation of the beam's behavior is achieved. The continuity equations for displacements and rotations of two adjacent members are formulated to derive the equations for the entire frame structure. The assembled equations are solved using the Finite Element Method (FEM). The numerical results obtained are compared with existing results in the literature and new results from 3D models in ANSYS. The present results for torsional modes are closer to the ANSYS results than the results of previous studies. An extensive analysis is performed to investigate the impact of various parameters, including stacking sequence, frame angle, relative frame length, and anisotropy ratios on the system response.

Evaluation of Polar-Coded Noncoherent Acoustic Underwater Communication

This article investigates several variants of noncoherent multiple-frequency-shift keying (MFSK) that uses Polar coding and evaluates their applicability to robust underwater acoustic underwater communication. A simplified data model is assumed, yielding a low-complexity MFSK symbol estimator, and symbol likelihoods are calculated using sparse pilot symbols. Results presented from field tests in the Stockholm archipelago indicate that a Polar code will decode with a low frame error rate (FER) if not signal-to-noise ratio (SNR) limited and that its performance is superior to convolutional coding. The reliability of short frames, with 0.16–0.5 s transmission time, and long frames, with spectral efficiency up to 0.1 b/s/Hz, are evaluated in the five replay channels of the Watermark benchmark over varying SNR and SNR per bit, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$E_{b}/N_{0}$</tex-math></inline-formula> . Synchronization is solved with a sliding hypothesis approach and the results show a FER <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\lesssim 1 \%$</tex-math></inline-formula> for <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">all</i> Watermark channels, where the robustness is significantly improved by either increasing the frequency separation of tones or lowering the code rate and extending the frame length. The performance compares favorably to methods found in the literature, particularly for the less-benign channels of the benchmark.

Frame Length Dependency for Fundamental Frequency Extraction in Noisy Speech

Frame Length Dependency for Fundamental Frequency Extraction in Noisy Speech

Recurrent neural network and federated learning based channel estimation approach in mmWave massive MIMO systems

AbstractSo far, various data‐driven approaches have been presented to obtain channel state information (CSI) in millimeter wave multiple‐input‐multiple‐output wireless networks. In almost all previous works, training and testing channels were assumed to have the same distribution, which may not be the case in practice. In this article, we address this challenge by proposing a learning framework that is a combination of a recurrent neural network (RNN) model and a deep neural network (DNN) for estimating CSI in a dynamic wireless communication environment. Furthermore, we use federated learning to train the learning‐based channel estimation model. More specifically, we introduce a two‐stage downlink pilot transmission procedure, where in the initial stage, long frame length downlink pilot signals are used to train the introduced RNN‐DNN model. Following that, users will receive shorter‐frame‐length pilot signals that can be used for CSI estimation. To speed up the training procedure of the proposed network, we first generate a pre‐trained model and then modify it according to the collected data samples. Simulation results demonstrate that, when the channel distribution is unavailable, the proposed approach performs significantly better than the most recent channel estimation algorithms in terms of estimation performance and computational complexity.

An adaptive cepstrum feature representation method with variable frame length and variable filter banks for acoustic emission signals

Acoustic emission technology is widely employed in defect detection, structural health monitoring, fault diagnosis, and other applications due to its benefits of non-contact, real-time, sensitivity, and flexibility. To implement efficient and timely intelligent monitoring, it is crucial to promptly and precisely gather the time-varying properties in the non-stationary acoustic emission signals. The current work proposes an adaptive cepstrum feature representation method that combines variable frame length and variable filter bank. The variable frame length is implemented based on the spectral distribution correlation, and the optimal frame length and frame rate are selected by iterative operations. The variable filter bank, on the other hand, characterizes the local measure features of variable frame length signals by setting dynamic weight distributions. The proposed adaptive cepstrum algorithm can be used as an auxiliary tool to initially analyze complex non-stationary signals, and thus effectively identify the time-varying and frequency components in acoustic emission signals. The superiority of the proposed method has been confirmed on simulation data. Furthermore, the practical performance of the proposed method is evaluated and illustrated based on the burst signal from laser shock peening processing and the mixed signal from pipeline leakage. The proposed method can be extended to other broadband signals and can be used for feature pre-processing before deep learning network modeling to improve recognition accuracy due to its high time–frequency resolution.

The Enrichment of miRNA-Targeted mRNAs in Translationally Less Active over More Active Polysomes.

miRNAs moderately inhibit the translation and enhance the degradation of their target mRNAs via cognate binding sites located predominantly in the 3'-untranslated regions (UTR). Paradoxically, miRNA targets are also polysome-associated. We studied the polysome association by the comparative translationally less-active light- and more-active heavy-polysome profiling of a wild type (WT) human cell line and its isogenic mutant (MT) with a disrupted DICER1 gene and, thus, mature miRNA production. As expected, the open reading frame (ORF) length is a major determinant of light- to heavy-polysome mRNA abundance ratios, but is rendered less powerful in WT than in MT cells by miRNA-regulatory activities. We also observed that miRNAs tend to target mRNAs with longer ORFs, and that adjusting the mRNA abundance ratio with the ORF length improves its correlation with the 3'-UTR miRNA-binding-site count. In WT cells, miRNA-targeted mRNAs exhibit higher abundance in light relative to heavy polysomes, i.e., light-polysome enrichment. In MT cells, the DICER1 disruption not only significantly abrogated the light-polysome enrichment, but also narrowed the mRNA abundance ratio value range. Additionally, the abrogation of the enrichment due to the DICER1 gene disruption, i.e., the decreases of the ORF-length-adjusted mRNA abundance ratio from WT to MT cells, exhibits a nearly perfect linear correlation with the 3'-UTR binding-site count. Transcription factors and protein kinases are the top two most enriched mRNA groups. Taken together, the results provide evidence for the light-polysome enrichment of miRNA-targeted mRNAs to reconcile polysome association and moderate translation inhibition, and that ORF length is an important, though currently under-appreciated, transcriptome regulation parameter.