esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Analysis
### Results and Discussion: No-Load Magnetic Density Distribution and Electromagnetic Force (EMF)  
It is necessary to analyze the no-load magnetic density distribution firstly, because the no-load air-gap magnetic density fundamental component amplitude can be seen as an elementary assessment index of the output torque capability. The no-load magnetic density distribution of the original and optimized structures and EMF are simulated at the rated speed of 3000 rpm. The simulation result of the magnetic field is symmetrical, and part of the motor nephogram of the no-load magnetic density distribution is shown in Figure 5.  
In Figure 5, the saturation of the rotor bridges in both structures can be seen. But compared with the original structure, the magnetic density of the stator teeth and yokes of the optimized structure is higher.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Scheme I** & **Scheme II \({}^{*}\)** & **Scheme III** \\ \hline \(O\) & 17 mm & 18 mm & 19 mm \\ \(D_{1}\) & 3.5 mm & 4.5 mm & 5.5 mm \\ \(D_{2}\) & 1.6 mm & 2 mm & 2.4 mm \\ \(L_{1}\) & 16 mm & 18 mm & 20 mm \\ \(L_{2}\) & 16 mm & 18 mm & 20 mm \\ \(H\) & 2.5 mm & 3 mm & 3.5 mm \\ \(W_{1}\) & 1.1 mm & 1.3 mm & 1.5 mm \\ \(W_{2}\) & 0.8 mm & 1 mm & 1.2 mm \\ Cogging torque (peak-to-peak value) & 4.3 N\(\cdot\)m & 2.9 N\(\cdot\)m & 1.4 N\(\cdot\)m \\ Rated output torque (average value) & 97.5 N\(\cdot\)m & 98.2 N\(\cdot\)m & 95.1 N\(\cdot\)m \\ Torque ripple & 9.07\% & 7.1\% & 8.6\% \\ \hline \hline \end{tabular}  
* The structural parameters adopted in this article are scheme II.  
\end{table}
Table 4: Comparison of different rotor structural parameters.  
Figure 5: The motor nephogram of no-load magnetic density distribution: (**a**) original structure; and (**b**) optimized structure.  
Further, the harmonic analysis of the air-gap flux density distribution is carried out and reveals that the air-gap magnetic fields mainly comprise the components of four pole pairs. The fundamental components of the air-gap magnetic densities are 0.78 T and 0.94 T, respectively, as shown in Figure 6.  
The EMF waveforms and harmonic components' distribution for the two different structures are shown in Figure 7. The fundamental component values of the EMFs for the two structures are 108 V and 116 V, respectively, and the effective values are 76 V and 82 V; the optimized structure is the higher one. The total harmonic distortion (THD) contents of the EMF waveforms are calculated, and the original one is 12.3%, while the optimized one is 3.4%, and the corresponding waveforms are closer to the ideal sinusoidal waveform.  
It can be seen from above that the optimized structure has a larger amplitude of the air-gap magnetic density and the EMFs fundamental component compared to the original structure, which can improve the output torque capability and torque density.
# Toxins of Prokaryotic Toxin-Antitoxin Systems with Sequence-Specific Endoribonuclease Activity
## 3 Physiological Roles
### Cell Death Pathways (DNA Replication Related)  
Starvation for thymine induces a specific type of cell death, called thymine-less death (TLD) [111]. While TLD was discovered over 60 years ago, the exact molecular mechanism behind TLD remains unknown. The lack of thymine in media induces DNA damage and DNA structural abnormalities [112; 113; 99; 114]. The continuation of RNA and protein synthesis, despite the cessation of DNA synthesis, also seems to be crucial for TLD, as the inhibition of RNA and protein synthesis rescues cells from undergoing TLD [99]. [PERSON] and colleagues suggested that the lack of thymine can induce a RecA/LexA-dependent SOS response [115]. Later, [PERSON] and colleagues proposed that depending on the severity of DNA damage, RecA/LexA can mediate an apoptosis like death (ALD) or the SOS-response [116].  
The exact role of TA systems on TLD also remains unclear. One study showed that during the growth in thymine-less media, the transcription of the _mazEF_ module became diminished, which led to the activation of genes such _as yfbU, ygcR, cdpP, slyD_ and _yfbD_[117]. Conversely, [PERSON] et al. showed that the transcriptional level of _mazEF_ increased in absence of thymine [116]. Additionally, while [PERSON] and colleagues showed an inhibition of translation rescues TLD, [PERSON]'s model suggested that the induction of nucleases led to cell death. Further investigation is required to decipher the exact mechanisms and physiological roles of each pathway.  
Replication arrest, caused by hydroxyurea (HU), induces DNA-damage independent cell death [118; 119]. HU inhibits class I ribonucleoreductase, which is required for the formation of deoxyribonucleotide triphosphate (dNTPs). Depletion of dNTPs results in the inhibition of DNA replication [119]. HU also upregulates iron uptake genes, which leads to increased hydroxyl radicals through the Fenton reaction [120]. The increased formation of hydroxyl radicals is crucial for HU-induced cell death in _E. coli_[120]. The expression of TA toxins is upregulated in HU-treated cells [121]. _mazEF_ is important in HU-induced cell death, as the deletion of _mazEF_ increased the cells' resistance to HU [120]. Furthermore, other TA systems, such as _relBE_, _mqsR_ and _higAB_, are also postulated to be important in HU-induced cell death [120]. [PERSON] et al. proposed that increased iron uptake and an increased membrane stress induced by MazF/RelE action are both required for hydroxyl radical-induced cell death [120].
## 3 Numerical Modeling
### Static Loading Conditions  
In this study, the bridge numerical models are subjected to three different static loadings. The static loading condition is a simple condition wherein the vertical load (dead load, live load) is assumed stationary and applied uniformly on top of the bridge and abutment. Case A loading considers the equivalent bridge girder total dead loads (167 kPa) and the approach backfill total dead loads (18 kPa) at the end of bridge construction. Case B loading considers the presence of the general traffic loads after the bridge construction. The general traffic loadings are equal to the live loads of typical vehicles and trucks in addition to the bridge girder and approach backfill total dead loads. The bridge's total dead load, including general traffic live load, is 217 kPa, and the approach backfill total dead load, including general traffic live load, is 30 kPa. Lastly, Case C loading considers the presence of the railway loads after the bridge construction. The railway loadings are equal to the rail track total dead loads and train live loads, in addition to the bridge girder and approach backfill total dead loads. The bridge's total dead load, including rail track total dead load and train live load, is 250 kPa and the approach backfill total dead load, including rail track total dead load and train live load, is 68 kPa [17]. For the calculation of static loadings, Plastic calculation type and Elastoplastic drained analysis are used wherein soil consolidation was not considered. Hence, the time of construction does not have substantial effects on the behavior of the numerical models. During phase construction, the pressure from the previous phase is carried until the completion of the structure.
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 3 Results
### Seismic Data Standards
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt}} \hline \hline
**Section** & **Seismic Data Standards** \\ \hline
**Article** & A31,A32,A33,A34,A35,A36,A37,A38 \\ \hline
**Focus** & FDSN, SEED standard. Global Seismographic Network (GSN). Metadata. Adaptable Seismic Data Format (ASDF). \\ \hline \multirow{6}{*}{**Methods used in experimental analysis**} & \multirow{3}{*}{**Mechanism**} & Control headers (ASCII) volume identifier headers, abbreviation dictionary headers, station headers, and time span headers. Large data set for managing metadata. ASDF uses C/Fortran and Python-based APIs coupling to SPECFEM3D GLOBE and ObsPy toolkits. \\ \cline{2-3}  & & Conversion protocol, compression protocol. Comparison of: SAC, miniSEED, SEG Y, PH5. ASDF libraries for reading, writing, conversion, and visualization. \\ \cline{2-3}  & \multirow{3}{*}{**Fomats**} & SEED, telemetry volume format. Time series data and related metadata. SEED, FDSN, StationXML, dataless SEED, ExtStationXML. Standards such as QuakeML, StationXML, W3C PROV, and HDF5 must resolve efficiency, data organization, data exchange, reproducibility, mining, and visualization and understanding of data. \\ \cline{2-3}  & \multirow{3}{*}{**Topologies**} & Seismic monitoring networks. These include configuration, firmware, sensor status, calibration parameters, installation information. The HDF5 format allows efficient and parallel I/O operations, integrates compression algorithms, and checks sums to guard against data corruption. \\ \hline
**Application Area** & SEED provides a special Telemetry Blockette, transmission of only the newest data. Permanent network globally distributed. The Station Information System (SIS) provides a valuable bridge between field equipment and the end user’s metadata. Inclusion of comprehensive meta information. \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Seismic Data Standards—Results. ties [43]. Additionally, current studies pretend to include comprehensive metainformation (event or station information) in the seismic files that could support standards such as Quake Markup Language (QuakeML), StationXML, W3C PROV, and HDF5. Moreover, it is suggested that applications should be developed on Python, and tools coupled to ObsPy.
## Materials and Methods  
The two study sites are both located in the state of Texas, in south central U.S. (Figure 1). The South Texas site is situated between the Edwards Plateau to the northwest and the Gulf Coastal Plains to the southeast, and 200 km from the Gulf of Mexico. It is predominantly rural and consists of nine sub-basins, totaling an area of 23,625 km\({}^{2}\) located in four major river basins: Nueces, San Antonio, Guadalupe, and San Antonio-Nueces. The climate is transitional humid subtropical, and the elevation is approximately 213 m above sea level. Vegetation is mostly oak and cedar woodland, grassland savanna, thorny brush, and riparian woodland. Most of the western half of the study area is part of the South Texas Plains, which stretch from the edges of the Hill Country into the subtropical regions of the Lower Rio Grande valley. The area is also prone to occasional severe weather from tornadoes and tropical cyclones and is prone to flooding. The average high temperature is 35 \({}^{\circ}\)C for both July and August, while the average low is 17 \({}^{\circ}\)C for December and January. The average annual precipitation for San Antonio is 836 mm. On average, the wettest months are May (119 mm) and June (109 mm) while the driest are January (41 mm) and February (43 mm).  
The second study site is centered on the city of Houston in east Texas, and as such is actively irrigated and managed. The site has a total area of 22,000 km\({}^{2}\) and consists of ten sub-basins located in four major river basins: San Jacinto, Trinity, Neches, and Neches-Trinity. The climate is humid subtropical, with mixed forested and temperate prairie grasslands. Generally, elevation rises around 25 cm per km, but coastal subsidence has caused the elevation to drop 3 m or more in some areas. Subsidence is caused by historical use of ground water and the extensive extraction of oil and natural  
Figure 1: Study Sites: (**left**) South Texas, (**right**) Houston.  
gas. Lakes, rivers, and a large network of bayous and manmade canals make up the surface water. Occasional severe weather includes flooding, tornadoes, and tropical cyclones. The average high temperature in July and August is around 34 \({}^{\circ}\)C while in December and January it is 17 \({}^{\circ}\)C. The normal annual precipitation for Houston is 1150 mm. On average, the wettest months are May (113 mm) and July (132 mm), while the driest, February (81 mm) and March (61 mm).
## 4 Materials and Methods
### Single-Cell RNA Sequencing (scRNA-seq) Analysis  
For all samples, cell suspensions were collected and processed for single-cell isolation and RNA library preparation. Approximately 10,000 cells per sample were captured and used for library construction using the 10\(\times\) Genomics Chromium Fixed RNA (Flex) Human Transcriptome Kit (PN-1000475, Genomics Chromium, Shirley, NY, USA). Library preparation was performed as per the manufacturer's protocol. Sequencing was performed on the NovaSeq X Plus (Illumina, San Diego, CA, USA). The raw data (i.e., reads in FASTQ files) were mapped using CellRanger 8.0.0 referencing to the GRCh38-2024-A probe set. The expression matrices were imported into MATLAB (R2024b) scGEAToolbox (v24.1) (RRID-SCR_001622) [41] and R Seurat package [42] for subsequent data analysis. Stringent quality control filtering was applied to each sample requiring a minimum of 1000 reads per cell, a maximum mitochondrial DNA ratio per cell of 15%, a minimum of 15 non-zero cells per gene and a minimum of 500 non-zero genes per cell. DecontX [43] was applied to remove ambient RNA contamination. To plot the expression profile for individual genes, average gene expression values were imported into Prism software as a column data table, and non-parametric one-way ANOVA was used to compare between groups. The differential expression (DE) analysis was conducted using a non-parametric Wilcoxon rank sum test. Comparisons were made between the control and each of the treatment groups: DIM-3,5-CI\({}_{2}\), siNR4A1, siNR4A2 and siNR4A1 and siNR4A2 (combined). Functional enrichment tests were conducted using the web interface of Enrichr [44] for the top 500 differentially expressed genes (ranked according to the \(p\) values).
## 5 Conclusions  
1. In response to the operational needs of the seedling belt-style sweet potato transplanter and the agronomic requirements for planting bare sweet potato seedlings, this paper uses LDPE film as the base material to design a new type of \"bell-mouth\" staggered seedling belt structure. Utilizing the heat sealing properties of LDPE film, sweet potato seedlings are fixed between films. The structural feature of this design is that it requires minimal force to separate the seedling belts, which facilitates smooth peeling.
2. Considering the fixation state of sweet potato seedlings in the seedling belt, a force analysis was conducted on the bonding points of the belt. Calculations revealed thatwhen the peel force at the bonding points is 13.2 N, the seedlings can be stably fixed. In single-factor experiments, the individual effects of each factor on the peel strength were examined, and the results showed that temperature, time, and pressure all have significant impacts on the peel strength. Comparing the separation states of seedling belts under different peel strengths, when the peel strength, \(W\), is around 13 N, the bonding points exhibit the best peeling state at this strength. The bonding points can peel smoothly without tearing the film, thus demonstrating that the optimal heat sealing effect of the seedling belt is achieved at a peel strength of 13.2 N.
3. The reasonable value ranges of influencing factors were determined through single-factor experiments. On this basis, a three-factor, three-level orthogonal experiment was conducted. The results indicated that the order of significance of each factor's effect on heat sealing strength is \(A\), \(B\), and \(C\). Through a comprehensive range and variance analysis, the optimal parameter combination was determined to be \(A_{2}B_{3}C_{3}\), that is, a heat sealing temperature of 140 \({}^{\circ}\)C, time of 1.2 s, and pressure of 0.4 MPa. Under these parameters, the experiment was repeated ten times, and the average peel strength of the seedling belt obtained after testing was 14.31 N, with the peeling state of the heat-sealed points meeting expectations.  
Conceptualization, Y.W. and K.L.; methodology, M.H.; software, K.L.; validation, B.Q. and Y.J.; formal analysis, M.H.; investigation, K.L.; resources, W.Z.; data curation, Y.J.; writing--original draft preparation, Y.W.; writing--review and editing, W.Y.; visualization, Y.J.; supervision, W.Z.; project administration, B.Q.; funding acquisition, W.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Key R&D Plan of Jiangsu Province (Grant No. BE2021311), the Jiangsu Province Agricultural Science and Technology Independent Innovation Fund (Grant No. CX(22)3094) and the China Agriculture Research System of MOF and MARA (Grant No. CARS-10-B19).  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors declare no conflict of interest.
## 3 Proposed Model
### Temporal Encoding Layer  
The temporal encoding layer consists of multiple layers of GRU. Its input is the entity embedding sequences at different time granularities, which incorporate attribute and high-order structural information. Controlled by the time window, it models the short-term, medium-term, and long-term historical information of entities separately. Finally, the outputs of GRUs at different granularities are concatenated and passed to the next layer. The multi-granularity temporal features provide abundant temporal information for TKGC tasks and help improve the model's prediction accuracy:  
\[\mathbf{H}_{i,t}^{e}=GRU\big{(}\mathbf{h}_{i,t-N_{1}},\mathbf{h}_{i,t-N_{1}+1},\ldots,\mathbf{h }_{i,t-1}\big{)}, \tag{7}\]  
\[\mathbf{H}_{i,t}^{m}=GRU\big{(}\mathbf{h}_{i,t-N_{2}},\mathbf{h}_{i,t-N_{2}+1},\ldots,\mathbf{h }_{i,t-1}\big{)}, \tag{8}\]  
\[\mathbf{H}_{i,t}^{l}=GRU\big{(}\mathbf{h}_{i,t-N_{2}},\mathbf{h}_{i,t-N_{3}+1},\ldots,\mathbf{h }_{i,t-1}\big{)}, \tag{9}\]  
\[\mathbf{\hat{H}}_{i,t}=\lambda_{1}\mathbf{H}_{i,t}^{e}+\lambda_{2}\mathbf{H}_{i,t}^{m}+ \lambda_{3}\mathbf{H}_{i,t}^{l}, \tag{10}\]  
where \(\mathbf{H}_{i,t}^{e}\in\mathbb{R}^{|\mathcal{E}|\times d}\), \(\mathbf{H}_{i,t}^{m}\in\mathbb{R}^{|\mathcal{E}|\times d}\) and \(\mathbf{H}_{i,t}^{l}\in\mathbb{R}^{|\mathcal{E}|\times d}\) denote the temporal aggregation generated from short-term, medium-term and long-term historical information, respectively. \(|\mathcal{E}|\) denote the number of entities and \(N_{1}<N_{2}<N_{3}\) represents the time window size, which is used to regulate the timescale of the input. \(\mathbf{\hat{H}}_{i,t}\in\mathbb{R}^{|\mathcal{E}|\times d}\) is a weighted sum of \(\mathbf{H}_{i,t}^{m}\), \(\mathbf{H}_{i,t}^{l}\) and \(\mathbf{H}_{i,t}^{l}\), controlled by predefined weighting coefficients \(\lambda_{1}\), \(\lambda_{2}\) and \(\lambda_{3}\), respectively, and represents the final output of the temporal coding layer.
# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Issues on Image-Based SHM
#### 4.4.2 Broad-Scale of Natural Frequencies to Be Utilized  
In general, SHM, structural engineers, and building managers may lack knowledge in collecting, classifying, and interpreting the inherent frequency of various materials or components. This is caused by the complexity of material behavior, the connection between other materials with the same and varying compositions, and the effects of different loading combinations. Loads may be predicted, but in practice, designing is limited to the conservative measures considered. Tracking the tensions of stay cables, which transmit the primary loads acting on the girder to the pylon, is often the most important aspect of vibration monitoring and dynamic feature identification for bridges [77]. This issue to adequately capture cable vibration measurements is conceivable using digital photogrammetry approaches that establish the geometric transition between the motion trajectory in the object space and that in the picture plane [78]. Now, as records of vibration are fittingly measured in the form of videos, the image processing procedure follows as taken from the works of [77]. First, one must separate colored digital images for different time instants and convert them from RGB to greyscale images. Then, an assigned threshold is taken from the converted binary images. In this stage, a boundary target for each binary image is set [78]. In the final step, the coordinates of a border's centroid for each binary image are calculated, and the displacement time history of cable vibration is established depending on where the target center was at various points in time. This method may be used in many situations where the materials and structural components have a history of displacement through time. Other tension, compression, and flexural-induced members may also use it; cables are not its only possible application.  
4.3 Difficulty in Securing High-Quality Devices, Use of Advanced Types of Sensors, Determining Enough Quantities of Sensors for a Strategic Location  
It is vital to implement sensors in most critical areas where damage and failure are highly expected to occur and manifest abruptly or through time. Low-cost Internet of Things platforms for SHM are the best solution for this problem. In order to detect an unusual change in the values of the monitored parameters and spikes in the values of the monitored parameters, SHM can be performed by integrating a temperature sensor, geophone sensor, pressure sensor, turbidity sensor, crack width sensor, gyroscope plus accelerometer sensor, strain sensor, temperature, and humidity sensor [79]. Technology integration [80] using the combined application of the interferometric survey of structures (IBIS-S) sensor and system and the high-frequency thermal (FLIR) camera to study the dynamic behavior can serve as references to increase reliability to estimate modal shapes that are closest to laboratory and actual field measurements useful for engineers [80; 81; 82].
# A Survey of Visual SLAM Based on RGB-D Images Using Deep Learning and Comparative Study for VOE
###### Abstract  
Visual simultaneous localization and mapping (Visual SLAM) based on RGB-D image data includes two main tasks: One is to build an environment map, and the other is to simultaneously track the position and movement of visual odometry estimation (VOE). Visual SLAM and VOE are used in many applications, such as robot systems, autonomous mobile robots, assistance systems for the blind, human-machine interaction, industry, etc. To solve the computer vision problems in Visual SLAM and VOE from RGB-D images, deep learning (DL) is an approach that gives very convincing results. This manuscript examines the results, advantages, difficulties, and challenges of the problem of Visual SLAM and VOE based on DL. In this paper, the taxonomy is proposed to conduct a complete survey based on three methods to construct Visual SLAM and VOE from RGB-D images (1) using DL for the modules of the Visual SLAM and VOE systems; (2) using DL to supplement the modules of Visual SLAM and VOE systems; and (3) using end-to-end DL to build Visual SLAM and VOE systems. The 220 scientific publications on Visual SLAM, VOE, and related issues were surveyed. The studies were surveyed based on the order of methods, datasets, evaluation measures, and detailed results. In particular, studies on using DL to build Visual SLAM and VOE systems have analyzed the challenges, advantages, and disadvantages. We also proposed and published the TQU-SLAM benchmark dataset, and a comparative study on fine-tuning the VOE model using a Multi-Layer Fusion network (MLF-VO) framework was performed. The comparison results of VOE on the TQU-SLAM benchmark dataset range from 16.97 m to 57.61 m. This is a huge error compared to the VOE methods on the KITTI, TUM RGB-D SLAM, and ICL-NUIM datasets. Therefore, the dataset we publish is very challenging, especially in the opposite direction (OP-D) when collecting and annotation data. The results of the comparative study are also presented in detail and available.  
Visual SLAM; visual odometry estimation (VOE); deep learning (DL); RGB-D images; TQU-SLAM benchmark dataset; comparative study