esa-sceva/satcom-chunk-collection huggingface dataset:
# The Simulation of Dielectric Barrier Discharge for Breakdown Voltage in Starch Modification
## 2 Modeling and Simulation Methods  
This study solved electric fields with COMSOL Multiphysics 5.3a software, using models designed based on various situations. COMSOL is used to simulate the distribution of the electric field within the gap between two electrodes using the finite element method (FEM) concept. Recently, complex electric field problems, particularly for non-uniform electric fields, have been simulated using this software. The model consists of two circular electrode plates, each 5 mm thick and with a radius of 4 cm, and quartz as the dielectric placed on the bottom electrode, as shown in Figure 1. The gap spacing changed between 0.01 and 1 cm at 760 torr of pressure.  
Before using the model to simulate each condition, it is necessary to identify the optimal mesh type for solving the partial differential equations. Consequently, nine mesh types of COMSOL, from extremely coarse to extremely fine, were examined to ensure that the simulation results were sufficiently mesh-independent. The optimal mesh type is crucial for enhancing the analysis's accuracy. The optimal mesh type was frequently determined using the grid independence test [42; 43].  
Voltage was applied to the high-voltage electrode. One of the key findings from the simulation was the electric field between electrodes. If the electric field was not constant, the electric field value on the left-hand side of Equation (9) could be substituted and compared with the value of the right-hand side. This voltage was a breakdown voltage if the value was equal to the value on the right part. After that, the Paschen curves were obtained for various studies by graphing breakdown voltage with the product of pressure and gap distance (_pd_).
## 5 Results
### Comparative Experiment of Different Models  
To further verify the effectiveness of LTSCD-YOLO, we conducted comparative experiments with other mainstream object detection algorithms, including the original models  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Group** & **A1** & **A2** & **A3** & **A4** & **A5** & **[EMAIL_ADDRESS]/\%** & **Params/10\({}^{6}\)** & **GFLOPs/G** & **FPS/(frame-s\({}^{-1}\))** \\ \hline
1 & — & — & — & — & — & 93.1 & 11.14 & 28.8 & 98.04 \\
2 & \(\surd\) & — & — & — & — & 92.8 & 6.53 & 17.3 & 96.15 \\
3 & — & \(\surd\) & — & — & — & 93.2 & 7.26 & 23.2 & 107.53 \\
4 & — & — & \(\surd\) & — & — & 93.8 & 7.59 & 19.7 & 104.17 \\
5 & — & — & — & \(\surd\) & — & 94.3 & 11.14 & 28.8 & 119.05 \\
6 & — & — & — & — & \(\surd\) & 93.5 & 15.82 & 32.9 & 98.75 \\
7 & — & \(\surd\) & — & — & \(\surd\) & 93.7 & 7.05 & 21.9 & 103.09 \\
8 & \(\surd\) & \(\surd\) & — & — & — & 93.0 & 2.90 & 12.3 & 103.74 \\
9 & \(\surd\) & \(\surd\) & \(\surd\) & — & — & 93.4 & 2.63 & 11.0 & 108.15 \\
10 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & — & 94.0 & 2.63 & 11.0 & 113.35 \\
11 & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & 94.5 & 2.40 & 9.8 & 115.35 \\ \hline \hline \end{tabular}  
* A1 represents EfficientNet_B0, A2 represents CCFM; A3 represents F-C2f; 4 represents Focal-EIoU; A5 represents RepHead. Group 1 is the baseline model using CSPDarknet, without any modifications.  
\end{table}
Table 4: Results of the ablation experiment.  
and improved models of the YOLO series as well as the DETR model, such as YOLOv3-tiny, YOLOv4-CSP, YOLOv5s, YOLOv6n, YOLOv7-tiny, YOLOv7, YOLOv8s, YOLOv8s-ghost, YOLOv9t, YOLOv9s, YOLOv10s, and DETR [41]. The results are shown in Table 5.  
According to the data in Table 5, compared with the YOLO series and the DETR series, the LTSCD-YOLO algorithm proposed in this paper achieves the highest precision (95.7%) and mAP50 (94.5%) with the smallest parameter and computational load, and has the second fastest detection speed. Although YOLOv3-tiny has the fastest detection speed, its precision, recall rate, and mAP50 are respectively 7.31%, 5.56%, and 6.14% lower than those of LTSCD-YOLO, and it has a larger parameter and computational load, making its overall performance inferior to LTSCD-YOLO. Although YOLOv9s achieves the highest recall rate of 89.9%, its detection speed is slower, and both the parameter count and computational load are much higher compared to LTSCD-YOLO, resulting in overall performance that is inferior to LTSCD-YOLO. Compared to LTSCD-YOLO, the DETR series has a much larger parameter count and computational load and slower detection speed, making it unsuitable for deployment in resource-limited space environments. The results indicate that LTSCD-YOLO surpasses the other algorithms in the table in overall performance, especially due to its small parameter count and computational load, high precision, and fast detection speed, making it highly suitable for detection tasks in space environments.
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 3 Methods
### Semantic Extraction Based on Bidirectional Long-Short Memory Network  
The core of the sentence representation method is the design of the representation network. Sentence representation based on the neural network has gradually become the main method in sentence representation. Although the recurrent neural network can retain the information between adjacent words, it cannot learn the information between the words far away in the sentence. The emergence of long-short term memory (LSTM) solves the problem that the recurrent neural networks cannot learn long-term information. The LSTM and RNN both have the chain shape of repetitive neural network modules. The difference is that the unit modules are different. As shown in Figure 1, three gates are added to the LSTM unit model: input gate, forgetting gate, and output gate.  
Figure 1: Schematic diagram of long short-term memory network unit module.  
LSTM first determines what information should be discarded at time \(t\) through the forgetting gate, then determines what information should be saved at time \(t\) through the input layer, then updates the temporary information according to the information of input gate and forgetting gate, and finally outputs the result of time t and the hidden layer state \(h_{t}\). The hidden layer state \(h_{t}\) will serve as input to the next neuron. Suppose that the input at time \(t\) is \(x_{t}\). The temporary information is \(C_{t}\). The output is \(o_{t}\). The calculation formula of each door is shown in Formulas (1)-(6).  
(1) Forget gate  
\[f_{t}=\sigma\Big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\Big{)} \tag{1}\]  
(2) Input gate  
\[i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\widetilde{C}_{t}=tanh(W_{C}[h_{t-1},x_{t}]+b_{C}) \tag{3}\]  
(3) Output gate  
\[C_{t}=f_{t}*C_{t-1}+i_{t}*\widetilde{C}_{t} \tag{4}\]  
\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[h_{t}=o_{t}*tanh(C_{t}) \tag{6}\]  
Among them, \(W_{f},W_{i},W_{o}\), and \(W_{C}\) are the weight parameter of each gate in the unit module; B is the weight parameter of each gate in the unit module; \(b_{f}\), \(b_{i}\), \(b_{o}\), and \(b_{C}\) are the corresponding bias variables; \(\sigma\) and \(tanh\) are the activation functions of the network; \(h_{t-1}\) and \(C_{t-1}\) were the state of hidden layer and cell at the last moment; \(\widetilde{and}\)\(C_{t}\) represents the information that should be updated in the cell at time \(t\).  
The disadvantage of LSTM in sentence representation modeling is that it only considers single direction word order when more fine-grained classification is needed.  
Bi-directional long-short memory network (BiLSTM) considers the hidden layer state \(h_{t}=[h_{Lt},h_{Rt}]\) of two directions of LSTM at the same time and solves the problem of bidirectional semantic dependency. The structure of BiLSTM is shown in Figure 2. The forward and backward network structures can be consistent or different from each other.  
Figure 2: Operation diagram of the bidirectional long-short memory network.
# Exploring the Data Efficiency of Cross-Lingual Post-Training in Pretrained Language Models
## 1 Introduction
Evaluating our method on four challenging Korean natural language understanding tasks, we find that cross-lingual post-training is extremely effective at increasing data efficiency. Compared to training a language model from scratch, data efficiency improvement of up to 32 times was observed. Further, XPT outperformed or matched the performance of publicly available massively multilingual and monolingual PLMs while utilizing only a fraction of the data used to train those models.
## 3 SDVN Traffic Analysis and Prediction
Generally, when the precision is high, the recall tends to be low; conversely, when the recall is high, the precision often decreases. The precision and recall of the above five algorithms are shown in Figure 6:2  
As shown in Figure 11a,b, the precision rates of the sequential model neural network, XGBoost classification algorithm, random forest, logistic regression, and integrated model are 85.60%, 90.70%, 87.97%, 91.27%, and 86.67%, respectively, and the recall rates are 90.68%, 99.15%, 99.15%, 97.46%, and 99.15%, respectively. Among them, the logistic regression algorithm has the highest precision rate of 91.27%; the recall rate of XGB Classifier, random forest, and integrated model is the highest, which is 99.15%. Figure 6:3 The Recall Rate and Precision Rate of Five Algorithms. Next, the predictive values of the number of traffic occurrence instances on the test set are compared between the sequential model neural network algorithm and the integrated model, as shown in Figure 6:4  
Figure 12a,b describe the traffic prediction of the sequential model neural network algorithm and the ensemble algorithm on the test set, respectively. Comparing Figure 12a,b, it can be seen that the flow prediction on the C and D controllers is quite different. The predictive flow of the sequential model neural network for the C controller is 24 instances, and the predictive flow for the D controller is 18 instances. The prediction flow of the integrated algorithm for the C controller is 17 instances, and the prediction flow for the D controller is 20 instances. Network traffic prediction can provide a basis for judging the load balancing of a SDVN, and then provide a reference for the iterative update optimization of SDVN controller dynamic deployment. For the difference in traffic prediction results, we can further judge the prediction advantages and disadvantages of different algorithms when studying the dynamic deployment of SDVN controllers in the future.
# An Analytical Model for Interference Alignment in Broadcast Assisted VANETs
## 5 Simulations  
### Simulation for Clustering Algorithm  
Our simulation scenario is shown in Figure 8, including 7 vertical streets and 3 horizontal streets in this scenario. Vehicles are generated by SUMO [13]. Simulation area is 500 m in width and 2000 m in length. There is one base station in this area. The time slots of each scheduling frame is set to 20. Gurobi is chosen to be the solver for the optimization [26].  
Clustering results based on RSSI and location are shown in Figure 9. The RSSI based clustering vehicles are grouped into ring-shaped area which is obviously not suitable for our scenarios since it does not take buildings into consideration.  
The clustering result based on \(\sigma\)-clustering algorithm is shown in Figure 10. Vehicles from the same intersection or blocked by the same building are grouped into the same cluster, which is more reasonable.  
Figure 8: Base Station and Clusters (\(H_{i}\) indicates cluster of vehicles with similar CSI).  
Figure 9: Clustering According to RSSI.
## 4 Methods  
### Sentinel-2  
The S2 data were obtained from the GEE. In particular, the collection COPERNICUS/S2_SR was used. A self-developed algorithm performed in the GEE was adopted to create the median composites. The S2 composite stack included bands, spectral indices and standard deviations. These input parameters are reported in Table 3. The S2 stack, including the DTM aspect and slope, was adopted as the input data during the classification while the S1 output layers served to better refine the urban and water classes. Each composite image was generated starting with the EO data available every 10 days for the period from 1 May 2020 to 30 September 2020 (t), i.e., the summer weather season, in order to correctly map the glacial surface of the territory falling within the ablation period and observe the vegetation during the phenological active season. It is worth noting that the generated composite images consisted of the median value for each pixel in the reference period t. For S2, we considered all the images that satisfied the condition in which each pixel had cloud cover equal to zero (the clouds and shadows were suitably masked and the pixel, if cloudy, was considered in the definition of the median value of the reflectance of each band). The S2 input data were reported in Table 3 as the input dataset for the k-nearest neighbor supervised classification considering all the classes. The input dataset was normalized. \begin{table}
\begin{tabular}{c c c} \hline
**ID** & **Bands/Index** & **Description** \\ \hline
1 & “B2” & Blue \\
2 & “B3” & Green \\
3 & “B4” & Red \\
4 & “B5” & Vegetation Red Edge 1 \\
5 & “B6” & Vegetation Red Edge 2 \\
6 & “B7” & Vegetation Red Edge 3 \\
7 & “B8” & NIR \\ \hline \end{tabular}
\end{table} 
Table 3: S2 input datasets. The spectral indexes reported in Table 3 were calculated as follows using the S2 coefficient reported in ([[https://www.indexdatabase.de](https://www.indexdatabase.de)]([https://www.indexdatabase.de](https://www.indexdatabase.de)), last access 7 November 2022):  
NDVI Normalized Difference Vegetation Index [45; 46; 47; 48; 49]  
\[\text{NDVI}=\frac{\text{NIR}-\text{RED}}{\text{NIR}+\text{RED}}\]  
BSI Bare Soil Index [50]  
\[\text{BSI}=\frac{(\text{SWIR}\ 1+\text{RED})-(\text{NIR}+\text{BLUE})}{(\text{ SWIR}\ 1+\text{RED})+(\text{NIR}+\text{BLUE})}\]  
NDWI Normalized Difference Water Index [51; 52]  
NDWI \[\text{NDWI}=\frac{\text{NIR}-\text{SWIR}\ 1}{\text{NIR}+\text{SWIR}\ 1}\]  
NDSI Normalized Difference Snow Index [53; 54; 55; 56]  
NDSI \[\text{NDSI}=\frac{\text{NIR}-\text{SWIR}\ 1}{\text{NIR}+\text{SWIR}\ 1}\]  
TCB (Tasseled Cap Brightness) [57; 58; 59; 60]
# Performance Evaluation of LoRa Communications in Harsh Industrial Environments
## 3 LoRa, Channel and Noise  
### LoRa: Modulator and Demodulator  
In this sub-section, we will present the LoRa modulator and demodulator that we used in the simulations. This presentation is based on the research done in the paper [36], where the authors designed a LoRa emulator.
# Adsorption of Coxsackievirus in Sediments: Influencing Factors, Kinetics, and Isotherm Modeling
## 2 Material and Methods
### Virus Adsorption Batch Experiment  
Virus adsorption batch experiments were conducted using the standardized batch equilibrium method under controlled laboratory conditions. In every experiment, 0.3 g of sediment particles were added to 12 plastic centrifuge tubes, each with a 4 mL volume. Following that, 1 mL of virus suspension at a specific concentration was added to each plastic centrifuge tube. Subsequently, the tubes were placed on a temperature-controlled shaker and agitated at a speed of 200 rpm. At specific time points (0, 5, 10, 15, 20, 30, 45, 60, 75, 90, 100, and 120 min), one centrifuge tube was randomly selected, and the supernatant was carefully collected. To assess the viral suspension activity, the supernatant underwent filtration using a 0.45 \(\upmu\)m aqueous filter. Three replicates were conducted at each time point, and the average value was calculated. To minimize experimental error, additional plastic centrifuge tubes with only 1 mL of viral suspension were utilized as blank controls. Table 1 presents a comprehensive overview of the detailed experimental scheme to facilitate data analysis. A control experiment of virus decay is first conducted in the study. In the control experiment, a 0.05 log reduction is observed within 120 min. Therefore, this study solely focuses on the deactivation attributed to adsorption.  
The adsorption decay curve of CA16 under different conditions was calculated using \(\log(C_{t}/C_{0})\), and Equation (1) was utilized to calculate the equilibrium adsorption capacity of CA16 on different sediment particles [44]:  
\[Q_{e}=\frac{(C_{0}-Ce)V}{W} \tag{1}\]  
where \(Q_{e}\) is the quantity of CA16 adsorbed by the medium at adsorption equilibrium (TCID5\({}_{0}\)/g); \(C_{0}\) is the initial viral activity titer of CA16 (TCID\({}_{50}\)/mL); \(C_{t}\) is the activity titer of the virus at time t (TCID\({}_{50}\)/mL); \(C_{e}\) is the activity titer of the toxin at adsorption equilibrium (TCID\({}_{50}\)/mL); \(V\) is the volume of solution (mL); and \(W\) is the mass of adsorption medium (g).  
Figure 2: Virus titer determination experimental procedure. (**a**) TCID50 assay experimental process; (**b**) TCID50 assay results.
## 6 Data Processing and Analysis
### Identification Confidence and Confirmation
To overcome the lack of reference standards, [PERSON] et al. [23] proposed the organic synthesis of linear and cyclic oligomers derived from neopentyl glycol and IPA units, both in their native and deuterated forms. To achieve this, they employed a stepwise synthesis approach, carefully controlling cyclization through a challenging macrolatonization step as the final stage [81]. This strategy ensured the production of pure analytical batches while optimizing recovery yields and minimizing the waste of costly labeled precursors. The uncertainties introduced by surrogate-based quantification and matrix effects have direct implications for risk assessments. For instance, semi-quantitative estimates may underestimate or overestimate exposure levels. Standardized protocols for standards selection, such as prioritizing structurally homologous compounds or applying ionization efficiency corrections, could mitigate these errors. To improve comparability and transparency, minimum quality reporting standards should be followed, such as clearly indicating the confidence level of identification (e.g., Schymanski levels) and specifying whether quantification is based on reference or surrogate standards. In summary, while analytical standards are indispensable for precise NIAS confirmation and quantification, their limited availability drives reliance on semi-quantitative methods, introducing uncertainties that complicate risk assessments. Future advancements in standard availability, coupled with harmonized protocols for surrogate selection and matrix effect compensation, are critical to enhancing the accuracy and reliability of NIAS characterization in plastic FCMs.
## 4 Simulation Studies  
### Simulation Conditions  
Computer simulation was performed to show the effectiveness and design procedure of the double sky-hook algorithm with a quarter and a full car suspension dynamic model. In this simulation, in order to include nonlinear behavior of actual semi-active suspension systems, we considered the dead time from an electronic circuit and a variable damper valve action as well as the ramp up time from the damping force development after the valve was closed. Figure 3 shows these time delays and its model using MATLAB Simulink. In addition, we included nonlinear characteristics of the damper, as shown in Figure 4. In the simulation, we used irregular random road profiles classified by the ISO [22]. Two different vehicle speeds with different road conditions were considered as follows:  
**Case (a):** Grade A (Very Good), \(S_{g}(\Omega_{0})=4\) with 80 km/h  
**Case (b):** Grade B (Very Poor), \(S_{g}(\Omega_{0})=256\) with 10 km/hFor the above simulation conditions, we simulated each vehicle dynamics model with a passive, conventional sky-hook, and double sky-hook damper.
# Prediction Models for Diabetes in Children and Adolescents: A Review
## 2 Prediction Models of Type 1 Diabetes (T1D)
### Metabolic Markers as Risk Factors  
For advancing the prevention of T1D, the identification and widespread use of reliable metabolic markers is of essential importance. For T1D, traditional risk factors are poor glycemic control, hypertension, dyslipidemia, obesity, albuminuria, unhealthy diet, and physical inactivity [36]. Novel biomarkers, like chronic inflammation, also contribute to risk.
# The Method of Multi-Camera Layout in Motion Capture System for Diverse Small Animals
## 4 Discussion
### Scalability of Multi-Camera System  
In order to obtain a higher motion capture accuracy rate for small animals with great complexity and uncertainty, it is necessary to increase the number of cameras. Although this paper only conducted theoretical research and experimental verification on a unit modular consisting of 4 cameras, this system is theoretically extensible. According to the principle of 3-D imaging technology and geometrical optics, it can be known that a greater number of cameras can be regarded as a combination of some unit modules. When faced with captured objects that have a large body and a wide range of motion, or the objects that have a small volume but a large area of motion, it is necessary to ensure that the complete movement of the objects in the entire movement space is fully recorded. This can be achieved by increasing the number of cameras, because a multi-camera system composed of four cameras could accomplish the motion capture mode of \"scratch capture of specific areas\", that means the capture object could be captured by at least one camera unit module when it moves in a certain area. A multi-camera system composed of \(N\) (\(N>=4\)) cameras could be divided into some module units. The union of intersecting volumes of each unit module forms a motion capture sampling area. For example, to capture the movement of a mosquito whose motion range is much larger than its own size, eight cameras had been adopted using two half-ring layouts. When the captured objects enter a certain area, there are specific intersecting fields of view of four cameras to envelop them for effective capture [16]. Nowadays, the low-cost and high-efficiency of small-animal-oriented multi-camera systems have attracted more and more attention. Although it is very difficult to achieve high capture accuracy with a small number of cameras, a reasonable optimization of the camera layout is a priority, which could reduce the occlusion, ensure the scope of observation and obtain high-definition images by appropriately setting parameters such as the position and observation direction of each camera [4]. Therefore, multi-camera layout methods that we established based on the matching of the demand-boundary of animal movements and the capture characteristics of the multi-camera system have great universality, versatility and extensibility.