esa-sceva/satcom-chunk-collection huggingface dataset:
# Concurrent Transmission Based on Distributed Scheduling for Underwater Acoustic Networks
## Appendix A  
To simplify the expression, let \(t_{j}\) denotes \(T_{d_{j}s_{i}}^{cls}\), \(a_{j}\) denotes \(D_{d_{j}s_{i}}\), \(b\) denotes \(T_{0}\), \(l\) denotes \(L^{cls}\), where \(1\leq j\leq M\). Then \(t_{1}\), \(t_{2}\),..., \(t_{M}\) are \(M\) independent random variables that follow the uniform distributions of \(\mathrm{U}\!\left(a_{j},b+a_{j}\right)\). Therefore the point \((t_{1},t_{2},\ldots,t_{M})\) is located in a hypercube with the side length of \(b\) in \(M\)-dimensional space and has a probability density function of \(p(t_{1},t_{2},\ldots,t_{M})=\frac{1}{b^{M}}\). Collisions will occur when \(\left|t_{j}-t_{k}\right|<l\), where \(1\leq j\leq M\), \(1\leq k\leq M\) and \(j\
eq k\). Then, the hypercube is divided into two parts by the hyper-planes of \(t_{k}-t_{j}=l\) and \(t_{k}-t_{j}=-l\). Part I is the union of the regions in the hypercube that are sandwiched between \(t_{k}-t_{j}=l\) and \(t_{k}-t_{j}=-l\). When \((t_{1},t_{2},\ldots,t_{M})\) falls into this part, a collision will occur. Part II is the union of the regions in the hypercube that are outside \(t_{k}-t_{j}=l\) and \(t_{k}-t_{j}=-l\). \((t_{1},t_{2},\ldots,t_{M})\) belongs to this part, meaning it is collision-free. Figure 11 shows a section of these two parts on the \(t_{j}-o-t_{k}\) plane. The sectional area of part II in the \(t_{j}-o-t_{k}\) plane will reach its minimum when the diagonal of the cube overlaps with line \(t_{k}-t_{j}=0\), i.e., \(a_{j}=a_{k}\).  
Let \(\Phi^{\mathrm{I}}\) and \(\Phi^{\mathrm{II}}\) denote parts I and II of the hypercube, respectively. Then, the probability of no collision can be given by:  
\[P(no\,\text{collision})=\int\limits_{\Phi^{\mathrm{II}}}\ldots\int\frac{1}{b^{ M}}dt_{1}dt_{2}\ldots dt_{M}=1-\int\limits_{\Phi^{\mathrm{I}}}\ldots\int\frac{1}{b^{ M}}dt_{1}dt_{2}\ldots dt_{M} \tag{1}\]  
Because \(\Phi^{\mathrm{I}}\) and \(\Phi^{\mathrm{II}}\) are piecewise functions of \(a_{j}\), it is hard to obtain a closed expression for this probability. However, the probability of no collision will reach a minimum when \(a_{j}=A\), where\(1\leq j\leq M\) and A is a constant. If we ignore the overlapping parts among the sandwiched regions of \(t_{k}-t_{j}=l\) and \(t_{k}-t_{j}=-l\) in the hypercube, where \(1\leq j\leq M\), \(1\leq k\leq M\) and \(j\
eq k\), there will be  
\[\begin{array}{ll}P(\textit{no collision})&>1-\sum\limits_{1\leq j\leq M,j \
eq k}\left(\iint\limits_{\Phi^{1}_{jk}}\frac{1}{b^{2}}dt_{j}dt_{k}\underbrace{ \int_{A}^{A+b}\frac{1}{b}dt_{1}\cdots\int_{A}^{A+b}\frac{1}{b}dt_{l}\cdots\int_ {A}^{A+b}\frac{1}{b}dt_{M}}_{1\leq l\leq M,l\
eq j,l\
eq k}\right)\\ &=1-\sum\limits_{1\leq j\leq M,j\
eq k}\frac{2b-l^{2}}{b^{2}}\\ &=1-C_{M}^{2}\left(\frac{2l}{b}-\frac{l^{2}}{b^{2}}\right)\\ &=1-\frac{M!}{(M-2)!2!}\left(\frac{2l}{b}-\frac{l^{2}}{b^{2}}\right)\\ &=1-M(M-1)\left(\frac{l}{b}-\frac{l^{2}}{2b^{2}}\right)\end{array} \tag{10}\]  
where \(\Phi^{1}_{jk}\) denote the sectional region of part I in the \(t_{j}-o-t_{k}\) plane. Equation (10) gives an approximate lower bound for the no-collision probability at \(s_{i}\).
# Urban Road Collapse Risk Assessment Study Based on InSAR Spatiotemporal Data +
## 3 Assessment of Road Collapse Risk
### Model Training Parameters  
This study employs Python 3.7 as the programming language and Pytorch 1.10.2 as the deep learning framework. The network training and testing environment is based on Windows 10, with an Intel(R) Xeon(R) Silver 4114 CPU @ 2.20 GHz and 2.19 GHz (two processors) and 32 GB of memory. The model training is conducted using an NVIDIA Quadro P5000 graphics processing unit (GPU).  
Given that the assessment of road collapse risk is inherently a binary classification task, distinguishing data points as either collapse or non-collapse states, this study employs the binary cross-entropy loss function during the network training process:  
\[L(y,\hat{y})=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}log(\hat{y}_{i})+(1-y_{i})log(1- \hat{y}_{i})]\]  
where \(N\) represents the total number of samples; \(y_{i}\) denotes the actual label of the \(i\)-th sample, taking a value of either 0 or 1; and \(\hat{y}\) indicates the probability predicted by the model that the \(i\)-th sample belongs to the positive class. \(L\) signifies the average loss over the entire dataset.  
During the model network training process, the evaluation dataset constructed in this study is divided into a training set and a test set in an 8:2 ratio, which are used for model training and performance assessment, respectively. Throughout the training phase, the initial learning rate is set to 1 \(\times\) 10\({}^{-4}\), with a batch size of 64, and exponential decay rates \(\beta_{1}\) and \(\beta_{2}\) are set to 0.9 and 0.999, respectively. The network employs the cross-entropy loss function to evaluate the network loss and selects the Adam optimization algorithm to optimize the loss function.
## 4 Simulation Evaluation
### Evaluation of SC-Sched
#### 4.2.2 Evaluation of Using Multiple Channels  
Here, we evaluate the effect of using multiple channels. Table 5 shows the results of three metrics and the reduction ratio of data collection time. The reduction ratio is defined as \(1-r\), where \(r\) is the ratio of data collection time with multiple channels to that without multiple channels.  
By using multiple channels, the data collection time is reduced by over 31% irrespective of the number of nodes. Currently, one channel is used for WuCs and another channel is used for data frames. In the future, we plan to further reduce data collection time by increasing the number of channels used for data communication.  
\begin{table}
\begin{tabular}{c|c|c|c c c} \hline \hline \multicolumn{2}{c|}{**Number of WuC**} & \multicolumn{1}{c|}{**Number of**} & \multicolumn{1}{c}{**Frame Delivery**} & \multicolumn{1}{c}{**Total Energy**} & \multicolumn{1}{c}{**Data Collection**} \\ \multicolumn{2}{c|}{**Transmissions**} & \multicolumn{1}{c|}{**Retransmission Slots**} & \multicolumn{1}{c}{**Ratio (\%)**} & \multicolumn{1}{c}{**Consumption (J)**} & \multicolumn{1}{c}{**Time (s)**} \\ \hline \multirow{8}{*}{Fixed} & 4 & 1 & 95.5 & 1.82 & 35.0 \\ \cline{2-6}  & 4 & 2 & 96.7 & 1.86 & 37.9 \\ \cline{2-6}  & 4 & 3 & 96.8 & 1.90 & 40.8 \\ \cline{2-6}  & 5 & 1 & 96.7 & 1.89 & 39.7 \\ \cline{2-6}  & 5 & 2 & 98.0 & 1.96 & 42.6 \\ \cline{2-6}  & 5 & 3 & 98.3 & 2.00 & 45.6 \\ \cline{2-6}  & 6 & 1 & 97.2 & 1.97 & 44.4 \\ \cline{2-6}  & 6 & 2 & 98.6 & 2.02 & 47.4 \\ \cline{2-6}  & 6 & 3 & 99.0 & 2.07 & 50.3 \\ \hline \multicolumn{2}{c|}{Adaptive} & Adaptive & 99.4 & 1.94 & 41.8 \\ \hline \end{tabular}
\end{table}
Table 4: Evaluation of dealing with frame loss.
# Zinc-Decorated and Nitrogen-Functionalized Hierarchical Porous Carbons for Carbon Dioxide Capture
## 3 Results and Discussion
### CO\({}_{2}\) Adsorption  
Figure 10a-c show the CO\({}_{2}\) adsorption isotherms under atmospheric pressure at different temperatures. The CO\({}_{2}\) adsorption performance increased with increasing CO\({}_{2}\) pressure, but decreased with increasing adsorption temperature, confirming that it is an exothermic reaction. The CO\({}_{2}\) adsorption capacities at 298 K and 101.3 kPa (Figure 10d) were ranked as follows: Z8-850 (3.57 mmol/g) \(>\) Z8-750 (3.40 mmol/g) \(>\) Z8-950 (3.25 mmol/g) \(>\) Z8-650 (2.83 mmol/g) \(>\) ZIF-8 (0.88 mmol/g). This indicated that although ZIF-8 had the largest SSA, it had the smallest adsorption capacity. Considering a typical untreated flue gas composition, the adsorption performance at 298 K and 15 kPa were recorded (Figure 10e), and the CO\({}_{2}\) uptake performance exhibited a different order: Z8-750 (1.26 mmol/g) \(>\) Z8-650 (1.25 mmol/g) \(>\) Z8-850 (1.13 mmol/g) \(>\) Z8-950 (0.93 mmol/g) \(>\) ZIF-8 (0.09 mmol/g). The particle sizes of the samples seems to be related to the amount of CO\({}_{2}\) adsorption at 15 kPa. This means that the CO\({}_{2}\) adsorption capacities of the samples are the result of a compromise between surface structure and surface chemistry. The Freundlich equation works well for curve fitting of the CO\({}_{2}\) adsorption data (Figure 10 and Table 2). The values of \(K_{F}\) were inversely proportional to the adsorption temperature. The values of \(n\) ranged from 0.69 to 1.96, suggesting favorable adsorption on all the ZDCs except for ZIF-8. Figure 10: _Cont._  
Figure 9: X-ray photoelectron spectroscopy (XPS) Zn 2p spectra: (**a**) ZIF-8; (**b**) Z8-650; (**c**) Z8-750; (**d**) Z8-850; (**e**) Z8-950. (**f**) Proportions of functional groups. adsorption thermodynamic properties can provide helpful information for understanding the mechanism of the spontaneous adsorption process. Adsorption is a spontaneous process if there is a decrease in the total free energy of the system. Moreover, the more spontaneous the CO\({}_{2}\) adsorption process is, the easier it is for CO\({}_{2}\) to be preferentially adsorbed. The three thermodynamic parameters of CO\({}_{2}\) adsorption on the samples are summarized in Table 2. The \(\Delta G\) values of CO\({}_{2}\) adsorption were negative, which indicated that the adsorption process occurs favorably and spontaneously. Despite this, the absolute value of \(\Delta G\) for ZIF-8 was the lowest compared with the ZDC samples at the same temperature. Except for ZIF-8, the largest absolute value of \(\Delta G\) was observed with Z8-650 at the same temperature, and the absolute value of \(\Delta G\) decreased with an increase in the carbonization temperature of the samples, implying that an increase in carbonization temperature is not conducive to CO\({}_{2}\) adsorption. In addition, the absolute value of \(\Delta G\) decreased when the adsorption temperature increased, implying that increasing the adsorption temperature is not beneficial to the adsorption process [44].
# A Systematic Deep Learning Based Overhead Tracking and Counting System Using RGB-D Remote Cameras
## 6 Conclusions
### Theoretical Implications  
Our proposed HASTAC model has various practical applications in security, visitor counting, queue management etc. Our system is efficient and applicable for people counting and crowd analysis etc.
## 4 Result and Discussion
### AI Results  
The models for classifying the occurrence or non-occurrence of cracks based on the analysis data were trained using AI, and the results are shown in Table 3. The models showed a high accuracy of 100%; however, overfitting occurred for accuracy. After classification learning, the occurrence or non-occurrence of cracks was classified, and their numbers were predicted using the prediction algorithms. As shown in Table 3, the accuracy of the prediction models was evaluated using MSE. GBM exhibited the highest value, followed by Random Forest, SVM, and ANN for wrinkle prediction. This confirmed that GBM has the highest accuracy among the wrinkle prediction models. In the case of  
Figure 8: Correlation graphs between drawbead position and wrinkle and crack formation.  
Figure 9: Comparison of FLD results between (**a**) no crack and (**b**) minimal number of wrinkles and cracks.  
cracks, Random Forest showed the highest accuracy, followed by GBM, SVM, and ANN. Therefore, the GBM model was employed for wrinkle prediction, and the Random Forest algorithm was applied for formability prediction to improve predictive performance. GBM and Random Forest use ensemble techniques, unlike the conventional machine learning models. They exhibit high predictive performance as algorithms that can exceptionally handle nonlinear relationships and interactions among multiple variables. Figure 10 is a curve that compares the actual analysis data and the predicted values; the predicted values were almost similar to the data. Overall, the predicted and actual analysis values showed similar tendencies, which indicates that the proposed models can be applied to predict the occurrence of wrinkles and cracks in the forming process.  
Based on this, a GUI for predicting wrinkles and cracks in real time after forming was constructed, as shown in Figure 11. When the values according to the drawbead position were entered into the GUI for drawbead formability prediction, the number of wrinkles and cracks were displayed. In this case, the bead force for each drawbead position can be configured, allowing for the prediction of sheet metal forming results without the need for finite element analysis, based on the set values. This indicates that the GUI can be understood and utilized even without expertise in machine learning and analysis, which is expected to reduce design time and costs compared to predicting forming quality through traditional analysis or experiments.  
Figure 10: Verification of modeling parameters.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Crack**} & \multicolumn{2}{c}{**Prediction [-]**} \\ \cline{2-4}  & **Classifier [\%]** & **Wrinkle** & **Crack** \\ \hline SVM & 100 & 0.158 & 0.123 \\ Random Forest & 100 & 0.154 & 0.038 \\ GBM & 100 & 0.141 & 0.042 \\ ANN & 100 & 0.549 & 0.388 \\ \hline \hline \end{tabular}
\end{table}
Table 3: MSE for wrinkle and crack prediction by machine learning model.
# A Survey of OCR in Arabic Language: Applications, Techniques, and Challenges
## 3 OCR Process
### Segmentation
Some approaches are used for the segmentation-free approach and HMM. There are some applications where OCR systems are used to ease use and increase work productivity in healthcare, education, banking, insurance, automatic exam paper checking, bills and invoices, newspapers, and comics. Some phases are discussed to process the document to get better accuracy, including image acquisition, preprocessing, segmentation,classification and recognition, and postprocessing. Moreover, methods used by previous researchers are matrix matching, fuzzy logic, structural analysis, and neural networks.
# Fingerprint-Based Localization Approach for WSN Using Machine Learning Models
## 3 Machine Learning-Based Localization System
* Decision Tree (DT) is based on the form of a tree structure, where it breaks down the RSS dataset into smaller subsets, while an associated decision tree is incrementally developed. Figure 4: The structure of the RSS dataset. Figure 3: The online phase, which includes estimating the position of the target node. Random Forest (RF) employs the ensemble learning method for regression, where it combines predictions from multiple ML algorithms to make a more precise prediction than a single ML one. The above four ML models were tailored to be suitable with RSS fingerprints through processing the RSS values and then estimating the localization information of the target node.
## 5 Evaluation and Results
### Results
A small set of 23 rules is able to block over 99% of the attacks. In the context of SQLi detection, the high precision means that nearly every detected attack is a true attack, with very few false positives (normal traffic wrongly classified as an attack). The high recall indicates that almost all SQL injection attacks are correctly identified and blocked, with very few false negatives (missed attacks). We provide a brief assessment of the performance of the clustering strategies based on the results of Table 7. DBSCAN excels at grouping SQLi queries with similar structures, even when obfuscated, by dynamically adapting to the dataset and identifying natural clusters without needing predefined cluster counts. It focuses on meaningful patterns while excluding outliers, though it may cluster noisy data if their structure appears similar. In contrast, TF-IDF identifies important terms across the dataset, emphasizing the unique features of SQLi attacks and reducing noise by de-emphasizing irrelevant terms. This approach enables precise clustering and rule generation, making it effective for targeting specific attack types with high precision and minimal false positives. Overall, TF-IDF provides a global perspective on query patterns, while DBSCAN offers localized similarity clustering. Our results suggest that both strategies could be used to identify the most effective SecRules that can be generated. Future investigations could explore integrating regex for initial pattern matching with machine-learning models to develop more computationally efficient classification systems.
## 2 Multiple-Criteria Decision Aid (MCDA)
The reader can easily find classifications in the literature based essentially on how the overall outcome of the preference aggregation is computed (2012; 2013). However, the reference to \"outranking methods\", \"multi-attribute value functions\", and \"multi-objective programming methods\" is old and misleading, because it does not consider the whole decision-aiding process (see [PERSON] (2014)); meanwhile, the use of any of these methods is not considered as the result of a methodological choice, but an arbitrary one. Nevertheless, most application papers, as well as surveys, use this obsolete classification. Indeed, most of the applications presented in the literature do not really justify why a certain method has been adopted (besides having an available software) and do not explain why that choice fits the problem's characteristics. Justifications to the presence of \"ordinal information\" or to \"compensation\" are misleading: all preferences are based upon ordinal information, and constructing a model based on differences in preferences is always possible; all methods introduce a level of compensation among the different criteria, while \"non compensation\" can be introduced using models considering differences in preferences (value functions: [PERSON] (2015), [PERSON] (2016), and [PERSON] (2017)). Since most of the papers surveyed in this research still use this type of classification based upon ready-made methods, we will keep differentiating the literature based upon such methods. However, one among our findings is that such classification and reference to unjustified methods results in poor utilisation and represents an obstacle in using such tools. The division of MCDA methods that has dominated the literature in recent decades and many authors continue to rely on, although it is getting outdated [24], has three approaches. The first approach is the full-aggregation approach [28]. Methods that fall into this category have as their main feature that each criterion is given a score, which is then aggregated to provide an overall score [29]. This approach assumes compensatory scores, in which a low score on one criterion is compensated by a high score on another; thus, AHP [30; 31; 32; 33], ANP [34; 35], MAUT [7; 29; 36], and MACBETH [37; 38; 39] apply the full-aggregation approach. The second approach is the outranking approach; in this approach, a higher score cannot compensate for a lower score [28]. As the incomparability condition is allowed, the ranking of alternatives may be partial, so two options with the same score but possibly different behaviours may be incomparable [29]. PROMETHEE [23; 40] and ELECTRE [41], among others, apply the outranking approach. The third approach is the goal-, aspiration-, or reference-level approach [28]. This approach sets a target on each criterion and then identifies the alternatives that are closest to the ideal goal or benchmark. TOPSIS [33; 42], goal programming [33], DEA [41; 43], and VIKOR [44; 45] apply the goal-, aspiration-, or reference-level approach.
# Coverage and Rate Analysis for Location-Aware Cross-Tier Cooperation in Two-Tier HetNets
## 5 Conclusions  
In this paper, using tools of stochastic geometry, we presented an analytical framework to evaluate the coverage probability and the average rate for location-aware CoMP transmission in  
Figure 6: The overall coverage probability and average rate for the proposed location-aware cross-tier cooperation (LA-CTC) scheme and location-aware cross-tier CoMP transmission (LA-CTCT) scheme with different cooperation thresholds. (**a**) Overall coverage probability; (**b**) Overall average rate.  
two-tier HetNets. A novel LA-CTC scheme was proposed and the mathematical coverage probability and average rate expressions were derived. The numerical results showed that the proposed LA-CTC scheme could improve the performance of a network significantly. In addition, the optimal network performance can be achieved by properly tuning the parameters of the proposed scheme. Moreover, the proposed scheme has been compared with the conventional MRP scheme, the CRE scheme and the LA-CTCT scheme, in terms of the coverage probability and the average rate. Numerical comparisons showed that the proposed LA-CTC scheme outperforms the other three schemes.  
This paper was funded by the National Natural Science Foundation of China (Grant No. 61271263).  
[PERSON] and [PERSON] conceived the proposed scheme. [PERSON] reviewed the manuscript. [PERSON] conducted the detailed derivation to evaluate the performance of the proposed scheme and wrote the manuscript. All authors read and approved the final manuscript.  
The authors declare no conflict of interest.
## 3 Methodology
### Overview of Model Architecture  
This paper introduces a hierarchical spatial-temporal attention networks (HSTAN) model, depicted in Figure 2. The model primarily comprises an input embedding layer, a hierarchical spatial-temporal self-attention module (STA-Blocks), and an output layer, as shown in the left part of Figure 2. The STA-Blocks, stacked in multiple layers, each include a temporal attention layer and a spatial attention layer. The temporal attention layer identifies the temporal correlation of historical data on future time points, while the spatial attention layer discerns the spatial dependency of locally adjacent nodes. The hierarchical structure of the residual association facilitates the capture of more profound spatial-temporal correlations, thereby enhancing the model's predictive performance. Lastly, a two-layer fully connected neural network is employed to transform the feature space and generate traffic forecasts for all nodes within the target road network.