esa-sceva/satcom-chunk-collection huggingface dataset:
# Handwriting Recognition Based on 3D Accelerometer Data by Deep Learning
## 4 Experiments and Numerical Evaluation
### Computational Time Analysis  
The total elapsed time during the training and testing was computed for each architecture using a GPU. Table 6 shows a comparison of the time statistics related to training and testing for different state-of-the-art methods and the proposed neural network architecture.  
[PERSON] et al. [4] were the only authors to report the result in testing. However, the results are not comparable, because each work used different datasets and computing resources. The CNN architecture was faster during the training stage from the three tested architectures. The CNN needed only \(5.68\) min to train the architecture using the complete database, while the LSTM required \(155.58\) min, almost double the time necessary to train  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Architecture** & **Training [min]** & **Testing [s]** \\ \hline DTW, [PERSON] et al. [4] & – & 0.9 \\ [PERSON], [PERSON] et al. [25] & – & – \\ [PERSON], [PERSON] and [PERSON] [14] & 16.156 & – \\ F-BiLSTM, [PERSON] et al. [19] & – & – \\ F-BiGRU, [PERSON] et al. [40] & – & – \\ FDSVN, [PERSON] et al. [13] & – & – \\ \hline CNN \({}^{\star}\) & 5.66 & 0.0009 \\ LSTM \({}^{\star}\) & 155.58 & 0.0263 \\ CNN-LSTM \({}^{\star}\) & 78.48 & 0.0123 \\ \hline \hline \end{tabular} \({}^{(*)}\) Proposed method. \((-)\) Non-available.  
\end{table}
Table 6: Elapsed time expended in training (in min) and testing (in s).  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **Accuracy [\%]** & **Precision** & **F\({}_{1}\)-Measure** \\ \hline DTW, [PERSON] et al. [4] & 95.00 & – & – \\ LR, [PERSON] et al. [25] & 94.60 & – & – \\ DTW, [PERSON] and [PERSON] [14] & 98.00 & – & – \\ F-BiLSTM, [PERSON] et al. [19] & 98.04 & – & – \\ F-BiGRU, [PERSON] et al. [40] & 99.15 & – & – \\ FDSVN, [PERSON] et al. [13] & 95.21 & – & – \\ \hline CNN \({}^{\star}\) & 99.45 & 0.97 & 0.97 \\ LSTM \({}^{\star}\) & 99.68 & 0.99 & 0.99 \\ CNN-LSTM \({}^{\star}\) & 99.55 & 0.98 & 0.98 \\ \hline \hline \end{tabular} \({}^{(*)}\) Proposed method. \((-)\) Nonavailable.  
\end{table}
Table 5: Comparison with the state-of-the-art related methods.  
the CNN-LSTM architecture. This discrepancy is because the time needed to recognize a character is very short, requiring a fraction of a second to process the signal representing the character. In this sense, the CNN is the fastest architecture, exhibiting a comparable performance to the proposed LSTM and CNN-LSTM architectures, but the CNN requires more samples to obtain such a performance.
# Small Dam Drainage with Nonwoven Geotextile after 40 Years of Exploitation
###### Abstract  
Synthetic materials such as nonwovens, mats and membranes have been commonly used in civil engineering for many years. The changes of geotextile characteristics in time (permeability, porosity, etc.) are poorly understood. Many authors have presented the changes in the properties of synthetic materials, mainly based on laboratory tests. The studies on geotextile samples taken from working hydraulic structures are not so many. Over 40 years, the reduction in the permeability of nonwoven geotextile was about four-fold. Probably the reason for that reduction is mechanical clogging caused by fine particles which have moved from the earth dam soil to the nonwoven geotextile. Over 40 years of operations, despite the reduction in permeability of the nonwoven geotextile, the drainage worked properly. The experiment was conducted in the field and laboratory and a two-dimensional numerical model built in software package HYDRUS (two-dimensional/three-dimensional (2D/3D)) Standard was proposed. The field measurements allow calibration and validation of the numerical model. The conducted simulations showed that at a maximum water level in the reservoir equal to 3.32 m, the water supply to the drainage is higher from upstream than from downstream. Particularly, water supply from above to the drainage is absent.  
earth dam; clogging; nonwoven geotextile; HYDRUS; seepage velocity; internal cracking +
Footnote †: journal: Applied Sciences
# Comparison of Oil Spill Classifications Using Fully and Compact Polarimetric SAR Images
## 3 Results
### Oil Spill Classification Based on Different Polarimetric SAR Modes
This result manifested the limitation of single polarimetric SAR mode in distinguishing mineral oil and biogenic films. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Ground Truth (Pixels)**} \\ \cline{2-4}  & **Oil** & **Sea** & **Total** \\ \hline
**Oil** & 5357 & 445 & 5802 \\
**Sea** & 193 & 5090 & 5283 \\
**Total** & 5550 & 5535 & 11,085 \\ \hline \end{tabular} Overall accuracy = 94.2445% (10,447/11,085), kappa coefficient = 0.8849.  
\end{table} 
Table 8: Confusion matrix achieved by SVM based on 9 dual-polarized (DP) mode features. Figure 6: Classification accuracy of different polarimetric SAR modes achieved by SVM, ANN and ML. \begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Class**} & \multicolumn{3}{c}{**Ground Truth (Pixels)**} \\ \cline{2-4}  & **Oil** & **Sea** & **Total** \\ \hline
**Oil** & 5378 & 363 & 5741 \\
**Sea** & 172 & 5172 & 5344 \\
**Total** & 5550 & 5535 & 11,085 \\ \hline \end{tabular} Overall accuracy = 95.1737% (10,550/11,085), kappa coefficient = 0.9035.  
\end{table} 
Table 9: Confusion matrix achieved by SVM based on 10\(\pi/2\) mode features.
# Energy Efficiency Perspectives of PMR Networks +
## 1 Introduction
Since the evaluation of power saving solutions should be based on actual network configuration and traffic load scenarios, this work extends the results presented in [13] by introducing a detailed energy consumption analysis focused on the traffic data of a deployed TETRA regional network. Finally, the performance of the considered systems is evaluated both from an economic and an environmental point of view, showing the positive impact of the energy management improvements in current TETRA/TEDS systems and the significant energy saving gain allowed by the LTE platform. The paper is organized as follows. Section 2 gives an overview of the state-of-the-art of TETRA system,  
Figure 1: Power consumption and traffic of a generic public cellular base station during a day. describing its main features and discussing the feasibility of the potential future evolution over the LTE system. Section 3 introduces a power consumption model for today's TETRA systems, starting from the work carried out in literature about \"green\" cellular systems. Sections 4 and 5 describe some feasible strategies to increase the energy efficiency of current TETRA and TEDS systems and discuss the power saving gain, including environmental and economic effects, that can be obtained by actually implementing a TETRA over LTE infrastructure. Finally, Section 6 concludes the work.
# Robust Additive Value-Based Efficiency Analysis
## 5 Case Study concerning Evaluation of Healthcare System in Poland
### Complete Efficiency Rankings of Vovodeships
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & **ZPM** & **POM** & **WM** & **PDL** & **LBU** & **WLKP** & **KP** & **MAZ** & **LBL** & **DSL** & **OPO** & **LDZ** & **SL** & **SW** & **MLP** & **PKR** \\ \hline \multirow{2}{*}{\(Ed\)} & 0.109 & 0.152 & 0.462 & 0.582 & 0.179 & 0.000 & 0.514 & 0.372 & 0.523 & 0.492 & 0.754 & 0.548 & 0.302 & 0.526 & 0.659 & 0.652 \\  & 2 & 3 & 7 & 13 & 4 & 1 & 9 & 6 & 10 & 8 & 16 & 12 & 5 & 11 & 15 & 14 \\ \multirow{2}{*}{\(ER\)} & 2.110 & 3.034 & 7.456 & 12.530 & 3.860 & 1.000 & 9.885 & 6.132 & 10.215 & 9.164 & 15.990 & 11.310 & 4.996 & 9.774 & 14.339 & 14.205 \\  & 2 & 3 & 7 & 13 & 4 & 1 & 10 & 6 & 11 & 8 & 16 & 12 & 5 & 9 & 15 & 14 \\ \multirow{2}{*}{\(CE\)} & 0.102 & 0.155 & 0.471 & 0.598 & 0.178 & 0.000 & 0.525 & 0.378 & 0.507 & 0.495 & 0.760 & 0.526 & 0.294 & 0.509 & 0.656 & 0.630 \\  & 2 & 3 & 7 & 13 & 4 & 1 & 11 & 6 & 9 & 8 & 16 & 12 & 5 & 10 & 15 & 14 \\ \multirow{2}{*}{\(SE\)} & 0.065 & 0.130 & 0.383 & 0.514 & 0.114 & -0.140 & 0.458 & 0.324 & 0.414 & 0.374 & 0.726 & 0.470 & 0.233 & 0.469 & 0.565 & 0.560 \\  & 2 & 4 & 8 & 13 & 3 & 1 & 10 & 6 & 9 & 7 & 16 & 12 & 5 & 11 & 15 & 14 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: The vivodeships’ efficiency rankings imposed by four different measures at the level of health improvement: expected distance (\(Ed\)), expected rank (\(ER\)), cross-efficiency (\(CE\)), and super-efficiency (\(SE\)).
## 6 Discussion
### Limitations and Future Work  
Notably, the current assessment results may rely on ECR for some vulnerabilities with a low probability of exploitation. This reliance stems from two factors.  
First, the fact that the current exploit probability prediction, as provided by EPSS, is highly accurate primarily for network vulnerabilities in IT environments. This means that when assessing vulnerabilities in OT/ICS environments, the exploitation information about exploiting vulnerabilities in those environments may not be fully considered.  
The second is the fact that the final score based on the EoE, which is calculated based on the ECR, ECA, and EUP that make up the current score, has a large difference in the range of scores for each index. This means that a vulnerability with a high EoE, but with a weak attacker's motive, may not be considered as dangerous as other vulnerabilities.  
Based on these limitations, future research aims to first analyze IIoT device vulnerability data in OT/ICS environments and develop a customized prediction model that can predict the likelihood of exploitation considering such environments. Then, we aim to improve the accuracy of the vulnerability exploitation risk score proposed in this study by conducting a study to derive measures to bridge the evaluation score gap with ECR.
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 2 Knowledge-Based Approaches
### Categories of Knowledge Representation and Reasoning Approaches
#### 2.1.2 Standard Logic-Based Formalisms  
These formalisms use logical rules and symbols to represent knowledge and perform deductive reasoning. They provide a precise and rigorous framework for expressing relationships and making inferences based on logical rules and constraints. Examples of such formalisms are the first-order logic and the propositional logic [12].
# Frequency Selective Surfaces: Design, Analysis, and Applications
## 3 Elements of Frequency Selective Surfaces
### Class 4: Combination Elements  
This class consists of a broad range of elements because it represents combinations of the elements from the other three groups to construct new elements. The different shapes of elements enable the creation of diverse frequency responses [111].
## 4 Review: Slant Links Rain Fade Models  
### Empirical Model  
#### 4.1.1 SAM  
The SAM model [32] has evolved through some previously published articles [101]. Here, the point rainfall rate at the ground is used. The model yields the attenuation in time series. However, it is not validated with heavy rainfall or with rainfall data from various locations worldwide. The quick steps of the SAM model are the following set of equations:  
(1) Calculate specific attenuation:  
\[\gamma_{\text{decay}}(R)=kR^{\alpha}\ (\text{dB/km}) \tag{15}\]  
If the rainfall rate is unavailable, use the International Radio Consultative Committee (CCIR) recommended rainfall rate (former CCIR is now merged with ITU-R).  
(2) The rainfall rate varies along with the link, and the rainfall rate can be defined as:  
\[R(l)=\begin{cases}R_{p}&R_{p}\leq 10\ \text{mm/h}\\ R_{p}e^{-\gamma_{\text{decay}}\ln(R_{p}/10)l\cos\theta}&R_{p}\geq 10\ \text{mm/h} \end{cases} \tag{16}\]  
where \(R_{p}\) is the point rainfall intensity, \(\gamma_{\text{decay}}\) is the decay profile along the horizontal axis, and \(l\leq L_{S}\) is defined by Equation (17):  
\[L_{S}=\frac{H_{R}-H_{S}}{\sin\theta} \tag{17}\]  
where rain height, \(H_{R}\), is approximated by Equation (18):  
\[H_{R}=\left\{\begin{array}{ll}4.8&\text{if }|\varphi|\leq 30^{\circ}\ \ \text{R}_{p}\leq 10\ \text{mm/h}\\ 7.8-0.1|\varphi|&\text{if }|\varphi|\geq 30^{\circ}\ \ \text{R}_{p}\geq 10\ \text{mm/h} \end{array}\right. \tag{18}\]  
Figure 3: Taxonomy of the rain fade models for Earth–space links [88; 89; 90].  
(3) Finally the rain attenuation is determined by Equation (19):  
\[A(R_{0})=\left\{\begin{array}{ll}R_{L}^{a}L_{S}&\text{if $R_{p}\leq 10\,\mathrm{mm}/ \mathrm{h}$}\\ \frac{(kR_{p}^{a})(1-\exp\bigl{[}-a\gamma_{\text{decay}}\ln\bigl{(}R_{p}/10 \bigr{)}L_{S}\cos\theta\bigr{]})}{a\gamma_{\text{decay}}\ln\bigl{(}R_{p}/10 \bigr{)}\cos\theta}&\text{if $R_{p}\geq 10\,\mathrm{mm}/\mathrm{h}$}\end{array}\right. \tag{19}\]  
Algorithm 1 shows the procedure for this model.  
```
1 Compute \(\gamma_{\text{decay}}\) /* Equation (15) */
2 Compute \(R(l)\) /* Equation (16) */
3 Compute Equation (18)
4 Compute \(L_{S}\leftarrow(H_{\text{R}}-H_{S})/\sin\theta\) /* \(L_{S}\) in km */
5 if\(R_{p}\leq 10\) mm/h then
6 Compute Equation (19)
7 else
8 Compute Equation (19)
9 Return \(A_{p}\)
```  
**Algorithm 1**SAM Model [32]  
Advantages  
It proposes a useful rainfall rate model that can yield the spatially distributed rainfall rate. The spatial distribution of rainfall concept later played a significant role in determining rainfall from microwave link attenuation [102] and real-time frequency scaling applications [103]. It extensively used slant-path attenuation, which combines the individual features of convective and stratiform types of rainfall.  
Limitations  
The usable frequency band is limited to 10-35 GHz.
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 5 Proposed Coexistence Solutions for Uncoordinated LTE-U and Wi-Fi Networks
### Reward Selective Experience Replay Based Q-Learning  
We have seen that the ER-based Q-learning solution stores experience \(\{s,a,r,s^{\prime}\}\) for all actions until the number of experiences reaches \(N_{E}\). After recording \(N_{E}\) experiences, each stored element \(\{s,a,r,s^{\prime}\}\) is used to update the Q-network using the steps described in Algorithm 3. In most circumstances, it is not recommended to use every experience in the database, so we must specify some sort of selection process. For this reason, we also investigate RE based Q-learning strategy in which we keep only the \(N_{E}\) best experiences with the highest attained reward in each action. In other words, the reward for each action is calculated using Equation (4), and the corresponding experience is used to update the Q-matrix values if the acquired reward is 100.  
```
1 Input: Set learning rate (\(\eta\)), discount factor (\(\gamma\)), initial \(\epsilon\)-greedy value (\(\epsilon\)), buffer size (\(N_{E}\)), number of replays (\(K\))
2 Output: Optimal policy \(\pi^{*}\)
3 for\(s\in S\), \(a\in A\)do
4 Q(s,a) \(\gets 0\)
5 end for
6\(R_{e}\leftarrow\) // Record of experiences in a fixed traffic load
7\(R_{E}\leftarrow\) // Set of record of experiences for dynamic traffic
8 while Convergence is not achieveddo
9 whileNumber of iterations \(\leq N_{E}\)do
10 Generate random LTE and WiFi target traffic loads
11 for\(\forall a\epsilon A\)do
12 // Observe all actions for a fixed traffic load
13 Execute action \(a\)
14 Update state \(s^{\prime}\)
15 Compute \(f_{f}\) based on the \(s^{\prime}\) and Equation (3)
16\(R_{e}\gets R_{e}\cup\{s,a,f_{f},s^{\prime}\}\)
17
18 end for
19 for\(\forall a\epsilon A\)do
20 Compute reward \(r\) for action \(a\) based on Equation (4)
21\(R_{e}\gets R_{e}\cup\{s,a,r,s^{\prime}\}\)
22 end for
23 // store the experiences
24\(R_{E}\gets R_{E}\cup R_{e}\)
25 end for // Update Q table based on recorded experiences
26\(Q\gets L(R_{E},K,\eta,\gamma,\epsilon)\)
27\(N_{E}\gets 0\)
28 end for
```  
**Algorithm 3**ER Q-learning based algorithm for dynamic selection of \(N_{\text{b}}\)  
In the RER based Q-learning, the main procedures used to update the Q-table are similar to the procedures used in ER based Q-learning scheme depicted in Algorithm 3. The difference between ER based Q-learning scheme and RER based Q-learning scheme is, in ER based Q-learning scheme every recorded experience is used to update the Q-table while RER based Q-learning scheme updates the Q-table based on experiences with best reward value. This reward based experience record update is adopted from [9].
## 4 Target Identification Method between Star Trackers  
The proposed target identification method in this paper is combined with the star tracker technology. First, the proposed maximum projection method is used for star detection, where the median frame image obtained during star detection is removed from the maximum projection image, and the stars in the image can be removed. Next, the target detection method based on the projection time information is used to detect the moving targets using the trajectory of the moving targets as well as the continuity of the time information, while eliminating the false targets. After the detection of the targets in frame groups is completed, the target information array of each frame group can be obtained, and then the inter-frame target trajectory association method is used for the trajectory association of the target information to form a complete target trajectory. Finally, the identification method of targets observed by the same orbital arc segment as well as different orbital arc segments by using the star tracker is analyzed.
# A Framework to Determine the Utilization of Vacant Taxis on HOV Lanes with the Optimal Deployment
###### Abstract  
For the purpose of determining whether vacant taxis are supposed to use high-occupancy vehicle (HOV) lanes, this paper builds a framework to explore the performances of a network before and after vacant taxis use HOV lanes and to detect the optimal deployment of HOV lanes. We use a variational inequality (VI) to describe travelers' mode choice following a logit model and the route choice abided by the user equilibrium principle on the network. A simple network is adopted to explore the network performances when vacant taxis are allowed to use/forbidden from using HOV lanes. Then we propose a framework presented by a bi-level model for the optimal deployment of HOV lanes in which the lower level is the variational inequality and the upper level aims at maximization of social welfare. This bi-level programming with a genetic algorithm combined with a surrogate assistant approach is applied to the simple network and a Sioux Falls network to analyze the network performances in the cases allowing/forbidding vacant taxis from using HOV lanes. The results reveal the paradox that social welfare may decrease when allowing vacant taxis to use HOV lanes compared to when forbidding vacant taxis from using HOV lanes. Considering optimal HOV lane deployment, this paradox still exists. However, it does not always happen due to the different parameters, e.g., flag-fall price. Therefore, the qualification of vacant taxis to use HOV lanes depends on the topology, attributes, and travel demand of the network. The results display that the models and the algorithm are effective in reality.  
urban traffic; vacant taxis; HOV lanes; utilization; optimal deployment of HOV lanes +
Footnote †: journal: Applied Sciences  
1
## 5 Ablation Study and Discussions
### Experiments on Real-World Non-Textile Images  
As discussed in the introduction, and also illustrated in Figure 1, repeated pattern images are everywhere, and they are different in terms of content diversity and topological regularity. The proposed method covering five steps/modules can be flexibly adjusted based on different target image types. It is interesting to explore whether the proposed method can also work on other fronto-parallel images with complex content and near-regular topology structure. Thus, the proposed method was tested on some real-world images, e.g., environmental images taken from the testing dataset of [12].  
Figure 10 compares the results from [PERSON]'s method and the current method, where the number of activation peaks for Algorithm 1 was set as \(k=20\); and the parameter \(\epsilon\) in the optimization module (Equation (7)) was set to 0.87. It can be shown that although the current method uses fewer filters than that of [PERSON], similar or better detection results are achieved on these environmental images. For example, the current method performed better on the fifth image that had a very strong illumination difference. It proves that the current method could achieve satisfactory results on fronto-parallel non-textile real-world images with significantly varied content by adjusting parameters. This supports  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Image Type**} & \multicolumn{2}{c}{**Alexnet + Canny [40]**} & \multicolumn{2}{c}{**Alexnet + BDCN [39]**} \\ \cline{2-5}  & **Accuracy (\%)** & **Overall Time (s)** & **Accuracy (\%)** & **Overall Time (s)** \\ \hline Computer-generated & 84.4 & 46.3 & 87.6 & 86.8 \\ Scans of actual fabrics & 65.8 & 39.4 & 65.6 & 41.0 \\ All images & 67.3 & 40.0 & 67.3 & 44.7 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Summary of quantitative results of using different boundary detection methods.  
the conjecture that most fronto-parallel repeated patterns can be detected by tactically integrating the modules with different degrees of coupling.