esa-sceva/satcom-chunk-collection huggingface dataset:
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 2 EMG Signals
### Feature extraction from an EMG signal  
A parameter of an EMG signal is a stable variable or a value from a mathematical or physical model ideally associated with the generation or detection of an MES process, such as length and depth of a fiber, electrode surface or distance between them, coefficients of the auto-regressive model, etc. [28]. After the signal acquisition stage, a processing stage extracts a series of parameters for the analysis of the EMG signal.  
After filtering the signal and digitalizing it, since the vector components do not have any meaning individually but only as a whole, it is necessary to characterize the vector representation of the signal. As a result, it is required to extract the features from the vector that represents the signal, and, from it, implement the vector classification.  
A feature of an EMG signal is a unique property, which can be observed or described qualitatively, such as being big or small, fast or slow, and sharp or smooth. An EMG variable is a physical amount that can be computed, reported and transmitted in a numeric form, and that can change as a function of time, such as voltage, frequency, velocity, and delay, amongst others. The variable is estimated during a finite time interval known as an epoch [28].  
Nevertheless, when the purpose of extracting the signal features is to control a certain device, it is necessary to obtain more information from each channel of the EMG signal or to assign a control function to a specific combination from the multi-channel system, which is the particular purpose of extracting characteristics from the signals [27].  
For the extraction of features, the signals can be processed in the time domain; they can also be transformed into the frequency domain, or represented in the time-frequency space or in time scale. This process consists in assembling a feature vector with different parameters of the signal. Choosing the proper parameters that will form the feature vector correctly is of vital importance since this is the starting point from which classification is made.  
That is, an MES is a time function, and, thus, it can be described in terms of its amplitude, frequency, or phase. Hence, for its study, the extracted features lie in different domains, such as time or frequency, and some of their variants. A description of the most common features is given in Appendix A.
## 4 Experimental Analysis
### Experimental Result and Analysis  
#### 4.3.1 The Effectiveness of the Feature Extractor in the Proposed Method  
To verify that our feature extractor can extract effective feature representations than a plain CNN structure without shortcut connections, we first compared the performance of our feature extractor combined with CTC and a plain CNN structure with CTC, which both eliminated sequential modeling. In order to verify our hypothesis more comprehensively, we conducted experiments on both simple dataset A and complex dataset B and measured the experimental results both on \(SRA\) and \(CRA\). The test results of the two models are shown in Table 3.  
From the result, we saw that the \(SRA\) of the skip connection structure model reached 96.9% and 83.1% for dataset A and dataset B, respectively. This was an improvement of 1.1% and 9.1% compared with the plain CNN structure model for the two datasets, respectively. In order to validate our hypothesis, the skip connection structure model can achieve a  
Figure 8: Some examples of memorial errors.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Network Model**} & \multicolumn{2}{c}{**Train on A\({}_{\mathbf{1}}\)**} & \multicolumn{2}{c}{**Train on B\({}_{\mathbf{1}}\)**} \\  & \multicolumn{2}{c}{**Test on A\({}_{\mathbf{2}}\)**} & \multicolumn{2}{c}{**Test on B\({}_{\mathbf{2}}\)**} \\ \cline{2-5}  & \(SRA\) & \(CRA\) & \(SRA\) & \(CRA\) \\ \hline
**The skip connection structure + CTC** & **0.969** & \(\mathbf{-13}\) & **0.831** & \(\mathbf{-95}\) \\ The plain CNN structure + CTC & 0.958 & \(\mathbf{-17}\) & 0.740 & \(\mathbf{-143}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Recognition accuracy of the skip connection structure + CTC and the plain CNN structure + CTC.  
better performance more comprehensively, we calculated the _CRA_ as a supplement for the _SRA_. The _CRA_ of skip connection structure model reached \(-13\) and \(-95\) for dataset A and dataset B, respectively. This was an improvement of 4 and 48 compared with the plain CNN structure model for the two datasets, respectively. As we discuss above, dataset A is simpler compared with B, resulting in easy recognition of other characters in A except for the small character recognition errors, while dataset B is more complex. In this way, the 9.1% accuracy improvement on B, a small portion of which is attributable to avoiding the loss of small object feature, is mostly attributable to the effectiveness of the residual net-work feature extraction advantage on the overall recognition performance improvement. As a result, there is a minor accuracy impact obtained from dataset A, while a greater accuracy impact obtained from dataset B. In conclusion, the improvement from the plain CNN structure model to the skip connection structure model proved that our novel feature extractor was capable of learning more effective features from characters with complex and diverse forms, which indicates our novel feature extractor is more suitable for the variable-length reading recognition task of multi-type digital instruments.
# Graph-Based Resource Allocation for Integrated Space and Terrestrial Communications
### Article Structure
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Definition** \\ \hline
5G & Fifth-generation wireless communications \\
6G & Sixth-generation wireless communications \\ ABRB & Almost blank resource block \\ AOI & Age of information \\ AP & Access point \\ BP & Belief propagation \\ BS & Base station \\ CAP & Cognitive AP \\ CP & Centralization point \\ CR & Cognitive radio \\ CSI & Channel state information \\ CSS & Cooperative spectrum sensing \\ CUE & Cellular user equipment \\ D2D & Device to device \\ DCA & Difference of two convex functions approximation \\ DL & Downlink \\ DRL & Deep reinforcement learning \\ DSA & Dynamic spectrum access \\ DUE & D2D pair \\ EE & Energy efficiency \\ FD & Full-duplex \\ GDFT & Graph Discrete Fourier transform \\ GNN & Graph neural network \\ GRIST & Graph-based Resource management for Integrated Space and \\ \hline GSP & Graph signal processing \\ Het-Net & Heterogeneous network \\ ICI & Inter-cell interference \\ IoT & Internet of Things \\ ISTN & Integrated space and terrestrial network \\ KKT & Karun-Kush-Tucker \\ LTE & Long-Term evolution \\ MC & Multiple connectivity \\ MCNF & Minimum cost network flow \\ MIMO & Multiple-input-multiple-output \\ ML & Machine learning \\ mmWave & Millimeter wave \\ NOMA & Non-orthogonal multiple access \\ NTN & Non-terrestrial network \\ OFDMA & Orthogonal frequency division multiple access \\ OSI & Open systems interconnection \\ PF & Proportional fair \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Table of acronyms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Acronym** & **Definition** \\ \hline PCE & Power consumption and externality \\ PU & Primary user \\ QoS & Quality of service \\ RA & Resource allocation \\ RAT & Radio access technology \\ RB & Resource block \\ SGD & Stochastic gradient descent \\ SINR & Signal-to-noise-plus-interference ratio \\ SSh & Spectrum sharing \\ SU & Secondary user \\ TN & Terrestrial network \\ TSR & Throughput satisfaction rate \\ UA & User association \\ UAV & Unmanned aerial vehicle \\ UC & User centric \\ UDN & Ultra-dense network \\ UE & User equipment \\ UL & Uplink \\ V2I & Vehicle-to-infrastructure \\ V2V & Vehicle-to-vehicle \\ WLAN & Wireless local area network \\ WSR & Weighted sum-rate \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Table of acronyms. \begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definition** \\ \hline \(AR\) & Averaged risk-averse rate \\ \(A\) & Adjacency matrix \\ \(\alpha_{u}\) & Ratio of guaranteed users \\ \(\mathcal{B}\) & Set of edges \\ \(\mathcal{B}\) & Incidence matrix \\ \(\beta\) & Fairness index \\ C & Number of output channels \\ \(\mathcal{T}\)\(\mathcal{T}\)\(\mathcal{C}\)\(\mathcal{U}\) & Target SINR of the CUEs \\ \(\mathcal{T}\)\(\mathcal{T}\)\(\mathcal{U}\)\(\mathcal{U}\) & Target SINR of the DUEs \\ \(\mathcal{T}\)\(\mathcal{S}\)\(\mathcal{U}\)\(\mathcal
## 4 The Proposed System  
The most important role of the radar is to detect flying birds, but it also provides parameters for bird identification (i.e., classification) [20,21]. The parameters provided by the radar system are: the distance in 3D of a target (m), the velocity of a target (m/s) and the trajectory of a target. The distance of a detected bird is used to estimate the size of the bird in meters. Velocity of a target bird is used for the final classification. The system also includes the aforementioned camera with the telephoto lens and a motorized video head. The camera is controlled by the application programmable interface (API) of the camera manufacturer. The system has three servers: the radar server, the video head steering server and the camera control server. Software for the radar server is supplied by the manufacturer of the radar but the software for the other two servers is result of our development work.  
We took series of images of a single target bird and each image is processed according to the schematic diagram of the system in Figure 3.  
Figure 1: Color temperature and corresponding red, blue and green (RGB) values presented according to Commission Internationale de l’Eclairage (CIE) 1964 10-degree color matching function.  
Figure 2: Data example of the White-tailed Eagle. The image on the left is an augmented image with the color temperature 5600 K\({}^{\circ}\). The original image is in the middle with color temperature 7600 K\({}^{\circ}\). The image on the right is an augmented image with the color temperature 9600 K\({}^{\circ}\).  
Segmentation is computed in parallel to image classification in order to obtain an estimate of the target bird size in pixels, i.e., despite that segmentation is computed simultaneously when the classification process is started, it is not part of the actual classification, but the result of the segmentation is used for assigning a value to the size estimate parameter. When the estimate in pixels is known, the target bird size estimate in meters can be calculated. We studied methods from simple threshold to fuzzy logic for solving the problem at hand i.e., a dark figure against bright background and vice versa as well. At the extremity, the background and the target can share several colors in the RGB color space. We achieved the best results by applying fuzzy logic segmentation compared to the threshold segmentation and the edge detection segmentation [22; 23]. In particular, we applied [PERSON]'s fuzzy inference method [24]. Figure 4a,b show an example of segmentation.
## 1 Introduction
Additionally, incorporating certain metal impurities during the synthesis makes it possible to obtain phosphorescent luminescence instead of the more common fluorescence [9]. Perhaps the most significant advantage of phosphorescent QDs over traditional phosphors is that since the excited states involved are different, QD-phosphorescence is not affected by the same parameters that affect conventional organic phosphorescent molecules, such as the presence of oxygen or heavy atoms and the necessity of a rigid environment. An example of such phosphorescent QDs is Mn\({}^{2+}\)-doped ZnS QDs. Compared to traditional QDs (like ZnS or CdSe), the Mn-doped QDs present a larger Stokes shift between the excitation and emission wavelengths and longer luminescent lifetimes in the order of milliseconds, both of which are characteristic of phosphorescent emission. Being able to discriminate between the background fluorescence of the sample and the phosphorescence emitted by the QDs is of great interest for sensing. Mn:ZnS QDs have previously been used for acetone detection in liquid samples [10] in the laboratory by measuring the quenching of the room temperature phosphorescence (RTP) emission of such QDs after direct interaction with the analyte. Nevertheless, it is necessary that these chemical sensing materials \"leave the laboratory\" and can be used to solve everyday problems. For this purpose, they must be easily incorporated into portable and easy-to-use instrumentation systems. The main desired requirements for the development of an instrumentation prototype include portability, low cost, minimal interference by ambient light, high sensitivity, and easy to be used by a non-specialized user. In this context, several interesting developments of portable fluorimeters have been reported for the detection of cyanobacteria [11], pH measurements of environmental water [12], fast ultra-trace detection of explosive vapors [13], chlorophyll analysis in marine environments [14], and rapid screening of aflatoxin M1 [15] and _Escherichia coli_ in drinking water [16]. In this work, we successfully incorporated phosphorescence-emitting Mn\({}^{2+}\)-doped ZnS QDs into a sol-gel solid matrix for the development of an acetone gaseous sensing phase based on the quenching effect that acetone has on their luminescence. The sol-gel sensing phase was then coupled to a luminescent portable device designed with a deep-UV LED as an excitation source and a photomultiplier module working as a light detector. The combination of the sol-gel immobilized QDs with the portable luminescent device allowed for the development of an affordable portable instrumental system for the determination of trace levels of acetone in gaseous samples.
## 1 Introduction  
Bi\({}_{1-x}\)Sb\({}_{x}\) are semimetals, which form a continuous series of solid solutions while varying the antimony concentration \(x\)[1; 2; 3; 4; 5]. Optical and electronic properties of these materials depend on many factors, including antimony content, temperature and a substrate material used (due to the difference in the thermal expansion coefficients of Bi\({}_{1-x}\)Sb\({}_{x}\) and the substrate). Such a system allowsnot only the study of the fundamental physical phenomena, but also use in many applications, such as thermoelectric, electronic and optical devices. The properties of thin films strongly differ from these of bulk media and are of great research interest [6, 7]. At the temperature of 290 K, the influence of a size effect is minimal: the limitation of a mean free path is associated with the concentration of charge carriers. Many studies are dedicated to investigation of dependency of Bi\({}_{1-x}\)Sb\({}_{x}\) sheet DC conductivity on antimony content, film thickness, and temperature [1, 2, 3, 4, 5]. In two recent works it was shown that thin bismuth-antimony films have tunable optical response in the THz frequency range [8, 9]. In the work of [PERSON] et al., the optical response of charge carriers was investigated in thin films of Bi and Bi\({}_{1-x}\)Sb\({}_{x}\) (\(x\) from 0 to 16 %) in a frequency range up to 50 THz, but the measurements were performed only for film thickness of 40 nm [8]. In the mentioned work, the dependencies of dynamical sheet resistivity (or conductivity) on antimony content and a frequency of THz radiation were not calculated, but it should be mentioned that a semiconducting Si(111) substrate used strongly affects these parameters. Using the Drude analysis, the authors have extracted the dependencies of the plasma frequency and damping rate of charge carriers on antimony concentration. They have also extracted the carrier density assuming that effective carrier mass (measured for bulk crystal at the helium temperature, but applied for thin film at the room temperature) depends on neither film thickness nor antimony concentration. However, the film thickness, temperature and a substrate material strongly affect this parameter [10, 11]. To obtain the correct values of the mentioned parameters, it is necessary to use a dielectric substrate which has low losses in the THz frequency range.  
In this work, we study the influence of thickness and antimony content \(x\) of thin-film Bi\({}_{1-x}\)Sb\({}_{x}\) solid solutions as well as substrate material on their dynamical THz conductivity and galvanomagnetic properties at room temperature. From THz measurements, we extract the plasma frequency, relaxation time, DC conductivity and effective carrier mass and compare the obtained values with the results of galvanomagnetic measurements. These dependencies are important for application in simulation of various thermoelectric, electronic and optical devices, such as detectors of THz radiation or components which can control the properties of radiation in the THz frequency range, i.e., filters, modulators, phase and polarization shifters, etc. [12, 13, 14, 15, 16].
## 2 Design of Radial Grouting Range
### Result Analysis
#### 2.2.3 Internal Force Analysis of Secondary Lining  
The surrounding rock's radial grouting plays an important role in the internal force of the tunnel's secondary lining (see Table 4). According to the design scheme, with the tunnel lining anti-seepage grade of P8, the thickness of the double-layer initial support is 80 cm. Without radial grouting, the maximum values of bending moment and axial force are 219 kN.m and 1602.26 kN, respectively. When the radial grouting range is 2 m, the maximum bending moment and axial force become 148 kN.m and 1079.51 kN, respectively. These figures are 32.42% and 32.63% lower than those without radial grouting. When the radial grouting range is 4 m, the maximum bending moment and axial force are 85.4 kN.m and 614.95 kN, respectively. These figures are 42.30% and 43.03% lower than those with the radial grouting range of 2 m. As for the radial grouting range of 6 m, the maximum bending moment and axial force are 75.3 kN.m and 531.43 kN, respectively. They are 11.83% and 13.58% lower than those with the radial grouting range of 4 m. Therefore, the radial grouting range has a small impact on the internal force of the secondary lining after exceeding 4 m.  
As indicated by the analysis above, radial grouting of the surrounding rock can effectively assist the initial support in bearing the peripheral stress and reduce the stress on the initial support. However, when the radial grouting range exceeds 4 m, there is less influence on the grouting range. Therefore, radial grouting within the range of 4.0 m to 4.5 m has to be controlled during design.  
Radial grouting is used for surrounding rock reinforcement in water-rich tunnels under high water pressure. Curtain grouting is also adopted for water plugging in front of the palm surface. Such design is further studied in the following section.
# Research on Dynamic Path Planning of Multi-AGVs Based on Reinforcement Learning
## 3 Global Path Planning Algorithm Based on the Improved Genetic Algorithm
### Optimization of the Fitness Function
#### 3.3.3 Weight-Based Polling Algorithm  
The sum of the current node's busy factor is greater than the maximum \(P_{max}\) value, and the outgoing AGVs will be suspended and enter the queuing session. Next, we calculate the weight of each AGV in the queue according to three dimensions: waiting time, task distance, and the performance level of the AGVs. The calculation formula is as follows:  
\[w_{i}=\frac{1}{(1+\vec{e}(-(T_{w}*T+D+G))\ )},\ i=1,2,\ldots,m; \tag{12}\]  
where \(\frac{1}{1+\vec{e}^{-x}}\) is a normalization function; \(T_{w}\) is the time reward parameter, which is used to increase the weight of AGVs with long waiting times; \(T\) is the waiting time; \(D\) is the task linear distance; and \(G\) is the rank of AGVs, with a maximum rank of 3 and a minimum rank of 1.  
Then, we propose a weight-based smoothing polling algorithm (Algorithm 1), which rationalizes the departure order of AGVs according to their weights. The specific algorithm is as follows:  
```
WBSP Procedure Input: Number of AGVs in waiting status \(N\), AGVs current set of weights \(Curr\), AGVs fixed set of weights \(Aw\), and Serial number of the best AGVs \(Best\). Output:\(Best\) for each AGVs \(i\)  Initialize the current weight of AGVs, \(Curr_{i}=0\)  Calculating the fixed weights of AGVs \(Aw_{i}\) endfor \(Aw_{best}=0\), \(Curr_{best}=0\) while\(i<N\)  Random selection of an AGVs \(i\) \(Curr_{i}=Curr_{i}+Aw_{i}\) if\(Curr_{Best}<Curr_{i}\) \(Best\ =\ i\) \(n=n+1\); endwhile  printBest endprocedure
```  
**Algorithm 1** Weight-based smoothing polling algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline _Serial Number_ & _Number of Passing AGVs_ & _Number of Times AGVs are Used_ \\ \hline
**1** & \(A_{\mathbf{1}}\) & \(M_{\mathbf{1}}\) \\
2 & \(A_{\mathbf{1}}\) & \(M_{\mathbf{2}}\) \\
3 & \(A_{\mathbf{1}}\) & \(M_{\mathbf{3}}\) \\... &... &... \\
**M** & \(A_{\mathbf{n}}\) & \(M_{\mathbf{n}}\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the K-L Algorithm.  
Based on the above algorithm, we can prioritize the AGVs based on the weight size. When AGVs are unable to depart due to peak congestion factor or for other reasons, they can be selected again according to the polling mechanism.  
The Final Fitness Function Is Calculated as Follows  
\[f=\sum_{i=1}^{num}dis(R_{i},R_{i+1})+P_{i}+S_{i} \tag{13}\]  
where \(P_{i}\) and \(S_{i}\) are the node busy factor and path smoothing factor, respectively; and \(dis(R_{i},R_{i+1})\) denotes the straight-line distance between node \(R_{i}\) and node \(R_{i+1}\).  
According to Equations (9)-(12), with fewer turns, the turn angle is smaller, and the node heat value is lower, resulting in a smaller \(f\) value. According to the wheel selection mechanism of the genetic algorithm, it is easy to obtain paths with small \(f\) values, thus preventing the algorithm from selecting paths with too many turns or paths prone to congestion.
## 4 Simulation Results
### Impact of Node Density  
To study the impact of node density on each approach, we varied the number of nodes from 100 to 600 nodes with different initial energy. Figures 6(a)-c show the number of rounds of each approach at  
Figure 6: Impact of the number of cells. (**a**) A grid of 6 \(\times\)6 cells; (**b**) A grid of 8 \(\times\)8 cells; (**c**) A grid of 10 \(\times\)10 cells.  
different node densities with initial energy 0.25 J, 0.5 J, and 1.0 J per node, respectively. The results show that the number of rounds of each approach increases significantly as node density multiplies except for the Direct protocol. CBDAS is also better than the other approaches as illustrated in the results. In CBDAS, as the node density multiplies, the quantity of nodes in each cell increases. The load of the cell head in each cell was thus shared among more nodes. As a result, the energy consumed by each node was not so significant that the number of rounds using CBDAS substantially increased. On average, CBDAS has approximately 1.6 times more rounds compared to PEGASIS, over the three different initial energy levels.