esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Validation
For example, for Pause Time 1, the HMSFO-EARS technique has revealed a higher THRO of 43 KBPS whereas the NSSROP, PRRP, PDORP, DSR, and LEACH methods have portrayed a lower THRO of 40 KBPS, 38 KBPS, 30 KBPS, 9 KBPS, and 3 KBPS, correspondingly. Furthermore, for Pause Time 10, the HMSFO-EARS method has revealed the higher THRO of 47 KBPS whereas the NSSROP, PRRP, PDORP, DSR, and LEACH techniques have depicted a lower THRO of 45 KBPS, 40 KBPS, 39 KBPS, 19 KBPS, and 9 KBPS, correspondingly. \begin{table}
\begin{tabular}{l l l l l l l} \hline \multicolumn{6}{l}{**No. of Alive Nodes (\%)**} \\ \hline
**No. of Rounds** & **HMSFO-EARS** & **NSSROP** & **PRRP** & **PDORP** & **DSR** & **LEACH** \\ \hline
1 & 100 & 100 & 99 & 99 & 98 & 99 \\ \hline
2 & 99 & 98 & 97 & 98 & 96 & 96 \\ \hline
3 & 97 & 95 & 89 & 88 & 84 & 86 \\ \hline
4 & 82 & 77 & 68 & 63 & 56 & 61 \\ \hline
5 & 70 & 66 & 45 & 40 & 26 & 32 \\ \hline \end{tabular}
\end{table} 
Table 4: NAN analysis of HMSFO-EARS system with existing approaches under different rounds. Figure 4: NAN analysis of HMSFO-EARS system under different rounds. Table 6 and Figure 6 show a comparative delay (DEL) inspection of the HMSFO-EARS with recent techniques. The outcomes show that the LEACH approach has demonstrated poor performance with high DEL values. Then, the PDORP and DSR techniques have showed moderately closer and reduced DEL values. However, the PRRP method has obtained reasonable DEL values, and the HMSFO-EARS approach has attained the lowest DEL values under all pause times. For example, with 10 pause times, the HMSFO-EARS model has provided the minimum DEL of 2.15 ms while the NSSROP, PRRP, PDORP, DSR, and LEACH models have attained an increased DEL of 3.81 ms, 4.88 ms, 11.99 ms, 22.99 ms, and 25.23 ms, correspondingly. \begin{table}
\begin{tabular}{l l l l l l l} \hline \multicolumn{6}{l}{**Throughput (KBPS)**} \\ \hline
**Pause Time** & **HMSFO-EARS** & **NSSROP** & **PRRP** & **PDORP** & **DSR** & **LEACH** \\ \hline
1 & 43 & 40 & 38 & 30 & 9 & 3 \\ \hline
2 & 44 & 41 & 37 & 34 & 10 & 3 \\ \hline
3 & 45 & 41 & 37 & 37 & 12 & 4 \\ \hline
4 & 46 & 42 & 37 & 39 & 14 & 4 \\ \hline
5 & 46 & 42 & 37 & 39 & 17 & 4 \\ \hline
6 & 47 & 42 & 38 & 39 & 17 & 5 \\ \hline
7 & 47 & 43 & 38 & 39 & 18 & 4 \\ \hline
8 & 47 & 44 & 38 & 40 & 19 & 6 \\ \hline
9 & 47 & 44 & 39 & 39 & 18 & 9 \\ \hline
10 & 47 & 45 & 40 & 39 & 19 & 9 \\ \hline \end{tabular}
\end{table} 
Table 5: Throughput analysis of HMSFO-EARS system with existing approaches under different pause times. Figure 5: Throughput analysis of HMSFO-EARS system under different pause times. From these values, it is reassured that the HMSFO-EARS model has accomplished better performance. The enhanced performance of the proposed model is due to the incorporation of the MSFO algorithm with the ABHC concept. Additionally, the inclusion of multiple parameters in the objective function helps in the optimal selection of routes to their destination.
## 4 Results and Discussion
### Temporal Features and Parameter Values
The key reason is, again, the synchronization between acquisition date and the tillering stage, which at this frequency and polarization has a short length. Suphan Buri was the one site where the parameter was set outside the suggested criterion. In summary, the temporal feature descriptors give bounding values for the selection of the rule-based parameters in most cases. Operator experience, local knowledge and ancillary data will improve the algorithms' ability to detect rice. The descriptors only bound the parameters on one side, and it would be desirable to have another set of descriptors to bound the parameters on the other side to complement and support operator skill and local knowledge.
###### Abstract  
The current paradigm in integrating intermittent renewable energy sources into microgrids presents various technical challenges in terms of reliable operation and control. This paper performs a comprehensive justification of microgrid trends in dominant control strategies. It covers multilayer hierarchical control schemes, which are able to integrate seamlessly with coordinated control strategies. A general overview of the hierarchical control family that includes primary, secondary, tertiary controls is presented. For power sharing accuracy and capability, droop and non-droop-based controllers are comprehensively studied to address further development. The voltage and frequency restoration techniques are discussed thoroughly based on centralized and decentralized method in order to highlights the differences for better comprehend. The comprehensive studies of grid synchronization strategies also overviewed and analyzed under balanced and unbalanced grid conditions. The details studies for each control level are displayed to highlight the benefits and shortcomings of each control method. A future prediction from the authors' point of view is also provided to acknowledge which control is adequate to be adopted in proportion to their products applications and a possibility technique for self-synchronization is given in this paper.  
microgrid; hierarchical control; primary; secondary control; droop control; frequency restoration; voltage restoration; grid synchronization +
Footnote †: journal: Applied Sciences
# Application of Deep Learning and Symmetrized Dot Pattern to Detect Surge Arrester Status
## 4 Methodology
### Convolutional Neural Networks  
In recent years, CNN has been extensively used for signal processing and image classification in areas such as face recognition [24], imaging medicine [25], and fault diagnosis [26], and there have been good effects in these applications. This study used CNN to identify the SDP of surge arrester fault states. The image format was an RGB image with a size of 64 \(\times\) 64 pixels. The CNN model architecture designed for surge arrester fault type recognition is shown in Figure 16. The architecture comprised two convolution layers, two pooling layers, one fully connected layer, and the ReLu activation function.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Acronyms** & **Definition** \\ \hline PD & Partial discharge \\ SDP & Symmetrized dot pattern \\ CNN & Convolutional neural network \\ \hline \hline
**Symbols** & \\ \hline \(\gamma(i)\) & Polar coordinate radius \\ \hline \(\alpha_{cw}(i)\) & The clockwise rotation angle of the initial line \\ \hline \(\alpha_{ccw}(i)\) & The counterclockwise rotation angle of the initial line \\ \hline \(x_{i}\) & Vibration value of time \(i\) \\ \hline \(x_{max}\) & The maximum value of the vibration value \\ \hline \(x_{min}\) & The minimum value of the vibration value \\ \hline \(\Delta T\) & Time interval (range value 1–10) \\ \hline \(\psi\) & Initial rotation angle \\ \hline \(k\) & Magnification of the rotation angle \\ \hline \hline \end{tabular}
\end{table}
Table 2: Symbols and acronyms used in Section 4.2.  
Figure 15: Schematic diagram of SDP.
## 3 Materials and Methods
### Proposed Distraction Detection System
#### 3.1.1 Temporal and Spatial Variance for Driver's Distraction Detection  
The study aims to calculate the 3D rotation vector \(R\) corresponding to the panning of a driver's head. If the 3D rotation vector R for the driver's head panning reaches above 20 degrees in a clockwise or anticlockwise direction, then the frame contains Distraction (D); otherwise, it contains No Distraction (ND). It has been observed that the rotation vector R changes with the change in features of the frame. The geometric features for frontal faces are calculated using fiducial facial points. In the case of non-frontal faces, the geometric features are calculated using interpolation based on motion vectors.  
The active appearance model (AAM) minimizes the residual error between the model appearance and the input image; hence, it often fails to accurately converge to the landmark points of the input image. To alleviate this problem, the active shape model (ASM), a fiducial facial point algorithm, is used, which gives 18 facial points, as shown in Figure 2b [69; 70]. The features being used for detecting distractions are explained in Sections 3.1.2-3.1.6.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref** & **Approach** & **Acc** & **Features** & **Limitations** \\ \hline
[45] & genetically weighted CNNs & 90.0\% & face, hand images & high training time \\ \hline
[55] & supervised descent method & 90.0\% & facial features & high complexity \\ \hline
[56] & data-mining & 95.4\% & gaze transition & large cycle time \\ \hline
[57] & gaze detection & 94.6\% & eye pose & large decision time \\ \hline
[62] & CNN, bayesian filter & 92.5\% & lane departure & high complexity \\ \hline
[63] & regression-based ML, fuzzy logic & 80.0\% & speed deviation & low accuracy \\ \hline
[64] & deep learning & 86.0\% & texting & high training time \\ \hline
[49] & gradient boosting & 95.0\% & hand and face images & high complexity \\ \hline
[65] & face-matching-based fuzzy expert system & 78.0\% & eyelid distance, eye closure rate & low accuracy \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of some existing distraction detection methods. Ref = Reference, Acc = Accuracy.
## 2 Materials and Methods  
### Chemicals and Reagents  
Pyrrole monomer (\(\geq\)98%) was purchased from Sigma-Aldrich (Shanghai, China). Sodium 4-[4'-(dimethylamino)-phenyldiazo] phenylsulfonate (\(\geq\)99.9%), iron (III) chloride (FeCl\({}_{3}\), \(\geq\)99.9%), iron (II) chloride tetrahydrate (FeCl\({}_{2}\)-4H\({}_{2}\)O, \(\geq\)99.9%), plytic acid (50% in water), and ammonium peroxulfate (\(\geq\)99%) were bought from J&K Chemical (Beijing, China). All solvents were of HPLC grade and purchased from Merck (Darmstadt, Germany). GO powder was procured from Xianfeng Nanomaterials Technology Co., Ltd. (Nanjing, China). Ultrapure water (18.2 M\(\Omega\)/cm) was prepared by a Synergy System from Thermo (Pittsburgh, PA, USA).  
Different analytical standards of insecticides, including isocarbophos (GBW(E)060544, 99.5%), quinalphos (GBW(E)060995, 99.8%), pirimor (GBW(E)060890, 99.5%), carbaryl (GBW(E)060223, 99.9%), myclobutanil (GBW(E)060979, 99.9%), diniconazole (GBW(E)060885, 99.5%), lambda-cylalothrin (GBW(E)061099, 99.5%), and bifenthrin (GBW(E)060548, 99.6%), were received from the National Institute of Metrology (NIM, Beijing, China). Thorate (99.3%), chlorporrifos (99.4%), phosalone (99.0%), 2,4'-DDT (99.2%), and mirex (99.0%) were purchased from AccuStandard Inc. (New Heaven, CT, USA). A mixed stock standard solution was prepared at a concentration of 10 \(\mathrm{\SIUnitSymbolMicro g}^{-1}\) in acetone. A series of working standard solutions at different concentrations were prepared by appropriately diluting of the stock standard solution with acetone to achieve the desired concentration. All the standard solutions were stored at \(-\)20 \({}^{\circ}\)C in the dark until use.
# Fusion of Linear and Mel Frequency Cepstral Coefficients for Automatic Classification of Reptiles
## 5 Conclusions and Future Work  
Automated methods to detect and identify species are particularly useful for biodiversity studies and conservation purposes. In this paper, a novel automatic method for the bioacoustic recognition of reptile species by a fusion of frequency cepstral features has been presented. Reptile acoustic characteristics have been analyzed to seek the more discriminant features to parametrize their acoustic signals. It has been concluded that MFCCs are able to represent the reptile call efficiently because their acoustic signals are emitted predominantly in low frequencies. However, some species can also produce sounds in high frequencies; hence, LFCCs have also been utilized to hold information regarding that part of the spectra. The experimental results have demonstrated that the fusion of both features allows a broad characterization of the signal, increasing the classification rate. It has been validated in over 27 different reptile species, achieving an average accuracy of 98.52% \(\pm\) 3.26. In addition, the proposed solution has been tested under low training sample conditions, proving the strength of the technique.  
Traditional reptile surveys rely on visual searching, which is costly and time-consuming. Therefore, this approach can lead to the development of new remote monitoring systems for reptile research. In addition, the authors are not aware of other studies that have assessed the use of reptile acoustic signals for their inter-species recognition. However, despite the promising results of this first research, it is necessary increase the corpus and extend the solution to the entire animal group. Furthermore, it would be useful to enhance the approach by recognizing individuals within the same species. Finally, this approach could be applied to classify animals with similar sound production mechanisms (such as frogs or birds) by adjusting the system parameters.  
[PERSON] conceived and performed the experiments. [PERSON], [PERSON] and [PERSON] contributed to the study design, analyzed the data, and wrote the manuscript. All authors have reviewed and approved the final manuscript.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Training (\%)** & **Accuracy (\%) \(\pm\) std** & **Precision** & **Recall** & **F-Measure** \\ \hline
5 & 85.50\% \(\pm\) 20.06 & 0.91 & 0.85 & 0.88 \\
10 & 91.03\% \(\pm\) 14.06 & 0.94 & 0.91 & 0.92 \\
20 & 94.81\% \(\pm\) 8.01 & 0.96 & 0.94 & 0.95 \\
30 & 96.86\% \(\pm\) 5.39 & 0.97 & 0.96 & 0.97 \\
40 & 97.88\% \(\pm\) 3.76 & 0.98 & 0.97 & 0.98 \\
50 & 98.52\% \(\pm\) 3.26 & 0.98 & 0.98 & 0.98 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Classifier performance by training set size.
# Video Interpolation-Based Multi-Source Data Fusion Method for Laser Processing Melt Pool
## 4 Results and Discussion
### Data Fusion Method Evaluation  
#### 4.2.1 Dynamic Analysis of Melt Pool  
Figure 10 displays the dynamic behavior of the melt pool captured by the coaxial visible-light monitoring module, revealing the dynamic evolution of the melt pool formation process. At the initial stage of processing, metal powder is ejected and the machine arm accelerates. Once the arm's speed reaches the scanning velocity, the laser begins to heat the metal powder and substrate, gradually forming the melt pool. Due to the substrate's low initial temperature, rapid heat dissipation, and the poor stability of the melt pool, a large plume is produced. As the size of the melt pool increases and the substrate temperature rises, the plume gradually decreases, and by 180 ms, when the width of the melt pool stabilizes, the variation in the plume also becomes stable.
# [PERSON] Glacier Status Analysis Based on Historical Data and Moving Detection Using RPAS
## 5 Data Capturing
### Historical Image Data  
In this part of the project, the aim was to show the possibility of using different types of image data for long-term monitoring of the state of the glacier and especially the new processing of historical aerial photographs into an orthophoto, which is compatible with modern data.  
There is aerial and satellite data available. Regular satellite imagery has been for civilian use since the 1980s. Historically, this is mainly data from Landsat satellites. Newer satellite data can be found from the Copernicus system. Here we were interested in freely available data only. These are available on Google Earth or on similar map servers, where they are already processed into a map form and are of lower quality, which does not matter in this case, because only the changing shape of the glacier front was analyzed. Professional original satellite data acquired from public funds are today on data archive accessible via access hub like [[http://scihub.copernicus.eu](http://scihub.copernicus.eu)]([http://scihub.copernicus.eu](http://scihub.copernicus.eu)) or [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)). Aerial image data are often used for business, mainly for mapping and typically are not free available on web. Really old historical aerial data can be found in museum data archives. The Knud Rasmussen Glacier area was photographed during the Danish Greenland expedition by the seventh Thule Expedition from 1932 to 1933. It was thus possible to study the condition of the glacier now and compare it with its condition almost ninety years ago. Thank to [PERSON] from the Department of Geoscience and Natural Resource Management, University of Copenhagen, we got a set of historical aerial images from the Danish Greenland expedition in the 1930s (Figure 9).  
Figure 8: A typical photo using NIR camera Canon PowerShot ELPH 110 HS (NIR).
# Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization
## 1 Introduction
### Related Works
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **System Setup** & **Objective** & **Design Variables** & **Decoupling** \\ \hline
[10] & Multi-user RIS-aided & Max. overall rate, & Resource allocation & Max-min allocation algorithm \\  & SISO OFDM network-UL & and minimum rate & & \\ \hline
[11] & Multi-user RIS-aided & Max. sum-rate & Resource allocation, & Combined deep Q networks (DQN) and deep deterministic \\  & MISO OFDM network-DL & Max. sum-rate & passive beamforming & policy-gradient (DDPG) \\ \hline
[12] & RIS-aided & Max. spectral efficiency & Passive beamforming & DRL-DQN \\  & SISO OFDM systems & Max. spectral efficiency & Passive beamforming & DRL-DQN \\ \hline
[13] & RIS-enhanced & & & \\  & two-way D2D & Max. Min. bidirectional & Sub-band allocation, & \\  & multi-pair & weighted sum-rate & the power allocation, and & \\  & OFDM communication systems & & & \\ \hline
[15] & RIS-aided THz & Max. weighted sum-rate & Hybrid analog/digital beamforming & \\  & MIMO-OFDMA system-DL & Max. weighted sum-rate & at the BS and reflection matrix & \\ \hline
[16] & Multi-user OFDMA system & & Power allocation, & \\  & with transmissive & Max. system sum-rate & subcarrier allocation, & \\  & RIS transceiver-Up & and transmissive RIS coefficient & & \\ \hline
[17] & Multi-user RIS-aided & Max. weighted rate, sum-rate, & \\  & MIMO OFDM network-DL & and geometric mean of rate & Active and passive beamforming & Closed form-based algorithms \\ \hline
[18] & RIS-aided & Max. weighted sum throughput & Active and passive beamforming & SCA, and iterative rank \\  & OFDMA-URLLC systems & Max. weighted sum throughput & Active and passive beamforming & \\ \hline
[19] & Multi-user RIS-aided & Max. users’ rate & Passive beamforming & Federated learning (FL), \\  & SISO OFDMA network-DL & Max. users’ random & Deep Neural Network(DNN) \\ \hline
[20] & Multi-user RIS-aided & & Passive beamforming, & \\  & MIMO OFDMA time-frequency & & AO, Lagrange duality \\  & MIMO OFDMA network-DL & Max. users’ common (minimum) rate & resource block as well as & method, SCA \\  & & power allocations & \\ \hline
[21] & Active RIS-aided & & Passive beamforming and & AO, FP \\  & cell-free network- & MISO OFDMA-Up & & \\  & & & \\ \hline This work & Multi-user RIS-aided & & Subcarrier and power allocations, & AO, Riemannian \\  & SISO-OFDMA network-DL & Max-EE & passive beamforming & gradient method (RGM), PSO \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review.
## 2 System Model  
In our system, we consider \(K+M\) vehicles equipped with mmWave mMIMO on a road as shown in Figure 1. Among them, \(K\) vehicles use ISAC signals for V2V communication, simultaneously sharing road safety information and sensing the motion parameters of target vehicles; \(M\) vehicles conduct I2V communication to receive data from the RSU. Considering that the vehicle trajectory may be aligned with the roadway, for simplicity, it is assumed that the RSU knows the motion parameters of all I2V vehicles. To improve spectrum utilization, it is assumed that the I2V link uses orthogonal frequency bands, while the V2V link reuses these orthogonal frequency bands. \(k\) denotes V2V vehicle, \(\mathbf{k}^{\prime}\) denotes the set of target vehicles for a source vehicle, \(K^{\prime}\) represents the size of set \(\mathbf{k}^{\prime}\), and \(m\) denotes I2V vehicle. For convenience of exposition, the vehicles are modeled as point targets following standard assumptions in the literature [33]. To avoid the interference between the echo signals and the transmitted signals, each vehicle is equipped with two separate mMIMO arrays with \(N_{t}\) and \(N_{r}\) antennas. For simplicity, it is assumed that the mMIMO array is a uniform linear array (ULA) [2]. The RSU is equipped with a mMIMO ULA with \(N_{t}^{R}\) transmit antennas. In the following subsections, we first present the general framework and then provide a detailed description of the signal model.
# Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks
## 3 Related Work
\begin{table}
\begin{tabular}{l l} \hline \hline
**Symbol** & **Definition** \\ \hline \(\lambda_{su}\) & SU reception threshold \\ \(\lambda_{pu}\) & PU reception threshold \\ \(S\) & SU received signal \\ \(H_{0}\) & PU’s hypothetical idle state \\ \(H_{1}\) & PU’s hypothetical active state \\ \(n(t)\) & additive white Gaussian noise (AWGN) \\ \(P_{d}\) & probability of detecting PU activity \\ \(P_{f}\) & probability of false detection of PU activity \\ \(S^{p}(t)\) & PU signal waveform \\ \(\lambda_{pu}\) & spectrum sensing decision threshold \\ \(n\) & number of SUs \\ \(P_{o}\) & SU’s transmission power \\ \(d_{o}\) & SU’s transmission reference distance \\ \(\alpha\) & path-loss exponential \\ \(Z_{o}\) & PU’s transmission radius \\ \(Z_{r,i}\) & base station’s keep-out radius \\ \(I_{i}\) & Interference on channel \(i\) \\ \(\ell_{i}\) & mobility-induced guard (mguard) distance \\ \(P_{i}\) & PU transmission power on channel \(i\) \\ \(\Re\) & transmission range of an SU transceiver pair \\ \(v\) & relative speed of SUs \\ \(T_{PU1}^{eff,i}\) & PU’s transmission period \\ \(\tau_{on,i}\) & PU’s arrival time \\ \(\tau_{off,i}\) & PU’s absence time \\ \(P_{on,i}\) & channel transition probability of ON to OFF state \\ \(P_{off,i}\) & channel transition probability of OFF to ON state \\ C & number of channels \\ \(T_{s,i}\) & period for spectrum sensing \\ \(T_{su,i}\) & period for channel switching \\ \(T_{i}\) & channel access period \\ \(r_{ret}\) & event radius \\ \(\overline{T_{r}}\) & maximum route negotiation time \\ \(R_{i}\) & achievable spatio-temporal data rate on channel \(i\) \\ \(\alpha_{i}\) & spatio-temporal availability of channel \(i\) \\ \(T_{su,i}\) & packet transmission period \\ \(\theta\) & scaling factor \\ \(E_{th}\) & SU’s node energy \\ \(E_{th}^{\min}\) & minimum allowed SU node energy \\ \(clc_{req}\) & data channels in route request packet \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations. PU, primary user; SU, secondary user.