esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In this work a compact WMPAS system for continuous gaseous chloroform monitoring using a home-made PA cell combined with eight microphones has been first demonstrated in detail. The application of WMPAS technology with a high-power DFB laser makes it possible to measure chloroform with high sensitivity in near infrared, and a detection sensitivity of 0.28 ppm with a time period of 20 s was achieved at room temperature (around 20 \({}^{\circ}\)C) and atmospheric pressure. To observe the influence of the adjacent absorption peak, we increased the laser wavelength covering range to 0.58 nm, which limited the modulation depth and sacrificed part of the signal intensity at the same time. The results indicate that WMPAS possesses excellent capability in the detection of gas with complex absorption cross-section.  
Different concentrations of chloroform gas were measured in sequence and the results have shown a good linear relationship between the 1f PA signal intensity and the gas concentrations. A \(R\)-square value of 0.999 was achieved when the gas concentration was less than 625 ppm, which proves the reliability of the system. Long-term monitoring was carried out to evaluate the stability of the system, and the measurement error was limited within \(\pm\)2%, which adequately illustrates the good stability performance of the system. A series of comparative experiments were also been carried out to prove the good anti-interference of dichloromethane on the detection of chloroform. The overall performance of the system has shown great promise for chloroform gas monitoring in industrial applications. In addition, the performance can be greatly improved if combines the system with multi-pass scheme [36].  
[PERSON] and [PERSON] designed/modified the experimental setup, [PERSON] performed the experiments and analyzed the data. [PERSON] wrote the software. [PERSON] wrote the manuscript. [PERSON] and [PERSON] supervised the work and finalized the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Key Research and Development Program of Zhejiang Province (grant number 2021C03178), NingboTech University (grant number 1141257B20200538), Zhejiang University NGICS Platform, and National Natural Science Foundation of China (11621101).  
Not applicable.  
Figure 8: Interference experiment of dichloromethane on chloroform. 1f PA signals of different kinds of gases are plotted with a scanning period of 20 s (colored area is the selected effective area).  
Data Availability Statement: Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors are grateful to [PERSON], and [PERSON] for valuable discussion and help. The authors declare no conflict of interest.
## 4 Results and Discussion
### Local Explanations of Prediction Model Based on SHAP  
The constructed prediction model was applied to a specific area, and the prediction results were visualized. Whereas global explanations focus on the contribution to the target variable, this approach enables explanations of targeted phenomena from a micro perspective. Combining this with domain knowledge allows for a logical interpretation.  
The model was applied to Urayasu City in Chiba Prefecture, which suffered extensive liquefaction damage in the 2011 Great East Japan Earthquake. The selected validation mesh for the area near the Urayasu City Hall was MESH5, with a MESH_CODE of 5339378124. The location of the target regional mesh is shown in Figure 11.  
The attribute data of the target mesh were used as the input explanatory variables. For the JMA seismic intensity at the engineering bedrock, I = 5.5 was used, corresponding to a JMA seismic intensity class 6 lower [46]. Figure 12 shows the results of the local explanation using the SHAP method.  
Figure 11: The location of the target regional mesh for local explanations based on SHAP.  
Local explanations can elucidate why liquefaction occurred in a particular area on a factor-by-factor basis. In this case, the JMA seismic intensity at the engineering bedrock (Ia_min_dI), AVS30, and B_Dist contributed to ground subsidence of 0.040, 0.009, and 0.008 m, respectively. Urayasu City in the Chiba Prefecture consists of reclaimed land and is a coastal area, therefore, its distance to the coast is short. For the same reason, it has sandy soil, and so the ground bearing capacity is low, and the SPT-N value is correspondingly low. Whereas previous evaluations of liquefaction were conducted through numerical simulations and ground surveys, the machine-learning model constructed in this study allows for the simple calculation of how much liquefied ground subsidence takes place in any regional mesh. Similarly, by changing the input seismic motion, liquefaction evaluation assuming a major earthquake is possible, and the factors contributing to ground subsidence can be interpreted from the explanatory variables used.  
Figure 12: Local description results for XGBoost model with MESH_CODE: 5339378124.
# Human Activity Signatures Captured Under Different Directions Using SISO and MIMO Radar Systems
### Contributions  
The problems faced by the aforementioned SISO and MIMO radar systems motivate us to develop a solution for a direction-independent HAR system. To illuminate the indoor environment from different perspectives, we propose to distribute multiple pairs of collocated transmitter-receiver radar antennas in an indoor environment for a direction-independent human activity detection system (see Section 4). This multi-perspective view of a distributed MIMO radar system will allow us to render direction-independence for HAR. The micro-Doppler signatures or radial velocity distribution (see Section 2) obtained using the proposed approach can help us to design HAR systems capable of classifying complex activities. Thus, the proposed framework is a step forward towards a more pragmatic and sophisticated radar-based HAR system.  
The principal contributions of this work are listed as follows:  
1. A basic multi-perspective \(2\times 2\) MIMO radar system is presented which can be easily scaled to a higher number of transmitter and receiver antennas for better performance.
2. We analyze the time-variant (TV) radial velocity distribution and TV mean radial velocity for the proposed distributed antenna configuration.
3. For the \(2\times 2\) MIMO radar configuration, we investigate the impact of a human falling incident and a walking activity on the measured radial velocity distribution and measured mean radial velocity.
4. We analyze the impact of two different activities performed in three different directions on the measured channel characteristics for a SISO and a \(2\times 2\) MIMO radar system. We corroborate the limitations of the SISO radar system by analyzing the real radar data.
5. We demonstrate the robustness of the proposed \(2\times 2\) MIMO radar system against different directions of the actual human walking and falling activity. We show that the proposed solution is able to detect the human gross motor activity in the horizontal _xy_-plane.
6. We analyze the performance of a radar-based passive step counter by integrating it with a SISO radar system. It is shown that the radar-based passive step counter, when used with a SISO radar system, may miss some human walking steps or detect false steps depending on the walking direction.
7. It is shown that by deploying the radar-based passive step counter with the proposed \(2\times 2\) MIMO radar framework, the step counter would accurately detect the number of steps for all considered human walking directions.
8. Finally, we quantify, compare, and numerically assess the performance of the SISO and \(2\times 2\) MIMO radar systems by using the dynamic time warping (DTW) [44] distance metric.
# Low Complexity Deep Learning Framework for Greek Orthodox Church Hymns Classification
## 2 Materials and Methods
### CNN Architectures  
CNNs have achieved impressive results in many research areas, including CV, natural language processing (NLP), audio signal processing, and object detection [35]. The building blocks of CNN architectures are the following:  
* Input layer: This layer accepts the input data in a form suitable for further processing. Usually, the image data are transformed into multi-dimensional arrays with three color channels.
* Convolution layer: Convolution layers are the building blocks of any CNN architecture. They perform the process of feature extraction. The main difference with a fully connected layer is that convolutional layers are characterized by the neuron's receptive field. This receptive field indicates that every single unit receives input from only a restricted area of the previous layer.
* Activation function: In the academic literature, the majority of the CNN architectures use as an activation function; either a rectified linear unit (ReLU) function or some kind of a variant. ReLU is mathematically defined as in [6] \[g(v)=\text{max}(0,v).\] (5)
* Pooling layer: Their purpose is to reduce the size of the incoming data in a computationally efficient manner.
* Flattening: This layer transforms the data into a 1D vector.
* Output layer: This layer outputs the model's prediction.  
Many CNNs utilize several techniques to improve their performance. Dropout [36] was introduced as a regularization technique. When Dropout is utilized, some layer outputs are ignored (\"dropped out\") in a random way, which results in a different layer behavior. The main consequence of this method is that, in each step during training, a different \"view\" of the configured layer takes place. In addition, the training process becomes noisy, since individual nodes within a layer are forced to take on more or less responsibility for the inputs.  
Batch normalization [37] is another widely used method for accelerating and stabilizing neural networks' training. Batch normalization usually calculates during training the mean and standard deviation of each input variable to a layer per mini-batch and uses these results to perform layer standardization. There is an ongoing discussion between ML researchers about the way that batch normalization affects training, particularly if it reduces internal covariance shift or smooths the objective function [38; 39].
## 4 Experiments, Results, and Discussion
### Experimental Results
#### 4.3.3 Ablation Study  
In this experiment, we conducted ablation experiments on our methods on the DOTA dataset to explore the improvement of each method on the network model, mainly including deep separable deconvolution, DCoTNet, EcaNet, optimized network input size, and CSL. Because this paper mainly focuses on the detection of rotating targets, the baseline model for comparison is mainly based on the detection of rotating targets. At the same time, we used Precision, Recall, and mAP as the evaluation indicators of the experiment. The specific experimental results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Backbone** & **Image Size** & **mAP** & **FPS** \\ \hline R2 PN [54] & VGG16 & - & 79.6 & - \\ RRD [55] & VGG16 & 384 \(\times\) 384 & 84.3 & - \\ SARD [56] & ResNet101 & 800 \(\times\) 800 & 85.4 & 1.5 \\ ROI Transformer [57] & ResNet101 & 512 \(\times\) 800 & 86.2 & 5.9 \\ R4 Det [58] & ResNet50 & 800 \(\times\) 800 & 88.17 & 8.6 \\ RepVGG-YOLO & RepVGG & - & 91.54 & 22 \\ DCE-YOLOX & CSPFocus & 768 \(\times\) 768 & 95.9 & 17.5 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Detection results of HRSC2016 dataset.  
Figure 9: The visualization results on DOTA dataset and HRSC2016 dataset. The first two lines are the DOTA dataset, and the third line is the HRSC2016 dataset.  
In the experiment, we found that, after the object detection frame is rotated, the mAP is lower than the horizontal object detection. This is due to the fact that performing detection of rotated objects requires additional consideration of the deflection angle of the object, and therefore the detection accuracy is degraded. Although the mAP is reduced, the actual detection results are greatly improved. The optimized network input size has greatly improved the performance of the network model, and the mAP has increased by 1.3%. This is because the large resolution input makes it easier for the network to identify small targets. The corresponding performance improvement will also increase the number of the network parameters and reduce the detection speed. ExaNet has no impact on the scale of the network model and will improve the detection accuracy of the network model in a limited way. It is a very useful plug-and-play network. DCoTNet extracts the semantic and location information of the target, which can improve the detection effect of the network, and the mAP is increased by 2.72%. Compared with ordinary oversampling, deep separable deconvolution can diversify the pixel values around the center pixel when the resolution is expanded, so that the feature map obtained is closer to the original map, thus improving the accuracy of the network model. The size of its convolution kernel has different effects on the results. After many experiments, we balanced the parameter amount and improved the performance. We set it to 4, which can not only provide detection accuracy, but also accelerate the convergence speed of training.
# Modeling and Performance of the IEEE 802.11p Broadcasting for Intra-Platoon Communication
## 4 Analytical Model
### Performance Measures  
In this section, packet dropping rate and delay are calculated based on our analytical model. The delay in the whole process includes queuing delay and back-off delay. In this paper, the time of transmitting packets to the recipient after successful access channel is ignored. Based on the average queue size and packet arrival rate, we can derive the queuing delay  
\[T_{que} =\frac{\sum_{i=0}^{K-1}\frac{ip_{i}^{que}}{\lambda T_{sv}+p_{0}^{ qtt}}+K(1-\frac{1}{\lambda T_{sv}+p_{0}^{qtt}})}{\lambda/(\lambda T_{sv}+p_{0}^{ qtt})} \tag{21}\] \[=K\frac{\lambda T_{sv}+p_{0}^{que}-1}{\lambda}+\sum_{i=0}^{K-1} \frac{ip_{i}^{que}}{\lambda}\]  
where \(\hat{T}_{sv}=\frac{dH(z)}{dz}|_{z=1}=\sum_{T=0}^{T_{m}}p_{st}^{T}\cdot T\tau\).  
In the case of successful transmission, the delay in the back-off process of the packet competitive channel is  
\[T=\frac{CW}{2}T_{slot} \tag{22}\] \[T_{slot} =\tau[1-(1-q_{em})p]^{N}+T_{c}\{1-[1-1(1-q_{em})p]^{N}-N(1-q_{em} )p[1-(1-q_{em})p]^{N-1}\}\] (23) \[+T_{e}p_{e}N(1-q_{em})p[1-(1-q_{em})p]^{N-1}+T_{s}(1-p_{e})N(1-q_{ em})p[1-(1-q_{em})p]^{N-1}\]  
Thus, the total delay of information transmission in a platoon is  
\[T_{delay}=T+T_{que} \tag{24}\]  
Meanwhile, packet dropping rate equals with the probability of packets transmission failure, and it can be expressed as  
\[P=p_{p}+p_{e}-p_{p}p_{e} \tag{25}\]  
To obtain delay and packet dropping rate of platoon communication, we assume that vehicles always have packets to be sent, i.e., \(q_{em}=0\). By solving Equations (6)-(8) and (10), we can get \(p\) and \(p_{f}\). Then, \(p_{st}^{T}\), \(p_{k}^{que}\) and \(q_{em}\) can be obtained by solving Equations (16), (17) and (19). If \(q_{em}\) is greater than \(1\times 10^{-6}\) (the set threshold), we update \(p\) and \(p_{f}\) to the probability in the condition of buffer may be empty. Otherwise, we do not need to update \(p\) and \(p_{f}\) because the possibility of buffer vacant is very low. Then, we can obtain delay and packet dropping rate by substituting Equations (22) and (23) into Equation (24).
# Review of the Research Progress in Combat Simulation Software
## 4 Typical Combat Simulation Software  
### EADSim  
EADSIM (Extended Air Defense Simulation) [12] is a multi-scenario simulation system for missile and space operations, providing warfighters with an overall solution for analysis, training, and operational planning. After nearly 30 years of development, EADSIM has become one of the most widely used simulation systems in the world. For example, [PERSON] et al. [43] utilized EADSIM for high detail virtual prototyping of defensive systems, and [PERSON] et al. [44] generated a fictitious asset-based defence scenario to illustrate the solution-space exploration across three simulation phases.  
Figure 7 shows the architecture of the EADSIM, which is divided into three parts: the simulation setup, the run-time models, and the post-simulation analysis.  
1. Simulation setup. With the help of a graphical interface, the users can set the basic parameters, check the parameter consistency and boundary, and generate operational scenarios.
2. Run-time model. In the C3I decision-making process, the C3I model deals with an entity's command and decision making; the flight processing model is responsible for updating the movement status of battlefield entities; the detection model simulates the working condition of the sensor; and the propagation model undertakes the simulation of network connectivity. In terms of system models, EADSIM supports operations such as air defense, air-to-ground, air warfare, electronic warfare, and cyber warfare and can model weapons, sensors, and a battlefield environment.
3. Post-simulation analysis. This module carries out statistical analysis on the simulation data, can realize the visualization of the combat simulation process, and can automatically generate reports such as engagement statistics, communication statistics, detection statistics, etc.  
Figure 7: EADSIM architecture.
## 3 Results
### Acoustic-Camera Noise Localization  
The gfai tech GmbH Noise Image software which accompanies the acoustic camera is capable of a range of post-processing, primary of which is producing sound pressure maps to identify the source location. By analysing a selection of data, it was clear that the majority were similar; hence a general example will be given outlining the cause of certain peaks visible on the frequency spectrum. Additionally, the peak in specific loudness for 1500 RPM (shown in Figure 8) at 3 Bark has been analysed. This configuration had the face vent open, half recirc and at 2000 RPM.  
The test selected for general analysis was the front view of the unit, with face vent open and half recirc at 2000 RPM. FFT of the waveforms recorded in this test is shown in Figure 10(a), a zoomed-in image in Figure 10(b). Figure 10(a) shows several high magnitude peaks between 0 and 1 kHz with additional peaks up to 10 kHz. A high magnitude of energy is also present at 20 kHz. Figure 10(b) is zoomed between 0 and 8 kHz. In this Figure peak, frequencies of 23 Hz, 190 Hz, 550 Hz, 720 Hz, 1020 Hz, 1800 Hz, 2800 Hz, and 3100 Hz can be seen. Although the exact frequencies in other tests vary, they typically look similar.  
The noise image software is able to isolate a frequency and identify its location. Unfortunately, when very low frequencies are concerned, such as 23 Hz, the wavelength is too large for accurate localisation. As the frequency increases, reliable localisation also increases. Figures 11(a)-11(b) show sound pressure maps for the other eight identified frequency peaks. It must be noted that the scales in these plots vary. During testing, it was noticed  
Figure 11: FFT of front face test at 2000 RPM, FFT up to 25 kHz (**a**) and up to 9 kHz (**b**).  
Figure 10: Sharpness at all RPMs of each vent configuration in rear view with half recirc (**a**) and full recirc (**b**).  
that 1500 RPM caused an 'annoying' noise which was different to that of the other, often louder, fan speeds. This was reinforced by the specific loudness plot in Figure 8 where a 3 Bark (\(\sim\)300 Hz) peak was present in 1500 RPM but not the other rates.  
Figure 14: Sound pressure map of front face test at 2000 RPM, frequency between 700 Hz and 750 Hz.  
Figure 12: Sound pressure map of front face test at 2000 RPM, frequency between 160 Hz and 210 Hz.  
Figure 13: Sound pressure map of front face test at 2000 RPM, frequency between 1700 Hz and 1900 Hz.  
To assess the cause of the 3 Bark peak at 1500 RPM, shown in Figures 8 and 9 the data from the front test of face vents at half recirc were analysed. The FFTs for this is shown in Figure 15 a,b where a clear peak at 300 Hz, which is significantly higher magnitude than any other frequency, can be seen.  
Figure 16 shows the sound pressure map for this frequency, locating its source towards the bottom of the unit.
## 5 Computational Experiments, Results, and Discussion  
The developed MOS was programmed using the C++ programming language. Like the previously developed NSGA-II approach in Abdelmaguid [4], the Embarcadero 32-bit C++ compiler, version 6.2, was used to generate the executable code. Computational experiments were conducted using a desktop computer with Intel Core i3-7100 dual-core processor running at a clock speed of 3.90 GHz, with a physical memory of 8 GB, that operated on Windows 10. Firstly, fine-tuning computational experiments were used to guide the selection of the parameters of the developed MOSS. Secondly, computational experiments were conducted to assess its performance by comparing its generated non-dominated solutions with exact Pareto front solutions obtained for small-sized instances and solutions obtained using the previously developed NSGA-II approach for small- and large-sized instances.
# Counteracting Selfish Nodes Using Reputation Based System in Mobile Ad Hoc Networks
## 1 Introduction
### Contributions  
This paper aims to exploit the consumption to contribution (C2C) information to detect and isolate selfish nodes in MANETs. Three main contributions of this work follow:  
* Novel Reputation-based Framework: a simple reputation-based framework is designed, which uses the C2C ratio of each node in the network for selfish node detection and cooperation enforcement. Nodes failing to cooperate are detached from the network to save resources of other nodes with a good reputation.
* Performance against Attacker Types: the proposed scheme is investigated against two types of attacks, namely, malicious attacks and partial droppers or selfish attacks.
* Performance Evaluation: it is demonstrated that the proposed scheme outperforms the benchmark scheme in terms of NRL (normalized routing load), PDF (packet delivery fraction), and packet drop in the presence of malicious and selfish attacks. Furthermore, the scheme identifies the selfish nodes quickly and accurately as compared to the benchmark scheme. Moreover, it is also shown that the proposed scheme is scalable to large scale networks.