esa-sceva/satcom-chunk-collection huggingface dataset:
# An Efficient Trust-Based Scheme for Secure and Quality of Service Routing in MANETs
## 1 Introduction
The proposed solution relies on the trust mechanism to provide reliable performance and secure links for data transmission and energy efficiency. The remainder of this paper is organized as follows. Section 2 contains the review of some related work. Section 3 presents the proposed secure and QoS routing scheme after an adversary model is described and Section 4 contains some evaluation results to show the advantage of the proposed scheme over two other comparable schemes in terms of some important matrices. Finally, Section concludes this paper.
## 2 Proposed Method
### Label Classifier Based on Local Minimum Mean Discrepancy
#### 2.3.1 Cross-Entropy Loss Function  
The function of cross-entropy loss in the label classifier is to measure the discrepancy between the predicted label and the real label and to optimize the differentiable parameters  
Figure 3: The proposed RASAN-W architecture in this article consists of three parts: a feature extractor \(G_{f}\) (orange), label classifier \(G_{f}\) (green), and domain discriminator \(G_{d}\) (blue). The basic framework of the feature extractor is ResNet-50 and the label classifier and domain discriminator are composed of different fully connected layers. The gradient reversal layer is inserted between the feature extractor and the domain discriminator to obfuscate the features of the source domain and the target domain, reducing the differences in feature distribution between domains.  
in the label classifier by minimizing the discrepancy to ensure classification accuracy in the bearing fault diagnosis process. The cross-entropy loss function is expressed as follows:  
\[L_{\text{cls}}(\theta_{f},\theta_{y}) =-\frac{1}{M}\overset{M}{\sum}J_{y}(\hat{y}_{i}^{s},y_{i}^{s})\] \[=-\frac{1}{M}\overset{M}{\sum}J_{y}(G_{y}(G_{f}(x_{i}^{s};\theta_ {f});\theta_{y});y_{i}^{s}) \tag{1}\] \[=-\frac{1}{M}\overset{M}{\sum}\sum_{i=1}^{C}I[y_{i}^{s}=c]\log(G_ {y}(G_{f}(x_{i}^{s};\theta_{f});\theta_{y}))\]  
where \(G_{f}(x_{i};\theta_{f})\) is the output features of the feature extractor for input source domain samples \(x_{i}^{s}\), \(\theta_{f}\) is the differentiable parameter in the feature extractor, \(M\) is the size of the input sample number for each training batch, \(G_{y}\) is the label classifier with differentiable parameters \(\theta_{c}\), and \(y_{i}^{s}\) is the label of the input source domain sample \(x_{i}^{s}\).
## 2 Online Measurement System Overall Architecture
### Optical Equipment  
The optical equipment contains 1 CMOS area array industrial camera, 1 industrial lens, 1 laser sensor, 1 industrial switch and single-mode optical fiber. These equipments were purchased from Hikvision, Hangzhou and China. The pixel count of the area array camera sensor is 4096 \(\times\) 3000. For the sake of the long-distance identification, a 12-million-pixel lens with 25 mm focal length is selected. To accurately trigger the camera's operation, an ultra-compact and sensitive red light laser sensor was selected.
# Speech Emotion Recognition Using Deep Learning Transfer Models and Explainable Techniques
## 3 Materials and Methods
### Gaussian Data Selection Mechanisms  
When acquiring speech data, matching the length of the data is difficult, even if the same lines are used. The data length varies depending on the speaker's pronunciation speed, interruptions, and noise. The consumption of computing resources increases with the increase in the data scale, which leads to inclusion of unnecessary sections, unrelated to emotions. Various data preprocessing methods are being studied to select and exclude unnecessary sections.  
Figure 3 illustrates the application of a Gaussian distribution and correlation coefficients to select unnecessary sections in the acquired data. The selection process excludes  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author** & **Purpose** & **Dataset** & **Model** & **Performance** \\ \hline
**[PERSON]** & Speaker’s place recognition & VCTK & CNN/Grad CAM & MAA: 0.351, \\  & & & & UAR: 0.356 \\ \hline
**[PERSON]** & Acoustic scene classification & DCASE 2016 challenge & ResNet/Grad CAM & 91.82\% \\
**[PERSON]** & Heartbeat sound classification & Heart sound database & 2D-CNN/Grad CAM & 86\% \\ \hline
**[PERSON]** & Predict sound recovery & Patients and vocal data & EfficientNet + \\  & & by DIRAMS & LSTM/Grad CAM & AUROC: 0.822 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Window Size** & **Overlapping Size** & **Window Type** & **Range of Frequency** \\ \hline STFT parameter & 1200 & 720 & Hann \"periodic\" & One-way \\ \hline \hline \end{tabular}
\end{table}
Table 2: Short-time Fourier transform (STFT) parameters to convert log-Mel spectrogram.  
Figure 2: Speech-data-to-spectrogram conversion process.  
heterogeneous data based on the correlation coefficients in the divided data section rather than simply removing sections with low or high data values by Algorithm 1. After calculating the mean and variance of the divided section, expressed as a Gaussian distribution, the correlation coefficients between all distributions are calculated, normalized, and selected based on the set threshold. Using the above method, sections having a low correlation with the data containing emotional features (i.e., sections that are heterogeneous from the Gaussian distribution of the emotional feature section) are excluded from learning. Accordingly, the data scale and computing resource consumption are reduced while maintaining classification accuracy.
## 4 Design of CNN-BLS Tremor Filter
### Algorithm of CNN-BLS  
#### 4.1.1 Broad Learning Network  
Professor [PERSON] and [PERSON] proposed a novel and simple network frame in [28]. Moreover, [PERSON] and [PERSON] further demonstrated the approximation capability of the BLS for generic functions and proposed several variants of the original BLS in [29]. Firstly, as shown in Figure 6, \(G=[x,y,z]\) is denoted as the input data, where \(X\), \(Y\), and \(Z\) are the three-axis input signals, respectively. The input data \(G=[x,y,z]\) are randomly mapped to \(n\) groups feature nodes, which can be expressed as the equation of the form  
\[Z_{i}=\phi\Big{(}GW_{f_{i}}+\beta_{f_{i}}\Big{)},\quad i=1,\ldots,n, \tag{9}\]  
Figure 5: Mathematical model of the tremor filter.  
where \(W_{f_{i}}\) and \(\beta_{f_{i}}\) are random weights and random biases. \(\phi(\cdot)\) represents a random mapping function.  
**Remark 1**.: _For the original BLS, the feature layer is obtained from a random mapping function, which regularizes the broad network. In addition, SAE is used to fine-tune the weights of feature extraction (\(W_{fi}\), \(\beta_{f_{i}}\)), and it has a good performance in classification. However, the original BLS feature extraction method cannot obtain enough information for time-series features that adjacent sequences have connections. Thus, a new network structure for feature extraction is proposed to replace the SAE of the original BLS._  
All generated feature nodes can be written as \(Z^{n}=[Z_{1},Z_{2},\ldots,Z_{n}]\), and then, are activated to be \(m\) enhancement nodes by a activation function \(\xi(\cdot)\). The j-th enhancement node is represented as  
\[H_{j}=\xi\Big{(}Z^{n}W_{e_{j}}+\beta_{e_{j}}\Big{)},\quad j=1,\ldots,m, \tag{10}\]  
where \(W_{e_{j}}\) and \(\beta_{e_{j}}\) are also random weights and random biases. Denote the input layer as  
\[I_{n}^{m}=[\begin{array}{c}Z_{i}\end{array}|\begin{array}{c}H_{j}\end{array} \end{array},\;i=1,\ldots,n,j=1,\ldots,m. \tag{11}\]  
Finally, the output of BLS can be represented as  
\[Y=[Z_{1},\ldots,Z_{n}\end{array}|\begin{array}{c}H_{1},\ldots,H_{m}]W^{*}=I_ {n}^{m}W^{*}. \tag{12}\]  
According to the pseudo-inverse calculation equation, the Equation (12) can be rewritten as the following form  
\[W^{*}=(I_{n}^{m})^{+}Y=((I_{n}^{m})^{T}I_{n}^{m})^{-1}(I_{n}^{m})^{T}Y. \tag{13}\]  
A ridge regression algorithm is used to avoid the inexistence of pseudo-inverse of \(I_{n}^{m}\)[37], and Equation (13) is rewritten as  
\[W^{*}=(\lambda E+(I_{n}^{m})^{T}I_{n}^{m})^{-1}(I_{n}^{m})^{T}Y. \tag{14}\]
## 3 Results
### Plume Parameters
The disappearance of the plume cluster at the tail end suggested that the ablation plume diverged at small angles, and a majority of particles maintained forward velocity, suggesting that more mass can reach the discharge channel during a pulsed discharge and is expected to achieve better performance in electromagnetic acceleration. Figure 8 shows the normalized plume relative luminosity of the axis distribution for the carbon-doped composite propellant samples. The maximum relative luminosity of the plume generated by laser ablation of the nano-carbon-doped composite propellant sample was observed within a range of 0-1 cm from the ablation surface. As the distance from the ablation surface increased, the relative luminosity fluctuated, but it consistently remained above 0.5 throughout the laser duration. For the graphite-doped composite propellant sample, the maximum relative luminosity of the plume was observed within the range of 0-2 cm from the ablation surface. Beyond this distance, the relative luminosity gradually decreased. Furthermore, the area exhibiting a relative luminosity greater than 0.5 was smaller compared to that of the nano-carbon-doped composite propellant sample. As for the graphene-doped composite propellant sample, the maximum relative luminosity of the plume consistently appeared within the range of 0-0.5 cm from the ablation surface. As the distance from the ablation surface increased, the relative luminosity decreased. Additionally, noticeable fractures in the plume were observed at the late stage of ablation, and there were multiple discontinuous areas after 3.5 ms.  
As mentioned before, the doped graphene facilitated ionization of the plume through reactions with the initiated products of PTFE. The optical emission in a vacuum arose from the excitation with ambient gases and the impact excitation of electrons, with the latter being the dominant factor [37]. This also explains why graphite-doped composite propellant generated a plume with a maximum relative luminosity closest to the ablated surface, followed by nano-carbon, and lastly, graphite. The increasing radiative emission over time was consistent with the deceleration of the plume as a consequence of the kinetic energy conversion. As the distance from the ablation surface increased, electron collision-induced radiation gradually superseded the chemical reaction, and the luminosity of the plume gradually diminished as the plasma density decreased.
## 4 Simulations
### System Framework  
The system framework of optimized routing in the community-based OppNets consists of three parts: data acquisition, methods, and results, as shown in Figure 3.  
Figure 3: System framework for optimized routing in the community-based Opportunistic Networks.  
In the part of data acquisition, the raw data can be obtained from three ways: open experimental data (e.g., the Infocom 2006 trace datasets), simulation and experimental data (e.g., ONE simulator), and actual data acquisition. These data may be incomplete, noisy, and inconsistent, and the use of data cleaning techniques to correct detected errors and omissions is needed. These improved data were saved to the cache of nodes to use again in the future. Then, three important data (including social features, historical encounters, and distances between nodes) can be gained after feature selection. It should be noted that social features of nodes are static internal and long-term characteristics and only need to be collected once, while historical encounters and distances between nodes are dynamic and need to be updated continually.  
In the methods, its main task is to the stated method in Section 3--to construct communities and deploy adaptive nodes to the network. In the result showing, its main task is to the system platform (e.g., the simulator ONE) to adjust the parameters of algorithms and evaluate the performance of the optimized routing.
# A Spatio-Temporal Model for Forest Fire Detection Using HJ-IRS Satellite Data
## 3 Methodology
### Overview of the Improved Algorithm
#### 3.2.1 Data Preprocessing  
The preprocessing procedure for the IRS images includes the following processes. (I) Resampling: Because the pixel resolution of the band 8 IRS images is 300 m, whereas the resolutions of the three other (band 5, band 6, band 7) are 150 m, we converted band 8 to 150 m by nearest-neighbor resampling, which assigns the digital number of the closest input pixel to the corresponding output pixel; (II) Geometric calibration: The image acquired on 1 April was chosen as the reference image, and the other images were corrected using geometric calibration with a correction error of 0.5 pixels or less; (III) Cutting out the region-of-interest (ROI): A 90 \(\times\) 90 km\({}^{2}\) square region (600 \(\times\) 600 pixels for HJ-IRS) was chosen as the validation site. A ROI between 48\({}^{\circ}\)11\({}^{\prime}\)49.0\({}^{\prime}\)N and 49\({}^{\circ}\)0\({}^{\prime}\)45.0\({}^{\prime}\)N latitude and 127\({}^{\circ}\)57\({}^{\prime}\)41.8\({}^{\prime}\)E to 129\({}^{\circ}\)10\({}^{\prime}\)39.4\({}^{\prime}\)E longitude covered the entire fire zone in the Yinanhe forest; (IV) Reflectance calculation and brightness temperature inversion: This process was used to convert the digital number (DN) values to radiances, reflectance and BTs. According to the absolute radiometric calibration tests conducted by the China Centre for Resources Satellite Data and Application, the DN values for bands 5 were converted to radiance values using Equation (3), whereas the DN values for bands 7 and 8 were converted to radiance values using Equation (4). The radiances for band 5 were converted to reflectance using Equation (5). The radiances for bands 7 and 8 were converted to BTs using Equation (6), which was derived using [PERSON]'s law.  
\[L=\frac{DN}{g} \tag{3}\]  
\[L=\frac{(DN-b)}{g} \tag{4}\]  
\[R=\frac{\pi\cdot L\cdot ds^{2}}{E\cdot\cos\theta} \tag{5}\]  
\[T=\frac{C_{2}}{\lambda\log(\frac{C_{1}}{\lambda^{3}\pi L}+1)} \tag{6}\]  
where \(L\) is the radiance, \(DN\) is the observed value, \(g\) is the gain coefficient, and \(b\) is the offset, which we obtained from the data item description of HJ-1B. In Equation (5), \(R\) is the reflectance, \(ds\) is the distance between the earth and the sun, \(E\) is the mean solar spectral irradiance, and \(\theta\) is the solar zenith angle. In Equation (6), \(T\) is the brightness temperature, \(\lambda\) is the center wavelength, and \(C_{1}\) and \(C_{2}\) are constants whose values are 3.741775 \(\times\) 10\({}^{-22}\) Wm\({}^{3}\cdot\upmu^{-1}\) and 0.0143877 mK, respectively, according to [25].
# Smart City Pilot Projects Using LoRa and IEEE802.15.4 Technologies
## 4 Smart Lighting Testbed
Finally, the coordinator forwarded these data to the control center by means of the cellular network (Figure 1b). The whole system has been successfully tested, however no smart parking service has been activated yet, because it is not among the highest priorities of the municipality. Indeed, activating such service would require further significant investments for the capillary deployment of magnetic sensors and, to a lesser extent, for the implementation of the hardware/software platform in charge of conveying to drivers, through the cellular network, the parking lot availabilities.
## 1 Introduction  
Humans rely on a healthy nervous system to control their movements. When their muscles react to nerve impulses (electrical signals) sent by the brain in cases of injury and nerve diseases, this important link can be broken. Understanding the signals between the muscles and the brain could have a significant impact on improving the lives of those suffering from nerve diseases or nerve damage. In this paper, we start by measuring the bioelectrical signals at the bicep and then develop a model using an artificial neural network (ANN).  
Several techniques are used to observe bioelectrical signals and electrical activity of the human body; some examples are the electrocardiogram (ECG), electroencephalogram (EEG) and electromyography (EMG). The peak amplitude of electromyography (EMG) signals is very low (a few microvolts), thus, such signals need to be amplified to sense it and it also needs to be converted from the analog (continuous) form to a digital (discrete) form to record it in a computer for further processing. This research will measure the muscle electrical activity using Surface Electromyography(SEMG), which involves placing electrodes on the skin to capture the electrical activity of the muscle and record it in a computer after amplifying and converting it to a digital signal [1].  
The current research will focus on measuring electrical activity of the bicep muscle during dynamic contraction (concentric and eccentric contraction). Such bicep activity is important in a variety of tasks involving rotation of the forearm around the elbow joint. The recorded weak electrical signals naturally contain unwanted information (that is noise) from wide spectrum of sources such as electronic equipment and physiological factors. This research will further process the SEMG signals to extract smooth signals by applying a low pass filter after determining the cutoff frequency utilizing the fast Fourier transform (FFT).  
The main objective of this research is to model nerve signals for the human bicep muscle for future smart systems. Since SEMG is nonlinear in nature, this research will use an artificial neural network (ANN) to predict it.
## 3 System Model
### Random Antenna Array  
To steer a beam in the desired direction, each sensor selects a boresight direction \(\theta\) from a uniform random distribution on \([0,2\Pi)\), completely independent of other sensors. We are interested in improving the overall connectivity to enable sensors to transmit to a larger distance. However, this increase in transmission range is bounded to a certain direction. Hence, the sensors disconnect the connections to other sensors located nearby and might end up being isolated if the mainbeam is too narrow. To overcome this problem, a CB that uses random beamforming by defining the probability of two randomly selected sensors in random deployment are connected via a multihop path is adopted in this paper. Since the deployment of sensors depends on a random distribution, the direction of any other sensor from a chosen sensor has a uniform distribution as well. Therefore, the antenna gain for any azimuthal angle \(\phi\) being uniformly distributed over \([0,2\Pi)\) is given as [18]  
\[AF^{\kappa}(\phi_{T},\phi_{R}/r,\theta)\triangleq\frac{1}{(2\Pi)^{3}}\int_{0}^ {2\Pi}\int_{0}^{2\Pi}(G(\phi,\phi_{T},k)G(\Pi+\phi,\phi_{R},k))^{\frac{2}{2}}d \phi_{R}d\phi_{T}d\phi \tag{9}\]  
where \(G(\phi,\phi_{T},k)\) and \(G(\phi,\phi_{R},k)\) are the transmit and receive antenna gains, respectively.
# Temporary Road Marking Paint for Vehicle Perception Tests
## 3 Experiment
### Data Evaluation  
The goal of the data evaluation was to analyze temporal changes in the visibility of the road markings over the course of a test drive with a maximum duration of four weeks.  
The data of the individual road marking lines recorded with the retroreflectometer were compared with the data of the asphalt. For the evaluation of reflectivity, the means of the four values collected per measurement per sample were determined. Subsequently, these means were compared to the recommended minimum values of the luminance coefficient for used road markings specified by the German Road and Transportation Research Association (Forschungsgesellschaft fur Strassen- und Verkehrswesen or FGSV) [7]. These values define the requirements for the visibility of road markings in daylight and nighttime conditions for Germany. Figure 5 shows the measurement results for daytime visibility (Q\({}_{\text{d}}\)). The dashed horizontal green line marks the minimum value, which was set at 130 mcd/(lx \(\times\) m\({}^{2}\)). For the values of nighttime visibility (R\({}_{\text{L}}\)), the threshold was set at 100 mcd/(lx \(\times\) m\({}^{2}\)), as can be seen in Figure 6. According to the FGSV, the determination  
Figure 4: Retroreflectometer measurement. The measurement device creates diffuse lighting similar to daylight conditions to measure diffuse reflectivity and directional light similar to the headlights of vehicles to measure retroreflectivity.  
of the luminance coefficient is only comparable when the marking surface is dry. Thus, the data were collected one day after removal, as the asphalt was initially wet from the removal with the high-pressure water cleaner.  
For camera sensors, a key attribute for visibility is the contrast between the road marking and the pavement. To analyze the contrast, we used RGB images created with a smartphone. The smartphone images allowed a better analysis of the contrast compared with the vehicle camera images due to the direct, parallel orientation toward the road marking on the ground. We converted the RGB images into grayscale images using the software GIMP (Version 2.10.24). We then created grayscale plots (8-bit scale) using the software ImageJ (Version 1.54d 30 March 2023) to analyze the difference between the asphalt and the paint. This was achieved by comparing the intensity of the pixels in a selected area of the road marking paint with the unmarked road surface. The gray values are dependent on the incidence of light [6]. Hence, this analysis only allows for a comparison  
Figure 5: Diffuse reflectivity (daytime).  
Figure 6: Retroreflectivity (nighttime).  
with the adjacent road surface but not with a measurement from another day, since weather differences affect diffuse lighting.