esa-sceva/satcom-chunk-collection huggingface dataset:
# Non-Mypopic Beam Scheduling for Multiple Smart-Target Tracking in Phased Array Radar Networks
## 2 Model Description and Problem Formulation
### Indexability and Whittle Index Policy  
Consider now the following structural property of subproblems (18), referred to as indexability in [16], which is formulated next as in (Section 3.2 in [30]). Note that target \(n\)'s subproblem (18) is indexable if there exists an index \(\lambda^{*}_{n}\): \((\mathbb{S}^{\text{L}}_{++},\mathbb{R}^{4})\to\mathbb{R}\) that characterizes its optimal policies, as follows: for any \(\lambda\in\mathbb{R}\), when the target is in the TEC state \(\mathbf{P}_{n}\) and dynamic state \(\hat{\mathbf{x}}_{n}\), \(\lambda^{*}_{n}(\mathbf{P}_{n},\hat{\mathbf{x}}_{n})>\lambda\) is a necessary and sufficient condition so that taking action \(a_{n}=1\) is optimal. Conversely, if \(\lambda^{*}_{n}(\mathbf{P}_{n},\hat{\mathbf{x}}_{n})\leq\lambda\), then the optimal action is \(a_{n}=0\).  
If each subproblem were indexable, the [PERSON] index policy would be to track up to \(K\) out of the \(N\) targets with largest index values, among those with non-negative indices. If the current index of a target is negative, it is not tracked.  
Yet, at present, it is unknown whether restless projects with the multi-dimensional non-linear filter such as those above are indexable, even for a single dynamic model (\(M=1\)). Indexability has only been established (in [40]) for the special case of target tracking with scalar Kalman filter dynamic and a single dynamic model, by applying the PCL-indexability approach for scalar-state projects in [22].
## 3 Results and Discussion
### Compressive Strength  
Figure 3 presents the experimental results of the compressive strength tests of the foamed concrete mixes. All the mixes had a compressive strength higher than 1 MPa, reaching 3.8 MPa for mix C/FA/FP50, which is superior to the strength of foamed concrete with the same density class found in the literature [6]. It is obvious from the results that the compressive strength of foamed concrete depends mainly on both mixture composition and dry density. Compared to the control mix with cement (mix C), the addition of fly ash (mix C/FA) increased the dry density and reduced the compressive strength. For mixes with the same binder content (mixes C/FA/FP50 and C/FA/FP100), the compressive strength increased with dry density. When the density was reduced, the compressive strength decreased significantly (mixes C/FA/FL50 and C/FA/FL100). Incorporation of fly ash as a cement replacement by 25 wt% had a marginal influence on the dry density, from 480 to 486 kg/m\({}^{3}\), as a result of the difference in specific gravity between cement and fly ash. However, significant effects were clearly detected in the results of compressive strength tests. Here, there was a reduction from the 3.35 MPa of the control mix (mix C) to 1.25 MPa for the mix with fly ash (mix C/FA). This strength  
Figure 2: Dry densities of foamed concrete mixes.  
degradation can be explained by the slow hydration rate of fly ash as compared to cement, which affects concrete strength negatively. By adding fly ash to the foamed concrete, the dry density increased by about 2%, while the compressive strength fell by about 62%.  
The use of fine lightweight aggregates affected the mechanical properties of the foamed concrete, as can be seen from the results of the compressive strength tests in Figure 3. No clear trend could be observed regarding the influence of fine lightweight aggregates on foamed concrete strength. The addition of fine perlite (with a specific density of 2.38 g/cm\({}^{3}\)) increased the compressive strength (mixes C/FA/FP50 and C/FA/FP100), as compared to the mix with fly ash (mix C/FA). This can be attributed to the high fineness and pozzolanic activity of fine perlite, as has been reported by other researchers [37]. On the other hand, fine Liaver did not affect the compressive strength of foamed concrete clearly. The density of foamed concrete with fine Liaver was lower than that of the control mix. A small improvement in compressive strength was detected in foamed concrete mixes with fine Liaver. Fine Liaver is a non-reactive material which is lighter than fly ash. As a result, in comparison to the fly ash mix (mix C/FA), the fine Liaver mix had a lower density and a marginally higher strength. Compared to the control mix, foamed concrete mixes with Liaver (C/FA/FL50 and C/FA/FL100) exhibited lower compressive strength. This can be attributed to the low cement content in the Liaver mixes, because of the addition of aggregate to the paste. In addition, the incorporation of fly ash in the Liaver mixes reduced their compressive strength as a result of Liaver's low hydration rate, as compared to cement.
# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 2 Methods
### Swin Transformer Extracts Branches
#### 2.2.3 Residual Feature Fusion Block  
Once the feature extraction phase is complete, we obtain features \(\text{MRDB}_{bs}\), \(\text{MRDB}_{pan}\), and \(\textit{swin}_{bs-pan}\). To effectively combine these features, we designed a feature fusion module, which will be discussed in this section. In the feature reconstruction stage, this paper selects the residual fusion block to reconstruct the features extracted from the three branches to reduce the feature loss caused during fusion. The residual fusion block consists of a \(1\times 1\), a \(3\times 3\) convolutional, and a ReLU layer, the exact structure of which is shown in Figure 5. CNN is utilized to extract fused features, and the ReLU layer is employed to enhance nonlinearities and improve the model's representation. The fusion process starts by concatenating the features extracted from the three branches, after which the image channels are converted to be consistent with HS using a convolution layer, as shown in Equation (6).  
\[F_{t}=H_{res}\Big{(}concat\Big{(}\text{MRDB}_{bs},\text{MRDB}_{pan},\textit{ Swin}_{bs-pan}\Big{)}\Big{)} \tag{6}\]  
where \(\text{MRDB}_{bs}\) denotes Hs features extracted using the MRDB, \(\text{MRDB}_{pan}\) denotes PAN features extracted using the MRDB branch, \(\textit{Swin}_{bs-pan}\) denotes HS and PAN features extracted by the Swin transformer branch, \(F_{t}\) represents the output of the three-stream feature extraction module, and \(H_{res}\) denotes the residual feature fusion operation.  
After the residual fusion block, we obtain a high-resolution hyperspectral image \(F_{t}\) that is consistent with the hyperspectral image band. To keep the original spectral information after multi-level fusion, we sum the Hs\(\uparrow\) and fusion characteristics, as shown in Equation (7).  
\[X_{t}=F_{t}+\text{Hs}\uparrow \tag{7}\]  
where \(F_{t}\) represents the output of the residual fusion block, Hs\(\uparrow\) represents the up-sampled Hs, and \(X_{t}\) represents the final output of the network.
# Deep Learning Forecasting Model for Market Demand of Electric Vehicles
## 5 Discussion
### Comparison of Proposed Model with Existing Methods
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Output** & **Methods** & **MAE** & **RMSE** & **MSE** & **R-Squared** \\ \hline \multirow{8}{*}{ALL} & DT & 2.9425 & 3.8339 & 16.879 & 85.54 \\ \cline{2-6}  & LR & 2.5215 & 3.1271 & 10.198 & 91.80 \\ \cline{2-6}  & Lasso-LR & 2.8003 & 3.6226 & 13.6815 & 89.48 \\ \cline{2-6}  & ElasticNet-LR & 2.7737 & 3.5811 & 13.2780 & 89.71 \\ \cline{2-6}  & Ridge-LR & 2.6116 & 3.2884 & 11.3243 & 91.12 \\ \cline{2-6}  & KNN & 2.8507 & 3.7130 & 16.6645 & 83.74 \\ \cline{2-6}  & SVM & 4.4557 & 5.9084 & 39.0444 & 75.60 \\ \cline{2-6}  & ABR & 2.6559 & 3.4348 & 13.6880 & 87.86 \\ \cline{2-6}  & Our (EVs-PredNet) & **2.0550** & **2.6285** & **7.4550** & **94.67** \\ \hline \multirow{8}{*}{BEV} & DT & 5.1888 & 6.4857 & 45.4623 & 84.12 \\ \cline{2-6}  & LR & 3.7475 & 4.7996 & 24.5736 & 91.95 \\ \cline{2-6}  & Lasso-LR & 4.2145 & 5.1830 & 28.5089 & 90.44 \\ \cline{2-6}  & ElasticNet-LR & 4.2165 & 5.1840 & 28.4818 & 90.43 \\ \cline{2-6}  & Ridge-LR & 3.8956 & 4.8768 & 25.5250 & 91.57 \\ \cline{2-6}  & KNN & 3.9573 & 5.1996 & 28.8485 & 89.54 \\ \cline{2-6}  & SVM & 8.9051 & 12.1546 & 170.360 & 54.51 \\ \cline{2-6}  & ABR & 4.7303 & 5.7607 & 36.6692 & 88.57 \\ \cline{2-6}  & Our (EVs-PredNet) & **3.6705** & **4.7297** & **24.4612** & **92.09** \\ \hline \multirow{8}{*}{HEV} & DT & 5.4582 & 6.8771 & 56.3469 & 68.73 \\ \cline{2-6}  & LR & 4.9470 & 6.2458 & 39.5813 & 81.74 \\ \cline{2-6}  & Lasso-LR & 5.3657 & 7.0166 & 50.9045 & 77.33 \\ \cline{2-6}  & ElasticNet-LR & 5.3378 & 6.9637 & 49.9973 & 77.65 \\ \cline{2-6}  & Ridge-LR & 5.2036 & 6.5875 & 44.2514 & 79.80 \\ \cline{2-6}  & KNN & 5.1340 & 6.6862 & 50.5554 & 74.55 \\ \cline{2-6}  & SVM & 7.8272 & 10.1949 & 113.3210 & 59.09 \\ \cline{2-6}  & ABR & 4.7991 & 6.1579 & 44.2127 & 76.21 \\ \cline{2-6}  & Our (EVs-PredNet) & **4.1358** & **5.1894** & **27.9417** & **86.69** \\ \hline \multirow{8}{*}{PHEV} & DT & 1.6284 & 1.9554 & 4.2977 & 71.89 \\ \cline{2-6}  & LR & 1.8791 & 2.3319 & 5.8026 & 65.17 \\ \cline{2-6}  & Lasso-LR & 1.8981 & 2.3743 & 6.0001 & 63.71 \\ \cline{2-6}  & ElasticNet-LR & 1.8788 & 2.36266 & 5.9102 & 63.75 \\ \cline{2-6}  & Ridge-LR & 1.8217 & 2.26636 & 5.4766 & 67.02 \\ \cline{2-6}  & KNN & 1.7032 & 2.24288 & 5.8266 & 52.06 \\ \cline{2-6}  & SVM & 2.1628 & 2.71467 & 7.6919 & 53.62 \\ \cline{2-6}  & ABR & 1.4383 & 1.87851 & 3.7734 & 75.39 \\ \cline{2-6}  & Our (EVs-PredNet) & **1.3801** & **1.75120** & **3.3972** & **78.07** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of proposed model with machine learning regression methods. that the proposed model outperforms standard methods and can be a valuable tool for forecasting electric vehicle demand.
## 3 Results
### Case Study
#### 3.1.3 Similar-Day Approach  
To rigorously evaluate the performance of the proposed day-ahead regional load forecasting methodology, four distinct days from each season of 2022 were selected. These days were carefully chosen to represent a variety of demand scenarios, including weekdays, weekends, and holidays, each presenting unique challenges for forecasting due to fluctuations in energy consumption patterns. This selection ensured that the evaluation encompasses a wide range of typical and atypical demand behaviors, providing a comprehensive validation of the model's overall performance when applied to different consumption patterns:  
* Weekdays typically exhibit predictable, higher demand during work hours, which gradually declines in the evening.
* Weekends often show decreased demand, with varying peaks based on the time of day and outside activities.
* Holidays exhibit irregular patterns, typically featuring decreased industrial and commercial activity but possibly increased residential consumption.
# Triaxial Test of Coarse-Grained Soils Reinforced with One Layer of Geogrid
## 1 Introduction
For geogrids used in embankment sites, it is recommended to maintain a separation of 20 cm between layers [28]. It is recommended to ensure that the optimal distance between geogrids does not exceed the diameter of the specimen [41]. Tests incorporating multiple geogrid layers simulate conditions found in bridge abutments [42]. This practice aims to replicate real-world scenarios, emphasizing that tests conducted with a large-scale triaxial device correspond closely to those performed under field conditions [43]. In geotechnical practice, it is common to apply high confining pressures, typically exceeding 100 kPa. However, it is essential to highlight a knowledge gap related to low confining pressures [44]. A comprehensive literature review indicated a more pronounced geogrid effect when lower pressures were applied to the samples [45]. The conducted studies have unveiled the intricate stresses generated by dynamic loads in road structures [46]. Hence, there is a critical need to analyze, calculate, and precisely select stress values within the structure, particularly in the context of a road embankment [47]. To address these considerations, the decision was made to employ static loads, aligning with the specifications outlined in standard documents. Subsequently, stress distribution analysis was conducted based on these static loads [48]. Based on these findings, the aim of the study is to conduct tests that closely simulate road construction conditions. Consolidated drained (CD) triaxial tests were carried out with small confining pressures of 20, 50, and 70 kPa, specifically calculated to replicate real-world road structures [48]. The primary objective is to investigate the influence of density and uniformity coefficient on cohesion, angle of internal friction, and the reinforcement effect for a single layer of geogrid-reinforced sandy and gravelly soils.
## 3 Methods
### Data Acquisition  
The prepared samples were placed in a Petri dish. To avoid diffraction and shadowing, samples were leveled using a surface leveler. The leveled samples were placed on the moving bed of the scanner. A white tile of 99.9% reflectance value was also placed on the moving platform along with samples for reference. The reference object was placed along with all samples for calculation of reflectance described in Section 3.3. The platform was then put in motion at a speed of 20 m/s while the frame rate of the camera was set at 60 Hz. The exposure was controlled manually using an aperture of the lens and kept constant for all samples. The following procedure was followed for data acquisition of samples.  
1. The camera's shutter was closed and 100 dark frames were acquired. A large number of frames were acquired to record the sensor's response fully. The mean noise spectra of the camera are shown in Figure 6.
2. The lab scanner transnational platform was moved to the position where the white reference is placed and the camera captures 100 frames of the white tile. The mean spectral response of white tile is shown in Figure 7.
3. The lab scanner transnational platform was moved to the sample's position and scanned the sample line by line. The reflected spectrum of the chili sample is shown in Figure 7.  
The numbers of dark and white frames along with the position of white reference, target start, and target stop were adjusted in Lumo Scanner Software to control the position of transnational platform and acquisition of samples. A total of 3 files were stored in.raw format for each sample: dark reference, white reference, and sample. Along with these files, a false-color RGB image was also  
Figure 6: Hyperspectral camera internal noise captured by acquiring dark frames.  
Figure 7: Reflected energy from the reference material and sample.  
formed using the spectral response of the object at 430 nm, 510 nm, and 670 nm to visualize the scanned sample. The sample and reference data were loaded in MATLAB in the form of a 3D cube using a multiband-read command. The cube consists of 224 spectral images having the radiance information of scanned object at the spectral range of 395 to 1000 nm with the spectral full width at half maximum of 5.5 nm.
## 3 Results and Discussions
### Order Picking Performance
#### 3.2.3 Performance of the Warehouses  
Warehouse 1 and 3 made up the majority of the most efficient subsets in almost all of the scenarios. In every scenario, at least 6 out of 10 subsets are from warehouse 1 and 3. They have a shorter a singles length, which might contribute to their good performance as order pickers spend less travel time within shorter aisles, especially when the traversal routing method is used where the picker has to cross the entire aisle that has at least one pick. In fact, the literature demonstrates that having shorter aisles by increasing the number of cross aisles can, to a certain level, decrease the order picking distance and be beneficial to the effectiveness of routing policies [36]. Our results therefore suggest that warehouse managers should consider adopting warehouse configurations that have short aisles by increasing either the number of aisles or cross aisles.
# TrojanDetector: A Multi-Layer Hybrid Approach for Trojan Detection in Android Applications
## 4 Experimental Validation
### Feature Selection  
Feature or variable subset selection selects the best predictive feature subset from the original feature set. We performed feature selection using a well-explored Feature Importance method to obtain the top-10 features based on their scores.  
Algorithm 1 shows the importance of every feature used in this work for different classifiers, i.e., 2a for DT, 2b for RF, 2c for LR, and 2d for SVM. The x-axis mentions all the features, while the y-axis shows the scores from 0 to 100. We select the top 10 features based on the score and use the reduced feature vectors (10-features long) to train and test our chosen classifiers.  
The importance of the features varies according to the chosen ML classifier and is given by the feature importance graphs of the corresponding classifier. Technically speaking, API (F1), system call (F2), CPU usage (F3), and dangerous permission used (F8) are consistently important irrespective of the chosen classifier. On the other hand, energy level (F4) and is-email-sent (F6) are of limited significance for any classifier.
## 4 Conclusions  
In this paper, a model considering multiple nonlinear factors is proposed. The complex nonlinear dynamic behavior of the coupling system is studied. The influence of the excitation current, radial stiffness, and friction coefficient on the dynamic characteristics of RBRS is analyzed. The following conclusions can be drawn from the study.  
1. For the RBRS coupling system, with the change of parameters, there are many kinds of motion states, including periodic-n, quasi-periodic, and chaotic motion. At the same time, the coupling  
Figure 30: Periodic-5 motion of RBRS at \(\omega\) = 52.2 rad/s: (**a**) Waveform, (**b**) Frequency, and (**c**) Axis orbit and Poincaré map.  
Figure 28: Chaotic motion of RBRS at \(\omega\) = 29.8 rad/s: (**a**) Waveform, (**b**) Frequency, and (**c**) Axis orbit and Poincaré map.  
Figure 29: Periodic-n motion of RBRS at \(\omega\) = 43.2 rad/s: (**a**) Waveform, (**b**) Frequency, and (**c**) Axis orbit and Poincaré map.  
system has intermittent chaos many times, and Hopf, saddle-node, period-doubling, and inverse period-doubling bifurcation appear alternately. The dynamic properties of the coupling system caused by different nonlinear factors are interactional.
2. When the excitation current of the generator \(I_{j}\) is small, the dynamic behavior of the system is relatively simple. As the rotational speed increases, the system transits from short-term periodic motion, chaotic motion, and quasi-periodic motion to periodic motion, successively undergoing saddle-node bifurcation, period-doubling bifurcation, inverse period-doubling bifurcation, and Hopf bifurcation. The increase of the excitation current \(I_{j}\) can inhibit the response amplitude of the system to some extent, but it cannot inhibit the motion state of the system, which makes the motion state of the system more complex, the chaotic motion wider, and the jump discontinuity enhanced. At higher speeds, the number of high-order bifurcation increases as the excitation current \(I_{j}\) increases.
3. Due to the influence of the radial stiffness \(k_{r}\) and the friction coefficient, the coupled system shows more complex nonlinear behavior. With the increase of radial stiffness \(k_{r}\), the motion complexity of the coupling system increases, the chaotic region increases, the response amplitude increases, and the vibration intensity increases. At the same time, in the high-speed region, the periodic motion region becomes narrow and tends to disappear. With the increase of the friction coefficient, the chaotic region increases first and then decreases, the complex motion and periodic motion alternate frequently, and the response amplitude increases gradually.  
The topic of this research is designed by [PERSON]; Formal analysis is completed by [PERSON]; The first version manuscript is prepared by [PERSON]; and this version of the manuscript is read, approved and substantially contributed by [PERSON], [PERSON], [PERSON], [PERSON].  
This research was funded by Fundamental Research Funds for the Central Universities (Grant number N170303008).  
The authors declare no conflict of interest.
# P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images
## 3 Experimental Results  
### Experimental Setup  
The U-Net model was run on a K80 GPU with a total of 12 GB of ram with the Python 3.5 anaconda package, tensor flow 1.2.1, and Keras 1.0. As the MIT-BIH AF database [29; 30] consists of ECG signals  
Figure 3: U-Net architecture and the parameters used in the proposed algorithm. Each box contains an n* n* k input and 2@Conv layers (which means that two consecutive convolution Layers are applied). The left-hand side is the contraction path (Encoder), where we apply regular convolutions and max-pooling layers, and the right-hand side is the expansion path (Decoder) where we apply transposed convolutions along with regular convolutions.  
in the time domain while the U-Net inputs must be images, the data were first converted to 2D ECG images. This makes this approach applicable to any printed ECG signal after background removal. A total of 690 one-channel input images of time-domain ECG signals were used, each of 10 s from the MIT-BIH AF database [29; 30]. They were divided into a training set and a testing set (2:1). Every image had corresponding manually annotated ground truth labeling. Four hundred and sixty images were used for training. This number of images is not nearly enough to feed a deep learning neural network. Therefore, we used a module called \"ImageDataGenerator\" in a Keras preprocessing image to perform data augmentation [48]. The remaining images (230 images) were used to test the trained U-Net model. The model takes less than 15 min to train over 20 epochs. This number of epochs was used as the test accuracy did not increase much for the last five epochs, as shown in Figure 4.  
In the encoder process of the U-Net model, each input image of size \(512\times 512\times 1\) was first fed to two convolutional layers of size \(2\times 2\) and strides of 2, and this process was repeated until an output image size of \(32\times 32\times 1024\) was obtained, while in the decoder process successive layers of deconvolution and merge layers were applied till the output image of size \(512\times 512\times 1\) was obtained. The output represents the segmented P-waves and their corresponding locations.  
Since there are only two classes (P and no P), each pixel within the image has a value between 0 and 1. Zero represents no P-waves, and 1 represents P-wave presence. We take 0.5 as the threshold to decide whether to classify a pixel as 0 or 1.