esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Previous Work
### Grayscale Representations  
Grayscale images offer a single-channel representation of raw bytecode or engineered features, making them lightweight and less memory-intensive.  
[PERSON] et al. (2022) [14] evaluated shallow classifiers trained on both grayscale and RGB images derived from.dex files. While training accuracy reached up to 94.3%, testing accuracy dropped to 85.7%, particularly in RGB cases--highlighting limited generalizability.  
[PERSON] et al. (2024) [15] proposed DexRay, converting DEX bytecode into grayscale vectors while preserving sequential code order. Unlike standard 2D CNNs that ignore byte ordering, DexRay maintained the instruction sequence, achieving an F1-score of 0.96 on a dataset of 158,000 Android apps.  
[PERSON] et al. (2024) [16] combined grayscale and RGB images generated from both static and dynamic features in a GAN-based setup. While classifiers trained on synthetic samples reached an F1-score of 0.8, adversarial examples still evaded detection, underscoring vulnerabilities.  
[PERSON] (2025) [17] introduced TuneDroid, a CNN-based framework that uses grayscale DEX images and employs Bayesian optimization to dynamically tune CNN configurations. TuneDroid achieved 99.44% validation and 98.00% test accuracy, outperforming untuned CNNs and static analysis baselines.  
[PERSON] et al. (2025) [18] used grayscale images to represent runtime behavior and network traffic features in DTDroid, achieving 95.14% accuracy. Their approach reinforces the feasibility of grayscale representations in dynamic settings.  
[PERSON] (2025) [19] developed an ensemble of six CNNs--ResNet50, EfficientNetB0, AlexNet, DenseNet121, ShuffleNetV2, and MobileNetV2--trained on grayscale images from the CICMalDroid 2020 dataset. The model, optimized with Bayesian fusion, achieved 99.3% accuracy.  
In follow-up work, [PERSON] and [PERSON] (2025) [20] converted multiple APK components (classes.dex, AndroidManifest.xml, resources.arsc) into grayscale images. Their CNN-based fusion model achieved 98.83% accuracy, 99.27% precision, and 99.15% recall, validating the efficacy of multi-source grayscale input.
# Measurement and Modeling of 3D Solar Irradiance for Vehicle-Integrated Photovoltaic
## 4 Discussion  
### Simplified Rating Method of VIPV Considering 3D Solar Irradiance  
The standard rating measurement for PV is done by a single measurement in the normal illumination. However, that of VIPV is not sufficient because it uses three-dimensional solar irradiance by a curved module and three-dimensional installation (frequently, the PV module on the car side is added).  
Figure 16: Daily-based comparison between the measured solar irradiance around the car (blue dots) and modeled (typical year from the METPV-11 solar database) solar irradiance on the car (orange dots): (**a**) car roof irradiance; (**b**) car side irradiance.  
Figure 17: Annually integrated angular distribution (weighted histogram of the leading solar beam).  
The ratio of solar resource measurement is useful for a quick rating of the VIPV system on the car by three-dimensional measurement. Possible steps are as follows:  
Figure 18: Monthly integrated angular distribution (weighted histogram of the leading solar beam).  
1. Measure the PV performance in five directions (see Figure 2).
2. The rating of the total performance in the specific area can be weighted by the normalized solar resources using Equation (23) and the values in Table 2, namely, \[P=aP_{z}+b(P_{x+}+P_{x-})+c\Big{(}P_{y+}+P_{y-}\Big{)}\] (23) where \(P\) is the rated power output. \(P_{z}\) is the measured power output by the illumination on the car roof. \(P_{x+}\), \(P_{x-}\), \(P_{y+}\), and \(P_{y1}\) are the measured power outputs by the illumination on the car sides in the direction defined in Figure 1. \(a\), \(b\), and \(c\) are weighting coefficients given by Table 2. Note that the coefficients a, b, and c in our measurement in Miyazaki, Japan are 0.925, 0.395, and 0.435, respectively. Equation (21) gives a total energy output of the entire PV system on the vehicle considering 3D solar irradiance around the vehicle comparable to GHI.
# Numerical Investigation of the Dynamics of 'Hot Spots' as Models of Dissipative Rogue Waves
## 5 Conclusions
The role of dispersion management in the dynamics of rogue wave in a dissipative medium remains to be investigated [38]. Secondly, coupled systems where the gain and loss are balanced between the cores can be considered in the future [41]. Furthermore, the sandwich effect of a linear and quintic gain (or loss) on the cubic gain can be studied too [41; 42; 43]. The effect of an oscillatory gain or loss can be investigated [43; 44]. Finally, the effect of an asymmetric gain or two hot spots can be pursued [22]. Computational studies of rogue waves have been actively pursued both from theoretical perspectives and in application settings [45; 46]. Although we focus on computational studies on robustness here, other approximation techniques like adiabatic approximation might also be valuable [47; 48; 49; 50; 51]. A simplified version for the case of weak energy input and comparisons with numerical simulations are discussed in Appendix A. Further efforts on studies of rogue wave systems with gain/loss will definitely be fruitful in the future. Conceptualization, K.W.C. ; Methodology, H.N.C. and K.W.C. ; Software, H.N.C. ; Formal Analysis, H.N.C. and K.W.C. ; Writing-Review & Editing, H.N.C. and K.W.C. Partial financial support has been provided by the Research Grants Council contract HKU17200815 and HKU17200718. The authors declare no conflict of interest. The adiabatic approximation is a commonly used scheme in estimating properties of wave dynamics or propagation through slowly varying media. While an elaborate analysis might be involved, the case of weak gain or loss can be illustrated through a combination of theoretical and numerical approaches here. For this purpose we consider  
\[iA_{t}+A_{xx}+\sigma|A|^{2}A=i\mu\beta_{0}A\] (A1)  
where \(\mu<<1\) will denote a weak gain/loss depending on the sign of \(\beta_{0}\). We assume that the wave dynamics will then be governed by a conventional rogue wave, \(u_{PB}\) given by the Peregrine breather (Equation (2)) multiplied by a slowly varying envelope of the form \(\exp(\mu z_{0}t)\) for a complex \(z_{0}\), i.e.,  
\[A=\exp(\mu z_{0}t)\;u_{PB}.\] (A2)  
Substituting this into the governing equation and retaining terms of order \(\mu\) only, we get  
\[\text{Re}(z_{0})=\beta_{0}.\] (A3)  
Numerical simulations with the actual governing equations as in the main text and direct evaluation using the adiabatic approximation Equation (A2) will now be compared (Figure A1). The absolute percentage errors at certain time instances are shown in Table A1. The results are in good agreement and the errors match the order of perturbation.
# Calibration of an Adaptive Genetic Algorithm for Modeling Opinion Diffusion
## 2 Materials and Methods
### Opinion Diffusion Modeling
#### 2.1.1 DeGroot Model  
The DeGroot model for opinion diffusion is a deterministic model that describes the process through which individuals or _agents_ update their opinions from time \(t\), through the influence of their social network contacts, to their opinions at time \(t+1\) using  
\[X(t+1)=WX(t), \tag{1}\]  
where \(X(t)\) is an \(N\times 1\) vector of opinions and \(W\) is an \(N\times N\) weight matrix. Each element in \(X(t)\), \(x_{i}(t)\in[0,1]\), represents the opinion of agent \(i\) at time \(t\) and each element of \(W\), \(w_{ij}\in[0,1]\), represents the weight that agent \(i\) places on the opinion of agent \(j\) when updating their current opinion. The elements in \(W\) are restricted by \(\sum_{j=1}^{N}w_{ij}=1\) so that \(w_{ij}\) can be interpreted as the proportion of the total influence on agent \(i\) exerted by agent \(j\). The model also incorporates the structure of the social network through an adjacency matrix \(A\), where \(a_{ij}=a_{ji}=1\) if agents \(i\) and \(j\) can directly influence each other and \(a_{ij}=a_{ji}=0\)otherwise, with a link between \(i\) and \(j\) in the social network being the simplest definition of \"ability to influence\". The adjacency matrix constrains the weight matrix by forcing \(w_{ij}\leq a_{ij}\), so the absence of influence implies zero weight. Though atypical for social network analysis, we include a self-link (\(a_{ii}=1\)) so that agents update their opinions based on their own current opinions.
## 5 Conclusions  
This study investigated the accuracy of two heuristic (MLP and CCNN) and decomposition (DWT and VMD) approaches for predicting dissolved oxygen (DO) concentration. To achieve this goal, the DO concentration and five chemical input parameters (CI, NOx, TDS, pH, and WT) in the St. Johns River, Florida, USA, were used. For training and testing the developed models, the total dataset was divided into 80% and 20%, respectively. Several statistical indices (e.g., RMSE, NSE, WI,  
Figure 14: Violin plots of observed and predicted DO concentration.  
\(\text{R}^{2}\), and MAE) and diagnostic analyses (e.g., Taylor diagram and violin plot) were used to compare the developed models.  
In the first stage, it was found that the CCNN-III (RMSE = 0.550 mg/L, NSE = 0.797, WI = 0.942, \(\text{R}^{2}\) = 0.825, and MAE = 0.185 mg/L) and MLP-III (RMSE = 1.261 mg/L, NSE = 0.736, WI = 0.919, \(\text{R}^{2}\) = 0.801, and MAE = 0.989 mg/L) provided the best results based on the standalone model category. In addition, a comparison suggested that the CCNN-III model performed better than the MLP-III model. In the second stage, however, it was found that the DWT-MLP-III (D6 and S6) (RMSE = 0.161 mg/L, NSE = 0.983, WI = 0.996, \(\text{R}^{2}\) = 0.983, and MAE = 0.061 mg/L) and VMD-MLP-III (\(K\) = 4 and \(\alpha\) = 10) (RMSE = 0.107 mg/L, NSE = 0.992, WI = 0.998, \(\text{R}^{2}\) = 0.993, and MAE = 0.034 mg/L) produced the best results based on the hybrid model category. Unfortunately, the CCNN-III did not improve the performance using the DWT and VMD approaches.  
M.Z.-K. conceived and designed the research; Y.S. implemented DWT and VMD; S.K. applied the CCNN model and wrote the original manuscripts; M.A.G. implemented the MLP model and performed analyses; S.S. and S.N. collected and analyzed the research data; N.W.K. analyzed the results; V.P.S. reviewed and edited the manuscripts.  
The authors would like to appreciate that this research was supported by a grant from a Strategic Research Project (20190153-001) funded by the Korea Institute of Civil Engineering and Building Technology.  
The authors declare no conflict of interest.
# Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
## 1 Introduction
Compressing DRL models enables low-power devices to perform inference using these models on the edge, increasing their applicability, reducing cost, enabling real-time execution, and providing more privacy. These benefits have recently been demonstrated in the context of communication systems and networks for the compression of DRL policies that dynamically scale NF replicas in software-based network architectures [5]. However, the original policy distillation [12] method was only designed for policies from DQN teachers, which can only perform discrete actions. Most subsequent research has also continued in the same direction by improving distillation for teachers with discrete action spaces [5; 13; 14; 15]. DQNs are also fully deterministic, meaning that, for a given observation of the environment, they will always choose the same action. But policies for continuous action spaces are generally stochastic, by predicting a distribution from which actions are sampled. In this paper, we therefore:  
1. Propose three loss functions that allow for the distillation of continuous actions, with a focus on preserving the stochastic nature of the original policies. 2. Highlight the difference in effectiveness between the methods depending on the policy stochasticity by comparing the average return and action distribution entropy during the evaluation of the student models. 3. Provide an analysis of the impact of using a stochastic student-driven control policy instead of a traditional teacher-driven approach while gathering training data to fill the replay memory. 4. Measure the compression potential of these methods using ten different student sizes, ranging from 0.6% to 100% of the teacher size. Benchmark these architectures on a wide range of low-power and high-power devices to measure the real-world benefit in inference throughput of our methods. We evaluate our methods using an SAC [11] and PPO [16] teacher on the popular HalfCheetah and Ant continuous control tasks [17]. Through these benchmarks, in which the agent needs to control a robot with multi-joint dynamics, we focus on an autonomous mobile robotics use case as a representative example of a power-constrained stochastic continuous control task. However, our methods can be applied to any DRL task defined with continuous action spaces, including the previously mentioned resource allocation tasks. These experiments demonstrate that we can effectively transfer the distribution from which the continuous actions are sampled, thereby accurately maintaining the stochasticity of the teacher. We also show that using such a stochastic student as a control policy while collecting training data from the teacher is even more beneficial, as this allows the student to explore more of the state space according to its policy, further reducing the distribution shift between training and real-world usage. Combined, this led to faster convergence during training and better performance of the final compressed models.
## 2 Materials and Methods
The water pump was operated from the grid as it was used for both the PV3 and PV4 panels, and its power consumption was not considered in the performance evaluation. Figure 4: Pictorial views of equipping the PV panels with PCM and LCPV: (**a**) PV2, (**b**) PV3, (**c**) PV4, and (**d**) PV4 with low-concentrator. Table 3 depicts the details of the instruments used in the experimental tests. The uncertainty in the performance parameters is calculated based on the given information in Table 3 for the independent readings and according to Equation (8) [36]. The uncertainty in each of the performance parameters is summarized in \[a=n\frac{r}{sin\theta_{acc}} \tag{1}\]0 Since electrical power is directly taken from the PV analyzer, accordingly, its uncertainty is \(\pm\)1%. The uncertainties of the electrical and overall efficiencies are \(\pm\)0.004 (\(\pm\)2.8% of the efficiency value) and \(\pm\)0.0344 (\(\pm\)8% of the efficiency value), respectively. These uncertainties were calculated based on the daily average experimental data of Day 3 of the PV/T-PCM, where the average inlet and outlet temperatures were 23.09 \(\pm\) 0.84 \({}^{\circ}\)C and 24.16 \(\pm\) 0.91 \({}^{\circ}\)C, respectively, and average solar irradiance was 730.68 W/m\({}^{2}\). The five days were in the same month where the averages recorded for the minimum and maximum solar irradiance were 730.68 W/m\({}^{2}\) on Day 3 and 774.4 on Day 5, respectively. The average measured minimum and maximum inlet cooling temperatures were 20.38 \({}^{\circ}\)C on Day 1 and 23.64 \({}^{\circ}\)C on Day 5, respectively. The variation in the ambient temperatures is shown in Figure 5a, which is limited to a maximum of 3 \({}^{\circ}\)C or 4 \({}^{\circ}\)C at each given time. These close experimental conditions make some kind of verification of the results and using the data of Day 3 to calculate the uncertainty seems reasonable. \[a=n\frac{r}{sin\theta_{acc}} \tag{1}\]1 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Uncertainty Value** & **Uncertainty Percentage** \\ \hline Electrical power & 0.722 W & \(\pm\)1\% \\ Electrical efficiency & \(\pm\)0.004 & \(\pm\)2.8\% \\ Overall efficiency & \(\pm\)0.0344 & \(\pm\)8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The uncertainty in the performance parameters. Figure 5: The weather conditions during five days in October 2022 in Riyadh from 9:30 to 16:30: (**a**) ambient temperature, and (**b**) total solar irradiance. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Apparatus** & **Model Name** & **Accuracy** \\ \hline Pyranometer & HUKSEFLUX–LP02 & \textless{}\(\pm\)1\% (100–1000 W/m\({}^{2}\)) \\ PV Analyzer & PROVA-200A & \(\pm\)1\% \\ Thermocouples (K-Type) & BESANTEK–BST TP01 & \(\pm\)0.4 \% \\ Solar Controller & EURONET-10A & \(\pm\)1\% \\ Gel Batteries & EURONET-EUR6512 (65 AH) & – \\ Pump & HYUNDAI-HWP0024 (0.15 HP) & – \\ Floodlight (DC 12-24V) & SHAHPAN-GRTCDY 120W & – \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The models’ name and accuracy of the apparatus used in the experimental setup.
# THz Rectennas and Their Design Rules
## 7 Fabrication Aspects
### Nano Imprint Lithography  
Referred to in the literature as nano imprint lithography (NIL), this type joins the mass production benefit of using photomask lithography and the high-resolution lithography techniques--laser, e-beam, etc.--which are known for their high resolution [107].  
The following briefly describes the generic fabrication method that can be used to produce nano-scale circuits or features on a substrate. The feature is fabricated in the typical way, which can incorporate many depositing and etching stages either in additive mode where the required material is deposited over the photoresist and then removing the photoresist and leaving the remaining material as the required layer; or in subtractive mode, where the material is deposited before the photoresist, which leaves the unwanted material exposed. This exposed material is etched away by plasma etching, for example, and the wanted layer will be under the photoresist layer, which can be lifted off at another stage, leaving the required material layer print. The photoresist mask itself will be fabricated based on the classical ways such as spin coating for depositing the photoresist layer. Additionally, ultraviolet, laser beam, e-beam, etc., or even another photomask above it and etching the unwanted photoresist areas by a photo-based process for etching of the photo-mask for additive and/or subtractive photo-masking can be used. The known techniques such as chemical vapor deposition (CVD), atomic layer deposition (ALD), etc., are used for depositing the material layer. Of course, the sequence of etching and depositing is relevant to the type, be it additive or subtractive processing. If further information is needed, the reader is advised to review [107] (Figure 16.1-3).  
While this procedure takes a screening style through the use of photo-masking, another very high resolution direct writing procedure is available. It is known, however, for the very long production time because the feature has to be produced in a linear writing style.
# A Web-Based Application for Smart City Data Analysis and Visualization
## 4 Experimental Protocol: System Implementation
### Dataset Management and Description
#### 4.1.3 Sound Levels  
Acoustic data helps characterize environmental noise pollution, a growing concern in urban planning. In the proposed system, filtering techniques are applied to the input to isolate traffic-related noise from other ambient sources such as wind, voices, or construction activity. This enhanced the sensor's ability to detect noise patterns directly associated with road activity [69]. The filtered audio input accurately represents traffic noise intensity in the monitored zone:  
* Decibel levels (dB) measure overall ambient sound intensity, typically sampled over time.
* Frequency bands advanced acoustic sensors break down noise by frequency to distinguish between sources such as traffic, construction, or natural elements.
## 4 Performance Analysis
### B. Average Bit Error Rate
 \tag{41}\]  
The above formula is simplified by \(I\)-function, and then the ABER of the \(N\)-hop link is:  
\[P_{b}=\frac{\delta}{2\Gamma(p)}\prod\limits_{i=1}^{N}\frac{\varepsilon_{\text{ mod},i}^{2}\varepsilon_{i}^{k_{i}}}{\Gamma(\alpha_{i})\Gamma(\beta_{i})}\sum \limits_{l=1}^{n}I_{2N+2,4N+1}^{4N,2}\left[\frac{N}{q_{l}}\prod\limits_{i=1}^{ N}\psi_{i}\left(\frac{\alpha_{i}\beta_{i}}{A_{\text{mod},i}\sqrt{\gamma_{i}}} \right)^{\frac{2l_{i}}{N}}\right](1,1,1),(1-p,1,1),J_{1} \tag{42}\]  
Similarly, taking Equation (21) into Equation (36) and performing a series of simplifications, we obtain the ABER of a single-hop link. Here, we directly give the expression for the ABER of a single-hop link, and do not repeat it in the text:  
\[P_{b}=\frac{\delta\varepsilon_{\text{mod},i}^{2}\varepsilon_{i}^{k_{i}}}{4 \Gamma(p)\Gamma(\alpha_{i})\Gamma(\beta_{i})}\sum\limits_{l=1}^{n}I_{4,5}^{4,2}\left[\frac{\alpha_{i}\beta_{i}}{A_{\text{mod},i}\sqrt{\gamma_{i}}q_{l}} \left| \begin{pmatrix}1,\frac{1}{2},1\\ \end{pmatrix} ,\left(1-p,\frac{1}{2},1\right)\right.\left(\varepsilon_{\text{ mod},i}^{2}+1,1,1\right),\left(z_{i}+1,1,k_{i}\right)\right] \tag{43}\]
# Deep Representation of EEG Signals Using Spatio-Spectral Feature Images
## 2 Dataset  
In this study, we use the PhyAAt dataset [34] to evaluate our proposed approach to analyzing deep representations in DNNs. The PhyAAt dataset provides a collection of 14-channel EEG signals recorded from 25 healthy subjects (labelled as S1 to S25) who underwent a total of 144 trials of an auditory attention experiment. Each trial consisted of three tasks. First, participants were presented with an audio message reproduced under different auditory conditions (listening task); afterwards, the participants transcribed the audio message (writing task), and finally, they enjoyed a resting period before the beginning of the following trial (rest task).  
The experimental auditory conditions included different levels of background noise, message lengths, and message semanticity, and the transcription of each audio message was used to define an auditory attention score. An Eproc-Emotiv device [37] was used to record 14-channel EEG signals from each participant. Electrodes were arranged following the standard 10-20 EEG electrode placement, the sampling rate was set to 128 Hz, and the average duration of the complete series of trials for a subject was 40 min. Finally, the time periods covering a single task were labeled according to the type of task, the auditory conditions, and the resulting attention score. The required ethical approval for the experiment was acquired.