esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Design of the Control System
### Driving Style Recognition  
The spectral clustering algorithm, derived from graph theory [37], is a method that utilizes the spectral properties of data to simultaneously perform dimensionality reduction and clustering. The basic idea behind spectral clustering is to convert the data into a graph representation and use the eigenvectors of the graph Laplacian to project the data into a low-dimensional space where clustering can be performed more efficiently. Spectral clustering is flexible and can handle complex and nonlinear data structures, so it is commonly used with driving style recognition. Spectral clustering is highly scalable, can handle large datasets, is computationally efficient, and is well suited to handle large amounts of driving data. In addition, spectral clustering is robust to noise and outliers, which makes it accurate in recognizing driving styles even when natural driving data is noisy.  
The combination of Bi-LSTM and a self-encoder is shown in Figure 2. The self-encoder compresses the data to low dimensions and later uses the compressed data as input, and the trained Bi-LSTM network learns the temporal dependencies between the sequence data points. In the training phase, the auto-encoder and Bi-LSTM networks are jointly trained, and this joint training process helps the model to learn a more informative and compact representation of the input data, which improves the performance of the driving style recognition task.  
Currently, the use of machine learning for driving style recognition is inefficient, costly, and weak in practical applications under large data conditions. Natural driving data is a continuous time series, and the state of the data at the current moment is related to the state of the moments before and after. Therefore, a spectral clustering driving style recognition method based on a Bi-LSTM autoencoder is proposed, which firstly determines the original labels from the cleaned data using k-means, and then uses a whale optimization algorithm combined with a sigmoid function to compress the size of the data set and use it for The selected features are fed into an auto-encoder with Bi-LSTM to learn the feature values and feature vectors required for spectral embedding, and spectral clustering is used to determine the driving style, as shown in Figures 3 and 4. The red and green lines in Figure 4 represent the flow of data at different layers.  
Figure 2: Schematic diagram of Bi-LSTM combined with autoencoder.  
In order to enable real-time feedback control of the vehicle based on the driver's driving style and road slope, this chapter proposes a model predictive control method with adaptive weights for the longitudinal control of the vehicle based on the driver's driving style.  
Figure 4: Model of driving style recognition.  
Figure 3: Framework of driving style recognition.  
style and changes in road slope, thus achieving real-time feedback on changes in driving operation requirements based on optimization control.
# Reversing File Access Control Using Disk Forensics on Low-Level Flash Memory
## 4 Models and Assumptions  
**System model**. We consider a computing device using flash memory as external storage. This includes servers and personal computers which use solid-state drives (SSD), and mobile/IoT devices which use SD/miniSD/microSD cards, MMC/eMMC cards, or UFS cards.  
**Adversarial model**. The adversary, e.g., a piece of malware, is able to compromise the OS of the victim device, obtaining root access. Abusing this high system privilege, the adversary is motivated to arbitrarily modify the access control for files across this device. For example, it may create a hidden user and grant this user arbitrary permissions of files, or it can delete or modify the existing file permissions of legitimate users. The device owner will be aware of this misbehavior over time (e.g., utilizing a low-level malware detection solution [31]). After having removing the malware, the user then needs to recover changes made by the adversary to the access control metadata. This seems impossible conventionally, because the adversary has compromised the OS before and tried to eliminate any traces accessible to the OS which may be relied on to get the access control metadata restored.  
**Assumptions**. Our design is based on a few assumptions: (1) The structure of the file system is intact. This assumption is reasonable as it would require the adversary with very high levels of knowledge about the operating system to compromise the file system's structure without completely destroying the victim device. (2) The flash translation layer (FTL) is secure. The FTL stays between the OS and the flash memory chips and it is typically isolated by the storage hardware from the OS. This hardware-level isolation ensures the security of the FTL. Re-flashing the flash memory firmware may disrupt this assumption, which however is destructive in nature and would result in a wipe of the drive.
## 6 Conclusions  
This work presents an efficient alternative method for estimating Vs30 values through HVSR inversion using an elitist genetic algorithm (GA).  
In the Greater Metro Manila Area, the estimation of Vs30 via HVSR inversion yielded satisfactory results, achieving a 76% match rate between the soil profile types based on refraction microtremor (ReMi) Vs30 values and those derived from inversion. The R\({}^{2}\) and r values indicate strong positive correlations, suggesting that the inversion effectively captures overall trends in the ReMi data. Although some sites exhibited underestimation, the overall findings show that this method offers a practical and efficient alternative for rapidly estimating Vs30 values.  
Results for the Leyte dataset likewise show a strong positive correlation between ReMi-derived and estimated Vs30 values as evidenced by high R\({}^{2}\) and r values. While the RMSE is higher than that of GMMA sites, the inversion still effectively captures the trend in Vs30 values derived from ReMi data. Additionally, power regression analysis highlights a strong inverse correlation between site period and estimated Vs30 values, with deviations primarily observed in lowland sites characterized by deep sediment layers. These findings demonstrate the viability of HVSR inversion for updating Vs30 maps in the Philippines. Future studies could explore additional geophysical data integration for further validation.  
Conceptualization, R.N.G.; methodology, R.N.G., A.T.S., J.M.L.C.T., and R.A.F.C.; software, R.N.G.; validation, R.N.G., formal analysis, A.T.S., J.M.L.C.T., and R.A.F.C.; investigation, A.T.S., J.M.L.C.T., and R.A.F.C.; resources, R.N.G. and A.T.S.; data curation, A.T.S., J.M.L.C.T., and R.A.F.C.; writing--original draft preparation, A.T.S., J.M.L.C.T., and R.A.F.C.;  
Figure 12: Correlation between site period and estimated Vs30 of Leyte sites using power regression.  
writing--reviewing and editing, R.N.G., A.T.S., J.M.L.C.T., and R.A.F.C.; visualization, R.N.G., A.T.S., J.M.L.C.T., and R.A.F.C.; supervision, R.N.G. and A.T.S.; project administration, A.T.S.; funding acquisition, R.N.G. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Republic of the Philippines Department of Sciences and Technology (DOST) and the Philippine Council for Industry, Energy, and Emerging Technology (DOST-PCIEERD) Grants-in-Aid (GIA).  
Not applicable.  
Data Availability Statement: Data are within the article.  
The authors would like to thank [PERSON], [PERSON], [PERSON], [PERSON]. [PERSON], [PERSON], [PERSON] and the local government unit of the province of Leye for their assistance in data acquisition; [PERSON] and [PERSON] for assistance in data acquisition and data processing; [PERSON] for further technical support; and DOST-PCIEERD for support in funding and [PERSON] for support in the publication of this research.  
The authors declare no conflicts of interest.
## 2 The Proposed Combined Method and Principles
### Principle of CNN  
A CNN is a type of feedforward neural network that is structured with five modules: the input layer, the convolutional layers, the activation layers, the pooling layers, and the fully connected layer [18; 19]. The layered architecture of CNNs excels at extracting spatial and temporal features from load data, facilitating precise predictions.  
The input layer serves as the starting point for data processing in load forecasting, handling historical load figures. These data are formatted to meet the requirements of the CNN, laying the groundwork for subsequent feature extraction. The convolutional layers are responsible for feature extraction from the input data. By applying various filters, these layers identify critical patterns in the data, such as cyclical fluctuations in power usage, which are essential for comprehending and predicting load patterns. Following the convolutional layers, the activation layers (typically ReLU) introduce nonlinearity, assisting the network in learning more complex data patterns, which is particularly beneficial in capturing and understanding irregular load variations in forecasting. Pooling layers are employed to reduce the dimensionality of the feature data, decreasing computational load while avoiding overfitting. In load forecasting, pooling layers refine and compress feature information, concentrating the model on the most crucial data characteristics. Finally, the fully connected layers represent the ultimate step in a CNN, integrating the features extracted by all previous layers to make the final prediction.  
The core of CNN lies in its convolutional layers, which extract diverse feature maps from the input data through convolutional computations with different kernels, as represented by formula (6).  
\[X^{m+1}=\sigma\big{(}\sum W^{m}\cdot X^{m}+B^{m}\big{)} \tag{6}\]  
where \(X^{m+1}\) represents the input to the next layer, \(\sigma\) signifies the activation function, \(W^{m}\) denotes the weight of the current layer, \(X^{m}\) represents the input to the current layer, and \(B^{m}\) indicates the bias.
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Ref.**} & \multirow{2}{*}{\begin{tabular}{c} **Bandwidth** \\ **(GHz)** \\ \end{tabular} } & \begin{tabular}{c} **Relative** \\ **Absorption** \\ **Bandwidth** \\ \end{tabular} & **Absorption** & \begin{tabular}{c} **Lumped** \\ **Elements/** \\ **Multilayer** \\ \end{tabular} & \
\begin{tabular}{c} **Polarization** \\ **(Both TE and** \\ **TM2)** \\ \end{tabular} &
\begin{tabular}{c} **Incident** \\ **Angle** \\ \end{tabular} \\ \hline
[39] & 7.93–17.18 & 73.67\% & \textgreater{}90\% & Yes/No & Both & 0–20\({}^{\circ}\) \\ \hline
[40] & 5.46–14.75 & 91.93\% & \textgreater{}90\% & No/Yes & Both & 0–30\({}^{\circ}\) \\ \hline
[41] & 6.80–11.80 & 53.76\% & \textgreater{}70\% & Yes/No & Both & 0–30\({}^{\circ}\) \\ \hline
[43] & 6.70–20.58 & 101.75\% & \textgreater{}90\% & Yes/Yes & Both & 0–30\({}^{\circ}\) \\ \hline
[44] & 6.21–19.31 & 102.66\% & \textgreater{}90\% & No/Yes & Both & 0\({}^{\circ}\) \\ \hline
[45] &
\begin{tabular}{c} Peaks at 14.50, \\ 16.50 \\ \end{tabular} & n/a & \textgreater{}90\% & No/No & Both & 0, 45\({}^{\circ}\), 90\({}^{\circ}\) \\ \hline
[46] & \begin{tabular}{c} 11.21–11.49 \\ 13.92–14.85 \\ 17.66–17.87 \\ \end{tabular} & \begin{tabular}{c} 2.46\% \\ 6.46\% \\ 1.18\% \\ \end{tabular} & \
\begin{tabular}{c} 2.46\% \\ 90\% \\ 1.18\% \\ \end{tabular} &
\begin{tabular}{c} \\ 30\% \\ \end{tabular} & No/No & Both & 0–45\({}^{\circ}\) \\ \hline This paper & 12.30–14.80 & 18.45\% & \textgreater{}90\% & No/No & Both & 0–45\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the absorption performance with other works.
# Research on Optimal Dispatching Strategy for Micro-Energy-Grid of Protected Agriculture
## 4 DR Model Considering Real-Time Tariff
### Case Study  
According to the load data for each period, the real-time price based on user's load can be calculated by Equation (14). The results are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Period** & **Price (Yuan/kWh)** \\ \hline High (8:00–11:00, 15:00–16:00, 19:00–22:00) & 0.6414 \\ Flat (12:00–14:00, 17:00–18:00) & 0.4289 \\ Low (23:00–7:00) & 0.2165 \\ \hline \hline \end{tabular}
\end{table}
Table 6: TOD tariff for agricultural electricity.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time** & **Load (kW)** & **Time** & **Load (kW)** & **Time** & **Load (kW)** & **Time** & **Load (kW)** \\ \hline
1:00 & 45.5 & 7:00 & 55 & 13:00 & 73.5 & 19:00 & 77 \\
2:00 & 42 & 8:00 & 97 & 14:00 & 94 & 20:00 & 92 \\
3:00 & 43.5 & 9:00 & 125 & 15:00 & 75 & 21:00 & 75.5 \\
4:00 & 45.5 & 10:00 & 55.5 & 16:00 & 126.5 & 22:00 & 42 \\
5:00 & 73.5 & 11:00 & 56 & 17:00 & 75 & 23:00 & 43.5 \\
6:00 & 95.5 & 12:00 & 96 & 18:00 & 55 & 24:00 & 44 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Typical daily load of an agricultural user.
# Enhancing Malware Detection via RGB Assembly Visualization and Hybrid Deep Learning Models
## 6 Discussion  
The experimental results demonstrate that our hybrid CNN model achieves superior performance compared to existing malware classification approaches, attaining 98.45% accuracy on the benchmark dataset.  
Several limitations warrant discussion. First, while our approach sets a new performance benchmark, the computational requirements exceed simpler methods like Random Forest. Second, the 1.15% accuracy gap between our method and the augmented CycleGAN + CNN approach (99.60%) suggests the potential for improvement through generative data augmentation, albeit at a higher computational cost.  
Interestingly, our method achieves this high accuracy without relying on computationally intensive data augmentation techniques, such as those employed by [PERSON] and [PERSON] (2022) [39], whose CycleGAN + CNN model reached 99.60% accuracy but at a significantly higher computational cost. This suggests that our assembly-to-image transformation provides a more efficient and scalable solution for real-world malware detection systems. However, the model does exhibit slightly reduced performance (~94%) on malware families with very few samples (<50), highlighting a limitation in handling rare variants. Future work could explore few-shot learning techniques to address this challenge while maintaining the model's efficiency and interpretability.  
These findings have significant implications for malware detection systems, demonstrating that the assembly-to-image transformation provides a robust representation which outperforms conventional byte-level approaches while maintaining computational feasibility. The results underscore the critical importance of preserving both structural and semantic relationships in malware code for achieving accurate classification. The success of our methodology opens several promising research directions, including the development of hybrid systems that combine our approach's superior accuracy with the computational efficiency of traditional machine learning methods like Random Forest, particularly for resource-constrained deployment scenarios. Future investigations will focus on: (1) enhancing visualization techniques to bridge the remaining performance gap with computationally intensive augmented methods, (2) integrating attention mechanisms to better capture long-range dependencies in code structure, and (3) extending this approach to other binary analysis tasks where preserving semantic relationships may prove equally beneficial. These advancements could lead to more versatile and efficient malware analysis systems capable of handling increasingly sophisticated threats while maintaining practical deployment characteristics.
# NASA Global Satellite and Model Data Products and Services for Tropical Meteorology and Climatology
## 2 Satellite Missions, Projects and Data
### Future Satellite Missions  
#### 2.5.1 Tropics  
Datasets from several future satellite missions that are related to tropical meteorology and climatology will be archived and distributed at GES DISC, including the Time-Resolved Observations of Precipitation Structure and Storm Intensity with a Constellation of Smallsats (TROPICS) Mission [36] and the Aerosol and Cloud, Convection and Precipitation (ACCP) Mission [37].  
As mentioned earlier, satellite-based global precipitation estimates depend on a constellation of both domestic and international satellites to increase spatial and temporal samplings. Multi-satellite precipitation datasets like IMERG mainly rely on high-quality PMW datasets collected from the satellite constellation. IR datasets are used to fill in data gaps when and where PMW data are not available. The TROPICS mission [36], consisting of six cube-shaped satellites and led by the MIT Lincoln Laboratory, will add more PMW observations in the tropics; however, their new high-frequency channels need to be further studied before they can be integrated into multi-satellite precipitation algorithms such as IMERG. The TROPICS mission will be the first NASA science mission to be implemented with CubeSats and the first proliferated CubeSat constellation mission funded by the U.S. government.
## 3 Results and Discussion  
### Plasma Diagnostics  
SDBD is based on the separation of metallic electrodes by a dielectric material, which is typically powered by an alternating current (AC) (0.05-500 kHz) high-voltage (HV) source [39]. The dielectric barrier limits current and collects surface charges to avoid sparks and arcs. Typically, SDBD micro-discharges are short-lived streamers that form either in volume or along the dielectric surface [40]. In dielectric barrier discharges, the production of a micro-discharge is invariably associated with charge accumulations on the dielectric surface and, subsequently, the generation of an electrical field in the opposite direction. This eventually causes a quick drop in current flow and the micro-discharge to self-terminate.  
The charge buildup causes a fresh micro-discharge in a new AC half-period; hence, the applied AC frequency must be sufficient to maintain the electrode surfaces with the charge collected because of the previous discharge event [41]. The generation of plasma at low temperatures and the high degree of diffusivity of SDBD are their primary benefits, which makes them effective surface treatments. The creation of plasma with low-temperature diffusivity is mostly dependent on the frequency and gas used to form the plasma [42]. Due to the plane-parallel geometry, huge effective plasma surface, and the ability to operate with just air as the supplying gas, we used air SDBD. When the discharge was ignited, transient microfilament streamers developed at random spots, and they transmitted a larger discharge current than the remainder of the discharge. However, because of the nature of an SDBD source, the total filament current was restricted, limiting the creation of hotspots. The design and application ease of this kind of plasma source was another factor in its selection. It does not need the addition of feeding gas, as a key aspect of future technology, and can be scaled up [43].  
Optical emission spectroscopy (OES) was used to measure the SDBD plasma emission spectra in the 200-900 nm range. Figure 1e shows the emission spectra up to 450 nm. In the spectra of SDBD plasma, the nitrogen second positive system (N2 SPS), nitrogen first negative system (N2 FNS), and OH radical were seen. (N2 SPS) spectral lines have appeared at 316, 337, and 357 nm. Moreover, at 389 and 405 nm, (N2 FNS) lines have developed. Furthermore, the OH radical has occurred at a wavelength of 308.9 nm. In addition, OES of air plasma often reveals the presence of excited nitrogen species, atomic oxygen, and hydroxyl radicals [44]. The reactive species created by non-thermal plasma induce an oxidative stress response that leads to harmful oxidative cell deterioration [45], therefore inhibiting microorganisms [46]. Hydroxyl radical is among the most powerful oxidants that decompose and oxidize toxic substances [47]. Alternatively, hydroxyl radicals enhance the microbicidal action [48]. Hydroxyl radicals have a serious influence on the double layer of glycerophospholipids and proteins that form the cell membranes of microorganisms [49].
# Situation-Aware Survivable Network Design for Tactical Environments
## 1 Introduction
The main difference in the proposed routing algorithm in comparison to the conventional routing algorithms is that it avoids the isolated single path. We allow duplicate control message and modify the packet format to find a branch node and select a routing path toward that node. Hence, the proposed routing algorithm provides reliable connectivity to a tactical network with the link disconnection problem. Our contributions in this paper are listed as follows:  
* We design and implement a situational backoff reset algorithm for meeting the requirements of military QoS (MQoS) by adjusting a length of the AIFS for avoiding priority inversion and conducting situational backoff reset for preventing collision;
* The novel branch node based routing algorithm for providing resilient connectivity by finding the branch node, which duplicates pass through when a source-destination pair discovers several end-to-end paths, is presented and analyzed. The remainder of this paper is organized as follows. Section 2 provides the related work of media access control (MAC) protocol and routing algorithms for WLAN. The situational backoff reset algorithm and the branch node-based routing algorithm are described in detail in Section 3. Section 4 presents simulation results and evaluates the performance of the proposed schemes in a tactical network. We conclude this paper in Section 5.
## 4 Performance Evaluation Metrics  
For evaluation purposes, multiple performance metrics were used, namely, Context Precision, Context Recall, Faithfulness, and Answer Relevancy as shown in Table 1.
## 4 Case Study
### Results and Analyses
The calculation results of the supply and return pipeline's temperatures at a continuous time period are shown in Figure 10. The results show that the temperatures at the end of the supply pipelines are approximately 92 \({}^{\circ}\)C, and the temperatures at end of return pipelines are approximately 44 \({}^{\circ}\)C. On the one hand, the temperature drop of pipelines is relatively small, and on the other hand, the temperature of heat load side is relatively stable, which can better meet the requirements of heat users. The optimization result of day-ahead optimal operating fuel cost is 877,653.58 CNY in the IES case, and the different types of fuel cost are shown in Figure 11 at a continuous time period. The change trend of various types of fuel costs per hour is noncorrespondence with the change trend of natural gas load, heat load, and electrical load demand. Since the _CHP_ unit undertakes the main heating  
Figure 8: Change of voltage and voltage angle in the electricity system. Figure 9: Change of node pressure in natural gas system. supply and part of the electricity supply, the _CHP_ unit's fuel cost (Cost-_CHP_) is nearly at maximum. The output of the thermal generating unit is relatively stable, and the fuel cost (Cost-GEN) is also relatively stable, which corresponds to the operation characteristics of the thermal generating unit. The price of heat and electricity is variable, while the price of natural gas is unchanged. From the results in Figure 11, it can be seen that natural gas plays a role of peak shaving and valley filling to a certain extent, so the fuel cost of natural gas (Cost-GAS) is relatively large.