esa-sceva/satcom-chunk-collection huggingface dataset:
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 4 Fuzzy Q-Charging Algorithm
### Charging Time Determination
* First, we calculate the safety energy charging time of all negative normal sensors (denoted as \(\Delta_{a_{1}}^{i},\dots,\Delta_{a_{n}}^{i}\)) and positive critical sensors (denoted as \(\Delta_{b_{1}}^{i},\dots,\Delta_{b_{b}}^{i}\)). * We then combine the values of \(\Delta_{a_{1}}^{i},\dots,\Delta_{a_{n}}^{i}\) and \(\Delta_{b_{1}}^{i},\dots,\Delta_{b_{b}}^{i}\) into an array denoted as \(\Delta_{c_{1}}^{i},\dots,\Delta_{c_{n+v}}^{i}\), where \(\Delta_{c_{1}}^{i},\dots,\Delta_{c_{n+v}}^{i}\) have been sorted by decreasing order (i.e., \(\Delta_{c_{1}}^{i}\geq\Delta_{c_{2}}^{i}\geq\dots\geq\Delta_{c_{n+v}}^{i}\)). We have an important observation that the value of \((e_{1}-e_{2})\) does not change when \(\mathcal{T}_{i}\) varies in the range from \(\Delta_{c_{p}}^{i}\) to \(\Delta_{c_{p+1}}^{i}\) (\(1\leq p\leq u+v\)). Therefore, the optimal value of \(\mathcal{T}_{i}\) can be easily determined by brute force search over \(\Delta_{c_{1}}^{i},\dots,\Delta_{c_{n+v}}^{i}\).
# A Self-Powered UHF Passive Tag for Biomedical Temperature Monitoring
## 2 Architecture of Proposed Self-Powered Passive Tag
### Low Frequency (LF) Section
#### 2.1.2 Voltage Buffer  
A voltage buffer is used to transmit the \(v_{osc}\) oscillation signal into the ASK backscatter modulator. The voltage buffer is designed to withstand the capacitive load \(C_{ind}\) imposed by the input of the ASK backscatter modulator. Figure 4 depicts the implemented buffer whose design is intended to set the same propagation delay during low-to-high and high-to-low transitions [53].
# AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification
## 5 Evaluation  
The AV-Teller framework was employed to evaluate the efficacy of antivirus detection mechanism. By leveraging system snapshots, it reconstructed diverse configurations, comprising various antivirus products combined with different browser settings. The evaluation prioritized simulating a representative and widely adopted environment, focusing on prevalent usage scenarios. Thus, antivirus settings remained at their default configurations, and browser extensions were excluded to eliminate potential biases introduced by atypical setup. This approach provided a consistent baseline for analysis. Integral to the AV-Teller framework, the Collector module systematically recorded detection events and system responses during experimentation. The collected data were processed by the Analyze Server, which generated critical performance metrics, including detection rates and false positives. Iterative analyses facilitated the identification of system vulnerabilities and the refinement of detection scenarios.  
To ensure the validity of the experimental setup, real-world usage patterns were considered, as supported by data from StatCounter [38], a web analytics service tracking global browser and operating system market shares. As of October 2024, StatCounter reported Chrome's dominance in the global desktop browser market, with a 65.25% share, establishing it as the most widely utilized browser. Similarly, Windows accounted for 73.39% of the desktop operating system market, with Windows 10 comprising 60.95% of that share. These statistics substantiate the selection of Chrome and Windows 10 as primary platforms for evaluation, aligning the experimental setup with prevailing user environments.  
For antivirus evaluation, we selected 28 products certified by AV-TEST and registered on VirusTotal. AV-TEST is an independent organization that evaluates and certifies antivirus products based on criteria such as detection performance and usability. VirusTotal aggregates results from multiple antivirus engines, reflecting their market usage and relevance. These criteria ensured the selection of widely used and credible antivirus products.
## 3 Results and Discussion
### Anisotropic Structures
#### 3.4.2 Test Results  
A ball rebound test was performed to evaluate the fabricated anisotropic structure. To ensure the reliability of the test, the average and standard deviation are shown in Figure 16, which were obtained after repeating the same test 3 times for each specimen type. For materials A and B, which were printed with only a single composite material, the rebound height ratios were 15.07% and 2.99%, respectively. For the anisotropic-1 specimen, only the two longitudinal axes parallel to the direction of impact of the steel ball consisted of material B and the rest consisted of material A, resulting in a rebound height ratio of 6.93%. On the other hand, for the anisotropic-2 specimen, which was subjected to the ball rebound test by rotating the same specimen by 90 degrees with respect to the printed plane, a rebound height ratio of 10.73% was shown. This was because among the regular octagonal sides, when the side parallel to the impact direction of the steel ball consisted of relatively soft material B, impact absorption was better than in other cases; therefore, it was possible to fabricate an anisotropic structure with a resilience difference of 155% depending on the impact direction during printing while selectively switching the material.  
Figure 14: Average rebound height ratios.  
Figure 15: Specimens with different anisotropic structures.
# Object-Based Mapping of Coral Reef Habitats Using Planet Dove Satellites
## 3 Results  
### Bathymetry Retrieval and Benthic Composition Classification  
The high-resolution bathymetry information generated from Planet Dove satellite images was consistent with known spatial variations from shallow to deep coral reef regions and out to the deep ocean. For instance, general depth trends were retrieved with an increasing depth gradient extending from land to ocean. These satellite-derived bathymetry measurements supported the mapping of benthic geomorphologic zones, such as shallow reef crest (depth \(<\)3 m), which are found along the coast of southeastern Dominican Republic (Figure 3). Comparing the mapped bathymetry to field-based water depth measurements, root mean square errors (RMSE) ranged from 1.37 m to 1.98 m, and \(R^{2}\) ranged from 0.70 to 0.91 (Catalina Island, RMSE = 1.98 m, \(R^{2}\) = 0.70; Saona Island, RMSE = 1.37 m, \(R^{2}\) = 0.91, east Dominican Republic, RMSE = 1.72 m, \(R^{2}\) = 0.73) (Figure 1a-c). The overall accuracy RMSE was 1.5 m, sufficient to support subsequent benthic classification steps. The NIR band was used to remove the water surface effect in the initial step of satellite image processing (Figure 2). Therefore, higher RMSE values at the Catalina Island study site were caused by poor image quality in the near-infrared band.  
The results of the image segmentation for the three study sites are shown in Figure 4. Different benthic habitat types were segmented based on the combined inputs of surface reflectance, bottom reflectance, and bathymetry. The final derived benthic habitat maps based on the classification scheme are shown in Figure 5. This scheme was developed for coral reef management applications, which provides a baseline to compare future change, and is useful for the identification of targeted conservation areas, marine spatial planning, and ecosystem service models (Figure 5) [23]. Coral reefs were classified into fore-reef and back-reef classes when considering bathymetry, reef crest, and the spatial proximity to the shoreline. Seagrass beds were mapped in shallow protected lagoons, such as the middle region of Saona Island and near-shore areas of the Punta Cana region in eastern DR (Figure 5).  
Figure 3: Bathymetric spatial information derived from Dove satellite images covering three study sites within the Arrecifes del Sureste marine sanctuary in southeastern Dominican Republic: Catalina Island, Saona Island, and eastern Dominican Republic. The bathymetry was derived from medium to high tidal stages.  
Figure 4: Image segmentation results of benthic features within the three study sites.
## 4 Results and Discussion
### Identification and Categorization of Factors
#### 4.2.5 Psychological and Behavioral Factors
Findingssuggest that the perceived usefulness, ease, and risk of adopting new technologies play a significant role in decision-making, highlighting the importance of cognitively linked factors. Institutional and behavioral factors are relatively modifiable, through which intervention has an opportunity to boost the likelihood of DAT adoption. For instance, public education on environmental damage and changing climate can raise awareness of sustainability. However, this social pressure may not be effective if there is insufficient stimulant. [PERSON] et al. [44] emphasized that a farmer still might adopt a new agricultural technology even though they may not perceive profitability. This is made possible through financial support and technical assistance. Institutional factors could indirectly reshape farmers' perceptions and increase actual rates of adoption.
## 3 Reflection of SOC into the System Frequency
As shown in Figure 1, the SOC signal enters into the comparators, which check whether the SOC drops below \(SOC_{min}\) or exceeds \(SOC_{max}\). If one of those events occurs, the output of the comparator triggers the pulse and generates '1' as the duration, which can be adjusted by the operator. Here, the duration is set as 1 h since the tertiary control time interval of \(P_{sched}\) is also considered as 1 h. This time interval can be adjusted by the operator. If \(P_{sched}\) is changed, the pulse (low/high SOC signal) is designed to be turned off so that the microgrid can be operated as scheduled data. Note that the output of the add/subtract component, '-1' (low SOC signal) or '+1' (high SOC signal) or '0', will be multiplied by \(P_{res}\). Since the low/high SOC signal is a step signal, \(P_{res}\) multiplied by the low/high SOC signal enters into the low-pass filter (LPF) to alleviate any step change. Otherwise, a large step change of \(f_{ref}\) occurs and may result in instability of the system. The switch shown in Figure 1 chooses node 'a' or 'b' to provide an active power reference as low-pass filtered \(P_{res}\) or \(P_{sched}\), respectively. The selection of the node is determined by the 'charging/discharging signal' shown at the bottom of Figure 1. Hence, \(P_{ref}\) can be expressed as:  
\[P_{ref}=\left\{ \begin{array}{ll}P_{sched}&,\ \text{if Charge/Discharge signal is '0'}\\ P_{res,LPF}&,\ \text{if High SOC signal is '1'}\\ -P_{res,LPF}&,\ \text{if Low SOC signal is '1'}\end{array} \right. \tag{8}\]  
where \(P_{res,LPF}\) is the low-pass filtered \(P_{res}\). The reason for predetermining \(P_{ref}\) as a specific constant value while the low/high SOC signal is turned on is that \(P_{ref}\) cannot be determined by some criterion considering the whole system. For instance, if communication is used, the charging/discharging active power reference can be determined by a central controller with consideration of the whole system. However, without communication, \(P_{ref}\) cannot be determined in a centralized way and hence should be determined locally. By only using local measurements and a local controller, it is impossible to calculate an optimal \(P_{ref}\). Thus, for the sake of convenience, when the low/high SOC signal is turned on, \(P_{ref}\) is predetermined as a specific value. The rest of the control scheme is the same as with conventional \(P\)-\(f\) droop control [35,36]. The parameters used in Figure 1 are shown in Table 2. \begin{table}
\begin{tabular}{c c c} \hline \hline \(SOC_{min}\) (\%) & \(SOC_{max}\) (\%) & Time Constant of Low Pass Filter (s) & Droop Coefficient, \(m_{PF}\) (pu) \\ \hline
20 & 90 & 0.0001 & 0.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters used in Figure 1.  
Figure 1: Proposed frequency reference controller for a droop-controlled grid-forming distributed generation (DG).
## 3 Modeling of Various Stakeholders in the Distribution Network (DN)
### Distribution System Operator (DSO) Model  
DSO is responsible for purchasing electricity from the wholesale market and prosumers, and then providing it to customers at retail prices. The economic model is as follows:  
_1) Energy Purchase Cost_  
The DSO has the responsibility of supplying electrical energy to customers by purchasing energy from both the wholesale market and the prosumer. Based on the laws of economics and the type of customer, the price from the wholesale market depends on the quantity and time. The cost of purchasing from wholesale market is expressed as (9), the DSO purchase energy from prosumers as a fixed price (such as local tariff price), and the cost is expressed as (10):  
\[C_{wholesale}=P_{wholesale}\times E_{wholesale} \tag{9}\]  
\[C_{renewable}=P_{tariff}\times\sum_{t}^{T}E_{renewable}^{t} \tag{10}\]  
where \(P_{wholesale}\) is the purchasing price from the wholesale market. The amount of electricity purchased from the wholesale market \(E_{wholesale}\) should satisfy the total demand of customers in the DN:  
\[E_{wholesale}=\sum_{i=1}^{N}{(x_{i}^{t}+x_{j}^{t})}-\sum_{j=1}^{J}E_{renewable,j}^{t} \tag{11}\]  
where \(\sum\limits_{t}^{T}E_{renewable}^{t}\) should be less than the total power of prosumer at time interval \(t\):  
\[\sum_{t}^{T}E_{renewable}^{t}\leq\sum_{t}^{T}\sum_{j}^{K}E_{renewable,j}^{t} \tag{12}\]  
_2) Carbon Income_  
The Kyoto Protocol stipulates that greenhouse gas emission reductions can be traded as intangible commodities [33]. The relationship between carbon emission gains and renewable energy generation over a period can be expressed as:  
\[H_{c}=a{\sum\limits_{t}^{T}E_{renewable}^{t}}^{2}+b{\sum\limits_{t}^{T}E_{renewable}^{t}} \tag{13}\]  
The derivation process of this model is described in Appendix A.  
_3) Retail Electricity Income_  
The DSO sell the electricity to the customer at the retail price, the income is:  
\[R_{retail}=\sum_{i=1}^{N}\sum_{j=1}^{K}\sum_{t}^{T}P_{retail}^{t}(x_{i}^{t}+x_{ j}^{t}) \tag{14}\]  
To sum up, the total revenues of the DSO can be expressed as:  
\[R_{DSO}=H_{c}-C_{renewable}+R_{retail}-C_{wholesale} \tag{15}\]
## 4 Implementation
### Implementation Results  
Our experiments were performed ten times with the MIMO-COOK scheme, and some rolling shutter cameras were used to verify the frame variation effect. The asynchronous process includes asynchronous decoding, merging data technique, and the detection of missing parts. The SN length also is chosen carefully for the overall process. For the Point Gray rolling shutter camera, the frame rate variation was between 20 fps and 60 fps. Figure 15 shows the setup of implementation, and Figure 16 shows the practical MIMO-COOK waveform. The experimental results are shown in Figure 16, and the results and parameters are shown in Table 2.  
Figure 17 depicts the implemented results; the experiments were performed ten times with different communication distances at the same exposure time of 100 us for comparing the COOK with our proposed scheme. Figure 17 shows that with the same communication distance and channel environment, the matched filter significantly improved the system performance more than the zero-crossing filter. From Figure 10, we can modify the pixel SNR by setting an exposure time to improve the system performance at a long communication distance; however, the high exposure time  
Figure 16: Rx interface.  
Figure 15: Setup for the MIMO-COOK implementation.  
will increase the probability that the fuzzy states of LEDs lead to a reduction in the communication bandwidth. From that, we can conclude that: by using the matched filter, the communication distance can improve without reducing the system performance. Besides this, to improve the system performance at longer communication distances, channel coding is recommended to mitigate the high ratio of fuzzy states.  
Table 2 shows the experimental results obtained by conducting experiments several times using different optical clock rates. The table shows that the data rate can increase by increasing the data sub-packet length and the optical clock rate; however, the clock rate and the data sub-packet length must suit the rolling shutter camera parameters, namely, the rolling rate of the rolling shutter camera and the size of images, as shown in Equations (7) and (8). We implement this scheme with two LEDs at the communication distance of 2 m, which can be visually assessed by the Supplementary Materials.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{4}{c}{**Transmitter Side**} \\ \hline
**Clock rate** & \(8\,\mathrm{kHz}\) & \(10\,\mathrm{kHz}\) \\ \hline
**RLL** & Manchester & 4B6B & Manchester & 4B6B \\ \hline FEC & & RS (15,11) & \\ LED type & & 12 V, 3 W & \\ Number of LEDs & & 2 & \\ Packet rate & & 20 packet/s & \\ \hline \multicolumn{4}{c}{**Receiver side**} \\ \hline Camera & & Point Gray rolling shutter camera & \\ Camera frame rate & & 60 fps & \\ \hline \multicolumn{4}{c}{**Data rate**} \\ \hline Uncode bit rate & 1.2 kbps & 1.8 kbps & 1.5 kbps & 2.25 kbps \\ Code bit rate & 0.88 kbps & 1.32 kbps & 1.1 kbps & 1.65 kbps \\ \hline \hline \end{tabular}
\end{table}
Table 2: MIMO–COOK parameters.  
Figure 17: BER of the COOK system with an exposure time of 100 us.
## 3 Model-Based System Engineering for Preliminary Design
### Integration of System Requirements within the Capella System Model
#### 3.3.4 Example of Traceability  
* Logical Component: Training and Development Module
* Linked Requirement: Develop and integrate locally manufactured space components.
- Linked Logical Function: On-Board Data Processing
* Traceability Link: The OBC component is directly linked to the training and development requirement through its logical function.  
Integrating system requirements into the Capella model and establishing robust traceability mechanisms ensured that every design decision aligned with the mission's objectives and that any changes in requirements or design could be managed effectively. This approach provided a transparent view of how requirements were met throughout the mission, incorporating detailed design activities for each subsystem.
# Automated Detection of Epileptic Seizures in EEG Signals via Micro-Capsule Networks
## 2 Methods
### Construction of the Micro-Capsule Networks
#### 2.2.2 Epilepsy Detection Based on the Micro-Capsule Network Model  
Targeting small-sample epilepsy EEG datasets, we propose a micro-capsule network architecture tailored for limited-data datasets. Reconstructing epileptic EEG signals exerts minimal influence on recognition accuracy. Excessively deep networks introduce excessive parameters, reducing training speed. To simplify the micro-capsule network and improve training efficiency, we omit the reconstruction layer. The capsule network's fully connected layers employ techniques like sparse connectivity and parameter sharing, significantly decreasing trainable parameters. Taking binary classification as an example, Figure 2 illustrates the micro-capsule network architecture, comprising convolutional, capsule, fully connected, and prediction layers.  
The proposed model employs a capsule network architecture to design an end-to-end network capable of effectively distinguishing different epileptic EEG signals. Taking binary classification (AB vs. E) as an example, the number of network layers and structural parameters of the micro-capsule architecture is detailed in Table 1.  
The layer architecture and structural parameters of the micro-capsule network are configured as follows. First, the \(1D\) EEG input undergoes dim  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Input Shape** & **Output Shape** & **Convolution Kernel** \\ \hline Input & (1, 342) & (19, 18, 1) & - \\ Con-1 & (19, 18, 1) & (19, 18, 8) & (4, 4) \\ Con-2 & (19, 18, 8) & (13, 12, 8) & (4, 4) \\ Primary Capsule & (13, 12, 8) & (312, 4, 1) & (4, 4), (4, 4), (2, 4, 16) \\ Label Capsule & (312, 4, 1) & (2, 16, 1) & - \\ Output & (2, 16, 1) & (1, 2) & - \\ \hline \hline \end{tabular} Hyperparameters of the micro-capsule network model employ three setting parameters: optimizer = Adam, learning rate = 0.0005, batch size = 32.  
\end{table}
Table 1: The layers and structural parameters of the micro-capsule network model.  
Figure 2: The number of network layer and structural parameters for micro-capsule network architecture.  
transforming into a \(3D\) matrix of shape \((19,18,1)\). This matrix is processed by the initial convolutional layer to generate primary features \((19,18,8)\). Subsequently, a second convolutional layer extracts abstract features \((13,12,8)\). These features are then routed to the primary capsule layer, producing high-dimensional capsule outputs \((312,4,1)\). Next, the deep feature vector is passed through the fully connected label capsule layer, where it is multiplied by a weight matrix to derive a refined eigenvector \((2,16,1)\). Finally, the prediction layer converts these features into classification probabilities either \((2,1)\) for binary tasks or \((3,1)\) for ternary classification enabling epileptic EEG signal identification.
# The Generalized Skin Depth for Polarized Porous Media Based on the Cole-Cole Model
## 3 Generalized Skin Depth Characteristic Analysis Based on the Cole-Cole Model
### Effect of the Polarization Parameters on the Generalized Skin Depth  
#### 3.2.1 Influence of the Conductivity on the Generalized Skin Depth  
Conductivity is an important parameter affecting the generalized skin depth. To analyze the effect of conductivity on the generalized skin depth, we chose the chargeability as \(\eta=0.5\) and the conductivities as \(\sigma_{\infty}=0.1\) S/m, \(\sigma_{\infty}=0.3\) S/m, and \(\sigma_{\infty}=0.5\) S/m. The calculation results are shown in Figure 2. From comparison with the classical skin depth formula, the absolute error is shown in Figure 3.  
Figure 1: The \(\eta=0\) generalized skin depth of polarized media ((**a**) calculation of depth, (**b**) relative error).  
As shown in Figures 2 and 3, the generalized skin depth increased with decreasing conductivity. Compared with the classical skin depth, the generalized skin depth was deeper. The absolute error increased with decreasing conductivity and frequency. The effect of conductivity on the generalized skin depth was mainly concentrated at f \(<\) 10 Hz; when the frequency f \(>\) 10 Hz, the absolute error between the generalized skin depth and classical skin depth was less than 5 m. As the frequency continued to increase, the final generalized skin depth and classical skin depth tended to coincide.