esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Background
### Trust Management in IoV
#### 3.1.4 Trust Metrics  
The proposed trust management approaches have been known to apply different metric methods to measure and evaluate trust value. These methods are:  
* Reputation-based metrics: This type of trust metric calculates trust value from the recommendations given by specific nodes in the network. These network nodes may have similar opinions about the node that has been propagated within the network. This metric method considers major opinions or global feedback regarding the node.
* Knowledge-based metrics: This technique calculates trust value based on direct or previous experience that the node has or has gained from another specific node. These metrics help identify selfish nodes that may be part of a network.
* Expectation-based metrics: These metric methods involve a node determining the trust of another node based on how it expects the node to act. Its expectations are based upon previous interactions with the node, received suggestions, or the initial prediction in the case of no prior communication.
* Node-properties-based metrics: Trust calculations make use of the main parameters of proximity such as location and distance with the considered node.  
Environmental-factors-based metrics: When measuring an IoV network, this metric takes into account environmental factors, including network density and topology.
# Operation Risk Assessment of Urban Dense Cable Passageway Based on Fuzzy-Analytic Hierarchy Process
## 2 Operational Risk Parameters for Urban Dense Cable Corridors
### Cable Channel Risk Parameters
#### 2.2.1 Blocked or Poorly Ventilated Passageways  
Blocked or poorly ventilated cable pathways are situations where obstructions prevent normal cable operation or poor air circulation in ancillary equipment along the cable-laying path. This condition may adversely affect the safe operation and life of the cable.
## 3 Results and Discussion
### Prediction Results of CNN-PSPA Models
#### 3.3.1 Optimal Parameters for CNN-PSPA Models  
Since the accuracy and the stability in the training of the CNN models were mainly dependent on the parameters of the convolutional layers and convolutional kernels, error values computed with different parameters were used to optimize the CNN-PSPA models. In order to construct a more optimal CNN-based model, four groups with different convolutional kernel sizes and kernel numbers were selected for evaluating the prediction errors. The average errors of the six CNN-PSPA models with different parameters are shown in Table 10. The differences in the average error between the maximum and minimum for each CNN-PSPA model are also listed in Table 10.  
Figure 11: Comparison of predicted and observed results.  
Table 10 shows that the average error values varied with the different parameters for all of the six CNN-PSPA models. The error differences of the six models ranged from 14.3%, 15.5%, 17.9%, 12.6%, 12.6%, and 21.2% to 23.0%, 24.5%, 23.9%, 22.5%, 22.1%, and 25.0%. Regarding the errors of the six models, the maximum change was 9.9% in the non-LHES model, and the minimum change was 3.9% in the non-BMIS model.  
The non-BMIS model was the least sensitive to the CNN parameters, while the non-LHES model was the most influenced by the parameters. From Table 10, the optimal values of the six models were extracted from the four group parameters for subsequent predictions. Among the six CNN-PSPA models, the best prediction model was the non-LHES model, which had a relative error of 12.2%. The model with the biggest error of all the six models was the non-BMIS model, which had an average error of 21.1%. The accuracy and stability of the trained CNN-PSPA models remained at a reasonable level for different network parameters.  
Table 10 shows that the sensitivity of the test results was inversely proportional to the error rate of the results. For example, the non-LHES model had the highest error fluctuation among the six CNN-PSPA models when trained with different parameters, but it had the lowest error value among the models. Compared to the five other models, it was more sensitive to the parameters within the control range. However, the models with higher errors did not fluctuate as much. This led to stable prediction results by the CNN-PSPA model, even with different training parameters.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 5 Evaluation
### Experimental Setup  
For simulation, we used ns-3. We simulated a random access network environment, and Table 1 presents some of the main parameters used. We consider the contention-based RA, where the TRxPs do not pre-allocate resources for users, users compete to seize an RA opportunity, and collisions occur in doing so. We also assume that users frequently trigger the RA and re-establish connections as in highly mobile environments. The mobility pattern follows a random uniform distribution. Each RA slot includes six physical resource blocks (PRBs), totaling 1.08 MHz. The number of available RA preambles in an RAO is 64. Users send a maximum of 50 preambles before they assume that the network is unavailable and then withdraw. The average tolerable delay is 100 slots, and the TRxPs can use any scheduler. In our case, we adopt the proportional fair scheduler. The main parameters used for the deep RL algorithm are summarized in Table 2. We use keras over Tensor-flowfor the implementation. There are four connected layers. Each hidden layer has 48 neurons. Neurons are activated according to a Relu activation function.
# Joint Maintenance Strategy Optimization for Railway
## 4 Numerical Analysis
### Analysis of Optimization Results  
To further illustrate the applicability of the proposed maintenance strategy optimization model, the results of different maintenance strategies and the numerical changes during the optimization process were further analyzed.  
The target value of the proposed maintenance strategy was analyzed quantitatively. It can be seen from Table 5 that the choice of reprofiling strategy has an influence on the number of reprofiling. When the profiling strategy selects (31.5, 32), it needs to be reprofiled 14 times in total, and when choosing (30, 32), only 6 reprofiles are performed on the wheel. Frequent reprofiling causes a heavy work burden for maintenance personnel and affects maintenance planning.  
Figure 10: Variation curve of expected life with maintenance cycles and maintenance strategies.  
Figure 9: Variation curve of cost rate with maintenance cycles and reprofiling strategies.  
The impact of maintenance intervals on cost rates was further analyzed, as shown in Figure 11, and when the reprofiling strategy (\(Sd_{L}\), \(Sd_{H}\)) = (28.5, 31) is fixed, with the length of the maintenance interval increasing, the maintenance cost rate also decreases first and then rises. Especially when the maintenance interval gradually increases from \(\Delta t\) = 2 to \(\Delta t\) = 4.5, the influence of the maintenance interval on the cost rate is evident. When the maintenance interval is short, frequent inspections need more expense. As the maintenance interval increases to a certain point, the effect of prolonging the life decreases, and the probability of the wheel flange thickness exceeding the limit increases, resulting in a higher maintenance cost rate.  
As the mileage increases, preventive and corrective maintenance probability increase, as shown in Figures 12 and 13. With the increase in reprofiling times, the probability of preventive maintenance continues to rise, and the probability of corrective maintenance decreases. Preventive maintenance eliminates tread surface damage and restore the wheel status. Since the effect of preventive maintenance is imperfect, the interval between two preventive maintenances is shortened gradually, reducing the failure probability of the tread surface.  
Figure 11: The curve of cost rate changing with the maintenance cycle.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multicolumn{3}{c}{**Maintenance Strategy Value**} & \multirow{2}{*}{**Reprofiling Times/Time**} & \multirow{2}{*}{**Expected Life/10\({}^{4}\) km**} & \multirow{2}{*}{**Cost Rate/(CNY/10\({}^{4}\) km)**} \\ \cline{2-2} \cline{5-6}  & \(\Delta t\) & \(Sd_{L}\) & \(Sd_{H}\) & & & \\ \hline
1 & 5 & 31.5 & 32 & 14 &  
To analyze the impact of the risk of failure on the maintenance joint strategy, Table 6 gives the optimal results under different failure risk values. With the increase in the risk of failure \(\alpha\), the maintenance interval and life expectancy of the wheelset increase, and the maintenance cost rate is reduced, as a lesser probability of failure risk tends to cost more.
# Blockchain Technology in Healthcare: A Comprehensive Review and Directions for Future Research
## Internet of Medical Things
### Blockchain Use Cases in the Internet of Medical Things  
**Lack of Standardization:** The acceleration of the healthcare industry towards adopting state-of-the-art IoMT technology is enormous. In addition to healthcare providers on premise medical devices, IoMT unlocks the facility of medical services to be accessed ubiquitously to guide patients invariably. This is also the building blocks of telehealth surveillance applications as wireless devices are associated with capturing bio-signals. Therefore, tamperproof transmission of medical data is very substantial [167]. This problem results from the high heterogeneity in communication protocols, medical devices and platforms, which could be termed as lack of standardization among IoMT manufacturers. Therefore, protecting such variable standardized transmission medium is difficult in the current perspective [168]. State-of-the-art technology, such as blockchain, could be applied to resist unauthorized access during data transmission over an unknown network as a diverse communication standard of a system does not affect blockchain's performance. Furthermore, tampering even a blockchain data block requires enormous computation power and consensus (51%) among its mining nodes before broadcasting to the whole network to add in the chain.
# Effect of pH Change on the Microalgae-Based Biogas Upgrading Process
## 2 Materials and Methods
### Determination of the Apparent Affinity of Microalgae  
The use of inorganic carbon by C. _sorokiniana_ was studied by photosynthetic activity (PA) kinetics (oxygen release) at pH 5.0 and pH 7.0, applying different dissolved inorganic carbon (DIC) concentrations into the electrode. The inorganic carbon was added in the form of NaHCO\({}_{3}\), partly converted into CO\({}_{2}\) as a function of the pH according to the chemical equilibrium NaHCO\({}_{3}\)/CO\({}_{2}\) in water. The initial oxygen release rate was registered for each NaHCO\({}_{3}\) concentration added [21]. The apparent affinity constant (KDIC) for inorganic carbon was calculated from a graph of 1/PA versus 1/[DIC], according to Equation (1).  
\[\frac{1}{PA}=\frac{K_{DIC}}{PA_{max}}\cdot\frac{1}{[DIC]}+\frac{1}{PA_{max}} \tag{1}\]
# A Novel Multi-Server Federated Learning Framework in Vehicular Edge Computing
## 6 Experimental Results
### Simulation Results  
In our experiments, we deployed three FL servers (\(\text{N}=3\)), each at a distinct RSU, and evaluated two scenarios: a shard-based dataset distribution with 48 clients in total, and a Dirichlet-based distribution with 100 clients in total. The clients were evenly distributed among the servers, with each server responsible for approximately one-third of the clients.  
To quantify the recognition quality for the proposed MS-FL framework, we record, for every class, the four canonical outcomes of a classifier:  
* True positives (TP)--images that truly belong to a class and are labeled as such by the model (e.g., a \"speed-limit-30 km h\" sign or a handwritten '8' identified correctly);
* True negatives (TN)--images that do not belong to the class and are correctly rejected;
* False positives (FP)--images that are erroneously assigned to the class (a \"speed-limit-50\" labeled as \"30\"; a '3' labeled as '8');
* False negatives (FN)--images that should have been assigned to the class but were missed by the model.  
From these counts, we report three widely accepted indicators:  
* Precision--the proportion of predicted positives that are indeed correct. High precision shows that the model rarely confuses one traffic sign (or digit) for another; \[\text{Precision}\ =\frac{\text{TP}}{\text{TP}+\text{FP}},\] (27)  
Figure 3: Sample images from the benchmarks: (**a**) one example per class from the 43 traffic sign categories in GTSRB [39]; (**b**) handwritten digits from MNIST [40].  
Recall--the proportion of actual positives retrieved by the model. A high recall means that very few relevant signs or digits are overlooked;  
\[\text{Recall}\,=\frac{\text{TP}}{\text{TP}+\text{FN}}, \tag{28}\]  
F1-score--the harmonic balance between precision and recall, providing a single, robust measure of overall classification effectiveness.  
\[\text{F1}=\frac{2\,\times\,\text{Precision}\,\times\,\text{Recall}}{\text{ Precision}+\text{Recall}}, \tag{29}\]
## 3 Experiments
### Engineering and Practical Applications  
To achieve the efficient screening of massive remote sensing data, we used PyQt5 (The version used in this study is 5.15.9) to encapsulate the code into application software. First, we designed an intuitive graphical user interface (GUI) according to user requirements to enable interaction between users and the algorithm. Subsequently, the developed optimization screening algorithm for remote sensing data was integrated into the PyQt5 framework to ensure the stable operation of the algorithm in the GUI environment. Finally, we used the PyInstaller tool to package the entire project into a standalone.exe file. This process not only simplifies the usage of complex algorithms but also allows non-professionals to operate easily through optimized interaction design. Nowadays, the application has been applied to many practical projects, such as pre-disaster data screening in Pakistan, national land change survey task area data optimization, the optimal selection of basic survey data on water resources, and the national land use dynamics coverage project. Verified by practice, it has significantly shortened the data screening cycle, improved the accuracy and efficiency of data processing, and strongly promoted the development of remote sensing data application.
## 3 Results and Discussion
### Regeneration and Reusability Study  
The regeneration and reuse of the sorbent material are essential for its economic viability. To evaluate the reusability of MWCNTs, adsorption-desorption experiments were carried out using 40 mg L\({}^{-1}\) of dye solution, shaken at 110 rpm for 2 h. The results showed that the removal efficiency of the MWCNTs for IV2R uptake dye remained nearly unchanged for the three consecutive series. The initial removal efficiency was 95.2% in 120 min, which reduced slightly to 92.1% after the third round, as shown in Figure 19. Hence, it could be concluded that the sorption capacity of MWCNTs remains unaffected with extended regeneration rounds. This decrease shows that the adsorbent represents a good material that can be used in industrial operations. The diminishing adsorption of IV2R dyes over the three cycles was brought about by the destructive influence of stripping agents and the weight loss in the adsorbent [64]. Furthermore, the resident IV2R dyes in the adsorbent (irreversible binding) resulted in a decrease in the number of available binding sites [65,66].  
Figure 19: Reusability study for removal of IV2R in various cycles.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Temperature (\({}^{\circ}\)C)** & \(\Delta\)**G\({}^{\circ}\) (kJ mol\({}^{-1}\))** & \(\Delta\)**H\({}^{\circ}\) (kJ mol\({}^{-1}\))** & \(\Delta\)**S\({}^{\circ}\) (J mol\({}^{-1}\))** \\ \hline
25 & \(-\)7.87669 & & \\ \hline
35 & \(-\)7.94698 & & \\ \hline
45 & \(-\)9.763 & & \\ \hline
55 & \(-\)10.5637 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Thermodynamic factors of the sorption of IV2R onto MWCNTs.  
[PERSON] et al. [67] achieved more than 85% removal of dyes, indicating the reusability of the adsorbent. Moreover, [PERSON] et al. [68] tested the regenerated adsorbent for up to five adsorption-desorption cycles and showed that the decline in efficiency was less than 5%, highlighting the favorable prospects of these fungi for Pb(II) elimination in industrial operations. [PERSON] et al. [69] studied the regeneration of MWCNTs for reactive red 3 BS, and 92.8% reuse efficiency was achieved after four cycles.
# InnCube--A Wireless Satellite Platform to Demonstrate Innovative Technologies
## 2 Mission
### Scientific and Technical Goals and Payloads
#### 2.4.5 WallE-2-Space  
The goal of the experiential payload WallE-2-Space is to examine previous researched Wall#E technology [15; 16] in a relevant space environment. The technology integrates energy storage functions into the supporting structures of a spacecraft, equipping the carbon fiber-reinforced plastics (CFRP) with solid-state battery materials at nanoscale and microscale levels. By partially replacing matrix polymer with active material for the anode and cathode, solid electrolyte and conductive additive, a storage function and electron transport can be achieved. In order to electrically separate the anode and cathode from each other, a separation layer made of a mechanically highly stressable glass fiber lies between these two layers of carbon fiber laminate (Figure 5).  
Figure 4: Structure of implemented radio protocol frame.  
Figure 5: Schematic structure of an energy-storing fiber composite structure (Wall#E) [13].  
Different experimental battery demonstrators are used for the satellite payload. The basis are CFRP structural batteries like that described above, with slightly varying layers based on the four types investigated in the original project. The layer structure of the Type I is based on the carbon fiber-based graphite-containing anodes, carbon fiber-based cathodes, and a commercial polyethylene oxide (PEO)-based separator. Type II consists of a layer structure of a carbon fiber-based cathode, the commercial separator and a lithium metal anode. Type III uses a glass fiber-based separator with a basis weight of 105 g m\({}^{-2}\) impregnated with an electrolyte solution of PEO, lithiumbis(trifluormethylsulfonyl)imide (LiTFSI), and acetonitrile. The same functional layers are used for type IV but with a basis weight of 25 g m\({}^{-2}\) for the separator to improve the performance. The CFRP structure batteries will be mounted on parts of the satellite structure between wall and solar panels as well as inside the CubeSat, with some reference cells in pouch design. The WallE-2-Space payload will act as a nominal consumer to the satellite power bus, with the payloads own power distribution and charge regulator used to cyclize the battery and record the experimental data.
# Innovative Strategies for Combining Solar and Wind Energy with Green Hydrogen Systems
## 2 Renewable Energy Integration
### Current State of Solar and Wind Energy Technologies  
#### 2.2.1 Significance of Solar and Wind in Advancing Sustainable Energy Transition  
To reach the goal of a clean energy society and address the global climate challenges by 2050, as envisioned by the United States Department of Energy, it is essential to develop  
Figure 1: Fossil and solar energy consumption in the (**a**) US and (**b**) worldwide in BTU (quads = 10\({}^{15}\) BTU) (USCB 2007).  
sustainable and efficient clean energy systems [17; 18]. This path underscores the necessity for a fundamental shift, driven by the pursuit of sustainable and environmentally friendly alternatives to the traditional reliance on fossil fuels. Solar and wind energy are crucial in the transition to a sustainable energy system, offering significant environmental, economic, and technological benefits. These sources--mainly solar and wind--are harnessed from the Earth's ongoing natural processes. The sun's radiant energy and the continuous movement of wind provide inexhaustible power that can meet humanity's energy needs indefinitely. Technological advancements and research have further bolstered the adoption and integration of these renewable energy sources, making them increasingly efficient, cost-effective, and accessible [19]. According to a UN report, the prices for renewable energy technologies are declining swiftly. Between 2010 and 2020, the cost of electricity from solar power dropped by 85 %, while onshore and offshore wind energy costs decreased by 56 % and 48 %, respectively. These decreasing prices enhance the appeal of renewable energy across the board, particularly for low- and middle-income countries, where most of the new electricity demand is expected. With these cost reductions, there is a significant opportunity for a substantial portion of the new power supply in the coming years to be generated from low-carbon sources [20]. These renewable energy sources play a vital role in lowering greenhouse gas emissions and combating climate change, as they produce electricity without relying on fossil fuels. Among them, solar energy stands out for its immense potential to generate renewable power. Additionally, unlike other renewable sources like biomass, which can have some negative environmental impacts [21], solar energy production is known to have no harmful effects on the environment [22]. This lack of emissions also reduces air and water pollution, leading to cleaner environments and improved public health.  
The integration of both solar and wind energy systems is gaining momentum as a strategy to balance the intermittent nature and geographical limitations of these renewable sources. This approach addresses the challenges of unpredictability and the technical constraints associated with each system, making them more reliable and viable on a larger scale. For example, the use of wind energy is limited at certain sites due to low wind speeds [23]. Hybrid solutions will therefore maximize their efficiencies and reliability [24].