esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey
## 7 Challenges and Future Research Directions
### Explainable Models  
RL and DRL have proven to successfully solve a range of sequential decision-making problems in networking and resource management. However, all approaches reviewed in this paper operate as black boxes (nontransparent and hard to interpret). They obfuscate their decision-making policy through complex Q-value functions or DNNs. Very few works concentrate on eXplainable RL (XRL) or eXplainable DRL (XDRL) that particularize the eXplainable AI concept (XAI). XAI intends to make AI-based solutions interpretable, manageable, and trustworthy [189]. XRL/XDRL is a relatively novel research field aimed at developing techniques to extract concepts from the RL/DRL agent's (e.g., perception of the environment, intrinsic/extrinsic motivations/beliefs, Q-values) [190]. We consider XRL and XDRL to be essential to achieving real deployments and commercial success of RL/DRLbased solutions in 5G/6G resource management since operators and tenants can gain access to explanations and justifications of the outcomes given by XRL/XDRL solutions.
# Digitalization Processes in Distribution Grids: A Comprehensive Review of Strategies and Challenges
## 2 Grid Planning
### Sustainability and Environmental Planning
#### 2.6.2 Battery Energy Storage Planning  
BESSs are key to transitioning towards sustainable power systems, enhancing renewable energy use, and reducing CO2 emissions. They address the intermittent nature of solar and wind power, ensuring a reliable energy supply, and are positioned as a cornerstone of the green power revolution.  
Study [112] highlights the role of BESSs in mitigating renewable energy intermittency and demand unpredictability, improving network performance and voltage regulation and minimizing power losses. This positions BESSs as strategic solutions for managing renewable energy variations, having benefits for both the economic and environmental aspects of power systems.  
The research in [113] introduces a two-stage optimization for integrating RESs and BESSs within distribution networks, aiming to reduce investment costs and improve voltage profiles and demonstrating the importance of coordinated planning and digital tools.  
Further discussions in [170] explore BESSs' contributions to generation capacity expansion and decarbonization, while [94] presents a planning method for a distributed BESS that addresses uncertainties in load forecasts and renewable outputs, enhancing power system efficiency. Additionally, Ref. [171] examines BESSs' role in grid stability in the global south and [114] investigates the simultaneous optimization of DG and BESSs for balanced reliability, cost efficiency, and sustainability in network planning.  
BESS integration marks a significant step in enhancing power system sustainability and efficiency, stabilizing the grid, and optimizing network performance. Strategic planning and advanced optimization make BESS investments more effective, significantly aiding voltage regulation and decarbonization efforts. Despite initial cost and regulatory challenges, BESSs are vital for a resilient and sustainable energy future.
## 4 Results and Discussion  
The wire bond solenoid inductor is modeled and simulated in High Frequency Structure Simulator (HFSS) as shown in Figure 4a. As can be seen, the metal base is implemented on a ROGERS4003 dielectric. It is recommended to use a relative permittivity of 3.55 for simulation. The wire bonds are modeled, as well, to form the solenoid inductor. Two excitation ports act as the terminals with a common reference ground. A three-dimensional solenoid inductor for both inductor types was designed in the HFSS EM simulation tool. Figure 4b shows the bond wire (bridge) connections on the base part. The PCB used in this work is ROGERS4003, with 0.8-mm dielectric thickness, a relative permittivity of 3.38, a loss tangent of 0.0027, a metal conductor thickness of 18 \(\upmu\)m and a metal conductivity of \(5.8\times 10^{7}\) S/m. The metal width at the base is 10 mL with a space of 10 mL. The bond wire is gold (Au) with 25 \(\upmu\)m diameter. Ferrofluid was injected into the channel, then it remains static in channel during measurement. The characterization of the inductance was performed based on the percentage of liquid in the channel. As explained in Section 3.2, ferrofluid was modeled as solid magnetic bar when the particles were magnetized.  
Using the simulated data, the discrepancy between measurement results and the ideal solution can be obtained. Figure 4a,b show the simulated 3D view of the wire-wound inductor and wire bond inductor, respectively.
# A Bounded Scheduling Method for Adaptive Gradient Methods
## 4 Experiments
### Image Classification  
#### 4.2.1 Simple Neural Network  
The MNIST database of handwritten digits has a training set of 60,000 images, and a test set of 10,000 images, which can be divided into 10 classes. We train a simple fully connected neural network with one hidden layer and a one-layer convolutional network with one convolutional layer and one fully connected layer for the image classification problem on the MNIST dataset. We run 100 epochs and decay the learning rate by 10 at 80 th epoch for fully connected neural network, then we run 75 epochs and decay the learning rate by 10 at 60 th epoch for convolutional network.  
Figure 3 shows the learning curve of each optimization method, which includes training accuracy and test accuracy. We find that all the optimization algorithms can achieve nearly 100% accuracy on the training set. However, the accuracy of each algorithm will be different on the test set. Among these algorithms, Adagrad converges fastest on the training set, but achieves lower accuracy on the test set and SGDM has a slightly better accuracy on the test set than Adam and Adagrad. Our proposed Bsadam has better convergence speed than SGDM in the early stage. Especially in the converging phase, the convergence speed of Bsadam is faster than all the compared algorithms on both training and test set. Moreover, the final test accuracy of Bsadam is as good as fine-tuned SGDM, which means that our algorithm can get faster training speed without sacrificing accuracy when training simple neural networks. We also run RMSProp and Nesterov with default setting on MNIST. We find that RMSProp has worst convergence speed and test accuracy, Nesterov has similar performance with SGD with momentum. So our method still has advantages over these methods.  
Figure 3: Training and test accuracy for fully connected neural network and one-layer convolutional neural network on MNIST.
# A Hybrid IVFF-AHP and Deep Reinforcement Learning Framework for an ATM Location and Routing Problem
## 4 A Real Case Study
### Sensitivity Analysis  
A sensitivity analysis emphasizes the effectiveness of the alternative evaluation methodology due to weight changes. This analysis based on altering the criteria weights is carried out to show the stability of the suggested method. For this analysis, four weight scenarios are created by changing the weights found with the IVFF-AHP method. Sensitivity aims to see the impact of changing the criteria weights. Table 9 shows the final weights of the alternatives according to each scenario.  
As Table 10 shows, the best (rank 1) and worst (rank 4) examples remain the same for all weighting scenarios. Therefore, the ranking results of the proposed methodology are very stable, even when the criteria weights are changed.
## 4 Simulation and Experiment
### Experimental Prototype  
To verify the effectiveness of the proposed DPSK communication method, a 350 W experimental prototype is established, as shown in Figure 15a. The related experimental parameters are listed in  
\begin{table}
\begin{tabular}{c l c} \hline \hline
**Parameters** & **Description** & **Simulation** & **Experiment** \\ \hline \(f\) & Operating frequency & 100 kHz \\ \(V_{\rm dc}\) & Voltage of DC power source & 60 V \\ \(R_{\rm L}\) & Load resistance & 10–25 \(\Omega\) \\ \(k\) & Coupling coefficient & 0.1–0.3 \\ \(L_{\rm P}\) & Primary coil inductance & 50 \(\mu\)H & 49.2 \(\mu\)H \\ \(C_{\rm P}\) & Compensated capacitor of primary side & 50 nF & 51.4 nF \\ \(L_{\rm s}\) & Secondary coil inductance & 50 \(\mu\)H & 50.4 \(\mu\)H \\ \(C_{\rm s}\) & Compensated capacitor of secondary side & 50 nF & 49.7 nF \\ \(R_{\rm P}\) & Equivalent resistance of primary coil & 0.1 \(\Omega\) & 0.18 \(\Omega\) \\ \(R_{\rm s}\) & Equivalent resistance of secondary coil & 0.1 \(\Omega\) & 0.18 \(\Omega\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of simulation and experiment.  
Figure 14: Simulated waveforms of transmitted bits (Data in), resonant currents (\(i_{\rm p}\) and \(i_{\rm s}\)) and received bits (Data out). (**a**) Forward communication with 25 kbps data rate. (**b**) Backward communication with 50 kbps data rate.
# Well-Being and Social Media: A Systematic Review of Bergen Addiction Scales
## 2 Method and Procedures
### Measuring Facebook and Social Media Addiction  
The study of Internet addiction phenomena grew along with the availability of ICT technologies. Since the late 1990s, researchers have investigated whether people became addicted and developed Internet-related addiction measures [28; 29]. As Facebook became more popular across broad segments of Internet users [30], dedicated measures were needed. For example, the Addictive Tendencies Scale was developed [31]. Nonetheless, it failed to consider all of the addiction's core components. To overcome this limitation, the Bergen Facebook Addiction Scale [23] was constructed. The scale is composed of six items, each of them reflecting one core aspect of addiction [32; 33; 34; 35], namely: (a) salience (i.e., the activity controls thinking and behavior), (b) mood modification (i.e., the activity is carried on to improve mood), (c) tolerance (i.e., increasing amounts of the activity are needed overtime), (d) withdrawal (i.e., unpleasant feelings emerge when the activity is reduced or interrupted), (e) conflict (i.e., the activity interferes in relationships and other activities), and (f) relapse (i.e., the tendency to return to earlier levels of the activity after abstinence).  
The BFAS shows an adequate dimensionality (\(\chi^{2}\)/df = 1.84; Comparative Fit Index [CFI] = 0.99; Root Mean Square Error of Approximation [RMSEA] = 0.05) [36] and reliability (Alpha coefficient is 0.83) [37]. For this reason, BFAS has been adopted and used as a psychometrically sound instrument for measuring Facebook-related addiction. Indeed, at the time of writing, the \"Web of Science\" tracking system reports 310 citations for BFAS, while Google Scholar 893.  
Despite the undoubted utility of the instrument, the Bergen Facebook Addiction Scale risks being too specific in the current environment in which social media proliferates and Facebook is no longer the only one on the pitch [38; 39]. For this reason, BSMAS was developed to capture the totality of all social network sites [24]. Although maintaining the same rationale (i.e., addiction criteria) and items' main structure, BSMAS uses the words \"social media\" instead of the word \"Facebook\" with social media being defined as \"Facebook, Twitter, Instagram and the like\". For the sake of clarity, we specify that in the literature \"Bergen Social Networking Addiction Scale\" (BSNAS) has been reported as an alternative name for this instrument's version. In any case, the internal consistency of the BSMAS results high (\(\alpha=0.88\)), while dimensionality information is currently missing but assumed similar to BFAS. Although Bergen's addiction scales were related to addiction's negative outcomes (e.g., poor sleep quality, anxiety, depression), well-being measures were not adopted as validity measures in the original works for BFAS, BSMAS, and BSNAS, but were rather tested in subsequent studies.
## 4 Discussion  
There was no significant difference in the setting times of the plain mortar assessed by the two methods. The initial setting times obtained from the penetration resistance test and the EMI sensing technique were 371 min and 370 min, respectively. The final setting times obtained from the penetration resistance test and the EMI sensing technique were 573 min and 540 min, respectively, showing a time difference of about 30 min. Based on these results, the EMI sensing technique can be said to be an appropriate method to evaluate the setting time of plain mortar.  
Since the mixture proportion of the plain mortar and the SFRMs is the same except for the amount of fiber, the setting time of SFRM should have a value similar to that of plain mortar in terms of cement hydration. In the penetration resistance test, both the initial and final setting time of the SFRM samples were accelerated by more than 2 h compared to those of the plain mortar. When a needle penetrates the mortar where the steel fibers are mixed, the steel fibers and the needle in the mortar may come into contact with each other to generate resistance, which increases the penetration resistance. The greater the amount of steel fibers in the mortar, the greater the resistance, so the fibers accelerate the setting time defined in ASTM C 403. Due to the additional resistance caused by the fibers, the setting times of SFRM measured by the penetration resistance test cannot represent the actual stiffness gain of the mortar phase in SFRM. It is noteworthy that the time gaps between the initial to final setting time (Table 2) are very similar regardless of the amount of fibers. This implies that the accelerated setting times measured by the penetration resistance are because the fibers affect the penetration resistance, and the actual stiffness gain of the mortar phase in SFRMs is the same for given material age.  
In the EMI sensing technique, both the initial and final setting time of the SFRM samples did not show significant difference compared to those of the plain mortar (less than 20 min difference). In EMI sensing technique, the stiffness gain of the mortar phase in direct contact with the PZT sensor affects the behavior of the EMI signal, but the fibers around the PZT sensor might have little effect on the EMI signal, so the setting times show consistent values regardless of the amount of fibers mixed in the SFRM.
# A Membrane Modified with Nitrogen-Doped TiO2/Graphene Oxide for Improved Photocatalytic Performance
## 3 Results and Discussion
### The Kinetic Analysis of the Photocatalytic Degradation of the Modified Membranes  
In order to quantify the effect of various modifications on the photocatalytic activity of the membrane, the first-order reaction kinetic equation \(\ln(C/C_{0})=-kt\) was used to fit the kinetics of MB degradation into the various surface-modified membranes (where \(C\) and \(C_{0}\) are the MB concentrations during the experiment and at the beginning of the experiment, respectively; \(k\) is the reaction rate constant, and \(t\) is the reaction time). The process of MB degradation by the modified membranes with different photocatalysts was consistent with first-order kinetics. Figure 10 summarizes the photodegradation dynamic curves of the TiO\({}_{2}\)/PVDF, NT/PVDF, TG/PVDF, and NTG/PVDF membranes under UV and sunlight, respectively. Under UV light, the degradation rate constants of the NTG/PVDF and TG/PVDF membranes were significantly higher than those of the TiO\({}_{2}\)/PVDF and NT/PVDF membranes (shown in Figure 10a). This is due to the synergy between GO and TiO\({}_{2}\). Graphene has a very high electron transport rate, which can accelerate the photogenerated electron  
Figure 9: The photodegradation of MB by the membranes in (**a**) darkness (**b**) under ultraviolet light and (**c**) under sunlight.  
transfer rate of TiO\({}_{2}\) after compounding it with GO, thereby improving the photocatalytic activity of the composite catalyst [24].  
Figure 10b showed that the combination of N, GO and TiO\({}_{2}\) clearly produced a significant synergistic effect in sunlight. The MB photodegradation rate constants of the NTG/PVDF, TG/PVDF, NT/PVDF membranes were significantly higher than the TiO\({}_{2}\)/PVDF membranes. This is because N-doping improves the visible light absorption of TiO\({}_{2}\). The effect of GO can be further improved by N-doping. The electron-hole recombination in TiO\({}_{2}\) can be effectively reduced, thereby significantly improving the photocatalytic performance of the NTG microfiltration membranes.  
The NTG/PVDF membranes and TG/PVDF membranes had very similar photocatalytic properties under UV, while the NTG/PVDF membrane performed better in sunlight than the TG/PVDF membranes, indicating that N-doping has no significant effect on UV light. However, this is not the case in sunlight. Nitrogen doping reduced the forbidden bandwidth of TiO\({}_{2}\). Thus, TiO\({}_{2}\) had a longer photoresponse range.
# A Review of Fault Diagnosing Methods in Power Transmission Systems
## 7 Fault Location Finding Methods
### ANN-based Algorithm for FL  
The fault location finding task can also be achieved in transmission networks by applying different kinds of ANNs as it shows self-organization, self-learning, high fault tolerance, fast processing, and non-linear function approximation. ANN data is trained by detailed coefficients obtained by DWT which are then employed for the Levenberg Marquardt algorithm to locate fault [135]. In [136], fundamental components of voltage and current signals are extracted by DFT. Different modular ANNs are employed and triplet vectors served as input for them. Best performance is obtained by features containing information of both voltage and current signals with respect to the fault location accuracy and training speed. Complex domain ANNs are simply the extensions of real domain ANNs whose input, output and hidden layers are all complex numbers. Mother wavelet, Db2, is employed as input to the complex domain ANN for finding the location of fault [137]. PNN reduced the error to 0 km for fault location for different circuit topologies including both loop and single-circuit topology [138]. [PERSON] et al. proposed a two-stage FL finding method using radial basis function (RBF) kernel-based SVM and scaled conjugate gradient (SCALCG) based ANN for fault location finding [139]. In the first stage, the fault area is estimated by measuring the magnitudes of fundamental harmonics of voltage and current signals which then fed as input to RBF-based SVM. In the second stage, high-frequency characteristics served as input for SCALCG based ANN to obtain precise fault location.
# Optimal Progressive Pitch for OneWeb Constellation with Seamless Coverage
## 3 Algorithm Design
### Complexity Analysis  
The genetic algorithms are adopted frequently, but there are no general analysis for all genetic algorithms. The complexity of genetic algorithms include the number of iterations and the cost of each iteration. For each iteration, crossover, mutation and selection operations are performed. Assume the size of the population is \(M\). The complexity of the above operators are \(O(M)\), \(O(M)\) and \(O(M^{2})\), respectively; therefore the overall complexity is equivalent to \(O(M^{2})\). It is difficult for the number of iterations to reach convergence and complex to find the complexity of iteration. Some interesting findings are in shown in [32; 33]. Since the complexity is hard to obtain, in many cases, it is demonstrated by simulations.
# Prediction of Cyanobacteria Using Decision Tree Algorithm and Sensor Monitoring Data
## 2 Research Method
### Evaluation of Sensor-Based Data Prediction Accuracy
Next, we evaluated the applicability of the category-type algae prediction model by applying it to the four sections, whereupon the DS-HC section was selected for further analysis owing to its frequent algae occurrences and high accuracy. Applying the RF model, we also analyzed the model prediction accuracy according to the training factors in the selected sections, using data from the algae alert system, algae and Chl-a prediction studies, and factors obtainable in the Nakdong River. The accuracy of predicting algae categories at the same time was compared with the accuracy of predicting them 7 days later. In Cases 1-4, input data used in previous studies were applied. Cases 5-8 were constructed using factors obtainable in the Nakdong River. Cases 7 and 8 comprised factors collectable in real time, which would allow the possibility of preemptive responses to algae outbreaks (Table 6). Figure 5: Determination of model accuracy per category using receiver operating characteristic curves ((**a**) S]-GM, (**b**) CG-GG, (**c**) DS-HC, (**d**) CH). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multicolumn{3}{c}{**AUC (Area under the Curve)**} \\ \cline{2-4}  & **Attention** & **Warning** & **Bloom** \\ \hline SJ–GM & 0.86 & 0.81 & 0.80 \\ \hline CG–GG & 0.87 & 0.75 & 0.92 \\ \hline DS–HC & 0.92 & 0.76 & 0.95 \\ \hline CH & 0.89 & 0.76 & 0.90 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Evaluation of the AUC of the category-type algae prediction model. We also compared the model accuracy according to training factors. The difference in accuracy according to the configuration of training factors ranged from 0.761 to 0.821 for predictions in the same time and from 0.751 to 0.814 for predictions after 7 days. Therefore, we were able to confirm the model prediction accuracy according to the composition of training factors. Additionally, the prediction accuracy appeared high when using collectible factors in real time, such as with Cases 7 and 8. This is because the water analysis factors were not included, making it possible to predict algae occurrence in real time. With water analysis, approximately 6 days are needed to obtain laboratory results for the water samples collected from the river, making it difficult to predict algae outbreaks in real time. Moreover, limitations exist in river environments where it is necessary to predict algae outbreaks and respond to them in advance. Based on the model prediction results of this study, we conclude that it is possible to predict algae outbreaks using the data of factors that can be measured in real time in the field.
# BDS Orbit Maneuver Detection Based on Epoch-Updated Orbits Estimated by SRIF
## 3 Data Processing and Validation  
In this section, first, a brief introduction is provided on the data and estimation strategy used for orbit determination based on the SRIF method and the corresponding orbit accuracy. Second, a detailed analysis of the results of the orbital maneuver detection is presented; the velocity change of satellites has been estimated and compared to indicate that the proposed method in this paper can accurately detect the start and end times of orbit maneuvers in post-processing and real time. Finally, the start and end times of maneuvering satellites for BDS satellites in 2022 are given.  
Figure 3: The schematic diagram for maneuvering end time detection in real-time.