esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Objective Path-Planning Approach for Multi-Scenario Urban Mobility Needs
## 2 Preliminaries
### A* Path-Planning Algorithm  
The A* algorithm is a heuristic search algorithm widely used in pathfinding and graph traversal. As a heuristic search algorithm, A* iteratively selects the optimal node and expands the search path to find the optimal route globally. By using a comprehensive cost evaluation function, the A* algorithm effectively reduces the number of intermediate nodes that need to be traversed, thus enhancing algorithm efficiency. The formula for the comprehensive cost evaluation function is as follows:  
\[f(n)=g(n)+h(n) \tag{1}\]  
where \(f(n)\) represents the comprehensive priority of node \(n\). \(g(n)\) denotes the accumulated path cost from the start node to the current node \(n\). \(h(n)\) is the heuristic estimation function, which estimates the cost from node \(n\) to the goal node.  
Algorithm 1 illustrates the A* path-planning algorithm, a widely used approach for finding an optimal path from a start node to a goal node in a network graph. This algorithm initializes two sets, openSet and closeSet. The openSet contains nodes that are pending exploration, while the closeSet contains nodes that have already been explored.  
In each iteration, the algorithm selects the node \(n\) from openSet with the lowest priority. If \(n\) is the goalNode, the algorithm traces back from goalNode through its parent nodes to construct and return the path from startNode to goalNode, successfully completing the search. If \(n\) is not the goalNode, it is removed from openSet and added to closeSet, marking it as explored. The algorithm then examines each neighbor \(m\) of \(n\). If \(m\) is already in closeSet, it is skipped, as it has already been explored. If \(m\) is not in openSet, it is added to openSet, with its parent set to \(n\), and its priority is calculated. The priority of \(m\) combines the known cost from startNode to \(m\) and the heuristic estimate from \(m\) to goalNode, balancing exploration with an efficient path toward the goal.  
This process repeats until openSet is empty (indicating that no path is found) or until the goalNode is reached, at which point, the path is returned. If no path is found after all nodes are explored, the algorithm returns a message, \"Path not found\", signaling that no valid route exists between the start and goal nodes in the given network graph [22].
## Appendix C Analysis of Memory Requirements for Sum-Table Methods  
Let the size of raw RF volume data is 1024 (axial) \(\times\) 128 (lateral) \(\times\) 50 (elevational). The memory usage for setting up sum-table is mainly associated with 3-D search range and the size of evelational of 3-D tracking kernel. Assuming that a 3-D tracking kernel is (69 (axial) \(\times\) 9 (lateral) \(\times\) 3 (elevational) and the search range is 18 (axial) \(\times\) 5 (lateral) \(\times\) 3 (elevational), we need to access to both the reference RF volume data \(f\) (\(1024\times 128\times 3\)) and the target RF data \(g\) (\(1024\times 128\times 5\)). According to Equations (4)-(6), the RF volume data required to set up the sum-tables of \(f\) and \(g\) are \(1024\times 128\times(3+1)\) and \(1024\times 128\times(5+1)\). Therefore, the memory consumption for the corresponding sum-table of \(f\) and \(g\) are \(1024\times 128\times(3+1)\times sizeof(float)=2\) MB, and \(1024\times 128\times(5+1)\times sizeof(float)=3\) MB, respectively.  
Since Luo-Konofagou method needs to set up a set of sum-tables at different shift locations in target signal \(g\) for calculation of numerator, the memory required for this part is \((18\times 5\times 3)\times(1024\times 128\times 5)\times(1024\times 128\times 5)\)\(128\times 4)\times sizeof(float)=540\) MB. Table 10 lists the memory usage for Luo-Konofagou method and [PERSON]'s method given different search ranges.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 3 Application of the Methodology
### Calculating Prior Probabilities of REs, CE, and OEs  
The occurrence probabilities of REs within the accident BN model are ascertained utilizing generic reliability databases [29; 31], FST-based expert judgment, frequentist estimation techniques, and the relevant literature [10; 15]. The comprehensive probability outcomes of all REs are presented in Table 1, serving as foundational prior failure data for the subsequent BN predictive analysis. Here, \(X_{6}\) is taken as an example to illustrate the calculation process of occurrence probability by the frequentist estimation (see Section 2.2.1), as shown in Table 2. Notably, the application of FST-based expert judgment for RE probability calculation has been reported in our previous research [7]. Concerning UEs, namely CE and OEs, their occurrence probabilities are derived through rigorous BN deductive reasoning. The corresponding results are compiled in Table 3. Specifically, the probability of leakage stemming from GDS failure (CE) is determined to be 5.23 \(\times\) 10\({}^{-2}\). Among the OEs, minor material losses (OE\({}_{1}\)) and short-duration jet fires (OE\({}_{3}\)) exhibit relatively higher occurrence probabilities, at 4.54 \(\times\) 10\({}^{-2}\) and 5.77 \(\times\) 10\({}^{-3}\), respectively. Conversely, the probabilities for major hazard scenarios, such as vapor cloud explosions (OE\({}_{5}\)) and long-duration jet fires (OE\({}_{6}\)), are significantly lower, at 1.53 \(\times\) 10\({}^{-8}\) and 1.26 \(\times\) 10\({}^{-4}\), respectively. This pronounced disparity primarily stems from the effective mitigation measures implemented by the multi-layered safety barriers installed in the station. Figure 5: The BN model for GDS accident scenario modeling.
# A Trusted Federated Incentive Mechanism Based on Blockchain for 6G Network Data Security
## 3 Isfl-Sys Architecture
### Effl Federated Learning Mechanism
#### 3.3.1 Standardizing the Magnitude of Local Model Updates  
In ETFL, we introduce a new aggregation rule for federated learning, where model updates can be seen as vectors containing both direction and scale. In this context, the Euclidean distance comprehensively measures the difference between vectors, accounting for both direction and magnitude. In our normalization approach, considering the limited computational resources of edge devices, we normalize and adjust the magnitude of each local model update. The specifics are as follows:  
Artificially setting the magnitude of all model updates to a fixed unit length, focusing solely on differences in the direction of model updates, can effectively conserve significant computational resources. Therefore, our aggregation rule normalizes the magnitude of model updates when calculating the global model update, while also considering the direction of model updates. In a \(n\)-dimensional space, if there are vectors \(x=(x_{1},\cdots,x_{n})\)  
Figure 4: Iteration steps of ETFL.  
and \(y=(y_{1},\cdots,y_{n})\), then the Euclidean distance \(d_{(x,y)}\) between these two vectors is defined as follows:  
\[d_{(x,y)}=\sqrt{\left(x_{1}-y_{1}\right)^{2}+\left(x_{2}-y_{2}\right)^{2}+\cdots +\left(x_{n}-y_{n}\right)^{2}}=\sqrt{\sum_{i=1}^{n}(x_{i}-y_{i})^{2}}. \tag{3}\]  
The result's range is not fixed and is influenced by the length, direction, and dimension of the vectors. In the above-mentioned approach, we use a fixed vector scale. Therefore, in the same dimension, the similarity between two vectors is only dependent on their direction.
# Repurposing Historic Drugs for Neutrophil-Mediated Inflammation in Skin Disorders
### Anti-Neutrophilic Drugs  
Dapsone, colchicine, and tetracyclines are established drugs within the dermatologist's therapeutic armamentarium; however, their use in neutrophil-mediated conditions is mostly off-label and still requires validation from robust clinical trials; conversely, our pathogenetic understanding of the mechanisms of neutrophil involvement in these disorders remains incomplete. Further research will also enable us to weigh the benefits of novel indications against the risks of adverse events with prolonged use.  
Dapsone is traditionally used as an antibiotic in leprosy and as an anti-inflammatory in dermatitis herepetiformis, owing to its emerging antineutrophilic effects. Colchicine is a historical anti-inflammatory agent used for gout and familial Mediterranean fever. Tetracyclines are a class of broad-spectrum antibiotics with potent anti-inflammatory effects that are approved for the management of acne and rosacea and used off-label in a diverse range of inflammatory skin conditions (Table 1).  
Table 2 summarizes the main mechanisms of action and the anti-neutrophilic effects of the investigated drugs.  
\begin{table}
\begin{tabular}{c c c} \hline
**Treatment** & **Use** & **Mechanism of Action** \\ \hline \multirow{8}{*}{Dapsone} & Dermatitis herepetiformis & \\  & Autoimmune bullous disorders & \\  & Sweet syndrome and pyoderma gangrenosum & \\  & Erythema elevatum diutinum & \\  & Urticarial vasculitis & \\  & Leukocytoclastic vasculitis & \\  & Pustular psoriasis & \\  & Hiddeneits suppurativa & \\  & Eosinophilic dermatoses: wells syndrome, granuloma faciale, & \\  & eosinophil annular erythema and chronic idiopathic urticaria & \\  & Cutaneous lupus erythematosusous & \\  & Recurrent erythema multiforme & \\  & Generalized granuloma annulare & \\  & Rheumatoid papular eruption & \\ \hline \multirow{4}{*}{Colchicine} & Autoimmune bullous disorders & \\  & Sweet syndrome and pyoderma gangrenosum & \\  & Pustular psoriasis & \\  & Hiddeneits suppurativa & \\  & [PERSON]’s syndrome & \\ \hline \multirow{4}{*}{Tetracyclines} & Acne & \\  & Rosacea & \\  & Autoimmune bullous disorders & \\  & Sweet syndrome and pyoderma gangrenosum & \\  & Erythema elevatum diutinum & \\  & Leukocytoclastic vasculitis & \\  & Hiddeneitis suppurativa & \\  & Pustular psoriasis & \\  & Wells syndrome & \\  & Lichen planus pilaris & \\  & Generalized granuloma annulare and cutaneous sarcoidosis & \\ \hline \end{tabular}
\end{table}
Table 1: Uses and mechanisms of action of anti-neutrophilic drugs for dermatological and autoimmune disorders.
## 4 Evaluation
### Overhead
#### 4.2.3 Performance  
The key performance metrics are the percentage of time spent by each block performing data processing, awaiting the next buffer and the service time of the framework managing the data delivery between different blocks. We also record the number of dropped buffers in the sink queues of each block output to identify performance bottlenecks in the pipeline (if any). The criteria for selecting the computing platform is its ability to execute all of the included processing blocks at greater than real-time.  
Results in Table 3 show that even a low-end embedded platform, such as the Raspberry Pi 4B, is capable of processing all the data in real time using the REIP SDK without dropping a single buffer. The service time overhead of REIP SDK remains well under 10% on all platforms, which is negligible.
## 4 Discussion
Another limitation is that we defined performance as one's ability to isometrically contract the knee extensor muscles, as maximal isometric contractions are common in the literature [4; 13; 53], while other studies have defined performance as dynamic muscle contraction, such as in vertical jumping [12], sprinting [15], or Wingate performance [18]. Our results only speak to the muscles' ability to produce isometric force following these modalities. Another limitation is the use of ultrasound to measure fascicle length and pennation angle as it is a 2D approximation of a 3D structure [64]. We did, however, have good reliability in all our measurements: cross-section area ICC = 0.98, echo intensity ICC = 0.98, fascicle length ICC = 0.88, pennation angle ICC = 0.97, and muscle thickness ICC = 0.98. This is in line with previous literature, where ICC > 0.7 [51; 65]. Future research should include different measures of performance, as the effects of these modalities may be more suited for dynamic contractions, or possible help with more aerobic type activities. Sonoelastography may shed more light on these modalities by measuring elasticity changes. Different muscles should also be addressed, as we did not find any improvement or detrimental effect for the quadricep muscle group. Muscles, such as gastrocnemius, may benefit from these modalities.
## 3 Results and Discussions
### Comparative Analysis of LIVs over Gaussian, Conical, and Flat Terrains
In the case of SL1, as shown in Figure 6a-c, the mountain's shielding effect attenuates the induced voltages on the overhead line and delays the peak voltage arrival time. Figure 6d-f depicts the variations in LIVs when lightning strikes at SL2, due to the symmetry of the terrain, Figure 6d,f show the identical results of LIVs at the close end and remote end of the line. The mountain's presence leads to a pronounced increase in the induced voltages, while also delaying the peak arrival time at both ends and the center of the line. This phenomenon can be explained by the fact that, as reported by [PERSON] et al. [51], due to the diffraction of EM waves, the lightning EM fields increase with longer peak arrival time as mountain height increases. The enhancement in LIVs can be attributed to the reflection of lightning EM waves between the mountainous terrain and the adjacent flat ground [22; 52]. As shown in Figure 6d,f, for Configuration B, the LIVs at both ends of the line exhibit an increase of approximately 72% and 58% for lightning strikes at SL2, respectively, compared with those over flat ground. And Gaussian-shaped mountainous terrain exhibits more pronounced enhancement effects on LIVs. This can be explained by the fact that surfaces with higher curvature possess stronger  
Figure 6: Comparison among the lightning-induced voltages for Configuration B over different terrains, including flat ground, Gaussian-shaped, and cone-shaped mountains. LIVs of line close end, center, and remote end when lightning strikes at (**a–c**) SL1, (**d–f**) SL2, (**g–i**) SL3, respectively. reflection of lightning EM waves [37]. The reflection of EM waves between mountain and flat ground is more intense, resulting in higher LIVs along the distribution line compared to those of the cone-shaped mountain However, in the case of SL3, the increased reflection of Gaussian-shaped mountain leads to a stronger shielding effect when lightning EM waves propagate cross the mountain, resulting in a larger attenuation in LIVs.
# Noise-Tolerant Data Reconstruction Based on Convolutional Autoencoder for Wireless Sensor Network +
## 3 Noise Model
### External Noise
#### 3.1.1 Rainfall Attenuation Model  
Rain attenuation plays a significant role in determining the dependability of a wireless network. Let \(k\) and \(\alpha\) be the frequency-dependent coefficients associated with linear polarization. The values of the variables \(k\) and \(\alpha\) may be determined by the use of curve-fitting-derived equations, which are applied to the connection frequency \(f\). Let \((k_{v},\alpha_{v})\) and \((k_{h},\alpha_{h})\) denote the vertical and horizontal local polarizations, respectively. \(k_{h|v}\) and \(\alpha_{h|v}\) are calculated based on the equation below.  
\[\log_{10}k_{h|v} =\sum_{j=1}^{4}\left(a_{j_{h|v}}\exp\left[-\left(\frac{\log_{10}f- b_{j_{h|v}}}{c_{j_{h|v}}}\right)^{2}\right]\right)+m_{k_{h|v}}\log_{10}f+c_{k_{h|v}}, \tag{1}\] \[\alpha_{h|v} =\sum_{j=1}^{5}\left(a_{j_{h|v}}\exp\left[-\left(\frac{\log_{10}f- b_{j_{h|v}}}{c_{j_{h|v}}}\right)^{2}\right]\right)+m_{\alpha_{h|v}}\log_{10}f+c_{ \alpha_{h|v}}, \tag{2}\]  
where \(a_{j_{h|v}}\), \(b_{j_{h|v}}\), \(c_{j_{h|v}}\), \(m_{k_{h|v}}\), \(c_{k_{h|v}}\), \(m_{a_{h|v}}\), and \(c_{a_{h|v}}\) are coefficients referenced from [22].  
The coefficients are determined as follows [23]:  
\[k= \frac{k_{h}+kv+(h_{h}-k_{v})cos^{2}(\theta)cos(2\tau)}{2}, \tag{3}\] \[\alpha= \frac{k_{h}\alpha_{h}+k_{v}+(k_{h}-k_{v}cos^{2}(\theta)cos(2\tau )}{2k}, \tag{4}\]  
where \(\theta\) and \(\tau\) represent the elevation and tilt angles of the signal, which are associated with the propagation route and vary from \(-90^{\circ}\) to \(90^{\circ}\).  
In this work, a rainfall attenuation model, as described in [23], was used to estimate the route loss of signals propagating inside an area experiencing rainfall. The rain attenuation \(\gamma_{\text{rain}}\) (dB/km) is calculated based on the power law relationship as follows:  
\[\gamma_{\text{rain}}=kR^{\alpha}, \tag{5}\]  
where \(R\) (mm/h) is the rain rate.
# A Review of Research on Spectrum Sensing Based on Deep Learning
## 6 Challenges and Perspectives
### Further Research on the Application of Self-Encoders in Spectrum Sensing  
The autoencoder is a type of artificial neural network that is used for semi-supervised and unsupervised learning. The network's advantages in feature learning, noise reduction, anomaly detection, and data compression make it an ideal tool for deep learning spectrum sensing, as it can be used to learn the most effective representation of data (i.e., features). Varied autoencoder applications have the potential to yield diverse outcomes in spectrum sensing. The deep autoencoder (DAE) proposed in [116] is capable of recognizing and eliminating malicious nodes in IoT through cooperative spectrum sensing, thereby reducing the risk of cognitive radio-assisted IoT jamming attacks. A subsequent study [117] presents another instance, where stacked self-encoders (SAEs) are combined with LSTM. The unsupervised extraction of hidden features from the spectrum data allows for a higher level of prediction accuracy. In addition, there are academics who have implemented noise-reduced autoencoders and integrated them with alternative approaches to safeguard against adversarial attacks. It is expected that additional studies will be conducted in the future to investigate different autoencoders for spectrum sensing in deep learning with the aim of achieving discriminative results.
## 5 Results of Analysis
### The Results of RMS Analysis  
The recorded signal was analyzed according to the methodology described in Section 2 of this study. Out of the 23 recorded events, the threshold of vibration perceptibility by people in buildings was exceeded in five cases. Three exceedances of the perceptibility threshold occurred for metro train passages, accounting for approximately 27% of all metro train passages recorded on the site. Two exceedances of the perceptibility threshold occurred for roadway passages, which in this case constituted 50% of all passages recorded from that road. In the case of passing trans, no exceedance of the vibration perceptibility threshold by people was observed.  
Below are examples of RMS analysis of individual events in 1/3-octave bands with the appropriate waveform (Figure 6--heavy truck passage, Figure 7--tramway passage, Figure 8--metro train passage).  
From the information provided in Figure 6, it can be inferred that the recorded vibrations do not violate the conditions for ensuring necessary comfort, but they are noticeable to individuals in the room. The threshold of vibration perception is exceeded for two frequencies: 12.5 Hz and 16 Hz, as better illustrated by the results given in Table 3, relating to the values of the HPVI. The passage of terms did not cause any exceedance of the threshold of vibration perception by people in any of the recorded events.  
The RMS analysis of the vibrogram caused by the passage of the metro train did not reveal any violation of the conditions for ensuring vibrational comfort to people in the rooms. However, exceedances of the vibration perception threshold occurred. In the example shown in Figures 3 and 4, the exceedance measured by the HPVI indicator was HPVI = 1.28 at a frequency of 40 Hz. In this example, compared to vibrations caused by passages on the roadway, vibrations caused by metro train passages shifted the frequency spectrum towards higher values.  
\begin{table}
\begin{tabular}{c c} \hline
**HPVI [-]** & **Center Frequency of 1/3 Octave Band [Hz]** \\ \hline
1.72 & 12.5 \\ \hline
1.49 & 16.0 \\ \hline \end{tabular}
\end{table}
Table 3: HPVI value with the middle frequency in 1/3 octave band for an example event (heavy truck passage in Figure 6).  
Figure 7: RMS analysis of tramway passage.  
Based on the RMS analysis, it can be concluded that in 22% of the events, there was an exceeding of values corresponding to the thresholds of vibration perception by people in buildings.
# A UAV Coverage Path Planning Method Based on a Diameter-Height Model for Mountainous Terrain
## 5 Conclusions  
In this paper, a coverage path planning method based on the DH model in 3D space is proposed for mountainous terrain. The proposed method provides good properties of conformal coverage effects, global shortest path optimization, safe obstacle avoidance, and UAV energy saving. The DH model is proposed for simplifying the 3D map construction of mountain terrain. A region segmentation method is proposed based on the DH model, and two coverage path patterns are adopted for covering the mountainous terrain, taking into account the mountainous features. A coverage path connection method with trajectory minimization and obstacle avoidance is proposed for the path connections. Simulation experiments have verified the feasibility and effectiveness of the proposed method, and the comparative experiment has also demonstrated the advantages of the proposed method in terms of UAV energy savings.  
Conceptualization, methodology, N.Z. and Q.Z.; software, L.Y.; validation, B.Z. and Y.W.; formal analysis, investigation, C.G.; resources, N.Z.; data curation, writing--original draft preparation, writing--review and editing, visualization, N.Z. and L.Y.; supervision, Q.Z.; funding acquisition, Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Program of Song Shan Laboratory (included in the management of Major Science and Technology Program of Henan Province, Grant No. 221100211000-01).  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Thanks to the anonymous reviewers for their thorough review and their instructive comments and suggestions.  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method Name** & **Energy Cost (Pixels)** \\ \hline The proposed CPP method based on the DH model & 74,510 \\ The classic zigzag coverage method & 230,274 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The energy consumption (total coverage path length) statistics of different coverage path planning methods.