esa-sceva/satcom-chunk-collection huggingface dataset:
### Intelligent Transportation System  
Intelligent Transportation Systems (ITS), which emerged as a result of the adaptation of information and communication technologies to the transportation sector, follow a parallel development with these applications in the world. ITS integrate telecommunications, electronics, control theory, and computer technologies with the transportation sector [27]. In addition, the developing sensor and communication technology causes improvements in the infrastructure of the roads and brings out the concept of smart roads [28]. Accordingly, ITS technologies continue to spread rapidly.  
Today, with the developing technology and fast mobility, many innovations are coming to the agenda in the transportation sector. Thanks to Intelligent Transportation Systems, ideal traffic conditions are created by ensuring coordination between different transportation types, and the efficiency and speed of the services related to passenger and vehicle movements are increased. Nowadays, smart cities and smart transportation are considered an inseparable whole [29]. It can be said that the management method of cities has changed with smart city technologies [30].  
Figure 1: Key steps of the research methodology.  
ITS provide significant benefits in the key points of the city with the data that they collect in areas with heavy traffic at hourly intervals. With these technologies, a well-organized traffic flow is achieved, and accidents and traffic jams can be prevented significantly. The use and correct positioning of these systems are very important, and, in this connection, central traffic management systems are one of the most important components of Intelligent Transportation Technologies in smart cities [27; 30; 31].
# Symmetric Keys for Lightweight Encryption Algorithms Using a Pre-Trained VGG16 Model
## 3 The Proposed Method
### NIST SP 800-22 Statistical Test Suite  
Algorithm 1 was randomly evaluated using the NIST SP 800-22 statistical test suite, confirming the randomness of the generated symmetric keys. Table 2 shows the P-value and pass rate from the suite's various randomness tests. Tests for frequency, block frequency, cumulative sums, runs, longest runs, rank, FFT, non-overlapping template, overlapping template, universal, approximate entropy, random excursions, random excursions variant, serial, and linear complexity were all included in the evaluation process. We generated and tested a total of 18,750,723 random bits from 290 different images. We evaluated the streams of 30 bits, 41 bits, and 43 bits using the default configuration of the NIST SP 800-22 suite. The results in Table 2 indicate that the symmetric keys generated are highly unpredictable and random, making it difficult for adversaries to guess them.  
The P-value (\(\geq\)0.01) is crucial for determining randomness and the pass rate [28]. Table 2 displays the P-values, all of which exceed the minimum threshold of 0.01. For the 30-bit, 41-bit, and 43-bit streams, the highest P-values are 0.999896, 0.986869, and 0.953553, respectively. The lowest are 0.213309, 0.227773, and 0.151616, respectively. This proves that Algorithm 1 can produce genuinely random numbers, which are useful for creating symmetric keys in lightweight cryptography.
# Energy-Efficient UAVs Deployment for QoS-Guaranteed VoWiFi Service
## Appendix C Particle Swarm Optimization Implementation
### Algorithm Steps  
The particle swarm algorithm begins by creating an initial population of particles defined by their position and velocity (i.e., function PSO_InitSwarm). Then, the algorithm iteratively evaluates and modifies each particle in the swarm:  
1. It chooses new velocities, based on the current velocity, particles' best location and the global best location.
2. It obtains a candidate new position for each particle (i.e., function _PSO_UpdateParticle_), considering the its best known location and its velocity.
3. It evaluates each particle, and determines the best known function value and its position.
4. It updates particles' and global best locations if improved.  
This procedure is repeated until an exit criteria (e.g., the best known objective function does not improve after a number of iterations) is satisfied.
# A Comprehensive Review of Micro UAV Charging Techniques
## 10 Conclusions  
The integration of contact-based and contactless charging techniques into UAVs has been a long process. In the context of contactless-based techniques however, there is a research gap with limitless development opportunities. The need for and feasibility of safe and fast charging techniques, such as MRC, CPT, IPT and LPT, will increase as demand for drone services grows. The future of UAVs is dependent on charging techniques that can charge multiple UAVs while saving money, time and stress on operating bodies. This study emphasizes previous research contributions to highlight the need for WPT in UAVs. In addition, a brief history of UAVs, as well as several characteristics and standardizations of UAVs, are discussed. Furthermore, this research covers the technical aspects of WPT techniques for drones by elaborating on existing WPT techniques and how these techniques can significantly improve autonomous operations. This paper comprehensively reviews the advancements in LPT techniques for UAVs, including DLC, SWIPT, SLIPT and related open issues. Finally, the article discusses potential challenges and future research directions. Future work on this research must focus on mathematical modeling of the electromagnetic field in order to investigate the most effective solutions for UAV charging. The research community should concentrate on multi-UAV charging and consider dynamic charging approaches. Optimization methods for UAV coil design, as well as designing multiple coils for a UAV for high power output and energy harvesting, are key research areas.  
Conceptualization, S.A.H.M. and M.A.K.; resources and data curation, N.Q.H.O. and M.A.K.; writing--original draft preparation; S.A.H.M., H.A., M.A.K. and J.Z.; writing--review and editing, S.A.H.M., N.Q.H.O., M.A.K. and J.Z. All authors acknowledge this work is not submitted or under consideration in any other journal. All authors have read and agreed to the published version of the manuscript.  
The authors declare that no funds, grants or other support were received during the preparation of this manuscript.  
This paper does not contain any studies with human participants or animals performed by any of the authors.  
Not applicable.  
The authors declare that they have no conflict of interest.
## 5 Performance Evaluation
### Real Dataset
#### 5.1.1 The Correctness of Clustering with SFDC  
To group together nodes with high spatial correlation to form a cluster, the Algorithm 1 is designed to heuristically obtain a rough approximation on correlation based on two indexes of geographical distance and Pearson's distance. The network is partitioned to several subareas in which the distance of any two nodes is smaller than \(max\_dis\). As the growth in sensor network scale, \(max\_dis\) will be set to be a larger value such that an optimal number of clusters is achieved. For a fixed scale sensor network, the smaller \(max\_dis\) leads to the more number of clusters. The optimal number of clusters by minimizing the communication cost of the network presented in [10] can be used to be a reference.  
Another parameter needed to be supplied is the communication radius \(R_{CH}\) of cluster head nodes and it depends on the application as well as the sensing hardware of sensor node. In general, the cluster heads have a larger communication radius than member nodes since they receive all data from member nodes and perform long range transmissions to the remote base station. We assume that all cluster head nodes have the same value of \(R_{CH}\) to guarantee the expedite communication within their clusters.  
As an example, Figure 1 illustrates the output of one of our clustering experiments with the default parameters \(max\_dis=15\), \(R_{CH}=25\), \(M=50\), \(N=52\), where the sensor network is organized into 6 clusters. All nodes marked with the same sequence number belong to the same cluster, and the cluster head nodes are marked in red. It can be see that the nodes adjacent to each other are grouped into one cluster and member nodes are close to the cluster heads in term of the Pearson's distance. Due to the Pearson's distance based on the historical readings is time-varying, the clustering map is not constant at different data collection phase. And hence it is reasonable to make the geographical distance that is constant as the primary metric without compromising the Pearson's distance.  
Figure 1: A clustering map is produced by implementing the Algorithm 1.
# Structural-Missing Tensor Completion for Robust DOA Estimation with Sensor Failure +
## 5 Simulation
### Comparison with Other Completion Methods  
To validate the effectiveness of the proposed structural-missing completion algorithm with serious sensor failure, in Figure 9, we compare the estimation accuracy of the proposed algorithm with those of the CPD-based completion method and Tucker-based completion  
Figure 8: Estimation of 8 sources.  
Figure 7: Source identifiability of the proposed algorithm.  
method, where the number of impaired sensors reaches 10, i.e., 20% of the sensors are impaired. As illustrated in the Introduction, the CPD-based completion method use the CP model to approximate the completed tensor, whereas the observed elements will also be modified. The Tucker-based completion method imposes the Tucker rank increment to complete the missing fibers, but requires exhausting rank searching and does not fit the tensor signal model.  
It is clear that the estimation accuracy of the proposed algorithm is superior to the other two completion methods. It is worth noting that the error between the manually set Tucker rank and the real Tucker rank will be amplified as the number of failed sensors increases, which results in degraded performance for the Tucker-based completion method. This highlights the advantage of the enforced low CP rank regularization for structural-missing tensor signal completion.
## 2 Methods  
Based on the HAIS framework, the proposed method focuses on optimizing the steps of offset, clustering, and set aggregation for ITS and realizes extreme offset, adaptive clustering, and ITS set aggregation, respectively. The specific process is shown in Figure 1, which includes preprocessing, extreme offset, mean shift, spatial mapping, set aggregation, and postprocessing. The process and function of specific links are as follows.  
1. Preprocessing. Data preprocessing included point cloud filtering, elevation normalization, dividing sub-plot (25 m \(\times\) 25 m), point cloud denoising, down-sampling, and coordinate normalization.
2. Extreme offset. The extreme offset deep learning method is used to perform a spatial transformation on the preprocessed point cloud, and each point is offset to the corresponding treetop to enhance the discrimination among different trees.
3. Mean shift. In view of the large density of treetops in the offset point cloud, the self-adaptive mean shift algorithm based on average neighboring distance is adopted to cluster the offset point cloud, divide the offset point cloud set, and complete the labeling.
4. Space mapping. The offset point cloud, after clustering and labeling, is mapped back to the original point cloud space to complete the preliminary segmentation.
5. ITS set aggregation. Considering the characteristics that the gradient change among different tree canopies is sharp, while the same tree canopy is gentle, the adjacent canopies with gentle gradient change are aggregated to reduce the over-segmentation error.
6. Postprocessing. The segmentation is completed after up-sampling and coordinate de-normalization. The flowchart of the proposed method is shown in Figure 1.  
Figure 1: Flowchart of the extreme offset segmentation method. (1) Preprocessing. (2) Extreme offset. Each point is offset to the corresponding treetop to obtain an offset point cloud with large discrimination among different trees. (3) Mean shift. The offset point cloud is clustered. (4) Space mapping. The offset point cloud, after clustering and labeling, is mapped back to the original point cloud space. (5) ITS set aggregation. The adjacent canopies with gentle gradient change are aggregated to reduce the over-segmentation error. (6) Postprocessing. The segmentation is completed after up-sampling and coordinate de-normalization.
## 3 Results and Discussion
### Sensory Evaluation  
It is generally believed that the faster the freezing rate, the shorter the time for the product to pass through the maximum ice crystal generation zone, and the less the damage of ice crystals to muscle tissue, the better the quality of the samples can be maintained during long-term storage, and the samples will be closer to the fresh state after thawing. However, some studies had found that the effect of freezing rate on the quality of river crabs was better when the freezing rate was faster, but the super-low temperature freezing can also lead to muscle tearing of some products. The changes in sensory scores of cuttlefish meat during storage for different freezing methods was shown in Figure 11. Fresh cuttlefish had a pleasant distinctive fresh taste, creamy white flesh color, and intact morphology without damage. [PERSON] et al. [30] found that denaturation and coagulation of myogenic fibrous proteins occurred during freezing, leading to changes in functional properties of the muscle, while fat oxidation affected the odor and color of the meat, thus reducing the sensory quality of the fish meat [31]. In this experiment, on the 3 rd day of freezing, the odor of the six groups of samples was similar, while the color and morphology had started to differ, and the morphology of the flat frozen cuttlefish was damaged due to the extrusion received from external forces, and the muscle of cuttlefish appeared uneven and sunken. Because of the fast freezing rate of liquid nitrogen and the small size of the ice crystals formed, it was still better than the other groups in terms of color and odor. However, the freezing rate of liquid nitrogen freezing is fast, and there will be temperature difference between the surface and the center of cuttlefish. Because of the thicker and larger size of cuttlefish, high water content, and high expansion pressure of freezing, local cracking phenomenon will occur during the freezing process, and the integrity of morphology will be destroyed. At the end of storage, the freshness of each group of cuttlefish disappeared and a less friendly odor appeared, and the color of the muscles of each group began to turn yellow, among which the local muscles of RF and FF groups showed browning. With the  
Figure 10: Change in MDA content of cuttlefish during storage at \(-18\)\({}^{\circ}\)C by different freezing methods. The letters “a-e” indicate significant differences (\(p<0.05\)). Error bars show standard deviation.  
extension of the storage period, the sensory scores of all groups became significantly worse. Overall, the BF group performed the best.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 4 Techniques of VLC-Based Indoor Positioning Systems
### Positioning Techniques
#### 4.2.3 AoA  
Different from RSS- and TDOA-based methods, we can use the AOA to determine a receiver's position. In [64], the receiver is a circular PD array composed of several PDs evenly distributed on a circle. The AOA from an LED to the receiver can be estimated according to the receiver's orientation and the knowledge of the locations of the PDs on the circular PD array, so as to determine the coordinates of the receiver. Since the Lambertian received power equation can be regarded as a function of AOA, we can derive the AOA from the received power strength. It is also possible to estimate the distances between the LEDs and the receiver accordingly.  
In [65], a visible light IPS is built based on a single transmitter and multiple tilted PDs. Inter-cell interference is negligible with a single transmitter. By measuring the received power strengths and thus the incidence angle gain differences due to different arrival angles and assuming that the tilted PDs are very close to each other so that they have the same coordinates, the receiver's coordinates on a horizontal plane (2D) are first estimated. Then, by comparing the received power strengths against each possible vertical coordinate, the vertical distance (height) of the receiver is estimated to provide the 3D coordinates.  
In [66], an optical LOS AOA-based positioning system is built. The mobile receiver contains an interior corner-cube formed by three orthogonal PDs to facilitate differential photo-current sensing and a retro-detection method [67] for estimating the azimuthal and polar angles of incident optical beams from LEDs to the receiver. Given the azimuthal and polar angles, a triangulation method is then applied to determine the 3D coordinates of the receiver. The experiment result with a sensitivity analysis of imbalanced signal strength to the positioning accuracy is provided and shows a much higher positioning accuracy than optical RSS-based positioning.
# Three-Dimensional Hyperbolic Mesh Generation Method Based on the Neural Network
###### Abstract  
Aiming at the limitations of the traditional hyperbolic mesh generation method, specifically the limited types of boundary control strategy along the advancing direction and the inability to control the outer boundary, this paper employs physics-informed neural networks with output range constraints to approximate the solutions of the governing equations that are used to generate the hyperbolic mesh. After transforming the form of the governing equations, the solution was fitted using the neural network driven solely by boundary data. By incorporating the governing equations and the boundary conditions into the loss function, the neural network method can directly control the boundaries along the advancing direction. For the outer boundary, a novel variance constraint strategy was proposed. Based on the proposed method, meshes were generated for three-dimensional surfaces and three-dimensional solids derived from the terrain surface. The quality of these meshes was compared with the traditional method. The results demonstrate that this method can effectively achieve boundary control during the hyperbolic mesh generation process and consistently produces high-quality hyperbolic meshes. Therefore, neural network-based hyperbolic mesh generation is an effective approach to achieving boundary control, which can further enhance the applicability of hyperbolic mesh generation methods.  
neural network; hyperbolic mesh generation; partial differential equation +
Footnote †: journal: applied sciences
## 4 Future Perspectives
### GNSS-Reflectometry (GNSS-R)  
The remote sensing of the Earth using reflected GNSS signals (L-band) such as the American GPS, started at the end of the 1990s. This method, called GNSS-Reflectometry (GNSS-R), uses indirect (_i.e._, reflected) signals for remote sensing of the Earth's surface [89]. While observations of the delays of the direct signal allow estimation of the water-vapor in the lower layers of atmosphere and could be conducted with existing instrumentation [90], processing of the reflected signals (normally considered as a noise in a GNSS-receiver) typically requires the construction of new instruments and new signal processing algorithms. Remarkably, relevant geophysical properties of the surface (e.g., soil moisture, water-body roughness and/or salinity retrieval, snow/ice cover determination, _etc._) can potentially be retrieved by this approach [17, 91, 92].  
GNSS-R can be implemented on Low Earth Orbit (LEO) satellites [93, 94], on aircrafts, or at the ground level [95]. In the former case, the spatial resolution is limited (similarly as with other satellite-based sensing instruments). In the two latter cases, it is possible to cover a relatively large area, considerably wider than with the point-based measurements, yet smaller than for space-based missions, with high resolution, as for regional-scale studies such as those occurring in the alpine environment.  
Figure 1 illustrates this concept in a simple local-scale bi-static experiment with a GNSS antenna mounted on a mast above the ground. In this setup, the extra path travelled by the reflected signal can be approximated by 2h sin 0, where h is the height of the antenna phase center above the reflection point and 0 the elevation of the satellite. As the navigation satellite is moving across the sky in a Keplerian orbit, the elevation angle and reflectance point is constantly changing on the ground. The power of the reflected signal depends on the surface, which allows the determination of some of its properties. Depending on the roughness of the surface, the reflection can be specular (or coherent) for a smooth surface, or diffuse (non-coherent) for a rough surface [96]. The height of the antenna can also be measured, which means that altimetry measurement can be performed. Considering that almost 20 satellites are potentially observable from any point on the Earth, and this number will almost double in the near future, a map of reflectance properties can be created for the surface around such instrument.  
As the extent of current signals from the existing American and Russian satellite systems (_i.e._, GPS and GLONASS, respectively) is world-wide, there is an enormous potential for exploring this concept and constructing a new type of low-cost remote sensing instrument.
## 4 Results and Discussion
### Cumulative Equivalent Plastic Strain near Column Ends  
Previous BRB-RCB tests [21; 22] have demonstrated that the severe cracking, appearing on the columns connected with the welded gusset plates, might be caused by the significant plastic responses near the column ends. In this section, the cumulative equivalent plastic strain (PEEQ) contours of the columns at 2% and 3% drift, respectively, are presented in Figures 20 and 21 to investigate the effect of different gusset connections on the damage of RCBs. From Figures 20 and 21, it can be drawn that the plastic responses were located near both ends of the pier column for the RCBs without BRBs. As for the BRB-RCBs with the welded gusset connection and bolted gusset connection, the top hinges were shifted from the column top to near the gusset tip due to the rigid gusset zone effect.  
As plotted in Figure 20 of the PEEQ value of the column at the 1/50 drift ratio, the plastic responses of the right pier column near the gusset tip can be successfully controlled by adopting the bolted gusset connection. As the story drift increased to 3%, as shown in Figure 21, the plastic hinges have been developed in the columns except for specimen NG-H. Compared with specimen TG, the damage level of the models with bolted gusset connections can be further decreased. Specim NG-H experienced the lowest plastic damage among all BRB-RCBs, followed closely by specimen NG-S, while specimen NG-H has the highest PEEQ value. With regards to the BRB-RCBs with slotted gusset plates suiting the mixed bolts, the right pier column suffered higher PEEQ strains when the non-slip bolt was located closer to the gusset tip. The major reason was that a greater frame-to-gusset interaction was induced as the non-slip bolt located further from the gusset corner. Both models with one-sided sliding bolts also sustained severe plastic responses near the gusset tip, although their stress levels at the gusset interfaces were relatively low as prescribed in Section 4.3. It indicates that simply reducing the slipping of the bolt group has a poor effect on controlling the plastic damage of the pier column.  
Figure 20: PEEQ on right pier columns at 2% drift.