esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed System
### Re-Identification  
We developed a pipeline for group re-identification based on the existing algorithm for person re-identification. In order to perform the re-identification of non-compliant groups, we used a lightweight and state-of-the-art person re-identification deep learning model termed as Omni-Scale Network (OSNet) [52]. We used a pre-trained model, which was trained on six widely used person re-identification image datasets: Market1501 [53], CUHK03 [54], MSMT17 [55], DukeMTMC-reID (Duke) [56,57], GRID [58] and VIPeR [59]. In this person re-identification technique, the database of existing images of the persons, from which the model has to re-identify the target person, is called 'Gallery', and the image of the target person is called the 'Query' image. For implementation, we used a library for deep learning person re-identification named Torchreid [60], which is based on a well-known machine learning library named PyTorch.  
Group re-identification was performed whenever a non-compliant group was detected through any CCTV camera to re-identify if it was previously detected by another camera while violating the physical distancing constraints. We cropped and extracted images of the persons in non-compliant groups detected by the person detection and tracking pipeline (Section 'Person Detection and Tracking') through a current CCTV camera for using them as query images in the re-identification module. The Gallery was based on the images of the persons present in moving non-compliant groups, which were tracked and extracted by the motion tracking module (Section 'Motion Tracking') We used the term re-identification score 'SI' to determine if the target non-compliant group was re-identified, where 'I' represents the unique identity of that group. It was based on how many persons were re-identified from the group based on the query images. '\(S_{l}=1\)' meant that one person from the query images was re-identified. The target non-compliant group was considered as re-identified in case of '\(S_{l}\geq 2\)', which means that at least two persons were re-identified from query images. The steps included in the pipeline developed for performing group re-identification are shown in Algorithm 1.  
Figure 6: Division of the FoV of a CCTV camera into regions.  
Where '\(l\)' represents the identity of the group, '\(k\)' represents the identity of the person and '\(D\)' represents the Gallery database of the persons belonging to the moving non-compliant groups. Whenever a non-compliant group was re-identified, the corresponding identities of the persons from that group were updated in the database to keep track of them.
## 3 Protocol Specification
### Global Pheromone Update Strategy  
The global pheromone update procedure will be applied when the FANT arrives at the sink node. The global pheromone includes information about a route obtained by long-term learning from a FANT. After arriving at the sink node, the FANT will be converted into a BANT. The BANT inherits all route statistic information from the FANT, including path length, energy levels and the visited node list along that path. Meanwhile, the sink node calculates the amount of pheromone value attached to this path according to the route statistics. We calculate \(\Delta\tau\) in the following manner:  
\[\Delta\tau=\frac{E_{min}\cdot E_{avg}}{e^{E_{init}}\cdot F_{ant}} \tag{8}\]  
\(E_{min}\), \(E_{avg}\) and \(E_{init}\) represent the minimum energy, average energy and initial energy in the current discovered path respectively, and \(F_{ant}\) denotes the length of the path. The BANT carries \(\Delta\tau\) at the start of its journey following the reverse path. Each node in the path updates the global pheromone trail (i.e., routing table) according to Equation (9).  
\[\tau_{ij}=(1-\rho)\cdot\tau_{ij}+\rho\cdot\Delta\tau_{ij} \tag{9}\]where \(0<\rho<1\) is the pheromone evaporation coefficient and \((1-\rho)\) is the pheromone residue factor, and \(\Delta\tau_{ij}\) is given by:  
\[\Delta\tau_{ij}=\left\{\begin{matrix}\xi\cdot\frac{E_{j}}{E_{init}\cdot B_{ant}} \cdot\Delta\tau&,i,j\in M\\ 0&,i,j\
otin M\end{matrix}\right. \tag{10}\]  
where \(0<\xi<1\) is a control coefficient, which normalises \(\Delta\tau_{ij}\) to the interval \((0,1)\). \(E_{j}\) is the residual energy of node \(j\), from which a BANT has come. \(B_{ant}\) denotes the path length from the sink node to the current node. The pheromone value is the function of both energy levels and the length of the path. As a result:  
1. The shorter path (less hops) will get a larger pheromone increment.
2. When the minimum energy value in this path is larger, the pheromone increment is also larger, which avoids more data traffic routed through this path.
3. The average energy of a relatively high path will get more attention, and therefore will attract more data flow.
4. The closer the node is, the more pheromones are obtained.
## 3 Results and Discussion
### Morphological Analysis
Only a few of the grains size distribution of bottom layer Mo films prepared at higher sputtering pressure were slightly deviated, which were mainly related to the higher sputtering pressure and shorter sputtering time. Thus the particles in Mo films do not have sufficient energy and time for nucleation and growth. The trend obtained in the width of the particle is more or less in agreement with the grain size variation observed with an X-ray diffraction analysis. However, the discrepancy observed in the grain size values from an X-ray diffraction and scanning electron microscopy imaging analysis could be attributed to the fact that X-ray diffraction is more sensitive to intragrain dislocations and low angle grain boundaries, and the scanning electron microscope mostly measure the distance between visible grain boundaries in the microstructure [25; 26]. The evolution of microstructure, grain size and compactness with sputtering process parameters (sputtering pressure and annealing temperature) is usually explained by comparing the microstructure with structure zone model for films deposited by magnetron sputtering [27]. The X-ray diffraction technique measures the crystalline region that coherently diffracts X-rays and is known to be the more precise technique for grain size determination [28]. Figure 3: Grain size distribution of bottom layer Mo films deposited at different bottom layer sputtering pressures: (**a**) 0.4 Pa, (**b**) 0.6 Pa, (**c**) 0.8 Pa, (**d**) 1.0 Pa, (**e**) 1.2 Pa, Mo bilayer films deposited at different bottom layer sputtering pressures: (**f**) 0.4 Pa, (**g**) 0.6 Pa, (**h**) 0.8 Pa, (**i**) 1.0 Pa, (**j**) 1.2 Pa and Mo bilayer films annealed at different temperatures: (**k**) 100 \({}^{\circ}\)C, (**l**) 150 \({}^{\circ}\)C, (**m**) 200 \({}^{\circ}\)C, (**n**) 300 \({}^{\circ}\)C, and (**o**) 400 \({}^{\circ}\)C.
## 3 Joint Optimization of Altitude and Energy Beamforming for a Single EHD
### Optimal Design with a Closed-Form Energy Beamforming Solution
Therefore, for simplicity of notation, we define the functions \(\Psi_{U}(h)\) and \(\Omega_{U}(h)\) such that \(P_{\text{LoS}}(\theta_{U})\triangleq\frac{1}{1+\Psi_{U}(h)}\) and \(\mathcal{K}_{U}=10^{0.1\mathcal{K}_{U}^{0B}}\triangleq\Omega_{U}(h)\), as follows:  
\[\mathrm{Tr}(\mathbf{X})\leq P,\ \mathbf{X}\succeq\mathbf{0},\]3 \[\Omega_{U}(h) =10 \begin{aligned} \delta_{3}\exp\left(b_{3} \arctan\left(\frac{h}{r_{U}}\right)\right)\end{aligned}  \tag{29}\]  
where \(\delta_{3}=a_{3}/10\). Then, by inserting (28) and (29) into (27), we have  
\[f_{U}(h)=\left(r_{U}^{2}+h^{2}\right)^{-\dfrac{1}{2}\left(\dfrac{a_{2}}{ \Psi_{U}(h)+1}+b_{2}\right)\dfrac{\|\mathbf{a}_{U}\|^{2}\Omega_{U}(h)+1}{ \Omega_{U}(h)+1}. \tag{30}\]  
As represented in (30), the objective function of the problem \(\mathbf{P}_{\mathcal{Y}}^{h}\) is non-convex, complex, and non-linear with respect to \(h\), making it generally challenging to obtain a closed form solution. Fortunately, the objective function of \(f_{U}(h)\) only has one variable \(h\) with the range of \(h\in[H_{\min},H_{\max}]\), so we adopt a 1D exhaustive line search method to obtain the optimal \(h\), i.e., \(h^{\star}\). The detailed procedure to obtain the optimal altitude of \(h^{\star}\) in the case of a single EHD is described in Algorithm 1. In this case, the expected required charging time becomes \(t^{\star}=\frac{Q_{U}^{\text{Req}}}{\text{Tr}(\mathbf{G}_{U}(h^{\star}))}\). ```
1:Input: Parameters of the channel statistics \((a_{1},b_{1},a_{2},b_{2},a_{3},c_{3})\), the minimum and maximum altitude of \(H_{\min}\) and \(H_{\max}\), the sample grid accuracy \(\Delta\), an EHD's horizontal distance and LoS component, i.e., \(r_{U}\) and \(\mathbf{a}_{U}\)
2:Set:\(H_{\min}\leq h\leq H_{\max}\) with the sample grid \(\Delta\), the iteration number \(Iter=1\)
3:for\(h=H_{\min},H_{\min}+\Delta,...,H_{\max}\)do
4: Obtain \(f_{U}(h)\) from (30)
5:\(Iter\gets Iter+1\)
6:endfor
7: Total number of iterations: \(Iter_{tot}\gets Iter\)
8: Obtain \(h^{\star}=\arg\max\limits_{H_{\min}\leq h\leq H_{\max}}f_{U}(h)\)
9:Output: The optimal altitude of \(h^{\star}\)
```  
**Algorithm 1** Algorithm to find an optimal solution for the problem \(\mathbf{P}_{3^{\prime}}^{h}\)
# Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback
## 5 Evaluation
### Conditions and apparatus  
\begin{table}
\begin{tabular}{l r r} \hline \hline  & **Familiarity [\%]** & **Age** \\ \hline Valid & 12 & 12 \\ Mean & 55.000 & 22.917 \\ Std. Deviation & 34.245 & 4.144 \\ Variance & 1172.727 & 17.174 \\ Minimum & 20.000 & 18.000 \\ Maximum & 100.000 & 31.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistics for the participants.  
This study's experimental setup was designed to explore the impact of haptic feedback and assistance on body ownership perception and task performance. Four distinct conditions were implemented:  
* Performing the task without haptic feedback or assistance (NN);
* Performing the task with assistance but no haptic feedback (NA);
* Performing the task with haptic feedback but no assistance (HN);
* Performing the task with both assistance and haptic feedback (HA).  
These conditions were used to evaluate the following hypotheses:  
* Haptic feedback enhances the sense of body ownership and improves user interaction with the remote environment.
* H. Assistance reduces the components of the illusory sense of virtual body ownership (IVBO).
* H. Assistance improves task performance, accuracy, and efficiency.  
The local site featured a virtual reality headset, a tracking system, and haptic gloves for remotely managing a robotic arm and gripper. At the remote site, a robotic arm with a parallel gripper and a head unit equipped with a stereoscopic camera was employed. Previous studies utilized a five-finger robotic hand for object manipulation, which posed challenges for inexperienced users, increasing task difficulty and learning time [9]. To address these issues, we chose a simpler parallel gripper to minimize task complexity and maintain ease of control. The assistance level was set to 3, as preliminary testing showed it offered an optimal balance between user control and system support.
# A Lightweight Slice-Based Quality of Service Manager for IoT
## 4 Our Proposed Slice-Based QoS Manager for IoT
### Data Plane Networking  
The goal of this work is to support WiFi, BLE and LoRa wireless technologies for IoT. In order to do so, the Gateway requires handling the reception of their signals, interpreting them and redirecting to the above architecture layers. Additionally, it is necessary to consider that the raw BLE and LoRa technologies may signify problems in the information exchanging process. Those problems can be solved by means of a renovated SDWI module, and only the LoRa physical interface is left for the gateway to be able to deal with the necessary radio services.
# A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree
## 3 Materials and Methods
### Hybrid Octree and 3D R*-Tree  
This part will describe the details of hybrid spatial index technology. Two steps are needed to build the hybrid structure, (1) building an octree on the whole Point Cloud data, (2) constructing 3D R*-tree on each leaf node of octree. Thresholds: \(leaf\_size\) and \(depth_{max}\) are required for building octree, \(Children_{max}\) is required for 3D R*-tree, where \(leaf\_size\) denotes the maximum number of points in a node, \(depth_{max}\) denotes the maximum depth of octree and \(Children_{max}\) denotes the maximum capacity of a 3D R*-tree node. \(node\_size\) denotes the actual number of points in the current node and \(depth\) denotes the actual depth of the octree. These thresholds are not as easy to set as previously thought. If set too small, computation will increase greatly. If set too large, it seems to be meaningless to establish the tree structure. Generally, thresholds are set according to the distribution of Point Cloud data and the number of searching neighbors. If \(k>Children_{max}\), it is impossible to find \(k\) neighbors in the same node and extra time will be taken to search adjacent nodes to find neighbors meeting the requirement.  
Concrete steps for constructing hybrid indexing method are described as follows:  
1. Find the maximum and minimum of \(x\), \(y\), and \(z\) over the whole Point Cloud data which are denoted as \((x_{min},x_{max},y_{min},y_{max},z_{min},z_{max})\). The root node is denoted as \((\frac{x_{min}+x_{max}}{2},\frac{y_{min}+y_{max}}{2},\frac{z_{min}+z_{max}}{2})\). Initializing octree: \(current\_node=root\);
2. For each \(current\_node\), judging \(node\_size>leaf\_size\) and \(depth<depth_{max}\), if so, subdivide \(current\_node\) into eight sub-nodes uniformly. Otherwise, stop subdividing. Computing Morton-code for all leaf nodes and sorting them by code. Go to (3);
3. A set of 3D R*-tree are constructed on each leaf node with \(id\). Initialize 3D R*-tree: \(root_{id}=leaf\_node_{id}\). Furthermore, insert the points inside \(leaf\_node_{id}\) into corresponding 3D R*-tree. At the same time, judge if \(node\_size>Children_{max}\). If so, implying that nodes in 3D R*-tree are too few to insert all points, then increase the number of cluster centers and dividing nodes of 3D R*-tree dynamically, until the number of points inserted is no more than maximum volume. Go to (4);  
Figure 3: Visualization of 3D R*-tree.  
4. Judge if \(points_{insert}<leaf\_size_{id}\), where \(points_{insert}\) is the number of points inserted into corresponding 3D \(R^{*}\)-\(tree_{id}\). If so, go to (3), otherwise, 3D \(R^{*}\)-\(tree_{id}\) is constructed. The overall hybrid structure is shown in Figure 4. The top-level is octree, and the bottom-level is a set of 3D R*-trees.
# A Location and Velocity Prediction-Assisted FANET Networking Scheme for Highly Mobile Scenarios
## 5 Simulation and Analysis  
In this section, we will present a series of simulation works to verify our proposed network clustering algorithm. The simulation scenario is set to a mobile UAV FANET scenario, where a swarm of UAVs proceeds at high mobility. Note that all nodes are at high altitudes and there are no obstacles around them. They proceed at predefined trajectories with influences from external winds and currents, which causes deviations from routines. Hence, their corresponding node motions are characterized by the proposed RPGM. Furthermore, higher motion velocity causes more severe disturbances to both positions and speeds. Nodes send CAMs periodically to maintain the topology structure. Once a link is disconnected, then a bunch of CAMs are required to establish a new link. The simulation is performed on the MATLAB platform in a Windows operating system. To start with, we set the initialization number of clusters \(K(t_{0})\) as 4 and generate 20 nodes per cluster randomly. Then, we initialize the node locations based on the Gaussian distribution with mean positions of \((0,0,80)\) m, \((0,80,40)\) m, \((80,0,40)\) m, and \((80,80,0)\) m, respectively. All nodes move in the same direction of \((\phi_{0},\theta_{0})=(45^{\circ},45^{\circ})\) at the initial time \(t_{0}\). The RPGM is used to model the movement of all nodes and the initialized group center speed is 30 m/s. The maximal random speed is set to 3 m/s. On this basis, the proposed Kalman-filter-assisted networking algorithm is utilized to reduce communication overheads. The detailed simulation parameter values are presented in Table 2.
## 4 Computational Experiments
### Ablation Experiments
#### 4.4.1 Effectiveness Verification of the Multi-Head Self-Attention Mechanism  
To demonstrate the contribution of the multi-head self-attention (MHSA), we, respectively, adopt a convolutional neural network (CNN) and LSTM to extract features in the static encoder to build comparison neural network models. The kernel size of CNN is set as 5. The comparison network models are trained first on the dataset _Training_R_1 and then tested on the dataset _Testing_R_1.  
Table 9 presents the testing results of the proposed network with MHSA and the comparison networks with CNN and LSTM, and the best values are bolded. It can be seen that MHSA performs better than CNN and LSTM in terms of MPR, and it takes the least computational time in the instances with 150, 200, and 250 tasks, indicating that the features extracted by MHSA are more beneficial for the subsequent solution construction. The main reason is that their feature extraction techniques are different. CNN uses a convolution kernel to scan the task information to extract local features, and the extracted features of a task are related to its own and adjacent tasks. LSTM processes task information in a sequencing order, and the extracted features of a task are relevant to its own and the previous tasks. As for CNN and LSTM, the sequencing order of the input tasks can influence the feature extraction results. In comparison, MHSA adopts multiple independent self-attention heads to extract diverse features, and each head focuses on different parts of the task sequence that are not related to the task order. Thus, MHSA has significant advantages over CNN and LSTM in feature extraction.
## 3 Result and Discussion
### Simulation Results of Wind Field Distribution  
Wind is the primary factor that affects the occurrence of drifting snow. The wind direction determines the movement direction of snow particles, and the wind speed determines the scale of drifting snow. Figure 11 illustrates the monthly average wind speed and direction distribution in the simulated area. The arrows in the figures represent the direction of wind speed. In region 1, the wind speed on the west side of \(102^{\circ}\) E is higher than that on the east side. In the three winter months, the wind speed in Kangding area is always higher than that in other surrounding areas. The average wind speed in January 2017 was higher than that in December 2016 and February 2017, with a maximum wind speed of \(11.41\) m/s. The wind direction around the railway is variable. In the section from Ya'an to Kangding, the wind direction is mainly eastward, parallel to the railway direction, with a small angle. On the west side of Kangding, the wind direction changes more frequently. Between \(101.6^{\circ}\) E and \(102^{\circ}\) E, it is mainly southwest wind. Between \(101^{\circ}\) E and \(101.5^{\circ}\) E, it is mainly south wind and southwest wind. In region 2, the whole area has high wind speed and higher than that in region 1, with a maximum wind speed of \(12.33\) m/s. The average wind speed in November is higher than that in the other two months. The wind direction is mainly southwest wind, forming a large angle with the railway. Figure 9: The distribution of average temperature in the simulation period. (**a**) Temperature of region 1 in December; (**b**) temperature of region 2 in December; (**c**) temperature of region 1 in January; (**d**) temperature of region 2 in January; (**e**) temperature of region 1 in February; (**f**) temperature of region 2 in February. According to the different wind speeds and directions along the railway, five points, P1 (102\({}^{\circ}\) E, 30.13\({}^{\circ}\) N), P2 (101.6\({}^{\circ}\) E, 29.98\({}^{\circ}\) N), P3 (101.1\({}^{\circ}\) E, 30.03\({}^{\circ}\) N), P4 (99.50\({}^{\circ}\) E, 30.37\({}^{\circ}\) N), and P5 (97.7\({}^{\circ}\) E, 31.15\({}^{\circ}\) N), are selected as representatives to study the wind speed and direction distribution in their surrounding areas.
# Fault Analysis and Protection Principle for the Distribution Networks Integrated with PV and BESS
## 2 Fault Characteristics of the Distribution Networks with PV and BESS
### FRT Control Strategy of PV and BESS
#### 2.1.2 Asymmetrical Phase-to-Phase Faults  
Since the new grid connection specifications require the adoption of a negative sequence current absorption strategy, during asymmetric phase-to-phase faults, the PV and BESS should not only output positive sequence reactive current but also absorb negative sequence reactive current to limit the negative sequence voltage at the PCC. During asymmetric faults, when the output reactive current does not exceed the limit, the positive sequence reactive current reference value and the positive sequence active current reference value are determined by the same method as during symmetric faults. However, unlike the conventional negative sequence current suppression control strategy, the negative sequence current absorption strategy specifies the negative sequence reactive current reference value through the grid connection standards. Nevertheless, when the reactive current exceeds the limit, proportional redistribution of A and B is required to prevent single-phase overcurrent. The corresponding FRT strategy is shown in Equation (2).  
\[\left\{\begin{array}{ccc}\left\{\begin{array}{ccc}I_{q,ref}^{+}=\left\{ \begin{array}{ccc}0&\frac{\sqrt{3}|\boldsymbol{u}^{+}|}{U_{N}}>0.9\\ 2\times(0.9-\frac{\sqrt{3}|\boldsymbol{u}^{+}|}{U_{N}})\times I_{N}&0.35<\frac{ \sqrt{3  
where \(I_{q,ref}^{+}\) and \(I_{q,ref}^{-}\) are the reference values before the redistribution of positive sequence reactive current and negative sequence reactive current; \(\mathbf{U}^{-}\) is the negative sequence phase voltage vector at PCC; \(I_{q,ref}^{+}\) and \(I_{q,ref}^{\prime-}\) are the reference values after the redistribution of positive sequence reactive current and negative sequence reactive current; \(I_{q,ref}^{\prime^{+}+}\) and \(I_{q,ref}^{\prime^{-}}\) are the actual reference values of positive sequence reactive current and negative sequence reactive current; \(I_{d,ref}^{\prime^{+}+}\) represents the reference value of positive sequence active power current.
## 4 The Proposed LSE Scheme
### Computational Complexity  
In this section, the complexity of the frequency-offset estimation schemes is discussed in terms of the number of arithmetic operations. The conventional scheme requires \(3N_{R}N_{f}\) complex multiplications and \((N_{R}-1)N_{f}\) complex additions to compute Equation (10). In Equations (14) and (15), \(N_{f}+1\) real multiplications and \(2N_{f}-2\) real additions are needed for each couple \((\hat{\epsilon},\hat{\xi})\). Since the proposed scheme uses only \(N_{p}\) pilot symbols selected from \(N_{f}\) full pilot symbols, the computational processing required in the proposed method can be identical to that used in the existing method, replacing \(N_{f}\) with \(N_{p}\). Table 1 compares the computational complexity between the existing and proposed methods. One readily find that the computational burden of the proposed method is lower than that of the conventional method because \(N_{p}\leq N_{f}\).