esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods
### Improved Branch-and-Bound Algorithm for Topology Comparison between Diagrams and Physical Entities
#### 2.4.1 MCS Algorithm Based on Branch-and-Bound Method  
Since the MCS is identified as an NP-hard problem, the most effective current method for its identification between two topological graphs is based on a heuristic algorithm known as the branch-and-bound method.  
The branch delimitation algorithm is essentially a depth-first algorithm. For two topological graphs G1 and G2, the algorithm starts with an empty subgraph and iteratively adds a pair of nodes in a specific manner, ensuring that the added pair still maintains the topological consistency of the subgraph. During each search iteration, the current search state is represented by \(a_{t}\), consisting of elements from both topological graphs. The pair (\(i\), \(j\)) represents the node numbers corresponding to the two subgraphs in each search step, where node \(i\) is the newly added node in G1 and node \(j\) is the newly added node in G2, denoted by \(b_{t}\). Decide whether the current state is to continue the search or return to the parent search node based on the results of the impact of the added node. Different search strategies can be employed for the search of new nodes, and these strategies influence both the efficiency and the focus of the algorithm. For instance, in [17], the degree of the node is used as a criterion for determining the priority of the search node. The higher the degree of the node, the more likely it is to be the next node.  
In fact, at each step of the search, it is possible to evaluate whether the nodes searched at each step satisfy the requirements by calculating the bounding value \(Bound_{t}\), and, for this purpose, the concept of \"domain\" is introduced, which divides the remaining nodes into several equal parts. In the set \(R\), which consists of all domains in a given state, \(R_{k}=[V_{1},V_{2}]\) is composed of two sets of nodes. In this context, \(V_{1}\) and \(V_{2}\) have the same connection patterns for nodes that have already been matched. To ensure the single-connectivity of the subgraph, the range of node pair selection, or the action space, \(A_{t}\), is confined to the domain, \(R^{(c)}\), that is connected to the already matched nodes. Based on the definition of domain and according to [12], the calculation method for \(Bound_{t}\) is \(\sum_{R_{k}\in\mathbf{R}}\min\left(|V^{\prime}_{k1},V^{\prime}_{k2}|\right)\).
# Handover Management for Drones in Future Mobile Networks--A Survey
## 2 Drone Networks
### Drones in 6G Networks  
Drones can be extremely useful in variety of civic and business applications when combined with 6G communication service that allow for smart automation and the integration of Artificial Intelligence (AI), paving the way for new services such as ultra-smart cities and Internet of Everything (IoE), Extended Reality (XR) (including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR)), autonomous connectivity (such as autonomous vehicles), Wireless Brain Computer Interaction (WBCI), and AI-based services [43]. Sixth Generation is expected to offer 100 times the wireless connectivity and multiple times the performance of 5G. The most significant innovations that will drive 6G are satellite connectivity, drones, connected intelligence with machine learning, the terahertz (THz) band, Optical Wireless Communication (OWC), wireless power transfer, and 3D networking [44; 45].  
For air communications, 6G can overcome a number of limitations associated with previous generations of wireless communications [46; 47; 48; 49; 50; 51; 52; 53]. Sixth Generation communications integrates a non-terrestrial network with 3D connectivity and ubiquitous AI-based services in 3D space, making it suitable for air communications. Sixth Generation technologies will provide seamless connectivity, high-accuracy positioning, ultra-high bandwidth, and real-time remotely controlled features in high-density aerial vehicle scenarios. Although drones may be affected by the utilization of terahertz (THz) bands due to high path loss and small coverage, the integration with satellite networks may solve the issue.
## 2 Methods
### Features Extraction for Oil Spills Detection  
Previous studies proved experimentally that various SAR features could assist oil spill detection and classifications [31]. In this study, ten features including single VV channel intensity, entropy, alpha angle, degree of polarization, ellipticity, pedestal height, copolarized phase difference (CPD), conformity coefficient, correlation coefficient and coherence coefficient are extracted from the covariance matrix (or coherence matrix and Stokes vector deriving from the covariance matrix) [32] of polarimetric SAR data. The ten features investigated in this study, and their behavior on clean sea surface and sea surface covered by different materials, are given in Table 1. Detailed definitions and their behavior on different targets are provided explicitly in [27].
# Pansharpening by Complementing Compressed Sensing with Spectral Correction
## 2 Materials and Methods
### Reconstruction Process  
Assuming that the low-resolution image and the high-resolution image have the same sparse representation, the sparse representation of the low-resolution image can be obtained. The sparse representation \(\alpha\) was estimated by solving the optimization problem of Equation (12).  
\[\underset{\alpha}{\operatorname{argmin}}\frac{1}{2}\|D^{low}\alpha-Y^{low}\|_ {2}^{2}+\lambda\|\alpha\|_{1} \tag{12}\]  
The reconstruction process is performed as follows. In this study, the resolution of RGB image was increased.  
1. The low-resolution RGB images are upsampled via bicubic interpolation to the size of the PAN image. The upsampled low-resolution intensity \(I_{up}^{low}\) is calculated using Equation (6) with the upsampled RGB image.
2. The feature map \(Y^{low}\) is obtained from the upsampled low-resolution intensity \(I_{up}^{low}\). After applying the four filters shown in Section 2.6 to \(I_{up}^{low}\), the feature map \(Y^{low}\in\mathbb{R}^{4p^{2}\times N_{patch}}\) is obtained, where the overlap of adjacent patches are \(p\times 1\) and \(1\times p\) for horizontal and vertical directions. \(I_{patch}^{low}\in\mathbb{R}^{p^{2}\times N_{patch}}\) is then obtained from \(I_{up}^{low}\) where the overlap of adjacent patches are \(p\times 1\) and \(1\times p\) for horizontal and vertical directions.
3. The sparse representation \(\hat{\alpha}\) is calculated using Equation (12).
4. The high-resolution intensity image \(I_{sr}\) is obtained from the sparse representation \(\hat{\alpha}\) and the high-resolution dictionary \(D^{high}\) by Equation (13), and it is normalized for each patch. The average value of the \(j\)th patch, \(\tilde{I}_{sr}(j)\), is calculated with the upsampled low-resolution intensity \(I_{patch}^{low}(j)\), and it is added to the patch of the high-resolution intensity \(I_{sr}(j)\) using Equation (14). \[\begin{array}{c}I_{sr}(j)=D^{high}\hat{\alpha}(j),\\ \hat{\alpha}\in\mathbb{R}^{Nd\times N_{patch}}\end{array}\] (13) \[\hat{I}_{sr}(j)=I_{sr}(j)+\tilde{I}_{sr}(j)\] (14)
5. Using the patches of the obtained high-resolution intensity \(\hat{I}_{sr}(j)\), the image is reconstructed. The mean value of the overlapped pixels is used as the value of the pixel in the adjacent overlapping patches.
## 3 Results  
### Results of Factors for Constructing Essential Networks  
We present the results of the eight factors used to construct the ecos spatial network and the NPP used to indirectly validate the Biome-BGC model in Figure 4. The first three factors (NDVI, MNDWI, and NPP) were extracted from MODIS products, where the unit of NPP is g-C m\({}^{-2}\). year\({}^{-1}\) and NDVI and MNDWI are dimensionless. The distribution of NDVI in the YRB ranged from 0 to 0.92, and its value showed a gradual increasing trend from northwest to southeast. The distribution of MNDWI in the YRB ranged from \(-0.72\) to 0.96, and the dark-blue part of the figure is the water body (Figure 4 MNDWI). The results of MNDWI show that the water bodies were more distributed in the western region of the YRB than in other regions. The NPP results were white in the northwestern region of the YRB because the NPP products of MODIS were filled with invalid values in these areas. These invalid values represent the non-vegetated land-cover type in these regions [35]. With the same trend as NDVI, NPP in the YRB showed a gradual increase from northwest to southeast. The slope of the YRB was extracted from DEM, and this operation was implemented by the 3D Analyst tool in ARCGIS 10.4. The DEM and slope results of the YRB show that the western region of the Yellow River Basin was at a higher elevation than the other regions, and the topography in the west was more complex than the other regions. The remaining four results of density analysis were involved in constructing the cumulative resistance surface of the YRB. From these density results, it can be seen that the southeastern region of the YRB had more human activities than the other regions. This also implied that the vegetation in the southeastern region of the YRB may be more disturbed by humans than other areas.  
Figure 4: Results of factors for constructing ecos spatial networks. The unit of NPP is g-C m\({}^{-2}\).year\({}^{-1}\). The unit of DEM is m. The unit of slope is \({}^{\circ}\). The other factors are dimensionless.
## 2 Manufacturing the Composite Main Reflector
### Verification of Manufacturing Process Variables
Unlike the general sandwich structure, the composite reflector is an edge-closed-type sandwich composite structure in which the outer surface is entirely composed of CFRP prepreg. If the sandwich composite is manufactured without the adhesive film using un-perforated honeycomb core, gas may remain inside of the core cell, as shown in Figure 4, and some of the gas inside of the cell may not be removed during the vacuum bagging process. The composite reflector is formed at a low curing pressure (0-10 psi) due to the strength characteristics of the aramid core. In this case, the pressure of the gas inside of the core cell increases under high-temperature curing conditions (170-180 \({}^{\circ}\)C), and if the internal pressure is higher than the curing pressure applied from outside of the structure, the bonding between the core and the sheet may not proceed smoothly (Figure 5). Therefore, in this study, perforation holes were applied on the honeycomb core wall surface for the composite reflector in Cases 3 and 4, as shown in Figures 6 and 7. Additionally, a hole capable of forming a venting path was machined into the outer surface of the main reflector and a co-curing process was performed. It was confirmed that no problems occurred in the production of an edge-closed-type sandwich composite including a venting path [20]. Figure 4: Pressure distribution during curing without venting path. The final variable for the manufacturing process was considered to be the additional layup process to laminate additional reinforcement to the fastening portion of the composite main reflector. As shown in Figure 8, the deployable reflector antenna includes deployable structures at the top, middle, and bottom of the antenna, and the deployable structures are fastened through the fastening interface of the main reflector. Accordingly, when a load, such as a launch load, is applied to the antenna structure in the stowed state, stress concentration may occur at the interface, and the interface stiffness with the lower  
Figure 5: De-bonding between face sheet (CFRP) and honeycomb core when curing without venting path. Figure 6: Pressure distribution during curing with venting path. Figure 7: Perforated holes in aramid honeycomb core. deployable structure may also affect the stiffness of the antenna when deployed. Therefore, Case 4 was manufactured by locally performing an additional 2-ply lamination.
## 2 Related Research  
### Usage of Android GNSS Measurements  
The development of Android GNSS measurements was highly impacted by the release in 2018 of the Xiaomi Mi8, the first device to embed a dual-frequency GNSS chip from Broadcom (BCM4775) [8]. Reception of multiple GNSS frequencies, such as L1 and L5, can enable advanced positioning techniques [9]. Consequently, many publications over the last five years have demonstrated diverse application topics for the usage of Android GNSS measurements: measurement quality [10; 11; 12; 13], high-precision positioning [10; 14; 15; 16], atmospheric error estimation [17; 18], sustainable water access through standalone positioning aiding [19; 20], spoofing/jamming mitigation [21; 22], etc.  
While the use of the Android GNSS measurements appears very fruitful and has high potential, acquiring the raw data is not trivial and requires specific apps and access to the device's developer options. In [11], a comprehensive review of the available Android apps for GNSS data acquisition is performed. The paper analyzes the raw measurements available in Android devices and mentions some of the problems associated with the processing. One of the issues pointed out is the measurement quality and unknown hardware characteristics, which prevent more precise positioning as of today. Similarly, as the receiver is greatly influenced by its environment, a lack of details about the acquisition protocol and lack of dataset diversity (e.g., harsh environments) are also highlighted as a limitation with currently available open data.
# Failure Type Prediction Using Physical Indices and Data Features for Solenoid Valve +
###### Abstract  
A high-speed solenoid valve is a key component of the braking system. Accurately predicting the failure type of the solenoid valve is an important guarantee for safe operation of the braking system. However, electrical, magnetic, and mechanical coupling aging mechanism; individual differences; and uncertainty of aging processes have remained major challenges. To address this problem, a method combining physical indices and data features is proposed to predict the failure type of solenoid valve. Firstly, the mechanism model of the solenoid valve is established and five physical indices are extracted from the driven current curve. Then, the frequency band energy characteristics are obtained from the current change rate curve of the solenoid valve by wavelet packet decomposition. Combining physical indices and frequency band energy characteristics into a comprehensive feature vector, we applied random forest to both predict and classify the failure type. We generate a data set consisting of 60 high-speed solenoid valves periodically switched under accelerated aging test conditions, including driven current, final failure type, and switching cycles. The prediction result shows that the proposed method achieves 95.95% and 94.68% precision for the two failures using the driven current data of the 3000 th cycle and has better prediction performance than other algorithms.  
failure type prediction; high-speed solenoid valve; wavelet packet decomposition; random forest +
Footnote †: journal: Applied Sciences
## 6 Numerical Results
### Analysis of Network Performance
#### 6.4.4 Latencies  
Figures 12 and 13 show the maximum and average latencies of the FH and MH flows, respectively, in network slices eMBB and URLLC as a function of the number of RUs. The results were obtained for different network scenarios assuming \(\mu\in\{1,2,3\}\), \(\gamma\in\{0.1,0.2,\ldots,0.9\}\), and \(M\in\{1.5,2.0,\ldots,4.0\}\).  
Figure 11: Comparison of network slice prioritization policies as a function of the number of RUs assuming \(\gamma=0.3\); the results averaged for \(\mu\in\{1,2,3\}\).  
Figure 10: Average number of active PPs as a function of the PP capacity for different numerologies and \(\gamma=0.3\); the results averaged for \(10\leq R\leq 70\).  
As seen in Figure 12, the maximum latency of FH flows in all network scenarios is kept below the imposed limits, i.e., 50 us for URLLC and 100  for eMBB. Concurrently, the average FH latency is between 25 and 40 us and does not change significantly with the number of RUs and the network topology. Figure 14 indicates higher MH latencies for eMBB than for URLLC, resulting from two opposite factors. On the one hand, URRLC MH flows are routed to a PP node, where the CU is placed, and are delayed by higher priority FH flows terminated at this node. On the other hand, eMBB MH flows are transported to a distant hub node, where the propagation latencies are higher for these flows.  
Figure 14: Overall packet transport latency as a function of the numerology applied.  
Figure 12: Maximum and average latencies of FH flows as a function of the number of RUs.  
Figure 13: Maximum and average latencies of MH flows as a function of the number of RUs.  
Figure 14 presents the packet transport network's overall latency (i.e., the sum of FH and MH latencies) as a function of the numerology (\(\mu\)) applied. The results were obtained and averaged for \(R\in\{10,20,\ldots,70\}\), \(\gamma=0.3\), and \(M=1.5\). The figure shows that the application of higher numerologies decreases the transport latencies. Besides, the overall transport latency is lower for network slice URRLC than for eMBB, which follows the results in Figures 12 and 13, and the difference is higher in smaller network topology MESH-20.
## 3 Proposed Work
### Watermark Encryption and Decryption  
In the proposed scheme, a symmetric cryptographic approach is used for watermark encryption. To encrypt the binary watermark, its pixels are partitioned into even (\(V_{even}\)) and odd (\(V_{odd}\)) position pixel vectors which is further shuffled according to a pseudo random key (\(PR_{Key}\)). Steps for watermark encryption are provided in Algorithm 3. Watermark partitioning and shuffling are explained below in detail. ```
0: Watermark (W) of size \(P\times Q\)
0: Encrypted watermark vectors \(V^{1}_{odd}\) and \(V^{1}_{even}\) of size \(P\times Q/2\), \(R_{key}\), \(PR_{Key}\) (\(P\times Q\) /2)
1: Partition W into even(\(V_{even}\)) and odd (\(V_{odd}\)) position pixel vectors. 2: Generate 8-bit binary intermediate key (\(IK_{B}\)). 3: Generate 128 bit \(R_{key}\) by hashing (\(IK_{B}\)) using MD-5. 4: Generate \(PR_{Key}\) (( \(P\times Q\))/2) from \(R_{key}\) using Algorithm 4. 5: Shuffle \(V_{odd}\) and \(V_{even}\) using \(PR_{Key}\) to get the encrypted vectors \(V^{1}_{odd}\) and \(V^{1}_{even}\). ```  
**Algorithm 3** Watermark encryption. Watermark Partition: A watermark image is partitioned into even and odd position pixel vectors ( \(V_{even}\) and \(V_{odd}\)) by scanning from top to bottom and left to right order in a raster scan line fashion. All even and odd position pixel values are appended to \(V_{even}\) and \(V_{odd}\), respectively, using Equation (3) and (4):  
\[V_{odd}\ (x)=\left\{ \begin{aligned} & W(row,col),\ if\ mod(col,2)\
eq 0\\ & Otherwise,\ ignored,\end{aligned} \right. \tag{3}\]  
Figure 3: Block diagram for the watermark extraction process. \[V_{even}\ (y)=\left\{ \begin{aligned} & W(row,col),\ if\ mod(col,2)=0\\ & Otherwise,\ ignored,\end{aligned} \right. \tag{4}\]  
where W (row, col) is the original watermark image. \(V_{odd}(x)\) and \(V_{even}(y)\) are odd and even position pixels of the watermark image, respectively. Further illustrations of watermark partitioning are provided in Figure 4.  
Watermark Shuffling: Vectors \(V_{even}\) and \(V_{odd}\) are shuffled by using a pseudo random key (\(PR_{key}\)) which is generated from a 128 bit \(R_{key}\). To ensure that \(R_{Key}\) cannot be cracked, a unique binary intermediate key (\(IK_{B}\)) is used as an initial parameter. \(IK_{B}\) is generated by using the following relations:  
\[S=ceil(sum(\mu LL,\mu LH,\mu HL,\mu HH,\  
```
0: Random key (128 bits)
0: Pseudo Random key (\((P\times Q)/2\) )
1: Initial conditions \(a_{0}\), \(b_{0}\), \(S_{1}\) and \(S_{2}\) are generated as follows: \[a_{0}=\sum_{K=1}^{20}\frac{Randomkey(K)}{2^{K}}\] \[b_{0}=(\sum_{K=21}^{50}\frac{Randomkey(K)}{2^{K-52}})\ mod1\] \[S_{1}= (\sum_{K=1}^{8}Randomkey(K)\times 2^{K}\] \[+\sum_{K=25}^{76}\frac{Randomkey(K)}{2^{K-24}}+b_{0})\ mod10+10\] \[S_{2}= (\sum_{K=121}^{128}Randomkey(129-K)\times 2^{K}\] \[+\sum_{K=77}^{128}\frac{Randomkey(K)}{2^{K-76}}\times S_{1})\ mod10+10\] \[a_{0}=(\ a_{0}+S_{2})\ mod1\]
2: The initial values \(a_{0}\), \(S_{1}\) and \(b_{0}\), \(S_{2}\) are used for the logistic and sine map, respectively.
## 1 Introduction
### The Current Study  
In this investigation, we primarily formulate our hypotheses based on the fundamental assumption of PLT [9; 29; 30; 31; 32]. According to PLT, in the low perceptual load condition, spare perceptual capacities should automatically spill over to process the features of the task-irrelevant singleton, leading to a significant N2 pc response (i.e., attentional capture). In contrast, under high perceptual load conditions, the perceptual capacity is exhausted by processing task-relevant complex stimuli, resulting in an early selection that ignores the task-irrelevant singleton and, therefore, an absence of N2 pc. However, it seems unlikely that processing capacity is ever completely exhausted. Thus, under certain experimental conditions, PLT's predictions may not always hold. If the N2 pc results of our study do not fully support PLT--for instance, by showing attentional capture under high perceptual loads or failing to show attentional capture under low perceptual loads in either participant group--then we should consider whether alternative theories, such as the SSH of controlled attentional capture [18; 22; 23; 25; 26; 33], play a role.  
According to the SSH, in the current study, we hypothesize that if individuals with depressive tendencies, as compared to matched controls, have a relative deficit in top-down attentional control processes in preventing attentional capture by a task-irrelevant singleton, then the electrophysiological results should reveal a weak (or absent) early \(\mathrm{P_{D}}\), followed (or replaced) by the N2 pc component, which reflects the process of orienting and focusing covert attention on peripheral object features. In particular, we predict that individuals with high state depression, as compared to low state depression, should not show an increased \(\mathrm{P_{D}}\) amplitude with an increase in perceptual load in the attempt to actively suppress task-irrelevant singleton features.  
To test our hypotheses, we employed a modified version of a delayed match-to-sample task [34]. Participants were required to encode and retain either one (low perceptual load) or four (high perceptual load) complex objects (abstract shapes) in VWM while ignoring a lateralized, task-irrelevant complex singleton distractor object (Chinese character). The singleton distractor object, a lateralized stimulus that differed in shape from the other task-irrelevant homogeneous items (circles) in a circular visual array, was presented during the retention interval. Participants' ability to resist its attentional capture was examined using primarily electrophysiological measures. By elucidating the interplay between VWM, selective attention, and depressive symptomatology, this study contributes to a deeper understanding of cognitive dysfunction in MDD.