esa-sceva/satcom-chunk-collection huggingface dataset:
# Motion Trajectory Prediction in Warehouse Management Systems: A Systematic Literature Review
## 5 Findings
### TSP-Based Methods
By using the algorithm, they increased productivity by as much as 41% compared to before using the algorithm. They tested the method on data from a real warehouse. The system's adaptability stands out, with its capacity to operate optimally across various warehouse layouts, bolstered by the introduction of fictive locations. However, there are tangible limitations: real-world factors such as item weight and fragility were somewhat sidelined, and while the algorithm's efficacy was proven in the study's context, its absolute generalizability across all warehouse layouts is yet to be established. Future endeavours by the research team are inclined towards further minimizing the distance covered by workers during their daily routines. By strategically positioning frequently picked items near exits and clustering commonly ordered items together, the team anticipates achieving greater efficiency in warehouse operations. The TSP-based methods for trajectory prediction in warehouses, as discussed in the presented studies, address the optimization of order picking scenarios. Referring to Table 6, it is evident that the primary evaluation method is simulation, as highlighted by the works of [PERSON] et al. [76] and [PERSON] [77]. While simulation-based evaluations give an understanding of the models' potential, they may not fully represent real-world complexities. Conversely, [PERSON] et al. [78] employed an empirical approach, testing their method with data from an actual warehouse. This real-world evaluation provides a direct assessment of the method's practical applicability.
## 3 Data Clustering Algorithm
### Clustering Implementation  
Spectral clustering is a well-established algorithm but can be carried out using many input arguments. The authors tried many of them and the trials were evaluated statistically. First, the similarity graphs are generated using two ways (number of nearest neighbors and according to a certain value that represent radius to search for the nearest neighbors). Then, the similarity graphs are represented using Laplacian matrix. The clustering results were evaluated for different forms of this matrix; without normalization, random-walk normalization, and symmetric normalization. Finally, two clustering methods (k-means and k-medoids) were tested to cluster the eigenvectors of the Laplacian matrix. In eachcase, the eigenvalues were checked, and the silhouette coefficients were calculated for performance evaluation [28].  
MATLAB is the selected platform to perform spectral clustering. The similarity graph was generated using kernel nearest neighbors, where, it connects two points \(i\) and \(j\), when either \(i\) is the nearest neighbor of \(j\) or \(j\) is the nearest neighbor of \(i\). These distances  
Figure 2: Architecture of the algorithm used to determine the proper number of clusters.  
are calculated using Euclidean formula, then, transformed with a scaled kernel with a value that is selected using heuristic procedure. The clustering method used to cluster eigenvectors of the Laplacian matrix is the K-medoids. A medoid in the K-medoids algorithm is the most centrally located point with minimum distance with respect to other points and is not influenced by the outliers or extremities [29,30]. Finally, the similarity graph is represented with normalized Laplacian matrix using random-walk.
# Design and Evaluation of Flexible Support Based on Space Mirror
## 4 Analysis and Evaluation
### Gravity Load Analysis  
During the ground mounting process of the mirror component, the mirror will be affected by gravity; the degree of its influence will be analyzed by simulation during the component design process and appropriate countermeasures will be taken [16].  
The coordinate values of all points on the mirror surface are obtained by simulation analysis and brought into MATLAB calculation to simulate the mirror surface shape. As shown in the Table 6, it is the root-mean-square (RMS) value of the surface shape of the mirror component after loading the gravity in X and Y directions through simulation analysis (since the Z direction is the vertical direction of the optical axis, not the direction of loading and adjusting, the mirror component is not affected by gravity in the stage of on-orbit operation; therefore, it is not included in the scope of this analysis).
# Sensors and Communication Simulation for Unmanned Traffic Management
## 6 Validation
### Public Communications Networks  
In this last scenario, we show the functioning of the communications network. The main evidence that the communications network is working properly is that it has already been able to carry traffic between the different actors in the previous scenarios. However, we propose the following scenario (Figure 18) to show that coverage and technology  
Figure 16: (**a**) Sensors range along with the position sent to the UTM system by each sensor. (**b**) Time evolution of the track numbers sent by the non-cooperative sensor.  
Figure 17: Comparison of the position received in the UTM system for both flights from non-cooperative (**a**), cooperative (**b**) sensors and telemetry from drone.  
restrictions are respected and that BVLOS-type operations work as expected. This figure shows a simulation environment with three mobile communications nodes and their respective coverages: two 4G technology nodes (CNode1 and CNode2) and a 3G technology node (CNode3). In this environment, we simulate two BVLOS flights: Flight 1 which is carried out by a drone using exclusively 4G technology and flight 2 which is flown with a drone compatible with 3G technology.  
In Figure 19a, we can see which base stations process the C2 traffic of the two drones in the scenario. We can see that nodes 1 and 2 carry all the traffic from flight 1, since it is only compatible with 4G technology. The drone is assigned to either node depending on the distances to both drones, as long as it is within communication nodes coverage. In fact, we can see in Figure 20 that the drone is out of range of both 4G technology base stations in part of the operation. This implies that, since the drone is not compatible with 3G, node 3 cannot relay its traffic and the C2 link is interrupted. The drone detects this interruption and automatically initiates a return to home (RTH) maneuver. Since the communications are interrupted, the GCS does not receive telemetry messages from the drone so it cannot send position updates to the UTM system. However, while executing the RTH maneuver, the drone re-enters the coverage zone of a 4G node, automatically re-establishing communications, which results in the UTM resuming telemetry reception.  
As for the second flight, we see that all the traffic is carried uninterruptedly by base station 3, which is the only one compatible with 3G technology, despite the existence of other closer base stations. At the same time, all simulated packets are experiencing the simulated communication disturbances. In Figure 19b, we show the delay experienced by UTM-received packages from flight 2 for three different network model configurations in three different executions. In all cases, we see a small variable delay for most of the packets (due to the simulated transmission delays) and some peaks for some consecutive packets, due to packet losses or bursts.  
Figure 18: Scenario 6.3 with two different BVLOS flight plans and 3 different CNodes.
# Application of USCCD on Girth Weld Defect Detection of Oil Pipelines
## 3 USCCD Pull Test Results for GW Circumferential Crack-Like Defects
### POD (Probability of Detection) and POI (Probability of Identification)
#### 3.1.1 Depth Sizing Accuracy  
Depth of defects can be reported only in two classes: \(<\)2.5 mm and \(\geq\)2.5 mm. For defects \(\geq\)1 mm (axial opening) \(\times\) 40 mm (circ.) in the GW, all of which are made as external, the depth estimation  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Defect Group** & **Total \# of Defects** & **\# of Defects Detected** & **POD** & **POD Interval @95\% Confidence Level** \\ \hline Defect in weld & 36 & 35 & 97.2\% & 85-99\% \\ Defect at weld & 66 & 61 & 92.4\% & 82-99\% \\ Total & 102 & 96 & 94.1\% & 84-99\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Probability of Detection (POD) summary of test results of Girth Weld (GW) crack-like defects.  
Figure 5: Pull test pipe facility and tool.  
accuracies were achieved as listed in Table 2. For defects \(\geq\)1 mm (axial opening) \(\times\) 40 mm (circ.) at the GW, the depth estimation accuracies were achieved as listed in Table 3. As the data analysis shows, all of the defects of 4 mm depth can be correctly classified in depth class \(\geq\)2.5 mm; while most defects of 1 and 2 mm deep were overestimated, especially for those in welds.
# A Trusted Lightweight Communication Strategy for Flying Named Data Networking
###### Abstract  
Flying Ad hoc Network (FANET) is a new resource-constrained breed and instantiation of Mobile Ad hoc Network (MANET) employing Unmanned Aerial Vehicles (UAVs) as communicating nodes. These latter follow a predefined path called'mission' to provide a wide range of applications/services. Without loss of generality, the services and applications offered by the FANET are based on data/content delivery in various forms such as, but not limited to, pictures, video, status, warnings, and so on. Therefore, a content-centric communication mechanism such as Information Centric Networking (ICN) is essential for FANET. ICN addresses the problems of classical TCP/IP-based Internet. To this end, Content-centric networking (CCN), and Named Data Networking (NDN) are two of the most famous and widely-adapted implementations of ICN due to their intrinsic security mechanism and Interest/Data-based communication. To ensure data security, a signature on the contents is appended to each response/data packet in transit. However, trusted communication is of paramount importance and currently lacks in NDN-driven communication. To fill the gaps, in this paper, we propose a novel trust-aware Monitor-based communication architecture for Flying Named Data Networking (FNDN). We first select the monitors based on their trust and stability, which then become responsible for the interest packets dissemination to avoid broadcast storm problem. Once the interest reaches data producer, the data comes back to the requester through the shortest and most trusted path (which is also the same path through which the interest packet arrived at the producer). Simultaneously, the intermediate UAVs choose whether to check the data authenticity or not, following their subjective belief on its producer's behavior and thus-forth reducing the computation complexity and delay. Simulation results show that our proposal can sustain the vanilla NDN security levels exceeding the 80% dishonesty detection ratio while reducing the generated end-to-end delay to less than 1 s in the worst case and reducing the average consumed energy by more than two times.  
UAV; FANET; NDN; VANET; trust; energy efficiency +
Footnote †: journal: Sensors
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 5 Conclusions  
In this study, an efficient grid-based geocasting scheme called GB-FERMA is proposed in WSNs. GB-FERMA was developed to deal with the geocasting through a grid-based structure and Fermat points theory. GB-FERMA selects the optimal relay nodes (gateways) via the Fermat points calculation based on the Fermat point theorem to achieve energy efficient data transmission. In the simulations, when the initial power is 0.25 J, the average energy consumption of GB-FERMA is about 53% of FERMA-QL, 37% of FERMA, and 23% of GEAR; however, when with the initial power is 0.5 J, the average energy consumption of GB-FERMA is about 77% of FERMA-QL, 65% of FERMA, and 43% of GEAR. The proposed GB-FERMA is more capable of reducing energy consumption and prolonging the lifetime of WSNs than FERMA-QL, FERMA, and GEAR.  
Conceptualization, N.-C.W.; methodology, N.-C.W., C.-Y.L. and S.-H.W.; software, M.-F.T. and S.-H.W.; validation, M.-F.T. and Y.-L.C.; formal analysis, N.-C.W. and C.-Y.L.; investigation, C.-Y.L. and S.-H.W.; resources, M.-F.T., C.-Y.L. and Y.-L.C.; data curation, M.-F.T. and Y.-L.C.; writing--original draft preparation, C.-Y.L. and S.-H.W.; writing--review and editing, [PERSON]-C.W. and Y.-L.C.; visualization, M.-F.T. and C.-Y.L.; supervision, N.-C.W.; project administration, N.-C.W.; funding acquisition, N.-C.W. and M.-F.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Ministry of Science and Technology of the Republic of China under grant MOST-110-2221-E-239-002 and the National United University under grant 112-NUUPRJ-01.  
Not applicable.  
The authors declare no conflict of interest.
# Semi-Supervised Time Series Anomaly Detection Based on Statistics and Deep Learning
## 2 Background Knowledge
### Discret Wavelet Transform  
Wavelet transform (WT) utilizes wavelets as the basis for decomposing signals or time series, where wavelets are functions that are defined over a finite interval of time and whose average value is zero [38]. Wavelets are also called mother wavelets. They are exemplified by [PERSON], [PERSON], [PERSON], and Daubechies, among others. WT is a very well-known and useful tool for extracting the time-frequency features of a time series.  
Let \(\psi(t)\) be a mother wavelet. The child wavelet \(\psi_{a,b}(t)\) of \(\psi(t)\) is defined according to (7).  
\[\psi_{a,b}(t)=\frac{1}{\sqrt{a}}+\left(\frac{t-b}{a}\right) \tag{7}\]  
where \(a\) is the dilation (scaling) parameter and \(b\) is the transition (shifting) parameter. High-scaled child wavelets correspond to lower frequencies and are used as low-pass filters. On the contrary, low-scaled child wavelets correspond to higher frequencies and are used as high-pass filters.  
WT can be divided into two categories, the discrete wavelet transform (DWT) and the continuous wavelet transform (CWT). Below, the DWT, which is adopted by this research, is introduced. As shown in Figure 1, the DWT consists of high-pass filters (HPFs) and low-pass filters (LPFs), both along with down sampling filters (DSFs). Time series data first go through high-pass filters to generate detail coefficients (denoted as cD) to represent high-frequency components of data. Moreover, time series data go through low-pass filters to generate approximation coefficients (denoted as cA) to represent low-frequency components of data. Afterwards, detail coefficients are retained, whereas approximation coefficients are further decomposed again into high- and low-frequency components until a specific number of decompositions is reached. Finally, all retained detail coefficients and the last approximation coefficients altogether constitute the result of the DWT.
## 6 Conclusions  
In this paper, we first introduce a novel network covert channel model based on end spreading in the end hopping system, namely CCES, and then propose a CCES-based scheme. According to the evaluation metrics, we implement experiments to practically verify the performance of the proposed CCES scheme, and the results are inspiring. In recent years, bandwidth is no longer the only public requirement for communication; information transmission security is being paid increasing attention to. As a kind of novel network storage and behavior covert channel with high robustness, CCES could transmit messages covertly and safely under a hostile environment, which provides a new perspective for communication security.  
Figure 8: (**a**) Efficiency under different hopping rates. (**b**) Efficiency and accuracy rate under different \(e\).  
We are currently experimenting with improving the transmission efficiency of CCES, such as the use of data compression and end information multiplexing. Additionally, we also focus on the research of autocorrelation among end spreading sequences, thereby realizing multi-user communications without the need for authentication. Except for end hopping, the application fields of CCES could also be promoted from end hopping to include IPv6, software-defined networks, and so on. In the future, we aim to deploy our system in real-world scenarios to validate its effectiveness and practical value.  
Conceptualization, Z.W. and F.L.; methodology, Z.W.; data curation, Z.W.; validation, F.L.; review & editing, L.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
All data included in this study are available upon request by contact with the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} BER & Bit error rate \\ BPS & Bits per second \\ CCES & Covert channel model based on end spreading \\ FHSS & Frequency hopping spread spectrum \\ LFSR & Linear feedback shift register \\ PLR & Packet loss rate \\ \end{tabular}
## 4 Intelligent Traffic Load Distribution
At the end of the second lap of the satellite constellation, ITLD selects the traffic load distribution that allows to achieve the highest throughput:  
\[G_{i}^{(ITLD)}= \begin{cases}G_{i}^{(mu)}&\text{if}\quad\mathcal{T}^{(mu)}> \mathcal{T}^{(u)}\\ G_{i}^{(u)}&\text{if}\quad\mathcal{T}^{(mu)}<\mathcal{T}^{(u)}\end{cases}  \tag{15}\]  
Consequently, ITLD throughput is  
\[\mathcal{T}^{(ITLD)}=\text{max}\,(\mathcal{T}^{(u)},\mathcal{T}^{(nu)}). \tag{16}\]  
Note that this intelligent distribution does not guarantee the maximum overall system throughput per lap. However, the proposed strategy is a feasible and a simple solution to increase the overall system throughput, considering the implementation limitations of typical IoT device hardware, and the energy consumption constraints associated with the on-board satellite signal processing. ```
1: According to the satellite spacing \(s\), obtain the number of positions \(M\) using (1);
2: Evaluating (11), compute the uniform traffic load distribution per position \(G_{i}^{(u)}\);
3: For \(G_{i}^{(u)}\), calculate the throughput per position \(\mathcal{T}_{i}\) with (6);
4: Determine the overall throughput with uniform load distribution, \(\mathcal{T}^{(u)}=\sum_{i=1}^{M}\mathcal{T}_{i}\big{(}G_{i}^{(u)}\big{)}\);
5: Find the load factor \(Q_{i}\) in each position using (12);
6: Following (13), compute the non-uniform traffic load distribution per position \(G_{i}^{(mu)}\);
7: Repeat step 4 now to find \(\mathcal{T}_{i}\) with \(G_{i}^{(mu)}\) for the proposed strategy;
8: Determine the overall throughput with non-uniform load distribution \(\mathcal{T}^{(mu)}=\sum_{i=1}^{M}\mathcal{T}_{i}\big{(}G_{i}^{(mu)}\big{)}\);
9: If \(\mathcal{T}_{i}^{(mu)}>\mathcal{T}_{i}^{(u)}\), then \(G_{i}=G_{i}^{(mu)}\). Else, \(G_{i}=G_{i}^{(u)}\). Thus, \(\mathcal{T}=\text{max}(\mathcal{T}^{(mu)},\mathcal{T}^{(u)})\). ```  
**Algorithm 1**ITLD--intelligent traffic load distribution.
# Mobile Robot Path Planning with a Non-Dominated Sorting Genetic Algorithm
## 3 Path Planning Problem
### Objectives
#### 3.1.2 Path Smoothness  
Path smoothness denotes the degree of sleekness of the path. The smoother the route, the less energy the robot consumes as it travels along the way. In this paper, it is defined by the average turning angle of the path. The corner formed by two consecutive line segments can be determined. Then, the average turning angle of the route can be calculated by Equation (2).  
\[\left\{\begin{array}{l}Angle[p_{i},p_{i+1},p_{i+2}]=\pi-\cos^{-1}\left( \frac{(x_{i+1}-x_{i})(x_{i+2}-x_{i+1})+(y_{i+1}-y_{i})(y_{i+2}-y_{i+1})}{dis( p_{i},p_{i+1})\times dis(p_{i+1},p_{i+2})}\right)\\ Smoothness(p)=\frac{1}{n}\sum\limits_{i=0}^{n-1}\{Angle[p_{i},p_{i+1},p_{i+2} ]\}.\end{array}\right. \tag{2}\]
# ColorMedGAN: A Semantic Colorization Framework for Medical Images
## 3 Experiment and Result
### Ablations Analysis
#### 3.1.1 Effectiveness of Edge Loss  
We conducted comparison experiments on two datasets, Oasis1 and Kaggle brain MR datasets [51]. The Kaggle image data has lower resolution and worse texture details, and texture loss is more severe when colorization on such data, with color overbleeding or edge loss. By introducing edge loss, it is possible to constrain the edge information globally in the image generation process. The experimental results in Figure 6 show that the edge loss is more prominent for poor-quality data sets. From the results of not adding edge loss constraints in the second column of Figure 6, the generated image outline color bleeding is severe, and the structural information of the image is seriously lost. After adding edge loss constraints (the third column of Figure 6), a clear image can be generated, and the color itself retains semantic information. Table 2 lists the relevant metrics. The image quality generated after introducing edge loss is better in each metric. The image has better similarity and better results at the level of abstract perception. On the one hand, it can prevent the color overflow phenomenon; on the other hand, adding the edge loss can guide the colorization network to pay more attention to the edge information in the global scope and generate higher-quality texture details in the shading process.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **SSMI \(\uparrow\)** & **PSNR \(\uparrow\)** & **FID \(\downarrow\)** \\ \hline w/o edge loss & 0.745 & 20.735 & 112.15 \\
**w edge loss** & **0.757** & **21.010** & **110.32** \\ \hline \hline \end{tabular} Bold fonts indicate better results.  
\end{table}
Table 2: Comparison of the influence of edge loss in ColorMedGAN.
## 5 Discussion  
Exploitation and exploration indexes are two important criteria and indicators in analyzing and evaluating the performance of optimization algorithms. The exploitation power means the ability of an optimization algorithm to provide a suitable quasi-optimal solution at the end of the algorithm iterations. Thus, in comparing the performance of several optimization algorithms on an optimization problem, the algorithm that can ultimately provide a quasi-optimal solution closer to the global optimal has higher exploitation power. The unimodal objective functions F1 to F7 have only one main solution and are therefore very suitable for evaluating the exploitation power of optimization algorithms. Comparison of the optimization results of these functions using the GBGBO and eight other algorithms in Table 2 shows the acceptable exploitation power of the GBGBO in solving optimization problems. Exploration power means the ability of an algorithm to accurately scan the search space of the optimization problem. In fact, among several optimization algorithms, an algorithm that can scan the search space well and is not limited to certain areas, as well as can pass locally optimal solutions, has higher exploration power. This indicator is especially important for optimization problems that have multiple local optimal solutions. The F8 to F23 objective functions had several local optimal solutions in addition to the main solution. Therefore, these objective functions were suitable for evaluating the exploration power of optimization algorithms. Analysis and comparison of the optimization results of the GBGBO and eight other algorithms on these objective functions, which are presented in Tables 3 and 4, indicated the acceptable exploration power of the proposed GBGBO algorithm in solving this type of objective functions. On the other hand, the results of the Friedman rank test showed that the acceptable power of the GBGBO in exploration and exploitation indices is not random.