esa-sceva/satcom-chunk-collection huggingface dataset:
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 6 Discussion
### Summary of Experiments  
In this paper, we simulated AODV, DSDV, and OLSR, which are representative routing protocols of FANETs, according to the mobility models when performing multi-UAV-based reconnaissance. As a result, the AODV protocol yielded reasonable PDR performance, while OLSR showed good end-to-end delay performance. Therefore, AODV appears to be suitable for scenarios in which UAV movement is irregular and packet delivery is important, while OLSR is applicable when UAVs need to deliver packets quickly.  
We also expected that the more UAVs forming the FANET, the denser the network connectivity; the denser the connectivity, the higher the PDR values. To verify this hypothesis, we conducted a simulation by increasing the number of nodes. However, in our simulation, the PDR decreased as the number of nodes increased. These simulation results help us infer that PDR performance does not improve as the node density increases. Rather, we observed that as the connectivity improves, more route searching overhead and routing table management overhead occur and affect the PDR performance. In FANETs, where topology changes are fast and irregular reconnaissance scenarios are used, the overhead is the largest cause of performance degradation.  
In regard to the mobility models, we found that the SRWP model is an adequate model for the reconnaissance scenario because the model shows a high reconnaissance rate and a high packet delivery ratio. However, the SRWP model has an insufficient reconnaissance rate in the outer part of the reconnaissance area. As an alternative model, the RDPZ and EGM models could be used because these models also show a reasonable reconnaissance rate and a reasonable packet delivery ratio. In contrast, the MP model should not be used in the reconnaissance scenario because nodes are flocked to a specific area, so the reconnaissance rate is low. The DPR model should also not be used because of its low packet delivery ratios.  
In addition, it was observed that the mobility model with good performance is different according to the GCS location. For example, SRWP shows high performance when the GCS is located in the middle of the reconnaissance area because, in the SRWP model, the node moves in a straight line from the source to the destination and has many chances to be near the GCS. RDPZ shows high performance when the GCS is located at the left top of the reconnaissance area because nodes move around the area evenly. MP shows high performance when the GCS is located at the bottom because nodes move to the bottom because of the mechanism of MP.
## 5 Other Topics: Applications and Technology  
Desalination of aquifers and marine water represents a viable choice to meet global domestic and industrial water needs. With the rise in sea levels and saltwater intrusion into groundwater, desalination becomes increasingly necessary. Removing dissolved salts from seawater to obtain fresh water of sufficient quality for many purposes generates highly concentrated salt waste, known as brine. [PERSON] et al. (Contribution 11) described a clean, low-impact technology for producing drinking water without producing pollution. [PERSON] et al. (Contribution 12) used very advanced technologies to define the decomposition of plastic materials in the marine environment, in particular bioplastics, such as starch-based compostable shopping bags. This leads to useful considerations for defining the use of increasingly rapidly degrading materials in the marine environment. [PERSON] and [PERSON] (Contribution 13) consider coupling scientific evidence from sediment cores with historical information as an effective way to reconstruct and quantify anthropogenic impacts in transitional and marine-coastal areas and thus establish the responsibilities for pollution that may also be used in the legal field. Good practices for selecting sampling sites and specific investigation techniques are key to understanding pollution histories and dynamics, along with reliable dating methods and analytical procedures. Examples are shown of how the results have been used in preliminary investigations or judicial debates to attribute responsibility for environmental pollution to past or present industrial management.
## 3 Dataset and Methods
### AD Definition during ML Model Construction (Substudy 2)  
Once the best-performing outlier detection method has been identified and the outliers eliminated (substudy 1), the obtained dataset, preprocessed and without outliers, is subjected to the previously presented data splitting and data scaling steps. Furthermore, a dimensionality reduction step is implemented to improve interpretability of the developed ML models. To achieve this, the same GA procedure as the one used in the first article is used to reduce the numbers of descriptors to a fixed number of 100 [15]. Finally, the Lasso models are trained and validated on the new dataset of reduced dimension. Note that the criterion of selection of the best-performing outlier detection method is based on compromise between high training/test performances and the lowest possible number of eliminated molecules.  
The main goal of this second substudy is to visualize where the test data are located with respect to the AD defined by the training data. Therefore, the AD of each model is visualized on AD plots showing the standardized residuals (Model-outliers) of the different molecules versus their anomaly scores (X-outliers). The test data should be within or close to the AD to have a reliable model.  
In addition to the above, tSNE 2D representations are provided in an attempt to identify the potential sources of high prediction errors (e.g., lack of training molecules in certain regions, presence of molecules with different structures but similar property values...). Note that the procedure of this substudy also applies to a potential hyperparameter optimization phase in order to check whether the validation data are indeed close to the training data. However, this study has not been pursued in the framework of the present article.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Configuration** & **Methods** & **Thresholds** \\ \hline
1. Effect of X-outlier elimination & _iForest_, _RF confidence_, _tSNE2D/\&NN_ & Elimination of 0\%, 10\%, 30\%, 50\% of the molecules with the highest anomaly scores \\ \hline
2. Effect of Model-outlier elimination & Standardized residuals & Elimination of the molecules with absolute standardized residuals above 1, 2, 3, 5 and 10 \\ \hline  & Layouts: & _RF confidence_: \\
3. Effect of X-outlier and & 2-Model-outlier then X-outlier & 150 kJ/mol for enthalpy, \\ Model-outlier elimination & 3-X-outlier then Model-outlier & 50 J/mol/K for entropy. \\  & _(RF confidence_ for X-outlier, & Standardized residuals: \\  & Standardized residuals for Model-outlier) & 2 kJ/mol or J/mol/K. \\ \hline  & & Elimination of the molecules with y-values outside the thresholds of \(Q1-1.5\times IQR\) and \(Q3+1.5\times IQR\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the methods and thresholds tested for each configuration in substudy 1.
## 2 Related Work
### Energy Consumption Model  
WSNs are most important for efficient energy use. It is not possible to charge or replace batteries when sensor nodes are sprayed in a given area. That is, the life of the sensor node depends on the life of the battery, which affects the life of the WSNs. The sensor nodes consume energy for sensor detection, computation of data, and transmission and/or reception between sensor nodes. In WSNs, a large mount of energy is used for data transmission, and much research has focused on the routing method for efficient use of transmission energy. The transmission energy model of LEACH is shown in Figure 3[36].  
In Equation (1), the energy consumption \(E_{tx}\) required to transmit \(k\) bits over the distance \(d\) is represented. \(E_{dec}\) is the radio electron energy, and \(E_{amp}\) is the transmitter amplification energy. The amplification energy is divided into a \(E_{fs}\)free space propagation model and \(E_{mp}\) multipath propagation model according to the critical distance \(d_{0}\).  
\[\begin{split}& E_{tx}=E_{elec}(k)+E_{amp}(k,d),\\ &=\begin{cases}kE_{elec}+kE_{fs}d^{2},&d<d_{0}\\ kE_{elec}+kE_{mp}d^{4},&d\geq d_{0}\end{cases}\end{split} \tag{1}\]  
LEACH distinguishes between the free space propagation model and the multipath propagation model using the \(d_{0}\) critical distance defined in Equation (2).  
\[d_{0}=\sqrt{\frac{E_{fs}}{E_{mp}}} \tag{2}\]  
If the transmission distance is smaller than \(d_{0}\), a free space propagation model is employed, and if it is large, a multipath propagation model is utilized. In the free space propagation model, power is consumed in proportion to the square of the transmission distance \(d_{0}\), and equation d in the multipath propagation model, power is consumed in proportion to the four square of the transmission distance \(d_{0}\). Equation (3) represents the energy consumption when receiving \(k\) bits of data.  
Figure 3: Energy propagation model of low-energy adaptive clustering hierarchy (LEACH).  
\[E_{rx}=E_{elec}\times k \tag{3}\]
# Human Clustering Based on Graph Embedding and Space Functions of Trajectory Stay Points on Campus
## 3 Results
### Individual K-Means Clustering in 4-Hour Scenario  
Within the 4 h scenario, the human network size is smaller than that withing the 2 h scenario. The individuals were grouped into 18 clusters based on their feature vectors, of which there were also fewer than in the 2 h scenario. Similarly, six parameter combinations were selected, with the first three biased towards DFS and the last three towards BFS. The analysis of these combinations highlights how different biases and parameter settings influence the clustering results (Figure 9). The subfigures (a) to (f) in Figure 9 correspond to different combinations of \(p\) and \(q\), respectively. The clustering results shown in Figure 9 are plotted against the first two major components after dimension reduction of the original feature vectors based on PCA processing.  
Figure 10 shows a 3D visualization of the clustering results with parameter settings of \(p=0.25\) and \(q=2\), which generate the best overall cluster metrics (DBS and CHS).  
For the overall DBS and CHS cluster metrics, Figure 9 presents the best performance. Figure 10 shows a 3D visualization of the clustering results for this parameter scenario (\(p=1\), \(q=0.25\)). Similarly the corresponding result is plotted against the first three major components after PCA dimension reduction.  
Within the 4 h scenario, Cluster 16 has the largest number of individuals. Most of the individuals stay in dormitories, followed by classrooms and laboratories. This cluster covers the widest range of space functions compared to other clusters. On the other hand, Cluster 18 has the fewest individuals and the fewest space functions covered by their stay points. Figure 11 shows the percentage of individuals in different functional spaces within each cluster, as well as the number of individuals in each cluster.  
The nodes with the most edges, each having 1828 connections, are nodes 985, 1772, 1892, and 2527. Compared with the 2 h scenario, nodes 985, 1892, and 2527 have 2098 connections each, which is just below the nodes with the highest number of connections. Among these four nodes, node 985 is in Cluster 8, while the remaining three were assigned to Cluster 16, which contains the most nodes. Notably, node 1892 shows regular patterns  
Figure 8: Distribution of clustering results across space functions within the 2 h scenario.  
of stay in both the dormitory and laboratory over the 9-day period. Almost every half-day work shift is spent in the laboratory, with a return to the dormitory after 11 p.m., suggesting that this individual may be a graduate student. In contrast, nodes 1772 and 2527, while having fewer records in the laboratory compared to node 1892, show similar patterns, spending significant time in classrooms during the daytime and evening work hours. Node 985, however, demonstrates more diverse space function patterns compared to the other three.  
Figure 9: Individual K-means clustering in the 4 h scenario.
# Converging Telco-Grade Solutions 5G and beyond to Support Production in Industry 4.0
## 5 The Telco Cloud Enabled 5G Applications
### Lessons Learned  
Table 7 summarizes the findings of a group of studies on Telco Cloud technology, as it was discovered that Telco Cloud is the most recent major advancement in cloud technology and a promising technology because it helps reduce the high cost of CAPEX and OPEX. It offers network operators a scalable infrastructure as an open environment for developers, with the cloud serving as a direct link between the user and the applications, and it can be used in a variety of 5G and 6G applications. More emphasis should be placed on research in the field of telecommunications cloud computing. According to the review of related papers, Telco Cloud is a modern technology, where it was noted that the first research paper related to Telco Cloud was in 2011 and that technology is not thoroughly researched; future research should be studied from several different perspectives to get a more paramount view, such as a customer, regulator, or application provider view. Because of the importance of cloud economic efficiency for service providers, cost structure research should be considered. Another critical aspect to thoroughly investigate is Telco Cloud security.
## 4 Numerical Results  
In this section, we try to prove the profitability and the effectiveness of the proposed MRP system when considering remanufacturing parts. At first, we test an MRP model without taking the recovery of used/end-of-life products into account, which means that all raw materials (components of level 2) are ordered and new. Second, we interpret the results given as compared with our proposed model. Based on the calculation of the expected cost in [PERSON] et al. [24], we calculate \(EC[[T_{PR},X]]\) to minimize the expected total cost (details in the Appendix A). Then, we determine the disassembly-assembly plan during the system's work horizon. From this disassembly-assembly plan, we find the vector of the system solicitation levels for each period and type of disassembly or assembly process. We calculate the optimal preventive maintenance number \(N^{*}\).  
We consider a finished product with the two-level assembly structure described in Figure 5.  
The procurement time of the different components (used/end-of-life component, disassembled component, components at level 2 and assembled component at level 1) follow a uniform probability law.  
For the calculation of the expected average number of failures, we adopt for \(\lambda_{n}(t)\) a Weibull distribution with two parameters \((\alpha,\beta)\). This distribution is generally used to describe the system's failure rate in industrial areas.
## 4 Simulation Experiments  
To validate the proposed system parameter identification method based on the integration of BLS and PSO, a series of simulations were conducted to assess its modeling performance under vibratory operating conditions. The proposed algorithm and associated simulation framework were implemented using MATLAB R2023b Specifically, the PSO optimization algorithm was programmed using MATLAB script files, while the BLS network leveraged MATLAB's built-in linear algebra routines and optimization toolboxes to ensure efficient matrix computations. The simulations were executed on a personal computer equipped with an Intel Core i7 processor, 16 GB RAM, and Windows 11 operating system. Such an environment clearly demonstrates the practical feasibility and real-time potential of the proposed identification algorithm for robotic manipulator control.  
First, friction torque was simulated to evaluate the method's stability in the presence of structural vibrations induced by nonlinear disturbances. Then, the total joint torque was analyzed and compared to verify the overall accuracy of the identification approach. Finally, the total torque estimation error was used to demonstrate the feasibility of the proposed identification framework.
## 2 Study Area and Data Preparation
### Data and Pre-Processing
#### 2.2.1 HJ-1 CCD Data  
The HJ satellite constellation consists of two satellites (HJ-1-A and HJ-1-B), and was launched on 6 September 2008. HJ-1-A is equipped with a CCD camera and a hyperspectral imager (HSI). HJ-1-B is equipped with a CCD camera and an infrared camera (IRS). The HJ satellite constellation can image the entire Earth at two-day intervals. Table 2 lists the sensors' parameters for the HJ satellite constellation.  
Three HJ CCD datasets were downloaded from the China Centre for Resources Satellite Data and Application ([[http://www.cresda.com/n16/index.html](http://www.cresda.com/n16/index.html)]([http://www.cresda.com/n16/index.html](http://www.cresda.com/n16/index.html))). These data were atmospherically corrected using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) Atmospheric Correction Model in the ENVI 5.0 software. The three HJ CCD datasets were then georeferenced using a second-order polynomial warping approach based on the selection of 45 ground control points (GCPs) from a 1:10,000 topographic map by the nearest neighbour resampling method, with a position error of within 0.67 HJ CCD pixels.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Study Area**} & \multirow{2}{*}{**Acquisition Date**} & \multicolumn{2}{c}{**Path/Row**} \\ \cline{3-4}  & & **HJ-1 CCD** & **GF-1 WFV** \\ \hline \multirow{3}{*}{Kuche} & 03/10/2013 & 41/64 & 58/173 \\  & 07/10/2013 & 42/64 & 56/174 \\  & 15/10/2013 & 45/64 & 58/173 \\ \hline \multirow{3}{*}{Luntai} & 03/10/2013 & 41/64 & 58/167 \\  & 07/10/2013 & 42/64 & 56/168 \\ \cline{1-1}  & 15/10/2013 & 45/64 & 56/168 \\ \hline \end{tabular}
\end{table}
Table 1: Images used in this research.
# Cross-View Outdoor Localization in Augmented Reality by Fusing Map and Satellite Data
## 3 Methods
### Data Overview  
The input data of the model were the KITTI dataset [32], as well as corresponding satellite and OSM data. The satellite data represent a 250 m by 250 m area around the ground-truth position and have a resolution of 1280 pixels by 1280 pixels, as extracted by [PERSON] et al. [13]. Furthermore, we extracted OSM data from the exact same position and area by utilizing the overpass API and mapping each node, line, and area to a specific semantic class.  
Before the input data were put into the encoders, translation and rotation noise were added to the image. The center 100 m by 100 m area, corresponding to an image of 512 by 512 pixels, was cut out. This area was made larger than the maximum translation noise to ensure the model worked properly even at the border of the translation noise. For the OSM data, the same preprocessing steps were taken to ensure the satellite and OSM data were exactly aligned.  
OpenStreetMap Extraction  
Following the work by [PERSON] et al. [12], we, instead of directly taking a screenshot-like image of OpenStreetMap, extracted its node, line, and area (closed-loop line objects) objects. Based on the tags included in these objects, we manually constructed rules to map useful objects to different classes. These classes included areas such as buildings or vegetation; the lines included different types of roads, and nodes included many different single-place positions including trees, doors, and signs. Although later, we show that the main contributing modality to the improvement seen when using the OSM data is the road data, we used all 3 modalities as input to the model. This was carried out by setting each of the 3 channels to a certain value based on the geometry of the input OSM objects and for each pixel of the OSM input (Figure 2). The resolution was 512 pixels by 512 pixels, which corresponded to about \(\Delta\)50 cm per pixel, with lines and nodes having a width of 4 pixels.
## 7 Conclusions  
* Under the bending condition within 90\({}^{\circ}\), the spring-back angle of the bimetallic tube is increased with the bending angle. This trend is the same as single-layered tubes. It indicates that the increase of plastic deformation degree with the increasing of bending angle does not matter for the spring-back of bimetallic tubes. The spring-back angle does not decrease with the increasing of the plastic deformation degree, and the bending angle is still the main effective factor of bimetallic tubes' spring-back. If the neutral layer shifting is neglected, the theoretical spring-back angles of bimetallic tubes will increase linearly with the bending angle. The theoretical spring-back angles considering the NLSE model are smaller and closer to experimental results.
* The section strain of bimetallic tubes is continuous at the contact between layers. Therefore, the neutral layer shifting of bimetallic tubes can be described by the position of the coincident strain neutral layer. While the outer material is stainless steel and the inner material is carbon steel, the greatest effective factor of the bimetallic tubes' neutral layer shifting is the layered thickness ratio. The effective secondary factor is the interlaminar friction coefficient. Generally, the larger the layered thickness ratio and the smaller the interlaminar friction coefficient, the larger the neutral layer shifting.
* When the interlaminar friction coefficient is below 0.3, the spring-back angle of bimetallic tube increase with the increase of the interlaminar friction coefficient. For those normal mechanical bonding bimetallic tubes, their interlaminar friction coefficients are less than 0.3 generally. In order to reduce the spring-back and improve the forming accuracy, suitable materials should be selected, and appropriate surface treatments can be done on the interface to reduce the friction coefficient.  
Figure 18: Effect of interlaminar friction coefficient on the spring-back angle.  
**Author Contributions:** [PERSON] and [PERSON] designed the simulations and experiments. [PERSON] proposed the spring-back method of bimetallic tubes and established the composite neutral layer model. [PERSON] and [PERSON] performed the numerical simulations and the regression analysis. [PERSON] performed the CNC tube bending experiments. [PERSON], [PERSON], [PERSON] and [PERSON] wrote the paper. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work has been funded by the National Natural Science Foundation of China (51905476) and the Key R&D Program of Zhejiang Province (2019C05 SAB51751).  
**Conflicts of Interest:** The authors declare no conflict of interest.