esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methodology
### Experimental Setup of a 15 \(\times\) 10 TEG Panel  
As illustrated in Figure 3, the series connections of 10 TEG modules are linked in parallel to produce 15 lines of TEG modules. Experimental work was done on a July day in Iraq. The aim of the proposed system is to generate and maximize output power from the thermoelectric modules (TEGs).  
Figure 2: A sustainable energy system using TEG panels and solar water heaters.  
The processing system consists of three Sections. Section 1 represents the hot surface of the system, where 30 pieces of an aluminum heating block are used. The dimensions of each piece are (40 \(\times\) 240 \(\times\) 10 mm) [26], with an identical cooling block on the opposite side of the panel. This was built as shown in Figure 4. Then the hot water coming from a solar water heater passes these pieces in parallel flow, with a constant velocity (0.8 m/s), to ensure a constant flow rate of water evenly entering the heating block, as shown in Figure 5. Then, this water goes into the house for domestic purposes after it heats the upper surface of the panel.  
Figure 4: (**a**) Schematic diagram of CLYXGS aluminum water block, and (**b**) individual pieces comprising an aluminum water block in the as-built system.  
Figure 3: (**a**) Single line of layer panel, and (**b**) Schematic diagram of a 15 \(\times\) 10 TEG panel.  
Section 2 represents the cold surface of the TEG panel, which is at the bottom of the panel. Here, the technology is the same as for the upper surface, which is an aluminum water-cooling block. However, here, normal tap water enters the cooling block, at a temperature that ranges from 25 to 34 \({}^{\circ}\)C. The flow of this water will dissipate the heat coming from the hot surface of the plate to the outside of the system. Then, this water is returned to the solar water heater. Here, an important new idea of this work emerges. This is to heat the water in two stages. The first stage is when it passes through a (TEG panel). The second time is in a solar water heater.  
Section 3 represents the many TEG modules, as shown in Figure 6. They are connected electrically in series and parallel to obtain the required voltage. Thermal paste is used to improve thermal contact between the module components and allow for a uniform temperature distribution at each side of the TEG module, as shown in Figure 7. Additionally, insulators are here between the wires and the electrical connections passing between the TEG pieces to ensure that no electrical short-circuit occurs.  
Figure 5: Pipes and integration of the system.  
Figure 6: (**a**) Schematic diagram, and (**b**) image of single TEG module implementation.  
Then, acrylic plates are installed on the two surfaces of the panel in a sandwich configuration so that all the connecting parts are installed correctly and evenly.  
The significant difference between this system and solar panels is that the former can be used continuously during the day and night hours. This is unlike solar systems that operate during daylight hours because they depend only on solar radiation.
# Sensitivity Analysis for Gaussian-Associated Features
## 5 Sensitivity Matrix Associated with a Least-Squares Sphere Fit to Data  
If a sphere is parametrized by \(\mathbf{a}=(x_{0},y_{0},z_{0},r_{0})^{\top}\) specifying its center coordinates \(\mathbf{x}_{0}=(x_{0},y_{0},z_{0})^{\top}\) and radius \(r_{0}\), the signed distance \(d(\mathbf{x}_{i},\mathbf{a})\) from a data point \(\mathbf{x}_{i}\) to the sphere given by \(\mathbf{a}\) is \(d(\mathbf{x}_{i},\mathbf{a})=r_{i}-r_{0}\), where \(r_{i}^{2}=(x_{i}-x_{0})^{2}+(y_{i}-y_{0})^{2}+(z_{i}-z_{0})^{2}\).  
The \(i\)th row Jacobian matrix \(J\) of partial derivatives of \(d(\mathbf{x}_{i},\mathbf{a})\) with respect to \(\mathbf{a}^{\top}\) is given by  
\[J(i,:)=-\frac{1}{r_{i}}[x_{i}-x_{0},\;y_{i}-y_{0},\;z_{i}-z_{0},\;r_{i}]=-[ \mathbf{n}_{i}^{\top},\;1],\quad\mathbf{n}_{i}=(\mathbf{x}_{i}-\mathbf{x}_{0})/r_{i}\]  
The \(4\times 4\) matrix \(H=J^{\top}J\) is given by  
\[H=\left[\begin{array}{cc}\sum_{i}\mathbf{n}_{i}\mathbf{n}_{i}^{\top}&\sum_{i}\mathbf{n} _{i}\\ \sum_{i}\mathbf{n}_{i}^{\top}&m\end{array}\right].\]  
For points \(\mathbf{x}_{i}\) approximately uniformly distributed around the sphere, \(H\) is approximately diagonal with  
\[H\approx m\left[\begin{array}{cccc}1/3&0&0&0\\ 0&1/3&0&0\\ 0&0&1/3&0\\ 0&0&0&1\end{array}\right],\quad H^{-1}\approx\frac{1}{m}\left[\begin{array}{ cccc}3&0&0&0\\ 0&3&0&0\\ 0&0&3&0\\ 0&0&0&1\end{array}\right].\]  
Thus, if \(V_{X}=\sigma_{R}^{2}I\) then, from (10), \(V_{A}=\sigma_{R}^{2}H^{-1}\), showing that the variances associated with \(\mathbf{a}\) vary with \(1/m\) and that the variances associated with \(\mathbf{x}_{0}\) are three times that associated with \(r_{0}\), for a uniform distribution of points around the complete sphere.  
The sensitivity matrix of \(\mathbf{a}\) with respect to \(\mathbf{x}_{1:m}\) is given by \(G_{A|X}=H^{-1}J^{\top}N^{\top}\) where  
\[J^{\top}N^{\top}=-\left[\begin{array}{ccc}\mathbf{n}_{1}\mathbf{n}_{1}^{\top}&\mathbf{n}_{ 2}\mathbf{n}_{1}^{\top}&\cdots&\mathbf{n}_{m}\mathbf{n}_{m}^{\top}\\ \mathbf{n}_{1}^{\top}&\mathbf{n}_{2}^{\top}&\cdots&\mathbf{n}_{m}^{\top}\end{array}\right]. \tag{17}\]  
Perturbing \(\mathbf{x}_{i}\) by \(\Delta\mathbf{x}_{i}\) causes \(\mathbf{x}_{0}\) and \(r_{0}\) to be perturbed by an amount that depends on extend to which \(\Delta\mathbf{x}_{i}\) is aligned with the normal \(\mathbf{n}_{i}\).
# A CNN Regression Approach to Mobile Robot Localization Using Omnidirectional Images
## 5 Experiments
### Results of the Fine Localization Stage
#### 5.2.1 Training Process of the Regression CNNs  
In the present subsection, instead of a classification task, we focus on addressing a regression task. That is, once the CNN is properly trained for room retrieval purposes, the next step consists in carrying out the transfer learning and re-training of the network with the aim of estimating the position of the robot in the ground plane (i.e., the coordinates \(X\), \(Y\)). In this sense, since the network was designed for classification, it is necessary to carry out some modifications in the architecture. Moreover, a new network is developed for each room and, in some cases, several networks for different parts of a single room, with the aim of improving the performance, as explained later in this section. The objective output of the network is not a one-hot vector, but two values: coordinate \(X\) and coordinate \(Y\). Hence, the labeling should be adapted for the new training. The coordinates will be output through two perceptrons with the aim of fitting best each one to its loss function. Additionally, the labels will be normalized, since the different ranges of the coordinates could affect the weights of the final error function. The labels for training are the \(X\) and \(Y\) coordinates, obtained from the ground truth of the database. The normalization procedure is performed independently for each coordinate and for each room. These values are ranged between 0 and 1.  
Concerning the transfer learning, as explained in previous sections, this technique is useful to save training time, since the most of the layers are already tuned to address a similar problem. In this case, the previous CNN was trained with the aim of obtaining robust holistic descriptors from the omnidirectional images and then use that information to carry out a classification (room retrieval) task, as shown in Section 5.1.2. In this new task, the objective consists in using the feature information to estimate the coordinates of the capture point of the test image. Therefore, the featuring part of the CNN can be kept and  
Figure 3: Trajectory maps. (**a**) The correct (blue) and the wrong (red) retrievals tackled by the developed CNN. (**b**) The room retrievals done along the trajectory. Each room has been assigned a different color (e.g., the red color indicates the images that have been retrieved by the CNN as belonging to the printer area). Apart from the expected mistakes in the transition areas, there is some confusion between the printer area (red color) and the office (green).  
only the classification part is modified. Figure 4 shows the the architecture that we use to perform the regression task. It is obtained after applying the proposed changes to the CNN developed for room retrieval (transfer learning). In this figure, we show with green color the layers that are kept from the classification CNN (both these layers and their parameters are kept), and with blue color the layers that are changed from the original classification CNN to obtain the regression CNN.
# Development and Application of Unesaled borehole Leakage Detection Device Based on Flow Method
## 2 Research and Development of Unsealed Borehole Leakage Detection Technology Based on Flow Method
### Selection of the Main Components of the Air Leakage Detection Device
#### 2.2.1 Expandable Capsule Pressure-Bearing Test  
As the main component of the air leakage detection device, the pressure-bearing capacity of the expandable capsule determines the usefulness and reliability of the device. The main material of the expandable capsule selected in this device is polyurethane resin; it has the characteristics of a light weight, corrosion resistance, and strong plasticity, which make the expandable capsule suitable for boreholes of any aperture and angle, and also improve the pressure-bearing capacity and service life of the capsule. Meanwhile, in order to avoid rupture of the expandable capsule during the operation of the device, it is necessary to simulate and investigate the maximum pressure that the expandable capsule can withstand in the borehole. The specific steps are as follows. After the expandable capsule is sent into the \(\Phi\)94 mm PVC pipe (consistent with the diameter of the commonly downhole borehole), slowly inflate the expandable capsule to observe the final rupture pressure of the capsule. In order to prevent the contingency of the structure, repeat the steps three times, and the rupture pressures of the selected expandable capsule are tested to be 0.40 MPa, 0.43 MPa, and 0.45 MPa, respectively (Figure 2). According to the test results, the pressure in the expandable capsule should be controlled below 0.4 MPa.  
Figure 1: Air leakage detection device. 1—Press-air joint; 2—Pressure limiting valve; 3—Pressure gauge; 4—Rubber plug; 5—Purple copper pipe; 6—Extraction system valve; 7—Extraction system connector; 8—Wet flow gauge; 9—Connecting rod; 10—Expandable capsule; 11—Borehole; 12—Capsular bag 1; 13—Capsular bag 2; 14—Sealing material; 15—Extraction pipe; 16—Measurement point 1; 17—Measurement point 2 Q\({}_{1}\)—Pure seam gas flow at measurement point 1 Q\({}_{1}\)—Pure seam gas flow at measurement point 2 Q\({}_{2}\)—Air leakage around the borehole.
# Sensor Fusion of GNSS and IMU Data for Robust Localization via Smoothed Error State Kalman Filter
## 2 GNSS and IMU Integrated Filter Design
### Error State Kalman filter
#### 2.2.1 Continuous Time Kinetic Model  
The nominal state kinematics refers to the system modelled with system noise or external perturbations in the form of  
\[\begin{array}{l}\dot{\mathbf{p}}_{t}=\mathbf{v}_{t}\\ \dot{\mathbf{v}}_{t}=\mathbf{R}_{t}(\mathbf{a}_{m}-\mathbf{a}_{bt}-\mathbf{a}_{n})+\mathbf{g}_{t}\\ \dot{\mathbf{q}}_{t}=\frac{1}{2}\mathbf{q}_{t}\otimes(\mathbf{\omega}_{m}-\mathbf{\omega}_{bt} -\mathbf{\omega}_{n}).\\ \dot{\mathbf{a}}_{bt}=\mathbf{a}_{\omega}\\ \dot{\mathbf{\omega}}_{bt}=\mathbf{\omega}_{\omega}\end{array} \tag{9}\]  
The acceleration and angular velocity measurements are represented by \(\mathbf{a}_{m}\) and \(\mathbf{\omega}_{m}\), respectively, while \(\mathbf{a}_{n}\) and \(\mathbf{\omega}_{n}\) represent the corresponding noise vectors, and \(\mathbf{a}_{\omega}\) and \(\mathbf{\omega}_{\omega}\) represent the bias vectors for acceleration and angular velocity, respectively. The equations driving the system dynamics in the nominal state are as follows, where the nominal state refers to the modeled system without any noise or perturbations:  
\[\begin{array}{l}\dot{\mathbf{p}}=\mathbf{v}\\ \dot{\mathbf{v}}=\mathbf{R}(\mathbf{a}_{m}-\mathbf{a}_{b})+\mathbf{g}\\ \dot{\mathbf{q}}=\frac{1}{2}\mathbf{q}\otimes(\mathbf{\omega}_{m}-\mathbf{\omega}_{b}).\\ \dot{\mathbf{a}}_{b}=0\\ \dot{\mathbf{\omega}}_{b}=0\end{array} \tag{10}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **True State** & **Nominal State** & **Error States** \\ \hline All states & \(x_{t}\) & \(x\) & \(\delta x\) \\ Location & \(p_{t}\) & \(p\) & \(\delta p\) \\ Speed & \(v_{t}\) & \(v\) & \(\delta v\) \\ Quaternion & \(q_{t}\) & \(q\) & \(\delta q\) \\ Rotation matrix & \(R_{t}\) & \(R\) & \(\delta R\) \\ Angular vectors & \(\theta_{t}\) & \(\theta\) & \(\delta\theta\) \\ Acceleration bias & \(a_{bt}\) & \(a_{b}\) & \(\delta a_{b}\) \\ Angular velocity bias & \(\mathbf{\omega}_{bt}\) & \(\mathbf{\omega}_{b}\) & \(\delta\mathbf{\omega}_{b}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The definition of variable symbols.  
Solving for the error state and simplifying all second\(-\)order infinitesimals. The kinetic equation for the error state can be obtained from Equations (9) and (10).  
\[\begin{array}{l}\textit{ffi}\dot{\mathbf{p}}=\textit{ffi}\mathbf{v}\\ \textit{ffi}\dot{\mathbf{v}}=\mathbf{R}(\mathbf{a}_{m}-\mathbf{a}_{b})\textit{ffi}\dot{\mathbf{ \theta}}-\textit{R}\textit{ffi}\mathbf{a}_{b}-\textit{R}\mathbf{a}_{n}\\ \textit{ffi}\dot{\mathbf{\theta}}=-(\mathbf{\omega}_{m}-\mathbf{\omega}_{b})\textit{ffi}^{ \top}-\textit{ffi}\mathbf{\omega}_{b}-\mathbf{\omega}_{n}.\\ \textit{ffi}\dot{\mathbf{a}}_{b}=\mathbf{a}_{\omega}\\ \textit{ffi}\dot{\mathbf{\omega}}_{b}=\mathbf{\omega}_{\omega}\end{array} \tag{11}\]
## 3 Results
### Computational Complexity  
Model training, testing, and application were performed on a desktop personal computer: Intel Core i7-8700 CPU 6 \(\times\) 3.2 GHz with 16 Gb RAM. The model training phase requires the most computing time compared to the testing and validation phases. The LM and LMI models were trained in 1-2 min, the NN model with 1000 epochs was trained in 2-3 min. The GPRexp/GPR2 models required about 120-240 min for the training model for a single target variable. After the optimal model parameters were identified, the computation of the continuous time series for a target variable at testing or application phase occur for 10-20 s. A 5-fold cross-validation was applied during model training to protect against overfitting. An increase in the number of iterations for cross-validation results in a rise in the computing time.
# Cluster Head Selection Method for Edge Computing WSN Based on Improved Sparrow Search Algorithm
## 6 Conclusions  
WSN plays an essential role in the data collection of the IOT. To reduce the energy consumption of sensor nodes and prolong the network's life, this paper constructs a two-layer protocol structure system model of WSN. Then, we design an optimization problem that balances distance and energy consumption based on the proposed model. Finally, we optimize the cluster head selection through the improved sparrow search algorithm, including chaotic initialization, sine-cosine mutation, and adaptive population adjustment strategy. This effectively reduces node energy consumption, improves network performance, and prolongs the network life cycle. Specifically, compared with WOA-RCP by the ICSSA-CHS algorithm, we reduce energy consumption by 26.8%, improve network packet transmission, and prolong the network lifetime. In the future, we will further study issues such as node data collection and fusion compression under the double-layer WSN structure to improve network efficiency.  
Conceptualization, S.Q. and A.L.; methodology, S.Q. and J.Z.; software, A.L.; validation, X.Z.; formal analysis, J.Z.; investigation, X.Z.; resources, S.Q.; data curation, A.L.; writing--original draft preparation, A.L.; writing--review and editing, J.Z.; visualization, F.C.; supervision, Y.W.; project administration, Y.W. and F.C.; funding acquisition, S.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by a fund project of the Equipment Development Department of the Central Military Commission grant number [No. 6140002010101, No. 6140001030111] and The APC was funded by Dalian University.  
Not applicable.  
The authors declare no conflicts of interest.  
Figure 7: The number of edge-end data packets received by different algorithms after the number of running rounds increases.
## 4 Results and Discussion
### Apparent Friction Energy Density
In this study, [PERSON]'s wear model was experimentally used to investigate the relationship between friction and wear in a state of mixed friction, where only the wear on the steel plate was taken into consideration. In this sense, the apparent friction energy densities (\(e_{R}^{*}\)), which describe the amount of friction work required for the separation of the friction-exposed material, and the wear intensities (\(I_{h}\)), which are expressed by the wear depth (\(I_{v}\)) divided by the wear path (i.e., friction distance, \(s_{R}\)), were determined according to Equation (5). As in Figure 7, there was an inverse relationship between the \(e_{R}^{*}\) and \(I_{h}\), indicating that the greases that exhibited the highest friction energy densities caused the lowest wear intensities on the steel plate. For example, reference grease sample R1 showed the highest value of \(e_{R}^{*}\) and the lowest intensity of wear on the steel plate. Figure 5: Friction coefficients versus wear volume on a steel plate for all model greases (wear path \(s_{R}=387\) m). Figure 6: Profilometric pictures obtained from the steel plates as a function of the lubricant grease (window sizes of 0.599 mm \(\times\) 1.111 mm, 0.461 mm \(\times\) 1.111 mm, 0.680 mm \(\times\) 1.111 mm, and 0.478 mm \(\times\) 1.111 mm). Figure 7: Apparent friction energy density (\(e_{B}^{*}\)) vs. wear intensity (\(l_{h}\)) for all the model greases. For further comprehension, the values of the three associated tribological parameters \(e_{R}^{*}\), \(I_{h}\), and \(\tau_{R}\) were plotted on a graph depicting the basic energy equation of wear for the model greases (Figure 8). As observed, all the model greases were in the range of 1, indicating that all the lubricating grease experiments in the tribometer were conducted in the quasi-fluid-friction/mixed-friction state, thereby leading to deformation on the steel plates. Finally, the results of the measured tribological parameters, i.e., \(e_{R}^{*}\), \(I_{h}\), and \(\tau_{R}\), showed that all the model grease samples applied some friction pairings, such as the toothed wheel or rolling bearing, according to a large number of studies looking at different machine elements [22].
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 2 Health Monitoring Techniques in Pipeline Industry
### In Pipeline Industry
Several methods could be implemented to reduce the loss that occurs as a result of hydrogen cracking such as (1) minimum stress on joints, (2) proper preheating and post-weld heating, (3) appropriate selection of filler material, and (4) careful handle and storage of filler metals [24]. Another prominent issue in the pipeline industry is welding. Friction stir welding is a solid-state joining process invented at the welding institute in the United Kingdom. The friction stir welding was found to be efficient for joining hard to weld metals and joining plates of different thicknesses or different materials. The typical way to connect two natural pipes is shielded metal arc welding. When welding, we first need to tack the two pipes together to keep them in place. Then, we apply the method of butt welding. At present, to produce a flame hot enough to melt steel in the energy industry, this process involves the use of two gases: acetylene and oxygen. Through applying this flame to the steel being welded, a permanent weld is made; if required, an additional filler rod can also be attached to the welding area. Oxygen acetylene welding does not require the formation of electric welding. There are several disadvantages of this method, such as a visible hole being created in the welding plates. It is also less flexible as compared to the arc welding process. FSW cannot weld non-forgeable materials; also, filler joints cannot be created by it. Gas welding, on the other hand, showcases some advantages. This technique has the advantage of balancing the temperature at low temperatures. In the case of I-joint, it could weld the metal of thickness up to 6 mm. Generally, the pipes and tubes are welded in confined spaces, so gas welding is preferable here over other methods, as it requires equipment that is not bulky in nature. Moreover, this equipment does not require any electric supply. The light generated by its flame could be used to spot joints before welding begins. A major disadvantage of gas metal arc welding is the creation of a spatter, which are droplets of molten matter near the welding arc. It occurs when the welding currents are extremely high or in the case when gas shielding is not sufficient. For avoiding spatter, the welding current should be reduced, and the welder should correct the polarity. Moreover, a check over shielding gas type and flow rate should also be done [25]. Other common problems associated with gas welding include porosity, slag inclusions, incomplete penetration and fusion, incorrect wire delivery, deformation, cracks, and undercut.
# Integrated Digital-Twin-Based Decision Support System for
## 4 Developing a DT Platform
### Physical Layer  
The physical layer serves as the foundation for real-time visibility, traceability, and interoperability in managing RMBs [43]. It includes various physical components such as modular units, resources, and human actors, along with advanced communication tools. In this study, modular units are treated as the core assets, each uniquely tracked throughout its lifecycle. Unlike permanent MC, RMBs require a system capable of managing their relocation and reuse, necessitating advanced physical layer technologies.  
This layer is equipped with IoT-enabled sensors, such as Radio Frequency Identification (RFID) tags and GPS devices, to continuously collect and transmit data on the status, location, and condition of the modular units. These data streams ensure the continuous monitoring of the modular units across their lifecycle stages, from fabrication and transportation to on-site assembly and reuse. The physical layer's capability to provide granular, real-time data for the DT system enables the robust management of resources and operations [44].
# Enabling Trustworthy Multicast Wireless Services through D2D Communications in 5G Networks
## 2 Related Work
describe in [10] a secure data sharing strategy able to guarantee D2D privacy and security in LTE-A networks. The basic idea is to encrypt data transmitted in the D2D link using a symmetric encryption algorithm and generating the private key, for data encryption and decryption, following the Diffie-Hellman key exchange protocol. The strength of this strategy lies in the intervention of the eNodeB that represents a trusted third party and protects against malicious behaviors such as the man-in-the-middle attack. In [9], security solutions proposed improving D2D in 5G networks are analyzed. In addition to reporting D2D security requirements, threats and solutions, this work hints at the role that the social relationships could play in improving the D2D security. This is an interesting starting point for future research aimed at exploiting concepts, such as social trust, to evaluate how much a network node can be reliable. In this work, we aim to add the security dimension to the D2D-SF solution we proposed in [5] in order to make it suitable to 5G networks and cope with typical D2D vulnerability attacks.