esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Description and Numerical Analysis of Failure Characteristics of Test Block
### Rupture and Numerical Analysis of Test Block under Different Hole Depths  
Figure 9 shows the failure diagram of test blocks with different drilling depths. The results show that the failure process of the test block with different hole depths is basically the same as that of the hole diameter. As the axial stress continues to increase, the microcracks generated around the hole increase, which leads to the acceleration of the main control crack propagation, and the test block is quickly and completely destroyed. From the failure state of the test block in Figure 9, the number of micro-cracks in the test block with a hole depth of 3 mm and 6 mm (such as Figure 9a,b) is relatively large, the main control crack propagation is not clear, the hole wall is retained intact, and the surface shedding is not obvious. The main control crack propagation, hole wall collapse, and surface shedding on the surface of the test block with depths of 9 mm, 12 mm, and 15 mm (Figure 9c-e) become more and more obvious, and the degree of damage also increases. Combined with the FLAC3D simulation in Figures 8-11, it can be seen that the deeper the drilling hole, the larger the radius of the plastic zone around the hole, and the smaller the vertical stress value at both ends of the top and bottom of the hole, but the vertical stress value does not decrease significantly. It can be seen from Figure 8b that as the hole depth increases, the stress peak above the hole shifts deeper.  
Figure 8: (**a**) is the vertical stress change curve of the monitoring points of different borehole diameter samples from shallow to deep, and (**b**) is the vertical stress change curve of the monitoring points of different borehole depth samples from shallow to deep.  
Figure 9: (**a**) is the drilling depth of a 3 cm sample failure diagram, (**b**) is the drilling depth of a 6 cm sample failure diagram, (**c**) is the drilling depth of a 9 cm sample failure diagram, (**d**) is the drilling depth of a 12 cm sample failure diagram, and (**e**) is the drilling depth of a 15 cm sample failure diagram.  
According to the experimental and numerical analysis results of different pore diameters and depths, it can be seen that the larger the borehole diameter, the smaller the peak strength and the larger the plastic zone range, which leads to more obvious main crack propagation. The larger the aperture is, the smaller the vertical stress value at both ends of the top and bottom of the hole is, but there is still a concentration of peak stress not far from the borehole, which indicates that the borehole pressure relief can only unload the stress around the borehole. With the increase in drilling depth, the vertical stress values at both ends of the top and bottom of the hole decrease slightly, and the strength decreases not obviously, indicating that the pressure relief efficiency of the hole depth is smaller than that of the hole diameter, and the larger the hole depth, the more the peak stress is transferred to the deep part.
## 3 Experimental Methods and Numerical Verification
### Fiber Optical Sensors--Fiber Bragg Gratings  
Fiber Bragg gratings are the most common optical fiber sensor used to measure strain and temperature changes. Thanks to their small size, they are common point-wise transducers that can also be easily integrated in a composite material (without any negative influence on its structure). The length of a single grating is in the range of 5-10 mm, and the external diameter is about 200-250 \(\upmu\)m. Because of its excellent multiplexing capabilities, it is possible to combine many FBGs to form a network of sensors [31].  
An FBG sensor has a specific structure formed in the core of an optical fiber, characterized by periodical changes in the refractive index value [37]. The measured value (deformation, temperature) is directly proportional to the displacement of the so-called Bragg wave (\(\lambda_{\text{B}}\)), which depends on a change in the grating period (\(\Lambda\)) resulting from the changes in fiber size (compressing, stretching) and thermal effects (thermal expansion of the material and the thermo-optic effect on the refractive index) [38].
## 3 Results
### Analysis of HS-GC-IMS Test Results
#### 3.2.2 Comparison of VOCs Fingerprints in Silage Based on HS-GC-IMS  
The VOC fingerprint spectra for the silage samples were generated using the Gallery Plot plugin, as shown in Figure 4. The development of GC-IMS fingerprint chromatograms provides a clearer understanding of the differences in VOCs among the samples. Table 3 presents detailed information on the identified compounds. In the complete fingerprint spectrum, each row represents the concentration value detected in a single sample, while each column corresponds to the same VOC across different samples. The content of each VOC is visualized, with the brightness of each square indicating the concentration level. Brighter squares represent higher concentrations of VOCs, while darker squares indicate lower concentrations. By comparing the differences between the fingerprints, a total of 32 VOCs were qualitatively identified, including 13 esters, 7 alcohols, 5 aldehydes, and 1 heterocyclic compound. The HS-GC-IMS results indicate that both samples contain distinct characteristic VOCs, with the abnormal sample exhibiting significantly more specific compounds than the normal one. This difference may account for the variation in their odors and the presence of a bitter almond taste. In the area labeled with a red frame, substances such as 3-methyl-1-pentanol, ethyl isovalerate, 1-pentane- 3-ol, benzaldehyde, 1-hexanol, hexyl acetate, 2-pentyl furan, and ethyl acetate were found in higher concentrations in the normal group samples. By contrast, the abnormal sample features elevated concentrations of compounds such as 2-pentyl-3-hexenal, 3-hexenoic acid ethyl ester, methyl salicate, 2-propenyl-2-pentenoate, octanoic acid ethyl ester, 3-pentanone, 1-butanol, 1-hexanol, ethyl propionate, and 1-propanol, as indicated in the yellow frame.  
According to the PCA analysis chart of VOCs detected by GC-IMS, the cumulative variance contribution of PCA1 (98.2%) and PCA2 (1%) was 99.2%, effectively distinguishing the samples between different groups. As shown in Figure 5, the GC-IMS results align with the E-nose analysis results, and the considerable distance between the two sample groups indicates that the VOCs in the two types of silage are significantly different.
## 3 Results
### Absorption and Distribution  
Table 3 presents absorption and distribution properties for the compounds. All compounds show favorable Caco-2 permeability (> -5.15) and high passive MDCK permeability, except L4, which has intermediate permeability. Compounds L2, L3, and L5 effectively inhibit plasma glycoprotein (PGP), with L4 showing moderate inhibition. All lead compounds show low human intestinal absorption (HIA < 30%, values < 0.1), indicating potential for poor intestinal absorption. Only L3 and L4 display favorable oral bioavailability at 20%, while other compounds exceed a permeability threshold of 0.7. High plasma protein binding (PPB > 95%) suggests a limited therapeutic index. Volume of distribution (VD) values (0.196-4.954 L/kg) indicate appropriate distribution, but only L3 has moderate blood-brain barrier (BBB) penetration, potentially affecting the CNS, while other compounds show minimal BBB penetration (<0.3).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Comp.** & **L1** & **L2** & **L3** & **L4** & **L5** & **MEY** \\ \hline MW & 360.150 & 350.160 & 308.080 & 348.070 & 254.110 & 495.070 \\ \hline Volume & 376.842 & 364.819 & 281.648 & 293.697 & 263.446 & 459.902 \\ \hline n-Rot & 5 & 3 & 3 & 3 & 2 & 5 \\ \hline nRing & 4 & 4 & 3 & 3 & 3 & 5 \\ \hline nHet & 5 & 5 & 7 & 9 & 4 & 10 \\ \hline Flexibility & 0.217 & 0.125 & 0.176 & 0.188 & 0.118 & 0.167 \\ \hline TPSA & 78.250 & 78.250 & 58.020 & 35.500 & 58.020 & 124.420 \\ \hline logS & \(-3.958\) & \(-3.931\) & \(-3.971\) & \(-5.746\) & \(-3.045\) & \(-4.776\) \\ \hline logP & 4.422 & 5.019 & 3.464 & 4.655 & 2.836 & 4.562 \\ \hline logD & 3.937 & 4.146 & 3.674 & 4.138 & 2.963 & 3.492 \\ \hline \end{tabular}
\end{table}
Table 2: Physicochemical properties of the top lead compounds.
## 4 Application of ISSA in Node Location in HWSNs  
### Node Localization Problem in HWSNs  
The node localization of HWSNs can be divided into two stages: distance estimation and coordinate calculation. Once the distances from each anchor point to the unknown node are obtained in the distance estimation phase, the coordinate calculation of the unknown node can be transformed into an optimization problem, as shown in Equation (9). The dimension of this optimization problem is 2, and the theoretical optimal value is 0. Therefore, the problem can be solved by meta-heuristic algorithms.  
\[F(x)=\sum_{j=1}^{Na}\left|\sqrt{\left(x_{1}-a_{j}\right)^{2}+\left(x_{2}-b_{j} \right)^{2}}-d_{j}\right| \tag{9}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**ISSA and PSO** &
\begin{tabular}{c} **ISSA and** \\ **Gold-SA** \\ \end{tabular} & **ISSA and HHO** & **ISSA and GTO** & **ISSA and SSA** \\ \hline
14/1/4/4 & 10/1/8/4 & 13/1/7/2 & 6/1/13/3 & 13/0/10/0 \\ \hline a/b/c/d a, b, c denote the number of benchmark functions whose ISSA performance is better than, weaker than, or equal to the comparison algorithm, respectively. d denotes the number of benchmark functions that cannot be judged in terms of statistical significance. & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical results of the performance advantages and disadvantages between ISSA and each algorithm.  
where \(Na\) indicates the number of anchor nodes, \((a_{j},b_{j})\) represents the coordinates of the \(j\)-th unknown node, and \(d_{j}\) is the distance from the unknown node to the \(j\)-th anchor node.
###### Abstract  
In current research, fully supervised Deep Learning (DL) techniques are employed to train a segmentation network to be applied to point clouds of buildings. However, training such networks requires large amounts of fine-labeled buildings' point-cloud data, presenting a major challenge in practice because they are difficult to obtain. Consequently, the application of fully supervised DL for semantic segmentation of buildings' point clouds at LoD3 level is severely limited. In order to reduce the number of required annotated labels, we proposed a novel label-efficient DL network that obtains per-point semantic labels of LoD3 buildings' point clouds with limited supervision, named 3 DLEB-Net. In general, it consists of two steps. The first step (Autoencoder, AE) is composed of a Dynamic Graph Convolutional Neural Network (DGCNN) encoder and a folding-based decoder. It is designed to extract discriminative global and local features from input point clouds by faithfully reconstructing them without any label. The second step is the semantic segmentation network. By supplying a small amount of task-specific supervision, a segmentation network is proposed for semantically segmenting the encoded features acquired from the pre-trained AE. Experimentally, we evaluated our approach based on the Architectural Cultural Heritage (ArCH) dataset. Compared to the fully supervised DL methods, we found that our model achieved state-of-the-art results on the unseen scenes, with only 10% of labeled training data from fully supervised methods as input. Moreover, we conducted a series of ablation studies to show the effectiveness of the design choices of our model.  
3D point cloud; autoencoder; label-efficient; LoD3 building; unsupervised deep learning +
Footnote †: journal: Applied Sciences  
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Footnote †: journal: Applied Sciences  
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Footnote 1: _Applied Sciences_
## 5 Simulation and Performance Evaluation
### Behavioral Analysis of the RSC  
The performance of the RSC was analyzed based on the following criteria: the number of live nodes, the total network residual energy, and the packet received ratio.  
1. **Live sensing nodes**: the total numbers of sensing nodes with an energy level greater than the threshold are called live sensing nodes.
2. **Total residual energy**: this is the total energy level of all live sensing nodes measured in joules (J).
3. **Packet received ratio**: the packet received ratio for each round is calculated as the ratio of the total number of packets received and the node count. The packet received ratio metric used statistical measures as referred to in Chen [31], are listed below:  
* Mean
* Median
* Mode
* Maximum
* Variance or standard deviation  
Figure 4 shows the number of live nodes, the residual power level of all the nodes, and the ratio of packets collected based on the time (number of rounds). As shown in Figure 4a, the number of live nodes decreased as the number of rounds increased. It was observed that all the nodes were alive until 700 s and started to die at about 1700 s. The energy level indicated that the proposed algorithm achieved improved load balancing. As shown in Figure 4b, the residual power level of the total deployed nodes decreased with the increase in duration. The sudden increase in energy consumption of a network was caused by the following: first, the nodes' count expired after certain duration; the long route needed to be identified by the packets to reach the sink node. Secondly, the frequent re-election process. The energy level was expended rapidly in both cases. Figure 4c shows that the rate of packets received started to decrease at the duration of 700 s. It was observed that up to 700 s, the packet collection rate was 100% and then it decreased subsequently. Even after 2000 s, almost 50% of the nodes were still alive, which gave a packet collection rate of 50%. This indicated that the nodes were alive in the first layer and showed that the RSC achieved load balancing.  
Figure 3: Routing for the RSC.
## 4 Results and Discussion
### Wdxrf
Meanwhile, small concentrations of Na\({}_{2}\)O, MgO, Titanium oxide (TiO\({}_{2}\)), and SO\({}_{3}\) content have been identified. The proportions of CaO allowed the grouping of ceramic materials into two categories: calcareous and non-calcareous [21]. The sherds are calcareous (>6% CaO), while the rest are non-calcareous. Most of the CaO comes from carbonate corrections. The K\({}_{2}\)O content is relatively high and could, in principle, come from several mineral phases; the most important could be illite (from paste), muscovite, biotite, and microcline (from clasts). Most of the K\({}_{2}\)O comes from illite; a relatively high Fe\({}_{2}\)O\({}_{3}\) content was also observed. Since the iron-bearing minerals (biotite) are very small, we conclude that most of the iron must be in the sherd paste, most likely in oxide form (hematite/geotheite in the red paste and probably magnetite in the black ones). The main oxides present in the materials were SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), as essential components for the high-quality production burnt bricks [22]. The mass ratio of SiO\({}_{2}\)/Al\({}_{2}\)O\({}_{3}\) should be between 0.5 and 4.5. The sum of flux agents Fe\({}_{2}\)O\({}_{3}\), CaO, Na\({}_{2}\)O, K\({}_{2}\)O, and MgO were all present in an amount above 10% by weight (higher than 14.50% by weight); this indicates that the inclusion of these materials will reduce the combustion requirement of the temperature of the products. The sum of the flux oxides is increased in all mixtures, compared to the clay raw material [23; 24]. In our experiments, chlorine was also identified. For the explanation of its presence, the fact that the ceramic can act as an ion exchanger should be taken into account, entrapping water-soluble ions, such as chloride anions, in its silicate matrix. The composition and structure of the clay determines the strength of the bond, with strongly bound chloride being able to be retained for thousands of years. As the ceramic is heated, the salt crystallizes on the surface and penetrates into the ceramic body, causing the ceramic to become brittle and prone to chipping. In this context, few scientific studies have been conducted to support these archaeological observations [24].
# Fast Loop Closure Selection Method with Spatiotemporal Consistency for Multi-Robot Map Fusion
## 5 Experiments Evaluation  
In this section, To verify the practicability of our method in the natural environments, several sets of experiments in two different scenarios were carried out based on the self-built mobile robot platform to determine whether the inter-robot measurements are accurate or not. Figure 3 shows the physical platform that a Leishen C-16 lidar and a MicroStrain 3 DM-GX3-25 IMU are installed on the ground mobile robot-TIABBOT TOM08Q2. We also implemented two other state-of-the-art methods, including PCM and MEWC, to compare the simulation and experimental results with our method. All the experiments and simulations are run in C++ and executed on an Intel NUC equipped with a Core i7-7567 CPU and 8 Gib memory using the robot operating system (ROS) in Ubuntu 18.04 Linux.  
The system framework of the multi-robot map fusion algorithm is shown in Figure 4. The system consists of three main modules: the local single-robot SALM module, the inter-robot loop closure detection module, and the multi-robot pose graph optimizer. Each robot builds its local map, and the inter-robot loop closure detection module estimates the relative pose among robots. The robust inter-robot loop closure selection method proposed in this paper selects the proper subset of measurements that satisfy the conditions. The  
Figure 3: Mobile robot platform.  
measurements contained in the subset are then inserted into the optimizer to fuse local maps into a global map.  
In order to qualitatively analyze the experimental results, the SLAM accuracy assessment tool evaluation of odometry (EVO) [38] is used to calculate the absolute trajectory error (ATE). In terms of accuracy, we use ATE as the metric to evaluate the maps generated by existing algorithms. We introduced some basic concepts in the loop closure, where TP, TN, FP, and FN correspond for true positive, true negative, false positive, and false negative, respectively. The true positive rate (TPR) and false positive rate (FPR) are defined as follows:  
\[TPR=\frac{TP}{TP+FN},\ FPR=\frac{FP}{FP+TN} \tag{9}\]
## 3 Related Works
### OTP Mechanism
#### 3.2.1 Correlation Information Generation  
Correlation information refers to random numbers that can be collected from time information, event occurrence information, and so on. This can be a complete value generated by the OTP generation method based on the current time, event occurrence, or a random number that has been transformed through additional processes, such as hashing or encryption. Correlation information is necessary for the unpredictability of OTP, similar to the noise source (or entropy source) required to generate cryptographically secure pseudorandom numbers. This process corresponds to the red part in Figure 1.
## 3 Dynamic Model of a Gear System  
As shown in Figure 4, a six-degree of freedom dynamical model that considers the time-vary mesh stiffness, transmission error, and bearing support stiffness is established in which the changes of TVMS are caused by a double-teeth spalling fault [12].  
Here are some assumptions: (1) the bearings at both ends of the shaft have the same stiffness and damping, (2) the gearbox casing is a rigid body, and (3) the shaft of the pinion and gear are parallel to each other. The equations of the dynamic model are given by Equation (17).  
\[\left\{\begin{array}{l}m_{p}\ddot{x}_{p}+c_{bp}\dot{x}_{p}+k_{bp}x_{p}+F_{pg} \cos\varphi_{pg}=0\\ m_{p}\ddot{y}_{p}+c_{bp}\dot{y}_{p}+k_{bp}y_{p}+F_{pg}\sin\varphi_{pg}=0\\ I_{p}\ddot{\theta}_{p}+r_{bp}F_{pg}=T_{M}\\ m_{g}\ddot{x}_{g}+c_{bg}\dot{x}_{g}+k_{bg}x_{g}-F_{pg}\cos\varphi_{pg}=0\\ m_{g}\ddot{y}_{g}+c_{bg}\dot{y}_{g}+k_{bg}y_{g}-F_{pg}\sin\varphi_{pg}=0\\ I_{\ddot{g}}\ddot{\theta}_{g}-r_{bg}F_{pg}=-T_{L}\end{array}\right. \tag{17}\]  
\(F_{pg}\) denotes the total mesh force, and it is expressed by:  
\[\begin{array}{ll}F_{pg}=&c_{m}\left[\begin{array}{l}r_{bp}\dot{\theta}_{ p}-r_{bg}\dot{\theta}_{g}-(\dot{x}_{p}-\dot{x}_{g})\cos\varphi_{pg}-\\ (\dot{y}_{p}-\dot{y}_{g})\sin\varphi_{pg}-\dot{e}_{pg}\end{array}\right]+\\ &k_{m}\left[\begin{array}{l}r_{bp}\theta_{p}-r_{bg}\theta_{p}-(x_{p}-x_{g}) \cos\varphi_{pg}-\\ (y_{p}-y_{g})\sin\varphi_{pg}-e_{pg}\end{array}\right]\end{array} \tag{18}\]  
where \(r_{bp}\) and \(r_{bg}\) are the base circle radius of pinion and gear, \(I_{p}\) and \(I_{g}\) are the mass moments of inertia of the pinion and gear, \(m_{p}\) and \(m_{g}\) are the masses of the pinion and gear, \(k_{bp}\) and \(k_{bg}\) are the bearing stiffness of the pinion and gear, respectively, \(c_{bp}\) and \(c_{bg}\) are the bearing damping of the pinion and gear, respectively, \(T_{M}\) is the input torque, \(T_{L}\) is the load torque, the time-varying meshing damping \(c_{m}\) is mentioned in Reference [49], and \(e_{pg}\) denotes the transmission error, which can be obtained by the equation below.  
\[e_{pg}(t)=e_{m}+e_{r}\sin(2\pi f_{m}+\phi)+\frac{\Delta f_{f}}{\cos\alpha}+ \frac{\Delta f_{pb}}{\cos\alpha} \tag{19}\]  
where \(e_{m}\) is the constant of the mesh error on the pitch circle, and \(e_{r}\) is the amplitude of mesh error on the pitch circle. \(a\) is the pressure angle,\(f_{m}\) is the mesh frequency of the gear, and \(\phi\) is the phase. \(\Delta f_{f}\) is tooth error and \(\Delta f_{pb}\) is the base pitch error, as shown in Figure 5.  
Figure 4: Dynamic model of the six-degree of freedom gear system.