esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Simulation Results
### Placement of the RN  
In order to observe the impact of the RN on the lifetime of the sensor network, a RN is positioned at the center of the sensor network field. When a RN is implemented, the CHs can transmit their collected data to the RN, if better energy conservation can be achieved. The RN will then transmit the collected data to the BS. In this part, the simulation outcomes with RN are presented.  
As presented in Figure 10, when a RN is used, the network lifetimes with the proposed algorithm over SEP protocol utilizing two and three energy levels (SEPWMBS-R and SEPWMBS-3L-R) are almost 2603 and 2832 rounds, respectively. In the conventional SEP protocol utilizing two and three energy levels (SEP and SEP-3L), the network lifetimes are approximately 2481 and 2502 rounds, respectively. Since the usage of a RN helps reduce the overall communication distances between the SNs and the BS, the network lifetimes increase when compared to the case without using a RN.  
A comparison of remaining energies in a large area network when a relay node is used is illustrated in Figure 11. While for SEPWMBS-R and SEPWMBS-3L-R, the network energies are completely depleted at rounds 2653 and 2823, respectively, under the conventional SEP and SEP-3L protocols, the total energy depletion occurs at rounds 2473 and 2653 rounds, respectively. These results are promising as they imply that RN usage may improve the overall energy efficiency of a WSN.  
Figure 10: Comparison of network lifetimes in a large area network with a RN.  
Figure 9: Comparison of remaining energies in a large area network.
# A Survey of Public Key Infrastructure-Based Security for Mobile Communication Systems
## 5 New Research Directions and Challenges
### Using Secure Algorithms  
The service providers can use efficient algorithms such as A5/3 or EPS-AKA, which have been improved to provide a secure system, or can use a newer PKI-based cryptosystem. All these schemes should be implemented on both the network side base transceiver station (BTS) or mobile switching center (MSC/VLR) and the user side (ME or MS/SIM). However, standardization for any modifications in the security or network architectures will require agreement and mutual aid of software and hardware industrialists. In the meantime they should carry out the applicable modifications to their systems. Therefore the cryptosystem should be applied on the mobile stations, and the agreement of mobile phone industrialists is also necessary. Though improvement for the adopted cryptosystems algorithms may not be convenient, if the cryptographic algorithms are substituted with the robust ones,  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **No. of Phases** & **Computational Cost** & **Total Running Time (ms)** & **Comment** \\ \hline
[16] & 4 & \(2 Td+9 Th+2\)Ts & 29.709 & Middle \\
[19] & 3 & \(4 Te+4 Th+5T\)s & 16.947 & Low \\
[30] & 4 & \(4 Te+2 Th+5 Td+Tp\) & 35.255 & Middle \\
[34] & 5 & \(Td+18 Th+17T\)s & 55.981 & High \\
[35] & 3 & \(7 Td+5 Th+11T\)s & 23.443 & Middle \\
[36] & 3 & \(11 Th+7T\)s & 33.478 & Middle \\
[37] & 3 & \(6 Th+8T\)s & 18.283 & Low \\
[38] & 4 & \(2 Td+3T\)s & 2.354 & Low \\
[41] & 8 & \(2 Te+9 Th\) & 29.020 & Middle \\
[42] & 2 & \(13 Te+10 Th+2T\)s & 41.201 & Middle \\
[55] & 4 & \(2 Te+2 Th+Tm+4 Tp\) & 94.16 & High \\
[56] & 6 & \(9 Tm+2 Th+4 Tp\) & 143.540 & High \\
[57] & 3 & \(5 Te+4 Th+2 Tm+5 Tp\) & 129.120 & High \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of computational efficiency in mobile security schemes.  
and the attacker can basically impersonate the real network operator and force the MS/SIM to disable the authentication or the ciphering mode. Hence it is also necessary to modify all security protocols.
# Fuzzy-Logic Dijkstra-Based Energy-Efficient Algorithm for Data Transmission in WSNs
## 5 Performance Evaluation
### Performance Metrics  
To evaluate the performance of the proposed scheme, the following performance metrics were used:  
1. Lifetime of the network
2. Network energy conservation and consumption
3. Delay
4. Percentage of packet loss
5. Network throughput  
To evaluate the performance of clustering routing protocols, the network lifetime was considered to be a significant index. There are several definitions of the network lifetime of a WSN. The time at which half of the network nodes are dead (HND) or when all the nodes in the network die (AND) can be referred to as the network lifetime index [33]. Additionally, the death of the first node (FND) in the network is an inflection point of the network, and it plays a vital role in the network lifetime analysis. Network energy conservation and consumption complement each other, and they are a major factor in reflecting the network performance. They can be defined as the proportion of the total amount of energy saved or used per node to transmit data to the BS. In this study, the packet delivery delay is defined as the physical distance between the nodes divided by the speed of an electromagnetic wave [34]. The percent packet loss metric represents that the relay nodes with energy less than the threshold level will not be able to participate in the data transmission process. It is defined in Equation (18). Throughput is the total number of data packets received at the BS per communication round [2].  
\[\text{Packet Loss }(\%)=\frac{\text{Number of packets transmitted }-\text{number of packets received}}{\text{Number of packets transmitted}}\times\text{100} \tag{18}\]
## 3 Results and Discussion
### Purslane Powder Characterization
#### 3.1.2 Particle Size Distribution (PSD)  
The results of the particle size distributions of the powders after grinding in three different mills are shown in Figure 3 as mass distribution functions, i.e., as the mass percentage of the particles in a certain size interval (\(\Delta Q_{3}\) (\(x\))/\(\Delta x\)). It can be seen from the results that the product from the ball mill has the highest proportion of fine particles (\(x\) < 100 \(\upmu\)m), followed by the knife mill. In addition, the products from these two mills have a negligible proportion of particles in the size range larger than \(x\geq\) 250 \(\upmu\)m. On the other hand, the product from the planetary ball mill has the lowest proportion of fine particles and the highest proportion of coarse particles (200 \(\upmu\)m < \(x\) < 500 \(\upmu\)m). The powders obtained in the ball mill and the planetary ball mill are characterized by bimodal functions, whereby this property is more pronounced in the product from the planetary ball mill. Both planetary and ball mills are based on the same comminution principle, namely the impact and friction principle. While the impact breaks the large particles into coarse fragments, the friction leads to the formation of fine particles. In this study, the presence of fines, which is a consequence of friction, i.e., abrasion, indicates that this mechanism is predominant in the ball mill. The bimodal size distribution with the second mode in the coarse fraction indicates that the coarse particles were not adequately ground in the planetary ball mill. Other authors also reported a bimodal distribution of the particles in the powder obtained with a planetary ball mill after a short grinding time and came to the conclusion that the bimodal distribution only changes to a unimodal distribution after a long grinding time (\(>\)60 min) [38; 39].  
The calculated values in Figure 3 show that the smallest average particle size is achieved in the ball mill and the coarsest in the planar mill. The higher values of the standard deviations in the planetary ball mill and the ball mill respectively indicate the presence of particles in the coarser fraction, which is due to incomplete, i.e., insufficient grinding.
# Ultraviolet-Assisted Modified Delignified Wood with High Transparency
## 3 Results and Discussion
### Analysis of Lignin Content  
The lignin, cellulose and hemicellulose mass fractions were used as assessment indicators to measure the lignin loss in delignified wood prepared under different treatment conditions in NW, DW-B, DW-B-U, DW-D and DW-D-U. The test results detailed in Table 2 show that in comparing the DW-B-U and DW-B groups or comparing the DW-D-U and DW-D groups, UV irradiation for a certain period of time accelerated the rate of lignin removal, resulting in clear wood with higher transparency. UV-assisted delignification of DW-B-U resulted in 17% higher delignification than the DW-B group, all other things being equal. The lignin content of the treated wood is reduced, while the cellulose content is slightly increased, as the major loss of lignin leads to a relative increase in cellulose content. On the other hand, although there was a significant loss of lignin, up to 45% of the lignin in the DW-B-U group was preserved while being rich in cellulose. This contributes to the mechanical properties of TW-B-U, and the picture in Figure 4c shows that TW-B-U has a high degree of transparency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Groups** & **LC (wt \%)** & **CC (wt \%)** & **HC (wt \%)** \\ \hline NW & 23.30 & 42.36 & 20.15 \\ DW-B & 17.63 & 43.50 & 18.69 \\ DW-B-U & 14.58 & 44.01 & 17.36 \\ DW-D & 7.78 & 45.22 & 17.94 \\ DW-D-U & 7.26 & 45.19 & 18.12 \\ \hline \hline \end{tabular} Abbreviations: LC—lignin content, CC—cellulose content, HC—hemicellulose content.  
\end{table}
Table 2: Lignin content, cellulose and hemicellulose content of natural wood NW, four groups of delignified wood DW-B, DW-B-U, DW-D, DW-D-U.  
Figure 5: Infrared spectral images of natural wood NW, four groups of delignified wood DW\(-\)B, DW\(-\)B–U, DW\(-\)D, DW\(-\)D–U and “transparent wood” TW\(-\)D\(-\)U.
## 4 Design and Implementation  
In this section, we evaluate the pain points derived from the research. The Value Flow Model presents the supply and communication between an organization and its users, as well as the value flow among multiple members in the whole system. Therefore, we built a Value Flow Model for the operation of this project in the early stage as shown in Figure 2, incorporating readers, the design studio, APP, and products. These construct a four-core value proposition. Librarians, staff on duty, equipment managers, and security staff are complementary value points. Maintenance workers, manufacturers, and the network service compose the supply chain. The construction of the value stream model in this phase helps us establish the overall direction of product and strategy design in the actual operation system.  
We also used an affinity diagram to derive design insights. This places designer insights and observations on paper, facilitating the aggregation of information. Figure 3 is based on specific observations of the project and information derived from an affinity dia  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{2}{c}{**Leverne’s Test for**} & \multicolumn{2}{c}{**\(t\)-Test for Equality of Means**} \\ \cline{3-6}  & & **F** & **Sig.** & **\(t\)** & **df** & **Sig. (2-Tailed)** \\ \hline \multirow{2}{*}{Light and dark feel} & Equal variances assumed & \multirow{2}{*}{2.634} & \multirow{2}{*}{0.106} & \(-4.440\) & 236 & 0.000 \\ \cline{2-6}  & Equal variances not assumed & & & \(-4.425\) & 227.140 & 0.000 \\ \hline \multirow{2}{*}{Light environmental satisfaction} & Equal variances assumed & \multirow{2}{*}{10.728} & \multirow{2}{*}{0.001} & \(2.381\) & 237 & 0.018 \\ \cline{2-6}  & Equal variances not assumed & & & \(2.352\) & 215.688 & 0.020 \\ \hline \multirow{2}{*}{Overall comfort} & Equal variances assumed & \multirow{2}{*}{7.258} & \multirow{2}{*}{0.008} & \(1.636\) & 237 & 0.103 \\ \cline{2-6}  & Equal variances not assumed & & & \(1.620\) & 143.450 & 0.107 \\ \hline \end{tabular}
\end{table}
Table 4: Group statistical comparison of being by the window or not.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & \multicolumn{2}{c}{**Whether with**} & \multirow{2}{*}{**N**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Std. Deviation**} & \multirow{2}{*}{**Std. Error Mean**} \\ \cline{3-4}  & & **Direct Sunlight** & & & & \\ \hline \multirow{2}{*}{**Light and dark feel**} & No & 150 & 4.59 & 0.744 & 0.061 \\ \cline{2-6}  & Yes & 85 & 4.98 & 0.74 & 0.08 \\ \hline \multirow{2}{*}{**Light environmental satisfaction**} & No & 150 & 3.87 & 0.662 & 0.054 \\ \cline{2-6}  & Yes & 85 & 3.48 & 0.781 & 0.085 \\ \hline \multirow{2}{*}{**Overall comfort**} & No & 150 & 3.95 & 0.535 & 0.044 \\ \cline{2-6}  & Yes & 85 & 3.73 & 0.679 & 0.074 \\ \hline \end{tabular}
\end{table}
Table 3: Group statistical comparison of with direct sunlight vs. not.  
gram. The information obtained is divided into three parts: user feeling, actual conditions, user needs.  
Figure 3: The affinity diagram.  
Figure 2: The value flow model.
## 1 Introduction  
The erosion of the seabed around offshore structures has become a primary factor limiting the operational safety of offshore engineering installations [1]. Timely and accurate identification and monitoring of the water-soil interface are crucial for addressing the seabed erosion issues affecting offshore structures. However, traditional monitoring equipment often suffers from low accuracy and high costs, making it challenging to meet practical needs [2, 3]. Developing an efficient and low-cost device capable of identifying the water-soil interface is key to solving this problem.  
Due to the differences in thermodynamic parameters between water and soil, such as density, specific heat capacity, and thermal conductivity, identifying these differences has become the focus of developing related equipment. In water media, heat transfer primarily occurs through thermal convection [4]. In the open sea environment surrounding offshore structures, seawater's higher specific heat capacity and thermal conductivity lead to rapid heat transfer from the heat source, resulting in a relatively fast temperature increase or decrease [5, 6]. In contrast, heat transfer in soil media occurs primarily through thermal conduction. Due to the low specific heat capacity and thermal conductivity of soil, the rate at which heat is transferred from the heat source to the soil is significantly slower than that in water media [7]. In its natural state, the temperature difference between the soil and the seawater on the seabed is very small, making it challenging to identify the water-soil interface [8]. However, by heating and cooling specific power sources in water and soil media, a significant temperature difference can be observed, as demonstrated in Figure 1.  
Hence, monitoring the temperature changes and their rates of change in heat sources within water and soil media during heating and cooling processes allows for effective identification of the water-soil interface. This capability enables dynamic monitoring of the erosion process of offshore structures under the influence of water flow, as depicted in the schematic diagram of the monitoring equipment in Figure 2.  
Figure 1: A schematic diagram of heating and cooling in water and soil media.  
Figure 2: Schematic diagram of monitoring equipment.  
To address this challenge, a theoretical model based on thermodynamic principles is firstly developed, which can be used to assess the water-soil interface. Then, the heat distribution pattern of a linear heat source in water-soil media under a constant heating power is analyzed, and the practicality and accuracy of this theoretical model are further verified through indoor experimental verification.
# Optimal Lyapunov-Based Sliding Mode Control for Slotless-Self Bearing Motor System
## 3 Proposed Approach
### The OLB-SMC for the \(y\)-Axis  
Select the proportional-derivative surface of SMC for the \(y\)-axis, the surface equation is given as follows:  
\[s(e_{y})=\dot{e}_{y}+\lambda_{y}e_{y} \tag{20}\]  
where \(e_{y}=y_{r}-y_{m}\). Similarly, the surface of SMC for \(y\)-axis should be selected as similar with the surface of controller for \(x\)-axis. The derivative of Equation (20) is  
\[\dot{s}(e_{y})=\ddot{e}_{y}+\lambda_{y}\dot{e}_{y} \tag{21}\]  
when considering \(\dot{s}(e_{y})=0\) and disturbance is zero, Equation (21) is rewritten as follows:  
\[\ddot{e}_{y}+\lambda_{y}\dot{e}_{y}=0 \tag{22}\]  
Combining the Equations (7) and (22) yieldsm  
\[\ddot{y}_{r}-(\frac{k_{nb}k_{b}\dot{u}_{d}}{m}-\frac{F_{ly}}{m})+\lambda_{y} \dot{e}_{y}=0 \tag{23}\]  
So, the equivalent term is calculated as follows:  
\[i_{dot}=\frac{m}{k_{nb}k_{b}}(\ddot{y}_{r}+\lambda_{y}\dot{e}_{y}) \tag{24}\]  
\[\dot{V}(s_{total}) =s_{x}(-k_{1x}s_{x}-k_{2x}\int s_{x}+\frac{F_{lx}}{m})+s_{y}(-k_{1y} -k_{2y}\int s_{y}+\frac{F_{ly}}{m})\] \[+s_{\omega}(-k_{1\omega}s_{\omega}-k_{2\omega}\int s_{\omega}+ \frac{T_{l}}{J}) \tag{30}\]  
For softening the calculations and proof of the stability of whole system the Equation (30) should be transferred to the equation such as follows:  
\[\begin{bmatrix}\dot{V}(s_{total})\\ z\end{bmatrix}=\begin{bmatrix}-k_{1x}&1\\ -k_{2x}&0\end{bmatrix}\begin{bmatrix}s\\ z\end{bmatrix}+\begin{bmatrix}\frac{F_{lx}}{m}\\ 0\end{bmatrix}+\begin{bmatrix}-k_{1y}&1\\ -k_{2y}&0\end{bmatrix}\begin{bmatrix}s\\ z\end{bmatrix}+\begin{bmatrix}\frac{F_{ly}}{m}\\ 0\end{bmatrix}+\begin{bmatrix}-k_{1\omega}&1\\ -k_{2\omega}&0\end{bmatrix}\begin{bmatrix}s\\ z\end{bmatrix}+\begin{bmatrix}\frac{T_{l}}{J}\\ 0\end{bmatrix} \tag{31}\]  
The system is stable when the eigenvalues of the matrix \(A_{x,y,\omega}\) is optimally located in the left side of the plane.  
\[A_{x,y,\omega}=\begin{bmatrix}-k_{1x,y,\omega},&1\\ -k_{2x,t,\omega},&0\end{bmatrix} \tag{32}\]  
The performance of the OLB-SMC for SSMB system is given as the following section.
# A Deep Learning Approach for Maximum Activity Links in D2D Communications
## 5 Deep Learning-Based Link Scheduling
### Deep Belief Network
#### 5.3.4 Training Process  
Algorithm 2 summarizes the training process of the learning framework. First, Algorithm 1 generates the training set. Then, the DBN is unsupervised and trained by a multi-layer RBM. In the pre-training phase, real network data with initial random weights are used to train the DBN. The BP method is applied to the supervised training. After multiple rounds of training, the weights in the DBN are fixed. Finally, the BP method is performed on all layers of fine-tuning.  
```
1. Input: Training Set; The number of layers: \(L\), weight of \(lth\) layer: \(w^{(l)}\);
2. for\(l=~{}1\cdots L\)do
3. initialization: \(w^{(l)}\to 0\);
4. extract feature \(h^{(l-1)}\);
5. train \(w^{(l)}\) of RBM of \(lth\) layer using \(h^{(l-1)}\);
6. end for
7. use BP method to adjust the weight;
8. fine-tuning the parameters of the whole layer
9. output: the state of D2D links
```  
**Algorithm 2**Training Process of a DBN
## 5 Validation: The Use of the Ontology to Extract a Historical Site Component from LiDAR Data
### Extraction of Topological Relations between Geometric Components  
The topological relations between the components are summarised as a 3*3 matrix of the dimensionally extended 9-intersection model (DE-9 IM) [42] to represent topological relations. As the description presented in [43], the matrix is composed of the dimension operation on the interior, the boundaries of the two planar regions that represent building elements, and the intersection line between the two plane regions in 3D space. Based on the representation of DE-9 IM for recording topological relations between planar regions, the semantic representation of the topological relations between the two planar regions is determined by analysing the relation between the intersection line and two planar regions, and the relations between the intersected parts composed of the intersection operation between planar regions and the intersection line. For example, the topological relations between the intersected parts could be \"point-point\" relations, \"point-line segment\" relations and \"line segment-line segment\" relations.  
For describing the topological relations in a semantically meaningful way, the formalised representation of topological relations between the planar regions is described by the four-word description \"T\({}_{\text{p1}}\)-T\({}_{\text{p2}}\)-T\({}_{\text{p3}}\)-T\({}_{\text{p4}}\)\". The first part T\({}_{\text{p1}}\) describes the overall topological relations between the two planar regions. It could be disjointed, met, and intersected. The second part T\({}_{\text{p2}}\) represents the relation between planar region A and the intersection line. It could be disjointed, met, and overlapped. The third part T\({}_{\text{p3}}\) representsthe relation between planar region B and the intersection line. It could be disjointed, met, and overlapped as well. The fourth part \(T_{P4}\) represents the relation between the intersected parts on the intersection line. It depends on the intersected parts created by the planar regions and the intersection line. When it is the relation between two points, it could be disjointed and equal. When it represents the relation between a line segment and a point, it could be disjointed, met and contained. When it represents the relation between two-line segments, it could be disjointed, met, overlapped, covered, covered by, contained, contained by, and equal. Finally, the topological relations between two planar regions in 3D space are formalised as semantic descriptions.  
Based on the segmentation results of HBH buildings from point clouds, the geometric information of the building components is calculated from segments, and the topological relations between the building components can be identified as well. The geometric information of building components and the topological relations between components make it possible to extract the semantic information related to the building structure constructed from basic geometric and topological information.
## 1 Introduction
The tests that were carried out in the laboratory and an outdoor test bed validated that the buried objects' vibration responses are well distinguishable both in the frequency range and vibration intensity due to the landmine having a larger compliance when compared to roots, stones, and other rigid clutters for the landmine structure contains an air cavity between the explosive cavity and upper casing. Since the vibration response is the key feature in distinguishing landmine from other rigid buried objects. When compared to the Ref. [26], we further studied the theoretical model of the prodder-object coupling system to investigate the effects of system parameters on the measured response signal features in this paper. A numerical analysis method is proposed to quantitatively investigate the resonance behavior of the prodder-object system and their influencing factors. The calculation result met well with the simulation and experimental data published in [26], which verifies the feasibility of this proposed numerical method. Based on the analysis results regarding system sensitivity, an optimal design for a better performing prodder is proposed, and the measured signal strength can increase 122.78% theoretically, which means greater depth of detection can be acquired, and a simulation experiment is conducted to verify the proposed optimal design with the optimization function of Adams software. This analysis procedure provides a reference for developing a good performing prodder.