esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Analysis of Experimental Results
### Analysis of Formation Law of Ground Surface  
In order to better observe the surface, microscopic observation on the processed surface was conducted by an ultra depth of field electron microscope. The test results are shown in Figure 8, where (a)-(d) and (a')-(d') represent the surface under the grinding parameters in Table 3.  
Figure 8: _Cont._  
Figure 7: Trend line of roughness change.  
From the detected results of ultra depth of field electron microscopy, it can be seen that the belt rows out the ravines on the surface of the welded seam, and the ravines distribute on the surface of the weld. The ground ribbed surfaces range in width from 20 to 40 \(\upmu\)m and in height from 3 to 10 \(\upmu\)m. The angles of the ribs are about 50 to 70 degrees. In the case of lower belt speed and grinding speed, the microstructure of the grinding surface is more prominent. The low feed speed is beneficial in prolonging the processing time between the strip and the workpiece surface and the overall surface treatment. The lower linear velocity of the friction band can avoid the damage of friction particles to the surface of the rib.  
In order to facilitate the study of the surface characteristics of the stiffened ribs, some stiffened surfaces were selected to simplify the geometric surface models analyzed and make the stiffened rib surface more scientific and reasonable. The fifth group and the third group of experiments have visible ground surface characteristics, so this paper extracted the geometric features of the two groups of surfaces. Two kinds of 3D models were modeled according to the geometric elements, referred to as type I and type II. The smooth surface was also modeled to contrast, called type II. Figures 9 and 10 are the geometric feature extraction models and simulation analysis models of the ground surface.  
Figure 8: Results of ground surface detection by ultra depth of field electron microscope.  
Figure 9: Ground surface of type I.  
Figure 9 shows the type I model. Figure 9 represents the microscopic view of the weld, while Figure 9 represents the profile of the weld section, and a standard model was used to fit the monitoring results. By analyzing the detection results, the average depth of the valley floor was equal to 10, and the average width between the two valley peaks was 40 ; thus, the model in Figure 9 was obtained. It can be seen from the figure that the reinforcing rib presents a gully shape. The width of the gully is about 40, the height is 10, and the angle is about 60-70 degrees. For this reason, we simplified the model, as shown in Figure 10. Figure 10 shows the type II model. Figure 10 represents the microscopic view of the weld, while Figure 10 represents the profile of the weld section. A standard model was used to fit the monitoring results. By analyzing the detection results, the average depth of the valley floor is equal to 3, and the average width between the two valley peaks was 20 ; thus, the model in Figure 10 was obtained.
# Embedded Based Real-Time Monitoring in the High-Pressure Resin Transfer Molding Process for CFRP
## 4 Results and Discussion
### Detection of Abnormal States
#### 4.2.2 Partial Impregnation  
In the HP-RTM process, the most important factor that influences the quality of the CFRP surface is the surface integrity of the mold surface transferred to the surface of the CFRP specimen and the resin flow. Figure 14 shows the signals and CFRP specimen when the un-impregnation occurred due to the mold having a defective surface. The carbon fiber layup used seven sheets and signals were acquired until the curing time of 500 s unlike in other experiments. In the curing step (3) shown in Figure 14a, the cavity pressure CP2 continuously increased but CP3-4 decreased to below 10 bar upon the completion of curing. The maximum reaction temperature of T1-2 was 4 \({}^{\circ}\)C and that of T3-4 was 2 \({}^{\circ}\)C in shown Figure 14b. In Figure 14c, the end-gap of the mold was set at 0.5 mm but the LVDT measured 0.2-0.4 mm. Figure 14d indicates the CFRP specimen that had the 3.04 mm thickness at the center (#2), the 2.52 mm thickness at the end (#4), and un-impregnation area between #3 and #4. Figure 15 shows a photo of the surface of the mold. In the curing step (2), the flow rate of the resin was decreased by the friction on the mold surface, which made the diffusion more difficult, and the resin stagnated on the mold's rough surface of the mold. The resin injected at a high pressure was impregnated normally in the region near the injection port but the resin flow was uneven as further from the injection port, so un-impregnation occurred. The stagnation of resin flow is provoked at the central region of the mold, which causes the amount of resin, pressure, and CFRP thickness to increase. Therefore, the resin was not uniformly distributed in the mold and the end-gap was opened 0.3 mm and thus did not reach the set value of 0.5 mm. The cavity pressure CP3-4 decreased because the end-gap did not close completely. The maximum reaction temperature of T3-4 was not attained by the lack of resin and the thickness of the specimen was smaller than the average value.  
Figure 14: Results of partially impregnated in the HP-RTM process: (**a**) Cavity pressure; (**b**) Temperature; (**c**) End-gap; (**d**) The CFRP thickness and specimen.
# Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation
## 3 Results and Discussions  
This section describes the results obtained in this study. The impact of the heatwave on LAI and SSM is first assessed using CGLS satellite derived data. To that end, the latter are expressed in monthly anomalies (difference to the mean scaled by the standard deviation). LDAS-Monde ability to represent LAI and SSM is then assessed using the correlation coefficient (R) and root mean square differences (RMSD).
## 2 Pseudo-Membranes, Systems, and Applications
### Preparing Ag/AgCl Pseudo-Membranes
#### 2.5.2 Thickness of the AgCl Layer  
The thickness of the AgCl layer during the anodizing process is estimated from the applied charge per m\({}^{2}\). After transport of 1 Faraday of electricity, 1 mol of AgCl is deposited on one side of the silver plate and 1 mol of Ag is converted to AgCl. From the densities (AgCl: 5.56; Ag: 10.50 g/cm\({}^{3}\)) and molar masses (AgCl: 143.32; Ag: 107.87 g/mol), it follows that this results in a decrease of 10.3 \(\upmu\)m for Ag and an increase of 25.8 \(\upmu\)m for AgCl. Therefore it is assumed that the AgCl layer is composed of pure crystalline AgCl without inclusion or pores.  
In our experiments, the maximum anodizing time was 30 min with a current density of 60 A/m\({}^{2}\), resulting in a transport of 1.12 Faraday, equivalent to a shrinkage of the silver plate of 11.5 \(\upmu\)m on each side. Thus the 200 \(\upmu\)m of material present is more than sufficient.
## 3 Performance Evaluation of RAN Network Slicing
### Relationship between RAN Network Slicing and Bandwidth  
The effect of increasing the number of users using the available bandwidth on throughput and latency was examined. Each UE has three active sessions: DL-autonomous vehicle (red band, URLLC), DL-4K Video (green band, eMBB) and UL-IoT (purple band, mMTC). The bandwidth usage of the applications according to the network slices is shown in Figure 5.  
The tests were performed with 1 UE, 2 UEs and 4 UEs respectively. Table 3 shows the test configuration for 4 UEs and their 12 different traffic flows. According to the simulation results in terms of data rate, the average data throughput for 1 UE (three different traffic flows) is 104.3 Mbps, for 2 UEs (six different traffic flows) the throughput is 101.2 Mbps, and for 4 UEs (twelve different traffic flows) the throughput is 77.4 Mbps. It can be seen that the throughput decreases as the number of active sessions increases. According to the simulation results in terms of latency, for 1 UE (three different traffic flows) the latency is 1.7 ms, for 2 UEs (six different traffic flows) the latency is 4.8 ms, for 4 UEs (twelve different traffic flows) the latency is 1.7 ms. Similarly, the latency increases as the number of sessions increases.  
Figure 4: Effect of QoS types on throughput.  
Figure 5: Bandwidth usage of applications.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**UE** & **Flow and Direction** & **Application Type** & **Slice** \\ \hline \multirow{4}{*}{UE-1} & Flow-1, DL & Autonomous Car & URLLC \\  & Flow-2, DL & 4K Video & eMBB \\  & Flow-3, UL & IoT & mMTC \\ \hline \multirow{4}{*}{UE-2} & Flow-4, DL & Autonomous Car & URLLC \\  & Flow-5, DL & 4K Video & eMBB \\  & Flow-6, UL & IoT & mMTC \\ \hline \multirow{4}{*}{UE-3} & Flow-7, DL & Autonomous Car & URLLC \\  & Flow-8, DL & 4K Video & eMBB \\  & Flow-9, UL & IoT & mMTC \\ \hline \multirow{4}{*}{UE-4} & Flow-10, DL & Autonomous Car & URLLC \\  & Flow-11, DL & 4K Video & eMBB \\  & Flow-12, UL & IoT & mMTC \\ \hline \hline \end{tabular}
\end{table}
Table 3: 4 UE—12 Different Traffic Flow Configuration.  
Figure 6 shows the effect of the number of users on throughput and latency according to the simulation results. According to these results, it can be assessed that the throughput and latency metrics will be improved if the resources of the users whose session is terminated are dynamically allocated to other users.
## 3 Numerical Simulation
### Theoretical Analysis and Algebraic Attack  
The image encryption system is based on optical and chaos theory. To prove its security, we must first test whether it can withstand the theoretical analysis against the weakness of the chaotic linear transformation and algebraic attack [41].  
In the triangle correlation algorithm (TAEA) used in this paper, although the Arnold transform is linear, the Tent map is nonlinear, and the two are not a simple cascade, so the whole encryption system is not linear. Therefore, the theoretical analysis method for the weakness of the linear chaotic encryption system described in the \"Cryptanalysis of Chaotic Ciphers\" [41] is not applicable. We can also assume that the system is secure in this respect.  
Algebraic attack methods are implemented based on key space or cycle, so we now analyze the key space of the encryption system. The 3D Arnold has six parameters \(a\), \(b\), \(c\), \(d\), \(e\), \(f\), and they're all eight bits, and there are 4 \(\times\) 3 \(\times\) 2 types of module movement methods. The variation range of \(a_{0}\) is set as 112. \(\mu\) and \(\theta\) are the double-precision floating point data, and the computer used for the simulation is 64-bit. So, the key space is \(2^{108}\times 2^{8}\times 2^{8}\times 2^{8}\times 2^{8}\times 2^{8}\times 2^{8} \times 24\times 112\times 2^{64}\times 2^{64}>2^{294}>10^{87}\). The encryption  
Figure 6: Experimental data: (**a**) final encrypted image combined of 30 th, 70 th and 100 th band; (**b**) decrypted image combined of 30 th, 70 th and 100 th band.  
system is computationally secure and can withstand violent attacks [42; 43] and algebraic attacks based on key space analysis.  
According to [PERSON]'s criterion [44], a good encryption algorithm should have enough key space. The key space comparison of several encryption algorithms is shown in Table 1. Compared with the encryption algorithm in literature [27; 45; 46; 47; 48], the key space of the encryption algorithm in this paper is larger, which can better resist the exhaustive attack.
## 2 Problem Statement
### Communication for a Reliable ASM  
Reliable communication is required for effective data sharing among aircraft, and it allows them to make decisions for airspace allocation accordingly. P2P topology is the most efficient communication architecture in terms of reliability [48]. Figure 3 shows the P2P communication between an aircraft and a ground station.  
Figure 3 illustrates a ground station and a \(k\) number of UAVs in a flight zone. Each aircraft sends its GPS data directly to all other aircraft, and each receives the GPS data from the others. Therefore, all aircraft are informed of the GPS data corresponding to all of them.  
In this methodology, the ground station acts as a supervisor, and each aircraft is considered a local node on the blockchain, which provides a distributed protocol for airspace allocation. Blockchain can serve a large number of local nodes (UAVs), while the limitation arises from other factors (e.g., regulations, size of the flight zone, the purpose of the UAVs, etc.) in advance. Although several ground stations can be involved, relying on the support of the blockchain, one grand station is enough for supervision.  
The ground station is the third party in a conventional ASM, which is considered the supervisor. In other words, UAVs perform the full propagation of data sharing by relying on the P2P topology, and the ground station is a redundant validator used to improve reliability and ground supervision. An algorithm (a smart contract), using shared GPS data, evaluates the aircraft trajectories, makes decisions, and then allocates safe airspaces. This process is shown in Figure 4.  
Figure 4 shows the locations and trajectories for the k number of UAVs collected by a smart contract. An imaginary mesh was used to create sectors along the \(x\) and \(y\) axes at a specific altitude. Aircraft are flying through predetermined trajectories while safe sectors are allocated to them. The smart contract collects data containing allocated sectors  
Figure 4: Conflict detection and airspace allocation procedure.  
Figure 3: P2P communication between aircraft and a ground station [43].  
to all UAVs. Its algorithm detects potential conflicts and allocates safer sectors to UAVs such that the risk of conflict is minimized. Allocated airspaces conduct a safe path for each UAS at each timestamp. A timestamp is a digital record that indicates the date and time at which a specific event (airspace allocation) was created, modified, or recorded. It serves as a chronological reference point, often represented in a standardized format, to establish the sequence and timing of events. Then, the conduct-safe trajectories are stored in a blockchain and are used to compute airspaces in further steps. The smart contract algorithm and the associated blockchain for ASM are explained in the following section.
## 2 Study Area and Data Acquisition
### Vegetation and Land Use Change Data  
Based on China's Vegetation Map (1:1,000,000) [37] published in 2001, the spatial vegetation data for the study region were extracted and regrouped into four land use types: cropland, forestland, grassland, and shrubland (Figure 2). This map, which presents the general land use condition in the starting year of the study period, was used as the basis to analyze the results.  
Two types of land use change data were collected: statistical data for the annual deforestation area during 2000-2012 from local yearbooks and data obtained by interpreting high-resolution images for a typical hilly area of 900 km\({}^{2}\) (30 \(\times\) 30 km, 36\({}^{\circ}\)45\({}^{\prime}\)15\({}^{\prime\prime}\)-37\({}^{\circ}\)1\({}^{\prime}\)26\" N, and 109\({}^{\circ}\)25\({}^{\prime}\)2\"-109\({}^{\circ}\)45\({}^{\prime}\)22\" E) in the middle area of the region (Figure 2). Two available 2.5 m resolution images (i.e., the SPOT image on 9 June 2003 and the ALOS image on 9 September 2009) were obtained to delineate land use types in 2003 and 2009 via visual interpretation. These two land use maps were then overlapped to extract the area change for each land use type between 2009 and 2003.
# An Overview on the Latest Nature-Inspired and Metaheuristics-Based Image Registration Algorithms
## 4 Revision of the State-of-the-Art
### [PERSON] et al.'s CRO-SL-Based Proposal  
In [67], the authors addressed medical IR scenarios by means of the Coral Reef Optimization (CRO) [68] algorithm with Substrate Layers (CRO-SL). The conducted experimental studies focused on both mono-modal and inter-modal medical IRs in which the scenarios belong to different kinds of 3D affine transformations. Besides, the authors adapted CRO-SL to tackle both the intensity and the feature-based IR approaches. The (CRO) algorithm is a recent contribution to the field of EAs which provides a global-search strategy based on the biology of the formation of coral reefs. Specifically, the algorithm is based on a reef of solutions to a given optimization problem (i.e., corals), that reproduce in the same manner as corals do in nature, by means of using the broadcast spawning, brooding and budding operators. In its canonical version, CRO lacks of exploitation capabilities. Then, the authors adapted CRO in order to address the IR problem by cleverly providing an enhanced version, named CRO-SL [68]. The new design of CRO-SL refers to a well-balanced combination of two crossover operators (i.e., BLX-\(\alpha\) and SBX), two evolutionary-based operators (i.e., HS and DE), and one mutation operator (i.e., GM). The proposed IR algorithm was tested against two gradient-based IR algorithms and other tree heuristic-based methods in the SoTA. From the experimental results, the authors concluded that CRO-SL provided a suitable trade-off between exploration and exploitation capabilities when dealing with medical IR scenarios.
## 4 Discussion  
### Discussion on the Results of WA-LSTM  
The convergence rate of the LSTM model is faster than that of the DNN model, and the convergence value is smaller, which indicates that temporal information has an important influence on action  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **DNN** & **LSTM** & **KAA-LSTM** \\ \hline Cross Entropy & 0.1991 & 0.1843 & 0.1274 \\ Accuracy & 0.9478 & 0.9775 & 0.9936 \\ Recall & 0.8832 & 0.9023 & 0.9525 \\ Precision & 0.9227 & 0.9343 & 0.9710 \\ F1-score & 0.9025 & 0.9180 & 0.9617 \\ \hline \hline \end{tabular} Note. DNN = Deep Neural Network; LSTM = Long Short-Term Memory; KAA-LSTM = Key Action Attentional LSTM.  
\end{table}
Table 1: The results of three comparative experiments.  
Figure 6: Comparison of experimental loss and accuracy between three groups.  
recognition and can better promote convergence. From the iterative process of accuracy, it can be seen that the LSTM model can achieve 98% accuracy in the training set and more than 95% accuracy in the validation set, while the DNN model can only achieve roughly 87% accuracy in both sets. This is because the DNN model does not take temporal information into account, making it difficult to distinguish between ambiguously transitional actions. In addition, after adding the workpiece feature on the basis of LSTM, the converged value is further reduced and the accuracy is further improved, indicating that the workpiece feature as attention information can effectively improve the performance of the original model.  
Stacking the truth label and the predicted label of each frame along the time line, we get the result shown in Figure 7. We can see that due to the lack of temporal information, the results of the DNN model are unstable, and there are many small segments, which will greatly affect the segment of action. As it takes account of the context information of the action, the LSTM model has better continuity in its results, and its performance is further improved after the addition of the workpiece attention mechanism. Its predicted results can better reflect the real values but have a small delay compared with the truth label. At the same time, it is easy to make recognition mistakes in the process of action cohesion when tools are changed. We consider the reason for this to be because all kinds of action have the same probability when tools are changed; the breakpoint of the action division when marking the truth value also causes interference in the experimental results. In conclusion, the proposed WA-LSTM model can significantly improve accuracy and make the prediction of results smoother.
# Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review
## 1 Introduction
### Remotely Operated Vehicles
A subset of work-class ROVs is the light work-class ROV, such as SEAPUP of the family of SEALION, which bridges the gap between observation-class and work-class ROVs [38]. These vehicles offer enhanced capabilities compared to observation-class ROVs but are not as large or powerful as full work-class models. Light work-class ROVs are equipped with more advanced tools and can perform moderate-depth operations, making them suitable for tasks like light maintenance, underwater inspections, and shallow-water construction projects [37]. They offer a balance between functionality and maneuverability, providing versatility for various underwater applications. Each type of ROV is designed with specific features to meet the demands of its intended tasks, from simple visual inspections to complex underwater operations. The choice of ROV depends on the nature of the mission, the operational depth, and the required payload capabilities.
# Buckling Analysis of CNTRC Curved Sandwich Nanobeams in Thermal Environment
## 3 Equilibrium Governing Equations  
### Classical Formulation of Curved Sandwich Beams  
Based on a classical formulation, the equilibrium equations of the problem are determined by means of the potential energy principle. In detail, the strain energy variation is defined as  
\[\int\limits_{-\ h/2}^{h/2}\int\limits_{0}^{L}\left[\sigma_{xx}^{(k)}\delta \varepsilon_{xx}\ +\ \sigma_{zz}^{k}\varepsilon_{zz}\ +\ \tau_{xz}^{(k)}(\
u_{xz})\gamma_{xz}\right]\mathrm{d}x\mathrm{d}z\ -\ \int\limits_{0}^{L}N_{2}\frac{ \partial u_{0}}{\partial x}\frac{\partial\delta u_{0}}{\partial x}\mathrm{d}x -\ \int\limits_{0}^{L}\left[k_{w}w_{0}\delta w_{0}+\ k_{g}\frac{\partial u_{0}}{ \partial x}\frac{\partial\delta u_{0}}{\partial x}\ +\ k_{NL}w_{0}^{3}\delta w_{0}\right]\mathrm{d}x \tag{17}\]  
in agreement with a quasi-3D theory, where \(k_{w}\) and \(k_{g}\) are the linear Winkler stiffness and the shear layer stiffness, respectively, and \(k_{NL}\) refers to the non-linear stiffness. The strain energy variation can be rewritten in terms of stress resultants as  
\[\int\limits_{0}^{L}\left[N_{xx}\frac{\partial\delta u_{0}}{\partial x}\ -\ M_{xx}\frac{ \partial^{2}\delta w_{0}}{\partial x^{2}}+P_{xx}\frac{\partial\delta\varphi_{ x}}{\partial x}+N_{xx}\frac{\delta w_{0}}{R}+Q_{x}\frac{\delta\varphi_{z}}{R}+R_{z} \delta\varphi_{z}+Q_{xz}\delta\varphi_{x}+Q_{xz}\frac{\delta\delta\varphi_{z}}{ \partial x}\right]\mathrm{d}x \tag{18}\]  
where  
where \(C_{ijkl}\) is an elastic constant and \(l\) is the material length scale parameter, here introduced to account for the strain gradient stress field; \(e_{0}a\) and \(e_{1}a\) are the nonlocal parameters defining the nonlocal elastic stress field. The nonlocal kernel functions \(\alpha_{0}(x,x^{\prime},e_{0}a)\) and \(\alpha_{1}(x,x^{\prime},e_{1}a)\) satisfy the conditions developed by [PERSON] [52], whereby the general constitutive relations can be defined as  
\[\Big{[}1\ -\ (e_{1}a)^{2}\
abla^{2}\Big{]}\Big{[}1\ -\ (e_{0}a)^{2}\
abla^{2} \Big{]}\sigma_{ij}=C_{ijkl}\Big{[}1\ -\ (e_{1}a)^{2}\
abla^{2}\Big{]}\epsilon_{kl}\ -\ C_{ijkl}l^{2}\Big{[}1\ -\ (e_{0}a)^{2}\
abla^{2}\Big{]}\
abla^{2} \epsilon_{kl} \tag{24}\]  
\[\Big{[}1\ -\ \mu\
abla^{2}\Big{]}\sigma_{ij}=C_{ijkl}\Big{[}1\ -\ \lambda\
abla^{2}\Big{]} \epsilon_{kl} \tag{25}\]  
where \(\mu=(ea)^{2}\) and \(\lambda=l^{2}\). In addition, the constitutive relations for a nonlocal shear deformable CNTRC curved sandwich nanobeam can be written as  
\[\sigma_{xx}\ -\ \mu\frac{\partial^{2}\sigma_{xx}}{\partial x^{2}}=\overline{Q }_{11}^{k}\bigg{(}\varepsilon_{xx}\ -\ \lambda\frac{\partial^{2}\varepsilon_{xx}}{ \partial x^{2}}\bigg{)} \tag{26}\]  
\[\sigma_{zz}\ -\ \mu\frac{\partial^{2}\sigma_{xx}}{\partial x^{2}}=\overline{Q }_{55}^{k}\bigg{(}\gamma_{xz}\ -\ \lambda\frac{\partial^{2}\gamma_{xz}}{ \partial x^{2}}\bigg{)} \tag{27}\]  
Based on a nonlocal strain gradient theory, the following equilibrium equations are obtained in terms of the displacement components by substitution of Equation (19) into Equation (21).