esa-sceva/satcom-chunk-collection huggingface dataset:
# PV Benefits Assessment for PV-Powered Charging Stations for Electric Vehicles
## 2 PV Benefits Assessment Methodology
### Phase 1: Maximum Pre-Sizing
#### 2.1.5 Fourth Step: Sizing of the Storage  
The solar irradiation \(G\) data for the optimum location, orientation \(\gamma\) and inclination are obtained from the PVGIS tool, whose data are available at present for every 10 min on any day, for the years from 2005 to 2016. The corrected irradiation value \(G^{\prime}\) is calculated according to a correction factor \(k\) and \(G\):  
\[G^{\prime}=k\cdot G \tag{5}\]  
where the factor \(k\) depends on \(\gamma\) and \(\alpha\), as summarized in Table 1.  
The total PV power \(P\) produced by the PVCS is calculated according to Equation (6):  
\[P=N_{PV-max}\cdot\frac{G^{\prime}}{G_{STC}\cdot P_{STC}} \tag{6}\]  
where \(P_{STC}\) is the PV panel power at standard test estimated from a database of different available ranges of PV panels. This power calculation is made for the best sunny day in terms of irradiation, according to data from PVGIS. The obtained power is corrected by considering the losses due to the infrastructure estimated at 14%. Then, by integration, the total energy produced in this best day \(E_{best-day}\) is obtained. Aiming at a good trade-off between cost and operation, the considered assumption is that the maximum capacity \(C_{max}\) of stationary batteries must allow to store at least half of the energy produced on this best day, hence, the following Equation (7):  
\[C_{max}=50\%\cdot E_{best-day} \tag{7}\]
# Drone Arc Routing Problems and Metaheuristic Solution Approach
## 3 Problem Definition
\begin{table}
\begin{tabular}{c c} \hline \hline
**Nodes** & **Coordinate Values (x, y)** \\ \hline Depot & \((0,-110)\) \\  
\hline \hline \end{tabular}
\end{table} 
Table 1: Coordinate values of DARP-1 nodes. Figure 1: DARP-DD example 1.  
the drone consumes 40 Joules when deadheading through an edge (\(1-2\)) and 80 Joules when servicing the same edge. Thus, the deadheading demand value for an edge (\(1-2\)) is 40 Joules, while the servicing demand value is 80 Joules. Please note that the non-required edges only have a deadheading demand value, because drones do not serve these edges. The mobility of the drone on the graph is determined as follows: The drone can directly traverse from the depot node to all nodes on the graph and from all nodes to the depot node. The edges where the drone can traverse from the depot to other nodes are shown with red dashed lines in Figure 2.  
As presented in Figure 2, the drone can directly traverse any node on the graph from the depot node by deadheading. On the other hand, the drone can directly traverse between all predefined nodes on the graph. In other words, after the drone has completed servicing the required edge, it can directly traverse to the node of any other required edge on the graph. The mobility of the drone between nodes is presented in Figure 3. Since the drone can perform a large number of deadheading between nodes, only a subset of them are presented in Figure 3.  
In Figure 3, the deadheading edges that drones can traverse between nodes are shown by the red dashed lines. For example, the drone can carry out deadheading between different components with edges such as (\(15-12\)), (\(9-3\)), and (\(14-1\)) as well as between  
Figure 3: Traversing between nodes in the DARP-DD. Figure 2: Traversing from the depot node to the other nodes in the DARP-DD. nodes of the same component such as the edge (\(13-16\)). Since the drone is capable of traversing between all nodes, the example consists of \(19\) nodes in total including the depot node and \(17\) required edges. The number of non-required edges in the graph is calculated by the number of nodes. The disconnected graph is converted to complete graph since the drone can directly traverse between all nodes on the graph. So, this graph will have \((n\times(n-1))/2\) non-required edges with \(n\) nodes. As a result, this example has \(171\) non-required edges. In the relevant problem, some critical areas or borders may need to be monitored multiple times by drones during surveillance. In this case, servicing the required edges that correspond to the relevant boundaries (areas) only once is not sufficient. So, we can generate an instance for the MS-DARP-DD using Figure 1. For example, on the graph presented in Figure 1, due to the special case of the boundaries represented by the edges (\(2-3\)) and (\(9-10\)), it may be necessary to monitor the required edge (\(2-3\)) three times and the required edge (\(9-10\)) two times. The related multiple services to the required edges can be performed by the same drone or different drones.
## 6 MIMO Antennas in Indoor Environment  
MIMO antennas, when installed within the inhouse region, suffer with the major issue of reduced channel capacity. Some efforts have been made to improve the capacity performance of MIMO systems. Applying frequency-selective (F-S) wallpaper to the walls to block undesired interference with the desired radio communication services is a potential method of resolving indoor situations [92; 93]. Considering both SISO and MIMO systems, the features and design of the new F-S wallpaper are reported in [94]. The wallpaper, which is built on symmetric and periodic metallic hexagons, is designed and applied to the ordinary walls in order to block 5 GHz transmissions without obstructing the other radio communication services. The wallpaper is seated using the periodic boundary finite-difference time-domain (PB-FDTD) approach based on the unit cell analysis method. In [95], interference levels are decreased using the wallpaper inside a 4 \(\times\) 4 MIMO system for indoor environments. This MIMO design consists of an array of half-wavelength dipoles for the receiving antenna and a cavity-backed dynamic meta-surface antenna (DMA) for the transmission of the signal. While the transmitter's location is fixed, the MIMO channels are simulated and the channel capacity is calculated at various receiver locations using the ray tracing approach. Compared to a MIMO system using a sub-aperture phased array for the transmitting antenna, the reported adaptive radiation pattern provided by the DMA can achieve excellent capacity improvement [95].
# Congestion-Aware Geocast Routing in Vehicular Delay-Tolerant Networks
## 2 State of the Art  
### Unicast Routing  
As described in [8], the Direct Delivery protocol is the simplest way to conceive a unicast delivery scheme. The node that carries the message forwards the message only if it establishes a direct contact opportunity with the target node.  
The Spray and Wait protocol [9], in turn, is based on the dissemination of a limited set of replicas of a given message to distinct contacted nodes during a first phase called the spray phase. Afterwards, during the wait phase, relay nodes assume the responsibility of delivering the message directly to the final recipient. However, regarding the spray phase, several heuristics can be envisioned to properly distribute messages.  
Finally, the Epidemic [10] protocol is classified as a flooding-based scheme, since the idea behind it is based on the provision of unlimited message replication, throughout the network, by exchanging all possible messages whenever contact opportunities arise.
# Method to Control the Deformation of Anti-Slide Piles in Zhenzilin Landslide
###### Abstract  
Anti-slide piles were used in the region of the Zhenzilin landslide in Sichuan, China. The horizontal displacement of these piles exceeds specifications. Deterioration in bedrock properties may cause deformation, thereby causing landslide destabilization. An approach was developed for the analysis of anti-slide pile in two bedrocks with different strengths below the slip surface. A relationship has been established between the modulus of subgrade reaction of the first weak bedrock and reasonable embedded length for landfill slopes with strata of various strengths. Furthermore, the influence of embedding length on deformation has been studied to determine the reasonable embedded length, which helps reduce deformation and ensure landslide stability. The results reveal that (1) at a constant embedded length, horizontal displacement increases with the thickness of the first soft bedrock, meanwhile the maximum shear force remains constant, and the bending moment first increases followed by subsequent decrease; (2) with an increase in the embedded length, horizontal displacement and the maximum shear force of the pile in the embedded bedrock decrease, whereas the bending moment increases; (3) the maximum internal forces and horizontal displacement increase with a decrease in the subgrade reaction modulus of the first weak rock; and (4) the reasonable embedded length of an anti-slide pile increases with a decrease in the subgrade reaction modulus of the first weak bedrock. The proposed approach can be employed to design anti-slide piles in similar landslide regions to control pile-head deformation.  
colluvial landslide; anti-slide pile; embedded length of piles; multilayered bedrock +
Footnote †: journal: Applied Physics Letters
###### Abstract  
In recent years, the ultra-dense low Earth orbit (LEO) satellite communication (UD-LSC) networks such as SpaceX and OneWeb are under rapid development to provide worldwide and broadband services. However, the deployment of thousands of LEO satellites into space leads to the shortage of the orbital and the frequency resources. Spectrum sharing between geostationary Earth orbit (GEO) satellite systems and LEO satellite systems seems to be a promising way to alleviate the problem of restricted spectrum resources. In this paper, a joint cooperative beam association and power allocation scheme for the UD-LSC network to share the same spectrum with a GEO satellite system is considered. By exploiting the cooperative transmission between multiple LEO satellites, we first propose a many-to-many match game-based beam association (MGBA) algorithm to obtain a stable matching between LEO satellites and beam cells, and then, we propose a successive convex approximation (SCA)-based power allocation (SPA) algorithm to iteratively acquire the sub-optimal power allocation matrix. Simulation results show that the proposed MGBA-SPA scheme outperforms other contrast schemes from the perspective of communication satisfaction, and it realizes the balance between the traffic request and the provided capacity of each ground beam cell.  
UD-LSC; spectrum sharing; beam association; power allocation +
Footnote †: journal: Electronics
# Geometry and Kinematics of the Central Fault Zone, Fula Sag, Central Africa Shear Zone
## 3 Datasets and Methodologies  
The seismic data utilized in this study comprise two 3D seismic surveys, namely the Fula and the Moga 3D seismic surveys, which were acquired between 2000 and 2003. Covering an area of 500 km\({}^{2}\), the surveys recorded a length of 5.0 s two-way travel time (TWT), with a line spacing of 9.375 m \(\times\) 9.375 m and a vertical sample rate of 2 ms. The polarity of the seismic data is positive. The dominant frequency ranges from 30 to 50 Hz, and the acoustic velocities of the AG to Zaraf section range between 2000 and 5000 m/s, resulting in an estimated vertical seismic resolution of 40-167 m. Stratigraphic correlation was performed using wells Fula-1, FN-67, Moga-8, Moga-11, and Moga-23 (Figure 1b), among more than 30 exploration and evaluation wells in the study area.  
Five key horizons, namely the top of the Amal Formation, the top of the Darfur Group, the top of the Bentiu Formation, the top of the AG\({}_{1}\) Member, and the top of the AG\({}_{2}\) Member, were interpreted across the research area to illustrate the lateral and vertical variations in the structure of the extensional faults (Figure 2). The fault system under investigation was labeled F\({}_{1}\) to F\({}_{13}\) (Figure 2). Based on the seismic interpretation results, an accurate description of the geometry for the Fula-Moga transfer zone is provided.  
After interpreting the horizons and faults, the 3-D fault planes were created and visualized using corner point gridding in the Structural Modeling module of PETREL 2020.2 software. Subsequently, the throw attribute of the main fault planes was generated in PETREL software using the 3-D throw mapping technique described by [PERSON] [57] and applied by [PERSON], et al. [58]. The throw represents the vertical component of the dip-separation of a normal or reverse fault, measured in a vertical cross-section perpendicular to the strike of the fault [59]. Throw-distance graphs (T-X) for individual main faults F\({}_{1}\), F\({}_{3}\), and F\({}_{2}\), associated with the CFZ, were generated every 400 m along strike using the exported throw data from the software. Finally, the maximum throw subtraction method was employed to quantitatively study the growth process of each fault segment and reconstruct the fault segmentation growth of the entire fault zone.
# A Comparative Study of Deep Learning Models for Dental Segmentation in Panoramic Radiograph
## 4 Experimental Results and Discussion
### Comparison with the State of the Art  
From the results described above, we highlight the results of the DoubleU-Net and Nano-Net models, both for training using data augmentation, as well as for training without data augmentation. Thus, this section presents a comparison of these models with others from the literature [15,18]. Table 5 presents the results obtained by the models proposed by [15,18], using the same dataset tested in this work.  
The U-Net* model proposed by [18] obtained the best Dice coefficient with 93.58%, followed by DoubleU-Net with data augmentation with 92.886%. TSASNet obtained the best accuracy, with 96.94%, while U-Net* [18] with 95.17% had the worst. Precision was a highlight of Nano-Net (True) with 96.326% but obtained the worst recall. For recall, the best result was U-Net* [18] with 93.91%, followed by 93.77% for TSASNet and 92.705% for DoubleU-Net (True).  
Regarding the number of parameters, TSASNet requires more than 78 million trainable parameters, which shows how costly this architecture can be for training. DoubleU-Net managed to lower this number to 29 million and the greatest prominence is for Nano-Net, which uses 235,000. The number of parameters trained by the U-Net architecture* [18] was not presented, but by analyzing the work of [18], their proposal uses an ensemble of two U-Net models, in which each model corresponds to approximately 31 million parameters. Thus, their proposal is expected to be close to 62 million trainable parameters.  
It is observed that U-Net* [18] has better results in two metrics, while TSASNet and Nano-Net (True) have better results in one. With the analysis, we see DoubleU-Net (True) has competitive results in almost all metrics and Nano-Net is competitive even with the number of trainable parameters being far below any other model.
# Random Field-Based Time-Dependent Reliability Analyses of a PSC Box-Grider Bridge
## 2 Basic Theory of Random Field  
### Discretization of Random Field  
The random field model \(H(w)\), denoted by \(\{H(w),w\in\Omega\}\), represents the variability in the spatial distribution of structural parameters, and \(\Omega\) indicates the definition domain of the random field [21]. Considering the effects of a random field in structural reliability analysis, the random field should be discretized into a set of random variables. At present, several methods have been developed for the discretization of random fields, including the midpoint method [22], interpolation method [23], local average method (LAM) [24; 25], weighted integral method [26], orthogonal expansion method [27], and optimal linear evaluation method [28]. However, each method does not have absolute advantages over other methods. The LAM means that the average characteristic of a random field in any discrete element represents its probability distribution in this element. This method can achieve the balance between calculation accuracy and efficiency and can be easily programmed in finite element software; therefore, it is widely used in practice. In this paper, the method is used to discretize the random fields of a structure. The applications of the LAM in the discretization of one-dimensional (1D) and two-dimensional (2D) random fields are described as follows.
## 4 Research Directions for System Designs and Optimization Concepts
### Optimization in PHY Security: Current Status and Main Issues  
The information-theoretic studies on the investigation and characterization of attainable rates of security performance for multi-dimensional secure wireless networks that are presented in [31; 81; 107; 108] have inspired the optimization and system design of many signal processing techniques. From the fundamental viewpoints of optimization objectives, four representative techniques have been adopted in this area: (1) convex optimization; (2) secure beamforming; (3) artificial noise (AN); and (4) zero-forcing (ZF) precoding.
## 3 MSA-LMS Adaptive Filter
### MSA-LMS Adaptive Filter Implementation and Analysis  
An adaptive filter updates the coefficient of the filter or the system parameters automatically by an adaptive algorithm so that the system reaches the optimum state.  
Adaptive filters are widely used in various fields of communication-signal processing, such as radar and active control of vibration and noise, owing to their self-tuning ability to change the signal to be tracked and the surrounding situation. The structure of the proposed MSA-LMS adaptive filter is shown in Figure 2.  
The proposed algorithm is verified by simulation for four types of signals: \"Incommensurate Sinusoids\", \"Periodic Sinusoids\", \"Beating\", and \"Amplitude Modulated\". The vibration reduction performance is found to be much improved. This result can be applied to an active control system for the reduction of vibration and noise in a next-generation powertrain; for example, it has recently been applied to an electric car having signals with complex spectra. The MSA-LMS algorithm is implemented in four stages: Stage 0, Stage 1, Stage 2, and Stage 3. If the same algorithm is used in the control system of each stage of the active period structure, improvement of tracking performance will not be expected regardless of the stage. Therefore, we proposed the above structure to allow a different control for each stage. Based on the MSA-LMS algorithm, Stage 1 is applied to active element 1 of an active control system, Stage 2 to active element 2, and Stage 3 to active element 3. As the stage increases, the number of channels of the adaptive filter is increased, and the frequency of the resultant signal of the LMS algorithm is additionally targeted to improve the tracking performance and minimize the error value.  
In the simulation performed in this study, the control time of the four stages is set as 1 s, and the sampling time is defined as 0.0001 s. Accordingly, Stage 0 has a range from 0 to 0.9999 s, Stage 1 from 1 to 1.9999 s, Stage 2 from 2 to 2.9999 s, and Stage 3 from 3 to 3.9999 s.
## 4 Results and Discussion
### Four-Class to Two-Class Classification
Overall, the results emphasize the need for the careful consideration of various factors, including the classification task design, representation of classes, selection of input features, network architecture, and optimization techniques, to achieve optimal classification accuracy. Further analysis, experimentation, and refinement of methods are warranted to advance our understanding and improve the accuracy of classification tasks.
# An Enhanced Privacy-Preserving Authentication Scheme for Vehicle Sensor Networks
## 4 Review of the Chuang-Lee's Scheme
### Registration  
**LE Registration**: In this procedure, an LE registers itself with the AS via the manufacturer or a secure channel. The secure key set \(\{PSK_{i},i=1,...,n\}\) is sent to the LE by the AS. Only this secure key set is required to be stored in the secure hardware of the LE. No other user information needs to be stored. Furthermore, the lifetime of each \(PSK_{i}\) is set to be short for robust security. When the lifetime of each trusted vehicle's key expires, this vehicle is required to perform the key update procedure with the LE. The procedure for the key set generation is depicted in Figure 2. It can be seen that the old \(PSK\) (e.g., \(PSK_{1}\)) cannot be used to derive the new \(PSK\) (e.g., \(PSK_{2}\)) because a one-way hash function is introduced in the key generation procedure.  
**Normal Vehicle Registration**: All vehicles except LEs need to perform this procedure when they are delivered to market. This registration procedure is performed only once by each vehicle.  
* \(U_{i}\to AS\): A user \(U_{i}\) chooses his password \(PW_{i}\) and sends its public identity \(ID_{i}\) and \(PW_{i}\) to the AS via the manufacturer or a secure channel.
* The AS evaluates the following parameters for \(U_{i}\) after it receives \(ID_{i}\) and \(PW_{i}\): \(A_{i}=h\left(ID_{i}||x\right)\), \(B_{i}=h^{2}\left(ID_{i}||x\right)\)\(=h\left(A_{i}\right)\), \(C_{i}=h\left(PW_{i}\right)\oplus B_{i}\), and \(D_{i}=PSK\oplus A_{i}\).
* \(AS\to U_{i}\): The parameters (i.e., \(ID_{i},B_{i},C_{i},D_{i},h\left(\right)\)) are stored in the OBU's secure hardware by the AS via a secure channel.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Notation** & **Definitions** \\ \hline \(x\) & A private key for the AS \\ \(x_{i}\) & A private key for user \(i\) \\ \(PSK\) & A pre-shared secure key set among the LEs and the AS \\ \(ID_{i}\) & The identification code for entity \(i\) \\ \(PW_{i}\) & The password for user \(i\) \\ \(AID_{i}\) & The alias for entity \(i\) \\ \(h\) & A secure hash function \\ \(\oplus\) & The XOR operator \\ \(\|\) & The combination of strings \\ \(P_{pub_{i}}\) & A public key for user \(i\) and \(P_{pub_{i}}=x_{i}P\) \\ \(p\) & A secure large prime \\ \(E(F_{p})\) & A secure elliptic curve \\ \(p\) & The primitive generator for \(G\) \\ \(G\) & The subgroup of \(E(F_{p})\) with order \(p\) \\ \(Z_{p}^{*}\) & The set consisting of all primes in \(\{0,1,...,p-1\}\) \\ \(r,y\)\(\in\)\(R_{2}^{*}\) & An element selected randomly from \(Z_{p}^{*}\) \\ \(SK_{ij}\) & A session key between entity \(i\) and entity \(j\) \\ \(MSG_{K\!U}\) & A key update message \\ \hline \hline \end{tabular}
\end{table}
Table 1: The notations.  
Figure 2: Key set generation scheme based on the hash–chain method.