esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Wind Direction and Velocity on PV Panels Cooling with Perforated Heat Sinks
## 3 Materials and Methods
### Numerical Modeling  
The ANSYS Fluent platform was used for numerical modeling in steady-state conditions. The geometric model and the mesh were created by using ANSYS Fluent-Meshing and ANSYS SpaceClaim.  
Given the particularities of the flow (that is not aligned with the mesh), the simulations were realized in the SIMPLE method. The second-order accuracy was used for solving the governing equations of the phenomena involved [8; 34].  
The solar radiation was modeled by the aid of the native module of Fluent--Solar Ray Tracing. The conversion of solar radiation into heat is synthesized as an absorption coefficient of the PV panel of \(\alpha\) = 0.7 [6; 8].  
The energy balance between the PV panel-heat sink ensemble and the surroundings were modeled by including both the convection and radiation effects [28]. The turbulence model used for the flowing air was the \(k\)-\(\varepsilon\) realizable model [34].  
The geometry of the model was created using SpaceClaim. For all studied cases, after preliminary analysis, the distance between fins was imposed to \(s\) = 150 mm. The fins are orthogonal to the heat sink sheet, as shown in Figure 1. The circular perforations had 30 mm or 60 mm in diameter and they are located at 100 mm one to another according to previous research works [6; 23]. In this way, a better air circulation is achieved near the heat sink and the cooling effect is enhanced.  
It is well known that the accuracy and correctness of the mesh lead to precise CFD (Computational Fluid Dynamics) [34]; therefore, in order to solve the highly orthogonal mesh that could result from a conventional mesh. The polyhedral mesh for surfaces and poly-hex mesh for volumes determined an increase of the simulation time, maintaining the same precision of the results. In order to achieve an appropriate mesh, different refinements are necessary [34]. For the present numerical modeling, the PV panel, heat sink and air volume had a mesh quality as presented in Table 2.  
In order to optimize the number of the cells of the mesh and the simulation time, a mesh independence study was realized. The preliminary simulations showed that 3 \(\times\) 10\({}^{6}\) cells for the base case and 5.5 \(\times\) 10\({}^{6}\) cells for cases with heat sinks determined accurate results compared to a higher number of elements (see Figure 2).  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Mesh Quality** \\ \hline Aspect ratio & 5.0–60 \\ Skewness & 0.4–0.7 \\ Orthogonal quality & 0.1–0.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The mesh quality of the simulation domain.
## 5 Results and Discussion
### Material Removal Rate  
Figure 14 shows the \(MRR\) comparison between the traditional CMP and the DEPP at different processing times; the dotted points in the graph are from the experimental data, and the curved lines show their fittings. From Figure 14, it can be seen that the \(MRR\) of sapphire substrates gradually decrease as time increases. This is due to the fact that during the initial polishing stage, the process mainly removes small protrusions generated by the former process, and the actual polishing area is extremely small. Therefore, the pressure is high. With the polished surface area increasing, the corresponding pressure decreases, so does the \(MRR\).  
Comparing the two polishing methods, the \(MRR\) of DEPP was significantly higher than that of the traditional CMP. The maximum increase is 71%, reaching 13 mg/h, and the minimum increase is 9.5%, reaching 4.6 mg/h. The reason behind this is that, subject to DEP force, the abrasives will move towards to and congregate on the surface of the sapphire substrate, increasing the number of abrasives that participate in polishing. In the same time, the movement of abrasives increases the pressure between the sapphire substrate and polishing slurry. The ability of removing the soft layer formed by chemical action is also improved. Therefore, the DEPP method greatly improves the polishing efficiency.
## 2 Methodology and Data
### Proposed Forecasting Framework
#### 2.1.2 Determining the Nonlinear Autoregressive (NAR) Neural Networks  
In this context, the NAR neural network not only inherits the characteristics of an MLP but also distinguishes itself by incorporating autoregressive elements. Specifically, the inputs supplied to the network's input layer are derived from previous values of the observed variable, enabling the network to capture temporal dependencies. This leads to a formulation of the NAR network in the form of the following Equation (1):  
\[X_{t}=f\big{(}X_{t-1},X_{t-2},\ldots,X_{t-p}\big{)}, \tag{1}\]  
where \(f\) represents an unknown generally nonlinear function, \(X_{t}\) denotes the time series containing the historical values of the observed variable, and \(p\in\mathbb{N}\) represents the number of inputs introduced into the network, corresponding to the number of lagged variables required to predict the future value, that, in our case, by the number of elements of the time series data, have to be used to approximate the function \(f\).  
In terms of architecture, the presented NAR network adopts a single hidden layer in line with previous studies demonstrating the capability of a single hidden layer MLP to approximate any function [56; 57; 58]. Activation functions such as sigmoidal or hyperbolic tangent are employed in the hidden layer, while the output layer utilizes the identity  
Figure 1: Example of MLP neural network structure.  
function, commonly used in regression tasks [59; 60]. The network can be defined using expression (2):  
\[net_{j} = \sum_{i=1}^{p}w_{i,j}X_{t-i}+\theta_{j}, \tag{2}\] \[X_{t} = \sum_{j=1}^{L}w_{j,\rho}\phi\big{(}net_{j}\big{)}+\theta_{o},\]  
where \(w_{i,j}\in\mathbb{R}\) represents the weight connecting input \(i\) to neuron \(j\) in the hidden layer, \(w_{j,o}\in\mathbb{R}\) represents the weight connecting neuron \(j\) of the hidden layer to the output, \(\theta_{j},\theta_{o}\in\mathbb{R}\) represent the biases applied to each layer, \(\phi\) denotes the activation function of the NAR network, and \(p\in\mathbb{N}\) and \(L\in\mathbb{N}\) represent the number of inputs and neurons in the hidden layer, respectively. Determining the optimal values of \(p\) and \(L\) is challenging, as this heavily depends on the nature of the observed variable and the researcher's experience.  
To compare different models, we consider a range of values for \((p,L)\in[1,20]\times[1,20]\), utilizing hyperbolic and sigmoidal tangent activation functions. The model selected as the optimal one is the one that achieves the lowest values in both the root mean square error (RMSE) and the mean absolute error (MAE) across the training set.
## 3 Results  
### Data Situation  
In order to obtain comprehensive and sufficient calculation results, this paper calculates the whole-network adjustment results of multi-source data according to the L-band satellite-ground two-way time synchronization and Ka-band inter-satellite two-way time synchronization of BDS on 1 March 2022. In addition, this paper makes a comparative analysis of \"one-hop\" reduction results for the same period. It is worth noting that GEO satellites mainly deliver SBAS, RDSS and other services, so they are without involvement in inter-satellite calculations. Therefore, this paper selects the data of 24 MEO satellites and 3 IGSO satellites for calculation. The data of all the 27 satellites are used for the inter-satellite calculation. The satellite-ground clock offset curve is shown in Figure 4, and ISL establishment during this period is shown in Figure 5.  
As shown in Figure 4, the satellite-ground clock offset curve changes steadily, and we can see from Figure 5 that satellite C38 and other 26 satellites all have established links. Based on such data, we verify the validity of the whole-network adjustment method.  
Figure 4: Curve of SGL clock offset.  
Figure 5: ISL between C38 and other satellites.
# Solution-Based Synthesis and Processing of Metal Chalcogenides for Thermoelectric Applications
## 2 Solution-Based Approaches for Synthesis of Metal Chalcogenides
### Hydrothermal or Solvothermal Method
synthesized Ce-, Y-, and Sm-doped Bi\({}_{2}\)T\({}_{3}\) nanopowders via hydrothermal treatment [51]. Doping with these rare earth elements can effectively decrease the thermal conductivity, especially for the Ce-doped material, which exhibits a ZT value up to 1.29 at 373 K. The thermoelectric properties of these nanostructured metal chalcogenides prepared by hydrothermal/solvothermal method and other solution-based approaches are summarized in Table 1.  
Figure 4: Scanning electron microscopy (SEM) images of nanostructured metal chalcogenides with different morphologies synthesized by the hydrothermal/solvothermal method: (**a**) Bi\({}_{2}\)Te\({}_{3}\) nanostring clusters; (**b**) Bi\({}_{2}\)Te\({}_{3}\) nanorod bundles; (**c**) Bi\({}_{2}\)Te\({}_{3}\) nanowires; (**d**) Bi\({}_{2}\)Te\({}_{3}\) nanoflowers; (**e**) Bi\({}_{2}\)Te\({}_{3}\) nanoparticles; (**f**) Cu\({}_{2}\)Se nanoplates; (**g**) SnSe microplates; (**h**) SnSe nanobelts. Reprinted with permission from the American Chemical Society [51], Elsevier Ltd. [54; 55; 56; 61; 66], and Wiley-VCH [53; 60].
###### Abstract  
With increases in train speed and traffic density, problems due to wheel polygons and those caused by wheel-rail impacts will increase accordingly, which will affect train operational safety and passenger ride comfort. This paper investigates the effects of polygonal wheels on the dynamic performance of the track in a high-speed railway system. The wheel-rail interaction forces caused by wheel polygons are determined using a dynamic vehicle-track model, and the results are entered into a slab track finite element model. The influence of the harmonic order and out-of-roundness (OOR) amplitude of wheel polygons on the transient dynamic characteristics of the track(von Mises equivalent stress, displacement, and acceleration) is examined under high-speed conditions. The results indicate that the vibration acceleration and von Mises equivalent stress of the rail increase in proportion to the harmonic order and the OOR amplitude and velocity of a polygonized wheel. The vibration displacement of the rail first increases and then decreases with a change in the harmonic order, and reaches a maximum at the ninth order. The dynamic responses of the concrete slab layer, cement-asphalt layer, and support layer increase linearly with the harmonic order and amplitude of wheel polygons and decrease from top to bottom. Through a combination of numerical simulations and real-time monitoring of rail vibrations, this study provides guidance on potential sensor locations to identify polygonized wheels before they fail.  
high-speed train; polygonized wheel; slab track; numerical simulation +
Footnote †: journal: Applied Sciences
# RPC-EAU: Radar Plot Classification Algorithm Based on Evidence Adaptive Updating
## 2 Related Work
### Intelligent Radar Plot Classification  
In radar data processing, a large number of clutter plots surround the real target, which increases the difficulty of accurate classification. Traditional methods for judging truth from falsehood are difficult to use to fully characterize the distinguishing rules of sample features. The birth and development of artificial neural networks have provided great support for solving this problem. Therefore, some radar plot recognition algorithms based on neural networks have been studied by experts and scholars.  
A fully connected neural network, also known as a feedforward neural network, is the most basic type of artificial neural network. In this network, neurons (or nodes) are organized into multiple layers, with each layer's neurons connected to both the previous and subsequent layers, but there is no connection between neurons within the same layer. The data propagates unidirectionally from the input layer to the output layer without feedback (or loop) connections. Each connection of a fully connected neural network has a weight, which is learned through training data. Each neuron calculates the weighted sum of all its inputs, and then obtains its output through an activation function such as ReLU, sigmoid, or tanh. The fully connected network structure for radar block classification is shown in Figure 2.  
Figure 2: The fully connected network structure for radar plot classification.  
Input layer: The number of nodes in this input layer depends on the feature dimension of the radar data. For example, assuming the input feature is \(F=[f_{1},\cdots,f_{M}]\), the number of input nodes needs to be set to \(M\).  
Hidden layer: The design of network hidden layers is usually related to two factors, the size of radar plots and the spatial distribution of feature data. There are no specific rules to follow, and usually exploratory experiments are used to guide the design.  
Output layer: The number of nodes in the output layer is generally set to 2, corresponding to the probabilities of being the target and clutter, respectively. Using \(P_{\text{target}}\) to represent the probability that the radar point is the target, and \(P_{\text{clutter}}\) to represent the probability of clutter, they satisfy the relationship \(P_{\text{target}}+P_{\text{clutter}}=1\).  
Fully connected neural networks have many advantages, such as an intuitive structure, high computational efficiency, and convenient configuration. Therefore, the research on its application in radar plots is very valuable.
# Retrieving Freeze/Thaw Cycles Using Sentinel-1 Data in Eastern Nunavik (Quebec, Canada)
## Materials and Methods
### Validation and Threshold Optimization  
To validate the estimated FT state, real-time temperature measurement data were used. A temperature threshold of \(0\,^{\circ}\mathrm{C}\) was set for the in situ data, which means that, when the measured temperature is higher than \(0\,^{\circ}\mathrm{C}\), the soil state is set as thawed; otherwise, the soil state is defined as frozen. Every FT state that was estimated from the S1 image was then compared to the FT state that was determined on the basis of the temperature data, which were measured on the same date as the S1 acquisition. For S1 acquisition dates, where no exactly matching measurement is available, the nearest measurement within \(\pm 5\) days (less than the shortest revisit time) was searched for usage.  
To make the outputs from different locations more comparable, the calculated seasonal scale factors \(\Delta(t)\) from Equation (1) were further normalized to the range of \([0,1]\). Furthermore, an optimization step was included in the working process. Instead of fixing a threshold \(T\) (Equation (2)) at a certain value, the threshold was tested in a range of \([0,1]\) with a step of 0.01 during the estimation process. The accuracies of the tested timeseries were compared with each other to find the most effective threshold.
## 3 Methodology
### Energy Consumption Model  
The energy consumed by the \(v\)th sensor node, \(EC_{v}\) in a round of data transmission is the summation of the energy consumed by the sensor node in sending and receiving data packets and is given in the Equation (30).  
\[EC_{v}(p,d)=E_{tx}(p,d)+E_{rx}(p) \tag{30}\]The energy consumed by a sensor node for sending and receiving data packets is given in Equations (31) and (33), respectively [33].  
\[E_{tx}(p,d)=\begin{cases}E_{elec}p\ell+E_{fs}pd^{2}\ if\ d\leqslant d_{o}\\ elecp\ell+E_{mp}pd^{4}\ if\ d>d_{o}\end{cases} \tag{31}\]  
\[E_{rx}(p)=E_{elec}p\tau \tag{32}\]  
where \(p\) is the number of bits per packet, \(d\) is the distance between the source node and the destination node, \(\ell\) is the number of packets sent by a sensor node per round, \(\tau\) is the number of packets received by a sensor node per round, \(E_{tx}(p,d)\) is the transmission energy, \(E_{rx}(p)\) is the reception energy, and \(E_{elec}\) is the electronic energy consumed to transmit or receive unit data of the packet. \(E_{fs}\), \(E_{mp}\) are the transmit amplifier efficiency and depend on the transmitter amplifier model (free space model is employed when \(d\leqslant d_{o}\), otherwise the multipath model is employed). \(d_{o}\) is the baseline distance and is obtained by equating the two expressions of \(E_{tx}(p,d)\) at \(d=d_{o}\) and is given as:  
\[d_{o}=\sqrt{\frac{E_{fs}}{E_{mp}}} \tag{33}\]
# Dynamic Controllability of Processes without Surprises
## 5 Sudden Termination Constellations and Patterns
### Sudden Termination Constellation and Pattern STP-5  
A _Sudden Termination Pattern STP-5_ (see Figure 8) is defined as follows: A contingent activity \(C\) and a semi-contingent activity \(S\) with constraints \(ubc(C.e,S.e,w)\) and \(ubc(S.s,C.e,v)\) as above are in an STP, iff there is no way to schedule the start of \(S\) and the start of \(C\), such that the value for the end of \(S\) can be fixed, when \(S\) starts. This means that for all admissible start times of \(S\) and \(C\) and all possible durations of \(S\) there is a possible duration of \(C\) such that one of the constraints is violated.  
Figure 8: Sudden Termination Pattern 5 (STP-5).  
Figure 7: Sudden Termination Pattern 4 (STP-4).  
**Definition 11** (Sudden Termination Pattern 5).: _Let \(P\) be a dynamically controllable process. Let \(S\) and \(C\) be activities in \(P\), with the duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(ubc(S.s,C.e,v)\) be constraints in \(P\)._  
\(S\) _and \(C\) are in a sudden termination pattern STP-5 iff \(\forall S.s,C.s\)\(\forall S.d_{min}\leq S.d\leq S.d_{max}\)\(\exists C.d_{min}\leq C.d\leq C.d_{max}:\ S.s+S.d>C.s+C.d+w\), or \(C.s+C.d>S.s+v\)_  
In the following we derive conditions to check whether an STP-5 might occur for a given pair of activities \(S\) and \(C\).  
**Theorem 9**.: _Let \(P\) be a dynamically controllable process. Let \(S\) be a semi-contingent activity, and \(C\) be a contingent activity in \(P\), with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(ubc(S.s,C.e,v)\) be constraints in \(P\)._  
_A sudden termination of \(S\) cannot occur, iff \(C.d_{max}+S.d_{min}-v<S.e-C.s\leq C.d_{min}+w\) holds in \(P\)._  
Proof.: Please refer to Appendix A.4.  
**Theorem 10**.: _Let \(P\) be a process. Let \(S\) be a semi-contingent, and \(C\) be a contingent activity in \(P\) with duration constraints \(d(S,S.d_{min},S.d_{max},sc)\) and \(d(C,C.d_{min},C.d_{max},c)\). Let \(ubc(C.e,S.e,w)\) and \(lbc(C.s,S.s,v)\) be constraints in \(P\)._  
_A sudden termination of \(S\) cannot occur, iff the constraints \(lbc(C.s,S.e,C.d_{max}+S.d_{min}-v)\) and \(ubc(C.s,S.e,C.d_{min}+w)\) hold in \(P\)._  
Proof.: The constraints express the condition described in Theorem 9. Thus in a process model including these constraints \(S\) and \(C\) cannot be in an STP-5.
## 4 Proposed Sing-Ant Methodology
### Antenna Clusters  
Antenna clusters are made up of the outer centric rings of antenna beams that surround the core and they are made up of 8 antenna beams, all scanning successively one after the other. The cluster's area of operation increases with each cluster, and as the circular area of coverage grows after each antenna is finished. Due to the large number of antennas, the sectors tend to have higher reliability and outrange the core. The mathematical operations required to work on the clusters are identical to the operations needed by the core. The structure of all the antenna cores is shown in Figure 2, where shows the examples of antenna clusters on the left side of the image, and a single isolated core is shown on the right side.  
Figure 2 shows that the antenna clusters are rings around the core. They are adept at determining the location of the target tag due to their lack of polarity, because, whilst they contain the same spread as the core cluster in that they calculate the difference in direction at 45\({}^{\circ}\) angles, they do not suffer from the same polarity drawbacks as the core does, and hence they can locate the target tag without having to be optimized.
# Geolocation and Tracking by TDOA Measurements Based on Space-Air-Ground Integrated Network
## 2 Theoretical Performance
### Crlb  
CRLB presents the best accuracy than an unbiased estimator could achieve. Due to the constrained conditions of the target's position and velocity on the earth, the derivations of CRLB can be classified into two types. In this section, we evaluate the two types of CRLB for passive geolocation and tracking based on SAGI network as follows. The first type of method is developed for the target denoted in the ECEF coordinate system. We first derive the FIM without using the constrained conditions. Then, the constrained CRLB is generated by the inversion of FIM and gradient matrix of the constraints [31]. However, the FIM may be occasionally nonreversible. For the purpose of more accurate estimation, the second type of the CRLB is derived for the target modeled in the geodetic coordinate system.
## 4 Vehicle Trajectory Prediction Model
### Deep Learning EDLN Model  
The encoder-decoder long short-term memory network (EDLN), as a variant of the long short-term memory (LSTM) recurrent neural network, has been shown by [PERSON] et al. [20] to effectively extract trajectory features in complex scenarios. Therefore, EDLN was chosen as the basis for the prediction model, which is primarily divided into three components: the encoder, the decoder, and the mixture density layer, as shown in Figure 5. The encoder and decoder of the EDLN are composed of LSTM stacks with the same structure and weights [21; 22; 23]. The encoder transforms historical trajectory data into a bridging vector, storing the behavioral features of interacting vehicles, while the  
Figure 4: Distribution of the risk fields for vehicle lane changing behavior. (**a**) Three-dimensional distribution of the field strength. (**b**) Field strength plane distribution.  
decoder outputs the vehicle's \(x\) and \(y\) coordinate vectors based on the encoded features of the bridging vector. Finally, the mixture density layer outputs the predicted distribution of the vehicle.  
Considering the prediction performance, the model uses two layers of LSTM, with a total of 512 neurons, and the initial learning rate was set to 0.001 [24; 25; 26; 27]. The number of Gaussian distributions in the mixture density layer was set to 6. Since evaluating the prediction model was crucial for determining its effectiveness and reliability, the learning rate was halved if the validation loss did not improve within 5 epochs. To prevent overfitting, training was stopped and the trained model was saved if the validation loss did not improve within 10 epochs.
# Recent Advances in Plasmonic Sensor-Based Fiber Optic Probes for Biological Applications
## 2 Physics of Surface Plasmons  
A quantum of a collective oscillation of free electrons that are confined evanescently on the surface of a metal surface induced by an electromagnetic field is known as surface plasmons (SPs), a term that was introduced by [PERSON] in 1957 [PERSON] (1957). SPs that propagate in the transverse magnetic (TM) direction (p-polarized) are known as surface plasmon waves (SPWs). In what follows, we discuss surface plasmon polaritons (SPPs) and localized surface plasmon polaritons (LSPPs) in detail.
## 4 Result and Discussion  
The algorithms are evaluated based on the previously described metrics of RMSE, MAE, and R\({}^{2}\). The predicted and the actual values are compared for train and test datasets. In a base case scenario, the predicted and the experimental values should yield a 45\({}^{\circ}\) slope line that can be illustrated as best fit curve to visually inspect the deviation.