esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Governing Equations
### Theoretical Equations
#### 2.2.2 Equations for the Specific Nodes  
As can be seen in Figure 2a, at the intersection of the spillway tunnel and the first air intake well, the energy equation and mass conservation equation can be expressed as  
\[p_{h}+\frac{1}{2}\rho_{a}{v_{h}}^{2}-(\zeta_{z}+\zeta_{k})\frac{1}{2}\rho_{a} {v_{h}}^{2}=p_{t}+\frac{1}{2}\rho_{a}{v_{t}}^{2},\text{ and} \tag{2}\]  
\[v_{h}d=v_{t}A_{a}, \tag{3}\]  
Figure 1: Schematic diagram of the multi-intake-well air supply system in a spillway tunnel. Note: Serial number of dot circles I–IV represent different connection nodes and V represents a gradually varied section.  
where \(p_{t}\) is the air pressure in the spillway tunnel; \(v_{t}\) is the air velocity in the spillway tunnel; \(a\) is the cross-sectional area of the air intake well; and \(A_{a}\) is the cross-sectional area of the water-free space of the spillway tunnel.  
As shown in Figure 2b, at the intersections of the spillway tunnel and the other air intake wells, the equations should be  
\[p_{h}+\frac{1}{2}\rho_{a}{v_{h}}^{2}-(\zeta_{z}+\zeta_{k})\frac{1}{2}\rho_{a}{v _{h}}^{2}=p_{t2}+\frac{1}{2}\rho_{a}{v_{t2}}^{2}, \tag{4}\]  
\[p_{t1}+\frac{1}{2}\rho_{a}{v_{t1}}^{2}-(\zeta_{z}+\zeta_{k})\frac{1}{2}\rho_{a }{v_{t1}}^{2}=p_{t2}+\frac{1}{2}\rho_{a}{v_{t2}}^{2},\text{ and } \tag{5}\]  
\[v_{h}a+v_{t1}A_{a1}=v_{t2}A_{a2}, \tag{6}\]  
where \(A_{a1}\), \(v_{t1}\) and \(p_{t1}\) respectively are the cross-sectional area, the air velocity and the air pressure of the water-free space of the spillway tunnel upstream of the air intake well, and \(A_{a2}\), \(v_{t2}\) and \(p_{t2}\) respectively are the cross-sectional area, the air velocity and the air pressure of the water-free space of the spillway tunnel downstream of the air intake well.  
As shown in Figure 2c, at the node where the aerator is located, the following can be obtained:  
\[p_{t1}+\frac{1}{2}\rho_{a}{v_{t1}}^{2}-(\zeta_{z}+\zeta_{s})\frac{1}{2}\rho_{ a}{v_{a}}^{2}=p_{a}+\frac{1}{2}\rho_{a}{v_{a}}^{2}, \tag{7}\]  
\[p_{t1}+\frac{1}{2}\rho_{a}{v_{t1}}^{2}-(\zeta_{z}+\zeta_{k})\frac{1}{2}\rho_{a }{v_{t1}}^{2}=p_{t2}+\frac{1}{2}\rho_{a}{v_{t2}}^{2},\text{ and } \tag{8}\]  
\[v_{t1}A_{a1}=v_{a}A+v_{t2}A_{a2}, \tag{9}\]  
where \(v_{a}\), \(p_{a}\) and A are the air velocity, the air pressure and the cross-sectional area, respectively, at the bottom of the air ducts, which are arranged symmetrically on both sides of one aerator to connect the top of the tunnel and the cavity under the jet.  
In addition, at the outlet of the spillway tunnel, air flows into the atmosphere. Therefore, there is  
\[p_{0}=0, \tag{10}\]  
where \(p_{0}\) is the air pressure at the outlet of the spillway tunnel.
## 3 Base Station Deployment and Self-Optimization Positioning Method
### Experiment and Results
#### 3.4.2 Cross-Regional Positioning Accuracy Comparison Experiment  
Traditional weighted average positioning algorithms do not incorporate regional selection distinctions. To validate that the precision of the transition zone-based base station self-optimization positioning method surpasses that of the traditional weighted average positioning algorithms, the ranging information dt is generated with errors based on the true value d using the following formula during simulation:  
\[d_{t}=\left\{\begin{array}{c}d+r\times e^{0.1\times|d-x_{a}|}\,0<x<x_{a}\\ d+r\times 0.03\,x_{a}\leq x\leq x_{b}\\ d+r\times(0.03+0.05\times|d-x_{b}|)\,x>x_{b}\end{array}\right. \tag{7}\]  
In the equation, \(r\) is a uniformly distributed random number within the range [\(-1\) m, \(1\) m]. If the generated error is less than 0, then the error is taken as \(r\times x_{a}\). Although noise in real-world scenarios is more in line with Gaussian noise models, in order to better compare the differences in positioning accuracy between the two methods, this paper uses a uniform distribution noise model to test the positioning accuracy performance of different methods in harsh site environments. But as a result, some cases with extremely large errors in the Gaussian distribution model will also be lost. However, based on Equation (1) mentioned earlier, when the distance measurement information is clearly unreasonable and the weight value is very small, it will be directly discarded and not  
Figure 4: Simulation results of self-optimizing positioning algorithm for base stations.  
applied to the positioning algorithm. Therefore, using a uniformly distributed noise model also has some rationality.  
Subsequently, 100 straight paths with lengths greater than \(1.75x_{b}\) are randomly generated within the central \(2x_{b}\times 2x_{b}\) area of a \(3x_{b}\times 3x_{b}\) venue. Trilateration positioning calculations are performed using both the traditional algorithm and the novel base station self-optimization positioning algorithm, and the mathematical expectations and variances of the errors are compared. The results are shown in Figure 5.  
From Figure 5, it can be observed that the mathematical expectation and variance of the base station self-optimization positioning method based on transition zone division are significantly lower than those of the traditional weighted average positioning algorithm. This indicates that the novel base station self-optimization positioning algorithm effectively enhances the precision and stability of positioning. By appropriately selecting base stations, avoiding the use of ranging information from short distances and discarding unreliable ranging information with weights less than 0.5, the base station self-optimization positioning method based on transition zone division effectively improves positioning accuracy and stability. Furthermore, it ensures a smoother transition in base station selection for mobile terminal nodes when crossing different areas.
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 5 Experiments
### 24-h-Ahead Forecasting
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Station** & **Metric** & **LR** & **FFN** & **LSTM** & **GRU** & **LSTM-4** \\ \hline \multirow{4}{*}{Cerrillos} & R2 & \(-0.153\) & 0.347 & 0.389 & 0.392 & **0.401** \\ \cline{2-7}  & RMSE & 20.99 & 15.79 & 15.28 & 15.24 & **15.12** \\ \cline{2-7}  & MAE & 12.85 & 7.20 & 6.57 & 6.85 & **6.39** \\ \hline \multirow{4}{*}{Cerro Navia} & R2 & 0.193 & 0.393 & **0.411** & 0.383 & 0.408 \\ \cline{2-7}  & RMSE & 22.43 & 19.46 & **19.17** & 19.63 & 19.21 \\ \cline{2-7}  & MAE & 11.80 & 7.40 & 7.26 & 8.38 & **6.67** \\ \hline \multirow{4}{*}{ElBosque} & R2 & \(-0.145\) & 0.387 & **0.403** & 0.399 & 0.402 \\ \cline{2-7}  & RMSE & 24.02 & 17.58 & **17.36** & 17.41 & 17.37 \\ \cline{2-7}  & MAE & 14.99 & 8.25 & 7.51 & 7.71 & **7.23** \\ \hline \multirow{4}{*}{Independencia} & R2 & \(-0.767\) & 0.133 & 0.282 & **0.377** & 0.352 \\ \cline{2-7}  & RMSE & 19.44 & 13.62 & **12.39** & 11.54 & 11.77 \\ \cline{2-7}  & MAE & 12.37 & 7.18 & 6.16 & 6.50 & **6.10** \\ \hline \multirow{4}{*}{LaFlorida} & R2 & 0.085 & 0.137 & 0.284 & **0.330** & 0.327 \\ \cline{2-7}  & RMSE & 16.20 & 15.73 & 14.32 & **13.86** & 13.89 \\ \cline{2-7}  & MAE & 8.73 & 8.32 & 6.78 & 7.25 & **6.72** \\ \hline \multirow{4}{*}{POTHiggins} & R2 & \(-0.122\) & 0.305 & 0.370 & **0.391** & 0.377 \\ \cline{2-7}  & RMSE & 18.86 & 14.84 & 14.13 & **13.90** & 14.05 \\ \cline{2-7}  & MAE & 11.70 & 7.62 & 6.65 & 6.74 & **6.37** \\ \hline \multirow{4}{*}{Pudahuel} & R2 & 0.112 & 0.360 & 0.383 & 0.373 & **0.393** \\ \cline{2-7}  & RMSE & 20.69 & 17.56 & 17.24 & 17.38 & 17.11 \\ \cline{2-7}  & MAE & 11.31 & 7.15 & 7.03 & 7.36 & 6.40 \\ \hline \multirow{4}{*}{Average} & R2 & \(-0.114\) & 0.295 & 0.360 & 0.378 & **0.380** \\ \cline{2-7}  & RMSE & 20.38 & 16.37 & 15.70 & 15.57 & **15.51** \\ \cline{1-1} \cline{2-7}  & MAE & 11.96 & 7.59 & 6.85 & 7.26 & **6.56** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Metrics of space-time prediction considering 7 stations, of 24 h in the future. Bold font indicates best results. We note that the most distant stations, in particular, Las Condes, Puente Alto and Talagante, but not Quilicura, have the poorest metrics. We believe that this was due to a variety of phenomena, such as the great distances between stations, the different emission sources and the altitude of each (see, for example, the works of [6; 51]).
## 3 Methodology
Figure 2: Flowchart for calculating the bias as stated in Equation (6). The availability of ground motions that can be effectively used is influenced by two factors: the usable spectral frequency of each record and scale threshold [56]. The scale threshold in Figure 2 is the maximum intensity measure (IM) of unscaled MSAS sequences. The IM of the ground motions after scaling with _SF_ should not exceed the scale threshold. Figure 3a illustrates the quantity of unscaled mainshock-aftershock ground motions that can be used. The number of usable ground motions in Figure 3a is computed considering the usable spectral frequency of each record, defined as exceeding 1.25 times the high-pass corner frequency used in the record processing, as specified in the NGA database [57]. Figure 3b,c display the number of scaled aftershock ground motions that can be used for a period of \(T=1.0\) s, considering both the usable spectra frequency of each record and the scale threshold for eight different scale indicators. The number of usable ground motions in Figure 3b,c is determined based on the following principle: for a given IM, the IM of the ground motions should not exceed the maximum IM in the unscaled database (i.e., benchmark database). It is important to note that when the aftershock ground motions are scaled up using the _PGA_ as the scaling indicator, the number of usable records decreases significantly, resulting in only 40 usable records for a scale factor of 10.0. However, for the other seven scale indicators, the number of usable records remains around or above 200 when the scale factor is 10.0.
# Coverage Strategy for Small-Cell UAV-Based Networks in IoT Environment
## 3 Problem Formulation
### Experiment 1: Symmetric Case  
This experiment aimed to determine the converging time of a UAV-based wireless network in the symmetric case where \(\lambda_{1}=\lambda_{2}=3\), using a specified learning rate and a set of beaconing periods. The experimental setup consisted of a UAV equipped with a wireless communication module and a ground station acting as a base station.  
The experiment was conducted using a set of beaconing periods ranging from 1 s to 10 s. The UAV initially flew in a random pattern, and the beaconing period was set to a random value between 0.1 s and 1 s. The learning rate had to be set to 0.1, and the UAV used a reinforcement learning algorithm to learn the optimal beaconing period that provided the best encounter rate. The UAV updated its policy based on the received feedback from the base station. The experiment was repeated for several iterations to determine the converging time to the specified beaconing period. The converging time was measured as the number of iterations required for the UAV to converge to the optimal beaconing period.  
The results of the experiment were analyzed and are presented in the form of graphs and tables. Figures 3 and 4 show the convergence of the UAV to the optimal beaconingperiod over time, while Table 2 presents the final probabilities for each set of beaconing periods.  
Table 2 shows the final values of the probabilities after convergence.  
The experiment provides insights into the development of future UAV-based wireless network systems and has practical implications for wireless network providers, IoT service providers, and UAV manufacturers.
## 2 Materials and Method
### Variable Selection Methods
#### 2.4.1 Classical Variable Selection Methods  
These are traditional methods for variable selection, with extensive literature in statistics. Here, key issues are briefly presented, and interested readers are referred to [PERSON] et al. [43] and [PERSON] [44] for further details.  
BSS involves fitting OLS models for all possible combinations of the p covariates, using either an exhaustive search or leaps and bounds algorithm [45]. It returns the best models with one covariate, two covariates, and so on, where the best model is the one with the smallest residual sum of squares (RSS) [43].  
In contrast, BE starts with a model containing all variables and iteratively removes the covariate that contributes the least to the model, one by one [43]. This process also produces the best model for each subset size, including the null model [45].  
FS starts with a null model (containing only the intercept) and iteratively adds one predictor at a time--the variable that most significantly reduces the RSS. This procedure also identifies the best model for each subset size, including the full model.  
For all approaches, a pre-specified stopping criterion such as AIC can be used to choose the best-fitting subset of variables [43; 44]. Once the best subset is selected, the regression coefficients for these variables are estimated using the least squares method.  
BSS is computationally intensive, while BE and FS are less intensive because they explore a smaller set of models [43]. BE is often preferred over FS, especially in the presence of collinearity and when the goal is to derive a suitable model for description [3; 46]. [PERSON] [46] demonstrated the advantages of BE over FS and concluded that BE eliminates variables only when appropriate. If a set of variables should be kept as a group, BE retains the whole set, whereas FS may fail to select the complete set. Other variants of BE and FS allow for omitted variables to be re-included and included variables to be removed, as summarized by [PERSON] and [PERSON] [47]. These variants were not considered in this study. AIC, BIC, and CV were used to select the single best model for each approach.
# Implementation of a Cross-Layer Sensing Medium-Access Control Scheme
## 1 Introduction
The compressive complex requests are used to identify the active sensor node which makes the scheme more efficient. Moreover, because the reconstruct process is executed in the complex field of the physical layer, no bit and frame synchronizations are needed. The scheme is implemented by Universal Software Radio Peripheral (USRP) practically. It provides higher throughput and more fairness compared to the CSMA and data link layer CS-MAC schemes. The paper is organized as follows. We introduce compressive sensing theory in Section 2 and outline the system model in Section 3. In Section 4, we discuss the design of CL CS-MAC. In Section 5, we explain the implementation of CL CS-MAC and experiments based on software-defined radio in detail. Finally, we provide concluding remarks in Section 6.
# Caching Transient Contents in Vehicular Named Data Networking: A Performance Analysis
## 6 Performance Evaluation  
To assess the performance of FDC, we performed a simulation campaign in two distinct vehicular scenarios: the same urban topology described previously, and a highway topology, which consists of a 2 km-long highway road segment, where 100 vehicles move at a maximum speed of 90 km/h. In both scenarios, we assume that a RSU in the middle of the topology acts as original producers of transient contents. The _FreshnessPeriod_ of Data packets varies uniformly in the range [5; 6; 7; 8; 9; 100] s to match a realistic and heterogeneous data traffic pattern.  
CEE and Random Caching (RC) with probability \(p=0.5\) are considered to be benchmark schemes. They were selected as the most representative baseline solutions in the literature for V-NDN. As with FDC, they have the virtue of simplicity and incur no overhead, being completely autonomous. This is a crucial feature in the vehicular domain. For the sake of a fair comparison, all the schemes implement LRU coupled with RFP-based replacement. By doing so, cache inconsistency is always null. The proposal as well as the benchmark schemes were implemented in ndnSIM [37].  
The main simulation settings are reported in Table 3.  
In addition to the cache hit ratio, the following metrics are considered:  
* _Content retrieval delay_, computed as the average time for retrieving a content.
* _Number of hops_, computed as the average number of hops travelled by the Interest packets for retrieving the content.
* _Number of Data packets_ as the total number of Data packets broadcasted by vehicles in the simulation. It, therefore, includes also re-transmitted and redundant packets.  
Results are averaged over ten independent runs and reported with 95% confidence intervals.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Content catalog size & 10,000 contents \\ \hline Content size & 100 Data packets \\ \hline Data packet size & 1000 bytes \\ \hline Content Popularity & Zipf distributed with \(\alpha\in[1-2.5]\) \\ \hline Propagation & Nakagami fading \\ \hline Scenario & Urban topology (Manhattan Grid of size 1 km\({}^{2}\)) \\  & Highway topology (2 km-long highway road segment) \\ \hline Number of vehicles & 100 \\ \hline Number of consumers & 20–50 \\ \hline Number of producers & 1 RSU \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main simulation settings.
# A Study of Adversarial Attacks and Detection on Deep Learning-Based Plant Disease Identification
## 2 Methods
### Pre-Trained DNN Models for Plant Disease Identification
#### 2.2.1 VGGNet  
VGGNet is a deep convolutional neural network (CNN) model proposed for the ILSVRC-2014 challenge [18]. The input of the model is a fixed-size \(224\times 224\) image, which passes through a stack of convolutional layers with \(3\times 3\) filter. The model also uses \(2\times 2\) max-pooling layer following some convolutional layers for down-sampling which reduces the input size of later layers. The end of the network consists of two fully connected layers with 4096 neurons each followed by a SoftMax layer. Depending on the number of convolutional layers of the network, there are two VGGNet architectures: VGG-16 and VGG-19. VGG-16 is considered in our study.
# A Photometric Stereo Using Re-Projected Images for Active Stereo Vision System
###### Abstract  
In optical 3D shape measurement, stereo vision with structured light can measure 3D scan data with high accuracy and is used in many applications, but fine surface detail is difficult to obtain. On the other hand, photometric stereo can capture surface details but has disadvantages, in that its 3D data accuracy drops and it requires multiple light sources. When the two measurement methods are combined, more accurate 3D scan data and detailed surface features can be obtained at the same time. In this paper, we present a 3D optical measurement technique that uses re-projection of images to implement photometric stereo without an external light source. 3D scan data is enhanced by combining normal vector from this photometric stereo method, and the result is evaluated with the ground truth.  
optical 3D measurement; active stereo vision; photometric stereo; image re-projection +
Footnote †: journal: Applied Sciences
## 2 Considerations to Set a Habitat Protection Strategy  
### Passive Shielding  
Radiation protection can take several forms, mainly divided into passive and active with electrostatic, magnetic and plasma shielding [3; 15]. This work studies the passive shielding which is based on the ability of a chosen material to stop or decelerate charged particles and attenuate the dose accumulated in the target placed behind the shield. For human outposts on the Moon, passive shielding from radiation also plays the role of structural support and protection from micro meteoroid impacts which altogether may require a habitat to be covered with a dense or thick material. The in-situ resource utilisation (ISRU) is key in achieving this in order to reduce transportation of materials from the Earth and mission costs [6; 16]. Regolith is abundant on the surface of the Moon and it may serve as the main material for protecting habitats from the space environment [16]. When processed, i.e., molten, sintered, mixed with binders, loose regolith can become a useful solid material [7; 17; 18]. This concept relies of the development of ISRU technologies.  
Following the As Low As Reasonably Achievable (ALARA) principle of NASA, additional materials such as aluminium, polyethylene, Kevlar, etc., are repeatedly investigated in literature to determine how their utilisation may optimise the radiation protection of habitats and in deep space [10; 19]. Hydrogen-rich materials, e.g., water and polyethylene, rank high among others [9; 10] because they have more nuclei in the path of the particles for the same shield thickness in mass per unit area. This helps to break up the heavy nuclei. These materials contain few neutrons which leads to fewer secondary neutrons and as neutrons are a big concern for the effective dose [1], it is an important factor in radiation protection. In this respect, hydrogen \({}^{1}\)H is the best option because it contains no neutrons (also called \"protium\"). Furthermore, nuclei with lower atomic mass have a smaller atomic charge, which makes them less effective in producing secondary emissions.
# A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks
## 5 Machine Learning for CML-Based Rainfall Measurement
### Potential of Deep Learning
#### 5.2.1 Lstm  
As one of the most popular DL algorithms for processing time-series data, the LSTM is equipped with a memory cell and several gates to overcome the gradient explosion or gradient disappearance problem of RNN. Figure 4 shows the cell structure of the LSTM, and Equations (10)-(15) give the basic mathematical computational steps of the algorithm [90]. First, the forgetting gate \(f_{t}\), input gate \(i_{t}\), and candidate value \(z_{t}\) are calculated by using the current input \(x_{t}\) and the previous hidden state \(h_{t-1}\). Second, the current memory cell state \(c_{t}\) is updated by combining \(f_{t}\), \(i_{t}\), \(z_{t}\), and the previous memory cell state \(c_{t-1}\). Finally, the information of \(c_{t}\) is transferred to the current hidden state \(h_{t}\) through the output gate \(o_{t}\).  
\[f_{t}=\sigma(W_{f}x_{t}+U_{f}h_{t-1}+b_{f}), \tag{10}\]  
\[i_{t}=\sigma(W_{i}x_{t}+U_{i}h_{t-1}+b_{i}), \tag{11}\]  
\[z_{t}=tanh(W_{z}x_{t}+U_{z}h_{t-1}+b_{z}), \tag{12}\]  
\[c_{t}=i_{t}\times z_{t}+f_{t}\times c_{t-1}, \tag{13}\]  
\[o_{t}=\sigma(W_{0}x_{t}+U_{0}h_{t-1}+b_{0}), \tag{14}\]  
\[h_{t}=o_{t}\times tanh(c_{t}), \tag{15}\]  
where \(\sigma()\) and \(\tanh\) (\(\cdot\)) are the sigmoid and hyperbolic tangent functions, respectively. The weight matrices \(W_{f}\), \(W_{i}\), \(W_{z}\), \(W_{o}\), \(U_{f}\), \(U_{i}\), \(U_{z}\), \(U_{o}\) and the bias vectors \(b_{f}\), \(b_{i}\), \(b_{z}\), \(b_{o}\) can be predicted in the training stage. Through the cooperation between the memory cell and the gates, LSTM is equipped with a powerful ability to predict time series with long-term dependence.