esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
The time-varying skywave ionosphere channel for hypersonic vehicles primarily depends on the vehicle's maneuvering characteristics. To describe the dynamic process of trajectory changes in hypersonic vehicles and their impact on ray-tracing results, the ray-tracing results at the first, middle, and last three moments during the re-entry period were selected for simulation. A simulation analysis was performed on the loss, delay, and Doppler effects for each channel over the entire re-entry time.  
In this section, the IRI-2020 ionospheric model served as input for propagate physical environment data, and the NRLMSIS-00 atmospheric background molecular model served as input for the ionospheric electron collision frequency model proposed by [PERSON] [55]. The vehicle launched from Xi'an and flew due south, with the flight trajectory based on the RAM C II re-entry experiment [56; 57; 58; 59; 60], and the elevation angle of the ray emission ranged from 0\({}^{\circ}\) to 90\({}^{\circ}\). Figure 4 shows that the range of the vehicle varied with altitude and speed. To ensure generality of the comparison between the ray-tracing results at midnight and noon, ionospheric model data from multiple seasons (vernal equinox, summer solstice, autumnal equinox, and winter solstice) at 00:00 a.m. and 12:00 p.m. were selected. The path with the minimum loss was defined as the main path of the channel (1 st path/path 1).  
Figure 3: Results of the 2D ray-tracing algorithm—wave’s path calculus for a set of elevation angles between 60\({}^{\circ}\) and 70\({}^{\circ}\): (**a**) reference [49]’s ray-tracing results; (**b**) the ray-tracing trajectory results of this algorithm.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 5 Numerical Simulation of Linear MIMO FSE Receiver for MDL-Impaired Optical Channel
### Transmitter and Linear MIMO FSE Receiver Parameters  
As described in Section 4, the transmitter uses a generalized PAM modulation and an square-root raised cosine for pulse shaping with a roll-off factor equal to \(\alpha\). We will show results of the numerical simulation for several values of \(\alpha\). The symbol period \(T\) has been set for a symbol rate \(R_{s}=64\) Gbaud. The FSE-based MIMO receiver has an oversampling factor \(r_{ov}=2\), and a number of equalizer taps \(N_{taps}=1000\) has been selected to ensure that it does not limit the receiver performance for the considered channel MDL.
# Evaluating Machine Learning and Remote Sensing in Monitoring NO2 Emission of Power Plants
## 3 Materials and Methods
### Study Area
#### 3.2.4 U.S. Power Plants Data  
Power plant metadata and location shapefiles were obtained from the Energy Information Administration (EIA). The shapefile was dated January 2020 and included 9768 power plants, 289 of which were coal-fired. \"Plant Code\", \"Plant Name\", \"Utility Name\" and \"Utility ID\", \"Sector Name\", \"Address\", \"Primary Source\" (e.g., coal, solar, etc.), power plant capacity per MW (megawatt), and the plants' latitudes and longitudes were among the features included for each power plant.
## 3 Shearer Positioning System Based on SINS  
Because the environment in which the shearer works underground is very complicated, high precision is required to meet shearer positioning needs. Before field application, it must undergo rigorous testing and experiments to determine whether it can meet the real-time, accuracy, and reliability requirements of underground positioning.
## 3 Methodology
### Random Forest Algorithm  
Random forest learning is commonly used in both classification and regression tasks. The technique involves constructing and training a set of decision trees, which are then utilized to make predictions or classify results. [PERSON] first introduced the algorithm in 2001 [96].  
Figure 1: The framework for predicting smart city success.  
The initial approach involved generating multiple subsets of the original dataset through bootstrapping, a sampling process using replacement values. This resulted in a new dataset, \(D_{i}\), containing n data points selected from the original dataset D, as shown in Equation (1).  
\[D=\begin{bmatrix}f_{1}&f_{2}&f_{3}&\dots&f_{n}\\ \begin{bmatrix}X_{11}&X_{12}&X_{13}&\dots&X_{1j}\\ X_{21}&X_{22}&X_{23}&\dots&X_{2j}\\ X_{31}&X_{32}&X_{33}&\dots&X_{3j}\\ \vdots&\vdots&\vdots&\ddots&\vdots\\ X_{i1}&X_{i2}&A_{i3}&\dots&X_{ij}\end{bmatrix}\end{bmatrix} \tag{1}\]  
The second step involved growing decision trees from each dataset \(D_{i}\). To grow the tree, the dataset was repeatedly split based on features [97]. A subset of features, denoted as \(m\) (where \(m<M\)), was randomly chosen. \(M\) refers to the total number of selected features. The most optimal feature for each split was chosen from this subset by maximizing different criteria, such as the Gini impurity or information gain. The method of random selection aided in reducing correlation among trees.  
The next action involved sub-dividing criteria. Beginning at node \(t\), it splits feature \(X_{j}\) into nodes \(t_{L}\) and \(t_{R}\). The splitting criteria \(\Delta\)G was expressed mathematically, as shown in Equation (2):  
\[\Delta G=G(t)-\left(\frac{|t_{L}|}{|t|}G(t_{L})+\frac{|t_{R}|}{|t|}G(t_{R}) \ \right) \tag{2}\]  
\(G(t)\) is an impurity measure, such as Gini impurity or entropy. Individual predictions were aggregated after all the trees were constructed to comprise the final projection. Classification typically yields predictions, while regression generates the mean [98; 99; 100], as shown in Equation (3).  
\[\hat{y}=\frac{1}{T}{\sum_{i=1}^{T}}\hat{y}_{i} \tag{3}\]  
where \(T\) is the total number of trees; \(\hat{y}\) is the prediction from the ith tree. This method generally minimizes model overfitting and improves model accuracy. Figure 2 presents the flow chart for RF.  
Figure 2: Random forest flowchart.
# Remote Sensing Technologies for Monitoring Argane Forest Stands: A Comprehensive Review
## 3 Benefits and Uses of Argane
### Socio-Economic Interest of Argane Stands  
The Argane stands are a highly valuable resource with diverse applications, and each part of the tree, including its wood, leaves, fruit, and oil, serves as a source of sustenance and income for those who depend on it. This tree is essential to the socio-economic, botanical, and ecological dynamics within rural communities [1,28].  
Wood: The Argane stands provide sturdy and resilient wood, widely used as a fuel source in the form of charcoal for heating, as well as in rural construction, joinery, and the crafting of traditional and household items [1].  
Livestock Use: All components of the tree are essential for livestock. Argane leaves offer valuable grazing for goats and camels, while the oil cake, rich in carbohydrates and proteins, is used to fatten livestock.  
Edible Argane Oil: Argane oil, extracted from the kernels, is a nutritious edible oil with a pleasing taste and exceptional dietary value. It contains approximately 80% unsaturated fatty acids and 20% saturated fatty acids, including significant amounts of linoleic and oleic acids and rich in polyphenols, tocopherols, and carotenes [29,30]. This oil has become one of the most valuable edible oils globally [31].  
Cosmetic Application: Argane oil is also used in cosmetic products for its therapeutic potential [16,32,33], serving as a moisturizing oil for the face, hands, and feet. In addition to these functions and uses, the Argane stands play a crucial role in sustaining the livelihoods of rural communities, helping to alleviate rural migration pressures. Thus, the Argane stands are a versatile and indispensable resource, supporting both the economic well-being of local residents and the preservation of traditional practices, while also playing a vital role in addressing rural depopulation.
# Secure Information Transmission with Self Jamming SWIPT
## 2 System Model
### Iterative Block Decision Feedback Equalization Receiver  
In this system model, it is assumed that both \(R\) and \(\xi\) employ IB-DFE to improve the accuracy of the information estimate as proposed in [18]. The IB-DFE based estimate for \(X_{l}^{(k)}\) at \(R\) is given as:  
\[\tilde{X}_{l}^{(j,k)}=Y_{l}^{(k)}F^{(j,k)}-\tilde{X}_{l}^{(j-1,k)}B^{(j,k)}, \tag{19}\]  
where \(j=0,1,...,J\), \(j\) is the number of iterations followed in the IB-DFE receiver, \(\tilde{X}_{l}^{(j-1,k)}\) is the previous iteration value of \(\tilde{X}_{l}^{(j,k)}\), and \(F^{(j,k)}\) is the iteration of the feedforward coefficient, given as:  
\[F^{(j,k)}=\frac{\tilde{F}^{(j,k)}}{\frac{1}{k}\sum_{k=0}^{K-1}\big{(}\tilde{F }^{(j,k)}H_{\mathcal{T}R}^{(j,k)}\big{)}}, \tag{20}\]  
where \(\tilde{F}^{(j,k)}\) is written as:  
\[\tilde{F}^{(j,k)}=\frac{H_{\mathcal{T}R}^{(j,k)*}}{\big{(}\frac{\rho_{N}^{2}}{ \sigma_{N,l}^{2}}\big{)}+|H_{\mathcal{T}R}^{(j,k)}|^{2}\big{(}1-\big{(}\rho^{( j-1)}\big{)}^{2}\big{)}}, \tag{21}\]  
and the correlation factor \(\rho^{(j-1)}=\frac{\mathbb{E}[\xi^{(j,k)}x_{i}^{(j,n)*}]}{\mathbb{E}[\tilde{x} _{i}^{(j,n)}|]}\), where \(x_{i}^{(j,n)}\) is time domain equivalent of \(X_{l}^{(j,k)}\) and \(\tilde{x}_{i}^{(j,n)}\) is the extrinsic information of \(x_{i}^{(j,n)}\). Furthermore, \(\{X_{l}^{(j,k)};k=0,1,...,K-1\}=\text{DFT}\ \{x_{i}^{(j,n)}g_{n};n=0,1,...,N-1\}\), and if the \(n^{\text{th}}\) time domain is inside the cycle prefix, then \(g_{n}\) is either one or zero. The feedback co-efficient \(B^{(j,k)}\) is written as:  
\[B^{(j,k)}=F^{(j,k)}H_{\mathcal{T}R}^{(j,k)}-1. \tag{22}\]In fact, the accuracy of \(\tilde{X}_{I}^{(j,k)}\) increases with each iteration up to its saturation point. Similarly, the accuracy of the information estimate can be improved at \(R\) and \(\xi\) for all the scenarios except \(\xi(a)\). For \(\xi(a)\), \(\tilde{X}_{I}^{(j,k)}\) is completely distorted by EH signals.  
The performance of the IB-DFE receiver can be improved at \(R(b)\) with improved feedforward input in the iteration. The feedforward estimate, \(\tilde{F}^{(j,k)}\), is derived in Equation (21), and this estimate can be improved by including the impact of \(\beta\sigma_{A}^{2}\) in the SINR of the information signal. Therefore, by using Equation (15) in (21), we get:  
\[\tilde{F}^{(j,k)}=\frac{H_{\mathcal{TR}}^{(j,k)*}}{\big{(}\frac{\sigma_{N}^{2} +\beta\sigma_{A}^{2}}{\sigma_{X,I}^{2}}\big{)}+|H_{\mathcal{TR}}^{(j,k)}|^{2} \big{(}1-(\rho^{(j-1)})^{2}\big{)}}. \tag{23}\]  
Here, unlike the eavesdropper, \(R(b)\) can take advantage of the available knowledge from the legitimate transmitter and improve the performance of \(R(b)\); therefore, \(R(b)\) with the improved feedback estimate can perform better than \(\xi\).
# Structural Crack Detection and Recognition Based on Deep Learning
## 3 Crack Recognition Based on Convolutional Neural Network
### Evaluation Index  
The experiment uses a confusion matrix to evaluate the network model, which is shown in Table 1. The total number of samples in each column is the predicted category, the total number of samples in each row is the actual category, and the total number of samples on the diagonal is the correct samples classified by the model.  
According to the test dataset, the ratio of the correct value predicted by the model to the total value is defined as the accuracy rate, as shown in Equation (1):  
\[Acc=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{FP}+\text{FN}+\text{TN}} \tag{1}\]  
Accuracy is the most common evaluation indicator, but for a certain case, it is difficult to derive the accuracy rate. Thus, a so-called \"precision rate\" can be used instead. The precision rate is defined as follows:  
\[P=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{2}\]  
In addition, the so-called \"recall rate\" and \"\(F_{1}\) score\" were used to evaluate the network model. The recall rate is the proportion of all real I (TP + FN) that are judged to be class I (TP), as shown in Equation (3):  
\[R=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{3}\]  
The relationship between the \(F_{1}\) score and the precision rate as well as the recall rate is shown in Equation (4):  
\[\frac{2}{F_{1}}=\frac{1}{P}+\frac{1}{R} \tag{4}\]  
From the above analysis and Table 2, under the premise of training 30 epochs at the same time, the ResNet18 model's accuracy, precision, recall, and F1 in the test set are better than those of the other two neural networks. It shows that the ResNet18 model has better performance than other models in the crack dataset, so we take the ResNet18 model as our final recognition model.
# Experimental Seaborne Passive Radar
## 4 Results of Moving Target Detection
### Scenario 3: PC-12, Fishing Boat, CASA C-295, F-16 (DVB-T Illumination)  
In Figure 18, we have shown a situation map with the cooperative PILATUS PC-12 (SUI812 [26]) aircraft passing in front of the ship, the fishing boat around 7 km from the front, and the CASA aircraft moving away at the back with an F-16 jet fighter. A signal from the DVB-T (191.5 MHz) transmitter was selected for illumination with horizontally polarized receiving antennas and the reference antenna from the side of the ship.  
Detection results for this situation are depicted in Figure 19. The echoes from the PC-12 aircraft flying nearly the PCL receiver (ca. 5 km) are clearly seen in the bistatic \(v/R\) plane around \(R=7.5\) km, with the bistatic velocity turning from positive to negative. The detection was not possible during a period from 170 to 177 s when the bistatic velocity was close to zero and the echo was lost in the clutter. Detection of the fishing boat cannot be  
Figure 17: Detection results of the CASA C-295 aircraft (subsequent time stamps, scene in Figure 16).  
Figure 18: Situation map with the cooperative aircraft and fishing boat.  
extracted from the \(v/R\) plots, and the distant F-16 jet fighter and CASA airplane are also not visible in this configuration.
## 3 Proposed Method  
### Construction of Proposed Measurement Matrix  
Our measurement matrix is applicable to signals of length \(N=4t-1\), where \(t\) is a positive integer and \(N\) is a prime number. The construction method is as follows:  
1. Let \(p=N\), the following numbers are obtained: \(\left(1^{2}\right)_{p^{\prime}}\)\(\left(2^{2}\right)_{p^{\prime}}\)\(\cdots\), \(\left(\left(\frac{p-1}{2}\right)^{2}\right)_{p}\).
2. If \(i=1,2,\cdots,p-1\) appears among the numbers obtained in step 1. Let \(s_{i}=0\); otherwise, let \(s_{i}=+1\).
3. Let \(s_{0}=0\); then, a Legendre sequence, \(\mathbf{S}=\left\{s_{0},s_{1},\cdots,s_{p-1}\right\}\), can be obtained.
4. A \(p\times p\) matrix \(\mathbf{Q}\) can be constructed using (10): \[\mathbf{Q}=\begin{bmatrix}s_{0}&s_{1}&\cdots&s_{p-2}&s_{p-1}\\ s_{1}&s_{2}&\cdots&s_{p-1}&s_{0}\\ \vdots&\vdots&\ddots&\vdots&\vdots\\ s_{p-2}&s_{p-1}&\cdots&s_{p-4}&s_{p-3}\\ s_{p-1}&s_{0}&\cdots&s_{p-3}&s_{p-2}\end{bmatrix}\] (10)
5. The top \(M,\left(1<M<p\right)\) rows from matrix \(\mathbf{Q}\) are selected. Then, a \(M\times p\) deterministic sparse measurement matrix is obtained. According to compressed sensing theory, when \(M\) exceeds a certain threshold, the signal can be reconstructed with high probability. Therefore, \(M\) can be set based on the sparsity of the signal. Typically, \(M\) is required to satisfy the condition \(M>\)\(C\)\(\mathit{Klog}(N/k)\), where \(N\) is the signal dimension and \(C\) is a constant related to the desired reconstruction accuracy.
### System Planning and Configuration
#### 2.2.1 Principles of System Planning and Configuration  
The configuration optimization of the PEDF system should adhere to the following principles [11; 22; 23]:  
* Energy matching principle: The system must ensure that there is a balance between photovoltaic sources, energy storage, the power grid, and load demand, effectively fulfilling all energy requirements.
* Flexibility principle: The PEDF system should be designed to accommodate flexible adjustments and expansions based on various application scenarios, guaranteeing adaptability, efficiency, and stability.
* Economic principle: An optimally configured PEDF system can reduce both construction and operational costs, significantly enhancing economic benefits and cost-effectiveness.
* Low-carbon principle: When configuring the PEDF system, it is essential to utilize solar energy resources, minimize dependence on the power grid, and strive for lower carbon emissions.
* Reliability principle: A well-considered configuration of the PEDF system and its interactive components should reduce the capacity needed for power electronic conversion devices, thereby improving overall reliability.
## 3 Results  
### Dose-Response Effects of Doxycycline and Minocycline on tPA and uPA Secretion in RT4 Cells  
To investigate if doxycycline and minocycline were able to stimulate the secretion of tPA and uPA by RT4 Schwann cell-like cultures, we measured the amount of plasminogen activators released in culture media by enzyme-like immunosorbent assay (ELISA) after treatment with either antibiotic for 24 h. We utilized increasing concentrations of doxycycline and minocycline (0, 10, 50, 100, 500 and 1000 ng/mL) to establish the optimal concentration to use in subsequent experiments (Figure 1). PACAP, which was used as a positive control, significantly increased tPA (*** \(p\) < 0.001 vs. Control) and uPA (* \(p\) < 0.05 and ** \(p\) < 0.01) levels in cultured media, confirming the dose- and time-dependent increase in plasminogen activators expression and activity of PACAP in RT4 cells in earlier studies [(22)].  
Experimental data demonstrated that both doxycycline and minocycline dose-dependently increased the levels of secreted tPA and uPA in the supernatant of RT4 cells, although uPA induction was less remarkable than tPA (Figure 1A,B,A',B', respectively). Doxycycline reliably increased tPA secretion at 50 ng/mL (* \(p<0.05\) vs. Control, Figure 1A), however, 100 ng/mL doxycycline was required to significantly increase uPA levels as well (** \(p<0.01\) vs. Control, Figure 1A').  
Minocycline treatment significantly increased tPA (** \(p<0.01\) vs. Control, Figure 1B) and uPA levels (* \(p<0.05\) vs. Control, Figure 1B') in the culture media at 50 ng/mL; however, similarly to doxycycline, minocycline activity also peaked at 100 ng/mL (** \(p<0.01\) vs. Control for both tPA and uPA). Accordingly, we identified 100 ng/mL as the most effective concentration of antibiotic to use in subsequent experiments.  
Figure 1: Dose–response effect of doxycycline and minocycline on tPA and uPA secretion in RT4 cells. (**A,B**) Levels of secreted tPA or uPA in the supernatant of RT4 cells treated with either doxycycline (**A,A’**) or minocycline (**B,B’**). RT4 cells were treated with the indicated treatments for 24 h. Cell supernatants were harvested and the concentration of tPA and uPA were determined by ELISA. Data shown are the result of two independent experiments using five different batches of cells (n = 5). * \(p<0.05\), ** \(p<0.01\) or *** \(p<0.001\), as determined by ANOVA followed by Dunnett’s _post-hoc_ test.
## 3 Applications
### Example Calculation
#### 3.2.6 Checking Calculation of Mortar Coating under Quasi-Permanent Limit States  
\(\psi_{qv}\) and \(\psi_{qw}\) are the quasi-permanent coefficient of vertical pressure generated by the ground vehicle loads and the internal water pressure, respectively. Here, \(\psi_{qv}=0.5\) and \(\psi_{qw}=0.72\). The maximum bending moment of the pipe at the spring-line was \(M_{pml}^{l}=-23.422\) kN\(\cdot\)m/m. The value of \(M_{pml}^{l}\) is negative, indicating that the mortar coating was subjected to tension. The absolute value was taken when substituting the following equations. The axial tension of the pipe at the spring-line was \(N_{pl}^{l}=602.911\) kN/m. The maximum tensile stress at the edge of the pipe at the spring-line was \(\sigma_{bs}^{l}=8.21\) N/mm\({}^{2}\). The design parameter of strain for the mortar coating was \(\alpha_{m}^{\prime}=4\). Therefore, \(\left(\alpha_{m}^{\prime}\varepsilon_{mt}E_{m}=13.95\) N/mm\({}^{2}\right)>\left(\sigma_{ss}^{l}=8.21\) N/mm\({}^{2}\right)\), indicating that the area of prestressed strands is able to meet the tensile requirement of the mortar coating under quasi-permanent limit states.  
Above all, this is reasonable of the calculation result of the area of prestressed strands, which is \(A_{st}=2223\) mm\({}^{2}\)/m. The center spacing of steel strands was \(l_{st}=A\times\frac{1000}{A_{st}}=62.99\) mm.
# A New Architectural Approach to Monitoring and Controlling AM Processes
## 4 A Closer Look at the Time-Based Functions
### Interfaces  
Figure 3 shows the data exchanged between function blocks and the applicable data-connection standards. For real-time control, sampled data or images are used to adjust the energy-input intensity which helps to control melt-pool formation and cooling. Real-time functions usually perform preprocessing, anomaly detection, and feature extraction on a per sample basis. Only extracted feature data is transferred to near-real-time functions. For the connections between control loops and IT systems, including the cloud, communication protocols are adopted by the AM machine vendors including OPC UA, MTConnect [39], and other Internet of Things standards.  
OPC Unified Architecture (OPC UA) is a new generation standard from the OPC foundation with a wide, install base in traditional manufacturing plants. Some AM system vendors have already adopted OPC UA to monitor the operational status of their machines. MTConnect is a manufacturing technical standard for communicating process data from numerically controlled machine tools to shop-floor control systems. AM system vendors are investigating MTConnect for process monitoring; but currently, it is a read-only protocol control. Emerging internet of things (IoT) standards are playing an important role to communicate in situ monitoring data. Many standardized session layer protocols, like Message Queue Telemetry Transport (MQTT), Constrained Application Protocol (CoAP), and Data Distribution Service (DDS), can be applied in the AM domain to get measurements from monitoring devices. The Real Time Streaming Protocol (RTSP) is a network-layer protocol designed to control streaming media servers which are used to establish and control exposure surface or melt-pool image recording.