esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Electromagnetic-Thermal Bidirectional Coupling Model Formulation of TPMLG
### Electromagnetic Finite Element Model of the TPMLG and Loss Calculation
The core loss is calculated by setting the three core loss coefficients of the stator material in the software and mainly depends on the magnetic flux density amplitude of the electromagnetic field analysis. Average eddy current loss of the permanent magnets (PMs) in single cycle is calculated as below:  
\[P_{edd}=\frac{1}{T_{e}}\int\sum_{i=1}^{n}\frac{J_{e}^{2}V_{e}}{\sigma}\mathrm{d}t \tag{4}\]  
where \(P_{edd}\) indicates eddy current loss; \(T_{e}\) represents time of each cycle; \(J_{e}\) represents eddy current density of each element; \(\sigma\) represents the electrical conductivity; \(V_{e}\) represents element volume; and \(n\) represents the number of total elements.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods
### Proof That Our Method Uses the Minimum Number of Drones
#### 3.6.2. Wastage of Coverage in _FillAlongXAxis_ and _FillAlongYAxis_  
As given in the methods _FillAlongXAxis_ and _FillAlongYAxis_, the radii of the different circles filling the rectangular region are different when deploying drones along the \(x\) and \(y\) axes. The boundaries of the rectangular region are then (0, 0) and (\(m,n\)).  
Consider a circle with a center \(E\) and radius \(r_{1}\), as in Figure 15. We want to find the center \(C\) such that the wastage is distributed as equally as possible. For this, we find a point \(C_{1}\) which is \(r_{1}+r\) distant from \(E\) and at an \(r\) vertical distance from the \(x\)-axis. Let \(B\) be (\(x_{1}\), 0) and \(C_{1}\) be (\(x_{2}\),\(r\)); now, \(C\) can be calculated as \(x=x_{1}+rcos\theta\) and \(y=rsin\theta\). To prove the efficiency of this approach, we can consider \(r_{1}=r\), meaning that \(E\) is (\(\frac{r}{\sqrt{2}}\), \(\frac{r}{\sqrt{2}}\)). Therefore,  
\[\begin{split}(x_{1}-\frac{r}{\sqrt{2}})^{2}+(\frac{r}{\sqrt{2}}) ^{2}&=r^{2}\\ \Rightarrow x_{1}&=\sqrt{2}r\end{split} \tag{8}\]  
The distance between \(E\) and \(C_{1}\) is \(r_{1}+r=2r\). Therefore,  
\[\begin{split}(x_{2}-\frac{r}{\sqrt{2}})^{2}&+(r- \frac{r}{\sqrt{2}})^{2}=4r^{2}\\ \Rightarrow x_{2}&=r(\frac{1}{\sqrt{2}}+\sqrt{4-(1 -\frac{1}{\sqrt{2}})^{2}})\\ \theta&=tan^{-1}(\frac{y_{2}-y_{1}}{x_{2}-x_{1}})\\ &=tan^{-1}(\frac{r}{r(\sqrt{4-(1-\frac{1}{\sqrt{2}})^{2}})-\frac {1}{\sqrt{2}}})\\ &\simeq 37.008^{\circ}\\ \angle ACB=45^{\circ}+\theta=82.008^{\circ}\text{(ACBE is a rhombus)}\\ \angle BCD=180^{\circ}-2\theta=105.984^{\circ}\text{(\bigtriangleup BCD is an isosceles triangle)}\\ 2(Area(SegmentAB))&=2(r^{2}(\frac{\angle ACB\pi}{360 ^{\circ}}-\frac{sin(\angle ACB)}{2}))\\ \simeq 0.46r^{2}\\ Area(SegmentBD)&=r^{2}(\frac{\angle BCD\pi}{360 ^{\circ}}-\frac{sin(\angle BCD)}{2})\\ \simeq 0.42r^{2}\\ \Rightarrow 2(Area(SegmentAB))&\simeq\ Area(Segment BD) \end{split}\]  
Here, the wastage of coverage (between the \(x\)-axis and the circles and between two circles) is distributed almost equally. A similar proof can be shown for the wastage with respect to the \(y\)-axis.  
Figure 15. Placing drones along the \(x\)-axis.
These alterations stimulate (i) the decomposition of soil organic carbon (SOC) and (ii) the fermentation and nitrification/denitrification processes, producing methane (CH\({}_{4}\)) and N\({}_{2}\)O, respectively. On the other hand, the increase in the soil temperature and moisture content due to film mulching contributes to the rapid loss of SOC. These two contrasted processes can result in enhancing, stabilizing, or decreasing SOC conservation [17; 18; 19; 20] depending on environmental factors, agricultural systems, crop types and field management practices [11; 13]. However, in general, as reported by 21. [PERSON] et al. [21], short-term studies cannot provide a clear picture of changes in agroecosystem support services due to agricultural systems since soil carbon requires at least 8-10 years to detect meaningful changes. The extensive use of plastic mulching films in agriculture has strong environmental impacts [22; 23] which can potentially be mitigated by using biodegradable materials that degrade in the environment by between 85% and 95% depending on their components [24]. However, aside from the general improvement in WUE when biodegradable mulching films are used [25], information on carbon and water balances in such scenarios is scarce, especially considering that the film is typically not collected and removed but left on the soil surface post-harvest until it decomposes. The eddy covariance (EC) technique for measuring scalar fluxes in the soil-crop-atmosphere continuum (see, e.g., [26]) is a suitable method for accurately establishing H\({}_{2}\)O and CO\({}_{2}\) balances at the ecosystem level [27; 28]. This capability of the EC technique can be notably enhanced to understand the separate active roles of the crop and the soil on H\({}_{2}\)O and CO\({}_{2}\) exchanges when these EC fluxes are partitioned into transpiration (T) and evaporation (E) and photosynthesis (P) and respiration (R), respectively. The well-established flux variance similarity (FVS) method can be utilized for this purpose; this technique was first introduced by [PERSON] et al. [29; 30]. FVS can distinguish between stomatal (T and P) and non-stomatal (E and R) fluxes when active plants are present on the soil surface [31]. The aim of this study was to assess the dynamics of canopy CO\({}_{2}\) assimilation and respiration, H\({}_{2}\)O crop transpiration and soil evaporation for a watermelon crop subjected to a Mediterranean semi-arid climate. Furthermore, water use efficiency has been determined over six months from planting to the next cultivation.
# Phishing Webpage Classification via Deep Learning-Based Algorithms: An Empirical Study
## 5 Conclusions and Future Works
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Experiment (Exp.)** & \
\begin{tabular}{c} **Number** \\ **of Epochs** \\ \end{tabular} & **FPR (\%)** &
\begin{tabular}{c} **FNR** \\ **(\%)** \\ \end{tabular} & **Precision** & **Recall** & **F1-Score** & **AUC** & **Accuracy** & **Time** \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \hline C8-1 & 50 & 3.50 & 3.39 & 96.61 & 97.09 & 96.85 & 99.51 & **96.56** & 54 \\ C8-2 & 100 & 4.81 & 4.62 & 95.38 & 96.43 & 95.90 & 99.30 & 95.30 & 103 \\ C8-3 & 150 & 2.93 & 5.18 & 94.82 & 97.70 & 96.24 & 99.32 & 95.79 & 153 \\ C8-4 & 200 & 5.17 & 3.84 & 96.16 & 95.85 & 96.00 & 99.34 & 95.57 & 204 \\ C8-5 & 250 & 3.63 & 4.97 & 95.03 & 97.13 & 96.07 & 99.29 & 95.61 & 254 \\ C8-6 & 300 & 3.41 & 5.66 & 94.34 & 97.40 & 95.85 & 99.22 & 95.30 & 302 \\ C8-7 & 500 & 4.57 & 4.16 & 95.84 & 96.31 & 96.08 & 99.28 & 95.66 & 503 \\ C8-8 & 700 & 3.91 & 6.53 & 93.47 & 97.23 & 95.31 & 99.06 & 94.53 & 704 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Performance metrics of LSTM for different number of epochs. (Number of kernels = 16, kernel size =3, learning rate = 0.005, dropout rate = 0.5, batch size = 0.5).
## 5 Performance Metrics  
For the analysis of the collected raw data, we have divided our study into three sections for each scenario: (1) positioning, assessing the native capacity of the device; (2) visibility, assessing the constellations/frequencies visible by each device; (3) measurements, assessing the raw GNSS observables' precision, which is relevant to advanced positioning applications. In this section, we explain the performance metrics chosen to evaluate these aspects in each scenario.
# Main Factors on Effect of Precise Measurement and Precise Tamping Based on BP Neural Network
## 4 Case Analysis
### Neural Network Training
#### 4.4.3 Evaluation Index  
In this paper, the coefficient of determination R (Equation (5)) was adopted to evaluate the effectiveness of the model. The coefficient of determination R was close to 1, indicating that the trained network model was trained well. Meanwhile, the median absolute deviation (_MAD_) and mean absolute percentage error (_MAPE_) evaluation indicators were used to aid the evaluation of the prognosis effectiveness of the target value. The foregoing define the deviation between the predictive value and true value, thus reflecting the prognosis capacity. The lower the error value, the better the prognosis effect and the higher the accuracy of the target data. The calculation formula is:  
\[\begin{cases}MAD=median(|v_{i}-v_{m}|)\\ MAPE=\frac{100\%}{n}\sum\limits_{i=1}^{n}\frac{|\hat{y}_{i}-y_{i}|}{y_{i}} \end{cases} \tag{15}\]  
where \(v_{i}\) is the \(i\) th value of \(\hat{y}-y\), \(v_{m}\) is the median value of \(v\), and \(n\) is the amount of data.  
Figure 11 is the result of fitting the target value of each dataset with the estimated value of the neural network. After experimentation, the data results after cleaning were trained based on the above structural neural network model. The correlation coefficient R could reach about 0.85 when the target value was the \(MTQ_{df}\). When the target value was the IR, R was only about 0.5. It shows that the neural network can fit \(MTQ_{df}\) well, but the processing effect of IR is not satisfactory. Therefore, the \(MTQ_{df}\) was selected as the only output of the neural network model. After 10,000 trainings, the mean of the MSE was 0.432, as is shown in Figure 12; the mean of the _MAD_ was 0.64, as is shown in Figure 13; and the mean of the _MAPE_ was 13.2%, as is shown in Figure 14. It can be seen that the deviation was small and the prediction effect was good. They met our precision needs for this study.
# An Upgraded Version of the Binary Search Space-Structured VQ Search Algorithm for AMR-WB Codec
## 4 Experimental Results
Accordingly, a total of 1.45 MB of memory is required for whole lookup tables in Table 8. The memory requirement is very small in this work. Finally, in our previous experiments, the linear prediction analysis and quantization occupied 8-13% of the total operation in an AMR-WB encoder, depending on the coding modes. In other words, this work could eliminate 7.5-12.2% of the total operation in the AMR-WB encoder.
## 3 Delay Spread, Doppler Shift, and Doppler Spread  
In this section, we define delay spread, Doppler shift and Doppler spread for the proposed system. Furthermore, we show that the RIS HWI has no impact on the Doppler shift and Doppler spread, although the delay spread is increased duo to the imperfection of the RIS hardware.
## 4 Simulation Experiment and Analysis
### Q-Learning Routing Algorithm Performance Evaluation
#### 4.2.1 Path Load  
Figure 14 depicts the maximum load of the selected business transmission path under different algorithms, where the reward function parameter in the Q-routing algorithm is \(a_{1}\) for 0.8, \(a_{2}\) for 0.2, \(a_{3}\) for 0. Figure 14a indicates that services are generated at two fixed nodes in a satellite network, while Figure 14b indicates requests for services between random source and destination node pairs. As shown in Figure 14a,b, when the number of services is small, the Q-routing routing algorithm has the lowest load, while for the Q-learning algorithm and the Dijkstra algorithm, the link load increases rapidly. This is because the link load is added to the reward function of the Q-routing algorithm as an important parameter, which allows the agent to consider the link load when exploring the optimal path, thus optimizing the overall traffic balance. The Dijkstra algorithm prefers the shortest path for packet forwarding, and the shortest path increases the link load due to its frequent use. The performance of the Q-learning algorithm is consistent with that of the Dijkstra algorithm before the link load reaches the threshold of 80%. As the business intensity increases, the Q-learning algorithm searches for other alternative paths to avoid the shortest path, so the link load it chooses later is between the Q-routing algorithm and the Dijkstra algorithm. From Figure 14, it can be seen that the Q-routing algorithm can optimize the link load and significantly increase the link resource utilization, thereby improving the service request acceptance rate.  
Figure 14: Comparison of service transmission path loads under different routing algorithms. (**a**) The source node and target node are fixed. (**b**) The source node and target node are random.
## 3 Proposal for Smart Energy Management
### System Overview  
Many daily life activities at work, home or during leisure time could be carried out with considerable savings in energy consumption and, on most occasions, this can be made possible with only a very small effort. The proposed system manages, in an intelligent way, the energy requirements of end users to make them match the energy resources available at each instant. However, not all processes or tasks can undergo an energy restriction without further consequences, and it may not be always possible to disconnect elements at will. Therefore elements can be differentiated (in a factory, at home or at the working place) according to their energy requirements:  
* Type 1: includes all machines or devices which need continuous and constant power; in general, machinery associated with continuous production processes.
* Type 2: devices with energetic inertia.
* Type 3: devices with some autonomy (e.g., those that can operate with batteries).
* Type 4: devices that can withstand a power outage at any time.  
Given that not all jobs or activities use resources of the same type, the system will have to differentiate between processes. As, depending on the application and circumstances, some processes will allow more or less changes in their power supply. To begin with, it will be necessary to ascertain the energy requirements of the machines or processes and their required availability.  
Furthermore, the system needs to obtain data from the electricity grid about energy generation, energy demand and costs in time. For this, real-time public data has to be collected over the Internet as provided by companies that operate the grid.  
Through an adequate analysis of this information, the system becomes capable of automatically deciding when a process must be initiated, or a machine has to stop. For example, it can determine when it is possible to pre-warm an industrial oven with a minor energy cost so that it will be ready when needed; or it can decide when and how to charge some batteries, thus making the most of any possible surplus in generated energy. Furthermore, the system may benefit from the development of studies on ways to improve predictions on the most adequate time slots to consume energy based on cost and availability. This is why some learning capabilities have been incorporated on it from the outset. Figure 1 describes the framework in which the system operates, and the relationships between its different constituent parts.  
The proposed system is divided into two parts: the hardware, which controls the power supply and captures information about real energy consumption and energy efficiency of the machinery; and the software, which captures information about the operation of electric networks, analyzes all collected data, and takes decisions that are executed by the hardware. Being the main subject of this paper, special emphasis will be put on the hardware development required for the WSAN being used.
## 4 Neural Network Architectures for Uniaxial Compressive Strength Prediction
### Radial Basis Function (RBF) Neural Network  
An RBF network is a single-hidden-layer feed-forward model that uses Gaussian basis functions centered on training patterns. Hidden neurons are added incrementally until a performance goal on the training set is achieved, and the output layer linearly combines these localized responses to predict UCS. RBFs train quickly and model highly nonlinear mappings, but they require careful tuning of the spread parameter to balance bias and variance [44].
## 5 Stability Analysis of High Slope of Carbonaceous Mudstone Based on Numerical Simulation
### Stability Analysis of Slope After Support
#### 5.4.3 Horizontal Displacement Analysis of Slope  
According to the data in Table 7 and the horizontal displacement contour maps in Figures 16 and 17, the deformation characteristics of the slope show significant changes after  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Working Conditions** & **Support Method** & **Stability Coefficient** \\ \hline Natural state & Before support & 1.15 \\  & After support & 1.57 \\ After soaking in full water & Before support & 1.03 \\  & After support & 1.40 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Statistical table of slope stability coefficient results before and after support under different working conditions.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Material Name** & **Unit Type** & **Elastic Modulus (kPa)** & \
\begin{tabular}{c} **Poisson’s** \\ **Ratio** \\ \end{tabular} &
\begin{tabular}{c} **Unit Weight** \\ **(kN/m3)** \\ \end{tabular} \\ \hline Anchor cable & Implantable truss & 1.95 \(\times\) 10\({}^{8}\) & 0.3 & 78.5 \\ Frame beam & Beam & 3.2 \(\times\) 10\({}^{7}\) & 0.2 & 25 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Parameters of prestressed anchor cable and frame beam.  
the implementation of the support project: in the natural state, the maximum horizontal displacement increases slightly from 70.3 mm to 74.0 mm, an increase of 5.33%, and an increase of 3.7 mm; while under saturated conditions, the displacement value decreases significantly from 996.2 mm to 111.1 mm, a decrease of 88.85%. These data changes fully prove that the support measures have a particularly prominent effect on controlling slope deformation, especially in displacement suppression under saturated conditions. In addition, the slight increase in displacement under natural working conditions may be due to the increase in sliding force caused by the self weight of the frame beam and its vertical component of prestress, as well as the relatively limited compression effect of the horizontal component of prestress under passive earth pressure.  
Figure 16: Horizontal displacement contour map of slope after support under natural working conditions.  
Figure 17: Horizontal displacement contour map of the slope after immersion in saturated water and support.  
Table 8 shows that the vertical settlement displacement and horizontal displacement exhibit similar evolution patterns. Specifically, under natural working conditions, the maximum vertical settlement displacement before and after support only increases by 1.01%; under saturated conditions, this indicator significantly decreased by 92.47%. This comparative result fully proves that the prestressed anchor cable + frame beam system can effectively restrain the vertical sliding of deep soil and ensure the overall stability of the slope. In addition, due to factors such as passive earth pressure, there is also a slight increase in vertical displacement under natural working conditions.
## 4 Monte Carlo Simulation
### MCS Framework
#### 4.1.1 Photon Initial Position  
To determine the initial direction cosines for an emitted photon, we first determine the polar \(\theta_{s}\) and azimuthal \(\Phi_{s}\) angles of the photon location with respect to the light source plane (or to the norm of the photodiode surface) and then convert \(\theta_{s}\) and \(\Phi_{s}\) to the direction cosines \(x_{r}\), \(y\), and \(z\), which are the \(x,\ y\), and \(z\) components of the unit Cartesian vector that points in the direction of photon travel.  
For ideal collimated light (e.g., a laser beam), we can initiate all photons at the same location and point them in the same direction. However, some variability should be included in the source location and direction for an actual source.  
For an ideal photon point source, one can initiate each photon at the centre of the source location. Hence, all photons will share the same initial radial position (\(r_{0},\ \theta_{0},\ \Phi_{0}\)). However, a Gaussian optical beam with a pulse width (\(w\)) can be considered irradiance at a \(1/e\) radius or full-width-at-half-maxima. The profile of relative irradiance \(I(r)\) [mm\({}^{-2}\)] as a function of radial position \(r\) is the following [73]:  
\[I(r)=\bigg{(}\frac{1}{\pi w^{2}}\bigg{)}e^{-\frac{r^{2}}{w^{2}}} \tag{58}\]  
Equation (58) is normalised concerning the maximum optical pulse intensity \(I_{0}\). The PDF that describes the beam profile as a function of radial position \(r\) is as follows [124]:  
\[p(r)=\bigg{(}\frac{2r}{w^{2}}\bigg{)}e^{-\frac{r^{2}}{w^{2}}},\ \mathrm{where}\ \int_{0}^{\infty}p(r)\ dr=1 \tag{59}\]  
Hence, the CDF of \(p(r)\) is  
\[P(r)=\int_{0}^{r}p\big{(}r^{\prime}\big{)}dr^{\prime}=1-e^{-\frac{r^{2}}{w^{2 }}} \tag{60}\]  
If we set \(P(r)\) equal to a random number \(q_{r}\), and solve Equation (60) for \(r\), it yields the value of \(r_{0}\) (the photon initial radial position at the source):  
\[r_{0}=w-ln(1-q_{r}) \tag{61}\]  
However, since \(w\), the pulse width is constant, less than one, and the \(ln(1-q_{r})\) effectively is a random variable less than one hence \(r_{0}\) is a random number. Hence, selecting a random value for \(r_{0}\) from the range of \(0\ \leq\ r_{0}\ \leq\ w\) is acceptable statistically. It is clear that if we assume that all emitted photons have the same \(r_{0}\), this means that we are assuming a point source.
## 4 Discussion
### Geographic Context Controlling the Spatial-Temporal Distribution
#### 4.1.1 Topographic Feature in Relation to Prehistoric Settlements
Furthermore, the terrain pattern within the study area is not expected to undergo significant changes across such a vast area, allowing for the identification of correlations between topographic features and prehistoric settlements. Regionally, an altitude of 100 m serves as a boundary between the middle and lower reaches of the Yuanjiang River. Archaeological sites below this elevation were located in the lower reaches, while those above were found in the middle reaches. Over time, the number of archaeological sites in the lower reaches increased steadily from the Paleolithic through the Shang and Zhou dynasties, maintaining a consistent growth rate. In contrast, the middle reaches experienced a significant decline in site number during the Neolithic, followed by a rapid resurgence in the Shang and Zhou periods. These cultural developments reflected the profound influence of geological environments on ancient societies. During the warmer Neolithic period, human activities thrived in the low-altitude plains, which provided more favorable conditions for growth compared to the more enclosed mountain basins. By the Shang and Zhou dynasties, the middle reaches underwent accelerated development, primarily fueled by agricultural advancements. The fertile river basins  
Figure 4: Distribution of archaeological sites by elevation (**a**) and distance away from water (**b**). became crucial for agricultural production, underscoring the role of altitude in shaping cultural transitions and settlement patterns. As agriculture emerged as the dominant mode of production, areas suitable for cultivation became key sites for cultural change and development [19; 25; 27; 31; 40].