esa-sceva/satcom-chunk-collection huggingface dataset:
# China's Water Intensity Factor Decomposition and Water Usage Decoupling Analysis
## 3 Results
### Water Intensity and Factor Decomposition Analysis
#### 3.1.2 Analysis of Decomposition Effect in Each Province
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Province**} & \multirow{2}{*}{\begin{tabular}{c} **Technological** \\ **Effect** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Industrial** \\ **Structure Effect** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Regional Scale** \\ **Effect** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Total Effect** \\ \end{tabular} } \\ \hline Beijing & \(-2.13\) & \(-1.41\) & \(0.24\) & \(-3.30\) \\ Tianjin & \(-1.20\) & \(-0.55\) & \(-0.05\) & \(-1.80\) \\ Hebei & \(-13.63\) & \(-2.69\) & \(-1.30\) & \(-17.61\) \\ Shanxi & \(-3.98\) & \(-0.55\) & \(0.09\) & \(-4.44\) \\ Inner Mongolia & \(-11.31\) & \(-3.78\) & \(1.25\) & \(-13.84\) \\ Liaoning & \(-8.06\) & \(-0.46\) & \(-2.69\) & \(-11.20\) \\ Jilin & \(-5.29\) & \(-2.75\) & \(-1.18\) & \(-9.22\) \\ Heilongjiang & \(-20.62\) & \(3.62\) & \(-7.05\) & \(-24.06\) \\ Shanghai & \(-6.20\) & \(-2.23\) & \(-0.52\) & \(-8.96\) \\ Jiangsu & \(-29.37\) & \(-10.35\) & \(4.59\) & \(-35.13\) \\ Zhejiang & \(-13.32\) & \(-4.31\) & \(0.84\) & \(-16.79\) \\ Anhui & \(-6.55\) & \(-9.00\) & \(2.03\) & \(-13.52\) \\ Fujian & \(-12.16\) & \(-3.63\) & \(1.45\) & \(-14.34\) \\ Jiangxi & \(-14.83\) & \(-5.38\) & \(2.71\) & \(-17.51\) \\ Shandong & \(-15.33\) & \(-4.35\) & \(-0.46\) & \(-20.13\) \\ Henan & \(-13.67\) & \(-3.87\) & \(1.15\) & \(-16.39\) \\ Hubei & \(-20.87\) & \(-3.82\) & \(2.68\) & \(-22.01\) \\ Hunan & \(-22.86\) & \(-5.70\) & \(2.65\) & \(-25.90\) \\ Guangdong & \(-30.22\) & \(-6.70\) & \(1.58\) & \(-35.33\) \\ Guangxi & \(-22.02\) & \(-3.59\) & \(1.41\) & \(-24.19\) \\ Hainan & \(-3.12\) & \(-0.68\) & \(0.21\) & \(-3.59\) \\ Chongqing & \(-4.79\) & \(-0.54\) & \(0.86\) & \(-4.46\) \\ Sichuan & \(-15.50\) & \(-3.49\) & \(2.25\) & \(-16.75\) \\ Guizhou & \(-7.78\) & \(-1.01\) & \(1.97\) & \(-6.81\) \\ Yunnan & \(-11.87\) & \(-1.51\) & \(1.46\) & \(-11.92\) \\ Tibet & \(-1.53\) & \(-1.23\) & \(0.58\) & \(-2.17\) \\ Shaanxi & \(-6.65\) & \(-0.99\) & \(1.33\) & \(-6.30\) \\ Gansu & \(-8.58\) & \(-1.20\) & \(-0.36\) & \(-10.15\) \\ Qinghai & \(-2.31\) & \(-0.21\) & \(0.20\) & \(-2.31\) \\ Ningxia & \(-6.76\) & \(-1.63\) & \(1.10\) & \(-7.29\) \\ Xinjiang & \(-34.05\) & \(-5.87\) & \(1.63\) & \(-38.29\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Three effects in each province in China.
## 3 Methodologies
### LSTM-RNN-Oriented RLU Prediction  
LSTM has been adopted as a popular solution for temporal sequence and long-range dependency modeling, having been applied in numerous studies on language modeling, speech recognition, and online handwriting recognition, among others. The reason for the preference of LSTM over RNN is its ability to resolve the vanishing/exploding gradients inherent in RNN training. Figure 8 presents LSTM in its basic structure, with the number of input layers, recurrent layers, and output layers all being 1. The innovation of LSTM is that it introduces different types of gates, such as the input gate \(i_{(t)}\), forget gate \(f_{(t)}\), output gate \(o_{(t)}\), and input modulation gate \(g_{(t)}\), and other components like hidden units \(h_{(t)}\) and memory cells \(c_{(t)}\).  
Information disposing is determined and performed by the forget gate \(f_{(t)}\). This gate utilizes a logistic activation function for inputs \(x_{(t)}\) and \(h_{(t-1)}\), of which the output is then provided to an element-wise multiplication operation. The gate is closed if the output is 0, and open if it is 1. The forget gate's calculation is:  
\[f_{t}=\sigma(W_{xf}x_{t}+W_{hf}h_{t-1}+b_{f}) \tag{8}\]  
Afterwards, new information is evaluated to decide whether it can be stored in the internal state. Initially, the \"input modulation gate\" \(g_{(t)}\), which acts as a tanh layer, forms a candidate state vector \(c_{(t)}\). Then, the input gate \(i_{(t)}\) determines which parts of \(g_{(t)}\) are to be supplemented to the long-term state \(c_{(t)}\). The two outputs are computed as follows:  
\[i_{(t)}=\sigma(W_{xi}x_{(t)}+W_{hi}h_{(t-1)}+b_{i}) \tag{9}\]  
\[g_{(t)}=\tanh(W_{xg}x_{(t)}+W_{hg}h_{(t-1)}+b_{g}) \tag{10}\]  
Deriving from Equations (8)-(10), the previous internal state \(c_{(t-1)}\) can be used to achieve the current state \(c_{(t)}\):  
\[c_{(t)}=f_{(t)}\otimes c_{(t-1)}+i_{(t)}\otimes g_{(t)} \tag{11}\]  
Eventually, the long-term state is then evaluated by the output gate \(o_{(t)}\) to determine which parts of it can be read and output at this time to both \(h_{(t)}\) and \(y_{(t)}\). After putting the internal state \(c_{(t)}\) through a tanh layer (to push the values to be between \(-1\) and \(1\)), it is multiplied by the output of the sigmoid gate to acquire the remaining state values. This is calculated as follows:  
\[o_{(t)}=\sigma(W_{xo}x_{(t)}+W_{ho}h_{(t-1)}+b_{o}) \tag{12}\]  
\[y_{(t)}=h_{(t)}=o_{(t)}\otimes\tanh(c_{(t)}) \tag{13}\]  
Figure 8: LSTM cell.  
with \(W\) and \(b\) as the layer weights and biases, respectively.
## 2 System Model and Performance Metric
### Performance Metric  
As a performance metric, we use the sum DoF, which is defined by  
\[\text{DoF}=\lim_{\text{SNR}\rightarrow\infty}\frac{R^{(u)}+R^{(d)}}{\log\text {SNR}}, \tag{3}\]  
where \(R^{(u)}\) and \(R^{(d)}\) denote the achievable sum-rates for uplink and downlink, respectively. Note that this DoF is the pre-log of the sum-rate capacity in the high SNR regime. In the next section, we describe our new hybrid opportunistic scheduling method for the cellular multi-antenna system with one full-duplex BS and multiple half-duplex MSs. We then show that it leads to an improved user scaling law (i.e., the reduced number of MSs) for guaranteeing the optimal DoF, compared to the prior work in [26].
## 3 Illustrative Numerical Results
### Technology Remarks  
In the proposed architecture, the _FSR_ of RRs in RSC drives the choice of the technology to be used (see Design Section). As for the PSC of our reference architecture (32 chann./50 GHz, 2 stages), the _FSR_ of last-stage RR is 200 GHz while for the RSC, the _FSR_ of each RR is 1.6 THz, corresponding to rings with radius = 7 \(\mu\)m, thus, the choice of silicon becomes mandatory [7]. Smaller rings, that is, wider _FSR_, are difficult to achieve in practice.
# An Improved Shoulder Line Extraction Method Fusing Edge Detection and Regional Growing Algorithm
## 2 Materials and Methods
### Accuracy Assessments  
To objectively evaluate and verify the superiority of the method in this paper, the following indicators were used to evaluate the edge detection method, the regional growing algorithm, and the method in this paper. Class pixel accuracy (CPA) is the percentage of correct pixels out of all the extracted result pixels; the closer the value is to 100%, the better. Pixel accuracy (PA) is the percentage of correctly extracted pixels in an image, that is, the proportion of correctly extracted pixels out of the total pixels; the larger the value, the better. The dice similarity coefficient (Dice) is a measure of set similarity, indicating the ratio of the area where two objects intersect the total area; the value range is (0, 1), and the effect is best when it is 1. Intersection over union (IOU) is the overlapping area between the extraction results and the real value divided by the joint area between the extraction results and the real value; the value is between (0, 1), and the larger the value, the better the effect. The formulas for each indicator are as follows:  
\[CPA=\frac{TP}{(TP+FP)}\times 100\% \tag{6}\]  
\[PA=\frac{(TP+TN)}{(TP+TN+FP+FN)}\times 100\% \tag{7}\]  
\[Dice(A,B)=\frac{2|A\cap B|}{|A|+|B|} \tag{8}\]  
\[IOU=\frac{|A\cap B|}{|A\cup B|} \tag{9}\]  
where \(A\) is the method extraction result; \(B\) is the manual visual interpretation result; \(TP=(A\cap B)\) is the method correctly extracting the region; \(FN=A-(A\cap B)\) is the target area missed by the method; \(TN=I-A\) is the real background area; and \(I\) is the set of image pixels.
# The Effect of Antagonist Abiotic Stress on Bioactive Compounds from Basil (_Ocimum basilicum_)
## 1 Introduction
Drought and flooding are key factors affecting productivity across world ecosystems [27]. The phenylpropanoid pathway is usually activated under stressful conditions (drought, flooding, temperature changes), leading to modifications in the composition of plant extracts [19]. The response of basil plants to abiotic stress has been extensively studied [6; 28; 29; 30; 31; 32; 33]; however, the physiological and biochemical response to antagonist drought\(-\)flooding stress on basil plants has not been reviewed. This study aims to evaluate the impacts of the antagonist abiotic stress of drought\(-\)flooding on basil plants. The photosynthetic parameters (net assimilation rate and stomatal conductance to water vapor), volatile organic compounds (VOC), chlorophylls content, flavonoids, and phenolic compounds are expected to change over the time of stress application (29 days).
## 3 Simulation Results and Discussion
### Pure Plane Wave  
Figure 4 shows a schematic view in which the propagation process of a plane wave in the pulse-echo mode with a reflecting boundary is unfolded. When the calculation results are displayed in the pulse echo mode, the two propagation regions overlap, making it difficult to identify. Therefore, in Figure 4 and thereafter, the back propagation region is developed and spreads to the right side of the reflection boundary for convenience of observation.  
The results of the pure longitudinal wave are presented here as references to other propagation and reflection problems. Plane waves are ideal waves without diffraction and attenuation. Figure 5 shows the variation in fundamental and second harmonic wave amplitudes in the forward and backward propagation directions. In the forward propagation, the displacement solution is given by Equation (2) for the fundamental and second harmonic waves. Since there is no diffraction and attenuation, the fundamental wave amplitude is constant, and the second harmonic amplitude linearly increases with propagation distance \(z\).  
The displacement solutions after reflection from the boundary are given by Equations (4) and (5), for the fundamental and second harmonic waves, respectively. Two kinds of boundary conditions are considered: the stress-free boundary (\(R=-1\)) and the rigid boundary (\(R=1\)). The same fundamental wave amplitudes are observed in Figure 5a after reflection from the rigid and stress-free boundaries because the reflection coefficient is \(|R|=1\).  
Figure 5b shows the reflected and newly generated second harmonic amplitudes and the total second harmonic amplitudes after being reflected from the two boundaries. The total second harmonic amplitude varies greatly depending on the boundary conditions used. It decreases linearly during backward propagation from the stress-free boundary and becomes 0 at the source position but continues to increase linearly in the case of the rigid boundary. This is known as the canceling and cumulative effects of the 2 nd harmonic, respectively, which occur depending on the boundary conditions.  
Figure 4: Unfolded schematic of plane longitudinal wave propagation in the pulse-echo mode.
## 4 Current Issues and Challenges of Realising the Tactile Internet
### Reliability of Connection  
The reliability in communication relies on the data received from the end user and feedback to the remote place. If the data received is incorrect, then the same is carried forward to the remote place. The TCP and UDP protocols have their drawbacks, which are not reliable for this type of communication. Sadly, this protocol algorithm leads to undesired propagation delay, and it is not acceptable to have a communication based on real-time information [63]. Therefore, there is a need to design a highly reliable infrastructure that will support audio and video data, with a packet loss probability of maximum 0.001% [4]. Therefore, modifications are required for existing protocols [63], or a new protocol should be created that goes with the requirements of 5G [8].
# Remaining Useful Life Prediction of Cutting Tools Using an Inverse Gaussian Process Model
## 2 Methods
### Remaining Useful Life Evaluation Model  
In the actual cutting process, cutting precision is not only related to the precision of the machine tool but also to the level of the operator, the state of the cutting tool, the state of the fixture, the material characteristics of the cutting workpiece, and the processing technology. However, these factors in the manufacturing process are often steady, while the cutting tool is gradually worn. Therefore, it is often the case that cutting precision is mainly dependent on the wearing level of the cutting tool. The influence of the cutting tool on cutting precision is directly reflected on the surface of a workpiece, which mainly has a significant influence on surface roughness. Geometric dimension precision is more related to the whole machining system, which can be improved by the adjustment of the operator. In addition, the mechanism affecting dimensional precision, such as the performance degradation of the spindle, will not change significantly in a short time. However, the tool wear will change significantly in a relatively short working time, resulting in abnormal surface roughness. Therefore, in this study, surface roughness is considered as the most significant precision index caused by tool wear in the short term. The time that the tool can normally work while still meeting the surface roughness requirements is defined as the remaining useful life (RUL). Assume that \(\mathrm{R_{k}}\) is the RUL corresponding to the equipment at the current measurement time \(t_{k}\); that is, the interval from time \(t_{k}\) to the time of fault occurrence. \[\mathrm{R_{k}}=\inf\{r_{k}\colon y(t_{k}+r_{k})>\omega|\mathbf{Y_{0:k}}\}, \tag{9}\]  
where \(\mathbf{Y_{0:k}}\) is the degenerate historical dataset from start time \(t_{0}\) to time \(t_{k}\). In addition, function \(\Lambda(t)\) should be updated under a different measurement time. The function \(\Lambda(t)\) corresponding to the measurement time \(t_{k}\)_is_\(\Lambda^{(t_{k})}(t)\):  
\[\Lambda^{(t_{k})}(t)=\Lambda^{(0)}(t+t_{k})-\Lambda^{(0)}(t), \tag{10}\]  
where \(\Lambda^{(0)}(t)\) is the function \(\Lambda(t)\) at the initial time.
## 4 Experiment
### Evaluation Metrics and Experimental Setup  
To quantitatively analyze the off-road semantic segmentation model constructed in this paper, we use the IoU (intersection-over-union), recall rate, and accuracy rate to evaluate the inference accuracy of the network model. In addition, we evaluate the efficiency of the network in terms of average inference time:  
\[\text{IoU}=\frac{T_{p}}{T_{p}+F_{p}+F_{N}} \tag{8}\]  
\[\text{recall}=\frac{T_{p}}{T_{p}+F_{N}} \tag{9}\]  
\[\text{precision}=\frac{T_{p}}{T_{p}+F_{p}} \tag{10}\]  
Among them, for a certain class of \(C\), \(T_{p}\) is the number of points that are correctly detected as \(C\); \(F_{N}\) is the number of points that are not detected as \(C\); and \(F_{p}\) is the number of points that are incorrectly detected as \(C\). Therefore, IoU can be used to evaluate the difference between the segmentation result and the true label, the recall can be used to analyze the missed detection rate, and the accuracy can be used to analyze the false detection rate.  
Figure 5: Simulated and real scenarios, where (**a**) is a real scenario from [30] and (**b**) is a simulated scenario built using the CARLA simulator.  
We use a computer with the Ubuntu18.04 operating system; its CPU is Intel@Xeon(R) E5-2678, the memory is 128 G, and it is equipped with four NVIDIA 3090 for neural network calculation acceleration. In our experiments, a single graphics card is used for training and inference.
# Drone Deployment Algorithms for Effective Communication Establishment in Disaster Affected Areas
## 3 Proposed Methods  
### Problem Statement  
We aim to formulate a drone deployment algorithm to establish a mobile network for disaster management. The main considerations can be summarized as follows:  
1. To develop a strategy for deploying drones to maintain the maximum possible average leftover energy and thereby increase the overall hovering time of all drones deployed.
2. To develop a region-filling method that guarantees coverage over the whole region with the minimum possible number of drones.
3. To deploy drones for communication establishment based on terrain and altitude analysis.
4. To determine the number and positions of base stations required to facilitate the network.
## Methods
### Statistical Analysis  
Hourly SSI estimated using ML approaches was compared with in situ hourly SSI. For quantitative evaluation of the hourly SSI derived from GK2A/AMI, we used the bias, RMSE, mean absolute error (MAE), normalized RMSE (nRMSE), and Pearson's correlation coefficient (R), as follows:  
\[\text{Bias}=\frac{1}{N}\sum_{i=1}^{N}(Est_{i}-Obs_{i}), \tag{12}\]  
\[\text{RMSE}=\sqrt{\frac{\sum_{i=1}^{N}(Est_{i}-Obs_{i})^{2}}{N}}, \tag{13}\]  
\[\text{MAE}=\frac{1}{N}\sum_{i=1}^{N}|Est_{i}-Obs_{i}|, \tag{14}\]  
\[\text{nRMSE}=\frac{\sqrt{\frac{\sum_{i=1}^{N}(Est_{i}-Obs_{i})^{2}}{N}}}{ \frac{\sum_{i=1}^{N}Obs_{i}}{N}}, \tag{15}\]  
\begin{table}
\begin{tabular}{c c c} \hline
**Specification** & **Parameter** & **Configuration** \\ \hline Conv1d Flatten layer & The number of filters & 16, 32, 64 \\ \hline Dense layer & The number of nodes & 100, 200, 300 \\  & The number of layers & 1, 2, 3 \\ \hline Regularization & L1 regularization parameter & \(10^{-3},10^{-5},0\) \\  & L2 regularization parameter & \(10^{-3},10^{-5},0\) \\ \hline \end{tabular}
\end{table}
Table 2: Parameters with structure of the convolutional neural network (CNN) model used to find an optimal model for estimating SSI derived from GK2A/AMI.  
\[\mathrm{R}=\frac{\sum_{i=1}^{N}\bigl{(}Est_{i}-\overline{Est}\bigr{)}\bigl{(}Obs_{i }-\overline{Obs}\bigr{)}}{\sqrt{\sum_{i=1}^{N}\bigl{(}Est_{i}-\overline{Est} \bigr{)}^{2}}\sqrt{\sum_{i=1}^{N}\bigl{(}Obs_{i}-\overline{Obs}\bigr{)}^{2}}}, \tag{16}\]  
where and \(Obs_{i}\) represent the estimated SSI from satellite data and observed SSI from the ground station, respectively; \(N\) is the number of data points; the subscript \(i\) denotes the \(i\)th data point; and \(\overline{Est}\) and \(\overline{Obs}\) represent the mean of the estimated SSI from satellite data and observed SSI from the ground station, respectively.
# Estimating Tail Probabilities of Random Sums of Phase-Type Scale Mixture Random Variables +
## 2 Preliminaries
### Scale Mixture of Phase-Type Distributions  
A random variable \(Z\) of the form \(Z:=W\cdot X\) is a scale mixture of PH-distributed if \(X\sim F\), where \(F\) is a PH distribution, and \(W\sim H\), where \(H\) is an arbitrary non-negative distribution. We call \(W\) the scaling random variable and \(H\) the scaling distribution. It follows that the CDF of \(Z\) can be written as the Mellin-Stieltjes convolution of the two non-negative distributions \(F\) and \(H\):  
\[B(z)=\int_{0}^{\infty}F(z/w)\,\mathrm{d}\,H(w)=\int_{0}^{\infty}H(z/x)\, \mathrm{d}\,F(x),\ \ \ z\geq 0\,.\]  
The integral expression above is available in closed form in very few isolated cases. Thus, we should rely on numerical integration or simulation methods for its computation.  
The key motivation for considering the class of scale mixture of PH distributions over the class of PH is that the latter class forms a subclass of light-tailed distributions while a distribution in the former class with scaling random variable having unbounded support is heavy-tailed ([11]). Hence, the class of scale mixture of PH distributions turns out to be an appealing tractable class for approximating heavy-tailed distributions. Recall that a non-negative random variable \(X\) has a heavy-tailed distribution if and only if \(\mathbb{E}[\mathsf{e}^{\theta X}]=\infty,\ \forall\theta>0\), equivalently, if \(\limsup_{x\to\infty}\mathbb{P}(X>x)\mathsf{e}^{\theta x}=\infty,\ \forall\theta>0.\) Otherwise, we say \(X\) is light-tailed.
# Multi-Criteria Optimization in Operations Scheduling Applying Selected Priority Rules
## 2 Scheduling Operations Based on Priority/Dispatching Rules
### Priority/Dispatching Rules
This coordinate mechanism is mainly useful when the goal is to reduce job lateness, minimize flow time and it also guarantees minimization of maximum and variance tardiness [23]. STR, MST (Minimum Slack Time) in [27], or MS (Minimum Slack) in [1] is a static rule concerning a specific operation, but a dynamic one with respect to a specific job [27], since the same job has assigned the different relative priorities to its individual operations. It chooses the waiting operation related to the job with minimum slack time. Slack time \(s_{j}\) of given job \(j\) with due date \(d_{j}\) and processing time \(p_{j}\) at time \(t\) is calculated by the expression (1):  
\[s_{j}(t)=d_{j}-p_{j}-t \tag{1}\]  
that represents the difference between the due date and the earliest possible finish time of the job [27]. The EDD rule or the STR rule ensures the optimal production performance when all due dates are sufficiently loose and spread out [1,23].
# Low Power Wide Area Network, Cognitive Radio and the Internet of Things: Potentials for Integration
## 6 Benefits of CR-LPWAN
### Technical Benefits to IoT Devices
#### 6.1.4 Increased Scalability  
The ability to seamlessly switch to licensed spectra will create additional bandwidth for LPWAN use. This increased bandwidth ultimately enables more end devices to transmit. At the moment, using a typical 200 kHz bandwidth and message bandwidth of 100 Hz, Sigfox networks can accommodate over 2000 IoT devices at a single transmission time. In fact, by deploying CR, it is noted that Sigfox networks may service up to 1 million end devices per base station [43], which is similar to Nwave, which can accommodate up to 1 million devices per base station [43]. Furthermore, the Ingenu technology used in Dallas/Forth Worth, US, serves more than 4.4 million people using only 17 access points [43]. This large number of users per base station can be further up-scaled by developing and adopting CR-LPWAN systems.
# Vocabulary Retention Under Multimodal Coupling Strength Index (MCSI): Insights from Eye Tracking
## 2 Literature Review
### Objectives  
This study pursues dual primary objectives: (a) conducting a comparative evaluation of learning efficacy across three sensory modalities--auditory-only stimulation, visual-only stimulation, and multimodal audiovisual stimulation; and (b) establishing a Multimodal Coupling Strength Index (MCSI) model that dynamically quantifies vocabulary recall performance through real-time eye-tracking metrics. Grounded in [PERSON]'s (2014) Multimedia Learning Theory, we advance two specific hypotheses: firstly, unimodal auditory and visual stimuli independently enhance vocabulary recall accuracy, while multimodal conditions generate superior lexical memory consolidation; secondly, the MCSI empirically demonstrates significant synergistic effects, confirming multimodal stimulation's cognitive advantage through quantifiable coupling strength metrics.