esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Study on Damage Constitutive Model of Rock under Thermo-Confining Pressure Coupling
## 4 Damage Constitutive Model of Rock
### Rock Damage Theory Based on the Drucker-Prager Criterion
#### 4.1.1 Rock Damage under Confining Pressure  
The damage variable can be defined in various ways. Given that material damage results in alterations to the microstructure and certain macroscopic physical properties, the failure of rock materials can be viewed as a process of damage accumulation. Consequently, the benchmark for measuring damage can be selected from both micro and macro perspectives. The damage to microelements within the rock under load is generally random. The damage variable \(D\) can be defined as the ratio of the number of damaged units \(n\) at a certain stress level \(q\) to the total number of units \(N\) in the initial state, as follows:  
\[D=\frac{n}{N} \tag{11}\]  
According to the Krajcinovic model [38; 39], the damage variable is the probability \(p\) of microelement failure. If the probability density of microelement failure is \(\phi(x)\), then \(p\) is the cumulative distribution function \(F\), expressed as:  
\[D=p=\int\limits_{0}^{F}\phi(x)dx \tag{12}\]  
Based on the assumption that the strength of rock microelements follows a Weibull distribution, its probability density function can be expressed as:  
\[\phi(F)=\frac{m}{F_{0}}(\frac{F}{F_{0}})^{m-1}\exp[-(\frac{F}{F_{0}})^{m}] \tag{13}\]where \(F\) represents the microelement strength; the parameter \(m\) represents the shape parameter of the Weibull distribution, indicating the uniformity of the internal strength of the rock material. The parameter \(F_{0}\) is the scale parameter of the Weibull distribution, reflecting the average strength level of the rock material.  
Substituting Equation (13) into Equation (12), the damage variable of rock under external load is:  
\[D=\int\limits_{0}^{F}\phi(x)dx=1-\exp[-(\frac{F}{F_{0}})^{m}] \tag{14}\]
## 5 Numerical Results
### Testing Performance of COA and ICOA on Different Adjustment Parameters
The improvement level of ICOA over COA is, respectively, 25.11%, 22.29%, 27.86%, and 21.69% for TVD case and is, respectively, 5.02%, 4.67%, and 0.72% for \(L\) index case. Clearly, the proposed ICOA approach could find a better optimal solution than COA for the same settings of control parameters. In relation to capability of handling constraints, the proposed method reported SR with 100% for TPL case and TVD case, and SR with \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \(N_{oxyate}\) & 9 & 6 & 4 & 3 \\  & \(N_{pack}\) & 2 & 3 & 4 & 5 \\  & \(N_{pop}\) & 18 & 18 & 16 & 15 \\  & \(N_{spi}\) & 20 & 21 & 20 & 20 \\ \hline \multirow{3}{*}{**ICOA**} & Min. (MW) & 4.5178 & 4.5154 & 4.5128 & 4.5238 \\  & Avg. (MW) & 4.5789 & 4.5997 & 4.7037 & 4.6868 \\  & Max. (MW) & 5.0749 & 5.4646 & 5.5696 & 5.5605 \\  & Std. dev. (MW) & 0.0868 & 0.143 & 0.2594 & 0.2649 \\  & CPU time (s) & 6.38 & 6.52 & 6.36 & 6.91 \\  & SR (\%) & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{**COA**} & Min. (MW) & 4.789 & 4.682 & 4.5777 & 4.595 \\  & Avg. (MW) & 5.072 & 5.1502 & 4.9205 & 5.2805 \\ \cline{1-1}  & Max. (MW) & 5.491 & 5.627 & 5.8416 & 5.5605 \\ \cline{1-1}  & Std. dev. (MW) & 0.1443 & 0.1985 & 0.2595 & 0.2920 \\ \cline{1-1}  & CPU time (s) & 8.3 & 8.4 & 8.2 & 8.5 \\ \cline{1-1}  & SR (\%) & 87 & 76 & 92 & 92 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results found by the proposed ICOA approach for TPL objective of the IEEE 30-bus system. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{3}{*}{**Method**} & \(N_{oxyate}\) & 9 & 6 & 4 & 3 \\  & \(N_{pack}\) & 2 & 3 & 4 & 5 \\ \hline \multirow{3}{*}{**ICOA**} & Min. (MW) & 0.09833 & 0.0948 & 0.0888 & 0.0971 \\  & Avg. (MW) & 0.1813 & 0.1848 & 0.1315 & 0.1594 \\  & Max. (MW) & 0.3479 & 0.5215 & 0.3231 & 0.3201 \\  & Std. dev. (MW) & 0.0516 & 0.0715 & 0.0416 & 0.0524 \\  & CPU time (s) & 6.88 & 6.38 & 6.39 & 6.73 \\  & SR (\%) & 100 & 100 & 100 & 100 \\ \hline \multirow{3}{*}{**COA**} & Min. (MW) & 0.1313 & 0.1220 & 0.1231 & 0.1240 \\  & Avg. (MW) & 0.2281 & 0.2047 & 0.1982 & 0.2112 \\ \cline{1-1}  & Max. (MW) & 0.4681 & 0.4433 & 0.4016 & 0.4181 \\ \cline{1-1}  & Std. dev. (MW) & 0.0716 & 0.0585 & 0.0800 & 0.0936 \\ \cline{1-1}  & CPU time (s) & 8.4 & 8.2 & 8.3 & 8.4 \\ \cline{1-1}  & SR (\%) & 88 & 89 & 90 & 79 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results found by the proposed ICOA approach for TVD objective of the IEEE 30-bus system. 98% for \(L\) index case while COA successfully handled the system with SR less than 100%. Namely, SR is 92% for TPL case, 90% for TVD case, and 76% for \(L\) index case.
## 3 Vibration Active Control
### Adaptive Algorithms
#### 3.1.2 Normalized Least Mean Square (NLMS) Algorithm  
The normalized least mean square (NLMS) algorithm is a variation of the conventional LMS. In the LMS, the value of the learning step \(\mu\) is a fixed value, while in the NLMS, this value is variable over time, in the form \(\mu(n)\)[32].  
Thus, in the case of NLMS, (14) can be rewritten as  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\mu(n)\mathbf{x}(n)e(n). \tag{15}\]  
It is then necessary to define the posterior error since the adaptive step value is variable. Thus,  
\[e_{p}=d(n)-\mathbf{w}^{\top}(n+1)\,\mathbf{x}(n). \tag{16}\]  
and substituting (15) into (16), yields  
\[e_{p}=\left[1-\mathbf{x}^{\top}(n)\mathbf{x}(n)\mu(n)\right]e(n). \tag{17}\]The value of \(\mu(n)\), which approximates the error value to its minimum, is given by  
\[\mu(n)=\frac{1}{||\mathbf{x}(n)||^{2}} \tag{18}\]  
in which \(||\mathbf{x}(n)||^{2}\) is the \(\ell_{2}\)-norm, i.e.,  
\[||\mathbf{x}(n)||^{2}=\sum_{i=0}^{M-1}|\mathbf{x}(n-i)|^{2}. \tag{19}\]  
Replacing (18) in (15) yields  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\frac{1}{||\mathbf{x}(n)||^{2}}\;\mathbf{x}(n)e (n). \tag{20}\]  
Expression (19) leads to an important regulation to determine the variation of the instantaneous squared error.  
Now, let us introduce a positive real scaling factor \(\mu\) in (18) in the same way of [33]. This scaling factor is important to control the change in the tap-weight vector from one interaction to the next without changing the direction of the vector. Hence, the updating weights in the NLMS is given by  
\[\mathbf{w}(n+1)=\mathbf{w}(n)+\frac{\mu}{||\mathbf{x}(n)||^{2}}\mathbf{x}(n)e (n). \tag{21}\]  
thus, we have a rate of convergence that is potentially faster than that of the LMS. Let us denote \(\mu(n)\) by \(\mu\) from this moment on for simplification.
# A Deep Learning Framework for Day Ahead Wind Power Short-Term Prediction
###### Abstract  
Due to the increasing proportion of wind power connected to the grid, day-ahead wind power prediction plays a more and more important role in the operation of the power system. This paper proposes a day-ahead wind power short-term prediction model based on deep learning (DWT_AE_BiLSTM). Firstly, discrete wavelet transform (DWT) is used to denoise the data, then an autoencoder (AE) technology is used to extract the data features, and finally, bidirectional long short-term memory (BiLSTM) is used for prediction. To verify the effectiveness of the proposed DWT_AE_BiLSTM model, we studied three different power stations and compared their performance with the shallow neural network model. Experimental analysis shows that this model is more competitive in forecasting accuracy and stability. Compared with the BP model, the proposed model has increased by 3.86%, 3.22% and 3.42% in three wind farms, respectively.  
discrete wavelet transform; autoencoder; bidirectional LSTM; wind power forecasting +
Footnote †: journal: Applied sciences
# Time Series Prediction of Sea Surface Temperature Based on an Adaptive Graph Learning Neural Model
However, these methods still cannot be used for SST time series because of the following difficulties:  
* Graph structure learning method: At present, most GNNs implement spatio-temporal series prediction based on predefined graph structure, but there is no graph structure displayed in SST time series data. The relationship between spatial points in the SST data set is hidden in the data, which needs to be mined instead of existing as prior knowledge. Therefore, how to mine the relationship between variables from the SST data and learn graph structure by deep learning remains a big challenge at present. * End-to-end framework: At present, most GNNs only update the hidden state of input sequence data in the learning process, neglecting to update the graph structure in time. Therefore, how to learn the graph structure and time series data simultaneously in a unified end-to-end framework is also a challenge. To solve the above problems, this paper proposes a graph neural network model (AGLNM) specially designed for spatio-temporal series prediction, which can automatically learn and model the relationship between variables. The AGLNM mainly consists of a graph learning module, graph convolution module, and time convolution module. The contribution points of this paper are as follows:  
* The graph learning module designed by this paper breaks the current limitation of GNN application in SST data sets without an explicit graph structure; the module can not only mine the hidden spatial-temporal dependencies in SST sequential data, but also process SST data without a predefined graph structure by automatically learning the graph structure. * This paper proposes an end-to-end framework which includes a graph learning module, graph convolution module, and time convolution module. In this framework, a graph loss mechanism is added to guide the graph structure to update to the optimal direction according to downstream tasks, which makes the final graph structure effectively aid in SST prediction. Compared with other models, the mean absolute error of this model is reduced by more than 13%, and it can be transplanted to data sets without graph structure. * In this paper, AGLNM is evaluated for the first time on two remote-sensing SEA surface temperature data sets, and compared with several representative time series prediction models. The experimental results show that the performance of AGLMN is better than other advanced models. The rest of this article is organized as follows. In Section 2, we formulate the question and presents the details of the proposed AGLNM. In Section 3, we evaluate the performance of the AGLNM and analyze the experimental results. Finally, Section 4 gives our conclusions.
## 1 Introduction
The V\({}_{\text{MG}}\), i.e., V\({}_{\text{G}}\) which correlates to I\({}_{\text{D}}\) = I\({}_{\text{MG}}\), may be determined as V\({}_{\text{MG}}\)= [log (I\({}_{\text{MG}}\)-n)/m] using the formula log (I\({}_{\text{D}}\)) = m.V\({}_{\text{G}}\) + n derived by the linear curve of subthreshold characteristic. V\({}_{\text{MG0}}\) and V\({}_{\text{MG}}\) are discovered using this method. The straight lines generated by the linear fittings of subthreshold properties are gradually applied to the appropriate midgap current I\({}_{\text{MG}}\). \(\Delta\)V\({}_{\text{ft}}\) is the factor of threshold voltage changes caused by FT, as:  
\[\Delta\text{V}_{\text{ft}}=\Delta\text{V}_{\text{MG}}=\text{V}_{\text{MG}}- \text{V}_{\text{MG0}} \tag{2}\]  
where V\({}_{\text{MG0}}\) and V\({}_{\text{MG}}\) are the midgap voltages in between irradiation. Vst is the component of threshold voltage shift caused by ST.  
\[\Delta\text{V}_{\text{st}}=(\text{V}_{\text{T}}-\text{V}_{\text{MG}})-(\text{ V}_{\text{T0}}-\text{V}_{\text{MG0}})=\text{V}_{\text{S}}-\text{V}_{\text{S0}} \tag{3}\]  
where V\({}_{\text{T0}}\) and V\({}_{\text{T}}\) are the threshold voltages of transistors before and after irradiation, respectively, and threshold voltage shift is \(\Delta\)VT = V\({}_{\text{T}}-\text{V}_{\text{T0}}\). V\({}_{\text{T0}}\) and V\({}_{\text{T}}\) are calculated from the saturation transfer characteristics as the junction of the V\({}_{\text{G}}\)-axis and the extrapolation linear area of the curves \(\sqrt{\text{I}_{\text{D}}}\) = \(f\)V\({}_{\text{G}}\). [12]. Optical thin films have been the focus of conducting film research. Because of its advantages, such as economic feasibility and nontoxicity, ZnO thin films have indeed been employed in a variety of applications [13,14]. According to reports, ZnO has (i) better quantum efficiency, (ii) great photochemical characteristics, and (iii) the capacity to produce a high-quality single crystal at a low price [15]. It is also a low-cost luminous and biofriendly oxide semiconductor device. Appropriately, ZnO is predicted to find widespread use in UV laser, biosensing, bio-imaging, drug development, and other fields. Doping substances with ZnO, such as lead (Pb), offers benefits. They discovered that Pb does have an impact on the size and shape of ZnO [16]. The inclusion of lead nanoparticles is accountable for the modified electrode's increased sensor activity. The significant improvement might be attributed to the synergistic impact of both Pb and ZnO nanoparticles [17]. In this work, ZnO-Pb thin and thick film were synthesised via chemical bath deposition (CBD) and their structural and morphological properties investigated. The samples were applied to use as low and high radiation detector-based EGFET.
# Statistical Method Based Waveform Optimization in Collocated MIMO Radar Systems
## 5 Simulation Results and Discussions  
In this section, we validate the closed-form expressions given in (3) and (21), and then we cast the later expression in the proposed algorithm of outage probability minimization. In the considered experimental set-up, we consider distinct transmissions and receive correlation matrices of target and clutter waveform denoted, respectively, as, \(\mathbf{R}_{Tt}\), \(\mathbf{R}_{T}\), \(\mathbf{R}_{Cl}\) and \(\mathbf{R}_{Cr}\). Furthermore, the correlation matrices are constructed using correlation coefficient \(\eta\) such that \(\mathbf{R}_{a,b}=\eta^{|a-b|}\), and \(0<\eta<1\). The SNR value considered throughout is 2 dB.
# Rapid Synthesis of Silver Nanowires in the Polyol Process with Conventional and Microwave Heating
###### Abstract  
Silver nanowires (AgNWs) represent an excellent material for many advanced applications due to their thermal and electrical properties. However, synthesising materials with the desired characteristics requires knowledge of the parameters affecting their size and an appropriate fabrication method. This paper presents a study on the synthesis of silver nanowires using the polyol process by conventional and microwave heating. Various polyols (1,2-ethanediol, 1,3-propanediol, 1,3-butanediol, 1,4-butanediol, 1,5-pentanediol) with different viscosities and dielectric properties were used as reductants. It resulted in nanowires with an average diameter of 119-198 nm. It was found that, in contrast to the viscosity and dielectric constant of the alcohol used, the heating method had a limited effect on the average diameter and length value of the final product. The performed studies indicate an optimal strategy for fabricating one-dimensional silver nanostructures using the polyol method.  
batch synthesis; silver nanowires; polyol process; microwave-assisted synthesis +
Footnote †: journal: Applied
# Recent Progress in Fast Distributed Brillouin Optical Fiber Sensing
## 4 Developments of the Fast BOTDA Schemes
### Optical Frequency-Agile Based Fast BOTDA
modulated by a sinusoidal microwave with a fixed frequency of \(\sim\)10 GHz output from a microwave source. Subsequently, the first order sideband is selected to be intensity-modulated into a 10-ns pulse by an optical filter, which is used as an up-converted pump pulse. For the left branch, a single-sideband modulator is driven by the frequency-agile microwave output from the AWG to generate a down-converted OFA-probe wave. As a result, a lower bandwidth requirement of only several hundred of MHz for the AWG is achieved as the frequency difference between the pump pulse and the OFA-probe wave, which can cover the BFS of the FUT. A damped oscillation with the central frequency of 12.3 Hz is measured by a sampling rate of 100 Hz (see Figure 7b).
# A Visual Analytics Framework for Inter-Hospital Transfer Network of Stroke Patients
## 3 Analysis of Data and User Task
### Hospital and Patient Transfer Data
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Attribute** & **Description** & **Notes** \\ \hline \multirow{6}{*}{Hospital} & HID & Unique ID for hospital & - \\  & Type & Type of hospital & Quaternary hospital, tertiary hospital, \\  & Address & Address of hospital & secondary hospital, and primary hospital \\  & Coordinate & Address of hospital & City, county, and district information \\  & & Location of hospital & GPS coordinates calculated \\  & & & based on the government office \\  & SCH & Stroke care hospital & - \\  & & designation status & - \\ \hline \multirow{6}{*}{Patient transfer} & Type & Type of patient transfer & “Stay” or “transfer and stay” \\  & HID (from) & Hospital ID from which & - \\  & & the patient is transferred & \\ \cline{1-1}  & & Hospital ID to which & - \\ \cline{1-1}  & & the patient is transferred & \\ \cline{1-1}  & & Patient count & Number of patients transferred & Total number of patients transferred \\ \cline{1-1}  & & & within the aggregation period \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hospital and patient transfer data. 0.43% of the total number of patients, this study only examined the two transfer types that accounted for the most (99.57%).
## 5 Discussion  
Although at first sight, it seems that just by looking with the naked eye at the RGB image it is possible to distinguish pixels belonging to an archaeological structure from pixels not in the structure, for computers performing this task is not so immediate. From the experiments, we can see the usefulness of our proposed method to more quickly identify archaeological structures in large areas of land with a lot of vegetation. Our method reduces archaeologists' pixel selection time when identifying large-image structures. Eventually,  
Figure 12: Detection results of the proposed method for the Dzibilchaltun, Xcambo, Mayapán, and Ake archaeological sites. (**a**) Satellite image of Dzibilchaltun, Mexico. (**b**) Structure detected of Dzibilchaltun, Mexico. (**c**) Satellite image of Ake, Mexico. (**d**) Structure detected of Ake, Mexico. (**e**) Satellite image of Xcambo, Mexico. (**f**) Structure detected of Xcambo, Mexico. (**g**) Structure detected of Mayapán, Mexico. (**h**) Structure detected of Mayapán, Mexico.  
our method allows the archaeologist not to waste time on this task, allowing them to perform more significant duties. Our experiments also showed that using appropriate filters and color spaces, and their synergy with a supervised classifier, made possible the detection of archaeological structures. The difference between our method and other approaches is that our method focuses on images with large areas of land and a lot of vegetation. Hence, using color spaces and filters was vital to highlighting information helpful to the classifier.  
The main limitation of our method is in images with shadows (due to vegetation, structures, and weather), which caused the classifiers to be unable to discriminate between archaeological structures and vegetation. Additionally, we noticed that some structures partially covered by grass, trees, or soil also caused classification errors. This type of image remains a challenge that deserves study in future work. However, as we showed in our experiments, the results obtained with our proposal are suitable for detecting archaeological structures in areas with a lot of vegetation which are usually difficult to access.
###### Abstract  
Considering the technological enhancements nowadays, antennas tend to be smaller in order to be easily integrated in devices. The most used antennas today in small high-tech devices close to the human body are planar antennas. In this paper, a Yagi Uda planar antenna operating at a frequency of 2.4 GHz is HF analyzed and optimized by increasing its bandwidth and gain while maintaining its initial dimensions. The methods used to optimize the antenna's operation are the use of different dielectrics, different numbers of directors, and different dimensions for directors, placing new conductor elements, all while keeping the same dimensions for its implementation on the planar device. The optimized structure of the planar Yagi Uda antenna has a 10% increase in bandwidth and a 30% increase in gain, reaching a peak value of 4.84 dBi. In our daily activities, we use devices with such antennas very often, so an analysis of the antenna's influence on the human body is performed: the SAR, electric and magnetic field and radiation power density are determined, represented and reported to the standards in force. For the frequency considered, the SAR should be below 4 W/kg for the head/torso when the exposure is more than six minutes, which is a value exceeded by the antenna in its near vicinity. The calculated maximum electric field limit is 0.349 V/m and the maximum magnetic field value is 28.441 V/m for an exposure between 6 and 30 min values, which is also exceeded in the immediate vicinity of the antenna. The results allow us to suggest that such an antenna should be placed further from the human body, or some protection should be placed between the body and the antenna. From the radiation power density point of view for the modeled antenna, it can be said that a distance from the antenna greater than 0.5 m is considered to be safe.  
dielectric; geometrical dimensions; S parameters; gain; SAR; electric field; magnetic field Article
## 3 The Loitering Detection Methods
### The Rectangle Loitering  
We use Equation (8) to establish an active rectangle frame to determine whether the pedestrian's activity area is in a rectangle frame. A rectangle trajectory is defined as a trajectory contained within a rectangle, as shown in Figure 7.  
\[(x_{i},y_{i})e\;rect((x_{1},y_{1}),(x_{2},y_{2})) \tag{8}\]  
In Equation (8), \((x_{i},y_{i})\) represents any coordinate in the \((t_{1},\cdots,t_{2})\) period, \((x_{1},y_{1})\) represents the coordinates of \(t_{1}\) moments, \((x_{2},y_{2})\) represents the coordinates of \(t_{2}\) moments, and \(rect(\left(\left(x_{1},y_{1}\right),\left(x_{2},y_{2}\right)\right)\) represents all coordinate points in the rectangular domain with \((x_{1},y_{1})\) and \((x_{2},y_{2})\) as diagonals.  
We propose a dynamic rectangular frame to judge loitering, which avoids judging the motion trajectory of each frame in the video. The detailed of our algorithm is described in the Algorithm 1, where \(T_{0}\) represents the initial threshold. In our experiments using PETS 2007 dataset, the time threshold \(T_{0}\) is set to 60 s. The time threshold should be set according to different scenarios.  
Input: the trajectory coordinates \(P(x,y)=\left\{(x_{1},y_{1}),(x_{2},y_{2}),\cdots(x_{n},y_{n})\right\}\)  
Output: loitering detection result  
1. Extract the trajectory coordinates \(p_{i}(x_{i},y_{i})\)
2. when \(T_{i}>T_{0}\), calculate the distance between \(P_{i}\) and \(P_{i-T}\), \(i>0\)  
3. Update \(\mathrm{P}_{i}\), calculate the size of activity area: \(S=|D_{xmax}-D_{xmin}|*\left|D_{ymax}-D_{ymin}\right|\)
4. Compare the area, if \(S<S_{threshold}\), a rectangle loitering is detected.  
**Algorithm 1** Rectangle loitering detection