esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### Data and Preprocessing
Daily tidal data of HYNPP ([[https://www.cnss.com.cn/html/tide.html](https://www.cnss.com.cn/html/tide.html)]([https://www.cnss.com.cn/html/tide.html](https://www.cnss.com.cn/html/tide.html)), accessed on 15 November 2024), spanning from April 2018 to November 2024, were acquired from the National Marine Information Centre, Ministry of National Resources, China. The tidal data were matched to the satellite image in time order, and the tidal state at the scene time of the satellite image and the tidal height were extracted and applied to thermal dispersion analysis. Criterion for Temperature Rise Intensity Grading  
To assess the thermal impact of the NPP, a background temperature approximating the natural temperature of the area in the absence of thermal discharge is essential [28; 30; 31]. Given the marine location of the heat dissipation areas, an adjacent-zone substitution method was employed to determine a reference temperature (\(T_{\text{Ref}}\)). Specifically, a \(600\text{ m}\times 600\text{ m}\) square zone located approximately 3 km from the intake, and positioned to avoid the influence of thermal discharge, was selected as \(T_{\text{Ref}}\) for calculating Temperature Rise Intensities (\(TRIs\)). To maintain consistency with sea water quality standards regarding temperature, \(TRIs\) were categorized into four levels using \(T_{\text{Ref}}\) as the baseline (Table 2). For descriptive convenience, areas exhibiting SST increases of 1-2 \({}^{\circ}\)C, 2-3 \({}^{\circ}\)C, 3-4 \({}^{\circ}\)C, and \(>\)4 \({}^{\circ}\)C were designated as \(R_{+1}\), \(R_{+2}\), \(R_{+3}\), and \(R_{+4}\), respectively. The sum of the areas for each _TRI_ category was designated as \(A_{\text{total}}\).
## 2 Methodology
### The Crop Discrimination Network with Multi-Scale Features (MSCDN)  
In the deep learning field, convolutional neural network (CNN) has become increasingly powerful to deal with the complicated classification and recognition problems. Recently, CNN has been widely adopted in remote sensing, for example, in image classification, target detection, and semantic segmentation. However, most classical CNNs only use one single convolution kernel to extract the feature images, the resulting single feature map in each convolutional layer make it difficult to distinguish the similar crops, consequently the overall crop classification performance degraded. Just as our previous work [17], the poor overall performance is devoted to the minor category of crops that possess the similar polarimetric scattering characteristics. Therefore, in this paper, a new multi-scale deep neural network called MSCDN is proposed, attempting to further improve the classification accuracy. The MSCDN not only extracts the features with different scales by using multiple kernels in some convolution layers, but also captures the tiny distinctions between feature maps of multi-scales.  
The architecture of the proposed MSCDN classifier is shown in Figure 3. The network of MSCDN mainly contains three parts: multi-scale feature extraction, feature fusion and classification. First, multiple convolutional layers and multiple kernels within a certain convolution layers extract feature maps with different scales. Second, the feature information of these diverse scales was fused together as the basis to feed the classification layer. Finally, the softmax layer is adopted to perform the classification.  
As shown in Figure 3, the MSCDN comprises seven convolutional layers, two max-pooling layers, four fully connected layers, one concat layer, and a softmax classifier. The Rectified Linear Unit (ReLU) and Batch Normalization (BN) layers are successively connected after Conv_1 to Conv_5. The aim of ReLU layer is avoid the problems of gradient explosion and gradient dispersive to further improve the efficient of gradient descent and back propagation. As for the BN layer, it is a normalized procedure for each batchsize of internal data for the purpose of standardizing the output data as the normal distribution with zero mean and unit variance, which can accelerate the convergences. The branches  
Figure 3: MSCDN classifier architecture.  
of Conv_6 and Conv_7 aim to reduce the depth of the output feature image from Conv_3 and Conv_4, and decrease the computational complexity. The detailed parameters of the convolution kernel for each layer and other parameters for the MSCDN structure are listed in Table 2.
###### Abstract  
We have investigated the energy transfers in the 1.54-\(\upmu\)m region of (Er\({}_{x}\)Sc)\({}_{2}\)O\({}_{3}\) epitaxial thin films grown on Si(111). The interplay of the energy transfers between Er ions in the different and the same symmetry sites makes the dynamics complicated. To suppress the energy transfer upconversion, low power and resonant excitation of the third crystal-field level (\({}^{4}I_{13/2}:Y_{3}^{\prime}\)) of the Er\({}^{3+}\) site with \(C_{3i}\) symmetry was employed. The time-resolved photoluminescence measurements of the Y\({}_{1}^{\prime}\)-\(Z_{1}^{\prime}\) transition indicate the existence of two decay components having fast (10-100 \(\upmu\)s) and slow (0.1-1 ms) relaxation times in the range of 4-60 K. The model calculation including the inter-site energy transfers, the temperature-sensitive and -insensitive non-radiative relaxations fits the experimental results well. Moreover, the long averaged inter-Er\({}^{3+}\) distance obtained by decreasing Er concentration was found to reduce two kinds of non-radiative relaxation rates and the energy transfer rates between Er ions very effectively.  
erbium; epitaxial film; population dynamics +
Footnote †: journal: Applied Physics Letters  
13 April 2018; Accepted: 20 May 2018; Published: 25 May 2018
# Comparative Study of Minimally Invasive Microwave Ablation Applicators
The first part of this study concerns the benefits of helical open waveguide structure and the comparison with monopole. Then, the work focuses on the precise simulation of all these designs as presented in previous works. To ensure accuracy, the dimensions, lengths, boundaries, frequency, properties, and materials used were closely aligned with those in the corresponding papers. This alignment is essential for conducting a fair and valid comparison of all indicative applicators considered. After performing the simulations and confirming that the results, specifically the temperature distribution, closely match those of the corresponding parsing studies, the final part of the study is undertaken. This involves implementing all designs within a two-compartment model (tumor and healthy tissue), which provides a reasonably accurate representation of anatomical geometry [49]. This paper aims at comparing the different microwave applicators used in microwave tumor ablation, focusing on their design principles and studying for the same tumor. These published studies have mainly focused on the liver using a two-compartment model, which applies different properties to tumor and healthy tissue. A two-compartment model is already a good approximation in terms of anatomical geometry. However, it is crucial to use accurate tissue properties for each compartment, as the predicted treatment outcome is highly sensitive to these parameters. Ultimately, this study compares the temperature distribution of different applicators when applied to the same tumor models as well as the shape of the ablated region and its impact on healthy tissue. To sum up, this work provides the first comparative study where multiple, published, microwave applicators are simulated under unified conditions. The novelty of this study lies in its approach: simulations are conducted under standardized conditions, including a two-compartment model that better approximates real experimental results.
## 3 Results and Discussion
### Global Assessment of WSs' Physicochemical Characteristics  
To complete the physicochemical assessment, a global analysis was supported by a cluster heat map to associate patterns to the blending technologies under study (Figure 8).  
The heat map clearly divided the WSs into two groups based on the ageing modalities (traditional and alternative); no separation of WSs according to the blending technology was made. These results are in line with and reinforce the ANOVA outcomes.  
The main analytical parameters involved in the differentiation of WSs from the traditional ageing technology and from the alternative one, regardless of the blending technology, were (i) pH, gallic acid, scopoletin, lightness (L*) and volatile acidity that exhibited positive correlations with the ageing in barrels; and (ii) umbelliferone, ferulic acid, ellagic acid, sinapaldehyde, coniferaldehyde, syringaldehyde, vanillin, furfural, total phenolic index, syringic acid, chroma (C), chromaticity coordinates (a* and b*), absorbance at 470 nm, total dry extract, fixed acidity and alcoholic strength that showed positive correlations with the ageing in stainless steel tanks using wood staves and MOX.  
The results also reveal the peculiar behaviour of 5-methylfurfural and HMF, as well as of total acidity, which did not present a marked trend according to the ageing technologies.
## 6 Results and Discussion
### Model Implementation and Training Protocols
#### 6.2.1 Radial Basis Function (RBF) Neural Network  
Implemented with MATLAB's newrb, the network grew hidden Gaussian units one at a time (up to 25) until the training MSE fell below 1 \(\times\) 10\({}^{-3}\) (spread = 0.8). Inputs were standardized (z-score) and outputs normalized to [0,1] via mapminmax. The model achieved R\({}^{2}\) = 0.972 and RMSE = 1.313 MPa on the hold-out set, as shown in Figure 13.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & **MATLAB** & \multirow{2}{*}{**Topology**} & **Data Split** & \multirow{2}{*}{**Preprocessing**} & **Key Hyperparameters** & **Performance Metrics** \\ \hline RBF & \multirow{2}{*}{newrb} & Up to 25 Gaussian & 80\% train/ 20\% test & \
\begin{tabular}{c} Inputs z-score; \\ outputs \\ mapminmax \\ [0,1] \\ \end{tabular} & \multirow{2}{*}{RMSE, R\({}^{2}\)} \\ \hline Bayesian & \multirow{2}{*}{trainibr} & 1 hidden & \multirow{2}{*}{80\%/20\%} & z-score; \\ NN & & 15 neurons & & & \\ \hline \multirow{2}{*}{
\begin{tabular}{c} SCG \\ network \\ \end{tabular} } & \multirow{2}{*}{trainscg} & 1 hidden & \multirow{2}{*}{70\%/15\%/15\%} & z-score; \\  & & layer, & & & \(\sigma\) = 1.0 \(\times\) 10\({}^{-6}\); \\  & & 15 neurons & & & \(\text{reg}=0.01\) \\ \hline \multirow{2}{*}{LM network} & \multirow{2}{*}{trainlm} & 1 hidden & \multirow{2}{*}{70\%/15\%/15\%} & z-score; \\  & & layer, & & & \(\mu\) = 1.0 \(\times\) 10\({}^{-3}\); \\  & & 15 neurons & & & \(\mu\) = 0.1; \(\mu\)\(\uparrow\) = 10; \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of Model Configurations, Training Functions, and Evaluation Criteria.  
Figure 12: Parallel coordinate plot of rock properties.
# An Assessment of Environmental RF Noise Due to IoT Deployment
## 6 Preparing Data for Analysis
### Calibration and Amplitude Correction  
A radio noise measurement system shall distinguish between the internally generated noise by the receiver and the external noise captured by the antenna. The collected raw data combined both variables. The internal noise measurement required inserting a 50 \(\Omega\) load at the antenna connection point. Subtracting the internal noise values from the collected raw data results in obtaining the actual external noise levels [19]. This paper measured the equipment noise (EN) for five minutes after replacing the receiving antenna with one 50 \(\Omega\) RF load. This procedure also applied data reduction techniques to EN [12].  
The results in this RNSS obtained lower EN levels than the antenna noise (AN) on the frequencies and candidate sites. The calibrated noise (CN) resulted from the subtraction, as follows: CN = AN \(-\) EN. The antenna factors were insignificant. The test results show that the losses due to RF cables, connectors, and RF adaptors were also negligible [12].  
The LNA gain, the filter insertion loss, and the digital gain set in the URSP constituted the RNSS front-end factor. The amplitude correction process subtracted the front-end factor from the CN data. Table 3 shows the values considered for the amplitude correction [12].
## 1 Introduction  
The application of remote sensing technology in geological research mainly focuses on the extraction of linear structures. Linear structures are a series of linear, arc, and fold linear (shape) image features [1; 2; 3; 4; 5; 6] displayed in remote sensing images related to geological processes. Linear structure extraction methods mainly include principal component analysis, image texture analysis, false color composite enhancement study area fracture, and surrounding color difference [7; 8; 9]. The linear structure extraction process of this method mainly depends on manual visual interpretation [10; 11].  
Due to the long time period necessary for visual interpretation and the need for interpretation experts to be present to accumulate a large number of field investigation experiences, the interpretation results are slow and time-consuming. To improve the efficiency of linear structure extraction, the standard Hough transform method is used to detect fracture structures with linear characteristics in remote sensing images. For image mitochondrial edge tracking, the linear structure in the image is combined with DEM data and then accurately interpreted to obtain the fracture surface. Methods such as that using gravity data combined with field investigation to identify linear structural features in images have gradually emerged [12; 13; 14; 15]. However, most of these methods are based on the edge detection technology of digital filtering, which is used to extract the mitochondrial features in the image. Generally, it is impossible to extract the mitochondrial features in the shadow area of the remote sensing image and in the area parallel to the incident angle of the sun. In addition, there are problems due to the fact that the line element automatic connection cannot reflect the line element interval and turn, and also cannot fully show the fracture change, fracture attribute characteristics, and so on.
## 3 Results
### Verification Studies
#### 3.1.2 Free Vibration Analysis of an Isotropic Beam  
The second verification case consists of analyzing the free vibration of a cantilever beam made of isotropic material, with a unit length, rectangular cross-section of height h and width b, modulus of elasticity E, density \(\rho\), 2 nd moment of area I, Poisson's ratio v = 0.25, with EI = 1, \(\rho\)I = 1, and h taking values such that L/h = 10 and L/h = 100. The results to be compared will be transformed into dimensionless frequency values using the expression  
\[\overline{\omega}=\omega\ L^{2}\bigg{(}\frac{\rho A}{EI}\bigg{)}^{\frac{1}{2}} \tag{5}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Exact [40]**} & \multicolumn{2}{c}{**Transverse Displacement [m]**} & \multicolumn{2}{c}{**Rotation [rad]**} \\ \cline{2-5}  & **FEM** & **Exact** & **Present** & **FEM** & **Exact** & **Present** \\ \hline
0 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 & 0.0000 \\
0.1875 & 0.0008 & 0.0008 & 0.0009 & 0.00880 & 0.00891 & 0.00891 \\
0.375 & 0.  
Thus, studies were carried out for two, four, and eight quadratic elements, in order to compare the present predictions with the reference obtained by the [PERSON] (TBT) and Euller-Bernoulli (EBT and EBT*) beam theories, where EBT corresponds to the Euler-Bernoulli beam theory considering rotational inertia, and EBT* corresponds to the Euler-Bernoulli theory considering rotational inertia to be negligible (the results are independent of L/h), for the structure under study, and using the finite element method. Looking at the results presented by [PERSON] [40] for the exact solutions according to both the Euler-Bernoulli and Timoshenko beam theories (Tables 3 and 4), it should be noted that when the rotational inertia is neglected (EBT*), the resonance frequencies are slightly higher than the other solutions. Tables 3-6 show the first four resonance frequencies of the cantilever beam, for L/h = 10 and L/h = 100, respectively, predicted by [PERSON]'s model, and the resonance frequencies obtained using the implemented model.  
4, it is out of step with the reference mode; however, the model, in terms of representation of vibration modes, is verified, since in this case the comparison is between linear elements (in the referenced author) and quadratic elements (in the present model).
## 2 Materials and Methods
### Primers, PCRs, and Gel Electrophoresis
rDNA consists of five regions in repeated gene clusters: the highly conserved ITS1 and ITS2 are separated by three coding regions: the central 5.8S subunit, the 18S and the 26S unit at each end of the rDNA cluster 400-900 base pairs long. (4,5) Fungal-specific primer pairs are used to detect and amplify the specific ITS region. In our case, fungal-specific primer pairs successfully amplified the specific region containing the whole ITS and ITS2, whereas ITS1 (f) + ITS2 (f) failed to amplify the ITS1 region. Based on this, we did not include the extended primer for the ITS1 region. (6) Extended primer pairs were used to amplify the ITS and ITS2 regions of fungal rDNA. (7,8) Extended primer pairs that targeted the whole ITS region failed to amplify at different annealing temperatures and PCR cycles. As a result, index PCR was unsuccessful. However, extended primer pairs successfully amplified the ITS2 region and attached the index primer to the index PCR. To add index primers to the samples, commercial index primers that could bind uniquely to the samples were provided by SciLifeLab ([[https://ngisweden.scilifelab.se/methods/illumina-amplicon-sequencing](https://ngisweden.scilifelab.se/methods/illumina-amplicon-sequencing)]([https://ngisweden.scilifelab.se/methods/illumina-amplicon-sequencing](https://ngisweden.scilifelab.se/methods/illumina-amplicon-sequencing)), access date 5 December 2021). Index PCR was performed on five randomly selected extended PCR products to confirm the attachment of
## 3 Localization Method
### Initial Solution
\[\mathbf{H}((k-1)M+i,(k-1)M+i)=\mathbf{h}((k-1)M+i)^{2}= \mathbf{q}^{T}\mathbf{q}-2\mathbf{q}^{T}\mathbf{X}^{T}\mathbf{s}_{i,k}+ \mathbf{s}_{i,k}^{T}\mathbf{s}_{i,k},\] \[\quad\mathbf{H}(l_{1},l_{2})\geq|\mathbf{q}_{1}^{T}\mathbf{q}_{2 }-\mathbf{q}_{1}^{T}\mathbf{X}^{T}\mathbf{s}_{i,2k}-\mathbf{q}_{2}^{T}\mathbf{ X}^{T}\mathbf{s}_{i,k_{1}}+\mathbf{s}_{i,k_{1}}^{T}\mathbf{s}_{i,2k}|,\] \[\quad\mathbf{H}((k-1)M+i,(k-1+K)M+i)\] \[\quad=\mathbf{Y}(2,1)/\tau+(k-1)\mathbf{Y}(2,2)/\tau-\mathbf{X}(:,2)^{T}\mathbf{s}_{i,k}/\tau-\mathbf{q}^{T}\mathbf{X}(:,1)^{T}\mathbf{s}_{i}+ \mathbf{s}_{i,k}^{T}\mathbf{s}_{i}. \tag{37}\] \[\quad\mathbf{H}((k-1+K)M+i,(k-1+K)M+i)\leq\mathbf{Y}(2,2)/\tau^{2 }-2\mathbf{X}(:,2)^{T}\mathbf{s}_{i}/\tau+\mathbf{s}_{i}^{T}\mathbf{s}_{i}.\] \[ \begin{bmatrix}1&\mathbf{h}^{T}\\ \mathbf{h}&\mathbf{H}\end{bmatrix} \succeq\mathbf{0},\] \[ \begin{bmatrix}\mathbf{I}_{N}&\mathbf{X}\\ \mathbf{X}^{T}&\mathbf{Y}\end{bmatrix} \succeq\mathbf{0},\] \[\left\|\mathbf{X}(:,1)\mathbf{q}-\mathbf{s}_{i,k}\right\|\leq \mathbf{h}((k-1)M+i),\quad i=1,\cdots,M.\]  
This problem can be solved using the _CVX Toolbox_[47]. Therefore, the initial solution for the GN iteration described above is \(\mathbf{\beta}^{(0)}=[\mathbf{X}(:,1)^{T},\mathbf{X}(:,2)^{T}/\tau]^{T}\). Suitable values of \(\eta_{1}\) and \(\eta_{2}\) are crucial for achieving good estimation performance. However, obtaining the optimal values analytically is challenging, as they depend on various factors such as the distance, relative velocity between the sensor and the source nodes, and the noise level [51]. In each scenario, the first set of measurements is utilized to select suitable parameters. Specifically, \(C_{1}\) values of \(\eta_{1}\) are denoted as \(\eta_{1,c_{1}}\), \(c_{1}=1,2,\ldots,C_{1}\), and \(C_{2}\) values of \(\eta_{2}\) are denoted as \(\eta_{2,c_{2}}\), \(c_{2}=1,2,\ldots,C_{2}\). For each pair of penalty factors, the (37) is computed in parallel. The \((\eta_{1,c_{1}},\eta_{2,c_{2}})\) giving the estimation \(\mathbf{\mathsf{X}}_{c_{1},c_{2}}\) that minimizes \(J(\mathbf{\beta})\) in (15) were selected for following simulations.
# Intelligent Total Transportation Management System for Future Smart Cities
## 3 Results--Examples of Sub-Model Developments
### Managing Drones as a Formation to Avoid Obstacles in Smart Cities
#### 3.2.1 Formation Model  
Formation motion means a group of vehicles moving together and following a leader vehicle. In such a case, the motion of the group of vehicles can be managed by only controlling the leader vehicle, as originally developed for advanced air traffic control. The formation flight modes are considered more than relevant for the coordination of drones, as conventional air traffic management techniques and tools are powerless in dealing with such a high number (and complexity) of movements. The model can be relatively easily adapted to the road or urban traffic systems.  
Consider a multi-drone system consisting of N drones and a leader. The motion dynamics of each \(i\)-th drone can be represented by a simple system of the linearized equation:  
\[\dot{s_{i}}=Ps_{i}+Qu_{i} \tag{9}\]  
where \(s_{i}\in R^{n}\) is the drone \(i\)'s state, namely it is a state vector, and \(u_{i}\in R^{n}\) is the drone \(i\)'s input, input vector, which can only use local information from its neighbor drones. Matrix \(P=[p_{\bar{y}}]\in R^{nxn}\) is a diagonally dominant matrix or state transition matrix, which means that  
\[\left|p_{\bar{u}}\right|\geq\sum_{j=1,j\
eq i}^{n}\left|p_{\bar{u}j}\right|\]  
for all \(i=1,\,\ldots\,\,,N.\) The matrix \(Q\) is of full column rank.  
The leader drone, labeled as \(i=0\), has linear dynamics as follows:  
\[\dot{s_{0}}=As_{0} \tag{10}\]  
where \(s_{0}\in R^{n}\) is the state of the leader drone. It can be noted that the leader's dynamics is independent from the others.  
With the assumption that the pair \((P,\,Q)\) is stabilizable, the drone Equations (9) and (10) are said to be achieved if, for each drone, \(i\in\{1,\,\ldots\,\,,N\}\), there is a local state feedback \(u_{i}\) of \(\{x_{i}\colon j\in N_{i}\}\) such that the closed-loop system satisfies \(\lim\limits_{i\to\infty}\left\|s_{i}(t)-s_{0}(t)\right\|=0\) for any initial condition \(s_{i}(0)\), \(i=0,\,1,\ldots\,\,,N.\)  
We use the control law for drone \(i\) as follows:  
\[u_{i}(x)=\sum_{j\in N_{i}}\left\|s_{j}-s_{i}-d_{ij}\right\|^{2} \tag{11}\]  
where \(d_{\bar{y}}\) is the desired inter-distance related to the position vector. A drone \(j\) is the neighbor of drone \(i.\)