esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Applications in SDN-based VANETs
### Cooperative Forward Collision Warning  
This application is utilized to help drivers of vehicles evade mishaps with different vehicles which are going ahead. This type of application includes the V2I and V2V types of correspondence. In the outcome structure, it gives the threat levels ahead.
## 5 Applications  
Diode laser based sensing nowadays is a well-established technique with a wide variety of applications in many different branches of industry and science. Because the emission linewidth of complex coupled DFB LD is typically well below 3 MHz, even the fine structures of absorption features--typically exhibiting linewidths of a few GHz--can be resolved, enabling a clear distinction of different gas features or even isotopes during one measurement cycle. Some key applications are presented in the following sections.
## 2 Materials and Methods
### Training Generative Adversarial Networks
The definitions are as follows:  
Figure 3: Diagram showing the output relationship of the discriminator. When there is little or no overlap between the two distributions, the JS divergence is constant, causing the discriminator to quickly converge, leading to the gradient vanishing. Additionally, during the training process, if the generator and discriminator cannot converge simultaneously, it can result in mode collapse, making the training process unstable. Therefore, this paper adopts WGAN-GP to improve the above-mentioned issues. WGAN-GP is an improved model based on WGAN, with the core being the replacement of the evaluation method for measuring the distance between the generated distribution and the real distribution. In WGAN, the Wasserstein distance is used, which replaces the JS divergence that is not suitable for measuring the distance between non-overlapping parts of the distributions [39]. Compared to JS divergence, the Wasserstein distance can reflect the distance between distributions when there is no overlap in the data distribution. Additionally, the Wasserstein distance has a continuous and smooth change, allowing for optimization of model parameters using gradient descent. By adding gradient penalty (GP) to WGAN, the training speed and the quality of generated samples are improved, further overcoming the problem of slow convergence in network training. The WGAN-GP model structure used in this study is illustrated in Figure 5. The generator takes a 100-dimensional sample from a uniform distribution \(U(-1,1)\) as input. The input is then fully connected to a hidden layer with 9216 nodes, which is reshaped into a 64 \(\times\) 6 \(\times\) 24 tensor. After the hidden layer, three deep deconvolutional layers are connected, which increase the dimensions of the input by extending its length and width while reducing the depth until the last layer reaches the desired dimension. The number of filters in these deconvolutional layers is 32, 16, and \(n\), where \(n=16\) represents the number of channels in the EEG data. The filter stride is 2 \(\times\) 2, and the filter size is 5 \(\times\) 5. The output of this generator has the same dimensions as the short-time Fourier transform of a 28-s EEG signal. The discriminator is composed of three convolutional layers, each with a filter size of 5 \(\times\) 5 and a stride of 2 \(\times\) 2. The respective numbers of filters in these layers are 16, 32, and 64. Throughout the training process, signals that bear resemblance to the original signal are generated by the generator. Optimization of the results is achieved by the discriminator through parameter adjustments in the convolutional layers. Features from the epileptic EEG signals are learned and extracted, culminating in the detection and differentiation between real and fake data. Figure 4: Analysis of the Jensen-Shannon divergence between the generated distribution and the real distribution.
# Numerical Investigations on the Blade Tip Clearance Excitation Forces in an Unshrouded Turbine
## 2 Numerical Setup
### Rotordynamic Coefficients Solution  
Based on the small motion theory, [PERSON] [24] presented a response-force model, as defined in Equation (9):  
\[-\left[\begin{array}{c}F_{x}\\ F_{y}\end{array}\right]=\left[\begin{array}{cc}K_{xx}&K_{xy}\\ K_{yx}&K_{yy}\end{array}\right]+\left[\begin{array}{cc}C_{xx}&C_{xy}\\ C_{yx}&C_{yy}\end{array}\right]\left[\begin{array}{c}\dot{x}\\ \dot{y}\end{array}\right]. \tag{9}\]  
The rotordynamic coefficients \(K_{ij}\) and \(C_{ij}\), including the direct stiffness (\(K_{xx}\), \(K_{yy}\)), the cross coupling stiffness (\(K_{xy}\), \(K_{yx}\)), the direct damping (\(C_{xx}\), \(C_{yy}\)), and the cross coupling damping (\(C_{xy}\), \(C_{yx}\)), could be calculated using the response forces and the corresponding rotor displacements and velocities. For \(K_{ij}\) and \(C_{ij}\), the first subscript (\(i\)) represents the direction of the response force and the second one (\(j\)) represents the direction of the rotor motion. The response forces in this paper are the excitation forces on the rotor blades caused by the rotor eccentricity, which are also known as the \"Thomas-Alford\" forces. By using the Fast Fourier Transform (FFT), Equation (9) can be transformed into the frequency domain expression as follows,  
\[-\left[\begin{array}{cc}G_{xx}&G_{xy}\\ G_{yx}&G_{yy}\end{array}\right]=\left[\begin{array}{cc}K_{xx}+\sqrt{-1}( \Omega C_{xx})&K_{xy}+\sqrt{-1}\Big{(}\Omega C_{xy}\Big{)}\\ K_{yx}+\sqrt{-1}\Big{(}\Omega C_{yx})&K_{yy}+\sqrt{-1}\Big{(}\Omega C_{yy} \Big{)}\end{array}\right]\left[\begin{array}{cc}D_{xx}&D_{xy}\\ D_{yx}&D_{yy}\end{array}\right], \tag{10}\]  
where \(D_{ij}\) and \(G_{ij}\) are the frequency-domain components of the rotor displacement and response forces, respectively. For \(D_{ij}\) and \(G_{ij}\), the first subscript (\(i\)) represents the excitation direction and the second one (\(j\)) represents the direction of response forces.  
Then, the corresponding impedances (\(H_{ij}\)) could be solved using Equation (11):  
\[\left\{\begin{array}{l}H_{xx}=\frac{(-G_{xx})\cdot D_{yy}-(-G_{yx})\cdot D_{ xy}}{D_{xx}\cdot D_{yy}-D_{yx}}\\ H_{xx}=\frac{(-G_{xx})\cdot D_{yx}-(-G_{yx})\cdot D_{xx}}{(D_{xy}\cdot D_{yx}-D_ {yx})\cdot D_{yx}}\\ H_{xx}=\frac{(-G_{yy})\cdot D_{yy}-(-G_{xy})\cdot D_{yy}}{D_{xy}\cdot D_{yx}}\\ H_{xx}=\frac{(-G_{yy})\cdot D_{xx}-(-G_{xy})\cdot D_{yx}}{D_{xx}\cdot D_{yy}-D_ {yx}\cdot D_{yy}}\end{array}\right.. \tag{11}\]  
Finally, the stiffness and damping coefficients could be solved using Equation (12):  
\[\left\{\begin{array}{l}K_{xx}=\text{Re}(H_{xx}),\ C_{xx}=\text{Im}(H_{xx})/ \Omega\\ K_{xy}=\text{Re}(H_{xy}),\ C_{xy}=\text{Im}\Big{(}H_{xy}\Big{)}/\Omega\\ K_{yx}=\text{Re}\Big{(}H_{yx}\Big{)},\ C_{yx}=\text{Im}\Big{(}H_{yx}\Big{)}/ \Omega\\ K_{yy}=\text{Re}\Big{(}H_{yy}\Big{)},\ C_{yy}=\text{Im}\Big{(}H_{yy}\Big{)}/ \Omega\end{array}\right.. \tag{12}\]
## 3 Results and Discussion  
### Effect of Storage Conditions on Total Acidity, Firmness, and Soluble Solids in Blueberries  
Total acidity, soluble solids, weight loss, and firmness were determined to understand the effect of the storage treatments on basic quality parameters (Table 1). No change was observed in soluble solids, while total acidity only differed between SC and S2. Total weight loss increased in a linear manner until S3, where weight loss greatly increased (Figure S1). Firmness tended to decrease during storage, from SC to S3, though only the firmness of S3 significantly differed among the storage treatments. Loss of firmness is a common feature during blueberry storage; it is closely linked to weight loss, except during low weight loss, when blueberry firmness often temporarily increases [9,23]. As found in this study, firmness loss in blueberries is also temperature-dependent, with greater rates of firmness loss occurring at warm versus cold temperatures. Firmness is very important to blueberry quality, as blueberries that are perceived to be soft are not as well-liked by consumers [2].
## 2 Materials and Methods  
### Participants and Study Design  
This was a cross-sectional study, and it was performed with the participation of adolescents enrolled in four high schools located in Iasi in the northeastern region of Romania. The selected schools included students from similar socioeconomic groups. The study sample included 1320 adolescents 14-18 years old, and written assent from the adolescents and written informed consent from their legal guardians were obtained. Data collection was performed via a face-to-face interview based on a structured, pre-tested questionnaire between January 2016 and December 2018. The participants completed the questionnaire anonymously in their classrooms at school under the guidance of the interviewers and the teachers. The information was obtained via an administered questionnaire which was divided into three sections: (1) the food frequency questionnaire (the short diet quality screener); (2) the Revised Physical Self-Perception Profile; and (3) physical activity.  
The short diet quality screener (sDQS) was used to estimate the overall diet quality [6]. In this study, the participants were asked to report their habitual intake of (1) cereals; (2) vegetables; (3) fruit; (4) dairy products; (5) red meat, poultry, and fish; (6) meat substitutes; (7) sweetened foods; (8) alcoholic beverages; (9) drinks with caffeine; and (10) fast food. Standardized portion sizes were used to quantify the frequency of food intake. Food frequency consumption was arranged into three frequency response categories: \"recommended daily consumption\", \"more than recommended daily consumption\", and \"less than recommended daily consumption\" according to the Romanian nutrition guidelines [7].  
The Revised Physical Self-Perception Profile (PSPP-R), a 6-item subscale of the body (perceived bodily attractiveness), was used to measure physical self-esteem [8]. The perceived benefits of physical activity were assessed using a scale consisting of 10 statements relating to its effects on health. The subjects' task was to assess their accuracy on a scale from 1 (definitely false) to 4 (definitely true).  
The body mass index (BMI) was calculated in kg/m\({}^{2}\) and converted to BMI percentiles. Using the WHO percentiles method, the adolescents were divided into normal weight (5 th-85 th percentile), underweight (under the 5 th percentile), overweight (85 th-95 th percentile), and obese groups (over the 95 th percentile).
## 3 Methodology
### TOPSIS Method  
The steps of the TOPSIS method are as follows [55; 56]:  
Step 1. Normalize the decision matrix:  
\[r_{ij}=\frac{x_{ij}}{\sqrt{\sum_{k=1}^{m}x_{kj}^{2}}},\;i=1,\ldots,m;j=1, \ldots,n \tag{21}\]  
where \(r_{ij}\) represents the normalized value of the jth criterion for the ith alternative \(A_{i}\).  
Step 2. Determine the weighted normalized decision matrix:  
\[v_{ij}=w_{j}r_{ij}\;i=1,\ldots,m;j=1,\ldots,n \tag{22}\]  
where \(w_{j}\) is the weight of the jth criterion.  
Step 3. Identify the positive ideal and negative ideal solutions:  
\[A^{+}=\left\{v_{1}^{+},\ldots,v_{n}^{+}\right\} \tag{23}\]  
\[A^{-}=\left\{v_{1}^{-},\ldots,v_{n}^{-}\right\} \tag{24}\]  
where A+ and A\(-\) represent the positive and negative ideal solution. If the jth criterion is a beneficial one, then \(v_{1}^{+}=\max\{v_{\bar{\mathrm{y}}},\,\mathrm{i}=1,\ldots,\,\mathrm{m}\}\) and \(v_{1}^{-}=\min\{v_{\bar{\mathrm{y}}},\,\mathrm{i}=1,\ldots,\,\mathrm{m}\}\). On the other hand, if the jth criterion is a cost criterion, then \(v_{1}^{+}=\min\{v_{\bar{\mathrm{y}}},\,\mathrm{i}=1,\ldots,\,\mathrm{m}\}\) and \(v_{1}^{-}=\max\{v_{\bar{\mathrm{y}}},\,\mathrm{i}=1,\ldots,\,\mathrm{m}\}\).  
Step 4. Compute the distances of each alternative from the positive ideal solution and the negative ideal solution:  
\[D_{i}^{+}=\sqrt{\sum_{j=1}^{n}\Bigl{(}v_{ij}-v_{j}^{+}\Bigr{)}^{2}} \tag{25}\]  
\[D_{i}^{-}=\sqrt{\sum_{j=1}^{n}\Bigl{(}v_{ij}-v_{j}^{-}\Bigr{)}^{2}} \tag{26}\]  
where \(D_{i}^{+}\) represents the distance between the ith alternative and the positive ideal solution and \(D_{i}^{-}\) represents the distance between the ith alternative and the negative ideal solution.  
Step 5. Determine the relative closeness to the ideal solution;  
\[C_{i}=\frac{D_{i}^{-}}{D_{i}^{+}+D_{i}^{-}} \tag{27}\]  
Step 6. Rank the alternatives in descending order according to their Ci values.
## 3 Methods and Model Setup
### Methods
#### 3.1.2 Momentum Equation  
The momentum conservation equation is the equation connecting pressure and deformation, which describes the momentum conservation of the continuum in a gravitational field.  
\[\frac{\partial\sigma_{ij}}{\partial x_{j}}+\rho g_{i}=\rho\frac{Dv_{i}}{D_{t}} \tag{3}\]  
Figure 3: **(a)** Magnetic anomaly map of the South China Sea basin and its surrounding area [52]; **(b)** Interpreted magnetic lineations. **(b)** was modified and supplemented according to references [41; 42], and major magnetic anomalies are marked with numbers.  
With Equation (3) and the relation between the total stress (\(\sigma_{ij}\)) and deviatoric stress (\(\sigma^{\prime}_{ij}\)), the Navier-Stokes motion equation describing the conservation of momentum for viscous fluid in the gravity field can be obtained:  
\[\frac{\partial\sigma^{\prime}_{ij}}{\partial x_{j}}-\frac{\partial P}{\partial x _{i}}+\rho g_{i}=\rho\frac{Dv_{i}}{Dt} \tag{4}\]  
where \(\rho\) is density (kg/m\({}^{3}\)), \(P\) is pressure in units of Pa, \(g_{i}\) is the \(i\)-th component of the gravity vector \(\overrightarrow{g}\), \(\frac{Dv_{i}}{Dt}\) is the substantive time derivative of the velocity vector (\(v_{i}\)).
# Earthquake-Induced Pounding of Medium-to-High-Rise Base-Isolated Buildings
###### Abstract  
During earthquakes, out-of-phase vibrations in adjacent buildings with limited distance may cause bounding between them. In recent years, the use of seismic isolation has expanded considerably as an effective approach to reduce seismic damage. However, the isolated building experiences large displacements during earthquakes, and there is a possibility of collisions with adjacent structures. The research on earthquake-induced bounding of base-isolated buildings has been mainly focused on interactions between low structures. In this paper, the influence of structural bounding on the response of medium-to-high-rise base-isolated buildings is investigated under different ground motions. The analysis has been focused on collisions between two insufficiently separated five-story and eight-story base-isolated and fixed base buildings aligned in three different configurations. The results of the study indicate that structural bounding may significantly increase the response of medium-to-high-rise base-isolated buildings during earthquakes. Moreover, substantial dependence of the structural behavior on the gap size between structures has been observed. The general trend shows the reduction in the bounding-involved response with the increase in the gap size value. The results indicate that the increase in the response of the base-isolated building is larger when the height of the structure is bigger. They also show that larger amplifications of peak accelerations of the upper stories can be expected due to collisions. On the other hand, the amplifications of the story shears have not shown any specific trend for different stories of the analyzed base-isolated building.  
seismic isolation; structural bounding; seismic gap; non-linear analysis Article
## 1 Introduction
A critical observation is that digitalization in asset management and maintenance extends beyond technological challenges. Practical management and decision-making aspects must guide the design and implementation of advanced solutions like IoT platforms and digital twins. Effective integration of system architecture and data models is essential for addressing the operational demands of maintenance and asset management. Established frameworks for maintenance and IT systems under Industry 4.0 provide a foundation, but highly integrated solutions merging these domains are still rare [21]. This paper answers the following questions:  
* How can a model-based approach for asset digitalization be effectively introduced to serve maintenance and asset management purposes? * How can this approach be incorporated into digital maintenance decision-making workflows?How can the integration of an IoT monitoring platform with short-term models (e.g., Condition-Based Maintenance) and long-term models (e.g., Asset Health Index) improve asset digitalization and management practices? This work's content is structured as follows: Section 2 discusses the key issues and approaches related to asset digitalization, including its definition, relevant frameworks, and architectures for advanced digitalization strategies. Section 3 outlines the proposed methodology, which is applied in Section 4 to a bridge maintenance case study using an IoT platform and AHI model. Finally, Section 5 summarizes conclusions and future applications.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 4 Vision-Aided PDR System Design
### Visual Place Recognition
#### 4.1.1 Design of VPR Network and Training  
Considering the real-time positioning and the simplicity of the device, it is not possible to use too complicated neural networks for the VPR here. The construction of a VPR network consists of  
Figure 4: The system architecture of vision-aided PDR (V-PDR).  
Figure 5: Visual Place Recognition Algorithm Diagram.  
two parts: design of structure and training of parameters. For the purpose of learning deep visual features to make exact matching, six convolution layers are used to learn enough deep features, and these features are combined by the fully connected layer. The activation function ReLu follows the convolution layer, and the max-pooling is selected. The structure of our CNN architecture is shown in Figure 6.  
A classification model is not used here because classification models need to fine tune to the different datasets in other places, which is impossible in practical use. Thus, we use metric learning as our VPR backbone network to train our VPR model. In the training process, RGB images with three-dimensional location labels of any size are resized i to \(256\times 256\). Small blocks with size of \(225\times 225\) are extracted from the resized images and then flipped horizontally to increase total data amounts, which helps to overcome overfitting. The parameters are trained by hard triplet loss gradient descent method and back propagation mechanism. The triplet loss function can be expressed as Equation (12).  
\[\sum\left[\left\|f(I_{a})-f(I_{p})\right\|_{2}^{2}-\left\|f(I_{a})-f(I_{n}) \right\|_{2}^{2}+\alpha\right]_{+} \tag{12}\]  
where the first item is the Euclidean distance between the positive and the anchor and the second item is the Euclidean distance between the negative and the anchor. \(\alpha\) serves as a margin parameter. \([x]_{+}=\text{max}(0,x)\) means the max distance between negative and positive queries. The purpose is to make the feature vectors of the same places more similar and make the distance between feature vectors in different places as large as possible.  
Note that, because this is not a classification model but an encoder that encodes the image into the feature vectors, it can be used directly without modification for other scenes. In any scenario, this neural network can encode images into 4096-dimensional vectors. Vectors encoded by the same neural network will have the same feature extraction method, thus the encoding of similar images will be similar, and the encoding difference of different images will also be very large. Thus, when this network is used in other scenarios, no secondary training is required.
# Design of a Phase Calibrator to Evaluate Non-Matched Microphones for Intensity Probes
## 5 Conclusions  
This work aimed at designing an acoustic phase calibrator to identify phase mismatches between class-1 microphones not intended for intensity measurements and provide the required corrections in a given frequency range. Numerical acoustical FEM simulations were carried out with commercially available numerical codes exploiting the structural-acoustic analogy. The model's results guided the prototyping phase, which led to three different configurations. Tests were conducted employing a specifically designed software that allowed performing both the intrinsic phase-mismatch extraction and correction. The configuration of choice was the cylindrical face-to-face (Configuration A-F-F), which was scaled to provide a more compact solution. Both the full-size Configuration A-F-F and its scaled version were compared with a state-of-the-art, laboratory standard acoustic coupler showing very good results in the [60-4000] Hz frequency range and encouraging one in the [4000-7000] Hz range. Future developments of the calibrator will be oriented to the extension of the frequency range and the creation of a standalone version.  
Conceptualization, N.R. and E.M.; methodology, E.M.; software, N.R.; hardware, A.E.; validation, N.R., E.M. and A.E.; formal analysis, E.M.; investigation, N.R.; resources, E.M.; data curation, N.R., E.M. and A.E.; writing--original draft preparation, N.R.; writing--review and editing, E.M.; supervision, E.M.; project administration, E.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability Statement: Data are contained within the article.  
Grateful thanks to the Italian company Sonora Srl. for the donation of the materials received for experimental campaigns and for providing expertise concerning both the software and hardware implementation.  
Figure 43: Comparison between the phase difference for calibrated measurement for prototypes and reference instruments in the [4000–7000] Hz frequency range.  
**Conflicts of Interest:** Authors [PERSON] and [PERSON] were employed by the company Sonora SrI. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.