esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Ground Response Estimation Based on Convolutional Neural Networks (CNN)
## 2 Earthquake Database
### Site Information  
A total of 12 sites that include 50 earthquake events, whose magnitude is higher than 6 and PGA is greater than 0.01 g, were selected (Figure 1). These sites have the records at both surfaces and at 100 m depth beneath the surface after the year 2008.  
Figure 1: The selected stations.  
Shear wave velocity (\(V_{S}\)) profiles and brief classifications of the soil layers of the selected sites were shown in Figure 2. The black triangles in the figure represent the installation locations of the seismometers.  
Figure 2: Soil profiles and shear wave velocities at the selected sites.  
According to a two-parameter site classification system from the National Earthquake Hazard Reduction Program (NEHRP), the sites can be classified into six classes based on the average shear velocity (\(V_{S,30}\)) and natural period of the soil deposit (\(T_{G}\)) [18] in the seismic design. The average shear velocity is the shear wave velocity profile up to 30 m (100 ft) depth beneath the ground. The average shear velocity and the natural period of the soil can respectively be calculated by Equations (1) and (2):  
\[V_{S,30}=\frac{30}{\sum_{i=1}^{n}\frac{D_{i}}{V_{Si}}} \tag{1}\]  
\[T_{G}=4\sum_{i}^{n}\frac{D_{i}}{V_{Si}} \tag{2}\]  
where \(n\) is the number of strata from the surface up to 30 m beneath the ground surface, \(D_{i}\) is the thickness of the \(i\)th stratum (m), and \(V_{Si}\) is the shear wave velocity of the \(i\)th stratum (m/s).  
According to the National Earthquake Hazard Reduction Program (NEHRP), the sites where \(V_{S,30}\) ranges from 180 to 360 m/s are classified as stiff soil. The sites where \(V_{S,30}\) ranges from 360 to 760 m/s are classified as soft rock, and those whose \(V_{S,30}\) are over 760 m/s are classified as rock. Table 1 presents the basic information of the sites, the values of average shear velocities, natural periods, and the classification of the soil in the sites.
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 3 Results and Discussion
### Application Examples
#### 3.2.1 Parallel RC Circuit  
A parallel RC circuit is the simplest equivalent electrical model of biological tissue. In this experiment, four RC circuit configurations were used, with the R value being fixed at 5.6 k\(\Omega\) and the C values set different at values of 1 nF, 10 nF, 33 nF, and 47 nF. These values are those listed on the components body. Measurement by using Precision LCR meters ST2830, the following values are obtained R fix is 5.45 k\(\Omega\), and C are 0.93 nF, 9.41 nF, 34.15 nF, and 51.46 nF simultaneously.  
In the experiment, measurements were made using a BNC connector, with both components soldered directly to the ends of the connector. We use 10 uA current value and several choices voltage amplification. Result of the experiment for each configuration is displayed at the Bode plot, Nyquist plot, and Excel's data format in real-time. Cumulative results is given in Figure 8 as Bode plot, both in magnitude (ohm) and phase difference (degree). Based on the data in Figure 8, the calculation of component values performed by the program gets the values as shown in Table 1. The comparison of the 'true values' and measured values of components give maximum error of 1.5%.
## 3 Results and Discussion
### Realized Gain  
The realized gain performance of the proposed multi-port MIMO antenna array highlights its suitability for future-generation wireless applications. The gain varied across different ports, showcasing the antenna's ability to deliver stable radiation characteristics. Among the ports, Port 10 achieved the highest realized gain of around 4.5 dBi at 5.9 GHz, ensuring strong signal reception and transmission, while Port 2 registered the lowest gain at approximately 0 dBi at 4.7 GHz, as presented in the Figure 9, indicating some variations in radiation efficiency. Despite these differences, the overall gain remained well within an acceptable range, supporting reliable connectivity, enhanced spatial diversity, and improved signal coverage. This multi-port design effectively operates across multiple frequency bands, making it an excellent candidate for next-generation 5G communication systems and beyond.  
Figure 8: Transmission coefficient of proposed ten-port MIMO antenna array. (**a**) Simulated transmission coefficient. (**b**) Measured transmission coefficient. (**c**) Transmission coefficient with hand. (**d**) Transmission coefficient with head.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Crop-Wise Analysis  
The crop-wise analysis [55] on the two datasets is shown in this subsection.
## 2 Related Work
### Task Scheduling Strategy
#### 2.3.1 Multiple Mobile Devices, Single Edge Server  
Academics study the subsystems in complex MEC settings independently in this scenario, as well as in the case where a single edge server offers services to several mobile devices. Because the edge server's computation and storage capacity cannot support all devices at the same time, task offloading should be based on the current resource situation, and resource optimization is dependent on the state of the system after all devices have offloaded their duties. As a result, many aspects inside the system influence and are coupled with the offloading choice, and an effective offloading strategy needs to be established in order for the system to operate efficiently.
## 5 Conclusions  
A light-load optimized WBG extension for existing silicon-based inverters and active rectifiers for different DC-link voltage levels, which (mainly dependent on converter specifications such as switching frequency and loading, operating hours and resulting energy savings) either prefers the utilization of GaN transistors or SiC-MOSFETs was proposed and discussed. The WBG circuit can farther be simplified and optimized in terms of the minimum required WBG semiconductors and thus, cost and payback time of the additional circuitry can be reduced. Furthermore, a design example and loss comparison of a 125 kW three-phase six-switch inverter with additional light-load SiC-MOSFET power stage have revealed that a WBG upgrade can significantly reduce semiconductor losses and boost system efficiency of the proposed solution under light- and no-load condition. A further improvement could be achieved by either using GaN transistors and/or increasing the switching frequency when the light-load circuitry is commencing in operation (compared to the IGBT-based setup). As a follow-up of the current project and its promising results, it is planned to design and realized such a light-load upgrade for a conventional 125 kW inverter and analyze the benefits of the upgraded system. Conceptualization, M.M. ; methodology, M.M. ; software, M.M. ; validation, M.M. and S.B. ; formal analysis, M.M. ; investigation, M.M. ; resources, M.M. and S.B. ; data curation, M.M. ; writing--original draft preparation, M.M. ; writing--review and editing, M.M. and S.B. ; visualization, M.M. ; supervision, M.M. ; project administration, M.M. ; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript. \begin{table}
\begin{tabular}{l c} \hline \hline
**Parameter** & **Value** \\ \hline SiC–MOSFET on resistance: & \(R_{\rm on,MOS}\) = 80 m\(\Omega\) \\ SiC–MOSFET max. blocking voltage & \(V_{\rm DS,max}\) = 650 V \\ SiC–MOSFET cont. drain current (\(T_{\rm C}\) = 25 \({}^{\circ}\)C) & \(I_{\rm DS,25}\)\({}^{\circ}\)C = 30 A \\ GaN transistor on resistance: & \(R_{\rm on,GaN}\) = 50 m\(\Omega\) \\ GaN transistor max. blocking voltage & \(V_{\rm DS,max}\) = 650 V \\ GaN transistor cont. drain current (\(T_{\rm C}\) = 25 \({}^{\circ}\)C) & \(I_{\rm DS,25}\)\({}^{\circ}\)C = 30 A \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Relevant SiC MOSFET and GaN transistor parameters. **Funding:** This research received no external funding. **Conflicts of Interest:** The authors declare no conflicts of interest. **Abbreviation**
## 3 The Proposed Recommendation System
### Overall Architecture and Implementations  
The overall architecture for our hybrid recommendation system is depicted in Figure 1. We use both Silk-road datasets and user data, including profiles, view history, and rating information. For content-based filtering, similarity measures (tf-idf and cosine similarity) are used. For collaborative filtering, the k-nearest neighbors (KNN) classifier and ALS factorization are used. In the collaborative filtering procedure, the user similarity is also calculated. As our recommendation system uses a hybrid approach, both methods (content-based filtering and collaborative filtering) are combined with additional online data. That is, users' item rating information and users' view history data are stored in the database, and the stored data are also intelligently used for calculating top-N recommendations.  
Implementing a hybrid recommendation system requires careful consideration of data preprocessing, feature selection, similarity calculation, and weighting strategies. The high-level algorithmic procedures for implementing a hybrid recommendation system based on content-based filtering and collaborative filtering are described as follows:  
1. Data collection * Gather user-item interaction data (ratings, reviews, etc.) for collaborative filtering. * Collect item features (types, keywords, etc.) for content-based filtering.
2. User profiling * Create user profiles based on their historical interactions using collaborative filtering techniques. * Analyze user preferences, behaviors, and similarities with other users.
3. Item profiling * Extract relevant features from items using content-based filtering techniques. * Represent items in a feature space based on their attributes.  
Figure 1: The overall architecture of the recommendation system.  
* Hybrid score calculation
* Calculate a similarity score between the target user and other users using collaborative filtering.
* Compute a similarity score between the target item and other items using content-based filtering.
* Weighted combination
* Assign weights to the similarity scores from collaborative filtering and content-based filtering based on their importance.
* Combine the scores using a weighted sum or other fusion techniques.
* Top-N recommendations
* Rank the items based on the combined scores from the previous step.
* Select the top-N items with the highest scores as recommendations for the target user.
# Dynamic Tensor Modeling for Missing Data Completion in Electronic Toll Collection Gantry Systems
## 3 Materials and Methods
### ETC Gantry Data Completion Method Based on Improved Tensor Decomposition
#### 3.2.1 Improved Dynamic Tensor Decomposition Model  
The sparse nature of traffic data presents significant challenges. The \"vehicle user-time stamp\" information matrix typically exhibits low density, which becomes increasingly problematic as the volume of highway vehicles and the frequency of traffic record timestamps surge, thereby diminishing the information density related to vehicles. The addition of a spatial dimension exacerbates this sparsity. Our approach involves analyzing tensor flow within a stipulated timeframe and introducing a tensor window to the data tensor flow. The tensor flow for a fixed period with a tensor window size of \(w\) is denoted as:  
\[\begin{split} D(t,w)=\left\{\begin{array}{l}\mathcal{X}_{(T-w+1)},\ldots,\mathcal{X}_{T}\\ \mathcal{X}_{t}\in R^{1_{1}\times_{2}\times_{r}\ldots,J_{N+1}}\end{array} \right\}\end{split} \tag{4}\]  
Figure 3: Diagrammatic representations of traffic data calculations based on the high-order tensor model: (**a**) layout of high-order tensor model; (**b**) dissection of the high-order tensor model to analyze traffic data (**c**) process of extracting specific vehicle information within the high-order tensor mode for traffic.  
As depicted in Figure 4a, the tensor flow \(\mathcal{X}_{t}\in\mathbb{R}^{\bar{I}_{1}\times{I_{2}}\times\ldots,I_{N}},(1\leq t \leq T)\) performs local processing using a sliding tensor window of size \(w\). Moreover, dynamic traffic flow information is captured using the dynamic tensor model. The decomposition of the Nth-order tensor flow \(\mathcal{X}_{t}\in R^{I_{1}\times{I_{2}}\times\ldots,I_{N}},(1\leq t\leq T)\), is articulated as:  
\[\begin{array}{c}\mathcal{X}_{t}\approx G_{t}\times_{1}U_{1}\times_{2}U_{2} \times\ldots\ldots\times_{N}U_{N}+E_{t}\\ G_{t}\in R^{I_{1}\times{I_{2}}\times\ldots\times{I_{N}}}\end{array} \tag{5}\]  
where \(U_{n}\) represents the factor matrix corresponding to mode \(n\) (the principal component of mode \(n\)); \(G_{t}\) is the core tensor, encapsulating independent features that articulate the interactions among various pattern principal components; and \(U_{1}\), \(U_{2}\), and \(U_{3}\) are the factor matrices for modes 1, 2, and 3, respectively. The constituents of the core tensor stream,\(G_{t}\), mirror the interplay between principal components and the temporal dynamics of this tensor series. The intricacies of tensor flow decomposition are exemplified in Figure 4b.
## 4 Analysis and Discussion of Results
For this case study, the results for the total period of the equipment's 10-year useful life, performing 120 cycles of 21 days, are shown in Table 10.  
The emissions of the improved project were less than 30% of the current situation. The difference in energy consumption was a reduction of almost 30%, and water consumption was reduced by almost 30%.
## 3 Test Results and Discussions
### Optimum Asphal Content  
The optimum asphalt content of the asphalt mixtures should provide a mix with sufficient asphalt film thickness to provide good durability and avoid excessive asphalt drainage. The optimum asphalt content is determined from the average value of the corresponding asphalt contents to the maximum stability, the maximum unit weight and 4% void rate. Higher asphalt content might lead to a poor bonding behavior for aggregate. However, a lower asphalt content of mixtures will potentially be susceptible to rutting and cracking.  
Figure 2: Thermal image of an asphalt mixture.  
Optimum asphalt contents of dense, SMA, and PA-graded mixtures will be decreased when the BOF slag substituted proportion is increased. In BOF slag, the proportion of SiO\({}_{2}\) is less than in natural gravel. However, CaO occupies a higher proportion [33; 34]. Since CaO is lipophilic, and BOF slag structure has higher surface area and porosity, which facilitates absorption by the asphalt cement to allow for the aggregate to bond with the asphalt more tightly [13], and has proven BOF slag to be lipophilic, it can be easily absorbed and bond with asphalt cement. [PERSON] et al. 2014 [35] undertook similar research, which used the boiling water test to demonstrate, after BOF slag contacted with asphalt cement, it will form hydration products and the bonding behavior of BOF slag and asphalt cement; therefore, it can decrease the asphalt content demand on asphalt mixtures.  
SMA and PA-graded were designed as the jumping gradation with less proportion of fine aggregate and need to fill a large number of asphalt cement in voids among aggregates; but with the substitution of BOF slag for natural aggregate, it could lower the quantity demanded for asphalt cement, as well as reduce the cost of asphalt binder.
# Deep-Learning Integration of CNN-Transformer and U-Net for Bi-Temporal SAR Flash-Flood Detection
## 3 Methodology  
### Network Architecture Overview  
This research introduced the Compact Convolutional Tokenizer-based Hybrid U-Net and Vision Transformer Model (CCT-U-ViT) (Figure 3). This innovative model features a hybrid architecture designed for Synthetic Aperture Radar (SAR)-based flood detection, integrating U-Net's spatial feature extraction with the Transformer's global context modeling. The system processes pre- and post-flood SAR images utilizing a U-Net encoder-decoder framework with skip connections; the encoder methodically extracts hierarchical features using 8, 16, 32, and 64 filters across four levels. Each encoder level corresponds to a decoder layer, facilitating the reconstruction of spatial details. At the bottleneck layer, equipped with 128 filters, the model employs three vital enhancement modules: a Compact Convolutional Transformer (CCT) tokenizer that transforms 2D pooled features into 1D tokens for sequential processing, position embedding for adding spatial awareness (a dense 128-dimensional structure), and transformer blocks that implement multihead attention mechanisms along with MLP and skip connections to capture long-range dependencies. The U-Net's spatial features are merged with the global features derived from the transformer via a feature fusion module, which concatenates, densifies (to 64 channels), and applies a dropout rate of 0.2 for regularization. Ultimately, a binary classification head with dense layers differentiates between flood and non-flood pixels, enabling the model to utilize local spatial patternsthrough the U-Net path and global contextual relationships via the transformer path to delineate flood boundaries precisely.
# Intelligent Transmission Control Scheme for 5G mmWave Networks Employing Hybrid Beamforming
## 2 Literature Survey
The deconvolution scenario, on the other hand, requires more time to train the parameters, which causes a rather slower training rate every epoch. The authors of [33] proposed an analog deep neural network (ADNN) structure, which can be applied to conventional RF components. This structure is integrated into an extended hybrid analog-digital deep neural network (HDNN). MIMO systems make implementation easier. * Nevertheless, a bottlenecking impact in the information flow from the digital DNN to the ADNN and vice versa may affect the HDNN, depending on the particular task. \begin{table}
\begin{tabular}{c c c c} \hline
**References** & **Method** & **Advantages** & **Limitations** \\ \hline \multirow{3}{*}{[26]} & Fast super-resolution convolutional & \multirow{3}{*}{Manages situations involving  excessive mobility} & Computationally demanding (processing images) \\  & & channel estimation & \\ \hline \multirow{2}{*}{[27]} & Sparse recovery CE & Dependable in situations with & Assumptions that are sensitive to \\  & & several paths & noise correlation \\ \hline \multirow{2}{*}{[28]} & \multirow{2}{*}{Quantum-based channel estimation} & Low overhead & Need quantum hardware \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._The authors of [34] simulated and studied the uplink in a two-hop 5G new radio cooperative system using relay nodes (RNs) in millimeter bands. They examined two uplink mmWave MIMO decode-and-forward (DF) relaying methods under the assumption that the uplink channel is fully understood and that the uplink channel is estimated using the least squares (LS) algorithm. * Here, using the new ratio for transmission achieves the lower throughput. To improve the performance of 5G mmWave networks, several new schemes are proposed herein along with the use of the terms RCTS-AMP-RSSI-AS for channel estimation, Mu-MATD3 for user scenarios, DAH-BT-LSTM for beam training, and LLCCS for congestion control. These schemes tackle the issues of path loss and directional communication to achieve improved data speed, latency, and connectivity.
### Background and Conceptual Framework
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Main Author** & **Year** & **Description of Innovation** & **Specification** & **Reference** \\ \hline [PERSON] et al. & 1995 & \begin{tabular}{c} Polymer materials inside vehicles contribute to \\ sound emission reduction, ensuring acoustic \\ comfort for passengers. \\ \end{tabular} & Polymer materials for vehicle & [74] \\ \hline [PERSON] et al. & 2004 & \begin{tabular}{c} Recommendations and directions regarding the \\ level of hearing protection required based on the \\ scenario and duration of operation of military \\ vehicles, along with noise combating strategies \\ (absorbent panels and active noise control) \\ \end{tabular} & Active and passive noise & [75] \\ \hline [PERSON] & 2005 & \begin{tabular}{c} Determining correlations between objective and \\ subjective methods of comfort during off-road \\ military vehicle operation \\ \end{tabular} & Correlations between objective and subjective methods in & [76] \\ \hline [PERSON] et al. & 2008 & \begin{tabular}{c} Development of thermoplastic composites with \\ enhanced acoustic and mechanical performance \\ for use in vehicle interiors \\ \end{tabular} & Thermoplastic composites for & [77] \\ \hline [PERSON] & 2009 & \begin{tabular}{c} PhD thesis: Development of reactive attenuators \\ for reducing sound pressure levels \\ \end{tabular} & Reactive attenuators for & [78] \\ \hline [PERSON] & 2011 & \begin{tabular}{c} PhD thesis: Development of a multifunctional \\ multilayer structure for noise attenuation and \\ total vehicle weight reduction \\ \end{tabular} & Noise attenuation in vehicles & [79] \\ \hline [PERSON] et al. & 2011 & \begin{tabular}{c} Development of sound-absorption materials from \\ hollow PET fibers for noise reduction inside \\ vehicles \\ \end{tabular} & Sound-absorption materials & [80] \\ \hline [PERSON] et al. & 2014 & \
\begin{tabular}{c} Thermoacoustic encapsulation of the engine \\ block for noise reduction and engine efficiency \\ improvement \\ \end{tabular} & Thermoacoustic encapsulation & [81] \\ \hline [PERSON] & 2012 &
\begin{tabular}{c} Use of materials (glass wool, jute, cotton, \\ polyester and polypropylene) to improve acoustic \\ and thermal comfort inside vehicles \\ \end{tabular} & Insulating materials for & [82] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: List of innovations of interest correlated with relevant publications on the evolution of noise reduction in vehicles. (Color legend: red for Google Scholar (GS) citations \(\geq\)79, orange for GS citations \(\geq\)42, black for GS citations \(\leq\)47.) Source: Created by the author.