esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
Based on sink and relay mobility, the proposed approach focuses on cooperation and relay optimization. Now, the sink is able to gather all of the information sensed from underwater nodes, including relay nodes. We deployed 15 neighbor identification, 15 neighbor head node selection, and 50 relay optimization nodes. With the help of these nodes, significant contributions were achieved with the introduction of the CR-NBEER protocol. A 10-sink approach was implemented, as illustrated in Figure 2. The simulations were carried out, and the values of all relevant parameters obtained via the simulations are given in the tables below.
## 5 Numerical Examples
### Comparative Experiments  
In this section, three comparative experiments are conducted. Firstly, the performance of the proposed methodology is compared with CPLEX in Section 5.2.1. Secondly, the influence of the variance of random variables is investigated in Section 5.2.2. Finally, we study the sensitivity of the optimal solution to the travel distance in Section 5.2.3.
# Research on Location Selection Model of 5G Micro Base Station Based on Smart Street Lighting System
## 4 Location Selection Model of 5G Micro Base Station
### Location Selection Model Construction  
In order to realize the intelligent interconnection between the devices in smart light poles, this paper proposed a general location selection model from three aspects: coverage, economic cost, and collaborative ability; that is, on the premise of meeting the basic network coverage rate and economic cost requirements, the collaborative ability between the target device and each mounted device are fully considered. Firstly, the location selection index was proposed to measure the system coordination degree and then construct the 5G micro-base station site selection model based on the objective function. Noting that the location selection index and model can be solved for the problem with multiple types of target devices mounted on smart street lighting as input, this paper used 5G micro-base stations as an example for the location selection study.
# Fuzzy Logic-Based Geographic Routing Protocol for Dynamic Wireless Sensor Networks
## 3 FLGR Protocol
### Message Format and Local Storage Structure
With the transmission of data messages in the network, each node can update its local network location table frequently. The local network location table is presented as follows:  
As shown in Table 4, each node stores the local network location table, including node ID, node location, and update time. The update time indicates when the last update of the node location occurs. If a node has a message to be sent, the fuzzy region of the destination node can be obtained by the interval between the update time of the destination node in the local network location table and the current moment (as shown in Formula (1)).
## 3 Adversarial Network with Distilled Knowledge from Statistical Method
### Distilling Knowledge from MMSE-Based Speech Enhancement Algorithm  
For the DNN-based speech enhancement method, the more the simulated speech available, the better the performance that can be obtained. In order to obtain a better generalization performance, the authors in [9] use 104 types of noise and 6 levels of signal-to-noise ratios (SNRs) simulated noisy speech to train the DNN. Yet, those simulated noisy speech cannot cover all the real cases.  
To improve the generalization performance of the generator network trained in the previous step, we distill knowledge from the traditional MMSE-based speech enhancement algorithm, other than building a more complex training set. We re-use the simulated noisy speech in the previous step, and feed the data to the MMSE-based speech enhancement method, e.g., OMLSA [13] which is one of the state-of-art MMSE-based method, to get the estimated clean speech, i.e., the enhanced speech. Then, the estimated clean speech and noisy speech constitute the new training data pair. In the view of machine learning, the procedure of obtain the new training pairs can be thought of as data augmentation. Subsequently, we re-train the discriminator network and the generator network with the weights of the previous step.  
This step is inspired by the knowledge distillation in a neural network [10], which uses a big model (i.e., the teacher) to teach a small model (i.e., the student) in the absence of more training data. Given the training data, the teacher model can generate more \"soft target\" to teach the student model. And the student model can learn what the teacher model has \"taught\". In this paper, the MMSE-based speech enhancement algorithm is the teacher, the DNN is the student. Given the simulated noisy speech, the DNN can learn the intrinsic mapping function of the MMSE-based method.
# Characterization of the Aroma Profile of Commercial Prosecco Sparkling Wines
## 4 Conclusions  
This research provides an extensive chemical characterization of the volatile compound profile of Prosecco wine. The aroma of Prosecco wines was characterized mainly by ethyl esters of straight-chain fatty acids and acetates. Other compounds that characterized the volatile composition of Prosecco wine were the fermentative compounds, such as fatty acids, phenylethyl alcohol, and the sulfur-containing compounds dimethyl trisulfide and dimethyl sulfide. Monoterpenes could have, in some samples, contributed to the floral notes, however, in general, their concentration remained lower than their odor threshold. Compositional differences were observed between the three PDOs analyzed, with the Asolo wines generally richer in terpen, norisoprenoids and sulfur compounds. Valdobbiadene was mainly characterized by benzenoids, while Treviso was mainly characterized by hexanoic acid and phenylethyl alcohol. Sensory analysis performed by mean of sorting task methodology which indicated that the wines were grouped to form two clusters not perfectly matching with the three PDOs considered. The two clusters were characterized by
# A Comprehensive Hybrid Deep Learning Approach for Accurate Status Predicting of Hydropower Units
## 5 Experimental Results and Discussion
### Ablation Experimental Results
The \(R^{2}\) improved from 0.801, 0.862, and 0.974 to 0.891, 0.913, and 0.994, showing enhanced prediction accuracy and trend alignment. These improvements suggest that the optimization network significantly enhances the model's predictive performance by enabling more reasonable hyperparameter selection. In Figure 12, removing the TCN, REST-LSTM network, GRU network, adaptive weighting network, and optimization network all resulted in a noticeable decline in the prediction accuracy of the unit flow rate. Figures 13 and 14 show that the removal of any of the five components had a more substantial impact on the prediction results for guide vane opening and maximum water guide tile temperature, leading to increased prediction errors and decreased fitting accuracy. This indicates that each component of the proposed hybrid model is crucial for achieving accurate predictions in these scenarios.
## 4 Implementation
### Exploration Algorithms
#### 4.1.2 Nearest Cell Exploration  
As the environment has 30 grid cells, the input to this algorithm is given by the cost to reach each unvisited cell but accessible from the agent initial cell. This cost is determined by the number of moves that the robot requires to reach a particular cell. Here the unit cost is 1 for 90-degree rotation, and four units for walking from one cell to the next one. This cost can represent the power consumption of the robot to produce these movements. Thus, this algorithm finds the cell that has a minimum cost to reach it (see Figure 3).  
Figure 3: Example case and decision function evaluation for nearest cell exploration.
# Calculation Method of Loose Pressure in Surrounding Rock Mass
## 5 Upper Bound Solution for Loose Pressure in the Surrounding Rock Mass
### Upper Boundary Solution for Loose Pressure for Extra-Large Section Tunnels
#### 5.1.3. Derivation of the Upper Bound Solution for the Loose Pressure
\tag{15}\]  
\(\alpha_{1},\ldots,\alpha_{m+n}\) can be calculated by the sine theorem:  
\[\left\{ \begin{array}{l}\alpha_{1}=\arcsin\left(\left|\frac{OA_{0}}{\alpha_ {0}A_{1}}\right|\sin\beta_{1}\right)\\ \alpha_{i}=\arcsin\left(\left|\frac{AA_{i-1}}{\lambda_{i-1}A_{i}}\right|\sin \beta_{i}\right),(i=2,\ldots,m)\\ \alpha_{j}=\arcsin\left(\left|\frac{AA_{j-1}}{\lambda_{i-1}A_{j}}\right|\sin \beta_{j}\right),(j=m+1,\ldots,m+n-1)\\ \alpha_{m+n}=\arcsin\left(\left|\frac{CA}{\alpha_{m+n-1}C}\right|\sin\beta_{ m+n}\right)\end{array} \right. \tag{16}\]  
(3) Calculation of the velocity field  
As seen in Figure 13, the velocity vector relationship of each slider meets the following recurrence correlations:  
\[V_{1}=\frac{\sin(3\pi/4+3\varphi_{t}/2)}{\sin(\pi/2-\alpha_{1}-\beta_{1})}V_{0} \tag{17}\]  
\[V_{0,1}=\frac{\sin(\alpha_{1}+\beta_{1}-3\varphi_{t}/2-\pi/4)}{\sin(\pi/2- \alpha_{1}-\beta_{1})}V_{0} \tag{18}\]\[V_{i+1}=\frac{\sin(\pi/2-\alpha_{i+1})}{\sin(\pi/2-\alpha_{i+1}-\beta_{i+1})}V_{i }(i=1,\ldots,m-1) \tag{19}\]  
\[V_{i,i+1}=\frac{\sin(\alpha_{i+1}-\alpha_{i}+\beta_{i+1})}{\sin(\pi/2-\alpha_{i +1}-\beta_{i+1})}V_{i}(i=1,\ldots,m-1) \tag{20}\]  
\[V_{m+1}=\frac{\sin(\pi/2+\alpha_{m+1}+\theta_{1})}{\sin(\pi/2-\alpha_{m+1}- \beta_{m+1})}V_{m} \tag{21}\]  
\[V_{m,m+1}=\frac{\sin(\alpha_{m+1}-\alpha_{m}+\beta_{m+1}-\theta_{1})}{\sin( \pi/2-\alpha_{m+1}-\beta_{m+1})}V_{m} \tag{22}\]  
\[V_{j+1}=\frac{\sin(\pi/2-\alpha_{j+1})}{\sin(\pi/2-\alpha_{j+1}-\beta_{j+1})} V_{j}(j=m+1,\ldots,m+n-1) \tag{23}\]  
\[V_{j,i+1}=\frac{\sin(\alpha_{j+1}-\alpha_{j}+\beta_{j+1})}{\sin(\pi/2-\alpha_{ j}-\beta_{j})}V_{j}(j=m+1,\ldots,m+n-1) \tag{24}\]  
\[V_{m+n+1}=\frac{\sin(\pi/2-\alpha_{m}+\theta_{4}-\theta_{1}+2\varphi_{t})}{ \sin(\pi/2-\theta_{4}-\varphi_{t})}V_{m} \tag{25}\]  
\[V_{m,m+n+1}=\frac{\sin(\alpha_{m}+\beta_{m+1}-\varphi_{t})}{\sin(\pi/2-\theta _{4}-\varphi_{t})}V_{m} \tag{26}\]  
(4) Calculation of external powers  
1.
# A Novel TLS-Based Fingerprinting Approach That Combines Feature Expansion and Similarity Mapping
## 3 Methodology
### Data Acquisition  
For known good domains, the Tranco list [18] was selected, as it offers several advantages over alternatives such as the historical Alexa or Umbrella rankings: it employs vote-counting rank aggregation across multiple source lists, it implements specific manipulation-resistance measures, and it provides reproducible daily snapshots with verifiable generation processes [18].  
Known active malicious and unknown domains were more difficult to source in bulk, due to their transient nature. A range of publicly accessible sources were used, as shown in Table 2, covering various threat intelligence platforms and attack vectors. To ensure data quality and representativeness, several controls were implemented. Domains were deduplicated and cross-validated across multiple threat intelligence platforms, with any inactive domains removed from the dataset, given that prior studies indicated that approximately 75% of malicious domain activity occurs within the first 30 days after registration [19], and that industry findings from Akamai also demonstrated that newly registered domains exhibit the highest rates of malicious activity within their first month [20]. A 30-day sliding window was used for data collection, to balance currency with sufficient volume for meaningful analysis.
# Determination of Point-to-Point 3D Routing Algorithm Using LiDAR Data for Noise Prediction
## 1 Introduction
In the proposed research, a customized solution is evolved for extraction of routes between any pair of points (source and destination) and accurate shortest routes are determined in 3D using a highly dense LiDAR terrain point cloud. The novel solution is applicable for sound propagation modelling in 3D and for any route determination applications in cities when previously labelled routes are not available. The paper not only developed a novel algorithm of 3D routing for sound propagation modelling. It further demonstrates the 3D noise mapping with the above algorithm. Thus, in summary, the contributions of the paper are:  
1. Development of a novel algorithm to extract 3D shortest routes between a pair of points (source point and receiver point). 2. Determination of 3D routes using unlabeled raw 3D LiDAR terrain points existing between source and receiver points. 3. Determination detailed routes with highest accuracy. 4. Accurate sound propagation modelling integrating noise data, and LiDAR terrain data with noise model. **Definition of terms used:**  
_Noise source_: Source point of noise. _Destination_: Destination is where noise impact is about to calculate. _Point-to-Point_: It is pair of source and destination. _Building Edges and corner_: Edges of a building and its corners. _Terrain data_: Information of buildings, ground, trees, and other attributes. _Terrain parameters_: Route length and path difference. _Ground and Non-ground points_: Points on local ground levels. Points above the ground are non-ground points. _Points of Intersection_: Intersection points between lines. _The route over the top of the building_: Route determined from cutting plane technique that runs over buildings joining a noise source and noise receiver. _The route around the sides of the building_: Route determined from cutting plane technique that originates from the source and terminates at a receiving location traversing around the building. Reflected Route: Route determined where the route is formed after reflection from the ground or the nearby walls of the building (if any). Building Edges array: An array that contain building edges. Upward Route: A component of the route over top. It is the route from the source to the tallest building point. Downward Route: A component of the route over top. It is the route from the tallest building point to the destination point. Intersection Array: An array contains the intersection points between the building and cutting plane. Right side Route: Route from the source to destination point following the right side of the building. Left side Route: Route from the source to destination point following the left side of building. Tonal frequency: Short term single frequency sound.
# Short-Term Passenger Flow Prediction of Urban Rail Transit Based on a Combined Deep Learning Model
## 2 Literature Review on Short-Term Passenger Flow Prediction of URT
The research results show that the hybrid model exhibited more effective performance in terms of prediction accuracy than the existing algorithms, such as autoregressive integrated moving average (ARIMA), nonlinear regression (NAR) and traditional LSTM model [28]. [PERSON] established a traffic flow prediction model using a seasonal autoregressive integrated moving average model (SARIMA) and support vector machines (SVM). The test results on a Beijing traffic data set show that a SARIMA-SVM combined model can improve the accuracy of passenger flow prediction and reduce errors [29]. In addition, the attention mechanism, as an effective method to improve the accuracy and interpretability of the model, is also used to combine with other deep learning models [30]. [PERSON] et al. verified that the attention mechanism can recognize the relevant input time steps in an LSTM/GRU, so as to improve the prediction performance of the model [31]. [PERSON] and [PERSON] combined GCN with an LSTM/GRU to establish a traffic speed / flow prediction model. The results show that the combined prediction model has high reliability and better prediction performance than the single LSTM/GRU model [32; 33]. [PERSON] et al. incorporates two external factors, weekday, and weekend, into an LSTM network, and verifies that adding external factors can effectively improve the prediction performance of the model, but few factors are considered [34]. Therefore, facing the problem of short-term passenger flow prediction of URT, this paper establishes a TCN-LSTM combined model, which couples external factors such as date attribute, weather conditions, and air quality to predict the short-term passenger flow of URT.
## 5 Direction-of-Arrival Estimation  
In the previous section, a low-dimensional SDP that is a function of the SH atomic norm minimization (SHANM) method for gridless sparse signal recovery on the manifolds in the SH domain is presented, which produced source localization. Here, it is demonstrated how the developed algorithm estimate the DoA using an SAA in free space.  
Assuming a far field, the point source estimate of the DoA using an SAA is defined as recognizing atoms from measurements in the SH domain, and therefore executable based on the SHANM method.  
The DoA estimation using the SHANM-based approach steps from the recovered covariance matrix  
\[\widetilde{\mathbf{R}^{\star}}=\widetilde{O}S\Big{(}\widetilde{\mathbf{Z}^{\star}} \Big{)}\widetilde{O}^{H}, \tag{31}\]  
where \(\widetilde{\mathbf{Z}^{\star}}\) represents the optimal solution of Equation (30). Extracting the DoAs using the SHANM-based method in Equation (31), the following analysis is given.  
Based on Proposition 1, \((\mathbf{Z}^{\star},\mathbf{M}^{\star})\) expressed as  
\[\begin{split} S(\mathbf{Z}^{\star})=\sum\limits_{i=1}^{I}q_{i}f( \psi_{i})\mathbf{f}^{H}(\psi_{i}),\\ \mathbf{M}^{\star}=\sum\limits_{i=1}^{I}q_  
where \(I_{n,m}^{\pm}=\sqrt{(n\mp m)(n\pm m+1)}\) and \(\mathbf{U}_{s}^{(-1)}\), \(\mathbf{U}_{s}^{0}\), \(\mathbf{U}_{s}^{(1)}\) is produced from \(\mathbf{U}_{s}\)[55], then  
\[\mathbf{D}_{1}\mathbf{U}_{s}^{0}=E\begin{bmatrix}\cap^{T}\\ \cap^{H}\end{bmatrix}, \tag{37}\]  
then \(\bigcap\) can be expressed as  
\[\bigcap=\left(E^{H}E\right)^{-1}\mathbf{E}^{H}\mathbf{D}_{1}\mathbf{U}_{s}^{0}. \tag{38}\]  
The computation of eigenvalues \(u_{i}\), \(i=1,2,\ldots,I\), of \(\bigcap\), the DoA of the _ith_ source can be obtained by \(\hat{\theta}_{i}=\tan^{-1}\lvert u_{i}\rvert\) and \(\hat{\varphi}_{i}=\arg(u_{i})\), respectively.
## 2 Materials and Methods
### Determination of Antimicrobial Activity  
The antimicrobial activity of the extracts was evaluated through the determination of the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) against 7 Gram-negative bacteria (_Escherichia coli_ ATCC 8739, _Pseudomonas aeruginosa_ ATCC 27853, _Pseudomonas agarici_ ATCC 25943, _Pseudomonas fluorescens_ (internal collection from the Escola Superior de Biotecnologia da Universidade Catolica Portuguesa), _Pseudomonas tolaasii_ ATCC 51309, _Salmonella enterica_ ATCC 13076, and _Yersinia enterocolitia_ DSM 11503) and 3 Gram-positive bacteria (_Bacillus cereus_ ATCC 14579, _Listeria monocytogenes_ ATCC 15313, and _Staphylococcus aureus_ ATCC 6538).  
The MICs were determined through the microdilution method and colorimetric assays. Firstly, the extracts were decontaminated by exposure to UV radiation for 20 min and dissolved in Mueller Hinton broth (80 mg/mL). Then, 100 \(\upmu\)L of each extract was placed in a respective well in a microplate, and serial dilutions were made in the following wells where 50 \(\upmu\)L of Mueller Hinton broth had previously been placed. Finally, 50 \(\upmu\)L of Mueller Hinton broth with an inoculum of 10\({}^{6}\) CFU/mL of the microorganism under study was added to all wells (except for the negative controls). For each extract and each of the microorganisms studied, two negative controls (extract dissolved in Mueller Hinton broth (40 mg/mL) and only Mueller Hinton broth) and a positive control (100 \(\upmu\)L of Mueller Hinton broth with 5 \(\times\) 10\({}^{5}\) UFCs) were studied. All microplates were incubated for 24 h at 37 \({}^{\circ}\)C, excluding _P. agarici_ (25 \({}^{\circ}\)C), _P. fluorescens_ (25 \({}^{\circ}\)C), _P. loaasii_ (25 \({}^{\circ}\)C), _Yersinia enterocolitia_ (30 \({}^{\circ}\)C), and _B. cereus_ (30 \({}^{\circ}\)C) [23]. After the end of the incubation period, 40 \(\upmu\)L of dye p-iodonitrotetrazolum chloride (INT) (0.2 mg/mL) was added to each well, and the microplate was incubated at 37 \({}^{\circ}\)C, 25 \({}^{\circ}\)C, or 30 \({}^{\circ}\)C for 30 min. Viable microorganisms change the color of INT from yellow/colorless to pink. Thus, it was considered that the MIC of each extract against each microorganism under study corresponded to the lowest concentration at which there was no color change [24].  
To determine the MBC, 50 \(\upmu\)L of the solutions, where there was no growth of microorganisms, was inoculated in plate count agar and incubated at 37 \({}^{\circ}\)C for 24 h. The MBC was considered to correspond to the lowest concentration, where there was no growth of microorganisms. All tests were performed in duplicate [24].
## 3 Results and Discussion
### In Vitro Cytotoxicity  
As for the cytotoxicity of the products released during material degradation, cell viability was analyzed after culturing cells in contact with eluates of the SA-G hydrogel samples for 1, 5, and 7 days. The time points were chosen according to the results of the degradation test (Section 3.1). Cell viability was not significantly affected by the presence of the degradation products (Figure 6). Indeed, at every time point, the cell viability was found to be higher than 90%. Additionally, no statistical difference was found between the viability of the controls and of the cells in contact with SA-G eluates at any time point (\(p\)-value = 0.07). This result suggests that the SA-G bionik is non-cytotoxic and, hence, can be loaded with cells for 3D bioprinting applications.  
Figure 5: **(a)** Flow curves showing the viscosity\(-\)shear rate behavior of SA-G bioinks with and without cells. Error bars represent the standard deviation. Analyses were performed on SA-G bioinks with no cells, with cells included at a density of 5 M cells/mL, and with cells included at a density of 10 M cells/mL; **(b)** Results of strain sweep tests displaying the storage (G\({}^{\prime}\)) and loss (G\({}^{\prime\prime}\)) moduli against the oscillation strain. G\({}^{\prime}\) and G\({}^{\prime\prime}\) are represented by square markers and circle markers, respectively. Analyses were performed on SA-G bioinks with no cells, with cells included at a density of 5 M cells/mL, and with cells included at a density of 10 M cells/mL. **(c)** Strain recovery test results; **(d)** Elastic recovery test results. G\({}^{\prime}\) and G\({}^{\prime\prime}\) are represented by square markers and circle markers, respectively. SA-G: sodium alginate-gelatin.