esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Viscous Effects on Flow Field Prediction
Figures 12-14 compare the iso-contours of the predicted distributions of the stream-wise (x-direction) velocities, of the three different viscous models, during a single cycle. As reported in several previous studies [3; 42; 43; 44], the re-entrant jet is the most critical mechanism causing unstable cavitation behavior. The re-entrant jet causes adverse velocity gradients on hydrofoil surfaces in a stream-wise direction, and then back-flow toward the leading edge occurs. This back-flow eventually breaks the cavity structures such that the most developed cavity structure is split into two, and the trailing edge cavities are shed downstream. As shown in the Figures 12-14, re-entrant jet phenomena can be seen in the suction side of hydrofoil. In order to compare the strength of re-entrant jet for three viscous cases, the time-averaged x-directional velocity profile is examined. Figure 15 shows the distribution of time-averaged x-direction velocities at different locations for the three viscous cases. As can be seen in Figure 15, all three cases exhibit similar velocity profiles, and velocity values, at the outer cavity region. However, in the inner cavity region, the profiles differ significantly. \begin{table}
\begin{tabular}{c c c c c} \hline
**Location** & **Experiments** & **Viscous Lagging** & **Full Viscous** & **Thin-Layer** \\ \hline \(x/C=0.2\) & 0.0195 & 0.0285 & 0.0280 & 0.0286 \\ \(x/C=0.4\) & 0.0658 & 0.0811 & 0.0738 & 0.0748 \\ \(x/C=0.6\) & 0.1166 & 0.1529 & 0.1  
Figure 3:0 Time-variation of predicted x-directional velocity: thin-layer case. Figure 3:1 Time-variation of predicted x-directional velocity: full viscous case. Figure 16 shows zoomed-in plots of the x-direction velocity profiles at \(x/C=0.4\) and \(x/C=0.6\). The viscous lagging approach exhibits a stronger re-entrant jet phenomenon than the thin-layer and full viscous cases. The over-predicted re-entrant jet results in larger shed cavity structures than the other two cases. Consequently, the viscous lagging approach exhibits significantly different results from the experimental result, in terms of the cavity distribution. In Figure 17, the required computation times per 50 time steps are compared for the three different viscous cases. As can be seen, the viscous lagging approach requires less computation time than the other approaches, and the thin-layer approach requires approximately half that of the full viscous approach. Based on the above results, it can be concluded that the thin-layer approach can be used as an alternative for the full viscous approach in terms of accuracy and efficiency. Figure 3:2 Comparison of zoomed time-averaged x-directional velocity distribution with experimental results by [PERSON] et al. [40]: (**a**) \(x/C=0.4\); and (**b**) \(x/C=0.6\). Figure 3:3 Time-averaged x-directional velocity distribution at five stream-wise locations: (**a**) viscous lagging; (**b**) full viscous; and (**c**) thin-layer cases.
# Dual-Path Beat Tracking: Combining Temporal Convolutional Networks and Transformers in Parallel
## 1 Introduction
In this task, the local temporal context is crucial, as the model needs to focus on the relationship between each frame and its neighboring context, rather than between frequency bins within a single frame. In this context, inter-frame features are less critical than the information derived from a frame and its neighboring frames, which is better captured through convolutional layers. Convolutions introduce an inductive bias that is useful for processing frequency information, as the number of frequency bins is generally smaller than the number of time frames in a song. Utilizing a Transformer to model frequency bins within the same frame would significantly increase computational cost without clear advantages for the task at hand. Therefore, we opt for convolutional layers to handle frequency-based features efficiently while maintaining the ability to model the critical temporal relationships for accurate beat tracking. In this paper, we propose a hybrid model that combines TCNs and Transformers in parallel to enhance beat tracking. The key objectives of our approach are to improve accuracy across musical genres by combining TCNs, which capture local, short-term dependencies, with Transformers, which model long-term structures. Specifically, the TCNs capture fine-grained rhythmic features like note onsets, while the Transformers account for broader rhythmic patterns, allowing the model to leverage both local and global cues for more robust beat tracking. To improve the model's interpretability, we use Grad-CAM [13] to analyze attention weights and convolution filters, providing insights into the rhythmic patterns the model learns. The Grad-CAM visualizations help to verify the model's ability to capture features across time sequences and reveal the important frequency bins that contribute to the model's decisions. Our model achieves competitive performance with fewer parameters compared with existing SOTA methods, showing that a more efficient approach can be both effective and interpretable.
# Sentiment Analysis of Online New Energy Vehicle Reviews
## 5 Experimental Results
### Recall Experimental Results Comparison  
Recall is a crucial performance metric for evaluating sentiment classification models. A high recall value indicates the model's ability to minimize false negatives and effectively capture positive sentiment instances, thereby enhancing the accuracy and reliability of the sentiment analysis. Evaluating the recall also helps gauge the model's capability to recognize different sentiment classes, particularly in scenarios with imbalanced data distribution.  
Figure 11 depicts a comparative analysis of the EDC model against models utilizing the word2 vec and BERT pretraining methods in terms of recall.  
Figure 11: Comparison of recall (Recall) values between EDC and different models. (**a**) Comparison with word2 vec word embedding model. (**b**) Comparison with BERT word embedding model.  
Figure 10: Comparison of precision (Pre) values between EDC and different models. (**a**) Comparison with word2 vec word embedding model. (**b**) Comparison with BERT word embedding model.  
Upon analyzing the recall rate graph, it is evident that the EDC model surpasses other models trained with word2 vec in terms of the recall rate. Throughout all epochs, the EDC model consistently maintains a recall rate above 97%, reaching a peak of 97.42%. In contrast, other models like FastText, TextCNN, and TextRNN exhibit varying recall rates. For instance, the FastText model starts with a recall rate of 81.31% in Epoch 1 and improves to 89.93% by Epoch 8. The TextCNN model starts at 87.73% in Epoch 1 and increases to 93.59% by Epoch 8. Similarly, the TextRNN model begins at 52.93% in Epoch 1 and improves to 91.51% by Epoch 8. These models demonstrate significant enhancements in recall rates as training progresses.  
When compared to models based on BERT, the EDC model showcases the superior performance in terms of the recall rate. Other models (BERT_CNN, BERT_RNN, BERT_RCNN, and BERT_DPCNN) exhibit relatively lower recall rates, peaking at around 94.86%. This indicates that the EDC model outshines other models when it comes to capturing a higher proportion of relevant sentiment instances in the automotive comment dataset. The EDC model exhibits robust feature extraction and representation capabilities when dealing with textual data, leading to remarkable performances in retrieval and identification tasks.
###### Abstract  
The increasing deployment of IEEE 802.11 ax (Wi-Fi 6) networks necessitates an accurate assessment of radiofrequency electromagnetic field (RF-EMF) exposure under realistic usage scenarios. This study investigates the duty cycle (DC) and corresponding exposure levels of Wi-Fi 6 in controlled laboratory conditions, focusing on bandwidth variations, multi-user scenarios, and application types. DC measurements reveal significant variability across internet services, with FTP upload exhibiting the highest mean DC (94.3%) under 20 MHz bandwidth, while YouTube 4K video streaming showed bursts with a maximum DC of 89.2%. Under poor radio conditions, DC increased by up to 5\(\times\) for certain applications, emphasizing the influence of degraded signal-to-noise ratio (SNR) on retransmissions and modulation. Weighted exposure results indicate a reduction in average electric-field strength by up to 10\(\times\) when incorporating DC, with maximum weighted exposure at 4.2 V/m (6.9% of ICNIRP limits) during multi-user scenarios. These findings highlight the critical role of realistic DC assessments in refining exposure evaluations, ensuring regulatory compliance, and advancing the understanding of Wi-Fi 6's EMF exposure implications.  
W -Fi 622020151528582  
smartphones, computers, and IoT devices [3]. The growth of Wi-Fi networks raises concerns about RF-EMF exposure [4; 5]. Accurate assessment is needed to ensure compliance with ICNIRP [6] and IEEE standards [7].
# ERS-1/2 and Sentinel-1 SAR Data Mining for Flood Hazard and Risk Assessment in Lima, Peru
## 1 Introduction
This approach is quite established in applications of Earth and environmental observations, risk management and security [42], and is easier to implement by a wider spectrum of users (e.g., staff from technical offices of local administrations, practitioners, early responders with duties of damage mapping). Therefore, the present study has been conceived according to an applied research perspective, by developing a method that is accessible to users and, as such, is more likely to be replicated and transferred in GIS mapping, in support of natural hazard assessment and disaster risk management. Additionally, because our approach of SAR data mining couples with a workflow of expert-led interpretation of the observed patterns through the integration with geospatial datasets, this method is suitable to complement more traditional GIS-based approaches of analysis of hazard, vulnerability and exposure to flooding (e.g., [43]). Section 2 outlines the environmental and meteorological settings of Lima, alongside the methodology to mine the SAR archives, extract the radar backscatter changes from each SAR image set, and label them according to two categories--i.e., \"Flooding\" and \"No Flooding\"--depending on whether these changes were due to flood events or anthropogenic activity (e.g., urbanization). Section 3 showcases a representative selection of the results of the geospatial analysis carried out within the GIS environment. Section 4 explains how the resulting flooding risk map can serve as an evidence base, gathered from satellite data, of the sectors of potential concern along the Rimac River, should flooding events of equal severity occur in the future. Accuracy and reliability of the results are finally discussed by comparison with the map product that was generated by the Copernicus Emergency Management Service (EMS) during the Risk and Recovery Activation EMSN-038 for flood delineation and damage mapping of the same flooding event happened in mid-March 2017 [44], as well as with the map of flooding susceptibility and fluvial erosion published by [PERSON] et al. [28].
## 1 Introduction
Their findings indicate that after 28 days of repairing cracks, the compressive strength increased by 76% compared with that of the unrepaired cracks. The tensile strength of the repaired cracks increased to 84% of the parent body's strength. In MICP technology, microorganisms decompose carbonate, which transforms into CO\({}_{3}^{2-}\) and combines with Ca\({}^{2+}\) to form the precipitation of CaCO\({}_{3}\). According to the reaction mechanism expressed in Formulas (1) and (2), the combination of 1 mol of Ca\({}^{2+}\) in 1 mol of CO\({}_{3}^{2-}\) generates 1 mol of CaCO\({}_{3}\). \[\mathrm{CO(NH_{2})}_{2}+\mathrm{H_{2}O}\rightarrow\mathrm{CO_{3}^{2-}+NH_{4}^ {+}} \tag{1}\]  
\[\mathrm{CO_{3}^{2-}+Ca^{2+}\rightarrow CaCO_{3}} \tag{2}\]  
In this experiment, the microbial group with the best growth activity within 72 h was selected. Using C\({}_{4}\)H\({}_{6}\)CaO\({}_{4}\)-H\({}_{2}\)O as an external Ca\({}^{2+}\) source, mineralization repair tests were carried out to investigate the strength of cement mortar specimens before and after high temperature damage and the influence of microbial activity on their repair performance. The effect of microbial mineralization on fracture repair was reflected by the comparison of chloride ion migration parameters before and after repair. Meanwhile, the microstructure and mineral composition of the repair products were analyzed. To provide a theoretical basis for microbial mineralization technology to repair high temperature damage defects in cementitious sand specimens and to provide a preliminary research basis for further application of MICP technology to concrete repair.
# Random Access Using Deep Reinforcement Learning in Dense Mobile Networks
## 4 RL Based Selection of TRxPs for RA
### System Parameters through SIB2  
(2) UBW and ACB are random variables that are initially broadcast by the TRxPs and later selected by the users to reduce the occurrence of collision and congestion, respectively. UBW is the original standard of RA. ACB is a recently adopted mechanism to control congestion in massive RA scenarios. They become important inference variables for learning because it is shown that proper selection of these parameters allows reduction of collision and congestion, and therefore has a direct impact on estimating the RA delay. Once users select the proper system parameters, the available resources for RA are made known to them according to the configuration from the TRxPs.
# A Lightweight Model for DDoS Attack Detection Using Machine Learning Techniques
## 4 Results and Discussion
### Evaluation Using Metrics
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline  & & **Accuracy** & **Class** & **Precision** & **Recall** & **F1 Score** & **Training time (ms)** & **Predict Time (ms)** \\ \hline \multirow{8}{*}{Case 1: TON-IOT with All Attacks} & Logistic Regression & 0 & 0.81 & 0.83 & 0.82 & \multirow{8}{*}{8.3514} & \multirow{8}{*}{0.0319} \\  & & 1 & 0.82 & 0.81 & 0.81 & & \\  & Random Forest & 100\% & 0 & 1.00 & 1.00 & & \\  & & 1 & 1.00 & 1.00 & & \\  & Naive Bayes & 77\% & 0 & 0.75 & 0.81 & 0.78 & \\  & ANN & 99\% & 1 & 0.79 & 0.73 & 0.76 & \\  & KNN & 99\% & 0 & 0.99 & 0.99 & & \\  & & 0 & 0.99 & 0.97 & 0.98 & \\  & & 1 & 0.95 & 0.99 & 0.97 & 2.208 & 26.923 \\ \hline \multirow{8}{*}{Case 2: TON-IOT with DDoS} & Logistic Regression & 100\% & 0 & 1.00 & 1.00 & 1.00 & \\  & & 1 & 1.00 & 1.00 & 1.00 & \\  & Random Forest & 100\% & 1 & 1.00 & 1.00 & 14.3046 & 0.3173 \\  & Naive Bayes & 100\% & 0 & 1.00 & 1.00 & 1.00 & \\  & & 1 & 1.00 & 1.00 & 1.00 & \\  & ANN & 100\% & 0 & 1.00 & 1.00 & \\  & KNN & 100\% & 1 & 1.00 & 1.00 & \\  & & 1 & 1.00 & 1.00 & 1.00 & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation using metrics for TON-IOT (15 features). time combined. This research has proven to be much more effective in terms of accuracy and the time factor for detecting the DDoS attacks in IoT devices. A stringent validation process was employed to evaluate the effectiveness of the proposed intrusion detection system for multiple-class classification. Specifically, a five-fold cross-validation technique was utilized to ensure a robust assessment of the model's performance. This method involved partitioning the dataset into five equal subsets or \"folds\". The model was then trained on four of these folds and validated on the remaining one, iterating this process five times so that each fold served as the validation set once. By employing five-fold cross-validation, the research ensured that the results were less susceptible to variations in the data, providing a more reliable and unbiased estimation of the model's ability to generalize to unseen data. This approach helped in reducing overfitting and offered a comprehensive view of how the model performed across different segments of the dataset, thereby adding rigor and credibility to the findings related to multiple-class classification of other attacks within lightweight IoT networks.
## 2 Jamming Classification and Mathematical Modeling
### Suppressive Jamming
It is typically modeled as a series of Gaussian-shaped pulse pairs, expressed as [20] \[z_{pl}[n]=Pp[n]\otimes\sum_{k=1}^{M_{p}}\alpha_{k}u(nT-\eta_{k})\text{exp}(j2 \pi f_{k}(nT-\eta_{k}))\] (5) In this expression, \(u(\cdot)\) represents the waveform of a single pulse, \(M_{p}\) is the number of pulses per unit time, and \(\alpha_{k}\), \(f_{k}\), and \(\eta_{k}\) denote the amplitude, carrier frequency, and time delay parameter of the \(k\)-th pulse, respectively.
# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 3 SIMMA System Architecture
### Sensor Node
The first of these are used to modify the transceiver's internal registers, which alter the transmission frequency, spreading factor, coding rate, and channel bandwidth [44; 45]. These settings will determine the range and data rate at which transmissions will be made, as well as the level redundancy used to add robustness to the transmission and avoid interference that could be found in the environment. On the other hand, the device operation configurations are the ones that implement the buoys' functionality, which includes the data capture frequency and the time period in which the LoRa transceiver will be turned on. This last feature is thought for scenarios where the buoys will be left for several days at the sea, and the data collection with the UAV would be done periodically at certain hours of the day. This helps reduce the energy consumed by the sensor node and, in consequence, extends its lifespan. All of the sensor nodes can be reconfigured to the initial start-up settings, either by command input or a physical hardware reset button. As the sensor node is thought to work for extended periods of time, low power consumption modes were directly implemented. Although these modes are always present, the longevity of the devices will be directly impacted by the data capture frequency. At higher frequency intervals, the device will capture almost complete streams of data, but will only last a short period of time and vice versa.
## 2 Materials and Methods
### Characterization of the Polymeric Systems
#### 2.3.3 Yield, Entrapment Efficiency, and Drug Loading  
The yield (\(\eta\)%), representing the percentage of CS and active substances (Gly, LA), which was transformed/loaded into microparticles, was calculated using the formula [34]:  
\[\eta(\%)=(m_{1}/m_{0})\times 100 \tag{1}\]  
where:  
\(m_{1}=\) the amount of obtained microparticles, in dry state (mg)  
\(m_{0}=\) the total initial amount of CS and active substances (Gly, LA) (mg)  
The entrapment efficiency (EE%) was calculated based on a validated High Performance Liquid Chromatography (HPLC) method, using a HPLC apparatus type Agilent Technologies 1200 Series, equipped with UV detector type DAD (Diode Array Detector) G1315A and injector type Rheodyne. The following parameters were used: acetonitrile and phosphate buffer, pH = 2.7, as mobile phase, gradient elution during 60 min, at a flow rate of 1 mL/min [35; 36], ODS (octadecyl-silica) Hypersil C18 column, \(250\times 4.6\) mm, 5 \(\upmu\)m particles size. Briefly, after removing the formed CS-Gly, CS-LA, and CS-Gly-LA microparticles, the TPP solution was analyzed in the mentioned chromatographic conditions. Based on the peak area corresponding to Gly and LA and on the corresponding specific calibration curves, the unloaded amount of active substances (Gly, LA) was determined. By difference between the initial (m\({}_{1}\)) and unloaded amount (m\({}_{2}\)) of active substances, the amount of loaded (m\({}_{3}\)) active substances into the chitosan matrix was calculated. EE% was calculated using the formula [34]:  
\[\text{EE\%}=(m_{3}/m_{0})\times 100 \tag{2}\]  
where:  
\(m_{3}=\) the amount of active substance loaded into CS microparticles (mg)  
\(m_{0}=\) the initial amount of active substance used for the preparation of microparticles (mg)  
Drug loading (DL%) was calculated using the following formula [37]:  
\[\text{DL\%}=(m_{3}/m_{1})\times 100 \tag{3}\]  
where:  
\(m_{3}=\) the amount of active substance loaded into chitosan microparticles (mg)  
\(m_{1}=\) the amount of microparticles obtained, in dry state (mg)  
All tests were performed in triplicate and the results were expressed as mean \(\pm\) SD.
# Inland Water Level Monitoring from Satellite Observations:
## 2 Methodological Framework  
In this section, the methodological framework of the scoping review is described, based on the stages followed by [PERSON] and [PERSON] [38] and [PERSON] et al.'s [37], to analyze the current state of different satellite missions, altimetry, or others, regarding the highly focussed research questions [40, 41]. The performances of different satellite missions are examined as well as the different retracking algorithms used over inland water bodies. The research protocol described in the following paragraphs was proposed by [PERSON] and [PERSON] [38] and [PERSON] et al. [42].
# Evaluation of Radio Communication Links of 4G Systems
## 2 Background
### Study and Simulation of the Radio Links of a 4G System
#### 2.2.2 Free Space Model  
At distance R, the power density p(R,\(\theta\),\(\varphi\)) (number of Watts/m\({}^{2}\)) is given by [14]:  
\[\text{p(R,\(\theta\),\(\varphi\)) = P}_{\text{a}}/4\pi\text{R}^{2}\ [\text{W}/\text{m}^{2}] \tag{9}\]  
Figure 1: Influence of distance and type of medium on propagation: (**a**) Cost 231 Hata model; (**b**) Okumura—Hata model.  
Figure 2: Influence of distance and type of medium on propagation.  
(R) is the distance between the transmitter and the receiver. From the calculation of the sphere of the isotropic antenna and the definition of the antenna gain, it can be shown that the distance between the transmitter and the receiver introduces attenuation of the propagation due to free space (Afs) at [14,20]:  
\[\text{Afs}=20\log(4\pi\text{d}/\lambda)\text{ [dB]} \tag{10}\]  
The variation of the power received according to the power supply from 1 Watt up to 1 kW is presented in Figure 3. The simulation parameters are as follows: P\({}_{\text{a}}\) = [1, 1000] Watt; d = 3 km and \(\lambda\) = 0.01 m.  
Note that the relationship between P and P\({}_{\text{a}}\) is linearly proportional because the function is a straight line.  
The variation of received power and attenuation vs. distance (with: Pa = 1000 Watt; d = [0, 30] m and \(\lambda\) = 0.1 m) are given in Figure 4.  
We notice a proportional relationship between attenuation and distance, while the relationship between power and distance is anti-proportional.  
The variation of attenuation according to wavelength for the parameters: Pa = 1000 Watts; d = 100 m and \(\lambda\) = [0.01, 10] m is presented in Figure 5:  
Figure 4: Relationship between power and attenuation vs. distance.  
Figure 3: Variation of received power vs. power supply.
## 3 Methods
### Data Preprocessing  
#### 3.1.1 Outlier Samples Rejection  
Due to improper operation during the acquisition process, value entry error, model misfit, and other reasons, there are usually a small number of outlier samples in the raw data. In accordance with the standard GB/T 37969-2019 [21] and the standard GB/T 29858-2013 [22], outlier samples must be eliminated prior to spectral preprocessing. The outlier samples are divided into two categories: high leverage value samples, whose spectra are significantly different from the average spectrum of the entire model sample, and samples whose predicted values are significantly different from the reference values. In this paper, high leverage value samples and reference value outlier samples are rejected by constructing a full-wavelength PLSR quantitative model in TQ Analyst (Thermo, Waltham, MA, USA).