esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Soil Microbial Community Structure Based on FAME Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Year** & **Treatments** & **Total FAME** & **Total Bacteria** & **Total Fungi** & **Gram Positive** & **Gram Negative** & **Actinomycetes** & **Saprophyic Fungi** & **AMF** \\ \hline  & Control & 94.04 (13.3) & 23.25 (3.1) & 14.88 (3.8) & 13.44 (1.6) & 3.63 (0.6) & 6.17 (0.9) & 13.18 (3.1) & 1.92 (0.9) \\
2018 & 5\%-Sur & 10.15 (4.7) & 24.33 (2.1) & 16.04 (2.6) & 14.57 (1.1) & 3.59 (0.3) & 6.16 (0.7) & 13.92 (2.4) & 21.22 (0.2) \\
5\%-Inc & 93.08 (19.1) & 22.45 (2.9) & 12.15 (2.0) & 13.25 (1.6) & 3.25 (0.5) & 5.94 (0.8) & 10.44 (1.6) & 1.71 (0.9) \\  & 10\%-Inc & 107.44 (12.2) & 27.79 (2.5) & 17.54 (3.0) & 16.30 (2.0) & 4.19 (0.5) & 7.29 (1.0) & 15.11 (2.7) & 2.43 (0.4) \\  & Control & 96.14 (20.0) & 23.59 (0.5) & 18.73 (3.0) & 13.48 (2.2) & 3.60 (0.6) & 6.50 (0.6) & 16.45 (3.2) & 2.27 (0.4) \\
2019 & 5\%-Sur & 139.52 (0.4) & 33.47 (2.9) & 26.24 (2.2) & 13.87 (1.9) & 5.11 (0.4) & 9.30 (0.9) & 24.25 (2.4) & 3.97 (0.6) \\  & 5\%-Inc & 109.24 (12.9) & 27.39 (2.8) & 20.02 (2.0) & 15.56 (1.7) & 3.96 (0.5) & 7.87 (0.6) & 16.57 (2.2) & 3.45 (0.5) \\  & 10\%-Inc & 115.69 (12.8) & 28.39 (2.2) & 18.98 (1.8) & 16.01 (1.9) & 4.43 (0.6) & 7.85 (0.7) & 15.57 (1.8) & 3.22 (0.9) \\  & Treat & ns. & ns. & ns. & ns. & 0.038 & ns. & ns. & ns. \\ ANOVA & Year & 0.0333 & 0.0222 & 0.00248 & ns. & 0.0473 & 0.017 & 0.000013 & 0.0073 \\  & TxJ & ns. & ns. & ns. & ns. & ns. & ns. & ns. & ns. \\ \hline  & Control & 38.08 (22.6) & 8.26 (2.8) & 6.56 (3.1) & 5.01 (3.3) & 1.48 (1.0) & 1.76 (0.6) & 5.73 (3.6) & 0.83 (0.1) \\
2018 & 5\%-Sur & 46.87 (35.1) & 10.29 (4.5) & 6.68 (2.5) & 5.50 (1.1) & 1.70 (0.5) & 3.07 (0.8) & 5.75 (3.1) & 0.93 (0.3) \\  & 5\%-Inc & 52.09 (33.5) & 9.02 (2.8) & 6.12 (3.5) & 5.44 (2.6) & 1.45 (0.6) & 2.16 (0.9) & 5.48 (2.5) & 0.64 (0.2) \\  & 10\%-Inc & 33.39 (35.7) & 8.22 (4.2) & 5.12 (4.0) & 4.68 (2.1) & 1.41 (0.3) & 2.13 (2.9) & 4.60 (2.6) & 0.51 (0.4) \\  & Control & 56.88 (25.1) & 10.73 (4.4) & 11.99 (2.4) & 6.07 (2.1) & 1.80 (1.0) & 2.85 (1.3) & 10.58 (0.3) & 1.40 (0.8) \\  & 5\%-Sur & 57.12 (27.3) & 13.83 (5.1) & 9.83 (1.3) & 7.78 (2.7) & 22.24 (1.1) & 3.80 (1.6) & 7.97 (2.4) & 1.85 (0.2) \\
2019 & 5\%-Inc & 56.82 (24.1) & 13.60 (4.1) & 10.62 (1.1) & 7.63 (1.9) & 2.16 (1.0) & 3.80 (1.3) & 9.93 (0.0) & 1.56 (0.2) \\  & 10\%-Inc & 63.91 (21.9) & 15.31 (5.9) & 11.68 (3.3) & 8.59 (3.0) & 2.50 (1.2) & 4.20 (1.8) & 9.72 (3.9) & 1.96 (0.7) \\  & Treat & ns. & ns. & ns. & ns. & ns. & ns. & ns. & ns. \\ ANOVA & Year & 0.0115 & 0.0004 & 0.0005 & 0.0015 & 0.0004 & 0.0001 & 0.0073 & 0.0001 \\  & TxJ & ns. & ns. & ns. & ns. & ns. & ns. & ns. & ns. \\ \hline \end{tabular}
\end{table} 
Table 1: Abundance of microbial fatty acid methyl ester (FAME) indicators groups with and without biochar addition. Numbers in bracket represent standard error of the means (\(n=6\)). Inc: incorporated; Sur: surface; non-significant (\(p\)-value > 0.05). The letter indicates statistical significance differences based on the [PERSON] LSD test with a \(p\)-value < 0.05.
## 4 The Simulation Framework
### Vehicles and \(\text{CO}_{2}\) Emissions' Estimation
The VSP parameter is dynamic over the course of time and must be calculated as vehicles change acceleration and speed. The frequency of VSP calculation is parameterizable in the framework; in this use case, it is calculated with a frequency of 10 Hz, i.e., using 100 ms time slots. Fuel consumption and CO\({}_{2}\) emissions are then calculated for each time slot, using the methodology of CONOX project task 1 [43] (p. 15), which describes a model to calculate emissions from the VSP parameter and vehicle characteristics, based on real-world driving measurements. According to this model, the instantaneous fuel consumption (in grams per hour) can be estimated with Equation (2). \[\mathrm{FC}=\frac{\left(\mathrm{A}\times\mathrm{VSP}^{2}+\mathrm{B}\times \mathrm{VSP}+\mathrm{C}\right)\times\mathrm{m}}{1000} \tag{2}\]  
FC: fuel consumption in g/h  
A, B, C: factors that depend on vehicle type  
m: vehicle mass in kg  
Factors A, B, and C depend on the type of car and fuel. This framework uses the generic data to be used per vehicle segment or for average diesel and gasoline vehicles, as suggested by the CONOX report [43] (p. 15). Table 2 shows the values for the three factors, for each vehicle type of the framework. Finally, having computed the fuel consumption, the last step is to calculate the CO\({}_{2}\) emissions for each time slot. This is performed by converting the consumed fuel mass to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vehicle Type** & **A** & **B** & **C** \\ \hline Small diesel passenger vehicle & 1.0601 & 168 & 379 \\ Small petrol passenger vehicle & 0.2403 & 227 & 595 \\ Big diesel passenger vehicle & 0.6787 & 174 & 348 \\ Big petrol passenger vehicle & 0.2471 & 210 & 609 \\ Medium commercial (van) vehicle & 1.3313 & 166 & 357 \\ Big commercial (van) vehicle & 1.4156 & 166 & 378 \\ Bus * & 1.4156 & 166 & 378 \\ \hline \hline \end{tabular}  
* The CONOX report has no values for buses; thus, these values were extrapolated from the big commercial vehicle data (the same factors were used). The potential error in these factors affects all the simulation rounds in the same way; therefore, it will not compromise the main goal of comparing different routing strategies.  
\end{table} 
Table 2: CONOX suggested values for factors A, B, and C.  
CO\({}_{2}\) emissions--assuming that most of the carbon in the fuel is converted to CO\({}_{2}\). The conversion factors are based on [44] (p. 7) and are, respectively, 3.163 kg of CO\({}_{2}\) per kg of diesel and 3.171 kg of CO\({}_{2}\) per kg of petrol.
## 2 Related Work
### Google Maps  
Google Maps [11] is a popular web-based mapping and navigational service that provides estimates of journey times. Google does not publish any formal numbers on the performance of Google Maps, so we need to examine the work of others. In [14], the authors compared the results from Google Maps and an ITS system in which data was sent through the road infrastructure between key routes in major parts of Hong Kong; i.e., Hong Kong Island (HK), Kowloon Peninsula (KL) and New Territories (NT). The time average difference for all the routes was 1.69 min on weekdays and 1.32 min on weekends. If we divided this by the average route distance, we get 0.45 and 0.34, respectively. These are extremely busy routes in Hong Kong. The authors in [15] showed how travel time can also be investigated with the Google API Data using the colours of Google Maps for the city of Krefeld in Germany. The results showed that the deviation of this method from the measured values is less than 6 percent.  
Perhaps the most recent study on this issue was conducted by Ancoris [16]. This company is a Google Cloud Partner and a Google Maps Premier Partner and has also achieved Google's Location-Based Services Partner Specialisation. The evaluation of Google Maps by Ancoris was conducted over 56 journeys of varying lengths with an average of 18.0 miles, which is 28.8 km. The average difference between the estimated time by Google Maps and the actual time was 1.8 min. The average journey time was 32 min, so the percentage difference is (1.8/32), which gives an accuracy of 5.6 percent. This is close to the findings of the Krefled study.
# Transfer Learning Approach for Human Activity Recognition Based on Continuous Wavelet Transform
## 3 Materials and Methods
### Salogram Generation  
The continuous wavelet transform (CWT) of a function \(x(t)\) can be defined by the following integral:  
\[X_{\omega}(a,b)=\frac{1}{|a|^{1/2}}\int_{-\infty}^{+\infty}x(t)\overline{\psi} \bigg{(}\frac{t-b}{a}\bigg{)}dt, \tag{1}\]  
where \(\psi(t)\) is called a mother wavelet, which is a continuous function in both the time and frequency domains; \(a\) is called a scale value, \(a>0,\ a\in\mathbb{R}^{+*}\); and \(b\) is called a translational value, \(b\in\mathbb{R}\). The operation of the complex conjugate is represented by an overline.  
The results of the CWT can be represented as a heat map, also known as a scalogram, by placing the \(a\)-values along the \(y\)-axis, the \(b\)-values along the \(x\)-axis, and the intensity of each point determined by (1). Figure 2 demonstrates a transformed accelerometer \(x\)-axis signal from the KU-HAR dataset. The parameters of the illustrated transform are the Morlet mother wavelet and scale values from 0 to 128.  
Figure 1: Class distributions of the whole preprocessed UCI-HAPT dataset (outer circle) and its subset (inner circle).  
Figure 2: An example of a transformed accelerometer signal.  
The CWT has a number of advantages over Fourier-related transforms, which are commonly used in HAR problems for feature extraction. First, it provides a more accurate representation of signals with sharp spikes and breaks, which are typical in wearable sensor signals and are often important characteristics for activity classification. Second, CWT overcomes the issue of the nonstationary nature of the signals by simultaneously representing temporal and local spectral information. Consequently, it is reasonable to employ the wavelet transform in HAR-related problems as a powerful technique for frequency and time domain feature extraction.  
To improve the models' accuracy and mitigate the overfitting and underfitting problems that frequently arise during pre-training and fine-tuning, we used various scalogram sets generated using CWT. The parameters considered for the CWT are the scale values ranging from 0 to 32, 64, 128, and 256 using Mexican hat and Morlet mother wavelets. Thus, the performance of the models was evaluated using eight CWT configurations.
## 6 Experimental Simulation and Analysis of the Results
### Selection of Parameter \(\beta\)  
As the weight coefficient of the two reference factors introduced in PEAFC-CL during the CH selection strategy, the parameter \(\beta\) determines the proportion of the residual energy ratio factor and the channel efficiency factor in the threshold. Twenty-one groups of data are obtained by taking a value between [0,1] at 0.05 unit intervals and regarding the death time of the first node in the network as the criterion; the bottom curve shown in Figure 8 is drawn based on curve fitting. The network lifetime is maximum when \(\beta\) is between 0.5 and 0.6. Meanwhile, specific to different values of \(\beta\) within [0,1], the average acceptance rate of a packet is regarded as the criterion for discovering that an inflection point appears when \(\beta\) is 0.8 or between 0.5 and 0.6, which indicates that the value at these two points plays an important role in the data acceptance rate of the network. Overall, it is best to set the parameter to 0.55.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(f\) & 2.4 GHz & \(P_{APP_{T}}\) & 3.36 mW \\ \(\alpha\) & 2.0–6.0 & \(P_{APP_{R}}\) & 11.13 mW \\ \(\sigma\) & 1–8 dB & \(P_{APP_{S}}\) & 5.56 mW \\ \(\eta_{amp}\) & 0.8 & \(R_{b}\) & 54 Mbit/s \\ \(E_{0}\) & 5.0 J & \(W\) & 1030 kHz \\ \(P_{Rmin}\) & 5.92 pW & \(P_{Smin}\) & 3.4 pW \\ \(p_{out_{R}}\) & 0.12 & \(p_{out_{Sstart}}\) & 0.01 \\ \(p_{out_{TS}}\) & 0.05 & \(N\) & 30, 40, 60, 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reference value of simulation parameters.  
Figure 7: Comparison of the statistics of the number of CHs formed.
## 2 System Model and Assumptions
### Receiver  
Utilizing the photoelectric effect [31], photons detected by a PD can be converted into electrons. We denote \(\tau\) as the duration of each symbol. Within a \(\tau\) slot, the electron count received by \(\text{User}_{k}\) is represented as \(Y_{k},\forall k\). Given the characteristics of photon counting, this process is a probabilistic measurement process [31] following a Poisson distribution [32]. The conditional probability density function (PDF) of this process is expressed as follows:  
\[\Pr(Y_{k}=y_{k}\mid\mathbf{S}=\mathbf{s})=\frac{(\lambda_{k})^{y_{k}}}{y_{k}!}\exp(- \lambda_{k}), \tag{5}\]  
where \(y_{k}=0,1,\cdots,\infty,\forall k\) refers to an integer that is non-negative, representing the possible received count, and the vector \(\mathbf{s}\) denotes the possible realization of \(\mathbf{S}\), encompassing \(2^{K}\) possibilities. \(\lambda_{k}\) represents the average photon count received at \(\text{User}_{k}\), which can be obtained by  
\[\lambda_{k}=\zeta\frac{\mathbf{g}_{k}^{\top}\mathbf{P}\tau}{\hbar v}+n_{\text{b}}= \zeta\frac{\mathbf{g}_{k}^{\top}(P_{\text{s}}\mathbf{W}\mathbf{S}+U\mathbf{I}_{\text{d}})\tau} {\hbar v}+n_{\text{b}}=\zeta\mathbf{g}_{k}^{\top}(n_{\text{s}}\mathbf{W}\mathbf{S}+\mathbf{d} )+n_{\text{b}}. \tag{6}\]  
where \(\hbar=6.626\times 10^{-34}\mathbf{\cdot}\mathbf{s}\), \(\zeta\), and \(v\) represent the [PERSON]'s constant, the quantum efficiency of the photon-counting process, and the frequency of visible light, respectively. Let \(P_{\text{b}}\) represent the incident background light power, whereas the average photon count generated by background radiation within a single \(\tau\) slot is denoted as \(n_{\text{b}}=\zeta\frac{P_{\text{s}}\tau}{\hbar v}\). For ease of expression, we denote the signal strength and bias strength as \(n_{\text{s}}=\frac{\hbar\tau}{\hbar v}\) and \(\mathbf{d}=\frac{U\tau}{\hbar v}\mathbf{I}_{\text{d}}\), respectively. Equation (6) is derived by substituting (2).
# Predictive Modeling of Urban Lake Water Quality Using Machine Learning: A 20-Year Study
## 3 Results and Discussion
### Interpretability of ANNs Using SHAP Values  
To enhance the interpretability of our ANNs model, we employed SHAP values, a powerful tool that provides a unified measure of feature importance. Below are the computed SHAP values for our model's features (Table 5):  
Interpretation:  
* DO (Dissolved Oxygen): With the highest positive SHAP value, DO is the most influential feature in predicting WQI. An increase in DO levels is associated with a higher WQI, indicating better water quality.  
\begin{table}
\begin{tabular}{c c} \hline
**Feature** & **SHAP Value** \\ \hline DO & 0.35 \\ \hline BOD & \(-\)0.25 \\ \hline pH & 0.15 \\ \hline Turbidity & \(-\)0.10 \\ \hline Nitrate & 0.05 \\ \hline Phosphate & 0.20 \\ \hline \end{tabular} Note: Positive SHAP values indicate a feature pushing the prediction higher, while negative values push the prediction lower.  
\end{table}
Table 5: ANN SHAP values.  
* BOD (Biochemical Oxygen Demand): BOD has a negative influence on the WQI. Higher BOD levels, which indicate more organic matter in the water, are associated with a decrease in the WQI, signifying poorer water quality.
* pH: The pH level has a moderate positive influence on the WQI. This suggests that neutral to slightly alkaline waters tend to have a better-quality index.
* Turbidity: Increased turbidity levels have a slight negative impact on the WQI, indicating that clearer waters are associated with better water quality.
* Nitrate: Nitrate levels have a minimal positive influence on the WQI. However, it is worth noting that extremely high nitrate levels can be detrimental to aquatic life.
* Phosphate: Phosphate levels have a positive influence on the WQI, but like nitrate, extremely high levels can lead to issues like algal blooms.  
In conclusion, our SHAP analysis provides a transparent and interpretable breakdown of feature influences on the WQI predictions made by our ANNs model. These insights are invaluable for stakeholders, offering a clear understanding of the key factors driving water quality in the studied region.
# Investigation of Segmented Halbach Array Design for Passive Magnetorheological Brake System
## 2 Permanent-Magnet Halbach Array Design
### Halbach Array Configurations
#### 2.2.1 Linear Halbach Array Configuration  
The linear Halbach array shown in Figure 2 consists of magnets arranged in a straight line, with their magnetization directions such that the north pole of one magnet faces the north pole of the adjacent magnet, forming an array. This configuration produces a strong, unidirectional magnetic field on one side of the array, concentrating the field strength in that region.
## 4 Experiments and Results: Performance Analysis
### Experimental Methodology: ns-3 Simulations
Our detailed analysis confirmed the feasibility and practical efficacy of the proposed solution in real-world VANET environments, thereby significantly contributing to the advancement of secure and efficient vehicular communication systems.
# A Network Traffic Abnormal Detection Method: Sketch-Based Profile Evolution
## 3 Sketch-Based Profile Evolution for Network Traffic Abnormal Detection
### Sketch-Based Network Traffic Structuring Process
The specific methods to extract application fingerprints are as follows: first, we use IP Address Transition (IAT) to divide network traffic, find the \"breakpoints\" of network traffic, divide network traffic according to communication sessions, and obtain a complete application fingerprint. Then, combine with the natural language processing method the tf-idf algorithm and N-gram model to calculate the utility of the applied fingerprint and an N-gram model-based \"tree\" to save the candidate set, and then extract the applied fingerprint from the structure of the \"tree\" to achieve the non-artificial dynamic extraction of the applied fingerprint. The structuring process needs to use the acquired application fingerprint. Let the network traffic be \(seq_{T}=\{p_{1},p_{2},p_{3},\cdots\}\), the \(N\) application fingerprints \(FP=\{fp_{1},fp_{2},\cdots,fp_{N}\}\)  
Figure 2: Sketch data stream summarization. extracted from the network traffic, with \(M\) apps \(APP=\left\{app_{1},app_{2},\cdots,app_{M}\right\}\). The relationship is shown in Equation (1):  
\[R_{map}:seq_{T}\to FP\to TG_{M\times N}=\left\{p_{i},\cdots,p_{j}\right\} \to TG_{mn}(1<m<M,1<n<N) \tag{1}\]  
The structured process also requires the classification of the traffic received. In a network environment, the packet exchange between two communication parties is a continuous process. The type, length, and IP address of packets is constantly obtained. The packet length is taken as an important feature analysis, and the Gaussian mixture model is used to solve the duplication problem of fingerprint application. By limiting clustering, the fingerprints of the same application with similar lengths are divided into the same cluster as far as possible to realize the traffic classification of different states. The same fingerprint is clustered according to its length, and the probability distribution of the fingerprint with a similar length is calculated to complete the classification of the same fingerprint. In restricted clustering, the fingerprints of the same IP address should be divided into a cluster as far as possible, so that the fingerprints in the same cluster are similar, which may come from the same application, to improve the classification effect. The TG map is generated through the network traffic structuring process, and an image can be generated according to \(TG_{M\times N}\), as shown in Figure 3:
# Evolution of the Seismic Forecast System Implemented for the Vrancea Area (Romania)
## 4 Discussion
Although NIEP's electromagnetic and acoustic sensors are capable of supporting earthquake localization, they are not yet integrated into the OEF system. Decision matrix analysis predicts seismic activity 3 to 6 days in advance. Similar results are reported in [29; 30], in the context of radon monitoring in a region of Pakistan characterized by deep seismicity and a distinct geological structure. Although the equipment and methods differ, the monitoring and data analysis were conducted over a limited period: \"Real-time measurements of radon/thoron and soil meteorological parameters (temperature, pressure, and relative humidity) were recorded every 15 min\" [30]. As in our case, one condition mentioned in the article for the potential detection of precursors is a ratio of R\({}_{E}\)/PZone > 1.5 (see Tables 3 and 4). For the Vrancea area and deep earthquakes, this corresponds to a minimum local magnitude of ML = 4.3, equivalent to Mw = 4. Using a geophysical data network within the context of OEF involves various limitations and challenges. Interruptions in the data flow can result in missed seismic events. Such interruptions may be caused by climatic conditions (e.g., strong winds, lightning, or flooding affecting the power supply to telecommunications relays). While protection systems exist, they also have limitations. One solution is to use multiple monitoring stations. Another challenge involves sensor placement. Sometimes sensors cannot be installed under optimal conditions due to a lack of infrastructure, which restricts site selection and may dictate the type of sensor used. For seismic stations, environmental noise or vibrations from geophysical equipment must be avoided. For example, the RTM 1688 radon monitoring device includes an air pump that generates noise and vibration, potentially affecting the reliability of continuous operation. Data interpretation also presents challenges and requires a computing infrastructure capable of supporting the demands of the software and methodologies employed--such as memory capacity, storage, processing speed, high-speed internet, and access to various databases. These requirements are all met by the NIEP OEF, but they come with significant costs, including for equipment, software updates, and trained personnel. The biggest challenge remains ensuring the system's continuous operation and convincing decision-makers of its importance. Data analysis algorithms and methodological approaches can be implemented and adapted easily--provided the necessary resources are available. Understanding the interaction between monitored environmental factors and their correlation with seismicity is an ongoing scientific challenge.