esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Industrial Consumption, BESS, and Grid Modelling
### BESS Model
#### 3.2.1 Electronic Converter Model  
For the purpose of this work, there is no need to model the power electronic converter in detail, since neither efficiency nor power converter ageing are incorporated into the economic evaluation of the methodology. Thus, an ideal voltage source converter (VSC) has been selected, with no conduction or switching losses and no power limitations, so the battery can dispatch as much power as required. As a result, the VSC is modelled as a controlled three-phase voltage source whose reference voltage is given by the converter control, which manages the power that flows in and out of the battery.  
The control of a VSC can be modelled as explained in [23], consisting of two levels: a high-level control and a low-level control. The high-level control is in charge of selecting, based on a specific strategy, the power dispatched or consumed by the battery. Several strategies have been proposed in the literature, from those based on rules [24] to more complex strategies [25; 26]. For this paper, a simple strategy based on rules has been selected, which sets the reference power for the battery (\(P_{BATT}\)) based on the maximum power that the industrial consumption can absorb from the grid (\(P_{LIMIT}\)), the actual power that the industrial load is demanding (\(P_{LOAD}\)), and the battery state of charge (\(SoC_{BATT}\)). The flow diagram for the high-level control is shown in Figure 4.  
Figure 3: Industrial consumption model in MATLAB Simulink.  
The BESS recharge profile has been defined as the minimum current that allows the BESS to recharge the same amount of energy that it has provided to the load along the day. Moreover, reactive power reference for the battery has been set to zero, so no contribution is expected from it.  
On the other hand, the low-level control is in charge of setting the voltage at the VSC terminals so that the power management commanded by the high-level control can be performed. The low-level control is a dq axis control, shown in Figure 5, which has been developed building on [27]. The control is duplicated for P and Q, with minor variations. Each branch accounts for an outer PI loop control, which sets the battery power equal to the reference power coming from the high-level control, outputting a reference current. Then, there is an inner PI loop control that adjusts the current flow through the VSC with the reference current, so a reference voltage is outputted. This voltage is finally regulated so that it corresponds to the VSC voltage at its terminals. In order to properly control the VSC, the inner control must be considerably faster than the outer control. Detailed explanation about the implemented dq axis control for a VSC can be found in [23; 27].
# Directional Suppression of Monotone Noises with A Parametric Array Loudspeaker
## 5 Experimental Evaluation for Directional Monotone Noise Suppression with PALs  
### Experimental Setup  
We performed practical ANC experiments to examine the effectiveness of the implementation of PALs for long-distance localized monotone noise suppression. The schematic for the experimental setups is shown in Figure 5a and a photograph of the practical setup is shown in Figure 5b. A generic loudspeaker functions as the noise source at the far end of the entire scene to provide ambient monotone noise for suppression. Either PAL or VCL, as discussed in Section 3, is implemented as the secondary source. A reference microphone is placed close to the noise source to sample the ambient noise and an error microphone is placed at the location of interest to monitor the real-time residual noise. For comparison purposes, as shown in Figure 5a, the error microphone is placed at 260 cm away from the PAL, which is 5.8 times further compared with the case when using VCL as the secondary source. The practical setup illustration in Figure 5b shows the experimental scene, in which the PC functions as the core to process ANC algorithms and data I/O streaming. The real-time residual noise is collected by the error microphone at the location of interest and digitized via the sound card to be streamed to the PC. A high-precision oscilloscope also monitors the residual noises. The signal generator modulates the PC streamed secondary source driving signal in real-time with the transducer carrier frequency of 40 kHz to power the PAL via the PA module. For verification purposes, the test monotone noise was selected as 1 kHz in the experiments.  
Figure 5: Experimental setup for the evaluation of the PAL for monotone noise suppression. (**a**) The schematic of the overall ANC scene using either PAL or VCL as the secondary source. (**b**) A photograph that illustrates the practical setup.
## 4 Simulation Results and Analysis  
### Simulation Parameters  
In the simulation scenario considered in this paper, a circular ground area with a radius of 700 km is selected as the target service region. This area is jointly covered by four LEO satellites and one GEO satellite, with each LEO satellite operating in a multi-beam mode and capable of activating up to four beams simultaneously. Over six time slots, the LEO satellite system can form up to 96 beam positions that can be scheduled under this configuration. In each time slot, the GEO satellite activates one beam and establishes a protection zone with a radius of 150 km around GEO user. Beam coverage from the LEO satellites is prohibited within this protection zone to avoid interference.The simulation parameters are summarized in Table 2.  
The antenna gain used in the simulation is calculated according to the following formula [28,29]:  
\[G_{b}^{i}=G_{0}\left[\frac{J_{1}(\mu)}{2\mu}+36\frac{J_{3}(\mu)}{\mu^{3}} \right]^{2} \tag{17}\]  
where \(G_{0}\) denotes the peak transmit antenna gain of the \(b\)-th beam, which is calculated as follows: \(G_{0}=\eta\frac{4\pi D}{(c/f)^{2}}\), where \(\eta\) is the antenna aperture efficiency, \(D\) is the antenna aperture diameter, \(f\) is the carrier frequency, and c is the speed of light. Then \(\mu=2.07123\frac{\sin\Theta_{b}}{\sin\Theta_{\rm{3 dB}}}\), where \(\Theta_{\rm{3 dB}}\) denotes the 3 dB beamwidth of the \(b\)-th beam, and \(\Theta_{b}\) represents the off-axis angle between the \(i\)-th user and the pointing direction of the \(b\)-th beam. \(J_{1}(\cdot)\) and \(J_{3}(\cdot)\) are the first- and third-order Bessel functions of the first kind, respectively.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline LEO Satellite Orbital Altitude & 500 km/600 km \\ Number of LEO Satellites & 4 \\ GEO Satellite Position & 40\({}^{\circ}\) E, 100\({}^{\circ}\) N \\ GEO Earth Station Location & 40\({}^{\circ}\) E, 100\({}^{\circ}\) N \\ Radius of Target Ground Area & 700 km \\ LEO Satellite Beam Radius & 50 km \\ Peak Antenna Gain of LEO Satellite & 35 dB \\
3 dB Beamwidth & 1.65\({}^{\circ}\) \\ Number of Beams per LEO Satellite & 4 \\ Number of Users to Be Served & 750 \\ Number of Time Slots & 6 \\ System Bandwidth & 400 MHz \\ Carrier Frequency & 20 GHz \\ Noise Temperature & 150 K \\ Maximum LEO Satellite Transmit Power & 800 W \\ Rician Fading Factor & 0.95 \\ Cloud/Rain Attenuation Coefficient & 0.1/0.05 \\ Maximum Beam Power & 250 W \\ \hline \hline \end{tabular}
\end{table}
Table 2: Simulation parameter settings.
## 5 Conclusions
### Implications  
Although the distributed real-time data management framework proposed in this study shows good performance and scalability in the test, it still has the following limitations:  
1. At present, the system has not yet integrated a real-time anomaly detection mechanism, and in the future, a lightweight model can be introduced at the edge node to achieve rapid response. Second, although the framework supports integration with edge AI, it lacks in-depth research on edge inference and model deployment. Further research can focus on the adaptability of federated learning or model compression technologies in practical applications.
2. Although a TSN-based OPC UA communication scheme is designed in this study, its performance in very low-latency scenarios (such as multi-device cooperative control) has not been fully verified, and it should be verified through more complex industrial experiments in the future.
3. In terms of security, the current solution mainly relies on the built-in mechanism of Kafka, and in the future, the system should introduce a zero-trust architecture, end-to-end encryption, and edge privacy protection policies to meet the multi-node deployment and data security requirements in the industrial Internet.  
This study provides the academic community with the implementation idea of a system integrating OPC UA, Kafka, and edge computing and proposes a reference architecture for data processing in intelligent manufacturing. For the industrial community, the framework can be applied to key scenarios such as equipment condition monitoring and predictive maintenance and provides a foundation for building low-latency and highly reliable edge intelligent systems in the future. In the future, it can continue to expand to edge AI integration, real-time anomaly detection, OPC UA TSN key control scenarios, and industrial data security protection mechanisms.  
Conceptualization, D.L.; methodology, D.L. and K.W.; software, K.W.; validation, Y.W.; formal analysis, Y.W. and Y.S.; investigation, D.L. and K.W.; resources, Y.W.; data curation, Y.S.; writing\(-\)original draft preparation, K.W.; writing\(-\)review and editing, D.L. and Y.W.; visualization, K.W. and Y.S.; supervision, Y.S.; project administration, Y.W.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by funding from the Shanghai Committee of Science and Technology (21010504000).  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
The authors would like to express their sincere gratitude to the Shanghai Institute of Technology.  
The authors declare no conflicts of interest.
## 3 Proposed LDPC Architecture  
We introduce our novel LDPC decoder architecture for 5G-NR standards in this section. The proposed decoder is designed based on the layered belief propagation (BP) algorithm with MS approximation and area optimization techniques. The feature details of each component are described in the following subsections.
## 2 Materials and methods
### RSM Design and Statistical Analysis  
A Box-Behnken design was used for response surface methodology (RSM) analysis and to describe the influences of the various parameters on adsorption efficiency of MB and CR onto BCs. As shown in Table 2, three main factors and levels were chosen: solution pH (X\({}_{1}\)), pyrolysis temperature of a produced BCs (\({}^{\circ}\)C) (X\({}_{2}\)), and dye adsorption temperature (\({}^{\circ}\)C) (X\({}_{3}\)). The experimental data were analyzed to fit a second-order polynomial model by Equation (1):  
\[Y=\beta_{0}+\sum_{i=1}^{k}\beta_{i}X_{i}+\ \sum_{i=1}^{k}\beta_{ii}X_{i}^{2}+ \ \sum_{i=1}^{k}\sum_{j=1}^{k}\beta_{ij}X_{i}X_{j}+\ \varepsilon \tag{1}\]  
where \(Y\) is adsorption efficiency; \(\beta_{0}\), \(\beta i\) (\(i\) = 1, 2, 3), \(\beta ii\) (\(i\) = 1, 2, 3), and \(\beta ij\) (\(i\) = 1, 2, 3; \(j\) = 1, 2, 3) are the various coefficients, while _Xi_ and _Xj_ are coded independent variables. The Minitab 16 Statistical software was used to analyze the data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Factor** & **Variables** & \multicolumn{3}{c}{**Levels of Variables**} \\ \hline  & & \(-\)1 & 0 & +1 \\ X1 & Solution pH & 4 & 7 & 10 \\ X2 & Pyrolysis temperature (\({}^{\circ}\)C) & 350 & 550 & 750 \\ X3 & Adsorption temperature (\({}^{\circ}\)C) & 20 & 30 & 40 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Coded and actual values of the various variables (pH, pyrolysis, and adsorption temperature) used in Box–Behnken design.
## 3 Mathematical Model
### Optimization  
To find the optimum parameters for an SLM process, we considered three different optimization algorithms: genetic algorithm, particle swarm optimization (PSO), and differential evolution. These algorithms are briefly described in the following subsections.
# Optimization of Offshore Saline Aquifer CO2 Storage in Smeaheia Using Surrogate Reservoir Models
## 3 Methodology  
Figure 3 shows the overall workflow. The study commences by examining the Smeaheia aquifer and the available numerical model. Afterwards, the aquifer model will be modified by incorporating pore volume multipliers, determining an appropriate well location using sensitivity analysis, and designing a new injection well. The third phase is formulating the Smeaheia optimization problem by identifying the objective function and constraints. SRMs are designed, trained, and validated based on the input parameters of objectives and constraints. Ultimately, GA is coupled with SRMs to efficiently solve the optimization problem within a remarkably short timeframe.
## 2 Materials and Methods
### Methods  
Figure 1 shows the ring shear apparatus used, a schematic illustration of the slide simulation, and the shear stress-strain relation to determine the peak and residual shear stress. The drainage-controlled ring shear apparatus has an annular-like shape of a sample box with an inner diameter of 110 mm and an outer diameter of 250 mm. Sample height is generally set for 65 to 75 mm. Owing to the dimensions of the ring shear box, it is possible to test a sample containing a large amount of coarse particles (e.g., 4500 g of sediments placed in the container). There is a rough (serrated) surface in both the upper and lower rings with 32 and 72 blades for inner and outer rough surfaces [24], respectively, to minimize slippage during shearing. The test procedure follows: sample setting, sample saturation, checking degree of saturation, consolidation, loading, and monitoring of shearing [25; 26; 27; 28]. First, the soil sample is placed into the ring shear box. Then, water is slowly infiltrated from the bottom of the ring shear box to saturate the soil sample and to remove the air in the ring shear box. To expel the air trapped in the ring shear box, a relatively long period of time is required for clayey soils (i.e., more than 7 days) and sandy soils (i.e., more than 1 h). For landslide materials that typically have coarse-grained sediments composed of sands and gravels, saturation time is generally set to less than 24 h. After soil saturation, the targeted normal stress is loaded slowly depending on the drainage condition selected. Drainage is selected to keep the open or closed drainage valve, which is located in the ring shear box. The shear speed is selected as 0.01, 0.1, 1, 10, or 100 mm/s. During shearing, the shear speed is continuously applied for all tests. In general, the shear speed is applied for 300-3000 s. After testing, soil samples were taken from the upper, middle (shear zone), and lower ring shear box to analyze the grain size distribution. The monitoring of shear stress, normal stress, displacement, and pore water pressure is optional. In this study, shear-displacement-dependent and shear-speed-dependent shear characteristics are examined. The shear resistance is measured for rotational lengths of 10, 30, 50, 100, and 110 mm at a shear speed of 0.1 mm/s. For a given shear speed, different shearing times are also applied for approximately 1-1000 s. The shear resistance obtained is compared with that obtained from the direct shear test. Detailed test results are discussed in the next section.
# Detection of Abnormal Symptoms Using Acoustic-Spectrogram-Based Deep Learning
## 4 Experiments and Results
### Acoustic Abnormal Symptom Detection Experiment Result  
In this study, spectrogram, Mel-spectrogram, and MFCC data obtained through preprocessing were converted into 160 \(\times\) 160-pixel image files in PNG format. These images were used as input for training and validation. The data were applied to the model architecture described in Figure 9, and three separate models were trained, each using a different type of input. Model performance was evaluated using a test dataset that was not included in training, and the results are summarized in Table 3.  
As shown in Table 3, the models trained on different feature types exhibited clear differences in classification performance. The spectrogram-based model achieved an accuracy of 95.22%, while the MFCC-based model showed a detection accuracy of 93.78%. The Mel-spectrogram-based model outperformed the others, achieving an accuracy of 97.13%. Model performance was evaluated using accuracy, precision, recall, and F1-score. To further assess the effectiveness of the proposed model, it was compared with an SVM, a plain CNN, and the ResNet50 model trained on Mel-spectrogram data. As shown in Table 4, the proposed model achieved an accuracy of 97.37% and an F1-score of 96.87%, exceeding the performance of both the SVM and the plain CNN model without residual connections.
## 3 Digital Intelligence and Intelligent Forensics
### Intelligent Forensics
#### 3.2.1 Social Network Analysis  
Social Network Analysis (SNA) draws on graph theory and other mathematical techniques to allow for the analysis of networks--in this case, networks of people. SNA can provide numerous insights into the network structure and highlight a series of metrics, such as degree centrality, which refers loosely to the centrality of a person in a network.  
The value of SNA in investigations has been demonstrated most particularly in the abundance of research conducted into the Enron email dataset which contains around half a million emails generated over three and a half years. On the release of the dataset in 2002 into the public domain [31], researchers were able to:  
* Discover hidden groups, _i.e._, \"a group of individuals planning an activity over a communication medium without announcing their intentions\" [32];
* Discovering organizational structure [33];
* Demonstrate how networks of people change during an emerging situation. In the Enron case for instance, the executives formed a tighter clique and information distribution became less coordinated during the collapse (Figure 3) [34].  
How changes in word usage over time can demonstrate key players and individual influence [35].  
SNA techniques allow the investigator to determine the density of communications, the strength of connections between two nodes or people and the \"influencing power\" of a person in a network. The same techniques can help to identify people who may not have been part of the original investigation thereby giving a greater \"intelligent\" insight into the investigation.  
Whilst such a visual analysis is useful in identifying patterns, the real science behind such systems is the graph based mathematical analysis that allows an investigator to identify patterns in group behavior and in particular, identify key parts of the network--such as nodes that are the most influential in a network. A number of open source tools and solutions exist to enable this analysis such as NetworkX [36], Pajek [37], and Gephi [38], as well as industrial solutions, such as i2 analyser [39].
# Ginger: Panacea or Consumer's Hype?
## 2 Pharmacology of Ginger
### Biological Activity
#### 2.3.5 Cancer Preventive Activity  
A small clinical trial using only 9 participants in the test group investigated the ability of ginger powder, at a daily dose of 2 g for 28 days, to prevent the development of colon cancer. All the participants had increased risk for this kind of cancer, previously determined by their medical background, and the action of ginger was measured by making a post-treatment sigmoidoscopy with collection of intestinal tissue for immunohistochemical studies. The colon crypts, tube-like formations that line the colon and produce mucus, were measured to determine their content in cancer and apoptosis biomarkers. Results showed that the treatment with ginger restored the levels of these biomarkers back to 'normality', that is to values typical of healthy individuals [76].  
The cancer preventive action of ginger also seems to be associated with reduction of inflammation, which is commonly linked with cancer. In a trial with 10 participants having increased risk for colon cancer, the intake of 2 g of ginger powder daily for 28 days reduced significantly the expression of the pro-inflammatory enzyme COX-1 in the intestinal wall (biopsy extraction) in comparison with the placebo group (\(n=11\)) [77]. Also noteworthy is the fact that the same dose was studied, in parallel, on healthy volunteers (that is, with no risk for colon cancer) and ginger did not reduce their levels of COX-1 [77].
## 4 Discussion
### Improvement of Lithology Discrimination Results  
The enhanced sampling methods significantly improve the lithology recognition performance of each model after processing the dataset. According to the results shown in Figure 10, the improvement effect of balancing processing on the SVM model is the most significant. However, due to the limitations of the model itself, the lithology prediction score of SVM is consistently lower than that of random forest and GBDT. The random forest model performs the best after enhancement, especially in the prediction tasks of siltstone and sandstone, which lack original data. The balanced dataset helps improve the prediction score of random forest. The improvement in the GBDT model is relatively small, and the effect of the balanced dataset on GBDT is limited.  
Compared to different data sampling methods, the proposed ADASYN+ENN method shows the most significant improvement in models. From the results shown in Figure 7, the effectiveness of the comprehensive method in processing the dataset is better than using ADASYN or SMOTE over-sampling algorithms alone. Over-sampling algorithms cannot accurately determine whether the baseline samples used to synthesize simulated data are errors generated during data collection and may increase noise in the dataset after synthesizing a certain number of samples. The key role of the ENN algorithm is to detect and remove potentially erroneous samples from the dataset. Therefore, the dataset processed by the comprehensive method combining ADASYN and ENN is more reliable.
# Joint Content Recommendation and Delivery in Mobile Wireless Networks with Outage Management
## 5 Performance Evaluation Framework  
To evaluate the performance of joint content recommendation and delivery algorithms, we consider two major metrics: total user interest and transmission outage probability. The first metric is related to the effectiveness of content recommendation, while the second metric is related to the reliability of content delivery. The total user interest is given by:  
\[I_{total}=\sum_{u=1}^{U}\sum_{f=1}^{F}r_{uf}x_{uf}. \tag{28}\]  
The transmission outage is defined as the probability that when each user randomly requests content from the recommended list (with \(N\) contents), the BS cannot deliver the request contents within a time constraint \(T_{s}\). An outage event can be defined with respect to the following mathematical problem:  
\[\begin{split}&\text{Find}\quad c_{uk},\rho_{uk}\\ \text{subject to}& l_{i}^{u}\leq B*\sum_{k=1}^{K}c_{uk }*\rho_{uk}*T_{s}\qquad\forall u\\ & B*\sum_{u=1}^{U}\sum_{k=1}^{K}\frac{f(c_{uk})}{\alpha_{uk}^{2}}* \rho_{uk}\leq P_{T}\\ &\sum_{u=1}^{U}\rho_{uk}\leq 1\\ & x_{uf}\in\{0,1\},\rho_{uk}\in\{0,1\}\end{split} \tag{29}\]  
where \(l_{i}^{u}\) represents the size of the file \(i\) requested by user \(u\). Because the file should be delivered within a time constraint \(T_{s}\), it is directly related to the instantaneous data rate requirement of user \(u\). The problem is whether there exist any feasible channel and power allocation policy (i.e., \(c_{uk}\) and \(\rho_{uk}\)) that can satisfy the instantaneous data rate requirements of all users. If the above problem is solvable, this means that content delivery is successful; otherwise, a transmission outage occurs. We note that the above outage metric is defined at the system level by considering multiple users.