esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on PM2.5 Concentration Prediction Based on the CE-AGA-LSTM Model
## 4 Experiments
### Experimental Data
#### 4.2.2 Prediction of PM2.5 Concentration Based on the AGA-LSTM Prediction Model
\[\text{MAE}=\frac{1}{n}{\sum\
olimits_{i=1}^{n}{\left|{{{\hat{y}}_{i}}-{{y}_{i}} }\right|}} \tag{7}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}{\sum\
olimits_{i=1}^{n}{\left({{{\hat{y}}_{i}}-{ {y}_{i}}}\right)^{2}}}} \tag{8}\]  
\[\text{R}^{2}=\text{SSR}/\text{SST}=1-\text{SSE}/\text{SST}=1-\frac{\sum \limits_{i=1}^{n}{\left({{y}_{i}}-{{{\hat{y}}_{i}}}\right)^{2}}}{\sum\limits_ {i=1}^{n}{\left({{y}_{i}}-\overline{y}\right)^{2}}},\overline{y}=\frac{1}{n} {\sum\limits_{i=1}^{n}{{y}_{i}}} \tag{9}\]  
Here, \({{{\hat{y}}_{i}}}\) and \({{y}_{i}}\) represent the predicted and true values of PM2.5 concentration, respectively. Tables 5-7 show MAE, RMSE and R\({}^{2}\) averages for the nine models over the five sample sets. Each column value indicates MAE, RMSE and R\({}^{2}\) values of the PM2.5 concentration of that model in 1-5 sample sets, and the results show that the proposed method in this paper (CE-AGA-LSTM) had the smallest MAE and RMSE and the largest R\({}^{2}\). Meanwhile, we found that the RF prediction model and the LSTM prediction model gave the same results as the CE-LSTM prediction model, while the CE-RNN prediction model had the worst performance. As can be seen from the above tables:  
* Compared with the RNN model, the LSTM model is more suitable for model prediction of time series data, and its unique \"gate structure\" better realizes the effective combination of short-term memory and long-term memory. * Although the LSTM model is more suitable for the prediction of time series data, for PM2.5 concentration prediction, long-term prediction in days, weeks and months is different from short-term prediction in seconds and minutes in data processing and prediction model construction; therefore, to meet the needs of the actual situation, according to the prediction of time, different prediction models should be designed. This is also a key direction of future research. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{8}{c}{**MAE**} \\ \cline{2-10}  & **LR** & **SVM** & **RF** & **ARMA** & **ST-LSTM** & **LSTM** & **CE+LSTM** & **CE+AGA-LSTM** & **CE+RNN** \\ \hline
1 & 16.826 & 25.516 & 15.747 & 16.735 & 15.521 & 15.9372 & 16.1229 & **14.2270** & 24.8390 \\
2 & 16.782 & 25.782 & 15.032 & 17.352 & 15.602 & 16.2981 & 15.5084 & **14.5400** & 22.1463 \\
3 & 17.855 & 26.792 & 15.248 & 17.395 & 15.272 & 16.2664 & 15.5939 & **14.4312** & 21.7606 \\
4 & 17.364 & 26.455 & 15.289 & 16.897 & 14.583 & 16.1936 & 15.3963 & **14.0790** & 20.4152 \\
5 & 17.522 & 26.374 & 14.553 & 16.823 & 14.890 & 15.5610 & 15.4919 & **15.2141** & 19.2932 \\ \hline Average & 17.2698 & 26.1838 & 15.1738 & 17.0404 & 15.1736 & 16.05126 & 15.62268 & **14.49826** & 21.69086 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Mean Values of MAE in five Experiments.
## 4 Experimental Design and Results
### Case 1: The Performance of HEMOGWO on Benchmark Functions
For the ZDT3 function, HEMOGWO achieves full coverage of the PF\({}_{\text{F}}\)ure, showcasing strong global search capability and distribution control, especially when dealing with the discontinuous PF structure. While SMOGWO, MOGWO, and NSGA-III follow the general trend, they still exhibit localized deviations. In comparison, the solution sets obtained by MOEAD_DE and MOPSO significantly diverge from PF\({}_{\text{F}}\)ure, reflecting poor convergence and inconsistent distribution. On the CF1 function, MOEAD_DE performs the best, with its solutions closely matching PF\({}_{\text{F}}\)ure and exhibiting the most uniform distribution among all compared algorithms. To comprehensively evaluate the overall performance of each algorithm and establish a ranking, the GD and IGD results from Tables 5 and 6 were subjected to the Friedman non-parametric test [30]. The test outcomes for both performance metrics are summarized in Table 7. As shown, the \(p\)-values for both GD and IGD are significantly lower than the predefined significance level of \(\alpha\) = 0.05, indicating that the differences in performance among the five algorithms are statistically significant for both indicators. Furthermore, the rankings of the algorithms are consistent across the GD and IGD metrics, with HEMOGWO securing the first place in both cases. This demonstrates that HEMOGWO outperforms the other algorithms in terms of both convergence accuracy and solution distribution. These statistical findings are in strong agreement with the numerical results reported in Tables 5 and 6.
# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 2 Materials and Methods
### Students' Behavior Data from Four Universities
#### 2.1.4 Data Analysis and Algorithm Tools  
The above data analysis and visualization show that the categories and distribution of student behaviors in different schools were different, so it is impossible to use a unified baseline to classify student behaviors. That is, there is no universal student classification method to label each student's category. In other words, the existing classification methods based on expert evaluation and educational standards are not objective and accurate enough and do not consider the diversity of student categories. Therefore, this is an unsupervised machine learning problem. Clustering is an effective method to solve unsupervised learning problems. We mainly used the K-CFSFDP algorithm to analyze the students' behaviors and compared with K-Means and CFSFDP clustering algorithms.  
The K-Means clustering algorithm is a classical algorithm that is widely used in fields such as data mining and knowledge discovery. The principle of K-Means clustering algorithm is simple with high efficiency, and it is adapted to big data sets. However, it has two major drawbacks. First, the k value needs to be set in advance, in most cases, the optimal number of categories for a dataset can not be determined in advance, so it is difficult to choose a reasonable value. For example, in this study, the number of student groups in each school may not be the same. Second, in the K-Means algorithm, it is necessary to determine an initial partition based on the initial cluster center and then optimize  
Figure 6: The scatter plot matrix of S3.  
Figure 7: The scatter plot matrix of S4.  
the initial partition, and the choice of this initial cluster center has a greater impact on the clustering result. Once the initial value is not selected well, it may not be able to obtain effective clustering results. For example, we cannot find a representative from each student category, especially when the number of student categories is unknown.  
We proposed the K-CFSFDP algorithm to determine the k value and cluster center based on the characters of the data set.
## 4 Analysis of the Anomalous Noise Events Present in the Urban and Suburban Environments
### ANE Durations  
In this section, we analyze the durations of the ANE subcategories for each of the two recording scenarios. In Figure 9 the boxplots of ANE durations are shown for each ANE subcategory. As it can be observed, in the suburban context the sounds of sirens constitute the longest ANEs, while the shortest ones are impulsive-like noises (labelled as 'door') in the A90 highway ring circumventing Rome. However, in these recordings brake noises, people, sounds of trucks and noise coming from the structure of portals also have significant durations.  
Figure 8: Histograms showing the accumulated ANE durations per category differentiating both locations Rome (suburban) and Milan (urban). The \(x\)-axis show the ANE category.  
Figure 9: Boxplots of the durations of each ANE type per category differentiating both locations Rome (suburban) and Milan (urban).  
Regarding the audio recorded in urban area within district 9 of Milan, Figure 9 shows that music, sirens and tramways and trains, airplanes and wind sound are the ones with longer durations. We should underline the fact that in the context of an urban environment like the one in Milan, events like sirens exhibit larger durations than those observed in the Rome A90 highway, because vehicles speeds are lower and, as a consequence, the duration of the passing of the vehicles with respect to the point of measurement is longer.
# Privacy-Preserving and Explainable AI in Industrial Applications
## 4 Industrial Impact and Remaining Challenges  
### Industrial Impact  
The use of artificial intelligence in industrial applications, and the use of techniques for privacy preservation and model explainability can impact industries in many ways:  
* improving productivity: by predicting the quality parameters of the product [67], manufacturers can swiftly modify the industrial process setup to fit the updated requirements. Thus, they can save time by using an AI method to provide the best setup which meets their needs;
* improving maintenance: AI algorithms can be used to identify anomalies, and they can also handle large quantities of data [68]. By training an AI model to behave like a device in its normal state, it will be able to identify events that are abnormal (anomalies), which are dangerous, and which can lead to accidents. Prevention is a key factor in reducing them;
* increasing security: the use of privacy-preserving methods for artificial intelligence algorithms will increase security for the client, as well as the provider, making sure that no entity can have access to the model expertise while using the model to generate predictions [69];
* increasing trust in predictions: to improve the accuracy, the model complexity must be increased, and this leads to models being regarded as black boxes. To identify the logic behind a prediction, explainability methods have been developed, and they can be used to identify key features from the input data, which lead to a certain prediction and, thus, an understanding of the model logic.
## 1 Introduction
In combination with agronomic and morphological observations, the use of microsatellite molecular markers has also highlighted the geographical distribution of allelic richness and phenotypic variation in the stress response of chestnut populations, defining suitable breeding areas [9; 10; 11; 12]. The chestnut agrobiodiversity influences nut composition, nutritional and technological traits, and the synthesis of secondary metabolites with high nutraceutical potential, as emerged from several chemical studies [4; 13; 14; 15; 16]. Therefore, it is very important to recognize the factors that mainly influence the chestnut quality to select the best cultivars and agrootechniques, according to pedoclimatic conditions and commercial use (e.g., fresh market, industrial processing, livestock feed, etc.). Moreover, the phytochemical characterization of fruits from _Castanea_ spp. may be very important for the development of effective strategies for the biodiversity conservation and traceability valorization of chestnuts. This study aimed to provide a traceability tool based on secondary metabolite quantification that may be effective and rapid in discriminating cultivars and selecting the most interesting one for a specific purpose. Chromatographic and spectrophotometric analyses were performed to define the nut composition (phytochemical fingerprint), as well as verify and quantify the content of antioxidant compounds useful to prevent chronic diseases and important in preservation and transformation processes [17; 18]. The obtained results were used to define the structure of a predictive and screening model by a multivariate statistical approach (principal component analysis) to distinguish cultivars with unknown origins based on genotype (\"species\" level), reducing the number of samples to be subjected to genetic analysis, with cost and time savings.
## 3 High-Energy Few-Optical Cycle Parametric Sources in the IR Spectral Region
### Perspectives in OPAs and OPCPAs Development
As opposed to the previously described approach based on synthesis of multiple sources, FOPA consists of dividing the spectrum of seed pulses from a single parametric source and in amplifying each spectral component independently from the others. The working principle of FOPA is depicted in Figure 9a. Spectral partitioning is obtained by spreading the pulse spectrum over a plane, the Fourier Plane (FP), by means of a 4-f setup. Then, different spectral components occupy a different position on the FP and can be amplified independently by using an array of non-linear crystals. Each element of this array is optimized for amplifying the corresponding spatial frequency, so that ultra-broadband phase matching is achieved, as shown in Figure 9b. As in the case of synthesizers, limitations in amplification bandwidth due to phase-matching in a single crystal are overcome. Moreover, since the incident energy is distributed over a large plane, seed and pump pulses with higher energy can be used, overcoming the restrictions imposed by limited crystal apertures. Up to now, \(\sim\)1.5-mJ pulses at 1.8 \(\upmu\)m with a sub-two cycle duration have been produced at a 100-Hz repetition rate by using the FOPA design [53]. Figure 8: Scheme of the OPCPA system. NLF, Non-Linear Fiber; DM, Deformable Mirror; PPLN, Periodically Poled Lithium Niobate crystal. Adapted with permission from [50], Copyright OSA Publishing, 2011. The development of powerful few-cycle parametric sources remains a challenging task from a technological point of view. Nevertheless, OPA and OPCPA systems will benefit from the introduction of the above-mentioned novel approaches towards the scalability of pulse energy and duration. Therefore, improvements are expected in the future both in terms of performances and in terms of system architecture.
# Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification
## 3 Proposed Method
### Weight Adjustment  
#### 3.2.1 Class Weight for Imbalanced Data  
As shown in Figure 3, the data are heavily imbalanced. Data imbalance stands as a significant challenge constraining the performance of ML models, particularly when the samples of the minority class are insufficient [52; 53]. To address this issue, common practices involve the utilization of undersampling and oversampling techniques. However, these methods come with risks of underfitting and overfitting, respectively, potentially limiting the generalization ability of the model.  
\[W_{kj}=1-\frac{C_{kj}}{\sum_{j}C_{kj}} \tag{1}\]  
In recent research, weighted classes have been recognized as one approach to addressing data imbalance [33]. Weighted classes can significantly reduce the bias in the data; thus, we utilize a method for calculating class weights. Equation (1) indicates the method for calculating the normalized weights for each class. In Equation (1), \(W_{ki}\) is the weight for each class in task \(k\), \(C_{ki}\) is the number of samples for each class label within the \(k\) tasks, \(k\) indicates target task, and \(j\) indicates class label. These weights are utilized to adjust the training of the model, taking into consideration the imbalance within each class, thereby aiding in enhancing the overall model performance.
# A Distributed File-Based Storage System for Improving High Availability of Space Weather Data
###### Abstract  
In space science research, the Indonesia National Institute of Aeronautics and Space (LAPAN) is concerned with the development of a system that provides actual information and predictions called the Space Weather Information and Forecast Services (SWIFS). SWIFS is supported by a data storage system that serves data, implementing a centralized storage model. This has some problems that impact to researchers as the primary users. The single point of failure and also the delay in data updating on the server is a significant issue when researchers need the latest data, but the server is unable to provide it. To overcome these problems, we proposed a new system that utilized a decentralized model for storing data, leveraging the InterPlanetary File System (IPFS) file system. Our proposed method focused on the automated background process, and its scheme would increase the data availability and throughput by spreading it into nodes through a peer-to-peer connection. Moreover, we also included system monitoring for real-time data flow from each node and information of node status that combines active and passive approaches. For system evaluation, the experiment was performed to determine the performance of the proposed system compared to the existing system by calculating mean replication time and the mean throughput of a node. As expected, performance evaluations showed that our proposed scheme had faster file replication time and supported high throughput.  
distributed file; peer-to-peer network; automated process; decentralized storage; data management +
Footnote †: journal: Applied Soft 2019, 9, 5024; doi:10.3390/app9235024
# A PMSG Wind Energy System Featuring Low-Voltage Ride-through via Mode-Shift Control
## 3 System under Consideration
### Inverter DC-LINK
\begin{array}{ccc}0&0&0\\ 0&0&-\frac{m}{2\dot{v_{dc}}}\\ 0&\frac{m}{2\dot{L}}&-\frac{R}{\dot{L}}\end{array} \right]\;\;\;\;\;\;\;\;\;\; \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \;\;\;\;\;\;\;\;\;\  
Equations (24)-(26) are rearranged to obtain the state-space sub-model shown in (26):  
\[\left[ \begin{array}{c}\dot{l}_{b}\\ \dot{v}_{dc}\\ \dot{l}_{grid}\end{array} \right]=A_{2}\left[ \begin{array}{c}\dot{u}_{b}\\ \dot{v}_{dc}\\ \dot{i}_{grid}\end{array} \right]+B_{2}\left[ \begin{array}{c}v_{r}\\ v_{grid}\end{array} \right] \tag{27}\]  
where  
\[A_{2}=\left[ \begin{array}{ccc}0&-\frac{1}{L_{dc}}&0\\ \frac{1}{c_{dc}}&0&-\frac{m}{2C_{dc}}\\ 0&\frac{m}{2L}&-\frac{1}{L}\end{array} \right]\;and\;B_{2}=\left[ \begin{array}[ ]{ccc}\frac{1}{L_{dc}}&0\\ 0&0\\ 0&-\frac{1}{L}\end{array} \right] \tag{28}\]  
The state-space averaging approach offers clear insight into the entire system dynamics, where:  
\[\overline{A}=A_{1}*d+A_{2}*(1-d)\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\; \;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;\;  
MPPT mode (during normal grid conditions):  
* MPPT for maximum wind energy harvesting. * DC-link voltage regulation. * Grid synchronization and active power injection into utility. * Grid current control to ensure high quality and unity power factor. LVRT mode (during voltage sag conditions):
* MPPT is seized and output active power is limited to eliminate grid current rises to avoid the tripping of the inverter overcurrent protection [31]. * DC-link voltage regulation. * Grid synchronization and reactive power injection into the grid, according to grid regulations, to support grid voltage during recovery. * Grid current high quality is preserved during LV conditions. * Any mismatch, between generator and grid powers, is stored in rotor inertia.
# Influence of Random Modulated Power Converter on G3 Power Line Communication
## 5 Results and Discussion  
The performance of the G3-PLC is assessed by considering the FER, the Channel Capacity, and the Channel Capacity Loss metrics. The \(FER\) indicates the percentage of data frames correctly received over the total data frames sent through the communication system, and it can be determined as:  
\[FER(\%)=\frac{(Sent\;frames-Received\;frames)}{(Sent\;frames)}\times 100 \tag{5}\]  
Figure 4: Frequency spectrum of the exploited G3-PLC signal.  
Figure 5: Experimental test bench.  
The Channel Capacity, \(C\), indicates the maximum bits of data that the communication system can transmit in the unit time [25], and is defined as:  
\[C=\int_{f_{1}}^{f_{2}}~{}~{}\log_{2}\Big{(}1+\sfrac{S(f)}{N(f)}\Big{/}N(f)\Big{)}~{}df \tag{6}\]  
where \(f_{1}\) and \(f_{2}\) are the lower and upper frequency bands of the G3-PLC, \(S(f)\) is the PSD of the G3-PLC, and \(N(f)\) is the PSD of the noise, which encompasses two contributions:  
\[N(f)=N\_0~{}(f)+N\_EMI~{}(f) \tag{7}\]  
where \(N\_0~{}(f)\) denotes the PSD of the background noise and \(N\_EMI~{}(f)\) denotes the PSD of the noise from the DC-DC converter. Eventually, the Channel Capacity Loss is defined as:  
\[C_{Loss}(\%)=\frac{(C_{0}-C_{G3})}{C_{0}}\times 100 \tag{8}\]  
where \(C_{G3}\) and \(C_{0}\) denote the Channel Capacity of the G3-PLC with and without the noise from the DC-DC converter (\(N\_EMI\)).
# The Effect of Seed Removal and Extraction Time on the Phenolic Profile of Plavac Mali Wine
## 3 Results and Discussion
### Plavac Mali Wines' Phenolic Profile
#### 3.1.1 Anthocyanins  
It is well known that anthocyanins are one of the most important bioactive compounds and have a strong influence on wine color intensity and antioxidant activity [27]. The significant contribution of anthocyanins to antioxidant capacity as well as a proportional correlation between the quantitative content of wine anthocyanins and antioxidant activity was shown in work by [28; 29]. As expected and in accordance with data in the literature [30; 31], the most represented anthocyanin was malvidin-3-\(O\)-glucose followed by delphinidin-3-\(O\)-glucose. Presented results are partly in accordance with published data, suggesting that longer maceration times decrease anthocyanin concentrations by their degradation and/or transformation to other chemical compounds [2; 3; 7]. As already posted in the paper [32], the maximum concentrations of total anthocyanins in Plavac mali wines were obtained after 8 days followed by a significant decrease after 17 days. In this research, the highest concentrations were also achieved after 7 days followed by a pronounced decrease after 21 days and no marked differences after 49 days of maceration. The same pattern was noticed between variants D and E (21 and 49 day macerations with seed removal). Even though in the work by [14; 18] seed removal did not cause any important total anthocyanins differences, these results showed that the combination of prolonged maceration time with seed removal regardless of the duration time had an additional effect on the anthocyanin concentration decrease, mainly malvidin-3-\(O\)-glucose. This result pointed out a significant impact of seed tannins in the preservation of monomeric anthocyanins, as shown in the work by [33] where wines supplemented either with a commercial seed tannin solution or fermented seeds had higher tannin and monomeric anthocyanin concentrations than the untreated wine. Even though the concentration of anthocyanins was much higher in the year 2014, as can be seen from Table 1 the impact of maceration time and seed removal on the decrease of anthocyanins was noted in both experimental years. Extremely high temperatures in Dalmatia in 2015 with several heat waves (max temp. higher than 38 \({}^{\circ}\)C) [34] against relatively moderate (average) temperatures in 2014 resulted in significantly lower total anthocyanins in wines from all treatments (as well as other groups of phenolic compounds). This corresponds other research suggesting that this is a result of the inhibition and reduced enzymatic activity in grapes related to the synthesis of anthocyanins [35] as well as other phenolic compounds [36].