esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Responses Measured by Optical Fiber Sensor for Structural Health Monitoring
## 2 Optical Fiber Sensing System
A schematic diagram of the integrated optical fiber sensing system is shown in Figure 3. The optical system consists of a broadband light source, \(2\times 1\) coupler, FBG sensor, LPFG optical filter and a photodetector. When the light of a broadband spectrum is guided through the optical fiber to the FBG, a narrow band of Bragg wavelength \(\lambda_{B}\) is reflected back from the FBG sensor. In this work, LPFG is used as an optical filter to convert the wavelength shift of the FBG into a change of light intensity. The light intensity is transferred to a voltage signal through a photodetector, leading to the determination of the strain applied to the FBG sensor.
# A Multimodal Perception Framework for Users Emotional State Assessment in Social Robotics
## 3 Experiments
### Validation of the TMP  
The performance of the developed TMP has been evaluated by comparing data acquired at the chest with data acquired at the fingers. In particular, the TMP system was compared with Biopac instrument (MP35 device from Biopac Systems Inc., Goleta, CA, USA), considered as a gold standard for the acquisition of physiological signal. Seven users were asked to attach Biopac Ag/AgCl electrodes in the thorax area and then to put their fingers in the TMP sensor in order to acquire ECG signals from both systems at the same time, during static position. Figure 8 shows the collected signals after being time aligned and then elaborated using a dedicated software tool (WFDB) available from Physionet ([[https://physionet.org/physionetools/matlab/](https://physionet.org/physionetools/matlab/)]([https://physionet.org/physionetools/matlab/](https://physionet.org/physionetools/matlab/))). This tool could be used within Matlab software and allows to extract useful parameter from ECG signals [56; 57]. In particular, we used the feature called _wqrs_, that attempts to locate QRS complexes in an ECG signal, in order to evaluate if it is possible to extract robust HR parameter also from signals gathered from user fingers.  
Then, a Bland-Altman method was used to quantify the maximum error of the TMP on extracting the HR value from the IBI parameter. This method allows to statistically compare two signals, using a graphical representation.  
In this graphical method, the differences between the two techniques are plotted against the averages of the two techniques. In the plots, horizontal lines represent respectively the  
Figure 7: Flowchart of the subjects information flow managed by the “ID Tracking Module”. This schema reports how the subject ID is detected and maintained, even in case of camera occlusion, thanks to the synergy between the two different acquisition systems modules: “Scene Analyzer” and “Physio Signal Processing”.  
mean of the difference of the two systems and lower and upper _Limits of agreements_. If the 95% of the difference value remain within the _Limits of agreement_, the two systems of signals could be considered statistically equivalent. Considering the Bland-Altman plot reported in Figure 9, it is possible to underline that the TMP system is a valid instrument for physiological signals evaluation, with a maximum error in HR evaluation of \(\pm 5\) bpm.  
Figure 8: Synchronised ECG signals acquired from thorax region with Biopac instrument (red line) and from finger site with TMP system (blue line). TMP signal is processed with WFDB tool and R peaks detected are marked with green * symbols.  
Figure 9: Bland-Altman plots of the Heart Rate value extracted from the TMP signal compared with the one calculated from Biopac signal.
## 3 System Model
### General Assumptions  
We consider a network slice provider (network operator) with a total capacity (bit rate) of C bps, which is shared between two network slice service providers (communication service providers), denoted by \(K=2\). Each service provider has its own network slice service users (communication service customers). The resource reallocation between the service providers is managed by the network slice provider's controller, which sends signals to determine the need for resource reallocation. The time between signal arrivals follows an exponential distribution with parameter \(\delta\). The decision-making process is based on three principles: maximum matching for equal resource partitioning, maximum share of signals resulting in resource reallocation, and maximum resource utilization. Each principle is assigned a weight, denoted by \(w_{1}\), \(w_{2}\), and \(w_{3}\), respectively. The motivations behind these weights will be discussed in Section 3.5. The general scenario is illustrated in Figure 2.  
The network slice service provider offers its users a service for transmitting elastic traffic with a minimum-bit-rate guarantee of \(b\). This means that the maximum number of all providers' users jointly transmitting elastic traffic is equal to \(N=\lfloor\frac{C}{b}\rfloor\). The delay in transmission is acceptable, but the maximum number of users for each provider waiting for delayed elastic traffic transmission is limited to \(R_{k}\) (size of the provider's buffer). Requests for elastic traffic transmission from users arrive at a rate of \(\lambda_{k}\) according to a Poisson process. The volume of elastic traffic transmitted by each user is exponentially distributed with an average of \(\mu_{k}^{-1}\). Each user is also impatient and spends an exponentially distributed amount of time in their provider's buffer with a parameter of \(\epsilon_{k}\) (abandonment rate).  
The system employs the Egalitarian Process Sharing discipline to manage traffic fluctuations. This approach ensures equitable allocation of resources. Resources within  
Figure 2: Considered scenario.
# Production Optimization in a Grain Facility through Mixed-Integer Linear Programming +
## 6 Experimental Evaluation
### Numerical Results
#### 6.4.3 Comparison with the Ad Hoc Heuristic Results  
Table 7 reports the number of the number of operator shifts, number of cleanings needed, maximum storage used, and overall cost for the solutions computed by the proposed MILP model implemented in CPLEX, and the comparison with the ad hoc heuristic method considered to be a reference baseline. The \(\Delta\) metric reports the reductions computed by the proposed method in each relevant indicator (in absolute value) and \(\Delta\%\) indicates the percent value of improvement over the heuristic result,  
The overall cost (objective function) is expressed in monetary units for all minimized and duly weighted elements that define the considered objective function. This unit is in accordance with the criteria applied by the company and in order to maintain confidentiality, because the reported optimization corresponds to a real harvest.  
The results in Table 7 indicate that the proposed MILP formulation implemented in CPLEX was able to compute significant improvements over the solution computed by the ad hoc heuristic. The MILP method computed plans that reduced the number of operator shifts by more than 12%, the number of cleanings by more than 35%, and the maximum storage needed for production by almost 30%. The computed results demonstrate the accuracy of the computed plans. Indicators directly imply a better management of resources, which is crucial for improving competitiveness and minimizing operation costs. The plans computed by the proposed MILP model improved the overall values of production cost by 10.4% (approximately USD 20,000) over the heuristic planning and by more than 30% (USD 60,000) over the original manual planning.
# Short-Term Traffic Flow Forecasting via Multi-Regime Modeling and Ensemble Learning
## 3 Methodology
### Regime Identification Based on Probabilistic Modeling
#### 3.2.1 Regime Identification
For simplicity, we denote \(\lambda=(A,B,\pi)\). To get the optimal \(\lambda\) according to the given observation sequence \(O=O_{1},O_{2},\ldots,O_{T}\), we defined and solved the training problem by computing the maximum probability \(p=(O|\lambda)\) with an iterative procedure. A modified Baum-Welch algorithm was introduced to learn the best \(\lambda\). The details of the algorithm are skipped here. Interested readers can refer to the work [42] for more details. The determined \(\lambda\) can be further utilized to uncover the hidden state sequence \(Q\), based on the given observation sequence \(O\). To this end, we implemented the Viterbi algorithm [43] to solve the problem. The algorithm aims to optimize the following objective:  
\[\delta_{t}(i)=\max_{q_{1},q_{2},\ldots,q_{t-1}}p[q_{1},q_{2},\ldots,q_{t}=s_{i},O_{1},O_{2},\ldots,O_{t}\big{|}\lambda] \tag{4}\]  
where \(\delta_{t}(i)\) is the highest probability along a single path at time \(t\). To obtain the state sequence, we needed to iteratively solve the following optimization problem by considering each \(t\) and \(i\):  
\[\delta_{t+1}(j)=\left[\max_{i}\ \delta_{t}(i)a_{ij}\right]\cdot b_{j}(O_{t+1}). \tag{5}\]  
The complete computing procedure of the Viterbi algorithm can be seen in the literature [44]. In summary, through the above modeling process, the regimes of traffic flow can be properly identified. The next section is to build the forecasting model by using the regimes to construct the representative features and data sets for the model training.
# A Siphon Drainage Method for Consolidation of Soft Soil Foundation
## 3 Numerical Simulation of Feasibility of Siphon Drainage in Soft Soil Consolidation
### Results and Discussion
#### 3.2.2 Pore Water Pressure at the Depth of 10 m from the Corner of Square Column  
The changing trend of pore water pressure at the corner of the square column with 10 m depth is nearly the same under different drain spacing conditions, as shown in Figure 7. Under the siphon drainage, the pore pressure is always decreasing. When the siphon drainage begins, the pore water pressure drops rapidly at first. Then, as time increases, the pore water pressure decreases gradually.  
Figure 6: Vertical distribution of soil pore water pressure at corner of square column at siphon hole spacing \(d=1\) m.  
When the hole spacing is the same, the larger the permeability is, the faster the pore pressure drops rate is. When the soil permeability is the same, the smaller the drainage hole spacing is, the faster the pore pressure drop rate is. From the details, for the soils in which the permeability is \(10^{-7}\) cm/s, it needs 74 days for 2 m hole-spacing condition to reduce the pore water pressure at the corner of the square column at 10 m depth into 40 kPa. As a comparison, it only needs 12 days for 1m hole-spacing condition to achieve the same pore water pressure. Thus, the spacing of siphon holes can be determined by the permeability of soft soils and the requirements of the construction period to achieve a balance between time and economy.  
Figure 8 shows the variation of pore water pressure with time at a depth of 10 m in the soil under different hole spacing. As the drainage hole spacing decreases, the dissipation of pore water pressure in the soil is more rapid in the early stage of drainage, and reaches a steady state faster. In terms of drainage effect, reducing the hole spacing will also reduce the water level to a lower level within a specified time. A better processing effect has been achieved.
# Prediction of Machine Inactivation Status Using Statistical Feature Extraction and Machine Learning
## 3 The Proposed Method
### Model Training  
Once statistical features were extracted and data normalized, learning models for predicting non-active states of the machines were constructed by combining the raw data from the sensor device and extracted features. Considering that we deal with a dataset that contains active and non-active states, we can formulate the predicting non-active states of machines as a classification problem. Thus, we constructed predictive models using the following state-of-the-art machine learning methods suitable for classification: Decision tree, kNN, random forest, and linear SVM.  
A decision tree is one of the most popular methods used for classification. The decision tree predicts the class of a target feature by producing simple decision rules inferred from the data [32]. In the context of the proposed method, these decision rules enabled us to understand and interpret non-active states of machines easily. kNN classification was one of the most fundamental and simple classification methods. It classifies the class of a target feature by using the distance between a test sample and the specified training samples [33]. kNN is suitable for datasets with numeric values. Thus, it can be easily adapted for our dataset as it contains numeric values obtained through statistical analysis. [PERSON] [34] introduced the random forest method, which was a combination of tree predictors. In other words, a random forest algorithm creates a decision tree on data samples, gets the prediction from each of them, and finally selects the best solution. Despite its complexity, the random forest algorithm achieves high accuracy because it creates multiple trees and enables us to choose a tree with the highest accuracy. Considering that predicting the non-active status of machines with high accuracy can bring benefits to the efficient production of manufacturing machines, we can efficiently utilize the random forest algorithm to solve our problem. SVM was another classification method that was widely used for the analysis of a large amount of data. One of the main advantages of SVM was that it was effective in high dimensional spaces. In the case of our dataset, we obtained 17 input features, including raw data from the sensor device. Considering that the proposed dataset was relatively high dimensional, we can benefit from using SVM for classifying the non-active state of the machines. Table 5 demonstrate the advantages and disadvantage of classification algorithm used in this study.
## 1 Introduction
In addition, there is a lack of research conducted on sugarcane cultivation areas in Sudan, and most of it focused more on irrigation methods and did not link the green water footprint with remote sensing indicators and machine learning algorithms to predict the green water footprint of sugarcane, which enables sustainable water management of irrigation water and its preservation to increase the agricultural area and achieve food security in Sudan. Therefore, the primary objectives of this study are as follows: (i) assess the response of sugarcane's green water footprint (GWFP) to climate variations over the period from 2001 to 2020; (ii) utilize four remote sensing indices to monitor the current status of sugarcane throughout the same timeframe and their impact on GWFP; (iii) develop and compare three machine learning models (SVM, RF, and XGB) individually and in a hybrid form over sugarcane; (iv) identify the most effective model under optimal conditions, achieving high accuracy and minimal error in predicting the GWFP of sugarcane. This investigation can thus introduce an innovative modelling approach that will bolster endeavours to address GWFP forecasting, which helps in implementing strategies to mitigate issues like water usage regulations and enhancing food safety protocols.
# Changes in Defensive Variables Determining Success in the NBA over the Last 10 Years
## 3 Results  
A total of 660 data points were included in this study, with no data loss observed. The hypothesis test results indicated statistically significant findings regarding defensive variables about wins and losses in the NBA league. The impact of defensive rebounding on game outcomes regarding wins and losses was assessed, revealing a moderate effect favoring wins (ES = 1.39, [95% CI = 1.22-1.56], \(p\) < 0.001). Specifically, defensive rebounding increased by 12% in the case of a win. Similar results were observed for total rebounds, which had a moderate effect on wins, showing an increase of 8% (ES = 1.51, [95% CI = 1.34-1.69], \(p\) < 0.001). Conversely, there was a 11% increase in the number of steals in the event of a win, with this increase having a small effect on the win (ES = 0.67, [95% CI  
In addition to hypothesis testing, six machine learning algorithms were used to forecast the impact of defensive variables on game outcomes in the NBA league. The XGBoost algorithm emerged as the most effective model in predicting the defensive variables influencing game results, achieving an accuracy rate of 86%. Conversely, the logistic regression algorithm exhibited the lowest predictive accuracy at 81%. The F1 scores and accuracy rates were comparable, suggesting consistent results. Detailed information on the success rates of the algorithm models is presented in Table 4.  
The XGBoost algorithm estimated the most crucial defensive variable influencing game wins in the NBA league. The results obtained with the XGBoost algorithm were consistent with the hypothesis tests, revealing that defensive rebounding is the most significant defensive factor impacting game wins, with an accuracy rate of 86%. Detailed information about the defensive variables affecting the game win is presented in Figure 2.  
The defensive variable with the highest factor loading and the most significant impact on winning was DREB (\(\lambda\) = 0.50), while the variable with the least impact on was offensive rebounds (\(\lambda\) = 0.06). Additionally, the impact of other defensive variables on winning and their factor loadings are as follows: ST (\(\lambda\) = 0.15), TREB (\(\lambda\) = 0.10) and BLK (\(\lambda\) = 0.08).
## 3 Materials and Methods
### Proposed Method
#### 3.3.2 Sliding-Window Attention Mechanism
For low-resolution feature maps (e.g., \(H/32\times W/32\times C_{4}\)), a window size of \(w=3\) and stride \(s=2\) are employed to accommodate sparser feature distributions. For each layer, the channel dimension \(C\) is reduced to \(C/4\) through linear projection, further lowering computational overhead. The sliding-window attention mechanism is combined with the sliding-window computation network by alternately stacking attention computations and convolutional operations. Convolutions enhance translational invariance and extract local features, while sliding-window attention captures long-range dependencies within windows. This synergistic design maintains computational efficiency while capturing both local and global information. Such a structure is particularly advantageous for multimodal data processing, such as integrating temporal and spatial features. The sliding-window attention mechanism adapts to varying data distributions across modalities, achieving efficient feature integration through hierarchical processing. This design improves model performance and enhances robustness and generalization in complex scenarios.
## 3 Results
No significant difference was found between all the groups when the Ra, Rq, and Rv values were observed (\(p>0.05\)). However, following the Rt value, the 5% DMAHDM-20% NACP group was significantly associated with more surface discrepancies compared to the experimental control (\(p<0.05\)). Figure 6 illustrates the SEM images of the composite samples following the simulated wear. All the investigated groups showed surface topography changes. Figure 4: (**A**) The metabolic activities and (**C**) lactic acid production of the biofilms grown over the baseline and aged samples (mean \(\pm\) SD; n = 9). Different letters indicate significant differences between groups (\(p<0.05\)). The normalized data of the (**B**) metabolic activities and (**D**) lactic acid reduction in percentage. * Asterisks indicate significant differences compared to baseline values before aging. (EC = experimental control; 3D = 3% DMAHDM; 5D = 5% DMAHDM; 3 DN = 3% DMAHDM-20% NACP; 5 DN = 5% DMAHDM-20% NACP). Figure 5: The live/dead images for the biofilms formed over the composites’ surface (n = 3). (**A**) A schematic drawing showing the accumulation of the biofilm over the composite surface. Live bacteria were stained green, and compromised bacteria were stained red. The higher amount of viable microorganisms was associated with the experimental control group (**B**), followed by the 3% and 5% DMAHDM (**C**,**D**). The minimum load of viable bacteria was observed over the DMAHDM-NACP groups (**E**,**F**).
## 3 Results
### Comparison with Previous Studies  
Next, we compared the results of the proposed method with those obtained from previous studies.  
Table 7 displays a comparison of the kappa values obtained by converting the average accuracy of the model using transposed convolution and those of past studies performing the classification using BCI competition IV dataset 2a. The mean kappa value for the proposed method across subjects was 0.62, which is the highest classification accuracy compared with that of the other studies. However, a statistically significant difference between the proposed and previous methods was only found for the method in [23] and not for the method in [7] and that in [6].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{4}{c}{**Evaluation Accuracy**} \\ \cline{2-5}  & **Transposed Convolution** & **1D CNN** & **2D by STFT** & **Time and ch** \\ \cline{2-5}  & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** & **Mean \(\pm\) SD** \\ \hline
1 & 0.8654 \(\pm\) 0.0216 & 0.7571 \(\pm\) 0.0208 & 0.6039 \(\pm\) 0.0278 & 0.6041 \(\pm\) 0.0356 \\
2 & 0.4757 \(\pm\) 0.0309 & 0.3979 \(\pm\) 0.0263 & 0.3419 \(\pm\) 0.0185 & 0.3194 \(\pm\) 0.0222 \\
3 & 0.9061 \(\pm\) 0.0116 & 0.8182 \(\pm\) 0.0204 & 0.6474 \(\pm\) 0.0354 & 0.5109 \(\pm\) 0.0364 \\
4 & 0.6715 \(\pm\) 0.0446 & 0.4916 \(\pm\) 0.0308 & 0.4174 \(\pm\) 0.0234 & 0.3382 \(\pm\) 0.0272 \\
5 & 0.5468 \(\pm\) 0.0392 & 0.4956 \(\pm\) 0.0288 & 0.2789 \(\pm\) 0.0239 & 0.2598 \(\pm\) 0.0251 \\
6 & 0.4987 \(\pm\) 0.0444 & 0.4569 \(\pm\) 0.0326 & 0.3451 \(\pm\) 0.0295 & 0.3149 \(\pm\) 0.0266 \\
7 & 0.8769 \(\pm\) 0.0227 & 0.7351 \(\pm\) 0.0299 & 0.6519 \(\pm\) 0.0304 & 0.4539 \(\pm\) 0.0399 \\
8 & 0.8202 \(\pm\) 0.0149 & 0.7443 \(\pm\) 0.0221 & 0.5998 \(\pm\) 0.0334 & 0.5049 \(\pm\) 0.0470 \\
9 & 0.7886 \(\pm\) 0.0227 & 0.7395 \(\pm\) 0.0235 & 0.6362 \(\pm\) 0.0279 & 0.5835 \(\pm\) 0.0458 \\ \hline Average & 0.7167 & 0.6262 & 0.5025 & 0.4322 \\ \hline \(p\)-value & - & \(<\)0.05 & \(<\)0.05 & \(<\)0.05 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Unseen evaluation data accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{4}{c}{**Evaluation Kappa Value**} \\ \cline{2-5}  & **Transposed Convolution** & **Method-[7]** & **Method-[23]** & **Method-[6] PW** \\ \hline
1 & 0.821 & 0.86 & 0.75 & 0.782 \\
2 & 0.301 & 0.24 & 0.37 & 0.407 \\
3 & 0.875 & 0.70 & 0.66 & 0.755 \\
4 & 0.562 & 0.68 & 0.53 & 0.528 \\
5 & 0.396 & 0.36 & 0.29 & 0.417 \\