esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Flexibility Digital Representation
### Data Modelling and Data Analysis  
The digital twin flexibility evaluation (DTFE) is mainly responsible for processing, modelling, and analysis and decision making of the collected data. The collected data comes from the constructed physical system, and the data information to be collected is positioned to the requirements of the system characteristic parameters. The model for DTFE consists of the digital twin static function model for system static characteristic parameters and the digital twin dynamic prediction model for system dynamic characteristic parameters. The static function model for system flexibility needs to define a series of parameters:  
\(vv_{il}\): judgment value of whether product variety \(v_{i}\) and product variety \(v_{l}\) are the same family. If \(v_{i}\) is of the same family as \(v_{l}\) or \(i=l\), then take \(vv_{il}\) as 1, and 0 otherwise;  
\(vp_{ij}\): judgment value of whether process type \(p_{j}\) is required for manufacturing product \(v_{i}\). It assumes value 1 if process \(p_{j}\) is required for the production of product \(v_{i}\), and 0 otherwise;  
\(pm_{jk}\): judgment value of whether the process type \(p_{j}\) is processed by the machine type \(m_{k}\). It assumes value 1 if the process type \(p_{j}\) is processed by the machine type \(m_{k}\), and 0 otherwise;  
\(pp_{il}\): the quantity of the same process type that product type \(v_{i}\) and product type \(v_{l}\) have, and \(pp_{il}\ \leq\ pp_{ii}\).  
\(pt_{sj}\): the judgment value of whether the sequence between process type \(p_{s}\) and process type \(p_{j}\) is changeable. When \(s\) does not equal to \(j\), and the sequence of process \(p_{s}\) and \(p_{j}\) can be changed, then taken \(pt_{sj}\) as 1, and 0 otherwise. When \(s\) is equal to \(j\), the sequence changeability between two identical processes represented by \(pt_{s,j}\) is meaningless in the actual production process, so take \(pt_{s,j}\) as 0;  
\(mm_{ko}\): judgment value of whether the machine type \(m_{k}\) and the machine type \(m_{o}\) are pluggable. When \(k\) is not equal to \(o\), it assumes value 1 if the machine type \(m_{o}\) is arranged in parallel with the machine type \(m_{k}\), take \(mm_{ko}\) to be 1 and 0 otherwise;  
\(vc_{i}\): output of \(v_{i}\) produced by the production system per hour, and the \(VQ\) matrix is a diagonal matrix;  
\(tr_{il}\): adjustment time required by the production system to make production transformation between products \(v_{i}\) and product \(v_{l}\);  
\(Si_{v_{i},v_{h}}\): similarity value between product variety \(v_{i}\) and product variety \(v_{h}\), the greater the value, the higher the similarity. if \(i=l\), then \(Si_{v_{i},v_{h}}=1\), and if \(i\
eq l\), then \(Si_{v_{i},v_{h}}\in[0,1]\).
# Explainable AI in Education: Techniques and Qualitative Assessment
## 3 Experimental Setup
### OULA Dataset  
In the fields of learning analytics and educational data mining, the OULAD [22] is a vast and well-known dataset. An array of student data from several kinds of modules, including demographics, assessments, and experiences (the data are organized into many CSV files such as studentInfo.csv, assessments.csv, studentRegistration, etc.) in the virtual learning environment, is available in this dataset, which was released by the Open University, the leading distance learning institution in the United Kingdom. The OULA dataset provides researchers with access to data from more than 32,000 students, and researchers can review a variety of areas of study, such as student performance (e.g., [15; 23; 24; 25; 26; 27]), dropout and retention prediction (e.g., [13; 28; 29; 30; 31]), and personalized learning paths (e.g., [32]). Recent research has assessed ML models deployed in educational contexts based on fairness, as well as performance, metrics. Several studies evaluate these models' fairness quantitatively to make sure that the predicted results do not unfairly favor or harm particular student groups based on factors like gender, socioeconomic position, or race. A few examples of fairness measures that have been studied to identify biases in educational data models include equalized odds and demographic parity. The studies that have used the OULAD and other notable datasets to assess student performance, retention, and dropouts were thoroughly compared using both performance and fairness metrics, as shown in Table 1. By examining a variety of contributing variables and parameters, a rising amount of studies in the recent literature have used ML approaches to simulate student performance. These studies demonstrate how well ML works when analyzing educational data, providing insightful information about predicting student success and assessing academic performance. As has been previously said, while these studies assessed interpretability using linear or non-linear ML models, they did not address the use of potentially widely accepted metrics for evaluating explainability. Therefore, we used the same dataset to predict student performance in a task involving binary classification. Following that, we went over the analysis process, which included assessing the performance and explainability of two ML models, including ANN and DTs. We used local interpretations, such as LIME and SHAP, to provide a deeper understanding of the model's decision-making process. We also assessed the explainability using two other measures to provide an understandable evaluation of the model's accuracy and transparency.
## 2 Materials and Methods  
### Patients and Samples  
All patients who underwent primary mpMRI/US-targeted prostate biopsy for suspected localized PCa at the National Cancer Institute (Lithuania) between November 2019 and February 2022 were included in the study. The Regional Bioethics Committee approved the study (No. 2019/11-166-654 and 2020-LP-68), and informed consent was obtained from all participants.  
All 1.5T and 3T mpMRI scans have been performed in the same institution according to the standardized protocol, while external images have been re-evaluated pre-operatively. The PI-RADSv2.1 scoring system was used to report mpMRI results. Contouring of the prostate margins and target lesions was performed by two experienced radiologists using the transverse T2 TSE images. The mpMRI/US biopsies have been performed with the \"Biojet\" fusion system (D&K Technologies, Barum, Germany) using the transperineal approach by a single interventional radiologist. Furthermore, we used this technique that avoids rectal wall puncturing, which leads to a near-zero risk of infection [14]. For all patients, a prostate biopsy was performed under general anesthesia. Only 1 g of Cefazolin was used during the procedure for the prevention of UTI, based on the findings of a literature meta-analysis [15].  
For all patients, lesion-targeted and systematic biopsies have been performed. Lesion-targeted biopsy 2-4 cores have been taken from each PI-RADS 3-5 lesion, followed by a systemic 12-core biopsy ignoring index lesions. Thus, the median number of cores taken from the lesions by the targeted prostate biopsy was 4 (DS = 1.68), while 12 cores in median (SD = 4.8) were taken during systematic sampling. In general, the median number of biopsy cores performed at one procedure was 16 (SD = 4.5). Slight biopsy variations were allowed in large-size prostates and/or large-size target lesions. All biopsy samples have been evaluated by urological pathologists from the National Center of Pathology (Lithuania) and reported according to the EAU recommendations [16]. Clinically significant PCa was defined as ISUP grade group \(\geq\) 2 detected at any biopsy core.
# Using the Guided Fireworks Algorithm for Local Backlight Dimming
## 2 Optimization Model of the Local Dimming Problem  
### The Previous Model of the Local Dimming Problem  
Image quality evaluation is an important step in the local dimming process. To evaluate the displayed image quality, the first step is to use a simulation method to calculate the gray levels of the displayed image. The gray level \(\hat{y}_{i}\) at pixel \(i\) of the displayed image can be calculated by [8; 21]:  
\[\hat{y}_{i}=t_{i}\times l_{i} \tag{1}\]  
where \(l_{i}\) is the backlight luminance level behind pixel \(i\), and \(t_{i}\) is the transmittance of the LC panel at pixel \(i\). The backlight luminance level \(l_{i}\) at the \(i\)th pixel is determined by the backlight blocks behind and around pixel \(i\). It can be calculated by [11; 22]:  
\[l_{i}=\sum\limits_{j=1}^{K}h_{i,j}\times r_{j} \tag{2}\]  
where \(K\) is the number of backlight blocks, \(h_{i,j}\) is the attenuation coefficient of the luminance from the \(j\)th backlight block to pixel \(i\), and \(r_{j}\) is the luminance level of the \(j\)th backlight block.  
In our previous work [12], the objective was to improve the image quality, with the constraint of maintaining the power consumption below a certain value. We used PSNR to evaluate the image quality; the higher the PSNR, the better the image quality. Therefore, the local dimming problem model proposed in [12] is:  
\[\left\{\begin{array}{c}\max:\text{PSNR}\\ \text{s.t.}PC<PC_{limit}\end{array}\right. \tag{3}\]  
where \(PC\) indicates the power consumption, and \(PC_{limit}\) is the constraint value of \(PC\). \(PC\) can be calculated by [23]:  
\[PC=\frac{\frac{1}{K}\sum\limits_{i=1}^{K}r_{i}}{r_{full}}\times 100\% \tag{4}\]  
where \(K\) is the number of backlight blocks and \(r_{i}\) is the luminance level of the \(i\)th backlight block. \(r_{full}\) is the maximum luminance level that the block can maintain, usually \(r_{full}=255\).
## 2 Use of Reliability for Optimum Inspection Planning
### Computational Procedure for Reliability-Based Optimum Inspection Planning  
Inspection planning for deteriorating structures can be optimized based on the assessment and prediction of reliability and probability of failure [2,6]. The associated computational procedure is illustrated in Figure 2. This procedure consists of three main phases: (a) prediction of damage propagation and service life considering inspection and maintenance interventions, (b) formulation of the objectives for reliability-based inspection planning, and (c) formulation and computation for single-and multiobjective optimization. These main phases require the establishment of prediction models associated with damage propagation and loading conditions, as well as estimation of the effects of inspections and maintenance on damage propagation and structural reliability. Optimization can be based on several objectives, such as maximization of the reliability index (or reliability) for a predefined period, minimization of the probability of failure for a predefined period, minimization of the total life-cycle cost, minimization of the damage detection-based probability of failure, and maximization of the service life-based safety margin. These objectives are formulated by considering the effects of inspection and maintenance on the service-life extensions and improvement of structural reliability. To select and apply the best solution for multiobjective optimization (i.e., the best optimum inspection and maintenance planning), decision making should be performed.  
Figure 2: Computational procedure for reliability-based optimum inspection planning for deteriorating bridges.
## 2 Materials and Methods
### Cell Morphology and Adhesion  
Cell adhesion was analyzed by imaging the morphology of adhered cells by staining the nuclei of hG-MSCs and actin cytoskeleton structures. Firstly, hG-MSCs were cultured on different materials with an initial seeding density of 5 \(\times\) 10\({}^{3}\)/well in 200 \(\upmu\)L of DMEM. After 3, 7, and 14 days, cells were fixed on the GPP, VSCM, and TCP surface by incubating with 4% paraformaldehyde (ThermoFisher Scientific, Waltham, MA, USA) for 15 min and afterwards permeabilized by treating with 0.1% Triton-X100 for 5 min at room temperature. After blocking by bathing in 1% bovine serum albumin for 30 min and triple washing with PBS, rhodamine-conjugated phalloidin (1:200, ThermoFisher Scientific, Waltham, MA, USA) was added. After incubation for 45 min and three washing steps, nucleus counterstain was performed with 4', 6-diamidino-2-phenylindole (DAPI, 1:1000 in PBS, Sigma-Aldrich, St. Louis, MO, USA) and incubated for 5 min at room temperature. The cell morphology and adhesion were observed under a fluorescence microscope using the ECHO Revolve fluorescence microscope (Echo, San Diego, CA, USA).
## 2 Design of the 3.7 kW WCS
### Design of the Input and Output Electrical Modules
#### 2.1.2 Design of PFC  
The main function of PFC is to correct the power factor and improve the system's efficiency. The Boost PFC is chosen to increase AC from 220 V to 425 V. Parameters of PFC are mainly including the boost inductor and PFC capacitor. The boost PFC utilizes the boost inductor to suppress current transient changes, so the boost inductor must be large enough to limit the output current ripple to a determined range, which is 15%. The peak of current flowing through the inductor can be obtained when reaching the maximum power and minimum voltage. The boost inductor \(L_{PFC}\) can be calculated as follows:  
\[L_{\text{PFC}}=\frac{\eta U_{\text{min}}^{2}}{0.15f_{s}P_{0}}\left(1-\frac{ \sqrt{2}P_{0}}{\eta U_{\text{PFC}}}\right), \tag{2}\]  
where \(U_{PFC}\) is the output voltage of PFC module, \(f_{s}\) is the switching frequency of mosfet, \(P_{max}\) is the maximum power and \(U_{min}\) is the minimum voltage after rectification.  
The PFC capacitor \(C_{PFC}\) is used to reduce the ripple of output voltage, and it can be calculated by the following formula:  
\[C_{\text{PFC}}=\frac{P_{0}}{2\pi\cdot 2f_{in}\Delta U_{\text{PFC}}U_{\text{PFC}}}, \tag{3}\]  
where \(f_{in}\) is the frequency of the public grid, and \(\Delta U_{PFC}\) is the ripple of designed output voltage.
## 4 Simulations and Analysis
### Comparison of Evaluation Measures  
In order to further evaluate the performance and robustness of the proposed method under different levels of noise, Figure 4 gives the evaluation results of the SINR, PESQ, STOI and SDI versus the input SNR (iSNR). In Figure 4, \"Noisy\" indicates the observed signal of the reference microphone.  
In Figure 4a-c, as the iSNR increased, the outputs of the SINR, PESQ and STOI of the IMB, HKB, SMF, BPR, GLC, IDL-SIM, NRP-TMMSE, PFLI, IDL-SIM and proposed method increased as well. However, the SINR, PESQ and STOI of the CMB and LNR  
Figure 4: The results of the evaluation measures under different iSNRs: (**a**) SINR; (**b**) PESQ; (**c**) STOI; (**d**) SDI.  
Figure 3: A comparison of beampatterns. The number of snapshots was 100 and the SNR was 10 dB.  
showed an increase and decrease procedure. The reason for this is that the problem of the self-cancellation of the main lobe was aggravated with the increase in the iSNR. Moreover, the proposed method obtained better results for the SINR, PESQ and STOI, except for the IMB. This indicates that the proposed method effectively improved the performance of the CMB under different iSNRs. In addition, Figure 4d shows the results of the SDIs. We can find that as the iSNR increased, the SDIs of the CMB, HKB, LNR and BPR showed decreases. The reason for this is that the problem of the self-cancellation of the main lobe is aggravated with the increase in the iSNR. Unlike the SDIs of the rest of the methods, which were more stable, the IMB used the real DOA of the target source, the SMF, GLC, PFLI and NRP-TMMSE were degenerated into the fixed beamformer, and the IDL-SIM and the proposed method corrected the steering vector of the target source backward. In addition, the proposed method obtained a lower SDI except for the IMB. This indicates that the proposed method has a stronger robustness under different levels of noise.
# A Cox Model-Based Workflow for Increased Accuracy in Activity-Travel Patterns Generation
## 4 Results
### Cox and Cox-Based Models Comparison
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trip Purpose** & **Model Type** & **C-index** & **MAE** & **RMSE** \\ \hline \multirow{2}{*}{Work} & 80/20 Split & 0.7005 & 2.2353 & 3.1947 \\  & 5-Fold CV & 0.6814 & 2.2546 & 3.2137 \\ \hline \multirow{2}{*}{Market} & 80/20 Split & 0.15 & 1.4444 & 1.7321 \\  & 5-Fold CV & 0.404 & 2.3028 & 2.9354 \\ \hline \multirow{2}{*}{Education} & 80/20 Split & 0.4848 & 3.2857 & 4.0883 \\  & 5-Fold CV & 0.6707 & 2.2667 & 2.9981 \\ \hline \multirow{2}{*}{Other} & 80/20 Split & 0.6842 & 1.8571 & 2.2361 \\  & 5-Fold CV & 0.651 & 2.4238 & 3.1286 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Cox-based machine learning model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Trip Purpose** & **Model Type** & **Criterion** & **Value** \\ \hline \multirow{2}{*}{Work} & [PERSON] without frailty & AIC & 3162.45 \\  & [PERSON] with frailty & WAIC & 1590.6 \\ \hline \multirow{2}{*}{Other} & [PERSON] without frailty & AIC & 171.83 \\  & [PERSON] with frailty & WAIC & 166.17 \\ \hline \multirow{2}{*}{Market} & [PERSON] without frailty & AIC & 285.44 \\  & [PERSON] with frailty & WAIC & 103.1 \\ \hline \multirow{2}{*}{Education} & [PERSON] without frailty & AIC & 257.65 \\  & [PERSON] with frailty & WAIC & 229.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Cox models comparison. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Trip Purpose** & **Model Type** & **C-index** & **MAE** & **RMSE** \\ \hline \multirow{2}{*}{Work} & 80/20 Split & 0.4319 & 4.5439 & 5.2035 \\  & 5-Fold CV & 0.4753 & 4.6526 & 5.2832 \\ \hline \multirow{2}{*}{Market} & 80/20 Split & 0.5 & 3.4341 & 3.7552 \\  & 5-Fold CV & 0.5164 & 3.2440 & 3.5082 \\ \hline \multirow{2}{*}{Education} & 80/20 Split & 0.7879 & 4.1424 & 4.8065 \\  & 5-Fold CV & 0.507 & 3.4398 & 4.0277 \\ \hline \multirow{2}{*}{Other} & 80/20 Split & 0.2632 & 6.2377 & 6.7870 \\  & 5-Fold CV & 0.4907 & 5.5641 & 6.3158 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Cox model with frailty evaluation results. The tests performed under Section 4.1 are just preliminary and serve to benchmark the relative performance of the Cox-based models in isolation. While they indicate the statistical superiority of the machine learning model, they do not yet reflect the models' real-world impact on synthetic data generation. Therefore, Section 4.2 advances this analysis by directly comparing the synthetic data outputs generated using each model against actual observed data. This deeper evaluation helps determine whether improvements in model structure translate into more realistic and reliable synthetic activity-travel patterns.
## 6 Results and Discussion  
### Random Forest Model Analysis  
Random forest is an ensemble modeling algorithm which uses a weak learner to give the best performance as depicted in Figure 2. These algorithms are supervised learners giving adamant accuracy in terms of correlation. The model is divided into twenty submodels to give maximum determination of coefficient as illustrated in Figure 2a. It can be seen that sub-model equal to 10 outbursts and gives a strong relationship. It is due to incorporation of a weak learner (decision tree), which then uses it in the ensembling algorithm. Moreover, the model gives an obstinate correlation of R\({}^{2}\) = 0.96 between experimental and predicted values and gives good validation results as illustrated in Figure 2b,c. In addition, the model performance shows less error as illustrated in Figure 2d. All the predicted data points lie in the same range of experimental values with an error less than 10 MPa. This shows that the random forest ensemble algorithm gives adamant good results.  
Figure 2: Model evaluation (**a**) Ensemble model with 20 submodels; (**b**) validation based on RF; (**c**) testing based on RF; (**d**) error distribution of the testing set.  
Statistical analysis checks are applied to check the model performance using random forest. This is an indirect method which shows model performance. These statistical analyses check the errors in the model; thus, RMSE, MAE, RSE, and RRMSE are used as shown in Table 7. The RF model is ensemble one and thus shows lesser error in the prediction aspect.
## 2 Materials and Methods
### Dynamic Shear Rheology (DSR)  
Temperature sweep (time cure) rheological tests were performed to analyze the mechanical response of modified bitumens vs. CTRL upon artificial aging process. Experiments were carried out using a controlled shear stress rheometer (SR5, Rheometric Scientific, Piscataway, NJ, USA) equipped with a parallel plate geometry (gap 2.0 \(\pm\) 0.1 mm, diameter 25 mm) and a Peltier system (\(\pm\)0.1 \({}^{\circ}\)C) for temperature control. Bitumen exhibits aspects of both elastic and viscous behaviors and is thus classified as a visco-elastic material [31; 32]. DSR is a common technique used to study the rheology of asphalt binders at high and intermediate temperatures [33; 34]. Operatively, a bitumen sample was sandwiched between two parallel plates, one standing and one oscillatory. The oscillating plate was rotated accordingly with the sample and the resulting shear stress was measured. The linear viscoelastic regime of both the reference free of additives (CTRL), and modified bitumens was checked by preliminary stress sweep tests. The temperature sweep tests were performed within the range 25-80 \({}^{\circ}\)C with ramp 1 \({}^{\circ}\)C/min in heating by applying the proper stress values to guarantee linear viscoelastic conditions at all tested temperatures. During the tests a periodic sinusoidal displacement at  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Asphaltene (\% _w/w_)**} \\ \cline{2-3}  & **Unaged** & **Aged** \\ \hline CTRL & 26.8 & 34.2 \\ Bitumen + LCS & 29.2 & 32.8 \\ Bitumen + VC & 28.2 & 35.3 \\ Bitumen + RH & 29.5 & 35.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The weight percentages of asphaltene fraction obtained from crude bitumen (CTRL) and bitumen modified with LCS, VC and RH additives, both unaged and subjected to artificial aging.  
constant frequency of 1 Hz was applied to the sample and the resulting sinusoidal force was measured in terms of amplitude and phase angle as the loss tangent (tan \(\delta\)). RSI Orchestrator(r) software was used to determine the complex modulus (\(G^{\bullet}\)), storage (\(G^{\prime}\)) and loss (\(G^{\prime\prime}\)) moduli, phase angle (\(\delta\)) or tan \(\delta=G^{\prime\prime}/G^{\prime}\). More details about the mechanical characterization can be found elsewhere [35].