esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Explainable AI (XAI) for the Prediction of Falls in the Older Population
## 2 Materials and Methods
### Random Forests and Feature Relevance  
Various applications of random forests in medicine have been described [19; 20]. In the present study, random forests were used as a classifier for the above-mentioned 46 features to predict simple falls, complex falls, or syncope among participants over a 6-year period. _RandomForestClassifier_ in the Scikit-learn Python package was used for the construction of the random forest models, and _GridSearchCV_ was implemented to tune the hyperparameters of the models. Shapley additive explanations (SHAP) and random forest feature importance were then used to explain the prediction models that were created.  
Feature importance can also be derived from the random forest models by calculating the mean decrease in impurity (MDI) [21]. The most important features are utilised earlier as splitting attributes at the top of the tree, and these continue downwards according to the order of their importance. The decision tree identifies the feature importance based on its Gini coefficient, which measures the probability of a particular variable being wrongly classified when it is randomly chosen. The lower the Gini coefficient of a feature is, the more important the decision tree deems the feature to be in its classification [22]. In the context of random forests, the final feature importance is derived from the average of the impurity decrease across all of the trees [21].  
With its origins from game theory, SHAP is a technique that can be applied to predictive models to enhance their explainability [23]. This is because with SHAP, the individual contribution towards each prediction can be observed in comparison to other techniques that only present the aggregate contribution. For each input feature, SHAP measures its contribution to the model. SHAP values also indicate if the feature contributes positively or negatively towards the output, which in this case is the prediction of a future fall.
## 3 Results  
### Preliminary Results  
The correlation coefficients of the independent variables are shown in Figure S2 (see Supplementary Materials). Four variables (VV, NDWI_Gao, NDWI_McFeeters, CHM_mean) were removed from the database before modeling. Table 2 presents 1-h fuel load statistics for the field plots divided by land-cover classes.  
The 1-h fuel load ranged from a low of 0 to 4.10 t/ha, with an average value of 0.63 t/ha. Deciduous and mixed forest vegetation recorded the highest mean values of fine dead fuel load (1.13 and 0.92 t/ha, respectively), while the lowest values were found in meadows and pastures with (0.29 t/ha) or without (0.14 t/ha) the presence of trees. High standard error (SE) and standard deviation (SD) values within the mixed forest plots indicated a high variability in fine dead fuel load values, probably related to the number of plots in this land-cover category. From a total of 251 plots, 7 plots were removed as outliers.  
\begin{table}
\begin{tabular}{c c c c c c}  & **Plots (N)** & **Mean (t/ha)** & **SD** & **SE** & **Range** \\ \hline
**Coniferous Forests** & 39 & 0.80 & 0.45 & 0.07 & 0.12–1.92 \\
**Deciduous Forests** & 52 & 1.13 & 0.66 & 0.09 & 0.05–4.10 \\
**Mixed Forests** & 10 & 0.92 & 0.66 & 0.21 & 0–2.09 \\
**Bushes or Shrubs** & 30 & 0.38 & 0.43 & 0.08 & 0–1.97 \\
**Sclerophyllus Vegetation** & 41 & 0.48 & 0.45 & 0.07 & 0–2.34 \\
**Meadows or Pastures (with Trees)** & 20 & 0.29 & 0.26 & 0.06 & 0.03–1.10 \\
**Meadows or Pastures (without Trees)** & 25 & 0.14 & 0.19 & 0.04 & 0–0.86 \\
**Natural Recolonization Areas** & 16 & 0.43 & 0.42 & 0.10 & 0.01–1.83 \\
**Reforestations** & 11 & 0.51 & 0.38 & 0.11 & 0.02–1.15 \\ \hline \end{tabular} N = number of plots; SD = standard deviation; SE = standard error.  
\end{table}
Table 2: 1-h fuel load statistics for land-cover classes.  
A total of 171 were randomly selected as a training set while the remaining 73 plots were used as a test set.
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 5 Experimental Evaluation
### State-of-the-Art Comparison for UP-Fall Dataset
(2017) utilized a basic fusion of visual and inertial features, our model integrates these modalities through a dynamic fusion mechanism, which optimally combines visual and inertial signal data. This method ensures a more robust representation of actions and helps the model capture complex temporal and spatial dependencies more effectively than traditional approaches. Additionally, we use advanced noise reduction and augmentation techniques that improve feature extraction, resulting in better performance even in noisy environments.
## 3 Results
### Quinolone Resistance Genes, Integrons, and Plasmid Incompatibility  
The \(qnrS1\) gene was found in 44.44% (12/27) of the strains. The \(aac(6^{\prime})lb\text{-}cr\) variant was observed in 22.22% (6/27) of the strains. The genes \(int1\) and \(int2\) were reported, respectively, in 25.9% (7/27) and 7.4% (2/27) for our isolates (Table 3). A total of 48.14% (13/27) of the strains showed a plasmid replicon type. Only \(IncF\) (11.1%; 3/27) and \(IncFIA\) (18.5%; 5/27) were identified among our collection; moreover, both co-occurred in 18.5% of isolates (5/27) (Table 3). The transfer of the \(bla_{CTX\text{-}M\text{-}15}/bla_{TEM\text{-}1}/qnrS1/aac(6^{\prime})lb\text{-}cr\) and \(int1\) genes has been frequently observed (Table 4). Similarly, the \(IncF\) and \(IncFIA\) plasmids were also successfully transmitted through conjugation (Table 5).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Strains** & \(bla\) **and PMQR\({}^{\ast}\) Genes Identified** & **Virulence Profile** & **Virulence Score** \\ \hline EC 1 & \(bla_{CTX\text{-}M\text{-}15}\), \(qnrS1\) & \(fmH\)-\(p\)\(p\)\(G\)\(l\)-\(hu\)\(i\)\(t\)\(A\)-\(tra\)\(T\)-\(m\)\(l\)\(X\)-\(usp\)\(omp\)\(T\) & 8 \\ EC 2 & \(bla_{CMV\text{-}42}\), \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{SHV\text{-}1}\) & \(fmH\)-\(p\)\(p\)\(G\)\(l\)-\(i\)\(t\)\(A\)-\(malX\) & 4 \\ EC 3 & \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{CTX\text{-}M\text{-}27}\), \(bla_{TEM}\) & \(\text{app}G\)\(l\)-\(omp\)\(T\) & 2 \\ EC 4 & \(bla_{CTX\text{-}M\text{-}27}\) & \(fmH\)-\(p\)\(G\)\(l\)-\(hu\)\(n\)\(l\)X-\(usp\)\(omp\)\(T\) & 6 \\ EC 5 & \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{TEM\text{-}1}\) & \(m\)\(l\)X-\(omp\)\(T\) & 2 \\ EC 6 & \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{TEM\text{-}1}\) & \(p\)\(apG\)\(G\)\(l\)-\(fu\)\(a\)\(t\)\(A\)-\(l\)\(x\)-\(omp\)\(T\) & 4 \\ EC 7 & \(bla_{CTX\text{-}M\text{-}15}\), \(qnrS1\) & \(fmH\)-\(p\)\(G\)\(G\)\(l\)-\(hu\)\(i\)\(t\)\(A\)-\(l\)\(x\)-\(usp\)\(omp\)\(T\) & 8 \\ EC 8 & \(bla_{CMV\text{-}42}\), \(bla_{TEXT\text{-}1}\), \(bla_{SHV\text{-}1}\), \(qnrS1\) & \(imH\)-\(p\)\(G\)\(l\)-\(ru\)\(G\)\(l\)\(l\)-\(cr\) & 2 \\ EC 9 & \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{TEM\text{-}1}\) & \(p\)\(apG\)\(G\)\(l\)-\(ma\)\(l\)X & 2 \\ EC 11 & \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{SHV\text{}}\), \(qnrS1\) & \(p\)\(apG\)\(G\)\(l\)-\(ha\)\(t\)\(T\)-\(malX\)-\(usp\)\(omp\)\(T\) & 6 \\ EC 12 & \(bla_{CTX\text{-}M\text{-}15}\), \(qnrS1\), \(aac(6^{\prime})lb\text{-}cr\) & \(p\)\(apG\)\(G\)\(l\)-\(hu\)\(n\)\(l\)X-\(usp\)\(omp\)\(T\) & 5 \\ EC 14 & \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{TEM\text{-}1}\) & \(p\)\(apG\)\(G\)\(l\)-\(ma\)\(l\)X-\(usp\)\(omp\)\(T\) & 4 \\ EC 15 & \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{TEM\text{-}1}\), \(bla_{SHV\text{-}1}\) & \(p\)\(p\)\(G\)\(l\)-\(hu\)\(i\)\(t\)\(A\)-\(l\)\(x\)-\(usp\)\(omp\)\(T\) & 6 \\ EC 16 & \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{TEM\text{-}1}\) & \(\mathit{limH}\)-\(p\)\(G\)\(l\)-\(hu\)\(i\)\(t\)\(A\)-\(m\)\(l\)\(l\)\(x\)-\(usp\)\(omp\)\(T\) & 5 \\ EC 17 & \(bla_{CTX\text{-}M\text{-}15}\), \(bla_{TEM\text{-}1}\) & \(\mathit{limH}\)-\(p\)\(G\)\(l\)-\(hu\)\(i\)\(t\)\(A\)-\(l\)\(x\)\(usp\)\(omp\)\(T\) & 5 \\ EC
## 1 Introduction
Moreover, many studies report that only Sn location is the best candidate for substituting in the Cu\({}_{2}\)Sn(Ge)Se(S)\({}_{3}\) structure, which limits the improvement of TE performance [3; 8; 10]. Based on those limits mentioned above, the composite was designed on the Cu-based ternary selenides matrix. Some studies confirmed that introducing a nanophase into the Cu-based semiconductor can improve the \(ZT\). [PERSON] et al. introduced graphite nanosheets into CuGaTe\({}_{2}\) and obtained the highest \(ZT\) of 0.93 for the CuGaTe\({}_{2}\) composite at 873 K, which is 21% higher than that of CuGaTe\({}_{2}\) matrix [12]. [PERSON] et al. reported that the incorporation of a suitable quantity of nanophase PbTe particles into SnSe matrix could efficiently enhance the TE performance of SnSe material [13]. TiO\({}_{2}\) has a band gap of 3.0 eV and has good thermal and chemical stability as well as large elastic modulus, which makes it a good candidate as a second phase. TiO\({}_{2}\) has been introduced into a filled-skutterudite matrix in previous studies [14; 15; 16]. However, introducing nano-TiO\({}_{2}\) into Cu\({}_{2}\)SnSe\({}_{3}\) has never been reported. In this study, the technological parameter was optimized to synthesize pure Cu\({}_{2}\)SnSe\({}_{3}\) material. The nano-TiO\({}_{2}\) was incorporated into the Cu\({}_{2}\)SnSe\({}_{3}\) matrix by ball milling method, and the TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) thermoelectric composites were prepared by spark plasma sintering (SPS). The thermoelectric properties of TiO\({}_{2}\)/Cu\({}_{2}\)SnSe\({}_{3}\) composites were investigated in detail.
# Optimizing Flight Delay Predictions with Scorecard Systems
## 5 Results
### Variable Descriptions and Reports
#### 5.2.2 Variable Month: The Month of Departure Time  
The analysis examines the impact of the departure month on various delay metrics, providing information on seasonal patterns and their influence on flight punctuality. The results are presented in Table 7.  
For that variable, it is useful to also present an evolution over time (Figure 4). The following presents the dynamism of DR statistics over every ten years:  
It can be seen that the delay rate is shifted in 2000 and warm months, \(5.5\leq\) Month \(<\) 8.5--partial score 288 (red color), are suddenly more delayed than before. It can also be observed that the shares are not stable (Figure 5), particularly for the 291 partial scores, Month \(<\) 5.5, and cold months, which show a higher number of flights.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Score** & **Condition** & **DR [\%]** & **Share [\%]** & **Total Flights** & **Delayed** & **On Time** \\ \hline
288 & \(63.5\leq\) DepDelay & 90.4 & 4.4 & 54,118 & 48,899 & 5219 \\
398 & \(39.5\leq\) DepDelay \(<\) 63.5 & 17.4 & 3.2 & 39,504 & 6871 & 32,633 \\
450 & \(21.5\leq\) DepDelay \(<\) 39.5 & 3.4 & 5.2 & 64,443 & 2169 & 62,274 \\
520 & DepDelay \(<\) 21.5 & 0.3 & 87.2 & 1,075,424 & 3231 & 1,072,193 \\ \hline \end{tabular} Source: Authors’ own elaboration.  
\end{table}
Table 6: Variable category report—departure delay (DepDelay).  
Figure 4: Delayed rate as a function of time for the variable of month. Source: Authors’ own elaboration.
## 2 Materials and Methods
### The Proposed Deraining Network
#### 2.2.1 Subpixel Convolution Up-Sampling  
For the input stage of the rain removal network designed in this paper, the input image was uniformly segmented into ten small images to enlarge the patterns and details of the rain streaks in the image, allowing the network to extract and utilize more global features. However, the resolution is reduced with the rain streaks enlargement. Therefore, subpixel convolution up-sampling was incorporated to replace the bilinear interpolation of DTDNet to improve the resolution of the image.  
Subpixel convolution up-sampling is an end-to-end up-sampling module. It can obtain a high-resolution image at a specified rate from a low-resolution image by setting the up-sampling ratio. Typically, a proximity pixel-filling algorithm is used in the up-sampling process in which mainly the effect of spatial factors is taken into account but the effect of channel dimension is ignored; however, the convolution channel dimension information is combined in the subpixel convolution up-sampling module. It can achieve super resolution and generate more realistic images, which can effectively solve the problem of reduced  
Figure 2: The framework of the deraining network.  
image resolution with image deraining [30]. The structure of subpixel convolution up-sampling is shown in Figure 3.  
The subpixel convolution up-sampling is used in both two stages, but in contrast to the first stage, the attention mechanism and pyramid multi-scale pooling layer are not used to fuse the features of the output of the two channels again in the second stage. Moreover, the number of RDB modules in the extension channel is reduced and the connection mode of this module is changed to step-by-step connection. The depth of the network is reduced through this structure. Combined with the activation function, the overfitting can be effectively alleviated.
## 4 Simulation Results and Analysis
### Predictive Model of Explicit MPC  
The explicit MPC is based on the state space model. Therefore, the integrated model of the turbofan engine and jet noise is linearized at steady-state points. Select \(u=\left[w_{f},\alpha_{fan},A_{microjet}\right]^{T}\) as the input of the state space model, \(x=\left[N_{h},N_{l},SPL\right]^{T}\) as the state of the state space model, and \(y=\left[N_{h},N_{l},SPL,\,F,\,T_{41}\right]^{T}\) as the output of the state space model, where \(N_{h}\) is the speed of high pressure rotor, \(N_{l}\) is the speed of the low pressure rotor, \(SPL\) is the total sound pressure level of jet noise, \(F\) is the thrust of the turbofan engine, and \(T_{41}\) is the inlet temperature of the high pressure turbine of the turbofan engine. In this paper, the climb phase of an aircraft is used an example. At this phase, \(\text{Ma}=0.2\) and the flying altitude is about sea level. When \(N_{l}\) is 100% rotation speed, the continuous state space equation of the integrated model is obtained by the small deviation method, and the following normalized system matrix is obtained:\[A_{c}=\left[\begin{array}{ccc}-0.856&-0.312&0\\ 1.656&-17.65&0.0005\\ 0&0&0\end{array}\right]\]  
\[B_{c}=\left[\begin{array}{ccc}0.534&0.427&0.003\\ 5.328&-35.197&-0.585\\ 6.151&-33.234&-0.560\end{array}\right]\]  
\[C_{c}=\left[\begin{array}{ccc}0.983&-0.006&0\\ 0.033&0.647&0\\ 0.008&0.159&0\\ 0.036&1.213&0\\ 0.202&-0.047&0\end{array}\right] \tag{35}\]  
\[D_{c}=\left[\begin{array}{ccc}0.011&0.009&0\\ 0.107&-0.704&-0.012\\ 0.035&0.218&-0.051\\ 0.136&2.053&-0.003\\ 0.474&0.745&0.015\end{array}\right]\]  
The state weighting matrix and the input weighting matrix for the explicit MPC controller are, respectively:  
\[\begin{array}{c}Q=\left[\begin{array}{ccc}1&0&0\\ 0&1&0\\ 0&0&1\end{array}\right]\\ R=\left[\begin{array}{ccc}0.01&0&0\\ 0&0.01&0\\ 0&0&0.01\end{array}\right]\end{array} \tag{36}\]
## 3 Results Analysis
### Route Discovery Delay  
Finally, to appraise the computational time overhead introduced by the TTHCA algorithm, the route discovery delay was calculated and compared with the original AODV protocol. MHA was also used in these experiments to demonstrate the reduced delay achieved by TTHCA in replacing \(RREP_{lim}\) with the threshold in (2). Route discovery delays were measured for both a wormhole infected MANET environment, where a 4-hop PM O-B wormhole was assumed, together with a wormhole-free network. The results in Table 3 are based on the average route discovery delay for 100 route discovery samples for each protocol. They show that as TTHCA required only one route sample to identify a wormhole infected route, no delay in the route discovery procedure was introduced compared with AODV in a wormhole-free network. If a wormhole existed, a graylist broadcasting delay was incurred after a wormhole route had been identified so as to locate a new healthy route. MHA in contrast, nearly doubled its route discovery time compared to AODV, regardless of whether a wormhole existed or not. This is because it mandates that at least two unique routes must be identified to detect a wormhole.  
Figure 9: Radio coverage variation tolerances for TTHCA in detecting a PM O-B wormhole (WH).  
Note, these results do not take account of either the practical device delays caused by the packet processing time measurements or the increased RREP packet length, though pragmatically both of these represent a very small overhead for both the TTHCA and MHA algorithms.
# Thermal Image Generation for Robust Face Recognition
## 3 Visible and Thermal Face Database  
In the literature, there are few thermal databases available that are public and also show variability in thermal images. Thermal databases generally present images acquired in a single session, contain few individuals, and have low resolution. We need a database with visible and thermal images with different characteristics and good resolution for our system. Therefore, we have selected the PUCV-VisibleThermal-Face (PUCV-VTF) database [46]. The PUCV-VTF database contains thermal and visible images acquired with two different sensors and allows us to study the fusion of these images. We acquired visible images with a 640 \(\times\) 480 pixels PS3 Eye camera, while we acquired the thermal images using a FLIR Tau 2640 camera with 640 \(\times\) 512 14-bit resolution. The database includes 76 individuals with five subsets, in which each subset has several images with different attributes. This gives a total of 12,160 images in both visible and thermal spectra. In addition, we added thermal images of 46 individuals to the database, obtained with the same thermal and visible camera, corresponding to images not included in the PUCV-VTF database. Therefore, the total number of images in this database is 15,668 images. Figure 2 shows some examples.  
All the database's images, visible and thermal, are aligned to the eyes' position and cropped to 256 \(\times\) 256 pixels, obtaining only the face. A visible face detector called multi-task cascaded convolutional networks, MTCNN [47], was used to get the bounding box coordinates. When observing the visible images of the PUCV-VTF database, their noise can be noticed, which can be detrimental to the training of our proposed generation model. The Deep Face Dictionary Network, DFDNet [48], is used to eliminate the noise, which corresponds to a deep face model that operates with a network of dictionaries to perform face restoration.  
DFDNet consists of two stages. Initially, it generates the deep components of the dictionaries from the high-definition image database using k-means. The model uses these generated dictionaries as candidate reference components. In the second stage, the model selects the dictionary with the most similar structure to the input for each degraded component. Each dictionary is then normalized using component AdaIN [49], based on the input components to eliminate style diversity in the distribution. The restoration process uses the selected dictionaries to guide the dictionary transform method. Then a confidence score is used in the chosen dictionary to generalize different levels of degradation through the fusion of the weighted value. Our experiments used 15,668 visible images to train the model and remove noise. Figure 3 shows some examples of the application of the DFDNet algorithm.  
Figure 2: Some examples of the PUCV-VTF database. (**top**) visible images, (**bottom**) thermal images.
## 5 Conclusions  
This paper presents an operational strategy for optimizing both full-length and short-turn routes using virtual coupling technology. We have developed an integrated optimization model that minimizes passenger travel time and operational costs, taking into account the variability in passenger flow [37; 38; 39; 40]. This model includes constraints related to station services, train operations, virtual coupling, rolling stock circulation, and passenger ride selection. Auxiliary variables are introduced to reformulate the model into a mixed integer linear programming model  
Given the complexities in accurately modeling passenger flow transitions--from new arrivals to those waiting for direct trains--we incorporated a train operation simulation into the genetic-simulated annealing algorithm. This integration allows for a more precise depiction of the dynamic passenger boarding and alighting processes, enhancing the model's effectiveness through the combined global and local search capabilities of the algorithm.  
In our case study of an urban rail transit line during morning peak hours, we explored the benefits of virtual over traditional mechanical coupling. The findings demonstrate that virtual coupling technology offers significant adaptability to fluctuations in passenger flow across different sections of the line. By enabling flexible coupling and uncoupling of trains, the technology adapts transportation capacity dynamically, which helps alleviate congestion and meets the spatial and directional demands of the passenger flow. Furthermore, the dynamic allocation of train formations through virtual coupling significantly enhances rolling stock circulation efficiency, effectively balancing the trade-offs between passenger waiting times, rolling stock procurement costs, and per-kilometer train operation costs. Ultimately, this approach optimizes the balance between passenger service quality and operational efficiency.  
Figure 11: Passenger Demand outside the Short-turn Route.  
**Author Contributions:** Conceptualization, F.L., Q.Z. and X.L.; methodology, F.L. and L.W.; validation, F.L. and L.W.; formal analysis, L.W.; resources, F.L.; data curation, F.L. and L.W.; writing--original draft preparation, J.H.; writing--review and editing, J.H.; funding acquisition, F.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by National Natural Science Foundation of China (52262043), Beijing Natural Science Foundation (L201013) and Fundamental Research Funds for the Central Universities (2022)BMC058).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
**Acknowledgments:** The authors thank everyone who contributed to this article.  
**Conflicts of Interest:** The authors declare no conflicts of interest.