esa-sceva/satcom-chunk-collection huggingface dataset:
# Multimodal Fall Detection Using Spatial-Temporal Attention and Bi-LSTM-Based Feature Fusion
## 3 Datasets
### Fall UP Dataset  
The UP-Fall dataset [26] is utilized in this study. This dataset represents a substantial resource for fall detection and activity recognition. It comprises a total of 11 distinct activities, each performed three times. The data were collected from a group of 17 healthy young adults (9 males and 8 females) aged 18 to 24 years, with an average height of 1.66 m and an average weight of 66.8 kg. The dataset includes 11 activities, covering six daily movements (walking, standing, sitting, picking up an object, jumping, and lying down) and five types of falls (falling forward with hands, falling forward on knees, falling backward, falling sideways, and falling while sitting on an empty chair), as shown in Table 1. These specific activities were selected based on the existing literature [51; 52]. Numerous activities that do not involve falling were included due to their prevalence in daily routines, with picking up objects being particularly prone to misinterpretation as a fall. Jumping was executed at 30 s intervals, picking up objects every 10 s, and all other daily activities were conducted every 60 s. Each falling scenario was enacted for 10 s.  
Because this is a multimodal dataset, the wearable sensor, context-aware sensor, and camera are used simultaneously to collect data. The wearable sensor collects data from a 3-axis accelerometer signal, as shown in the visualization in Figure 1, 3-axis gyroscope, and ambient light sensor. The wearable sensor is positioned in five different locations: on the left wrist, under the neck, inside the right pants pocket, at the center of the waist (on the belt), and on the left ankle, as shown in Figure 2. The reason for attaching it to the left wrist and pocket is to simulate how people normally wear a smartwatch or carry a cell phone. The subjects also wore an electroencephalograph (EEG) headset to acquire EEG signals. Context-aware sensors and cameras were installed, as shown in the figure. Two cameras were installed at a height of 1.82 m from the floor, one for the side view and the other for the front view. In our study, the only sensor data used were acceleration data. This dataset does not have data for the right pockets of Subjects 5 and 9, so our study experimented  
\begin{table}
\begin{tabular}{c l c l} \hline \hline
**Class No.** & **Class Description** & **Class No.** & **Class Description** \\ \hline
1 & Falling forward using knees & 7 & Standing \\
2 & Falling forward using hands & 8 & Sitting \\
3 & Falling backward & 9 & Picking up an object \\
4 & Falling sideward & 10 & Jumping \\
5 & Falling sitting in an empty chair & 11 & Laying \\
6 & Walking & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Activity ID and description of UP-Fall dataset.  
with data from Subjects 1-17, excluding Subjects 5 and 9. Figure 2 below visualizes the acceleration sensor position of the data used in this study. This figure shows the wrist acceleration data of Subject 1 Activity1 Trial camera 1.
## 4 Results and Discussion
### Hydrogeological Evaluation of the Fracture Network  
The fractures' calibrated aperture coefficient (A) values are significantly higher in the sandstone than claystone. In the sandstone body, the A values range from 1 \(\times\) 10\({}^{-4}\) to 3 \(\times\) 10\({}^{-4}\) (Figure 4C). So, the hydraulic aperture of a fracture in KSF with a diameter of 1 m can vary from 0.1 to 0.3 mm. In the claystone, the fracture aperture coefficient ranges between 6 \(\times\) 10\({}^{-6}\) and 1 \(\times\) 10\({}^{-4}\), which means that the hydraulic aperture of a 1 m fracture in BCF varies between 6 \(\upmu\)m and 0.1 mm. The effective porosity log follows the anomalies seen in the aperture coefficient log. The upper section of the borehole has high effective porosities compared to the lower half of the well. In the sandstone, the highest porosity value is 7% at 300 m, while the porosity in the claystone does not reach 1% (Figure 4D).  
Based on the poro-perm diagram, two trends describe the hydraulic behaviour of the well (Figure 7A). The permeability data were derived from hydraulic measurements of the borehole, while the porosity data were estimated based on the DFN model and calibrated aperture coefficients. Both trends show an excellent fit (R\({}_{1}\) = 0.996, R\({}_{2}\) = 0.972). The upper and lower parts of the borehole lie along with two distinct poro-perm trends, indicating the characteristics of the two different formations. The orange trend is predominant in the BCF, while the blue trend is dominant in the KSF.  
The FZI values show the same characteristics as the poro-perm diagram (Figure 7B). The sandstone forms a single HFU in the upper part of the borehole based on the FZI values. The transition zone between the two formations exhibits its own HFU. In the lower part of the borehole, the FZI values in the claystone vary considerably. It contains two well-defined zones with higher FZI values (630-700 m and 775-845 m), while the FZI values between these zones are much lower (Figure 7B).  
Figure 6: Vertical east–west sections of typical simulated fracture network geometry patterns (**A**,**B**) based on ten individual runs of the BAF–3 well. Different colours indicate the communicating fracture groups within the well. The width of the sections is 150 m.
## 2 Results and Discussion
### Competitive Sequestration of Palladium Solutions Containing Other Ligands  
Inspired by these results, we investigated whether a heptane solution of **1** could competitively sequester palladium species from polar organic solutions in the presence of other ligands that are  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Entry**} & \multirow{2}{*}{**Metal**} & \multirow{2}{*}{**Solvent**} & \multirow{2}{*}{**Time/h**} & \multicolumn{2}{c}{**Contents/ppm 1**} & \multicolumn{2}{c}{**Sequestration**} \\  & & & & **Initial** & **Final** & **Efficiency/\%** \\ \hline
1 & Co & water & 4 & 26.0 & 17.4 & 33.1 \\
2 & & water 2 & 4 & 26.0 & 2.64 & 89.8 \\
3 & Ni & water & 4 & 26.0 & 17.9 & 31.2 \\
4 & & water 2 & 4 & 26.0 & 3.72 & 85.7 \\
5 & Cu & water & 2 & 21.6 & 0.360 & 98.3 \\
6 & & MeOH & 2 & 14.4 & 0.0250 & 99.8 \\
7 & Ru & water & 4 & 26.0 & 1.02 & 96.1 \\
8 & & water 2 & 4 & 26.0 & 0.0200 & 99.9 \\
9 & Pd & water & 2 & 500 & 0.270 & 99.9 \\
10 & & water & 0.25 & 22.5 & 0.120 & 99.5 \\
11 & & water 2 & 1.5 & 26.8 & 0.0250 & 99.9 \\
12 & & CH\({}_{3}\)CN & 0.25 & 50.0 & 2.90 & 94.2 \\
13 & & CH\({}_{3}\)CN & 1.5 & 50.0 & 0.160 & 99.7 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Results of metal sequestration by **1** from model solutions under biphasic conditions.  
Figure 1: Schematic representation of copper sequestration from aqueous solution.  
commonly used in catalytic reactions. According to ICP-OES results (Table 2, initial Pd contents 50 ppm) high levels of Pd were sequestered by 1 in 4 h in most cases. Sequestration efficiency tended to increase with time and generally exceeded 96% except for samples where Pd was complexed by P(o-Anisyl)\({}_{3}\), P(o-Tolyl)\({}_{3}\), RuPhoS, DPPF and Hermann's ligand. Even in those cases _ca._ 90-95% of Pd could be removed with **1** if the extraction time was increased.
# Cheating Detection in Online Exams Using Deep Learning and Machine Learning
## 3 Results
### Analysis Findings for Scenario-1 Forecast Models  
For prediction, the deep learning model DNN algorithm was used. The analysis was initiated with the parameters that provided the best performance model in the algorithm, using the K-10 cross-validation method and a multi-layer structure. The goal was to achieve the highest R\({}^{2}\) and the lowest values for regression performance metrics, namely MAE and RMSE. The findings related to the analysis are summarized in Table 4.  
In Table 4, the performance measurement values of the DNN model in regression analysis using the K-10 cross-validation method are shown. The model with the best performance was proposed using a 5-layer structure. The R\({}^{2}\) value of 0.809 on the test dataset for detecting students who cheated in online exams indicates that approximately 80.9% of the variance in the dependent variable can be explained by the independent variables. The high R\({}^{2}\) value for the model's training performance suggests that the model explains the variability in the training data well. Overall, the test performance values of the DNN model with five and six layers have shown good results. It is observed that when the number of layers increases to seven or more, there is a decline in performance values. As the number of layers increases beyond five, MAE values start to rise, indicating that the model begins to make more errors in its predictions.  
Figure 2 shows that the proposed model detects the cheating students well in the prediction analysis performed with a 5-layer DNN with K-10 cross-validation. The visualized form of the performance success of the 5-layer DNN model presented in Table 4 is shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \
\begin{tabular}{c} **Educational** \\ **Performance (R\({}^{2}\))** \\ \end{tabular} &
\begin{tabular}{c} **Test Performance** \\ **(R\({}^{2}\))** \\ \end{tabular} & **MAE** \\ \hline Layer = 2 & 0.861 & 0.782 & 8.879 \\ \hline Layer = 3 & 0.861 & 0.796 & 8.757 \\ \hline Layer = 4 & 0.884 & 0.786 & 8.337 \\ \hline Layer = 5 & 0.919 & **0.809** & 8.079 \\ \hline Layer = 6 & 0.920 & 0.803 & 8.592 \\ \hline Layer = 7 & 0.904 & 0.766 & 9.300 \\ \hline Layer = 8 & 0.899 & 0.768 & 9.467 \\ \hline Layer = 9 & 0.710 & 0.706 & 9.350 \\ \hline \hline \end{tabular}
\end{table}
Table 4: DNN model prediction metric analysis.  
The layers and parameters used in the 5-layer DNN model analysis, which gives the best prediction among the deep learning algorithms, are visualized and presented in Table 5.
## 2 BiCA-LSTM-Informer Framework
### Multi-Task Output  
To achieve unified modeling and efficient prediction across different time series tasks, a multi-task output module based on fused features (referred to as the multi-task output module) is designed. This module separately handles regression and classification tasks while sharing a common feature representation.  
The multi-task output module takes the fused feature representation \(H^{\text{fusion}}\) as input and connects it to two parallel branches: one for regression and another for classification. These branches operate independently during both training and inference.  
For continuous numerical prediction tasks, the regression branch employs a fully connected layer that directly outputs a continuous value vector without applying an activation function:  
\[\hat{y}_{\text{reg}}=W_{\text{reg}}H^{\text{fusion}}+b_{\text{reg}} \tag{13}\]where \(W_{\text{reg}}\in\mathbb{R}^{d_{\text{fusion}}\times P}\) and \(b_{\text{reg}}\in\mathbb{R}^{P}\) denote the weight matrix and bias term, respectively; \(P\) represents the dimension of the regression target or the prediction horizon.  
For the classification task--aimed at determining the category to which the time series belongs--the classification branch first maps the fused features through a fully connected layer, followed by the application of the Softmax activation function to produce the probability distribution over classes. The computation proceeds as follows:  
\[z_{\text{cls}}=W_{\text{cls}}H^{\text{fusion}}+b_{\text{cls}} \tag{14}\] \[\hat{y}_{\text{cls}}=\text{softmax}(z_{\text{cls}}) \tag{15}\]  
where \(W_{\text{cls}}\in\mathbb{R}^{d_{\text{fusion}}\times C}\) and \(b_{\text{cls}}\in\mathbb{R}^{C}\) are the learnable parameters, and \(C\) denotes the number of classes.  
Both output branches follow the hard parameter-sharing paradigm. While they share the front-end feature extractor, each branch is trained and inferred independently, minimizing inter-task interference. This design effectively mitigates overfitting and enhances training efficiency.
# Quantum Key Distribution for 5G Networks: A Review, State of Art and Future Directions
## 5 Conclusions and Future Works
Currently, QKD networks are merely accomplished using Trusted Repeater Approach (TRA), and it is expected that the optical quantum repeaters will ultimately be accessible for practical use. TRA is important in assisting routing for QKD networks and solving the distance limitations of QKD links. However, the are some restrictions that need to be resolved for a QKD network to be combined with conventional IP networks and used in everyday life. One method for extensive application of QKDtechnology is by using an approach such as SDN-QKD to integrate QKD networks with telecommunication networks. Previous research [130] proved that the implementation of a new quantum technique for secret key transmission such as multiphoton approach in networks will improve the key transmission efficiency. Therefore, for future research, the implementation multiphoton approach during the interchanged of information are required to ensure that the key remains a secure between parties in the 5G networks. Authors contributed equally to this work. Conceptualization, M.H.A. and N.Z.H. ; methodology, Z.A.Z. ; validation, M.H.A., Z.A.Z. and N.Z.H. ; formal analysis, M.H.A. ; investigation, M.H.A. and N.Z.H. ; writing--original draft preparation, M.H.A. ; writing--review and editing, M.H.A. and N.Z.H. ; visualization, N.Z.H. ; supervision, Z.A.Z. ; project administration, Z.A.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research is self-funded by the authors since it is part of the requirement in University Putra Malaysia for postgraduate students. Not applicable. We thank the anonymous reviewers for their valuable comments and suggestions, which helped us to improve the content, organization, and presentation of this paper. The authors declare no conflict of interest.
# Comparison of CS-Based Channel Estimation for Millimeter Wave Massive MIMO Systems
###### Abstract  
Compressed sensing (CS) has great potential in channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To solve such a CS-based channel estimation problem, three categories of algorithms, namely convex relaxation algorithms, greedy iteration algorithms and Bayesian inference algorithms, are widely used. In this paper, with a unified massive MIMO framework, comprehensive comparisons among three categories of algorithms are presented in the perspective of the estimated accuracy, which is affected by the received signal-to-noise ratio (SNR), the number of resolvable paths, angular quantization error, the number of pilot symbols and hardware impairments. Specifically, it shows that convex relation algorithms achieve the best estimation accuracy at the high SNR range and it is mainly affected by the received SNR and transmitter's hardware impairments. At the low SNR range, greedy iteration algorithms outperform others and the estimated accuracy is then limited by the angle quantization error. Furthermore, a tradeoff between the estimated error and complexity is achieved in Bayesian inference algorithms, a;though its estimated error is sensitive to the number of available pilot symbols.  
m 25 August 2019; 9, 4346; doi:10.3390/app9204346
## 3 Results
### FT-IR Analysis  
The interaction between polymers can be observed through the results of infrared spectroscopy (IR), as shown in Figure 8. It can be observed that the miniplate with pure PLA material (SPLA0) has several wave peaks at 1042 cm\({}^{-1}\), 1079 cm\({}^{-1}\), 1127 cm\({}^{-1}\), 1180 cm\({}^{-1}\), 1451 cm\({}^{-1}\) and 1745 cm\({}^{-1}\). Peaks at 1042 cm\({}^{-1}\), 1079 cm\({}^{-1}\), 1127 cm\({}^{-1}\), and 1180 cm\({}^{-1}\) indicate the presence of -C-O bonds, while 1451 cm\({}^{-1}\) signifies CH\({}_{3}\) bonding, and 1745 cm\({}^{-1}\) indicates the presence of the -C=O group, referred to in the previous study [27]. The addition of 10% PEG into the PLA matrix (SPLA0P10) shows three peaks indicating the absorption of -CH\({}_{2}\)- stretching vibrations at wave numbers 2882, 2944 and 2994 cm\({}^{-1}\), as confirmed [PERSON] et al. [37]. The increased intensity at 1382 and 1452 cm\({}^{-1}\) represents the deformation of C-H peaks, indicating the presence of -CH\({}_{2}\) chains from PEG. Characteristics of C=O stretching and C-O stretching from ester groups in PLA and PLA/PEG 10% are shown at wave numbers 1180 and 1747 cm\({}^{-1}\). The influence of adding PEG plasticizer with different concentrations (10% and 20%) to the PLA/starch blend can be observed from the wave numbers 2888 and 2994 cm\({}^{-1}\), as well as 1747 cm\({}^{-1}\), where the peak ratio increases in C-O and C=O in the 10% PEG addition (SPLA20P10) compared to the 20% PEG addition (SPLA20P20). This suggests that a portion of the O-H groups from PEG is likely to bond with the C=O groups in PLA [37].  
Figure 7: SEM images of different miniplate specimens comparing compositions: (**a**) SPLA0P10, (**b**) SPLA20P0, (**c**) SPLA20P10 and (**d**) SPLA20P20 with injection molding temperature of 180 \({}^{\circ}\)C.  
The interaction between sago starch and PLA can be observed in the wave number range of 3000-3500 cm\({}^{-1}\), where there is no increase in waves in pure PLA (SPLA0) but an increase is observed in PLA/sago starch (SPLA20P0). This indicates the presence of -OH bonds in PLA/sago starch ([38; 39]). When compared to the addition of PEG to PLA, the wave peaks become higher in that wave number range, indicating the influence of PEG plasticizer on both PLA and the PLA/sago starch blend ([38]). The occurrence of interactions, such as hydrogen or dipolar interactions, between two polymers will manifest as changes in the observed IR spectrum. FT-IR testing on miniplate products from injection molding aimed to investigate conformational changes in the PLA/sago starch/PEG composite. Previous research by ([36]) explained that starch gelatinization and retrogradation are sensitive to changes in polymer conformation and molecular-level changes. The C-C and C-O stretching regions (800-1300 cm\({}^{-1}\)) are highly sensitive to retrogradation processes, where retrogradation kinetics involve structural changes at the molecular level by measuring the ratio between selected peaks ([40]).
## 4 Results and Discussion  
### Composition of End-of-Life PV Panels  
As seen in Table 2, the silicon content in the cells was 64.5-65.2% (\(w\)/\(w\)), while the silver content was 1.1-1.4% (\(w\)/\(w\)). Cell composition was in good agreement with the report by [PERSON] et al. [20]. The aluminum content of the cells was about 5.5-7.2 times greater than the silver content. The silver content in the metallic ribbons was much higher, between 4.6% and 6.2% (\(w\)/\(w\)), which was attributed to the silver band attached to the PV ribbon for electrical conduction [34].  
The mass composition (wt.%) of the panel components (excluding the mass of the backsheet, which was about 11% of the panel mass) is presented in Table 3, while the losses due to milling are calculated hereafter. Although the metal ribbons and cells accounted for 1-1.16% and 5.1-5.2% of the panel mass, respectively, the silver content of the metal ribbons was comparable to that of the cells due to the higher silver concentration.  
The total mass loss due to milling was 8.19% (\(w\)/\(w\)). The milled sample (the mixture of cells and metal ribbons) was subjected to XRF analysis and the Ag mass loss in the sample was calculated to be 30%. In the powder sample, there were negligible mass losses for Si and Al, but there were significant losses for Cu, Sn and Pb. This could be attributed to the very fine particles lost in the dust [Cu (75%), Sn (66%), Pb (71%) and Ag (30%)]. Thus, mass loss due to milling was not proportional for all elements, and Ag loss was significant. This implies that leaching using panel flakes instead of powder may be preferable.
## 4 Example of Application: Performance Simulations using the EIPS framework
### Performance Simulations Analysis Using EIPS Framework
#### 4.3.2 Averaging Kernels and Information Content Estimation  
To analyse the performance of MW system configurations (A, B and C) considered in this case study, three performance metrics have been estimated (these metrics are described in Section 3.2.4.2) using the EIPS information and error and analysis model and these are: Averaging kernels, Information content and Retrieval errors. Averaging kernels for temperature and humidity for all the MW system configurations (A, B and C) have been estimated and are shown in Figure 7.  
Figure 6: Simulated temperature and humidity Jacobians for two MW instrument configurations (A and B): Temperature and Humidity Jacobians for configuration A (ATMS-type) (shown in two figures in the top panel) and for configuration B (AMSU-A-type) (shown in side-by-side two figures in the bottom panel).  
To analyse the relative information content provided by all three MW system configurations, DFS values as information content have been estimated and are presented in Table 3.  
Figure 7: Estimated averaging kernel matrices (using Equation (8)) for temperature and humidity for all the MW system configurations considered in this case study: A (ATMS-type) (top), B (AMSU-A) (middle) and C (TES-based MW) (bottom) calculated using EIPS information and error analysis model. In these presented contour plots of averaging kernel matrices (for temperature and humidity), the “model levels” refers to the 70 Met Office UM model levels as mentioned in Section 4.1.  
Note that, by the definition given in Rodgers (1988), the DFS is the number of independent pieces of information in the measurement. Therefore, the estimated DFS values in Table 3 are unit-less numbers. Also, note that the impact of the fast code error as compare to the line code on the DFS values is less than 0.1 percent. Results in Table 3 shows that when comparing configurations A and B, configuration B (an AMSU-A type instrument) provides relatively high temperature information as compared to configuration A (an ATMS-type instrument). This can be mainly attributed to the lower radiometric noise of the AMSU-A-type instrument (see NETD values in Table 2). In the case of humidity, configuration A provide more information content (DFS = 2.24) on humidity as compared to configuration B (DFS = 0.67). This improvement is mainly because configuration A contains five extra humidity sounding channels around the 183.31 GHz water vapour absorption line (see Table 2 for channels). While comparing all three configurations, we observe that configuration C (a TES-based MW instrument) provides the highest information content on temperature and humidity (see Table 3) among all the configurations. As configuration C has exactly the same channel configuration as that of configuration A, this improvement in information content over configuration A can be directly attributed to the much lower radiometric noise in each channel of configuration C.
# Intelligent Diagnosis of Bearing Failures Based on Recurrence Quantification and Energy Difference
## 3 Experimental Implementation
### Extraction of Data Energy Features  
The time-domain signals were transformed into the frequency domain using Fourier transform, and the power spectral density was calculated for various frequency ranges. By constructing a feature matrix based on the differences in energy, we completed the feature extraction work