esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Figure 5: Graphical representation of the average MA2-FM score observed during each of the three assessments conducted at different time points: before starting inpatient treatment (T0), after completing inpatient treatment (T1), and after finishing tele-rehabilitation (TR). The figure visually shows the changes in mean scores over time across the three assessment points. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Mean (Standard** & \multicolumn{2}{c}{**Mean (Standard** & \multirow{2}{*}{_p_} \\  & **Deviation)**} & & **Mean (Standard)** & \multirow{2}{*}{_p_} \\ \hline MA2-RM\_T0 & 21.3 (3.40) & MA2-RM\_T1 & 24.6 (2.51) & 6.55 & **\textless{0.001}** \\ \hline MA2-RM\_T0 & 21.3 (3.40) & MA2-RM\_T2 & 24.3 (2.81) & 5.24 & **\textless{0.001}** \\ \hline MA2-RM\_T1 & 24.6 (2.51) & MA2-RM\_T2 & 24.3 (2.81) & 1.31 & 0.215 \\ \hline MA2-FLU\_T0 & 11.3 (3.15) & MA2-FLU\_T1 & 14.6 (3.82) & 2.799 & **0.016** \\ \hline MA2-FLU\_T0 & 11.3 (3.15) & MA2-FLU\_T2 & 14.9 (3.44) & 2.974 & **0.012** \\ \hline MA2-FLU\_T1 & 14.6 (3.82) & MA2-FLU\_T2 & 14.9 (3.44) & 0.175 & 0.864 \\ \hline \hline \end{tabular} The table shows all the possible changes in different primary outcome measures that occurred longitudinally between the three different assessment points: changes between T0 and T1, changes between T1 and T2, and changes between T1 and T2. T0: Assessment conducted at baseline; T1: Assessment conducted before discharge; T2: Assessment conducted at the end of TR; MA2: Melbourne Assessment 2; RM: Range of movement score; FLU: Fluency of movement score. MA2_RM: Range of movement scores as measured by Melbourne Assessment 2; MA2_FLU: Fluency of movement score as measured by Melbourne Assessment 2.  
\end{table} 
Table 2: Statistically significant results showed by Durbin-Conover pairwise comparison test in primary outcome measures.
## 2 Methodology
### Signal Preprocess and Determination of State Boundaries
#### 2.3.2 State Boundary Identification
Each feature vector is assigned to the cluster whose centroid is closest, and after assignment, the centroids are updated by calculating the mean of all feature vectors in the cluster:  
\[C_{k}^{new}=\frac{1}{N_{k}}\sum_{i=1}^{N_{k}}e_{i} \tag{6}\]  
where \(N_{k}\) is the number of data points in cluster \(k\), and \(e_{i}\) are the feature vectors assigned to cluster \(k\). After updating the centroids, the clustering assignment and centroid update steps are repeated until convergence, meaning that the centroids no longer change significantly. Once clustering is complete, each cluster corresponds to a specific operational state of the equipment. The clusters help to classify the operating states of the equipment, and the range of each cluster's feature vector defines the operational state intervals.
# AI-Driven Phishing Detection: Enhancing Cybersecurity with Reinforcement Learning
## 3 Classification of Phishing Detection Strategies
### Mathematical Advantages of RL over Traditional ML  
#### 3.5.1 Dynamic Adaptation Without Retraining  
Traditional ML models require periodic retraining to adapt to new phishing patterns, which are resource intensive. In contrast, RL agents continuously learn from new data without retraining.  
\[Q(s,a)=\alpha\big{[}r+\gamma\cdot a^{\prime}maxQ\big{(}s^{\prime},a^{\prime} \big{)}-Q(s,a)\big{]}\]  
This continuous update mechanism allows the model to adapt to real-time threats, improving detection performance as new attack vectors emerge.
# Role of Bimodal Water Retention Curve on the Unsaturated Shear Strength
## 3 Mathematical Equation
### Proposed Equation
For matric suctions > \(AEV_{2}\):  
\[\tau=ct+(\sigma-u_{a})tan\ \phi\ \prime+AEV_{2}tan\ \phi^{b_{2}}+[e\times(u_{a}-u_ {w})-(0.2+fine)\times AEV_{2}]b_{2}\Theta^{k_{2}}tan\phi\ \prime \tag{11}\]  
\[k_{2}=[log\ (u_{a}-u_{w})-log\ AEV_{2}]^{y} \tag{12}\]  
\[b_{2}=-0.245\{ln\ [\ s_{2}\big{(}I_{p}+4.4\big{)}\big{]}\}^{2}+2.114\{ln\ [\ s_{2} \big{(}I_{p}+4.4\big{)}\big{]}\}-3.522 \tag{13}\]\[y=0.502\,ln\big{(}I_{p}+2.7\big{)}-0.387 \tag{14}\]  
Equations (8) and (11) were developed for the estimation of the unsaturated shear strength of soil. The proposed equations require four (4) variables of water retention curve and three (3) soil properties. The required variables of water retention curve include \(AEV_{1}\), \(AEV_{2}\), \(s_{1}\) and \(s_{2}\). The required soil properties include plasticity index (\(I_{p}\)), void ratio (\(e\)) and percentage of fines.
# Long-Term Prosthetic Aftercare of mandibular Implant-Supported Overdenture
###### Abstract  
_Background:_ Two of the most popular resilient attachment systems for mandibular implant-supported overdenture (MISOD) are locator and ball attachments. The purpose of the present retrospective cohort study was to assess the long-term prosthetic aftercare and oral hygiene status in dedentulous patients rehabilitated with MISOD. _Materials and Methods:_ Forty-five consecutive patients were included (22, group A- ball vs. 23, group B- locator attachments). Attachment incorporation into the MISOD was conducted in a direct (chair-side) intraoral technique at the time of denture insertion. Routine follow-up included yearly visits. The number of visits requiring prosthetic aftercare, either during the follow-up or during the additional visit, was recorded. Outcome parameters included--prosthetic aftercare--the number of aftercare (primary outcome parameter) visits, and dental treatment received (pressure sores) relief, liner changes due to loss of retention, loss of retention due to debris accumulation, denture repair--secondary outcome parameters); oral hygiene--gingingival index (primary outcome parameter). _Results:_ The mean follow-up of the entire study population was 84 \(\pm\) 21 months, range 39-120 months. Statistical analysis revealed a lower need for prosthetic aftercare interventions in group A (\(p\) < 0.001). The mean number of visits dedicated to pressure sores relief (6.09 \(\pm\) 1.04 vs. 3.03 \(\pm\) 0.77, \(p\) < 0.001) and liner exchange due to loss of retention (5.6 \(\pm\) 1.03 vs. 2.09 \(\pm\) 1.04, \(p\) < 0.001), were significantly lower in group A. Debris (food/calculus) accumulation inside the attachment was noted only for the locator's group (\(p\) < 0.001). No statistically significant differences between the groups were noted for denture repair (\(p\) = 0.318). Oral hygiene also exhibited statistically significant differences in favor of group A (2.3 \(\pm\) 0.3 vs. 1.03 \(\pm\) 0.2, \(p\) < 0.001). _Conclusions:_ It can be concluded that using ball attachments for MISOD yields less need for aftercare treatments and improved oral hygiene status over the years.  
ball attachment; locator; long-term prosthetic aftercare; mandibular implant-supported overdenture
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 2 Learning-Based Approaches
### RGB-D-Based Approaches  
Compared with the learning-based approaches mentioned above, which only use the RGB information, RGB-D-based learning approaches combine color information and depth information to measure the pose of objects, and are able to solve the problem of insufficient information in approaches that only use color information. Additionally, due to the emergence of lots of RGB-D datasets, an increasing number of studies on RGB-D-based learning approaches are being performed. In [43], [PERSON] et al. proposed a novel method named DenseFusion that provided a two-stage method for measuring the 6D pose. In the first stage, DenseFusion uses a heterogeneous network to deal with the RGB data and point cloud data, and to save their original structure. In the second stage, a full convolutional network is used to map each pixel in RGB crop to colored feature space and uses a network based on PointNet to map each point in the point cloud to geometrical feature space. Then it merges the feature points in the colored feature space and the geometrical feature space and outputs a 6D pose estimation result. In addition, it finally refines the result by loop learning. In [44], [PERSON] et al. proposed a 6D pose estimation framework named G2L-Net. Firstly, it extracts the coarse point cloud from RGB-D images. Then, the point cloud is added into the network to achieve 3D segmentation and object translation predictions. Finally, the fine point cloud is transferred into a local canonical coordinate to estimate initial object rotation. In [45], PVN3D was proposed, in which the method based on 2D key points was extended to 3D key points, making full use of geometric constraint information of rigid objects and improving the accuracy of the 6D estimation significantly. In [46], a method named CosyPose was proposed, which used multiple cameras to estimate 6D pose. Firstly, it estimates the 6D pose of objects in each image and then matches the individual 6D object pose hypotheses across different input images in order to jointly estimate the camera viewpoints and 6D poses of all objects in a single consistent scene.
# A Novel Real-Time Battery State Estimation Using Data-Driven Prognostics and Health Management
## 5 Experimental Setup and Methodology
### Hardware and Software  
The offline training for the data-driven PHM was performed on a NVIDIA 1 xQuadro RTX8000-48 GB GDDR6 GPU (graphical card), sourced in the UK.  
The SOC estimator using EFK and UKF is computed using Matlab and Simulink R2023b. For the battery model estimation, the Parameter Estimator toolbox [46] is used.  
The data-driven PHM SOC estimator is computed using Keras [47] with a Tensorflow-backend, with Python version 3.9.7.
## 4 Application Example
### Influence of the Cross-Sectional Area of the Air Intake Wells  
There are three cases of the cross-sectional area of the air intake well, in which the area is 0.5 times, 1.0 times and 2.0 times the original cross-sectional area. The air velocity and air pressure in the different cases are shown in Figure 12. The corresponding total ventilation rates of the air supply system are 3752, 4537 and 4842 m\({}^{3}\)/s, respectively.  
The air velocity in the air intake well decreases, but that in the spillway tunnel increases. Moreover, there are sharp decreases in the sub-pressure in the spillway tunnel and air intake well. This result is because the increase in the air intake well cross-sectional area decreases the local resistance coefficient, making the air flow much more smoothly than before. Therefore, irrespective of the cost of cavern excavation, it is better to choose a larger cross-sectional area.  
The analysis indicates that the change in the air intake well cross-sectional area has a large influence on the ventilation rate, air velocity and pressure in both the air intake well and the spillway tunnel. Considering the structure safety and stability, it is better to adopt a larger cross-sectional area of the air intake well to allow the air to flow smoothly and decrease the air velocity and air pressure simultaneously.
## 3 Results
### In Vivo Activities
#### 3.3.6 Effects of CFPS on the Lipid Peroxidation  
The cardiac MDA level of all treated groups is illustrated in Figure 5. ISO injection induced heart oxidative stress as manifested by the increased MDA level, an indicator of  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Control** & **ISO** & **ISO + Pid** & **Iso + CFPS** \\ \hline cTn-I (ng/mL) & 0.44 \(\pm\) 0.05 & 2.26 \(\pm\) 0.13 * & 1.32 \(\pm\) 0.03 * & 1.45 \(\pm\) 0.12 *,\(\beta\) \\ CK-MB (U/L) & 57.43 \(\pm\) 1.30 & 128.9 \(\pm\) 1.50 * & 95.35 \(\pm\) 1.11 * & 102.3 \(\pm\) 1.00 *,\(\beta\) \\ LDH (U/L) & 58.03 \(\pm\) 1.66 & 166.7 \(\pm\) 1.32 * & 129.9 \(\pm\) 1.45 * & 134.4 \(\pm\) 0.72 *,\(\beta\) \\ \hline \hline \end{tabular} All values are given as mean \(\pm\) SD (\(n\) = 8). * \(p<0.05\): controls vs. ISO. * \(p<0.05\): CFPS + ISO or Pid + ISO vs. ISO. * \(p<0.05\): CFPS + ISO vs. Pid + ISO vs. Pid + ISO vs. Pid + ISO.  
\end{table}
Table 4: Effect of CFPS fraction on cardiac biomarkers.  
Figure 4: Plasma fibrinogen level variation in control and experimental rats. Values are given as mean \(\pm\) SD for groups of 8 animals each. * \(p<0.05\): controls vs. ISO. * \(p<0.05\): CFPS + ISO or Pid + ISO vs. ISO. * \(p<0.05\): CFPS + ISO vs. Pid + ISO.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Control** & **Iso** & **ISO + Pid** & **Iso + CFPS** \\ \hline TC (mg/dL) & 86.19 \(\pm\) 1.18 & 118.6 \(\pm\) 2.11 * & 93.48 \(\pm\) 0.66 * & 100.1 \(\pm\) 1.26 *,\(\beta\) \\ TG (mg/dL) & 40.26 \(\pm\) 0.72 & 56.69 \(\pm\) 1.44 * & 48.58 \(\pm\) 0.90 * & 51.04 \(\pm\) 0.91 *,\(\beta\) \\ LDL-c (mg/dL) & 29.25 \(\pm\) 0.63 & 64.99 \(\pm\) 0.87 * & 35.82 \(\pm\) 1.04 * & 40.76 \(\pm\) 0.46 *,\(\beta\) \\ \hline \hline \end{tabular} All values are given as mean \(\pm\) SD (\(n\) = 8). * \(p<0.05\): controls vs. ISO. * \(p<0.05\): CFPS + ISO or Pid + ISO vs. ISO. * \(p<0.05\): CFPS + ISO vs. Pid + ISO vs. Pid + ISO vs. Pid + ISO.  
\end{table}
Table 5: Effect of CFPS fraction on lipidic profile.  
lipidic peroxidation. However, the administration of CFPS or pidogrel revealed a significant reduction in MDA levels (\(p<0.05\)) when compared to isoproterenol alone. Significant differences in the profiles of enzymes were observed between the CFPS + ISO group and the Pid+ISO group.
## 6 Results and Discussion
### Solutions Quality
The Bermann-Hommel post-hoc test is then applied to find all possible hypotheses that cannot be refused. Table 9 shows the results of these tests, and Figure 13 shows the graph corresponding to these \(p\)-values. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**AR**} & \multicolumn{3}{c}{**GGA \(p\)-Values**} \\ \cline{3-4}  & & **Unadjusted** & **Bergmann-Hommel** \\ \hline \hline CPLA & 3.06 & 0.00581 & 0.02327 \\ \hline ACO-TS & 3.17 & 0.00365 & 0.01460 \\ \hline MOGA & 3.89 & 0.00011 & 0.00106 \\ \hline BCO-SHH & 3.89 & 0.00011 & 0.00106 \\ \hline GGA & 1 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 8: \(p\)-values for multiple comparisons among methods. Figure 12: \(p\)-values graph of the compared methods. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**AR**} & \multicolumn{3}{c}{**GGA \(p\)-Values**} \\ \cline{3-4}  & & **Unadjusted** & **Bergmann–Hommel** \\ \hline LC03 & 2 & 0.11752 & 0.11752 \\ HM4 & 2.7 & 0.00174 & 0.00523 \\ GGA & 1.3 & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 9: \(p\)-values for multiple comparisons among methods. The \(p\)-values obtained by the post-hoc test point out that the GGA method is only statistically different from the HM4 algorithm, and the comparison with the LC03 algorithm indicates that they have a similar statistical performance. However, the GGA has the better average ranking than the LC03 method.
## 5 Case Study
### Substation Network Modeling  
The electric substation in this case study can be conceived of as divided into nine interrelated components, joined by eight connecting edges, as shown in Figure 2. The nine components are: (A) current transformers, (B) power transformers, (C) insulators, (D) earthing systems, (E) isolation switches, (F) circuit breakers, (G) surge arrestors, (H) distribution busses and (I) the control building. Each of these components, with the exception of the control building, consists of several identical functioning units, the number of which and the number of parallel transmission lines in each connecting edge are set forth in Table 1.
# 2D Gaze Estimation Based on Pupil-Glint Vector Using an Artificial Neural Network
## 3 Experimental System and Results
### Gaze Estimation Accuracy Comparison of Different Methods
#### 3.5.2 Gaze Estimation Accuracy Considering Error Compensation  
Figure 11 shows the comparison of gaze estimation accuracy and standard deviation calculated respectively through the proposed method and other NN (Neural Network) methods considering error compensation. The proposed method can provide an accuracy of 1.17\({}^{\circ}\), 0.79\({}^{\circ}\), 0.47\({}^{\circ}\), and 0.36\({}^{\circ}\) respectively, when a four-, six-, nine-, or 16-marker calibration model is utilized for calibration. When considering error compensation, the improvement percentage of gaze estimation accuracy for four-, six-, nine-, and 16-marker calibration models is 9.3%, 11.2%, 9.6%, and 7.6%, respectively. The specific results are shown in Table A2 of the Appendix.  
Figure 10: Comparison of gaze estimation accuracy results between proposed method and other methods without considering error compensation.
# Time Series Anomaly Detection for KPIs Based on Correlation Analysis and HMM
## 4 Experiment
### Abnormal Screening of KPIs Based on 1D-CNN-TCN
#### 4.1.2 Results of Anomaly Screening Based on 1D-CNN-TCN
In the experiment, after repeated tests, under the premise of ensuring a good balance between time consumption and detection effect, the final hyperparameters are determined as shown in Table 4. For example, the process of kernel size parameter adjustment is shown in Figure 6. We evaluated different kernel sizes through MAE, and finally determined the kernel size to be 20. \begin{table}
\begin{tabular}{c c} \hline \hline
**Hyperparameter Name** & **Value** \\ \hline kernels of CNN & 128 \\ kernel sizes of CNN & 2 \\ kernels of TCN & 64 \\ kernel sizes of TCN & 20 \\ number of iterations & 100 \\ dropout rate & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The hyperparameters and value of the 1D-CNN-TCN. Figure 5: KPI curve of abnormal nodes detected during fault period. (**a–r**) The anomaly detection results of 18 KPIs in two periods. The red nodes represent the anomaly point, and the two blue lines represent the starting times of two different faults.
###### Abstract  
Free-space optical (FSO) communication has been studied for next-generation network systems. Because an FSO system establishes point-to-point communication links, maintaining alignment among the transceivers is a critical challenge. In addition, atmospheric turbulence causes significant signal loss in FSO vertical links. Even in clear weather conditions, transmitted optical signals suffer significant scintillation losses due to random variations. Thus, the effect of atmospheric turbulence should be considered in vertical links. In this paper, we analyze the relationship between pointing error and scintillation from the aspect of beam divergence angle. Furthermore, we propose an adaptive beam that optimizes its divergence angle according to the pointing error between the communicating optical transceivers to mitigate the effect of scintillation due to pointing error. We performed a beam divergence angle optimization and compared it with adaptive beamwidth. The proposed technique was demonstrated using simulations, which revealed an enhanced signal-to-noise ratio and the mitigation of the scintillation effect. The proposed technique would be useful in minimizing the scintillation effect in vertical FSO links.  
free-space optical communications; adaptive beamwidth; atmospheric turbulence; free-space optical vertical link +
Footnote †: journal: Sensors
## 3 Proposed System Model for Optimal Energy Management
### Distribution System Operator Module with Advance Communication Infrastructure  
The distribution system operator (DSO) provides an advanced communication infrastructure, i.e., AMI. The AMI consists of a data concentrator, communication module, and meter data management system. The communication module has two further modules: one on the consumer side and the other on the DSO side. The communication modules are connected to smart meters and are responsible for delivering and updating user energy consumption data. The energy usage data is received by the data concentrator and then regulated to the meter data management system for further processing. Here, the meter data management analyzes the user's energy consumption, processes the data to extract useful information, and then communicates this data to the DSO. This real-time sharing of information with the DSO empowers them to maintain, improve and detect power outages. The DSO is also responsible for regulating varying pricing signals. The ECS based on GWDO schedules the power usage pattern of users to minimize peak energy consumption, cost, PADR, user discomfort, and carbon emissions by efficiently utilizing DGs. Thus, the role of AMI is a communication system between DSO and end users.
## 3 Theory and Methods
### Neural Network Methods
#### 3.2.2 GRU Neural Network  
The internal structure of an LSTM neural network is more complex, and it is difficult to adjust its parameters, which leads to longer time spent on model training. [PERSON] et al. proposed a simplified version of an LSTM called GRU. The cell structure of a GRU neural network is shown in Figure 9.  
Figure 8: Schematic structure of the LSTM neural network.  
Figure 9 illustrates that the GRU's model architecture is comparatively more straightforward than that of the LSTM model. In practice, the GRU model requires less training time than the LSTM model, offering similar predictive accuracy. GRU integrates the input gate and forget gate of LSTM into a single update gate, reducing the memory module to only two gating components: the update gate and the reset gate. The update gate, represented as \(Z_{t}\), manages the degree to which previous data state information is incorporated into the current state. A higher value of the update gate indicates that the present neuron retains more historical information, while the previous neuron retains comparatively less. The primary function of the update gate is to refresh the memory and discern long-term patterns in the water quality data sequence. The formula for the update gate to capture information can be expressed as the following:  
\[Z_{t}=\sigma(W_{Z}\cdot[h_{t-1},X_{t}]) \tag{11}\]  
The reset gate, denoted as \(R_{t}\), plays a crucial role in assessing the extent of historical information retention. A lower reset gate value signifies a higher retention of past historical data, which is conducive to capturing the short-term patterns in the water quality parametric data. The formula for the reset gate to obtain information is the following:  
\[R_{t}=\sigma(W_{r}\cdot[h_{t-1},X_{t}]) \tag{12}\]  
where \(h_{t}\) denotes the output state of the unit at moment \(t\) and \(\widetilde{h}_{t}\) denotes the implied state value of \(h_{t}\). This value is utilized for storing the data of the present unit and transmitting it to the subsequent unit, while the estimated value for the previous moment's output is computed as  
\[\widetilde{h}_{t}=\tanh\bigl{(}W_{\widetilde{h}}\cdot[r_{t}*h_{t-1},X_{t}] \bigr{)} \tag{13}\]  
The forecasted outcomes of the water quality parameter data can be expressed as  
\[h_{t}=(1-Z_{t})*h_{t-1}+Z_{t}*\widetilde{h}_{t} \tag{14}\]  
where \(X_{t}\) denotes the input data value at the current moment \(t\), \(h_{t-1}\) is the output value of the water quality parameter data in the memory cell at moment \(t-1\), \(W_{2}\), \(W_{r}\), and \(W_{\widetilde{h}}\) denote the weight matrix in the cell, \"[]\"denotes the connection of two matrices, \"*\"  
Figure 9: Structure of the GRU model.  
denotes the matrix product, \(\sigma\) is the activation function, and tanh is the bisecting curve of the activation function.