esa-sceva/satcom-chunk-collection huggingface dataset:
# Interactions in Augmented and Mixed Reality: An Overview
## 3 Research Results
### 3.3. Haptic-Based Modality
#### 3.3.3 Controls-Based  
[PERSON] et al. [148] presented IncreTable, a mixed reality tabletop game that exploits the _digital controls handling_ interaction method. The users can perform interactions by utilizing digital pens with an embedded camera that tracks Anoto patterns printed on a special backlit foil. [PERSON] et al. [149] employed a joystick-based interaction to help the user drive a remote robot. The virtual 3D axes (x,y,z) were color-displayed on top of the real robot, which the user observed from a camera. Through the color indications of the robot's local axes system, the user could decide how to move the joystick to complete a task. [PERSON] et al. [150] used MR applications to train students in medical examinations of vulnerable patients (e.g., children with speaking disabilities). They have created two interfaces for interaction; a natural interface that utilizes a tablet for drawing shapes and a mouse-based interface for the same purpose. A final survey showed that the participants preferred the natural interface from the mouse-based interaction. Finally, [PERSON] et al. [151] proposed HiKeyb, a typing system with a style of mixed reality. They used a depth camera, a head-mounted display (HDM) and a QWERTY keyboard to enhance the immersive experience of typing in MR.  
A practical method that allows haptic interactions is _capacitive sensing_. This method uses the measurable distortion that an object with electrical characteristics (such as capacitance) creates within an electric field oscillating at low frequency [152]. [PERSON] et al. [153] proposed Touche; a novel swept frequency capacitive sensing technique that can detect a touch event and recognize complex human hands and body configurations with extremely high accuracy. [PERSON] et al. [154] proposed ElectroRing, a 3D-printed wearable ring similar to an active capacitive sensing system that can detect touch and pinch actions. Finally, HydroRing [155] was presented by [PERSON] et al., which is yet another wearable device that can provide tactile sensations of pressure, vibration, and temperature on the fingertip, enabling mixed-reality haptic interactions using liquid travelling through a thin, flexible latex tube.
## 5 Experimental Results
### Example Analysis
It can be seen that in the initial stage of the algorithm operation, the makespan will be optimized quickly as a higher priority goal, and the number of machine shutdowns and handling batches will be gradually reduced in the jump. The number of machine shutdowns and the number of handling batches will change during the search for the minimum makespan, which will still decrease in the subsequent search process, thus realizing the overall optimization of the scheme. In order to prove that the algorithm designed in this paper is still effective in solving large-scale application examples, a new adaptation example is designed for experiments. This example includes five types of jobs, 20 jobs, six processes, five work centers, and 12 machines. The processing time is adapted from the problem Sm01.1 from the article [7]. The design strategy of setup time and handling time refers to the same small-scale example as above. The processing time of the example is evenly distributed within the interval [10,30]. The setup time of the example is evenly distributed within the interval [0,4]. The handling time of the example is evenly distributed within the interval [1,7]. Figure 13 shows the scheduling Gantt chart of the optimal solution, and the information form is explained in the same way as Figure 9. The objectives of the optimal solution  
Figure 11: Iterative results of the total number of machine shutdowns. Figure 10: Iterative results of the makespan. are as follows: the makespan is 185, the total number of machine shutdowns is 17, and the total number of handling batches is 54.
## 2 Materials and Methods
### Statistical Analysis  
Statistical analysis was performed using SPSS version 20.0 for Windows. Before performing the comparative analysis, the normal or non-normal distribution of the quantitative variables was tested using the Shapiro-Wilk test. A linear mixed model with repeated-measures analysis was used to investigate the differences in outcomes in terms of time (baseline, postintervention, and one-week follow-up) and group (DN and DF). Analysis was performed on an intention-to-treat principle. Change scores from baseline were cal  
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **ICC (95\% CI)** & **SEM** & **MDC** \\ \hline ER ROM (\({}^{\circ}\)) & 0.92 (0.88, 0.95) & 2.29 & 6.35 \\ IR ROM (\({}^{\circ}\)) & 0.93(0.89, 0.97) & 1.83 & 5.07 \\ HA ROM (\({}^{\circ}\)) & 0.90 (0.83, 0.97) & 2.27 & 6.29 \\ \hline Extensibility (\({}^{\circ}\)) & 0.92 (0.9, 0.94) & 2.23 & 6.19 \\ Tone (Hz) & 0.98 (0.96, 0.99) & 0.29 & 0.80 \\ Stiffness (N/m) & 0.97 (0.95, 0.98) & 8.99 & 24.91 \\ \hline \hline \end{tabular} ICC: intraclass correlation coefficient; CI: confidence interval; SEM: standard error of measurement; MDC: minimal detectable change; ER: external rotation; IR: internal rotation; HA: horizontal abduction; ROM: range of motion.  
\end{table}
Table 1: Test–retest reliability of the outcome variables.  
culated for postintervention and one-week follow-up. A \(p\)-value < 0.05 was considered statistically significant. The magnitude of the differences was classified according to the value of Cohen's d. The effect size was considered small if the Cohen's d value ranged from 0.2 to 0.5, moderate from 0.5 to 0.8, or large if the value was greater than 0.8 [35].
## 4 Analytical Framework for Heterogeneous Service Provisioning
### Problem Overview and Healthcare Service Requirements
#### 4.1.6 Delay Estimation and Latency Modeling  
Accurate estimation of latency is essential for delivering critical healthcare services such as real-time ECG monitoring or fall detection. Rather than relying solely on analytical queueing approximations, we adopt a regression-based ML model--specifically, a gradient-boosted decision tree (GBDT)--to predict delay functions \(d_{s}(g_{s,i}^{t})\). The model is trained on time-series network metrics, including past traffic volumes, queue lengths, and packet loss events, allowing it to generalize to unseen traffic conditions and rapidly adapt to disruptions such as link failures or bursty demand.
## 4 Results and Discussions
### Normalization Results  
To verify the effect of the normalization process, we compared the reconstructed Sentinel-2 images before and after the normalization process for all six images shown in Figure 9. It was apparent that the differences between the reconstructed images before and after the normalization process were not considerable for all six images. As discussed above, the main reason was that the synthetic Sentinel-2 images were well predicted and resembled their observations. The normalization process only made minor changes to those replacement pixels from the synthetic Sentinel-2 images and brought them to match better to their true observation based on the cloud-free pixels of the observations. Overall, the images after the normalization looks smoother and more natural than the reconstructed images without the normalization. For example, we saw some differences between the cloud-free pixels and the replacement pixels on the image dated 5 October 2018 before the normalization process. However, the differences were greatly reduced on the same image after the normalization.
## Appendix A Global Optimization  
\"[PERSON] et al. [17] summarizes that global optimization methods have been quite interesting in different fields, including well-log inversion, geophysical inversion, and reservoir history matching. The most important advantage of these methods is their higher chance to find the global optimum of the objective function than other methods such as gradient-based algorithms. Consequently, they are mainly of benefit when gradient-based methods fail due to significant nonlinearity and discontinuities in the model parameter space in which situation the local optimization methods can get trapped in the local optimums of the objective function. The global optimization approaches such as simulated annealing work via direct search in model parameter space and they only need the value of the objective function to update model parameters in each iteration. In other words, they do not require the computation of the objective function's derivatives. This is another advantage of these methods compared to the gradient-based approaches. Furthermore, the global optimization techniques do not depend on a prior model and they can be basically initialized with a uniform or (if known) prior distribution of model parameters. Any form of prior model information can be easily incorporated. An additional advantage of global optimization techniques is their ability to deal with non-Gaussian random fields which are the essence of any reservoir modeling and characterization studies that combines various data from different sources. Finally, because of the stochastic nature of such algorithms, the mean solution, the most probable answer, and more generally the corresponding uncertainty of model parameters can be easily evaluated with different randomly chosen initial models. Despite having such unique characteristics, these algorithms cannot be expected to always perform perfectly since parameters have to be carefully selected for the specific problem in hand. For example, in simulated annealing it is crucial to appropriately choose the starting temperature and cooling schedule. Besides, the effectiveness of global optimization techniques is largely controlled by designing a rapid forward modeling operator to generate simulated data, e.g., synthetic resistivity well logs simulated by PCM technique. The availability of powerful computers and parallel computing algorithms are also necessary to perform global optimization techniques efficiently.
# Unsupervised Classification of Human Activity with Hidden Semi-Markov Models
## 3 Results
### Model Validation
\begin{table}
\begin{tabular}{l c c c} \hline \hline  & **Magnitude** & **Duration** \\ \hline
**Activity** & \(\mu\) & \(\sigma^{2}\) & \(\lambda\)**(s)** \\ \hline Walking & 0.17 & 0.058 & 8 \\ Tasks & 0.04 & 0.001 & 308 \\ SB & 0.01 & 0.0001 & 366 \\ Sleeping & 0.002 & 3 \(\times 10^{-6}\) & 452 \\ MPA & 0.11 & 0.005 & 254 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Acceleration magnitude and duration for each activity estimated by the HSMM. Figure 3: [PERSON]–[PERSON] plots for method agreement. The methods compared are the classification of activity by [PERSON] et al. [7] and by the HSMM. PA denotes the overall physical activity, as calculated by summing the time spent in moderate activity, walking and light tasks. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Outcome** & **Activity** & **Reference** & **Inferred** \\ \hline BMI (kg/m\({}^{2}\)) & SB (h/day) & 3.27 *** & 4.51 *** \\ BMI (kg/m\({}^{2}\)) & PA (h/day) & \(-3.43\) *** & \(-2.74\) *** \\ BMI (kg/m\({}^{2}\)) & Sleep (h/day) & 0.16 & \(-1.77\) * \\ WC (cm) & SB (h/day) & 9.96 *** & 12.54 *** \\ WC (cm) & PA (h/day) & \(-11.46\) *** & \(-7.62\) ** \\ WC (cm) & Sleep (h/day) & 1.51 & \(-4.92\) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Linear regression coefficients for the associations between overall physical activity (PA), sedentary behaviour (SB) and sleep (Sleep) with anthropometric measures: BMI (body mass index) and WC (waist circumference). The coefficients represent the increase in outcome for each extra hour/day spend in each activity. The time spent in PA is calculated as the sum of time in moderate activity, walking and light tasks. The reference coefficients were estimated by using the labelled data by [PERSON] et al. [7], while the inferred coefficients were estimated by using our HSMM approach. (*: \(p\)-value \(<\) 0.05, **: \(p\)-value \(<\) 0.01, ***: \(p\)-value \(<\) 0.001).
# Current-Signal-Based Fault Diagnosis of Railway Point Machines Using Machine Learning
## 2 Methods
### Experimental Setup and Data Preparation  
The experimental data in this study were obtained from PT KAI (Persero), the largest railway operator in Indonesia. In order to simulate the warning and failure conditions, the current data of a railway point machine were obtained by conducting the experiments on an RPM test bench so it did not interrupt railway operation. The research object was an NSE-type RPM, the most commonly used type of RPM in Indonesia. The NSE-type RPM has a 120 Vdc motor to operate the moving rod in order to change the point direction. Figure 2 shows the experimental setup.  
This work employed a WCS1700 current sensor, which was linked to a NodeMCU ESP8266 microprocessor. The current sensor reading was calibrated with a 3041A Precision Multi-Product Calibrator Transmille. After calibration, the current sensor was installed in the neutral line of the RPM equipment's power line input. Figure 3 shows the installation  
Figure 1: The proposed research methodology using current signals and machine learning for fault diagnosis of railway point machines.  
Figure 2: Experimental setup: (**a**) workstation laptop, (**b**) current sensor, (**c**) NSE-type RPM, (**d**) RPM test bench.  
connection of the current sensor in the neutral line of the RPM equipment. The working current always passes through the neutral line when the RPM is operating in a normal direction and can also reverse direction. So, the current sensor can monitor the RPM during every type of operation.  
The current data were acquired while the RPM was in operation and recorded to a.csv file on the workstation laptop. The data acquisition was performed at a sampling frequency of 10 Hz under three categories of RPM conditions: (1) normal, (2) warning, and (3) failure. Normal conditions occurred when the RPM performed its functions in good condition. Warning conditions occurred when the clutch assembly of the RPM contained old grease mixed with mud and needed to be serviced. The clutch assembly is a critical component of the RPM that engages and disengages the power transfer between the gearbox and the moving rod. The warning conditions of the clutch were considered because they cannot be examined through conventional maintenance such as visual inspection and are commonly found in RPM operation [11]. Figure 4 depicts the difference between the RPM clutch assembly under warning and normal conditions. Failure conditions occurred when the RPM was unable to travel into the end position due to an obstruction. The collected time-series current signals were processed and filtered using time-domain feature extraction and scalable hypothesis testing. The filtered features were utilized as the dataset for machine learning modeling in railway point machine fault diagnosis.  
Figure 3: Illustration of current sensor installation.
## 3 Results
### End-User Communication and Interaction with Technology  
As shown in Table 1, the interaction between end-users, including patients, their families, healthcare practitioners, and pharmacists, with technology plays a critical role  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
in transplant patient care. Patients and their families primarily utilize mobile health applications and telehealth platforms to track vital health information, manage medication adherence, and maintain communication with healthcare providers [20; 22; 29]. These technologies empower patients to take an active role in managing their health, with features such as medication reminders and health alerts [30]. Pharmacists and healthcare practitioners, in turn, leverage these digital tools to remotely monitor patients' medication, health metrics, and overall well-being, ensuring continuous care post-discharge [31; 32; 10; 33]. Additionally, the use of AI-driven tools allows healthcare providers to predict long-term risks, such as graft failure, enabling early intervention [34]. The integration of these technologies fosters improved communication, enhances patient self-management, and supports efficient care coordination, particularly for patients in rural or remote areas [35; 36].
# Investigating 2-D and 3-D Proximal Remote Sensing Techniques for Vineyard Yield Estimation
###### Abstract  
Vineyard yield estimation provides the winegrower with insightful information regarding the expected yield, facilitating managerial decisions to achieve maximum quantity and quality and assisting the winery with logistics. The use of proximal remote sensing technology and techniques for yield estimation has produced limited success within viticulture. In this study, 2-D RGB and 3-D RGB-D (Kinect sensor) imagery were investigated for yield estimation in a vertical shoot positioned (VSP) vineyard. Three experiments were implemented, including two measurement levels and two canopy treatments. The RGB imagery (bunch- and plant-level) underwent image segmentation before the fruit area was estimated using a calibrated pixel area. RGB-D imagery captured at bunch-level (mesh) and plant-level (point cloud) was reconstructed for fruit volume estimation. The RGB and RGB-D measurements utilised cross-validation to determine fruit mass, which was subsequently used for yield estimation. Experiment one's (laboratory conditions) bunch-level results achieved a high yield estimation agreement with RGB-D imagery (r\({}^{2}\) = 0.950), which outperformed RGB imagery (r\({}^{2}\) = 0.889). Both RGB and RGB-D performed similarly in experiment two (bunch-level), while RGB outperformed RGB-D in experiment three (plant-level). The RGB-D sensor (Kinect) is suited to ideal laboratory conditions, while the robust RGB methodology is suitable for both laboratory and in-situ yield estimation.  
Kinect sensor; RGB; RGB-D; image segmentation; colour thresholding; bunch area; bunch volume; point cloud; mesh; surface reconstruction +
Footnote †: journal: Sensors
# Wavelet Transform Application for/in Non-Stationary Time-Series Analysis: A Review
## 3 Wavelet Transformation for Non-Stationary Time-Series: State-of-the-Art
### Application of WT in Hydrology
First, the Morlet and Paul CWT have been used to analyze the periodicity and variability of the precipitation data over the period between 1964 and 2007. Second, the scale-average wavelet power has been applied to carry out the cluster analysis. Then, the correlation distance between the different stations together with 16 statistics, such as the geometric mean, the standard deviation and the kurtosis, have been calculated.
## 4 Convolutional/LSTM-Based Resource Allocation Framework
### Convolutional/LSTM Neural Network
\begin{array}{c} \mathbf{x}_{j}\\ \mathbf{h}_{j-1}\end{array} \right]+\left[ \begin{array}{c}\mathbf{b}_{i}\\ \mathbf{b}_{f}\\ \mathbf{b}_{o}\end{array} \right]\right) \tag{4}\]  
\[\mathbf{s}_{j}=\mathbf{f}_{j}\odot\mathbf{s}_{j-1}+\mathbf{i}_{j}\odot\varphi (\mathbf{W}_{gh}\mathbf{i}_{j-1}+\mathbf{W}_{gx}\mathbf{x}_{j}+\mathbf{b}_{g}) \tag{5}\]  
\[\mathbf{h}_{j}=\mathbf{o}_{j}\odot\varphi(\mathbf{s}_{j}) \tag{6}\]  
where \(\mathbf{W}_{ix}\), \(\mathbf{W}_{ih}\), \(\mathbf{W}_{fx}\), \(\mathbf{W}_{fh}\), \(\mathbf{W}_{ox}\), \(\mathbf{W}_{oh}\), \(\mathbf{W}_{gx}\), \(\mathbf{W}_{gh}\) are weight matrices for the corresponding inputs, \(\mathbf{b}_{i}\), \(\mathbf{b}_{f}\), \(\mathbf{b}_{o}\) and \(\mathbf{b}_{i}\) are the bias vectors, \(\odot\) denotes the Hadamard product, \(\varphi(\bullet)\) and \(\sigma(\bullet)\) denote the tanh and sigmoid activation functions. Finally the Feed Forward layer outputs the normalized processing capacities \(\mathbf{\hat{c}}_{nS}^{a,nor}\) and the de-normalization one provides the not normalized processing capacities \(\mathbf{\hat{c}}_{nS}^{a}\) to be allocated in the allocation period. They are achieved from the following expression:  
Figure 3: LSTM Cell Block \(LCB_{j}\). \[\hat{\mathbf{c}}_{j}^{a}=C_{min}\mathbf{o}_{N_{VNFI}^{a}}+\hat{ \mathbf{c}}_{j}^{a,nor}(C_{max}-C_{min}) \tag{7}\] \[j\in[n..n-L+1]\]
## 3 Results
### Characteristics of Spatial and Temporal Changes in NPP
#### 3.1.2 Characteristics of Interannual Changes in NPP Values  
We separately calculated the mean values and interannual changes in NPP in eight cities and counties in the northwest Liaoning region from 2001 to 2021 (Figure 4). In general, the yearly average NPP in the northwest Liaoning region displayed a varying upward trend, with relatively stable changes, while the cities and counties had similar trends. Over the two decades, the annual average NPP value in 2021 was significantly higher than in other years, and it was also higher in 2012. Before and after 2012, there was a sequence of growth succeeded by a reduction. Comparing the annual average NPP values in different regions, Huludao City had a higher NPP value than other areas, with 11 years exceeding 400 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), indicating vigorous vegetation growth. From 2001 to 2012, Fuxin City had an overall lower mean annual NPP than other regions, and from 2013 to 2021, Xinmin City had the lowest value in the region. Calculating the disparity between the highest and lowest values showed that the region with the most substantial rise in the yearly average NPP was Chaoyang City, possessing a figure of 167.1 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\), while the region with the smallest change was Changtu County, with an increase of 124.5 gC\(\cdot\)m\({}^{-2}\cdot\)a\({}^{-1}\). The increase in NPP values in descending order of magnitude was as follows: Chaoyang City, Fuxin City, Kangping County, Faku County, Huludao City, Xinmin City, Jinzhou City, and Changtu County.  
Figure 4: Interannual variation in average NPP for different regions in the northwest Liaoning Province from 2001 to 2021.
# Fast Fault Line Selection Technology of Distribution Network Based on MCECA-CloFormer
## 2 Characteristics Analysis of Single-Phase Grounding Fault  
The simplified equivalent network of a resonant grounding system during a single-phase grounding fault occurrence is illustrated in Figure 1, for a distribution network comprising n feeders, with the fault taking place on the ith feeder. Here, \(u_{0}\) represents the zero-sequence voltage of the bus bar, \(u_{0i}\) denotes the zero-sequence voltage of the power supply, and \(i_{L}\) signifies the equivalent zero-sequence current of the arc suppression coil. The variables \(i_{01}\), \(\ldots\), \(i_{0n}\) denote the zero-sequence currents of respective feeders, while \(C_{01}\), \(\ldots\), \(C_{0n}\) represent the zero-sequence capacitances of these feeders. The directions of zero-sequence currents in faulted and healthy lines, as illustrated in the figure, indicate their respective flow patterns.  
Figure 1: Simplified zero-sequence equivalent network of a resonant grounding system.  
From the diagram, expressions for the zero-sequence currents in both the faulty and healthy feeders can be derived, as represented by the following equations:  
\[i_{0m}=C_{0m}\frac{\mathrm{d}u_{0}}{\mathrm{d}t} \tag{1}\]  
\[i_{0i}=-(i_{L}+\sum\limits_{m=1,m\
eq i}^{n}i_{0m})=-(i_{L}+\sum\limits_{m=1,m \
eq i}^{n}C_{0m}\frac{\mathrm{d}u_{0}}{\mathrm{d}t}) \tag{2}\]  
As Equation (1) illustrates, the healthy feeders, being under the same zero-sequence voltage condition, exhibit identical expressions for their zero-sequence currents and display a high degree of waveform similarity. Equation (2), on the other hand, reveals that the magnitude of the zero-sequence current in the faulty feeder equals the sum of the zero-sequence currents from all healthy feeders combined with the current from the arc suppression coil. Comparing this with Equation (1), it becomes evident that there exists a disparity in the waveforms of the zero-sequence currents between the healthy and faulty feeders. The subsequent work in this paper will focus on exploring these waveform discrepancies.