esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Limitations  
In this study, we showed the feasibility of EMG employment for fatigue detection and motor recovery evaluation for inpatients. However, the reduced sensitivity to deeper structures leads to some limitations. Indeed, the activity of deeper muscles that may contribute to the task could not be measured by surface EMG. This problem can be addressed using more invasive procedures or with EMG array-based acquisition and processing techniques that can identify the discharge of multi-channel action potentials by individual muscle units. Alternatively, some works proposed a non-invasive methodology for muscular fatigue detection based on electrodermal activity (EDA) and heart rate variability (HRV) [19] that may be related to EMG and may provide a multi-parameter analysis allowing a complete characterization of the patient. Furthermore, our study considers only the median frequency as biomarker of muscle fatigue, as it is the standard and most used method in the field. Other measures, such as root mean square (RMS) or other spectral features of the EMG signal and NIRS can be included in future work that may provide further insight on muscle fatigue and show correlations among multiple domains of analysis.  
Moreover, since the hip fracture is an accidental event, we could not compare the results to the performance of the patient before the event. Patients may have had a different training level that may be correlated to the MVC increase during rehabilitation and also to fatigue development.  
Finally, for detecting muscular fatigue and perhaps provide a comprehensive analysis of the patient's condition, we should consider tools specifically designed to assist this vulnerable population once frailty has been identified. Given that frailty is the cumulative effect of various conditions and is the most significant factor in regaining independence, addressing this gap is crucial [47; 48].
# Incorporating Drone and AI to Empower Smart Journalism via Optimizing a Propagation Model
## 3 Proposed Drone-Enabled AI Framework
### The Proposed Conceptual Architecture
#### 3.1.5 Two-Ray Model  
The PL of this model can be calculated as:  
\[\mathrm{PL}=\rho_{\mathrm{LoS}}\ \times\mathrm{PL}_{\mathrm{LoS}}\ +\ \rho_{\mathrm{NLoS}}\ \times \mathrm{PL}_{\mathrm{NLoS}} \tag{17}\]  
\[\rho_{\mathrm{LoS}}\ =\mathrm{a}-\frac{\mathrm{a}-\mathrm{b}}{1+\left[\frac{ \theta-\mathrm{c}}{\mathrm{d}}\right]^{\mathrm{e}}} \tag{18}\]  
\[\rho_{\mathrm{NLoS}}\ =1-\mathrm{P}_{\mathrm{LoS}} \tag{19}\]  
\[\mathrm{PL}_{\mathrm{LoS}}\ (\mathrm{dB})=20\log\frac{4\,\pi\left(\mathrm{f} \right)\!\left(\mathrm{d}\right)}{\mathrm{c}}+\ \eta_{\mathrm{LoS}} \tag{20}\]  
\[\mathrm{PL}_{\mathrm{NLoS}}\ (\mathrm{dB})=20\log\frac{4\,\pi\left(\mathrm{f} \right)\!\left(\mathrm{d}\right)}{\mathrm{c}}+\ \eta_{\mathrm{NLoS}} \tag{21}\]  
where PL is Path Loss (dB), \(\mathrm{PL}_{\mathrm{(LoS)}}\), \(\mathrm{PL}_{\mathrm{NLoS}}\) refer to path losses at LoS and NLoS, respectively, \(\rho_{\mathrm{LoS}}\), \(\rho_{\mathrm{NLoS}}\) refer to the probability of having LoS and NLoS, respectively at an elevation angle, d is Distance of Transmission (km), \(f\) is Carrier Frequency (GHz), and a, b, c, d, and e are ITU-R correction factors' parameters for different environment types.  
Regardless of calculating the link budget parameters, each of the five selected models predicts values for five parameters: PL, RSS, fade margin (FM), signal-to-interference-noise ratio (SINR), throughput (Th), and footprint coverage. These are outputs, while inputs are transmitter power (\(\mathrm{P_{t}}\)), receiver power (\(\mathrm{P_{r}}\)), receiver sensitivity (\(\mathrm{R_{s}}\)), transmitter antenna gains (\(\mathrm{Gt}\)), receiver antenna gains (\(\mathrm{G_{r}}\)), (\(\mathrm{L}\)) losses, (\(\mathrm{h_{t}}\)) transmitter altitude, which represents drone altitude, and (\(\mathrm{\theta}\)) elevation angle. Calculating RSS helps estimating the coverage range when the signal weakens as the receiver travels away from the transmitter. FM represents the value of additional path loss over the median path loss that will cause the system performance to fall below an acceptable level. It provides a margin of safety in the event of a temporary attenuation or fading of the received signal power. SINR is used to measure the quality of a wireless link and bit error ratio. Throughput (T) is one of the performance indicators, which is affected with PL, distance, and shadowing. RSS, SINR, and Th can be mathematically expressed as per Equations (22)-(25):  
\[\mathrm{RSS}=\mathrm{P_{t}}+\mathrm{G_{t}}+\mathrm{G_{r}}-\mathrm{P_{L}}-\mathrm{L} \tag{22}\]  
\[\mathrm{FM}=\mathrm{P_{r}}-\mathrm{R_{s}} \tag{23}\]  
\[\mathrm{SNIR}\ (\mathrm{dB})\ =\frac{\mathrm{RSS}}{\mathrm{N+I}} \tag{24}\]  
\[\mathrm{Th}=\mathrm{B}\times\log(1+\mathrm{SNIR}) \tag{25}\]
# Performance Analysis of the Korean Positioning System Using Observation Simulation
## 2 KPS and GPS Constellations  
The constellation configuration of KPS is proposed to provide continuous satellite navigation services in regional coverages, including East Asia and Oceania. The initial constellation of KPS comprises seven satellites of two different orbit types. The three GEO satellites have a semi-major axis of about 42,164 km and have the same orbital period as Earth's rotation period. They are deployed on Earth's equator with an inclination of 0 degrees at about 78\({}^{\circ}\)E, 128\({}^{\circ}\)E, and 178\({}^{\circ}\)E, respectively. The four IGSO satellites in the constellation have the same orbital period and semi-major axis as GEO satellites. They are centered at 128\({}^{\circ}\)E (i.e., equator crossing point) with a nominal 90 degrees separation in the argument of latitude and right ascension of the ascending node (RAAN). They also have an inclination of 43\({}^{\circ}\) and an eccentricity of about 0.075. In addition, KPS IGSO satellites have different orbital planes, but all have the same ground track repeating daily.  
The GPS satellites in the constellation for orbit simulations are deployed in six equally spaced orbital planes. GPS consists of 24 MEO satellites with four satellites in each plane. The MEO satellites have circular orbits with an eccentricity of zero, a semi-major of 26,587 km, and an inclination of 55\({}^{\circ}\) with an altitude of 20,200 km [21]. The orbital period for GPS MEO satellites is precisely half a sidereal day (~11 h 58 m).  
In this study, we first consider the orbit simulation with 24 GPS and 7 KPS satellites. Table 1 lists the orbit parameters and designed constellations for GPS and KPS satellites. It includes the number of satellites, semi-major axis, inclination, eccentricity, and distribution of the three different orbit types.
# Study on the \"Two-Zone\" Heights in Lower Slice Mining Under Thick Al Alluvium and Thin Bedrock
## 4 Height Prediction of 'Two Zones' of Overlying Strata in Lower-Layer Working Face  
### 'Two Zones' Height Empirical Formula  
Coal seam mining causes the overlying strata to form a caving zone and water-conducting fractured zone, commonly known as 'two zones'. The height and distribution of the 'two zones' of overlying strata are closely related to the lithology, structure, and physical and mechanical properties of the overlying strata. The research on the height of the 'two zones' is mainly based on field observation. In the past, based on a large number of field observations, empirical formulas were summarized for calculating the height of 'two zones' for the slice mining of thick coal seams. There are design methods for safety coal (rock) pillars for coal mining near water bodies proposed in the 'construction, water, railway and main roadway coal pillar setting and coal mining specification' [23] (hereinafter referred to as the three-underground mining norm) and the 'two-zones' height empirical formula proposed in the 'hydrogeological engineering geological exploration code for mining area' [24] (hereinafter referred to as the 'exploration specification').  
The roof of the lower slice working face in the first panel to the east is situated between the medium-hard rock layer and the soft rock layer, with a maximum cumulative mining thickness of 6.93 m. According to the calculation method of the development height of the 'two zones' of the overburden rock outlined in the 'three-under mining norm' and the 'exploration specification', the theoretical height of the 'two zones' development after mining in the lower-layer working face of the East No. 1 panel can be obtained, as presented in Tables 1 and 2.  
Based on the two empirical formulas mentioned above, when substituting the coal seam mining thickness M and the number of coal seam slicing layers \(n\), it can be observed that after the mining of the lower slicing working face in the eastern first panel, the development height of the roof caving zone ranges from 11.24 m to 27.72 m, while the development height of the water-conducting fracture zone ranges from 41.58 m to 71.73 m.
## 3 Results and Discussion
### NIRS/NIRS-Based Hyperscanning Research  
NIRS and fNIRS hyperscanning are common methodologies for experimental studies, given their increased ecological validity compared to other neuro-imaging methods. While NIRS has a lower temporal resolution compared to EEG, it has a significantly higher spatial resolution [114]. Table 3 shows a summary of 44 NIRS/fNIRS-based hyperscanning papers. The most common application is cognition, representing seventeen of the 44 studies (38.6%). Studies with decision-making task applications comprised thirteen of the 44 studies (29.5%), closely followed by applications in motor synchronization, composed of twelve studies (27.3%). Applications in education were the least common, with only two studies (4.6%). Some examples of fNIRS/NIRS hyperscanning experimental tasks include computer games, motor tasks, synchronization games, musical activities, and facilitated discussion/brainstorming sessions.
# Development of the Virtual Reality Application:
## 2 Methodology
### Developing and Optimizing a VR Application in Unity3d
#### 2.3.2 Choice of Lighting Method  
The second step is using baked lighting instead of real-time lighting. Real-time lighting requires every triangle to be drawn multiple times in order to display the mesh, the texture that is applied on the mesh, and the shadows cast by the mesh or nearby meshes. Although this can produce more realistic lighting and shadows, it is very intensive on hardware and may produce lower than necessary performance, because VR is already intensive on the GPUs. On the other hand, baked lighting is precomputed and has a very small performance overhead, while also providing acceptable levels of lighting and shadow quality [27].  
In Unity3d, lighting is separated into direct and indirect (Figure 14). Every ray cast from the light source that hits a mesh is considered direct lighting, and every surface that is lit from the bounces of those rays is considered indirect lighting [27].  
Baked lighting was chosen in order to save as much performance as possible, given the fact that each frame is drawn twice for any VR application (once for each eye). For this method a Lightmapper was needed, which is a piece of software that uses light sources in a scene to precompute direct and indirect lighting and shadows cast from all sources, and then compresses the data into textures called lightmaps. These lightmaps were then overlaid on the textures applied to each mesh to give the illusion of lighting and shadows. Unity3d does offer a Lightmapper that is built into the engine, but it was decided to use a Unity Asset Store package named \"Bakery GPU Lightmapper\" because of its superior  
Figure 13: Ships from the VR application “Ships of Navarro”. Screenshot from Unity3d.  
speed and end result. Bakery's standard shader is equivalent to Unity3d's standard shader with a few extra features. For example, Unity3d's built-in Lightmapper does not include lightmap specular highlights while Bakery GPU Lightmapper does. This makes scenes using Bakery a lot more vibrant and realistic [28].
# Evaluating the Latest IMERG Products in a Subtropical Climate: The Case of Parana State, Brazil
## 2 Material and Methods
### Data
#### 2.2.3 Performance Analyses
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Index** & **Unit** & **Equation * & **Best Value** \\ \hline Determination coefficient (R\({}^{2}\)) & - & R\({}^{2}=\frac{\left\{\sum\limits_{i=1}^{n}(P_{i}-P)(\text{O}_{i}-\text{O}_{i}) \right\}^{2}}{\sum_{i=1}^{n}(P_{i}-P)^{2}\sum_{i=1}^{n}(\text{O}_{i}-\text{O} _{i})^{2}}\) & 1 \\ \hline Mean error (MBE) & mm & MBE =\(\frac{1}{n}\sum\limits_{i=1}^{n}(P_{i}-\text{O}_{i})\) & 0 \\ \hline Mean absolute error (MAE) & mm & MAE = \(\frac{1}{n}\sum\limits_{i=1}^{n}|P_{i}-\text{O}_{i}|\) & 0 \\ \hline Root of the mean square error (RMSE) & mm & RMSE = \(\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}(P_{i}-\text{O}_{i})^{2}}\) & 0 \\ \hline Kling–Gupta Efficiency (KGE) & - & KGE = \(1-\sqrt{(r-1)^{2}+(\alpha-1)^{2}+(\beta-1)^{2}}\) & 1 \\ \hline Coefficient of skewness (SK) & - & SK = \(\frac{3\left(\text{P}_{i}-\text{M}_{\alpha}\right)}{\sigma}\) & 0 \\ \hline Probability of detection (POD) & - & POD = \(\frac{\text{Hits}}{\text{Hits}+\text{Misses}}\) & 1 \\ \hline Critical success index (CSI) & - & CSI = \(\frac{\text{Hits}}{\text{Hits}+\text{FalseAlarm}+\text{Misses}}\) & 1 \\ \hline False alarm ratio (FAR) & - & FAR = \(\frac{\text{FalseAlarm}}{\text{Hits}+\text{FalseAlarm}}\) & 0 \\ \hline \hline \end{tabular}  
* where, O is the observed data of gauges of order \(i\); P i is the estimated order data (IMERG) of order \(i\); \(r=r\)Pearson, \(\alpha\) is the ratio between simulated variance and observed variance, and \(\beta\) is the ratio between simulated mean and observed mean; P is the average of the estimated data (IMERG) \(\widehat{\text{O}}_{i}\) is the average of the observed data (gauges); M\({}_{i}\) is the median error of satellite precipitation estimates; Hits are the days when IMERG and the station recorded rain; FalseAlarm are the days when IMERG recorded rain, but the gauges did not; Misses are the days when IMERG did not register rain, but the gauges did.  
\end{table} 
Table 1: Summary of statistical indices used to evaluate the satellite precipitation products. gauge precipitation amount class. In the perfect scenario, the matrix should be antidiagonal and present a value equal to 1 along the antidiagonal line and 0 for the all the other elements [43].
###### Abstract  
To measure the pushing distance of a hydraulic-powered roof support, and reduce the cost from a non-reusable displacement sensor embedded in pushing a hydraulic cylinder, an inertial sensor is used to measure the pushing distance, and a Kalman filter is applied to process the inertial data. To obtain better estimation performance, an improved fruit fly optimization algorithm (IFOA) is proposed to tune the parameters of the Kalman filter, processing noise covariance \(Q\) and observation noise covariance \(R\). The key procedures of the proposed method, including state-space model, fitness function, and Kalman filter implementation, are presented. Finally, an artificial signal is utilized to verify the feasibility of the proposed method, and the tuning results of other algorithms, particle swarm optimization (PSO), genetic algorithm (GA), basic FOA, and 3D-FOA are compared. The proposed method is also applied in the pushing distance estimation scenario. The simulation and application results prove the effectiveness and superiority of the proposed method.  
Kalman filter; fruit fly optimization algorithm; hydraulic powered roof support; state-space model +
Footnote †: journal: Applied Sciences
## 4 ITS Architecture Proposed for the FMCS
### Reference ITS Architectures Review
#### 4.2.1 ARC-IT (Architecture Reference for Cooperative and Intelligent Transportation)  
This American architecture establishes 12 areas of transportation services [77]. The two ARC-IT services of interest for this work are Public Transportation Transit Vehicle Tracking, and Transit Fleet Management (abbreviated as PT01 and PT06). The first one focuses on monitoring the current location of the transit vehicle using an Automated Vehicle Location System, while PT06 allows scheduling and automatic traffic maintenance monitoring.  
Both architectures keep certain similarities in their design because they share several of the physical objects that compose them, such as the Transit Management Center (TMC), in charge of managing the fleet of transit vehicles, and providing operations, maintenance, customer information, planning, and management functions for transit property in coordination with other modes and transport services.
# Attribute Relevance Score: A Novel Measure for Identifying Attribute Importance
## 4 Simulation Results
### Experiment #2: Benchmark Dataset
#### 4.2.1 Results #2.1  
Examining the results without the noise term \(\eta\) offers additional insight into the ARS measure's effectiveness in isolating informative dependencies. To highlight this, we present the ARS behavior derived from Equation (15) after excluding \(\eta\). This \"noise-free\" version of ARS is evaluated using three distinct base models: decision trees, linear regression, and k-nearest neighbors (k = 5). By removing the noise component, we aim to reveal how well each model captures the inherent relationships between \(\mathbf{x}_{1}\), \(\mathbf{x}_{2}\), and \(\mathbf{x}_{3}\) and the target variable \(\mathbf{y}\), thereby underscoring the relative strengths of each approach in detecting true signal (see Figure 2).  
**Performance on benchmark dataset without noise for different base models**: Figure 2 illustrates the ARS measure's sensitivity to both informative variables (\(\mathbf{x}_{1}\), \(\mathbf{x}_{2}\), and \(\mathbf{x}_{3}\)) and noninformative variables (\(\mathbf{x}_{4}\), \(\mathbf{x}_{5}\), and \(\mathbf{x}_{6}\)) under different base models (decision trees, linear regression, and k-nearest neighbors) with the noise term \(\eta\) excluded. The decision tree and k-nearest neighbors models demonstrate a stronger capacity to capture the inherent dependencies, particularly for the nonlinear relationships in \(\mathbf{x}_{1}\) and \(\mathbf{x}_{2}\). In contrast, the linear regression model struggles to detect these dependencies, reflecting its limitations with nonlinear data. For the noninformative variables \(\mathbf{x}_{4}\), \(\mathbf{x}_{5}\), and \(\mathbf{x}_{6}\), ARS consistently returns zero across all quartiles, with only occasional nonzero outliers, highlighting ARS's robustness in distinguishing relevant features from noise. This behavior underscores ARS's reliability in filtering out noninformative attributes, even in the absence of noise.
## 4 Evaluation of Application Potential of Supercritical Technology in Offshore Heavy Oil Reservoirs
### Cumulative Oil Production Evaluation Method  
By using a multiple linear regression method, a multiple regression model was established between y function of adaptability evaluation of different reservoir types and key parameters such as oil viscosity, reservoir permeability, reservoir pressure, and reservoir thickness; the formula is shown in Formula (2). Based on the cumulative oil production evaluation method, the application potential of supercritical technology in offshore heavy oil reservoir is ranked (Table 15).  
\[\text{y}=-0.00114\text{ a}+0.00084\text{ b}+0.53766\text{ c}-0.11004\text{ d}+0.22176\text{ e}+0.49122 \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Rank** & **Single Sand Body Oil Field** & **y Value** \\ \hline
1 & LD 21-2 IV oil group & 14.74 \\ \hline
2 & PL19-3 11/13 area & 12.74 \\ \hline
3 & LD 16-3 & 12.19 \\ \hline
4 & KL 10-2 & 10.60 \\ \hline
5 & QHD 27-3 & 10.49 \\ \hline
6 & LD 27-2 & 9.65 \\ \hline
7 & NB 35-2 & 9.34 \\ \hline
8 & PL 13-2 & 9.14 \\ \hline
9 & LD 27-1 & 8.77 \\ \hline \end{tabular}
\end{table}
Table 15: Evaluation of adaptive screening criteria based on oil production method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Oil Field** & **Reservoir** & **Reservoir** & **Water** & **Reservoir** & **Oil Viscosity/CP** \\  & **Thickness/m** & **Pressure/MPa** & **Energy/times** & **Permeability/mD** & **Oil Viscosity/CP** \\ \hline LD 21-2 IV oil group & 40 & 15.3 & 10 & 1966 & 2980 \\ \hline PL19-3 11/13 area & 25 & 12 & 2 & 1161 & 438 \\ \hline KL 10-2 & 8 & 13 & 1 & 3000 & 935 \\ \hline QHD 27-3 & 7 & 10.6 & 1 & 4000 & 440 \\ \hline LD 27-2 & 8 & 13.1 & 0.1 & 2300 & 1383 \\ \hline NB 35-2 & 8 & 8.5 & 5 & 4600 & 707 \\ \hline PL 13-2 & 8.3 & 11 & 0 & 1957 & 661 \\ \hline LD 27-1 & 8 & 11.8 & 0 & 1600 & 1038 \\ \hline JX 1-1 & 10 & 11.6 & 5 & 1000 & 593 \\ \hline QHD 33-1S & 5 & 10.6 & 1 & 2078 & 750 \\ \hline KL 9-5 & 10 & 10 & 10 & 1726 & 910 \\ \hline LD 32-2 & 15 & 12.1 & 60 & 3130 & 498 \\ \hline \end{tabular}
\end{table}
Table 14: Geological parameters of offshore heavy oil reservoirs.  
In the Formula (2), \(y\) is the cumulative oil production, 10\({}^{4}\) m\({}^{3}\); a is the formation oil viscosity, cP, b is reservoir permeability, mD, c is the reservoir pressure, MPa, d is the multiple of water, and e is the effective reservoir thickness, m.
## 5 Technical Challenges and Future Research Directions  
Grassmann manifold learning faces several key challenges: (i) addressing nonlinearity, (ii) selecting appropriate metrics, and (iii) reliance on single-manifold representations. Firstly, to represent datasets as points on a Grassmann manifold, data orthogonality must be ensured. This requirement aligns well with the nonlinear nature of many datasets. However, most clustering methods still rely on Euclidean metrics, which may not fully capture the intricacies of the Grassmann structure. Future research must carefully integrate nonlinearity to broaden the scope of Grassmann manifold applications.  
Once a dataset is represented as Grassmann points, it becomes essential to utilize metrics specifically designed for the Grassmann manifold to ensure effective analysis. In this work, we outlined three Grassmannian approaches to address this challenge, offering practical guidelines for selecting the most suitable method based on their respective strengths and limitations. Nevertheless, further research is required to establish optimal metrics tailored to various datasets and experimental conditions. Additionally, most existing manifold learning studies rely on a single manifold to represent datasets. However, datasets can often be better characterized on Grassmann manifolds of varying dimensions. Metrics such as the generalized Grassmannian distance [60], which calculates distances between manifolds, facilitate this flexibility. Previous research [41] has demonstrated the potential of using multiple manifolds, and similar approaches are becoming increasingly important as the truncation dimensions of Grassmann manifolds can vary depending on the characteristics of the data.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Approaches** & **Merits** & **Demerits** \\ \hline \multirow{2}{*}{Intrinsic} & Exploit the true manifold & Inflexible, the existing algorithms \\  & geometric characteristics & have to be substantially modified, \\  & & if not designed afresh \\ \hline \multirow{2}{*}{Ex-Tangent space} & Can adopt any commonpurpose & Problematic if the mapping point \\  & clustering algorithm in principal & is too far away from reference point, \\  & & e.g., Karcher mean \\ \hline \multirow{2}{*}{Ex-Kernel embedding} & Give reasonably good performance, & Inflexible, limited to kernel-based \\  & can adopt various Grassmann kernels & clustering methods, inherit open \\  & & problems of kernel learning methods \\ \hline \multirow{3}{*}{Semi-intrinsic} & Enable exploiting true manifold & Slow, require forward and reverse \\  & geometric characteristics without major & mappings between manifold and \\  & alteration on the existing algorithms & tangent space iteratively \\ \hline \hline \end{tabular}
\end{table}
Table 6: Merits and demerits of three umbrella Grassmann MTS clustering algorithms.