esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Method
#### 2.3.1. FOD Spectral Data Processing  
FOD is a branch of mathematical analysis introduced in the late 17 th century by [PERSON] and [PERSON] [14; 15; 16]. Unlike classical integer-order calculus (such as first-order or second-order derivatives), FOD extends the operation to any real-number order, offering a more precise description of dynamic characteristics in complex systems, including subtle variations in spectral reflectance signals. After more than three centuries of development, several definitions of FOD have been proposed from different perspectives, such as the Riemann-Liouville, Grunwald-Letnikov, and Caputo definitions [17; 18]. Although a unified formula for FOD has not yet been established, the Grunwald-Letnikov definition is widely adopted for its ease of use in numerical calculations and engineering applications [19; 20; 21].  
In this study, the Grunwald-Letnikov definition was used to carry out FOD as follows [22].  
For a function \(f(x)\), the Grunwald-Letnikov fractional derivative of order \(\alpha\) is defined as follows:  
\[D^{\alpha}f(x)=\underset{h\to 0}{lim}\frac{1}{h^{\alpha}}\sum_{k=0}^{ \infty}{(-1)^{k}(\binom{\alpha}{k})f(x-kh)} \tag{1}\]  
where  
\(D^{\alpha}f(x)\) denotes the fractional derivative of order \(\alpha\) of \(f(x)\);  
\(\alpha\in R\) represents the fractional order of differentiation;  
\(h\) represents a positive real number approaching zero;  
\(\binom{(\alpha}{k})\) represents the generalized binomial coefficient, defined as follows:  
\[\binom{\alpha}{k}=\frac{\Gamma(\alpha+1)}{\Gamma(k+1)\Gamma(\alpha-k+1)} \tag{2}\]  
\(\Gamma(x)\) denotes the Gamma function, which extends the factorial to complex and real number arguments.  
The Grunwald-Letnikov formulation conceptualizes fractional differentiation as an infinite series, approximating the true derivative through a limiting process. This approach  
Figure 2: Methodology flowchart.  
provides a direct link between integer-order and fractional-order calculus, offering intuitive insights into the nature of fractional derivatives. In practical applications, the infinite series is typically truncated to a finite number of terms for computational feasibility:  
\[D^{\kappa}f(x)\approx\frac{1}{\hat{n}^{\alpha}}\sum_{k=0}^{N}{(-1)^{k}{\Big{(} \binom{\alpha}{k}\Big{)}}f(x-kh)} \tag{3}\]  
where \(N\) is the chosen number of terms for truncation. The selection of \(N\) involves a trade-off between computational efficiency and accuracy.  
The spectral data of fresh tea leaves were preprocessed using the FOD method to enhance the spectral features. Different orders of derivatives (from 0.1 to 1.0) were applied to determine the optimal order for modeling the quality indices.
# A Robust and Secure Watermarking Approach Based on Hermite Transform and SVD-DCT
## 3 Materials and Methods
### Hamming Code  
Linear block codes, defined in coding theory, are a kind of error-correcting code, where a linear combination of codewords is also a codeword. Hamming codes are efficient error-correcting binary linear block codes used to detect and correct errors when data are stored or transmitted.  
For a \((7,4)\) Hamming code, the encoding operation is performed by the \(4\times 7\) generator matrix shown in Equation (3) [47]:  
\[G=\begin{bmatrix}1&1&0&1&0&0&0\\ 0&1&1&0&1&0&0\\ 0&0&1&1&0&1&0\\ 0&0&0&1&1&1&0\end{bmatrix}. \tag{3}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline \(\mathbf{r}\) & \(\mathbf{R}\) \\ \hline
8 & 0.903 \\ \hline
32 & 0.981 \\ \hline
64 & 0.994 \\ \hline
128 & 0.999 \\ \hline
256 & 1 \\ \hline \end{tabular}
\end{table}
Table 1: Examples showing the relation between the number of singular values (\(r\)) used to reconstruct the image and the correlation coefficient value obtained.  
Figure 7: Zoom of the image approximation using a different number of singular values (\(r\)) of SVD.  
When we talk about a \((7,4)\) Hamming code, we are referring to a code that generates seven bits for four input bits. Through a combination, e.g., lineal, of rows of \(G\) and the modulo-2 computation, the codewords are obtained for each input element, where the code corresponds to the length of a row of the matrix \(G\). Thus, \(\vec{c}=\vec{m}G\) is the codeword for \(\vec{m}=\left[\begin{array}{cc}m_{1}&m_{2}&m_{3}&m_{4}\end{array}\right]\) as the input message [47].  
For the decoding process, the \((7,4)\) Hamming code has associated with it a \(3\times 3\) parity check matrix \(H\), with \(\vec{v}H^{T}=0\) if \(\vec{v}\) is a codeword. Thus, for the matrix \(G\) of Equation (3), the corresponding parity check matrix \(H\) is given by Equation (4):  
\[H=\left[\begin{array}{cccccc}\vec{n_{1}}&\vec{n_{2}}&\vec{n_{3}}&\vec{n_{4}} &\vec{n_{5}}&\vec{n_{6}}&\vec{n_{7}}\end{array}\right]=\left[\begin{array}{ ccccccc}1&0&1&1&1&0&0\\ 0&1&0&1&1&1&0\\ 0&0&1&0&1&1&1\end{array}\right], \tag{4}\]  
where \(\vec{n_{k}}\) is the column vector \(k\), and it can be verified that \(GH^{T}=0\).
## 4 Discussion
One of the main methodological challenges in exercise oncology trials is the lack of uniformity in the definition of control groups and the variability in intervention timing. In this meta-analysis, control conditions ranged from inactivity to light interventions, and most studies implemented exercise during active treatment, with few conducting it in later phases. These inconsistencies, together with the limited number of studies in certain categories and the absence of clear trends in the forest plot, precluded formal subgroup analyses. Altogether, these findings highlight the need for more precise and standardized reporting of both control group composition and intervention timing in future trials. The analysis was performed using a random-effects model, appropriate for accounting for variability in study design, exercise type, intervention duration, and population characteristics. The heterogeneity observed was moderate, suggesting some variability in the results but not enough to compromise the validity of the pooled effect. Sensitivity analyses demonstrated a high degree of stability in the exercise effect on cancer-related fatigue, with consistently significant results. Notably, heterogeneity dropped substantially when the study by [PERSON] et al. [62] was excluded, suggesting that this study may contribute to overall variability, possibly due to its large reported effect size. Nevertheless, the consistent effect across studies confirms the robustness and clinical applicability of therapeutic exercise, regardless of modality. Potential publication bias was assessed via visual inspection of the funnel plot and [PERSON]'s test, with no significant asymmetry detected. The symmetrical distribution of studies and non-significant standard error coefficient support this conclusion. The comprehensive search strategy further reduces the risk of omitting relevant studies, reinforcing the validity and robustness of the meta-analysis and the reliability of the observed effects of therapeutic exercise on cancer-related fatigue. This meta-analysis has some methodological limitations that should be considered. First, the impossibility of blinding participants in exercise interventions may introduce performance and detection biases. Additionally, several studies did not adequately describe the randomization process, limiting risk-of-bias assessment. Moderate statistical heterogeneity was observed, likely due to differences in participant characteristics, study designs, interventions, and fatigue measurement tools. This methodological variability affects comparability and external validity. While subgroup analyses by exercise type, cancer type, and sex were performed, the limited number of studies in each subgroup may have affected the robustness of these results. Lastly, the scarcity of long-term follow-updata limits our understanding of the sustainability of exercise benefits. Therefore, although the results are promising, they should be interpreted with caution within the context of these limitations.
###### Abstract  
Despite the occupational nature of truckers predisposing them to cardiovascular diseases and risk factors, data is scarce on their prevalence of obesity and its association with developing hypertension (HBP) and diabetes in South Africa. Extracted from the original cross-sectional study of a sample of 312 employees in a logistics company, a census of 96 male truckers was used to determine the prevalence of obesity and its association with HBP and diabetes. WHO STEPwise approach was used to collect data, including systolic (SBP) and diastolic (DBP) blood pressure and anthropometry measurements. HBP is defined as SBP/DBP \(\geq\) 140/90 mmHg, overweight/obesity as BMI \(\geq\) 25 kg/m\({}^{2}\), abdominal obesity by waist circumference as WC \(\geq\) 90 cm, waist-to-hip-ratio as WHR \(\geq\) 0.90, and waist-to-height ratio as WHR \(\geq\) 0.5. Data were analysed using SPSS 22. The mean age of truckers was 46 years, 29% were smokers, and 57% alcohol users, while 26% were physically inactive. The prevalence of overweight (44%) and obesity (30%) were observed, while abdominal obesity was high; WC (59%), WHR (65%), and WHR (80%). HBP (57%) was prevalent and diabetes was 14%. The odds of developing HBP were high for obese truckers by WC [AOR = 4.68; CI = 1.92-11.34)] and by WHR [AOR = 5.49 CI = 1.74-17.27), while diabetes was associated with WHR (AOR = 1.19; CI = 1.19-31.21). This study showed an associative link between obesity, HBP, and diabetes among the truckers, which is informative for a relevant prevention programme tailored to their needs.  
overweight/obesity; abdominal obesity; hypertension; diabetes; truck drivers; logistics company; South Africa +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Empirical Temperature-Based Models
#### 2.3.2 [PERSON] and [PERSON]'s Model  
[PERSON] and [PERSON] (BC) presented a model based on two assumptions. The first assumption is that of a linear relationship existing between the absorbed and incoming solar insolation, while the second assumption is that of disregarding the heat flux coming from the soil for a daily period because it is close to zero. However, it is convenient to mention that the sensible heat produced by diurnal temperatures is higher than that produced during the nighttime [10]. This is understandable because solar irradiance heats air masses more due to short-wave radiation, whereas at night, there is less long-wave emission from the Earth to the atmosphere, reducing the temperature [29]. Furthermore, under ideal conditions, the temperature is minimum just before sunrise, resulting in a significant difference between daily maximum and minimum air temperature. This phenomenon permits the modeling of solar insolation as a function of temperature difference [10]. Under these assumptions, the authors presented their model:  
\[\frac{H}{H_{0}}=a\Big{[}1-e^{(-b\Delta T^{c})}\Big{]},\]  
where \(a\), \(b\), and \(c\) are empirical coefficients. \(a\) represents the maximum ratio between the extraterrestrial and terrestrial global solar insolation \(H/H_{0}\); \(b\) and \(c\) determine how soon the model achieves such a maximum by temperature increases \(\Delta T\). Empirical coefficients represent the regional characteristics from arid to humid environments [10].  
Temperature difference depends on the daily maximum \(T_{max}\) and minimum \(T_{min}\) temperatures for a day \(D\) given in \({}^{\circ}\)C. [PERSON] and [PERSON] concluded that the minimum temperature averages of two consecutive days reduces the effect from hot or cold air masses, in turn, avoiding overestimated and underestimated solar insolation values. Accordingly, [PERSON] et al. stated that advection is not a common phenomenon in tropical zones; therefore, the temperature change \(\Delta T\) estimation in these regions is \(\Delta T(D)~{}=T_{max}(D)~{}-~{}T_{min}(D)\). [PERSON] et al. also highlighted that this equation is better for sites with high altitudes, as in the study case [14].  
Another weather effect modifying the solar insolation estimation is rain. The effect of rain decreases, setting \(\Delta T(J)\) equal to 0.75 times the estimated \(\Delta T(D)\). If \(\Delta T(D-1)\) is less than \(\Delta T(\mathrm{D}-2)\) by about 2 \({}^{\circ}\)C, the first factor is multiplied by 0.75 [10]. However, when the rainy period is long, the relation between solar insolation and \(\Delta\)T reaches an equilibrium. Therefore, it does not require adjustments [36].
# Springback Behavior of AA 7075-T6 Alloy in V-Shaped Bending
## 3 Results and Discussions
### Effect of Sheet Metal Thickness  
Von Mises stress distribution of sheet metal of different thicknesses is given in Figure 9. The simulations were performed at room temperature, at a constant bending radius (BR: 4 mm) and bending angle value (BA: 90\({}^{\circ}\)), and the springback behavior and bending load of sheet metals of different thicknesses were investigated. As the material thickness increases, the amount of elastic deformation and therefore the springback values decrease. It has been observed that the springback decreases when the sheet metal thickness increases [28]. The bending load increases while the springback decreases.  
Simulation results of sheet metal at different thicknesses are given in Table 4. While the bending load was 2318 N in sheet metal with a thickness of 1 mm, the bending load increased 5.4 times to 12,556 N when the thickness increased to 3 mm. Springback decreased as the sheet thickness increased. While it was 8\({}^{\circ}\) in the 1 mm thick part, it decreased to  
Figure 9: Von Mises stress distribution of sheet metal of different thicknesses before and after springback: (**a**) t = 1 mm, (**b**) t = 2 mm, (**c**) t = 3 mm.  
0.2 mm in the sheet metal with 3 mm thickness. Von Mises stress value also increased as the sheet thickness increased [46; 47]. The highest stress was observed in the 3 mm thick sheet.  
Variation in springback with sheet metal thickness is given in Figure 10.
# Proactive Scheduling and Routing of MRP-Based Production with Constrained Resources
## 2 Related Works  
Traditional Material Requirements Planning (MRP), along with its successors such as MRP II (Manufacturing Resource Planning) and Enterprise Resource Planning (ERP), has long served as the foundation of production planning in manufacturing environments. However, these systems have often been criticized for their lack of responsiveness to real-time operational constraints and limited optimization capabilities. As [PERSON] [1] notes, classic MRP struggles to handle capacity constraints and frequently generates infeasible production schedules when resource availability fluctuates. To address these limitations, researchers have proposed various enhancements to MRP-based systems. For instance, [PERSON] and [PERSON] [2] introduced the concept of Demand-Driven MRP (DDMRP), which improves adaptability in conventional planning models through buffer-based scheduling. Nevertheless, DDMRP still lacks integrated optimization and routing features when resources are constrained. In recent years, attention has shifted towards the integration of mathematical optimization into production planning. [PERSON] [3] provides a comprehensive overview of scheduling models and emphasizes the growing use of mixed-integer linear programming (MILP) to model complex production systems under resource and precedence constraints. [PERSON] [4] and [PERSON] [5] propose hybrid approaches that link MRP data with MILP models for batch-level scheduling. While these hybrid MRP-MILP approaches [4,5] integrate optimization with MRP data, they typically lack three key capabilities: (a) full traceability of demand sources at the component level down to customer orders; (b) dynamic routing under real-time resource constraints; and (c) comprehensive resource coverage (tools, labor, software). Moreover, [PERSON] et al. [6] explored dynamic scheduling using real-time shop-floor data, highlighting the importance of responsiveness but without offering an integrated solution for MRP-driven contexts. Similarly, [PERSON] and [PERSON] [7] examined the role of decision support systems in manufacturing but did not extend their work to the mathematical optimization of scheduling.  
Recent advancements in production planning leverage Industry 4.0 technologies, such as digital twins [8] and reinforcement learning [9; 10], to enhance real-time decision-making [11]. However, these approaches often address isolated subproblems (e.g., single-machine scheduling) or lack integration with MRP's demand-driven logic. Our work bridges this gap by combining MILP optimization with MRP traceability while accounting for dynamic resource constraints and addressing them holistically (tools, labor, software, etc.), a challenge acknowledged but not fully addressed in contemporary research [12; 13].
## 4 Discussion  
### Data Acquisition and Preprocessing  
Typically, healthy green vegetation exhibits strong reflectance in the red/infrared boundary near 0.7 \(\upmu\)m. The reflectance is nearly constant from 0.7 to 1.0 \(\upmu\)m and gradually decreases for longer wavelengths as seen in Figure 5. The study area of PVNP comprises eight forest zones, with dry forests being the most abundant, and the layered structure of the vegetation largely responsible for the variation in the reflectance curve [(41)]. The endmember reflectance curve of uncharred soil is considerably less variable since it is affected by moisture content, soil texture, surface roughness, the presence of iron oxide, and organic matter [(56)]. These factors are less dominant than the absorbance features observed in vegetation reflectance spectra. The soil around PVNP have lower moisture content, attributed to a low reflectance, which is seen to be lowered further in charred soil collected from the post-fire image of 2015.  
Several studies used hyperspectral data to collect endmember fractions together with field plot data and/or Landsat multispectral data (For example, [(57)]). In this study, we had to rely on Landsat 7 ETM+, Landsat 8 OLI/TIRS and high resolution ortho-imagery due to the limitation of hyperspectral data between the pre-fire and post-fire period. However, the reflectance spectra of four endmember fractions of this study (Figure 5) are comparable with the spectra gathered using hyperspectral data [(57; 58)]. For example, as [PERSON] et al. [(57)] reported, the charred soil reflectance is slightly higher than charred vegetation and reflectance is increased from 0.7 \(\upmu\)m to 1.7 \(\upmu\)m wavelength by 0.2 reflectance (from 0.1 to 0.3), which clearly showed from this study. [PERSON] et al. [(58)] found in their study that stressed vegetation is often seen to have a slightly higher reflectance in the near infrared area mostly around 1.0-1.8 \(\upmu\)m. This study reflected similar results for charred/scorched vegetation where a slight increase in reflectance was noticed between 1.5 and 1.9 \(\upmu\)m from the darker colored burned vegetation.
# An Intelligent Collaborative Charging System for Open-Pit Mines
## 6 Simulation Experiments and Result Analysis
### Intelligent Collaborative Charging Method for Explosive Loading Scheme  
The operational workflow of the intelligent cooperative loading system comprises three key stages:  
(1) Parking Position Screening (Coverage Radius + Greedy Set Cover)  
The process initiates by inputting the borehole coordinate set, candidate parking positions, and the maximum reachable radius of loading robots. For each parking position, Euclidean distances to all boreholes are computed to identify covered borehole indices within the radius. A greedy algorithm is then applied to iteratively select the position covering the maximum number of uncovered boreholes, with selected positions being stored in a deduplicated array after removing their mapped coverage.  
(2) Explosive Truck Route Optimization  
After determining the minimal covering position set, a Traveling Salesman Problem (TSP) is constructed by incorporating an origin point. Consistent with conventional ap  
Figure 12: Actual stopping points in conventional explosive charging truck scheme.  
Figure 13: Traveling path of conventional explosive charging truck.  
proaches, path planning employs nearest-neighbor heuristics for initial route generation, followed by 2-opt algorithm iterations to minimize total travel distance and eliminate path crossings, thereby simulating the shortest operational route.  
(3) Loading Robot Path Planning  
For each selected parking position, its associated uncovered boreholes form a TSP instance. Identical path planning algorithms are executed for all robots to calculate their respective trajectories.  
The detailed traveling paths are illustrated in Figures 14-16.
## 2 Methodology and Selection Criteria  
A comprehensive search of scientific databases, including PubMed, Scopus, and Web of Science, was conducted by two independent authors ([PERSON] and [PERSON]) to collect relevant articles. The data were subsequently extracted and evaluated by two independent reviewers ([PERSON] and [PERSON]).  
The selection and exclusion criteria were carefully defined to ensure the relevance and quality of the included studies. The following criteria were applied:  
* Relevance: studies must focus on wearable devices used in sports contexts, particularly those capable of monitoring body movements, biophysical parameters, and marginal gains in athletic performance.
* Publication Year: to ensure currency, studies published within the last 10 years were prioritized, with seminal works from earlier periods included if deemed crucial.
* Study Design: Preference was given to randomized controlled trials, systematic reviews, and meta-analyses. Observational studies were included if they provided unique insights.
* Language: only articles published in English were considered.
* Peer Review: all included studies must have undergone peer review.
* Device Specificity: studies must focus on wearable devices specifically designed or adapted for sports applications.  
Exclusion Criteria Included
* Studies focusing solely on medical applications of wearable devices.
* Non-peer-reviewed articles, including conference abstracts and dissertations.
* Studies with inadequate methodological quality, as assessed by the reviewers.  
A three-step process was followed for the selection of documents (Figure 1):  
* Title evaluation: titles were screened for relevance to the topic.
* Abstract evaluation: abstracts of potentially relevant articles were reviewed.
* Full-text evaluation: the full text of each selected article was thoroughly examined.  
Figure 1: Methodology for selecting documents.  
This rigorous selection process ensured that only the most relevant and high-quality studies were included in our review, providing a comprehensive yet focused overview of wearable technologies in sports performance monitoring.  
Documents were divided into original articles (65%) and review articles (35%). Details of the keywords used for each database (Pubmed, Scopus, WOS) are shown in Figure 2.  
Following the described steps, each document was either accepted or rejected based on the above criteria.  
If a document's results were unclear, a further analysis of its contents was conducted by seeking information from external sources. Despite its clarity, one document was rejected by the review without explanation.  
A total of 55 documents were included in this review.