esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Group Feature Selection Method  
In the context of high-dimensional datasets, particularly within microarray gene expression datasets, the emergence of group structures among features is prevalent and attributed to various factors. Notably, genes that share membership in the same biological pathway, or genetic markers originating from identical genes, can be conceptualized as constituting a group [43]. Features belonging to the same membership group exhibit similar characteristics. Consequently, a strong correlation is often observed between features within the same group [44]. In such datasets, there is a preference for selecting entire features within the same group rather than individual features. Thus, feature ranking and selection in this scenario refer to group feature ranking and selection.  
Numerous methodologies have been developed by researchers to address group feature selection [13]. These methods are designed to select group features rather than individual features. However, the existing group feature selection methods do not provide information on the relative importance of group features, while various feature ranking-based selection techniques for individual features exist [27]. Therefore, only a few methods have demonstrated the ability to select group features based on their rankings.  
[PERSON] and [PERSON] [14] developed a method for group feature ranking and selection for high-dimensional datasets. In this method, the relief method was initially applied to each feature group to eliminate irrelevant individual features. Subsequently, [PERSON]'s linear discriminant analysis (FDA) was employed to reduce the dimensions of each group to a singular dimension, thus capturing the essence of the group features. Finally, a random forest method was used to assess the relative importance of these features. The features were ranked based on their relative importance, and those that surpassed a specified threshold were selected. The experimentation phase involved real-world microarray gene expression datasets characterized by a binary-class target variable. This method was specifically developed to rank and select group features in the context of binary-class datasets. Consequently, group feature ranking and selection for multiclass datasets remain challenging.  
In response to the challenges and limitations outlined above, this study extends the framework proposed by [PERSON] and [PERSON] [14] and introduces a novel permutation-based feature ranking and selection method explicitly developed for multiclass datasets. This study does not transform group features into a single dimension; instead, it selects the entire group. By adopting this approach, the information is preserved in its original form. Furthermore, this method addresses multiclass datasets rather than binary class datasets.
## 3 Results and Discussion
### In-Vitro Bioaccessibility Evaluation  
This assay was carried out using the aqueous extract of ginger obtained under optimal conditions from the experimental design applied. Bioaccessibility indices were calculated from each response evaluated (Y1, Y3, and Y4), and the values found were 31.54% from DPPH assay (Y1), 35.10% from FRAP test (Y3), and 35.77% from total phenolic content (Y4). Considering the DPPH measure (lipophilic character) and the lower total phenolic content, terpen can be the main antioxidant compounds present in the aqueous ginger extract.  
There are no previous studies on the bioaccessibility of aqueous ginger extracts; however, the values obtained in the present study are lower than results found by [PERSON] et al. [41] from pulp of green and purple cactus pear (69-83%) and [PERSON] et al. [42] from four varieties of apple (65%). Some authors have suggested that the low bioaccessibility of phenolic compounds could be because they are found linked to macromolecular compounds, such as proteins, polysaccharides, or soluble fiber or forming mineral complexes with reduced solubility [42; 43; 44; 45].  
The low bioaccessibility of antioxidant compounds from ginger aqueous extracts (DPPH and FRAP assay) is related to the absorption of phenolic compounds (flavonoids or terpenes); which can suffer structural changes or produce secondary metabolites during the digestion process [42; 43; 44; 45]. Additionally, the bioaccessibility of phenolic compounds can be influenced by the change of pH during gastric and intestinal digestion (acid to alkaline media) that produce alterations in the phenolic polarity; the linkage with other substances; and the hindrance of large molecules to cross the cell membrane semipermeable barrier [47; 48].
## 4 Discussion
### Current State of the Use of FES in UMN Diseases
The analyzed papers, although FES was used on A to E grade ASIA impairments, showed that even for a complete SCI, a motor response can be obtained during FES. The therapy parameters used a frequency of 35 to 40 Hz, and a pulse width between 300 and 400 microseconds for the lower extremity was used. The intensity of the FES device was adjusted from 20 mA to 130 mA and to visible contraction [25]. An essential feature of FES in SCI is described in the research carried out by [PERSON] et al. [41]. Their results show that a 20 min FES therapy session can determine the increase of muscle stiffness, but in 40 min sessions, fatigue appears and negatively influences motor response; therefore, maladaptive feedback seems to manifest in complete SCI patients when FES is overused, somehow depicting a similar response of the upper motor neuron response as in MS [17], with hyperstimulation of the central nervous system or motor deficit units recruiting in a long repetitive task. Moreover, recent research also suggests the need to adjust FES parameters according to muscle fatigue and type of UMN injury, by lowering the frequency of stimuli [35]. Therefore, the duration of an FES session, either as therapy for rehabilitation of normal movement pattern or as neuroprosthesis for UMN locomotor impairments, should be carefully considered in future research, especially in clinical practice. Otherwise, results suggest that clinicians should identify the optimal duration of the FES training session in SCI patients. A limitation of the studies carried out on patients with incomplete SCI is that every study pursues a specific objective (walking speed, grasping ability of the upper extremity, reduction of spasticity); furthermore, there is heterogeneity regarding ASIA scoring in subjects which used FES. Consequently, the evaluation scales and tests are different, the severity of the spine injuries are mixed, and comparison of the results is difficult. In chronic post-stroke patients, no modification of muscle spasticity was observed during ten therapy sessions of 10 min done over 6 weeks [27]. However, in the research performed on incomplete SCI [29] (eight of ten participants had a C score or higher on ASIA), where 30 min of FES were applied, the results suggested a significant change in hip abductors and knee extensors within four hours after therapy. Recent research on the effects of FES on SCI spasticity suggests that twenty sessions of FES combined with cycling decrease spasticity [38]; therefore, the results of the two studies in our review may be influenced by the FES research and therapy protocol and not by efficacy.
## 2 Materials and Methods
### Details on the Static Study Experiments  
Three-point compression tests (Figure 3) were conducted on different samples, either cut out from an industrial standard MP package (Figure 4), or from our handmade MP plates.  
Figure 3: Schematics: 3-point compression of a beam.  
Figure 2: Cones and pyramids: 3D-printed moulds and samples.  
With a beam under a 3-point compression load, the maximum deflection at point \(C\), \(Y_{c}\), is:  
\[Y_{c}=\frac{FL^{3}}{48 EI} \tag{1}\]  
where:  
* \(EI\) is the Flexural rigidity of the beam, with \(E\) being [PERSON]'s modulus and \(I\) is the moment of inertia of the beam's cross-section about the axis perpendicular to the applied load.
* \(L\) is the length of the beam.
* \(F\) is the force applied on point \(C\).  
Thus, we obtain the value of the Young modulus from the deformation:  
\[E=\frac{FL^{3}}{48 ITY_{c}} \tag{2}\]  
The moment of inertia \(I\) for a rectangle with width \(b\) and height \(h\) is given by  
\[I=\frac{bh^{3}}{12} \tag{3}\]  
Substituting Equation (3) into Equation (2), we finally get the expression for [PERSON]'s modulus:  
\[E=\frac{FL^{3}}{4 bh^{3}Y_{c}} \tag{4}\]  
The static test bench, depicted in Figure 5, is constructed from aluminium profiles and PLA 3D-printed components. As shown in Figure 6, the sample is positioned horizontally on a base made of two parallel profiles and placed on a scale capable of measuring forces up to 100 N, allowing for manual recording of the force applied to the sample. A second assembly, also constructed from profiles, is securely clamped to the table and includes a screw mechanism that can be manually turned using a 3D-printed crank. This crank has a hexagonal design, enabling precise adjustments, with each sixth of a full rotation corresponding to a displacement of 0.16 mm based on the pitch of the selected screw. To enhance surface contact with the sample and minimize damage during testing, a 3D-printed half-spherical screw tip is attached. The samples used in the static tests are \(95\,\mathrm{mm}\times 30\,\mathrm{mm}\) plates, either cut from industrial MP packaging or fabricated using the previously described production method.  
Figure 4: Commercial packaging, from which we extracted flat samples for the static study.  
The homemade plates feature different mass percentages of white glue, ranging from none up to 25%, which are used to obtain thin sheets with different Young moduli. Each of those plates is then cut into five samples, and each of these samples is tested using the 3-point compression test bench previously described: by applying a given displacement at the centre of the sample and measuring the resulting force, the Young's modulus can be calculated using Equation (4). These values are then averaged over the five samples extracted from each plate.  
Figure 5: Tailored 3-point compression test bench.  
Figure 6: Moulded-pulp plate sample in the compression test bench.
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 2 Overview and Theoretical Foundations
### Workflows and Pipelines for the Management of Data/Compute-Intensive Applications  
Industrial IoT systems generate complex workflows that fundamentally differ from traditional scientific computing pipelines in their operational requirements and constraints. Where scientific workflows might prioritize absolute computational accuracy with flexible timelines, industrial workflows must balance computational precision with hard real-time constraints, fault tolerance, and seamless integration across heterogeneous hardware platforms. These workflows typically manifest as directed acyclic graphs (DAGs) that orchestrate data ingestion, preprocessing, distributed computation, and decision-making across the entire manufacturing ecosystem. A typical smart factory deployment might involve thousands of such workflows operating concurrently, processing data streams from production line sensors, quality control systems, and supply chain monitoring tools.  
The computational demands of these industrial workflows have led to innovative hybrid architectures that combine the strengths of High-Performance Computing (HPC) systems with data-intensive scalable computing (DISC) frameworks. HPC systems, traditionally the domain of scientific simulations, are finding new applications in industrial scenarios such as digital twin implementations and complex process modeling. Meanwhile, DISC frameworks like Apache Spark [9] have become indispensable for handling the data-parallel aspects of industrial analytics, particularly for the real-time processing of sensor data and equipment telemetry. However, this convergence of computing paradigms introduces significant challenges in resource management, particularly when workflows must span from resource-constrained edge devices to powerful cloud-based computation nodes while maintaining strict quality-of-service guarantees.
# Design of a Bionic Spider Robot with a Two-Degrees-of-Freedom Leg Structure and Body Frame
## 4 Simulation Experiment Analysis
### Initial Verification  
In this study, we developed a biomimetic spider robot prototype using the Fisher Technik system, as shown in Figure 27, which validated our proposed new leg design with two degrees of freedom and hexagonal body structure. The two-degree-of-freedom leg structure combines a multi-link mechanism and a dual-motor system, enhancing the control of leg movement and improving leg strength. The hexagonal body structure and triangular gait were carefully designed to align weight with support points, thereby enhancing stability.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Criteria**} & **Sketch of the Overall Structure of the Lunar Exploration Hexapod Robot [44]** & **Hexapod Robot Model HexaV4 [28]** & **Two-DOF Design (Our Study)** \\ \hline \multirow{2}{*}{Control Complexity} & High, requiring advanced gait optimization algorithms. & High, requiring heuristic algorithms for gait switching. & Low, with simplified control architecture using a multi-link mechanism. \\ \hline \multirow{2}{*}{Energy Consumption} & Optimized for stability and energy efficiency using an improved triangular gait. & Reduced through heuristic gait switching. & Lowered through a simplified design and dual-motor system. \\ \hline \multirow{2}{*}{Maintenance Costs} & Moderate, due to complexity in gait optimization systems. & Moderate to high, depending on the complexity of heuristic control systems. & Low, with fewer moving parts and a simpler design. \\ \hline \end{tabular}
\end{table}
Table 7: Comparative analysis.  
Figure 26: Foot trajectory during DE changes.
# An Optimization Route Selection Method of Urban Oversize Cargo Transportation
## 1 Introduction  
As an important part of modern logistics, oversize cargo transportation refers to a series of applications of suitable vehicles, ships, and other transportation tools, using the corresponding safety measures and the necessary administrative permission, via roads, waterways, railways, and other transportation methods, from the place of departing to the place of delivery. A series of professional operations such as moving, loading and unloading, transportation, and storage of oversize cargos with non-dismantling characteristics is carried out.  
Due to the development of infrastructure construction, the number and scale of construction projects are increasing, and the large-scale equipment supporting them is becoming bigger, heavier, and of higher value. The demand for the oversize cargo logistics market continues to increase.  
Due to actual transportation demands, there are situations where oversize cargoes are transported through cities. This kind of transportation has caused great problems for urban traffic. Generally, it is necessary to block roads in advance and carry out necessary demolition operations. At the same time, because the transportation speed of oversize cargoes is generally slow, it will also cause long-term lane occupation over time. Furthermore, the inability to select a suitable transportation route quickly and accurately is an unsolved problem of current oversize cargo transportation. In order to minimize the interference of oversize cargo transportation on urban traffic from the space dimension and the timedimension, as well as make the route selection of oversize cargo transportation faster and more reasonable, this paper proposes a model of oversize cargo transportation route selection based on the optimization weight-technique for order preference by similarity to an ideal solution (TOPSIS) method. The purpose of the research is to construct a calculation and grade division method for urban oversize cargo transportation routes.  
Our research uses the entropy weight method for initial weight calculation, the cloud model for weight optimization, and the TOPSIS method for route selection. Since the entropy weight method can fully reflect the objectivity of the weights, it is selected as the initial weight calculation. Since the cloud model can perform a good conversion between subjective and objective expressions, the cloud model is introduced to optimize the weights of the entropy weight method. Since the TOPSIS method has a good application effect for multi-objective planning problems, it is used to calculate the final result. In the summary section, the calculations of the TOPSIS method, entropy weight-TOPSIS method, and the optimization method used in our research are compared and analyzed with the actual transportation selections in the past five years.  
This paper is divided into five parts for writing, as shown in Figure 1:
# An Efficient Multiscale Scheme Using Local Zernike Moments for Face Recognition
## 3 MS-LZM Face Recognition Scheme
### Dimensionality Reduction and Distance Calculation  
By using the method described in Section 3.1, a feature vector is computed for each facial point. In this study, Whitened Principal Component Analysis (WPCA) [51], which is an unsupervised dimension reduction method, is used to both reduce the size of these feature vectors and improve face recognition performance.  
Figure 4: **(a) Sixty-eight facial points detected using the algorithm suggested by Kazemi et al. [59]; (b) 21 facial points used to extract facial features.**  
In the dimension reduction process with PCA, an orthogonal projection matrix \(U\) is learned by using training data, and the dimensions of the feature vectors are reduced by using this matrix. \(U\) consists of eigenvectors corresponding to eigenvalues that are sorted from large to small. Hence, rather than the distinguishing properties, the general properties of the face images are encoded with the first vectors in the \(U\) matrix [2], and the effect of these encoded values on the face recognition performance is high [62]. In order to overcome this problem, WPCA is a widely-used method. In WPCA, each eigenvector in \(U\) is divided by the square root of its own eigenvalue, and in this way, the effect of the distinguishing properties of face images is enhanced.  
In this paper, the similarity values between facial points are used to calculate the similarity between two facial images. First, the similarities between the feature vectors whose dimensions are reduced by WPCA and belong to the same facial point are calculated. Then, these similarity values are summed up, and thus the similarity between the two facial images is obtained.  
There are many similarity and distance functions commonly used in literature to calculate similarity values. Since these functions have direct effects on performance [63], extensive research has been conducted to determine the most appropriate method. As a result, it is found that the best performance is obtained using weighted angle-based similarity, which is used in [46] to compute the distance between the vectors projected into WPCA subspace, given as:  
\[s(X,Y)=\frac{\sum_{i=1}^{N}z_{i}x_{i}y_{i}}{\sqrt{\sum_{i=1}^{N}x_{i}^{2}\sum_ {i=1}^{N}y_{i}^{2}}},\quad z_{i}=\sqrt{1/\lambda_{i}}, \tag{13}\]  
where \(X\) and \(Y\) are the feature vectors and \(\lambda_{i}\) is the eigenvalue of the \(i\)th vector of matrix \(U\). As noted above, the features computed using the eigenvectors corresponding to the large eigenvalues are not useful for face recognition. For this reason, in (13), we reduce the contribution of features that include general information about the faces, using inversely proportional weights to the eigenvalues.
# High-Performance Computing and Parallel Algorithms for Urban Water Demand Forecasting
## 2 Materials and Methods
### Study Area and Data Collection  
The dataset used in this study consists of water level measurements from urban reservoirs in the Thessaloniki region (Greece). In previous work, the focus was on 21 central reservoirs in Eastern Thessaloniki, which were monitored over a 15-month period. For the  
Figure 1: Short-term time series forecasting methodology applied to water tank level data [16].  
present study, the dataset scope was expanded to 48 reservoirs, covering a broader area of approximately 300 km\({}^{2}\) across metropolitan Thessaloniki.  
Each reservoir's water level was recorded using a SCADA (Supervisory Control and Data Acquisition) system, provided by EYATH S.A. at one-minute intervals, resulting in a high-resolution univariate time series of water levels. This high-frequency sampling over roughly two years generated an extensive dataset consisting of approximately \(10^{7}\) individual readings. The expanded dataset captures suburban water dynamics, including storage reservoirs and intermediary distribution facilities, reflecting a city-wide perspective on water demand patterns across residential, commercial, and industrial zones. By doubling the reservoir count and increasing geographic coverage, the dataset now represents a more comprehensive view of Thessaloniki's full water network, which serves over one million inhabitants.  
To ensure computational feasibility and align with operational forecasting needs, the raw data were aggregated to hourly intervals by averaging the 60 one-minute readings within each hour. This transformation preserves essential temporal trends while making large-scale analysis more tractable for modeling and forecasting applications.  
The dataset also contains missing values and anomalies caused by sensor outages or telemetry errors. The proportion of missing timestamps per reservoir ranged from approximately 1.5% to 16% before data cleaning. Across the entire dataset, the overall missing value ratio was around 3%, meaning roughly 3 out of every 100 hourly entries were absent due to telemetry issues. Additionally, outliers accounted for approximately 1-2% of the recorded values, varying by reservoir.  
The two-year span makes the forecasting task realistic and challenging. All raw data were stored in a Hadoop Distributed File System (HDFS) cluster for reliable and scalable access. Each large data file is broken into 128 MB blocks in HDFS, with replication (three copies per block by default) to ensure fault tolerance and high availability [18]. Although our experimental deployment uses a single physical node (described below), HDFS was configured to mimic a distributed environment, thereby facilitating seamless future scaling to multiple nodes.
# A Digital Twin for Intelligent Transportation Systems in Interurban Scenarios
## 2 State of the Art on Digital Twins for Intelligent Transportation Systems  
This section presents the state of the art of transportation digital twins. Before characterizing the latest studies in this area, it is important to know where the concept of digital twins comes from. The first public mention of the concept of digital twin was made in 2002 by [PERSON] at the Society of Manufacturing Engineers conference in Michigan [7]. The digital twin that [PERSON] proposed was a model to manage the product lifecycle. Although already defined, the term digital twin was established by [PERSON] of NASA in 2010, where it was used to improve the physical model of the spacecrafts [8]. Since then, digital twins have been developed in many different areas, such as industry 4.0 [9], agriculture [10], healthcare [11], ITS [6], and urban intelligence [4], among others. In ITS, several studies, depicted in the following subsections, have been carried out involving transportation digital twins. Some of them are merely a proof of concept, while others have been implemented in small environments to test their capabilities and limitations. Nowadays, we even have real implementations of digital twins of cities, which are mainly used for urban planning purposes. Transportation digital twins can be classified into four categories: urban scenarios, interurban scenarios, autonomous vehicles, and other prototypes.