esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Literature Survey and Critical Analysis
### Taxonomies and Literature Analysis
#### 3.2.3 Taxonomy 3: Multi-Objective Optimizer
In the experimental phase, the authors of the papers carried out different experiments varying the crossover and mutation values, so that in many cases, more than one value was provided for these operators. Therefore, we decided to group them by quartiles, with the first quartile Q1 being the probability of crossover set between [0, 0.25), Q2 between [0.25, 0.5), Q3 between [0.5, 0.75), and Q4 between [0.75 and 1]. The crossover operator was not always given, sometimes because the algorithm does not make any use of it (as in, e.g., PSO-based proposals). However, when used, there were a high number of papers that did not provide detailed information: in 39.34% of cases, the crossover operator was not specified; in 24.59% of the cases, the crossover probability was high, belonging to Q4; in 16.39% of the cases, the crossover parameters were in the third quartile; 1.6% and 4.92% had crossover probabilities belonging to the second quartile and the first quartile, respectively. It is remarkable that a large number of papers did not provide the crossover parameter settings, so they cannot be reproduced in new studies. This practice detracts from the scientific value of the publications because the statements and findings cannot be validated by the community. A well-conducted study should transparently inform about the operators and algorithms in use, so that interested researchers can replicate and advance the general knowledge in the field. Moreover, it is striking that this type of information did not appear when, in many cases, the studied problem was synthetic and there was no commitment to confidentiality. Apart from that, the probability values used more were the higher ones (i.e., in the Q4 range), as expected. Figure 6: Most utilized algorithms for benchmarking and comparison. we defined, as in the crossover operator, quartiles, with the first quartile Q1 being the probability of mutation set between [0, 0.25), Q2 between [0.25, 0.5), Q3 between [0.5, 0.75), and Q4 between [0.75 and 1]. In general, the mutation probabilities were low, as can be told from the most-used quartile for this parameter (34.2% in the first quartile and 15.8% in the second one). Similar to the crossover, a significant fraction of the papers (39.34%) did not clearly indicate the mutation parameters chosen for the experimental part of the study, which aggravates even further the reproducibility problems exposed previously.
## 1 Motivation
### Energy Transition towards Dispersed Generation  
Nowadays, the energy sector faces several challenges related to various domains covering the whole value chain of electricity delivery, such as energy generation, transmission, and distribution, as well as consumption and dispersed generation. Dispersed generation is the decentralized electricity production using small-scale, diverse energy sources located closer to the point of consumption, promoting grid resilience and sustainability. As a consequence of the implementation of European and worldwide climate strategies (e.g., EU Green Deal, Paris Agreement), the energy sector is progressing through extreme energy transitions, which bring a significant increase in electricity demand (e.g., due to the electricization of mobility) on one side and the strong decarbonization of electricity generation on the other side, for instance. This results in the massive installation of dispersed generation resources (mainly renewables) to the demand side. According to European Network of Transmission System Operators for Electricity (ENTSO-E) Ten-Year Network Development Plan (TYNDP) development scenarios [1], the volume of dispersed generation will significantly increase as depicted in Figure 1. The last three bars on the right side visualize actual global ambitions.  
The electricity volume provided by conventional generating units connected to transmission power systems will be continuously shifted to distribution power systems, where dispersed generation is located. According to TYNDP development scenarios, renewable energy generating units (i.e., photovoltaic, wind) will constitute the majority of generating elements. Unfortunately, these renewable generating units are mostly uncontrollable, as shown in some analyses from countries with a highly dispersed generation penetration. For example, Great Britain has reached a high penetration of dispersed generation units [2], representing approximately 35% of the generation mix, as depicted in Figure 2.  
The controllable generating units embrace only 41% of overall dispersed generation, while uncontrollable ones represent 59%. This phenomenon drives a significant increase in operational uncertainty at a distribution system level, which implies a notable escalation of volatile power flow exchanges among various parts of distribution power systems. A sufficient operational flexibility volume is required by Distribution System Operators (DSOs) to manage volatile power efficiently flows to maintain reliable and secure distribution system operation, preventing congestion or power quality issues. From a DSO perspective, various stakeholders, operational processes, and tools provide the required operational  
Figure 1: Primary energy supply mix in the COP 21 scenarios for EU27 (adapted from [1]).  
Figure 2: Generation mix in UK transmission and distribution systems (adapted from [2]).  
flexibility, where effective management of technology asset groups (e.g., microgrids, energy communities) located at the demand side represents one possibility discussed in this article.
## 1 Introduction
Also, the selected test needs to be valid and reliable to effectively track changes in an athlete's performance [29]. The same principles apply to a technology that is used to assess vertical jump characteristics. Originally, force platforms that measure ground reaction force exerted by the subject were classified as 'gold standard' [30]. They allow the analysis of kinematic variables of the jump (e.g., countermovement depth, time to peak force) in addition to kinetic variables (e.g., jump height, mean force) [31]. Despite providing a lot of potentially useful data, both laboratory-based and portable force platforms are expensive [32], and often the practitioners are forced to use the alternatives. Fortunately, there is a variety of technologies that can be implemented as an effective replacement to a force platform. Infrared platforms [33], contact mats [34], inertial devices [35] or even a smartphone video application [36] may be used as a more affordable option to track vertical jump height. However, it should be noted that the results from different devices are not interchangeable, in order to evaluate changes in jump height over time [37]. Thus, it is suggested to always perform the measurements with the same device to avoid misinterpretation of received results. In this pilot observational study, we analyzed changes in CMJ height during two competitive seasons. So far, authors have conducted investigations that contained several testing procedures through the competitive season [38; 39] and also weekly CMJ testing [40]. In our study, CMJ testing was performed weekly on a contact mat to assess NMF and monitor athletes' well-being and enhance the decision-making process to optimize prescribing training microcycles. In accordance with the authors [26; 27; 28], the general instructions of jump's execution were unchanged between two competitive seasons. However, we introduced a differentiating factor in jump execution between the seasons and observed changes in jump height (JH). Thus, the main goal of our study was to evaluate changes in jump performance in professional volleyball players during two competitive seasons. Another aim of this study was to determine whether the execution method of the jump during the same test can change the results of JH in CMJ.
# Vision-Based Hand Rotation Recognition Technique with Ground-Truth Dataset
## 1 Introduction  
A growing body of evidence shows substantial increases in diseases related to cognitive ability, such as mild cognitive impairment and dementia [1, 2]. Because there is currently no cure for cognitive-related diseases, the best solution is to manage them with continuous treatment and monitoring based on early detection. The first step is regular cognitive screening to determine whether the patient needs subsequent clinical examination for further neuropsychological testing [3]. However, commonly used tests such as MMSE have several problems, including long retest duration resulting from a strong learning effect [4, 5, 6]. Moreover, these tests have been repeatedly shown to be related to educational attainment [7, 8, 9], with results decreasing as age increases [10, 11, 12]; they are also affected by social class and socioeconomic status [13, 12, 14].  
In the case of rapidly progressive dementia, a single misdiagnosis can lead to fatal consequences for the patient [15]. Preemptive solutions whereby people can easily testthemselves to see whether they need serious tests are helpful. To solve this problem, several researchers have developed cognitive ability measurement technology based on behavioral data that can be assessed without a heavily trained professional and that also often has lower learning effects [16; 17]. However, these methods also have shortcomings. For example, gait tracking data require a large space because there is a risk of patient accidents, such as falls [18; 19; 20]. However, existing cognitive testing studies using hand movements [21; 22; 23] have limitations in quantitatively collecting and managing patient condition data because the tester qualitatively judges the accuracy of hand movements. Additionally, in one study that measured hand behaviors using virtual devices [24; 25], psychological barriers arose from using unfamiliar devices.  
Our goal is to devise biometric behavioral measures that can be systematically captured and that reduce spatial constraints, have a lower learning curve, reduce subjects' accident risk, have fewer instructions, require less equipment, and be less physically invasive. To be clear, we are not attempting to replace clinical testing, but we hope to come up with a screening method that is measured through a digital system. During this research, we selected a continuous wrist-rotating movement, defined its measurement through a common webcam device, and conducted an experiment with a dataset with a robot arm capable of precise control. As wrist-rotating movement is a continuous behavior, it is hard to make a person rotate at a certain speed or angle. Therefore, we have constructed a ground-truth dataset using a robot. After defining the hand gesture recognition methodology for rotation, we assigned various tasks to the dataset: defining 30 types of different rotation setting to mimic human movements; and we tested the accuracy of our algorithm to calculate the rotation movements. We calculated the prediction accuracy and tested the feasibility of the tasks.
## 2 Experimental Apparatus and Procedure
### Three DPF Samples with Different Types of Damage (Crack, Melting and Hollow)  
The aging DPFs were secured with the support of the Korea Automobile Environmental Association (KAEA). Then, imaging was performed using an X-ray radiograph generator (REX-650 RF/IRF-600-150, 70 kV-125 kV, LISTEM Co., Wonju, Korea) to photograph and analyze various types of DPF damage. Figure 7 shows a photograph of the interior of the DPF obtained using an X-ray generator. After adjusting the distance between the X-ray generator and IP to 100 cm, the filter part of the DPF was placed in the center of the IP. The image was captured while varying the tube voltage and tube current of the X-ray. This study could obtain an appropriate photographic image using a tube voltage of 125 kV.  
\begin{table}
\begin{tabular}{c c} \hline
**Item** & **Particle Analyzer** \\ \hline Particle size range & 5 nm-1 μm \\ Spectral Elements & 38 (1 μm) (at 16/decade, 32/decade available) \\ Size Classification & Electrical Mobility \\ Dilution Factor Range & 1–6 (1 st Dilution), 1–3000 (1 st and 2 nd) \\ Maximum 1 st dilution/ Heated Line temperatures & 150 \(\times\) \\ Sample Flow rate & 8 slpm (1 μm range), All at 0 \(\times\)C + 100 kPa \\ Time Response & T\({}_{10\text{-}90\text{-}200}\) 200 ms (1 μm range) \\ Maximum Concentration & \(\approx\)10\({}^{11}\) dN/dlogDp/cc (diluter on) \\ \hline \end{tabular}
\end{table}
Table 3: Specification of DMS-500 for measuring diesel particle number density.  
Figure 5: Vehicle operation setting on chassis dynamometer.  
Table 4 lists the three types of DPF damage observed in the X-ray images. Vehicle-driving experiments were performed by installing a damaged DPF device. In this study, the failure types in cordierite DPF substrates were classified into the following: cracks in the material inside the DPF, melting, and hollow fractures.
Sensors_**2014**, _14_, 8996-9026; doi:10.3390/s140508996  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Reliable Geographical Forwarding in Cognitive Radio Sensor Networks Using Virtual Clusters**  
**[PERSON] * and [PERSON]**  
UTM-MIMOs Centre of Excellence in Telecommunication Technology, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Malaysia;  
E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +60-7553-5394.  
_Received: 14 March 2014; in revised form: 23 April 2014 / Accepted: 7 May 2014 / Published: 21 May 2014_  
**Abstract:** The need for implementing reliable data transfer in resource-constrained cognitive radio _ad hoc_ networks is still an open issue in the research community. Although geographical forwarding schemes are characterized by their low overhead and efficiency in reliable data transfer in traditional wireless sensor network, this potential is still yet to be utilized for viable routing options in resource-constrained cognitive radio _ad hoc_ networks in the presence of lossy links. In this paper, a novel geographical forwarding technique that does not restrict the choice of the next hop to the nodes in the selected route is presented. This is achieved by the creation of virtual clusters based on spectrum correlation from which the next hop choice is made based on link quality. The design maximizes the use of idle listening and receiver contention prioritization for energy efficiency, the avoidance of routing hot spots and stability. The validation result, which closely follows the simulation result, shows that the developed scheme can make more advancement to the sink as against the usual decisions of relevant _ad hoc_ on-demand distance vector route select operations, while ensuring channel quality. Further simulation results have shown the enhanced reliability, lower latency and energy efficiency of the presented scheme.  
**Keywords:** cognitive radio _ad hoc_ wireless networks; cognitive radio sensor network; geographical forwarding; reliable routing; routing protocol; opportunistic routing; virtual clustering
## 2 Methodological Evolution
### Algorithmic Innovation
#### 2.2.2 Computational Domain Decomposition  
The Extended PINN (XPINN) architecture exemplifies how spatial-temporal partitioning enables scalable solutions for industrial-scale problems. By dividing the domain into overlapping subregions with shallow networks and enforcing interface flux continuity, XPINN achieves superlinear speedup (5\(\times\)) in 3D turbine blade flow simulations [26]. Adaptive sampling enhancements, such as residual-based attention mechanisms (RAR) [28], increase point density by 80% in high-gradient regions, reducing shock-capturing errors to below 3% in aerospace applications. This approach directly translates to real-world impact, as demonstrated in NASA's hypersonic tests where TFE-PINN reduces wall heat flux prediction errors by 62% compared to traditional LES methods [30].
## 4 Materials and Methods
### Research and Development Method  
One of the most important aspects of this research is to continuously include input from the potential end users of the proposed platform in order to achieve true user-friendliness. Systems described in previous sections have not completely succeeded in this point because of their complexity or their lack of understanding of some key requirements, such as the ability of working with an unstable Internet connection. To build a cloud platform that properly addresses the requirements stated in previous sections, we relied on User-Centered Design (UCD) principles. A UCD process requires the understanding of the target users, their tasks and environments. It incorporates the user feedback by directly involving them during the design and development. UCD principles have been used successfully together with agile development processes [71; 72; 73].  
To further emphasize the user-centered approach, lean development methodologies [14] were adopted to identify, satisfy and test the requirements of the platform. At its core, lean development methodology is based on Lean Startup principles [74] and it focuses on formulating a hypothesis and then quickly testing the assumptions surrounding it. It achieves this by establishing an iterative development process intent on building a Minimum Viable Product (MVP) around that hypothesis as quickly as possible. By working in concrete MVPs we will learn about the crucial design decisions and trade-offs that impact the final behavior of the KM platform. Moreover, the lean philosophy makes it possible to minimize the company's costs, eliminating any and all types of waste, and to increase profitability, giving more value to the products and services developed [75]. Then, the usage of this methodology is consistent with the context being worked on in Chile, since among the main barriers to the entry of ICTs in construction SMEs are the issues of small ICT investments [11] and worker's skepticism about the value they bring [76].  
In this methodology, every iteration defines a research cycle divided into three distinct phases (Build, Measure and Learn) in which many people have professional training that focuses on one single element of this feedback loop (shown in Figure 1). Due to this, by using this approach it allows this research to easily and properly integrate multidisciplinary perspectives to propose a solution for KM in construction companies. The formation of an interdisciplinary team is particularly relevant as construction experts and professionals have greater insights into the construction industry's needs, while key factors for the success of mobile services are identifying and investigating how users are influenced and then how they behave, and finally revealing what they really expect (i.e., needs and preferences) [77].  
Details of the Build-Measure-Learn cycles performed by this research are described in Section 4.2. Later, details of the empirical evaluation performed in the Measure phase of the second and third iterations are described in the Section 4.3.
# Design of Safety Evaluation and Risk Traceability System for Agricultural Product Quality
## 3 Construction of Risk Traceability System
### Traceability System Design
#### 3.2.2 System Function Module Design  
According to the system construction objectives and demand analysis, the system needs to be built to complete the basic database, data collection, risk evaluation, data display, user management, system configuration, and other functions.  
According to the idea of top-level design in system engineering, a modular design method is used to construct the main functional modules of the system, as shown in Figure 5, System Functional Module Diagram. The functions of the system modules are as follows: (1) System user module. The module provides the registration, login, and user maintenance functions for three levels of users of agricultural product quality and safety traceability at the supervisory department level, enterprise user level, and consumer level. (2) Data acquisition module. This module provides the uploading function of the data and information of the supervisory department and enterprise users for the testing data of agricultural products, the evaluation of the quality of agricultural products, the environmental testing data, and other data and information. Regulatory authorities and relevant enterprises play a supportive role in the data collection process. (3) Risk evaluation module. This module mainly focuses on the risk evaluation of the obtained data information related to the quality and safety of agricultural products based on the  
Figure 4: System technology stack diagram.  
limit value standard. It has functions such as analyzing the results of risk evaluations and independently highlighting risky data information. (4) Data traceability module. This module provides three levels of user traceability record information. (5) Statistical query module. This module uses risk assessment methods combined with recorded agricultural product monitoring data to perform scoring analysis on the quality and safety status of agricultural products. After statistical analysis, the scoring results are displayed. (6) System configuration module. This module mainly provides modified configuration functions for system operation and maintenance.
## Appendix A Frequency-Dependent Constitutive Equation for the Dissipative Structure
### Exterior Neumann Boundary Value Problem Related to the Helmholtz Equation  
The geometry is defined in Figure 11. The equations of the exterior Neumann problem related to the Helmholtz equation (see Equations (1)-(3)) are rewritten in terms of a velocity potential \(\psi(\mathbf{x},\omega)\). Let \(\mathbf{v}(\mathbf{x},\omega)=\mathbf{\
abla}\psi(\mathbf{x},\omega)\) be the velocity field of the fluid. The acoustic pressure \(p(\mathbf{x},\omega)\) is related to \(\psi(\mathbf{x},\omega)\) by the following equation:  
\[p(\mathbf{x},\omega)=-i\omega\,\rho_{\mathrm{E}}\,\psi(\mathbf{x},\omega)\quad \text{in}\quad\Omega_{E}\,, \tag{120}\]  
where \(\rho_{\mathrm{E}}\) is the constant mass density of the external fluid at equilibrium. Let \(c_{\mathrm{E}}\) be the constant speed of sound in the external fluid at equilibrium, and let \(k=\omega/c_{\mathrm{E}}\) be the wave number at frequency \(\omega\). The exterior Neumann problem is written as  
\[\
abla^{2}\psi(\mathbf{x},\omega)+k^{2}\,\psi(\mathbf{x},\omega)=0\quad\text{ in}\quad\Omega_{E}\,, \tag{121}\]  
\[\frac{\partial\psi(\mathbf{y},\omega)}{\partial\mathbf{n_{y}}}=v(\mathbf{y}) \quad\text{on}\quad\Gamma_{E}\,, \tag{122}\]  
\[|\,\psi\,|=O(\frac{1}{R})\quad,\quad\left|\,\frac{\partial\psi}{\partial R}+ ik\,\psi\,\right|=O(\frac{1}{R^{2}})\,, \tag{123}\]  
with \(R=\|\mathbf{x}\|\to+\infty\), where \(\partial/\partial R\) is the derivative in the radial direction and where \(v(\mathbf{y})\) is the given normal velocity field on \(\Gamma_{E}\). Equation (121) is the Helmholtz equation in the external acoustic fluid, Equation (122) is the Neumann condition on external fluid-structure interface \(\Gamma_{E}\), and Equation (123) corresponds to the outward Sommerfeld radiation condition at infinity. For arbitrary real \(\omega\
eq 0\), the boundary value problem defined by Equations (121)-(123) admits a unique solution denoted \(\psi^{\mathrm{sol}}\) that depends linearly on the normal velocity \(v\)[97; 98].
# A B5G Non-Terrestrial-Network (NTN) and Hybrid Constellation Based Data Collection System (DCS)
## 7 Simulations and Experiments
### DCS Receiver Simulations  
The DCS equipment is based on the SDR platform, and each application corresponds to a GRC flow. The specific GRC flow design was shown in Figure 9, and the results of simulation are shown in Figure 15 individually.  
A series of discrete pulses about PPM modulation belongs to ADS-B. A GMSK signal spectrum belongs to AIS. A two-symbol constellation about BPSK belongs to special military applications. Spread spectrum is the key technology of LORA.