esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Snow Load Characteristics on a Complex Long-Span Root Based on Snow-Wind Tunnel Tests
###### Abstract  
A considerable number of studies have been carried out for predicting snowdrifts on roofs over the years. However, few studies have focused on snowdrifts on complex long-span roofs, as the complex shape and fine structure pose significant challenges. In this study, to simplify the calculation requirements of snow load on such roofs, work was conducted to decompose the snowdrift on a complex roof into snowdrifts on several simple roofs. First, the snow-wind tunnel test similarity criteria were investigated based on a combined air-snow-wind experimental system. Thereafter, with reference to the validated experimental similarity criteria, a series of snow-wind tunnel tests were performed for snowdrifts on a complex long-span structure under the conditions of different inflow directions. Finally, based on empirical orthogonal function (EOF) analysis, the snowdrifts on the complex roof were decomposed into basic characteristic distribution modes, including snowdrifts caused by the local and overall roof forms. The snow distribution under a specific inflow direction could be derived from the weighted combination of the basic characteristic modes, based on the wind direction coefficients. Therefore, it is possible for the snow load on a complex roof to be estimated preliminarily based on the snow distributions on several simple roofs.  
**Keywords:** snow load; complex roof; snowdrift; EOF analysis; characteristics decomposition
###### Abstract  
(1) Background: Body Jump is a novel group fitness program with musical support, which is performed rebounding in a minitrampoline. Although the number of practitioners has increased exponentially in recent years, this activity's short-term effects on physical fitness and body composition in women have not yet been studied. (2) Methods: 27 healthy young women were randomly divided into a Body Jump(r) group (BJ) and a control group (CG). BJ performed three classes per week for one month. The week before and after the intervention, the anthropometric assessments were carried out to estimate the body composition, and different performance tests were performed to assess the jumping capacity (countermovement jump (CMJ) and squat jump (SJ) tests), the muscular strength (1 RM test) and the aerobic fitness (UKK test). (3) Results: VO\({}_{2}\) max (\(p\) = 0.001), CMJ flight height (\(p\) = 0.023), SJ flight height (\(p\) = 0.003) and the 1 RM value in the half-squat exercise (\(p\) = 0.009) were significantly increased in BJ. In CG, there were no statistically significant differences after the intervention period. Regarding the changes in body composition, a significant enhancement in several parameters were found in BJ, such as the sum of skinkolds (\(p\) = 0.003) and the percentage of fat mass (\(p\) = 0.002), while no changes were found in any of the anthropometric variables in CG. (4) Conclusions: carrying out the Body Jump(r) program three days per week for one month had positive effects on physical fitness and body composition in a group of healthy young women. This training program can be an effective option for enhancing, in the short term, these fitness parameters and the body composition of these recreational users into the fitness centers.  
physical activity; health; anthropometry; gym workout; group fitness program +
Footnote †: journal: Applied Sciences
## 1 Introduction
This research provides a strategic approach for simultaneously obtaining the V&V test result and the reliability information, as well as a realistic solution to address the obstacle of obtaining prior information on model parameters, which hinders the application of classical accelerated test plans. While prior information is essential for designing classical accelerated test plans, this research presents a method for obtaining prior information on the lifetime model parameters while also obtaining V&V test results and reliability information on the product. The degradation path and acceleration models with standardized stress variables related to this research are presented through illustrative examples, utilizing existing PADT articles [4; 5; 6; 7].
# Survey: Using Augmented Reality to Improve Learning Motivation in Cultural Heritage Studies
## 4 Questions Posed and Research Methodology  
For UNESCO, museums, and research studies are highly relevant in the effort to preserve CH, in that many buildings have structural risks and limited access [2]. Likewise, UNESCO considers it important to use technological tools for learning (but only if used in an appropriate way [16]) because through these tools CH and places can be preserved for future generations. From this, the following research questions were framed:* What advantages, disadvantages, limitations, and challenges do AR technologies have for the subject of CH?
* What advantages and disadvantages do AR technologies have compared to other technologies such as VR and MR?
* What tools or frameworks for developing applications in AR are used to create CH content?
* Does the use of AR improve student learning motivation in topics related to CH?
* To improve student motivation, what characteristics are considered in applications currently available, and which ones are missing?
## 2 System Overview
### Hardware Integration  
The FPTLIR is equipped with two lithium batteries that power its various systems for normal operation. These systems include the flight system, travelling and pressing system, vision system, and other sensors, as shown in Figure 2. The flight system primarily executes the flight function of the FPTLIR to accomplish missions both on-line and off-line. The flight component serves as the driving unit of the flight system, generating the lift for the FPTLIR  
Figure 1: Schematic diagram of FPTLIR structure: (**a**) complete machine structure; (**b**) flight mechanism; (**c**) travelling mechanism.  
by rotating the rotor motors in both clockwise and counterclockwise directions to drive the blades, which has an integrated electronic velocity controller. GPS is the position sensor used to obtain the position information of the FPTLIR. The data transmitter is a device that enables the transmission of flight data. The flight controller serves as the control unit of the flight system and contains sensors such as barometers, accelerometers, and others. It analyzes pre-set flight state data and converts them into velocity control commands transmitted to the Electronic Speed Controller (ESC), which controls the speed of the rotor motors. After completing the on-line mission, the FPTLIR relies on rolling components to hang onto the power line. The FPTLIR uses an STM32 microcontroller to control the travelling motor that drives the active travelling wheel, allowing it to move along the power lines. The STM32 can also control the pressing motor to adjust the force between the main pressing wheel, power line, and rolling component to prevent slippage during travel on power lines. The machine's vision system plays an important role in identifying and detecting power lines and fixtures. It consists of two cameras that capture images, a vision processor that processes the image data, and an image transmitter that facilitates transmission back to the host computer.  
This paper focuses on the energy consumption and execution time of the FPTLIR during flight missions. In this case, the flight system operates normally while the traveling and pressing system is in standby mode, requiring only minimal energy supply. It is important to mention that the energy usage of the vision system is quite low compared to the energy requirements of flight missions. Therefore, in the following sections, the static energy consumption is used to estimate the energy usage of the travelling and pressing systems, together with the vision system.
## 2 Materials and Methods
### Statistical Analysis  
Comparisons of the morphological parameters between the measurements of the five trials of the SIBPAIR method were performed using one-way repeated measurement analysis of variance (ANOVA), while comparisons of measurements between raters (senior vs. junior rater) and sessions (session 1 vs. session 2) were performed using a one-way repeated measurement ANOVA. Reliability of these measurements was assessed in terms of intraclass correlation coefficients (ICC) using a one-way random-effects model (ICC1,k) for the SIBPAIR method, a two-way mixed-effects model (ICC3,1) for intra-rater assessments, and a two-way random-effects model (ICC2,_k_) for inter-rater assessments, while a two-way mixed-effects model (ICC3,k) was used for inter-session reliability [42; 43]. The values of ICC indicated high (0.81-1.00), good (0.61-0.80), moderate (0.41-0.60), fair (0.21-0.40), and poor (below 0.20) reliability, respectively [44]. The standard error of measurement (SEM) was also used to quantify the reproducibility of an assessment for the SIBPAIR method and the raters. The SEM is an index of the precision of the measurements in the units of the measured quantity, and was calculated using the square root of the within-subject variance (\(SEM=s\cdot\sqrt{1-r}\); is the standard deviation of the measurements and \(r\) is the ICC). A statistical significance of 0.05 was set for all analyses. All statistical analyses were performed using a statistical software package (SPSS v.21; SPSS Inc., Chicago, IL, USA).
## 2 Materials and Methods
### Postural Control Tasks  
Participants were asked to stand as still as possible, with their medial malleoli aligned to a center horizontal line and calcanei aligned to a height-dependent stance width, during the performance of the sensory organization test (SOT), while the COP data were recorded (Figure 1). The SOT is a clinically used standardized instrumented balance test performed using the SMART EquiTest-Clinical Research System (SECRS, Neurocom, a division of Natus). The SOT is designed to assess a patient's use of sensory systems that contribute to balance and identify any abnormalities in the systems [25]. The six conditions of the SOT manipulate or eliminate information normally delivered to the patient's eye, head, feet, and joints. Specifically, there are three trials per condition and 20 s per trial in the SOT. The SOT measures an individual's ability to suppress the misleading information from the conflicting senses and use the remaining sensory input to maintain an upright stance [26]. Thus, in this study, the SOT introduces visual and somatosensory perturbations using sway-referenced mechanical ankle rotations, as part of the different sensory and minor mechanical perturbations presented to participants. To quantify postural control performance in the SOT, the SOT equilibrium score [0-100] was calculated using the following formula: SOT score = ((12.5 \(-\) (Maximum y-direction sway \(-\) Minimum y-direction sway)/12.5) \(\times\) 100, which quantifies how well the participant's sway remains within the expected angular limits of stability during each SOT condition, with higher scores indicating higher performance. Furthermore, self-reported functional capacity, physical activity, and psychological function were evaluated to help control for characterization of the three different groups. Self-reported functional capacity and physical activity were evaluated using a subset of the Established Populations for Epidemiologic Studies of the Elderly questionnaire [27]. In particular, stooping, crouching, or kneeling difficulty was assessed using a 0-4 scale ranging from 0 = no difficulty at all to 4 = just unable to do it [28], and brisk activity frequency per week. Lastly, the fall risk of the participants was assessed by the Falls Efficacy Scale-International (FES-I), with scores ranging from 16 to 64, with a higher score indicating a greater fear of falling [29].  
Figure 1: Sensory organization test (SOT) conditions (1–6), adapted from Neurocom.
## 4 Implemented MCDM Methods  
The purpose of MCDM methods is to define which of compared alternatives (cases) \(A_{1}\), \(A_{2}\),..., \(A_{n}\) is the best, or the order of alternatives in respect to importance of purpose. Evaluation process is characterized by criteria (indicators and factors) \(R_{1}\), \(R_{2}\),..., \(R_{m}\). The MCDM methods are based on the decision matrix \(R=\|r_{ij}\|\) statistical data of criteria, values of technological parameters or expert evaluations, and vector of criteria importance (weights) \(\Omega=(\omega_{j})\), where \(I=1\), \(2\),..., \(n\); \(j=1\), \(2\),..., \(m\); \(m\) is number of criteria; and \(n\) is number of evaluated alternatives.  
The main idea of MDCM methods is joining of evaluation criteria values and their weights to single evaluation characteristics--i.e., criteria of the method. MCDM methods implement the maximized (beneficial) criterion, in the case where maximum value of criteria corresponds the best one (profit, for example), and the minimized one, when the best value of criteria is minimal (expenses, for example).  
Recently, many MCDM methods are available [55]. Every method has its own logics, advantages and deficiencies. This paper presents research that implemented the main ideas of MCDM methods. Obtained experimental results were analyzed using several MCDM methods: EDAS (Evaluation Based on Distance from Average Solution) [45], SAW (Simple Additive Weighting) [46], TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) [46,47,56] and COPRAS (Complex Proportional Assessment) [47,57]. SAW combines the criteria values and weights to obtain a single point of reference for evaluation, i.e., the method's criterion. Nevertheless, SAW requires rearranging indicators from minimization to maximization values. COPRAS compensates this deficiency and separately evaluates influence of maximized and minimized indicators. TOPSIS defines alternatives by the distances between the best and worst values of indicators. EDAS evaluates distance of each alternative from the cases built using criteria mean values.  
Weight of criteria were obtained using methods of entropy [48], criteria loss CILOS (Criterion Impact LOS) [49,50] and generalized IDOCRIW [50] (Integrated Determination of Objective Criteria Weights). These methods have been used to solve mechanic [58], environmental [59], and civil engineering [60,61] problems.
# Analyses of GLONASS and GPS+GLONASS Precise Positioning Performance in Different Latitude Regions
###### Abstract  
The orbital inclination angle of the GLONASS constellation is about \(10^{\circ}\) larger than that of GPS, Galileo, and BDS. Theoretically, the higher orbital inclination angle could provide better observation geometry in high latitude regions. A wealth of research has investigated the positioning accuracy of GLONASS and its impact on multi-GNSS, but rarely considered the contribution of the GLONASS constellation's large orbit inclination angle. The performance of GLONASS in different latitude regions is evaluated in both stand-alone mode and integration with GPS in this paper. The performance of GPS is also presented for comparison. Three international GNSS service (IGS) networks located in high, middle, and low latitudes are selected for the current study. Multi-GNSS data between January 2021 and June 2021 are used for the assessment. The data quality check shows that the GLONASS data integrity is significantly lower than that of GPS. The constellation visibility analysis indicates that GLONASS has a much better elevation distribution than GPS in high latitude regions. Both daily double-difference network solutions and daily static Precise Point Positioning (PPP) solutions are evaluated. The statistical analysis of coordinate estimates indicates that, in high latitude regions, GLONASS has a comparable or even better accuracy than that of GPS, and GPS+GLONASS presents the best estimate accuracy; in middle latitude regions, GPS stand-alone constellation provides the best positioning accuracy; in low latitude regions, GLONASS offers the worst accuracy, but the positioning accuracy of GPS+GLONASS is better than that of GPS. The tropospheric estimates of GLONASS do not present a resemblance regional advantage as coordinate estimates, which is worse than that of GPS in all three networks. The PPP processing with combined GPS and GLONASS observations reduces the convergence time and improves the accuracy of tropospheric estimates in all three networks.  
GLONASS; GPS; double-differenced; static PPP 2022
## 4 Deep Reinforcement Learning-Based Efficient Federated Learning Policy Optimization
### Resource Optimization
#### 4.1.2 Experience-Driven Allocation  
FL struggles to aim for fast convergence speed, accuracy satisfaction, training delays, and local energy utilization, which are important features in mission-critical and resource-constrained IoT networks. Experience- and data-driven knowledge can greatly achieve these features by reactively and proactively analyzing the future congestion states, task execution times, and allocation properties. DRL has driven resource optimization in various aspects, but one of the most resource-critical fields is that of network slicing applications. Ref. [75] leveraged the data-driven DRL capability to offer efficient resource optimization and service reliability in E2E network slicing. By intelligently applying the actions to adjust the resource allocation properties in each slice, the agent can evaluate the performance through the reward function and optimize the policy throughout exploration.  
In [76], the authors jointly tackled the model learning and energy consumption by well-formulating a resource allocation problem for FL applications. The learning model primarily interacted with handling the communication overhead and computation resources. The energy model considered the unit energy consumption of each selected client for executing the model updates, and loss function described the model error estimation following the selected algorithm types. The joint problem models were solved by experience-driven actor-critic DRL, which learned the underlying pattern for a well-defined weighted sum balancing policy. The DRL agent was merged at the FL parameter server. Figure 8 shows the architecture of an experience-driven agent in a central FL server, which (1) observes the states of communication/computation features from the clients, (2) applies actions on federated settings and configures sufficient CPU-cycle amount for selected clients, and (3) evaluates the feedback from the environment and stores the batches in experience buffers.  
The primary components of the proposed agent are described as follows.  
* _State_: a set of experienced (historical) network bandwidth information was observed as a primary feature, which is the most influential factor in FL model transmission cost. Future allocation requires prior knowledge of available and used properties.
* _Action_: by determining the values on CPU-cycle frequency, the observed states are altered after applying the new actions. The action selection is indicated through the optimal policy function that is approximated by deep networks.
* _Reward_: the FL system at \(t\)-iteration was feedbacked by \(r_{t}\) of state-action pair \((s_{t},a_{t})\) efficiency. The evaluation metric aims to measure the system costs and scores according to the min-max possibilities.
## 6 Conclusion  
Through the past three decades, steady advancements have been made by various authors, employing many techniques for autonomous Star-ID. Although many complex arrangements have found to be successful, the trend for faster and more reliable methods saw rapid advancement in the 1990's. Star-ID techniques tend to fall in one of four categories separated by two decisions: whether or not to use star brightness information, and whether to select any stars for a given star pattern or use ordinal information from brightness or distance. Once the goal of solving the Lost-In-Space case from a single image in real-time became possible without _a priori_ information, advancements in the Star-ID field turned towards improving the robustness of Star-ID to errors in detecting stars and measuring their features.
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 2 MIMO FMCW Radar Principle and System Analysis
### Fundamentals of FMCW Radar
Generally, in radar systems, a larger antenna aperture correlates with enhanced angular resolution [20]. The angular resolution of a radar can be estimated by the following equation [21]:  
\[\Delta\theta=\frac{\lambda}{D}, \tag{7}\]  
where \(\Delta\theta\) represents the angular resolution in radians, and \(D\) denotes the aperture size of the antenna array.
## 3 Results and Discussion
### Applicability of Empirical Relations to Predict the Rock Mechanical Properties
#### 3.4.2 Evaluation of the Rock Mechanical Properties  
According to the established empirical relationship and the calibrated P-wave interval transit time and density curve, the mechanical parameter profile of the rock along the longitudinal distribution of the formation was obtained. At the same time, in order to evaluate the consistency between the laboratory-measured and calculated rock mechanical parameters, the laboratory-measured rock mechanical parameter points were placed on the graph. Figure 16 shows an example of the prediction profile of the rock mechanical parameters for shale oil reservoirs from the Permian Lucaogou Formation in the Junggar Basin (\(a\), uniaxial compressive strength; \(b\), Young's modulus; \(c\), Poisson's ratio; \(d\), cohesive strength, \(e\), internal friction angle). From Figure 16, it can be seen that the calculated rock mechanical property log curves had a good correlation with the laboratory-measured values.  
Figure 16: Predicted rock mechanical properties using Equations (23)–(27) based on the well logging data ((**a**) uniaxial compressive strength; (**b**) Young’s modulus; (**c**) Poisson’s ratio; (**d**) cohesive strength; (**e**) internal friction angle).  
Therefore, it was concluded that there is a real correlation between physical (such as _DEN_ and \(V_{\text{P}}\)) and rock mechanical properties (such as _UCS_, \(C\), \(\varphi\), _E_s and \(\
u_{\text{s}}\)), supporting the engineering use of correlations, such as optimized drilling operations, wellbore stability analysis, wellbore trajectory optimization, hydraulic fracturing design, etc.
# An Edge-Based Approach to Partitioning and Overlapping Graph Clustering with User-Specified Density
## 4 Proposed Partitioning and Overlapping Clustering Method  
The clustering method introduced in this study is designed for partitioning and overlapping clustering of connected, undirected, and unweighted graphs. A distinguishing feature of this method is its flexibility in enabling users to define the desired density for the resulting clusters, ranging from 0 to 1. This section provides a detailed description of the proposed clustering methods, including the pseudocode implementation and an illustration of an example on a simple random graph.