esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Applications of the Measurement System
### Experimental Procedure  
Applying the frequency converter, the speed of the \"drive motor\" is increased in small, equal steps from zero. At each step, the angular speed and the torque on the rotor of the analyzed motor are measured. (During the measurements, the angular acceleration of the rotor is zero.) After that, the measured torque can be given versus the measured angular speed. Since the measured torque is the braking torque of the rotor, finally, we obtain the function \(M_{\textit{brake}}(\omega)\). It must be emphasized that the measurement of the braking torque, when applying the above procedure, is only possible if a torque meter with a very low nominal (rated) torque and value of accuracy class is applied (typically: <0.5 Nm and <0.5%). If such a torque meter is unavailable, or if we do not have a torque meter, the experimental setup and procedure in References [28; 29] for the simultaneous measurement of the braking torque and moment of inertia can be applied.  
In Figure 15, an experimental setup for measuring the moment of inertia of the rotor of an electric motor is presented.  
Figure 14: Experimental setup for the measurement of the braking torque on the rotor of an electric motor.  
Experimental Procedure  
During the measurement, the three-phase induction motor is plugged into Socket 5 and used as a \"drive motor\". Its shaft is connected to the shaft of the analyzed motor through a releasable clutch. Applying the frequency converter, the speed of the \"drive motor\" is increased until it reaches its maximum speed. After that, the clutch is released, and the rotor of the analyzed motor slows down until it stops. During the retardation, its angular speed is measured by the optical LED sensor. From the measured angular speed vs. time function, its angular acceleration vs. time function can be calculated by derivation. After that, its angular acceleration can be given as the function of its angular speed (\(\epsilon(\omega)\)). From function \(\epsilon(\omega)\) and the previously measured \(M_{brake}(\omega)\) function, the moment of inertia of the rotor can be calculated as:  
\[J_{rotor}=\frac{M_{brake}(\omega)}{\epsilon(\omega)} \tag{2}\]  
In the case of a successful experiment, the calculated moment of inertia has a constant value in a wide angular speed range.
# Research on Interval Optimal Scheduling Strategy of Virtual Power Plants with Electric Vehicles
###### Abstract
This type of method has the feature of good robustness, but the optimal scheduling result depends on whether the typical scenario has a good representation. Finally, there are combinatorial optimization methods. References [24; 25; 26] proposed a two-stage robust optimization scheduling model, in which the uncertainty of the robust model is optimized in the first stage and the worst operation scenario is found within the optimized uncertainty set in the second stage. Reference [27] considered the uncertainty factor, built a two-stage model of robust optimization based on affine rules, and used mixed integer linear programming to solve it. Reference [28], under the probabilistic scenario based on the CVaR risk measure, a typical probabilistic scenario approach is used to transform the stochastic problem. Reference [29] proposes a multi-stage robust pairwise dynamic programming algorithm for uncertainty, and the combined optimization method can synthesize the advantages of a single optimization method, but there are disadvantages such as a complex optimization process and not being easy to implement. From the engineering application point of view, there is a lack of an optimal scheduling strategy for virtual power plants that can overcome uncertainty but is simple and feasible. The advantages and disadvantages of the different methods are summarized in Table 1. Unlike other methods, the interval number method can describe uncertainty and has a simple form, which only needs to grasp the upper and lower bounds or midpoints and radii of uncertainties. Moreover, it can transform random variables into interval numbers through confidence level and fuzzy numbers through intercept level, which has good generality. The following comparison table represents a summary of the advantages and disadvantages of each approach to uncertainty and indicates the advantages of interval numbers to describe uncertainty factors. In this paper, considering the engineering applications, the interval number method is adopted as it is an extremely simple and efficient way to deal with uncertainties and is suitable for a large number of engineering practice scenarios. The interval number method is considered for introduction into the optimal scheduling problem of virtual power plants to ensure the feasibility of engineering applications. Specifically, for the virtual power plant incorporating electric vehicles, the interval number is used to describe the stochastic fluctuations of system uncertainties, and the optimization objectives are to improve the operating economy, environmental protection, and grid load smoothing, and to build a multi-objective interval optimal dispatching model considering the constraints of electric-vehicle characteristics and power balance. The optimal scheduling scheme is obtained by using the improved NSGA-II algorithm to perform genetic iterations to obtain the Pareto solution set and hierarchical analysis.
## 4 Experimental Results and Analysis
### Mask Wearing Detection

Table 4: The detection results of the model deployed on the Android device. Figure 19: Training curve graph of the YOLOv5 model with the spatial-frequency fusion module added. Figure 18: Training curve of the original YOLOv5 model. 2.10% and 2.63% on CIFAR100 and Tiny ImageNet, respectively. In addition, MobileNetv2 and ResNet are selected for mask-wearing classification in diverse scenarios. MobileNetv2 achieved an accuracy of 95.49%, while ResNet achieved 98.14%. By applying the proposed enhancement, the ResNet mask-wearing status classification model culminated in a substantial accuracy increase by 1.58%. YOLOv5 was used for mask-wearing detection. Intending to maximize the utilization of image features, a spatial frequency fusion module was proposed, effectively boosting the performance of the model. The experimental results reveal that the YOLOv5 model strengthened with spatial-frequency fusion achieves an [EMAIL_ADDRESS] improvement of 6.95% when detecting incorrect mask wearing, and [EMAIL_ADDRESS] is increased by 2.20%. Nevertheless, it is essential to acknowledge the limitations inherent in the model demonstrated in this study. For example, for the model to perceive detailed features, we have added many branches, but some of them may be redundant, thereby increasing the complexity of the model. So, the next step is to prune and quantify the model to further compress its size and deploy it in practical applications. Conceptualization, S.C. and S.L. ; methodology, S.C. and S.L. ; software, S.C.; validation, S.C. and F.L. ; formal analysis, S.C. and S.L. ; investigation, S.C.; resources, S.C. and F.L. ; data curation, S.C. and F.L. ; writing--original draft preparation, S.C. and F.L. ; writing--review and editing, S.L. ; visualization, S.C.; supervision, S.L. ; project administration, S.C. All authors have read and agreed to the published version of the manuscript. This work has been partly supported by the Joint Research Fund in Astronomy under a cooperative agreement between the National Natural Science Foundation of China and the Chinese Academy of Sciences (No. U2031104), Guangdong Basic and Applied Basic Research Foundation (No. 2023A1515011340). Not applicable.
# Design and Optimization of Non-Coplanar Orbits for Orbital Photovoltaic Panel Cleaning Robots
###### Abstract
However, employing such sophisticated techniques in harsh non-vacuum environments necessitates the consideration of additional external factors, which undoubtedly increases the complexity and cost of the research and development process. It is therefore particularly important to select an effective design method, as this will influence both the effectiveness and the complexity of the design. [PERSON] et al. [24] discussed the Analytic Hierarchy Process (AHP), a structured decision-making framework that assists researchers in evaluating and comparing various design alternatives through multiple levels of criteria and sub-criteria. While applying AHP to non-coplanar trajectory design appears to facilitate the selection of an optimal design method, it is not fully effective as a decision-making tool due to its limited dynamic decision-making capabilities and reduced adaptability to uncertain environments. In conclusion, compared to the studies and findings of other researchers, Bezier curves seem to be a good method for designing non-coplanar orbits, simplifying the mathematical expressions and reducing the computational workload, making it more efficient in practical applications. In order to fill the research gap concerning non-coplanar orbits for photovoltaic panel cleaning robots, this paper proposes a method for design and optimization utilizing Bezier curves. By analyzing the motion law of the cleaning robot, trajectory data for a single working cycle and the trajectory during the spanning motion phase are computed. Considering the challenges associated with accurately controlling the shape of trajectory design, this study proposes the Bezier curve method with scaling parameters and completes the optimization of non-coplanar trajectories using a nonlinear planning method based on the Sequential Quadratic Programming (SQP) algorithm. The remainder of this paper is organized as follows: Section 2 presents a three-dimensional model of the cleaning robot, detailing the complex work scenarios it may encounter during operation. Additionally, the trajectory model, smoothing processing model, and optimization model are established. Section 3 verifies the validity of the proposed methods through case-based calculations and dynamic simulation results. Finally, Section 4 summarizes the entire paper and discusses future research directions.
# Augmented Reality in Heritage Apps:Current Trends in Europe
## 1 Introduction  
At the turn of the century, when information and communication technologies (ICT) had already acquired great importance in most sectors of society, the first proposals for their use in educational and heritage contexts began to appear. The spread of access to the Internet and mobile devices, such as tablets and smartphones, resulted in these new technologies being democratized. Indeed, this led to the development of tools that provided the general public with previously unprecedented access to information, with mobile applications (henceforth simply \"apps\") being the most widespread manifestation of this trend. Today, the educational potential of these types of devices and programs is no longer debatable [1], since their use in both formal and non-formal educational settings has become an undeniable fact. Mobile learning and e-learning have made it possible to adapt content to every type of user, by improving pedagogical processes and allowing for a type of learning which is social and collaborative and in which communication is multilateral and horizontal [2].  
Expanding beyond their original use in industry during the nineteen-nineties [3], more complex elements, such as virtual reality (VR), augmented reality (AR), and variations on these have been taken up more widely for a variety of uses over the past decade. The spread of these tools in recent decades has led to an abundance of research and publications on the potential of AR [4]. Nonetheless, as other authors have pointed out [5], there have been relatively few studies addressing the use of AR in education; there is not only a widespread lack of theoretical reflection on the issue [6], but also a belated application of this new technology in education when compared to its use in other fields [7].  
In fact, only a 2016 study from Spain has really begun to delve into the issue by showing that only 12.6% of analyzed apps with heritage content made use of AR [8].  
Accordingly, this is an important fledgling field of study in which work must continue to be done, especially given the advantages that various virtual tools can offer in the area of education and heritage [9]. So, the main purpose of this exploratory study is to address the current state of European heritage education apps that make use of AR. As far as we are aware, this is the first Europe-wide study on this topic and it will include a brief examination of applications for mobile devices that are currently available, with a description of their main features and the use that they make of AR for heritage purposes. The specific objectives of this study were:  
1. To find and inventory heritage-related open-access apps of European origin that use AR.
2. To carry out a statistical-descriptive analysis of these apps in order to outline their main characteristics.
3. To identify predominant trends in how the identified heritage apps make use of AR.
## 3 Results and Discussion  
### Total Aerobic Mesophilic Viable Count of Endive Lettuce along the Washing Process Chain  
Typical processing of fresh-cut salads includes the removal of outer leaves, cutting, washing/disinfection, centrifugal drying, and packaging. The washing step is mainly conducted after cutting of the produce, resulting in leakage of produce ingredients into the wash water which may increase the consumption of sanitizers. It is also proposed that cutting may enable a better attachment of microorganisms onto the surface of products, as well as the internalization of microorganisms [21]. Therefore, a washing-before-cutting approach was proposed by different authors [7, 21]. [PERSON] et al. [7] found a 2 log unit reduction of the initial microbial load of iceberg lettuce after pre-washing of whole-lettuce head using chlorine. [PERSON] et al. [21] found a 0.8 log cfu/g higher reduction of _E. coli_ O157:H7 on iceberg lettuce when applying the washing-before-cutting process in comparison to the commonly applied cutting-before-washing process. In this study, the total aerobic mesophilic viable count (TVC) of the entire raw material ranged between 7.5 log cfu/g and 7.7 log cfu/g (Table 1). After storage, the untreated material showed TVC values between 7.4 log cfu/g and 8.3 log cfu/g (Table 2). For the application of plasma-treated water during washing, the washing-before-cutting process as well as the cutting-before-washing process, was applied. Washing with water was applied using the washing-before-cutting method (trial A). The TVC was significantly reduced along the processing chain from 7.7 log cfu/g to 6.5 log cfu/g (Table 1), revealing a reduction of 1.2 log units (Table 3). After storage at 2 \({}^{\circ}\)C for seven days, the TVC of endive lettuce at the end of the washing was 7.5 log cfu/g and significantly lower than the stored endive raw material with 8.3 log cfu/g (Table 2), which still means a reduction of 0.8 log units (Table 3). The application of plasma-treated water at different processing steps reduced the TVC of the final endive lettuce product to 6.9 log cfu/g, 6.5 log cfu/g, 6.0 log cfu/g, 6.6 log cfu/g, and 6.6 log cfu/g for trials B, C, D, E, and F, respectively (Table 1). The highest reduction on the final endive lettuce product was achieved for trial D (PTW in pre-bath, 180 s) after processing (1.6 log reduction), followed by treatment F (PTW in pre-bath (180 s), pre-rinsing (30 s), and second wash (180 s)) with a reduction of 1.1 log unit (Table 3). However, there was no significant difference in the TVC reduction between washing with water and the addition of plasma-treated water at different processing steps with the exception of trial B (PTW in pre-rinsing, 10 s), which showed a significantly lower reduction of the TVC after processing (Table 3).
## 3 Case Study
### Data Pre-Processing
#### 3.2.1 Continuous Series Segmentation  
Both types of raw data are partially continuous and need to be divided into continuous series before interpolation. One hour is chosen as the criterion to determine whether the data is continuous or not. The adjacent moments in the original data with time interval more than one hour are intermittent points, and the time series between two intermittent points are continuous time series.
# Performance Evaluation of CF-MMIMO Wireless Systems Using Dynamic Mode Decomposition
## Appendix A Delay Correlation
### Scattering Function  
The scattering function \(S_{\tilde{h}}(\
u,\tau)\) characterizes how signal components are scattered in a multipath environment. It quantifies the power distribution of the received signal in terms of Doppler frequency shift (\(\
u\)) and delay (\(\tau\)):  
\[S_{\tilde{h}}(\
u,\tau)=\mathbb{E}\Big{[}|\tilde{h}(\
u,\tau)|^{2}\Big{]}. \tag{15}\]This function relates to the time-frequency correlation \(R_{\hat{h}}(\Delta t,\Delta f)\) through its two-dimensional Fourier transform:  
\[S_{\hat{h}}(\
u,\tau)=\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}R_{\hat{h}}( \Delta t,\Delta f)e^{-j2\pi(\Delta t\
u-\Delta f\tau)}d\Delta td\Delta f. \tag{10}\]  
This general framework allows the recovery of special cases like time or frequency correlations when stationarity conditions are relaxed and reveals relationships such as Doppler spectrum \(S_{h}(\
u)\) and Power Delay Profile (PDF) \(S_{h}(\tau)\):  
\[S_{h}(\
u)=\int_{-\infty}^{\infty}S_{\hat{h}}(\
u,\tau)d\tau, \tag{11}\]  
\[S_{h}(\tau)=\int_{-\infty}^{\infty}S_{\hat{h}}(\
u,\tau)d\
u. \tag{12}\]
# Analysis of the Aggregate Effect on the Compressive Strength of Concrete Using Dune Sand
## 1 Introduction
[4] reported that the compressive strength of concrete improved until the DS replacement ratio increased to 10%. [PERSON] et al. [9] reported that the compressive strength increased until the DS replacement ratio attained a certain value, but it decreased at higher replacement ratios. Thus, some studies have reported that the compressive strength of concrete decreased as the DS replacement ratio increased, whereas others have suggested that the maximum compressive strength of concrete is realized at a specific DS replacement ratio. In general, the properties of an aggregate that affect the strength of concrete include the strength, shape, surface texture, maximum size, and particle size of the aggregate [10]. Although the DS used in previous studies had similar shapes and surface textures [1], the strength of the DS or properties of other aggregates that were used with the DS in the concrete mix differed among the studies; thus, the effect of DS on the compressive strength of concrete may have varied among the studies. Hence, further experimental studies under various conditions are required for a comprehensive quantitative analysis of the properties of DS and other aggregates, as well as the properties of DS combined with other aggregates, which affect the compressive strength of concrete. In this study, concrete was produced using CS and DS from the Middle East region, and the effects of these aggregates on the strength of concrete were examined. To this end, the compressive strength properties of concrete for different unit water contents and volumetric DS to FA ratios (DS-FA ratio) were investigated for different ages. In addition, the effect of volume changes of each aggregate on the compressive strength properties of concrete were quantitatively analyzed based on the measured compressive strength at each age.
## 2 Methodologies
### Whale Optimization Algorithm (WOA)
\(t\) is the current iteration number, and  
\[\overrightarrow{A}=2\overrightarrow{a}.\overrightarrow{r}-\overrightarrow{a}, \tag{9}\]  
\[\overrightarrow{B}=\Big{|}\overrightarrow{C}.\overrightarrow{Z^{*}}(t)- \overrightarrow{Z}(t)\Big{|}, \tag{10}\]  
where \(\overrightarrow{A}\) is one coefficient vector, \(\overrightarrow{a}\) is a vector that decreases linearly from two to zero throughout the computing process, \(\overrightarrow{r}\) is a random vector in the range of [0, 1], \(\overrightarrow{Z}(t)\) is the current position vector and \(\overrightarrow{C}\) is another coefficient vector which can be expressed as  
\[\overrightarrow{C}=2\overrightarrow{r}, \tag{11}\]  
Figure 5: Flow chart of the WOA. When \(p<50\%\) and \(|\overrightarrow{A}|\geq 1\), the random search is used to update the position, which is expressed as  
\[\overrightarrow{Z}(t+1)=\overrightarrow{Z}_{rand}-\overrightarrow{A}.\left| \overrightarrow{C}.\overrightarrow{Z}_{rand}-\overrightarrow{Z}(t)\right|, \tag{12}\]  
where \(\overrightarrow{Z}_{rand}\) is a random position vector. Here, \(|\overrightarrow{A}|\geq 1\) emphasizes that the exploration shows that the WOA algorithm performs a global search. Lastly, when \(p\geq 50\%\), the spiral upward movement is used to update the position, which is expressed as  
\[\overrightarrow{Z}(t+1)=\overrightarrow{Z^{*}}(t)+\overrightarrow{B^{\prime} }.e^{bl}.\cos(2\pi l), \tag{13}\]  
where \(b\) represents a constant spiral size, \(l\) is a random number in the range of \([-1,1]\) and  
\[\overrightarrow{B^{\prime}}=\left|\overrightarrow{Z^{*}}(t)-\overrightarrow {Z}(t)\right|, \tag{14}\]  
After the position updating operation, the process returns to step (2). The previous study [32] shows that, because the exploitation and exploration phases have been conducted separately and in almost half of the iterations each, the WOA can solve the global optimization easily and with a high convergence speed. Moreover, the adaptive change in the \(\overrightarrow{A}\) allows the WOA to make a smooth transition between exploitation and exploration. It is worth noting that the two main internal parameters, \(\overrightarrow{A}\) and \(\overrightarrow{C}\), need to be adjusted in the WOA.
# Review and Comparison of Emerging Routing Protocols in Flying Ad Hoc Networks
## 3 Communication Architecture of FANETs
### UAV Ad Hoc Network  
The simplest one is the UAV ad hoc network, as shown in Figure 1. We can see that the backbone UAV play a part of a gateway node to maintain linking between base station and other sub-UAVs. Gateway UAVs will take along wireless communication equipments, which can work under the conditions of low power consumption, close communication with UAVs, high power, and long communication with ground stations [33]. To maintain sustained connectivity, all connected UAVs in FANET need to have similar movement patterns, such as speed and direction. When carrying out autonomous aerial reconnaissance missions, a large number of similar small UAVs can be deployed and this communication architecture can be used to complete the missions [34].