esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Comparison of Several Methods  
In order to compare the accuracy of the method proposed in this paper with other methods, the ten water samples were modeled by the following methods: the FDPE-PLS, MSC combined with FDPE-PLS, DC combined with FDPE-PLS, 254 nm single-wavelength model, MSC combined with 254 nm single-wavelength model and 254 nm-546 nm dual-wavelength method. The fitting results of the COD calculated by these models and the COD measurements were shown in Figure 19.  
Figure 19 shows that DC combined with FDPE-PLS has the best performance, whose \(R^{2}\) was 0.99 and the average relative error was 0.07. This result can prove the effectiveness of the method proposed in this paper.  
Figure 19: The comparison between different modeling methods.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Samples** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline COD (mg/L) & 1.28 & 2.97 & 4.33 & 7.24 & 11.62 & 12.83 & 16.14 & 23.19 & 27.61 & 31.52 \\ \hline Turbidity (NTU) & 49.55 & 47.92 & 41.77 & 35.12 & 29.64 & 25.03 & 18.88 & 13.91 & 10.45 & 6.54 \\ \hline \hline \end{tabular}
\end{table}
Table 10: The COD and turbidity values of ten actual samples.  
Figure 18: The relationship between actual COD values and predicted COD values.
# Remote and Proximal Sensing Techniques for Site-Specific Irrigation Management in the Olive Orchard
## 2 Materials and Methods
### Multispectral Imagery Acquisition and Analysis  
Images were acquired one day before the beginning of the irrigation differentiation (DOY 187) using a multispectral camera (Tetracam ADC-lite, Tetracam, Inc., Gainesville, FL, USA), mounted on a S1000 UAV octocopter (DJI, Shenzhen, China) able to fly autonomously over a predetermined waypoint. NIR-RG images were recorded in the visible red (R), green (G), and near-infrared (NIR) domain with nominal wavelengths of 520-600, 630-690, and 760-900 nm, respectively.  
Images were acquired at noon under clear sky conditions, the flight altitude was 70 m above ground level (AGL), as reported by [PERSON] et al. [25]. Before the UAV flight, a set of eight ground control points (GCPs) were placed in the orchard and georeferenced using a Leica GS09 real-time kinematic GPS (Leica Geosystems A.G., Heerbrugg, Switzerland). Multispectral images were first mosaicked using Autopano Giga 3.5 Software (Kolor SARL, Challes-les-Eaux, France), then georeferenced and orthorectified using the ground reference points (ArcGIS software, ESRI, Redlands, CA, USA) ad reported by [PERSON] et al. [25]. The NDVI index [49] was calculated using the map algebra technique implemented in ArcGIS software (ESRI, Redlands, CA, USA) using the following equation:  
\[\text{NDVI}=(\text{NIR}-\text{RED})/(\text{NIR}+\text{RED})\]  
where NIR and RED are the reflectance values in the near-infrared (630-690 nm) and red (760-900 nm) bands, respectively.
# A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
## 4 Results
### User Evaluation of Scalp Diagnosis Web Platform  
The questionnaire in Table 8 was used to evaluate the user-friendliness, user experience, and accessibility of the Scalp Diagnosis Web Platform. This survey is based on the system usability scale (SUS) and is designed to efficiently and effectively assess user experience. Each item is rated on a 5-point Likert scale where users can select from \"Strongly Disagree\" (1 point) to \"Strongly Agree\" (5 points) for each statement. Additionally, the analysis of each item based on the survey results from 10 male and 10 female users, aged between 20 and 40, is presented in Table 8.  
The web application received very positive feedback overall, with all the scores ranging between 4.4 and 4.8. This indicates a high level of user satisfaction across the various aspects of the application. In terms of ease of use and intuitiveness, the statement \"Using this web application is simple and intuitive\" scored 4.5, showing that users found the application easy to navigate and use without significant difficulty. The design aspect received a particularly high score of 4.7, indicating that users were very satisfied with the layout, esthetics, and overall usability of the web application. Notably, the statements \"This web application helps me easily accomplish the tasks I want to perform\" and \"I would recommend this web application to others\" both scored 4.8, highlighting the application's effectiveness in helping users achieve their goals and the confidence users have in recommending it to others. Additionally, the statement \"I am willing to use this web application again\" scored 4.7, demonstrating that users found significant value in the application and are highly inclined to reuse it. On the other hand, the statement \"It did not require much effort to understand the functions of the application\" scored slightly lower at 4.4. This suggests there may be room for improvement in making the functions more intuitive or providing better onboarding and help features. Similarly, the statements \"The response speed of the web application is fast and smooth\" and \"When errors occurred during use, I could easily resolve them\" scored 4.5 and 4.6, respectively. While these are strong scores, they indicate that further optimization in speed and more user-friendly error handling could enhance the overall user experience.
## 7 Discussion
### Theoretical Contributions
Use of the Inertia Matrix  
The application of solid mechanics concepts, specifically the use of the inertia matrix, in the identification and modeling of multivariable nonlinear systems using Takagi-Sugeno (T-S) techniques presents the following advantages:  
* Division of data space into exact inertia regions:
* The inertia matrix allows the division of the data space into homogeneous regions with respect to inertia, facilitating the precise and relevant segmentation of the input space. * Determination of principal axes:
* By calculating the eigenvectors of the inertia matrix, the principal axes of the data system are identified. This enables the proper orientation of multidimensional membership functions, ensuring that fuzzy rules are applied in the most significant regions. * Center of gravity for each rule:
* Using the inertia matrix, the center of gravity of the data in each region can be calculated. This center of gravity is used to define the central points of the fuzzy rules, enhancing model accuracy and preventing the creation of unnecessary rules. * Reduction in identification errors:
* Segmentation based on inertia and the strategic placement of rules at highly relevant points contribute to reducing identification errors by ensuring that fuzzy rules are applied more effectively within the data space.
# Real-time Conflict Resolution Algorithm for Multi-UAV Based on Model Predict Control
## 3 Modeling of the Conflict Resolution Problem
### Conflict Resolution Strategy Based on Synergetic Heading Angle Control Rule  
After maintaining the predicted conflict groups, in order not to cause chain reactions of new conflicts, conflicting UAVs are sorted by their flight status [21]. Define the following rules: (1) The UAV with the earlier arrival at the conflict position has the higher priority; (2) The UAV closer to its target has the higher priority; (3) The UAV with higher speed has the higher priority. In the process of resolution, the UAV with the highest priority can fly straight to its target (if it does not cause subsequent conflicts), while the UAV with the lowest priority needs to receive the trajectory of the UAV with the highest priority as a reference track and maneuver until all conflicts are resolved.  
To ensure that the two conflicting UAVs are able to handle the conflict autonomously, and to guarantee the consistency of the action, Ref. [15] introduced a control rule of synergetic heading angle for cruising UAVs with constant speed. During the process of conflict resolution, UAVs with higher and lower priorities (UAV H.P. and UAV L.P.) are commanded to turn to the same side of their current directions under this rule. Denote \(\Delta\psi=\psi_{L}-\psi_{H}\), shown in Figure 6, the rule can be described as: if \(\Delta\psi\in[0,\pi]\), then increase the heading angles of both UAVs; if \(\Delta\psi\in[\pi,2\pi]\), then decrease the heading angles of both UAVs.  
Figure 5: State Machine of UAV flight.
## 3 Differential Evolution for Feature Selection  
Another evolutionary algorithm suitable for feature selection is Differential Evolution (DE) adapted for discrete or binary domains. DE is a population-based stochastic optimization algorithm that has demonstrated strong performance on complex, nonlinear, and high-dimensional problems [9]. Its general procedure is outlined in Algorithm 1.  
```
1:Initialize population \(P=\{x_{1},x_{2},\ldots,x_{NP}\}\) with random values
2:Evaluate fitness \(f(x_{i})\) for all \(x_{i}\in P\)
3:for\(g=1\) to \(G_{\max}\)do
4:for each individual \(x_{i}\) in \(P\)do
5: Randomly select distinct \(x_{a},x_{b},x_{c}\in P\) such that \(i\
eq a\
eq b\
eq c\)
6:for\(j=1\) to \(D\)do
7:\(v_{ij}=x_{aj}+F\cdot(x_{bj}-x_{cj})\)
8: Generate \(r\sim\mathcal{U}(0,1)\)
9:if\(r<CR\)then
10:\(u_{ij}=v_{ij}\)
11:else
12:\(u_{ij}=x_{ij}\)
13:endif
14:endfor
15:if\(f(u_{i})\leq f(x_{i})\)then
16:\(x_{i}\gets u_{i}\)
17:endif
18:endfor
19:endfor
20:return Best individual in \(P\)
```  
**Algorithm 1** Differential Evolution (DE)Originally designed for continuous parameter optimization, DE has been successfully adapted to feature selection tasks by encoding candidate solutions as binary vectors that represent feature subsets [10]. In the context of feature selection, each binary vector represents a subset of features and the objective is to optimize the fitness function \(f\) (wrapper classification accuracy). DE offers a flexible approach for feature selection with two control parameters (\(F\) and \(CR\)). It evolves the population over successive generations using three main operators:  
* Mutation: Weighted difference of two randomly selected population vectors \((x_{b},x_{c})\) with a third one (\(x_{a}\)). This binary version [10] uses XOR as the difference operator and the \(F\) weighting parameter modulated by a random choice as the product and addition operators. This avoids bias on the appearance of 0 and 1 values. \[v_{i}=x_{a}+\mathrm{F}\cdot(x_{b}-x_{c})\] (3)
* Crossover: To increase diversity, elements from the mutant vector (\(v_{i}\)) are combined with the target vector with \(CR\) probability to create a trial vector. \[u_{ij}=\begin{cases}v_{ij}&\text{if rand}_{j}\leq CR\\ x_{ij}&\text{otherwise}\end{cases}\] (4)
* Selection: The fitness of the trial vector is evaluated, and it replaces the target vector in the next generation if it yields a better fitness value.
## 4 Case Studies
### Three-Dimensional Reconstruction of the Models  
The point cloud obtained from the geomatics surveys must be classified in objects which the structure under examination consists of. The processes necessary to perform this task must take into consideration several parameters, such as noise, occlusions, the association between faces of neighbouring objects, etc. We carried out this task in Rhinoceros software because it has more tools and plug-ins for 3D modelling. The key point of this software application is the possibility of generating a profile of the structure and, especially, to build a surface that can be adapted to the point cloud obtained in geomatics surveys. Once the point cloud was imported into Rhinoceros, it was possible to reanalyse it using the Arena plug-in. In this way, the density of the points of the PC was decreased and, consequently, it was possible to assess if there were any holes in the 3D model. Within the Rhinoceros software, the tools available to users are quality, point size and the visual analysis tools (render, ratio, opacity). This allowed for editing the point cloud of the structure. Subsequently, the point cloud was dissected into several planes in space. This operation allowed sections in strategic points of the structure, such as the arches of the bridge (Figure 9), to be performed.  
The plug-in allowed saving the sections in a specific layer. As a result, the sections were displayed as \"construction plans\" (see Figure 10).  
Sections that are transverse and longitudinal to the structure were used to create NURBS. Using the EvoluteTools PRO plug-in, it was possible to generate NURBS surfaces (Figure 11a). This plug-in allowed us to shape NURBS surfaces on objects of the structure, exploiting both the sections and the  
Figure 8: Three-dimensional point cloud of San Cono bridge (visualization in Agisoft Metashape software).  
Figure 9: Scheme of the position of the sections.  
Of course, the time of the clustering task was related to the complexity of the structure. In this way, it was possible to create surfaces that represent the elements of the structure (vault, stack, retaining walls and superstructure of the bridge), as shown in Figure 12a. Using the same procedure just described for the masonry bridge, it was possible to build a 3D model of the San Nicola in Montedoro church too (Figure 12b).  
Lastly, thanks to the development of the Grasshopper plug-in, it was possible to model similar structural elements (or parts of them) in 3D. Thus, it was possible to parameterize both from the geometric point of view and from the point of view of the type of material. For example, Figure 13 shows the parameterization of the arch of the bridge using the tools developed in Grasshopper.
## 4 Approach
### Synchronization  
Whenever an atomic model gets interchanged with a network model and vice versa, synchronization errors can occur. This is the result of already scheduled autonomous events, that now may become invalid. Looking at the mode of operation of the implemented abstract simulator, one can see that subsequently to an autonomous event the imminent model gets rescheduled with its time advance function. This little detail can be used to save complicated synchronization procedures: if the exchange of models can only occur during events, the subsequent rescheduling of the containing model will minimize the chance of falsely scheduled models.  
However, depending on the concrete implementation of the Future Event Schedule (FES), perhaps there are still internal events scheduled that are now too early or too late. For example, when the simulator switches from a detailed, slower advancing network model to the respective atomic one, it can happen that internal events of one of the network's faster advancing components are still inside the FES. The easiest solution to this is of course a future event schedule, which keeps at most one event of every atomic model inside itself. Through the encapsulation of the network model inside what appears to be an atomic DEVS model and the immediate rescheduling after execution of the internal event, the model would be scheduled correctly. However, in practise, this is not always that easy. Different areas of application can demand for different scheduling strategies and the possibility exists that not all of them can respect the requirements of this approach. OMNeT++ already is designed with several possible scheduling classes. To maintain maximal flexibility of the proposed approach, it is therefore necessary to answer the problem directly. Thus, the proposed new kind of model has to keep track of its own schedule on its own and decide on how to react to a falsely scheduled autonomous event independently. While models can keep track of their own model time through their time advance function and the fact that they get informed about the elapsed time since the last internal event (which supposedly followed a correct time advance of the model), when the occurrence of internal events loses reliability, this is no longer sufficient. Hence, the model needs the ability to acquire the global simulation time. This can be achieved by, for example, allowing the single models to access the simulation environment or the simulation kernel, as OMNeT++ does, or it can be accomplished by providing the global simulation time to the model with every function call to one of the state transition functions or the output function.
## 3 Comprehensive Evaluation Properties and Implementation Procedures
### Scaling Robustness  
Scaling robustness [26] implies that an IM is monotonic and can be proportionally adjusted. Concurrently, this adjustment should not introduce any bias in the results of the structural dynamic time history analysis. Typically, scaling robustness is assessed using the \(p\)-value derived from the F-test, with a significance level set at 5%. A higher \(p\)-value indicates a lower dependency of the structural dynamic response on the proportional adjustment factor, implying greater scaling robustness for the corresponding IM. Specifically, if the \(p\)-value exceeds 0.05, the slope of the regression line is near zero, affirming the scaling robustness of the IM. Conversely, a \(p\)-value of 0.05 or less negates this robustness. Given that scaling robustness also considers the impact of the adjustment factor on the structural dynamic response, and IMs and DMs satisfy the logarithmic distribution of Formula (3), \(\ln(DM)\) and \(\ln(\rm Scale\ Factor)\) are used to assess IM scaling robustness when \(\theta_{\rm max}\) is the DM.
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 3 Preliminaries
### Software-Defined Network  
Due to VANETs' salient features, it is difficult to supply coordinate services according to diverse quality of service (QoS) requirements. As a result, building a programmable network architecture to support the inter-operation among entities in VANETs becomes vital [28]. Software-defined network (SDN) is an important network architecture to support network management. The core concern of SDN is how to separate the system (control plane) and the sending capacities (data plane) [29], which are able to improve network performance in terms of network management, control, and data handling.  
As shown in Figure 2, SDN consists of a control plane and data plane. The control plane is responsible for generating internal exchange paths, boundary service routes, as well as handling the network state change events. The data plane only provides a simple data forwarding function, which can quickly process matching packets to meet the increasing demand of traffic. A standard protocol (e.g., openflow) is used for communication between the two planes. Openflow switch is the core component of the whole network, which stores a flow table that is generated, maintained, and distributed by an external controller to support data forwarding. There are two modes for distributing the flow table: active mode and passive mode. In active mode, the SDN controller actively sends the flow table data collected by itself to open the flow switch. Then, the open flow switch is able to directly  
Figure 1: VANETs architecture.  
forward data according to the flow table. The advantage of active mode is that the data plane shortens the waiting time for controller operation and greatly reduces the forwarding delay. However, this mode has high requirements for the capacity of the flow table. In passive mode, after receiving the packet, the open flow switch first finds the forwarding target port on the local flow table. If there is no match, the open flow switch forwards the data packet to the controller. The control layer determines the forwarding port and issues the flow table. The advantage of passive mode is that the network equipment does not need to maintain all flow tables. Only when the actual traffic is generated can the flow table records be obtained from the controller. When the records expire, the corresponding flow tables are deleted, so the storage space is greatly saved.
## 3 Results
### Self-Reported Overall Fatigue and Musculoskeletal Pains by the Degree of Hazard Exposure
#### 3.3.3 Self-Reported Upper Limb Pain by the Degree of Hazard Exposure
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Factor**} & \multirow{2}{*}{**Variables**} & \multicolumn{3}{c}{**ADHE**} & \multirow{2}{*}{\(\chi^{2}\) **test**} \\ \cline{3-5}  & & \(<\)**2** & **2–4** & & \(\geq\)**4** \\ \hline \multirow{4}{*}{Physical hazard} & Vibration & 17.5\% & 10.0\% & 29.7\% & \(\chi^{2}\) = 15.953, \(p\) \(<\) 0.001 * \\ \cline{2-5}  & Noise & 16.0\% & 18.9\% & 32.5\% & \(\chi^{2}\) = 14.832, \(p\) = 0.001 * \\ \cline{2-5}  & High temperature & 16.1\% & 17.9\% & 40.9\% & \(\chi^{2}\) = 28.776, \(p\) \(<\) 0.001 * \\ \cline{2-5}  & Low temperature & 20.7\% & 18.9\% & 34.4\% & \(\chi^{2}\) = 6.223, \(p\) = 0.045 * \\ \hline \multirow{4}{*}{Ergonomic hazard} & Awkward posture & 15.0\% & 15.9\% & 31.5\% & \(\chi^{2}\) = 16.641, \(p\) \(<\) 0.001 * \\ \cline{2-5}  & Manual material handling & 19.0\% & 22.1\% & 29.1\% & \(\chi^{2}\) = 4.169, \(p\) = 0.124 \\ \cline{2-5}  & Standing posture & 20.0\% & 11.2\% & 26.3\% & \(\chi^{2}\) = 9.123, \(p\) = 0.010 * \\ \cline{2-5}  & Repetitive motion & 15.8\% & 21.1\% & 41.2\% & \(\chi^{2}\) = 22.824, \(p\) \(<\) 0.001 * \\ \hline \multirow{4}{*}{Chemical and biological} & Fumes and dust & 18.4\% & 20.8\% & 39.5\% & \(\chi^{2}\) = 27.679, \(p\) \(<\) 0.001 * \\ \cline{2-5}  & Vapor & 19.2\% & 31.4\% & 39.0\% & \(\chi^{2}\) = 10.947, \(p\) = 0.004 * \\ \cline{2-5}  & Skin contact with chemical & 19.4\% & 25.0\% & 43.9\% & \(\chi^{2}\) = 12.965, \(p\) = 0.002 * \\ \cline{2-5}  & Tobacco smoke & 20.5\% & 28.3\% & 47.4\% & \(\chi^{2}\) = 8.532, \(p\) = 0.014 * \\ \cline{2-5}  & Infection & 21.0\% & 34.8\% & 36.8\% & \(\chi^{2}\) = 4.735, \(p\) = 0.094 \\ \hline \hline \end{tabular}  
* * Significant at 0.05. ADHE = adjusted daily hazard exposure time.  
\end{table} 
Table 8: Distribution of subjective upper limb pain by the degree of hazard exposure.
# Research of a Multi-Frequency Waveform Control Method on Double-Wire MIG Arc Welding
## 1 Introduction
(2019) found that if the peak period of trailing current was delayed by 0.5 ms compared to the leading current, more satisfactory welding quality and more stable welding process would be obtained. In our previous work [PERSON] et al. (2019), the SFIP form was used, and symmetrical transition periods were added. Then the welding process was much steadier, and the welding seams with more much satisfactory quality can be obtained. More works focused on improving the current waveforms control to eliminate the negative phenomena and obtain welding seam with satisfactory quality, however, the work cannot change the working frequency of two wires currents, and the process did not concern the effects if the two currents were not symmetrical. In this work, we tried to change one wire current by means of multi-frequency modulation, through a series of theoretic analysis, experimental validations and corresponding result analyses by auxiliary mathematical tools, such as orthogonal experiments, probability density function, and range analysis, were conducted. Corresponding results showed that the proposed multi-frequency waveform control method can improve the double-wire MIG arc welding technology, not only in process stability, but also in obtaining beautiful welding seam with satisfactory quality. The rest of this paper is organized as follows. In Section 2, the principle of the double-wire MIG arc welding is outlined. Section 3 introduces the proposed multi-frequency current waveform control method, based on the previous employed methods. Then, Section 4 provides the experimental setup and program. Section 5 gives the detailed experimental procedures, and experimental results and corresponding analyses. In this section, other auxiliary mathematical tools are introduced and applied in the experiments. Finally, Section 6 offers some concluding remarks and suggestions for future work.