esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussions  
### Feature Permutation  
We conducted a feature permutation test for the CNN model to understand the extent to which each input variable influenced the performance of the model when estimating SSI from GK2A/AMI data (Figure 11). The ESR, with the highest mean decrease in accuracy, was ranked as the most important feature. When the ESR was randomly permutated, the RMSE of the CNN model increased to 1.219 MJ m\({}^{-2}\). If a clear sky occurs, because there is no sky covered by clouds, when the ESR is incident on the Earth's surface, it is not affected by clouds. Thus, in clear-sky conditions, the SSI increases as the ESR increased. Furthermore, unless the sky is obscured by thick clouds, a higher ESR generally increases the SSI, even if scattered SSI is considered, and direct SSI cannot theoretically exceed the ESR. Hence, because ESR absolutely affects SSI, it was demonstrated to be the most important feature in the CNN model. The CNN model had the second and third highest feature permutations of 0.959 MJ m\({}^{-2}\) (IR12.3) and 0.926 MJ m\({}^{-2}\) (IR13.3), respectively, and  
Figure 10: Variation of accuracy by comparison between GK2A/AMI-derived SSI estimates using the CNN model as the reference model and in situ SSI from ASOS stations operated by KMA with respect to (**a**) in situ SSI, (**b**) in situ visibility, (**c**) in situ daylight, and (**d**) in situ cloud amount; the blue, green, and red lines represent RMSE, bias, and nRMSE, respectively, and the gray bars denote the number of matchups.  
their difference from the ESR was small compared with the other feature permutations. This implied that the structure of the CNN model was closely related to the ESR and the other input variables, such as IR12.3 and IR13.3, which implies that the structure of the model was complex. Among the top three most important features, including the ESR, the CNN model included infrared channels. Because the SSI is a parameter affected by clouds and atmospheric factors including aerosols, the model should reflect cloud attenuation and atmospheric factors. To reflect atmospheric attenuation, the CNN model was trained with an increased focus on infrared channels.
# Effect of Different Drying Methods on the Quality of _Oudemansiella raphanipes_
## 2 Materials and Methods
### Drying Methods  
There are three different drying methods, namely hot air drying (HD), infrared radiation drying (ID), and vacuum freeze drying (VD). In the preparation of the above materials, three 50 g samples of _O. raphanipes_ were taken as experimental samples. Table 1 shows the parameter settings of each drying method. Specific operations are as follows.  
During hot air drying (HD), 50 g _O. raphanipes_ was laid flat in the oven. In order to dry them fully, the samples were placed in a single layer without overlapping. The temperature was set at 50 \({}^{\circ}\)C, and the convection rate was 0.3 m\({}^{3}\)/s. At the beginning of drying, samples were taken at intervals of 2 h to determine the moisture content. When the dry base moisture content of the sample was less than 13%, the drying was ended.  
During infrared radiation drying (ID), weigh 50 g of _O. raphanipes_ and lay it on the drying screen in the form of a single layer. Select the far infrared radiation mode, with radiation intensity of 2 W/cm\({}^{2}\), temperature set at 50 \({}^{\circ}\)C, radiation distance between the far-infrared radiation plate and the material tray of 10 cm, and the wind speed above the material at 1.5 m/s. Samples were taken at an interval of 2 h to determine the dry base water content of the sample. When it was less than 13% of the safe water content, the drying was finished.  
When vacuum freeze drying (VD) was performed, 50 g of _O. raphanipes_ was also weighed and placed in a low-temperature freezer (\(-\)20 \({}^{\circ}\)C) for quick freezing for 6 h and then frozen in an environment of \(-\)80 \({}^{\circ}\)C for 12 h. The sample was placed on the material shelf of a vacuum freeze-drying machine (cold trap temperature of \(-\)50 \({}^{\circ}\)C and vacuum degree of 1.5 hPa). Samples were taken at intervals of 2 h to determine the moisture content. When the dry base moisture content of the sample was less than 13%, the drying was finished.  
Dry moisture content of _O. raphanipes_: dry moisture content W = (m\({}_{1}\) - m\({}_{2}\))/m\({}_{2}\) \(\times\) 100%, where m\({}_{1}\) represents the starting mass of fresh material and m\({}_{2}\) represents the mass of dried material.  
In order to reduce the error of the subsequent test, the final water content of the three dry samples is controlled at the same level below 13%.
## 2 Materials and Methods
### Antioxidant Activity Assays
#### 2.7.1 DPPH  
Twenty microliters of phenolic fractions from Aronia cells biomass extracts were mixed with 280 uL DPPH (1,1-Diphenyl-2-picrylhydrazyl) radical. Blank sample without extract was prepared as well. The reaction time was 15 min at 37 degC, then the absorbance at \(\lambda\) = 517 nm was recorded and used to calculate the percentage of DPPH radical inhibition. Trolox (6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) was used as standard.
## 2 System Design
### Transmitter
The mechanism of the proposed modulation scheme is that, by calculating the number of different frequencies had between two consecutive frequencies, we exported the data symbol with four total frequencies or executed an error correction if a mixed symbol frame occurred, as shown in Figure 3. To solve the error of two mixed frequencies, the state of the first bit in every three bits was replaced during the design of the modulation scheme. This design helped us to identify the frame with a mixed symbol and rectify the data signal. To maintain the synchronization control, a splitter frequency for supporting the system was inserted to identify the frame that would contribute to the beginning and ending frequency. A shutter speed threshold defined in the current study related to the FSOOK modulation technique was manually added to the splitter frequency above the camera image sensor. The splitter frequency was mostly 10 kHz, which maintained the average luminance equilibrium when the image was captured by the image sensor.
## 2 Method
### Optimizers
#### 2.2.1 Adam Optimizer  
The Adam optimization algorithm is an extension to stochastic gradient descent [47]. Its core idea is to compute individual adaptive learning rates for different parameters from the estimations of the first and second moments of the gradients. The detailed computing steps are given as Algorithm 1.  
```
Input: parameters, learning_rate, \(\beta_{1}\), \(\beta_{2}\), \(\epsilon\) \(m\leftarrow\text{zeros\_like}(parameters)\)\(\triangleright\) Initialize 1 st moment vector \(v\leftarrow\text{zeros\_like}(parameters)\)\(\triangleright\) Initialize 2 nd moment vector \(t\gets 0\)\(\triangleright\) Initialize timestep while not converged do \(t\gets t+1\) \(\text{gradient}\leftarrow\text{compute\_gradient}(parameters)\)\(\triangleright\) Compute gradient of the objective function \(m\leftarrow\beta_{1}\cdot m+(1-\beta_{1})\cdot\text{gradient}\)\(\triangleright\) Update biased first moment estimate \(v\leftarrow\beta_{2}\cdot v+(1-\beta_{2})\cdot(\text{gradient}^{2})\)\(\triangleright\) Update biased second raw moment estimate \(m_{\text{hat}}\gets m/(1-\beta_{1}^{\prime})\)\(\triangleright\) Bias-corrected first moment estimate \(v_{\text{hat}}\gets v/(1-\beta_{2}^{\prime})\)\(\triangleright\) Bias-corrected second moment estimate \(parameters\gets parameters-\text{learning\_rate}\cdot m_{\text{hat}}/(\sqrt{v_{ \text{hat}}}+\epsilon)\)\(\triangleright\) Update parameters endwhile
```  
**Algorithm 1** Adam Optimization
# Three-Dimensional Formation Control for Robot Swarms
## 4 Formation Controls
### Formation Controls for Expanding the Network
: _Consider the case where all ARs maneuver under Algorithm 1. While an AR \(u_{i}\) maneuvers along a path, say \(P\), until reaching \(f_{u_{i}}\), the maneuver of \(u_{i}\) does not lead to disconnected networks. While \(u_{i}\) maneuvers along \(P\), \(u_{i}\) applies local communication (one hop communication) only. As \(u_{i}\) arrives at \(f_{u_{i}}\), we are able to locate \(u_{i}\) in the global coordinates._  
Proof. : In Algorithm 1, all ARs rendezvous at the leader before the formation controls are initiated. Therefore, the maneuver of \(u_{i}\) does not lead to disconnected networks. An AR \(u_{i}\) maneuvers along a path, say \(P\), until reaching \(f_{u_{i}}\). Let \(\{m_{1}\to m_{2}\to\ldots\to m_{end}\}\) represent the order of ARs along \(P\) until reaching \(f_{u_{i}}\). After reaching \(m_{j}\) (\(j\leq end-1\)), \(u_{i}\) maneuvers toward \(m_{j+1}\). Furthermore, after reaching \(m_{end}\), \(u_{i}\) maneuvers toward \(f_{u_{i}}\). One proves that while \(u_{i}\) maneuvers along \(P\), \(u_{i}\) applies local sensing measurements only. At the instant when \(u_{i}\) encounters \(m_{j}\), \(u_{i}\) derives the relative coordinates of \(m_{j+1}\). \(m_{j}\) is a neighbor of \(m_{j+1}\). Thus, \(u_{i}\) applies local sensing measurements to maneuver from \(m_{j}\) to \(m_{j+1}\). This local measurement is feasible under Assumption (A1). One proves that when \(u_{i}\) arrives at its associated position \(f_{u_{i}}\), we can locate \(u_{i}\) in global coordinates. We prove by deduction. In Algorithm 1, \(i\) starts from \(N\) and decreases to \(1\) as time elapses. At the initial stage of Algorithm 1, one considers the case where \(i\) is \(N\). The global coordinate of \(u_{N}\) is derived from the addition of the following two vectors:  
1. The global coordinate of \(u_{1}\). 2. The vector from \(u_{1}\) to \(u_{N}\). The vector from \(u_{1}\) to \(u_{N}\) is available utilizing the proximity sensor of \(u_{N}\). Additionally, the leader \(u_{1}\) is localized in global coordinates. Next, consider the case where \(i\
eq N\). For \(i\
eq N\), the global coordinate of \(u_{i}\) is calculated as the addition of the following two vectors:  
1. The global coordinate of \(m_{end}\). 2. The vector from \(m_{end}\) to \(u_{i}\). The vector from \(u_{i}\) to \(m_{end}\) is available utilizing the proximity sensor of \(u_{N}\). This theorem is proved.
## 1 Introduction  
Multi-constellation GNSS now offers a wide range of services, signals and frequencies to choose from. The L1 C/A signal remains the leading signal of the well-established GPS and is used in almost all GPS devices and technology. L2C, the new civilian-use GPS signal, is used to improve the accuracy of navigation, offers a signal that is very easy to track and provides signal redundancy for adverse situations. The improvement brought about by using L2C in GPS-based ionospheric research and monitoring has already been explored elsewhere [1; 2]. In this study, we try to compare the positioning performance of L2C with traditional GPS signals and show that these signals have improved reliability in comparison to the traditional ones for field applications such as that on an excavator.  
[PERSON] et al. [3; 4] analysed the performance of Galileo L10 (tenth launch) in terms of Signals-in-Space (SiS) availability and accuracy and concluded with their numerical results that these satellites comply with their target values in terms of SiS performance. This demonstrates the reliability, robustness and consistency of the signal transmission for Galileo and is probably due to (apart from other technical improvements) the use of highly stable Passive Hydrogen Masers on board the satellites [5]. In this study, we also try to study Galileo signal positioning performance using signals transmitted on the E1 and E5b frequencies. This paper is based on the work conducted in the project SiL 2.0 and will focus on the study of multi-constellation/multi-frequency GNSS signals as received on top of construction equipment as part of the SiL 2.0 dissemination solution.  
Section 2 briefly talks about the SiL 2.0 project; Section 3 gives the details of the data collection with the excavator. Section 4 shows the results. Section 4.1 deals with signal degradation due to foliage and compares different GNSS signals on the basis of degradation of their signal strength indicators. Section 4.2 deals with signal degradation due to reflective environments and discusses how a metallic edge could bring about such a degradation. Section 5 gives the conclusions.
## 2 Materials and Methods
### Model Validation  
During the prediction of results against the field-measured (SPAD and Y\({}_{\text{A}}\)) data, the obtained dataset (90 samples) was randomly divided into two subsets of 63 samples (70% of the whole dataset) and 27 samples (30% of the whole dataset) for RF signature development and cross-validation, respectively. The 10-fold cross-validation resampling technique was used to tune the model hyper-parameters and to evaluate each model, considering the limited number of samples in the dataset [54]. The best-optimized models were tested against the remaining dataset (30%) that was not involved in the training-optimization phase. The range of the actual yield (Y\({}_{\text{A}}\)) for each subset was inspected, based on the normal distribution curve, to ensure that the two subsets were not radically different. The strength of the developed models was determined through the coefficient of determination (R\({}^{2}\)) and the root mean square error (RMSE). The best-fit ML algorithms were utilized for the determination of the optimal crop growth stage useful for early forecasting of carrot yield.  
Figure 2: Methodological flow of carrot crop monitoring and yield using machine-learning (ML) approach.
# Research on AGV Path Planning Integrating an Improved A* Algorithm and DWA Algorithm
## 2 Alternative Implementation of the A* Algorithm
### Optimize the Way Child Nodes Are Selected  
The storage space of the A* algorithm includes all current points in the path planning process and the surrounding child nodes [23]. The point with the lowest evaluation function cost is selected as the next path node. The selection of child nodes involves determining whether a location is an obstacle grid; if it is, no child node can be generated at that location. Therefore, AGVs that do not account for obstacle grids are prone to tangential contact with obstacle vertices or even collisions with the intersecting vertices of two obstacles during task execution. Figure 1 illustrates an AGV colliding with obstacle vertices during movement.  
To address the issue of robots easily colliding tangentially with obstacle vertices during the path search process of the traditional A* algorithm, this paper optimizes the selection method for child nodes. For obstacles encountered in the path planning process, this paper removes the child nodes around the obstacle nodes, preventing their selection. As shown in Figure 2, node \(n\) is the parent node, and nodes \(n_{1}\) to \(n_{8}\) are the child nodes. The optimized child node selection method is as follows:  
a. When the obstacle node is \(n_{2}\) or \(n_{7}\), the left and right child nodes of the obstacle node are removed, i.e., nodes \(n_{1}\) and \(n_{3}\) or nodes \(n_{6}\) and \(n_{8}\);  
b. When the obstacle node is \(n_{4}\) or \(n_{5}\), the upper and lower child nodes of the obstacle node are removed, i.e., nodes \(n_{1}\) and \(n_{6}\) or nodes \(n_{3}\) and \(n_{8}\);  
c. When the obstacle node is \(n_{1}\), \(n_{3}\), \(n_{6}\), or \(n_{8}\), the child nodes are not processed;  
By applying the above child node selection method, the A* algorithm can effectively prevent an AGV from cutting diagonally over obstacle vertices during the path planning process, reducing the risk of collisions between the AGV and obstacles in the printing factory. Figure 3 shows the optimized AGV movement path.  
Figure 1: AGV movement path in A* algorithm.  
Figure 3: AGV movement path in optimized A* algorithm.
## 2 Background and Related Work
Their method can detect devices with a limited computational resource in a system. This work is a practical method but suffers from a lack of real-time analysis on a platooning platform for IoT within a 5G network, which is the focus of this work. In [32], the authors explored an Azure scaffold environment combining various analyses on the BSs to detect an RBS in an LTE network. However, this method does not address the real-time interference in the dedicated resources of 5G networks. The work described in [18] designed an automated system to discover RBS in a GSM/LTE mobile network that controls the BS traffic data and recognises the invasion in the system. Although this work is a novel and interesting addition to the field, it does not handle traffic data over the 5G network and does not work for a platooning platform for IoT devices. In addition, [33] proposed a time-efficient symbol-based statistical Radio Frequency (RF) fingerprinting scheme to realise signal pattern anomalies and detect intrusions in the network. This work is impractical for large-scale IoT devices and does not address the actual propagation conditions considered in this study. Finally, FBSleuth [34] is a recently designed BS-based tool that can identify RBS devices based on minor differences in the emitted signals caused by hardware imperfections. This defence software lacks monitoring of the platooning data traffic traversing between multiple BS in a 5G network that is the domain of our work. The literature reviewed here indicates a wide range of RBS detection schemes and some of their drawbacks. Although the approaches are varied, they all operate by some kind of data analysis, hence, the availability of a large quantity of realistic MR data is a key component in the development of RBS detection techniques. It is the provision of this data that is the focus of our work. This motivates the potential future use of Machine Learning (ML) approaches that require a very large dataset that covers many different scenarios. This motivated the development of a simple simulator that can generate such datasets which is the focus of this study.
## 5 Experimental Evaluation
### Baseline Methods  
We compare TAIM against several mainstream FL strategies: FedAvg (standard averaging) [48], FedProx (adds a proximal term to mitigate heterogeneity) [49], FedTrust (trust-weighted aggregation) [50], and Krum (robust aggregation against outliers) [51]. These baselines serve as the foundation for our comprehensive comparison and analysis.
## 3 Collaborative Monitoring Algorithms for Situational Assessment
### Collaborative Traffic Trend Prediction Approach
#### 3.2.3 Traffic Trend Prediction  
To facilitate visualization of the evaluation results, the standardized data matrix \(D^{*}\) and the combined weight of indicators \(W^{*}\) are weighted to obtain the road traffic trend value \(F\), as shown in Equation (32).  
\[F=D^{*}W^{*}=\left[\begin{array}{ccc}d_{11}^{*}&\cdots&d_{1M}^{*}\\ d_{21}^{*}&\cdots&d_{2M}^{*}\\ \vdots&\ddots&\vdots\\ d_{N1}^{*}&\cdots&d_{NM}^{*}\\ \end{array}\right]_{N\times M}\left[\begin{array}{c}w_{1}^{*}\\ w_{2}^{*}\\ \vdots\\ w_{M}^{*}\\ \end{array}\right]_{M\times 1}=\left[\begin{array}{c}f_{1}\\ f_{2}\\ \vdots\\ f_{N}\\ \end{array}\right]_{N\times 1} \tag{32}\]  
Normalize the traffic trend value \(F\) and construct the normalized road traffic trend value \(F^{*}=\left[\begin{array}{c}f_{1}^{*}\\ f_{2}^{*}\\ \vdots\\ f_{N}^{*}\\ \end{array}\right]_{N\times 1}\) as shown in Equation (33).  
\[f_{j}^{*}=\frac{f_{j}}{\sum_{j=1}^{N}f_{j}} \tag{33}\]
## 2 Materials and Methods
### Estimation of the Adsorption Capacity  
The adsorption capacity of PMMA + TOCs films (TOCs = (**1**)-(**5**)) was determined by measuring changes in the absorbance of the dye solution (methylene blue (MB), rhodamine B (RhB) and crystal violet (CV)) (C = 1.0 \(\times\) 10\({}^{-5}\) M). The composites (26 \(\times\) 26 mm) and 30 mL of the dye solution were placed in a laboratory bottle in a dark place. Spectra were recorded (JASCO Deutschland GmbH, Pfungstadt, Germany) in the range of 400-700 nm at 0, 1, 3, 5, 9, and 24 h by taking 3.5 mL of the solution into a quartz cuvette. The absorption maxima were recorded at 664, 590, and 554 nm for MB, RhB, and CV, respectively. The percentage of MB decolorization was calculated using the following equation:  
\[\%\text{ dye decolorization}=\frac{(C_{0}-C_{t})}{C_{0}}\cdot 100\% \tag{1}\]  
where \(C_{0}\) is an initial dye concentration and \(C_{t}\) is a dye concentration at a given time \(t\)[54].
# Design and Fabrication Technology of Metal Mirrors Based on Additive Manufacturing: A Review
## 3 Design Technology of Additively Manufactured Metal Mirrors
### Topology Optimization (TO)  
TO is one of the structural optimization methods. It is derived from topology and used to calculate the optimal material spatial distribution for given problems. In general, the topologically optimized part can theoretically meet the load demand and achieve the optimal material distribution under a specific algorithm, which can obtain the optimal load-bearing structure under a particular volume fraction [55]. In generative design, multiple solutions are calculated simultaneously to create the most optimal solutions based upon functional and non-engineering requirements, such as manufacturability [56; 57]. The results produced by TO are often very complex and challenging to manufacture directly. So, the combination of TO and AM technologies has the potential to bring significant synergy benefits. Now, AM metal mirrors based on TO are analyzed as follows.  
First, [PERSON] et al. from the University of Arizona, USA, proposed a process chain for manufacturing metal mirrors based on AM and hot isostatic pressing in 2015 [30]. They utilized DMLS and EBM technologies to print AlSi\({}_{10}\)Mg and Ti6 Al4V, respectively.  
Figure 17: Mass reduction factor versus modal frequency of several design models [33].  
Figure 18: Internal lightweight structure (**a**) and mirror sample (**b**) [37].  
After that, the products' quality and method feasibility obtained by the two routes were compared. The mirror model is a cylinder with a bottom diameter of 4 in and a height of 38.1 mm. And the mirror structure was topologically optimized by applying a binding force to the mirror surface to achieve a light weight (Figure 19). After further grinding and polishing, the aluminum mirror was found to have a surface roughness of 22 nm and a surface accuracy of PV 255 nm. However, the Ti mirror did not reach expectations because of the subsurface porosity in the mirror (Figure 20).  
Subsequently, [PERSON] team used DMLS to print an astronomical X-ray mirror. Then the TO of the arches lightweight design was carried out, and the final lightweight rate reached 62% through iterative calculation. The mirror's diameter was 40 mm, with a 4 mm thickness in total and a 1 mm thickness of the front and back surfaces which formed the sandwich structure. While satisfying the lightweight requirement, its support was a continuous ring structure to reduce the print-through effect and improve its structural stiffness (Figure 21). Finally, the optimized mirror's mass was reduced from 9.2 g to 6.4 g, but the surface roughness increased from 2.47 nm to 5.22 nm [18; 35; 36].