esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Assessment of Precise MGEX Orbit and Clock Products
### PPP Performance  
In order to further verify the quality of precise MGEX orbit and clock products, PPP solutions were performed in the static and kinematic modes. TUM products were not evaluated by PPP, since TUM does not provide precise clock products. Observations at stations CUSV, DARW, DGAR, IISC, KARR and KAT1 on 21 October 2019 were processed for PPP solutions in the GPS-only (G), BDS-only (C), Galileo-only (E) and GPS + QZSS (GJ) modes, respectively. The distribution of the six stations are shown in Figure 12. Since GPS was established for the longest time, PPP results in GPS-only mode were computed for comparison. In the case of BDS-only PPP, ionosphere-free linear combinations of B1 and B2 were processed with COD and GFZ products applied, while the combination of B1 and B3 were processed with ESM, SHA and WUM products applied. Considering that ESA-estimated PCO/PCV values were used in the generation of ESM and GFZ BDS products, those corrections for BDS-2 satellites were used in BDS-only PPP when ESM and GFZ products were applied. However, since the ESA-estimated PCO/PCV values for BDS-3 satellites were not publicly available, MGEX PCO/PCV values were used for BDS-3 satellites instead. Note that BDS-2 and BDS-3 are processed as one system in this section. The ISBs were estimated as random walk processes in multi-GNSS PPP with GFZ products [44], while being estimated as constants with other products. To assess the positioning performance, PPP solutions using IGS final orbit and clock products were performed first to estimate the coordinates of the six stations. The estimated coordinates were introduced as a reference to compute positioning errors in this section.  
Figure 12: Distribution of the stations used in precise point positioning (PPP) solutions.
## 4 Results
### Presentation of Timing Results  
The experiments were run on a GPU-enabled workstation with its technical characteristics given in Appendix B.  
Given that our shadow casting algorithm uses an \"early termination\" criterion (as explained earlier in Section 3.2), tiles of different nature can lead to different run times. As such, tiles with more obstructions (typically urban areas) will have shorter processing times than tiles with fewer obstructions in more open areas.  
Figure 5: Screenshot of the web interface of the solar cadaster of the Greater Geneva area (with an example of a monthly solar thermal profile for a given building).  
Figure 6: Potential total photovoltaic (PV) peak power by commune (i.e., municipality) of the Greater Geneva area.  
For our timing experiments, we selected 2 representative tiles for 3 types of environment (6 tiles total). The types of environment that were chosen were \"urban\", \"rural\", and \"mountainous\".  
For the purposes of the timing evaluation, we defined appropriate masks for each tile (Figure 7). Masks of urban tiles exclude everything that is not the roof of a building. In rural and mountainous areas, we exclude forested areas as well as water bodies (rivers) from the analysis.  
From each mask, we deduced a coverage ratio, i.e., the number of image pixels included (i.e., mask = 1) for the analysis divided by the total number of pixels in the tile, expressed as a percentage.  
As we can observe from the results (Table 1), the relevance of applying masks, when it is a matter of assessing solar radiation on buildings only, is particularly evident in urban areas where the coverage ratio is low and the computing time saving is therefore important (factor 2). The same observation can be made when it is appropriate to remove forested areas (as in particular in the tile Mountainous 1).  
Overall, the processing time for the tiles (without using a mask) ranges between 1 and 2 h, which is notably less than the first experiments (40 h per tile with a simpler sky model in the first attempts in 2012, and 16 h per tile for our cloud computing experiments in 2016).  
For the purposes of the solar cadaster of the Greater Geneva area (that spans 294 tiles, although some tiles on the border have a significant amount of missing data), the entire analysis was completed in roughly 15 days (without usage of a mask), tiles being processed in sequence.  
It is also apparent, according to these results that using a mask, in the presence of areas that are not needed for analysis can lead to significant speedups. This is the case when it is appropriate to remove water (except if solar installations are planned on water surfaces) and forested portions in mostly unbuilt areas or focus on actual rooftops in heavily urbanized areas.  
We should also note that, since the processing of each tile is independent from each other, the workload can be distributed across multiple GPU-enabled machines, for a theoretically linear speedup (minus input-output overhead).
## 1 Introduction
In addition, a hybrid model that combines EMD and BiLSTM to enhance PM\({}_{2.5}\) concentration prediction performance was proposed by [15]. The EMD method has proved to be useful in decomposing the components from non-linear and non-stationary signals. However, EMD retains the problem of mode splitting and mode mixing [16]. To address this, advanced versions of EMD have been proposed. Ensemble Empirical Mode Decomposition (EEMD) for a noise-assisted method was proposed by [17]. The idea of the EEMD consists of adding different series of white Gaussian noise into the original signal. However, EEMD still has numerically negligible errors and, when different types of white Gaussian noise are added, the number of IMFs can alter [18]. To resolve this problem, Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) was proposed by [19]. In this method, at each stage of the decomposition, a different noise is added to the residue of the current stage instead of white Gaussian noise [20]. The advanced versions of EMD can be used in various applications such as a predicting approaching wind speed based on EEMD and LSTM [21], a predicting approach for crude oil prices based on CEEMDAN, and LSTM with news sentiment index developed by [22]. In this research, a hybrid framework based on the combination of Principal Component Analysis (PCA), EMD and LSTM is proposed to predict one step ahead the closing price of the stock market in Thailand. The proposed framework is divided into two parts: the features engineering part and the prediction model part, with a total of five processes. Concretely, first, the news sentiment index score is created by using Financial Sentiment Analysis with Bidirectional Encoder Representations from Transformers (FinBERT). After that, PCA is used to reduce the dimension of the technical indicator and as an input feature for the prediction model. Next, the closing price of the stock market is decomposed into several IMFs via EMD. LSTM is applied to predict each IMF along with the news sentiment score and principal components from PCA. Finally, the prediction values of each IMF are composed to obtain the final closing price of the stock market.
# A New Propagation Channel Synthesizer for UAVs in the Presence of Tree Canopies
## 2 Theoretical Background  
### Po  
When applying common PO as given in [8] or [9], the equivalent electric \(\mathbf{J}_{\text{eq}}\) and magnetic \(\mathbf{M}_{\text{eq}}\) currents on the part of the object's outer surface illuminated by the incident fields \(\mathbf{E}_{\text{inc}}\) and \(\mathbf{H}_{\text{inc}}\) need to be calculated using the free space wave number \(k_{0}\), the incident plane wave direction \(\mathbf{\hat{k}}_{\text{inc}}\) in the form of a unitary vector form \(\mathbf{k}\), and the outward pointing unit normal vector \(\mathbf{\hat{n}}\) at each surface sample \(\mathbf{r}^{\prime}\) (see [7]).  
Now, to obtain the fields scattered from the surface samples of areas \(dS^{\prime}\), we utilize the near field propagators from [8] and, at point of interest \(\mathbf{r}\), we obtain the scattered magnetic field \(\mathbf{H}_{\text{scat,J}}\) based on \(\mathbf{J}_{\text{eq}}\), the scattered electric field \(\mathbf{E}_{\text{scat,M}}\) based on\(\mathbf{M}_{\text{eq}}\), the electric field \(\mathbf{E}_{\text{scat,J}}\) associated with \(\mathbf{H}_{\text{scat,J}}\), and the magnetic field \(\mathbf{H}_{\text{scat,M}}\) associated with \(\mathbf{E}_{\text{scat,M}}\). For more details see [7]. These scattered fields represent the blockage of the original fields \(\mathbf{E}_{\text{dir}}\) and \(\mathbf{H}_{\text{dir}}\), which would be present at \(\mathbf{r}\) under LoS propagation conditions without any obstruction. The corresponding blockage fields may thus be summed as:  
\[\mathbf{E}_{\text{block}}=\mathbf{E}_{\text{scat,J}}+\mathbf{E}_{\text{scat,M}} \tag{1}\]  
\[\mathbf{H}_{\text{block}}=\mathbf{H}_{\text{scat,J}}+\mathbf{H}_{\text{scat,M}} \tag{2}\]  
and, for the perfectly absorbing obstacle, we have:  
\[\mathbf{E}_{\text{B}}=\mathbf{E}_{\text{dir}}+\mathbf{E}_{\text{block}} \tag{3}\]\[\mathbf{H}_{\text{B}}=\mathbf{H}_{\text{dir}}+\mathbf{H}_{\text{block}}. \tag{4}\]  
By this approach, illustrated below in Figure 1, we obtain the diffraction pattern around a perfectly absorbing object for the case of the coherent field including both the amplitude and phase terms. Unfortunately, for dielectric objects, PO only accounts for reflections in the backscatter direction and disregards any field passing through. This is the consequence of \(\mathbf{E}_{\text{block}}\) and \(\mathbf{H}_{\text{block}}\) relying on \(\mathbf{M}_{\text{eq}}\) and \(\mathbf{J}_{\text{eq}}\), which are, in the forward scatter direction, unaffected by the reflected fields. This disadvantage of PO is eliminated within the new PO/MST model [7] described later in Section 2.3.
## 3 Results
### Raman Spectroscopy  
Raman spectroscopy is one of the most commonly used characterization techniques for carbon-based materials. This technique can be efficiently used to monitor the number of layers, quality, doping level, and confinement of the materials [43]. In this work, Raman spectroscopy was employed in order to study the structure of the GNPs used and to verify their presence in the samples produced (Figure 8).  
The presence of three characteristic peaks of graphitic structures can be observed in the spectrum of the GNPs and GNPs-PCL membranes (0.5% and 2%), corresponding to bands D, G, and 2D. The D and G bands are presented in all poly-aromatic hydrocarbons. The G peak is related to the stretching bond of all pairs of sp\({}^{2}\) atoms in both rings and chains. The D peak is due to the breathing modes of sp\({}^{2}\) atoms in rings [43]. The 2D band is representative of graphite and allows us to obtain information regarding the number of sheets that constituted the GNPs. This information can also be determined through the I2D/IG calculation. The D band represents the degree of defects present in the structure. Then, through the characteristic Raman peak intensity ratio ID/IG, it is possible to obtain qualitative and quantitative information regarding the structure, size and defects of the carbon-based materials [44].  
Figure 7: Thermogravimetric Analysis (TGA) (**a**) and Derivative Thermogravimetry (DTG) (**b**) curves of GNPs, PCL, and GNPs-PCL membranes with different percentages of GNPs.  
The spectrum of the GNPs powder shows the D band peaking at approximately 1350 cm\({}^{-1}\). Compared with the G band, the D band presents a relatively low intensity with an ID/IG ratio of 0.08, indicating that these GNPs possessed few structural defects. Regarding the G band with higher intensity, it peaked at 1580 cm\({}^{-1}\). The 2D band peaked at \(\sim\)2720 cm\({}^{-1}\), showing a lower intensity than the G band (I2D/IG = 0.52). This indicates that the GNPs were composed of graphene multilayers, as can also be observed in the amplified FESEM images in Figure 6d,f. As observed in Figure 8, regardless of the concentration of GNPs arranged in the membranes, the peaks for the D, G, and 2D bands are positioned in the same wavenumber as the GNPs powder. Besides this information, comparing the PCL membrane spectrum with the GNPs-PCL, it can be concluded that the GNPs were correctly incorporated onto the surface membranes.
# CS-FL: Cross-Zone Secure Federated Learning with Blockchain and a Credibility Mechanism
## 5 Experiments and Analysis
### Results and Analysis
#### 5.2.4 Performances on Complicated Scenarios  
In order to verify the comprehensive performance of CS-FL and other three methods, a complicated scenario is set up to simulate practical cross-zone EC scenario, which includes three nodes each for the DPA, MPA, PCA, CCA, and IFA models. As shown in Figure 11, CS-FL demonstrates higher accuracy on both datasets compared to the other methods. The probabilities of CS-FL being successfully attacked are significantly lower than other methods, as shown in Table 3. These multidimensional improvements in defense capabilities and privacy protection are attributed to the introduction of the blockchain network and proposed credibility mechanism.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Attack Success Rate (\%) 1**} & \multirow{2}{*}{**Accuracy**} \\ \cline{3-5}  & & **DPA** & **MPA** & **PCA** & **CCA** & **IFA** & \\ \hline \multirow{4}{*}{MNIST} & FedAvg & 80 & 87.5 & 85 & 85 & 75 & 94.5\% \\  & FedCS & 80 & 85 & 7.5 & 80 & 75 & 95\% \\  & Oort & 5 & 75 & 82.5 & 85 & 75 & 96.5\% \\  & CS-FL & 5 & 20 & 5 & 17.5 & 17.5 & 98\% \\ \hline \multirow{4}{*}{CIFAR-10} & FedAvg & 75 & 82.5 & 85 & 82.5 & 75 & 64.5\% \\  & FedCS & 77.5 & 80 & 7.5 & 75 & 75 & 65.5\% \\ \cline{1-1}  & Oort & 5 & 72.5 & 80 & 80 & 75 & 74.5\% \\ \cline{1-1}  & CS-FL & 5 & 20 & 7.5 & 20 & 15 & 77.5\% \\ \hline \hline \end{tabular}  
* The attack success rates are obtained by averaging the results of the same experiment conducted five times.  
\end{table}
Table 3: Comparison of attack success rates and model accuracy.  
Figure 10: The SCI values of ENs using the IFA model on the CIFAR-10 dataset.  
Figure 9: The accuracy of FL task using the IFA model on the CIFAR-10 dataset.
## 3 Machine Learning Modeling
### Fundamental Theory of PCA, LSTM, and GA
#### 3.1.3.1 GA  
In this study, MSE was taken as the objective function for calculating the values of the hyperparameters in the LSTM, and the genetic algorithm (GA) was applied to LSTM optimization. GA was inspired by gene recombination, gene crossover, and gene mutation in nature [53; 54; 55]. Its principle is illustrated in Figure 6. This algorithm mainly involves binary encoding and decoding of the population, calculation of genetic operators, and calculation of individual fitnesses [56; 57]. The specific calculation procedure is as follows.  
1. Population initialization: binary encoding is utilized to convert feasible solutions in the problem space into genotype string structures in the genetic space, and the initial population is generated.
2. Individual evaluation: the fitness function values of individuals in the initial population are calculated.
3. Genetic operator calculation: new individuals are generated through three paths, namely selection operator, crossover operator, and mutation operator.
4. Whether the iteration conditions are met is identified. If not, Step 2 is conducted; otherwise, the optimal individual is decoded, and the optimal solution is output.
# Non-Contact and Non-Intrusive Add-on IoT Device for Wireless Remote Elevator Control
## 3 Materials and Methods
### ESP32-S2-WROVER  
The IoT device is based on an ESP32-S2-WROVER module developed by Espressif Systems (Shenzhen, China). This module has an ESP32-S2 microcontroller that includes an Xtensa LX7 32-bit microprocessor running at 240 MHz; 16 MB of usable flash memory for storing software and data; 320 kB of internal SRAM; and 37 general purpose input/output pins. The ESP32-S2 microcontroller also embeds an onboard antenna that supports a 2.4 GHz WiFi connection over the 802.11 b/g/n protocols. The WiFi connection is used to link the device to a wireless network. Additionally, the microcontroller includes standard peripheral modules, such as UART, SPI, I2C, and I2S communication modules, PWM and pulse signal units, an ADC, a DAC and a temperature sensor. Finally, this control module also supports a full-speed USB 2.0 on-the-go (OTG) connection.
## 3 Proposed ATMALS-GSK Algorithm
### Atmals-Gsk
#### 3.2.6 Overall Operation of ATMALS-GSK

Table 10: Comparison of the average and standard deviation results (Mean \(\pm\) STD) of ATMALS-GSK with GSK and its modifications over 51 runs for \(D=30\) on CEC 2017. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Fun.** & **GSK**[16] & **AGSK**[46] & **APCSK**[45] & **FDBAGSK**[36] & **eGSK**[49] & **ATMALS-GSK** \\ \hline
23 & \(5.42\times 10^{2}\pm 1.39\times 10^{2}\) & \(6.65\times 10^{2}\pm 2.22\times 10^{3}\) & \(7.69\times 10^{2}\pm 3.06\times 10^{3}\) & \(6.19\times 10^{2}\pm 2.23\times 10^{3}\) & \(4.51\times 10^{2}\pm 1.14\times 10^{3}\) & \(4.39\times 10^{2}\pm 1.27\times 10^{3}\) \\ \hline
24 & \(6.34\times 10^{2}\pm 1.39\times 10^{2}\) & \(7.26\times 10^{2}\pm 2.94\times 10^{3}\) & \(8.40\times 10^{2}\pm 4.04\times 10^{3}\) & \(6.90\times 10^{2}\pm 2.231\times 10^{3}\) & \(5.20\times 10^{2}\pm 8.56\) & \(5.14\times 10^{2}\pm 1.35\times 10^{3}\) \\ \hline
25 & \(5.56\times 10^{2}\pm 4.62\times 10^{3}\) & \(5.52\times 10^{2}\pm 3.40\times 10^{3}\) & \(5.63\times 10^{2}\pm 3.10\times 10^{3}\) & \(5.22\times 10^{2}\pm 4.06\times 10^{3}\) & \(5.63\times 10^{2}\pm 3.89\times 10^{3}\) & \(5.45\times 10^{2}\pm 2.74\times 10^{3}\) \\ \hline
26 & \(1.27\times 10^{3}\pm 9.18\times 10^{3}\) & \(3.59\times 10^{2}\pm 7.34\times 10^{2}\) & \(2.80\times 10^{3}\pm 1.80\times 10^{3}\) & \(3.00\times 10^{3}\pm 4.95\times 10^{2}\) & \(1.16\times 10^{3}\pm 4.43\times 10^{2}\) & \(1.35\times 10^{3}\pm 3.55\times 10^{2}\) \\ \hline
27 & \(5.92\times 10^{2}\pm 8.29\times 10^{3}\) & \(5.28\times 10^{2}\pm 1.63\times 10^{3}\) & \(5.62\times 10^{2}\pm 3.51\times 10^{3}\) & \(5.24\times 10^{2}\pm 1.94\times 10^{3}\) & \(6.09\times 10^{2}\pm 1.26\times 10^{2}\) & \(5.71\times 10^{2}\pm 2.78\times 10^{3}\) \\ \hline
28 & \(4.94\times 10^{2}\pm 2.24\times 10^{3}\) & \(4.99\times 10^{2}\pm 3.50\times 10^{3}\) & \(4.93\times 10^{2}\pm 1.91\times 10^{3}\) & \(4.83\times 10^{2}\pm 2.286\times 10^{3}\) & \(4.93\times 10^{2}\pm 3.43\times 10^{3}\) & \(4.88\times 10^{2}\pm 2.39\times 10^{3}\) \\ \hline
29 & \(3.60\times 10^{2}\pm 2.23\times 10^{3}\) & \(1.00\times 10^{3}\pm 1.36\times 10^{2}\) & \(1.01\times 10^{3}\pm 1.16\times 10^{3}\) & \(8.32\times 10^{2}\pm 1.26\times 10^{2}\) & \(4.37\times 10^{2}\pm 5.82\times 10^{3}\) & \(5.01\times 10^{2}\pm 4.19\times 10^{3}\) \\ \hline
30 & \(5.96\times 10^{5}\pm 2.24\times 10^{3}\) & \(5.83\times 10^{3}\pm 6.20\times 10^{3}\) & \(5.86\times 10^{3}\pm 5.35\times 10^{3}\) & \(5.86\times 10^{3}\pm 1.60\times 10^{4}\) & \(6.09\times 10^{3}\pm 3.27\times 10^{4}\) & \(5.92\times 10^{3}\pm 1.85\times 10^{4}\) \\ \hline \end{tabular}
\end{table} 
Table 12: Comparison of the average and standard deviation results (Mean \(\pm\) STD) of ATMALS-GSK with GSK and its modifications over 51 runs for \(D=100\) on CEC 2017. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Fun.
## 1 Introduction
### Related Work  
#### 1.1.1 AR-Based Maintenance Content  
AR content related to maintenance and repair is being researched in various fields to enhance the efficiency of maintenance operations. AR training is extensively studied in education, industry, vocational training, and healthcare fields. It is frequently used in medical training, industrial maintenance management, and assembly fields [5; 6].  
[PERSON] investigated methods to integrate AR and VR technologies into maintenance training programs [7]. They proposed overlaying digital information onto physical equipment to enhance education, provide real-time guidance, and offer interactive simulations for trainees. [PERSON] et al. [8] developed computer education tools, such as AR, to enhance the practical skills of students in railway universities and provide hands-on experience for designing, implementing, and improving tools. [PERSON] et al. [9] created a customized VR system for elevator maintenance practical training to address issues like inherent risks, high educational costs, and spatial constraints. They employed interactive methods to help students master technical skills related to elevator structure, operational principles, and governor adjustment. [PERSON] et al. [10] analyzed the application of AR in vocational education to complement standard curricula and provide specific learning experiences.  
In the industrial maintenance sector, [PERSON] et al. [11] applied AR and VR in the context of ergonomics across education, aviation, and maintenance domains, offering a new approach to education through immersive environments and interactive simulations. [PERSON] et al. [12] introduced a customized AR Holorailway system to support assembly operations within the railway industry. By integrating AR technology into assembly processes, they provided workers with real-time guidance and visual data, increasing efficiency, accuracy, reducing errors, and enhancing productivity. [PERSON] et al. [13] proposed integrating AR and VR technologies into maintenance and repair workflows for automotive and machinery equipment to enhance the efficiency, accuracy, and reliability of technicians, emphasizing the potential for innovation in maintenance and repair practices. [PERSON] et al. [14] developed AR learning content focusing on pneumatic flow in railway brake operation devices. They described the development process, including considerations for AR content design and implementation, and evaluated the effectiveness of AR learning content.