esa-sceva/satcom-chunk-collection huggingface dataset:
proposed a method based on a quality evaluation to find the optimal seam in UAV aerial image stitching [4]. [PERSON] et al. proposed a new fast-stitching algorithm for UAV aerial images [5]. [PERSON] et al. proposed a seamless stitching method for UAV remote sensing images, which combined SIFT feature points and overlapping transition Poisson fusion [6]. [PERSON] et al. proposed an automatic registration algorithm for UAV remote sensing images based on deep residual features [7]. [PERSON] et al. proposed a real-time stitching algorithm for UAV aerial images based on genetic algorithm optimization [8]. [PERSON] et al. proposed a new seam-cutting method based on superpixels for UAV image stitching [9]. All these stitching methods extract the same name points in overlapping areas by matching algorithms and then calculate the spatial transformation relationship between adjacent area array images and complete the stitching of images on the basis of transformation. When an area array camera acquires images, it acquires all pixels in the field of view at the same time, so when considering the transformation relationship between adjacent images, it is based on the geometric relationship of area array imaging. The transformation model between adjacent images adopts the perspective projection model, and it is necessary to extract four or more control points in the overlapping area for calculation. However, an aerial scanning image with a wide field of view is obtained by scanning with linear CCD. That is, an image is scanned and imaged in a time sequence, which does not meet the perspective projection model of the area array camera, and the image will be deformed locally during scanning. Therefore, it is necessary to consider not only the overall transformation relationship but also the local uneven deformation during scanning between adjacent scanned images. In view of the acquisition characteristics of wide-field aerial scanning images and the low accuracy of platform attitude and position measurement, a method of region mosaic processing of wide-field aerial scanning images is studied and given in this paper. The overall processing technical flow is shown in Figure 1. Firstly, the geometric correction of each image is carried out according to the imaging geometry model of wide-field aerial scanning. Based on correction, the overlapping areas of adjacent images are extracted by SIFT operator, and the translation, scale, and rotation models of images are used for rough stitching. Then, the normalized cross-correlation is used for matching in the local overlapping areas to realize fine stitching. The main contributions of this paper are the following:  
* A transformation model of \"scale + rotation\" is proposed in the calculation of the geometric transformation of two adjacent images;
* The realization of rough geometric stitching between adjacent images through the whole scale and rotation transformation processing by introducing the scale and rotation parameters between adjacent images;
* A solvation of the local geometric non-uniformity deformation between adjacent images is demonstrated with better ways and results.
## 4 Experimental Setup and Simulation Results
### Boxplot Analysis for Global Optimization Analysis  
The boxplot in Figure 13. represents the fitness function values corresponding to the population at the final iteration (generation). The fitness functions for EHO and PPA are distributed over a narrow range, except for a few far points (red + markers) for EHO. Also, the means for PPA and EHO are much less than for PSO. The lowest value of the objective function is found using the PPA-based algorithm which validates the result from Figure 9. The smaller range of objective function signifies that all the population has reached the global optimal solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **PSO** & **EHO** & **PPA** \\ \hline Objective Function & 0.325094 & 0.321471 & 0.2918 \\ \hline DC link controller Settling time (sec) & 0.1250 & 0.0900 & 0.1270 \\ \hline Id controller settling time (sec) & 0.1490 & 0.1060 & 0.0720 \\ \hline Iq/Q controller settling time (sec) & 0.1030 & 0.1550 & 0.1670 \\ \hline DC link controller Peak Overshoot (\%) & 18.9178 & 17.9912 & 17.9831 \\ \hline Id controller peak overshoot (\%) & 4.7317 & 5.0402 & 1.580398 \\ \hline Iq/Q Controller peak overshoot (\%) & 89.1056 & 91.9191 & 96.4454 \\ \hline Convergence Speed (iteration) & 21 & 18 & 13 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Objective function, time responses and convergence speed of different algorithms.  
Figure 12: Eight-axis spider graph for index comparison among the algorithms.  
Figure 13: Box plot for the objective function of the final generation population for every optimization technique.
## 3 Results  
As a benchmark, the results of feature-based and end-to-end ML models were evaluated on the original, unaltered WESAD dataset. For reference, using feature-based models, [PERSON] et al. detected anxiety with an F\({}_{1}\)-score of 0.91 and an accuracy of 0.92 for binary classification (stress vs. non-stress) using all modalities with the LDA model [8]. Using end-to-end models, [PERSON] et al. detected anxiety with an F\({}_{1}\)-score of 0.73 and an accuracy of 0.79 using the FCN architecture [39]. For context, a random guess would yield a 0.50 accuracy and 0.48 F\({}_{1}\)-score, while a weighted guess would result in 0.30 accuracy and 0.42 F\({}_{1}\)-score.
# Continuous Latent Spaces Sampling for Graph Autoencoder
## 5 Experiments
### Results  
We compared our colaGAE approach with several SOTA graph SSL models; as presented in Table 2, the results demonstrate that our approach achieves either the best or competitive performance compared to existing models. Our approach outperforms the previous best SOTA on four out of eight datasets by a small margin, with an average improvement of approximately 0.4%. Specifically, on the first three datasets, we observe an average relative improvement of 0.2% over the previous SOTA.  
Previous research has suggested that GCLs outperform GAEs due to the limited available pretext tasks in GAEs. However, the success of our colaGAE model demonstrates that mutual isomorphism is a promising pretext task for GAEs. The results suggest that leveraging topological information plays a more crucial role in graph SSL.  
We note that CCA-SSG performs poorly on arXiv and MAG, as it is essentially a dimension reduction method, where the ideal embedding dimension should be smaller than the input one, as reported in [18].  
During evaluation, colaGAE functions as a GCN-encoder while outperforming all baselines. Notably, colaGAE surpasses supervised GCN by an average of approximately 2.5%, indicating that prior information such as graph topological structure is critical for  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Cora** & **CiteSeer** & **PubMed** & **Photo** & **Computer** & **arXiv** & **MAG** & **Coauthor-CS** \\ \hline MLP & 47.90 \(\pm\) 0.40 & 49.30 \(\pm\) 0.30 & 69.10 \(\pm\) 0.20 & 78.50 \(\pm\) 0.20 & 73.80 \(\pm\) 0.10 & 56.30 \(\pm\) 0.3  
graph SSL. Its downstream performance on the large arXiv and MAG datasets further verifies the effectiveness of our colaGAE model.  
We used t-SNE to visually demonstrate the effectiveness of the node embeddings learned by our proposed model. To provide a comparative analysis, we generated embeddings from the supervised GCN. The results are presented in Figure 2, where each dot corresponds to the embedding of a node and the color represents its true label. Our analysis indicates that our proposed model can identify classes more accurately than the supervised GCN, as the boundaries between different classes are much clearer in the former.
## 2 Experimental
### Instrumentation and Software  
A UV-Vis spectrophotometer (Agilent diode-array, CA, USA) with 10 mm matched quartz cells was used to measure the absorbance of the samples. A centrifuge (Thermo Scientific, ST8 Benchtop, Waltham, MA, USA) was used for the separation of the supernatant. The pH of the prepared solutions was adjusted using a Jenway pH meter (Jenway, Staffordshire, UK).  
The fully dried air-dried _Aloe vera_ (ADAV) and the thermally treated _Aloe vera_ (TTAV300) samples were sprayed with gold, and the surface morphology of the samples was investigated using scanning electron microscope (SEM, FEI, Quanta 200, Thermo Scientific(tm), Waltham, MA, USA) under an accelerating voltage of 30 kV and by using a standard secondary electron (SE) detector, while the distribution of elements on the surface was determined by energy dispersive X-ray (EDS) detector.  
Fourier transform infrared radiation (FT-IR, Bruker Alpha, MA, USA) was used to recognize the functional groups on the surface of the prepared adsorbent. Spectra were collected in the range of 400-4000 cm\({}^{-1}\) with a resolution factor of 4 cm\({}^{-1}\). The Raman spectrum of the ADAV and the TTAV300 were acquired in the range from 50-3500 cm\({}^{-1}\) using a DXRTM2 Raman microscope (Thermo Scientific(tm), Waltham, MA, USA) with a laser beam at 532 nm as the excitation source and 10 mW power. A thermal gravimetric analyzer (TGA, PerkinElmer-TGA400, Waltham, MA, USA) was employed to investigate the thermal stability of the prepared AV at a temperature range of 50-800 \({}^{\circ}\)C.  
Analysis of the adsorbent's surface area was performed using a Micromeritics ASAP2020 Accelerated Surface Area and Porosimetry System (Micrometrics, Norcross, GA, USA). Degassing of samples was initially employed followed by investigating N\({}_{2}\) adsorption-desorption. Using the N\({}_{2}\) isotherms collected at 77 K and employing the Brunauer-Emmett-Teller (BET) equation, surface area was determined. Pore volume was established using the t-plots and the Barrett-Joyner-Halenda (BJH) equation.  
Minitab(r)18 software was purchased from Minitab Inc., State College, Pennsylvania, USA. The software was used to make the list of experiments according to the selected design.
# Integrating Process Re-Engineering Models in Cement Production to Improve Energy Efficiency
## 2 Materials and Methods
### Data Collection and Pre-Analysis  
The overall approach used to collect important data was to ensure that energy consumption data from cement plants were well pre-assessed and screened with reference to the total energy consumed for each plant. The input energy data were well understood in order to provide accurate insights into energy consumption and efficiency. Therefore, energy consumption data were collected from two semi-automated cement production plants in Zambia.  
Production data from Plant A was collected from August 2022 to July 2023, as indicated in Table 1. The table shows the energy consumption for both electricity usage and fuel consumption, totalling 13,900.66 MJ, with an average of 1158.39 MJ per month. The overall clinker output for the year was 512,138 tons, with an average monthly production of 42,678 tons. The total cement production was 632,664 tons, with an average monthly production of 52,722 tons.  
The production from Plant B, as shown in Table 2, was collected from April 2023 to March 2024, presenting the total energy consumed and the amounts of clinker and cement produced. Over this period, Plant B consumed a total of 1425.96 MJ, averaging 1188.3 MJ per month. Clinker production totalled 145,479 tons, with an average monthly production of 12,123.25 tons, while cement production amounted to 206,861 tons, averaging 17,238.42 tons per month. The data reflect consistent energy consumption and production rates throughout the year.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Total Energy Consumed**} & \multicolumn{2}{c}{**Production**} \\ \cline{3-5}  & & & **Clinker** & **Cement** \\ \cline{3-5}  & & **(MJ)** & **(ton)** & **(ton)** \\ \hline
1 & April 2023 & 1133.60 & 11,641 & 17,037 \\
2 & May 2023 & 1212.6 & 12,360 & 16,109 \\
3 & June 2023 & 1120.3 & 10,905 & 15,208 \\
4 & July 2023 & 1476.4 & 2711 & 13,967 \\
5 & August 2023 & 1150.5 & 13,575 & 13,065 \\
6 & September 2023 & 1140.3 & 13,345 & 19,818 \\
7 & October 2023 & 1127.8 & 13,181 & 21,779 \\
8 & November 2023 & 1137.6 & 12,341 & 18,856 \\
9 & December 2023 & 1172.3 & 13,190 & 19,730 \\
10 & January 2024 & 1250.2 & 14,540 & 17,007 \\
11 & February 2024 & 1152.3 & 14,636 & 17,780 \\
12 & March 2024 & 1185.7 & 13,054 & 16,505 \\ \hline  & **Total** & **14,259.6** & **145,479** & **206,861** \\  & **Average** & **1188.3** & **12,123.25** & **17,238.42** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Production data for Plant B collected over a period of 1 year.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Date**} & \multirow{2}{*}{**Total Energy Consumed**} & \multicolumn{2}{c}{**Production**} \\ \cline{3-5}  & & & **(MJ)** & **(ton)** & **(ton)** \\ \hline
1 & August 2022 & 1038.63 & 47,672 & 55,618 \\
2 & September 2022 & 1087.96 & 42,997 & 55,058 \\
3 & October 2022 & 1090.49 & 52,584 & 66,265 \\
4 & November 2022 & 901.27 & 67,2
## 3 Results and Discussion
### Thermal Stress Indicators  
The leading indicator of thermal comfort used in the studies (n = 6) was the temperature and humidity index (THI). Only one study used air temperature as an indicator of thermal stress. The equations that studies used to evaluate the THI are described in Table 3. The THI emerged in 1959 to assess thermal discomfort in humans [55]. This concept was later extended to farm animals, and since then, several researchers have considered the THI a standard method to assess heat stress in dairy cows [56; 57]. The THI is the most common thermal comfort indicator used because it considers air temperature and relative humidity, which are variables easily measured. [PERSON] et al. [58] investigated nine thermal indices related to dairy cows, including THI, black globe temperature index, equivalent temperature index, effective temperature for dairy cows, respiratory rate predictor, temperature-humidity adjusted, heat load index, comprehensive climate index and the equivalent temperature index for cattle. The authors concluded that most heat indices could not accurately predict heat stress in confined lactating dairy cows. In addition, [PERSON] et al. [58] identified that the THI, black globe temperature index, temperature-humidity adjusted, comprehensive climate index and equivalent temperature index for cattle were closer to the actual heat stress conditions during the experimental period [58]. In another study, [PERSON] et al. [59], when reviewing 16 heat stress indices for dairy cows, the authors concluded that there was significant variation across the different breeds, access to shade, the cooling system, the physiological responses of dairy cows and the other climatic variables. The authors also reported that heat stress indices must carefully choose different production contexts. Among the 16 indices observed, they identified that the black globe temperature and humidity index, global climate index, respiratory rate index and the adjusted temperature-humidity index performed best among those studied [59]. However, it is worth noting that different climatic conditions involve equations to determine the THI [60]. Thus, indices with higher humidity weights are more suitable in humid climates, while in semi-arid environments, indices with more emphasis on ambient temperatures are recommended [60]. THI is a general and indirect indicator that does not consider individual cows. Thus, its results may be inadequate to meet livestock needs [61]. Therefore, associating the THI with behavioral and physiological parameters is more adequate to assess whether cows are in heat stress. This is because there are differences between the breeds of dairy cows, as well as age and their milk production, in addition to the geographical location of the barn and the types of housing, thus making it difficult for conclusive analyses and future comparisons [62].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
# State of the Art of Telecommunication Systems in Isolated and Constrained Areas
## 3 Comparison of the Solutions
### Criteria for Comparing Solutions
#### 3.1.5 Telecommunication System Deployment Time and Permanence  
The last parameter to evaluate the best solution is whether the system deployed can be operated on an occasional or permanent basis and deployment time. The occasional solution can be interesting when, for example, a natural accident of an environmental type occurs in an isolated region. The establishment of an occasional telecommunication system will make it possible to locate individuals, possibly in danger. An LTE network provides a vital connection to organize help. The case of the hurricane that swept through Puerto Rico in October 2017 is an example of the contribution of an occasional telecommunication system. The Loon balloon fleet very quickly deployed [77] on-site during this tragic event made it possible to restore the network to some people in distress. They could then send an SMS or an email, be located, and then be saved. The balloons provide a permanent solution when the materials used to make the envelope limit the helium leakage over time. Then, the balloons must land to be re-inflated only after an extended period, thus giving the system permanence on site. This is the case for Loon balloons or for the Stratobus. Regarding the deployment time, it is relatively low and well established, as illustrated in Figure 13.  
The balloon is brought to the launch site, inflated, and allowed to take off. Depending on the launch location chosen, this time will be the team's traveling time, the preparation time of the equipment, which is on average two days, a day for the launch, and the team's return. A week-long campaign is usually enough. For the drones, it is merely a matter of sending the team in situ. On the other hand, the deployment time of a telecommunications tower is longer. We often talk about a few weeks depending on the geography of the implementation site in an isolated environment. The slowest solution to deploy is an optical fiber. The ground must first be prepared (brush cutting, possibly blasting, etc.) and trenches made, over distances which can be significant. It is then necessary to install the \"ends of optical fibers\" and carry out the junctions. It can take months. Optical fiber installation took four months between Emeril and Schefferville in Northern Quebec. It is a typical isolated environment, and the fiber was deployed over more than 250 km along the railroad operated by the Tshuetin railway company. It is, therefore, the most prolonged solution to implement.
## 4 Results: Algorithms Performance Analysis Using Global Optimum Results through Parallel Computing
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & **Average Fitness** & **Fitness Variance** & **Minimum Fitness** & \(d_{opt}\) \\ \hline HHO-DEEPSO– & & & & \\ HyDE-DF & & & & \\ \hline WOA–HyDE-DF & 1.7136 & 0.0040 & 1.5922 & 0.94 \\ \hline HyDE-DF [33] & 1.9149 & 0.00484 & 1.8087 & 0.51 \\ \hline I-GWO–HyDE-DF & 1.9409 & 0.01141 & 1.7343 & 0.74 \\ \hline CE\_CMAES & 2.2223 & 2.8819 & 1.6073 & 0.86 \\ \hline I-GWO [39] & 2.3614 & 0.15998 & 1.8423 & 0.66 \\ \hline CBBOCauchyDEEPSO & 2.8736 & 0.03623 & 2.3893 & 0.77 \\ \hline HHO [35] & 2.9503 & 0.02331 & 2.6991 & 0.74 \\ \hline WOA [40] & 3.1859 & 0.08721 & 2.2133 & 0.77 \\ \hline SSA [41] & 3.4358 & 17.502 & 2.2231 & 0.96 \\ \hline GOA [42] & 49.724 & 246.05 & 23.628 & 1.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Metaheuristic algorithm results over 20 runs. Figure 4: Fitness evolution for some tested algorithm. Some parameters' combinations with non-adaptative DE for HyDE-DF show better fitness (1.52 inclusive), but their results present high variance, and they were not repeatable. The results showed on average an inferior performance. Tables 6 and 7 show the results of the run for the HHO-DEEPSO-HyDE-DF solution reported in Table 4. The green cells represent fixed variables. Table 6 shows the results for bids or offer quantities for each agent. Table 7 represents prices. Positive and negative signs indicate bids and offers. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Phase**} \\ \hline \multicolumn{2}{c}{**HHO**} & \multicolumn{2}{c}{**DEEPSO**} & \multicolumn{2}{c}{**HyDE-DF**} & \multicolumn{1}{c}{**Average Fitness**} \\ \hline
**Iterations** & **Individuals** & **Iterations** & **Individuals** & **Individuals** & **Individuals** \\ \hline
7 & 3 & 60 & 3 & 5 & 1.6192 \\ \hline
5 & 3 & 60 & 3 & 5 & 1.6219 \\ \hline
5 & 15 & 70 & 11 & 5 & 1.7339 \\ \hline
7 & 11 & 50 & 7 & 10 & 1.8373 \\ \hline
7 & 15 & 50 & 3 & 10 & 2.0692 \\ \hline
7 & 15 & 60 & 3 & 10 & 2.1056 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Some tuning combinations (HyDE-DF iterations necessary to complete 10,000 fitness function evaluations).
## 3 A Wiener Process-Based Approach of RUL Prediction with Unequal Interval Data
### Model Parameters Estimation  
In this article, the unknown model parameters are donated by a vector; the vector is written as \(\Theta=\left[\mu_{\theta},\sigma_{\theta}^{2},Q,\sigma^{2}\right]^{\mathsf{T}}\). The log-likelihood function of \(X_{0\pm}\) at \(T_{k}\) for \(\Theta\) is expressed as:  
\[L_{k}(\Theta)=\ln[p(X_{0\pm}|\Theta)] \tag{14}\]  
where \(p(X_{0\pm}|\Theta)\) is the joint PDF of \(X_{0\pm}\) for \(\Theta\). The maximum likelihood estimation of \(\Theta\) based on \(L_{k}(\Theta)\) is formulated by:  
\[\hat{\Theta}_{k}=\underset{\Theta}{\text{argmax}}L_{k}(\Theta) \tag{15}\]  
In this paper, \(\theta\) is a hidden variable. Therefore, it is difficult to maximize the Equation (14) directly. The EM algorithm [23] is used to solve this problem. The following two steps can achieve the estimation of \(\Theta\):  
1. E-step \[\ell(\Theta|\hat{\Theta}_{k}^{(i)})=\mathbb{E}_{\theta_{k}|X_{0\pm},\hat{ \Theta}_{k}^{(i)}}\{\ln p(X_{0\pm},\theta_{k}|\Theta)\}\] (16) where \(\hat{\Theta}_{k}^{(i+1)}=[(\mu_{\theta})_{k}^{(i)},(\sigma_{\theta}^{2})_{k}^ {(i)},(Q)_{k}^{(i)},(\sigma^{2})_{k}^{(i)}]^{\mathsf{T}}\) is the result in the ith iteration based on \(X_{0\pm}\).
2. M-step \[\hat{\Theta}_{k}^{(i+1)}=\underset{\Theta}{\text{argmax}}\{\ell(\Theta|\hat {\Theta}_{k}^{(i)})\}\] (17)  
The EM algorithm needs to iterate many times. Generally, the iteration is terminated when the difference between \(\ell(\Theta|\hat{\Theta}_{k}^{(i)})\) and \(\ell(\Theta|\hat{\Theta}_{k}^{(i+1)})\) is less than a small value. A series of optimal estimation of model parameters are obtained through the iterative process; the model parameters are denoted by \(\{\hat{\Theta}_{k}^{(0)},\hat{\Theta}_{k}^{(1)},\ldots,\hat{\Theta}_{k}^{(i) },\hat{\Theta}_{k}^{(i+1)}\}\). The details of the EM algorithm and realization are summarized in Appendix B. The convergence property of the EM algorithm has a detailed demonstration in [8]. It will not be repeated here.