esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 2 Materials and Methods
### Multi-Channel Expression Recognition Network
#### 2.1.1 MainNet  
The backbone of MainNet is built according to ResNet18 [23] and integrates the ConvNext Block. At the same time, considering the large amount of network parameters, the deep structure of ResNet18 is replaced by the Ghost [24] module to complete the main feature extraction network construction. Finally, in order to obtain more complete facial features, feature fusion is performed again using the AFF [25] structure. The fusion process is shown in Figure 2.  
The corresponding sizes of the three fused layers are 512 \(\times\) 5 \(\times\) 5, 256 \(\times\) 10 \(\times\) 10, and 128 \(\times\) 20 \(\times\) 20, and the results are pooled by global tie for expression prediction. The formula is as follows:  
\[ASFF0=a_{i,j}^{0}\cdot X_{i,j}^{0\to 0}+\beta_{i,j}^{0}\cdot X_{i,j}^{1\to 0}+\gamma_{i,j}^{0}\cdot X_{i,j}^{2 \to 0} \tag{1}\]  
_ASFF0_ is used for resizing the feature maps from _Level_1 and _Level_2 to the same size as _Level_0, where \(X_{i,j}^{1\to 0}\) represents the feature map after _Level_1 reset, \(X_{i,j}^{2\to 0}\) represents the feature map after _Level_2 reset, and \(X_{i,j}^{0\to 0}\) represents the feature map from _Level_0.  
The values \(a_{i,j}^{0}\), \(\beta_{i,j}^{0}\) and \(\gamma_{i,j}^{0}\)\(\in\) [0,1] are the weight parameters of each feature map corresponding to _ASFF0_; the generation of the weight parameter is to reduce the dimensions of the resized _Level_1_resized_, _Level_2_resized_, and _Level_0, and then splicing. The 1 \(\times\) 1 convolution is used to calculate \(\lambda_{a_{i,j}}^{0}\), \(\lambda_{\beta_{i,j}}^{0}\), \(\lambda_{\gamma_{ij}}^{0}\) from _Level_1_resized_, _Level_2_resized_, _Level_0, respectively. Finally, perform _SoftMax_ in the channel dimension and compare it with _Level1_resized_, _Level_2_resized_, and _Level_0 multiply respectively. The formula is as follows:  
\[a_{ij}^{0}=\frac{e^{\lambda_{a_{ij}}^{0}}}{e^{\lambda_{a_{ij}}^{0}}+e^{\lambda _{\beta_{i}}^{0}}+e^{\lambda_{\gamma_{ij}}^{0}}} \tag{2}\]  
Figure 2: MainNet structure.  
Figures 3 and 4 show the feature visualization of whether to add the AFF module. Here, only 64 channels are randomly selected for display. Comparing Figures 3 and 4, it can be clearly seen that the feature information after adaptive feature fusion retains more complete facial information and discards some invalid channel information, which is more flexible for information fusion and aids to improve the accuracy of facial expression recognition.
## 3 Mathematical Modeling
### Leader (RE Company) Model  
In order to maximize its own revenue, the RE company guide the charging and discharging power of the BSS by optimizing the charge/discharge prices. The RE company's revenue is expressed as Equation (1) below:  
\[\sum_{t=1}^{N_{t}}\left(P_{RE}^{t}-P_{\text{c}}^{t}+P_{\text{d}}^{t}\right) \rho_{RE}^{t}\Delta t+\sum_{t=1}^{N_{t}}P_{\text{c}}^{t}\rho^{t}\Delta t-\sum_{ t=1}^{N_{t}}P_{\text{d}}^{t}\rho^{t}\Delta t \tag{1}\]  
where \(N_{t}\) refers to the number of time frames partitioned, \(\Delta t\) is the length of a single time interval, \(P_{\text{RE}}^{t}\) is the active power output of RE at time \(t\), \(\rho^{t}\) is the charge/discharge price that BSS buys/sells energy from/to the RE company at time \(t\), \(\rho_{\text{RE}}^{t}\) is the price at which the distribution company purchases energy from the RE company at time \(t\), \(P_{\text{c}}^{t}\) and \(P_{\text{d}}^{t}\) denotes the charging and discharging power of BSS, respectively.  
Equation (1) consists of three parts: the first part is the income from the sale of electricity to the distribution company, the second part is the income from the sale of electricity to BSS, and the third part is the cost of purchasing electricity from the BSS. Under the condition that \(\rho_{\text{RE}}^{t}\) decided by the distribution company and the forecast value of renewable energy are known, the income from the sale of this part of the electricity is fixed. Therefore, the payoff function of the RE company can be expressed as Equation (2).  
\[\text{max}\sum_{t=1}^{N_{t}}\left(P_{\text{c}}^{t}-P_{\text{d}}^{t}\right)\rho ^{t}\Delta t-\sum_{t=1}^{N_{t}}\left(P_{\text{c}}^{t}-P_{\text{d}}^{t}\right) \rho_{RE}^{t}\Delta t \tag{2}\]  
The strategy space is defined by the following constraints:  
(1) Charge/discharge price constraint  
\[\rho_{\text{min}}^{t}\leq\rho^{t}\leq\rho_{\text{max}}^{t} \tag{3}\]  
where \(\rho_{\text{max}}^{t}\) and \(\rho_{\text{min}}^{t}\) is the upper and lower limit of \(\rho^{t}\), respectively.  
(2) Average charge/discharge price constraint  
\[\frac{1}{N_{t}}\sum_{t=1}^{N_{t}}\rho^{t}\leq\overline{\rho} \tag{4}\]  
In order to restrain the market power of the RE company, the average charge/discharge price constraint is introduced. The upper limit of the average charge/discharge price \(\overline{\rho}\) is set to protect the benefits of BSS. When the RE company raises the charge/discharge price of some time periods, it needs to reduce the prices of others, and uses the price differences in different time periods to guide the charging and discharging behavior of BSS.
## 5 Experiments
### Experimental Introduction  
Electronic products are highly reliable and long-lasting, making it difficult to analyze them with traditional testing methods. Therefore, it is necessary to design accelerated life test plans that can shorten testing time without affecting the failure mechanism. Through experiments, combined with existing research findings, it has been discovered that the contact performance of relays is significantly affected by environmental factors, especially temperature conditions. As the temperature increases, the rate of performance degradation accelerates [30; 31]. This experiment is based on the IEC electrical standards, to improve  
Figure 19: Relay electromagnetic module structure diagram.  
Figure 18: Relay design module schematic diagram.  
the progress of life testing and consider the requirements for the lowest temperature, sets T1 to 40 \({}^{\circ}\)C. Since the relay structure contains a large number of plastic components, its maximum operating temperature is 90 \({}^{\circ}\)C, so testing should be conducted at 90 \({}^{\circ}\)C. Therefore, the temperature stress level is set to 4, with the values of two temperature stresses calculated using the Arrhenius Equations (35) and (36). Two intermediate temperatures can be calculated as 49 \({}^{\circ}\)C and 64 \({}^{\circ}\)C.  
\[T_{j}=\left[1/T_{1}+(j-1)\Delta\right]^{-1}\,\ j=2,3,...,n-1 \tag{35}\]  
\[\Delta=(1/T_{n}-1/T_{1})/(n-1) \tag{36}\]  
This experiment features 8 workstations, with each relay having 4 sets of contacts connected to power and 4 sets not connected. The experiment is conducted at selected temperature stress levels, and inspection and replacement are carried out after each round of testing. At a temperature stress of T1 (40 \({}^{\circ}\)C), the test cycle is 200,000 times. After the test, a relay is sequentially removed from the test station to measure its electrical and mechanical performance, followed by the use of a non-contact 3D analysis system to detect and record the surface morphology data of the contacts. In the subsequent test, the vacant workstation is replaced with a standard new relay. The 8 relays at the start of the experiment undergo 8 rounds of testing, completing a total of 1.6 million cycles of operation. The disassemble and replacement of test samples are carried out in the above cycle sequence until truncated failure. At temperature stresses of T2 (49 \({}^{\circ}\)C), T3 (65 \({}^{\circ}\)C), and T4 (90 \({}^{\circ}\)C), the test cycles are set at 150,000 times, 100,000 times, and 50,000 times respectively, with the relay testing and replacement method after each test cycle being consistent with that at a temperature stress of T1 (40
# Recent Advances in the Growth and Characterizations of SILAR-Deposited Thin Films
## 5 Metal Oxide Thin Films
### Iron Oxide Films  
Formation of the amorphous phase and hydrous (water contact angle smaller than 10\({}^{\circ}\)) could be observed in Fe\({}_{2}\)O\({}_{3}\) films [147]. These materials could be considered potential candidates for supercapacitor applications due to their excellent supercapacitance values (178 F/g at a scan rate of 5 mV/s). The annealing effect on the Fe\({}_{3}\)O\({}_{4}\) films was studied by [PERSON] and co-workers [148]. The crystallite size and transmission in the UV-visible region increased, and spherical morphological agglomeration could be observed with the annealing of the films. The iron(II) sulfate (FeSO\({}_{4}\)) and NH\({}_{4}\)OH were used to produce magnetite (Fe\({}_{3}\)O\({}_{4}\)) films under various deposition temperatures [149]. The XRD studies showed a cubic phase, Fd3m space group, and prominent growth direction (311) plane. Researchers also described the average crystallite size (56.34 to 34.73 nm) as reduced, but the dislocation densities (3.15 to 8.29 \(\times\) 10\({}^{14}\)/m\({}^{2}\)), microstrain (35.12 to 46.85 \(\times\) 10\({}^{-4}\)), and intensities of the emission peaks in photoluminescence spectra increased by increasing the deposition bath temperature from 55 \({}^{\circ}\)C to 85 \({}^{\circ}\)C. Based on the photoluminescence response, four peaks, around 360 nm (transition between vibrational energy), 442 and 492 nm (intraband defects of doubly ionized oxygen), 520 nm (singly ionized oxygen vacancies), and 592 nm (oxygen interstitials) could be detected in the obtained films. The Fe\({}_{2}\)O\({}_{3}\) films were synthesized on glass slides as reported by [PERSON] [150]. The refractive index and band gap values were 1.45-3.23 (400-700 nm, visible range) and 2.62-2.68 eV, respectively. The XRD and FESEM analyses revealed non-crystalline and porous morphology, respectively.
## 6 Deployment Stage
### Robustness Analysis: Model Performance in Noisy Environments  
In order to evaluate the applicability of the proposed solution in real-world applications, the FDT is evaluated under very severe noise conditions. The scenarios from Table 1 are repeated with SPM input signals (\(\mathrm{i_{in}}\), \(\mathrm{i_{out}}\), and \(\mathrm{v_{out}}\)) that are corrupted by white noise, which achieves a signal-to-noise ratio (SNR) value below 10 dB. SNR values are calculated using the signals' switching frequency spectral component as the reference signal (\(\mathrm{i_{inAC}}\), \(\mathrm{i_{outAC}}\), and \(\mathrm{v_{outAC}}\)).  
Figure 26 shows the SNR values for Scenario 1, along with the input signals' ripple (\(\mathrm{\Delta i_{in}}\), \(\mathrm{\Delta i_{out}}\), and \(\mathrm{\Delta v_{out}}\)) under both noisy and noise-free conditions, and the noise component itself.  
Figure 27 presents the response of the proposed model (17) to the noisy input signals depicted in Figure 26.  
Figure 27: Model (17) performance under noisy input signals (Figure 26).  
Figure 27 illustrates the robust performance of model (17), even under highly noisy conditions, as demonstrated by a recall of 1.0, an accuracy greater than 0.99, a precision exceeding 0.98, and an F1 score of 0.99.  
Scenario 2 is analyzed following the procedure established for Scenario 1. Therefore, Figure 28 shows the SNR values for Scenario 2, along with the input signals' ripple (\(\Delta\)i\({}_{\text{in}}\), \(\Delta\)i\({}_{\text{out}}\), and \(\Delta\)v\({}_{\text{out}}\)) under both noisy and noise-free conditions, and the noise component itself.  
Figure 29 presents the response of the proposed model (17) to the noisy input signals depicted in Figure 28.  
Figure 29 confirms excellent model robustness under noisy conditions, with the model achieving perfect recall (1.0) and accuracy, precision, and F1 scores greater than 0.99.
# Skin Lesion Segmentation Method for Dermoscopy Images Using Artificial Bee Colony Algorithm
## 1 Introduction
The results in Reference [6] established that social group optimization (SGO) is very active in extracting lesion sectors from hairy skin tumor images compared to the alternatives studied for detection accuracy. [PERSON] et al. [22] used region growing and neutrosophic clustering algorithms to detect melanoma from ISIC 2017 dataset images (International Symposium on Biomedical Imaging). [PERSON] et al. [23] utilized the bag of words (BOW) model in semantic annotation based on Support Vector Machines (SVM) classifier to develop the automatic skin lesion categorization. In Reference [24], new boundary features are presented based on the color variation of the pigmented lesion images to influence higher performance in skin tumor diagnosis with the multilayer perceptron neural network. [PERSON] et al. [25] used knowledge transfer learning for skin tumors lesion screening with deep learning to reach high confidence, whereas for difficult lesions performance is still little better than chance. [PERSON] et al. [26] developed a new algorithm with a self-generating neural network (SGNN) and features descriptive of the tumor color to get high accuracy with the classifier model for melanomas and benign case. [PERSON] et al. [27] evaluated various categories of the classifier of the feature extraction phase and several feature selection algorithms were compared for the categorization of melanoma. The contribution of this paper was to develop melanoma detection using the artificial bee colony (ABC) algorithm [28]. In this work, three steps were used to detect the melanoma in the dermoscopy images. In the pre-processing step, the red channel of the colored dermoscopy image was selected. For smoothing, 2D median filter with size 20 \(\times\) 20 and morphological filtering based on Gaussian kernel was used. In the next step, the ABC algorithm was used to find the optimum threshold value for the melanoma segmentation. Finally, the estimated optimum threshold value was used for the melanoma segmentation like the thresholding method as used in the Otsu method [29]. To show the robustness of this work, the proposed approach was evaluated for four public dermoscopy image datasets: PH2 [4], ISBI2016 challenge [5], ISBI 2017 challenge [30], and Dermis datasets [31].
## 4 Analysis of Energy Storage System
### Temperature Control Strategy  
Based on the analysis on Sections 4.1 and 4.2, the storage temperature of the battery needs to be kept above 0 \({}^{\circ}\)C to prevent the battery from low-temperature loss of the battery capacity. The heating system in the battery storage compartment ensures that the temperature of the battery can be constant within the ideal operating temperature range and reduces energy consumption by means of intermittent starting. The proportion-integral-differential (PID) algorithm was chosen to design the temperature control strategy.  
The chosen heating system is a complex system with larger time lag and inertia. The mathematical model of the temperature control system in this study is described by a first-order inertia lag link. The transfer function of the heater can be expressed as follows.  
\[G(s)=ke^{-\tau s}/(Ts+1) \tag{14}\]  
where \(k\) is static gain; \(T\) is time constant; \(\tau\) is pure lag time.  
This study uses an incremental PID control algorithm. A step input signal is applied to the controlled object to measure the step response of the controlled object, and the approximate transfer function of the controlled object can be obtained by the flying up curve method. Parameters of the transfer function can be seen in Table 7.  
The flowchart of the temperature control strategy is given in Figure 7.  
After the PID was initialized, the target temperature for battery storage \(T_{a}\) was set first and then the real-time temperature of battery storage \(T_{r}\) was obtained. Generally, \(T_{a}\) is set to be higher than 0 \({}^{\circ}\)C. If \(T_{a}<T_{r}\), the battery storage temperature could be considered suitable. If \(T_{a}>T_{r}\), the heating system will be started and the difference between the target temperature for battery storage \(T_{a}\) and the real-time temperature of battery storage \(T_{r}\) will calculated, which is marked as \(e\). If \(e<3\), the incremental PID control algorithm will be used to heat the battery storage room. If \(e>3\), the full power heating will be activated to quickly reach the target temperature for battery storage. After heating, the difference between \(T_{a}\) and \(T_{r}\) will be evaluated again. If \(T_{a}\leq T_{r}\), the heating system will end the heating of the battery storage compartment.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline \(k\) & 0.8 \\ \(T\) & 48.75 \\ \(\tau\) & 11.2 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Parameters of the transfer function.
## Abbreviations  
\begin{tabular}{l l} RNN & Recurrent neural networks \\ LSTM & Long short-term memory \\ GA & Genetic algorithm \\ GA-LSTM & GA with long short-term memory \\ BilstM & Bidirectional LSTM \\ GRU & Gated recurrent units \\ GI & Google index \\ NN & Neural network \\ SVR & Support vector regressions \\ EU-LFS & European Union Labor Force Survey \\ SARIMA & Seasonal autoregressive integrated moving average \\ SETAR & Self-exciting threshold autoregressive \\ ETS & Error, trend, seasonal \\ NNAR & Neural network autoregression \\ RMSE & Root mean squared error \\ MAE & Mean absolute error \\ MAPE & Mean absolute percentage error \\ BMA-PAR & Bayesian model averaging with periodic autoregressive \\ SARMA & Seasonal autoregressive moving average \\ PMEANS & Seasonal (or periodic) MEANS \\ BPAR & Bayesian PAR \\ VAR & Vector autoregressive \\ SVAR & Spatial VAR \\ NN & Neural network \\ NNS & Neural network seasonal \\ SpVAR & Spatial vector autoregressions \\ SpNN & Spatial neural network \\ SpANN & Spatial artificial neural network \\ FARIMA & Fractional autoregressive integrated moving Average \\ FARIMA-GARCH & FARIMA with generalized autoregressive conditional heteroskedasticity \\ ANN & Artificial neural network \\ SVM & Support vector machine \\ MARS & Multivariate adaptive regression splines \\ ARMA & Autoregressive moving average \\ BIC & Bayesian information criterion \\ FTS & Fuzzy time series \\ NMSE & Normalized mean square error \\ ARIMA & Autoregressive integrated moving average \\ ARNN & Autoregressive neural network \\ ARIMA-SVM & ARIMA with support vector machine \\ ARIMA-ANN & ARIMA with artificial neural network \\ ARIMA-ARNN & ARIMA with autoregressive neural network \\ PSO & Particle swarm optimization \\ ARMAX-GARCHX & Stochastic linear autoregressive moving average \\  & with exogenous variable with GARCHX \\ \end{tabular}
## 3 Results and Discussion
### Elemental Analysis Results  
The main elements of the three carbon materials were detected by Elemental Analysis (EA) of C, H, N, O, S, and the ash, as shown in Table 3. The ash contents of RHA and BC were, 6.13% and 8.38%, respectively, lower than that of SC, which was 38.51%. The reactive element C contents of RHA, BC, and SC were 87.00%, 72.30%, and 52.18%, respectively. The C content in RHA and BC was higher, which resulted in their high denitration rate. Although the C relative content of SC was the least, the initial denitration rate of SC was much higher than those of RHA and BC, which means that the C element in this biochar is a better reducer of C-NO. This may result from the presence of a higher number of aromatic hydrocarbons and various reactive functional groups in SC. As for the O/C ratio which will inhibit the reduction of C-NO [17], the relative content in BC and SC was relatively high. This partly explain why the denitration rate decreased rapidly with time. RHA and BC contain a little amount of N element, which does not exist in SC. According to the literature [16; 17; 18], the N element induces the formation of NFGs, which promote C-NO reduction. The positive effect of NFGs on the reduction of biochar is attributed to two properties: NFGs promote C-NO reduction under aerobic conditions and inhibit the reaction of biochar with O\({}_{2}\). In summary, the denitration rate decreases faster in the presence of a high O/C ratio, and the high relative content of N element can induce NFGs, which catalyze C-NO reduction.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Sample** & **C** & **H** & **N** & **O** & **S** & **Ash** & **O/C** \\ \hline RHA & 87.00 & 1.79 & 0.61 & 4.45 & 0.02 & 6.13 & 0.05 \\ BC & 72.30 & 2.70 & 0.80 & 15.31 & 0.51 & 8.38 & 0.21 \\ SC & 52.18 & 0.16 & 0.00 & 8.37 & 0.78 & 38.51 & 0.20 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main elements and their relative contents (%) in the carbon materials analyzed.
# _Pistacia Atlantica_ Desf., a Source of Healthy Vegetable Oil
## 3 Results and Discussion  
Oil yield represented more than 40% of seed dry weight (DW). This yield was quite similar to the levels reported earlier [28; 29] for seeds harvested in western Algeria under different weather conditions. [PERSON] et al. [34] observed higher oil content in seeds from the extreme west of Algeria. The yield we obtained can be considered high by comparison with those of oils from other oilseed crops (rapeseed and sunflower, for example), taking into account the fact that this species was not submitted to breeding programs to increase this trait [35]. Nevertheless, the climatic conditions observed in these studies were different from those reported in our study. This fact indicates that the oil yield from _Pistacia_ seeds is dependent not only on environmental parameters but also on genetic factors [35; 36]. Moreover, the oil extraction methods influence greatly the oil content [37]. The results of FA composition and oil content are presented in Table 2. In general, there was a predominance of monounsaturated fatty acids (MUFA) that represented more than 43% of total FA. The content of PUFA was slightly lower than that of SFA (Table 2). The major FAs were oleic (41%), linoleic (26.8%), and palmitic (26.7%) acids. Clearly, the oil was rich in unsaturated FAs (71.3%); the remaining FAs were saturated (28.7%). These levels are lower than those reported in other studies [28; 29; 34]. The differences could be due to genotypic and environmental [38; 39; 40] factors as well as to the methods used for the extraction and measurements of oil and fatty acids [35; 41; 42]. The ecotype seeds used in the cited studies were collected in 2006 in western Algeria, whereas the seeds used in our study were harvested in southern Algeria. Several factors like genotype, environmental conditions, type of soil, nutrients availability, year of cultivation could be involved in these differences [22; 38; 41; 43]. In some species, oil content decreases with the increase of temperature [35; 43; 44]. These parameters could also contribute to modifying fatty acid composition. Indeed, Djelfa region is known as an arid zone, with only 185 mm of rainfall recorded in 2016 (Table 1). Unfortunately, we cannot compare our data with those of previous studies [28; 29; 34] in western Algeria, since climatic data are lacking in these reports. The oil of _P. atlantica_ was composed of 75% of unsaturated fatty acids (Table 2) and therefore appears to be a very healthy oil for humans, possibly contributing to lowering the risk of cardiovascular diseases. This composition is interesting, which is lower than those reported in Chia [43], and indicates a higher polyunsaturated FA level than in oils from sunflower, rapeseed, brown mustard, oak, soya, and Apiaceae species [22; 26; 35; 45; 46]. Oils rich in polyunsaturated fatty acids reduce the