esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Performance Metrics  
The prediction accuracy of optimized ANN models (i.e., ANN-HHO, ANN-PSO, and ANN-GA) and traditional ANN and WM model for SD prediction was evaluated through mean absolute error (MAE), root mean square error (RMSE), coefficient of correlation (CC), Willmott index (WI), mean absolute percentage error (MAPE), and through graphical interpretation (line, scatter, and box plots, and Taylor diagram). The MAE, RMSE, CC, WI, and MAPE performance metrics are expressed as follows:  
I. Mean absolute error [53; 54]  
\[MAE\ =\ \frac{1}{N}\sum_{i\ =\ 1}^{N}\big{|}SD_{pre,i}-SD_{obs,i}\big{|} \qquad(0\ <\ \text{MAE}\ <\ \infty) \tag{8}\]  
II. Root mean square error [55; 56; 57]  
\[RMSE\ =\ \sqrt{\frac{1}{N}\sum_{i\ =\ 1}^{N}\big{(}SD_{obs,\ i}\ -SD_{pre,i}\big{)}^{2}} \qquad(0\ <\ \text{RMSE}\ <\ \infty) \tag{9}\]
## 3 Performance Characterization and Reliability Assessment
### Shear Pull Test
Figure 12: Shear pull test results showing (**a**) individual sample measurements and (**b**) average shear test values for each configuration (L1, L2, and L3), demonstrating that L1 and L3 configurations exhibited superior bonding strength compared to L2.
## 2 Experimental Method and Experimental Setup  
### Experimental Method  
For the numerical calculation of chemical reaction dynamics, CHEMKIN [18] is a common tool, which is suitable for analyzing the chemical reaction process. Its main function is to solve a series of chemical problems involved in combustion through the analysis and calculation of the chemical reaction mechanism. Because the software package has the characteristics of reasonable structure, good reliability and easy to transplant, it has become the mainstream calculation software in the field of combustion chemistry dynamics. Some thermodynamic data are provided in the form of the NASA chemical equilibrium code, which are showed as the following Equations (1)-(6) [19]. Seven coefficients (\(a_{1k}\,\sim\,a_{7k}\)) are given in a database file (e.g., therm.dat), which is provided by the mechanism file.  
\[\frac{C_{pk}^{0}}{R}=a_{1k}+a_{2k}T_{k}+a_{3k}T_{k}^{2}+a_{4k}T_{k}^{3}+a_{5k} T_{k}^{4} \tag{1}\]  
\[\frac{H_{k}^{0}}{RT_{k}}=a_{1k}+\frac{a_{2k}}{2}T_{k}+\frac{a_{3k}}{3}T_{k}^{ 2}+\frac{a_{4k}}{4}T_{k}^{3}+\frac{a_{5k}}{5}T_{k}^{4}+\frac{a_{6k}}{T_{k}} \tag{2}\]  
\[\frac{S_{k}^{0}}{R}=a_{1k}nT_{k}+a_{2k}T_{k}+\frac{a_{3k}}{2}T_{k}^{2}+\frac{ a_{4k}}{3}T_{k}^{3}+\frac{a_{5k}}{4}T_{k}^{4}+a_{7k} \tag{3}\]  
\[C_{vk}^{0}=C_{pk}^{0}-R \tag{4}\]  
\[U_{k}^{0}=H_{k}^{0}-RT_{k} \tag{5}\]  
\[G_{k}^{0}=H_{k}^{0}-T_{k}S_{k}^{0} \tag{6}\]  
The chemical reaction mechanism used in this paper comes from two parts. [PERSON] et al. [20] developed a reduced mechanism, which contains 207 species and 1026 steps. The mechanism is based on five components (C\({}_{6}\)H\({}_{5}\)CH\({}_{3}\), iC\({}_{5}\)H\({}_{12}\), iC\({}_{8}\)H\({}_{18}\), nC\({}_{5}\)H\({}_{12}\), nC\({}_{7}\)H\({}_{16}\)) gasoline surrogate mechanism, which comes from Lawrence Livermore National Laboratory (LLNL) [21]. This 207-species skeletal mechanism is validated under a wide range of conditions compared to the rapid compression machine (RCM) experimental data, and it has also been mentioned or verified widely in other research [22; 23; 24]. The mixture oil in this mechanism has similar properties to local oil products in Changchun, China, so the mechanism is suitable for this study and for future research.  
The NO\({}_{x}\) mechanism comes from GRI mech3.0 [25], which has been widely verified by experiments [26; 27; 28]. In order to study the effect of gasoline-water blended on NO\({}_{x}\), this work combines the NO\({}_{x}\) mechanism in GRI mech3.0 and Chen mechanism to fulfill the research. And the composite mechanism is provided with Supplementary materials.
## 3 Results and Discussion
### Data Fusion  
The possibility of integrating the information present in the different dataset of each individual model allows an improvement in the discrimination between the differently treated samples. Therefore, a strategy of merging all the analytical data obtained was implemented. The data fusion strategy consisted of concatenating the original dataset so as to be able to analyze the resulting array as if it were a single data block. The results of the metabolic profile (concentration of primary and secondary metabolites and ions), antioxidant activity, chemical classes of the volatile fraction obtained by SPME-GC-QTOF/MS, were concatenated in a single array and the individual data normalized. Principal components analysis (PCA) biplot visualization (Figure 5) summarized the overall relations between the all variables in all samples.  
The total variation was 79.13% (PC1: 51.47% and PC2: 27.66%). PCA showed a clear separation between all samples, indeed it showed that CV is clearly discriminated from the others as well as HD, confirming the quantitative and qualitative differences of red _Topepo_ differently fertilized.  
Figure 4: 3D-PCA representation based on the volatile profiles of the different red _Topepo_ sweet peppers obtained by UFGC (CV: vegetable compost; CO: olive compost; CTR: control; HD: horse dung)
## 3 Software and Experiment
### Implemented Features for Automated Measurement and Analysis
#### 3.4.1 Batch File Generator (BFG)  
Generating a batch file makes process workflow more effective due to hundreds of steps processed in a very short time. The operator can define parameters e.g., Theta and Tilt angles (horizontal and vertical) or a step size. The application generates all positions according to the selected mode and saves them into the batch file imported into Argus. BFG generates steps with constant values of steps parameters and define variable values for: StepNr, ThetaPos, TiltPos, OutputFile (Figure 11), where StepNr is a unique value identifying step, ThetaPos horizontal angle and TiltPos vertical angle. The output file contains measured data for every single step and is generated during a measurement. The batch file is used for measurement as well as analysis.  
Figure 10: Designed Coarse and Fine RBS/C evaluation algorithm.  
There are two alternatives implemented for steps generation (Figure 12):  
* mode A; The measurement starts in the first step by increasing the theta angle (quadrant 1). When the angle reaches its maximum, increasing of the tilt angle is performed (quadrant 2). Then, the theta angle is decreasing (quadrant 3) down to its minimum. The last movement is performed in quadrant 4, when the tilt angle is decreasing down to its minimum.
* mode B; Before the measurement starts, the goniometer is moved into the starting position and in the step after that into the first measuring position. The measurement is performed from the lowest to the highest angle incrementally.  
Figure 11: Batch file generator window.  
Figure 12: Goniometer and steps generation modes.  
Both of the modes were tested and evaluated during the experiments to eliminate the deviation caused by mechanical backslash of the gears of the stepper motors and for the highest measurement accuracy. The comparison of both modes is summarized in Section 4. One direction goniometer planar displacement of goniometer represents 1 quadrant, so finally there are 4 quadrants of measured values.
# An Efficient Library for Reliability Block Diagram Evaluation
## 3 RBD Computation Library-librbd  
As already stated in Section 1, our aim was to develop a library with the following characteristics:  
* To be highly optimized;
* To support the most common OSes, i.e., Windows, Mac OS and Linux;
* To support the numerical computation of the reliability curve for all RBD basic blocks;
* To be available as a free software.  
In order to meet the third goal, librbd implements the resolution formulas for series, parallel, Koo\(N\) and bridge RBD blocks over time by accepting the following parameters:  
* Number \(N\) of components within the block;
* Number \(T\) of temporal instants to be analyzed;
* Reliability values \(R\) for the modeled components over the requested time instants.  
In order to meet the first two goals, several optimizations have been designed and implemented, as described in Section 3.1. Finally, in Section 3.2 we validate the results obtained using librbd by comparing the reliability curves of several blocks with the ones obtaining by using SHARPE tool.
###### Abstract  
The aim of this study was to develop an innovative material by combining the properties of magnetic biochar derived from orange peels and hydrogel beads (BCA-Mag/AB) and to apply this material for the adsorption of methylene blue dye in water. The BCA-Mag/AB were synthesized using physical crosslinking methods and was characterized using various techniques. The surface functional groups were analyzed using FTIR, the crystal structure was examined through XRD, the surface area was determined by BET, the pore distribution was assessed via BJH, thermal stability was evaluated with TGA, surface morphology was investigated using SEM, and the surface element percentages were analyzed with EDX. The specific surface area obtained through nitrogen adsorption was found to be 455.4 m\({}^{2}\)/g, and the total pore volume was 0.268 cm\({}^{3}\)/g. The removal of methylene blue dye on BCA-Mag/AB was investigated concerning three variables using Box-Behnken Design (BBD) as follows: A: BCA-Mag/AB dosage (0.02-0.1 g); B: contact time (20-150 min); and C: initial pH (2-10). Adsorption kinetic and isotherm analyses indicated that the adsorption of methylene blue onto BCA-Mag/AB was governed by pseudo-first-order and Freundlich models. The adsorption mechanisms of methylene blue on the BCA-Mag/AB surface were associated with electrostatic interaction, hydrophobic interaction, and \(\pi\)-\(\pi\) stacking. The results suggest that BCA-Mag/AB could be an effective and locally available candidate adsorbent for the removal of dye pollutants from contaminated industrial wastewater.  
adsorption mechanism; hydrogel bead; methylene blue dye; magnetic biochar; water treatment 2025
## 3 Results and Discussion
### Data Processing using Machine Learning Methods
#### 3.4.4 Convolutional Autoencoder  
A neural network consisted of alternating layers, Conv2D [54] and Max-pooling [55]. Binary cross-entropy was chosen as a loss function [56]. Training was performed on a batch size of 196 samples with a total number of 150 epochs and cross-validation at the end of each epoch--the same image was used as an input and as a target. During the training process, the model learned to reconstruct the initial image as accurate as possible. During the reconstruction process, the image passed through each layer with a successive decrease in dimension (encoder layers) to reach the middle layer of the autoencoder. These transformations were held on the convolutional and max-pooling layers. Then, to restore the initial dimensions, the image passed through the convolution and upsampling layers (decoder). Example of an input and an output is shown in Figure 9.  
Since there were eight neurons in the middle layer, the output would be eight different sets of parameters. If the input data had a pattern, for example, the substrate might consist of several areas with low amplitude arranged in a line; a neural network would learn this fact and encode it in a more compact form. It allowed us to explore the extracted dependencies corresponding to different experimental parameters of the surface. For example, surfaces with low mean roughness corresponded to a more steady distribution of the analyte and a less distinct spectrum. Examples of codes are shown in Figure 10.  
Figure 8: Diagram of the data augmentation process.  
Figure 9: Original image (**a**) and the output (**b**) of the autoencoder.
## 2 Materials and Methods
### System Architecture
#### 2.2.2 Software Description
The developed RTU utilizes a PIC16F887 microchip and an 8-bit microcontroller with eight analog input channels. The controller programming and project development are performed similarly to the previously developed version, as it has been found that the programming tools available for these controllers are easy to use. Temperature sensors read the temperature from the switch points at each end of the railway station, while deposit sensors are mounted on each switch point and record the presence or absence of snow deposits on their surface. This information is transmitted in the form of an electrical voltage, that varies between 0 and 5 V. The four relays are interfaced with the PC16F887 microcontroller (Microchip, Chandler, AZ, USA), to control the switch point heating system and platform lighting system. Figure 10 depicts the setup of the control system applied in the field. Figure 8: Block diagram of the GSM-based control system of the railway station. GSM = global system for mobiles; RTU = remote terminal unit. Figure 7: Switch heating remote control device, mounted on a metal panel. The AC/DC motor was interfaced to be able to act in both directions on the 20 kV three-pole LEA separator. The GSM modem was interfaced with the RTU and the PC unit on which the SCADA system is installed. Both units communicate serially with the GSM module through AT command sets. The GSM infrastructure for communication between the dispatch post and the railway station was chosen, because it has the lowest implementation cost, and safety and efficiency in operation. Figure 10: Setup of the control system applied in the field. RTU = remote terminal unit. Figure 9: Diagram of a remote terminal unit. In this SCADA implementation, unique proprietary protocols were used for communication between field devices and the main terminal unit. The security of SCADA systems is based on the secrecy of these protocols, which we have defined as our own protocols for both transmission and reception.
# Estimation of Direct Normal Irradiance at Antarctica for Concentrated Solar Technology
###### Abstract  
The estimation of the average daily, monthly and annual direct normal solar irradiation (DNI) was done in the region hosting the Mario Zucchelli Station, in the bay of Terra Nova (Antarctica). Estimates are based on measurements of direct normal (DNI), horizontal global (GHI) and horizontal diffuse (Diff.HI) irradiations, observed by a solar-metric acquisition station installed during the XXVIII scientific expedition carried out in the austral summer 2012/2013 as part of the National Plan of Research in Antarctica (PNRA). The contemporary observations of all three irradiations allowed the setting up of the model for the estimation of the DNI starting from the values of the GHI only, validated for the weather conditions of the Antarctic region. Subsequently, the long-time data reconstruction of the DNI values thanks to the availability of several years' hourly measurements of GHI at the Mario Zucchelli base has been carried out using the meteorological acquisition data installed both at the base and in places scattered around it. The final results make feasible the estimation of solar potential for concentrated solar technology according to long measurements of GHI. Overall, we propose a clean technology based on a renewable power plant and a specific example with a tendency toward a decreased human carbon footprint in the atmosphere of this protected area.  
Antarctica; solar radiation observations; direct normal irradiance; global horizontal irradiance; diffraction model; concentrating solar technology +
Footnote †: journal:  
_Article_
# A Survey of Applications of Deep Learning in Radio Signal Modulation Recognition
## 2 Related Work  
Researchers worldwide have done a lot of work on radio modulation recognition based on DL. They have proposed a variety of network models for radio AMR based on DL. In 2016, [PERSON] [18] first proposed AMR technology based on Deep Neural Network (DNN). Subsequently, many modulation recognition methods based on DL have been developed. To facilitate the training and testing of the network, [PERSON] et al. [19] established a benchmark dataset using GNU'S Not Unix (GNU) radio. [PERSON] et al. [19,20] proposed two methods to recognize the signals of the dataset. One is CNN [19], which is used in the field of modulation recognition and the other is to apply the Recurrent Neural Network (RNN) [20] to modulation recognition. By 2017, some new methods had been developed. [PERSON] et al. [21] proposed an AMC recognition scheme based on Deep Belief Network (DBN). [PERSON] et al. [22] first proposed a non-negative constraint training method based on Auto-Encoder (AE). [PERSON] et al. [23] proposed a two-layer GRU based on circular neural network with appropriate parameters. In 2018, [PERSON] et al. [24] proposed an AMC with Long Short-Term Memory (LSTM) based on improved RNN.  
Until recent years, more and more methods have been studied in this field and a large number of methods based on DL have been proposed. In 2022, [PERSON] et al. [25] proposed a wireless modulation classification algorithm based on CNNs in which the radon transform (RT) of constellation diagrams with different modulation types is used as input. Soon after, [PERSON] et al. [26] conducted a more in-depth study in this direction. An AMC approach based on 2D transforms and CNN was proposed. Various transform methods were used. [PERSON] et al. [27] proposed a new AMC method that combines Gabor filtering, thresholding and CNN. [PERSON] et al. [28] proposed MCformer, a transformer for AMC based on DNN. MCformer makes use of the convolution layer and self-attention mechanism. [PERSON] et al. [29] proposed a multi-scale feature network with large kernel size and squeeze-and-excitation mechanism for AMC. [PERSON] et al. [30] proposed two AMC methodsbased on DL using image classification technology. One uses constellation images and image classification technology and the other uses Graphic Representation of Features (GRF) technology.  
From them, it is found that using DL to recognize the modulated radio signals has many advantages. The recognition accuracy is greatly improved compared with the traditional modulation recognition methods. The recognition methods based on DL solve the problems of dependence on manual characteristics extraction, low robustness and difficult model deployment in the traditional radio signal recognition methods. They overcome the shortcomings of traditional linear classification methods, achieve accurate recognition of radio-coded modulation signals and effectively enhance the classification performance of radio modulation.
# Abnormal Activity Detection Using Pyroelectric Infrared Sensors
## 7 Experimental Evaluation  
In order to evaluate the performance of our proposed method, experiments were carried out on a real data set collected from a wireless sensor network. Our proposed method is referred to as SC+OSVM in the experiments. Two other approaches were also used for comparison. We list these three methods as the following:  
* SC+OSVM--The method proposed in our study, mainly including self-turning spectral clustering and One-Class SVM.
* SC+iForest--The difference between this method and the SC+OSVM is that we use isolation forest to replace One-Class SVM for abnormal detection. Isolation forest is an alternative algorithm for abnormal detection [36].
* OneHMM--All the normal training samples are modeled by only one HMM, which corresponds to not applying spectral clustering to the unlabeled samples. A threshold is set to distinguish normal and abnormal activities.
# Energy-Efficient Power Scheduling Policy for Cloud-Assisted Distributed PV System: A TD3 Approach
## 2 System Model
### Energy Storage Device Operation Model  
The cloud-assisted distributed photovoltaic (PV) system is a novel architecture that integrates PV generation, energy storage devices, and cloud computing. In this system, the information of PV energy \(E_{i,t}^{pv}\), electric loads \(E_{i,t}^{load}\) and energy storage \(SOC_{i,t}\) in each park \(i\) for each time slot \(t\) will be collected and uploaded to the cloud server. The cloud server acts as a park manager that can optimize the operation of the energy storage devices and the internal market transactions among the parks. The park manager can calculate the optimal buying or selling information for each park, which would be uploaded to the internal market, after selecting the optimal charging or discharging power of the energy storage device. Let \(\varphi_{i}(i\in\{1,2,...n\})\) denote the charging or discharging power of park \(i\) in the time slot \(t\), where \(\varphi_{i}>0\) means that the energy storage device chooses to charge from the grid or PV generation, and \(\varphi_{i}<0\) means that the energy storage device chooses to discharge to the grid or load demand. Then the energy storage action vector of all parks in the time slot \(t\) can be defined as \(\phi=[\varphi_{1},\varphi_{2},\ldots,\varphi_{n}]\). The objective of the park manager is to maximize the total profit of all parks by determining \(\phi\) for each time slot, subject to some constraints such as power balance, energy storage capacity, and market clearing.
# Adaptive Equalization for Dispersion Mitigation in Multi-Channel Optical Communication Networks
## 5 Conclusions  
An optical fiber system accommodates many users, where they can avail high capacity and data rate services. However, multi carrier OCN faces severe dispersion effects such as CD and PMD losses. In this paper, we use DFE and FFE techniques to compensate these losses in OCNs. These mentioned techniques optimize the existing OCNs and meet the requirements of end users' high data rate demands. Furthermore, in this paper, performance analyses with and without DFE/FFE equalization for CD and PMD compensation are compared. Moreover, the effect of equalization for pre and post compensation of CD and PMD is investigated. Using adaptive equalization techniques at high data rates, the proposed system achieves significant dispersion compensations in terms of improved BER and Q-factor.  
Figure 11: BER eye-diagram with equalization.  
Figure 10: BER eye-diagram without equalization.  
**Author Contributions:** Conceptualization: F.A. and S.A.; methodology, F.A.; software: S.A, and Z.H.A.; validation: F.M. and Z.H.A.; formal analysis, F.A. and U.H.; investigation: S.A. F.M. and U.H; data curation: Z.H.A.; writing--original draft preparation, F.A., F.M. and S.A.; supervision: F.M. S.K.; project administration: F.M. and and S.K.; funding acquisition: S.K.  
**Funding:** This work (S2666095) was supported by project for Cooperative R&D between Industry, Academy, and Research Institute funded Korea Ministry of SMEs and Startups in 20.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Fault Diagnosis for a Class of Robotic Systems with Application to 2-DOF Helicopter
## 4 Study Case
### 2-Dof Helicopter  
Consider the Quanser(r) 2-DOF helicopter, a testing prototype that is on a fixed pedestal (that does the pivot function) and has two motors, one front for lift and one rear or tail for yaw, Figure 2.  
Figure 2: Schematic of the 2-degree of freedom (DOF) helicopter.  
The system model can be written in normal way through following nonlinear second order equation, matrix form:  
\[D(q)\ddot{q}+N(q,\dot{q})+g_{v}(q)=\tau, \tag{17}\]  
where \(\tau\) is the input vector, \(g_{v}(q)\) is the vector of gravitational pairs, \(D(q)\) is the inertial matrix, symmetric and positive definite, \(N(q,\dot{q})\) models the quadratic effects in velocity generated by centrifugal and Coriolis forces. For the Quanser\({}^{\textregistered}\) system, it is convenient to define the generalized coordinate vector \(q=[\phi,\psi]^{T}\), where \(\phi\) and \(\psi\) are the pitch and yaw angles. Consider \(\tau=[U_{p},U_{y}]^{T}\), and  
\[D(q) = \begin{bmatrix}m_{1}{l_{1}}^{2}+m_{2}{l_{2}}^{2}&0\\ 0&m_{1}{l_{1}}^{2}\text{cos}^{2}(\phi)+m_{2}{l_{2}}^{2}\text{cos}^{2}(\phi) \end{bmatrix},\] \[g_{v}(q) = \begin{bmatrix}m_{1}g{l_{1}}\text{cos}(\phi)-m_{2}g{l_{2}}\text{ cos}(\phi)\\ 0\end{bmatrix}.\]