esa-sceva/satcom-chunk-collection huggingface dataset:
# Function Optimization and Parameter Performance Analysis Based on Gravitation Search Algorithm
## 4 Conclusions  
Based on the basic principle of the gravitational search algorithm (GSA), the algorithm flowchart is described in detail. The optimization performance is verified by simulation experiments on six test  
Figure 4: Simulation results of six typical test functions. (**a**) Griewank function \(f_{1}\); (**b**) Quartic function \(f_{2}\); (**c**) Rastrigin function \(f_{3}\); (**d**) Schwefel function \(f_{4}\); (**e**) [PERSON]’s Foxholes function \(f_{5}\); (**f**) [PERSON]’s Foxholes function \(f_{6}\).  
functions. \(G_{0}\) as a role of step in the particle position growth, through the simulation analysis of functions, when \(G_{0}\) is 100, the algorithm is relatively inclined to be in a more stable state, which makes the optimal results more stable. The parameter \(\alpha\) played a key role in control algorithm convergence rate. When the parameter value is small, the convergence speed of the algorithm is relatively slow. Under the same number of iterations, the conditions need for an optimal solution are worse; thus, a higher number of iterations is needed to obtain the optimal solution. However, the high value will easily cause the algorithm convergence speed to be too fast and become caught into the local solution, which will reduce the accuracy of the solution. As a result, the value 15 is appropriate. The simulation results show that the convergence speed of the algorithm is relatively sensitive to the setting of the algorithm parameters, and the GSA parameters can be used to improve the algorithm's convergence velocity and improve the accuracy of the solutions.  
This work is partially supported by the National Key Technologies R & D Program of China (Grant No. 2014 BAF05B01), the Project by National Natural Science Foundation of China (Grant No. 21576127), the Program for Liaoning Excellent Talents in University (Grant No. LR2014008), the Project by Liaoning Provincial Natural Science Foundation of China (Grant No. 2014020177), and the Program for Research Special Foundation of University of Science and Technology of Liaoning (Grant No. 2015 TD04).  
[PERSON] participated in the concept, design, interpretation and commented on the manuscript. [PERSON] participated in the data collection, analysis, algorithm simulation, draft writing and critical revision of this paper.  
The authors declare no conflict of interest.
## 2 Electric Appliance Utility Function and Energy-Saving Cost
### Time-Sensitive Utility Function  
The time-sensitive utility function quantifies customers' satisfaction with the actual appliance operation status of time-type appliances, such as electric lights. We define the operating status \(s_{i}(t)\) of appliance \(i\) at the moment \(t\) as a binary variable, corresponding to two statuses: On and off. For the sake of simplicity, the time-sensitive utility function is expressed as time utility.  
It is generally believed to be high energy efficiency for electrical appliances to operate at the time when the user needs them. However, for practical engineering implementation, it is difficult to decide whether the user needs to use the appliance at a particular time. Instead, the historical electricity consumption data can be mined to calculate the operating probabilities of an appliance, which is regarded as the probability that the user needs to use the appliance, marked as \(P^{on}\).  
As a result, the time utility function is shown as Equation (1):  
\[U_{i}^{time}(t)=\left\{\begin{array}{ll}\rho(P_{i,t}^{on}(H))&s_{i}(t)=1\\ 1-\rho(P_{i,t}^{on}(H))&s_{i}(t)=0\end{array}\right.. \tag{1}\]In this equation, \(U_{i}^{time}(t)\) represents the time utility value of appliance \(i\) at the moment of \(t\), and \(H\) is the parameter that is set to infer \(P_{i,t}^{on}\), which is the operating probability of appliance \(i\) at the moment of \(t\). The function \(\rho(x)\) is the system preference function, which is introduced to represent decision preferences given from a decision-making system, defined as Equation (2). It can be seen from Equation (1) that when the operating probability of an electrical appliance is more significant at a certain time, user satisfaction is higher.  
\[\rho(x)=\left\{\begin{array}{ll}1&x\in((2\alpha)^{-\frac{1}{3}}+0.5,1]\\ \alpha(x-0.5)^{3}+0.5&x\in[-(2\alpha)^{-\frac{1}{3}}+0.5,(2\alpha)^{-\frac{1} {3}}+0.5]\\ 0&x\in[0,-(2\alpha)^{-\frac{1}{3}}+0.5)\\ \end{array}\right.. \tag{2}\]  
In Equation (2), \(\alpha\) is the decision preference coefficient, and \(\rho(x)\) is the system preference function of converting the objective probability into the preference probability, in which \(x\) is the objective probability value. In general, things that have an extremely small probability are often regarded as impossible events. On the contrary, things that have a significant probability are seen as absolute events. In conformity with this, the output \(\rho(x)\) in Equation (2) is the decision preference probability, which ignores impossible events and affirms very likely events. The preference extent depends on \(\alpha\), which ranges from 4 to infinity. A bigger \(\alpha\) corresponds to a larger preference extent.
# Inducing Optimality in Prescribed Performance Control for Uncertain Euler-Lagrange Systems
## 3 Problem Formulation and Preliminaries  
Consider an \(n\)-degree-of-freedom Euler-Lagrange system that obeys the following dynamic model:  
\[M(q)\dot{q}+C(q,\dot{q})\dot{q}+G(q)=\tau \tag{1}\]  
where \(q\), \(\dot{q}\in\mathbb{R}^{n}\) denote the generalized state vector (e.g., position and velocity), \(M(q)\) is a positive definite inertia matrix, \(C(q,\dot{q})\) is the matrix that describes the Coriolis centrifugal phenomena, \(G(q)\) is the vector describing the influence of gravity, and \(\tau\) is the torque that acts as the system's input.  
**Assumption 1**.: _The states \(q\) (position) and \(\dot{q}\) (velocity) are available for measurement, but \(\ddot{q}\) (acceleration) is not._  
We also define the Lipschitz continuous functions \(f(q,\dot{q})\in\mathbb{R}^{n}\) and \(g(q)\in\mathbb{R}^{n\times n}\) to describe the system drift dynamics \(f(q,\dot{q})=-M^{-1}(q)(C(q,\dot{q})\dot{q}+G(q))\) and the input vector field \(g(q)=M^{-1}(q)\), respectively. Thus, system (1) may be reformulated as:  
\[\ddot{q}=f(q,\dot{q})+g(q)\tau \tag{2}\]  
**Assumption 2**.: _The system (2) is robustly stabilizable [24], that is, there exists a continuous control law \(\tau(q,\dot{q})\) and a compact set \(\Omega\subset\mathbb{R}^{n}\) such that for any initial condition \(\ddot{q}(0)=[q(0),\dot{q}(0)]^{T}\), the solutions of the closed-loop system starting from \(\ddot{q}(0)\) exist for all \(t\geq 0\) and satisfy \(\|\ddot{q}(t)\|_{\Omega}\leq\beta(\|\ddot{q}(0)\|_{\Omega},t)\), where \(\beta\) is a class \(\mathcal{K}\mathcal{L}\) function and \(\|\bullet\|_{\Omega}\) denotes \(\|\bullet\|_{\Omega}=\inf\{\|\bullet-\zeta\|\,:\,\zeta\in\Omega\}\)._  
**Assumption 3**.: _No a priori knowledge regarding \(f(q,\dot{q})\) and \(g(q)\) is available besides the Lipschitz continuity._  
Our objective is to design an optimal (with respect to a state and input integral cost) control strategy within a compact workspace \(\Omega_{q}\times\Omega_{\dot{q}}\subset\mathbb{R}^{2n}\) that drives the system towards a fixed configuration \(\bar{q}_{d}\in\Omega_{\dot{q}}\) with predefined transient and steady-state performance (i.e., minimum convergence rate and steady-state error). Before we proceed with the formulation of the optimal control problem, we first give a brief presentation of the PPC technique.
## 6 Results and Discussion
### Test Using UNSW2018  
The model was tested to observe whether it could detect DDoS attacks from the UNSW2018 dataset [17]. The input parameters comprise 100,000 for regular traffic and 100,000 for DDoS attacks, in which each involves a 100,000 flow status interval. The data were split into 80% for training and 20% for testing. Attacks were labeled with 1 and 0 for the regular traffic, and the model was trained for 10 epochs (Table 6; Figure 6).  
Similar to the previous dataset, the single autoencoder model was less accurate in both training and validation, achieving 67.02% for training and 62.11% for validation. The CNN-autoencoder model suffered from overshooting, in which the training accuracy reached a high of 95.54%, while the validation accuracy was around 61.14%. The BDLSTM-autoencoder model achieved the highest result, reaching a high of 99.95% in training and 99.94% in validation, in which the two are very close. Based on these results, we selected the BDLSTM-autoencoder model as the primary DDoS classifier, subject to more testing in the future.  
Figure 5: Training and validation accuracy for DDoS detection of the BDLSTM-autoencoder model using different activation functions at output layer (**a**) sigmoid, (**b**) RelU, (**c**) SoftMax, and (**d**) tanh for 80-10-10 splitting.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Loss** & **Accuracy** & **Val. Loss** & **Val. Accuracy** \\ \hline ANN-Autoencoder & 0.5746 & 0.6453 & 0.5787 & 0.6512 \\ CNN-Autoencoder & 0.1338 & 0.9611 & 0.0880 & 0.986 \\ BDLSTM-Autoencoder & 0.0020 & 0.9995 & \(4.9197\times 10^{-4}\) & 0.9994 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of tests using the UNSW2018 dataset for three models.  
Figure 6: Accuracy results for (**a**) the CNN-autoencoder model, and (**b**) the BDLSTM-autoencoder model.
# A Novel Non-Coherent \(Q\)-Ary TH-PPM Transceiver
## 3 Performance Analysis  
In this section, the BER performance, both of the conventional TH-PPM transceiver and the new TH-PPM transceiver, under ideal synchronization conditions are analyzed first. Then the BER performance under non-ideal synchronization conditions is proposed for constant time offset and jitter, respectively. Furthermore, the BER performance with a change in the shape of the pulse is analyzed.
## 3 Results and Discussion
### Negative Differential Resistance
Figure 4: (**a**) Current density—Voltage (J-V) characteristics of a monolayer Alq\({}_{3}\):5% [Pt] based OLED. (**b**) Band diagram of a monolayer Alq\({}_{3}\):5% [Pt] based OLED in OFF-state (**i**) and ON-State (**ii**). In OFF-state, under negative applied voltages, electron transport is injection limited by energetic barrrier (\(\varphi_{\text{e}}\)). In ON-state, negative charges are trapped on [Pt] sites. Compensated positive charges are created in the vicinity of the interface with the anode leading band bending (\(\Delta\varphi_{\text{e}}\)) of energy profile (i.e., a decrease in electron injection barrier from anode to Alq\({}_{3}\) layer). Under negative applied voltage, the band bending would lead to enhanced electron injection from anode to Alq\({}_{3}\) layer. The monolayer devices displayed poor luminance performance (below 10 Cd/m\({}^{2}\)) compared to bilayer structures with the NPD layer (inset of Figure 5). Bilayer devices with 10 nm and 55 nm thicknesses of NPD exhibited a luminance of 15 Cd/m\({}^{2}\) and 35 Cd/m\({}^{2}\) at 14 V respectively. The intensity of NDR is decreasing with the enhancement of NPD thickness (Figure 5). For monolayer devices, the stored charges on trapping states lead to the modification of both charge injection and conduction. For bilayer devices, NPD acts as electron blocking layer due to its lower value of LUMO level [29] and Alq\({}_{3}\) as electron transport layer [30]. It can be inferred that the thin layer of NPD limits electron extraction from Alq\({}_{3}\) to the anode and promotes hole injection. Consequently, the amount of stored negative charges is reduced due to hole injection leading to the decrease in NDR peak. That is confirmed by the enhancement in luminance of devices with NPD layer (i.e., recombination of trapped electrons with holes). The NDR mechanism in organic devices is still discussed and depends on the device structures and/or the nature of the contacts [15; 21; 31]. Further electrical analyses are needed to identify clearly the main physical mechanism responsible for the NDR characteristic of [Pt] based devices. Nonetheless, the measurements reported in this work suggests that the NDR behavior in Alq\({}_{3}\):5% [Pt] could be related to electron accumulation within Alq\({}_{3}\) layer. Preliminary measurements have been carried out in order to evaluate retention time of space-charges (Figure S4 of Supplementary Information). It is observed that there is no obvious degradation for both the J\({}_{\text{ON}}\) and J\({}_{\text{OFF}}\) in the retention time of 1 \(\times\) 10\({}^{4}\) s, indicating the potential application in resistive memory device. Although their luminance performance should be improved, the electrical switching behavior of this type of OLEDs could likely open the development of potential applications such as light memory (which would combine the dual functions of the organic light emitting diodes and the organic bistable memory devices).
# An Adaptive Ensemble Approach to Ambient Intelligence Assisted People Search
## 4 Evaluation with Benchmark Databases
### Classifier Combinations  
Given a face photo of target person, we combined the classifiers by adjusting the mixture matrix to combine classifiers, as shown in Equation (14). The only unknown value was the prior belief \(p(w_{nm})\) for each classifier. The global classifier was trained on a large LFW database with 5400 samples. The LFW database also provided another 600 samples for verification. We took the recognition rate as the prior belief \(p(w_{nm})\) for each global classifier.  
The local classifier was trained on a small YaleB database. We used the same database for training and verification, and we took the recognition rate as the prior belief \(p(w_{nm})\) for each local classifier. The local classifier usually obtained a higher prior belief \(p(w_{nm})\) value than the global one.  
The mixture matrix was adjusted for different target people, to highlight the important face features. In a linear classifier (Equation (10), the important features should receive a large weight value \(w_{n}\). It was easier to understand our idea, if we drew all of the facial features on a figure. The 3 DMM face features contain face shape information in a matrix of 24,273 \(\times\) 3; each column is a triple-element in the XYZ format. If this shape information is used to train a linear classifier, it can obtain a 24,273 \(\times\) 3 weight value \(w_{n}\). With all of the \(w_{n}\) drawn onto a figure, the result looks like a face mask, as in Figure 4.  
Figure 4: Examples of classifier combination.  
Figure 4 shows the strategy of the classifier to distinguish targets from 28 YaleB people. The blue areas are the positions of the largest (top 10%) positive weight values \(w_{n}\), and the red areas are the positions of the smallest negative values \(w_{n}\). The figure shows that Global SVM extracted more large values of \(w_{n}\) on the eyebrow area, whilst the local SVM focused on the jaw area. It is interesting to note that the local SVM picked up many points in the nose wing area, while the global SVM nearly completely ignored that area.  
Our method adaptively used different linear classifiers for different targets when combining the global SVM and local SVM classifiers. As shown in Figure 4, the nose wing area was selected by our method for the target boy \"YaleB33\", as this was an apparent feature for recognizing this person. However, for the case of the target girl \"[PERSON]\", nose wing area should not be considered as an apparent feature. This was also consistent with the strategy that is normally adopted by a human, i.e., a focus on more apparent features such as a tall nose bridge for recognizing the target boy \"[PERSON]\", but not the girl \"[PERSON]\".
# A Robust Intelligent Controller for Autonomous Ground Vehicle Longitudinal Dynamics
## 2 Vehicle Modeling
### Longitudinal Aerodynamic Drag Force  
The following expression describes the aerodynamic drag force:  
\[\mathfrak{f}_{zero}=\frac{1}{2}\rho c_{ad}\mathcal{A}_{f}(v_{w}\pm v_{x})^{2} \tag{2}\]  
where \(\rho\) is the air density, \(c_{ad}\) is the coefficient of aerodynamic drag, \(\mathcal{A}_{f}\) is the area of the vehicle's front surface, \(v_{w}\) is the wind velocity, and \(v_{x}\) denotes the vehicle's longitudinal velocity.
# Internet of Things Geosensor Network for Cost-Effective Landslide Early Warning Systems
## 2 Landslide Early Warning Systems
### The Internet of Things and Landslide Monitoring
They have been in use for rockfall monitoring [43; 58; 59], but also for shallow rotational landslides. [PERSON] et al. (2018) developed a MEMS-based subsurface sensor with a volumetric water content sensor and a tiltmeter [60]. This concept, which is based on that proposed by [PERSON] et al. (2010) [61] has also been applied for other LEWS/sites [52; 54; 62].
# Internal Flow Prediction in Arbitrary Shaped Channel Using Stream-Wise Bidirectional LSTM
## 2 Methodology
### Network with Stream-Wise Bidirectional LSTM  
The integration of the SB-LSTM module in the network is explained using the example of a module with SB-LSTM applied to U-Net [27], used later in the experiments. The structural overview of U-Net with SB-LSTM is shown in Figure 7. In the middle part of the structure, where encoding ends, the latent space not only serves as an input for X2 Nearest Neighbor interpolation, indicated by the blue arrow, for the first decoding process, but also acts as an input for SB-LSTM to extract additional lateral connection information. To adjust the size for application in the network, \(1\times 1\) convolution and nearest neighbor interpolation have been added to the path for SB-LSTM. The added lateral information is concatenated with the latent space and used as an input for the convolution operation in the decoding process, along with the latent space of the original path. The detailed process of the operation of this network is described below.  
channels is reduced to 32 using a \(1\times 1\) convolutional layer and the compressed information is fed into the SB-LSTM module. After receiving the geometrical connection features from the SB-LSTM module, the channels are decreased to 256 using a \(1\times 1\) convolutional layer. The height and width are then upsampled using nearest neighbor interpolation with a scale factor of two. We have selected nearest neighbor interpolation to utilize the relations of adjacent pixels for a continuous upsampling process. The feature map shortly before the SB-LSTM module is also upsampled and both outputs are concatenated channel-wise. Moreover, the feature map from the encoder with the same spatial size is also aggregated to provide channel shape information. Two \(3\times 3\) convolutional layers with ReLU are then sequentially used for feature extraction and channel size reduction. This mechanism is applied repeatedly until the shape of the final output becomes \(192\times 192\times 32\). Finally, a \(3\times 3\) convolutional layer is used to adjust the number of channels from 32 to 1. To predict \(u\), \(v\), and \(P\), three decoders with the same architecture are employed, all sharing the features extracted by the SB-LSTM module.
## 2 Results and Discussion  
### Plant Material and Extraction  
To obtain an extract rich in cannabinoids and terpen, it was crucial to analyze the effects of extraction parameters such as the type of organic solvent, temperature, time, and pressure on the composition of the products isolated in the process. For the extraction of cannabinoids and terpenes from the Futura 75 hemp variety, five types of organic solvents (acetone, methanol, ethanol, isopropanol, hexane) were used, along with two binary solvent mixtures hexane/isopropanol (7:3, \(v/v\)) and hexane/ethanol (7:3, \(v/v\)). These binary systems were selected based on literature data, which indicated that a combination of nonpolar and polar solvents could improve the efficiency of cannabinoid and terpene extraction [29]. Processes of extractions were performed using two methods: pressurized extraction and magnetic stirring at temperatures of 25 \({}^{\circ}\)C and \(-\)55 \({}^{\circ}\)C. [PERSON] and colleagues demonstrated that ethanol extraction at low temperatures ranging from 0 \({}^{\circ}\)C to \(-\)80 \({}^{\circ}\)C enables the isolation of cannabinoids and terpenoids while reducing the presence of waves and chlorophyll [30].  
The results showed that the highest yield for pressurized extraction (0.46 g) was achieved using methanol as the solvent at 25 \({}^{\circ}\)C and a pressure of 2 bar. These findings align with literature data indicating that the methanol-based extraction of bioactive compounds from hemp yields higher efficiencies compared to ethanol and other organic solvents [27] (Table S1, Supplementary Materials). Contrary to initial expectations, the use of binary solvent systems did not significantly enhance the extract yield, as shown in Table S1 (Supplementary Materials). Furthermore, applying 2 bar of pressure during extractions with acetone, hexane, and binary solvent mixtures resulted in lower yields compared to processes conducted at 1 bar.  
In the second experimental approach, the extraction process was conducted using a magnetic stirrer (180 rpm) for 10, 20, and 40 min at temperatures of 25 \({}^{\circ}\)C and \(-\)55 \({}^{\circ}\)C, using the same organic solvents as in the pressurized method. The results are summarized in Table S2 (Supplementary Materials). Similar to the pressurized method, the highest extraction yield was achieved with methanol at 25 \({}^{\circ}\)C, and the yield increased with longer extraction times.  
The extracts obtained from both methods were subjected to a winterization process to remove waxes (Section 3.2.2). Based on the results (Tables S1 and S2, Supplementary Materials), it was observed that, regardless of the extraction method, the largest mass reduction of 48% after winterization was recorded for extracts obtained using hexane at 25 \({}^{\circ}\)C. These findings confirm that decreasing the polarity of the organic solvents used in heme extraction increases the wax content in the extracts, which is subsequently removed during the winterization process [31].
###### Abstract  
The state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are critical indicators for assessing battery reliability and safety management. However, these two indicators are difficult to measure directly, posing a challenge to ensure safe and stable battery operation. This paper proposes a method for estimating SOI and predicting RUL of lithium-ion batteries by charging feature extraction and ridge regression. First, three sets of health feature parameters are extracted from the charging voltage curve. The relationship between these health features and maximum battery capacity is quantitatively evaluated using the correlation analysis method. Then, the ridge regression method is employed to establish the battery aging model and estimate SOI. Meanwhile, a multiscale prediction model is developed to predict changes in health features as the number of charge-discharge cycles increases, combining with the battery aging model to perform multistep SOH estimation for predicting RUL. Finally, the accuracy and adaptability of the proposed method are confirmed by two battery datasets obtained from varying operating conditions. Experimental results demonstrate that the prediction curves can approximate the real values closely, the mean absolute error (MAE) and root mean square error (RMSE) calculations of SOH remain below 0.02, and the maximum absolute error (AE) of RUL is no more than two cycles.  
lithium-ion batteries; state of health; remaining useful life; charging feature extraction; ridge regression; maximum battery capacity; aging model; multiscale prediction model +
Footnote †: journal: Applied Sciences