esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Collection and Analysis of Volatile Organic Compounds (VOCs)  
Flower volatiles were collected and analyzed as described in [PERSON] et al. [16]. Flower material (0.3 g) excised from a given host plant was kept in a glass bottle (200 mL) for 2 h prior to capturing the volatiles emitted using a solid-phase microextraction fiber (a \(\sim\)50/30 \(\upmu\)m DVB/CAR/PDMS StableFlex fiber). Volatiles were extracted for 40 min at 80 \({}^{\circ}\)C then the fiber head was quickly removed. The collected volatiles were analyzed by gas chromatography mass spectrometry (GC-MS) (HP6890/5975C, Agilent Technologies, CA, USA). The chromatographic column was a ZB-5 MSI 5% phenyl-95% dimethylpolysiloxane elastic quartz capillary vessel column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\upmu\)m). The gas chromatograph was operated at an initial temperature of 40 \({}^{\circ}\)C for 2 min then increased at 5 \({}^{\circ}\)C/min to 255 \({}^{\circ}\)C, which was maintained for 2 min. To identify compounds, we compared the mass spectra of compounds with those in databases ([PERSON] 2005 and [PERSON] 275) and their constituents were confirmed through conijections with authentic standards.  
Behavioural Responses of T. hawaiiensis to the Main G. jasminoides Volatile Organic Compounds (VOCs)  
The VOC mixture from _G. jasminoides_ was the most attractive to _T. hawaiiensis_ as assessed by the Y-tube olfactometer bioassays. Since linalool, (Z)-3-hexenyl tglate and (E3,E7)-4,8,12-trimethyltrideca-1,3,7,11-tetraene were the most abundant compounds identified in the VOC profile of _G. jasminoides_, the behavioral responses of _T. hawaiiensis_ to these compounds were tested further in six-arm and Y-tube olfactometer bioassays.
## 4 Results
### Baselining Methods
#### 4.4.4 Method IV  
The new method IV, proposed by the authors, combines the historical data used in method III, with historical and actual irradiance measurements. As a result, the baseline is better able to follow the pattern of the reference profile. However, from Figure 13, it can be observed that this is typically with a downward bias in case curtailment option 1 is applied. Compared with methods I, II and III, our new method shows an improvement in accuracy for all curtailment options. For curtailment option 2a and 2b, the profile has the highest accuracy, with a mean absolute error of maximum 4.27 W in the first week of August. Furthermore, the historical flexibility activations introduced in curtailment option 2b have less impact on the outcome of method IV compared to method III.
# Deep Reinforcement Learning-Based Deployment Method for Emergency Communication Network
## 2 Relevant Work
### Heuristic Deployment Method  
To address hub location challenges in complex scenarios, researchers widely employ heuristic and metaheuristic algorithms to approximate optimal solutions. These methods transform the problem into a combinatorial optimization framework through mathematical modeling, designing iterative search strategies based on objective functions and constraints.  
[PERSON] et al. [13] propose stratified single-allocation hub location problems with various extensions and develop an efficient neighborhood-search algorithm to solve them, demonstrating the importance of considering demand stratification in hub-and-spoke network design. [PERSON] et al. [14] study the multi-allocation hub-and-spoke network design problem for flight flow routing (HURO) aiming to maximize airline profit utility and propose a two-level algorithm framework with a constant approximation ratio and approximation algorithms for related variants. Simulation and field experiments demonstrate the effectiveness of the proposed methods. [PERSON] et al. [15] employed the greedy algorithm to solve the hub location problem. They dynamically adjusted the hub positions through phased decision-making to maximize the coverage of demands. The core of their approach lies in integrating two-stage planning with hub movement strategies and optimizing the Q value calculation by efficiently solving the maximum flow problem.  
Heuristic methods, with their global search capabilities and convergence guarantees, have become dominant for large-scale hub location problems. However, traditional heuristic algorithms suffer from poor scalability and insufficient real-time performance, limiting their applicability to offline policy generation in dynamic environments.
## 3 Design Parameters
### Design Parameters for MR Shock Absorber  
In this work, the design parameters of an MR shock absorber are chosen on the basis of the lightweight landing gear system of a Beechcraft Baron B55 by considering the weight of 230 kg for each main landing gear. It is noted here that the original weight of the Beechcraft Baron B55 is 680 kg for each main landing gear. Hence, the design parameters of the prototype are appropriately adjusted, and the pressure drops are confirmed using the proposed design model. Table 1 provides the detailed design specifications of the MR shock absorber designed and manufactured in this work. It is noted that the optimization is performed on the design variables for the MR core to set the parameters using the equations in [22]. Based on the magnetic field analysis results and design parameters, the controllable forces are calculated as 0.9025 kN, 1.661 kN, and 2.095 at the input current of 0.5 A, 1.0 A, and 1.5 A, respectively, based on a stroke velocity of 2 m/s.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Symbol** & **Value** & **Unit** \\ \hline Orifice diameter & \(D_{o}\) & 43.75 & mm \\ Orifice gap & \(t_{o}\) & 2.450 & mm \\ Bypass diameter & \(D_{b}\) & 54.60 & mm \\ Bypass gap & \(t_{b}\) & 2.400 & mm \\ Length of the orifice and bypass & \(L\) & 130.0 & mm \\ Entry length & \(L_{e}\) & 85.00 & mm \\ Length of the pole 1 & \(L_{p,1}\) & 5.500 & mm \\ Length of the pole 2 & \(L_{p,2}\) & 11.00 & mm \\ Length of the pole 3 & \(L_{p,3}\) & 11.00 & mm \\ Length of the pole 4 & \(L_{p,A}\) & 5.500 & mm \\ Wire diameter of the solenoid coil & \(d_{coil}\) & 0.361 & mm \\ Inner diameter of the solenoid coil & \(D_{coil}\) & 20.00 & mm \\ Height of the solenoid coil & \(h_{coil}\) & 4.000 & mm \\ Number of turns of the solenoid coil & \(N_{turn}\) & 300 & - \\ Inner diameter of the main strut & \(D_{1}\) & 57.00 & mm \\ Outer diameter of the piston & \(D_{2}\) & 50.70 & mm \\ Viscosity of MR fluid & \(\mu\) & 0.290 & Pa-s \\ Density of MR fluid & \(\rho\) & 3.510 & g/cm\({}^{3}\) \\ Initial yield stress of MR fluid & \(\tau_{0}\) & 166.3 & Pa \\ Product of friction factor and Reynolds number for the orifice & \(f\cdot Re\) & 95.98 & - \\ Total loss coefficient for the orifice, compression & \(\Sigma k_{o,c}\) & 2.779 & - \\ Total loss coefficient for the orifice, rebound & \(\Sigma k_{o,r}\) & 3.233 & - \\ Total loss coefficient for the bypass, rebound & \(\Sigma k_{b,r}\) & 14.25 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications of MR Shock absorber.  
Figure 5: Pressure result calculated with CFD tool, ANSYS Fluent: **(a)** pressure change along the \(z\)-axis, compression condition, flow through the orifice, and comparison with major pressure loss; **(b)** pressure change along the \(z\)-axis, rebound condition, flow through orifice, and bypass simultaneously.
# DLSTM-Based Successive Cancellation Flipping Decoder for Short Polar Codes
## 3 The Proposed SC Flipping Algorithm
### Analysis of Error Propagation  
In SC decoding, a polar code that is decided to be incorrect has multiple error bits. Figure 5 shows that the error bits caused by channel noise are much smaller than the error bits caused by error propagation. However, error propagation affects the BER, not the BLER. To improve the block error-correction performance, the error bits caused by the channel noise should be flipped. As observed in Figure 5, most of the error decisions are caused by the error propagation of the first error bit, thus it is required to construct a flipping set efficiently.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Challenges of Implementing Energy-Aware Security  
The implementation of energy-aware security in IoT systems is facing several challenges. First, resource constraints have emerged as a primary concern. For example, security algorithms running on IoT devices must be energy-efficient, since IoT devices typically are equipped with relatively limited computing power and storage capacity. Second, the heterogeneity discussed above poses a challenge to applying targeted security policies to an entire IoT system consisting of various devices of different functionality. Third, environmental dynamics and uncertainty compel IoT devices to operate in unpredictable and harsh environments, which require security mechanisms to be capable of securing devices under diverse conditions. Fourth, modern IoT systems must feature real-time monitoring and response, as well as quick adaption to cope with evolving cyber-threats. Therefore, gaining in-depth insights into these challenges for implementing energy-aware security mechanisms in IoT systems is desired to provide guidelines for further growth.
###### Abstract  
This paper carries out a numerical simulation investigating three different modulation formats: carrier suppressed return to zero (CSRZ), modified duobinary return to zero (MDRZ) and return to zero (RZ). The purpose of this investigation is to find the optimum modulation format for the hybrid optical code division multiple accesses-dense wavelength division multiplexing (optical CDMA/DWDM) system with the implementation of an electro-optic phase modulator (EOPM) at a data rate of 40 Gbps per channel, with a transmitted power of 22 dBm and transmission distance of 105.075 km. The results revealed that CSRZ was superior to MDRZ and RZ and was more tolerant to optical fiber nonlinearity. Furthermore, unlike the DWDM systems, the performance of the proposed hybrid system based RZ format was better than the performance of MDRZ. Hence, the CSRZ modulation format is the best candidate for the optical CDMA/DWDM with EOPM module due to its high performance.  
optical CDMA; DWDM; RZ; MDRZ; CSRZ; FWM; ISI; EOPM Article Performance Comparison of Different Modulation Formats for a 40 Gbps Hybrid Optical CDMA/DWDM System against ISI and FWM  
1 School of Engineering, University of Tasmania, Hobart, TAS 7001, Australia1  
2 Faculty of Engineering Technology and Science, Higher Colleges of Technology, Madinar Zayed Campus, Abu Dhabi P.O. Box 25026, United Arab Emirates2  
3 Faculty of Engineering, Multimedia University, Cyberajaya 63100, Malaysia3  
Correspondence: [EMAIL_ADDRESS] (N.A.); [EMAIL_ADDRESS] (T.E.)3
## 3 Cutting-Edge Online Fault Diagnosis Techniques for Al-Caps
### Data-Based Online Fault Diagnosis Techniques (MB-ON-FDTs)
Therefore, the kurtosis, skewness, standard deviation (STD), root mean square (RMS), energy entropy (EE), and crest factor (CF) are extracted from the fourth wavelet coefficient. The authors frame the problem as a classification task, with the models being trained to distinguish between five states: healthy, aged, critical, very critical, and high alarm. In [50], the authors propose a method that integrates a SVM classifier with time-frequency analysis of conducted EMI to assess the condition of DC-link capacitors in a three-phase inverter. The EMI signals are analyzed during switching events using the continuous wavelet transform (CWT), which generates distinctive switching patterns. An analytic Morse wavelet is used for the analysis, and is configured with a symmetry parameter of 3, a time-bandwidth product of 60, and 10 voices per octave. The extracted patterns serve as input features for training a SVM classifier, which enables it to categorize the capacitor's condition into five severity levels. In [51], the authors propose a random forest classifier (RFC) that triggers an alert when the ESR of the output filter capacitor in a boost converter exceeds a predefined threshold. The input features include the capacitor voltage amplitude at the converter switching frequency, extracted using the STLSP algorithm, along with the load resistance and duty cycle. The RFC is trained to classify the condition as either a healthy al-cap or an aged al-cap. In [52], the authors use a logistic regression (LR) model to assess the condition of the output filter capacitor in a 3 dIBC. The capacitor current is reconstructed using the control signal and inductor currents. The raw data are processed through a bank of four filters to isolate the four most significant spectral components. Features are then extracted from these components using an RMS filter. Finally, these features are fed into the LR model, which, after training, classifies the capacitor as either a healthy al-cap or an aged al-cap.
## 4 Results and Discussion
### Influence of Plasma-Treated SiO\({}_{2}\) Nanofilers on Silicone-Rubber Nanocomite
#### 4.2.1 Electrical Tree Parameters
The plasma treatment improved the uniformity of the treatment and resulted in less agglomeration, which may have been due to the repelling effect of the nanoparticles. In general, when a plasma discharge is applied to nanoparticles, ions and electrons will be generated. The electrons become attached to the nanoparticles and repel one another due to electrostatic force. The nanoparticles become separated by strong intermolecular force, which prevents the formation of agglomeration and the attraction of the nanoparticles [10]. In addition, the improvement in dispersion in our study can be attributed to the surface modification of the nanoparticles. The plasma-treated nanoparticles improved the oxygen atom content due to the plasma treatment at the atmospheric-pressure condition. The high concentration of oxygen on the surface of the nanofiller resulted in a hydrophilic characteristic, which led to higher compatibility when mixed with the silicone-rubber matrix. This resulted in better adhesion of the nanofiller-polymer interfaces.
# Effects of the 12 May 2021 Geomagnetic Storm on Georeferencing Precision
## 1 Introduction
The only study of the positioning error that considers a moderate geomagnetic storm was presented by [PERSON] et al. [57] (see Table 1). [PERSON] et al. analyzed three geomagnetic storms in Solar Cycle 24. For the analysis, they used 17 March 2017 as a reference day for all the analyzed storms, which is a quiet day (Kp = 1\({}^{+}\)). This work neither analyzes the conditions on the previous/posterior days for each storm nor removes the noise sources, such as other geophysical events (earthquakes) or interference from other radio emissions. In addition, in this work, the root means square (RMS) statistics per component (North, East, and Up) were obtained per latitude (high-, mid-, and low-). Thus, the reported 3D RMS was calculated with the components that combine different stations located at a similar latitude range. In this work, we present the positioning error analysis of the 12 May 2021 geomagnetic storm (Dst peak = \(-\)61 nT, Kp = 7, AE\(\sim\)1500 nT), which can be classified as moderate in terms of Dst but strong in terms of Kp. Although the study is at a global scale, unlike previous studies, we focus on the southern sector of America due to the large number of GNSS stations now available in this part of Latin America. We studied the spatial and temporal dependence of the higher errors estimated in the GNSS receivers for this storm. We also studied the time needed for some geodesic-quality GNSS stations to reduce the positioning error thanks to the algorithms that detect and correct the effects of TEC disturbances. By using three stations, one close to Madrid (Spain) and the other two in Chile (one close to Santiago and a second one 400 km south of it), we studied the effects on the positioning accuracy for two other moderate storms (27 March 2017 and 5 August 2019) to verify the methodology used in the 12 May 2021 study.
# A Novel Deep Learning Method for Predicting RNA-Protein Binding Sites
## 2 Materials and Methods
### The Convolutional Neural Network in HPNet  
CNNs are artificial neural networks that have been applied in many fields. Typically, a CNN consists of three layers, as shown in Figure 2: the convolutional layer, the pooling layer, and the fully connected layer. The function of the convolutional layer is to automatically extract features from the input using filters. A single convolutional layer may have poor feature extraction capabilities, while increasing the number of convolutional layers can make it possible to extract more complex features. The pooling layers mainly sample the features learned in the convolutional layers to emphasize the most critical features and prevent overfitting. The CNN adjusts the weight parameters in the model backward using gradient descent and improves its accuracy through iterative training.  
The CNN's input in this experiment was the RNA sequences' one-hot encoding matrix. The filters moved over the input matrix with a particular step size to perform the dot product operation. Padding had to be added to the input matrix to ensure that the output matrix was the same size as the input matrix during the convolution operation. Given an input matrix \(\mathrm{M}\in\mathbb{R}^{\mathrm{h}\times\mathrm{w}}\) and a filter \(\mathrm{F}\in\mathbb{R}^{\mathrm{n}\times\mathrm{m}}\), the output matrix has a size of \(\mathrm{X}\in\mathbb{R}^{(\mathrm{h}-\mathrm{n}+\mathrm{I})\times(\mathrm{w}- \mathrm{m}+1)}\).  
Figure 1: Overall structure diagram of HPNet architecture.  
Figure 2: Structure diagram of the convolutional neural network (CNN) model.  
The activation function is essential for a CNN to learn nonlinear features. Therefore, this research employed the rectified linear unit (ReLU) activation function, defined as follows:  
\[\text{ReLU(x)}=\left\{\begin{array}{l}0\text{ if x }<0\\ \text{x otherwise}\end{array}\right. \tag{3}\]  
Then, the output of the convolutional layer was fed into the pooling layer and filtered using a pooling operation to highlight the most critical features and prevent overfitting. There are two popular pooling techniques: max pooling, which selects the most significant value in the region to represent the area; and mean pooling, which determines the mean value for the part to serve as a representation. A sensible pooling operation can reduce the numbers of features and neurons and the computational burden. However, an oversampling area may result in information loss and a sharp feature reduction. In this experiment, mean pooling was used.  
Finally, the high-level features obtained from pooling were flattened and fed into the fully connected layer for prediction.
# Automated and Controlled System for Analysis of Residual Limbs Thermograms of Transtibial Amputees
## 3 Methodology
### Control Solution
#### 3.4.2 Tracking Trajectory Problem
: Consider the Lyapunov function candidate  
\[V_{c}=\frac{1}{2}\ln\left(\frac{\Delta^{+}}{\Delta^{+}-\Delta_{1}^{2}}\right)+ \frac{1}{2}\|\bar{\Delta}\|^{2}+\frac{1}{2}\|\bar{\Phi}\|^{2}. \tag{8}\]  
The full time derivative of Equation (8) is given by  
\[\dot{V}_{c}=\frac{\Delta_{1}\dot{\Delta}_{1}}{\Delta^{+}-\Delta_{1}^{2}}+\bar {\Delta}^{\top}\bar{\Delta}-\bar{\Phi}^{\top}\dot{\Phi}. \tag{9}\]  
Taking the derivatives \(\dot{\Delta}_{1}=\bar{\Delta}_{2}+\
u_{1}\) and \(\dot{\bar{\Delta}}=[\dot{\Delta}_{2},\dot{\bar{\Delta}}_{3}]\), we can express (9) as follows:  
\[\dot{V}_{c}=\frac{\Delta_{1}(\bar{\Delta}_{2}+\
u_{1})}{\Delta^{+}-\bar{ \Delta}_{1}^{2}}+\bar{\Delta}_{2}\big{(}\dot{\Delta}_{2}-\dot{\
u}_{1}\big{)} +\bar{\Delta}_{3}(\dot{q}_{3}-\dot{\
u}_{2})-\bar{\Phi}^{\top}H(q)\bar{\Delta} +\gamma_{\Phi}\bar{\Phi}^{\top}\dot{\Phi}. \tag{10}\]  
Considering \(q_{3}=\bar{\Delta}_{3}+\
u_{2}\) and substituting \(\dot{\Delta}_{2}\), \(\dot{q}_{3}\) from (5) and the controls (6) and (7) into (10) yields  
\[\dot{V}_{c}=-\gamma\frac{\Delta_{1}^{2}}{\Delta^{+}-\Delta_{1}^{2}}+\frac{ \Delta_{1}\bar{\Delta}_{2}}{\Delta^{+}-\Delta_{1}^{2}}\]  
Now, adding and subtracting \(\dot{\xi}_{v}\) and \(\dot{\xi}_{u}\) we have  
\[\dot{V}_{c}=-\gamma\frac{\Delta_{1}^{2}}{\Delta^{+}-\Delta_{1}^{2}}+\phi_{2} \bar{\Delta}_{2}\bar{\Delta}_{3}+\dot{\xi}_{v}-\gamma_{\Phi}\bar{\Delta}_{2}^ {2}+\bar{\Delta}_{2}\bar{\phi}_{1}\sin q_{1}+\phi_{2}N(\zeta_{v})\dot{\xi}_{v}\]  
Taking the following inequality, \(-\frac{\varphi}{\Gamma-\varphi}\leq-\ln\left(\frac{1}{\Gamma-\varphi}\right)\), which is valid \(\forall\varphi\in[0,1)\)[24], eliminating terms and considering that \(\dot{\Phi}=\Phi-\dot{\Phi}\), the derivative (11) satisfies the following inequality:  
\[\dot{V}_{c}\leq-\gamma\ln\left(\frac{\Delta^{+}}{\Delta^{+}- \Delta_{1}^{2}}\right)+\dot{\xi}_{v}-\gamma_{\Phi}\bar{\Delta}_{2}^{2}+\phi_{ 2}N(\zeta_{v})\dot{\xi}_{v} \tag{12}\] \[+\phi_{5}N(\zeta_{u})\dot{\xi}_{u}+\dot{\xi}_{u}-(\gamma+(\phi_{2 }^{+})^{2}/2)\bar{\Delta}_{3}^{2}-\gamma_{\Phi}\bar{\Phi}^{\top}\dot{\Phi}+ \gamma_{\Phi}\bar{\Phi}^{\top}\Phi+\phi_{2}\bar{\Delta}_{2}\bar{\Delta}_{3}.\]  
Applying [PERSON]'s inequality for the last two elements in (12) and considering the value for \(\gamma_{\Phi}\), such that  
\[\dot{V}_{c}\leq\gamma\left(\ln\left(\frac{\Delta^{+}}{\Delta^{+}-\Delta_{1}^{2 }}\right)+\|\bar{\Delta}\|^{2}+\|\bar{\Phi}\|^{2}\right)+\dot{\xi}_{v}+\varphi _{2}N(\zeta_{v})\dot{\xi}_{v}\]  
Then \(\dot{V}_{c}\leq-2\gamma V_{c}+\beta\),  
with \(\beta=(\phi_{2}N(\zeta_{v})+1)\dot{\xi}_{v}+(\phi_{5}N(\zeta_{u})+1)\dot{\xi}_ {u}+3\gamma_{\Phi}^{2}\max_{k=1,3,4}\{(\phi_{k}^{+})^{2}\}\). Finally, multiplying by \(e^{2\gamma t}\) both sides and integrating over \((0,t)\), yields to  
\[V(t)\leq V(0)e^{-2\gamma t}+\int_{0}^{t}\beta(\tau)e^{-2\gamma(t-\tau)}d\tau.\]Considering the definition of \(\beta\), Lemma 1, and [PERSON]'s Lemma [25], the proof is completed.
## 3 Case Study
### Additional Case  
To further test the stability of the proposed model, another group of data (data collection B) was used. These data were measured from the intersection entrance B of an arterial road in Chongqing (including 2016 samples), as shown in Figures 2 and 6. Table 3 provided the relevant information of them. For simplicity, only the error indexes are given in Table 4. The intuitively results are shown in Figure 7. From Table 4 and Figure 7, the main results we  
* TVF-EMD was better than EMD in dealing with data nonlinearity and non-stationary. The forecasting result proves that the forecasting accuracy of TVF-EMD based method was higher than EMD based method.
* The hybrid models could take advantage of the superiority each component model. The results display that the forecasting accuracy of the hybrid models was higher than that of the single models.
* The ARIMA model usually presented the high performance for the data with significant linear features. However, for short-term traffic volume data with high nonlinear characteristics, the LSSVM model may have better forecasting performance.