esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Analysis by LPM  
### Electromagnetic Analysis  
The electromagnetic modeling can be described by an equivalent circuit, which is demonstrated in Figure 1. By solving [PERSON]'s voltage and current laws, the flux density in the armature can be expressed as a dimension of the electromagnetic circuit and material property, which includes the B-H curve and coercive force. The B-H curve indicates that the permeability changes with flux density in the armature and magnet house. When the position of the armature end changes, the reluctance of the air gap between armature and permanent magnet is changed, which means the different position contributes to a different flux density and force on the armature. Consequently, the electromagnetic characteristic such as inductance and force factor are nonlinear. Hence, to handle the nonlinear characteristic property of the B-H curve, the under-relaxed Newton-Raphson method is adopted to solve the [PERSON]'s current and voltage laws [21; 22; 23].  
By post processing flux density, the cogging force, force factor, and inductance can be obtained which are depicted in Figure 2.  
\begin{table}
\begin{tabular}{c c c} \hline
**Domain** & **Proposed Method** & **Previous Method** \\ \hline Electromagnetic & LPM & FEM \\ Mechanical & LPM & FEM \\ Acoustic & LPM & BEM \\ \hline \end{tabular}
\end{table}
Table 1: Comparison of simulation methods in the paper.  
Figure 1: The equivalent circuit of the electromagnetic part.  
Figure 2: Electromagnetic parameters vs displacement.  
The cogging force, force factor, and inductance expressions are listed in the following equations:  
\[F_{cogging}(x)=2\mu_{0}A_{g}F_{M}^{2}\frac{\big{(}D_{eff2}+D_{eff1}+4D_{eff} \big{)}\big{(}D_{eff2}-D_{eff1}+2x\big{)}}{\big{[}\big{(}D_{eff1}-x\big{)}\big{(} D_{eff2}+x\big{)}+D_{eff}\big{(}D_{eff1}+D_{eff2}\big{)}\big{]}^{2}}\] \[L_{E}(x)=\mu_{0}A_{g}N^{2}\frac{\big{(}D_{eff2}+D_{eff1}\big{)}}{ \big{(}D_{eff1}-x\big{)}\big{(}D_{eff2}+x\big{)}+D_{eff}\big{(}D_{eff2}+D_{eff1 }\big{)}} \tag{1}\] \[Bl(x)=\frac{\mu_{0}A_{g}}{2}\frac{F_{M}\big{[}\big{(}D_{eff2}+D_ {eff1}+4D_{eff}\big{)}\big{(}D_{eff2}+D_{eff1}\big{)}+\big{(}D_{eff2}-D_{eff1} +2x\big{)}^{2}\big{]}}{\big{[}\big{(}D_{eff1}-x\big{)}\big{(}D_{eff2}+x\big{)} +D_{eff}\big{(}D_{eff2}+D_{eff1}\big{)}\big{]}^{2}}\]  
where  
\[D_{eff1}=\mu_{0}A_{g}(R_{M}+R_{H11})+D\] \[D_{eff2}=\mu_{0}A_{g}\Big{(}R_{M}+R_{H2}+\tfrac{1}{2}R_{H} \Big{)}+D\] \[D_{eff}=\mu_{0}A_{g}(R_{A}+R_{i}+R_{ii})\]  
where \(F_{cogging}\), \(Bl\) and \(L_{E}\) are cogging force, force factor, and voice coil inductance, respectively, which are expressed by the armature position \(x\). \(R_{A}\), \(R_{H}\) are the reluctance of the armature and magnet house. \(F_{M}\), \(A_{g}\), \(N\) and \(D\) are the magnetomotive force of the magnet, the area of the magnet, the coil turns, and the air gap width respectively.  
The mathematic equation for the electrical part is given in the following equation:  
\[Z(s)=R_{E}+sL_{E} \tag{2}\]  
where \(R_{E}\) is the electrical voice coil resistance at DC and \(L_{E}\) is the voice coil inductance.
## 2 Materials and Methods
### Analytical Methods  
The untargeted analyses of the volatile compounds were performed on an Agilent 7890A gas chromatograph connected to a 5975C mass spectrometer (Agilent Technologies, Palo Alto, CA, USA) with an electron ionization source (EI) set at 70 eV. Chromatographic separation was achieved on a Phenomenerex ZB5 capillary column (30 m \(\times\) 0.25 mm \(\times\) L.0.25 mm) coated with 5% phenylpolysiloxane at a constant He flow rate of 1.2 mL min\({}^{-1}\). The injector was maintained at 250 \({}^{\circ}\)C. The temperature program was set as follows: 50 \({}^{\circ}\)C kept for 1 min, ramp of 10 \({}^{\circ}\)C min\({}^{-1}\) up to 300 \({}^{\circ}\)C and kept constant for 10 min. The transfer line, ion source and quadrupole were held at 280 \({}^{\circ}\)C, 230 \({}^{\circ}\)C and 150 \({}^{\circ}\)C, respectively. The mass spectrometer was set in full-scan (SCAN) mode for non-target screening, and full-scan spectra were collected from 50 to 550 \(m/z\). The mass spectra obtained in full scan for each chromatographic signal were compared with those contained in the NIST2 library (Version 2.2, June 2014). The selected ion monitoring (SIM) mode was used for the semi-quantitative analysis of chosen compounds. For each analyte, two ions were monitored in SIM mode, using the most abundant one for quantitation.  
The presence of target emerging contaminants was evaluated by using liquid chromatography coupled to tandem mass spectrometry [20]. Chromatographic separations were performed by an Agilent Liquid Chromatograph Series 1200 SL. HPLC was equipped with a biphenyl Raptor column (2.1 mm \(\times\) 50 mm, 1.8 \(\upmu\)m by Restek, Milan, Italy) held at 60 \({}^{\circ}\)C. An Agilent 6430 MSD triple-quadrupole mass spectrometer (Agilent Technologies, Santa Clara, CA, USA) equipped with an electrospray ion source (ESI) was employed for detection and used both in positive and negative ionization modes. Multiple reaction monitoring (MRM) was set to enhance sensitivity by choosing two transitions for each analyte (Table S1), using the most abundant one for quantification.  
Positive acquisition involved a chromatographic run in isocratic conditions with a 0.2 mL min\({}^{-1}\) flow rate and mobile phase composition of Milli-Q water:ACN = 60:40 (_v/v_). As for the analytes acquired in the negative mode, separation was performed in isocratic conditions as well. The mobile phase was Milli-Q water:ACN = 50:50 (_v/v_) containing 0.1% of acetic acid, and the flow rate was of 0.3 mL min\({}^{-1}\). Three technical replicates were performed for each sample and the injection volume was of 10 \(\upmu\)L.  
Three points calibration curves were drawn for each analyte in the 0.01-10 \(\upmu\)g L\({}^{-1}\) range. The performances of the instrumental method are reported in Table 1. The LOD and LOQ were calculated by considering a signal to noise ratio of 3 and 10, respectively; dilution factor and final volume of the extracts were considered to obtain LOD and LOQ values in the POCIS sampler.
## 6 Conclusions  
The study proposes the SEB-A method, which is innovative in that it considers the horizontal advection when applying the energy balance theory to estimate ET through remote sensing. The advantage of the SEB-A method is that it simplifies the ET process and regards the ET as regulated by two different driving forces: the local driving force and the exotic driving force. The local driving force is generated by radiation effects and can be described by the energy balance equation, whereas the exotic driving force is caused by the horizontal advection and can be expressed indirectly through ground-based EC observations. The idea makes the ET retrieval much simpler for the exact air aerodynamic resistance and for the quantification of the exotic ET. Model application and validation indicated that ET estimates from the SEB-A method had high accuracy, with an R\({}^{2}\) of 0.713, a MAE of 39.3 W/m\({}^{2}\) and an RMSE of 54.6 W/m\({}^{2}\) between estimates and measurements. In addition, ET estimates had percent errors within 20% at more than 80% of the locations and could well demonstrate ET differences caused by heterogeneous underlying surfaces. The sensitivity results showed that the SEB-A method requested accurate estimation of \(Rn-G\), but it was insensitive to the difference between surface and air temperatures.  
Since the influence of horizontal advection on ET is ubiquitous in practice, it is necessary to consider the exotic ET caused by the horizontal advection. The SEB-A method puts forward the way of utilizing ground-based EC measurements to quantify the exotic ET, which is verified with high accuracy. If remote sensing data and ground-based measurements including meteorological variables and surface fluxes are available, the SEB-A method can perform well. However, the method cannot work well when the ground-based measurements is insufficient.  
**Acknowledgments:** This work is supported by the National Natural Science Foundation of China (grant numbers 41571356, 41271380, and 41171286) and the National Basic Research Program of China (grant number 2013 CB733406). The authors wish to thank Professor [PERSON] of the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences for his kind assistance in providing the experimental data of measuring the air aerodynamic resistance under the condition of no advection.  
**Author Contributions:** [PERSON] wrote the manuscript with contributions from all co-authors; [PERSON] supervised the work and revised the manuscript; [PERSON] proposed and developed the research design; [PERSON] and [PERSON] provided some useful advice for the study; and [PERSON] coordinated data acquisition and data processing.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Ransomware Dynamic Analysis Studies Utilizing both Machine Learning and Deep Learning
### Ransomware Counteraction
#### 6.2.3 Detection
There are still limitations attached to each work. The limitations of existing detection work are listed below:  
1. Most of the conducted studies for the ransomware detection fall under conventional class where ransomware is detected after the encryption starts. 2. In the literature there is not a single study that deals with population drift concept while considering the pre-encryption early detection. High number of irrelevant and redundant system calls used to bypass the detection. * Developed ransomware studies used different number of logs from different ransomware families. * The ransomware detection systems are platform dependent. A system developed for windows API cannot be implemented for cloud and mobile devices. * Ransomware detection study cannot detect the ransomware which encrypt data using its own native code. * Not all the detection studies available in the literature are practical to implement. Some of the presented studies are empirical or supplement detection systems. * Honeypot methods are not fully reliable as there is no guarantee the honeypot folders will always be accessed by the attack. * Analyzing the samples for limited or ample time made the detection studies inadequate to implement. * Dealing with little amount of data or massive data with high redundant values. * Some of the studies did not explain well about the analysis performed for the detection. * There are few studies that detected ransomware for the backup systems. More studies are needed in this domain. * Datasets used to train data are synthetic and are extracted from specific sources, i.e., pseudo real world events. A summary of conventional and early ransomware detection is given in Table 6. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & \multicolumn{5}{c}{**Detection Studies w.r.t. Pre and Post Encryption**} \\ \hline \multirow{2}{*}{**Sr#**} & \multirow{2}{*}{**Study**} & \multicolumn{5}{c}{**Conventional**} & \multicolumn{2}{c}{**Early/Pre-Encryption**} \\ \cline{3-8}  & & **ML** & **DL** & **ML and DL** & **ML** & **DL** & **ML and DL** \\ \hline
1 & [94] & ✓ & & & & \\ \hline
2 & [20] & ✓ & & & & \\ \hline
3 & [89] & & ✓ & & & \\ \hline
4 & DRTHIS & & & & & \\ \cline{2-8}  & [62] & & ✓ & & & \\ \hline
5 & RanSD & & & ✓ & & \\ \hline
6 & DeepRan & & & & & ✓ \\ \cline{2-8}  & [85] & & & & & ✓ \\ \hline
7 & [90] & & ✓ & & & \\ \hline
8 & RAPPER & & & & & \\ \cline{2-8}  & [81] & & ✓ & & & \\ \hline
9 & [119] & ✓ & & & & \\ \hline
10 & RANDS & & & & & \\ \cline{2-8}  & [96] & ✓ & & & & \\ \hline
11 & [97] & & & & ✓ & \\ \hline
12 & [7] & ✓ & & & & \\ \hline
13 & [126] & ✓ & & & & \\ \hline
14 & [98] & ✓ & & & & \\ \hline
15 & PEDA & & & & ✓ & \\ \cline{2-8}  & [79] & & & & & ✓ \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of Ransomware studies w.r.t Pre and Post Encryption.
## 3 OPDG Problem Formulation
### Objective Functions
#### 3.1.2 Minimization of Voltage Deviation  
The voltage deviation (VD) of the system is minimized based on this objective function so that the voltage profile is improved, which is given by  
\[OF_{2}:\min(\frac{VD}{VD_{b}}), \tag{14}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Function** & **Formula** &
\begin{tabular}{c} **Input** \\ **Domain** \\ \end{tabular} & **No. of Parameters** \\ \hline \(f_{1}(x)\)1 & \(-20\exp\!\left(-0.2\sqrt{\left\{\frac{1}{d}\sum\limits_{i=1}^{d}x_{i}^{2}\right\} -\exp\!\left(\frac{1}{d}\sum\limits_{i=1}^{d}\cos(2\pi x_{i})\right)+20+\epsilon}\) & [\(-32.768\),\(32.768\)] & 20 \\ \(f_{2}(x)^{2}\) & \(\sum\limits_{i=1}^{d}\frac{x_{i}^{2}}{\lambda 000}^{-d}\sum\limits_{i=1}^{d}\cos\left(\frac{x_{i}}{ \sqrt{i}}\right)+1\) & [\(-600\),\(600\)] & 20 \\ \(f_{3}(x)^{3}\) & \(10d+\sum\limits_{i=1}^{d}\!\left[x_{i}^{2}-10\cos(2\pi x_{i})\right]\) & [\(-5.12\),\(5.12\)] & 5 \\ \(f_{4}(x)^{4}\) & \(\sin^{2}(\pi w_{1})+\sum\limits_{i=1}^{d-1}\left(w_{i}-1\right)^{2}\!\left[1+ 10\sin^{2}(\pi w_{i}+1)\right]+\) & [\(-10\),\(10\)] & 20 \\  & \((w_{d}-1)^{2}\!\left[1+\sin^{2}(2\pi w_{d})\right]\) & & \\ \(f_{5}(x)^{5}\) & \(\sum\limits_{i=1}^{d}\left(\sum\limits_{j=1}^{d}\left(j+\beta\right)\left(x_{j }^{i}-\frac{1}{j}\right)\right)^{2}\) & [\(-\)d,d] & 5 \\ \(f_{6}(x)^{6}\) & \(\sum\limits_{i=1}^{d}ix_{i}^{2}\) & [\(-10\),\(10\)] & 30 \\ \(f_{7}(x)^{7}\) & \(\sum\limits_{i=1}^{d}\sum\limits_{j=1}^{i}x_{j}^{2}\) & [\(-65.54\),\(65.54\)] & 20 \\ \(f_{8}(x)^{8}\) & \(\sum\limits_{i=1}^{d}\left[\left(\sum\limits_{j=1}^{d}x_{j}^{i}\right)-b_{i} \right]^{2}\) & [0,d] & 4 \\ \(f_{9}(x)^{9}\) & \(\sum\limits_{i=1}^{d-1}\!\left[100(x_{i+1}-x_{i}^{2})^{2}+(x_{i}-1)^{2}\right]\) & [\(-5\),\(10\)] & 4 \\ \(f_{10}(x)^{10}\) & \((x_{1}-1)^{2}+\sum\limits_{i=2}^{d}i(2x_{i}^{2}-x_{i-1})^{2}\) & [\(-10\),\(10\)] & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Benchmark functions.  
where \(VD_{b}\) is the voltage deviation of the base-case. The VD is formulated as  
\[VD=\sum_{i=1}^{n}{\left({V_{i}-V_{r}}\right)^{2}}, \tag{15}\]  
where \(n\) denotes the number of buses in the system, \(V_{i}\) is the voltage magnitude at bus \(i\), and \(V_{r}\) is the rated voltage which is equal to 1.0 p.u. [24].
# A Safety Prediction System for Lunar Orbit Rendezvous and Docking Mission
## 2 Mission Objectives and Characteristics  
### Mission Objectives  
During rendezvous and docking in the lunar orbit, the navigation surveillance camera configured on the chaser is used to monitor and observe the rendezvous and docking process of the two spacecraft. After entering the phase of ground measurement and control, we use surveillance cameras to perform target imaging, feature points extraction, tracking and matching and safety prediction. To accomplish the mission, the following problems need to be solved:  
1. Image feature detection and matching in uncertain environments. It needs to use image information to capture the target and extract related feature points, and further calculate the relative position and attitude of the two spacecraft;
2. Safety prediction and judgment of the docking process. It needs to construct a knowledge base for safety judgments, predict the possible docking process in the current pose and evaluate the probability of successful docking;
3. Visualized data to assist flight controllers in carrying out status analysis and mission decision-making. It is not only necessary to output the rendezvous safety judgment result, but also to visually display the safety judgment basis and criteria.  
Figure 1: Autonomous rendezvous and docking system.
# The Impact of Contact Force on Signal Quality Indices in Photoplethysmography Measurements
###### Abstract
However, the signals obtained in reflectance mode are more susceptible to motion artifacts [24], variations in contact force (CF) [25; 26; 27], and other factors that affect the signal quality compared to transmission mode [28; 29]. The CF applied between the probe and the skin significantly influences the quality of the recorded signals. Fluctuations in CF can cause signal distortions, especially in portable devices, where maintaining a constant pressure is challenging. The common artifacts related to CF include variations in signal amplitude and morphology due to inconsistent contact between the sensor and the skin [25; 27; 30; 31; 32]. An excessive CF can compress blood vessels, reducing the amplitude of the PPG signal, while an insufficient CF can result in poor sensor contact and increased noise [30; 33; 34]. Maintaining an optimal CF ensures signal stability and reliability for accurate HR measurements, oxygen saturation (SpO\({}_{2}\)), and other physiological variables. This work aims to evaluate the impact of CF on the signal quality index (SQI) in PPG measurements. It extends the results presented in [35], which explored the relationship between CF variations, tissue mechanics, and multi-wavelength PPG (MW-PPG) signal evolution, highlighting the influence of tissue viscoelastic characteristics on signal morphology. In contrast, the present work evaluates the impact of CF on six statistical SQI metrics. Figure 1: LED and PD placement for transmission and reflectance modes PPG. The left image illustrates the transmission mode, and the right image depicts the reflectance mode, both using a typical configuration with two LEDs (red and infrared) commonly applied in PPG applications. By identifying the optimal CF ranges based on these metrics, this research enhances the accuracy and reliability of PPG-based diagnostic methods, providing a more comprehensive and applicable view for the clinical field. The primary contributions of this work are as follows:  
1. The identification of the impact of CF variations on the SQIs in PPG signals. 2. The identification of the optimal CF ranges through normalization. The paper is structured as follows: Section 2 provides context for the current study. Section 3 details the characteristics of the methodology used to analyze the data. The research results are presented in Section 4. Following this, Section 5 delves into the analysis and interpretation of the results, exploring their meaning and implications in the field of study. The conclusions are presented in Section 6.
# Radiation Qualification by Means of the System-Level Testing: Opportunities and Limitations
## 4 Limiting Factors of Qualification
### The Worst Case TEST Conditions
#### 4.1.3 General Remarks Regarding the Worst Case Test Conditions  
Presented results may lead to the more general conclusions, that each system might have its own, specific worst-case configuration (such as internal state (ON/OFF/idle), input/output voltage, output load, clock frequency, number of system-specific operations per time unit, etc.) that is mostly related to the system internal design and might be difficult to predict without thorough analysis (acquiring more complex for more complex systems). In addition, it might be expected that for some systems only testing in different configurations will show what is the worst-case configuration. For some systems it may be possible to simplify this problem by testing in real conditions only, instead of worst-case conditions, therefore limiting the number of required tests. However, in many cases, e.g., for systems with many internal modes or functions, real conditions cannot be covered by testing in one configuration only.  
On the other hand, environmental worst-case conditions might be different for different parts in the system (depending on the component technology), therefore it might be required to irradiate the system in different temperatures or dose rates to meet the worst-case condition for all the components onboard.  
For example, when considering temperature, it is confirmed that for TID testing of components, both higher and lower temperatures might be the worst case, depending on the component type. For the SEE testing, the worst case condition for SEL is the high temperature, whereas for Single Event Burnout it is the low temperature. SEU testing is typically performed at room temperature, however, some specific IC designs may show up to have SEU rates dependent on the temperature [28]. Therefore, it may turn out that for complex designs, including different types of components, SEE testing should be performed over the full temperature range for the system. For TID, it should be sufficient to irradiate the system at one temperature and characterize it in different temperatures. In the example of PoL tests at CHARM mixed-field (see [24]), it was expected to observe SEL in the PoL with modified components--one of the components was known to be sensitive to SEL at LET as low as 1.2 MeV\(\cdot\)cm\({}^{2}\)/mg and its anti-latchup protection was removed. However, the latchup characterization of the component was performed at 125 \({}^{\circ}\)C and the test at CHARM was at room temperature, therefore not the worst case for SEL--this could be the reason why SEL was not observed during mixed-field tests at CHARM, although the high energy hadron fluence was as high as 3.47 \(\times\) 10\({}^{11}\) cm\({}^{-2}\) and the CHARM secondaries typical maximum LET is 15 MeV\(\cdot\)cm\({}^{2}\)/mg.
# Comparative Analysis of Deep Learning Methods for Real-Time Estimation of Earthquake Magnitude
## 2 Data and Methods
Exams [19] is based on a hybrid convolutional neural network structure. Spatial features are extracted through two-dimensional convolutional layers, then processed through multiple layers of one-dimensional convolution, and finally features are fused for magnitude regression. In this study, we describe the construction and processing of the dataset in detail and elaborate on the architecture of each deep learning model and its characteristics. The study's main objective is to reveal each model's strengths and weaknesses in feature extraction and learning capability by comparing the performance differences in different deep learning models in earthquake magnitude estimation. This will provide a theoretical basis for practical applications and guidance for further optimization of model selection. The significance of the study lies in the fact that magnitude estimation is an important topic in seismological research and its accuracy directly affects the effectiveness of disaster prevention and mitigation work. By analyzing the performance of each model in depth, we can identify the most suitable model for handling magnitude estimation and help researchers better understand the strengths and weaknesses of the model in the data learning process. In addition, the response characteristics of different model architectures to data processing and parameter settings provide rich practical guidance for future research, enabling subsequent studies to optimize the models more efficiently, thus improving the accuracy and reliability of earthquake magnitude estimation.