esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Effect of MLE on Plant Pigments  
#### 3.3.1 Chlorophyll a Contents  
Figure 3A shows the effect of MLE application on chlorophyll a content of wheat facing Cd stress. Results revealed that cadmium treatments of 500 uM and 1000 uM (under light) significantly reduced the chlorophyll a content by 2.3% and 2%, while, on the other hand, application of 3% MLE along with combinations of 500 uM and 1000 uM cadmium solutions increased the chlorophyll a content by 18.13% and 5.3% compared to respective Cd treatments. While compared to control lower content was observed.
## 3 Proposed V-I Characteristic Model of Electrical Arc
### Curve-Fitting Method for Defining Polynomial V-I Characteristics of Electric Arc  
The V-I characteristic of the electric arc in arc furnaces has been empirically categorized into four distinct zones. The constraints used to define these zones are presented in Figure 5a,b.  
Considering the positive and negative phases of the arc current, Area 1 and Area 2 exhibit symmetry relative to Area 3 and Area 4. Polynomial expressions are formulated  
Figure 5: The V–I characteristic of the EAF: (**a**) the four distinct voltage–current characteristic zones; (**b**) the conditional parameters of the EAF characteristic zones.  
utilizing the curve-fitting method for the positive region of the characteristic, as shown in Figure 5 (for Area 1 and Area 2).  
* For the case where \((di/dt)>0\), \(i>0\), and \(V_{a}>0\), the V-I characteristic is represented by an eighth-degree polynomial expression, as formulated in Equation (6): \[V(i)=p_{1}.i^{8}+p_{2}.i^{7}+p_{3}.i^{6}+p_{4}.i^{5}+p_{5}.i^{4}+p_{6}.i^{3}+p_{7 }.i^{2}+p_{8}.i+p_{9}\] (6)
* For the case where \((di/dt)<0\), \(i>0\), and \(V_{a}>0\), the V-I characteristic is represented by a third-degree expression, as formulated in Equation (7): \[V(i)=p_{1}.i^{3}+p_{2}.i^{2}+p_{3}.i+p_{4}\] (7)  
Based on these assumptions, the polynomial V-I characteristic of the arc is illustrated in Figure 6. The calculated polynomial coefficients derived from curve fitting and are presented below:  
* The eighth-degree polynomial expression coefficients (with 95% confidence bounds) are calculated as follows: \(p_{1}=-8.248.e^{-34}\), \(p_{2}=2.4973.e^{-28}\), \(p_{3}=-3.127.e^{-23}\), \(p_{4}=2.095.e^{-18}\), \(p_{5}=-8.1052.e^{-14}\), \(p_{6}=1.824.e^{-9}\), \(p_{7}=-2.285.e^{-5}\), \(p_{8}=0.1391\), and \(p_{9}=1.1695\).
* third-degree expression coefficients (with 95% confidence bounds) are calculated as follows: \(p_{1}=2.631.e^{-12}\), \(p_{2}=-2.3453.e^{-7}\), \(p_{3}=0.0081\), and \(p_{4}=0.5906\).
# IBA myQA SRS Detector for CyberKnife Robotic Radiosurgery Quality Assurance
## 2 Materials and Methods  
A prototype of myQA(r) SRS detector was provided to the authors' Institute in October 2021. The active detector area is composed of a 12 \(\times\) 14 cm\({}^{2}\) CMOS solid-state sensor matrix, with 0.4 mm resolution. Also, no interpolation of measurement results is necessary since the pixels are contiguous and there is no spacing between them. These features can greatly improve the detector's ability to effectively identify treatment dosimetric problems even when measuring the small dose distributions typical of SRS, as for example in the case of trigeminal neuralgia and other functional treatments (characterized by very high doses delivered to a small brain area of 4 to 5 mm in diameter). The thickness of the active area is 750 \(\upmu\)m and the detector array is embedded in an ABS insert (electron density relative to water = 1.030) at a depth of 19.3 mm from the upper face. The array can be allocated in a cylindrical ABS phantom, topped by a hemispheric cap for non-coplanar arc delivery. Additional tools include dedicated inserts for film and for small field dosimetry chambers. The link between the detector system and the PC equipped with the myQA(r) Platform acquisition software is provided by an RJ-45 Ethernet cable. An image of myQA(r) SRS detector and cylindrical phantom can be seen in Figure 1.  
The machine DQA protocol chosen to test the device performance followed the authors' Institute practice for periodic verification of beam parameters such as output factors, off-axis ratios, delivered dose linearity and reproducibility, and patient plan verification. The institutional protocol is based upon national and international published guidelines [3; 8; 9; 10]. The actual size of the IrisTM variable aperture collimator was verified as recommended by the manufacturer and as part of the authors' standard QA program. Finally, two final dosimetry checks were performed to fully characterize the device, namely the detector's reading dependence on photon dose rate and radiation beam angle of incidence.
## 4 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Selector** & **Classifier** & **Class** & **Precision** & **Recall** & **f1-Score** & **Support** & **Features** \\ \hline \multirow{2}{*}{Model (LR)} & \multirow{2}{*}{Bagging (SVC)} & 2 & \(0.90\pm 0.20\) & \(0.64\pm 0.16\) & \(0.73\pm 0.16\) & \(4.40\pm 0.49\) & \multirow{2}{*}{43} \\  & & 3 & \(0.74\pm 0.10\) & \(0.92\pm 0.16\) & \(0.82\pm 0.12\) & \(4.80\pm 0.40\) & \\ \hline \multirow{2}{*}{Univariate} & \multirow{2}{*}{SVC} & 2 & \(0.68\pm 0.17\) & \(0.64\pm 0.24\) & \(0.65\pm 0.20\) & \(4.40\pm 0.49\) & \multirow{2}{*}{20} \\  & & 3 & \(0.71\pm 0.17\) & \(0.75\pm 0.08\) & \(0.73\pm 0.12\) & \(4.80\pm 0.40\) & \\ \hline \multirow{2}{*}{Variance threshold} & \multirow{2}{*}{AdaBoost} & 2 & \(0.70\pm 0.22\) & \(0.64\pm 0.24\) & \(0.65\pm 0.20\) & \(4.40\pm 0.49\) & \multirow{2}{*}{107} \\  & & 3 & \(0.72\pm 0.16\) & \(0.76\pm 0.15\) & \(0.73\pm 0.12\) & \(4.80\pm 0.40\) & \\ \hline \multirow{2}{*}{Model (LR)} & \multirow{2}{*}{Gaussian NB} & 2 & \(0.81\pm 0.19\) & \(0.64\pm 0.16\) & \(0.70\pm 0.14\) & \(4.40\pm 0.49\) & \multirow{2}{*}{43} \\  & & 3 & \(0.72\pm 0.09\) & \(0.83\pm 0.15\) & \(0.77\pm 0.10\) & \(4.80\pm 0.40\) & \\ \hline \multirow{2}{*}{Univariate} & \multirow{2}{*}{ExtraTrees} & 2 & \(0.85\pm 0.20\) & \(0.64\pm 0.16\) & \(0.71\pm 0.15\) & \(4.40\pm 0.49\) & \multirow{2}{*}{20} \\  & & 3 & \(0.73\pm 0.10\) & \(0.88\pm 0.16\) & \(0.79\pm 0.11\) & \(4.80\pm 0.40\) & \\ \hline \multirow{2}{*}{Percentile} & \multirow{2}{*}{AdaBoost} & 2 & \(0.80\pm 0.22\) & \(0.82\pm 0.22\) & \(0.79\pm 0.19\) & \(4.40\pm 0.49\) & \multirow{2}{*}{11} \\  & & 3 & \(0.84\pm 0.20\) & \(0.80\pm 0.22\) & \(0.81\pm 0.19\) & \(4.80\pm 0.40\) & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Best pipelines’ performance parameters. All evaluated feature selection methods select the skewness. Complexity features are selected using a univariate or percentile approach, while the zone variance, business and surface to volume ratio are selected when learning from an LR model. The percentile-AdaBoost pipeline is used the complexity and skewness features in the cross-validation. Increasing the number of folds in the cross-validation scheme may provide more valuable insights.
# HTTP Adaptive Streaming Framework with Online Reinforcement Learning
## 5 Conclusions  
In this study, we present an adaptive streaming scheme based on online reinforcement learning. The proposed scheme aims to provide a network-aware adaptive streaming scheme by improving the performance of the existing learning-based adaptive streaming scheme from being degraded according to the network environment. The proposed scheme uses a state-of-the-art RL algorithm, trains the neural network model by improving the state and reward, and adapts the neural network model to the changing network environment by introducing network classification and QoE regularization. Because it is a framework that improves its performance based on the existing learning-based adaptive streaming technique, the proposed scheme can be used to extend learning-based ABR algorithms regardless of the learning method. Through the experimental results, we confirmed that the proposed scheme successfully improves QoE according to the changing network environment. In future research, we plan to study how to further increase the convergence speed of the model and how to allow the model to have generality by sharing parameters between updated models in different network environments.  
Conceptualization, J.K. and K.C.; methodology, J.K.; simulation, J.K.; validation, J.K. and K.C.; formal analysis, J.K.; investigation, J.K.; resources, J.K. and K.C.; data curation, J.K.; writing--original draft preparation, J.K.; writing--review and editing, J.K. and K.C.; visualization, J.K. and K.C.; supervision, K.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (No. 2020R1F1A1048627, Collaborative Media Streaming Model Based on Mobile Edge Computing). It was also supported by the Kwangwoon University Excellent Researcher Support Program in 2022.  
Not applicable.  
The authors declare no conflict of interest.
# A Survey on Unmanned Surface Vehicles for Disaster Robotics: Main Challenges and Directions
## 5 USV Applications for Preventive Maintenance and Disaster Response  
This section presents USV applications for the phases before a disaster (preparedness and mitigation phases), specifically structure inspection for preventive maintenance (Section 5.1), as well as applications for disaster after-effects (response phase), specifically structure inspection for damage assessment (Section 5.2) and search & rescue of people in water (Section 5.3).
## 2 Methods and Results  
### Materials and Set-Up  
In order to confirm the consistency and reliability of the force gradient method, we used lead zirconate titanate (PZT) and triglycine sulfate (TGS) samples, which are ferroelectric materials with polarization domains [33; 34; 35; 36; 37]. Moreover, it is possible to adopt a cantilever with high stiffness and frequency (\(K\sim 42\) N/m, 330 kHz) levels, such that it can minimize the effect of humidity and enhance the spatial resolution [19].  
As shown in Figure 1, we established an EFM system combined with the DDS, in order to generate more precise side-bands. A custom-built DDS system was inserted in commercial atomic force microscopy (AFM) equipment (nTRACE, Nanofocus Inc., Seoul, Korea). In the DDS circuit, a DDS-based programmable waveform generator (AD9834, Analog Dialogue, Norwood, MA, USA) is used, and it operates at 5.5 V with a 50 MHz clock. SR830 and SR844 LIA (Stanford research systems) are used as the external LIA instead of an internal LIA for EFM. Then, the frequency (\(f_{e}\)) of the AC bias voltage and the frequency (\(f_{o}\)) for mechanical oscillation were regulated by the DDS. Ultimately, a combination of the angular frequencies of \(\omega_{o}\) and \(\omega_{e}\) results in the side-bands of \(\omega_{o}\pm\omega_{e}\). As we directly assign a side-band frequency to the EFM lock-in amplifier, the EFM signals can be extracted. Additionally, we were able to optimize the recipe and carry out the various measurements in FM mode, because the side-band frequency of any cantilever is easily generated by the DDS. The great virtues of digital methods are that they generate less noise than analog methods and offer fine frequency resolutions [38].  
Figure 1: Schematic set-up of the electrostatic force microscopy (EFM) using a direct digital synthesizer (DDS). The vibration of the oscillator with a natural frequency of \(\omega_{o}\), alternating current (AC) voltage with a frequency of \(\omega_{e}\), and EFM signals with a side-band frequency of \(\omega_{o}\pm\omega_{e}\) were operated by the DDS.  
We measured the surface profiles of the zirconate titanate (PZT) and triglycine sulfate (TGS) samples using the conventional EFM method and the modified frequency-detection method. First, the PbZr\({}_{x}\)Ti\({}_{1-x}\)O\({}_{3}\) (PZT) films were deposited onto silicon substrates via radio frequency (RF) magnetron sputtering. The polarization direction of each domain in the PZT films was randomly oriented. This enabled us to construct arranged domains on the PZT film by applying positive or negative voltage to the probe [36; 39]. Additionally, TGS grown by evaporation from aqueous solution was chosen owing to its natural ferroelectric properties [40].
## 5 Particle Swarm Optimization Algorithm  
### Traditional Particle Swarm  
Particle swarm optimization (PSO) is a meta-heuristic intelligent optimization algorithm based on social behavior heuristics, which is inspired by the foraging behavior of birds and often used in optimization problems. The update rules for the position and speed of the traditional particle swarm are as follows:  
\[v_{i}(k+1)=\delta v_{i}(k)+c_{1}r_{1}(p_{i}-x_{i}(k))+c_{2}r_{2}(g-x_{i}(k)) \tag{14}\]  
\[x_{i}(k+1)=x_{i}(k)+v_{i}(k+1) \tag{15}\]  
where \(\delta\) is the inertia weight, indicates the degree of memory of the particle's previous velocity; \(c_{1}\) and \(c_{2}\) are learning factors, which are the weights
# Stability of Running Stride Biomechanical Parameters during Half-Marathon Race
## 3 Results  
Figure 1 shows a scattered plot connecting individual data points across the four race segments with lines. The different colors for the data points and lines represent different athletes from the sample. The lack of a clear trend in any of the four plots suggests that the measured variables (GCT, LSS, VO, SL) do not consistently increase or decrease throughout the segments of the race for the individuals. Figure 1: Individual variation in biomechanical parameters GCT, LSS, VO, and SL across four race segments. Each colored point represents a different athlete. Solid black lines indicate mean value of each parameter for each segment. The descriptive statistics of four biomechanical parameters measured across all four race segments are shown in Table 2. The mean GCT is relatively stable, ranging narrowly from 208.5 to 210.0 ms; SD, which measures the spread of the data around the mean, decreases slightly across the segments, from 17.4 ms in the first segment to 14.1 ms in the last, suggesting less variability in GCT as the race progresses. The CV, which is the SD expressed as a percentage of the mean, also decreases, although within the threshold of very good consistency (CV \(<\) 10%). Regarding LSS, the mean value decreases slightly throughout the race from 11.28 to 11.10 kN/m, while SD and SE show minimal changes, suggesting consistent variability and precision across race segments, with good CV values for all segments. The mean VO is very stable, showing minimal fluctuation between 7.36 and 7.41 cm across the race segments, and the SD and SE are relatively constant, suggesting that the amount of spread and precision in measuring VO does not change much through the race. CV is also good for all race segments (CV \(<\) 10%). Finally, the mean SL shows negligible variation, with a slight decrease in the third segment before returning to the initial value in the last segment, while the SD decreases as the race goes on, suggesting SL becomes more consistent among the athletes for a measure of centimeters. The CV for all parameters remained relatively low and constant across all race segments, implying that the athletes maintained a steady and consistent pattern across the race. Overall, the results suggest that the absolute values for each of these variables do not change dramatically across the race segments. Table 3 presents the results of the RM ANOVA analyzing the differences across four race segments for four different measurements: GCT, LSS, VO, and SL. [PERSON]'s \(W\) test indicated that sphericity is violated for GCT, LSS, and SL (\(p<\) 0.05), and therefore the \(df\), \(MS\), and \(p\) values of the RM ANOVA are corrected with the \(\epsilon\) Greenhouse-Geisser correction. There were no significant effects of race segment on GCT (\(F=0.96\), \(p=0.38\)), VO (\(F=0.23\), \(p=0.87\)), and SL (\(F=1.07\), \(p=0.35\)), all with a small effect size (\(\eta^{2}\) 0.001 to 0.002). Regarding the LSS, the RM ANOVA result showed a significant effect of race segment (\(F=5.52\), \(p=0.03\)) with a small effect size (\(\eta^{2}=0.004\)).
## 5 On-Site Monitoring and Analysis of Surface Deformation Law
### On-Site Monitoring Data
#### 5.1.1 Monitoring Point Layout  
According to the numerical simulation results in Section 3.2, larger settlement occurs at the edge of the jet grouting zone due to soil compression. Therefore, transverse ground settlement monitoring points were placed at the beginning of the reinforcement zone (near the launching shaft edge) (CDBC719-1, 2, 3) and at the end of the reinforced zone (CDBC709-1, 2, 3, 4), with a total of seven monitoring points. The monitoring layout is shown in Figure 8.  
The schematic diagram and field picture of settlement monitoring instrument are shown in Figure 9.
## 2 Methods
### Initial Path Planning of Parallel Parking  
#### 2.4.1 Analysis of Experienced Driver's Parking Process  
The parallel parking process of experienced drivers when there are vehicles in front and behind the waiting parking space is analyzed as shown in Figure 10. First of all, the driver selects appropriate starting position of parking, shifts into reverse gear, turns the steering wheel to the right limit position, and the vehicle runs along the arc. When he observes that the rear of the vehicle is close to the right side line of the parking space, he quickly turns the steering wheel to the left limit position and continues to reverse. When both sides of the vehicle body are parallel to the side line of the parking space, he quickly turns the steering wheel back to the right, stops, and the parking is over. The vehicle path is similar to the S-curve. The parallel parking mode is reversing. Combined with the Ackerman steering principle, taking the midpoint of the rear axle as the reference point, from which the trajectory of any point of the body is obtained, the parking path of the vehicle is analyzed.  
Figure 10: Arc- line-arc parallel parking path.  
The conditions for planning the parking path include:  
1. Safety, i.e., the vehicle will not collide with the surrounding obstacles when parking according to the planning path so as to ensure the safety of human and vehicles.
2. Meeting the constraints of path curvature, i.e., the minimum curvature radius of the path is greater than the minimum turning radius of the vehicle so as to track the path.
3. Curvature continuity, i.e., the curvature of the parking path is continuous everywhere, because continuous curvature can ensure the continuous steering control of the vehicle and avoid the situation of pivot steering.
## 2 Problem Description
### Higher-Level Planning
#### 2.1.1 Allocation of Patients to Operating Room  
On the higher level, the available information of arriving surgery patients, such as arrival date, due date, preoperative time, surgery duration, etc., is used to insert the patients into different days of the planning horizon. The capacity of all resources is considered infinite at a higher level, and all patients are placed in suitable planning horizons. Equation (1) explains the objective function of higher-level planning.  
\[obj=\text{maximize}\left(\sum_{p=1}^{P}H_{pd\tau}\right)\,\forall 0<d\leq D\ \ \forall\tau \tag{1}\]  
\[\sum_{d=1}^{D}H_{pd\tau}=1\,\forall p\in P\ \forall\tau \tag{2}\]  
\[H_{pd\tau}=1\ \forall p\in P\ \forall DD_{p}\leq d\geq EPST_{p}\ \forall\tau \tag{3}\]  
\[\text{EPST}_{p}=A_{p}+t_{p}^{pre-op}\ \forall p\in P \tag{4}\]  
The objective function (see Equation (1)) is aimed to maximize the number of patients inserted into each day of the planning horizon. Equation (2) gives the constraint that each patient can be inserted for one day of the planning horizon only. Equation (3) gives the constraint that a patient can be assigned to a planning horizon only if the patient's earliest possible start time (EPST) is the day d of the planning horizon. It is worth mentioning that \((DD_{p}\leq d\geq EPST_{p})\) in Equation (3) means that a patient can be inserted into a day equal to EPST or greater than EPST in the planning horizon. It also states that the patient's due date is considered while assigning a patient to a day. This equation has practical significance when there is a larger number of patients and limited capacity. Equation (4) shows the method to calculate the EPST of a patient. The earliest possible start time of a patient is calculated from the arrival time of the patient and after the time required for the preoperative activities of the patient. For the patients arriving at the ICU and ward, the preoperative time is zero, and patients are inserted into the planning horizon based on their arrival date using Equation (4).
# A Survey on Satellite Communication System Security
## 8 Conclusions  
In this paper, we have comprehensively classified potential attacks on satellite communication systems (SCSs), examining the unique characteristics and vulnerabilities associated with each type. In particular, the broad coverage and wireless nature of SCSs not only enhance service accessibility but also increase the susceptibility to severe threats such as DoS and DDoS attacks. These attacks pose the highest risk due to their potential to completely disrupt services. Additionally, the challenges associated with the maintenance of these systems heighten their vulnerability to eavesdropping and spoofing, compromising the integrity and confidentiality of communications.  
Through a sophisticated classification framework, we evaluate the existing defenses against these threats, such as blockchain, cryptography, and quantum communication methods, and identify critical areas. Additionally, we explore and summarize the policies of various countries. Our investigation into the policies of various countries has revealed a diverse range of approaches, reflecting differing national priorities and capabilities, which highlights the need for international cooperation.  
Finally, we compiled a list of potential future security issues and provided directions for future research, such as AI, integrated protocols, SDNs, and quantum communication.  
Conceptualization, M.K. and Y.L.; methodology, M.K.; validation, M.K.; formal analysis, M.K.; investigation, M.K.; resources, M.K.; data curation, M.K.; writing--original draft preparation, M.K.; writing--review and editing, S.P., M.K. and Y.L.; visualization, M.K.; supervision, Y.L.; project administration, Y.L.; funding acquisition, Y.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a Korea Research Institute for defense Technology planning and advancement (KRIT) grant funded by the Korea government (DAPA (Defense Acquisition Program Administration)) (No. KRIT-CT-22-021, Space Signal Intelligence Research Laboratory, 2022).  
Data Availability Statement: Data sharing is not applicable.  
**Acknowledgments:** We use free vector images that are \"Designed by Freepik\" in our figures in this paper.  
**Conflicts of Interest:** The authors declare no conflicts of interest.