esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Models and Data
### Synthetic Snow Dataset  
#### 2.3.1 Random Data  
Fifty-thousand values for the relative azimuth angle, viewing zenith angle, solar zenith angle, snow grain size, and impurity concentration were used to generate a synthetic training dataset. The angular data were formed with a uniform random distribution in the solar zenith angle, viewing polar angle, and relative azimuth angle, while the grain size and the impurity concentration were formed with a log-normal random distribution. The log-normal distributions were chosen to cluster random data around values that are more likely to be found in nature. A summary of the dataset is shown in Table 1. This dataset was used to simulate radiances and irradiances, employing the AccuRT computational tool.
# Monitoring of Surgically Treated Upper Arm Fracture by Implanted Antenna at 402 MHz
## 3 The Method
### Biocompatibility  
Implantable antennas must be biocompatible to ensure patient safety and prevent implant rejection. Isolating the metallic radiator from the human tissue by employing a biocompatible substrate and inserting a thin layer of low-loss biocompatible coating is a technique used to preserve the biocompatibility of the antenna (biocompatible encapsulation) [18].  
Antennas insulation by capsules is used in capsule endoscopy for the diagnosis of the human digestive system. A swallowable electronic radio-telemetry capsule was first developed in 1957 and was used to measure temperature and blood pressure [19].  
Various designs have been used in the related literature for biocompatible antennas. Table 2 lists some of the antenna types, the operating frequency, and antenna parameters.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Tissue Thickness** & **Dimension in mm** \\ \hline Bone & 12.5 \\ Muscle & 27.5 \\ Fat & 8.5 \\ Skin & 1.5 \\ Length of model & 350 \\ Fracture thickness & 2 \\ Split thickness & 3 \\ Fixation plate length & 100 \\ Fixation plate width & 8 \\ Fixation plate thickness & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Dimensions of the model and fixation plate.
## 4 Discussion  
The performed computational experiments and sensitivity analysis have shown that it is possible to develop an efficient hybrid approach, combining PSO and DE. The key to efficient performance here seems to be in the usage of personal best positions of particles instead of current positions because personal best points contain important information for the search process.  
Although within this study only one baseline algorithm was considered, namely mQSO, it is possible to interpolate the proposed algorithmic scheme for any other PSO-based algorithm, and most of the used approaches for dynamic optimization are based on a type of swarm intelligence algorithm derived from particle swarm optimization. Thus, the findings of this study could be considered rather general; however, additional experiments in other scenarios are required to prove the concept.  
It is rather obvious that setting the probability of applying DE to a fixed value during the whole algorithm run is not the best possible option, and a specific adaptation mechanism for \(P_{DE}\) should be developed. In our preliminary experiments with several adaptation schemes, we were not able to come up with an efficient method that would significantly improve the performance of the GMPB test suite. The reason for that was that DE mutation generates successful solutions more often so allowing the \(P_{DE}\) to be adapted simply increases this probability to the upper threshold. This, in turn, results in too high \(P_{DE}\) values, which decrease the search efficiency, as was shown in the experiments in this  
Figure 5: Bar plot Friedman ranks of tested algorithms, aggregated over four settings.  
study. In other words, the adaptation scheme should consider this fact and be less greedy for improvements.  
In addition to \(P_{DE}\) adaptation, application of DE operators burdens researchers with additional parameters to be tuned, namely the scaling factor \(F\) and crossover rate \(Cr\). In this study, it was shown that the sensitivity to these values is not very large, probably thanks to the rotation-invariant nature of PSO and applied sampling of \(F\) values from the Cauchy distribution. Nevertheless, incorporating known adaptation schemes, such as success-history adaptation, or developing new specific methods is another direction of further studies.  
Finally, it should be mentioned that in this study only one differential mutation strategy was tested, i.e., the classical rand/1, although other schemes are known to be superior in certain cases. If some of them would be shown to perform better than rand/1 for dynamic environments, this will allow making another step in the direction toward efficient DOPs solvers.
# Influence of Multiple Factors on the Wettability and Surface Free Energy of Leaf Surface
###### Abstract  
The wettability of plant leaves directly reflects leaf hydrophilicity, which is the key factor that influences the adhesion of liquid pesticide as well as affects plant protection products (PPP) efficacy. Generally, the wettability of leaf surface is quantified by the contact angle and surface free energy (SFE), which are mainly dependent on leaf surface properties, liquid properties and other spraying parameters. Therefore, the aim of this paper was to investigate the SFE of rice and rape leaves with the variation of leaf status, leaf surface, and probe liquid as well as the influence of droplet falling height, solid surface, and PPP concentration on the wettability. The results showed that: (1) the dispersive components of SFE of rice and rape account for a large proportion which are closely related to their hydrophobicity--the abaxial of rape new leaf and the adaxial of rape old leaf are easier to wet comparing with rice and rape leaves in other statuses; (2) the increase of droplet falling height had a significant effect on improving the wettability between wax surface and adjuvant solution, while it had little improving effect on the wettability between wax surface and water; (3) the wettability of different solid surface varied greatly, and the order of wettability from good to bad is water-sensitive paper (WSP), wax, rape leaf, and rice leaf; (4) the effect of PPP concentration on the leaf surface wettability is significant, the contact angle decreased with the increase of PPP concentration, and the wettability of microemulsion is better than that of suspending agent and wettable powder. In conclusion, the SFE and wettability of crop leaf surface determine the suitable type of PPP, studying the influence of multiple factors on leaf surface wettability can provide a reliable reference for providing scientific guidance as well as improving the effective utilization of PPP.  
wettability; surface free energy; contact angle; droplet falling height; plant protection products +
Footnote †: journal: Applied Sciences
# Real-Time Simulator for Dynamic Systems on FPGA
## 5 Other Examples of Dynamic Systems
### Flow System in a Tank  
This dynamic system represents a water tank connected to a pump, as presented in [40]. The pump, operating at a constant flow rate \(q_{m}\), feeds into an inlet valve controlled continuously by \(V_{e}(t)\), which in turn determines the input flow \(q_{e}(t)\) into the tank. The outlet valve, also with continuous control \(V_{o}(t)\), governs the outflow \(q_{s}(t)\) from the tank. The water level in the tank is represented by the variable \(n(t)\). The system has two inputs--the inlet valve control \(V_{e}(t)\) and the outlet valve control \(V_{o}(t)\)--and one output, the water level \(n(t)\) in the tank. The system is modeled by the following differential equation:  
\[A\frac{dn(t)}{dt}=q_{e}(t)-V_{o}(t)q_{s}(t) \tag{31}\]  
where  
\[q_{e}(t)=q_{m}V_{e}(t) \tag{32}\]  
and  
\[q_{s}(t)=a\sqrt{2 gn(t)}, \tag{33}\]  
In these equations, \(q_{m}\), \(A\), \(g\), and \(a\) represent the pump flow rate (m\({}^{3}\)/s), the cross-sectional area of the tank (m\({}^{2}\)), gravitational acceleration (m/s\({}^{2}\)), and the cross-sectional area of the tank's outlet tube (m\({}^{2}\)), respectively [40; 41].  
Equation (31) can be expressed by  
\[n(t)=\left\{\begin{array}{l}u(t)\text{ para }u(t)<n_{max}\\ n_{max}\text{ para }u(t)\geq n_{max}\end{array}\right., \tag{34}\]where \(n_{max}\) is the maximum water level of the tank, and \(u(t)\) is given by Equation (26), where \(v_{0}\) is the initial condition of the water level of the tank, and Equation (27) can be expressed as  
\[h_{vs}(s,v(s))=\frac{1}{A}\Big{(}q_{m}V_{e}(t)-V_{o}(t)a\sqrt{2 gn(t)}\Big{)}. \tag{35}\]  
As described in Section 3, the proposed methodology is designed to simulate generic systems and is not limited to the specific example presented in Section 4. To clarify the use of RTSDS-FPGA, the simulation of the tank system in hardware is similar to the example discussed in Section 4. The primary modification required is to change the DSM hardware, which will now be replaced by the configuration presented in Figure 26. This further emphasizes the generic nature of the proposed methodology, as outlined in Section 3 and illustrated in Figure 1, demonstrating its adaptability to various dynamic systems.
# A Hybrid Data-Fusion System by Integrating CFD and PNN for Structural Damage Identification
## 2 Novel Hybrid Data-Fusion System
### Single PNN Damage Detection Model
#### 2.2.1 Data Preprocessing  
Before features extraction, data preprocessing is necessary. The reason for this is that collecting signals from raw measurement inevitably contain noises, measurement errors and even are influenced by imprecision of measurement facility, etc. First of all, the measured signals are converted from analog to digital form. Subsequently, with the help of either basic or advanced data preprocessing techniques, the digitized data are processed, such as threshold, filtering, averaging, etc. For better results, some advanced data processing techniques, such as wavelet transform (WT) and wavelet packet analysis, Hilbert-Huang transform (HHT) and principal component analysis (PCA), can also be used  
Figure 1: Architecture of the hybrid data-fusion system.  
Figure 2: Single PNN classifier model for damage detection.  
for data de-noising and compressing. Here, the filtering and averaging method were used for data preprocessing.
# Asymptotic Analytical Solution on Lamb Waves in Functionally Graded Nano Copper Layered Wafer
## 3 Solution to the Problem
### Ordinary Differential Equations with Variable Coefficients and Power Series Solution  
The solutions to the governing equations for Lamb waves propagating in an FG nano copper wafer are expressed as:  
\[u_{1}^{(1)}=U_{1}^{(1)}(x_{3})exp(ikx_{1}-\omega t),u_{3}^{(1)}=U_{3}^{(1)}(x_{ 3})exp(ikx_{1}-\omega t),\ \text{and}\ u_{2}^{(1)}=0 \tag{15}\]  
where \(i\) is the imaginary unit, \(\omega\) is the frequency and satisfies \(\omega=ck\), \(k\) and \(c\) are the wave number and wave velocity, respectively, \(U_{1}(z)\) and \(U_{3}(z)\) are the unknown amplitudes of the displacement, and superscript \(\langle l\rangle\) represents the \(l\)th wafer for the layered wafer (\(l=1\sim N\)). This superscript is not present in the simple wafer. The relationship between the wave number and wave length \(l\) is expressed as \(kl=2\pi\). Given that the form of the governing equation is similar, the superscript is ignored when solving the above equations. Substituting Equation (15) to Equation (5) yields:  
\[ \begin{split}\mu\frac{d^{2}U_{1}}{dx_{3}^{2}}+\frac{dy}{dx_{3}} \frac{dU_{1}}{dx_{3}}+i(\lambda+\mu)k\frac{dU_{3}}{dx_{3}}+\left(\rho c^{2}- \lambda-2\mu\right)k^{2}U_{1}+i\frac{d\mu}{dx_{3}}kU_{3}=0\\ (\lambda+2\mu)\frac{d^{2}U_{1}}{dx_{3}^{2}}+i(\lambda+\mu)k\frac{dU _{1}}{dx_{3}}+\frac{d(\lambda+2\mu)}{dx_{3}}\frac{dU_{3}}{dx_{3}}+ik\frac{d \lambda}{dx_{3}}U_{1}+\left(\rho c^{2}-\mu\right)k^{2}U_{3}=0\end{split}  \tag{16}\]  
Equation (6) is a set of a set of ordinary differential equations with variable coefficients. In this study, power series method, which both the coefficients and solution are expressed as power series form, is applied for solving the equations. Considering that the Lame parameters and mass density are functions of thickness, they can be expressed in power series form as:  
\[\lambda=f_{1}\Big{(}\frac{x_{3}}{h}\Big{)}=\sum_{m=0}^{\infty}a_{n[1]}\Big{(} \frac{x_{3}}{h}\Big{)}^{n},\mu=f_{2}\Big{(}\frac{x_{3}}{h}\Big{)}=\sum_{m=0}^{ \infty}a_{n[2]}\Big{(}\frac{x_{3}}{h}\Big{)}^{n},\ \text{and}\ \rho=f_{3}\Big{(}\frac{x_{3}}{h}\Big{)}=\sum_{m=0}^{ \infty}a_{n[3]}\Big{(}\frac{x_{3}}{h}\Big{)}^{n} \tag{17}\]  
where \(a_{n[1]}\), \(a_{n[2]}\), and \(a_{n[3]}\) are the \(n\)th coefficients of the Taylor series of \(\lambda\), \(\mu\), and \(\rho\), respectively. To solve Equation (16) with variable coefficients, the solution is assumed to also follow the power series form. \[U_{1}=\sum_{m=0}^{\infty}s_{n}\Big{(}\frac{x_{3}}{h}\Big{)}^{n},U_{3}=-i\sum_{m= 0}^{\infty}t_{n}\Big{(}\frac{x_{3}}{h}\Big{)}^{n} \tag{18}\]  
By substituting Equations (17) and (18) to Equation (16) and equating the coefficient of \((x_{3}/h)^{n}\) to zero, the following recursive equations are obtained. \[
## 5 Simulation Result and Discussion
### Simulation Based on Different Numbers of MEC Servers
These findings indicate that the proposed Lagrange approach is slightly superior to conventional methods in reducing costs, particularly as the number of MEC servers increases.
# Research on Dynamic Searchable Encryption Method Based on Bloom Filter
## 3 Preliminaries  
### Forward and Backward Privacy  
The definition of forward-backward security was first proposed by [PERSON] et al. [21] with the first scheme to support dynamic keywords. Then, forward-backward security was first formalized by [PERSON] et al. [10; 11]. As for the definition of forward security, [PERSON] et al. argue that an update does not reveal information about the updated keywords. Importantly, the server does not know if the updated file matches the keyword that the user searched for. The definition is as follows:  
**Definition 1**.: _The update leak function \(L^{Uplt}\) can be written as:_  
\[L^{Uplt}(op,in)=L^{{}^{\prime}}(op,\{ind_{i},\mu_{i}\}) \tag{1}\]  
For \((op,in)\) pairs, op is the update query and in is the input. \(\{ind_{i},\mu_{i}\}\) is a collection of update files, in which \(\mu_{i}\) is the key of the update and \(ind\) is the updated document. If the update function can be written as the above expression, then the \(L\) adaptive secure SSE scheme is forward private.  
[PERSON] et al.'s [10] definition here extends forward privacy as proposed by [PERSON] et al. [21]. Also, this definition focuses only on adding documents, not updating them.  
In backward security, the server cannot associate the currently searched keyword with the results of a previous search. That is, every time a user adds a document \(ind\) corresponding to a keyword to the database, then it is removed later [21]. After this series of operations, when searching this keyword, the search result will not appear in the document ind, so the SSE scheme is backward safe. [PERSON] et al. defined three types of backward security in their paper: backward privacy with insertion pattern, backward privacy with update pattern, and weak backward privacy. In this paper, backward security is judged according to the second one: backward privacy with update mode. It leaks the documents currently matching w, when they were inserted, and when all the updates on w happened (but not their content).
## 2 Materials and Methods
### Research Object  
The subject of the research was an automated guided vehicle designed to transport products and materials (Figure 2). The vehicle is a three-wheeled structure equipped with two driving wheels and one independent turning wheel. The vehicle is designed to move on smooth surfaces due to the lack of flexible suspension. It is set in motion and steered by two independently driven wheels.  
The technical parameters characterizing the AGV are as follows:  
* The mass of the vehicle with batteries is 200 kg;
* The maximum working speed is 1 m/s;
* The vehicle is able to carry loads of up to 100 kg;
* The vehicle dimensions 1.24 \(\times\) 1.04 m;
* The driving wheels are made of metal around the circumference with a rim of vulcanized rubber;  
Figure 2: The automated guided vehicle that is the subject of this research: (**a**) rear view of the vehicle; (**b**) front view of the vehicle.  
The on-board steering system comprises an on-board PC computer, acquisition cards and a computer program with an implemented control algorithm written in C++;
* Sensors are used to measure the distance, angular position and deviation from a given direction. Two types of sensors are used in the AGV: MHK40 encoders and LT3 Banner laser rangefinders.  
Figure 3 shows a diagram of the AGV with its accessories.  
The sensor used is the time-of-flight laser sensor. The LT3 sensor uses pulsed laser technology to produce highly accurate results. The sensor's laser operates at a frequency of one million pulses per second. The microprocessor records the time needed for the impulse to travel to the object and back. Every millisecond, a thousand pulses are recalculated and their averaged value is output. The long detection range allows the recognition of very small or difficult-to-recognize objects, even if the observation is carried out from a considerable distance. The sensor parameters are presented in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Range/Value** \\ \hline measurement range & diffusion Mode: 0.3–5 m \\  & reflective Mode: 0.5–50 m \\  & low level: max 1 mm at 200 mm \\  & high level: max 5 mm at 200 mm \\  & settling time & 1 s \\   response settling time & 1; 10; 100 ms \\  & diffusion mode: 20 mm \\  & reflective mode: 40 mm \\  & diffusion mode: \(\pm\)30 mm (0.3–1.5 m) \\  & \(\pm\)20 mm (1.5–5.0 m) \\  & reflective mode: \(\pm\)60 mm (0.5–50 m) \\  & diffusion mode: \(<\)2 mm/\({}^{\circ}\)C \\  & reflective mode: \(<\)3 mm/\({}^{\circ}\)C \\  & 0–10 V \\  & 1 kI \\   short-circuit and reverse polarity protection & YES \\   supply voltage & 12–24 V \\ current consumption & UT = 24 V \\   ambient temperature & 0 \(\ldots\) +55 \({}^{\circ}\)C \\ \hline \hline \end{tabular}
\end{table}
Table 1: Laser rangefinder parameters.  
Figure 3: The scheme of the automated guided vehicle.
## 4 Results and Discussion
### Analysis of Firefighters' Motion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Movement Analysis Item** & **a. General Vest** & **b. Cooling Vest** & \(p\) & **Unplanned** \\  & & & & **Comparison Result** \\ \hline Slowly swing the right hand up and & 0.67 \(\pm\) 0.68 & 0.68 \(\pm\) 0.72 & 0.000 * & b \(>\) a \\ down by parallel with the body & 0.53 \(\pm\) 0.71 & 0.54 \(\pm\) 0.78 & 0.000 * & b \(>\) a \\ \hline Make a fist on the right hand and & 0.64 \(\pm\) 0.69 & 0.63 \(\pm\) 0.78 & 0.000 * & b \(>\) a \\  & the body & & & \\ \hline Cross the arms over the chest to do the & 0.57 \(\pm\) 0.59 & 0.58 \(\pm\) 0.67 & 0.000 * & b \(>\) a \\ motion of opening arms & & & & \\ \hline Raise hands up and keep parallel and & 0.56 \(\pm\) 0.45 & 0.53 \(\pm\) 0.91 & 0.000 * & b \(>\) a \\  & to touch knees & & & \\ \hline Put feet together and then move & & & & \\  outwards to the same width & 0.61 \(\pm\) 0.59 & 0.62 \(\pm\) 0.87 & 0.000 * & b \(>\) a \\  & as shoulders & & & \\ \hline Jump in place & 0.65 \(\pm\) 0.89 & 0.67 \(\pm\) 0.93 & 0.000 * & b \(>\) a \\ \hline Lift the leg to vertical 90\({}^{\circ}\) & 0.56 \(\pm\) 0.23 & 0.57 \(\pm\) 0.82 & 0.000 * & b \(>\) a \\ \hline Lift the right leg outwards to the & 0.52 \(\pm\) 0.67 & 0.55 \(\pm\) 0.79 & 0.000 * & b \(>\) a \\  & right waist & & & \\ \hline Squat in place, raise both hands up & & & & \\  with parallel, and then move from the & 0.45 \(\pm\) 0.81 & 0.46 \(\pm\) 0.78 & 0.000 * & b \(>\) a \\  & top of head to touch knees & & & \\ \hline By simulating the rescue situation of & & & & \\  firefighters, lift an injured person, & & & & \\  weighted 73 kg, and move backward & 0.57 \(\pm\) 0.59 & 0.58 \(\pm\) 0.78 & 0.000 * & b \(>\) a \\  & for 3 m & & & \\ \hline Raise both hands closed, move & & & & \\  downward to knees, and swing to the & 0.58 \(\pm\) 0.87 & 0.60 \(\pm\) 0.76 & 0.000 * & b \(>\) a \\  & right behind the neck & & & \\ \hline Squat and move forward for 3 m & 0.47 \(\pm\) 0.83 & 0.48 \(\pm\) 0.62 & 0.000 * & b \(>\) a \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Analysis of motion during tasks (Likert-type scale; mean \(\pm\) standard deviation; and unit: %).
# On-the-Fly Repairing of Atomicity Violations in ARINC 653 Software
## 2 Background
### On-the-Fly Repairing of Atomicity Violations
The method of inserting condition variables, which changes thread scheduling, puts the thread in the waiting state before the access event of the shared variable in which the error occurred. Then, it resumes the execution of the waiting thread after processing with the shared variable access of other thread capable of correct interleaving. The time-out method allows the execution timing of the threads to be changed by using delaying techniques (e.g., using sleep() or yield()) and leads to fixed-time overheads as delayed times. However, it is crucial to use appropriate latency to handle access faults because the errors may not be fixed by applying low latencies. Conversely, applying high latencies may lead to high-cost overheads. The method of inserting locks is more suitable for repairing atomicity violations.
###### Abstract
Further, soundscapes (i.e., the acoustic environment as perceived and evaluated by humans) have been studied using GIS in a variety of outdoor environments [27; 28; 29; 30]. In this paper, we describe a k-means clustering analysis of 51 geospatial layers relevant to outdoor geospatial acoustic modeling in the continental United States (CONUS) [26]. The geospatial layers include descriptors of anthropogenic activity, landscape structure and characteristics, land use, land cover, and climate. K-means clustering was selected, in part, because of its simplicity, efficiency, relatively low computational cost, and common use as a clustering method [31]. Additionally, the application of k-means to our data set produces clear, human-interpretable clusters. Maps of the clustering results as well as correlations between the geospatial layers and clusters are used to identify geospatial characteristics of the different clusters. In particular, we make connections between the clusters and _geospatially_ distinct environments. This interpretation of the clusters aids in determining _acoustically_ distinct environments. Although the clusters are not tied to specific acoustic characteristics, their distinct geospatial characteristics likely correspond to distinct acoustic environments. Areas corresponding to the same cluster may share acoustic characteristics despite being separated by potentially large physical distances. We discuss how the clustering results can guide acoustic data collection for the problem of predicting average outdoor sound levels over continental scales using supervised machine learning. We previously used the 51 geospatial features and acoustic training data from 496 unique sites to train a supervised machine learning model to predict outdoor sound levels over the CONUS [26]. In this paper we identify poorly sampled clusters in the training data from which to target future data collection efforts.