esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In the work in [20], PAN fibers were electrospun from dimethylformamide (DMF). By varying the parameters, such as polymer concentration, feed rate, and applied voltage, fibers with different sizes were produced. The authors received straight and beaded structures. The beads appeared when the fiber diameter was \(\leq\)350 nm. [PERSON] and coauthors [18] fabricated poly(acrylonitrile) beaded-free nonwovens with fibers at a diameter of 77 nm. The parameters involved in fiber production referred to process temperature, solution concentration, applied voltage, collector rotation speed, tip-to-collector distance, and solution feed rate. The design of experiment (DOE) was used to upgrade the electrospinning process. Obtained structures was tested against cigarette smoke and particles released during 3D printing. Filtration efficiency was 99.26% in the first case and 99.26% in the second one. Through the control of ambient humidity as well as the spinning solution concentration, the bead-on-string PAN structures were produced by [PERSON] et al. [21]. The best filtration efficiency was 99%, with a pressure drop of 27 Pa. The multilayered PAN structure of fibers at \(\approx\)850 nm and \(\approx\)15,000 nm in diameter were fabricated by [PERSON] et al. [16]; a 99.99% filtration efficiency at a pressure drop of 35 Pa was achieved. A 99.999% filtration efficiency against particles of 300 nm was obtained when using polyethersulfone/polyamide 66 membrane (PES/PA66); the average fiber diameter of the considered yarn was 520 nm [22]. A large number of different factors, incomplete reporting of the electrospinning process conditions, and limited knowledge about the role of these parameters make it difficult to obtain nonwovens of the desired structure. Therefore, it appears necessary to provide an appropriate, accurate mathematical model able to explain the system behavior with various components' influences. The nonlinear algebraic [23,24], differential, and differential-algebraic [25] models have been commonly used in the literature; usually, the (i) stable jet portion, (ii) whipping instability, or (iii) entire jet models are considered [19,26]. The axial length scale \(\chi\) stands as an example of criterion for which the systematical calculation method has not been clearly investigated [23,24]. The main goal of this work is (i) to estimate the axial length scale with respect to specified experiment conditions of poly(acrylonitrile) electrospinning and (ii) to analyze how this parameter may leads to the nonwovens of fibers at certain sizes and functionalities (able to collect fine particulate matter fractions (PM\({}_{2.5}\))).
## 2 Experiment and Methods
### Numerical Simulation for Multilayer Structure Mold  
To evaluate the heating performance of the multilayer structure mold, numerical simulation was performed by effectively coupled electrical and thermal finite element analyses. CNT web film, which serves as a heat source, generates Joule heat when electricity flows through it. Joule heat is calculated as Equation (1).  
\[\hat{Q}=\sigma\cdot J^{2} \tag{1}\]  
\[\hat{Q}+\
abla\cdot[k(T)\
abla T]=\rho C(T)\frac{dT}{dt} \tag{2}\]  
\(J\) is current density, and \(\sigma\) is electrical resistivity. The temperature change of CNT web film is calculated by the transient heat conduction equation as shown in Equation (2). \(\rho,k,\) and \(C\) refer to the material's density, thermal conductivity, and specific heat. In the multilayer structure mold, each mold layer is tightly attached to the other layers. Hence, the thermal contact conductance of each layer was fixed (Table 2).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Material** & **Component** & **Density** & **Conductivity** & **Specific Heat** & **Resistivity** \\  & **(g/cm\({}^{3}\))** & **(W/m\(\cdot\)K)** & **(J/g\(\cdot\)K)** & **(Ohm\(\cdot\)cm)** \\ \hline NAK80 & Core plate & 7.8 & 41.33 & 0.481 & \(2.63\times 10^{-5}\) \\ \hline CNT web film & Heater & 0.41 &
\begin{tabular}{c} (In-plane) 14.65 \\ (Out-of-plane) 600 \\ \end{tabular} & 0.716 & \(2.49\times 10^{-3}\) \\ \hline C1100 & Bus bar & 8.89 & 390.79 & 0.385 & \(1.7\times 10^{-8}\) \\ \hline Glass fiber fabric & Electrical insulator & 1.26 & 1.5 & 0.65 & \(1\times 10^{25}\) \\ \hline ISOL600 & Insulator & 1.63 & 0.33 & 0.88 & - \\ \hline \hline \end{tabular}
\end{table}
Table 1: Properties of the materials for the multilayer test mold.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Materials** & **Thermal Contact Conductance (W/m\({}^{2}\cdot\)K)** \\ \hline Nak80 & Electric insulator & 2900 \\ \hline Electric insulator & CNT web film & 30,000 \\ \hline CNT web film & Insulator & 1000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Thermal contact conductance of multilayer structure mold.
## 3 Principals of Routing Protocols and Their Breakdowns
### Geographical Information-Based Routing Protocols
#### 3.2.7. Gedar [51]  
For UWSNs, the geographic and opportunistic routing (GEDAR) protocol is suggested, which combines geographic and opportunistic routing with depth adjustment-based topology management for communication recovery over void regions. GEDAR uses a greedy forwarding technique to choose the next-hop forwarder group of neighbors based on the neighbor and sinks nodes' geographic information. The unique void node recovery approach, which transfers void nodes to new depths to overcome communication void areas and minimize needless transmissions, is the most significant component of the GEDAR protocol. The authors chose two methods for selecting forwarding nodes: a timer-based system and a control-based system. A timer-based system leads to more energy usage of the node and pairing with the cooperative routing nature, which uses the nodes closer to sink more than other nodes unbalanced energy consumption.
# Influence Analysis of Liquefiable Interlayer on Seismic Response of Underground Station Structure
## 2 Numerical Calculation Method  
### Geometric Model and Site Condition Setting  
A typical subway station is a two-story, three-span structure. The cross-sectional dimensions of the main part are shown in Figure 1. The top plate of the station structure is buried at a depth of 5 m, the width of the structural model is 21.34 m, the height is 14.89 m, the size of the central column of the station structure is 0.6 m \(\times\) 1.1 m, and the distance between the longitudinal columns of the central column is 9.12 m.  
Figure 1: Standard cross-section of station structure (unit: mm).  
In this study, seven different site conditions were constructed, and three sets of numerical models were set up for comparison and analysis according to different variables. The influence of the location of the liquefiable interlayer on the dynamic response of the structure was investigated in cases a, b, and c; the influence of the relative density of the interlayer was investigated in cases b, d, and e; and the influence of the thickness of the interlayer was investigated in cases b, f, and g. The water level is assumed to be at the ground surface in each model, and the liquefiable interlayer is a sandy soil, while the rest of the site soil is non-liquefiable clay. A simplified model of the soil-station structure and the arrangement of the monitoring points are shown in Figure 2. The variable \(D\) is the burial depth at the top of the liquefiable interlayer, and \(H\) is the thickness of the interlayer. The distribution of the soil layers in each case is shown in Table 1.
# Development of Energy-Based Impact Formula--Part I: Penetration Depth
###### Abstract  
Predicting the damage to a concrete panel under impact loading is difficult due to the complexity of the impact mechanism of concrete. Based on the experimental results obtained by various researchers, the energies involved in the impact mechanism are classified into seven categories: Kinetic energy, deformed energy of a projectile, elastic penetration resistance energy of the panel, overall deformed energy of the panel, spalling-resistant energy, tunneling-resistant energy, and scabbing-resistant energy. Using these impact mechanisms and the energy conservation law, a new energy-based penetration depth formula is proposed to predict the penetration depth. This is validated using 402 impact test results, which include those with high-strength concrete, ultra-high-performance concrete (UHPC), or steel fiber-reinforced concrete, those under very high-velocity impact, and those with a very low ratio of target panel thickness to projectile diameter. It is found that the new impact formula predicts the penetration depth quite well.  
impact formula; penetration depth; concrete panel; blast impact; modified NDRC; energy-based +
Footnote †: journal: Applied Sciences
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Received Signal Strength Indicator(RSSI)  
The intensity of the received signal (RSSI--received signal strength indicator) is used to know the propagation of the radio wave [42; 43; 44]. The surrounding environment, the growth of crops and thedifferent antenna heights will influence the measurement of RSSI [37]. In that sense, the models used to predict the RSSI between two transceivers are called propagation models [45].
# Low-Cost, Compact, and Rapid Bio-Impedance Spectrometer with Real-Time Bode and Nyquist Plots
## 3 Results and Discussion
### Application Examples
#### 3.2.2 Mixed Water and \"Yakult\" with Different Concentration  
In this experiment we use interdegitated electrode (IDT). The IDT electrode is very suitable for the measurement of impedance of small amounts of liquid material. More about the use of IDT electrodes can be seen in several references, and not discussed in this paper. The liquid sample used in this experiment is a mixture of mineral water and milk (\"Yakult\") in several types of concentrations. The experiment results are given in Figure 9.  
Figure 9 presents impedance values of samples in magnitude Bode plot, and Figure 9 is in Nyquist plot. It appears that the electrical impedance of mineral water without Yakult addition has the highest value, this is because water is a material with weak electrolyte properties so the impedance value is relatively high. The addition of Yakult material of 0.4 mL, 0.8 mL, 1.2 mL, 1.6 mL, and 2.0 mL to the water causes the electrical impedance of the mixture liquids to decrease. In here, the greater the volume of Yakult added to the water, the greater the decrease in impedance that occurs. This is because Yakult is an electrolyte solution, so the conductivity of the sample will increase, which means the total resistance or impedance will decrease. Furthermore, using the Nyquist plot given in Figure 9, we can study the phenomenon of ion transport and bulk electrolyte resistance, which is very important in material characterization by using electrical impedance spectroscopy method.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Prec. RLC Meter** & \multicolumn{2}{c}{**Dev. BIS**} & \multicolumn{2}{c}{**Error (\%)**} \\ \hline
**R (k\(\Omega\))** & **C (nF)** & **R (k\(\Omega\))** & **C (nF)** & **C (\%)** & **R (\%)** \\ \hline
5.45 & 0.93 & 5.472 & 0.938 & 0.40 & 0.86 \\
5.45 & 9.41 & 5.489 & 9.341 & 0.72 & 0.73 \\
5.45 & 34.15 & 5.377 & 34.653 & 0.64 & 1.47 \\
5.45 & 51.46 & 5.415 & 52.602 & 1.34 & 1.17 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Measurement result and error calculation  
Figure 8: Bode plot of RC experiments: (**a**) impedance magnitude (ohm), and (**b**) phase (degree).  
Figure 9: Impedance values of Yakult solution: (**a**) in Bode plot, and (**b**) in Nyquist plot.
## 3 Spacecraft Dynamics
### Spacecraft Model  
The satellite model considered here represents a realistic Earth Observation (EO) spacecraft and consists of a parallelepiped central platform along with two solar arrays, each measuring 1 \(\times\) 3 m (in turn composed of two 1 \(\times\) 1 m sub-panels). The panels are connected to the central hub, which is considered rigid with respect to the flexible appendages. The spacecraft's reference frame has its origin G located at the vehicle's CoG (refer to Figure 8), while two body reference frames for the panels originate in the attachment points \(P_{1}\) and \(P_{2}\). The solar panels are realized as a one-layer equivalent composite material whose material properties were derived in Section 2.4. Moreover, a yoke structure replicates the panel's attachment to the platform. Since the central hub is assumed to be rigid, the flexible substructures are directly assembled in MSC Nastran. Rigid body element connections are utilized to link them to a single node coinciding with point G. The relevant data, including inertial properties, modal participation factors, and natural frequencies of the flexible structure, computed with respect to point G, are imported into a Matlab environment to implement the dynamics of the flexible spacecraft, as described in Section 3.1. The inertial properties of the system are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Mass (kg)** & \multicolumn{3}{c}{**Inertia (kg m\({}^{2}\))**} & \multicolumn{3}{c}{**Size(m)**} \\ \hline  & \(J_{xx}\) & \(J_{yy}\) & \(J_{zz}\) & \(X\) & \(Y\) & \(Z\) \\
300 & 125 & 125 & 50 & 1 & 1 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Spacecraft inertial properties.  
Panel-shaped structures exhibit three distinct constrained modes, which involve bending and torsion along the main coordinated axes. However, when these structures are installed on a satellite that also hosts other flexible appendages, their elastic modes interact with the overall system dynamics. Figure 9 provides a depiction of the assembled spacecraft's modes, along with an overview of the complete system. The modal behavior of the system is characterized by both symmetric and asymmetric bending modes. Translation maneuvers trigger the symmetric bending modes, while attitude maneuvers excite the asymmetric bending modes.
# Robotic Coverage Path Planning for Ultrasonic Inspection
## 3 Streamlines  
Physical flow, for example, fluid flow in a liquid, is described by a vector field. If the vector field does not change over time, we call it a static vector field. The trajectory of a very light particle (with zero-mass) through the flow follows a streamline. Given a start point and a vector field, it is possible to calculate the respective streamline. The position of the particle evolves through the vector field such that the velocity of the particle in each point corresponds to the value of the vector field at that position. For a vector field \(\mathbf{v}\), a streamline \(\mathbf{s}_{t}=\mathbf{s}_{t}(t)\) is defined as follows:  
\[\frac{d\mathbf{s}_{t}}{dt}\times\mathbf{v}(\mathbf{s}_{t})=0 \tag{1}\]  
where \(t\) is the parameter of the streamline. In general, at any point along the streamline \(\mathbf{s}_{t}\), the vector \(\mathbf{v}(\mathbf{s}_{t})\) is parallel or anti-parallel to the derivative \(\frac{d\mathbf{s}_{t}}{dt}\). Therefore, both vectors differ at most in their absolute size:  
\[\frac{ds_{x}}{dv_{x}}=\frac{ds_{y}}{dv_{y}}=\frac{ds_{z}}{dv_{z}} \tag{2}\]  
where \(\mathbf{s}_{t}=(s_{x},s_{y},s_{z})^{T}\) and \(\mathbf{v}=(v_{x},v_{y},v_{z})^{T}\). The streamline is not defined where the vector field is zero. Figure 1 shows an example for a streamline in 2D. The first step for calculation of a streamline is to select a seed point. At this point, the respective vector from the vector field is investigated. Via numerical integration, the streamline is expanded point by point, following the parallel and anti-parallel direction of the vector field in each point. In the following sections, we outline how streamlines are used as a basis for the trajectory of a sensor and the corresponding robot path.
## 4 Simulation Analysis
### Estimated Performance at SNRs  
In addition to evaluating the algorithm's estimation performance at various frequencies, the effects of noise on frequency estimation are also assessed. The quantized frequency point is set to \(f_{i}=19.2\) Hz.  
Figures 4 and 5 present the comparison results, where (a) displays the data for SNR values ranging from \(-20\) dB to \(20\) dB, and (b) offers an expanded view of (a) for SNR values between \(-10\) dB and \(10\) dB. The ASIQ-Rife and PAI-Rife algorithms show an improved estimation accuracy compared to the original Rife algorithm under certain SNR conditions. Nevertheless, substantial errors are still evident at lower SNR levels. Conversely, the SP-Rife algorithm capitalizes on the stability of phase angles and spectral refinement techniques, effectively reducing the impact of noise on the frequency estimation. Consequently, the SP-Rife algorithm consistently achieves the most stable mean frequency estimation in low-SNR scenarios.  
Figure 4: The mean of SP–Rife and other algorithms at different SNRs. (**a**) The results at SNR ranging from \(-20\) dB to \(20\) dB are presented to demonstrate the overall noise resilience of different algorithms. (**b**) The results at SNR ranging from \(-10\) dB to \(10\) dB are provided to compare the estimation performance of different algorithms under varying SNRs.  
Figure 5: The standard deviation of SP–Rife and other algorithms at different SNRs. (**a**) The results at SNR ranging from \(-20\) dB to \(20\) dB are presented to demonstrate the overall noise resilience ofdifferent algorithms. (**b**) The results at SNR ranging from \(-10\) dB to \(10\) dB are provided to compare the estimation performance of different algorithms under varying SNRs.  
The calculations reveal that, within an SNR range of \(-10\) dB to \(10\) dB, the Rife algorithm has an average standard deviation of approximately \(0.063\). Among the two enhanced Rife algorithms, the PAI-Rife algorithm performs better, with an average standard deviation of about \(0.013\). The SP-Rife algorithm delivers the best performance, with an average standard deviation of approximately \(0.008\). Compared to the Rife algorithm, the PAI-Rife algorithm shows a notable improvement of \(87.3\%\) in noise resilience, while the SP-Rife algorithm surpasses PAI-Rife with a \(38.5\%\) improvement in noise resilience. In low-SNR conditions, this enhancement is even more significant, with the standard deviation nearing the CRLB.
# The Impact of GCP Chip Distribution on Kompsat-3A RPC Bias Compensation
###### Abstract
Moreover, a study on stereo WorldView-2 [17] highlighted that GCP distribution outweighs the number of GCPs in geometric processing. While prior studies have emphasized the importance of GCP distribution [11; 17], their recommendations vary due to differences in satellite characteristics regarding orbit and attitude information stability and reliability. This study focuses on Kompsat-3A, providing a PAN (panchromatic) resolution of 0.55 m, an MS (multi-spectral) resolution of 2.20 m, and a TIR (thermal infrared) resolution at 5.5 m [18]. The specified positional accuracy for Kompsat-3A is 70 m (CE90), although its actual accuracy surpasses this specification [16]. Kompsat-3A also requires post-processing for RPC bias compensation using GCPs. Previous studies have indicated that GCPs need to be used efficiently in terms of time and cost [13]. Consequently, we investigated the impact of GCP distribution on Kompsat-3A. While abundant GCPs are needed across the entire image space, gathering this information is expensive. Therefore, we implemented an automated procedure to extract GCP image chips with ground coordinates from precise aerial orthoimages. Kompsat-3A image coordinates for these chips were determined using robust image-matching techniques. Subsequently, experiments were conducted based on diverse GCP distribution scenarios using this dense GCP information. Lastly, the study continued to determine the minimum GCP distribution requirement. The paper is structured as follows: in Section 2, the methodology is described, and the experimental results are presented in Section 3; the discussion and conclusion are presented in Sections 4 and 5.
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 1 Introduction
In this paper, we continue these studies in relation to another type of multimedia data--audio carriers. We studied the basic characteristics of an audio stegosystem for various spreading sequences. In our experiments, we estimate the bit error rate (BER) of hidden information data. This is the most important indicator of the quality of recovered messages. In addition, we evaluated the distortions of the audio container, which characterize the quality of multimedia data after hiding information messages in them. Therefore, we used the mean squared error (MSE) and the peak signal-to-noise ratio (PSNR). In this article, we demonstrated that the variety of ways of generating chip codes have different results for BER, MSE, and PSNR. It becomes an area for possible optimization, as the choice of the method of forming the spreading sequences is very important for various computer applications. Thus, the main goal of our work is to find the best chip codes that would minimize the BER for comparable PSNR.
# Reliability of a Portable Fixed Dynamometer During Different Isometric Hamstring Assessments
###### Abstract  
Hamstring strain injuries are one of the most common non-contact injuries in sport. Hamstring strength and asymmetry are two key modifiable risk factors for hamstring strain injuries; therefore, it seems important to find reliable tests for assessing hamstring strength. The purpose of this study was to determine the within- and between-session reliability of a portable fixed dynamometer for measuring hamstring strength using three different protocols. Fourteen male participants completed three hamstring isometric protocols across three testing occasions separated by seven days. Peak force, mean force and impulse all had good to excellent within- and between-session reliability for the standing hamstring, supine 90:90 and standing 90:20 assessment (CV = 2.6-11.7%, ICC = 0.74-0.99), while peak rate of force development had moderate to excellent relative consistency (ICC = 0.64-0.90) and unacceptable absolute consistency (CV = 17.1-36.6%). The 90:20 assessment produced significantly higher values (33.4-47.3%) compared to the standing and 90:90 assessments for peak force, mean force and impulse. It appears that a portable fixed dynamometer can reliably measure a range of force-time metrics during three different hamstring assessments; however, the results of the tests cannot be used interchangeably and practitioners comparing hamstring force capability between individuals/research studies need to be cognizant of this and proceed with caution.  
strain gauge; load cell; diagnostics; force; hamstring injuries +
Footnote †: journal: applied sciences
# Analysis of the Water Quality Improvement in Urban Stream Using MIKE 21 FM
## 2 Theoretical Background
### Overview of Ozone Treatment and Stream Water Quality Factors
#### 2.1.2 Overview of Ozone Treatment  
The ozone treatment purifies waste water via oxidative decomposition reaction. Ozone is an unstable gas composed of three oxygen atoms with higher sterilizing rate. Furthermore, there is no controversy over the dangers of using the remaining materials [24]. It kills germs and viruses by destroying their cell membranes, has a harmful material decomposition function, and does not leave any harmful secondary byproduct other than oxygen.  
In addition, it is known to have far-reaching effects, such as color, odor, TPC, chemical oxygen demand (COD) removal, manganese oxidation, and increased dissolved oxygen [24; 25]. The ozone treatment facility installed at the Gulpo sewage treatment plant uses the high-efficiency ozone oxidation method [26].