esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model  
### Network Model  
As shown in Figure 1, the sensor nodes periodically sense the data and transmit them to BS in a multi-hop manner. The network model includes a fixed BS in the center, which is used to manage nodes information. \(N\) sensor nodes are randomly distributed in a square area \(L\times L\), and the communication radius of each sensor node is set to \(R\). Meanwhile, the energy of the battery carried by each node is limited and all initial energy is \(E_{S}\). The topology of the whole network is composed of an undirected graph \(G(V,C)\), where \(V=\{1,2,\ldots,i,\ldots,N\}\) denotes all sensor nodes and \(C=\left\{C_{12},C_{13},\ldots,C_{ij}|i\
eq j,i,j\in V\right\}\) is a set of directed path links between nodes. The set of forwarding neighboring nodes of node \(i\) is given by:  
\[FN(i)=\left\{j|d_{ij}<R,d_{js}<d_{is},i,j\in V\right\} \tag{1}\]  
where \(d_{ij}\), \(d_{js}\) denote the distance from node \(i\), node \(j\) to BS respectively.  
To facilitate the description of the network model, the following reasonable assumptions are presented as follows:  
1. Once the network is deployed, the location of the sensor nodes will not move any more. The sensor nodes can estimate the distance to other nodes based on the received signal strength and positioning module [23]. Then the node uploads the location information to BS.
2. All sensor nodes are isomorphic, with function of routing, sending and receiving packets, and the initial status is the same.
3. Each node has a unique identifier (ID), which maintains a buffer to store information such as residual energy, next-hop ID, packet ID, sender IDs etc. The information is updated in real time as the forward neighbor changes [22].  
It is noted that the buffer length of each node is limited, and data packets are processed in first-in first-out (FIFO) mode.
5. The time when the first node dies due to energy exhaustion is defined as network lifetime.
## 3 Results and Discussion
### Effect of CSS on the Qualitative Characteristics of Burger Samples  
#### 3.1.1 Compositive Characteristics  
Results from proximate compositional analyses of meat are shown in Table 1 (uncooked burgers); data agree with the literature [29; 30]. Generally, macro-elements checked do not change so much. Moisture, proteins, and lipids remained at around their normal values; ashes had a small significant increase reaching the highest point of 2.69% for 3% CSS sample. TDF for samples (calculated as reported in Section 2.3) are 1.70% and 3.40%, for 1.5% and 3% CSS, respectively.  
pH of breast fillets used for the burger production was 5.93 \(\pm\) 0.04, not differing with reported values which indicate values around 5.89-6.00 [31]. Formulated burgers (uncoked and cooked) registered pH values, shown in Table 2, in line with other sources [32]. Cooked burgers had similar pH and a\({}_{\text{w}}\) values.  
In eating a newly formulated burger (45 g) with a CSS inclusion of 1.5%, 16.54 mg of calcium and 65.7 mg of potassium can be assumed. Harvard Health Publishing in 2019 reviewed the daily intake of calcium for women between 50 and 71 years old fixing this at 1200 mg [33]. The same value was established by the National Institute of Health (NIH) which also defines limits [34] for men at 1000 mg. Potassium was fixed at 3400 mg and 2600 mg for males and females from 19 to 50 years old, respectively.  
For marketing within the European Union (EU), it is very important to define whether by-products, such as CSS, need to obtain an approval as ingredient for novel foods [34], with special reference to legal status within the EU and potential options for producers to obtain approval according to Novel Food [35; 36; 37].
## 3 Results
### Memory Resource Analysis  
On spaceborne GNSS receivers, memory resources are constrained, and gravity field models with excessively high orders may result in memory overflow. As a result, optimizing memory use while preserving orbit determination accuracy has become a significant topic that demands in-depth investigation. The structure of the gravity field in the programming implementation is depicted in Table 8:  
The gravitational mass coefficient of the celestial body is approximately 3,986,004,415 m\({}^{3}\)/s\({}^{2}\), and the Earth's reference radius is about 6,378,136.46 m. The order and degree of the spherical harmonic coefficients are the main subjects of this investigation. In this comparison, KB (kilobytes) is used as the unit of measurement to examine the memory space occupied by different orders, with a particular focus on the space occupied by the code (.text), static variables and global variables (.bss), and the memory used by the gravity field definition structures. The memory usage for different orders is shown in Table 9 and Figure 9.  
The experimental results reveal that when the order grows from 40 to 80, the memory space required by static variables, global variables, and structures gradually increases, as does the memory demand throughout the computation process. Comparing memory consumption at different orders reveals that the existing system's memory allocation  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Type** & **Name** & **Meanings** \\ \hline double & GMConst & Gravitational Mass Coefficient of Celestial Bodies \\ double & refRadius & Earth Reference Radius \\ char & maxDegree & Order of Spherical Harmonic Coefficients \\ char & maxOrder & Degree of Spherical Harmonic Coefficients \\ double & C[] & Sine Terms of Spherical Harmonic Coefficients \\ double & S[] & Cosine Terms of Spherical Harmonic Coefficients \\ \hline \hline \end{tabular}
\end{table}
Table 8: Structure of gravity field.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Order** & **40** & **50** & **60** & **70** & **80** \\ \hline.text & 19.93 & 20.02 & 20.02 & 20.02 & 20.02 \\.bss & 41.28 & 57.30 & 76.43 & 98.70 & 124.10 \\ Size (struct) & 13.50 & 20.74 & 29.57 & 39.96 & 51.91 \\ Total & 74.71 & 98.06 & 126.02 & 158.68 & 196.03 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Memory usage for different orders (Unit: KB).  
Figure 9: Memory usage for different orders.  
is sufficient to enable gravity field model calculations up to order 80, although this is approaching the memory limit. To allow calculations at even higher levels, memory allocation must be further optimized or system memory resources increased to ensure that the calculations run smoothly. This analysis gives critical information for carrying out high-order gravity field model calculations in resource-constrained contexts.
# Polycaprolactone Blended Cosmetic Microneedle for Transdermal Hispidin Delivery System
## 3 Results and Discussion
### Cell Viability Test with Hispidin, PCL, PLA, and Mixture  
The transdermal MN delivery system passed through the epidermis and underwent biodegradation. Once hispidin was eluted from the MNs as a solution, the effect of solution blends had a similar antibacterial activity. To determine the concentration of hispidin at which no cytotoxicity was present, B16F10 melanoma cells were treated with hispidin, PCL, and PLA in various concentrations for 24 h. The final concentration of DMSO used was less than 0.1%, which is a concentration that does not affect the cell viability. The MTT assays revealed that there were 103%, 95%, 91%, and 90% levels of viable B16F10 melanoma cells after treatment with hispidin at concentrations ranging from 1-20 uM (Figure 3a). PLA and PCL were dissolved in 1% DMSO for 24 h at 55 \({}^{\circ}\)C, giving concentrations of 10, 100, 1000, and 10,000 ppm for both materials used for the cell viability test. PLA at concentrations ranging from 10-10,000 ppm showed values of 101%, 98%, 91%, and 90% viability for B16F10 melanoma cells (Figure 3c). Furthermore, PCL at concentrations ranging from 10-10,000 ppm demonstrated values of 121%, 122%, 119%, and 120% viability for B16F10 melanoma cells (Figure 3b). Hispidin (1-20 uM) mixed with PLA/PCL at the same concentration (10,000 ppm) showed levels of 100-120% cell viability (Figure 3d). These results suggest that hispidin, PCL, and PLA, as well as their mixtures, exhibited almost identical cell viability compared to the control.  
Figure 2: In vitro skin permeability of hispidin with PLA/PCL blended MNs. (**a**) Ultraviolet-Visible (UV–Vis) spectroscopy of hispidin, (**b**) PCL, (**c**) PLA, and (**d**) MN in vitro tested sample.
# Research Progress on Flexible Oxide-Based Thin Film Transistors
## 3 Approaches to Flexible Oxide-Based TFTs
### Flexible TFTs under Mechanical Stress  
When bending flexible devices, mechanical stress will be generated as the substrate and films of the TFTs come under tensile or compressive strain. TFT properties will then also be degraded. Furthermore, when the radius of curvature and the mechanical stress increase beyond tolerance, cracks in the substrate emerge and TFTs will fail. Hence, it is necessary for researchers to comprehend the impact of mechanical stress on flexible device performance [173; 174; 175; 176; 177; 178; 179; 180; 181; 182; 183]. In fact, with the widely extending use of flexible applications, bending behaviors of the TFTs will become more and more complex. Three types of practical applications can be distinguished: the first involves a fixed bending radius, like Samsung Galaxy S mobile phones and curved TVs. In the second kind, the bending is mostly in one direction, like in wristband-like displays, wristwatches and flexible TVs. In the last one, bending occurs in any direction randomly, like in wearable electronics and skin sensors. In this section, bending test platforms and methods are introduced in Section 3.2.1. Then, the degradation of devices under the tensile and compressive strain is discussed in Section 3.2.2. Finally, two methods for the optimization of TFT design are introduced.  
Figure 10: (**a**) schematic fabrication flow of the gas sensitive TFT, (**b**) layout of the TFT, (**c**) photograph of the fabricated sensors on the flexible polyimide substrate [172].
## 4 Simulation and Analysis  
This paper constructed a dynamic model of the telescope pointing mechanism in Matlab/Simulink (version R2021b) software, introduced disturbance noise into the model, and validated the effectiveness of the control method through numerical simulations. The reference signal in the simulation was derived from the respiratory angle variation in the TianQin constellation plane, using the calculated respiratory angle variation from orbital data as the ideal reference angle, as shown in Figure 2. The main parameters used in the simulation are listed in Table 1.  
In the numerical simulations, the tracking errors of the telescope pointing mechanism were compared under three scenarios: without DOB, traditional DOB design, and optimized DOB design, as shown in Figure 16a. In the graph, it can be observed that the tracking accuracy of the telescope pointing mechanism can be improved by approximately one order of magnitude using the HODOB method compared to the traditional  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline \(J_{t}\) & 15 kg\(\cdot\)m\({}^{2}\) & \((F_{E}^{O})_{max}\) & 5.7 \(\times\) 10\({}^{-9}\) N \\ \(J_{s}\) & Diag[800,800,100] & \((M_{E}^{O})_{max}\) & 3 \(\times\) 10\({}^{-11}\) Nm \\ \(\omega_{N}\) & 2.9439 rad/s & \(b_{1}^{S}\) & 3.6 \(\times\) 10\({}^{-1}\) m \\ \(\xi\) & 0.9058 & \(\mu\) & 398,600.44 km\({}^{3}\)/s\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key simulation parameters.  
Figure 15: Comparison of disturbance suppression performance between optimally designed filter Q(\(s\)) and traditional filter design Q(\(s\)).  
DOB method. Converting the tracking errors into power spectral density, the simulation results in Figure 16b demonstrate that the use of HODOB significantly outperformed the traditional DOB in suppressing disturbance noise. This ensures that the stability of the telescope pointing system meets the pointing requirements of 10 \(\text{nrad}/\text{Hz}^{1/2}\) for the TianQin mission.  
To verify the effectiveness of the proposed composite control method, a comparison was made between the HODOB-based \(H_{\infty}\) and the HODOB-based PID. Figure 17 illustrates the comparison results of the stability of the telescope pointing system under these two control methods. It can be observed in the figure that, within the detection frequency range, the proposed composite control method performed better in terms of pointing stability compared to the HODOB-based PID. In other words, the proposed composite controller exhibited greater robustness against multi-frequency band disturbances, thereby further enhancing the control performance of the telescope pointing system.
## 5 Result and Discussion
### Optimization Results and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**NSGAII**} & \multicolumn{3}{c}{**HO-NSGAII**} & \multicolumn{3}{c}{**Distance Gap**} & \multicolumn{1}{c}{**Optimization Rate**} \\ \cline{2-9}  & **m** & **D1** & **D2** & **m** & **D3** & **D4** & & \\ \hline \multirow{3}{*}{Test01} & 10 & 297.166 & 313.129 & 10 & 282.322 & 282.322 & 30.807 & 9.8\% \\  & 11 & 268.431 & 279.614 & 11 & 259.177 & 259.177 & 20.437 & 7.3\% \\  & 12 & 267.611 & 278.794 & 12 & 257.177 & 257.177 & 21.617 & 7.8\% \\ \hline \multirow{3}{*}{Test02} & 10 & 264.747 & 266.757 & 10 & 257.671 & 257.671 & 9.086 & 3.4\% \\  & 11 & 260.156 & 264.145 & 11 & 247.409 & 247.409 & 16.736 & 6.3\% \\  & 12 & 258.336 & 261.426 & 12 & 243.066 & 243.066 & 18.36 & 7.0\% \\ \hline \multirow{3}{*}{Test03} & 9 & 396.447 & 426.526 & 9 & 356.199 & 356.199 & 70.327 & 16.4\% \\  & 10 & 293.853 & 303.414 & 10 & 255.034 & 255.034 & 48.38 & 15.9\% \\ \cline{1-1}  & 11 & 282.503 & 292.064 & 11 & 242.796 & 242.796 & 49.268 & 16.8\% \\ \cline{1-1}  & 12 & 275.586 & 285.147 & 12 & 233.884 & 233.884 & 51.263 & 17.9\% \\ \cline{1-1}  & 13 & 273.586 & 283.147 & 13 & 232.884 & 232.884 & 50.263 & 17.8\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Data comparison. Figure 7: Comparison of delivery distances for the same number of vehicles. The HO-NSGAII algorithm introduced in this study offers a solution to the challenge of material location and path planning in scenarios with obstacles. In comparison to the conventional NSGAII algorithm, the proposed algorithm demonstrates enhanced effectiveness in obstacle scenarios. It is capable of generating a more optimal distribution scheme with shorter distribution distances for a consistent number of vehicles, resulting in more practical outcomes.
# A Novel GNSS Interference Detection Method Based on Smoothed Pseudo-Wigner-Hough Transform
## 2 GNSS Signal and Interference Models
\begin{array}{ll}y(t)&=\sum\limits_{i=1}^{N_{c}}r_{i}(t)+\eta(t)\\ &=\sum\limits_{i=1}^{N_{c}}A_{i}\widetilde{c}_{i}(t-\tau_{i})d_{i}(t-\tau_{i} )\cos[2\pi(f_{IF}+f_{d,i})+\varphi_{i}]+\eta(t)\end{array}  \tag{7}\]  
where \(f_{IF}\) is the IF of the GNSS receiver, and \(\widetilde{c}_{i}(t-\tau_{i})\) is the spreading code sequence after filtering of the GNSS receiver front-end. Here, the effect of the GNSS receiver front-endfilter is neglected, assuming the simplifying condition \(\widetilde{c}_{i}(t)\approx c_{i}(t);\eta(t)\) is the disturbing component after down-conversion and filtering, \(\eta[t]=j[t]+\omega[t]\).
## 5 Experiments  
### Collaborative Framework Performance Tests  
#### 5.1.1 Experimental Setup  
Different experiments were carried out in order to determine the performance of the most innovative component of the proposed system: its collaborative framework. In such experiments, communication delay was measured when multiple AR nodes sent their position to all their neighbors continuously.  
The following hardware was used for the tests:  
* One pair of Microsoft HoloLens glasses.
* Router Linksys Wireless-G (2.4 GHz).
* Router Archer C3200 (5 GHz).
* Desktop computer.  
In order to recreate a multi-user scenario with \(n\) HoloLens glasses, a simulator was created using a Universal Windows Platform (UWP) application [62], which can be executed on a desktop computer. Such a kind of application is the most similar execution environment that can emulate in a desktop computer the functioning of HoloLens glasses, since the libraries developed for the sharing system can be executed natively.  
The number of simultaneous devices can be selected on the HoloLens simulator. Once such a number is configured, the application starts to send the coordinates of the indicated amount of simulated glasses in parallel, using threads in a way that the network load for the actual glasses is comparable to the one that would exist if the same number of real glasses were sending packets over the network.  
In order to evaluate the performance of the application, the main parameters that were measured were latency and packet lost. It is important to note that, in the type of application presented in this article, the delay of each individual packet is one of the most relevant parameters to provide a good user experience. In contrast to traditional real-time applications where network stability and jitter can have more impact than latency, this type of application has to be synchronized with the real world, meaning that when other users make a real gesture to interact with an object, the remote user has to perceive that the action is taking place in real time and synchronized with the real-world gesture. Therefore, it is more critical that each packet arrives as fast as possible instead of being stable over time.
# Fish Detection in Fishways for Hydropower Stations Using Bidirectional Cross-Scale Feature Fusion
## 2 Materials and Methods
### Loss Function  
Loss functions are crucial for model detection accuracy. The CIoU [29] loss function is used for bounding box regression, as described by the formula in Equation (9). The Intersection over Union (IoU) measures the intersection ratio to the union of the predicted and ground truth boxes. This formula, \(P^{2}\big{(}b,\ b^{gt}\big{)}\) represents the Euclidean distance between the centers of the predicted and ground truth boxes, \(c\) is the diagonal length of the smallest enclosing rectangle that contains both the predicted and ground truth boxes, \(h\) and \(h^{gt}\) denotes the height of the predicted and ground truth boxes, respectively, while \(w\) and \(w^{gt}\) represent their widths.  
\[L_{CIoU}=1-IoU+\frac{P^{2}\big{(}b,b^{gt}\big{)}}{c^{2}}+\frac{4}{\pi^{2}} \bigg{(}arctan\frac{w^{gt}}{h^{gt}}-arctan\frac{w}{h}\bigg{)} \tag{9}\]  
The CIoU loss function presents several limitations in the context of fishway fish detection. It introduces additional calculations for diagonal distance and correction factors, increasing computational complexity and potentially prolonging training and inference time. To address the limitations of CIoU in fishway detection tasks, this study uses the WIoU [30] loss function as a replacement. The formula for the WIoU loss function is given in Equation (10), where \(\alpha\) and \(\delta\) are hyperparameters that can be adjusted based on different models, \(L_{IoU}^{*}\) represents the monotonic focus coefficient, \(r\) is the non-monotonic focus factor, and \(\beta\) denotes outliers.  
\[L_{WIoU}=r\times\exp\Bigg{(}\frac{\left(b_{C_{x}}^{gt}-b_{C_{x}}\right)^{2}+ \left(b_{C_{y}}^{gt}-b_{C_{y}}\right)^{2}}{C^{2}}\Bigg{)}\times(1-IoU);r=\frac {\beta}{\delta\alpha^{\beta-\alpha}},\ \ \beta=\frac{L_{IoU}^{*}}{L_{IoU}} \tag{10}\]  
The WIoU loss function adds a focusing coefficient to the attention-based predicted box loss. The additional computational cost mainly comes from the calculation of the focusing coefficient and the mean statistics of the IoU loss. However, it is faster because it does not involve the calculation of aspect ratios. Furthermore, it performs well in small object detection, enabling more accurate evaluation of small object detection results and reducing missed and false detections. This is particularly important for multi-scale fishway fish detection tasks.
# A Content Hiding Method for Digital Hologram Using Multiple Fresnel Diffraction
## 5 Conclusions  
In this paper we proposed an encryption scheme for a DH to hide the information contained in the DH by encrypting as small an amount of data as possible with the maximal encryption efficiency. It is a signal processing method that uses the Fresnel transform such that the first transform is done to change the DH into the corresponding 2D object image and the second one is to concentrate the energy of the object in the image. The second transform uses the characteristic of the Fresnel transform that as the diffraction distance increases, the relative size of the object region decreases with the same resolution, which results in reducing the amount of data it is necessary to encrypt to hide all the information. In addition, we adopted a parameter called scaling factor that scales the area to be encrypted to make the encryption efficiency maximal.  
Experimental results told us that only 0.0058% of the original data needs to be encrypted to hide the information unrecognizably, and it is smaller amount than any existing signal processing method. In addition, ours can apply to an already generated DH, which makes it useful for more applications than the ones that must be applied during generation of a DH. Consequently we expect that the proposed method will be used in more applications with very high encryption efficiency.  
The proposed method is for the Fresnel hologram, so if it is applied to other types of holograms, it will have different results.  
**Author Contributions:** Conceptualization, Y.-H.S. and Y.-H.L.; software, Y.-H.L.; validation, Y.-H.S. and D.-W.K.; writing-original draft preparation, Y.-H.S. and D.-W.K.; writing-review and editing, Y.-H.S.; supervision, D.-W.K.; funding acquisition, D.-W.K. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2019R1F1A105455212). The present Research has been conducted by the Research Grant of Kwangwoon University in 2020.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{2}{c}{**The Previous Method [13]**} & \multirow{2}{*}{**Proposed Method**} \\ \cline{2-3}  & \(L_{TH}(7),E_{TH}(95)\) & \(L_{TH}(7),E_{TH}(95)\) & \\ \hline NC & 0.65 & 0.8 & 0.0008 \\ \hline Encryption Ratio & - & 0.032\% & 0.0058\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with previous research.