esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Design and Analysis
### Modal Analysis and Sensitivity Analysis
The design parameters in group one are \(H_{1}\), \(N_{1}\) and \(N_{2}\) which have almost the same sensitivity tendency, and this means that the two working modal frequencies will change in the same direction upon their perturbation, so they play a minor role in frequency degeneration. \(C_{1}\) and \(C_{2}\) are in group two, with different sensitivities to the two working modes, which means Mode A is more sensitive to parameter \(C_{1}\) and Mode B is more sensitive to parameter \(C_{2}\). Thus, the two modals' frequencies can be separately regulated by adjusting \(C_{1}\) and \(C_{2}\), respectively. The degeneration process was performed in three steps. In step one, by adjusting \(N_{1}\) roughly, the two modal frequencies will have great changes and appear in the same frequency range. In step two, altering \(C_{1}\) and \(C_{2}\), respectively, can make the two modal frequencies close enough, which is dominated by the whole degeneration process. In step three, by changing \(H_{1}\) and \(N_{2}\) slightly, the resonant frequency can be degenerated accurately. Finally, the resonant frequencies of Mode A and Mode B were degenerated at 24,640 Hz and 24,653 Hz, respectively. The final structure parameters of the proposed motor are listed in Table 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{L_{1}}\) & \(\mathbf{L_{2}}\) & \(\mathbf{L_{3}}\) & \(\mathbf{F_{1}}\) & \(\mathbf{F_{2}}\) & \(\mathbf{H}\) & \(\mathbf{V}\) \\ \hline
38.0 & 3.0 & 35.0 & 20.0 & 20.0 & 36.0 & 36.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Initial values of the stator’s main parameters (mm).
## 3 Results
### Performance Evaluation  
In the field of ECG diagnosis, the most common performance evaluation is sensitivity (Sen), specificity (Spec), and accuracy (Acc) respectively. To facilitate comparison with related studies, we also use them to evaluate our method. The expression is shown as (14), (15) and (16):  
\[\text{Sensitivity}=\text{TP}/(\text{TP}+\text{FN}) \tag{14}\]  
\[\text{Specificity}=\text{TN}/(\text{TN}+\text{FP}) \tag{15}\]  
\[\text{Accuracy}=(\text{TP}+\text{TN})/(\text{TP}+\text{TN}+\text{FP}+\text{ FN}) \tag{16}\]  
The positive sample is MI (label as 1) and the negative sample is HC (label as 0). Among them, TP, FP, TN, and FN, respectively, represent the number of sick samples diagnosed correctly as sick. Healthy samples are wrongly diagnosed as sick, healthy samples are correctly diagnosed as healthy, and sick samples are wrongly diagnosed as healthy.
# Multi-LEO Satellite Stereo Winds
## 4 Discussion
### Validation
#### 4.1.3. Reanalysis Winds  
To validate the retrieved stereo-wind vectors, we use European Center for Medium Range Weather Forecasting (ECMWF) fifth-generation Reanalysis (ERA5) [59] wind profiles for the dates in question, collocated in space and time with the stereo-retrieval sites using a nearest neighbor assignment. The ERA5 model updates hourly and has a grid spacing of 0.25\({}^{\circ}\), which allows for collocation to better \(\pm\)15 km, so each stereo-wind template will contain a grid point simultaneous to within \(\pm\)30 min. ERA5 profiles are recorded at 37 distinct pressure levels. The geopotential is provided at each pressure level, from which we calculate the geopotential height. We interpolate a reanalysis wind to compare with each stereo wind at the stereo height using the wind versus the geopotential height at the 37 model levels. Figure 13 provides an example of one comparison and Table 3 provides aggregated statistics across all overpasses of the pole for each case in comparison with ground-point error statistics. The best results correspond to cases where the ERA5 data are most simultaneous with the stereo winds (e.g., Figure 13). The statistics exclude a small outlier population (absolute difference > 15 m/s). The outlier population size depends on the time difference between the stereo and reanalysis winds. In Figure 13, the reanalysis and observed winds are essentially simultaneous and the excluded outlier population is \(<\)1%, but, when the difference exceeds 20 min, it can be \(>\)3%. The low bias and \(\sim\)2.5 m/s standard deviation are similar to results found in a study comparing ERA5 with radiosonde winds collected in a field campaign at high latitudes [60]. Another study [34], comparing MISR stereo winds to GOES and MODIS winds, shows similar differences (2-4 m/s) between datasets. The ERA5 comparisons in Table 3 show larger errors than the ground points and are also likely to overestimate the stereo-wind errors.  
Figure 13. Collocated ERA5–stereo-wind comparisons are shown on log-density plots for an Arctic overpass of Sentinel-3B and Terra on 9 July 2021 23:00Z. Statistics (mean (u), standard deviation (\(\sigma\)), and correlation (R)) exclude ground points and a small outlier population with \(>\)15 m/s difference (11 outliers out of 17,428). Stereo winds that are more likely misclassified ground points, identified as near-surface calm winds in the stereo dataset, are also excluded.
## 3 The Interaction Landscape of _exo-xis_ Region Proteins
### Yeast Two-Hybrid Studies  
Host metabolic and stress response pathways are the most plausible targets of _exo-xis_ proteins. Within those pathways, _exo-xis_ proteins may exert wide ranging effects through interactions with RNA polymerase, regulatory sequences and response regulators. To date, potential protein partners for two \(\lambda\)_exo-xis_ proteins have been identified as from a yeast two-hybrid (\(\gamma\)ZH) study that screened \(\lambda\) phage protein baits with a library of either phage or bacterial protein preys [31; 32]. In Figure 2, an interaction map is presented for Orf63, a protein conserved among \(\lambda\) and \(\text{Stx}^{+}\) phages, and Ea8.5, a protein exclusive to \(\lambda\). The map  
Figure 1: The _exo-xis_ regions of \(\lambda\) and two Shiga toxin producing phages (933W, \(\Phi\)24\({}_{8}\)) associated with clinically relevant \(E\). _coli_ infections. Solid bars indicate a typically conserved core set of genes. Other genes are indicated by shaded bars.  
shows that both Orf63 and Ea8.5 are associated with the Ren and Q as partners. Ren is a transcriptional activator that allows prophages to be superinfected by a similar phage but not by unrelated phages [33; 34]. Q is an anti-termination factor that permits middle and late phage genes to be expressed by preventing the formation of pausing and terminator hairpins in the emerging RNA at the exit-channel of RNA polymerase (RNAP) [35]. Stf and Tta are tail-associated structural proteins and may not be true partners of Ea8.5. One promising interactor for Orf63 from the Y2H assay is Yqhc, a transcription factor that stimulates expression of YqhD, an enzyme that amplifies the oxidative stress response. If Orf63 bound and inhibited Yqhc, neutrophil mediated oxidative attack in the gut would be diminished thereby creating a more favorable environment for the phage to develop [24]. Turning to Ea8.5, the identification of MinE as a potential protein partner may suggest a role in altering the cell division process. Since MinE helps maintain the Z-ring assembly in the middle of the cell [36; 37], Ea8.5 in turn, may sequester it or bias MinE towards one conformation. In support of this role, Ea8.5 is known from early functional studies for its ability to help synchronize cell division [18]; however, its structure suggests that it may serve as a transcription factor instead [38]. Further biochemical and biophysical studies with purified proteins and protein fragments are necessary to reveal the precise function role of Ea8.5 and other _exo-xis_ proteins.  
Aside from Orf63 and Ea8.5, no other _exo-xis_ protein partners were identified by the Y2H studies. Since the Y2H method only identifies binary protein-protein interactions, it is possible that some _exo-xis_ proteins participate in large protein assemblies. Alternatively, some _exo-xis_ proteins may have a nucleic acid as a ligand instead of a protein. With these possibilities in mind, it would be worthwhile to extend the Y2H studies by using contemporary proteomic approaches that combine direct capture with mass spectrometry-based detection.
# Security Protection of 3D Models of Oblique Photography by Digital Watermarking and Data Encryption
## 5 Experimental Results and Analysis
### Analysis of Imperceptibility
#### 5.2.1 Watermark Extraction from the Unattacked Model  
The embedded watermark data is a 48 \(\times\) 48 pixel binary image. The watermark extraction is performed on the unattacked 3D model data after embedding the watermark. The extracted watermark information is shown in Figure 7. When the model data is not attacked, the extraction result is consistent with the embedded image.  
Figure 7: Embedded watermark (**a**) and extracted results (**b**).
## 3 Adaptive Computation
### Adaptive Hardware Usage
The look up table is obtained from receiver simulations and power analysis to ensure that the least power settings, which will ensure bit error rate are entered into the table. Prior to reception of a packet, the digital data path is set to operate at the highest resolution of 8-bits and 30 MHz. During packet preamble, in parallel with timing acquisition, the IDSE block estimates the signal and interference conditions and determines the optimal \(Q_{dig}\) and \(f_{s}\) settings. At the end of the preamble, the datapath's settings are adjusted to this new value so that the rest of the data symbols in the packet are received with this optimal setting for the receiver. A slice of the look up table is shown in Table 1 for two different interference and signal conditions (high and low values). It shows significant power differences between the best case and worst case input signal conditions. \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Low SNR** & **High SNR** \\ \hline High Interference & 8-bits, 15 MHz, 3.3 mW & 1-bit, 2 MHz, 0.5 mW \\ Low Interference & 2-bits, 15 MHz, 2.49 mW & 1-bit, 2 MHz, 0.49 mW \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Bit resolution and sampling rate settings for minimum power dissipation for different signal and interference strengths. Figure 9: Architecture of a power scalable receiver. The receiver’s data path can work at a number of different word lengths and sampling frequencies. An adaptivity control unit is added to determine the right word length and sampling frequency which minimizes overall power consumption.
# Study of Motion Control and a Virtual Reality System for Autonomous Underwater Vehicles
## 4 Development of the AUV Motion Control Virtual Reality System  
### Overall Design  
Before development of the virtual design, one needs to consider the tasks the system should perform, such as showing vivid virtual scenes, changing scene element attributes according to demand, and obtaining numeric parameters of scene elements in real time. The development task of designing the system comprises of three parts:  
1. Construction and display of 3D AUV and virtual environment scene: A 3D modeling software is used to generate the AUV 3D model and other environmental element models. Each element is appropriately deployed to generate a 3D virtual scene of underwater robotic motion.
2. AUV motion control realization and output display: This paper provides two methods for the realization and output of the AUV motion control. The first method directly uses Visual C++ programming in the virtual reality system to realize the AUV motion control algorithm and displays the results on the AUV motion visual  
Figure 6: Heading response of the neuro-fuzzy and PID controllers with model perturbation.  
Figure 7: Rudder order outputs of the neuro-fuzzy and PID controllers.  
simulation interface. In the second method, the system first uses MATLAB to design the AUV motion control algorithm, and then uses the MATLAB engine to execute the commands and perform the data transmission between MATLAB and the virtual simulation system under the Visual C++ environment. The output of the AUV motion control system designed using MATLAB can be shown in the virtual reality system.
3. Design of AUV motion control virtual reality system: The key functionalities of a virtual reality system are a) how to \"control\" the virtual scenes, b) how to integrate and synchronize the AUV motion control and virtual scene under the same software platform, and c) how to realize the poses of the AUV 3D model motion and the \"communication\" of the control algorithm.  
The choice of the software used for the development of the virtual reality system is as follows: (a) MilkShape 3D: The 3D model drawing software can flexibly call the OpenGL (open graphics library) library for drawing. (b) MATLAB: MATLAB has powerful operational capabilities and can implement complex AUV motion control algorithms, such as neural networks and fuzzy control. (c) Visual C++: Visual C++ combines many development tools together as an integrated development platform.
## 1 Introduction
Moreover, these sensors typically have time responses in the order of a few seconds, up to some minutes [18; 19; 20; 21; 22]. In general, in sensor design there is an inherent compromise between the level of sensitivity, the time response and the gas path length. In this work a C\({}_{2}\)H\({}_{2}\) sensor based on the Line Locked Tunable Laser Absorption Spectroscopy (LL-TLAS) and a dual path correlation spectroscopy method is presented. In order to implement the LL-TLAS technique, an erbium doped tunable fiber laser was designed and implemented. This laser used a silicon wafer as a selective filter that allowed us to finely tune the laser emission until it is matched to one ro-vibrational line of the target gas. Afterwards, the laser remains in line-locked mode by keeping the wafer temperature constant. This is achieved by driving a thermo-electric cooler with a proportional-integral-derivative (PID) control program. This allowed the laser to have a very high wavelength accuracy in the order of 0.12 pm, which is a mandatory requirement for this kind of sensing application. Moreover, the sensing arrangement is complemented with a simple dual path correlation spectroscopy stage that helps to strongly minimize errors due to laser intensity fluctuations. The achieved sensor sensitivity was of 521 ppm for the concentration range from 0 to 20% by using a 10 cm cell with a time response within the sub-minute order. Experimental measurements supported by numerical simulations are provided demonstrating the suitability of the tunable fiber laser for the implementation of this type of sensing application.
## 3 Problem Definition and Network Model
### Network Model  
To model an LEO satellite network, we utilize undirected graph \(G(N,E)\), where \(N\) represents the set of satellites and \(E\) represents the set of all possible links connecting the satellites. Additionally, we define a set of commodities, denoted as \(M(o_{i},d_{i},D_{i})_{i\in I}\), characterized by their source, destination, and demand. We summarize the main symbols and their explanations in Abbreviations section.  
In order to ensure that each commodity can only choose one path to forward traffic in the end,  
\[t_{ei}\in\{0,1\},\ \ \forall i\in I,\ \ \ \ \forall e\in E. \tag{1}\]  
which \(t_{ei}\) indicates whether there is traffic on link \(e\) for commodity \(i\). In order to ensure that the traffic can pass through the inter satellite link normally, we set the optimization objective as a constraint:  
\[\min_{t_{ei}\ \triangle_{e}}\sum_{e\in E}\triangle_{e}. \tag{2}\]  
where \(\triangle_{e}\) represents the overflow on link \(e\). To guarantee that the incoming and outgoing traffic of each commodity flowing through each satellite node is the same, we have the following constraint:  
\[\sum_{e\in E^{+}(n)}t_{ei}\ =\ \sum_{e\in E^{-}(n)}t_{ei},\ \ \ \forall i\in I,\ \forall n\in N. \tag{3}\]  
where \(E^{+}(n)\) represents the incoming traffic of node \(n\), and \(E^{-}(n)\) represents the outgoing traffic of node \(n\). In order to ensure reasonable traffic overflow when traffic passes through link \(e\), we have the following constraint:  
\[\sum_{i\in I}t_{ei}D_{i}\ -\ c_{e}\leq\triangle_{e}\,\ \ \forall e\in E. \tag{4}\]
# Bat Optimized Link State Routing Protocol for Energy-Aware Mobile Ad-Hoc Networks
## 5 Simulation and Result
### Simulation Model and Parameters  
The simulation of the proposed BOLSR protocol is discussed in this section. The aim of the experimental tests of the simulation is to improve the MANET performance and prolong its lifespan [42]. The objective is to use the BOLSR to find the path with the highest levels of energy and a minimum delay. Afterwards, three routing protocols along with the BOLSR are tested and their results are compared. Figure 7 represents the main steps that were used to implement the simulation of the BOLSR and other protocols. The simulation model consists of four main modules. Firstly, the MANET environment Module A was employed to create simulation environments. This module was achieved by integrating the main characteristics of MANET, such as network size, a number of nodes, bandwidth and so on. The next step was implementing the routing protocols as Module B in which several optimization algorithms were used to address the progress of the solution. Three different scenarios (node speed, number of nodes, and simulation time) with various parameters were implemented in Module C to assess the performance quality of the protocols. The scenarios were altered in each set of runs of a particular protocol. In Module D, the performance metrics of PDR, E2E, Routing overhead ratio (ROR), and EC were implemented to evaluate the protocols. Finally, a results visualization graphical user interface module is included to view the results and related analysis.  
Figure 6: Snapshot of the simulation.  
Figure 7: The mobile ad hoc network (MANET) simulation model.  
The setting of the simulation's parameters was adopted from [4; 6; 7; 8; 9; 25; 44]. Table 2 describes all the parameters used for the setting of the simulation.
## 4 Iv-Ssim
### Image Properties Similarity  
In the second step of IV-SSIM calculation, all the abovementioned local statistics are used for the estimation of the similarity of three properties of images \(I\) and \(J\): intensity \(L\), contrast \(C\), and structure \(S\).  
\[L_{c}^{I\to J}(x,y)=\frac{2\cdot\mu_{c}^{I}(x,y)\cdot\left[\mu_{c}^{I}(x,y)+s _{c}^{I\to J}\right]+C_{1}}{\mu_{c}^{I}(x,y)^{2}+\left[\mu_{c}^{I}(x,y)+s_{c}^ {I\to J}\right]^{2}+C_{1}}, \tag{9}\]  
\[C_{c}^{I\to J}(x,y)=\frac{2\cdot\sigma_{c}^{I}(x,y)\cdot\sigma_{c}^{I}(x,y)+C _{2}}{\sigma_{c}^{I}(x,y)^{2}+\sigma_{c}^{I}(x,y)^{2}+C_{2}}, \tag{10}\]  
\[S_{c}^{I\to J}(x,y)=\frac{\sigma_{c}^{I\to J}(x,y)+C_{3}}{\sigma_{c}^{I}(x,y) \cdot\sigma_{c}^{J}(x,y)+C_{3}}, \tag{11}\]  
where \(s_{c}^{I\to J}\) is the global offset (cf. Section 3.2) of component \(c\) between images \(I\) and \(J\), defined as:  
\[s_{c}^{I\to I}=\frac{1}{W_{C}\cdot H_{c}}\sum_{y=0}^{H_{c}\to 1}\sum_{x=0}^{W_{c} -1}(l_{c}(x,y)-J_{c}(x,y)), \tag{12}\]  
and \(C_{1}\), \(C_{2}\), and \(C_{3}\) are constants providing numerical stability:  
\[C_{1}=\left(K_{1}\cdot(2^{b}-1)\right)^{2},C_{2}=\left(K_{2}\cdot(2^{b}-1) \right)^{2},C_{3}=\frac{C_{2}}{2}, \tag{13}\]  
where \(b\) is the bit-depth of the image, and:  
\[K_{1}=0.01,K_{2}=0.03. \tag{14}\]  
\(W_{c}\) and \(H_{c}\) denote the width and height of the image (i.e., width and height of its \(c\)-th color plane). The comprehensive description regarding the calculation and usage of \(C_{1}\), \(C_{2}\), \(C_{3}\), \(K_{1}\), and \(K_{2}\) coefficients can be found in [40].  
In the third step, all property similarities are combined, resulting in local quality scores for component \(c\):  
\[Q_{c}^{I\to J}(x,y)=\left[L_{c}^{I\to J}(x,y)\right]^{\alpha}\cdot \left[C_{c}^{I\to J}(x,y)\right]^{\beta}\cdot\left[S_{c}^{I\to J}(x,y) \right]^{\gamma}, \tag{15}\]  
where \(\alpha\), \(\beta\), and \(\gamma\) are parameters describing the relative importance of \(L_{c}^{I\to J}\), \(C_{c}^{I\to J}\), and \(S_{c}^{I\to J}\), respectively. As proposed in [11], the values \(\alpha=\beta=\gamma=1\) are used.
## 3 Results and Discussion
### Compression Tests  
The compression tests revealed significant differences in the load-bearing capacity of the samples. PLA-FDM specimens withstood 2.97 kN, ABS-FDM specimens withstood 0.95 kN, and ABS-Injection molded specimens exhibited the highest resistance, enduring 3.93 kN. The graphical representation of these compression test results is presented in Figure 6. These findings clearly demonstrate that the ABS plastic injection molded samples exhibited the highest resistance to compressive load.  
The superior strength of injection-molded parts can be attributed to the critical ironing and holding phases within the plastic injection process. These phases, which involve compressing the material within the mold, effectively prevent the formation of internal hollow structures or voids, thereby enhancing the material's overall strength. We observed a direct correlation: as the presence of hollow structures in the samples decreased, resistance to fracture increased and vice versa. This suggests that the plastic injection molding method promotes ductile behavior under load due to its significantly reduced internal void content.  
Conversely, a subsequent examination of the SEM images of the 3D-printed samples revealed a large cavity structure. This phenomenon is hypothesized to result from either an uneven distribution or an inadequate adhesion of the molten polymer during filament extrusion in the FDM process. The study's findings indicate a direct correlation between material  
Figure 5: Compression test.  
strength and the presence of voids and microstructure irregularities: as these parameters increase, the material's strength diminishes. The data obtained through scanning electron microscopy (SEM) corroborates the outcomes of previous studies, thereby validating our research approach and providing a robust evidence base for the conclusions drawn.