esa-sceva/satcom-chunk-collection huggingface dataset:
# Adapting Satellite Soundings for Operational Forecasting within the Hazardous Weather Testbed
## Materials and Methods
### Datasets
#### 2.1.3 Gridded NUCAPS  
Gridded NUCAPS [3; 25] displays horizontal \"slices\" through a swath of NUCAPS soundings at specific pressure levels, which includes humidity and temperature as well as additional derived fields, such as lapse rate and stability indices for severe weather potential (Figure 2d). The horizontal display enables quick visualization of hotspots or areas of interest with convective potential. As such, they provide the context, or situational awareness, prior for the interrogation of individual Skew-T diagrams. Additionally, gridded NUCAPS can be overlaid on other imagery, such as GOES ABI data and can be compared to model fields.  
When initially introduced to the 2017 HWT, gridded NUCAPS was filtered so that all retrievals colored either yellow or red were removed from the display to avoid introducing spurious features. This, however, introduced gaps which the forecasters strongly disliked. They are used to evaluating model fields with smooth, uninterrupted spatial gradients and indicated that they prefer the ability to mentally filter unrealistic values [26]. So, in later HWT demonstrations gridded NUCAPS was not filtered based on quality control thresholds and forecasters were encouraged to overlay the color-coded sounding data to see retrieval quality and examine unbroken gradients in stability indices, temperature and moisture. This feedback-training cycle improved the usability of gridded NUCPAS prior to operational implementation in AWIPS version 19.2.1.
## 2 Operation Principles and Analysis Methods
### Sensitivity Analysis and General Design Flowchart
Figure 13: The measured transmission magnitude results under different incident angles. (**a**) in the _xoz_ plane. (**b**) in the _yoz_ plane.
# Optimization of Design Parameters Using SQP for an Agricultural Pipe Extraction Device
## 2 Materials and Methods
### Optimal Design Method  
The optimal design involves finding the parameters in the direction of minimizing the value of the objective function of the system, which takes into account the constraints proposed by the designer. In this study, the SQP algorithm was used as an optimization algorithm. The SQP algorithm is a nonlinear optimization design algorithm that has  
Figure 3: Schematic diagram of the device.  
the advantage of increasing the convergence speed and improving the accuracy through quadratic modeling of the objective function ([PERSON] et al., 2018) [7].  
The algorithm for the SQP method is represented by Equations (3)-(5).  
Minimize  
\[\frac{1}{2}d^{T}H^{k}d+\
abla f(x_{k})^{T}d \tag{3}\]  
Subject to  
\[\
abla g_{i}(x_{k})^{T}d+g_{i}(x_{k})=0(i=1,\ldots,m) \tag{4}\]  
\[\
abla g_{i}(x_{k})^{T}d+g_{i}(x_{k})\ \leq 0(i=m_{e}+1,\ldots,m) \tag{5}\]  
Here, \(d\) is the search direction vector, \(k\) is the number of iterations, \(H\) is the Hessian matrix, \(f(x_{k})\) is the objective function, and \(g_{i}(x_{k})\) is the constraint expression. The initial value \(x_{0}\) of the design variable and the initial matrix \(H_{0}\) of the Hessian matrix are obtained. After obtaining the search direction vector, a new \(x_{k}\) is selected through Equation (6). This process is repeated, and when the optimal design variable is found, the algorithm is terminated ([PERSON] et al., 2013) [10].
## 3 Results and Discussion
Figure 4 illustrates a comparison between the experimentally observed intensity of Raman scattering, and a theoretical calculation. As shown in this picture, for the band at 1853 cm\({}^{-1}\) (curve 2 in Figure 4), a satisfactory agreement for the bound state of the 4A\({}_{1}\)(LO) polar mode with \(\
u_{0}\) = 876 cm\({}^{-1}\) is achieved at s = 1000, \(\Delta\) = 0.1 \(\omega_{0}\), \(g_{4}\) = 4.61\(\cdot\)10\({}^{-18}\), and \(\Gamma\) = 7.53 10\({}^{12}\). For the 1963 cm\({}^{-1}\) band (curve 3 in Figure 4), a good agreement is obtained at the values s = 1000, \(\Delta\) = 0.1 \(\omega_{0}\), \(g_{4}\) = 7.54\(\cdot\)10\({}^{-18}\), and \(\Gamma\) = 7.53\(\cdot\)10\({}^{12}\). Near the center of the Brillouin zone, the dispersion curve of the 4A\({}_{1}\)(z)-mode occupies a small area, which explains the choice of the parameter \(\Delta\) in Equation (5).
# Camera-Based Vital Sign Estimation Techniques and Mobile App Development
## 4 Discussion
### Challenges in Camera-Based Bio-Signal Detection  
The first challenge is the quality of the image sequences captured by the camera. This issue is related to various camera-specific characteristics such as white point, brightness, gamma, focus, and the color correction matrix. To accurately detect facial color under different lighting conditions, these camera parameters must be properly considered. The second challenge concerns the recording environment and database limitations. Recently, non-contact bio-signal measurement methods using cameras have been actively researched, and several related mobile applications have been released [22]. However, due to issues such as illumination level, flicker, variations in skin tone across ethnicities, and user movement, the reliability of these methods remains relatively low.
# Landslide-Tunnel Interactions and Control Countermeasures under an Orthogonal System
## 3 Numerical Simulation of Tunnels and Landslides
### Stress Distribution of Surrounding Rock  
The vertical stress distribution of the surrounding rock after tunnel excavation is shown in Figure 8. Under the action of the mountain's self-weight, the stress distribution is evenly distributed from top to bottom. The maximum stress appears near the right side of the mountain, reaching up to 3.2 MPa. After tunnel excavation, the disturbance caused by the upper landslide results in stress concentration on both sides of the tunnel. The stress on the right side of the tunnel is significantly greater than that on the left side, and the stress concentration is obvious. Owing to the influence of the upper right sliding zone area, the stress is mainly concentrated in the right arch waist and the tunnel crown. Therefore, the tunnel crown and haunch position on the tunnel's near-mountainside are critical areas for controlling surrounding rock stability.  
Figure 8: Stress distribution in the vertical direction of the surrounding rock (unit: MPa).  
Figure 6: Initial stress equilibrium (unit: Pa).  
Figure 7: Distribution of plastic zone of the surrounding rock. (**a**) Before excavation. (**b**) After excavation.
## 3 Results
### Rotary Control
#### 3.1.1 Identification of Significant Factors Influencing the Evaluation Parameters
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Factor** & **ToT** & **Acc** & **SUSQ** & **NASA TLX** & **UEQ\({}_{\text{P}}\)** & **UEQ\({}_{\text{E}}\)** & **UEQ\({}_{\text{D}}\)** \\ \hline
**F** & — & — & — & — & — & 0.004 & — \\
**G** & — & — & — & 0.004 & — & — & — \\
**H** & — & — & — & — & — & — & — \\
**J** & — & 0.017 & 0.006 & — & — & — & — \\
**K** & — & — & — & — & 0.002 & — & 0.019 \\
**AC** & \textless{0.001} & — & — & — & — & — & — \\
**AG** & — & — & — & — & — & — & 0.040 \\
**BE** & — & — & 0.006 & 0.002 & \textless{0.001} & — & — \\
**EF** & — & — & — & — & — & 0.006 & — \\
**EH** & — & — & — &.003 & — & — & — \\
**HK** & — & — & — & — & — & \textless{0.001} & — \\ \hline \hline \end{tabular}  
* A: angular resolution; **B**: diameter; **C**: inclination; **D**: haptic feedback, **E**: acoustic feedback; **F**: visualized hand; **G**: shape; **H**: position participant; **J**: input device; **K**: VR environment.  
\end{table} 
Table 5: _Cont._In addition to the angular resolution factor, the factor input device also significantly influenced the positioning accuracy. Test participants who used the Valve Index controller made about two more errors than test participants who used the HTC Vive controller. The input device factor also has a significant influence on the perceived presence. Users of the HTC Vive controllers rated presence 1.4 points higher than users of the Valve Index controllers. The factor angular resolution had the strongest influence on mental workload. Averaged over all test runs, the mental workload was rated significantly lower at an angular resolution of 5\({}^{\circ}\) (NASA = 2.0) than at an angular resolution of 45\({}^{\circ}\) (NASA = 6.9). The factor angular resolution also significantly influenced the dimensions efficiency and perspicuity of the UEQ. Test participants rated the efficiency dimension significantly better (UEQ\({}_{\text{E}}\) = 2.3) when they used rotary control with an angular resolution of 5\({}^{\circ}\). Rotary control with an angular resolution of 45\({}^{\circ}\) achieved a mean UEQ\({}_{\text{E}}\) score of 0.7. The test participants also rated the user experience better when the rotary control was tested in the detailed VR environment (crane). The visualization of the hand in the VR also had a significant influence on the user experience. The test participants rated the dimension perspicuity 0.6 points higher when no hand was shown in the VR.
# Reversible Data Hiding in Encrypted Color Halftone Images with High Capacity
## 1 Introduction
Designing RDH algorithm for encrypted halftone image is a challenging problem. In this paper, we focus on the topic of RDH in encrypted color halftone images (RDH-ECH), where the original cover image can be restored from the stego image after extracting the watermark. To overcome the problem that in the encrypted halftone images little redundancy exists, we adopt dual cover images in the embedding process. The role of dual images are different from the two shares in visual cryptography [29; 30]. Dual images are used to create redundancy for data hiding. We also combine the resulting stego images for facilitating transmission. Because of the reversibility, the proposed method can be applied to data hiding applications such as the healthcare industry and online distribution systems. Unless owning the secret keys, attackers cannot restore the original halftone image and watermark losslessly. The remaining part of this paper is organized as follows. Section 2 introduces the necessary background of the proposed algorithm which is wet paper code scheme. The proposed scheme is elaborated in Section 3. The experimental results are presented in Section 4, which includes test of lossless recovery, security, visual quality, embedding capacity, and computational complexity. Finally, Section 5 concludes this paper. Unless otherwise stated, all vectors are column vectors by default.
## 2 Theoretical Model
Using the finite-difference method and the nonlinear optimization algorithm, we simulated the initial droplet profile \(\Omega_{0}\), the initial front contact angle \(\theta_{1-0}\), the initial rear contact angle \(\theta_{2-0}\), and the initial droplet contact line length \(l_{0}\) for a droplet on the inclined hydrophilic rough surface (for details, see Sections S5.3 and S5.4 in Supplementary Materials). Model 3 for the Dynamic Front Contact Angles \(\theta_{1-s}\) and the Dynamic Rear Contact Angles \(\theta_{2-s}\) When a Droplet Evolves Its Contact Line Length on an Inclined, Hydrophilic Rough Surface  
The initial state of the droplet is the whole minimum relative energy state, but it is certainly unstable if \(\theta_{1-0}\geq\theta_{\rm a}\) or \(\theta_{2-0}\leq\theta_{\rm r}\). As is shown in Figure 5a, when \(\theta_{1-0}\geq\theta_{\rm a}\) and \(\theta_{2-0}>\theta_{\rm r}\), the front end of the droplet advances while the rear end stays pinned, leading to drop stretching. Alternatively, as shown in Figure 5b, when \(\theta_{1-0}<\theta_{\rm a}\) and \(\theta_{2-0}\leq\theta_{\rm r}\), the rear end of the droplet retracts while the front end stays pinned, leading to drop contracting. When the droplet's contact line moves, the front contact angles and the rear contact angles will change with the change of the droplet contact line length. Every contact line length corresponds to the droplet's dynamic front contact angle \(\theta_{1-s}\), and the droplet's dynamic rear contact angle \(\theta_{2-s}\). According to minimum energy dissipation principle, the droplet has local minimum total potential energy at every contact line length, whether stretching or contracting. In Model 3, we first set the length as \(l_{\rm given}\). Second, with the constraint \(l_{\rm given}\), we minimized the local total potential energy \({E^{\prime}}_{\rm w-2}\) and calculated the dynamic droplet shape \(\Omega_{s}\), \(\theta_{1-s}\), and \(\theta_{2-s}\) corresponding to \(l_{\rm given}\). Using the algorithm for continuously changing the set \(l_{\rm given}\), we can calculate \(\theta_{1-s}\) and \(\theta_{2-s}\) at every point during the droplet moving period. (for the algorithm and the flow chart, see Sections S6.1 and S6.2 in the Supplementary Materials).
## 5 Results
### Study on Controller Functionality
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & \multirow{2}{*}{\(s\) **(m)**} & \multirow{2}{*}{\(-a_{average}\)} & \multirow{2}{*}{\(\lambda_{average}\) (\%)} & \multirow{2}{*}{\(\psi_{\text{p+p}}\)} & \multirow{2}{*}{\(\delta_{\max}\) (\({}^{\circ}\))} & \multirow{2}{*}{\(\psi_{\max}\) (\({}^{\circ}\)/s)} \\  & & & & & & **Controlled/** \\  & & & & & No Control & No Control \\ \hline \multirow{3}{*}{**Straight split-\(\upmu\)**} & \multirow{3}{*}{47.16} & \multirow{3}{*}{\(-4.25\)} & FL & 18.41 & 46.91 & \multirow{3}{*}{\(175.00/449.96\)} \\  & & & FR & 13.97 & 46.10 & & \\  & & & RL & 14.20 & 45.37 & \\  & & & RR & 14.20 & 45.61 & \\ \hline \multirow{3}{*}{**Straight split-\(\upmu\)**} & \multirow{3}{*}{102.51} & \multirow{3}{*}{\(-3.31\)} & FL & 12.74 & 32.51 & \multirow{3}{*}{\(60.16/450\)} \\  & & & FR & 7.34 & 27.63 & \\  & & & RL & 10.62 & 24.85 & \\  & & & RR & 5.10 & 18.15 & \\ \hline \multirow{3}{*}{**Cornering split-\(\upmu\)**} & \multirow{3}{*}{44.74} & \multirow{3}{*}{\(-3.14\)} & FL & 4.71 & 28.26 & \multirow{3}{*}{\(-182.46/-450\)} \\  & & & FR & 15.96 & 35.04 & \\ \cline{1-1}  & & & RL & 4.40 & 23.24 & \\ \cline{1-1}  & & & RR & 13.05 & 28.58 & \\ \hline \multirow{3}{*}{**Cornering split-\(\upmu\)**} & \multirow{3}{*}{20.19} & \multirow{3}{*}{\(-5.23\)} & FL & 19.29 & 49.36 & \multirow{3}{*}{\(363.52/450\)} \\  & & & FR & 14.32 & 46.95 & \\ \cline{1-1}  & & & RL & 17.30 & 48.16 & \\ \cline{1-1}  & & & RR & 13.77 & 46.49 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Controller functionality results. Figure 11: Braking on a curved split-\(\upmu\) road with a half dry–half icy surface profile angle curves: \(1\)—steering wheel angle \(\xi\), \(2\)—yaw rate \(\psi\). In short, the comparison simulation, where the controller was turned on and turned off, shows the importance of the proposed solution in terms of vehicle safety. Different complex maneuvers were studied. The driver is able to remain on the road, following the path. When the controller is turned off, steering is impossible and, the vehicle starts to spin around the COG, causing unfortunate car accidents.
# A Review of CO\({}_{2}\) Laser-Mediated Therapy for Oral Mucosal Lesions
## 3 Results
### CO\({}_{2}\) Laser Treatment in Case of Oral Lichen Planus  
In a clinical study published by [PERSON] et al. (2012), 28 patients (with 57 oral lichen planus lesions) were randomly divided into two groups and treated with either low-level laser therapy (LLLT) or CO\({}_{2}\) laser surgery. After 3 months, the clinical response showed 100% partial to complete improvement in the case of LLLT, and 85% in the case of CO\({}_{2}\) laser surgery. Thus, the results revealed that LLLT may be superior to CO\({}_{2}\) laser therapy ([PERSON] et al., 2014).  
[PERSON] et al. (2014) performed a clinical trial in order to analyze the impact of deceased CO\({}_{2}\) vaporization on the malignant transformation of erosive oral lichen planus (erosive OLP). With the participation of 171 patients with erosive OLP, the study demonstrated that some factors (such as symptomatic analgesic treatment in the case of erosive OLP) have significantly higher risk associated with the occurrence of malignant transformation. The authors emphasized the potential impact of the CO\({}_{2}\) laser in the management of patients with erosive OLP ([PERSON] et al., 2014).  
[PERSON] et al. (2019) evaluated the efficacy of CO2 laser vaporization therapy on refractory to conservative treatments for oral lichen planus (OLP). A total of 16 patients with clinically and histologically diagnosed OLP underwent conservative treatment for 3 months (adrenal cortex hormone ointment and azulene sulfonate sodium hydrate gargle). Patients who showed no improvement with conservative treatment underwent CO2 laser vaporization therapy (3 W, continuous-wave mode). The numerical rating score (NRS) decreased at all 11 sites (100%) and 10 sites (90.9%) at 1 year after irradiation, compared to pre-irradiation scores [54].  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
## 6 Refined Authenticated Key Exchange Protocol with Point Halving Optimization  
In this section, we propose a refined variant of the GN-AK authenticated key agreement protocol, designed to integrate an enhanced scalar multiplication technique based on double-base chains (DBCs) incorporating point halving. This variant redefines the scalar computation process, with respect for specifications from [28; 29], while preserving the core structure of mutual authentication and session key derivation. The notation and step identifiers have been updated for clarity and differentiation from the original scheme.
## 3 Methodology
### Model Training  
To avoid any unexpected results while calculating the embedding vector for individual faces, preprocessing entails cropping faces out of the original photos. Face Detection Algorithms (such as Viola-Jones), Local Binary Patterns, and AdaBoost are used to locate the bounding boxes of all faces. For face detection, the Viola Jones technique is employed to run on every image in the original dataset and then produce a new dataset with just the face clipped out. Using preprocessing, the background noise that was impacting the accuracy of the facial embedding predictions was successfully eliminated. A convolutional network was utilized to reduce the image size from its original 224 \(\times\) 224 \(\times\) 3 pixels to its final output size of 128 \(\times\) 1 pixels. Here, the identical framework proposed in the original paper for the Facenet model is employed.  
A 3D convolutional filter is the first layer of the network, followed by 2-batch normalization and max-pooling layers. Following that, there are 10 Inception modules. Each Inception module contains 1 \(\times\) 1, 3 \(\times\) 3, and 5 \(\times\) 5 convolutional filters. The combined output of all these filters is then sent on to the next layer. Following processing by each of the 10 Inception Modules, the final image has 771,024 dimensions. After all the channels in the final image have been averaged, the result is a vector with 1024 dimensions.  
Subsequently, these 1024 output units pass through the output ReLU activation layer of a dense fully linked network with 128 output units. The total results from each node's output are mapped onto a 128-dimensional vector. These additional layers allow the network to acquire more nuanced knowledge about the image's content. As a means of accelerating backpropagation, residual connections are inserted between layers. The Facenet model's initial training weights have been used in this application of Transfer Learning.  
To sum up, the input image (of size 2,242,243) is fed into the network, and the resulting output vector (of size 128) contains the facial embedding of the original image. The network consists of 24 layers and was developed with efficiency in computation in mind. Facial embeddings are constructed using the same 128 dimensions as those used in the original article since those dimensions allowed the authors to reach greater accuracy.
###### Abstract  
This paper introduces a novel free space optics (FSO) communication system for future-generation high-speed networks. The proposed system integrates orbital angular momentum (OAM) modes with an optical code division multiple access (OCDMA) technique. Two OAM beams are used (\(L\)G\({}_{0,0}\) and \(L\)G\({}_{0,10}\)), each of which is used for transmitting three independent channels. Each channel is assigned by fixed right shift (FRS) codes and carries 10 Gbps of information data. The performance of the proposed model is evaluated under different foggy and dust storm conditions. Furthermore, the performance of two cities with different geographical locations, Alexandria city in Egypt and Srinagar city in India, is investigated to demonstrate its ability to be implemented in future generations. Bit error rate (BER), eye diagrams, received optical power (ROP), and channel capacity are used for studying the performance of the proposed system. The observed simulation results show successful transmission of 60 Gbps overall capacity with the longest propagation FSO range for Alexandria city, which is 1400 m. Because dust storms have a large attenuation when compared to different foggy conditions, the proposed model had the shortest propagation range of 315 m under low dust (LD), 105 m under moderate dust (MD), and 40 m under heavy dust (HD). Furthermore, the cloudy weather conditions that affect Srinagar city, which is considered a hilly area, make our suggested model achieve 1000 m.  
space division multiplexing; orbital angular momentum; free space optics; optical code division multiple access; fixed right shift; bit error rate +
Footnote †: journal: _Energies_  
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Footnote 0: _E-mail:_: _et al._
# Entity Embeddings in Remote Sensing: Application to Deformation Monitoring for Infrastructure
## 2 Data and Software
However, it is essential to emphasise that, in the context of this study, which primarily focused on identifying anomalous deformations leading up to TSF failure, the importance should be placed on the actual deformation signal observed at each specific time instance (i.e., LOS deformation), rather than relying on the average deformation rate. While attributes such as the deformation rate might hold value in different scenarios, considering the specific goal of this research, LOS deformation may serve as a more-direct and -sensitive metric. The deep learning training was implemented using the abstraction library _FastAI_[42] and the _PyTorch_ framework [43]. The Gaussian Process Regression was implemented in _GPyTorch_[44].
## 3 Smoothing and Untangling Approach
### Element Stitching Operation
According to the expression of quality function, low-quality elements especially inverted elements account for larger weights in determining the final node positions. This is designed to accelerate the convergence speed of the approach and to improve the ability of mesh untangling. Generally speaking, increasing the value of exponent \(\eta\) will lead to an improved worst element quality as well as a slightly lower average element quality. Therefore, \(\eta=2.0\) have been used as a default value to obtain a balanced optimization result. Note that the default values for \(\eta=2.0\) and \(c=2.0\) usually work well. However, in some cases, they still need to be carefully selected in order to produce a better result.