esa-sceva/satcom-chunk-collection huggingface dataset:
# Pose Measurement Method Based on Machine Vision and Novel Directional Target
## 3 Methods
### Algorithms Related to Target Image Processing  
#### 3.2.1 Camera Self-Calibration  
The sub-pixel coordinates of the feature points are the primary data for the whole pose measurement, which is mainly obtained by combining the pixel coordinate parameters on the image with the intrinsic parameters of the camera itself, so obtaining accurate camera parameters is crucial for the whole measurement system.  
The traditional camera calibration method generally adopts [PERSON]'s calibration method, and the camera calibration target scale generally adopts a checkerboard target. However, after the relative pose relationship between the camera and the target changes, it will also cause a slight change in the camera parameters. This can be ignored in the usual measurement of low-precision requirements. Still, it is necessary to re-calibrate the camera for high-precision measurement. It is troublesome to recalibrate the camera every time the measurement target changes. To solve the above problems, combined with [PERSON]'s calibration method, the original checkerboard grid target is replaced with the existing new cooperative target. After the target is moved or the measurement pose is changed, it is only necessary to follow the operation procedure of [PERSON]'s calibration method to take several pictures of the new cooperative target in different poses. Then, the self-calibration of the camera can be completed.  
Calibration step: after installing the new cooperative target and camera in a suitable pose, the new collective target is manipulated to transform the pose. After the change, the camera takes pictures and saves them, and after taking 15 or more photos, the feature points are extracted and sorted, and the camera parameters can be obtained through Equations (1)-(14). Figure 4a is the visual display centred on the camera, and Figure 4b is the visual display centred on the target. The three-coloured arrows indicate the pose of the camera relative to the target mark at this time; the centre of the three hands indicates the optical centre of the camera, the blue arrow indicates the pointing of the camera lens, the green arrow points to the top of the camera, and the red arrow points to the left side of the camera.
## 6 Numerical Results
### Tuning of OSNR-LA-PSA Method  
In order to tune the OSNR-LA-PSA method, we executed it assuming different values of cooling rate \(\rho\) and initial temperature coefficient \(\tau\), where \(0.8\leq\rho\leq 0.999\) and \(0.1\leq\tau\leq 10\). For each pair of \(\rho\) and \(\tau\), we evaluated \(20\) different problem instances, namely, \(10\) randomly generated demand sets for each MCF scenario (MCF-6 and MCF-7), assuming \(|\mathcal{D}|=200\), \(XT^{\mathrm{REF}}=-57\) dB/km, and \(10^{4}\) algorithm iterations. Next, the results were averaged.  
\begin{table}
\begin{tabular}{l c} \hline \hline Attenuation coefficient (\(\mathrm{a}\)) (dB/km) & \(0.21\) \\ Chromatic dispersion (\(C\)D) (\(\mathrm{ps/nm/km}\)) & \(16.7\) \\ Nonlinear index (\(n_{2}\)) (\(\mathrm{W}^{2}/\mathrm{m}^{-1}\)) & \(2.3\times 10^{-20}\) \\ Effective area (\(A_{\mathrm{eff}}\)) (\(\upmu\)m\({}^{2}\)) & \(80\) \\ EDFA noise figure (\(NF\)) (dB) & \(5\) \\ Required SNR for BPSK (dB) & \(6.8\) \\ Required SNR for QPSK (dB) & \(9.8\) \\ Required SNR for 8 QAM (dB) & \(14.3\) \\ Required SNR for 16 QAM (dB) & \(16.5\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Transmission system parameters.  
In Table 5, we present the average values of \(z\) in a function of the OSNR-LA-PSA parameters in the studied networks. Although the results do not differ much, still, the best performance (i.e., the lowest value of \(z\)) is achieved for \(\rho=0.9\) and \(\tau=1\) in each network. Therefore, we apply these best values of \(\rho\) and \(\tau\) in the following sections.
# Optical Turbulence Profile Forecasting and Verification in the Offshore Atmospheric Boundary Layer
## 2 Optical Turbulence Forecasting
### Integrated Astroclimatic Parameters  
The integrated value of \(C_{n}^{2}\) helps us to understand the atmospheric optical quality in terms of seeing \(\varepsilon\), as well as the coherence length \(r_{0}\), that depends on the local conditions at altitude \(h\). In the adaptive optics (AO) system, the seeing \(\varepsilon\) is a crucial parameter to identify the most effective window to be used to retrieve \(r_{0}\)[40]. Meanwhile, the wind speed and \(C_{n}^{2}\) profiles are employed to calculate \(\tau_{0}\)[41]. All the parameters are given as follows to give the calculation results reference value in astronomical observation, and the point light mode is used here:  
\[r_{0}=\left[0.423\left(\frac{2\pi}{\lambda}\right)^{2}\int_{0}^{\infty}C_{n}^{ 2}(h)dh\right]^{-3/5} \tag{21}\]  
\[\varepsilon_{FWHM}=5.25\lambda^{-1/5}\left(\int_{0}^{\infty}C_{n}^{2}(h)dh \right)^{3/5} \tag{22}\]  
\[\tau_{0}=0.057\lambda^{6/5}\left(\int_{0}^{\infty}|V(h)|^{5/3}C_{n}^{2}(h)dh \right)^{-3/5} \tag{23}\]
## 2 Proposed Spectrum-Sharing Scheme
### Strategic Bargaining  
Licensed users compete with each other, in terms of spectrum price, which will directly affect the strategies of unlicensed users. Once licensed users make their decisions, unlicensed users will buy spectra from whichever licensed user that will let them obtain maximum utility. Then, licensed users self-adaptively adjust their prices according to unlicensed users' information, and unlicensed users again provide their best responses. These actions and reactions will either continue until some prescriptive deadline arrives or will end with an agreement. Of the many applications of strategic interactions, one ubiquitous situation naturally comes to mind: Strategic bargaining. We start the analyses with an easy case, in which only one round of bargaining is taken into consideration.
## 3 Rock Mass Model Based on Digital Image Recognition  
### Principle of Image Recognition and the Numerical Model  
The image can provide information for the joint distribution of the jointed rock mass and a basis for its modelling. Based on the image of the RDFN model generated using MATLAB code, digital image recognition technology can be applied to generate the numerical particle flow code (PFC) models via the following steps: firstly, the image is read into the MATLAB program (as shown in Figure 5a); secondly, different types of pixels are classified to obtain the spatial distribution coordinates of the bedrock model and joint model through image pixel recognition. The data for bedrock and joint are shown in Table 3. Finally, the joints and rock models are grouped and assigned values to enable the mechanical modelling of the RDFN model based on the particle flow discrete element method, as shown in Figure 5b.  
Figure 4: The RDFN models of varied geometry types. (**a**) The Linear DFN model. (**b**) The Fractal RDFN model. (**c**) The Fourier type RDFN model. (**d**) The mixed RDFN model.  
Figure 5: Digital image and the PFC simulation model. (**a**) The colour DFN model diagram. (**b**) The PFC numerical calculation model.
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 2 Results
### Effects of the Radiation Received by Grapes on the VOCs of the Resulting Wines  
A total of 30 VOCs were identified: 18 esters, four alcohols, four fatty acids, two hydrocarbons, one lactone, and one aldehyde. Only six of these individual VOCs were also found in grapes (Table 3). The most abundant VOCs were alcohols (approximately 65-75% of total VOCs), followed by esters (25-35%), fatty acids (around 2%), and hydrocarbons (less than 0.1%). The total concentrations of alcohols and esters were significantly affected by the radiation regime (Figure 2). Regarding total alcohol concentrations, PA samples showed significantly lower values than P samples, while the remaining samples were not significantly different from either P or PA samples. As for total ester concentrations, PA samples showed significantly higher values than the remaining samples, which showed similar values. Total hydrocarbons and fatty acids showed no significant response to radiation regimes.  
Fifteen individual VOCs (50% of the total) were significantly influenced by the radiation regime: eight esters, three fatty acids, two alcohols, one hydrocarbon, and one aldehyde (Table 2). Wine samples made from grapes that had received some kind of UV radiation showed higher concentrations of some specific VOCs, such as ethyl decanoate, ethyl octanoate, methyl octanoate, hexanoic acid, octanoic acid, nonanoic acid, 2-methyl-1-butanol, and tetradecane. This suggested some effect of either UV-A, UV-B, or the combination of both. However, for other VOCs (ethyl dodecanoate, ethyl nonanoate, tetradecanoic acid ethyl ester, 3-methyl butanol, and n-nonaldehyde), concentrations in P samples were higher than (or similar to) those found in samples made with UV-exposed grapes.  
No significant difference between PAB and PAB\(\uparrow\) samples was found for the total concentrations of VOCs families (Figure 2), but a few individual VOCs did show differences (Table 2): 2-methyl-1-butanol and nonanoic acid concentrations were higher in PAB\(\uparrow\) than in PAB samples, whereas 3-methyl butanol, ethyl nonanoate, and n-nonaldehyde showed opposite results.
## 2 Materials and Methods
### Study Framework  
The framework of the voltage management strategy proposed in this study is depicted in Figure 1. The proposed method achieves optimal BESS planning via network-level optimization. Hence, a bi-level optimization algorithm is used for BESS planning to obtain the optimal daily (24-h) charging/discharging scheduling of batteries to improve distribution network voltage. The algorithm is divided into two optimization levels: upper-level optimization and lower-level optimization. NNA represents the upper-level optimization, while LP is used in the lower-level optimization. The following are the main steps in the voltage control strategy:  
* The algorithm receives as input data the PV power output, load data of each house, and prespecified available BESS.
* The NNA produces the initial population for the optimization problem. The explanation of NNA is presented in Section 3. NNA pattern solution populations are represented as a vector containing battery sizes in kWh (\(M_{\text{max}}\)) and kW (\(S_{\text{max}}\)) and location of each BESS, which are the variables to be optimized in the upper-level optimization problem. In other words, the vectors' dimension is three times the number of BESS that are deployed in the network and \(S_{\text{max}}\) are real variables, while the location (\(L\)) is an integer variable as it represents the house number. If we have, for instance, three available batteries to be installed in the network, nine decision variables are optimized by NNA. \[v=\left[S_{\text{max}}^{1}\;S_{\text{max}}^{2}\;\ldots\;S_{\text{max}}^{N}M_{ \text{max}}^{1}\;M_{\text{max}}^{2}\;\ldots\;M_{\text{max}}^{N}L^{1}\;L^{2} \ldots L^{N}\right]\] (1) where \(S_{\text{max}}^{1}\), \(M_{\text{max}}^{1}\), and \(L^{1}\) are BESS capacity in kW, BESS capacity in kWh, and the location of the first BESS, respectively. The size of vector \(v\) increases as the available batteries increase.
* Using the LP, the BESS schedule for the following day is determined and solved in MATLAB (see Section 2.2). After that, the Open Distribution System Simulator (OpenDSS) software receives the ideal daily charge/discharge of BESS [31] and measures its effectiveness on the distribution grid. The role of OpenDSS is to model and simulate the distribution system. The power flow constraints are satisfied since the power flow is conducted in real-time simulation through the OpenDSS.
* The evaluation is accomplished using the fitness function, Equation (2), which minimizes voltage deviation. Three-phase load flow in OpenDSS is used to obtain the voltage magnitudes. This technique is continued until the desired number of iterations has been reached. \[Fitness\;(v_{i})=\frac{1}{2}(\sum_{i=1}^{N}\sum_{t=1}^{24}(1.05-\text{V}_{i,t} )^{2}+(0.95-\text{V}_{i,t})^{2})\] (2) where \(N\) is the PV systems buses in the distribution network, and \(\text{V}_{i,t}\) is the voltage magnitude at bus \(i\) at time \(t\).
## 2 Model Formulation and Implementation
In all cases, the successive ratio mesh was used in the vertical direction of the wall, and the first grid point adjacent to the wall was carefully placed to ensure that the condition of nondimensional wall distance \(y^{+}<1\) was satisfied. The no-slip condition was applied to the solid walls. However, in different models, the wall treatments were different. Specifically, for the SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\)-SAS and SST-\(k\)-\(\omega\)-\(\varphi\)-\(\alpha\) models, \(u_{i}=0\), \(k=0\), \(\varphi=0\), \(\alpha=0\), and \(\omega_{w}=3v/\left(\beta_{0}y_{1}^{2}\right)\)[14]. For the SST-SAS model, an automatic near-wall treatment method was used [17]. At the outlets, a zero-gradient condition was applied for each variable except pressure. For the periodic boundary conditions occurring in this study, the pressure drop across the periodic planes was set to zero. At inlets, the values of the variables were problem-dependent and are stated later in the paper for each case. Additionally, the vortex method [18] was applied to generate random fluctuations at the inlets.
## 2 Related Work
### Registration Methods
#### 2.1.2 Feature Point-Based Methods  
Feature point-based registration techniques are designed to extract high-precision key point pairs from point clouds. Numerous researchers have adapted the principles of image matching to the domain of three-dimensional registration. For instance, [PERSON] et al. [31] enhanced the traditional two-dimensional Harris corner detection algorithm, resulting in the development of a rapid feature point extraction algorithm known as 3D-Harris, specifically optimized for three-dimensional contexts. [PERSON] et al. [32] introduced a curvature-based key point selection strategy for simultaneous localization and mapping (SLAM) tasks involving laser point clouds, which effectively identified both planar and edge key points for application in three-dimensional point cloud registration. Furthermore, [PERSON] et al. [33] proposed the binary feature descriptor B-SHOT, which facilitates efficient matching of key points in three-dimensional point clouds while significantly minimizing memory usage. The advancement of feature point detection has catalyzed further exploration into efficient feature description methodologies. [PERSON] et al. introduced the Point Feature Histogram (PFH) [34], which employs a multidimensional histogram toencapsulate the curvature information of a sample point, integrating geometric features within the k-neighborhood to formulate a feature descriptor. However, the analysis of relationships among neighboring points results in high computational complexity, which adversely affects efficiency, particularly when handling large-scale datasets. To mitigate this challenge, [PERSON] et al. proposed the Fast Point Feature Histogram (FPFH) [15], which omits the relational information between neighboring points of the target point and utilizes a weighted approach to represent point distances, thereby reducing the time complexity from quadratic to linear. Additionally, [PERSON] et al. [35] introduced a Strictly Rotation-Invariant (RRI) point cloud feature descriptor, which extracts rotation-invariant features through a series of mathematical computations involving the center point and its adjacent points, thereby ensuring strict rotation invariance. These feature description methodologies have significantly influenced the design of feature extraction networks within the realm of deep learning.
## 3 Methodology
### Label Encoding and Feature Extraction  
The extracted emotions are in the form of text as categorical values thus further requiring the label encoding wherein each textual (emotion) category is assigned a numeric value that can be used as distinct classes during the classification task. Following are the numeric codes we have assigned to each emotion category--admiration = 1, anger = 2, annoyance = 3, approval = 4, amusement = 5, caring = 6, confusion = 7, curiosity = 8, disappointment = 9, disapproval = 10, disgust = 11, desire = 12, embarrassment = 13, excitement = 14, fear = 15, gratitude = 16, grief = 17, joy = 18, love = 19, neutral = 20, nervousness = 21, optimism = 22, ptide = 23, realization = 24, remorse = 25, sadness = 26, surprise =27. We did not find any tweet related to relief, so we did not perform encoding for it. Moreover, to train the ML models on the text data it is required to vectorize (assigning numerical values to words and letters), hence, we have used (Tf-idf) [58] as a feature extractor to transform the text into the sparse matrix of numerical values. In tf-idf, the tf represents the \"number of times a word appears in a document divided by the total number of words in that document [59] and idf represents the logarithm of the number of the documents in the corpus divided by the number of documents where the specific term appears [59]\".
# Tamper and Clone-Resistant Authentication Scheme for Medical Image Systems
## 5 Watermarking Analysis and Security Evaluation
### Experimental Results  
The performance of the proposed method was evaluated using four grayscale medical images in the DICOM format, \"Chest\", \"T-spine\",\" Hands\", and \"Skull\" of the size of \(512\times 512\) pixels as host images. A binary watermark of size \(16\times 16\) is generated from the host images to be embedded. The experiment is performed on a computer with an Intel Core i5, 2.6 GHz CPU, 4 GB memory, windows 10 and MATLAB 2016b (the MathWorks, Natick, MA, USA).  
The proposed watermarking system's performance is evaluated in terms of imperceptibility and robustness against various attacks. To measure the imperceptibility of the watermark, SSIM (Structural Similarity Index) and PSNR (Peak Signal to Noise Ratio) values are used. To measure the robustness, BER (Bit Error Rate) and NC (Normalized Correlation) values are used. The original images used to investigate the performance of the proposed method are presented in Figure 14.
## 1 Introduction
The gel film thickness significantly influences the instability of the displacement pattern. Moreover, other features of finger patterns, such as the finger number and width, were not examined. These features are expected to depend on parameters, such as the injection rate and gap width. In this study, we aimed to (1) investigate the effects of gel film formation on the fingering instability of miscible fluids and (2) develop a mathematical model of the sequential growth of gel film formation at the tip of the finger. The experiments were conducted using a Hele-Shaw cell. An aqueous citric acid solution and skim milk were used as the fluid pair. The mixing of the two fluids resulted in gel formation, resembling the isoelectric coagulation of the yogurt fermentation process [35; 36]. Moreover, the two fluids have similar viscosities to ensure that displacement instability does not occur unless there is gel formation. The paper is organized as follows. Details of the experimental setup, fluid pair, and experimental procedures were given in Section 2. In Section 3, the typical structure of the fingering pattern induced by gel formation was discussed, and the diffusion coefficient was estimated. The changes in displacement pattern with parameters, such as citric acid concentration, injection rate, and the Hele-Shaw cell gap, were captured in phase diagrams. In addition, the displacement pattern upon reverse injection was assessed. The sequential growth of gel film formation at the fingertip was modeled mathematically. Finally, the findings were summarized in Section 4.
## 5 Rotor Temperature Modeling and Prediction
### Enhancement of Motor Optimization and Adaptability of RIME-XGBoost  
This study intentionally focused on developing a robust baseline model using commonly available operational parameters (speed, torque, d-q currents/voltages) that are standard across most PMSM implementations. It offers a practical value for EV manufacturers without requiring additional sensor infrastructure.  
The RIME-XGBoost framework offers two inherent adaptability advantages:  
(1) The optimization approach enables efficient fine-tuning with small new datasets when applied to different motor designs.  
(2) The hybrid model can naturally incorporate additional input parameters (e.g., coolant flow rate, ambient temperature) through simple feature engineering.  
RIME-XGBoost establishes a data-driven predictive model for PMSM rotor temperature by leveraging easily monitored operational variables (e.g., d-q-axis currents/voltages, torque, speed). It circumvents the need for direct rotor temperature sensors. Thus, it reduces hardware costs while enhancing the intelligence of thermal management systems. The integration of RIME-XGBoost with adaptive thermal management policies can provide a foundation for motor optimization, for instance, enabling the exploration of smaller cooling systems or higher magnetic saturation thresholds.  
The adaptability of RIME-XGBoost to new PMSMs arises from the integration of RIME's optimization capabilities and XGBoost's robust ensemble learning framework. The hybrid approach achieves transfer learning through hyperparameter adaptation.  
RIME employs a gradient-free optimization mechanism that facilitates rapid convergence to optimal configurations. By dynamically re-optimizing key parameters (number of trees, tree depth, and learning rate), the model can strike a balance between retaining prior knowledge and adapting to specific domains.  
The experimental results on medium- and small-sample datasets demonstrate the adaptability of RIME-XGBoost. This hybrid approach ensures transfer learning across different conditions while maintaining efficiency and accuracy.