esa-sceva/satcom-chunk-collection huggingface dataset:
# Fivadmi: A Framework for In-Vehicle Anomaly Detection by Monitoring and Isolation +
## 4 Implementation of FIVADMI
### Monitoring Scheme
#### 4.3.2 Rules for Behavioural Properties  
Figure 14 shows rules expressed in EC, to detect anomalies in vehicle speed. However, it should be noted that similar rules can be specified to detect anomalies in coolant temperature, ambient temperature, and intake air temperature.  
Rule SR1 signifies the creation of the fluent _OBD_Data(dID, dSpd, dTS)_ with empty values at the beginning of the monitoring process. This fluent will be used to store CAN frame source ID, the vehicle speed reading contained in the CAN frame and the time stamp of receipt.  
Rule SR4 is used to update the fluent OBD_Data. Each time a CAN frame is received (signified by the predicate _Happens(CAN Frame(clD, cSpd), cTS)_), the values of _dID_, _dSpdg_ and _dTS_ in the fluent OBD_Data are updated which is signified by the predicates _Initiates(CAN Frame(clD, cSpd), equalTo(dID, cID),cTS)_, _Initiates(CAN Frame(clD, cSpd), equalTo(dSpd, cTS)_)_ and _Initiates(CAN Frame(clD, cSpd), equalTo(dTS, cTS), cTS)_.  
Rules SR2 and SR3 are used to detect anomalies in acceleration and deceleration respectively. This is done by calculating acceleration (or deceleration) of the vehicle as follows:  
\[Acc=\frac{(v2-v1)}{(t2-t1)} \tag{1}\]  
where v2 and v1 are vehicle speeds at time points t2 and t1 respectively. If the calculated acceleration (or deceleration) is greater (or lower) than an acceptable threshold, an alarm is raised.  
According to the rule SR2, the CAN frame that has arrived (signified by the predicate _Happens (CAN_Frame (clD, cSpd), cTS)_) contains the speed value and time stamp of the current CAN frame. The fluent OBD_Data (signified by _HoldsAT (OBD_Data(dID, dSpd, dTS), cTS)_) contains the speed value and time stamp of the previous frame. The rule checks that both current CAN_Frame and the previous CAN_Frame have valid speed value and different time stamps; the acceleration is computed. An alert is raised if the acceleration is above a threshold.  
Rule SR3 checks the anomaly in deceleration, which can be explained in a similar way as the Rule SR2.
## 2 Overview of Original Articles
The authors recommended the use of highly accelerated approaches, using compressed sensing instead of accelerated parallel imaging, given the time cost of the acquisition. The usefulness of 4D flow MRI in congenital cases has been demonstrated in multiple studies. The 4D flow recommendations for congenital diseases highlight several of its applications [4]. Contribution #7 explored the capacity of 4D flow MRI for quantifying blood flow turbulence in Tetralogy of Fallot patients, a common congenital disease. The authors aimed to compare the level of turbulence in repaired Tetralogy of Fallot patients versus healthy controls. Turbulence was found to be abnormally elevated in these patients. However, the clinical value of this turbulence remains to be demonstrated. Myocardial perfusion imaging has been proven useful in identifying coronary artery disease [7]. Contribution #8 proposed an attention-based feature-fusion VGG19 network for the detection of coronary artery disease using 486 patient records. This network demonstrated a high agreement with expert diagnosis and illustrated the potential of artificial intelligence tools in supporting clinical diagnosis. Similarly, Contribution #9 introduced a deep learning approach based on deep fuzzy cognitive maps to diagnose coronary artery disease. These innovative works illustrate the practical applications and the potential impact on daily clinical practice. Doppler ultrasound is by far the most accessible imaging tool used in clinical practice. Contribution #10 used Doppler echocardiography to conduct a serial study monitoring the performance of dexamethasone therapy in premature neonates. Their findings demonstrated that the therapeutic strategy improved the respiratory status of patients as well as their heart function. Device innovation is also an important component of the health care system in improving patient management. Contribution #11 highlights a dedicated device development for arterial embolization. The caterpillar device was demonstrated to be safe and efficient in performing arterial occlusions in this pilot study.
# Estimation of Methane Gas Production in Turkey Using Machine Learning Methods
## 2 Materials and Methods
### Machine Learning
#### 2.2.3 Support Vector Regression  
Support vector machines (SVMs) are a supervised learning algorithm used for both classification and regression analyses. The algorithm was developed by [PERSON] [30]. It can be used for continuous dependent variables and categorical variables. It is a supervised learning method that analyzes data, recognizes models and patterns, and is used in classification and regression analysis. Using the training data, it produces a mapping function between input and output. SVMs are high dimensional and distributed. Unlike classical models, the parameters are not predefined, and their number varies according to the training data. By keeping the error value in the training data constant, the confidence interval is minimized [31].  
The line dividing the data set into two is called the hyperplane. It creates one or more hyperplanes in multidimensional space. Although it is possible to draw infinite hyperplanes, it is important to determine the optimal hyperplane, in other words, the most appropriate hyperplane. The distance between the hyperplane and the nearest data point in the data set is called the margin. The aim is to select a hyperplane with the highest margin between the hyperplane and a point in the data set. This is to increase the chances of correctly classifying new data. There should be no data points within the separated region [32].  
A data set may often contain a set of data that cannot be linearly separated. In this case, in order to classify the data set, it is necessary to pass the data from a two-dimensional view to a three-dimensional view. This process is called the two-dimensional process. It takes a low dimensional input field and transforms it into a higher dimensional field. Maximizing the distances between the nearest data point and the hyperplane helps to determine the correct hyperplane. This is called the margin distance. Another important reason for choosing a high margin hyperplane is robustness. If we choose a low margin hyperplane, the probability of misclassification will be high [33].
## 2 The Model for GNSS/INS/LEO/5G Integrated Navigation
### Simulation Method of Each Signal Source
#### 2.3.2 IMU Model Simulation  
The IMU error model in this paper is:  
\[\begin{array}{l}\hat{f}_{ib}^{b}=b_{a}+(I_{3}+M_{a})f_{ib}^{b}+w_{a},\\ \tilde{\omega}_{ib}^{b}=b_{g}+\left(I_{3}+M_{g}\right)\omega_{ib}^{b}+G_{g}f_{ ib}^{b}+w_{g}\end{array} \tag{25}\]  
where \(\hat{f}_{ib}^{b}\) is the output specific force of IMU. \(f_{ib}^{b}\) is the theoretical value of specific force. \(b_{a}\) is the acceleration deviation. \(M_{a}\) is the scale factor and cross-coupling error of the  
Figure 8: Global visibility analysis of GNSS+LEO constellation.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**Value**} \\ \cline{2-3}
**Parameters** & **GNSS** & **LEO** \\ \hline Satellite positioning time interval (s)— \(\Delta t\) & 1 & 1 \\ Mask Angle (deg, it can be adjusted according to different scenarios) & 10 & 10 \\ Zenith ionosphere error SD (m)— \(\delta\rho_{I}\) & 2 & 1.5 \\ Zenith troposphere error SD (m)— \(\delta\rho_{T}\) & 0.2 & 0.15 \\ Space signal error SD (m)— \(\delta\rho_{SIS}\) & 1 & 1 \\ Code tracking error SD (m)— \(\delta\rho_{cl}\) & 0.5 & 1 \\ Satellite clock error SD(m)— \(\delta\rho_{sc}\) & 0.5 & 1 \\ Distance rate tracking error SD (m)— \(\delta\rho_{ct}\) & 0.02 & 0.05 \\ Initial estimated position of receiver (m, in EECF coordinate system) & [0,0,0] & [0,0,0] \\ Receiver clock offset (m)— \(\delta\rho_{c}\) & 10,000 & 10,000 \\ Receiver clock drift (m/s)— \(\delta\hat{\rho}_{cd}\) & 100 & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 3: GNSS/LEO navigation model parameters.  
accelerometer. \(w_{a}\) is the random noise of the accelerometer. \(b_{g}\) is the gyroscope bias. \(\hat{\omega}_{ib}^{b}\) is the IMU output angular rate. \(\omega_{ib}^{b}\) is the theoretical value of angular velocity. \(M_{g}\) is the gyroscope scale factor and cross-coupling error. \(G_{g}\) is the acceleration of gravity-related gyro bias. \(w_{g}\) is the gyroscope random noise.  
Table 4 shows the simulation parameters of the IMU model. In this paper, INS navigation calculation is carried out under the ECEF coordinates system, and the IMU error model value (Table 4) is added based on the theoretical value of specific force \(f_{ib}^{b}\) and theoretical value of angular velocity \(\omega_{ib}^{b}\) obtained by simulation, which is the measured value of IMU.
## 4 Clustering, Classification, and Quantification of Coal Based on Machine Learning
### Classification Models  
Classification is a type of supervised learning, which learns the regularity between the LIBS spectra and labels to predict the unknown coal samples, mainly including K-nearest neighbor (KNN), random forest classification (RFC), etc. The classification model can also  
Figure 6: K-distance plot (**a**) and clustering results (**b**) of DBSCAN clustering.  
be used before the establishment of the regression model, because the fitting degree may not be so good for a huge database due to various sub-categories. Thus, the classification model can divide the coal samples into different categories based on the similarity, and then build the regression model within the same categories to obtain a higher fitting degree.
## Materials and Methods
### Material Inks  
Organic semiconductor inks: A p-type donor-acceptor (D-A) polymer semiconductor and an n-type low molecular semiconductor were used as the semiconductor materials. Each ink was prepared to be printed by a dispenser system. For the p-type semiconductor, based on D-A polymer (MOP-01, Mitsubishi Chemical Corporation, Tokyo, Japan) [23; 24; 25; 26], mestilpene was used as a solvent. The p-type semiconductor was dissolved to 0.01 wt % at room temperature. Benzobisthiadiazole (BBT) based semiconductor TU-3 [18; 27] (Future Ink Corporation, Yonezawa, Japan) and poly(\(\alpha\)-methylstyrene) (PaMS: Sigma-Aldrich, Merck KGaA, Darmstadt, Germany) were blended for the n-type semiconductor, and 1-methylnaphthalene was used a solvent. The concentrations of these materials were 0.045 wt % for TU-3 and 0.015 wt % for PaMS, and they were dissolved on a hot-plate at 120 \({}^{\circ}\)C for 30 min. The p-type and n-type organic semiconductors used in this paper are materials that are poorly soluble in solvents such as 1-methoxy-2-propyl acetate (PGMEA), which are often used as a solvent for gate dielectric material. Therefore, we believe it will be possible to replace readily printable gate dielectrics.  
Hydrophobic bank ink: As a hydrophobic bank ink for defining the printing area of the organic semiconductor, Teflon (AF1600X, Dupont, Wilmington, DE, USA) was dissolved in Fluorinert (FC-43, 3M Company, Maplewood, MN, USA) to 1 wt %.
# Distributed Resources Allocation Method for Space-Ground Integrated Mobile Communication System
## 5 Physical Network Optimization
### Action Space  
The action space is defined as the allocation of slices for the new access mobile devices which can be divided into two steps:  
* Give the slice choice \(\mathbf{B}_{kb}\) which indicates that the MS will select which specific slicing network to access, and also select several specific slicing resources in this network \[\mathbf{a}(\mathbf{t})=\mathbf{B}_{kb},k\in K,b\in B.\] (40)
* Renew the slice state from \(\mathbf{A}_{kb}(t)\) to \(\mathbf{A}_{kb}(t+)\).  
For example, the current number of \(\mathbf{A}_{ij}(t)\) is 0 which means it is available now; the action may allocate it to the new MS and set it to 1.
## 5 Simulation Outcomes and Analysis  
### Virtual Setting and Appraisal Criteria  
We selected the square two-dimensional area as the node deployment environment in this section and conducted simulation experiments on MATLAB R2020b. In order to have the relevant performance tested, the following algorithms have been compared: DV-Hop [9], CS-DV-Hop [29], NSGA-II-DV-Hop [35], BFO-GSO-DV-Hop [30], and CMWN-DV-Hop.  
Experiments were carried out with three factors as variables considering the different deployment environments of wireless sensor networks. Nodes were randomized within a square area, so each set of simulation experiments was run 100 times. The parameter settings for the simulation experiment are given in Table 1. Figure 8 shows the network deployment diagram with 100 nodes randomly distributed.  
The simulation experiments were conducted based on two evaluation metrics. The first is the localization error, and the other is the average localization error. The specific calculation equations are shown below.  
\[\text{Localization error}= \frac{\sum\limits_{i=1}^{N_{u}}\text{error}_{i}}{N_{u}} \tag{30}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Node distribution & Random \\ Anchor node ratio & 20\% (5–30\%) \\ Communication radius (R) & 25 (15–40) \\ Number of nodes & 100 (50–100) \\ Segmentation factor (a) & 2, 4, 6, 10 \\ Deployed environment & 100 m \(\times\) 100 m \\ Transmission signal power \(P_{s}\) & 20 dBm \\ Frequency of signal \(f\) & 5 GHz \\ Path loss exponent \(\eta\) & 4 \\ Reference distance \(d_{0}\) & 1 m \\ Transmitting energy consumption & 1.50 mJ \\ Computational energy consumption & 0.20 mJ \\ Receiving energy consumption & 1.15 mJ \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of parameters.  
Figure 8: Schematic of random node deployment.  
\[\text{Average localization error}=\frac{\sum\limits_{i=1}^{N_{u}}\text{error}_{i}}{N_{u }\cdot R}\times 100\% \tag{31}\]  
In Equation (30), \(\text{error}_{i}=\|X_{i}-X_{i0}\|_{2}\), and \(R\) indicates the radius of communication. The total count of unknown nodes is represented visually by \(N_{u}\). The estimated coordinates of node \(i\) are \(X_{i}=[x_{i},y_{i}]^{T}\). The virtual position of unknown node i is denoted by \(X_{i0}=[x_{i0},y_{i0}]^{T}\).
## 3 Results
### Sample Characterization
#### 3.1.3 XRD Analysis  
The structural analysis of activated carbon samples after heavy metal adsorption (Figures 8 and 9) provides crucial insights into their effectiveness and interaction mechanisms. X-ray diffraction (XRD) analysis is a commonly employed technique to identify the crystalline phases and structural changes in adsorbents. By analyzing the XRD patterns, it is possible to determine the presence (and crystalline nature) of adsorbed metals. This offers valuable information on the adsorption behavior and material properties.  
Figure 5: Energy Dispersive X-ray (EDX) spectrum of date seed activated carbon before adsorption.  
Figure 6: Energy Dispersive X-ray (EDX) spectrum of date seed activated carbon after lead adsorption.  
Figure 7: Energy Dispersive X-ray (EDX) spectrum of date seed activated carbon after copper adsorption.  
The XRD pattern of activated carbon with adsorbed copper (Figure 8) exhibits peaks located around 43.8\({}^{\circ}\), 51.31\({}^{\circ}\), and 74\({}^{\circ}\). These peaks closely match the characteristic peaks of copper reported in the literature, corresponding to the (111), (200), and (220) planes of FCC copper [55]. The literature confirmed the use of the standard JCPDS card (No. 04-0836) for copper, indicating that the peaks in the activated carbon sample can also be indexed to these crystal planes of copper [56]. This alignment with the literature confirms the presence of copper in the activated carbon and supports its structure as FCC copper [55,56].  
The broad peak between 20\({}^{\circ}\) and 30\({}^{\circ}\) shows the amorphous nature of the activated carbon. This peak, which can be observed in both activated carbon samples, is due to amorphous carbon. There is no characteristic peak of lead observed in the XRD pattern of Pb-adsorbed activated carbon.
## 3 Materials and Methods
### Overall Structure  
The overall architecture is shown in the Figure 1. Our system consists of a mobile phone-assisted diagnosis program, a breast ultrasound report generation method and a breast ultrasound classification model.  
* Mobile-assisted diagnostic program: Patients use the assisted diagnostic program provided by us to take and upload breast ultrasound reports, and after subsequent processing, acquire the classification outcomes for benign and malignant breast ultrasound images.
* Breast ultrasound report generation method: As mentioned earlier in this article, it is difficult for classification models to directly classify the benign and malignant breast ultrasound reports. There is a lot of noise in the photos taken by users. Therefore, we use segmentation models, rotation classification models and generation models to segment and denoise breast ultrasound reports.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Dataset** & **Number** & **Size** \\ \hline ElecPic & 16091 & \(224\times 224\) \\ Photo & 11021 & \(224\times 224\) \\ Defor-Photo & 11021 & \(224\times 224\) \\ Spot-Photo & 11021 & \(224\times 224\) \\ Rot-Photo & 11021 & \(224\times 224\) \\ Mix-Photo & 11021 & \(224\times 224\) \\ GenPic & 11021 & \(224\times 224\) \\ \hline \hline \end{tabular}  
* ElecPic—Original ultrasound images exported from the hospital; Photo—A single ultrasound image taken with a mobile phone and cropped; Defor-Photo—Ultrasound images containing deformation noise; Spot-Photo—Ultrasound images containing a spot of light; Rot-Photo—Ultrasound images containing rotational noise; Mix-Photo—Ultrasound image mixed with three types of noise.  
\end{table}
Table 2: The dataset used in this article.  
* Breast ultrasound report classification model: The classification model includes two inputs. One input is a high-quality image from the generative model, and the other input is the original low-quality image segmented by the segmentation model. They are both input into the classification model, and after going through the feature extraction module, channel attention module and classification module, they obtain the final classification results.
# Can OCT Angiography Be Made a Quantitative Blood Measurement Tool?
## 7 OCTA Measurements of Hemodynamics
### Flow Quantification
The power spectral density, \(\mathrm{P_{d}}\), derived from the temporal autocorrelation function, is expressed as:  
\[\mathrm{P_{d}}(\mathrm{f})=\frac{2|\mathrm{K}|^{2}}{\pi\mathrm{w_{e}^{4}}( \frac{\mathrm{v_{e}^{2}}}{\mathrm{w_{e}^{2}}}+\frac{\mathrm{v_{e}^{2}}}{ \mathrm{w_{z}^{2}}})}\mathrm{P_{A}}\exp[-\frac{\pi^{2}(\mathrm{f}-\frac{2 \mathrm{w_{e}}}{\mathrm{w_{e}^{2}}})^{2}}{(\frac{\mathrm{v_{e}^{2}}}{\mathrm{ w_{e}^{2}}}+\frac{\mathrm{v_{e}^{2}}}{\mathrm{w_{z}^{2}}})}]. \tag{22}\]  
In the presence of static scattering (Figure 6), the autocorrelation takes the form:  
\[\mathrm{R}(\tau)=\ R_{\mathrm{d}}(\tau)+\mathrm{R_{s}}(\tau), \tag{23}\]  
where \(\mathrm{R_{s}}(\tau)\) is the autocorrelation of the static component, with a much longer decorrelation time than the autocorrelation of the dynamic component, \(\mathrm{R_{d}}(\tau)\). In practice, \(\mathrm{R_{s}}(\tau)\) is usually constant over time scales of interest. Aside from the Doppler shift, the un-normalized autocorrelation \(\mathrm{R_{d}}(\tau)\) provides two essential observables: the decorrelation rate, which is sensitive to speed, and power (\(\mathrm{P_{A}}\)), which is sensitive to the RBC density. Recent work has proposed to quantify OCTA using difference algorithms measured at several time delays [20; 78], providing the ability to measure blood flow speed. Since difference and correlation algorithms are related by Equation (16), these algorithms essentially estimate the un-normalized autocorrelation. As highlighted in Equation (23), static scattering, if present, must also be taken into account in parametric estimations based on the autocorrelation. Finally, a major limitation of existing models is that they do not account for multiple scattering. In particular, multiple dynamic scattering events (Figure 2C, green) increase the decorrelation rate relative to the single scattering models described above, as each dynamic scattering event causes momentum transfer [79]. In such cases, the decorrelation rate depends on the number of scattering events, which in turn is impacted by the RBC density. Thus, with multiple intravascular scattering events, decorrelation rate is not a \"pure\" metric of speed. Therefore, decorrelation rate is not a good metric of speed within macrovessels where multiple scattering dominates, but may perform better in capillaries where hematocrits are lower and singly backscattered light prevails (Figure 2B).
# Forces in the Interaction of Light with Matter
## 3 Optical Forces in the Laser Cooling and Trapping of Atoms
### Cooling Forces Arising from Polarization Gradients  
Soon after the experimental confirmation of Doppler cooling, new experimental data revealed clear violations of the Doppler limit. Further theoretical research confirmed the existence of a polarization-based mechanism for slowing atomic motion, called _polarisation gradient cooling_ or simply _Sisyphus cooling_. This mechanism is based on light, whose polarization varies in space. The interaction of this light field with the atom entails electronic transitions involving three atomic levels, not two, as in the previous mechanisms. The result is again a continuous, repetitive cycle of kinetic energy loss from the atom, as the atom climbs potential hills, leading to a very strong deceleration of its motion. When the polarization gradients are formed by two counter-propagating laser beams with mutually orthogonal polarization (the so called \(lin\perp lin\) configuration), the damping force is given by \(F_{lin\perp lin}=-av\) with \(a=-3\hbar k^{2}\Delta/\Gamma\), (\(\Delta=\omega_{L}-\omega_{0}<0\)). When the polarization gradients are formed by two counter-propagating laser beams with opposite circular polarization (the so called \(\sigma^{+}-\sigma^{-}\) configuration), the damping force is given by \(F_{\sigma^{+}-\sigma^{-}}=-bv\) with \(b=-120\hbar k^{2}\Delta/\Gamma/[17(5\Gamma^{2}+4\Delta^{2})]\), (\(\Delta<0\)) [141].