esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### The Proposed Model
The geometry map encodes the distances from each pixel to the four corner points of the predicted quadrilateral, with 8 output channels corresponding to the \((dx,dy)\) offsets of the four vertices. This allows the coordinates of the quadrilateral vertices to be reconstructed from pixel-wise offsets and enables the model to effectively capture the geometric structure of text regions with various orientations and shapes. It is worth noting that, to prevent excessively large offset values from causing training instability, the geometry outputs should be normalized, typically by applying a sigmoid activation followed by scaling. Additionally, to ensure consistent vertex ordering, a fixed-point sequence (e.g., starting from the top-left corner in a clockwise direction) should be used during both label generation and model prediction. In the text recognition task, the fused visual features are further processed by a classification branch. Unlike the traditional Convolutional Recurrent Neural Network (CRNN) [16], we adopt a lightweight fully convolutional structure as the classification head to ensure overall efficiency and enable end-to-end training. In this branch, the input features are first passed through two convolutional layers to extract semantic information, followed by a \(1\times 1\) convolution that expands the channel dimension, resulting in the recognition feature map \(\mathbf{f}_{\text{cls}}\) with a shape of \(B\times C\times H\times W\), where \(B\) denotes the batch size, \(C\) is the number of channels, and \(H\) and \(W\) are the spatial dimensions. To perform character classification, \(\mathbf{f}_{\text{cls}}\) is flattened along the spatial dimensions and fed into a fully connected network with dropout, producing a set of class probabilities for each spatial location. The flattened feature has a shape of \(B\cdot H\cdot W\times N\), where \(N\) is the number of character categories. The softmax function is applied along the class dimension to normalize the output into a valid probability distribution for each pixel. Finally, the output is reshaped back to \(B\times N\times H\times W\), restoring the spatial layout of the predictions. As a result, each pixel position carries a complete probability distribution over all character classes, enabling dense, pixel-level character recognition. \[\mathbf{f}_{\text{cls}}=\text{Softmax}\Big{(}\text{FC}\Big{(}\text{Conv}_{1 \times 1}\Big{(}\text{Conv}_{3\times 3}^{(2)}(\mathbf{f}_{\text{det-rec}}) \Big{)}\Big{)}\Big{)} \tag{3}\]
# Numerical Simulation of Red Blood Cell-Induced Platelet Transport in Saccular Aneurysms
## Mathematical Model
### Constitutive Relations
#### 2.2.2 Stress Tensor of the RBCs  
Here, the RBCs are assumed to behave as a non-linear fluid with viscosity dependent on volume fraction and shear rate,  
\[\mathbf{T}_{s}=\Big{[}-p\phi+\beta_{20}\Big{(}\phi+\phi^{2}\Big{)}tr\mathbf{D}_{s} \Big{]}\mathbf{I}+\mu_{s}(\phi,\ tr\mathbf{D}_{s})\phi\mathbf{D}_{s} \tag{7}\]  
\[\mu_{s}(\phi,\ tr\mathbf{D}_{s})\phi=\Bigg{(}\mu_{\infty}(\phi)+(\mu_{0}(\phi)-\mu _{\infty}(\phi))\frac{1+ln\Big{(}1+k(\phi)\big{(}2 tr\mathbf{D}_{s}{}^{2}\big{)}^{ 1/2}\Big{)}}{1+k(\phi)\big{(}2 tr\mathbf{D}_{s}{}^{2}\big{)}^{1/2}}\Bigg{)} \tag{8}\]  
The value of these parameters are fitted using the experimental measurements reported by [PERSON] [29] (See Figure 1), where  
\[\mu_{0}=537.002\phi^{3}+55.006\phi^{2}-0.129\phi \tag{9a}\] \[\mu_{\infty}=27.873\phi^{3}-21.218\phi^{2}+14.439\phi\] (9b) \[k=11 \tag{9c}\]  
where the unit of \(\mu_{0}\) and \(\mu_{\infty}\) is cP. It is noted that Equations (9a,b) imply that when the hematocrit approaches zero the viscosity of RBCs reduces to zero. One of the outstanding advantages of the current viscosity model is that we use a larger set of experimental data, where the hematocrit ranges from 8.6% to 70.2%; therefore, this correlation is, in a sense, more general and better suited for studying problems when the local hematocrit varies heavily in the flow region [22]. Similar to the plasma, the term \(\beta_{20}\big{(}\phi+\phi^{2}\big{)}tr\mathbf{D}_{s}\) disappears automatically, because we consider the RBC component to be incompressible.
## 3 The Proposed Attack Path Discovery
### Assumptions  
The proposed attack path discovery method considers the following assumptions:  
* The assets within the healthcare ecosystem are dependent upon each other for the healthcare service delivery;
* Each asset may link with single or multiple confirmed vulnerabilities published by the National Vulnerability Database (NVD) or CVE, which are required to be considered for the attack path generation. CVE contains a huge list of published vulnerabilities that assist in determining vulnerabilities related to specific healthcare assets;
* The threat actor needs a certain profile in terms of attacker capability (knowledge and skill) and access vector (local, adjacent, network, and physical) to exploit a vulnerability and discover an attack path;
* Each user within the healthcare system performs certain functionalities based on the roles and responsibilities. Threat actors could take advantage of target user profiles to execute an attack;
* Each attack path includes several variables, such as entry point asset, intermediate point (if any), target point asset, dependencies among the assets, and underlying characteristics of the vulnerability within the assets;
* The methodology follows the CVSS for attack path generation and vulnerability estimation. It mainly considers the base score metric values for generating the attack path.
# Modelling, Analysis and Performance of a Low Inertia AC-DC Microgrid
## 4 Simulation Results
### Comparison between LPF- and HPF-Based VI Controllers  
This case study is dedicated to comparing the dynamic performance of the proposed control system based on HPF to produce virtual inertia with a scheme based on LPF. The two controllers are tuned to have the same damping coefficients by setting their gains.  
Figure 11: Model validation: (**a**) frequency, (**b**) DC link voltage.  
The poles responsible for the controller are tuned to have the best possible damping in each controller. Finally, the two controllers are compared together under the same conditions of contingency by increasing the load torque of the motor load by 3% in the AC microgrid side.  
Figure 12a shows the frequency response of the AC microgrid, which indicates relatively low-frequency oscillations when LPF is used. However, the frequency stabilizes with a lower steady state error compared to the HPF, which did not encounter high-frequency oscillations. Moreover, Figure 12b demonstrates that the generated inertial response for frequency support comes at the cost of the DC link voltage; in the case of LPF, the DC microgrid voltage is reduced below its nominal value. In contrast, the proposed controller based on HPF results restores the DC link voltage to its nominal value after providing the required inertial support without utilizing a secondary control loop. It is worth mentioning that both the HPF and LPF controllers damp the frequency oscillations compared to the case of disabling the inertia loop.
## 1 Introduction
At the same time, the intrinsic random function (IRF) [15; 16] is used to replace the variation function which requires prior statistics in the Kriging process, such that the whole model does not need prior information in the calculation process. * In the process of model calculation, a constant size state vector is maintained (the size being the number of sensors, N, in the sensor network) and the available sampling data \(n_{t}\) at time t is obtained through the data selection matrix \(\mathbf{W}_{t}\) for correction and calculation. This method solves the problem of data inconsistency caused by different sampling data at different times. At the same time, for a constant extra space storage  
Figure 1: General processing of spatio-temporal data. required \((O(N^{2}))\) and computational complexity \((O(n_{t}^{3}))\) in the cyclic calculation process, the computational complexity can be further reduced by setting \(W_{t}\). * Through the derivation process, similar to that for a Kalman filter, the model realizes a similar function to Kalman smoothing [17], such that the estimated data can eliminate the random systematic error, to a certain extent. Exponential smoothing prediction [18; 19] is introduced, in order to improve the prediction ability of the model. The remainder of this article is organized as follows: Section 2 provides related background information. Section 3 describes the FUKSS model in detail. For this model, in Section 3.1, we propose the state equation and derive the recursive equation. A specific formulation of the state-space model is described in Section 3.1.2, while the detailed calculation process is described in Section 3.1.3. In Section 3.2, we describe the selection of various functions for our model in detail. In Section 3.3, we introduce cubic exponential smoothing prediction to realize the multi-step prediction by the model. Based on this model, we conclude the paper with some numerical examples in Sections 4.1 and 4.2, an application of the algorithm in Section 4.3, and a discussion of the method in Section 5. Section 6 presents the conclusion of our work. Notations: Matrices are in upper case bold, and column vectors are in lower case bold. The notation \((. )^{T}\) is the transpose operator, \(\bar{Z}_{t}(x)\) is the estimate of \(Z_{t}(x)\). The special cases to be noted are: the regional variable functions \(Z\) and \(Y\) based on the stochastic process. \(Z_{t}(x)\) and \(Y_{t}(x)\) represent an implementation of a random process at time \(t\) and position \(x\). In this case, bold capital \(\mathbf{Z}_{t}\) and \(\mathbf{Y}_{t}\) represents positions \((x_{1},x_{2},\ldots)\) at time \(t\), which represents the \(n*1\) vector. Sampling data lowercase \(x\) represents the position information of a point, and a capital \(\mathbf{X}\) represents the data set composed of \(n\) sampling points. An identity matrix of size \(N*N\) is denoted by \(\mathbf{I}\). \(\left\|\,x_{a}-x_{b}\right\|_{2}\) represents the second order universal number between \(x_{a}\) and \(x_{b}\), namely the Euclidean distance. Specific variable settings in the manuscript can be found in Nomenclature part.
# Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavior
## 6 Implementation and Results
### Considered Kinodynamic Model of Robot  
#### 6.1.1 Car-Like Robot  
As illustrated in Figure 2, the states of the car-like robot can be given by the center position of the rear wheel axle \(p_{a}=[x_{a}\,y_{a}]^{T}\), its orientation \(\theta\) and steering angle \(\phi\). Its state-transition equations are given by:  
\[\dot{x}_{a}(t) =v_{s}\cos\theta(t) \tag{16}\] \[\dot{y}_{a}(t) =v_{s}\sin\theta(t)\] \[\dot{\theta}(t) =v_{s}k\]  
where \(p_{a}(0)\) is the current position of a robot. The admissible velocities space due to acceleration constraints is a disc in velocity space, its radius is equal to \(\eta a_{m}\) and its center is at \(\dot{p}_{a}(0)\). The admissible velocities space due to speed constraints is a disc in velocity space, its radius is equal to \(v_{m}\) and its center is at the origin. The intersection of these two convex spaces will give us an admissible velocity space \(U_{ad}\).
## 3 Results
### Ablation Study  
External data were used to inspect how ParaNet+ performs on surgically verified samples. The external dataset consists of 100 participants. All participants had at least one abnormal finding on surgery. Therefore, there are no subjects populating the normal class. Consequently, only sensitivity and false-positive rates are assessed in this group. On surgery, 118 abnormal PGs were identified and excised, 86 corresponding to solitary adenomas (in 86 patients), and 32 to multiglandular disease (in 14 patients).  
The experts identified 93/100 abnormal cases on the patient level and 99/118 PGs. Eleven findings of the human reader were false positives on the PG level.  
ParaNet++ identified 99/100 cases on a patient basis and 103/118 on a PG basis. There were 13 false-positive PG findings. On both patient and PG bases, the model yielded somewhat higher sensitivity than the human reader (99.0% vs. 93.0% and 87.3% vs. 83.9%, respectively) with comparable FPRs (11.2% vs. 10.0%, respectively). The results are also summarised in Table 5.  
The model successfully detected solitary adenomas (97.7% sensitivity). However, it performed sub-optimally in cases with multiglandular disease (59.4% sensitivity). It is worth noticing that the human readers performed better than the model for the latter case, yielding a sensitivity of 65.6%. On the other hand, some false positives from the model, judged with reference to experts' diagnoses, proved to be true positives based on the surgical findings.
# PAGE--Practical AES-GCM Encryption for Low-End Microcontrollers
## 2 Related Work  
### Galois Counter Mode (GCM) of Operation  
GCM mode is the most commonly used encryption mode that protects message confidentiality, authentication, and integrity. GCM mode performs encryption using CTR mode and generates Message Authentication Code (MAC) using GHASH function using 128-bit binary multiplication. Security is ensured because the receiver can know the integrity of the message by comparing the MAC value before decrypting the ciphertext.  
AES-GCM is a well known Authenticated Encryption with Associated Data (AEAD) encryption algorithm that sets a specific part of the message as associated data and transmits it without encryption. Even though the associated data is not encrypted, it is still often used in network packet encryption since it can be still used to verify the authenticity and integrity of the message. Currently, AES-GCM is adopted by NIST (National Institute of Standards and Technology) authentication encryption standard (SP 800-38D) and Transport Layer Security (TLS) as authentication encryption.
# Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers
## 3 Proposed Model
### Dataset  
The dataset used in this study was obtained from the UCI Heart Disease Data Repository [34]. It comprises a total of 14 features, with the target variable being the dependent variable. The independent variables include age, sex, cp (chest pain), trestbps (resting blood pressure), chol (serum cholesterol), fbs (fasting blood sugar), restecg (resting electrocardiographic results), thalach (maximum heart rate achieved), exang (exercise-induced angina, with zero representing absence and one representing presence), oldpeak (ST depression induced by exercise relative to rest), slope (the slope of the peak exercise ST segment), ca (number of major vessels colored by fluoroscopy), and thalal (thalassia).  
Before constructing the model, a comprehensive analysis and visualization of the dataset were conducted to gain valuable insights into the distribution of values. This initial exploratory analysis facilitated a deeper understanding of the data, allowing us to make informed decisions during the subsequent modeling phase. It is important to note that \"angina\" (exercise-induced angina) represents whether or not the patient experienced angina during the stress test, with values of zero indicating no angina and one indicating the presence of angina.  
Table 1 presents the distribution of different chest pain values in relation to the target variable.  
Table 2 displays the sample distribution of high and low blood sugar values against the target variable.  
In Table 3, the sample distribution of exang (exercise-induced angina) values with respect to the target variable can be depicted.  
\begin{table}
\begin{tabular}{c c} \hline
**Chest Pain Distribution** & **Percentage** \\ \hline Asymptomatic with Heart Disease & 45.6\% \\ Asymptomatic without Heart Disease & 17.1\% \\ Angina with Heart Disease & 3.07\% \\ Non-Angina without Heart Disease & 30.3\% \\ Atypical Angina with Heart Disease & 3.05\% \\ \hline \end{tabular}
\end{table}
Table 1: Sample Chest Pain Distribution against Target.  
\begin{table}
\begin{tabular}{c c} \hline
**Blood Sugar Distribution** & **Percentage** \\ \hline High Blood Sugar with Heart Disease & 48.89\% \\ High Blood Sugar without Heart Disease & 51.11\% \\ Low Blood Sugar with Heart Disease & 44.96\% \\ Low Blood Sugar without Heart Disease & 55.04\% \\ \hline \end{tabular}
\end{table}
Table 2: Sample blood sugar distribution against target.  
Figure 2: Flowchart of concatenated hybrid ensemble voting classifier.  
The distribution of continuous variables, including age, cholesterol, oldpeak, thalach, and trestbps, is visualized through the scatter plots in Figure 3. This figure helps us identify potential data patterns and outliers, which can influence the choice of appropriate modeling techniques and preprocessing steps.
# Gearbox Fault Diagnosis Based on ICEEMDAN-MPE-AWT and SE-ResNeXt50 Transfer Learning Model
## 2 ICEEMDAN-MPE-AWT Denoising
### Mpe  
Permutation Entropy (PE) is an approach used to quantify the complexity and randomness of a time series or chaotic dynamical system. It was introduced by [PERSON] et al. [22]. The count of permutation entropy is simple and robust, and the requirement of time series length is not high. Even if the time series is very short, it is possible to compute. However, the fault characteristic information of a gearbox is spread across multiple scales, and the permutation entropy of a single scale will miss the characteristic information of other scales. MPE is a multiscale complexity method that can measure the randomness of signals at different scales. It first coarse granulates the original time series by a multiscale algorithm, then constructs the multiscale time series and calculates the permutation entropy of each scale. MPE is calculated as below.  
1. For one-dimensional time series \(Z=(z_{1},z_{2},\ldots z_{n})\), the coarse-grained time series can be constructed by Equation (8): \[y_{j}^{(s)}=\frac{1}{s}\sum_{j=s}^{i=(j-1)s+1}z_{i},\;1\leq j\leq\frac{n}{s},\] (8) where \(s\) is a positive integer called the scale factor.
2. The time series reconstruction of  
MPE has three parameters: embedding dimension \(m\), scale factor \(s\) and delay \(\tau\). The delay \(\tau\) has little influence on the time series calculation, which is generally 1. For the embedding dimension \(m\), a value that is too small will lead to too few permutation states in the phase space reconstruction process, and it is difficult to accurately measure the dynamic mutation of the time series. A value that is too large will lead to too many permutation states and uniform distribution, which is insensitive to small changes in the system and also increases the computational cost. According to the research of [PERSON] et al. [23], the optimal representation effect of MPE could be obtained by selecting \(m=5\), \(m=6\), or \(m=7\). Therefore, \(m=6\) was selected in this study. A standard method for choosing the scale factors is not uniform, and, generally, a value greater than or equal to 10 can be selected. In this study, \(s=11\) was selected.
# Performance Analysis of BDS-5G Combined Precise Point Positioning
## 4 Conclusions  
A combined BDS and 5G PPP model was proposed, and the effects of different numbers and different geometrical configurations of 5G base stations participating in PPP on the convergence speed of three directions (XYZ) and the tropospheric zenith wet delay from  
Figure 14: Convergence of different base station geometries for Scene 2 (dynamic precise point positioning [PPP]), (a) X direction, (b) Y direction, (c) Z direction, (d) Zenith wet delay.  
Figure 15: Convergence speed and standard deviation (STD) of the errors for different base station geometry configurations under Scene 1 and Scene 2 (dynamic precise point positioning [PPP]), (a) Convergence speed under Scene 1, (b) Convergence speed under Scene 2, (e) STD under Scene 1, (d) STD under Scene 2.  
both static and dynamic perspectives were investigated. From the experiments, it can be seen that:  
1. As the quantity of 5G base stations participating in the positioning solution increases, the convergence time and the STD of the errors after convergence both tend to decrease. The beneficial effect brought about by the participation of 5G base stations in positioning is also evident with a smaller number of visible satellites. In particular, when a single BDS system cannot converge within 1 h or when there is a large post-convergence error in some cases, the involvement of 5G base stations can significantly speed up convergence and achieve better post-convergence positioning accuracy.
2. In experiments with different geometrical configurations, there is a significant advantage for speed of convergence and STD of the errors after convergence of the various configurations in their dominant directions compared with the relatively balanced configurations. The advantage is also evident when the number of satellites is low. The dominant geometric configuration in each direction is suitable where rapid convergence is required in a particular target direction.
3. The research on BDS and 5G combined precise point positioning technology is conducive to the rapid realization of indoor and outdoor seamless positioning and is of great significance to the development of the PNT community.  
[PERSON] and [PERSON] provided the initial idea for this work and wrote this manuscript; [PERSON] designed the algorithm. [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the analyses of results. [PERSON] and [PERSON] contributed to the collection and analysis of field test data. [PERSON] and [PERSON] drew the pictures. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 41974032.  
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.  
The work was supported by the Chinese Academy of Sciences (CAS) programs of \"High Level Talents\". Thanks to [PERSON] and his research team from the National Time Service Center, Chinese Academy of Sciences, for providing the 5G measured data.  
The authors declare no conflict of interest.