esa-sceva/satcom-chunk-collection huggingface dataset:
## 2. Mobility Models and Related Work
### Random Waypoint Mobility Models (RWM)  
This model was first proposed and evaluated by [PERSON] and [PERSON] [15] and later became a benchmark for wireless networks. Implementation of these models includes two major factors, i.e., pause time [0 to T\({}_{\text{pause}}\)] and velocity [0 to V\({}_{\text{max}}\)]. Each mobile node starts its movement by selecting any random location as a destination and then starts moving towards it. After reaching that location, it pauses for a certain time and then moves towards its further destination. The velocity and direction of each node are independent of any other node in the network. The whole process of moving and pausing is repeated until the simulation ends. The movement traces are shown in Figure 2.
###### Abstract  
The pre-operative localisation of abnormal parathyroid glands (PG) in parathyroid scintigraphy is essential for suggesting treatment and assisting surgery. Human experts examine the scintigraphic image outputs. An assisting diagnostic framework for localisation reduces the workload of physicians and can serve educational purposes. Former studies from the authors suggested a successful deep learning model, but it produced many false positives. Between 2010 and 2020, 648 participants were enrolled in the Department of Nuclear Medicine of the University Hospital of Patras, Greece. An innovative modification of the well-known VGG19 network (ParaNet+) is proposed to classify scintigraphic images into normal and abnormal classes. The Grad-CAM++ algorithm is applied to localise the abnormal PGs. An external dataset of 100 patients imaged at the same department who underwent parathyroidectomy in 2021 and 2022 was used for evaluation. ParaNet+ agreed with the human readers, showing 0.9861 on a patient-level and 0.8831 on a PG-level basis under a 10-fold cross-validation on the training set of 648 participants. Regarding the external dataset, the experts identified 93 of 100 abnormal patient cases and 99 of 118 surgically excised abnormal PGs. The human-reader false-positive rate (FPR) was 10% on a PG basis. ParaNet+ identified 99/100 abnormal cases and 103/118 PGs, with an 11.2% FPR. The model achieved higher sensitivity on both patient and PG bases than the human reader (99.0% vs. 93% and 87.3% vs. 83.9%, respectively), with comparable FPRs. Deep learning can assist in detecting and localising abnormal PGs in scintigraphic scans of patients with primary hyperparathyroidismism and can be adapted to the everyday routine.  
deep learning: explainable artificial intelligence; parathyroid glands; hyperparathyroidismism 202316435. [[https://doi.org/10.3390/a16090435](https://doi.org/10.3390/a16090435)]([https://doi.org/10.3390/a16090435](https://doi.org/10.3390/a16090435))
# A Chaotic System with Infinite Equilibria and Its S-Box Constructing Application
## 5 Conclusions  
A chaotic system with an infinite number of equilibrium points has been investigated in this work. Dynamics of the system have been discovered by using numerical simulations and circuital experiments. The system with infinite equilibrium displays attractive behaviour such as chaos and multistability. By using such a system, we have presented a new S-Box algorithm and proposed two S-Boxes. Different tests have been implemented to confirm the good performance of two S-Boxes, which are appropriate to cryptographic applications. The comparison of the proposed S-Boxes with some studies in the literature is presented. As a result of the comparison in the literature, it has been shown that the proposed S-Boxes have the best max, min and avg nonlinearity values after the AES algorithm and have good values in other criteria. We will apply such S-Boxes to develop a secure communication system for medical devices in our future works. In addition, constructing the fuzzy controller to stabilize the chaotic behavior of the system will be studied.  
Conceptualization, X.W.; Formal Analysis, X.W.; Investigation, A.A. and U.C.; Methodology, A.A. and V.-T.P.; Project Administration, X.Q.N.; Software, U.C. and V.-T.P.; Supervision, D.V.H.; Writing--Original Draft, D.V.K.; Writing--Review and Editing, X.Q.N.  
The author [PERSON] was supported by the National Natural Science Foundation of China (No. 61601306) and Shenzhen Overseas High Level Talent Peacock Project Fund (No. 20150215145C). [PERSON] was supported by Sakarya University Scientific Research Projects Unit under Grants 2016-09-00-008, 2016-50-01-026.  
The authors declare no conflict of interest.
# Urban Traffic State Sensing and Analysis Based on ETC Data: A Survey
## 2 ETC Systems: Historical Development and Current Applications
### Global ETC Development History Overview
Another important feature of China's ETC development is the uniformity of the technical standards and system compatibility. Unlike the American model of states building independent systems before integration, China has emphasized the formulation and implementation of unified national standards from the beginning. The advantages of this unified standard approach were fully demonstrated during the nationwide networking process, providing a technical foundation for data interconnection and cross-regional applications [11].
## 4 Simulation and Analysis of the MRAI-LDPC Decoding Algorithm
### Decoding Performance
#### 4.1.1 Experiment 1  
Consider the \(F_{16}(225,147)\) regular NB-LDPC code with finite-field construction [21], which has column weight \(\rho=14\), row weight \(\gamma=14\), and code rate \(R=0.65\). The parameters for several decoding algorithms are set as follows: (1) the threshold of the AlgB algorithm is set to 7; (2) for the wtd-AlgB algorithm, the threshold is set to 7 and the Hamming distance coefficient is \(\theta=(2.1,\ 2.0,\ 1.0,\ 1.0)\); (3) for the presented MRAI-LDPC algorithm, the threshold \(T_{H}\) is set to 7. Meanwhile, we use the bit error rate (BER), the frame error rate (FER), and the average number of iterations (ANI) as the performance metrics. The BER and FER simulation results are shown in Figure 4.  
The solid line in Figure 4 represents the BER performance, while the dashed line represents the FER performance. It can be seen from Figure 4 that:  
* The BER performance and FER performance of the presented MRAI-LDPC algorithm are superior to the AlgB algorithm and the wtd-AlgB algorithm, thanks to the introduction of the channel received information, which improves the total reliability;
* The presented MRAI-LDPC algorithm achieves significant performance gains. For example, at a BER of \(10^{-5}\), the MRAI-LDPC decoding algorithm has about 0.4 dB BER performance gain over the wtd-AlgB algorithm. When FER is \(10^{-4}\), it has an FER performance gain of about 0.15 dB and 1.15 dB compared with the wtd-AlgB and wtd-AlgB algorithms, respectively;  
Figure 3: The flow diagram of the MRAI-LDPC decoding algorithm.  
In addition, with the increase in SNR, the BER and FER of the MRAI-LDPC decoding algorithm decrease more rapidly, and the performance gain is more obvious.  
As depicted in Figure 5, the presented MRAI-LDPC algorithm has the fastest convergence speed among these decoding algorithms, especially in low SNR regions. For instance, at an SNR of 4.0\(-\)4.5 dB, the average number of iterations (ANI) of the MRAI-LDPC significantly decreases by 70\(-\)85% compared to the AlgB algorithm. When the SNR is 4.0 dB, the ANI of the wtd-AlgB algorithm is 38, while the MRAI-LDPC algorithm requires only 28 iterations on average.  
Figure 4: The BER and FER performance of the \(F_{16}(225,147)\) QC-LDPC code.  
Figure 5: The average number of iterations under the \(F_{16}(225,147)\) QC-LDPC code.
## 2 Experimental Setup
### AC Breakdown Test  
The insulation strength and short-term breakdown time of epoxy/Al2O3 micro-composites were obtained according to IEC 60243-1 [29]. The overall test setup is shown in Figure 4. To evaluate the AC insulation characteristics, the setup consisted of a high-voltage source and test electrodes. As shown in Figure 4, the electrode arrangement with embedding must be made for films and boards of up to 3 mm [29]. For the short-term breakdown test, both the ramp test (RT) and step-up test (ST) were considered.  
Figure 3: Relative permittivity and dissipation factor measuring device: (**a**) circuit diagram of dielectric property measurement; (**b**) actual measurement equipment.  
Figure 2: Epoxy/Al2O3 micro-composites.  
In this study, the RT method was performed such that the electrical stress level is increased in a continuous manner as shown in Figure 5a. In our case, the voltage was ramped up at a rate of 1 kV/s so that the insulation breakdown would occur within 10 to 20 s [29]. The insulation BDS values measured during this test were statistically processed using the Weibull distribution, and the BDS at the 40 th percentile was set as the initial voltage for the ST. The ST method incrementally increases the stress level and is classified into 60 s, 10 min, and 30 min STs based on the duration of voltage increase cycles. In this study, the voltage was increased every 60 s. According to the table provided by IEC 60243-1, the voltage was increased by 0.5 kV at 60 s intervals during the breakdown test, as shown in Figure 5b. Since both RT and ST are tests that evaluate the dielectric strength of epoxy/Al2O3 micro-composites by penetration, transformer oil was used as the surrounding medium to prevent creepage discharge, as shown in Figure 4.
# Unsupervised Learning-Based Spectrum Sensing Algorithm with Defending Adversarial Attacks
## 4 Implementation of USDAA Defense Algorithm
### Implementation of Primary User Adversarial Attacks and Defense  
The process of the primary user adversarial attacks and defenses is shown in Figure 2. The target model \(F_{\beta}\) is trained using clean samples \(D\) and labels, and its network structure is identical to the encoder. The generation process of adversarial samples of two kinds of white box primary user adversarial attacks is shown in the left part of the network in Figure 2. According to FGSM and PGD, two kinds of white box attack algorithms, clean samples \(D\) are input into the target model \(F_{\beta}\) to obtain adversarial samples \(D^{adv}\). The right side of Figure 2 shows the defense process, and the defense network \(R_{\theta}\) is obtained by the methods in Sections 4.1 and 4.2. When the adversarial samples \(D^{adv}\) are input to the defense model \(R_{\theta}\), it can correctly sense whether there is a primary user signal.
# Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation
## 4 Illustrative Examples  
This section demonstrates the performances of the proposed algorithm by two illustrative examples. In the paper, we mainly compare the estimation performance based on the following benchmarks:  
\[\text{MSE}_{1}(m)=\frac{1}{K}\sum_{k=1}^{K}\left(x(k)-\widehat{x}(k|k)\right)^{2 },m=1,\ldots,M, \tag{40}\]  
\[\text{MSE}=\frac{1}{M}\sum_{m=1}^{M}\text{MSE}_{1}(m), \tag{41}\]  
\[\text{MSD}_{\text{p}}(k)=\frac{1}{M}\sum_{m=1}^{M}\left((x(k)-\widehat{x}(k|k)) ^{2}+(y(k)-\widehat{y}(k|k))^{2}+(z(k)-\widehat{z}(k|k))^{2}\right),k=1,\ldots,K, \tag{42}\]  
\[\text{MSD}_{\text{v}}(k)=\frac{1}{M}\sum_{m=1}^{M}\left((\hat{x}(k)-\widehat{ \widehat{x}}(k|k))^{2}+(\hat{y}(k)-\widehat{y}(k|k))^{2}+(\hat{z}(k)-\widehat {z}(k|k))^{2}\right),k=1,\ldots,K, \tag{43}\]  
\[\text{TAMSD}_{\text{p}}=\frac{1}{k_{2}-k_{1}}\sum_{k=\overline{k}_{1}+1}^{k_ {2}}\text{MSD}_{\text{p}}(k), \tag{44}\]  
\[\text{TAMSD}_{\text{v}}=\frac{1}{k_{2}-k_{1}}\sum_{k=\overline{k}_{1}+1}^{k_ {2}}\text{MSD}_{\text{v}}(k), \tag{45}\]  
where \(K\) is the entire time steps in every Monte Carlo run and \(M\) represents the total Monte Carlo runs.
## 2 Design of the Generator
### Safety Considerations  
The nodes and individual parts of this device are protected against overcurrent and overvoltage. Although the variable high-voltage power supply has internal current limits (250 mA), a resistor R1 = 5 k\(\Omega\) is connected in series with the capacitors to protect the power supply against the overcurrent. In addition, a diode DI and an inductor L1 are used to protect the power supply from the negative voltage that can occur when the capacitors are discharged.  
To ensure the precise control of the electroporation device, the accurate and safe selection of the pulse parameters for each operating mode is crucial. Therefore, various restrictions are implemented in both the software design and the hardware. The software limits parameters such as the maximum charging voltage, the number of pulses, and the pulse repetition time.  
To avoid voltage dips during relay switching, the operation of the relay is synchronized with the high-voltage supply. A separate interruption event is integrated into the software to anticipate a change in the operating module that leads to the high-voltage source being switched off. When the user changes the operating module, the power supply is deactivated, and after confirmation of its status, the relay is switched. This implementation ensures safe commutation of the capacitors and prevents voltage dips during mode selection.  
In addition, charged high-voltage capacitors are a potential hazard. If they are switched off for any reason after charging, they can retain a lethal charge over a long period of time. To eliminate this risk, leakage resistors (10 M\(\Omega\) resistors connected to the capacitors) are installed at the terminals to ensure a constant discharge even when disconnected from the pulse circuit. In addition, the device has a forced discharge of the capacitors, which is activated by the \"Cancel\" button and directs the discharge through the intended circuit.  
In addition, ballast resistors (R2, R3) of 5 \(\Omega\) and an induction coil (L2, L3) of 5 \(\mu\)H are connected in series with the cuvette to limit the current flow through the thyristors and to control the rise time of the current in the event of an electrical short circuit in the cuvette.
# Measures and Methods for the Evaluation of ATO Algorithms
## 3 Approach for ATO Testing
### Extraction of Challenging Scenarios for Performance Evaluation
#### 3.3.2 Base Scenario  
Before logical scenarios are defined allowing the variation of parameter values, a minimal scenario definition is a crucial requirement for the lengthy scenarios necessary for testing the performance of ATO algorithms. This minimal definition is introduced in this paper and will be referred to as the _base scenario_. It can be provided either by designing a track from the scratch or by making use of already existing reference tracks.  
A base scenario consists of a minimal set of scenario components to allow a logical scenario to function in principle. These minimal defined scenario components are:  
* correctly designed track layout including track characteristics,
* presence of critical infrastructure elements,
* accurate use and positioning of safety system components,
* definition of a (standard) timetable,
* comprehensive train model and characteristics.  
Bringing back the layer model for the railways, elements of the static layers one and two as well as parts of layers three and six need to be defined as a base scenario before logical scenarios can be set up. According to the simulator used and which default values need to be defined, these minimal requirements can be more comprehensive.
## 3 Results
### Microscopic Characteristics of Fiber-Reinforced Cement-Stabilized Soft Clay  
To elucidate the strengthening mechanism, SEM analyses were conducted on the samples of FCSSC with a curing age of 7 days, a fiber content of 0.3%, and a fiber length of 5 mm. Meanwhile, the morphology of both the intact and damaged samples after uniaxial compression was observed and compared using microscopic scanning. Previous studies have indicated that the active components in cement and fly ash, such as calcium silicate minerals, react with clay during the curing process, leading to the formation of cementitious products [1; 7; 8]. This reaction enhances the strength and stability of the soil. As depicted in Figure 6a, before failure, the stabilized soft clay contains C-S-H gel and Ca(OH)\({}_{2}\), with clay particles tightly wrapped, covered, and bonded by numerous hydrates, thereby enhancing the density of the stabilized soft clay. In Figure 6b, the hydration gel substance adheres to the fiber's surface, causing an overlap between the fibers and the hydration product, thereby improving the bonding force and mechanical interlocking force in the cement mortar. Simultaneously, the curing agent may reduce the water absorption of fibers and slow down the decay or degradation process of fibers, thereby enhancing the durability of fiber-rich soils. Figure 6c reveals the microscopic morphology of the fractured cement mortar sand. Notably, the fibers exhibited no breakage post sample failure but underwent pulling, suggesting fiber displacement or rotation. This signifies that the fibers possess high tensile strength and act as a bridge among the clay-cement particles inside the specimens of FCSSC. This inhibits the expansion of compression cracks, thus allowing cracks to expand on both sides of the fibers. Consequently, the overall severity of deformation and compression fracture is reduced in the sample. Regarding the mechanism of action, after reaching the peak compressive strength of the specimens of FCSSC, exceeding the optimal fiber content and length leads to overlapping and entanglement, causing non-uniform dispersion in the soil. This may result in weak interfaces, reducing the adhesive strength of the fibers and hydration products. Thus, glass fiber-reinforced cement-stabilized soft clay must contain an optimum combination of fiber content and length.  
Figure 6: Microstructural morphology of cement sand material at different states: (**a**) hydration products before destruction, (**b**) fibers in the sample before destruction, and (**c**) fiber pulling after failure.
## 2 Review of the MCC and Its Related Sparse Algorithms
\begin{array}{l}\min\frac{1}{2}\|\boldsymbol{\hat{g}}\left(n+1\right)- \boldsymbol{\hat{g}}\left(n\right)\|^{2}+\theta_{\mathrm{ZA}}\|\boldsymbol{ \hat{g}}\left(n+1\right)\|_{1}\\ \text{subject to }\hat{e}\left(n\right)=\left[1-\alpha\exp\left(-\frac{\sigma^{2} \left(n\right)}{2\sigma^{2}}\right)\right]e\left(n\right)\end{array} , \tag{9}\]  
where \(\|\bullet\|_{1}\) is the \(l_{1}\)-norm, and a regularization parameter \(\theta_{\mathrm{ZA}}\) is used for controlling its ability. Based on the LMM, the ZA-MCC's cost function is [33]  
\[ \begin{array}{l}J_{1}\left(n\right)=\frac{1}{2}\|\boldsymbol{\hat{g}}\left( n+1\right)-\boldsymbol{\hat{g}}\left(n\right)\|^{2}+\theta_{\mathrm{ZA}}\| \boldsymbol{\hat{g}}\left(n+1\right)\|_{1}+\lambda_{\mathrm{ZA}}\left(\hat{e }\left(n\right)-\left[1-\alpha\exp\left(-\frac{\sigma^{2}\left(n\right)}{2 \sigma^{2}}\right)\right]e\left(n\right)\right)\end{array} , \tag{10}\]  
where \(\lambda_{\mathrm{ZA}}\) is the multiplier parameter of the ZA-MCC algorithm. By using LMM, the updating Equation of the ZA-MCC algorithm is [33]  
\[\boldsymbol{\hat{g}}\left(n+1\right)=\boldsymbol{\hat{g}}\left(n\right)+\beta _{\mathrm{ZA}}\exp\left(-\frac{e^{2}\left(n\right)}{2\sigma^{2}}\right)e \left(n\right)\mathbf{x}\left(n\right)-\theta_{\mathrm{ZA}}\mathrm{sgn} \left[\boldsymbol{\hat{g}}\left(n\right)\right], \tag{11}\]  
where \(\theta_{\mathrm{ZA}}\) is a zero attracting ability controlling parameter that is used for giving a tradeoff between the estimation error and \(l_{1}\)-norm constraint, and \(\beta_{\mathrm{ZA}}\) is the step-size of the ZA-MCC algorithm. However, we noticed that the zero attraction term \(-\theta_{\mathrm{ZA}}\mathrm{sgn}\left[\boldsymbol{\hat{g}}\left(n\right)\right]\) uniformly attracts all the channel coefficients to zero. Therefore, its performance might be degraded when it deals with less sparse channels. Then, a reweighting factor is introduced into the zero attraction term \(-\theta_{\mathrm{ZA}}\mathrm{sgn}\left[\boldsymbol{\hat{g}}\left(n\right)\right]\), resulting in a RZA-MCC algorithm whose updating equation is [33]  
\[\boldsymbol{\hat{g}}\left(n+1\right)=\boldsymbol{\hat{g}}\left(n\right)+\beta _{\mathrm{RZA}}\exp\left(-\frac{e^{2}\left(n\right)}{2\sigma^{2}}\right)e \left(n\right)\mathbf{x}\left(n\right)-\theta_{\mathrm{RZA}}\frac{\mathrm{sgn }\left[\boldsymbol{\hat{g}}\left(n\right)\right]}{1+\varepsilon\left| \boldsymbol{\hat{g}}\left(n\right)\right|}, \tag{12}\]  
where \(\varepsilon\), \(\theta_{\mathrm{RZA}}\) and \(\beta_{\mathrm{RZA}}\) are the reweighting controlling factor, zero attraction controlling parameter and the RZA-MCC's step-size, respectively. It can be seen from the upgrading equation that \(-\theta_{\mathrm{RZA}}\frac{\mathrm{sgn}\left[\boldsymbol{\hat{g}}\left(n \right)\right]}{1+\varepsilon\left|\boldsymbol{\hat{g}}\left(n\right)\right|}\) acts as the zero attractor which exerts different zero attraction to the channel coefficients that depends on their magnitudes. Figure 1: Structure diagram of sparse channel estimation.
# Research on Wellbore Trajectory Optimization and Drilling Control Based on the TD3 Algorithm
## 2 Related Work
### TD3 Network
Firstly, the critic network receives the state information and the action of the actor network calculation as input; then, it performs the fusion calculation in the middle layer and calculates the value evaluation through two separate Q networks. The average value of the final two Q values represents the evaluation value of the action in this state. The minimum value of the Q network output is taken as the target Q value, which further enhances the stability and accuracy of the Q network model. The critic network output layer uses a linear activation function to directly predict the Q value, and the calculation formula is as follows (25). \[Q(s,a)=\omega^{T}x+b \tag{25}\]  
where \(x\) is the input of the final layer of the critic network, \(Q(s,a)\) is the value function obtained by network prediction, \(\omega\) is the weight, and \(b\) is the offset.
# Gaussian-Distributed Spread-Spectrum for Covert Communications
## 4 Detectability of GDSS Signals
### Energy Detector  
Energy detection is another commonly used method for detecting communication signals. The basic idea behind energy detection is to compare the energy of the received signal to a pre-defined threshold. If the energy of the received signal exceeds this threshold, the signal is assumed to be present. One of the advantages of the energy detection method is its simplicity and low computational complexity. However, energy detection can suffer from high false alarm rates in low SNR environments, where the noise can easily exceed the detection threshold. Therefore, energy detectors are generally not effective for detecting signals, such as DSSS and GDSS signals, operating under the noise floor. As the GDSS system requires more power than the DSSS system to achieve the same BER performance, it is expected that GDSS signals can be more likely detected compared to DSSS signals using an energy detector in high SNR conditions.