esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Timing Synchronization Algorithm Based on PSSS for LTE-V2X
## 1 Introduction
A drawback was the substantial computational workload. In [20], [PERSON] et al. adopted the block cross-correlation method, dividing the local signal and received signal into several blocks, performing separate correlations and then summing them. This enhances resistance to frequency offset. However, due to the cumulative effect of noise, its performance is poorer in low SNR conditions. From these works, we can learn that existing symbol timing synchronization methods based on the PSSS have shortcomings, such as susceptibility to channel state variations and high complexity. Therefore, building upon the foundation of the aforementioned research, this paper proposes an improved symbol timing synchronization algorithm based on the PSSS. Our main contributions are outlined as follows:  
1. We investigate and analyze several commonly used time synchronization algorithms based on the PSSS. We perform theoretical analyses on aspects such as synchronization signal detection performance and algorithm complexity. We propose an improved symbol timing synchronization algorithm based on the PSSS, aiming to enhance the robustness and accuracy of synchronization signal detection in LTE-V2X. We conduct simulation analyses to compare its performance with existing algorithms, demonstrating effective improvement in robustness under high frequency offset and low SNR scenarios in vehicular networks. 3. We establish a hardware-in-the-loop simulation platform and compare the performance of the proposed improved algorithm with other existing algorithms in different scenarios and vehicle speeds. We validate, in high-speed mobile scenarios, that the proposed algorithm maintains a high CDR even under low SNR conditions. The remainder of this paper is organized as follows: First, we introduce the main properties of the PSSS in Section 2. In Section 3, we analyze the limitations of several existing symbol timing synchronization algorithms based on the PSSS. Building on this analysis, we propose an improved algorithm with the aim of enhancing the robustness and accuracy of synchronization signal detection in Section 4. Section 5 is dedicated to verifying the performance of the proposed improved algorithm through software simulation and hardware-in-the-loop simulation. Finally, in Section 6, we summarize our conclusions and provide a brief outlook on future work.
# The Development of Key Technologies in Applications of Vessels Connected to the Internet
## 2 Internet of Vessels Architecture
### The Technical Architecture of the Perception Layer  
As one of the most important fundamentals in the IoV system, the intelligent perception platform contains the ship electronic authentication center, the intelligent ship terminal, sensors and image detection, and the identification system. Based on the collection of the ship positioning data, operating data, waterway traffic data and emergency data, the perception layer can provide the data support for the upper layer [17]. The function of the ship electronic authentication center is to identify that the operating ships are legitimate. The intelligent ship terminal is the core of the perception layer. The RFID module, GPS/Bedou module and sensor module are integrated in this part to provide data (cargos, ships, ports) collection and exchange with the shore-based facilities and servers. The technical architecture of this layer is shown in Figure 5.  
In Figure 5, it can be seen that the IoV system could obtain the dynamic perception of the maritime information in real-time in a variety of ways. The object of the dynamic perception covers a wide range of roles including waterways, ports, ships, intersections, inland river-way service centers, refuse collection points, ship locks, bridges, fleets and marine service stations. Every perceiving subject has its own perceiving object and contents. For instance, in the Yangtze river, in order to realize the construction of the IoV system, the state of the navigation environment which includes the channels, hydrometeorology and navigation structures should be perceived. Generally, the buoys identify the boundary of the channels and provide the information on traffic flow direction, channel obstructions, channel width and channel depth. Therefore, the GNSS (Global Navigation Satellite System) is installed in the buoys to monitor their position. At the same time, [PERSON] [13] proposed a kind of estimation method to calculate the floating threshold of buoys in the safety area. The use of a high precision positioning method for buoys helps the management center control the information on the channels at any time. Besides, with the feature image learning technology based on the BP (back propagation) neural network and the night visibility monitoring technology based on the dual differential luminance algorithm, it could distinguish the trend of bad weather and provide the meteorology information to the management center. Similarly, lots of different sensors are mounted on the navigation structures to  
Figure 5: The technical architecture of the perception layer. (**a**) The perception technology and equipment of the perception layer; (**b**) The perception information of the perception layer.  
collect the information about the ships, hydrometeorology and channels. Thus, based on the perception technology, the specific perceiving objects and contents are listed in Table 1[13].
# Efficient Estimation of Generative Models Using Tukey Depth
## Appendix A Copulas  
If we are in the situation where we do know about the marginal distributions but relatively little about the joint distribution, and want to build a simulation model, then the copula is a useful method to deriving such a joint distributions respecting all those given marginal distributions. Here we present some basic ideas about copulas and introduce the two copulas that we used in this paper. For further details on the topic, we refer the reader to [45; 46].  
Definition A: A \(p\)-dimensional copula \(C\colon[0,1]^{p}\to[0,1]\) is a function which is a cumulative distribution function with uniform marginals.  
The following result due to [PERSON] (1959) says that one can always express any distribution function in terms of a copula of its margins.  
Theorem A1 ([PERSON]'s theorem): _Consider a \(p\)-dimensional cdf \(F\) with marginals \(F_{1},\ldots,F_{p}\). There exists a copula \(C\), such that_  
\[F(x_{1},\ldots,x_{p})=C(F_{1}(x_{1}),\ldots,F_{p}(x_{p})) \tag{1}\]  
_for all \(x_{i}\) in \([-\infty,\infty]\), \(i=1,\ldots,p\). If \(F_{i}\) is continuous for all \(i=1,\ldots,p\) then \(C\) is unique; otherwise \(C\) is uniquely determined only on \(\text{Ran}\;F_{1}\times\cdots\times\text{Ran}\;F_{p}\), where \(\text{Ran}\;F_{i}\) denotes the range of the cdf \(F\)._  
In our work, we make use of the following two bivariate copulas. The bivariate Gumbel copula or Gumbel-Hougaard copula is given in the following form:  
\[C_{\gamma}(u_{1},u_{2})=\exp\Bigl{\{}-[(-\ln u_{1})^{\gamma}+(-\ln u_{2})^{ \gamma}]^{\frac{1}{\gamma}}\Bigr{\}},\]  
where \(\gamma\in[1,\infty)\). The Clayton copula is given by  
\[C_{\gamma}(u_{1},u_{2})=\Bigl{(}\max\{u_{1}^{-\gamma}+u_{2}^{-\gamma}-1,0\} \Bigr{)}^{-\frac{1}{\gamma}},\]  
where \(\gamma\in[-1,\infty)\setminus\{0\}\).
# Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients
## 3 Proposed Method
### Stacking Ensemble Model Construction
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Group** & **Model** & **Precision** & **Recall** & **F1 Score** & **Specificity** & **AUC** \\ \hline \multirow{7}{*}{Average} & Logistic Regression (LR) & 0.7529 & 0.5748 & 0.6526 & **0.7679** & 0.7303 \\ \cline{2-6}  & XGBoost (XGB) & 0.7292 & 0.7501 & 0.7409 & 0.5108 & 0.7554 \\ \cline{2-6}  & PyCaret & 0.7249 & 0.7756 & 0.7493 & 0.6558 & 0.7031 \\ \cline{2-6}  & H2O.ai & 0.6965 & 0.7849 & 0.7389 & 0.6167 & 0.6888 \\ \cline{2-6}  & Average of All Single Classifiers and AutoML & 0.7206 & 0.6601 & 0.6787 & 0.5978 & 0.6812 \\ \cline{2-6}  & **Proposed Model (CSS Type 3)** & **0.7789** & **0.8183** & **0.7981** & 0.6348 & **0.7736** \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average performance results of Type 3 severity for all groups. Similar to the performance observed for Types 1 and 2, an increase in the number of clinical variables from Groups 1-5 resulted in improved F1 and AUC scores. However, the improvement in our proposed model from Group 2 to Group 3 was limited, with only a 1.5% increase in the F1 score and a 4.4% increase in the AUC score. Similarly, the performance of other single classifiers and AutoML approaches was observed to be lower in comparison. This suggests that a patient's current or past medical history may not be as crucial as significant predictive variables when patient symptom information is available. Groups 4 and 5, which included the blood test results as clinical variables, showed significant performance improvements. Thus, all experiments confirmed that blood test results could be used as important variables for predicting the severity of COVID-19. Moreover, while the AutoML approaches PyCaret and H2O.ai demonstrated performance enhancements in Groups 1 and 2, they did not consistently outperform our proposed model. Specifically, PyCaret showed notable F1 scores, such as 0.7304 in Group 1 and 0.7517 in Group 2, by utilizing various single classifiers, including Logistic Regression and Gaussian Naive Bayes. Similarly, H2O.ai, through its ensemble strategy that incorpo
## 2 Materials and Methods  
### Materials  
Fennel essential oil and its main component anethole are used as inhibitors.  
* Fennel fruit essential oil: Processed fruits of sweet fennel, harvested in 2020. The essential oil was obtained by water distillation in a laboratory glass apparatus of the British Pharmacopoeia. The essential oil has the following main components: anethole (73.72%), fenchone (17.09%), and estragole (2.67%) [32].
* Anethole: An isolate purchased from the pharmacy network in the town of Razgrad, Bulgaria was used.
* Steel samples: A plain steel material grade ASTM A36 [33](EN S235 JR, St44-2) with a density of 7850 kg/m\({}^{3}\) and with the following composition was used: 0.021% C; 0.012% Si; 0.292% Mn; 0.0095% P; 0.0067% S; 0.02% Cu, and balanced Fe. Sample bodies measuring 14 \(\times\) 14 \(\times\) 1 mm were cut from the sheet material mechanically. Each sample was processed manually with 400 grit sandpaper to obtain a mirror-like surface, and then washed with deionized water, degreased and dried with 95% ethanol and hot air. The steel tiles were stored in a desiccator with a silica gel dryer to prevent subsequent oxidation.  
Solutions: Aggressive solutions of 1 M HCl were prepared by diluting 37% HCl with distilled water. Solutions of fennel fruit essential oil and anethole at concentrations of 1.0 mL/L, 1.5 mL/L and 2.0 mL/L in 1 M HCl were also prepared. The conditions for conducting the experiments--the choice of temperature, time, amount of inhibitors, and concentration of the aggressive environment with hydrochloric acid--are according to our preliminary unpublished data and according to data from the literature.
# A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy
###### Abstract  
Diabetic retinopathy (DR) is a complication of diabetes and is known as visual impairment, and is diagnosed in various ethnicities of the working-age population worldwide. Fundus angiography is a widely applicable modality used by ophthalmologists and computerized applications to detect DR-based clinical features such as microaneurysms (MAs), hemorrhages (HEMs), and exudates (EXs) for early screening of DR. Fundus images are usually acquired using funduscop cameras in varied light conditions and angles. Therefore, these images are prone to non-uniform illumination, poor contrast, transmission error, low brightness, and noise problems. This paper presents a novel and real-time mechanism of fundus image enhancement used for early grading of diabetic retinopathy, macular degeneration, retinal neoplasms, and choroid disruptions. The proposed system is based on two folds: (i) An RGB fundus image is initially taken and converted into a color appearance module (called lightness and denoted as _J_) of the CIECAM02 color space model to obtain image information in grayscale with bright light. Afterwards, in step (ii), the achieved \(J\) component is processed using a nonlinear contrast enhancement approach to improve the textural and color features of the fundus image without any further extraction steps. To test and evaluate the strength of the proposed technique, several performance and quality parameters--namely peak signal-to-noise ratio (PSNR), contrast-to-noise ratio (CNR), entropy (content information), histograms (intensity variation), and a structure similarity index measure (SSIM)--were applied to 1240 fundus images comprised of two publicly available datasets, DRIVE and MESSIDOR. It was determined from the experiments that the proposed enhancement procedure outperformed histogram-based approaches in terms of contrast, sharpness of fundus features, and brightness. This further revealed that it can be a suitable preprocessing tool for segmentation and classification of DR-related features algorithms.  
CIECAM02 color model; contrast enhancement; diabetic retinopathy; fundus angiography; nonlinear contrast enhancement; histogram-based enhancement procedures +
Footnote †: journal:  
Article
## 4 TDC-VSG Control Strategy
### Parameter Design of the TDC-VSG  
Predicated on Equation (12), the closed-loop transfer function of the APL system in the TDC-VSG under given power variations is derived as follows:  
\[\Phi(s)=\frac{\Delta P_{\text{ref}}}{\Delta P_{\text{out,G}}}=\frac{K_{\text{P}} (T_{\text{T}}s+1)}{T_{\text{T}}J_{\omega}\omega_{0}s^{3}+a_{1}s^{2}+b_{1}s+K_{ \text{P}}} \tag{14}\]  
Based on classical control principles and the theoretical analysis in Figure 6, the stability performance of the TDC-VSG is primarily influenced by the dominant poles \(s_{\text{T}1}\) and \(s_{\text{T}2}\). Thus, the transient damping parameter \(D_{\text{T}}\) can be designed using the dominant pole method [29] and frequency domain analysis. Based on Equation (14), it can be deduced as follows:  
\[\Phi(s)=\frac{(T_{\text{T}}s+1)}{k_{\text{P}}(T_{\text{T}}s+1)(s^{2}+2\xi \omega_{\text{s}}s+\omega_{\text{s}}^{2})}=\frac{(T_{\text{T}}s+1)}{k_{\text{ P}}(T_{\text{T}}s+1)(s^{2}+\frac{D_{\text{T}}+K_{\omega_{\text{s}}}}{J_{ \omega}}s+\frac{3 EL_{\text{g}}}{J_{\omega}\omega_{0}\chi})} \tag{15}\]  
where \(k_{\text{p}}\) is the coefficient; \(\xi\) is the damping ratio of the APL; and \(\omega_{\text{s}}\) is the natural frequency of the APL. The concrete expression of the damping ratio \(\xi\) and natural frequency \(\omega_{\text{s}}\) are shown as follows:  
\[\left\{\begin{array}{l}\xi=\frac{D_{\text{T}}+K_{\omega}}{2}\sqrt{\frac{ \omega_{0}X}{J_{\omega}EL_{\text{g}}}}\\ \omega_{\text{s}}=\sqrt{\frac{3 EL_{\text{g}}}{J_{\omega}\omega_{0}X}}\end{array}\right. \tag{16}\]  
Based on classical control theory, analyzing the frequency domain index relationships of the TDC-VSG yields the expressions for phase angle margin \(\gamma\) and cutoff frequency \(\omega_{\text{c}}\) of the APL as follows:  
\[\left\{\begin{array}{l}\gamma=\text{arctan}\frac{2^{\gamma}}{\sqrt{\sqrt{1+ 4^{\zeta}4^{4}-2\zeta^{2}}}}\\ \omega_{\text{c}}=\omega_{\text{s}}\sqrt{\sqrt{1+4\xi^{4}}-2\xi^{2}}\end{array}\right. \tag{17}\]  
To ensure the transient stability of the APL system in the TDC-VSG, the transient damping coefficient \(D_{\text{T}}\) must satisfy the phase angle margin of \(30^{\circ}<\gamma<70^{\circ}\). Using Equations (16) and (17), the feasible region for \(D_{\text{T}}\) is illustrated in Figure 8. Hence, this paper adopts \(\gamma=57.3^{\circ}\), \(\xi\approx 0.707\), and \(D_{\text{T}}\approx 17.32\).  
Figure 8: The selectable regions of \(J_{\omega}\) and \(D_{\text{T}}\) under phase angle constraints.
# Analysis and Optimization of Output Low-Pass Filter
## 2 Filter Resonance and Damping
### Damped Filter Response and Configuration Comparison  
Figure 4 illustrates the performance of four different damped low-pass filter configurations. For two specific damping resistances, \(Z_{0}\)\(\Omega\) and 2 \(Z_{0}\)\(\Omega\), the figures show how the gain and phase delay of the filter change as the input frequency varies. The input frequency is measured relative to the resonance frequency and plotted on a logarithmic scale from 0.01 to 100.  
When the damping resistance is equal to the characteristic impedance, FC 1 and FC 3 exhibit identical gain and phase delay plots similar to FC 2 and FC 4 (Figure 4a). With an increase in damping resistance, the gain and phase delay plots exhibit distinct characteristics (see Figure 4b). FC 1 and FC 3 demonstrate similar behavior, whereas FC 2  
Figure 3: A low-pass filter with four damping variations, combining capacitors and inductors.  
and FC 4 also show comparable characteristics. The phase delay of an \(n\)th-order low-pass filter approaches \(-90\)\(n^{\circ}\) at higher frequencies, whereas the gain generally diminishes at a rate of \(-20\)\(n\) dB per decade. This shows that FC 1 and FC 3 behave as first-order low-pass filters, while FC 2 and FC 4 behave as second-order low-pass filters. This shows that FC 2 and FC 4 are expected to generate a lower THD than FC 1 and FC 3.  
Figure 5 analyses the effect of damping resistance on the filter's quality factor and phase delay at resonance for various \(R_{D}\) values (normalized relative to the characteristic impedance, \(Z_{0}\)). This analysis supports the results that are presented in Figure 4. The selected filter configuration and the damping resistance both affect the \(Q\) factor and phase delay of the filter at its resonant frequency. The phase delay of FC 2 and FC 4 remains constant to the ratio of \(R_{D}\) to \(Z_{0}\). This result suggests that in second-order designs, the required \(Q\) factor can be achieved by adjusting \(R_{D}\) without affecting the phase delay at resonance.  
Figure 4: Analysis of the normalized gain and phase margin of a CL low-pass filter under two conditions: (**a**) \(R_{D}=\ Z_{0}\) and (**b**) \(R_{D}=2\ Z_{0}\). FC 1 and FC 3 are first-order filters, while FC 2 and FC 4 are second-order filters. The frequency has been normalized with respect to the resonant frequency, \(f_{R}\).  
Figure 5: Filter gain (**a**) and phase delay (**b**) at the resonant frequency, \(f_{R}\), for each FC are presented based on the damping resistance, \(R_{D}\). It is normalized, meaning \(R_{D}\) is expressed in relation to \(Z_{0}\).
## 2 Region, Data, Method
### Conceptual Model Used in Data Interpretation  
A conceptual model of noise behavior at a network of stations in a seismically active region was proposed in [5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 35] using seismic records and theoretical modeling. It was shown that the high values of the multi-fractal parameters \(\Delta\alpha\), \(\alpha^{*}\) and the \(\beta\) value, as well as the low values of the minimum normalized entropy of the squared orthogonal wavelet coefficients \(En\), are due to an increase in the number of outliers in the original seismic records. For example, an increase in the number of outliers in the time series of a continuous seismic signal can occur when seismicity is activated during the aftershock stages of strong earthquakes. On the other hand, the consolidation of individual elements of the geological environment and the weakening of near-surface movements may manifest itself in a decrease in the number of high-amplitude outliers in seismic records and will be reflected in high values of entropy \(En\) and low values of \(\Delta\alpha\), \(\alpha^{*}\), and \(\beta\).  
In seismically active areas, the formation of a large, consolidated block contributes to the accumulation of energy in it and, consequently, increases the danger of a strong earthquake. Such large, consolidated blocks can be long-existing areas of seismic quietness distinguished by a decrease in the number of weak earthquakes or \"seismic gaps\" [45; 46]. Formation of a large, consolidated block can also be accompanied by a decrease in the diversity of the transfer and resonance properties of the geological environment and loss of the multi-fractality of noise time series and, accordingly, a decrease in \(\Delta\alpha\)\(u\)\(\alpha^{*}\) parameters [5; 6; 7].  
Such a model of noise parameters' behavior during the preparation for strong earthquakes was used in interpreting the results of processing data from a network of stations in Kamchatka [35; 36; 39], as well as in the present work. We also note that this simple model of the behavior of seismic noise parameters during the preparation of a strong earthquake is in good agreement with the data on the decrease in the multifractal parameters \(\alpha^{*}\) and \(\Delta\alpha\) in the regions of future earthquake sources with \(M_{w}\) = 8.3 and 9.0 in Japan [8].  
In accordance with the general patterns of complex systems' behavior before catastrophic changes, we also considered an increase in coherence in the variations of the four-dimensional series of the statistical parameters of seismic noise as a possible sign of strong earthquake preparation, diagnosed in time-frequency diagrams [12].
# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 3 Classification of Energy Management Techniques for Prolonging WSN's Lifetime
### Energy Consumption Analysis of WSN  
To comprehensively study the range of energy management techniques applicable to WSNs, it is important to first analyze the power dissipation characteristics of a sensor node and identify the factors that influence energy consumption both at the node level and across the entire network. Conducting a thorough and systematic analysis of energy consumption within a sensor node is crucial for identifying the key parameters that impact WSN energy consumption. This analysis aids in gaining a deeper understanding of how various energy management strategies function to effectively minimize energy consumption and extend the lifespan of WSNs. In this subsection, we undertake an examination of WSN energy consumption by first modeling the energy consumption of a sensor node. Subsequently, we outline the parameters influencing energy consumption within a WSN, both at the node level and across the network as a whole, as well as the sources of energy wastage.
## 5 Results and Evaluation
### Answering the Research Question  
With all these results, the answer concerning the best visual clue is rather hard. While, in the domain of input performance, the size animation clue performed best, the static clues showed better usability and were preferred by the participants. Additionally, the gain of 4% typing speed in comparison to the static clues is rather small. If the problem is viewed from a performance perspective, the best clue is the size animation clue ahead of the font clue. But combining all results shows that this is not really the answer this paper was looking for. These might be the best-performing visual clues in the domain of typing speed, but the user experience is also a performance metric and similarly important. Therefore, the answer to the question \"Which visual clue leads to the best typing performance in a novice experience setting\" is the clue the user prefers to use in this setting, as long as it is not the wiggle animation clue. The differences are too small to choose a single visual clue to be used with all people and therefore using the one the user likes the most is the best choice.