esa-sceva/satcom-chunk-collection huggingface dataset:
# A Police and Insurance Joint Management System Based on High Precision BDS/GPS Positioning
###### Abstract  
Car ownership in China reached 194 million vehicles at the end of 2016. The traffic congestion index (TCI) exceeds 2.0 during rush hour in some cities. Inefficient processing for minor traffic accidents is considered to be one of the leading causes for road traffic jams. Meanwhile, the process after an accident is quite troublesome. The main reason is that it is almost always impossible to get the complete chain of evidence when the accident happens. Accordingly, a police and insurance joint management system is developed which is based on high precision BeiDou Navigation Satellite System (BDS)/Global Positioning System (GPS) positioning to process traffic accidents. First of all, an intelligent vehicle rearview mirror terminal is developed. The terminal applies a commonly used consumer electronic device with single frequency navigation. Based on the high precision BDS/GPS positioning algorithm, its accuracy can reach sub-meter level in the urban areas. More specifically, a kernel driver is built to realize the high precision positioning algorithm in an Android HAL layer. Thus the third-party application developers can call the general location Application Programming Interface (API) of the original standard Global Navigation Satellite System (GNSS) to get high precision positioning results. Therefore, the terminal can provide lane level positioning service for car users. Next, a remote traffic accident processing platform is built to provide big data analysis and management. According to the big data analysis of information collected by BDS high precision intelligent sense service, vehicle behaviors can be obtained. The platform can also automatically match and screen the data that uploads after an accident to achieve accurate reproduction of the scene. Thus, it helps traffic police and insurance personnel to complete remote responsibility identification and survey for the accident. Thirdly, a rapid processing flow is established in this article to meet the requirements to quickly handle traffic accidents. The traffic police can remotely identify accident responsibility and the insurance personnel can remotely survey an accident. Moreover, the police and insurance joint management system has been carried out in Wuhan, Central China's Hubei Province, and Wuxi, Eastern China's Jiangsu Province. In a word, a system is developed to obtain and analyze multisource data including precise positioning and visual information, and a solution is proposed for efficient processing of traffic accidents.  
Keywords:lane level map; high precision positioning; traffic management; insurance survey; Internet of Vehicles (IoV) +
Footnote †: journal: Sensors
# Seismic Performance of Reinforced Concrete Short Columns Subjected to Freeze-Thaw Cycles
## 2 Experimental Program
### Pseudo-Static Test Setup and Loading Procedure  
The cantilever test setup is shown in Figure 5. The column was mounted vertically with the bottom of the RC foundation which was restrained to the strong floor. Two linear variable displacement transducers (LVDTs) were installed separately at the loading point and at the centerline of the foundation to monitor the lateral displacement and the slip of the foundation. Two additional pairs of LVDTs were used for measuring shear displacements and rotations at the plastic region where the length was evaluated as 150 mm [33].  
All columns were tested under the combined axial load and cyclic lateral loads. The axial load was applied via a hydraulic jack before the horizontal one and kept constant throughout the testing process.  
Figure 4: Column surface after freeze–thaw applications.  
Figure 3: SEM images of concrete specimens after different numbers of FTCs.  
The horizontal cyclic action was controlled by a 500 kN MTS hydraulic actuator, which was 100 mm away from the top face of the specimen to ensure shear span ratio equal to 2.5. Since the yielding point during the loading process could be unclear if the columns showed shear behavior, the specimens were tested under the same predefined displacement control, as shown in Figure 6. The displacement was represented by the drift ratio which was defined as the lateral displacement divided by the column height. When the peak drift was smaller than 0.48%, the cyclic loads were applied once. After that, each drift level was repeated three times to evaluate the in-cycle strength and stiffness degradation.
## 3 Adaptive Time-Frequency Analysis  
This section introduces adaptive time-frequency analysis, including three key methodologies and their optimization models: empirical mode decomposition (EMD), variational mode decomposition (VMD), and empirical wavelet transform (EWT). Each method and its associated optimization framework will be systematically analyzed from three perspectives: definition, practical applications, and strengths and weaknesses. This comparative examination aims to clarify their theoretical foundations, implementation challenges, and suitability for diverse signal processing scenarios.
## 5 Conclusions and Future Work  
### Conclusions  
This study primarily centered on identifying vulgar language in social media posts. As the common approach to vulgar remark detection is using simple lists of vulgar keywords, we firstly proposed a method to automatically extract such vulgar keywords from raw data and used those keywords in simple keyword-matching baseline classification methods. The automatic keyword extraction method was able to successfully extract vulgar keywords and additionally successfully filter out half of the non-vulgar words, which allowed the method to reach a satisfying approximately 70% accuracy in detecting vulgar sentences. Next, we also employed machine learning (ML) and deep learning (DL) classifiers, including logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), multinomial naive Bayes (MNB), simple recurrent neural network (simpleRNN), and long short-term memory (LSTM). These classifiers were coupled with various feature extraction techniques like CountVectorizer, TF-IDF Vectorizer, Word2 Vec, and fastText. Our dataset consisted of 2485 comments, balanced between vulgar and non-vulgar remarks. To evaluate the performance of the proposed methods, we ran experiments on this dataset. The results indicated that LR with CountVectorizer or TF-IDF Vectorizer, as well as simpleRNN with Word2 Vec and fastText, were particularly effective in detecting vulgar comments.
## 3 Background
### Radio Signal Propagation
The multipath fading effects in the received signal is a function of both space and time; \(PL^{mp}=\Omega_{dB}\) and are modeled as a Gaussian random variable (\(\Omega\sim\mathcal{N}(0,\sigma_{\Omega}^{2})\)) with zero-mean and a variance \(\sigma_{\Omega}^{2}\). The probability density function (pdf) of the \(PL^{mp}\) can be structured using Rician fading, Rayleigh fading or Nakagami-m fading models for LOS, NLOS or indoor distributions, respectively [30]. The parameters \(\eta\), \(\sigma_{\psi}\) and \(\sigma_{\Omega}\) together define the radio environment [29]. A radio environment usually contains noise signals (\(PN\)) from other radio sources operating in the same frequency band or higher power signals operating at any radio frequency nearby the radio receivers. As the noise in the environment is a non-stationary process because of many factors, including the mobility of the node, it is highly unpredictable and cannot usually be modeled. In order to account for the noise, a widely-used measure is the signal-to-noise ratio (SNR), which assesses the received radio signal level with respect to the noise level in the surroundings. The SNR is defined as follows:  
\[SNR=\frac{RSS_{W}}{PN_{W}}=10^{(\frac{SNR_{dBm}}{10})}\text{ where, }SNR_{dBm}=RSS_{dBm}-PN_{dBm} \tag{3}\]  
The \(RSS_{W}\) is the absolute power (expressed in W) and \(RSS_{dBm}=10\log(RSS_{mW})\) is the RSS value in decibels referenced to the RSS of one milliwatt of the absolute power. The concept of conversion from absolute power to dBm applies also to the SNR and the noise signal. While the RSS is an indicator of the intended signal's power, the SNR is an indicator of the intended signal power with respect to the noise power in the environment. The RSS can be measured directly (in terms of dBm) in most of the commercial wireless devices using a metric called the radio signal strength indicator (RSSI) (even though the RSSI metric may report different measurements, such as the absolute signal power (dBm), a relative signal level (unit-less), the SNR (dBm) or the bit error rate (BER) (%), due to its vendor-specific nature, the wireless devices used in this paper report absolute signal power (dBm) in RSSI) [31]; however, this is not the case for SNR measurements. In some devices, the noise power level (which is usually inaccurate [31]) and RSS values are reported separately, and hence, the SNR can be calculated using both of these measurements. To avoid device dependency and to widen the applicability of our algorithm, we use the RSS measurements instead of the SNR measurements.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 3 State of the Art in Linear Computation Coding
### Scalar Functions
One can also search for repeating bit patterns in the representation of \(t\) and reuse the initial computation for later occurrences [23; 24; 25]. This search, however, is known to be non-polynomial (NP)-complete, if performed in an optimal way [26]. One can represent \(t\) by multiple bases at the same time [27]. However, the conversion into the multiple base number system does not come for free. Area and power savings were studied in [28] for 32-bit and 64-bit integer arithmetic. Only for 64-bit arithmetic improvements were reported. An approach relevant to our work is reported in [29]. The authors address multiple constant multiplication. That is the parallel computation of several scalar linear functions. The authors utilize common terms among the several functions and report significant savings. In some sense, our work can be understood as a generalization of [29] to linear functions with vector-valued arguments. When targeting very low precision, simply using tables can also be a promising alternative [30].
## 2 Physiological Controller Development  
### Software Model of CVS  
Figure 1 shows the CVS-IRBP interaction system model utilized to evaluate the proposed control system. This model was developed using MATLAB Simulink tools (The MathWorks Inc., Natick, MA, USA). The model was composed of different numbers of  
variables that represented lumped parameter blocks. Each parameter was distinguished by its own unique block, such as pressure (\(P\)), diodes (\(D\)), inertances (\(L\)), elastances (\(E\)), and resistances (\(R\)). The CVS-RBP model consisted of several parts, including pulmonary circulation, systemic circulation, and heart compartments. The motor used as the IRBP in this investigation was a VentAssist\({}^{\text{TM}}\) centrifugal pump; this pump belonged to Ventracor Ltd., Sydney, Australia.  
In this model, the hydraulic and electrical characteristics of the pump were modeled based on three differential equations. These included the motor windings, motor electromagnetic torque, and the hydraulic pump. These equations could be described as:  
* The electrical motor winding: \[v=k\omega+RI+L\frac{dI}{dt}\] (1) where \(v\) is the motor terminal voltage, \(k\) is the back of the electromotive force, \(\omega\) is the impeller speed, \(R\) is the resistance for the motor winding, \(I\) is the motor current, and \(L\) is the inductance for the motor winding.
* The motor electromagnetic torque: \[T=J\frac{d\omega}{dt}+aQ^{2}\omega+bQ\omega^{2}+c\omega+d\omega^{3}\] (2) where \(T\) is the electromagnetic torque; \(Q\) is the pump flow rate; \(J\) is the moment of inertia of the impeller; and \(a,\ b,\ c\), and \(d\) are the polynomial coefficients that were obtained by the least squares method using the experimental data under a steady flow condition.
* The hydraulic pump: \[\Delta P=e+fQ^{3}+g\omega^{2}\] (3) where \(\Delta P\) is the pump differential pressure and \(e,\ f,\) and \(g\) are constants.  
The pressure and volume of each heart compartment were modeled for each chamber based on the cardiac cycle. During this cycle, the elastance function (\(E(t)\)) was varied  
Figure 1: Cardiovascular system CVS-IRBP interaction model. \(M\): pump motor; \(P\): pressure; \(D\): diodes; \(L\): inertances; \(E\): elastances; \(R\) : resistances; \(L_{in}\): inlet cannulae inertances; \(L_{out}\): outlet cannulae inertances; \(R_{suc}\): suction resistance; \(R_{in}\): inlet cannulae resistances; \(R_{out}\): outlet cannulae resistances.  
according to a linear function during end-systole and an exponential function during end-diastole. The elastance function of this model could be expressed as follows:  
\[E(t)=\begin{cases}sin^{2}\big{(}\pi t/T_{sys}\big{)}&t<T_{sys}\\ 0&t\geq T_{sys}\end{cases} \tag{4}\]  
Here, \(T_{sys}\) is the systolic interval. This model was validated and tested using data from animals in both in vitro and in vivo environments [18; 19].
## Appendix: Force Constant of the Girifalco Interaction Potential  
In this appendix, the energetics of a graphene plane physisorbed on a layered crystal is developed on the basis of the Girifalco potential [90]. In the Girifalco approach, each carbon of the graphene interacts with the atomic planes of the substrate through a 6-12 Lennard-Jones potential \(C_{12}/r^{12}-C_{6}/r^{6}\), the sums over the atomic pairs being replaced by integrals. The interaction of a C atom with a given atomic plane of the substrate at distance \(d\) is  
\[V_{1}(d)=\frac{b}{10d^{10}}\;-\;\frac{a}{4d^{4}} \tag{26}\]  
The interaction of a C atom with a massive substrate follows by summing Equation (26) over an infinite set of planes parallel to the surface. Assuming a regular inter plane spacing \(h\) yields  
\[V(z)=\frac{b}{10h^{10}}\,\zeta_{10}(z/h)\;-\;\frac{a}{4h^{4}}\,\zeta_{4}(z/h) \tag{27}\]  
where \(z\) is the distance of graphene above the surface and \(\zeta_{s}(x)=\sum_{n=0}^{\infty}(x+n)^{-s}\) is a generalized Riemann function (\(x>0\), \(s>1\)).  
For a given substrate, when the parameters \(a\) and \(b\) are known together with the bulk interlayer spacing \(h\), the adsorption distance \(z_{0}\) of the graphene plane is obtained by minimizing Equation (27). The force constant \(K\) (Table 4) of the interaction potential is the second derivative of \(V(z)\) at equilibrium, multiplied by the number \(N\) of C atoms per unit area in graphene.  
To obtain the Grifalco parameters for graphite, Equation (27) is applied to a C atom located in the bulk, namely in between two semi-infinite multilayers, while setting \(z=h\). In this geometry, the bulk energy per C atom writes \(E=B/10h^{10}-A/4h^{4}\) where \(A=a\,\zeta_{4}\) and \(B=b\,\zeta_{10}\) with \(\zeta_{s}\equiv\zeta_{s}(1)\) is the usual Riemann function. The interlayer distance leading to the minimum of energy is \(h=(B/A)^{1/6}\). A second relation is obtained from the elastic constant \(C_{33}\) that corresponds to the second derivative of the bulk energy per unit volume at the equilibrium distance, which leads to \(C_{33}=6 NA/h^{5}\). Using the value of \(h\) given in Table 1, \(N\) = 38.18 nm\({}^{-2}\), and \(C_{33}\) = 38.7 GPa (Table 2), one obtains \(A\) = 4.48 meV nm\({}^{4}\) and \(B=6.38\times 10^{-3}\) meV nm\({}^{10}\). The \(a\) and \(b\) parameters listed in Table 1 follow from the definition of \(A\) and \(B\).  
For the case of graphene on BN, _ab-initio_ RPA data of graphene on a single BN layer have been used [91]. In this reference, six positions of the graphene layer on top of the BN layer were considered while assuming a common parameter for the two hexagonal lattices. The average potential well and separation distance are 34.5 meV/atom and 0.345 nm, respectively. Fitting Equation (26) on these data leads to the parameters listed in Table 1.
## 3 Results
The results of the field studies are presented in Table 4. Field tests were limited to an actual horizontal soil displacement of 2.7 mm, although they confirmed a higher bearing capacity, even compared to conical foundations [9], which are also effective in areas of possible seismic activity and in mining areas (Figure 13). In this case, the horizontal forces caused by horizontal tensile deformations of the soil mass can be similarly eliminated by installing a special sliding joint located between the conical foundations and the foundation structure. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Foundation Model Type**} & \multicolumn{2}{c}{**Horizontal Tension**} & \multicolumn{2}{c}{**Settlement Under Load, mm**} \\ \cline{3-5}  & **Strains of Soil, mm/m** & **100 N** & **150 N** & **200 N** \\ \hline \multirow{3}{*}{Conical foundation with an aperture angle of 90 degrees from [9]} & 0 & 0.75 & 2.65 & 9.54 \\  & 3 & 1.02 & 3.30 & 11.24 \\  & 6 & 1.32 & 3.90 & 12.19 \\  & 9 & 1.55 & 4.50 & 13.51 \\ \hline \multirow{3}{*}{Cone-top pile foundation} & 0 & 0.75 & 1.90 & 8.40 \\  & 3 & 0.80 & 2.53 & 9.65 \\  & 6 & 0.85 & 3.16 & 10.75 \\  & 9 & 0.90 & 3.79 & 11.82 \\ \hline \multirow{3}{*}{Shallow isolated foundation from [9]} & 0 & 1.30 & 7.25 & - \\  & 3 & 1.50 & 11.44 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the numerical modeling for loading–settlement interaction. Figure 12: Results of laboratory and numerical studies on load–settlement curve for cone-top pile foundation model and isolated shallow foundations: 1, 2—curves obtained for isolated shallow foundation for horizontal tensile strains \(\varepsilon\) = 0 and 3 mm/m; 3, 4, 5, 6—curves obtained for cone-top pile foundation for horizontal tensile strains \(\varepsilon\) = 0, 3, 6 and 9 mm/m. The field test results demonstrate that all foundation types exhibited increased settlement with rising vertical loads, and the presence of horizontal tension strains in the soil (approximately 2.6-2.7 mm/m) consistently led to greater settlements, across the board. At a load of 100 kN, the cone-top pile foundation showed the highest settlement, of 10.09 mm under strain, compared to 8.52 mm without strain, representing an 18.4% increase. Similarly, the conical foundation experienced a 16.6% increase in settlement under strain, while the shallow isolated foundation showed a 19% increase at 75 kN. These results indicate that horizontal soil deformation significantly affects vertical settlement, with conical and cone-top pile foundations still performing better overall than shallow foundations, particularly under higher loads and strained conditions.
# Time Optimization of Unmanned Aerial Vehicles Using an Augmented Path
## 3 Proposed Augmented Path-Time Architecture
### Curve of Descent  
The Brachistochrone curve or curve of the fastest descent is used to reach the destination point. It is the most efficient curve in case of descent as it increases the efficiency and decreases the time of flight by a great margin.  
The equation to be used in the algorithm is:  
\[X=a\;(\theta-\sin\;\theta), \tag{2}\]  
\[Y=a\;(1-\cos\;\theta), \tag{3}\]  
where a is the distance of the centre to an endpoint and \(\theta\) is the angle between the path and the angle of the UAVs.  
The brachistochrone curve is the fastest descent as with all the forces acting in favour of the drone and for this curve, only a small amount of adjustment and forces by the motorsare required as all the others are provided by gravity and wind. The curve is free to move on the \(z\)-axis as the curve has specific angles and to accomplish it accurately, the drone has to have free movement along an axis to overcome any threat or damage in its way. The small step guarantees a higher gradient in altitude than any other curve, which makes it more prone to damage from things like buildings, animals, and stones so an axis free to move will prove to be an optimum solution.
## 6 Results and Discussion
### Optimization and Computational Results
In the case of the IHS algorithm, the precision has been improved thanks to the increase of the exploitation with the new pitch adjustment rules (21) and (22) but because \(PAR\) continues to increase until the end, somehow IHS diverged from the optimal region. In the case of the GHS algorithm, it converged quicker to the best solution thanks to the use of the best vector in the improvisation process, however, the GHS failed to further converge because of the lack of refinement rules using \(BW\) this behavior is known as the premature convergence. Finally, in the case of the SGHS algorithm, it can be noticed that a gradual convergence took place thanks to the self-adaptation of \(HMCR\) and \(PAR\) every \(LP=10\) improvisations. Moreover, the reinstatement of the refinement rules using the bandwidth parameter \(BW\) allowed SGHS to obtain more precise solutions than those of the IHS algorithm. The Water Cycle Algorithm presents close results to the HS algorithm, but the new variants of the HS algorithm are better in comparison to other previous algorithms tested on the OWC system. The parameters obtained from the mean case of feasible results from 20 trials of optimization using the HS, IHS, GHS and SGHS algorithms are given in Table 3. The \(K_{p}\), \(K_{i}\) and \(K_{d}\) values are within a close range, proving that the algorithms converge to the same interesting search region.
## 4 pLEO Time Scale Realizations
### Implicit Ensemble Mean (IEM)  
Given the foregoing, at any time \(t_{k}\), the implicit ensemble mean \(\overline{x_{0}}|X(t_{k})\) and \(X(t_{k})\triangleq\{x_{1}(t_{k}),x_{2}(t_{k}),\ldots,x_{N}(t_{k})\}\) is defined as the conditional mean of the ideal clock state \(x_{0}(t_{k})\) or in vector form of  
\[x_{0}(t_{k})\triangleq\left[\begin{array}{c}p_{0}(t_{k})\\ f_{0}(t_{k})\\ r_{0}(t_{k})\end{array}\right] \tag{66}\]  
given any \(i\)-th physical clock state \(x_{i}(t_{k})\) where  
\[x_{i}(t_{k})\triangleq\left[\begin{array}{c}p_{i}(t_{k})\\ f_{i}(t_{k})\\ r_{i}(t_{k})\end{array}\right] \tag{67}\]  
and \(i=1,\ldots,N\) of all real member clocks, given all the available clock difference measurements up to time \(t_{k}\), and given no prior information on the ideal clock.  
According to the definition of the ideal clock presented in Section 2, we can define the bias state for each ensemble clock as the difference \(b_{i}(t_{k})\) between the physical clock state \(x_{i}(t_{k})\) and that of the ideal clock. For example, this bias state represents how much each member clock deviates from the ideal clock's time  
\[x_{i}(t_{k})=x_{0}(t_{k})+b_{i}(t_{k})\,, \tag{68}\]  
which leads to  
\[x_{i}(t_{k})=x_{0}(t_{k})+\hat{x}_{i}(t_{k})+e_{i}(t_{k})\,, \tag{69}\]  
where \(e_{i}(t_{k})\) are the errors in the estimate \(\hat{x}_{i}(t_{k})\), i.e.,  
\[\hat{x}_{i}(t_{k})\triangleq\left[\begin{array}{c}\hat{p}_{i}(t_{k})\\ \hat{f}_{i}(t_{k})\\ \hat{r}_{i}(t_{k})\end{array}\right] \tag{70}\]  
as governed by (59), (64) and (65) of the bias \(b_{i}(t_{k})\).  
In estimation theory, the IEM estimation is expressed as the conditional mean of \(x_{0}(t_{k})\). However, this value is not computed explicitly because it relies on all the physical clock states \(X(t_{k})\), which are often difficult to obtain from a single orbital platform in the pLEO clock system. Nevertheless, any \(i\)-th corrected clock can be made accessible to its participating orbital platform, subject to certain limitations of inter-satellite links. Essentially, any physical clock that has been corrected for its estimated bias can serve as a reliable representation of the IEM estimation with high accuracy, for example, in practical applications  
\[\overline{x}_{0}(t_{k})|X(t_{k})=x_{i}(t_{k})-\hat{x}_{i}(t_{k})\,. \tag{71}\]