esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In [23], the AOA-based two-dimensional station deployment strategy is extended to a three-dimensional scenario, and it is proposed that when \(N\) stations (\(N\geq 4\)) are spatially equidistant from the target, the optimal geometry between the sensor and the target is a uniform distribution of \(N\) stations on a unit sphere with the target at the center of the circle. For the CRLB minimization problem in the TDOA localization scenario, [24; 25] investigated the optimization of ground station deployment for TDOA localization utilizing the GA algorithm and particle swarm algorithm, with CRLB minimization as the objective function, and produced superior results. In [26], TOA-based sound source localization was investigated, and the adaptive GA algorithm was employed to determine the optimal sensor placement scheme based on the probability distributions of target occurrence in the area. Against this background, we propose a hybrid source localization technique that incorporates TOA and TDOA measurement information to boost the localization precision of moving targets to the maximum extent possible. To the best of our knowledge, most of the research has centered on individual TOA or TDOA localization algorithms, performance analysis, and placement optimization, but the hybrid source localization technique has received less attention. First, the fundamental architecture of the hybrid source localization system is developed and investigated by combining TOA and TDOA measurement information. Secondly, an iterative localization algorithm based on the least squares criterion is proposed, and the performance of localization error and CRLB is quantified through theoretical analysis and experimental simulations. Finally, the average CRLB value of target line trajectory localization error is used as the performance estimation objective function, and a GA algorithm is introduced to search for the optimal station placement scheme for TOA and TDOA hybrid source localization, the general principles of optimal station placement are given, and the localization performance is simulated and validated under various scenario configurations. The rest of this paper is structured as follows. Section 2 analyzes the TOA/TDOA system model, while the structure of the proposed TOA/TDOA algorithm and simulation results are discussed in further detail. In Section 3, we derive the positioning error and CRLB and study their properties. Section 4 presents the simulation results of TOA, TDOA, and TOA/TDOA positioning accuracy to demonstrate the proposed system's placement optimization. Finally, Section 5 provides conclusions.
## 2 Materials and Methods
### Influence of System Parameter Mismatch on the Transmission of LFF Waveforms
As the mismatch of linewidth enhancement factor and the pump current of the response laser can both induce an effect on the output LFF waveforms, to quantify which external operating parameter has a more significant effect on the transmission of the system, the mismatch is analyzed using the mismatch degree, which is defined in Equation (10),  
\[Mismatch(\gamma)=\frac{\left|\gamma^{\text{R}}-\gamma^{\text{D}}\right|}{ \gamma^{\text{D}}}, \tag{10}\]  
where \(\gamma^{\text{R}}\) and \(\gamma^{\text{D}}\) represent the external parameters of the response laser and the drive laser, respectively. As can be seen in Figure 9a, the red line is always higher than the black line, which means that the mismatch of the linewidth enhancement factor always has a limited impact on the system transmission, while the mismatch of the pump current \(I_{\text{R}}\) can cause a more serious decrease in CC. This can also be proven by the difference in the average spike rate _Diff_, which is defined in Equation (11),  
\[Diff=|rate_{\text{R}}-rate_{\text{D}}|, \tag{11}\]  
where \(rate_{\text{R}}\) and \(rate_{\text{D}}\) represent the average spike rate of the response and the drive laser, respectively. A larger _Diff_ means a greater difference between the output of the drive laser and the response laser. From Figure 9b, the red line is always lower than the black line, which means that the mismatch in pump current \(I_{\text{R}}\) can lead to a more severe increase in _Diff_. Therefore, the system transmission is more sensitive to the current mismatch, and to ensure the effectiveness of the transmission, efforts should be made to ensure that the pump current of the drive and response laser should be as consistent as possible. Figure 8: Effect of linewidth enhancement factor mismatch on system transmission. (**a**) Event probability error, (**b**) pulse rate, and (**c**) correlation coefficient. From the above results, it can be found that parameter mismatch significantly influences the transmission quality. To discover methods that can guarantee LFF transmission, we investigated the influence of injection strength \(x_{\text{R}}\) on \(CC\) when the parameters of two lasers are mismatched. In Figure 10, the red line denotes \(\alpha_{\text{D}}=5\) and \(\alpha_{\text{R}}=6\), while the rest of the parameters are the same. Similarly, the black line represents that \(I_{\text{D}}=15\) mA and \(I_{\text{R}}=14.6\) mA. From this figure, CC increases with \(x_{\text{R}}\) for both mismatch conditions. The difference in the average spike rate also decreases when the injection strength is enhanced, as shown in Figure 10b. These results mean that increasing the strength can enhance the robustness of the LFF transmission, even though the parameters of the two lasers are different.
# Experimental Study of LoRa Transmission over Seawater
## 3 Materials, Experimental Setup and Methods
### Experimental Locations and Setup  
The experimental locations were found using BotRf [26], a software tool for wireless link planning that uses the Longley-Rice model to estimate path loss on a given trajectory using digital elevation maps. The locations were selected based on maximum attainable LOS and obstructed LOS link distance in Gulf of Trieste. The process of finding suitable test locations was carried out through several iterations, considering also the physical suitability for equipment deployment. Geographical details of the sites that fulfill such requirements, and which were used in the experiments are given in Table 1, while Figure 4 provides details on the map.  
The simulation results obtained using BotRf are summarized in Table 2, while the terrain profiles are shown in Figure 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Site** & **Latitude** & **Longitude** & **Elevation (above Sea Level)** & **Channel Condition** & **Link Length** & **Toponym, Country** \\ \hline A & 45.703800 & 13.720100 & 56 m & - & - & Miramare, Italy \\ C & 45.521777 & 13.589979 & 133 m & Line-of-Sight & 22.6 km & Pacug, Slovenia \\ D & 45.477700 & 13.557300 & 64 m & Obstructed path & 28.2 km & Kanegra, Croatia \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geographical details of the experimental sites.  
Figure 3: Physical appearance of: (**a**) ODT, (**b**) device deployed in the field experiments.  
The transmitters were deployed at Site A during all experiments, while the receiving nodes were deployed at Site C in the first group of experiments (LOS) and at Site D (obstructed LOS) in the second group of experiments.
# Multi-Source Data-Driven Terrestrial Multi-Algorithm Fusion Path Planning Technology
## 3 Multi-Objective Path Planning
### Optimization Objectives
Let the area avoidance factor be \(a(n_{i},n_{i+1})\), where a value of 1 indicates that the edge is within an avoidance zone. The calculation of the avoidance zone index is as follows:  
\[f_{\text{avoidzone}}(p)=\sum_{i=1}^{k-1} \begin{cases}d(n_{i},n_{i+1})&\text{if }a(n_{i},n_{i+1})=1\\ 0&\text{otherwise}\end{cases}  \tag{10}\]  
The objective is to minimize this value in order to enhance the safety and traffic efficiency of the route. The aforementioned optimization objectives are designed to comprehensively assess the quality of a route from multiple key dimensions, in order to meet the diverse needs of vehicle route planning within intelligent transportation systems. In different application scenarios, the importance of each optimization objective may vary. By introducing a dynamic weight adjustment mechanism, the system is able to flexibly adjust the relative importance of each objective according to the specific requirements of a given scenario, thereby providing more optimized route selections.
## 6 Conclusions  
Based on the quantitative analysis of the performance indices in the IR and PFR stages, this paper proposes a parameter tuning method adaptive to load changes for VSG-PEIs performing frequency control. From the previous, the conclusions are made.  
Figure 17: Adaptive parameter determination according to various demands.  
Figure 18: Dynamic responses with adaptive parameter tuning.  
(1) The frequency responses of PEIs include two stages: the inertia response and the primary frequency response. When subjected to load disturbances, the transfer function of the dynamic system of VSG-PEIs performing frequency response can be derived from the control structures.  
(2) By the properties of the fast and slow poles and zeros, the dynamic system can be simplified into a second-order system. The performance indices are deduced by the inverse Laplace transformation.  
(3) In the frequency response, the maximum deviation and the magnitude of the ROCOF are two key indices that determine the performance. The magnitude of the ROCOF is determined by the load deviation and the inertia constant. The nadir is determined by the load step, the inertia constant, and the speed droop.  
(4) By the properties of the performance indices, adaptive parameter tuning can be implemented. The inertia constant is obtained by the ROCOF standard and the measurement of the load step. Then, the speed droop \(R\) is obtained by the nadir standard and the inherited setting of \(H\). Finally, the desired \(H\) and \(R\) are checked to satisfy the standards of the settling frequency and time.  
By the simulation results, the parameter settings of the VSG-PEIs are adaptive to the step change of the demand. Consequently, the proposed method can fully satisfy the predetermined standards in various conditions.  
In parallel scenario, as the overall properties of frequency response are determined by all the responsive sources (in the same way as the frequency response dominated by online synchronous machines in traditional power systems), the calculated inertia and speed droop characteristics can be used to (1) examine the overall frequency stability and (2) allocate parameter references for coordinative operation.  
This study was carried out by [PERSON] under the supervision of [PERSON]. The editing was mostly done by [PERSON] All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China-Hebei on the project of \"Research on inertia characteristics and control strategy for primary frequency and voltage regulation of DFIG\" (no. E201802134).  
The authors declare no conflict of interest.
## 6 Simulation Results
### First Stage: One Day Evaluation
#### 6.1.1 Ramp Saturation Method  
The simulation results for the RS method are shown in Figure 11. It can be noted that, as PRR\({}_{\text{max}}\) increased, the SOC variations and the power at the ESS (P\({}_{\text{ess}}\)) were reduced. A summary of the simulation results for the RS method is shown in Table 3.  
Figure 10: Coto Laurel data vs simulation data.
# Performance Analysis of FSO-UWOC Mixed Dual-Hop Relay System with Decode-and-Forward Protocol
## 4 System Performance Analysis  
### Outage Probability and Its Asymptotic Behavior  
When the end-to-end SNR \(\gamma\) falls below a given threshold \(\gamma_{th}\), the system transmission is interrupted and the probability of this event is calculated as the OP of the system [33]. Clearly, the OP of the FSO-UWOC mixed dual-hop transmission system can be described as:  
\[P_{out}=F_{\gamma^{DF}}(\gamma_{th}) \tag{29}\]  
For the sake of analysis, let the average electronic SNRs of the FSO and UWOC links satisfy \(\mu_{r}=\rho_{r}=\overline{\gamma}\rightarrow\infty\), with \(\frac{\gamma}{\mu_{r}}=\frac{\gamma}{\rho_{r}}\to 0\). Based on Equation (27), the asymptotic expression for the OP of the FSO-UWOC mixed dual-hop system is given by:  
\[P_{out}=F_{\gamma^{DF}}(\gamma_{th}) \tag{30}\]  
\[P_{out}^{\infty}\approx F_{\gamma_{1}}^{\infty}(\gamma_{th})+F_{\gamma_{2}}^{ \infty}(\gamma_{th}) \tag{31}\]  
where \(F_{\gamma_{1}}^{\infty}(\gamma_{th})\) and \(F_{\gamma_{2}}^{\infty}(\gamma_{th})\) represent the asymptotic values of \(F_{\gamma_{1}}(\gamma_{th})\) and \(F_{\gamma_{2}}(\gamma_{th})\) as \(\overline{\gamma}\rightarrow\infty\). The asymptotic expression of \(F_{\gamma_{1}}(\gamma_{th})\), using [34] (Equation 07.34.06.0006.01), can be written as:  
\[ \begin{array}{ll}F_{\gamma_{1}}^{\infty}(\gamma_{th})&=\frac{\Gamma(\alpha- \xi_{1}^{2})\Gamma(\beta-\xi_{1}^{2})}{\Gamma(\alpha)\Gamma(\beta)}(\eta_{1} \alpha\beta)^{\xi_{1}^{2}}\Big{(}\frac{\gamma}{\mu_{r}}\Big{)}^{\frac{\xi_{1}^{ 2}}{\gamma}}\\ &+\frac{\xi_{1}^{2}}{\Gamma(\alpha)\Gamma(\beta)}\frac{(\gamma-\alpha)}{(\xi_{ 1}^{2}-\alpha)\alpha}(\eta_{1}\alpha\beta)^{\alpha}\Big{(}\frac{\gamma}{\mu_{ r}}\Big{)}^{\frac{\xi_{2}^{2}}{\gamma}}\\ &+\frac{\xi_{1}^{2}}{\Gamma(\alpha)\Gamma(\beta)}\frac{(\gamma-\beta)}{(\xi_{ 1}^{2}-\beta)\beta}(\eta_{1}\alpha\beta)^{\beta}\Big{(}\frac{\gamma}{\mu_{r}} \Big{)}^{\frac{\xi_{2}}{\gamma}}.\end{array}  \tag{32}\]  
Letting \(\Delta_{1}=\frac{\Gamma\left(\alpha-\xi_{1}^{2}\right)\Gamma\left(\beta-\xi_ {1}^{2}\right)}{\Gamma(\alpha)\Gamma(\beta)}(\eta_{1}\alpha\beta)^{\xi_{1}^{2}}\), \(\Delta_{2}=\frac{\xi_{1}^{2}}{\Gamma(\alpha)\Gamma(\beta)}\frac{\Gamma(\beta- \alpha)}{(\xi_{1}^{2}-\alpha)\alpha}(\eta_{1}\alpha\beta)^{\alpha}\), \(\Delta_{3}=\frac{\xi_{1}^{2}}{\Gamma(\alpha)\Gamma(\beta)}\frac{\Gamma(\alpha -\beta)}{(\xi_{1}^{2}-\beta)\beta}(\eta_{1}\alpha\beta)^{\beta}\). Similarly, \(F_{\gamma_{2}}(\gamma_{th})\) can be asymptotically expressed as:  
\[F_{\gamma_{2}}^{\infty}(\gamma_{th})=\frac{\Gamma\big{(}a-\xi_{2}^{2}/c\big{)}}{ \Gamma(a)}\frac{1}{(bA_{0})^{\xi_{2}^{2}}}\bigg{(}\frac{\gamma}{\rho_{r}}\bigg{)} ^{\frac{\xi_{2}^{2}}{\tau}}+\frac{\xi_{2}^{2}}{c\Gamma(a)}\frac{1}{\big{(}\xi_{ 2}^{2}/c-a\big{)}a}\frac{1}{(bA_{0})^{a}\tau}\bigg{(}\frac{\gamma}{\rho_{r}} \bigg{)}^{\frac{\varpi}{\tau}} \tag{33}\]  
Letting \(\Delta_{4}=\frac{\Gamma\big{(}a-\xi_{2}^{2}/c\big{)}}{\Gamma(a)(bA_{0})^{\xi_{ 2}^{2}}}\), \(\Delta_{5}=\frac{\xi_{2}^{2}}{c\Gamma(a)}\frac{1}{\big{(}\xi_{2}^{2}/c-a\big{)} a}\frac{1}{(bA_{0})^{\kappa_{2}}}\).
# Fast Gaussian Filter Approximations Comparison on SIMD Computing Platforms
## 4 Approximations
### FIR Approximations  
The Finite Impulse Response (FIR) filters use a finite number of input samples to produce each output sample. In the context of our paper, FIR filters use an approximated version of the truncated Gaussian function as the impulse response and can be expressed in the following form:  
\[y[i]=\sum_{n=i-M}^{i+M}h[i-n]\,x[n],\quad i=0,\ldots,N, \tag{4}\]  
where \(2M+1\) is the size of the mask, and \(N\) hereinafter is the length of the signal. So, in a general case, exploiting symmetry in the filter \(h\), FIR approximation to Gaussian filtering requires roughly \(1+M\) multiplications and \(2M\) additions per output sample. Researchers suggested different methods to reduce the computational complexity by specifying a special kind of \(h\). Below, we describe some of the approximations presented in various sources.
# Detecting Suspicious Texts Using Machine Learning Techniques
## 5 Experiments
### Measures of Evaluation  
Various statistical and graphical measures are used to calculate the efficiency of the system. The following terminologies have been used for evaluation purposes:  
* True Positive \((\tau_{p})\): Texts \((t_{i})\) correctly classified as suspicious \((C_{s})\).
* False Positive \((\Phi_{p})\): Texts \((t_{i})\) incorrectly classified as suspicious \((C_{s})\).
* True Negative \((\tau_{n})\): Texts \((t_{i})\) correctly classified as non-suspicious \((C_{ns})\).
* False Negative \((\Phi_{n})\): Texts \((t_{i})\) incorrectly classified as non-suspicious \((C_{ns})\).
* Precision: It tells how many of the \(t_{i}\) are actually \(C_{s}\) among the \(t_{i}\) that are classified as \(C_{s}\). Precision is calculated by Equation (12): \[P=(\tau_{p})/(\tau_{p}+\Phi_{p})\] (12)
* Recall: It gives the value of how many text documents \(t_{i}\) classified correctly as \(C_{s}\) among total suspicious texts. Recall can compute by using Equation (13): \[R=(\tau_{p})/(\tau_{p}+\Phi_{n})\] (13)
* \(f_{1}\)-score: This is a useful evaluation metric to decide which classifier to choose among several classifiers. It is calculated by averaging precision and recall, which is done by Equation (14): \[f_{1}\text{-}score=(2*P*R)/(P+R)\] (14)  
As the dataset is balanced, the receiver operating characteristics (ROC) curve is therefore used for the graphical evaluation. The trade-off between the true and false positive rate is summarized by it for different probability thresholds.
# Effect of Micropiles on Clean Sand Liquefaction Risk Based on CPT and SPT
## 2 Material and Methods
### SPT-Based Simplifeid Liquefaction Assessment (Site II)
\begin{tabular}{c} **Depth** \\ **USCS** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **\(\gamma\)** \\ **(KN/m3)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **\(\sigma_{\
u 0}\)** \\ **(KPa)** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **\(\sigma^{\prime}_{\
u 0}\)** \\ **(KPa)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **CSR** \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} **Before Grouting** \\ **(N\({}_{1}\))\({}_{60\text{CS}}\)** \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} **After Grouting** \\ **FSR** \\ \end{tabular}  } \\ \hline \multirow{9}{*}{BH-II-1} & 1 & 2.0 & SP & 18 & 4 & 36.00 & 31.09 & 0.23 & 35 & 2.00 & 5.00 & 41 & 2.00 & 5.00 \\  & 2 & 4.0 & SP-SM & 22 & 11 & 80.0 & 55.48 & 0.29 &  
The SPT-based liquefaction assessment in site II was also carried out according to the NCEER-97 specifications. The calculation was performed by applying MSF and \(r_{d}\) correction factors by NCEER and Liao and Whitman, respectively. Other parameters and assumptions are similar to the Boulanger and Idriss 2014 method. Figure 8 highlights the effect of using micropiles to reinforce soil against liquefaction following the NCEER and Eurocode 8 procedures. In these procedures, the base curve liquefaction assessment is adjusted for \(\sigma^{\prime}_{\
u\text{0}}=100\) KPa and \(CRR_{M=7.5}\). This curve predicts the liquefaction triggering using the CSR and \((N_{1})_{60 cs}\) quantities. Figure 8a reports the liquefaction assessment of the existing soil, before installing micropiles based on the NCEER methods. Figure 8b shows the liquefaction potential of the improved soil layers after grouting using the NCEER. After the soil remediation using micropiles, the cases without the potential of liquefaction triggering were increased from 32% to 82%. In the third liquefaction analysis, the SPT-based liquefaction assessment for site I was conducted as specified by the Eurocode 8. The existing sandy soil in this site was categorized as the type C subsoil in the liquefaction analysis. The fines contents' corrections were performed referring to the NCEER. Parameters such as GWL (before and during the seismic movement), \(\text{a}_{\text{max}}=0.35\) g, while \(\text{r}_{\text{d}}\) and MSF factors remain constant, similar to the performed analysis based on [PERSON] and [PERSON] 2014. The results of the liquefaction analysis before and after micropile implementation based on the Eurocode 8 approach are presented in Figure 8c,d. The number of cases in the safe \"No Liquefaction\" region increased by 22% after soil improvement by grouting. Figure 7: Corrected SPT values before and after jet grouting using micropiles in the soil profile containing SP-SC, SP-SM, SC, and SM.
# Integration of Sentinel 1 and Sentinel 2 Satellite Images for Crop Mapping
## 2 Materials and Methods
### Sentinel 1 Data  
The Sentinel 1 data came from Google Earth Engine's (GEE) Level 1 Ground Range Detected (GRD) Interferometric Wide Swath (IW) product [20]. Sentinel 1 radar observations were projected onto a standard 10-m grid in GRD output. GEE preprocessed the data with the Sentinel 1 toolbox. Thermal noise reduction, radiometric calibration, and terrain correction were all part of the preprocessing. Sentinel 1's key characteristics were the VV and VH polarized backscatter readings (in decibels, dB). The orientation of the transmitted radar beam has a significant impact on backscatter. Because of the considerably varied viewing orientations of the ascending and descending satellite overpasses, these were split and treated as supplementary observations. We used an improved [PERSON] filter ([PERSON], 1981) with a damping value of 1 and a kernel size of 7 \(\times\) 7 to minimize radar speckle in the pictures (Figure 3). To avoid the influence of changes in the angle of incidence on the return values, we employed two methods:  
Ignore any observations with incidence angles less than 32\({}^{\circ}\) or greater than 42\({}^{\circ}\) because their geometries differed too much from the average incidence angle in our region of 37\({}^{\circ}\). According to Equation (1), the remaining backscatter values observed at the angle of incidence \(\theta\) are converted to backscatter values viewed at a reference angle \(\theta_{ref}\) (1).  
\[\sigma^{0}_{\theta_{ref}}-\frac{\sigma^{0}_{\theta}cos^{2}\left(\theta_{ref} \right)}{cos^{2}(\theta)} \tag{1}\]  
In this equation, \(\sigma^{0}_{\theta}\) is the measured incidence angle \(\theta\) backscatter intensity, and \(\sigma^{0}_{\theta_{ref}}\) is the predicted backscatter intensity under a reference angle \(\theta_{ref}\) of 37 \({}^{\circ}\)C. This simplified adjustment was based on [PERSON]'s law of optics and assumed scattering processes. According to [PERSON]'s law, an ideal Lambert's reflector reflects the quantity of light equal to  
Figure 2: Location of the study area.  
the cosine of the angle of incidence of the radiation source in any direction; hence, according to this rule and for our categorization, the earth's surface is not an ideal [PERSON]'s reflector. After the severe incidence angle was covered, two to five statements were recorded for each location over 12 days, resulting in a 12-day return mosaic from the combined visits of Sentinel 1A and Sentinel 1B. Because each satellite has a 12-month repeat visit, after the severe incidence angle was covered, two to five statements were recorded for each location over 12 days. All recorded backscatter values inside the 12-day window for each pixel were transformed from their dB values to the original values, averaged, and converted back to the 12-day mosaic backscatter values in dB.