esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Radioactivity Analysis  
The specific activity of \({}^{40}\)K in the investigated wine samples is reported in the Table 3.  
\({}^{40}\)K levels ranged from a low 1595 Bq/L for S18 to a high of 1837 Bq/L for S4.  
Potassium is a mineral element present in grapes and wine, also used as a fertilizer in the vineyard [22]. Much of the potassium in wine comes from potassium in the soil where the vines are grown. As the vines grow, they absorb the potassium in the soil; for this reason, many grape growers add potassium to the soil because it can produce a large crop of wine grapes [23]. Also, the potassium sorbate, used to preserve certain types of wine, can increase the potassium content [24]. The vinicification process directly influences the amount of potassium kept in the alcoholic beverage [25]. Some processes, such as ion exchange in which potassium is exchanged with sodium, decrease the amount of potassium in the drink. Removal of potassium ions helps prevent crystallization [26].  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Gamma Spectrometry**} \\ \hline  & \multicolumn{2}{c}{**Activity Concentration (Bq/L)**} \\ \hline
**Sample ID** & \({}^{40}\)**K** & \({}^{137}\)**Cs** \\ \hline S1 & \(1784\pm 62\) & \(<\)0.10 \\ S2 & \(1826\pm 63\) & \(<\)0.09 \\ S3 & \(1758\pm 61\) & \(<\)0.08 \\ S4 & \(1837\pm 126\) & \(<\)0.09 \\ S5 & \(1794\pm 174\) & \(<\)0.10 \\ S6 & \(1798\pm 167\) & \(<\)0.10 \\ S7 & \(1670\pm 174\) & \(<\)0.07 \\ S8 & \(1825\pm 136\) & \(<\)0.08 \\ S9 & \(1724\pm 68\) & \(<\)0.10 \\ S10 & \(1830\pm 149\) & \(<\)0.10 \\ S11 & \(1714\pm 108\) & \(<\)0.09 \\ S12 & \(1709\pm 68\) & \(<\)0.08 \\ S13 & \(1776\pm 120\) & \(<\)0.08 \\ S14 & \(1679\pm 60\) & \(<\)0.10 \\ S15 & \(1623\pm 116\) & \(<\)0.08 \\ S16 & \(1697\pm 126\) & \(<\)0.09 \\ S17 & \(1717\pm 134\) & \(<\)0.09 \\ S18 & \(1595\pm 66\) & \(<\)0.08 \\ S19 & \(1723\pm 120\) & \(<\)0.09 \\ S20 & \(1712\pm 156\) & \(<\)0.09 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The specific activity of \({}^{40}\)K and \({}^{137}\)Cs in the investigated wine samples.  
Regarding \({}^{137}\)Cs, its activity concentration in all analyzed samples was lower than the minimum detectable activity value, as reported in the Table 3, thus excluding an anthropogenic radioactive contamination of the investigated samples.
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 5 Conclusions
As a consequence, three mathematical models were proposed to formulate different types of continuous-space multi-facility location problem and the corresponding globally optimal solutions are obtained, which is different from the previous clustering-based methods. To illustrate the effectiveness and efficiency of the proposed exact method, several illustrative instances from a benchmark were used to compare the performances of different methods. The comparison results show the superiority of the proposed exact method in the decision-making of the continuous-space multi-facility location problems. Despite the above contributions and insights, this work still has several limitations. This study only investigated a continuous-space multi-facility location problem based on the Manhattan distance. Moreover, it is difficult to determine the global optimum when the problem size is quite large. Accordingly, some meaningful problems of interest can be explored in more depth in future. It is important to find the candidate locations of the facilities for the Euclidean distance. Besides, we also need to develop a heuristic algorithm to determine the facility locations based on all of the candidate locations when the problem size is large. Furthermore, the maximum distance (i.e., blast radius) from the facility to the demand point will be considered in the continuous-space multi-facility location problem. Conceptualization, X.G. and C.P. ; software, X.G. and X.C. ; writing--original draft preparation, X.G. and E.X. ; writing--review, editing, and revising, X.G., C.P., G.H. and D.Z. ; funding acquisition, C.P. All authors have read and agreed to the published version of the manuscript. The work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (NRF-2017R1A2B4004169). Not applicable. Data Availability StatementData is contained within the article. The authors declare no conflict of interest.
## 4 Proposed Protocol
### Network Performance  
The indicators used to evaluate the proposed scheme have been already introduced: number of hops, number of neighbors, and RSSI. Additionally, different transmission ranges have been considered to assess the performance of the system in terms of energy consumption and average number of hops. The energy consumption in UWSNs is usually due to network operations such as processing, gathering, forwarding, and receiving data. Therefore, the total energy consumption is the energy dissipated due to these actions in the nodes.  
Number of hops refers to intermediate nodes that a packet must visit to reach the destination which is the gateway. This parameter has a direct relationship with the distance, which is the third indicator, obtained from the RSSI. Distance is related to energy consumption. Due to the law of transmission power proportional to the square of the distance, multi-hop communication is preferred. Nevertheless, the energy used in a node for receiving and processing a message, and not for transmission, might modify this criterion. However, the larger the number of hops, the larger the end-to-end delay.  
Number of neighbors is the second indicator the select the forwarding node. This indicator is a measure of the priority of selecting the forwarding node. The lower number of neighbors implies a greater chance of a node to be selected as forwarding node.
## 1 Introduction
This paper puts forward an online monitoring and diagnosis method for early faults of truck suspension systems. The average correlation signal based stochastic subspace identification (ACS-SSI) is adopted because of its advantages, including the low testing hardware requirements, high identification accuracy, and suitability for random vibration conditions. However, the challenge is dealing with the impact of noise and high damping ratios, so the vehicle vibration model with 11 degrees of freedom (DOFs) is established to verify the performance under different signal-to-noise ratios (SNR) and damper damping ratios, and then the modal parameters of the vehicle are identified by both numerical simulation and experiments. Moreover, the sensitivity of each modal parameter is evaluated, and a fault diagnosis method based on the modal energy difference method is established. Finally, the feasibility of the developed fault diagnosis method is validated by experimental tests carried out on a full-scale vehicle. The rest of this paper is organized as follows. Section 2 introduces the principles of ACS-SSI briefly, and verifies its robustness under noise and high damping ratios. Section 3 proposes the failure identification method and builds the 11 DOF model to simulate the diagnosis process. Section 4 conducts the real-scale experiment. Finally, the paper ends with conclusions in Section 5.
## 4 Validation and Case Study  
### Validation Using a 3D Shaking Table Test of a Three-Story Reinforced Concrete Frame  
#### 4.1.1 Basic Information  
To validate the reliability of the proposed hybrid framework, a 3D shaking table test was conducted with a three-story reinforced concrete frame [20] as a case study. The collapse of the structure was simulated using three methods: (1) FE simulation, (2) BCB simulation, and (3) the proposed hybrid framework.  
The details of the structure are shown in Figure 7. The concrete information is shown in Table 1, and the rebar information is shown in Table 2. The structure was subjected to the amplitude-scaled El-Centro ground motion record during the shaking table test, and the load cases are shown in Table 3. The 5%-damped pseudo-acceleration spectra of Load Case 5 are shown in Figure 8. More details can be found in [20]. The first mode is a translation with a fundamental period (elastic period) \(T_{1}\) = 0.316 s; the second mode is a translation with a fundamental period \(T_{2}\) = 0.316 s; and the third mode is the planar torsion, with a fundamental period \(T_{3}\) = 0.178 s.  
Figure 7: Dimensions and reinforcements of the model structure (units: mm). [20] (Reproduced with permission from [PERSON], from _Study on spatial collapse responses of reinforced concrete frame structures under earthquake_; published by Tongji Univerity, 2006.).
## 3 Numerical Scheme
### Framework
#### 3.1.2 Grid Functions and Finite Difference (FD) Operators  
All the time varying quantities defined in Section 2 are now discretised into series of integer multiples of the time step, and defined at times \(t=nk,n\in\mathbb{N}\). \(k\) is the time step in s; \(F_{s}=k^{-1}\) is the desired sample rate in Hz. Typically, audio sample rates are used, such as \(F_{s}=44.1\) kHz.  
\[\mathsf{L}w_{l}^{n}=0 \tag{39a}\] \[\mathsf{L}u_{l}^{n}=0 \tag{39b}\]  
where \(\mathsf{L}\) is a finite difference discretisation of the differential operator \(\mathcal{L}\) defined in Equation (2), using the finite difference operators described in Section 3.1.2:  
\[\mathsf{L}=\rho\delta_{tt}-T\delta_{xx}+EI_{0}\delta_{xx}\delta_{xx}+\lambda_{1 }\rho\delta_{t}.-\lambda_{2}\rho\delta_{t-}\delta_{xx} \tag{40}\]  
The use of the backward first order time FD operator in the second damping term allows for the isolated string scheme to stay explicit; this results in simplified computations, at the cost of a slightly altered stability condition.  
The discrete simply supported boundary conditions are given as:  
\[w_{0}^{n} =w_{N_{\mathfrak{D}}}^{n}=0 \tag{41a}\] \[\delta_{xx}w_{0}^{n} =\delta_{xx}w_{N_{\mathfrak{D}}}^{n}=0 \tag{41b}\]
# Hamiltonian Monte Carlo with Random Effect for Analyzing Cyclist Crash Severity
## 4 Results
### Drivers' Actions
#### 4.1.2 Driver Emotional Condition  
This attribute highlights the driver's conditions at the time of crashes, compared to being \"apparently normal\". Those include feelings such as being fatigued, ill or being under emotional conditions such as being angry, sad or depressed. The result is in line with the work in the literature that stress, and emotional states elevate crash risk [25]. In another study, by means of a driving simulator, the impacts of feelings of anger on driving choices and abilities were examined [26]. The results highlighted that angry drivers show a higher likelihood of crossing through an intersection at a yellow traffic light and tend to drive faster.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Attributes**} & \multicolumn{2}{c}{**Effect Estimates**} & \multicolumn{2}{c}{**CI**} \\ \cline{2-5}  & **Mean** & **SD** & **2.50\%** & **97.50\%** \\ \hline Intercept & \(-4.26\) & \(0.74\) & \(-5.66\) & \(-2.90\) \\ Alcohol involvement & \(1.27\) & \(0.49\) & \(0.32\) & \(2.20\) \\ Bikers’ age & \(0.52\) & \(0.27\) & \(0.0008\) & \(1.08\) \\ Hit and run & \(-1.79\) & \(0.52\) & \(-2.82\) & \(-0.83\) \\ Bikers crossing marked line & \(-1.74\) & \(0.48\) & \(-2.70\) & \(-0.87\) \\ at an intersection & & & & \\ Vehicle maneuver, turning & \(-0.96\) & \(0.30\) & \(-1.57\) & \(-0.41\) \\ Number of lanes & \(0.70\) & \(0.270\) & \(0.19\) & \(1.24\) \\ Light conditions & \(0.60\) & \(0.33\) & \(-6.89\) & \(0.22\) \\ Posted speed limit & \(1.16\) & \(0.64\) & \(0.031\) & \(2.45\) \\ Driver emotional condition & \(0.99\) & \(0.38\) & \(0.2\) & \(1.73\) \\ Bikers crossing or entering the road & \(1.03\) & \(0.\,0.41\) & \(0.27\) & \(1.85\) \\ Bikers travel with the direction of traffic & \(1.14\) & \(0.43\) & \(0.31\) & \(2.02\) \\ \hline \multicolumn{5}{c}{**Random Effects**} \\ \hline \(\tau_{1}\) & \(-0.001\) & \(1.01\) & \(-2.061\) & \(2.030\) \\ \(\tau_{2}\) & \(-0.06\) & \(1.0\) & \(-1.93\) & \(1.98\) \\ \(\lambda\) & \(-18.01\) & & & \\ \hline \multicolumn{5}{c}{\(WAIC_{random\ effect}=173\) vs. \(WAIC_{standard}=463\)} \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modeling results of bikers crash severity, HMC technique.
## 5 Simulations
### Simulation Result Analysis  
#### 5.3.1 The Influence of the Moving Model on the MSFC Algorithm  
This section mainly discusses and analyzes the performance of the MSFC algorithm in different mobile models. The simulation employed different mobile models to demonstrate the performance of the MSFC algorithm. We respectively selected the mobile models of Random Walk (RW), Random Way  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Simulation Parameters** & **Values** \\ \hline Simulator & Opportunistic Network Environment (ONE) \\ Mobility model & Shortest path map-based movement \\ Communication area & 2500 m\(\times\)3600 m \\ Nodes’ speed & 1–25 m/s \\ Simulation time & 12 h \\ Initial energy & 100 J \\ Transmit range & 20 m\({}^{2}\) \\ Nodes’ buffer & 10, 15, 20, 25, 30, 35, and 40 MB \\ \hline \hline \end{tabular}
\end{table}
Table 1: The experimental setting of the simulation environment.  
Point (RWP), and Shortest Path Map-Based Movement (SPMBM) to evaluate the message transmission efficiency. The delivery ratio of the MSFC algorithm in different mobile models is depicted in Figure 5. The MSFC algorithm worked best when using SPMBM as a mobile model. When the simulation time reached 12 h, the delivery ratio of MSFC could reach 95%. However, for the other two models, when the simulation time was 12 h, the delivery ratio of RWP was 0.9, and the delivery ratio of RW was 0.84.  
Next, Figure 6 shows the average end-to-end delay of the MSFC algorithm in different models. As can be seen from the result, the MSFC algorithm had the lowest average end-to-end delay, when this algorithm selected SPMBM as the mobile model. Consequently, when the simulation time reached 12 h, the average end-to-end delay of the MSFC algorithm was 35. However, for the other two models, when the simulation time was 12 h, the average end-to-end delay of RWP was 66, and the average end-to-end delay of RW was 46.  
Figure 5: Delivery ratio and time for MSFC in different models. Random Walk (RW), Random Way Point (RWP), and Shortest Path Map-Based Movement (SPMBM)  
Figure 6: Average end-to-end delay and time for MSFC in different models.  
Next, the average overhead of the MSFC algorithm is demonstrated in Figure 7 in different mobile models. With the increasing of simulation time, the average overhead decreased at the same time. Moreover, when the mobile model of the MSFC algorithm was SPMBM, the average overhead of the network was the lowest.  
The impact of the simulation time and mobile model on the energy consumption is exhibited in Figure 8. Regardless of the simulation time, the MSFC algorithm had the lowest energy consumption when the mobile model was SPMBM.  
Figure 8: Energy consumption and time for MSFC in different models.  
Figure 7: Average overhead and time for MSFC in different models.
# Fine-Tuning of Pre-Trained Deep Face Sketch Models Using Smart Switching Slime Mold Algorithm
## 4 Experimental Results and Analysis
### Evaluation on Large-Scale Benchmark Problems (CEC'2010)
#### 4.1.3 Convergence Evaluation
\begin{tabular}{c} **S\({}^{2}\)SMA** \\ **(Proposed)** \\ \end{tabular}  & **SMA** & **ESMA** & **LSMA** & **AOSMA** & **AOA** \\ \hline \multirow{4}{*}{F9} & Best & \(4.07\times 10^{9}\) & \(9.71\times 10^{9}\) & \(7.34\times 10^{9}\) & \(1.37\times 10^{10}\) & \(1.63\times 10^{10}\) & \(2.1\times 10^{11}\) \\ \cline{2-8}  & Median & \(4.62\times 10^{9}\) & \(1.12\times 10^{10}\) & \(8.6\times 10^{9}\) & \(1.6\times 10^{10}\) & \(2.05\times 10^{10}\) & \(2.22\times 10^{11}\) \\ \cline{2-8}  & Worst & \(5.47\times 10^{9}\) & \(1.36\times 10^{10}\) & \(9.8\times 10^{9}\) & \(1.86\times 10^{10}\) & \(2.55\times 10^{10}\) & \(2.35\times 10^{11}\) \\ \cline{2-8}  & Mean & \(\mathbf{4.62\times 10^{9}}\) & \(1.13\times 10^{10}\) & \(8.62\times 10^{9}\) & \(1.6\times 10^{10}\) & \(2.05\times 10^{10}\) & \(2.23\times 10^{11}\) \\ \cline{2-8}  & Std & \(2.81\times 10^{8}\) & \(8.49\times 10^{8}\) & \(6.85\times 10^{8}\) & \(1.01\times 10^{9}\) & \(2.17\times 10^{9}\) & \(7.27\times 10^{9}\) \\ \hline \multirow{4}{*}{F10} & Best & \(9874.878\) & \(11,724.88\) & \(11,405.84\) & \(11,488.99\) & \(11,704.62\) & \(16,819.37\) \\ \cline{2-8}  & Median & \(10,182.29\) & \(12,223.82\) & \(11,845.07\) & \(11,925.26\) & \(12,586.2\) & \(17,079.4\) \\ \cline{2-8}  & Worst & \(10,664.76\) & \(12,635.02\) & \(12,259.19\) & \(12,260.33\) & \(13,429.73\) & \(17,306.07\) \\ \cline{2-8}  & Mean & \(\mathbf{10,201.98}\) & \(12,226.61\) & \(11,853.7\) & \(11,917.62\) & \(12,551.05\) & \(17,081.
# Generalized Nonlinear Chirp Scaling Algorithm for High-Resolution Highly Squint SAR Imaging
## 3 Experimental Results and Discussion
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multirow{2}{*}{**Dimension**} & \multirow{2}{*}{**Performance**} & \multicolumn{3}{c}{**Target Index**} \\ \cline{5-6}  & & & **1** & **2** & **3** \\ \hline \multirow{5}{*}{ENLCSA} & \multirow{2}{*}{Range} & Res/m & 0.8854 & 0.8854 & 0.8854 \\  & & PSLR/dB & \(-13.2545\) & \(-13.2675\) & \(-13.2818\) \\  & & ISLR/dB & \(-9.9274\) & \(-9.8464\) & \(-9.8847\) \\ \cline{2-6}  & \multirow{2}{*}{Azimuth} & Res/m & 1.1294 & 1.0221 & 1.0219 \\  & & PSLR/dB & \(-7.3320\) & \(-12.4446\) & \(-9.3459\) \\  & & ISLR/dB & \(-9.6334\) & \(-9.3022\) & \(-6.2756\) \\ \hline \multirow{5}{*}{GNLCSA} & \multirow{2}{*}{Range} & Res/m & 0.8828 & 0.8854 & 0.8854 \\  & & PSLR/dB & \(-13.2864\) & \(-13.2761\) & \(-13.3001\) \\ \cline{1-1}  & & ISLR/dB & \(-9.3836\) & \(-9.8400\) & \(-9.8404\) \\ \cline{1-1} \cline{2-6}  & \multirow{2}{*}{Azimuth} & Res/m & 1.0115 & 1.0135 & 1.0188 \\ \cline{1-1}  & & PSLR/dB & \(-12.7645\) & \(-12.7299\) & \(-12.0016\) \\ \cline{1-1}  & & ISLR/dB & \(-9.5608\) & \(-9.6637\) & \(-9.0291\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Focusing performances for ENLCSA or GNLCSA. Figure 6: Focusing performances of different algorithms on 11 points marked in Figure 4. The blue solid lines with ‘o’ marker are the results of ENLCSA. The red dash lines with ‘*’ marker are the results of the GNLCSA. (**a–c**) represent the peak side-lobe ratio (PSLR), integrated side-lobe ratio (ISLR) and resolution, respectively. The normalized values of PSLR and ISLR for a precise focusing point (the antenna pattern is a rectangle) are \(-13.27\) dB and \(-9.60\) dB, respectively. From Figure 6, it can be concluded that the focusing performances of the GNLCSA are much better than the ones of the ENLCSA. PSLR and ISLR of GNLCSA are close to the normalized value. In addition, the GNLCSA is also more stable than the ENLCSA. Video SAR has great advantages in the applications of ground moving target indication (GMTI), for it can form a series of consecutive frames. Video SAR needs images at different squint angle to form the video sequence. Also, it requires a high focusing quality and a fast imaging ability for each frame. The simulation results show that the ENLCSA is not accessible to the focusing requirements of the scene size (4 km \(\times\) 4 km) for squint SAR (the squint angle is 45 degree). It means that the ENLCSA cannot be applied in the video SAR with the parameters in Table 3, but the GNLCSA can be done.
# Dynamic Decoupling and Trajectory Tracking for Automated Vehicles Based on the Inverse System
## 4 Simulation Results and Analysis
### Verification of Tracking Performance
#### 4.2.3 Scenario 3: Lane Change with Deceleration/Acceleration on a Wet Road Surface  
In scenario 3, the vehicle decelerates or accelerates at the initial speed of 20 m/s when changing lane on a wet road whose road adhesion coefficient is 0.35.  
Figure 10a shows that the three methods all still achieve trajectory tracking on a low-adhesion road. Compared with Figure 7, even though the road conditions are worse, the lateral and yaw tracking errors of the proposed method decrease with speed reduction. However, the lateral and yaw tracking performance of the MPC get worse. In Figure 10b, the pure-pursuit and MPC fail to track the target in longitudinal, lateral and yaw motion. However, the proposed method still follows the constrained vehicle states at the lower-adhesion road condition, guaranteeing the driving stability.  
Figure 8: The vehicle dynamic states tracking results in scenario 2.  
Figure 9: Trajectory tracking results in scenario 2.  
As we can see from Figure 11, the proposed method always keeps the vehicle in the safe area of the sideslip phase-plane like the pure-pursuit and MPC; however, in Figure 11, since the tracking performance becomes worse, the phase curve of the pure-pursuit and MPC are over the stability boundaries, which means that the proposed method significantly improves the handling stability and has fewer tracking errors compared with the pure-pursuit and MPC algorithms.  
Figure 12 shows the error statistics results of the longitudinal, lateral and yaw directions of the three tracking controllers under four working conditions, as well as the trajectory tracking error index \(P\) of the planar motion weighted by the three directions.  
\[P=w_{1}MSE_{Y}+w_{2}MSE_{Y}+w_{3}MSE_{yaw}, \tag{41}\]  
Figure 11: The sideslip phase-plane diagram: (**a**) steering with deceleration; (**b**) steering with acceleration.  
Figure 10: Trajectory tracking results on low-adhesion road: (**a**) steering with deceleration, (**b**) steering with acceleration.  
Considering that three directions are equally important in planar motion tracking, the three weights should be the same. However, due to the unit, the mean square error of yaw tracking is very large, so it is reduced by a certain proportion and the weighted vector is \(\mathbf{w}=\left(1,1,pi/180\right)^{T}\).  
As shown in Figure 12a, except for scenario 2, the \(MSE_{V}\) of the proposed method is the smallest. The accurate velocity tracking illustrates that the dynamic decoupling of the proposed tracking method can effectively reduce the longitudinal interference from other motion directions. Moreover, compared with the other two methods, the proposed method still performs well in the lateral and yaw tracking. In summary, as shown in Figure 12d, the trajectory tracking error index of the proposed method proposed is always the lowest, indicating that the proposed tracking method is more suitable for performing trajectory tracking tasks under the coupled conditions than the other two methods.