esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental Setup and Measurements  
Figure 2 shows the schematic of experimental setup used to observe the propagation characteristics of prebreakdown streamers. A needle-sphere electrode system, with a high voltage tungsten needle (tip radius of \(35\pm 5\) um) opposing a grounded sphere, was located within a test cell made of transparent Perspex sheet, which facilitates the streamer observation. A ten stage impulse generator with an energy of 27.5 kJ was used to provide 1.2/50 us standard lightning impulse voltage. The positive impulse breakdown strength were measured with an electrode gap of 25 mm according to the standard procedures for testing lightning impulse breakdown strength (IEC60897-1987). Then, an Intensified Charge Coupled Device (ICCD) camera was used to capture streamer propagation images, which were performed with an electrode gap of 45 mm under peak voltage of 75 kV. The camera works with the help of the laser to meet the need of the shadowgraph technique. A trigger unit was used to synchronously trigger the impulse generator and camera.  
To further investigate the microscopic effect mechanism of different types of nanoparticle materials, the charge transport processes of pure oil and nanofluids were measured by the thermally stimulated current (TSC) method. The experimental setup used was shown in Figure 3. At atmosphere pressure, switch S\({}_{3}\) was closed and a negative direct current (DC) field of 2 kV/mm was applied to the oil sample with a thickness of 0.2 mm for 20 min at 313 K. Then, after the temperature was lowered down to 248 K, switch S\({}_{3}\) was opened and switch S\({}_{1}\) was closed to discharge the equivalent capacitor of electrodes for one minute. Finally, switch S\({}_{1}\) was opened and switch S\({}_{2}\) was closed to begin the measurement of TSC by raising temperature at a rate of 2 K/min. A Keithley 6514 electrometer provides an accuracy of 10\({}^{-16}\) A on the current measurement, and the accuracy of temperature measurement is about \(\pm\)0.2%.  
Figure 3: Schematic of experimental setup for measurement of thermally stimulated current.  
Figure 2: Schematic of experimental setup for measurement of breakdown strength and prebreakdown streamers.
## 2 Numerical Simulation
### Experimental Validation
**1**: PC; **2**: data acquisition unit; **3**: flowmeter; **4**: outlet valve; **5**: water tank; **6**: inlet valve; **7**: pressure gauge; **8**: pump; **9**: torque meter; **10**: motor. It can be seen from Figure 6 that the deviation between the head values predicted by the steady-state numeration and the experimental measurements is controlled within 3.60%, while the deviation between the head values predicted by the unsteady numeration and the experimental measurements is 1.80%, i.e., only half of that of the steady-state numeration. The maximum deviations between the efficiency values predicted by the steady-state and unsteady numeration and the estimate experimental measurements under each flow condition are 1.65% and 0.81%, respectively. Further analysis reveals that at the rated flow rate \(Q_{d}\), both the steady-state and unsteady numeration can achieve high prediction accuracy. However, the greater the deviation of the flow rate from the rated point, the larger the deviation between the calculation results and the experimental values; this phenomenon is particularly obvious in the low flow conditions. Taking the head coefficient as an example, at the rated flow rate \(Q_{d}\), the deviations between the steady-state and unsteady numeration and the experimental values are 0.40% and 0.28%, respectively. At a lower flow rate of 0.8 \(Q_{d}\), these deviations increase to 3.60% and 1.80%, respectively. Overall, the numerical calculations in this study are highly reliable, and the unsteady numeration can provide better prediction accuracy than the steady-state numeration, especially in the low flow rate conditions. This is because under low flow rate conditions, the internal flow field of the centrifugal pump is complex, with unsteady phenomena such as backflow, vortices, boundary layer separation and reattachment, strong pressure pulsation, and significant interaction between the impeller and volute [25; 35]. These phenomena are ultimately reflected in the external characteristic parameters, such as the head and efficiency, and can only be accurately captured by an unsteady numerical model with real rotation of the impeller mesh during the solving process.
# Effect of Particle Size on Mechanical Property of Bio-Treated Sand Foundation
## 3 Results and Discussion
### The Relationship between the UCS and CaCO\({}_{3}\) Content  
Under unconfined compression strength conditions, the failure modes of sand columns were divided into tensile failure, shear columns, and tensile-shear composite failure [36]. As shown in Figure 8, the test results of the UCS of MICP-treated sand samples presented that almost all the failure modes were tensile-shear composite failure, which was mainly manifested as the damage had legible tensile failure surface and shear-slip surface. The main crack was dominated by the tensile failure surface.  
The relationship between unconfined compressive strength \(q_{\text{u}}\) and calcium carbonate content in the column is presented in Figure 9. The unconfined compressive strength \(q_{\text{u}}\) improved with the increase of average calcium carbonate content in the sand column, which verified the research of [PERSON] [37] and [PERSON] [16] that calcium carbonate content was positively correlated with the mechanical strength of the bio-treated sand column. The calcium carbonate content in the solidified sand column can be used to estimate the UCS value \(q_{\text{u}}\) of the sample to some extent.  
Figure 9 plots the relationship between the unconfined compressive strengths and average calcium carbonate contents of the specimens. The theory of [PERSON] [38] and [PERSON] [39] suggested that the cementation between sand particles cannot offer strong support at low calcium carbonate levels.  
Figure 8: Failure mode of unconfined compressive strength test of bio-treated sand column.  
Figure 9: Relationship between the unconfined compressive strengths and average calcium carbonate content.  
The UCS of the column improved rapidly with the increase of calcium carbonate content when the cementation can begin to support sand particles. Therefore, the relationship between unconfined compressive strength \(q_{u}\) and calcium carbonate content in the column can be fitted by an exponential function (6).  
\[q_{u}=3.68\times 10^{-3}\overline{\delta}^{4.63} \tag{6}\]
###### Abstract
Investigated the combined effect of signal attenuation and ambient solar radiation on end-to-end diffused line-of-sight and point-to-point line-of-sight UOWC systems under the eight stratification scenarios. This was evaluated for horizontal links and downlinks (transmitter is vertically above the receiver, with the receiver directly facing up in direct view of sunlight) by varying depth and distance. 3. Shared novel insights on the wavelength preferences for the signal (400-700 nm) for the optimum optical signal-to-noise ratio (O-SNR) under the conditions described above, that seems to be connected to the transmitter and receiver parameters such as transmit power, beam divergence, and the receiver (photodetector) wavelength responsivity curve. The results have been shown with trends discussed for four selected profiles for brevity, in order of increasing turbidity. 4. Demonstrated the variability of the maximum achievable link distance with depth based on the O-SNR (0 dB distance) correlated to the signal wavelength that achieves it. Figure 1: A conceptualized offshore wireless optical network incorporating underwater optical wireless communication and free-space optical (FSO) links. Shared insights on how these findings may be used to establish cooperative UOWC links for the optimal SNR, based on water profile, depth relative to the deep chlorophyll maximum, link orientation, and distance between the nodes. These findings may be useful for establishing UOWC within individual sites, such as aquaculture farms, or renewable energy sites if the site-specific water quality parameters are known and where the region of the UUV mission or connectivity of the UOWC network would span depths of the euphotic and disphotic zones. 6. Provided re-evaluated coefficients to ensure the attenuation model detailed in [PERSON] et al. [33] is consistent with [PERSON] et al. [34].
## 3 Results and Validation  
### Correlation between Landslides and Influencing Factors Based on SWARA  
Table 2 lists the correlation between landslides and influencing factors based on SWARA. In this table, the SWARA weight for slope is highest of 0.19 for the class of \(>\)47.6\({}^{\circ}\), indicating the highest possibility of landslide occurring. For aspect, the highest SWARA weight of 0.3 is obtained in the direction of south. In terms of altitude, the class 882-1032 m (0.33) was assigned with the highest SWARA weight of 0.33. The highest SWARA weights of 0.51 and 0.55 for plan and profile curvatures are achieved in the classes of \(>\)0.001 and \(<\)-0.001, respectively. For the three factors of SPI, STI and TWI, the highest SWARA weights of 0.32, 0.7, and 0.6 are reached in the classes of 800-1000, 30-40 (50-60), and 3-5, respectively. The lithology of the class C resulted in the best SWARA weight of 0.36. For the distance to fault and river, the highest SWARA weights 0.11 and 0.21 are computed for the distances of 0-500 and 600-750, respectively. The classes of forest and 0.35-0.45 for land use and NDVI were given the best SWARA weight of 0.53 and 0.4, respectively. The soil of ACu has the greatest SWARA weight of 0.54 and the SWARA weight for rainfall is highest of 0.21 in the classes of 1500-1600 mm. Also, one of the main advantages of this method is that considered the relative weights of each influencing factor compare to each other. Result shows that slope angle and Plan curvature have a highest and lowest impact on the landslide occurrences respectively (Table 3).
# Performance Analysis of Two-Hop mmWave Relay Nodes over the 5G NR Uplink Signal
## 3 Link-Level Co-Operative Simulator
### Network Model: Topology and Communication Channel
The 3 GPP Urban Macrocell (Uma) path loss model is employed to represent the outdoor sub-6 GHz LOS bckhaul link; therefore, the expressions can be given by  
\[\text{PL}_{\text{BL}}(\text{dB})= \begin{cases}\text{PL}_{1}+\sigma_{\text{BL} }&10\text{ m}\leq\text{d}_{\text{BL}}^{1}\leq\text{d}_{\text{BL}}^{1}\\ \text{PL}_{2}+\sigma_{\text{BL}}&\text{d}_{\text{BL}}^{1}\leq\text{d}_{\text{ BL}}^{1}\leq 5\text{ km}\end{cases} , \tag{12}\]  
where \(\text{d}_{\text{BL}}^{1}\)(m) is the distance from NR-RN to the gNodeB, as shown in Figure 8. Besides, \(\text{d}_{\text{BL}}^{1}\) (m) is the breakpoint distance, which can be obtained as in [22], besides, \(\text{PL}_{1}\)(dB) and \(\text{PL}_{2}\)(dB) are the path losses that can be given by  
\[\text{PL}_{1}(\text{dB})=28.0+22\log_{10}(\text{d}_{\text{BL}}^{2})+20\log_{1 0}(\text{f}_{\text{c}}), \tag{13}\]  
and  
\[\text{PL}_{2}(\text{dB})=28.0+40\log_{10}(\text{d}_{\text{BL}}^{2})+20\log_{1 0}(\text{f}_{\text{c}})-9\log_{10}((\text{d}_{\text{BL}}^{1})^{2}+(\text{h} _{\text{RN}}-\text{h}_{\text{gN}})^{2}), \tag{14}\]  
where \(\text{d}_{\text{BL}}^{2}\)(m) is the distance from the NR-RN antennas to the gNodeB antennas and \(\text{h}_{\text{gN}}\) and \(\text{h}_{\text{RN}}\) are the heights of the gNodeB and NR-RN in (m), respectively. \(\sigma_{\text{BL}}\) represents the shadowing term with zero mean and a standard deviation of 6 dB, and fc is the carrier frequency in GHz.
# Map-Matching-Based Localization Using Camera and Low-Cost GPS for Lane-Level Accuracy +
## 1 Introduction
Besides the expensive price issue, their drifts rapidly increase with time, and regular calibration is required [4; 5]. To bridge the gaps, fusing sensors [6; 7] such as visual information, digital map, and GNSS to improve localization accuracy of vehicle has emerged as a potential solution and has become one of the hottest issues in the past years [8; 9; 10; 11]. Currently, there is much interest in the vehicle localization based on high-definition maps [2; 3; 4]. Digital map is used as a powerful complementary system for improving the performance of the vehicle localization. It is generated by providing sufficient information to localize vehicle's positions relative to the map. Common approaches have been proposed involving the combination of LIDARs, IMU, GPS and high-resolution digital maps as solutions. Fusing the measurements from vision, GPS and LIDAR have been proposed in [12; 13; 14]. However, the LIDAR price is still one of the big issues preventing this kind of solution from becoming wide-spread commodities [15]. Therefore, in this work, we propose a low-cost solution for lane-level localization using a vision-based system combined with map-matching method and a low-cost GPS to achieve high precision lane-level localization. The main contributions of this paper are:  
* We propose a low-cost localization system using vision-based method;
* We combine map-matching method and low-cost GPS to achieve high precision lane-level localization;
* We carry out extensive experiments in real-time and a real environment. In general, three major steps are required: creating a reference map, finding the corresponding road segments, and positioning the vehicle on the map. The rest of the paper is structured as follows. Related work is discussed in Section 2. Section 3 presents our proposed approach for localization. Section 4 describes the experiments and discusses the results. Section 5 draws some conclusions and future directions.
## 5 Implementation
### Configuring the Software  
In MATLAB, the environment was configured to receive data from ThingSpeak. Then, an interface was used to communicate with the ThingSpeak API and retrieve real-time data from the sensors. We utilized advanced algorithms--such as particle swarm optimization (PSO) or grey wolf optimization (GWO)--to analyze the data and determine which traffic signal should be prioritized based on current traffic conditions.
## 3 Results and Discussion
### Analysis of Interaction Energy
As demonstrated in Figure 6b, the energy ratio (ER) decreased significantly with the increase in the interfacial water-layer thickness. This phenomenon suggests that the interfacial bonding performance between C-S-H and SiO\({}_{2}\) aggregates was diminished due to the existence of water molecules. Moreover, experimental studies have confirmed this conclusion. As an example, [PERSON] et al. [35] discovered that the deterioration of the peeling fracture energy was proportional to the amount of moisture at the bonding-layer-concrete interface.
# Optimization of Cellular Automata Model for Moving Bottlenecks in Urban Roads
## 2 A Cellular Automaton Model for Urban Roads Considering Vehicle Heterogeneity
### Rules for Updating Vehicles at Intersections  
The cellular division and vehicle trajectories at the intersection are shown in Figure 3. The cells at the intersection are divided into two types: (1) outer intersection cells (cells 1 to 12); (2) inner intersection cells (cells 13 to 16). Vehicles traveling straight, turning left, and turning right follow different trajectories to cross the intersection. For example, left-turning vehicles on Lane 1 enter Lane 5 along the path of cells 5-14-13-16-9; vehicles traveling straight can proceed directly along the path of cells 5-14-13-12; and right-turning vehicles enter Lane 8 via cell 4. The vehicles on the remaining lanes follow the same updating rules. Within the intersection, the speed of vehicles can be either 0 or 1. At the intersection, the position update of vehicles follows the car-following rules and intersection traffic rules established in this paper. Vehicles determine their speed for the next time step as either 0 or 1 based on these rules and then move forward accordingly, updating their cell position along the route. After the position update, the original cell occupied by the vehicle becomes vacant, while the new cell where the vehicle is located becomes occupied.  
This study establishes the traffic rules for signal-controlled intersections as follows: the through and left-turn movements in the north-south direction constitute one phase, and the through and left-turn movements in the east-west direction constitute another phase. In the event of a conflict between a left-turn and a through movement, priority is given to the through movement, with alternating passage. Taking a left-turning vehicle from east to south as an example, if a vehicle on Lane 1 occupies cell 13 and intends to turn left onto Lane 5 via cell 16, while a vehicle on Lane 3 occupies cell 11 and intends to continue straight ahead via cell 16, a conflict will arise at cell 16 between the two vehicles. According to the intersection traffic rules established in this study, the through vehicle on Lane 3 will be allowed to proceed and occupy cell 16 on a priority basis, provided that a safe distance is maintained from the preceding vehicle. The left-turning vehicle on Lane 1 will then enter cell 16 after the through vehicle has passed. Subsequently, vehicles on the two lanes will alternately occupy cell 16 to cross the intersection. If cell 13 is unoccupied or occupied by a through vehicle on Lane 1, there is no conflict with the through vehicle on Lane 3, and the latter may pass through the intersection without considering the priority or alternating passage rules, as long as a safe distance is maintained. Similarly, if cell 11 is unoccupied, the left-turning vehicle on Lane 1 may also pass through the intersection without considering the priority or alternating passage rules, as long as a safe distance is maintained.
# Enhancement of Low-Light Images Using Illumination Estimate and Local Steering Kernel
###### Abstract  
Images acquired in low-light conditions often have poor visibility. These images considerably degrade the performance of algorithms when used in computer vision and multi-media systems. Several methods for low-light image enhancement have been proposed to address these issues; furthermore, various techniques have been used to restore close-to-normal light conditions or improve visibility. However, there are problems with the enhanced image, such as saturation of local light sources, color distortion, and amplified noise. In this study, we propose a low-light image enhancement technique using illumination component estimation and a local steering kernel to address this problem. The proposed method estimates the illumination components in low-light images and obtains the images with illumination enhancement based on Retinex theory. The resulting image is then color-corrected and denoised using a local steering kernel. To evaluate the performance of the proposed method, low-light images taken under various conditions are simulated using the proposed method, and it demonstrates visual and quantitative superiority to the existing methods.  
low-light image; enhancement; Retinex; steering kernel; image processing Article
## 4 Experiments and Results Analysis
### Data Set  
The data collected from Foursquare [26] is used in our experiment to evaluate the proposed approach. It contains two long-term, real-time check-in datasets in New York City (NYC) and Tokyo (TK). New York City dataset contains consecutive check-ins from July 2012 to December 2013. Tokyo dataset contains consecutive check-ins from April 2012 to May 2013. The statistics of these two datasets are displayed in Table 2. New York City dataset contains 1083 users, 227,428 check-ins, and 38,333 locations. Tokyo dataset contains 2293 users, 573,707 check-ins, and 61,858 locations.  
For POI group recommendation, we adopt a method commonly used in academic simulation proposed by [PERSON] et al. [27] to generate preferred POIs for a group. The specific methods are as follows: If user \(u_{1},u_{2},u_{3}\) in less than an hour time to visit POI \(l\), and the three users are friends, then the three-person group \(u_{1}\), \(u_{2}\),\(u_{3}\) preferred to the location \(l\)As observed from Table 2, the number of two-person group of New York City and Tokyo is 2055 and 2129. In contrast, the number of multi-person group is lower than that of two-person group. In the experimental phase, we do POI group recommendation for two-person group and multi-person group, respectively.
# Coupled Vibration Analysis of Submerged Floating Tunnel System in Wave and Current
## 3 Case Study  
### Parameter Values in the Numerical Model  
There are no practical engineering applications of SFTs around the world. Some basic parameters of the structure and the fluid loads for the case study are selected in line with references [22; 34].  
In order to obtain the resonance performance of the tube-cable system, the dimensions of the structures are artificially adjusted in accordance with the following certain conditions:  
1. The first-order vibration frequency of the SFT tube is twice the fundamental frequency of the \(i^{\#}\) cables (i.e., \(\omega_{t1}\omega_{i1}\) = 2:1), which leads to the principal parametric resonance [18; 21];2 When the vortex-induced frequency is assumed to be consistent with the cable frequency (i.e., \(2\pi f_{vi}=\omega_{i1}\)), significant VIV of the cables occurs [13; 30]. After the determination of \(\omega_{i1}=3.493\) rad/s in Equation (9), the flow velocity \(v_{0}=1.18\) m/s can be further calculated by Equation (4).  
It is also worth noting that, after the anchor-cables are connected to the tube body, the taut cables play the role of elastic supports. [PERSON] et al. [23] concluded that, if the support stiffness provided by the cables and the bending rigidity of the tube meet a certain condition (Equation (10)), the natural frequency of the tube is increased, but the same vibration mode is retained as in the tube without the cables.  
\[\frac{k_{i}h^{4}}{24E_{t}I_{t}}\leq 0.05 \tag{10}\]  
where \(k_{i}=E_{ci}A_{ci}/I_{i}\), is the stiffness of the anchor cables; \(h\) is the cable interval.  
Figure 2 gives the detailed process to obtain the basic parameters in this case study (some essential parameters in the \"dashed box\" in Figure 2 are selected from the above references [22; 34]).  
Eventually, the total length of SFT \(L\) is equal to 150 m, and 2 pairs of cables with identical parameters are symmetrically installed in the \(L/3\) and \(2\,L/3\) positions. The particular values of the SFT parameters are given in Table 1.