esa-sceva/satcom-chunk-collection huggingface dataset:
# Target Tracking Based on a Nonsingular Fast Terminal Sliding Mode Guidance Law by Fixed-Wing UAV
## 1 Introduction
As aforementioned, this study aims at developing the problem of moving target tracking for the sake of reaching the desired distance as fast as possible. In order to achieve the finite time convergence in the sliding phase, the modified nonsingular fast terminal sliding mode (NFTSM) guidance law, whose sliding mode manifold is a nonlinear function, is applied to solve the problem of ground moving target tracking for fixed-wing UAV in a planar environment. Contrary to the NFTSM technology used in the problem of missile guidance [24], this paper steers the UAV to maintain a desired distance to the target and make the azimuth angle a variable. The overall structure of this paper is organized as follows: Section 2 mainly introduces the UAV dynamic model and the relative motion model. In Section 3, a modified NFTSM manifold is analyzed and designed. Furthermore, the ground moving target tracking approach for fixed-wing UAV based on modified NFTSM guidance law is proposed in this section. Subsequently, some simulation result analysis is provided in Section 4, and and the conclusions are described in Section 5.
## 3 Micro-CT Image and Characterization of the Pore Structure
### Probabilistic and Quantitative Characterization Methods  
#### 3.2.1 Low-Order Probability Functions  
For the characterization of the pore distribution of the foamed concrete samples, probabilistic methods can be used. Here, two types of probabilistic functions, two-point correlation and lineal-path, are used to characterize the pore distribution within the foamed samples. These probabilistic functions are shown to be effective in describing the spatial distributions of pores [11; 20; 36]. The two-point correlation function for a pore phase, \(P_{pp}(r)\), is the probability that any two randomly-placed points are positioned in the pore region (Figure 6a); this function is utilized to characterize the clustering level of pores [20; 22]. The probability of placing both points in the pore region converges to the porosity as the distance between two points approaches zero, and \(P_{pp}(r)\) approaches the product of the porosity as \(r\) increases as:  
\[\lim_{r\to 0}P_{pp}(r)=f_{p},\qquad\lim_{r\to\infty}P_{pp}(r)=[f_{p}]^{2} \tag{1}\]  
where \(f_{p}\) is the porosity of the foamed specimen.  
The lineal-path function, \(L_{p}(r)\), is also used to describe the pore distribution, and this function can be used as a compensation function of \(P_{pp}(r)\) for the different pore characterizations. \(L_{p}(r)\) is the probability of finding a whole random line with length \(r\) positioned in the pore region and can provide information about the continuous connectivity of the pores [20; 23]:  
\[\lim_{r\to 0}L_{p}(r)=f_{p},\qquad\lim_{r\to\infty}L_{p}(r)=0 \tag{2}\]  
In the general limits \(L_{p}(r)\), the function converges to zero as the line length increases.
## 4 Results and Discussion
### Wave Spectrum  
For the hydrodynamic loads, the time-domain wave excitation for the computation was implemented using a white-noise wave spectrum [18], as illustrated in Figure 10. In the subsequent simulation, each computation was carried out for a time duration of 8000 s with a time step size of 0.1 s. In this study, the response amplitude operator (RAO) was defined as the ratio of the cross-spectral density of the surface elevation (input) and the corresponding system response (output) to the auto-spectral density of the surface elevation, and can be expressed as:  
\[RAO=G(\omega)=\frac{S_{xy}(\omega)}{S_{xx}(\omega)} \tag{36}\]  
where \(G(\omega)\) indicates the frequency response function (FRF) or the RAO; \(S_{xx}(\omega)\) and \(S_{xy}(\omega)\) are the auto-spectral and cross-spectral densities in the frequency domain.  
The post-processing of these computations in the time domain was repeated for different load cases and degrees of freedom. Since the wave energy exists in 0.05 to 0.25 Hz, the RAOs were only  
Figure 10: The schematic of the white-noise spectrum.  
Figure 9: The comparison of damping coefficients between different mesh numbers and the UOU data in (**a**) surge and sway, (**b**) heave, (**c**) roll and pitch, and (**d**) yaw modesdiscussed in this frequency region, which was meaningful for investigating the motion response in irregular waves and for code-to-code comparison.
# Experimental and Numerical Study of Orifice Coefficient of Cargo Tank Design of LNG Vessels
## 4 Numerical Simulation
### Simulation Result of LNG  
Using the CFD model verified in the previous section, the leakage rate was calculated by applying the material properties of LNG (density: 498.5 kg/m\({}^{3}\) and dynamic viscosity coefficient: \(4.45\times 10^{-4}\) kg/m-s) [17]. The flow rate of LNG per minute was analyzed by the CFD model according to the crack area and loading condition. When the properties of LNG were applied, the leakage rate per minute was calculated for each condition, as shown in Table 5. The results show that they are similar to those applied to water. This is believed to be due to LNG's density and viscosity being lower than water under the flow conditions of incompressible fluid flow. The CFD analysis of LNG also represented the results of specimen No. 1. As shown in Figure 15, for each load condition, the pressure drops sharply as LNG is released into the air. The velocity distribution of fluids can also be observed to increase rapidly in the area of leakage as shown in Figure 16.  
Figure 15: Pressure distribution for LNG flow of \(0.5\times 7\) mm\({}^{2}\) crack. (**a**) LC 1 (0.7 bar). (**b**) LC 2 (2 bar). (**c**) LC 3 (3 bar).  
Figure 16: Velocity distribution and streamline for LNG flow of \(0.5\times 7\) mm\({}^{2}\) crack. **(a)** LC 1 (0.7 bar). **(b)** LC 2 (2 bar). **(c)** LC 3 (3 bar).  
When the 3 pressure conditions were applied, the amount of LNG leakage was analyzed by the CFD method to the 5 case crack specimens as shown in Table 5. By analyzing the amount of leakage rate for each crack condition, as in Figure 17, it is confirmed that the amount of leakage increases nonlinearly as the area of the crack increases, and that the amount of leakage rate increases rapidly as the size of the crack increases. In addition, if the Reynolds number(Re) for the orifice flow is calculated in the 60 mm diameter pipe, which is the condition of the experiment and CFD analysis, and compared with the amount of leakage, as shown in Figure 18a, the amount of leakage would increase rapidly as the size of the crack increases. Depending on the aspect ratio (AR) of the orifice, it was confirmed that the amount of leakage flow increases in proportion to the area of the crack specimen, as shown in Figure 18b.  
Figure 17: Office flow rate of each pressure conditions for rectangular crack.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimen No.** & **Dimension [mm]** & **Load Case** & **LNG Leakage Rate [kg/min]** \\ \hline  & & LC1 & 0.32 \\
0 & \(0.5\times 3.5\) & LC2 & 0.57 \\  & & LC3 & 0.73 \\  & & LC1 & 0.67 \\
1 & \(0.5\times 7\) & LC2 & 1.17 \\  & & LC3 & 1.51 \\  & & LC1 & 1.40 \\
2 & \(1.0\times 7\) & LC2 & 2.34 \\  & & LC3 & 3.11 \\  & & LC1 & 3.26 \\
3 & \(1.0\times 14\) & LC2 & 4.98 \\  & & LC3 & 6.56 \\  & & LC1 & 9.33 \\
4 & \(2.0\times 14\) & LC2 & 16.30 \\  & & LC3 & 20.38 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison between experimental and CFD results with water.
# A Deep Learning-Based System for Automatic License Plate Recognition Using YOLOv12 and PaddleOCR
## 5 Results and Validation
### Performance Summary  
The results confirm the performance of the proposed model, which, in the final training epoch, achieved a precision of 0.998, a recall of 0.940, an F1 score of 0.968, an [EMAIL_ADDRESS] of 0.981, and an [EMAIL_ADDRESS]:0.95 of 0.737 on the validation set, with minimal loss indicators of stable and efficient convergence.  
To validate its performance beyond the training data, the model was tested on a dedicated test set. The analysis of these results revealed an optimal confidence threshold of 0.5, at which the model achieved a precision of 0.996, a recall of 0.961, an F1 score of 0.978, and a peak [EMAIL_ADDRESS]:0.95 of 0.776. These values confirm the model's strong generalization capability under diverse, real-world conditions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **F1 Score** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline YOLOv11 & 0.974 & 0.961 & 0.967 & 0.979 & 0.769 \\ YOLOv12 & 0.996 & 0.961 & 0.978 & 0.980 & 0.776 \\ \hline \hline \end{tabular} Evaluation performed at a confidence threshold of 0.5 and an IoU threshold of 0.5.  
\end{table}
Table 6: Comparison of YOLOv12 and YOLOv11 model performance on the test set at a confidence threshold of 0.5 and IoU of 0.5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Epoch** & **Precision** & **Recall** & **F1 Score** & **[EMAIL_ADDRESS]** & **[EMAIL_ADDRESS]:0.95** \\ \hline YOLOv11 & 20 & 0.998 & 0.929 & 0.962 & 0.973 & 0.753 \\ YOLOv12 & 20 & 0.998 & 0.940 & 0.968 & 0.981 & 0.737 \\ \hline \hline \end{tabular} Values are taken from the final training epoch (epoch 20) for both models, evaluated on the validation set.  
\end{table}
Table 5: Performance comparison between YOLOv11 and YOLOv12 models at the final training epoch.  
Moreover, the model maintains a solid balance between precision and recall within a representative confidence threshold range of 0.3 to 0.5, making it easily adaptable to various application requirements, from maximum detection coverage to high operational reliability.  
Overall, YOLOv12 demonstrated better performance than YOLOv11 on the issue analyzed in this study.
## 3 Methodology
### TRFM Trajectory Prediction Model  
The TRFM trajectory prediction model consists of two parts: the encoding layer and the decoding layer. The structure of the model is shown in Figure 1, where both the encoder and decoder cores are composed of TRFM units. Each TRFM unit comprises two sub-layers: Self-Attention and Feed Forward. Self-Attention calculates the dependency between each position in the sequence and all other positions, while Feed Forward further enhances feature representation while maintaining consistent output dimensions with the input. In this paper, we use differential values for longitude and latitude instead of directly using them as the model's input and output. The output's differential values are calculated through the absolute regression of latitude and longitude, yielding the true predicted trajectory coordinates.
# The Response of NDVI to Climate Change in the Lowest and Hottest Basin in China
## 3 Results and Analysis
### Temporal and Spatial Dynamic Changes in NDVI  
#### 3.2.1 Analysis of Annual Interannual Variation Characteristics in NDVI  
From the perspective of interannual variation (Figure 4), the NDVI in the Turpan-Hami Basin ranged between 0.068 and 0.084 from 2001 to 2020, with an average value of 0.077. The annual NDVI trend rate in the Turpan-Hami was 0.0008\(\cdot\)a\({}^{-1}\), indicating a significant upward trend overall in the context of global warming. Although the annual increase was minimal, the cumulative growth over the 20-year period reached 0.016, indicating that the overall vegetation coverage and growth conditions in the region have gradually improved over the past 20 years. The figure also reveals that while the NDVI values fluctuated significantly between 2001 and 2020, the amplitude of these fluctuations decreased after 2010, showing a relatively stable upward trend. Despite some interannual variations in specific years, the overall upward trend in NDVI values was maintained.  
Figure 4: _Cont._On a seasonal scale, the Turpan-Hami Basin exhibited a significant upward trend in NDVI values from 2001 to 2020, both annually and across all individual seasons (spring, summer, autumn, and winter) (Figure 4). The average NDVI values for spring, summer, autumn, and winter were 0.07, 0.10, 0.08, and 0.06, respectively, with annual growth rates of 0.0006, 0.0008, 0.0007, and 0.0010, respectively. It can be observed that the interannual changes in spring and autumn NDVI were similar, with relatively slower growth rates compared to summer and winter. Summer exhibited the highest NDVI values, while winter had the lowest, consistent with the seasonal growth characteristics of vegetation. Notably, winter exhibited the highest annual growth rate, showing the most significant increase, despite weaker vegetation growth activity during this period. The rise in winter NDVI indicates an improving vegetation ecological environment during that season.
# Floating Photovoltaic Plant Monitoring: A Review of Requirements and Feasible Technologies
## 3 PV Floating Monitoring Issues
### Monitoring FPV Systems for Evaluating the Water Environment Impact on Energy Production
#### 3.1.2 PV Module Temperature  
The temperature of floating photovoltaic modules is affected by the different operating conditions compared to those on the ground, due to both the proximity of the water and the different wind conditions (soil roughness factor). Therefore, to evaluate the cell temperature by means of mathematical models, additional measurements of the local environmental variables will have to be carried out, specifically ambient temperature and wind speed, which may depend on the size of the basin and may be different compared to locations on land. In particular, water temperature is particularly important, as it has been included in multivariate regression models [37]. Vice versa, the role of humidity on cell temperatures, which could be important for FPV, has not been extensively studied [38], apart from by [PERSON] and [PERSON] [39], who consider relative humidity to calculate the contribution of cooling by radiative exchange. In their model, [PERSON] et al. [40] took vapor pressure into account to calculate the apparent temperature of the model. Other researchers, however, fully neglected the impact of relative humidity [41].  
Figure 2: Right-hand rule convention for pitch, yaw, and roll movements [32].
## 4 Conclusions  
* The solution pH significantly affected the adsorption capacity of the tested activated carbons. For the WD carbon (HNO\({}_{3}\)) the adsorption decreased slightly with increasing the solution pH. For carbons WDA and WD (HCI), the effect of the reaction on the chromium adsorption capacity was greater and definitely more beneficial at a lower pH.
* The action of inorganic acids (chemical modification) on activated commercial carbon WD-extra caused a change in its sorption properties. The use of hydrochloric acid contributed to the increase in the adsorption capacity of chromium (VI), by increasing the specific surface area of carbon as a result of the mesoporous ash removal.
* Modification with nitric acid (oxidizing) caused a decrease in the adsorption capacity, most likely associated with a change in the chemical nature of the carbon surface and partial destruction of the pore structure due to carbon oxidation.
* The adsorption time had a significant effect on the efficiency of chromium (VI) removal by the specific coal. For carbon WD(HCI) the adsorption equilibrium occurred after 2.5 h. The maximum reduction of chromate ions with respect to WDA occurred after 4.5 h.
* Analyzing the determined isotherms, it can be concluded that as the temperature increased, the adsorbents showed better adsorption properties, whereas the highest adsorption capacity of chromium (VI) was demonstrated by WD(HCI) modified carbon.
* Adsorption under flow through conditions showed that a modified WD(HCI) carbon bed worked much more efficiently than WDA. The filtration cycle to the bed breakthrough point lasted twice as long, and the chromium compounds were removed almost entirely. Adsorption capacities were obtained \(\mathrm{P_{bWD(HCI)}}\) = 2.25 mg/g (breakthrough point) and \(\mathrm{P_{eWD(HCI)}}\) = 4.35 mg/g (after exhaustion).
* In the light of the carried-out research, the modified WD (HCI) carbon effectively removed chromium (VI) compounds from water, which makes it possible to use it in water treatment systems.  
A.P. conceptualization and methodology; [PERSON] and [PERSON] performed the adsorption experiments; [PERSON] formal analysis; [PERSON] and [PERSON] carried out the measurements and calculations; [PERSON] and [PERSON] writing--review and editing.  
This scientific work was implemented within the frame of the project \"Maintaining research potential of the Rzeszow University of Technology\", number: DS.BO.19.001.  
The authors declare no conflict of interest.
## 2 FF-OCM Using SMMF  
The time-domain FF-OCM with four-phase shifting was utilized [29]. The OCM system (Figure 1a) was modified to reduce the light incident into the charge-coupled-device (CCD) camera by reflections at the SMMF facet. The light from a halogen lamp, collimated with a microscope objective lens (\(\times\)10, NA 0.25, focal length 16.6 mm), was incident onto the band-pass filter, polarizer, half-wave plate to change the angle of polarization, and non-polarizing beam splitter (BS) through the aperture. The reference arm consisted of a quarter-wave plate, variable neutral-density (ND) filter, dispersion compensator, objective lens (Edmund, DIN, \(\times\)10, focal length 16.6 mm, working distance 6.3 mm, NA 0.25), and a reference mirror with a lead zirconate titanate (PZT). The quarter-wave plate converted linearly polarized light into circularly polarized light.  
The signal arm consisted of the same objective lens (\(\times\)10), the SMMF, and the sample. The illumination power was 13 \(\upmu\)W at the exit of the SMMF. The polarizer was set in front of the CCD camera (AVT, Manta G-033, 656 \(\times\) 492 pixels, cell size 9.9 \(\upmu\)m \(\times\) 9.9 \(\upmu\)m, bit depth 8-12 bits, maximum speed 88 fps). To reduce the light reflected at the facet of SMMF into CCD using polarizations, the vertical polarization components perpendicular to the incident polarization into SMMF were imaged as OCM images.  
OCM images were obtained at 17 fps without depth scanning. For the SMMF, the multimode fiber was cut using a fiber cleaver (Fujikura, CT-22). Photographs of both facets and the 7.4 mm long SMMF are shown in Figure 1b. The two facets are clean with no cracks around the cladding as shown in Figure 1c,d.
# Design and Diagnostics of High-Precision Accelerator Neutrino Beams
## 8 Decay Section  
The length of the decay volume is generally optimized for a significant decay fraction of the pions. Since a pion travels about 56 m per GeV, the length of the decay volumes is of the order of several hundred meters. A quite extreme example is CNGS, where the average neutrino energy was 17 GeV. The facility thus required a 1 km long evacuated tunnel pointing to Gran Sasso (Italy). Due to the long baseline, the tilt of the tunnel was large: in this case, a possible ND would have been located hundreds of meters below the sea level, with a substantial increase of the cost. As demonstrated in Section 10.3, the benefit of an ND for a pure \(\
u_{\tau}\) appearance was marginal compared with the cost and the facility ran without an ND. Conversely, NuMI--serving MINOS and NO\(\
u\)A --had a 675 m long decay volume with a near detector located 900 m after the target. DUNE has to face an issue similar to CNGS since the baseline is 2300 km but an ND is mandatory for the precision measurement of CP violation. The challenge was solved by creating an artificial hill above the target station where the beam is steered down toward the Sanford Underground Research Facility (SURF), in South Dakota. In this case, the decay volume is just 194 m long and the near detector is located in a relatively shallow hall 574 m far from the target. This solution is feasible because the neutrino energy of DUNE (\(\langle E_{\
u_{\mu}}\rangle\simeq 3\) GeV) is much smaller than CNGS (17 GeV).  
Several accelerator neutrino beams designed during the previous century used non-evacuated decay volumes. To prevent the interactions of secondaries with air, the volume was replaced by Helium, which has an interaction length of 426 m. This solution is no more viable in contemporary physics because of the tremendous increase of the beam intensity, which produces tertiaries in helium and a dangerous air activation by tritium. Therefore, all tunnels are now evacuated at the mbar level or below using root pumps that can handle large volumes at a moderate cost.
## 2 Background: Electric Vehicle Charging Systems and Standards
### EV Charging Procedure  
In this section, we describe the procedure of EV charging as defined in ISO/IEC 15118 and related standards. Our test system aims to verify if the target EVSE conforms to this procedure. For brevity, we focus on the DC (Direct Current) charging procedure. However, our test system can test AC (Alternating Current) chargers, as well.  
Overview  
Figure 4 describes the overall procedure of EV charging. Relevant standards are indicated in parentheses. When the user connects the charging cable: (i) the EV and EVSE detect the flow of current and start the control pilot protocol, as defined in IEC 61851. By the protocol, both parties can recognize different states throughout the charging procedure. Once EV and EVSE are ready: (ii) they initiate a PLC connection by the SLAC protocol as defined in HPGP. Once the PLC connection is established: (iii) the EV broadcasts an SDP packet onto the LAN (Local Area Network) to identify the IP address and port number of the EVSE. After receiving the information: (iv) the EV makes a TCP connection to the EVSE, secured by TLS.  
At this point, the EV and EVSE can securely exchange TCP packets to control the charging procedure. We divided the procedure into three phases: Setup, Charging and Finalization. In the Setup phase, the EV and EVSE negotiate various parameters, including protocol version, user identity, service information, payment methods and charging parameters. In the Charging phase, they make an actual transfer of energy from the charger to the EV. During the transfer, they continuously exchange their status until the termination condition is met or an interruption is requested for any reason. In finalization, they perform safety checks before disconnection.  
During the procedure, the EVSE reports to the operator regarding the charging session. The interaction between EVSE and the operator is not well defined yet in any standards. However, the technical report IEC 61850-90-8 describes the information model for data exchange between the EVSE and the operator. See Figure 5 for the detailed message sequence during the setup, charging and finalization phases.  
Figure 3: Charging procedure overview with relevant standards in parenthesis.