esa-sceva/satcom-chunk-collection huggingface dataset:
# Nonlinear Vibration Isolation via a NiTiNOL Wire Rope
###### Abstract  
Vibration isolators with both stiffness and damping nonlinearities show promise for exhibiting compound advantages for broadband vibration isolation. A nonlinear isolator with a NiTiNOL wire rope is proposed with cubic stiffness, hysteretic damping, and pinching effects induced by geometric constraints, inner frictions, and phase transitions, respectively. A combined method of a beam constraint model and a Bouc-Wen model is presented to characterize the restoring force of the NiTiNOL wire rope. The frequency responses of the nonlinear isolator were analyzed through a harmonic balance method with an alternating frequency/time domain technique. The generalized equivalent stiffness and the generalized equivalent damping ratio were defined for a comprehensive understanding of the nonlinear characteristics. The isolator exhibited a stiffness-softening-hardening characteristic. The pinching effect, the Bouc-Wen hysteresis, and the cubic stiffness mainly influenced the equivalent stiffness at the initial value, the small displacements, and the large displacements, respectively. The rate-independent damping ratio increased and then decreased with increasing displacement, and the parameters influenced the damping ratio change in different ways. Compared to an isolator with a steel wire rope, the isolator with a NiTiNOL wire rope exhibited less initial stiffness and a stronger damping effect, and thus, better vibration isolation performance. The relationships of the peak displacement transmissibility and the resonant frequency with the excitation amplitude were both non-monotonic due to the non-monotonic changes of the stiffness and the damping ratio. The minimum peak transmissibility, the lowest resonant frequency, and their corresponding excitation amplitudes depended on the isolator parameters. The isolator demonstrated stiffness-softening and stiffness-hardening types of jump phenomena with different parameters.  
vibration isolation; wire rope; NiTiNOL; Bouc-Wen model; pinching effect +
Footnote †: 2021: 11, 10032. [[https://doi.org/10.3390/app112110032](https://doi.org/10.3390/app112110032)]([https://doi.org/10.3390/app112110032](https://doi.org/10.3390/app112110032))
## 2 Materials and Methods  
License plates are easily confused with surrounding objects such as road signs, logos, commercial, road-marking symbols, etc. Therefore, it is essential that the data provided for license plate detection algorithms is as clear as possible, with minimal incorrect and potentially confusing information. Vehicle detection algorithms can be used as a solution, with the potential to provide better overall accuracy of the system. However, they may show poor results in situations where the license plate is very close to the camera, and such systems can be rather slow [26].  
The solution proposed in this paper involves changing the identification process from standard car-number-letter recognition to finding the predicted license plate. It detects plate corners, corrects plate position, and then performs letter recognition. This approach differs from other algorithms in that the resulting image size is relatively small compared to the vehicle, enabling the use of a compact and fast network for plate corner detection. These coordinates are then used to perform a perspective transformation that corrects any distortion or rotation and returns the license plate to its original position, making precise license plate recognition easier for the second algorithm. The proposed solution is illustrated in Figures 1 and 2.  
All experiments were conducted using publicly available databases: AOLP [27], Caltech Cars [28], EnglishLP [29], OpenALPR [30], UFPR-ALPR [31], and Platesmania data that were published in article [32]. All these datasets are different and their summary is presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Title** & **Size** & **Number of Records** & **Number of Records for Testing** & **Country** \\ \hline AOLP & \(352\times 240\) & \(2049\) & \(1268\) & Taiwan \\ CaltechCars & \(896\times 592\) & \(124\) & \(124\) & USA \\ EnglishLP & \(640\times 480\) & \(509\) & \(509\) & Europe \\ OpenALPR & various & \(445\) & \(445\) & Europe \\ UFPR-ALPR & \(352\times 240\) & \(2049\) & \(1268\) & Taiwan \\ Platesmania & various & \(722\) & \(446\) & USA, China, Europe \\ \hline \end{tabular}
\end{table}
Table 1: Used datasets.  
Figure 1: Proposed two-step license plate recognition algorithm: the first network filters out candidate license plates, while the second network detects precise license plates. The final output is a clear image of only the license plate, which can then be used for the next step: character recognition. The goal of this work is to improve the license plate detection algorithm.  
Figure 2: Potential number detection and corners identification: (**a**) original picture with a car; (**b**) the original coordinates are increased and the number is cut out; (**c**) the four corners of the number are found.
# Research on Grain Moisture Model Based on Improved SSA-SVR Algorithm
## 3 System Modeling Method Construction
### Improved Sparrow Search Algorithm
Formula (13) is the optimization of the normal sparrow search algorithm, determined by \(f_{i}\) and \(f_{best}\). In Formula (13), when \(f_{i}\) is greater than \(f_{best}\), the updated formula is difficult to use to complete the early warning task due to its own location limitations, so the reverse learning mechanism is added and the idea of reverse learning is introduced to replace Formula (14), as follows:  
\[X_{i,j}^{t^{\prime}}=ub+lb-r_{3}.X_{i,j}^{t}\,\ f_{i}>f_{best} \tag{14}\]  
In the formula, \(ub\) and \(lb\) are the upper and lower limits of the current test function, respectively, and \(r_{3}\) is a random number with the interval [0, 1]. Step seven: update the historical optimal fitness;  
Step eight: End the loop when the maximum number of iterations is reached. Otherwise, perform steps three to seven. The specific significance of using the improved SSA-SVR algorithm for establishing the grain moisture model is as follows:  
(1) Automated parameter optimization. Efficient parameter selection: the SSA can automatically find the optimal SVR parameters (such as penalty parameter C, insensitive loss \(\epsilon\), and RBF kernel \(\gamma\) parameter). (2) Improved prediction accuracy. Precise model fitting: with optimized parameters, the SVR model can fit the training data more accurately, thereby improving the prediction accuracy of grain moisture content while maintaining generalization ability. (3) Enhanced model generalization ability. Avoiding overfitting: the combination of SSA-optimized SVR by selecting appropriate parameter configurations not only accurately fits the training data but also avoids overfitting issues, ensuring that the model has good generalization ability on unknown data. (4) Handling nonlinear problems. Strong nonlinear fitting ability: SVR is particularly good at handling nonlinear relationships, which is often a typical nonlinear problem in predicting grain moisture content. Combined with SSA, the kernel function parameters of SVR can be effectively adjusted to enhance its modeling ability for complex nonlinear relationships. (5) Strengthening the algorithm's search capability. Global search optimization: the SSA with Circle chaotic mapping and the reverse learning mechanism has stronger global search capability, helping to avoid local optima and potentially discovering SVR model parameters that are more suitable for the grain moisture model. In conclusion, using the optimized SSA combined with SVR for training the grain moisture model enables automated parameter optimization, improves prediction accuracy and generalization ability, effectively handles nonlinear problems, adapts to the complexity of the data, and provides a powerful method for establishing grain moisture content models.
# Leading Edge Blowing to Mimic and Enhance the Serration Effects for Aerofoil +
## 4 Use of Leading Edge Blowing to Enhance the Serration Effects (Hybrid Device)
### Acoustic Results
Another useful quantity to describe the noise reduction performance of the hybrid device is the Overall Sound Power Level (OAPWL). In the current analysis, the sound power is integrated between 0.1 and 20 kHz. Figure 11b shows the contour map of the \(\Delta\)OAPWL produced by the difference in OAPWL between the serration-only (\(\lambda 45A30\)) and hybrid (\(\lambda 45A30\) + \(Q\)'= 0.5-4.5 L/min), as a function of \(Q\)'and \(U\) at \(\alpha\) = 0\({}^{\circ}\). Essentially, the \(\Delta\)OAPWL defined here represents a quantity that measures the further level of noise reduction on top of the level that is already achieved by the serration-only case. The contour map emphasises the very sensitive nature of the blow rate \(Q\)'in the hybrid device. The blowing does not provide quantifiable benefits when the inflow velocity \(U\) is relatively high. Very high level of blowing itself is also not recommended. Generally, it works best when the inflow velocity is relative low and couples with a low blow rate \(Q\) '. From the figure, the upper limits of the optimal combination for \(Q\)'and \(U\) are 2 L/min and 45 ms\({}^{-1}\), respectively. We refer to this area as the \"_effective zone_\". Velocity profiles similar to those shown in Figure 4b have also been performed for the hybrid device. The nearest streamwise measurement point relative to the orifice at the serration tip is about 2 mm. Using the measured streamwise velocity at this location as the reference, the \(Q\)'can be converted to \(U_{peak}\) for the exit jet. Within the _effective zone_, the most optimal blowing level that achieves the largest level of \(\Delta\)OAPWL (\(\sim\)2.5 dB) is to apply an exit jet of \(U_{peak}\) between 10 and 13% of the inflow velocity. Again, a reminder that the \(\Delta\)OAPWL measures the further level of noise reduction above the serration-only case. To the best knowledge of the authors, this  
Figure 11: (**a**) Comparison of \(\Delta\)PWL as a function of normalized frequency \(f\)'produced by the baseline and either the serration-only (\(\lambda 45A30\)) and hybrid leading edge device (\(\lambda 45A30\) + blowing \(Q\) ) at \(U\) = 40 ms\({}^{-1}\) and \(\alpha\) = 0\({}^{\circ}\). The leading edge blowing range covers 0.5 \(\leq Q\)'\(\leq\) 4.50 L/min, and (**b**) contour map of \(\Delta\)OAPWL produced by the difference in OAPWL between the serration-only (\(\lambda 45A30\)) and hybrid (\(\lambda 45A30\) + \(Q\) ), as a function of \(Q\)'and \(U\) at \(\alpha\) = 0\({}^{\circ}\). is the first report where the interaction noise reduction by a serrated leading edge can be enhanced by combining it with leading edge blowing.
## 3 Results and Discussion
### Event 2 (28 October 2021)  
On 28 October 2021, there were two intense solar flares observed. One of these flares was categorized as type M with an intensity level of M8.77, while the other was of type X with an intensity of X1.06. The M-type flare was observed during the nighttime hours. As a result, the focus of the study is directed towards the X-type flare, which attained its peak intensity at 15:35 UTC. The initiation of this flare took place at 15:17 UTC, and its conclusion was marked at 15:48 UTC. The origin of this X-type flare was traced back to the solar region known as AR12887, situated at coordinates S28 E10.  
The impact of this event was visible at the three designated stations, namely, UNPM, SPIG, and UCOE. These stations encountered notable disturbances during the time of the flare. It is worth noting that Satellite 24 displayed an unusual noise pattern at 17:48 UTC. This occurrence is potentially linked to noise originating from the mentioned satellite, especially considering the considerable geographical separation between the stations where the noise was detected.  
Event 2 exhibited comparable features to the previous one, even though, in this instance, we had fewer satellites accessible across all stations. Nonetheless, a pronounced disturbance was evident, coinciding with the solar flare's occurrence. However, during the calm day, the alterations appeared random, lacking a consistent pattern attributable to flare-induced disruptions (Figure 7).  
Figure 4: Results from the X1.59 solar flare event that occurred on 3 July 2021, in Region AR12838 (Zone N24 W88) for the OXPE station (PRN 14). The upper graph shows two irradiance measurements within the X-ray part of the electromagnetic spectrum, which were obtained through solar observations. These measurements originate from the XRS-A and XRS-B channels. The XRS-A channels detect wavelengths ranging from 0.05 to 0.4 nm, represented by the solid blue line, while the XRS-B channels cover wavelengths from 0.1 to 0.8 nm, represented by the solid red line. In the middle graph, the vTEC (vertical total electron content) is depicted as a solid red line measured in TECU, and it is accompanied by wavelet details at level 8 (WD8), represented by blue column bars. The last graph displays the wavelet details at all levels (WD D1-D8) together. The X-axis represents the time in UTC for a 2-hour period, with each grid corresponding to 30 min.  
Figure 5: The wavelet results for the same event with accordance to all the six stations (OXPE, SPIG, SSNX, UCOE, UNPM, UTEO), each with three PRNs. The specific details for each station align with those depicted in Figure 4.  
Figure 6: Visualization of Total Electron Content Wavelet Analysis from GNSS Signals for the Event on 3 July 2021
## 2 Datasets and Methods  
### Datasets  
We used the Global Inventory Modeling and Mapping Studies (GIMMS) third-generation NDVI dataset derived from Advanced Very High Resolution Radiometer (AVHRR) sensors (NDVI3g) at 1/12 degree (0.0825 degree) spatial resolution and a 15-day interval, spanning July 1981 to December 2011. This dataset was developed under the framework of the GIMMS project. The GIMMS NDVI datasets have been corrected to minimize various deleterious effects, such as calibration loss, orbital drift and volcanic eruptions, and are suitable for identifying long-term trends in vegetation activity [36, 39]. We generated monthly NDVI by applying the maximum value composite method from the two NDVI images of each month, to reduce noise in the NDVI data [51]. Any pixels with a 30-year averaged NDVI value less than 0.1 were considered non-vegetated areas [37, 52] and were masked out.  
We used air temperature at 2 m above ground level and precipitation rate, taken from NCEP Climate Forecast System Reanalysis and its version 2 (CSFV2) products [53, 54], with spatial resolution 0.5 degree and 6-hour intervals. This dataset spans the period from January 1979 to December 2011. To match the resolutions and scales of meteorological data with the NDVI3g data, we averaged the meteorological data to monthly and yearly values, and resampled them to a spatial resolution of 1/12 degree using the nearest neighbor method.  
Figure 1: (**a**) Regional vegetation map of China. [55] DCF, cold temperate deciduous-coniferous forest; MF, temperate coniferous and broadleaf mixed forest; DBF, warm temperate deciduous broadleaf forest; EBF, subtropical evergreen broadleaf forest; RF, tropical monsoon and rain forest; TG, temperate grassland; TD, temperate desert; PAV, Qinghai-Tibet Plateau alpine vegetation; (**b**) Changing trend in mean annual temperature and (**c**) changing trend in mean annual precipitation.  
The vegetation map of China, based on different landforms, climate, soil and human activity, produced by the Institute of Botany, Chinese Academy of Sciences [55], was used for analysis of different regions. China was separated into eight vegetation regions (Figure 1), as follows: (1) cold temperate deciduous coniferous forest (DCF); (2) temperate coniferous and broadleaf mixed forest (MF); (3) warm temperate deciduous broadleaf forest (DBF); (4) subtropical evergreen broadleaf forest (EBF), (5) tropical monsoon and rainforest (RF); (6) temperate grassland (TG); (7) temperate desert (TD) and (8) Qinghai-Tibet Plateau alpine vegetation (PAV). This regionalized map has been relatively stable over the past three decades, with less uncertainty than land-cover or land-use data
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 3 Data Acquisition Methods
### Sampling Location and Duration
#### 3.1.2 Sampling Point Installation  
The selection of monitoring locations is based on site conditions and residents' water usage patterns. Water quality monitoring equipment is installed in key areas, including the following:  
* **Kitchen sinks**: Reflecting water used for drinking and cooking.
* **Bathroom sinks**: Monitoring water used for personal hygiene.
* **Toilets**: Assessing the quality of water used for flushing [21].
* **Utility sinks**: Providing insights into water use in dormitory common areas.  
By integrating strategically selected monitoring points with an optimized sampling interval, the proposed monitoring framework enhances the accuracy, completeness, and representativeness of the dataset. Future research will explore further optimization strategies, such as wavelet analysis, to enhance detection capabilities and improve system performance.
# The Necessity of LED to Ambient Light Ratio Optimization for Vehicular Optical Camera Communication
## 3 LED to Ambient Light Ratio Optimization
### A Top-Down Explanation on How L2A Ratio and LED Density Determine Bit Detection Accuracy
#### 3.3.3 Effect of L2A Ratio and LED Spacing on Extinction Ratio and Interference
(c) Effect of LED Interval and Communication Distance on LED Spacing  
As mentioned earlier, LED spacing is the amount of space between LEDs in the images. From the camera geometry, LED spacing is determined as:  
\[spacing=\frac{intv_{LED}\times f}{d}, \tag{16}\]  
where \(intv_{LED}\) is the LED interval, which is defined as the inter-distance between LED chips in the LED panel, \(f\) is the lens focal length, and \(d\) is the communication distance. From Equation (16), it can be seen that LED spacing is directly proportional to the LED interval \(intv_{LED}\) and lens focal length \(f\) increase while inversely proportional to the communication distance \(d\).
###### Abstract  
Major challenges affecting polarizers for communication systems include the inability to perform over a wide bandwidth with a simple design. Orthogonal outgoing polarization for polarization-diverse applications and stable performances for oblique incidence angles are also major requirements. This paper presents the design of a polarizer that can perform over a wide range of bandwidths in dual frequency bands. The unit cell is uniquely designed using a split circular ring resonator enclosed in a square ring with the addition of three-square patches. As a result, the incoming linearly polarized \(x(y)\) wave is converted into a transmitted LHCP (RHCP) wave in the Ku and Ka bands. The operational bandwidths are 11.05-16.75 GHz (41%) and 34.16-43.03 GHz (23%). The proposed polarizer is ultrathin, works in dual wide-bands, is polarization-diverse, and maintains performance over \(\pm 45^{\circ}\) and \(\pm 30^{\circ}\) oblique incidences, which makes it a strong candidate for many communication systems.  
Keywords:dual band; linear-to-circular converter; periodic array +
Footnote †: journal: Sensors
## 10 Comparative Analysis of Propagation Models
### Computational Time Comparison
Based on their computational complexity, the models evaluated in this study can be ordered in increasing complexity as follows: **SUI model** (\(\mathcal{O}(1)\)), which applies simple closed-form expressions; **ITU-R P.1546-6**, which relies on interpolation procedures and empirical corrections (\(\mathcal{O}(1)\)-\(\mathcal{O}(\log n)\)); **ITU-R P.452-17**, which combines terrain profile analysis, Bullington constructions, and statistical and climatological corrections (\(\mathcal{O}(n)\), where \(n\) denotes the number of profile points along the path); and finally, the most computationally intensive, **PEM (Parabolic Equation Modeling)**, which solves parabolic partial differential equations using numerical methods (e.g., Split-Step Fourier method: \(\mathcal{O}(N\log N)\) or Crank-Nicolson method: \(\mathcal{O}(NM)\), where \(N\) and \(M\) represent the resolution of the computational grid). This latter model has the highest computational and memory demands, yet it also offers the most accurate representation of terrain effects. These considerations are summarized in Table 15.
# Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network
## 5 A Case Study of UAV NTN Airborne Network in Mountainous Area
### Discussion
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application** & **Item** & **Results** \\ \hline \multirow{2}{*}{Multi robot control} & Robot control instruction delay & less than 20 ms \\  & Robot control range & 50 km \\ \hline \multirow{4}{*}{High-definition video streaming} & Channels supported of 4k & \multirow{2}{*}{16} \\  & video stream & \\ \cline{1-1}  & Video stream delay & less than 70 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Robot application of UAV airborne network coverage field trial results. edge. A heat map of user QoS might be a useful input for the online trajectory adjustment algorithm. In the next stage of research, we will consider the UAV trajectory adjustment method based on a QoS heat map to improve the user communication quality in UAV NTN for better practical usability.
# Structural Performance of R-Type Steel Damper Used in Reinforced Concrete Members
## 2 Experimental Program
### Phase I Test Program  
#### 2.1.1 Design of Phase I Test Specimens  
As shown in Figure 1, two types of dampers were designed in this study. In order to prevent eccentricity, four steel dampers were connected to the front and rear of each specimen. Tension tests of the steel used in the damper were performed according to ASTM E8 [20]. As a result, the yield and tensile strengths were 245 and 420 MPa, respectively, and the yield strain was 0.00147, as shown in Table 1.  
As shown in Figure 1a, Specimen D1 comprises four angled U-shaped dampers. This specimen was designed to compare the structural performance and characteristics of the rectangular-type (R-type) steel damper proposed in this study. The steel damper used in the experiment had an energy dissipation part and a load transmission part. The energy dissipation part dissipates external energy through the yielding of the steel. It comprises a vertical member and upper and lower horizontal members. The upper horizontal member allows the vertical member to stably undergo plastic deformation through bending while dissipating energy through plastic deformation. The lower horizontal member is directly connected to the load transfer plate to improve the load resistance capability of the damper and to dissipate energy. Figure 1 shows the dimensions and shape of this damper.