esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In this paper, on the basis of the fact that local adaptive thresholding estimates a different threshold value for each pixel according to the grayscale information of neighboring pixels [19], we proposed a novel saliency cuts method based on locally adaptive triple-thresholding using integral images, in order to assist the image recognition process of the visually impaired. Firstly, we obtained a saliency map using our previous saliency detection method [15]. Secondly, the binary saliency mask was obtained as the image of a four-region seeds by using adaptively triple-thresholding. Then, the seeds were fed to the GrabCuts [20] method and a robust binary mask was generated. The main contribution of the proposed method was as follows:  
* Salient object extraction based on locally adaptive triple thresholding using an integral image. * We combined the GrabCuts algorithm with the generated four-region seeds to refine the segmentation results. * One of the applications of salient object extraction is detected outer boundary, and inner edges of the salient object were illustrated on tactile graphics to facilitate the learning process of visually impaired. The proposed method cuts the salient object and detects the outer contours and inner edges of salient objects so that the visually impaired can easily understand the content of an image scene. Figure 1 demonstrates the proposed method: (a) input image, (b) saliency map generated using our previous approach [15], (c) the proposed saliency cuts, and (d) salient objects extracted using the mask. The remainder of the paper is organized as follows: Section 2 reviews conventional methods for salient object detection and saliency cuts. Section 3 presents the saliency cuts approach in detail. The experimental results based on popular benchmark databases are shown and discussed in Section 4. Section 5 highlights some limitations of the proposed method. Finally, Section 6 concludes the paper by summarizing our findings.
# Crack Initiation and Propagation Fatigue Life of Ultra High-Strength Steel Butt Joints
## Nomenclature  
\begin{tabular}{l l} \(a\) & Crack depth (mm) \\ \(a_{f}\) & Final crack depth for crack propagation calculation (mm) \\ \(a_{init}\) & Initial crack depth for crack propagation calculation (mm) \\ \(a_{th}\) & Threshold crack depth between crack initiation and propagation (mm) \\ \(a/c\) & Crack aspect ratio (-) \\ \(c\) & Half surface crack length, half width of elliptical crack (mm) \\ \(C_{P}\) & Crack growth rate coefficient according to Paris law (\(\Delta\)K in MPa\(\sqrt{\text{mm}}\); da/dN in mm/cycle) \\ \(E\) & [PERSON]'s modulus (MPa) \\ \(FAT\) & Fatigue class according to IIW, stress range \(\Delta\sigma\) at \(N=2\cdot 10^{6}\) load cycles and \(P_{S}=97.7\%\) (MPa) \\ \(K\) & Stress intensity factor (MPa\(\sqrt{\text{mm}}\)) \\ \(K_{f}\) & Fatigue notch factor (-) \\ \(K_{t}\) & Stress concentration factor (-) \\ \(k\) & Inverse slope of S/N-curve (-) \\ \(M_{a}\) & Weight function parameters for deepest point of a surface crack \\ \(m_{a}(x,a)\) & Weight function for deepest point of surface crack \\ \(M_{c}\) & Weight function parameters for surface point of a surface crack \\ \(m_{c}(x,a)\) & Weight function for surface point of surface crack \\ \(m_{p}\) & Slope of crack growth rate curve according to Paris law (-) \\ \(N\) & Load cycle number (-) \\ \(N_{f}\) & Load cycle number at specimen burst fracture (-) \\ \(N_{initial}\) & Load cycle number before start of image acquisition (-) \\ \(N_{interval}\) & Interval load cycle number between two image acquisitions (-) \\ \(N_{init}\) & Load cycle number at crack length \(a_{th}\), threshold between crack initiation and propagation (-) \\ \(N_{cp}\) & Load cycle number of crack propagation until burst fracture (-) \\ \(N_{k}\) & Transition knee point of S/N-curve (-) \\ \(P_{S}\) & Probability of survival (-) \\ \(R\) & Load stress ratio (-) \\ \(s_{y}\) & Displacement in y-direction (specimen loading direction) (pixel) \\ \(s^{\prime}{}_{y}\) & Gradient of displacement in y-direction (-) \\ \(s^{\prime}{}_{y,\text{thres}}\) & Threshold for gradient of displacement in y-direction (-) \\ \end{tabular}  
\begin{tabular}{l l} \(SSE\) & Sum of squared errors (-) \\ \(t\) & Sheet thickness (mm) \\ \(1:T_{\sigma}\) & Scatter index of S/N-curve, ratio of stress range \(\Delta\sigma\) at \(P_{S}=10\%\) and \(P_{S}=90\%\) (-) \\ \(\rho\) & Weld toe radius (mm) \\ \(\rho^{\ast}\) & Microstructural support length (mm) \\ \(\theta\) & Weld flank angle (\({}^{\circ}\)) \\ \(\sigma\) & Stress (MPa) \\
**Recurring indices** \\ eff & Effective value \\ \(n\) & Nominal value \\ _notch_ & Notch value \\ \(\Delta\) & Range, difference of upper and lower value \\ \end{tabular}
# Effect of Demand Side Management on the Operation of PV-Integrated Distribution Systems
## 3 Results and Discussion
### Case IV: With DLC, without DG and CVR  
Mainly, the algorithm measures the power of each group over 24-h period. It starts by comparing each load with its pre-declared threshold (P\({}_{\text{TH}}\)) during an interval of 30 min. Once the consumed power by the loads/users exceeds this threshold, load control is taken. DLC curtails 10-30% of the load, maintaining the loads with the highest priority active. Finally, if consumption drops below P\({}_{\text{TH}}\), the algorithm automatically connects the curtailed percentage back to the load.  
In Figure 10, one can see the power of loads 10, 21 and 30 along the 24-h simulation with a varying load shape. According to Table 4, the consumed power of each load was considerably reduced  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Bus/Load (\#)** & \begin{tabular}{c} **Reactive Power** \\ **without CVR (kvar)** \\ \end{tabular} & \begin{tabular}{c} **Reactive Power** \\ **with CVR (kvar)** \\ \end{tabular} & \
\begin{tabular}{c} **Reactive Power** \\ **Reduction (kvar)** \\ \end{tabular} &
\begin{tabular}{c} **Reduction** \\ **Percentage (\%)** \\ \end{tabular} \\ \hline B10 & 576.19 & 533.64 & 42.55 & 7.38 \\ B12 & 2179.3 & 2006 & 173.3 & 7.95 \\ B14 & 453.79 & 423.95 & 29.84 & 6.58 \\ B15 & 704.23 & 656.91 & 47.32 & 6.72 \\ B16 & 514.20 & 478.55 & 35.65 & 6.93 \\ B17 & 1651.4 & 1533.5 & 117.9 & 7.14 \\ B18 & 250.1 & 233.15 & 16.95 & 6.78 \\ B19 & 942.5 & 879.4 & 63.1 & 6.69 \\ B20 & 195.62 & 182.06 & 13.56 & 6.93 \\ B21 & 3152.6 & 2930.5 & 222.1 & 7.04 \\ B23 & 443.42 & 414.5 & 28.92 & 6.52 \\ B24 & 1844 & 1719 & 125 & 6.78 \\ B26 & 607.67 & 566.42 & 41.25 & 6.79 \\ B29 & 238.29 & 222.92 & 15.37 & 6.45 \\ B30 & 494.28 & 460.45 & 33.83 & 6.84 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Case III: Reactive power with/without CVR.  
Figure 9: Case III: Voltage vs. time with/without CVR.  
when exceeding its tie-line threshold, as several loads were curtailed according to the aforementioned algorithm. In Figure 11, a small increase in voltage is observed as 10-30% of consumed load is curtailed, as if the load has become lightly loaded. As a general result, the advantages of this algorithm are lowered system losses and lowered consumed power.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Bus/Load (\#)**} & **Active Power** & **Active Power with** & **Active Power** & **Reduction** \\  & **without DLC (kW)** & **DLC (kW)** & **Reduction (kW)** & **Percentage (\%)** \\ \hline B10 & 1608.8 & 1399.8 & 209 & 12.99 \\ B12 & 3110 & 2567.3 & 542.7 & 17.45 \\ B14 & 1716.4 & 1392 & 324.4 & 18.90 \\ B15 &
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 5 Testing and Result Analysis
Further, after semantic aggregation algorithm processing, some abnormal or low-precision observations  
Figure 8: Statistical results of completeness and redundancy of crowdsourced fusion mapping on-cloud on different semantic elements. Figure 9: The influence of different observation times on the accuracy and completeness of fusion mapping, the horizontal axis represents the observation times (aggregation times), the left vertical axis represents the relative precision (unit m), and the right vertical axis represents the semantic element completion rate. are filtered out, thus obtaining a high-precision lightweight semantic map as shown in Figure 10c. In this experiment, we utilized our crowdsourced semantic mapping method to automatically build a semantic map covering approximately 25 km in a complex circular overpass scenario, as shown in Figure 11. The semantic elements include various types of lanes (real lane lines, virtual lane lines, road edges, etc. ), ground arrows, traffic signs, etc. To verify the actual usability of this map, we loaded the map onto a vehicle and conducted semantic positioning tests. The results show an average lateral error of 15 cm, a longitudinal error of 35 cm, and a heading angle error of 0.5 degrees, as shown in Figure 12. These results confirm the reliability and practicality of the crowdsourced mapping method proposed in this paper. Figure 11: Automatic global consistency semantic map rendering. Figure 10: Effect of fusion mapping on-cloud process. (**a**) The original fragment map with multiple LSMs unified to the global coordinate system; (**b**) multiple fragment maps via GBA_SGICP algorithm optimization and semantic location adjustment; and (**c**) fragment map after aggregation and vectorization of semantic elements.
## 3 Results
### Test-2  
The results related to Test-2 are shown in Figure 5. Consistent with Section 3.1, the mean \(K_{R}\) values and the corresponding standard deviations (\(\sigma_{R}\)) are reported using bar charts (with uncertainty \(\pm\sigma_{R}\) intervals).  
The values of \(K_{R}\) that characterize the non-leaking conditions are consistent with the ones measured in Test-1. Furthermore, like for Test-1, leak L3 could not be distinguished from the background noise. Conversely, the behavior of all the three transducers for leaks L1 and L2 appeared fairly different from the previous test. In particular, sensor Ard seems to be now capable of detecting both leaks with an adequate robustness, since a sizeable increment in \(K_{R}\) was observed.  
Regarding sensor Aax, the sensitivity to leak L1 appears significantly lower than in Test-1. Nonetheless, both leaks L1 and L2 could be still identified effectively.  
Finally, sensor Hyd also exhibited a reduction in the sensitivity to leak L1, but leak detection could still be achieved satisfactorily for both leaks L1 and L2.  
The different behavior in comparison to Test-1 can reasonably be attributed to the modified conditions of the surrounding medium, namely the extremely high soil moisture content, which may alter the signal attenuation of the pipe. However, it is worth noting that the experimental results for the non-leaking state seem to not be affected by the changed boundary conditions.  
Figure 4: Mean kurtosis values (\(K_{R}\)) in Test-1 (\(\pm\) standard deviation (\(\sigma_{R}\)) intervals are indicated by the orange vertical bars), for sensors: (**a**) radial accelerometer sensor (Ard); (**b**) axial accelerometer sensor (Aax); (**c**) hydrophone sensor (Hyd).
# Gesture-to-Text Translation Using SURF for Indian Sign Language
## 3 Research Methodology
### Feature Extraction  
The next phase after image collection is the extraction of features. This phase involves three stages: image feature identification, clustering, and codebook preparation for the BoVW model. Initially, the scale-invariant feature transform (SIFT) method was used to detect significant image features [27].  
However, we later switched to using resilient features provided by the speeded-up robust features (SURF) technique [28]. SURF is a groundbreaking feature extraction technique that offers improved speed, which is essential for designing real-time gesture recognition applications. The technique achieves its speed advantage by using a box filter approximation, which approximates the Laplacian of Gaussian (LoG) used in SIFT. SURF is also more robust than SIFT for image scaling and rotation. It uses a scale-space pyramid and a rotation-invariant descriptor to handle variations in scale and rotation, respectively. In contrast, SIFT also uses a scale-space pyramid, but its descriptors are not inherently rotation-invariant and require additional computation to achieve rotation invariance. For these reasons, we implemented SURF in our study.  
Figure 7 shows what the image's SURF features look like. The next stage is to build a visual language by grouping all the identical SURF elements. It is improbable that humans can locate all relevant feature descriptors. The K-means clustering strategy may be effective in this scenario.  
The K-means algorithm is a regularly used unsupervised clustering algorithm [29]. Its purpose is to divide n features into k clusters and use the cluster mean to forecast a new feature for each cluster (centroid). K-means clustering takes a long time and much memory because much work is done with SURF features from 42,000 photographs. As a result, micro-batch K-means, comparable with K-means but require less memory and are faster, were deployed [30].  
After micro batch K-means has learned all SURF characteristics, it puts all related features into a bag with k clusters (visual words). A K value of 280 was used because there are 35 classes to categorize. A series of actions are taken to produce the desired outcome: a visual word, a feature vector representation of an image, and each vector dimension correspond to a visual word in the image.  
In picture classification and retrieval activities, these vectors are employed. To evaluate and categorize photos, these ideas and methods are utilized in combination in the manner shown in (Figure 8). This is carried out by extracting significant aspects from the images, putting related features together, and then representing the images with a set of visual words. The recurrence of every visual word linked with the image in overall visual words is used to compute the histogram.  
Figure 7: Character “D” after SURF implementation.
# Role of Radio Telescopes in Space Debris Monitoring: Current Insights and Future Directions
## 3 Radio Telescope Measurements with Active Transmitters
### Goldstone Orbital Debris Radar  
The Goldstone Orbital Debris Radar operated by NASA, is a highly capable system used for monitoring the orbital debris environment. It has supported multiple space debris observation campaigns [39]. Between October 1994 and May 1997, the Goldstone Solar System Radar, together with a 35-m parabolic antenna acting as the receiver, operated in beam-park mode. During this period, approximately 110 h of observations were conducted, resulting in the detection of 3476 objects [22]. Figure 5 presents a summary of this measurement campaign, where the minimum radar cross-section (RCS) of the detected objects is plotted against their corresponding altitudes. A particularly noteworthy finding from this study was the unexpected identification of a concentration of debris around 2900 km in altitude, a group not previously observed by other radar systems. Additionally, very few objects were detected below 300 km, which the authors attributed to the geometric limitations of the intersecting radar beam [22]. In a related study, [PERSON] et al. [40] compared debris flux measurements from three radar systems: the Goldstone Radar, Haystack Imaging Radar, and Haystack Auxiliary Radar (HAX) before the year 1998, during 2001, and after 2003, the last solar maximum. Furthermore, the findings from the measurement campaigns conducted between 2016 and 2017 are reported here [41]. Thanks to several upgrades in bandwidth and the integration of dual-polarization capabilities over the last few years, the detection efficiency of the system has improved significantly, enabling the detection of debris as small as 3 mm.  
Figure 4: Results from the first COBEAM experiment performed in 1996: (**a**) Detection rate (detections per hour) versus altitude, shown in 30 km bins, comparing performance of the TIRA L-band radar and the Effelsberg radio telescope. Effelsberg demonstrates significantly higher detection rates across all altitude ranges due to its superior sensitivity. (**b**) Detection rate versus range rate in 100 m/s bins over the interval \(-1\) to 1.8 km/s. The TIRA detections peak around 0.1 km/s, indicating more detections of near-stationary or slowly moving objects, while Effelsberg shows a dominant detection rate near 1 km/s, corresponding to faster-moving targets in its field of view [34]. Reprinted from Advances in Space Research, Copyright 1999, with permission from Elsevier.
# Priority-Driven Resource Allocation with Reuse for Platooning in 5G Vehicular Network
## 4 System Modeling and Resource Allocation Algorithm
### System Model
The denominator of the SINR expression accounts for both noise and interference. The term \(\sum_{r^{\prime}\
eq i}x_{rj}P_{0}h_{r^{\prime}k}^{(j)}\) represents the interference experienced by vehicle \(k\) when another vehicle \(i^{\prime}\) is using the same resource block. This interference is caused by concurrent transmissions sharing the same time-frequency resource block, which reduces the SINR of the target vehicle. Interference can significantly degrade communication performance by causing signal distortions or collisions, further reducing the available bandwidth for the vehicle. The higher the number of concurrent transmissions, the more interference is added to the received signal, leading to a decrease in SINR and a potential increase in communication delay. In this case, the channel gain between two vehicles using resource block \(j\) can be modeled using the complex Rayleigh fading channel coefficient \(h_{0}^{(j)}\), the distance \(d_{i,k}\) between vehicles \(i\) and \(k\), and the path loss exponent \(\beta\)[44]. \[h_{i,k}^{(j)}=\left|h_{0}^{(j)}\right|^{2}(d_{i,k})^{-\beta} \tag{5}\]  
As mentioned earlier, the base station can infer that the next packet destination for a platoon vehicle is the subsequent vehicle in the platoon through the resource request message. Using this information, the base station can calculate the channel gain between vehicles, including those within the platoon. Based on this calculation, the base station reuses the resource only when the SINR of the platoon vehicle, reduced by the interference power of the vehicle reusing the resource, remains higher than the threshold SINR required for reliable communication.
## 2 Characteristic Signal Selection and Wavelet Denoising Processing
### Wavelet Denoising
#### 2.2.4 Selection and Improvement of the Threshold Function  
The basic idea of wavelet threshold denoising is to set a threshold value in advance. The signal is transformed by wavelet transform to obtain the wavelet analysis coefficient and compare it with the initial threshold. If the wavelet coefficient is less than the threshold, it is considered that the coefficient is mainly caused by noise, and it is removed to achieve the purpose of denoising. The wavelet threshold denoising method mainly includes wavelet soft threshold denoising and wavelet hard threshold denoising. The wavelet soft  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Wavelet Basis**} & \multirow{2}{*}{**Noise Reduction Result SNR/dB**} & **Noise Reduction Result** \\  & & **RMSE/Hz** \\ \hline db1 & 10.1958 & 0.00920 \\ db2 & 11.1603 & 0.00820 \\ db3 & 11.6899 & 0.00770 \\ db4 & 11.9757 & 0.00750 \\ db5 & 12.1826 & 0.00730 \\ db6 & 12.3382 & 0.00720 \\ db7 & 12.4406 & 0.00710 \\ db8 & 12.5423 & 0.00700 \\ db9 & 12.5185 & 0.00700 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Denoising results of different wavelet bases.  
threshold denoising method can make the signal waveform smoother, but there are large signal distortion errors at the same time, which is very unfavorable to the signal feature analysis. Although there is a micro oscillation in the wavelet hard threshold denoising method, the signal obtained by its reconstruction can have a better approximation, be relatively more accurate, and have a small relative error. Therefore, the wavelet hard threshold method is adopted to conduct denoising processing, and the signal standard deviation is determined by the estimation method to determine the threshold value. The estimated threshold value is shown in Equation (3).  
\[thr=\delta\sqrt{2}\log(N) \tag{3}\]  
\(N\) is the length of the acoustic emission signal and the vibration signal, and \(thr\) is the estimated value of the signal threshold. Parameter \(\delta\) is shown in Equation (4).  
\[\delta=\frac{median\left(\left|d_{j}(k)\right|\right)}{0.6745} \tag{4}\]  
_median_ is the operation command in Matlab R2022b, it is used to calculate the noise variance, \(d_{j}(k)\) is the \(k\)th wavelet coefficient of the \(j\)th layer wavelet, it is the noise variance. When the wavelet coefficient is not less than the set threshold value, it remains unchanged; if the requirement is not met, it is set to 0, as shown in Equation (5).  
\[d_{j,thr}=\left\{\begin{array}{l}0,\left|d_{j}\right|<thr\\ d_{j},\left|d_{j}\right|\geq thr\end{array}\right. \tag{5}\]
# Active Flow Control of a Supercritical Airfoil and Flap with Sweeping Jets
## 4 Conclusions  
In this paper, a two-dimensional straight-wing model of the supercritical airfoil NASA SC(2)-0410 was used to study the flow-control effect of sweeping jets with different momentum coefficients at \(Re=2.0\times 10^{5}\) (\(V=10\) m/s). Based on the experimental results, the aerodynamic performance of the wing model and flow-separation characteristics of the wing flap have been demonstrated. The flow control effectiveness and control mechanism of sweeping jets on the model were investigated, and specific conclusions are as follows:  
1. A large angular deflection of a simple flap causes flow separation on the upper surface of the wing and flap, which limits further improvement in the wing lift coefficient using only flap deflection during the takeoff stage.
2. The lifting effect of the sweeping jet flow control is enhanced with the increase in the jet momentum coefficient of the sweeping jet. In this study, the flow control based on the sweeping jet actuator can generate a maximum increase of approximately 33% in the lift coefficient of the wing model with \(C_{\mu}=8.2\%\).
3. The use of a sweeping jet at the front of the flap can effectively improve the lift coefficient of the whole wing, which comes from the extra lift generated by the jet at the flap and from the greater negative pressure near the leading edge on the upper wing surface owing to the induced flow caused by the sweeping jet.  
The research results of this paper will help engineers and researchers understand how to better optimize the high-lift system during the takeoff stage and provide validation data for the flow control technology design and CFD simulation on AFC technology.  
Conceptualization, K.C. and Y.G.; methodology, L.L.; formal analysis, S.L.; investigation, S.L. and R.F.; resources, W.W.; writing--original draft preparation, S.L.; writing--review and editing, L.L. and S.L.; funding acquisition, Y.G. and W.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Civil Aircraft Research Projects and the Priority Academic Program Development of Jiangsu Higher Education Institutions.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors would like to thank all those who participated in the study, including [PERSON], [PERSON], and [PERSON].  
The authors declare no conflict of interest.  
The area of the section at the throat of the actuator.  
Wing chord length.  
The lift coefficient of the model.  
The surface pressure coefficient of the model.  
The momentum coefficient of the sweeping jet.  
Spanwise spacing distance of the actuators.  
The frequency of the sweeping jet.  
Wing span.  
The lift of the wing model.  
The mass flow rate of the sweeping jet.  
The static pressure of the incoming flow.  
The dynamic pressure of the incoming flow.  
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