esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Algorithm
### Proposed Efficient Packet Scheduling Algorithm
Step 7: The impact of any short-term undesired fluctuations is reduced by updating the average data rate according to the following smoothing exponential filtering equation, for every user \(k\)  
\[R_{k}(i,j,s)=wR_{k}(i,j,s)+(1-w)\overline{R}_{k}(s) \tag{3}\]  
where \(w\) is a weighting factor used to average the data rate of user \(k\). Step 8: A check is then conducted to determine if there are sufficient resources remaining that can be allocated in the current round. If there are, the algorithm returns to Step 4; otherwise, it returns to Step 1 for preparing for the start of the next round. A flow chart of the proposed algorithm is also presented in Figure 2. It should be note that the proposed algorithm is employed in the BS for operation. Figure 3 shows a typical transmission scenario for the algorithm in a case of a partitioned packet; more specifically, the illustrated case involves six users and two CCs aggregated in a BS. It is assumed that \(\eta=b_{1}/b_{\text{total}}\). It is then further assumed that user-5 is an NRT user, while the other users are  
Figure 2: Flow chart of the proposed algorithm. RT users. Thus, according to our definition, only the 1 st CC can be used by user-5 for transmission, whereas the RT users can use both CCs for parallel transmissions. However, since the size of the packet for user-5 is larger than that of the considered RB, it is partitioned during its first transmission. The remaining portion of this packet is then provided a RB for transmission during the next round. It should be noted that all those packets scheduled in the current round illustrated in Figure 3 would have already been buffered in the transmission queue illustrated in Figure 1.  
Last, but not least, the proposed algorithm's worst-case performance with regard to the time complexity as a function of the number of CCs \(c\), the number of the \(i\)th CC's RBs \(b_{i}\), with \(i=1,2,\ldots,c\), and the number of users \(n\) is provided as follows. The dominant cost is on the operation of the _fairness vector_ calculation, which iterates \(\sum_{i=1}^{c}b_{i}\) times and requires \(n\) comparisons in each stage, and thus the worst-case cost can be calculated as \(O(n\sum_{i=1}^{c}b_{i})\). It is important to note that, in current environments, the value of \(c\) typically ranges from 2-5, with the likelihood of it being equal to or lower than 3 being particularly high. As such, the corresponding level of complexity is also typically low.
## 5 Conclusions  
A portable long-range laser rangefinder performance was investigated based on commercially available silicon photomultipliers (SiPM) with multi-pixel architecture as the detector for time-of-flight (ToF) measurements. Experimental results based on our own  
Figure 10: Average raw data (**top**) and processed data (**bottom**). The range found with our own LRF is 3418 m away from the LRF. The number of averaged raw data frame, N is 4000.  
prototype show that SiPMs along with low-power pulsed laser diode yield promise long-range LRF performance owing to their exceptional properties such as high sensitivity, multi-pixel architectures and relatively low-voltage operation. We achieved a long-detection range over 3.5 km by employing a relatively low power laser with a pulse energy of a few uj at 0.905 um and 3 dB enhancement in SNR by the implemented signal extraction algorithm based on Wiener filtering. Our measurement resolution was not good because of using a 100 ns long laser pulse which corresponds to the uncertainty of 30 m. Although a LRF using a time-to-digital converter has better resolution, our uncertainty can be reduced if SNR is increased. In order to increase our measurement sensitivity, we need to have better SNR than those obtained. One way of doing this is to use a pulse coding scheme when sending laser light. Since we have implemented FPGA to control everything, this will be our future plan. Improvement can also be achieved by using proper optical design in the beam shaping of the laser output and also in the receiver FOV. Although SiPM could be vulnerable to stray light which results as an uncorrelated noise added to its intrinsic primary noise (related to dark count rate), our tests show that SiPM along with optimized optics and digital filtering promise to realize low-power and low-cost practical long-range LRF and LiDAR applications.  
Conceptualization, H.B., C.C. and M.T.; methodology, E.O., H.B. and A.Y.; software, M.T. and H.B., validation, C.C. and A.Y.; formal analysis, M.T.; investigation E.O., H.B. and M.T.; resources, E.O. and C.C.; writing--original draft preparation, H.B.; writing--review and editing, H.B. and M.T.; supervision, H.B.; project administration, E.O. and M.T.; funding acquisition, C.C. and E.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Scientific Research Council (BAP) of Gazi University, grant number 18/2015-03.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.
# Effective Implementation of Edge-Preserving Filtering on CPU Microarchitectures
## 7 Experimental Results
### Effective Implementation on CPU Microarchitectures
Therefore, we can achieve significant acceleration by applying the proposed methods for preventing the occurrence of denormalized numbers and selecting the implementation approaches of weight calculation and the appropriate data type. Finally, we compared the fastest implementation with OpenCV, which is the de facto standard image processing library [49]. Figure 19 shows the computational times of our implementation and the OpenCV's implementation with its speedup ratio. Notably, the computational time is plotted on the logarithmic scale. Our method is 2-14-times faster than OpenCV. In the OpenCV implementation, the distance function of the range kernel is not the L2 norm, and the kernel shape is circular for acceleration (see Appendix A); therefore, PSNR is low. By contrast, our implementation slightly approximates the kernel LUT, and the kernel shape is rectangular. Therefore, the proposed methods are more practical. Figure 17: Computational time when using unsigned char (8U) and single precision floating point number (32F) with respect to kernel radius: (**a**) bilateral filter; (**b**) non-local means filter. Note that the computational time is plotted on the logarithmic scale. These results were obtained on an Intel Core i9 7980 XE. \(\sigma_{r}=4\), \(\sigma_{s}=4\); and image size is 768 \(\times\) 512. Figure 18: Speedup ratios of various proposed implementation approaches and that of scalar implementation: (**a**) bilateral filter; (**b**) non-local means filter. The types of implementation considered herein are parallelized using multi-cores. These results were obtained on an Intel Core i9 7980 XE. \(\sigma_{r}=4\), \(\sigma_{s}=4\), \(r=3\sigma_{s}\); and image size is 768 \(\times\) 512.
# Radiometric Calibration of the Near-Infrared Bands of GF-5-02/DPC for Water Vapor Retrieval
## 4 Results
### Radiometric Calibration Results of the DPC at the NIR08 Band
In addition, the expected value of \(k1\) is 1.07, which is consistent with the above calibration result.
## 3 Theoretical Comparisons Between Estimators
Step (4): Estimates the parameters using the estimators under study. Step (5): Evaluate the estimators' performance using \(MSE\), using Equation (28). Step (6): Iterate Until Completion:
* Increment the replication counter: \(r=r+1\). * If \(r=1000\), stop the simulation; otherwise, return to Step 3. The simulation results are summarized in Tables 1-3. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \(\omega\) & \multicolumn{4}{c}{2} & \multicolumn{4}{c}{4} & \multicolumn{4}{c}{6} \\ \hline n & 30 & 50 & 100 & 200 & 30 & 50 & 100 & 200 & 30 & 50 & 100 & 200 \\ \hline \multicolumn{8}{c}{\(\gamma=0.8\)} & \multicolumn{8}{c}{\(\gamma=0.8\)} & \multicolumn{8}{c}{\(\gamma=0.8\)} & \multicolumn{8}{c}{\(\gamma=0.8\)} \\ \hline \(\hat{\alpha}^{\text{PIG}-MLE}\) & 2.563 & 1.956 & 1.892 & 1.814 & 2.656 & 1.984 & 1.960 & 1.906 & 2.704 & 2.007 & 1.993 & 1.982 \\ \hline \(\hat{\alpha}^{\text{PIG}-REE}\) & 1.347 & 0.965 & 0.925 & 0.919 & 1.531 & 0.981 & 0.957 & 0.939 & 1.680 & 1.000 & 0.981 & 0.972 \\ \hline \(\hat{\alpha}^{\text{PIG}-LE}\) & 1.328 & 0.975 & 0.935 & 0.904 & 1.524 & 0.988 & 0.957 & 0.925 & 1.899 & 1.000 & 0.990 & 0.964 \\ \hline \(\hat{\alpha}^{\text{PIG}-ISE}\) & 1.281 & 0.958 & 0.957 & 0.946 & 1.327 & 0.962 & 0.963 & 0.960 & 1.451 & 1.004 & 0.986 & 0.976 \\ \hline \(\hat{\alpha}^{\text{PIG}-TPE}\) & 1.267 & 0.982 & 0.914 & 0.902 & 1.294 & 0.995 & 0.986 & 0.929 & 1.309 & 1.006 & 1.000 & 0.974 \\ \hline \(\hat{\alpha}^{\text{PIG}-KLE}\) & 1.126 & 0.952 & 0.919 & 0.906 & 1.251 & 0.976 & 0.947 & 0.914 & 1.008 & 1.000 & 0.963 & 0.955 \\ \hline \(\hat{\alpha}^{\text{PIG}-MKLE}\) & **1.096** & **0.931** & **0.908** & **0.901** & **1.103** & **0.948** & **0.947** & **0.918** & **1.201** & **0.965** & **0.952** & **0.949** \\ \hline \multicolumn{8}{c}{\(\gamma=0.9\)} & \multicolumn{8}{c}{\(\gamma=0.9\)} & \multicolumn{8}{c}{\(\gamma=0.9\)} & \multicolumn{8}{c}{\(\gamma=0.9\)} \\ \hline \(\hat{\alpha}^{\text{PIG}-MLE}\) & 3.933 & 2.229 & 1.952 & 1.895 & 4.830 & 2.253 & 1.993 & 1.937 & 4.993 & 2.714 & 2.022 & 2.019 \\ \hline \(\hat{\alpha}^{\text{PIG}-RRE}\) & 2.268 & 1.140 & 0.952 & 0.921 & 2.927 & 1.197 & 0.977 & 0.940 & 3.061 & 1.289 & 0.999 & 0.986 \\ \hline \(\hat{\alpha}^{\text{PIG}-LE}\) & 2.209 & 1.150 & 0.958 & 0.920 & 2.900 & 1.212 & 0.986 & 0.938 & 3.036 & 1.305 & 1.003 & 0.988 \\ \hline \(\hat{\alpha}^{\text{PIG}-ISE}\) & 1.964 & 1.134 & 0.976 & 0.948 & 2.411 & 1.156 & 0.996 & 0.968 & 2.492 & 1.171 & 1.011 & 1.009 \\ \hline \(\hat{\alpha}^{\text{PIG}-TPE}\) & 1.914 & 1.136 & 0.956 & 0.953 & 2.298 & 1.169 & 1.002 & 0.976 & 2.369 & 1.179 & 1.015 & 1.015 \\ \hline \(\hat{\alpha}^{\text{PIG}-KLE}\) & 1.628 & 1.024 & 0.956 & 0.906 & 1.913 & 1.105 & 0.972 & 0.940 & 1.992 & 1.120 & 1.003 & 0.990 \\ \hline \(\hat{\alpha}^{\text{PIG}-MKLE}\) & **1.549** & **0.952** & **0.935** & **0.902** & **1.784** & **1.008** & **0.945** & **0.928** & **1.897** & **1.699** & **0.971** & **0.950** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Estimated MSE values for \(q=4\).
# Surround by Sound: A Review of Spatial Audio Recording and Reproduction
## 2 Binaural Recording and Rendering  
Humans have only two ears to perceive sound in a 3D space. Hence, it is intuitive to use two locally separated microphones to record audio as it is heard; and when played back through headphones or a stereo dipole, a 3D sound sensation is created for the listener. This is known as binaural recording, a specific approach of the two-channel stereo recording, where two microphones are placed at two ears either on a human head (known as _listening subject recording_) or an artificial head (known as _dummy head recording_).  
The first binaural audio system was demonstrated in 1881 on a device called a Theatrophone introduced by a French engineer [PERSON]. However, there was not too much interest in this technology. It was not until 1974 that the first clip-in binaural microphones for a human subject was offered by Sennheiser and the first completely in-ear binaural microphones were offered by Sound Professionals in 1999. Nowadays, with the widespread availability of headphones and cheaper methods of recording, there has been a renewed interest in binaural spatial sound.  
The sounds received in two ears are scattered and shaped by the human head, torso and ear geometry, resulting in spatial cues for binaural hearing being made available. This is fully captured by the HRTF, a filter defined in the spatial frequency domain that describes sound propagation from a specific point to the listener's ear, and a pair of HRTFs for two ears include all of the localization cues, such as the inter-aural time difference (ITD), inter-aural level difference (ILD), and spectral cues [17].  
To generate spatial audio effects in binaural rendering, we can either use the recordings directly to simulate real objects or generate virtual objects by convolving a mono signal with the HRTFs corresponding to the virtual source positions. These are common practices used in Augmented Reality (AR) [3] and Virtual Reality (VR) [18].
## 3 Results and Discussion
### Compressive Strength
also increased the amount of C4A3\(\overline{5}\) found in the samples. However, unreacted C4A3\(\overline{5}\) was left over because of the \(w/b\), which was insufficient for C4A3\(\overline{5}\) to sufficiently hydrate. SF showed the effect of improving compressive strength in all the CSAe substitution mixtures considered. SF showed an improvement effect from the early age of 1d compressive strength. It is believed that this contributes to the strength shape by forming a dense matrix due to the filler effect. The effect of improving the mechanical performance of SF is evident as the substitution rate of SF increases from 5% to 10%. In Figure 2, it can be seen that the compressive strength of 1 d was low regardless of the CSAe substitution rate, but increased significantly after 3 d. Looking at the compressive strength ratio of 3 d to 1 d, the G90F0C10, G85F5C10, and G80F10C10 of the 10% CSAe sample were 325.1%, 320.0%, and 310.3%. G80F0C20, G75F5C20, and G70F10C20 were 311.8%, 275.3%, and 267.8%, and G70F0C30, F65F5C30, and G60F10C30 were 418.8%, 397.7%, and 444.9%. The increase rate of compressive strength from 1d to 3d was the highest. Therefore, when looking at the rate of strength increase, it can be considered that the hydration delay of slag affects up to 3 d. According to previous studies, CSA shows fast and high-strength values during its early ages. However, under the conditions of this study, the improvement in compressive strength of the early age of CSAe was delayed by about 3 d. This may have been related to the high slag mixing ratio. Slow slag hydration affects strength development. Therefore, the hydration reaction of slag in early age may have delayed the strength development. In future studies, additional methods or admixtures should be used to promote the early age hydration of slag. Table 3 shows the rate of change of the strength values of samples with and without SF. SF substitution showed the highest increase in compressive strength of 1 d in all CSAe substitution rates. It is believed that SF filling and nucleation promoting effects acted in the early hydration process. From Table 3, it can be seen that the compressive strength improvement effect is further improved as the replacement rate of SF increases from 5% to 10%.
## 5 Experimental Design  
### Feature Extraction  
Features are the embodiment of certain salient properties in a dataset that function as the key to distinguish data information, while feature extraction is the selection of representative features in the dataset. In the pre-processed wireless sensing data, the noise signal was processed, and the rest of the information containing features that reflected human action on the signal perturbation was extracted from the pre-processed data. To train the classifier, the extracted features were used to distinguish the data action belonging to the category. In Wi-Fi action recognition, the common feature extraction methods include statistical features, Doppler shift features, wavelet transform features, and time-frequency map-based features. Time-domain-based feature extraction was deployed in this study. The common time-domain features are listed in Table 1 (below):  
where \(s_{i}\) is the sampled data point amplitude of the signal, while \(N\) is the number of sampled data points per sample.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Time Domain Features** & **Calculation Method** \\ \hline Average & \(\overline{s}=\frac{1}{N}\sum_{i=1}^{N}s_{i}\) \\ \hline Standard deviation & \(\rho_{t}=\left(\frac{1}{N}\sum_{i=1}^{N}(s_{i}-\overline{s})^{2}\right)^{\frac{1 }{2}}\) \\ \hline Skewness & \(\frac{1}{N}\sum_{i=1}^{N}\frac{(s_{i}-\overline{s})^{3}}{\rho^{3}}\) \\ \hline Steepness & \(\frac{1}{N}\sum_{i=1}^{N}\frac{(s_{i}-\overline{s})^{4}}{\rho^{3}}\) \\ \hline Maximum & \(s_{\max}\) \\ \hline Minimum value & \(s_{\min}\) \\ \hline Peak & \(s_{\max}-s_{\min}\) \\ \hline Root mean square & \(RMS=(\frac{1}{N}\sum_{i=1}^{N}s_{i}^{2})^{\frac{1}{2}}\) \\ \hline Amplitude factor & \(s_{\max}/RMS\) \\ \hline Waveform Factor & \(RMS/\left(\frac{1}{N}\sum_{i=1}^{N}[s_{i}]\right)\) \\ \hline Impact Factor & \(s_{\max}/\left(\frac{1}{N}\sum_{i=1}^{N}[s_{i}]\right)\) \\ \hline Yield Factor & \(s_{\max}/\left(\frac{1}{N}\sum_{i=1}^{N}[s_{i}]\right)^{2}\) \\ \hline Energy & \(\sum_{i=1}^{N}s^{2}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Feature extraction formula.  
Figure 8: After smoothing treatment.
# A Study on the Vehicle Routing Planning Method for Fresh Food Distribution
## 3 Algorithm Design
### Combining GA with PSO to Solve Double-Layer Model
The function fitness \(F=\text{min}Z\). The overall algorithm steps are as follows (as shown in Figure 3). Step 1: Read the data and set the number of iterations to 1. Step 2: Encode to generate an initial population and transfer the initial location plan to the lower level model. Step 3: Solve the lower level model, allocate customer points based on the spatio-temporal distance and solve the vehicle routing problem for a single parking lot corresponding to the front warehouse. Step 4: Calculate the fitness value of the upper level model algorithm based on customer point allocation and corresponding vehicle path results. Step 5: Conduct selection, crossover and mutation of the upper level algorithm (location selection based on 0-1 coding), using the tournament selection method to perform single-point crossover and two-point mutation to obtain new offspring. Step 6: Repeat steps 3 to 5, solve the lower level model in sequence and calculate the fitness value of the function. Step 7: Increase the number of iterations by 1 and repeat the above steps until the algorithm reaches the termination condition. Output the optimal solution with the lowest total cost.
###### Abstract  
Source facilities can manage stormwater runoff pollutants while also limiting runoff volume in China's sponge city construction. However, there is no apparent link between source volume control and runoff pollution reduction. This research uses SWMM to develop a model based on the basic data of a city's catchment zone in northern China in order to statistically examine the response relationship between the initial runoff volume capture of sponge city construction and the reduction effect of stormwater runoff pollution. Based on the dimensionless constant of the runoff pollution control curve, we suggested a novel approach for evaluating runoff pollution control effectiveness. This method was used to examine the response relationship between runoff volume control and runoff pollution control in three typical initial runoff volume capture facilities in the study area, namely bioretention, permeable pavement, rain barrels, and terminal storage tanks, under various design rainfall conditions. The dimensionless constant (Dc) has good practical application value and can quantitatively quantify the control effect of various facilities on stormwater runoff pollution. The Dc value of the source facilities is higher than that of the terminal control facilities, indicating that the source facilities have a higher ability to reduce rainwater runoff pollution than the terminal facilities, particularly the infiltration facilities. The research ideas and evaluation methods proposed in this paper provide an alternative approach for selecting and optimizing facilities during the planning and designing stages of sponge city construction, which can also be used in the monitoring and evaluation stage after completion to carry out a more effective evaluation of sponge facilities' effects.  
Keywords:dimensionless constant of runoff pollution control (Dc); initial runoff volume capture; runoff pollution control; sponge city construction +
Footnote †: journal: Applied Sciences
## 5 Parametric Analysis of Coplanar Dual Cracks
### Evaluation of Crack Equivalent Rules  
Based on the crack propagation patterns obtained in the previous section, the combination rules of coplanar double cracks can be used to determine the equivalent crack size. This approach facilitates the prediction of the fatigue life of steel wires with multiple cracks. Using the crack size model shown in Figure 9, change the stress amplitude to 630 MPa and calculate the initial stress intensity factors for the double cracks and the equivalent crack, as shown in Figure 16a,b. Based on the information obtained from the figure, it is observed that the stress intensity factors at the crack front of the equivalent crack are greater than those of the dual cracks, both for cases without corrosion pits and with corrosion pits. Additionally, the stress intensity factor for cracks with corrosion pits is greater than that for cracks without corrosion pits. The relationship curve between crack size and fatigue life obtained through calculations is shown in Figure 16c. The calculation results of the equivalent crack tend to be conservative, which may be due to neglecting the separate development stages of the dual cracks. Figure 17 uses numerical simulations to compare the differences in fatigue life between the double-crack model and the equivalent crack model. The results indicate that the equivalent crack calculation method is relatively conservative. Using the equivalent crack model can simplify the calculation of the steel wire's fatigue behavior to a certain extent, and it is feasible to use this result to evaluate the fatigue life of the steel wire.  
Figure 15: Fatigue life under dual-crack interaction. (**a**) Double crack. (**b**) Double cracks with corrosion pits.  
Figure 16: Comparison between coplanar dual cracks and equivalent cracks. (**a**) Initial SIF for a crack without corrosion pit. (**b**) Initial SIF for a crack with corrosion pit. (**c**) Corrosion fatigue life curve.