esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Lifelong Learning Optimization Algorithm in Dense Region Fusion
## 4 Results and Analysis
### Horizontal Comparison Experiment with Other Algorithms
#### 4.3.2 Comparison between DLLO-DRF and Other Algorithms in the Cement Pole Damage Dataset  
As shown in Table 12, DLLO-DRF ranks first for three or more metrics among seven detection algorithms. For the \(AP\) metric, DLLO-DRF ranks first among eight detection algorithms. For the \(AP_{50}\) metric, DLLO-DRF ranks first among eight detection algorithms. For the \(AP_{75}\) metric, DLLO-DRF ranks first among three detection algorithms. For the \(AR\) metric, DLLO-DRF ranks first among eight detection algorithms. In contrast, for Uniform soup, there are two, five, and two algorithms ranking first for the \(AP\), \(AP_{75}\), and \(AR\) metrics, respectively, and for Greedy soup, there are one and two algorithms ranking first for the \(AP\), \(AP_{50}\), and \(AR\) metrics, respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Object Algorithm** & **Fusion Method** & \(AP\) & \(AP_{50}\) & \(AP_{75}\) & \(AR\) \\ \hline \multirow{3}{*}{ATSS} & Uniform soup & 25.7 & 63.5 & 11.6 & 37.5 \\  & Greedy soup & 26 & 64.4 & 12.3 & 37.5 \\  & DLLO-DRF & **26.5** & **66** & **12.5** & **39.8** \\ \hline \multirow{3}{*}{DDOD} & Uniform soup & 28 & 64.6 & 14.6 & 40.7 \\  & Greedy soup & 29.3 & 65.8 & 16.3 & 41.5 \\  & DLLO-DRF & **30.1** & **67.2** & **16.6** & **42.4** \\ \hline \multirow{3}{*}{GFL} & Uniform soup & 17.3 & 39.9 & 10.8 & 35.4 \\  & Greedy soup & 17.8 & 41.6 & 10.5 & 36 \\  & DLLO-DRF & **19.3** & **46.9** & **11.6** & **36.6** \\ \hline \multirow{3}{*}{Cascade R-CNN} & Uniform soup & 28.9 & 67.9 & 11.9 & 36.6 \\  & Greedy soup & 29.3 & 68.2 & 13.1 & 37 \\  & DLLO-DRF & **30.2** & **70.1** & **14.4** & **37.6** \\ \hline \multirow{3}{*}{Faster R-CNN} & Uniform soup & 27.9 & 71.3 & 12.3 & 36 \\  & Greedy soup & 28.6 & 71.9 & 11.9 & 36.9 \\  & DLLO-DRF & **31.1** & **73** & **14.2** & **38.1** \\ \hline \multirow{3}{*}{Dynamic R-CNN} & Uniform soup & 26.6 & 66.5 & **12.1** & 36.8 \\  & Greedy soup & 26.9 & 66.5 & 10.7 & **36.9** \\  & DLLO-DRF & **27.7** & **68.8** & 11.5 & 36.5 \\ \hline \multirow{3}{*}{Grid R-CNN} & Uniform soup & 28.8 & 64.5 & 17.5 & 36.4 \\  & Greedy soup & 28.5 & 64.5 & 14.2 & 37.6 \\  & DLLO-DRF & **29.8** & **65.2** & **23.3** & **38.4** \\ \hline \multirow{3}{*}{Libra R-CNN} & Uniform soup & 25.7 & 62.3 & 11.5 & 35.4 \\  & Greedy soup & **26.7** & **63.8** & 11.7 & 36.6 \\  & DLLO-DRF & 26.6 & 62.9 & **13.2** & **37.2** \\ \hline \multirow{3}{*}{Sparse R-CNN} & Uniform soup & 25.1 & **67** & 9.5 & 39.7 \\  & Greedy soup & 24.1 & 66.1 & 6.9 & 38.9 \\ \cline{1-1}  & DLLO-DRF & **25.4** & 65.2 & **11.5** & **40.9** \\ \hline \hline \end{tabular}
\end{table}
Table 11: Comparison of DLLO-DRF and other fusion methods in the bird’s nest foreign object dataset. The better results are indicated using bold and underline.
## 2 Seawater Channel Modelling Elements
### UOWC System Layout
#### 2.1.1 Optical Transmitter Unit  
The optical transmitter (Tx) unit, as depicted in Figure 3A, includes the information digital generator; the digital modulator, which controls the modulation format and the transmission baud rate; the optical pulse generator (laser diode (LD) or light-emitting diode (LED)); and the optical coupler unit, which is used as a beam expander and/or culmination. The output of the Tx is a stabilised optical power that carries digital information. The emitted optical pulse's temporal and spatial profile characteristics are the basis of defining the transmitted optical pulse shape (\(h_{p}(t)\)). In some of the literature, \(h_{p}(t)\) is termed the optical source shape. \(h_{p}(t)\) could take a Gaussian, rectangular, or Lambertian profile, depending on the device type (LD or LED) used for the optical source.
# Hybrid Filter Based on Fuzzy Techniques for Mixed Noise Reduction in Color Images
## 4 Conclusions  
A new technique for the reduction of a mixed combination of Gaussian-impulse noise has been introduced. The mixed noise was suppressed by applying first a switching method based on a fuzzy metric intended for the suppression of impulses, and then a fuzzy average filtering developed for the attenuation of Gaussian noise. A comparative study with state-of-the-art methodologies was carried out using the objective qualitative measures MAE, PSNR, and NCD. The experiments revealed that the new filtering technique exhibits very good filtering properties, outperforming state-of-the-art filtering solutions in terms of MAE, PSNR, and NCD. Although the consecutive utilization of two distinct filters increases the computational load, the proposed filter has a structure which makes it adequate for parallel computing. Future work will involve the development and implementation of parallel methods based on the proposed filter. Since parallel techniques offer great computational efficiency, real-time processing can be achieved.  
**Author Contributions:** Supervision, J.A.; writing--original draft preparation, J.A. and L.S.; conceptualization, J.A. and L.S.; funding acquisition, J.A.; methodology, L.S. and J.A.; writing--review and editing, J.A.; software, L.S.; validation, J.A.; investigation, J.A. and L.S.; resources, J.A. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was funded by the Spanish Ministry of Science, Innovation, and Universities (grant RTI2018-098156-B-C54) and it was co-financed with FEDER funds.  
**Acknowledgments:** The authors would like to thank the editor and reviewers for their comments and suggestions, which helped to improve the quality of the paper significantly.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Follow-Up Development Plan  
Due to the prevalent cloudy and rainy conditions in Hainan Province, the applicability of the launched satellites (HN-1 01/02 and WC-1 01/02) is limited in this region. The remaining six satellites of the HSC, including both hyperspectral (SY-1 01/02) and SAR (SS-1 01/02) satellites, will be developed during the second stage to achieve high-frequency coverage observations.  
In particular, the two SY-1 hyperspectral satellites will be equipped with large-width and high-resolution hyperspectral cameras with push-scan imaging and staring video working modes. This imaging procedure is applicable for Earth observation purposes and can provide detailed spectral information for key applications in the fields of agriculture, marine and forestry resources.  
The two SS-1 SAR satellites are planned to integrate Earth observations and ship AIS acquisition functions, in particular under adverse weather conditions. They will be equipped with C-band SAR with multiple imaging modes, such as sliding, bunching strip, and scanning. The two SAR satellites should help overcome the lack of remote sensing data caused by clouds and rain, while also facilitating the dynamic monitoring of ship, marine, and forestry resources.
## 7 Net Axial Stress
### Net Bending Stress
When Equation (12a-g) are applied to channel section B, the bending stresses at each perimeter point are  
\((d^{4}/a)(\sigma_{z1}/FL)=(1/0.026395\times 7/8)+(1/0.0053615\times 11/15)=169.9\), \(|\sigma_{z1}=1105.65\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z2}/FL)=(1/0.026395\times 7/8)=33.1502\), \(|\sigma_{z2}=215.69\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z3}/FL)=(1/0.026395\times 7/8)-(1/0.0053615\times 4/15)=16.587\), \(|\sigma_{z3}=-107.92\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z4}/FL)=-(1/0.0053615\times 4/15)=-49.74\), \(|\sigma_{z4}=-323.6\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z5}/FL)=-(1/0.026395\times 7/8)-(1/0.0053615\times 4/15)=-82.888\)\(|\sigma_{z5}=-539.31\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z6}/FL)=-(1/0.026395\times 7/8)=-33.105\), \(|\sigma_{z6}=-215.69\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z7}/FL)=-(1/0.026395\times 7/8)+(1/0.0053615\times 11/15)=103.63\), \(|\sigma_{z7}=674.27\times 10^{-3}F\)  
where again it can be seen from a similar distribution to Figure 8 that with \(F\) = 100 N, these stress levels are elastic. The increase in this section's thickness dimension compensates for the greater length in maintaining acceptable stress levels under these loads (\(F_{x}\) = \(F_{y}\) = 100 N). Channel C:  
\(d/a=3\), \(X^{\prime}/a=1/5\), \(k_{x}=0.006251\), \(k_{y}=0.0004334\), \(L\) = 340 mm and \(d\) = 47.625 mm, \(a\) = 15.875 mm. When Equation (12a-g) are applied to channel section C, the bending stresses at each perimeter point become  
\((d^{4}/a)(\sigma_{z1}/FL)=(1/0.006251\times 3/2)+(1/0.0004334\times 4/5)=2085.83\), \(|\sigma_{z1}=2188.4\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z2}/FL)=(1/0.006251\times 3/2)=239.96\), \(|\sigma_{z2}=251.76\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z3}/FL)=(1/0.006251\times 3/2)-(1/0.0004343\times 1/5)=-221.51\), \(|\sigma_{z3}=-232.4\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z4}/FL)=-(1/0.0004343\times 1/5)=-461.47\), \(|\sigma_{z4}=-484.16\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z5}/FL)=-(1/0.006251\times 3/2)-(1/0.0004343\times 1/5)=-701.43\), \(|\sigma_{z5}=-735.92\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z6}/FL)=-(1/0.006251\times 3/2)=-239.96\), \(|\sigma_{z6}=-251.76\times 10^{-3}F\)  
\((d^{4}/a)(\sigma_{z7}/FL)=-(1/0.006251\times 3/2)+(1/0.0004334\times 4/5)=1605.91\), \(|\sigma_{z7}=1684.9\times 10^{-3}F\)  
Again, with \(F\) = 100 N, the stress levels in Figure 8 are linear elastic. However, it should be noted here that these are not net axial stress values. The latter follow from the addition of axial stress arising from the fixing constraint to be considered next. Only then can the safe loading be assessed from the net shear and net axial stress combination. Figure 8: Net bending stress distribution for channel section.
# Microwave and Ultrasound Based Methods in Sludge Treatment: A Review
## 4 Microwave Irradiation
### Microwave-Alkaline Combined Treatment  
Although microwave irradiation can be used for several purposes on its own in wastewater and sludge treatment, numerous studies have shown that to increase its efficiency, MW technology should be combined with chemical co-treatments, such as the use of alkalies. It was concluded in several papers that the effects of microwave and alkaline treatment are additive [42; 43; 44]; [PERSON] et al. for example found that MW-alkaline combination in the treatment of sludge resulted in the increase of solubilization rate by 20% in contrast to the standalone treatments. [PERSON] et al. found that by the combined alkaline-MW treatment of sludge, the disintegration degree (DD) reached 65.9% (at 38,400 kJ/kg TS and pH 11) and the anaerobic batch experiment showed that the combined pre-treatment significantly improved volatile fatty acids accumulation and shortened its time requirement [45]. [PERSON] et al. concluded that the maximum solubilization ratio (85.1%) of volatile suspended solids in sludge could be achieved at MW heating to 210 \({}^{\circ}\)C with 0.2 g NaOH/g SS [46]. They also investigated the cumulative methane production (CMP) during thermophilic anaerobic digestion and found that the optimal settings of combined pre-treatments in terms of CMP were MW heating to 170 \({}^{\circ}\)C and 0.05 g NaOH/g SS, with 1 min of holding time. In a study by [PERSON] and [PERSON], it was shown that with the combination of MW and alkatic treatment, the overall increase in biogas production exceeded 228% [47]. [PERSON] and [PERSON] found that the SCOD/TCOD ratio could be increased by combining microwave irradiation with alkaline treatment up to 0.37, however, the level of pH plays a key role in the entire process, with pH 12.5 being the optimal one [43]. [PERSON] et al. also investigated the effects of different pH (ranging from pH 2-12) on the change in SCOD/TCOD ratio and found that at any given total irradiated microwave energy level (70 kJ/L, 150 kJ/L, 230 kJ/L) a pH of 12.0 resulted in the highest SCOD/TCOD ratio (0.33) [48]. [PERSON] et al. came to a similar conclusion; when combining microwave treatment with alkaline, increasing the alkaline dosage and microwave energy intensity, DD could be enhanced up to a certain point (about 50%) [49].  
Although several types of alkatic compounds can be used in wastewater and sludge treatment (such as NaOH, KOH, Ca(OH)\({}_{2}\)), their effects on the efficiency of the whole process can be different. In 2012 [PERSON] and [PERSON] concluded that in terms of the rate of sludge solubilization, by using NaOH 52.5% solubilization could be achieved, while with KOH this rate was shown to be 4% less, 48.5% [50].
# Application of Terminal Audio Mixing in Multi-Bandwidth End-to-End Encrypted Voice Conference +
## 2 Homomorphic Encryption
### Big Integer-Based Mixing Method  
In the next step, the homomorphic encryption algorithm is employed. To ensure that the audio plaintext length matches the maximum length for encryption and achieves maximum encryption efficiency, we concatenated each sample of the audio track as follows.
# IVUS Image Segmentation Using Superpixel-Wise Fuzzy Clustering and Level Set Evolution
## 5 Discussion
We carried out several tests and chose the values \(\mu=0.2\), \(\beta=1\), and \(\
u=\pm 0.03\), yielding acceptable results for both borders. The highlight of the SFCME-LSE method lies in its effective utilization of pathological structures and simple implementation. Impediments in IVUS image segmentation are utilized to provide topological constraints on the boundary. Compared to the time-consuming extraction of features and training of classifiers [3; 4; 5; 6; 7; 8], unsupervised clustering with post-assignment is fast and requires no labeled data. The iterative update of indicators \(D_{g}\), \(D_{a}\), and \(D_{s}\) can quickly mine valuable information in the image. Moreover, the results in Section 4 indicate the adaptability of the proposed framework to different datasets. Inevitably, there exist several limitations in the proposed method. The inaccuracy in the detection of the MAB derives from the loss of contour information in the shadow area. Segments obtained from radial linear interpolation may not coincide with the ground truth well, as shown in Figure 11 1. Hypoechoic or echoless soft plaques may contain large amounts of lipids and necrotic tissue and appear in the IVUS image as areas of low intensity. Some soft plaques are significantly similar to the lumen, causing the segmentation error of the LIB, as depicted in Figure 11 2. In the mentioned circumstances, the clustering and LSE techniques cannot accurately locate the boundary with indistinguishable intensities and textures on both sides. Future studies can be conducted to solve these problems by deep learning architectures or optical coherence tomography (OCT)-guided IVUS image segmentation, if there are enough annotated data.
# Quantitative Characterization of Complex Systems--An Information Theoretic Approach
## 3 Understanding Entropy--An Overview
It is to be noted that information theory helps to precisely establish a measure of information defined in terms of probabilities based on some given evidence [13]. Several attempts in the literature are observed on the use of entropy as a tool in understanding uncertainty in domain specific complex systems. To name a few, examples include: in the biological domain, the use of entropy for predicting diverse states of kidney failure disease considering the effects of hypertension and stress [14], and assessing causal relationships between pairs in gene regulatory networks [15]; in image segmentation, a generalized application of entropy in continuous and discrete variables [16]; analysis of financial markets by analyzing the relationship between nine different stock indices from U.S, Europe, and China using entropy measures [17]; detecting fatigue cracks in aluminum alloys [18]; fabric defect detection [19]; and, more interestingly, the quantification of information in cosmological probes of large scale structures [20]. A lack of a unifying approach for characterizing complex systems across domains justifies the framework used to characterize complex systems based on the concept of information entropy, underlying the assumption that entropy of a complex system is a function of its components and their interactions.
# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 3 Problem Formulation and the Approach
### Revisiting FairMOT
#### 3.1.3 Object-Embedding Branch  
The branch aims to generate features that can distinguish objects. Ideally, the distance between different objects in different frames should be larger than the distance between the same objects in different frames. In this way, we can make good use of appearance features to distinguish different objects. In the experiment, we apply a convolution layer with 128 kernels based on the backbone features to extract identity-embedding features for each location. The resulting feature map is \(E\in R^{128\times W\times H}\). The Re-ID feature \(E_{x,y}\in R^{128}\) of an object at (\(x\), \(y\)) is extracted from the feature map.
## Appendix A Region-Based Classification Performance Comparisons  
We performed an extensive classification analysis to determine the effectiveness of using the wavelet features compared to other feature extraction approaches. Since theperformed classification results include too many values, detailed information about the results is listed here.  
Table 14 presents the ML performance comparisons of the regions in 1 s and 2 s segmentations. Among the features, wavelet features performed better in the regions for the metrics AUC, accuracy, and precision with the 1 s segmentation with the EC-data. With the 2 s segmentation, we found that the wavelet features performed higher in the regions. All regions performed better with the wavelet features for accuracy except for the temporal region. It also showed slightly better performance compared to the EEG frequency band features with SVM, KNN, and LR.  
Table 15 presents the ML performance comparisons of the regions in 1 s and 2 s segmentation with the EO-data. With EC- and EO-data, we found that wavelet features performed better in all the regions for the metrics (AUC, accuracy, and precision) in both 1 s and 2 s segmentation. Please note that the results of the F1 scores are included in the main paper.
## 5 Discussion  
### Global Deployment Optimization  
In many areas of the 3-HC deployment (see Figure 8), the coverage multiplicity is equal to nine, which means nine GSs are visible to the satellite at the same time, when it is positioned in the corresponding area. The concentration of GSs in these areas is due to moving the GSs which were not on land, particularly those in oceans. The positions of the GSs are optimized according to the maximum coverage. However, where they are not applicable due to being in oceans and seas, the GSs must be moved to the nearest land. As seen in Figure 6, there is an obvious concentration of GSs in the Pacific region, the part of the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Deployment** & **Parameters** & **Mean SPDOP** & **Max SPDOP** & **Min SPDOP** \\ \hline
15 GSs & GSs only & 2.058 & 3.143 & 1.516 \\  & GSs and ISLs & 0.439 & 0.453 & 0.435 \\
20 GSs & GSs only & 1.403 & 2.063 & 1.077 \\  & GSs and ISLs & 0.422 & 0.437 & 0.409 \\
25 GSs & GSs only & 1.330 & 1.945 & 1.011 \\  & GSs and ISLs & 0.409 & 0.425 & 0.396 \\ \hline \hline \end{tabular}
\end{table}
Table 7: SPDOP statistics for 14 days of observation.  
Figure 13: The mean SPDOP of 15 LEO satellites with optimal deployment for 14 days of observation of 15 GSs with ISLs.  
Earth most occupied by seas. This concentration creates a high level of coverage multiplicity compared to other regions. Table 9 shows the observing rate of 3-HC deployment with simple, double, triple and quadruple coverage. In the optimal deployment of 3-HC, the best obtained observing rate is 97.37%, which means that all satellites at any epoch are visible by at least two GSs, while 97.37% of the observing duration (24 h) means each satellite is visible by at least three GSs.  
For this deployment, quadruple-heavy observing coverage (4-HC) was achieved 92.01% of the time. Hence, within 92.01% of the observing duration, each satellite is visible by at least four GSs. With threefold coverage of GSs for each satellite at each epoch, the SPDOP value could be determined if only the satellite position was considered for the observation equation.
## 3 Results
### Simulation  
Figure 4a shows the simulated transmission spectra when the dielectric constant of the substrate was varied from 1.5 to 3. This range of dielectric constants covers most typical glass substrates. When the dielectric constant of the substrate varies from 1.5 to 3, the position of the first peak (\(Z=0.7\)) stays nearly unchanged, while the second peak (\(Z=0.85\)) undergoes a slight blue-shift (15 nm).
# A Fault Analysis Method Based on Association Rule Mining for Distribution Terminal Unit
## 2 DTU Fault Analysis
### Data Analysis of DTU Fault  
DTU fault data usually have the same format as Table 3.  
Each data item contains the time, device ID, device brand \(B\), fault type \(T\), and fault elimination measure \(F/M\). In which, fault type \(T\) refers to six types as shown in Table 1. Fault elimination approach contains information pertaining to the DTU fault module \(M\) (Figure 2) or factors \(F\) (Table 2). For instance, fault elimination approaches can be 'wireless signal issues and solved by restarting the communication module'; in this case, factor \(F\) is 'wireless signal quality' and the fault module is 'communication module'.  
According to DTU fault data, brand \(B\), fault type \(T\), factors \(F\), and fault module \(M\) are key elements. Therefore, item set \(FA\) containing these four elements was constructed as (1) to facilitate fault analysis:  
\[FA= \{B,\ T,\ F,\ M\} \tag{1}\]  
in which, there are \(p\) brand \(B\), \(q\) types of fault \(T\), \(i\) factors \(F\), and \(j\) fault modules as shown in Equations (2)-(5).  
\[B= \{B_{1},B_{2}\ldots,B_{p}\} \tag{2}\] \[T= \{T_{1},T_{2}\ldots,T_{q}\}\] (3) \[F= \{F_{1},F_{2}\ldots,F_{i}\}\] (4) \[M= \{M_{1},M_{2}\ldots,M_{j}\} \tag{5}\]