esa-sceva/satcom-chunk-collection huggingface dataset:
# Positioning Using IRIDIUM Satellite Signals of Opportunity in Weak Signal Environment
## 5 Experimental Results
### Positioning Based on Iridium Satellite SOPs in Weak Signal Environment

Table 1: Error statistic results of positioning with the ring alert signal in weak signal environment. Figure 11: Sky plot of the first visible IRIDIUM NEXT satellite. The positioning performance can be improved with height aiding, especially in the east-direction. The position error mean in the east-direction can be 21 m. However, the fluctuation range of position error in the east-direction is still larger than for north-direction. This is caused by the special IRIDIUM orbital characteristic. As a result, the 2D position error RMS in this case is 198 m. The positioning results presented above are based on the ring alert signal. Then, the receiver position can still be obtained after performing the same step for the primer message signal. The statistical positioning results of height aiding are shown in Table 3.  
The 2D position error RMS in this case is 163 m. It can be seen that the positioning error mean value of north-direction in Table 3 is close to the corresponding error mean in Table 2. However, the error mean value of north-direction in Tables 2 and 3 is different. On the other hand, the positioning error RMS values calculated with the primer message signal are more optimistic than for the ring alert signal. This is because the power of the ring alert signal is generally stronger than for the primer message signal. As a result, the Doppler-shift measurement accuracy of the primer message signal is better than for the ring alert signal, and the fluctuation of positioning error is smaller. Considering the Doppler-shift measurement accuracy for different satellites and different moments is different, the Kalman filtering (KF) is used to process the least squares (LS) solutions from different Doppler measurement combinations. As a result, the position estimations calculated with the two channel signals are both about 110 m away from the real location. The positioning errors in east-west direction are more optimistic than for south-north direction in two cases. The common features of positioning error are caused by the satellite orbital error.
# Rainfall Standard of Disaster Prediction for Agricultural Droughs in S. Korea
## 2 Materials and Methods
### Damage from Agricultural Droughts
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Region Index**} & \multicolumn{1}{c}{**Municipality**} & \multicolumn{1}{c}{**Population**} & \multicolumn{1}{c}{**Area**} \\ \hline
**Number** & **Acronyms** & & **(Thousand People)** & **(km\({}^{2}\))** \\ \hline
42 & GW & Gangwon-do & 1540 & 16,902.2 \\
41 & GG & Gyeonggi-do & 13,240 & 10,381.1 \\
48 & GN & Gyeongsangnum-do & 3360 & 11,815.8 \\
47 & GB & Gyeongsangbuk-do & 2670 & 19,128.7 \\
29 & GJ & Gwangju Metropolitan City & 1460 & 501.2 \\
27 & DG & Daegu Metropolitan City & 2440 & 883.5 \\
30 & DJ & Daejeon Metropolitan City & 1470 & 539.9 \\
26 & BS & Busan Metropolitan City & 3410 & 993.5 \\
11 & SE & Seoul & 9730 & 605.6 \\
36 & SJ & Sejong Metropolitan Autonomous City & 340 & 465.5 \\
31 & US & Ulsan Metropolitan City & 1150 & 1144.6 \\
28 & IC & Incheon Metropolitan City & 2960 & 1156.4 \\
46 & JN & Jeollanam-do & 1870 & 15,434.2 \\
45 & JB & Jeollabuk-do & 1820 & 8131.3 \\
50 & JJ & Jeju Province & 670 & 2051.3 \\
44 & CN & Chungcheongnum-do & 2120 & 8744.1 \\
43 & CB & Chungcheongbuk-do & 1600 & 7406.9 \\ \hline  & \multicolumn{1}{c}{Sum} & \multicolumn{1}{c}{51,850} & \multicolumn{1}{c}{106,285.8} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: State of study area. Figure 2: Damage from agricultural droughts by municipality (1965–2018). \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multicolumn{2}{c}{**Agricultural Drought**} & \multirow{2}{*}{**Damage Area (ha)**} & \multirow{2}{*}{**Region Index of Drought Damage**} \\  & **Damage Month Period** & & & \\ \cline{2-4}  & **Start** & & & \\ \hline
1965 & 06 & 09 & 89,134 & JN, GG, GN \\
1966 & 06 & 09 & 44,857 & JN, GN \\
1967 & 06 & 09 & 420,488 & JN, GN, JB, CN, JJ \\
1968 & 06 & 09 & 470,423 & JN, JB, GB, GN, CN, JN, GG \\
1969 & 06 & 09 & 5977 & GB, JN \\
1970 & 06 & 09 & 6015 & JN \\
1971 & 06 & 09 & 12,774 & JN, JJ \\
1972 & 06 & 09 & 13,545 & JN \\
1973 & 06 & 09 & 48,493 & GB, GN \\
1975 & 06 & 09 & 37,401 & GN, GB, CN, CB, GG \\
1976 & 06 & 09 & 28,218 & GN, GB, CN, JN, CB \\
1977 & 06 & 09 & 60,246 & SE, BS, GG, JN, CB, CN, JB, JN, GB, \\
1978 & 05 & 06 & 18,563 & SE, GG, JN, CB, CN \\
1981 & 05 & 07 & 145,457 & CB, CN, JB, JN, GB, GN \\
1982 & 05 & 07 & 231,244 & CB, CN, JB, JN, GB, GN \\
1994 & 07 & 09 & 253,803 & CB, CN, JB, JN, GB, GN, JJ, DG, GJ, DJ \\
1995 & 07 & 08 & 20,370 & CB, CN, JB, JN, GN \\
2013 & 07 & 08 & 7626 & JN, GB, GN, JJ \\
2015 & 06 & 06 & 7358 & IC, GG, JN, CB, GB \\
2016 & 07 & 08 & 39,826 & CN, JN, JB, GB \\
2017 & 05 & 07 & 9549 & JN, GN \\
2018 & 07 & 08 & 22,507 & JB, JN, GB, GN, CB, CN, GG, JN, IC, \\  & & & & \\ \hline Count & & 22 & 1,993,874 & 108 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Damage from agricultural droughts by year.
## 1 Introduction
By exploiting the dense topology of LEO satellites and jointly considering the angular domain and power domain, the cooperative beam association and power allocation scheme for multiple LEO satellites serving the same coverage area is adopted. * A multiple LEO satellite to multiple ground beam cell association problem is formulated, and a many-to-many match game-based beam association (MGBA) algorithm is proposed for the spectrum sharing between the GEO and UD-LSC networks. Different from the non-cooperative game scheme in [12], we set the preference function of both LEO satellites and all beam cells as the sum satisfaction of beams to avoid selfish power allocation results. Then, with the matched beam association results, a multi-satellite power allocation optimization problem is formulated with non-convex interference constraints, and a successive convex approximation (SCA)-based power allocation (SPA) algorithm is proposed to maximize the satisfaction of beams. * Simulations and discussions for the spectrum sharing between the GEO and UD-LSC networks are presented in this paper. Simulation results show that the proposed MGBA-SPA scheme can provide better satisfaction results compared with other schemes and achieve the balance between the traffic requests and provided capacity for all beam cells. The rest of this paper is organized as follows: Section 2 describes the coexistence model of the GEO satellite systems and the UD-LSC network. Then, the signal model and the interference model are given. The joint beam association and power allocation problem are also formulated. In Section 3, the many-to-many match game-based beam association algorithm and the successive convex approximation-based power allocation algorithm are proposed. Section 4 presents and analyzes the simulation results. Section 5 concludes this paper.
# Fish Detection and Classification for Automatic Sorting System with an Optimized YOLO Algorithm
## 4 Conclusions  
This work aims to propose an optimal approach for detecting and classifying fish intended for the aquaculture industry, especially for making automatic sorting processes. In this work, we created and presented a real video dataset of freshwater fish running on a conveyor, which is the first and only, as far as the authors know. The dataset includes eight types of freshwater fish that are grown and eaten most often in Thailand and nearby countries. Some of these fish are native to Thailand. This work uses YOLOv4, the most viral algorithm for object detection, and a relatively new version. Several studies were conducted to determine the level of accuracy, and finally, an approach was proposed.  
This approach utilizes YOLOv4, optimized with a combination/custom labeling technique, and extracted images as training data. From the test results on the video of eight types of freshwater fish running on a conveyor with a total duration of 25 min and 37 s at a speed of 505.08 m/h, the model could produce an accuracy of 98.15%. These results are considered quite good and can even be improved in the future. By using real videos of freshwater fish running on a conveyor, this work is expected to contribute to the development of fish detection and classification, especially for the automatic sorting process in the fish industry, which is very close to real conditions.  
Conceptualization, A.K. and T.S.; methodology, W.T. and N.N.; software, A.K. and G.S.; validation, W.T. and G.S.; formal analysis, A.K. and N.N.; investigation, W.T. and G.S.; resources, T.S. and N.N.; data curation, A.K. and T.S.; writing--original draft preparation, A.K.; writing--review and editing, A.K., T.S., W.T., N.N. and G.S.; visualization, A.K. and N.N.N.; supervision, W.T. and G.S.; project administration, W.T.; funding acquisition, T.S. All authors have read and agreed to the published version of the manuscript.  
[PERSON]'s Institute of Technology Ladkrabang Research Fund: KREF206314.  
Not applicable.  
The dataset created and the results videos can be found at: [[https://drive.google.com/drive/folders/10](https://drive.google.com/drive/folders/10) rEUIYdiDWbvDyDV46 WSrreGYd83 OTuw7 usp=sharing]([https://drive.google.com/drive/folders/10](https://drive.google.com/drive/folders/10) rEUIYdiDWbvDyDV46 WSrreGYd83 OTuw7 usp=sharing) (accessed on 1 March 2023). The python codes and repository are shared at: [[https://github.com/[PERSON]/Fish-Recognition-for-Auto-Sorting--Feature-Extraction](https://github.com/[PERSON]/Fish-Recognition-for-Auto-Sorting--Feature-Extraction)]([https://github.com/[PERSON]/Fish-Recognition-for-Auto-Sorting--Feature-Extraction](https://github.com/[PERSON]/Fish-Recognition-for-Auto-Sorting--Feature-Extraction)) (accessed on 1 March 2023).  
We want to thank AMI (Advanced Manufacturing Innovation)-KMITL, Bangkok, Thailand, for granting the CiRA-Core software license, and declare that this work was fully supported by [PERSON]'s Institute of Technology Ladkrabang (KMITL).  
The authors declare no conflict of interest.
## 1 Introduction
prepared and identified 17 peptides from the macroalgal species _Palmaria palmata_, and SDITRPGGNM showed the highest oxygen radical absorbance capacity and ferric reducing antioxidant power activity with values of 152.43 \(\pm\) 2.73 and 21.23 \(\pm\) 0.90 nmol TE/umol peptide, respectively [16]. MCLDSCLL (P1) and HPLDSLCL (P2) showed potent antioxidant activities against DPPH\(\bullet\) and 2,2\({}^{\prime}\)-azinobis-(3-ethylbenzothazoline-6-sulfonic acid) (ABTS) radical (ABTS\({}^{+}\bullet\)) and inhibiting copper-catalyzed human low-density lipoprotein (LDL) oxidation [21]. GAERP, GEREANVM, and AEVG from cartilage protein hydrolysate of spotless smoothhound exhibited good scavenging activities on DPPH\(\bullet\), HO\(\bullet\), ABTS\({}^{+}\bullet\), and O\({}_{2}^{-}\bullet\). Furthermore, GAERP, GEREANVM, and AEVG could protect H\({}_{2}\)O\({}_{2}\)-induced HepG2 cells from oxidative stress by decreasing the content of malonaldehyde (MDA) and increasing the levels of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and glutathione reductase (GSH-Rx) [12]. These studies indicated that seafood-derived peptides had strong antioxidant activity and could be served as ingredients in functional food and food systems to protect food quality by reducing oxidative stress. Hairtail (_Trichiurus japonicus_) belongs to cutlassfish family of Trichiuridae and is found throughout tropical and temperate waters worldwide. In China, hairtail is one of the four major aquatic products and wildly distributed in the Yellow Sea, the Bo Hai Sea, and the East China Sea. In our previous research, hairtail hydrolyses chelation with iron (Fe-FPH chelate) had higher hemoglobin regeneration efficiency (HRE), longer exhaustive swimming time, and higher SOD activity. Additionally, Fe-FPH chelate was found to significantly decrease levels the MDA content, visibly enhance the GSH-Px activity in liver and reduce blood lactic acid of rats [22; 23]. Therefore, the aim of this work was to (i) optimize the two-step sequential enzymolysis technology; (ii) isolate and identify the antioxidant peptides; and (iii) evaluate the activities of isolated peptides form protein hydrolysate of hairtail muscle in vitro.
# Acoustic Emission Activity for Characterizing Fracture of Marble under Bending
## 3 Results and Discussion
### Behavior of Repaired Specimens
This lower activity (and the longer plateaus in the beginning of the test) can be attributed to the lower elasticity of epoxy. The stiffness of this layer is much lower than marble, allowing it to deform without cracking for longer period than marble. Therefore, it withstands the imposed deflection with limited AE. Additionally it has been seen that the type of epoxy may influence this behavior. In recent experiments in concrete repaired by flexible healing agent, the AE development was smooth since the bonding took place in small and frequent events. On the other hand, rigid epoxy follows a slightly different trend by fewer but larger debonding events [24]. Excluding the specimens that exhibited random behavior (13 of type MA and two of type MB) one can again separate the AE behavior in the successive stages. Results are included in Table 2 above. It is  
Figure 10: (a) Cumulative AE history for repaired MA specimens following three stages. (b) Cumulative AE history for repaired MA specimens without similar three stages. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**MA**} & **Maximum** & \multirow{2}{*}{**UPV * (m/s)**} & **Maximum** & \multirow{2}{*}{**\(n\) = Load(REP)/Load (-)**} \\  & **Load (kN)** & & **Load(REP) (kN)** & \\ \hline no stages in AE & 2.24 & 4713 & 1.46 & 0.66 \\
3 stages in AE & 2.33 & 4846 & 1.75 & 0.76 \\ total & 2.30 & 4798 & 1.63 & 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the bending tests for MA type of repaired marble. evident that the parameter values are much closer between the different marble types for each stage. For the first stage received at low load, average frequencies are almost identical and RA values are very close. It should be recalled that in the first loading, marble MB exhibited 39% higher frequency, while the RA value of B was 58% lower than MA (see Table 1). Now these differences are nearly eliminated; frequencies are essentially the same and the average RA of MB is only 23% lower than MA. Differences in rise time and duration are similarly compressed. The AE parameters show indeed that in the repaired specimens, the importance lies primarily in the bonding between epoxy matrix and less in the marble itself. The difference of 23% in RA although may seem substantial, does not allow successful separation of the two classes, due to the overlap. Indeed in AE, the experimental scatter of the parameter values is high and a difference between averages of the order of 20% of even 50% is not enough to separate different populations [25]. However, it can still be seen that type MA with higher RA value in the preliminary stage of loading exhibited lower maximum load. Therefore, there is certainly potential in the study of AE in the repaired specimens as to predict the final behavior. It should be kept in mind that it is possible to apply pattern recognition combining not only two but many features which offer the highest characterization capacity or even apply principal component analysis.
## 3 Results
### Thinning
In 2009, two years after thinning, measured TER was predominantly higher throughout the year during easterly winds. However, PnET-simulated monthly TER hardly differed for 0% and 30% thinning and was most similar to measured fluxes from the west sector (Figure 10a). In contrast, PSIM-simulated TER showed a clear _reduction_ for 30% thinning, compared with 0% thinning between January and November 2009, and higher simulated TER values after June than those measured from the west (Figure 10b). In 2012, 5 years after the thinning, measured TER values were similar in the western and eastern sectors, except that the summer peak in the eastern sector lagged behind that of the western sector by a month. PnET-simulated TER values were substantially lower than measured values throughout the year and, unlike in 2009, simulated thinning decreased TER slightly between May and October 2012 (Figure 10c). As in 2009, PSIM-simulated TER was reduced by the 30% thinning, particularly in summer months (Figure 10d). For the above comparison of simulated and measured data, model efficiency (ME) values were consistently >0; coefficient of determination (CD) values were >1 in all PSIM simulations except 0% thinning in 2009 and were <1 in all PnET simulations (Table 7). PSIM produced better simulated monthly results, giving slightly higher CD values and similar or lower RMSE than PnET, except for the 2009 simulations with 0% thinning. However,  
Figure 10: Monthly TER at the Straits Inclosure for 2009 (**a,b**) and 2012 (**c,d**). Measured EC TER data are separated into data originating from the eastern (thinned) sector and western (unthinned) sector [45]. Equivalent simulated TER data are shown for PnET (**a,c**) and PSIM (**b,d**) with 0% and 30% thinning after separating results depending on the daily wind direction. for both years, the unthinned model output gave a better match to the western sector measured data than the thinned model output did to the eastern sector measured data.
# Harmonic Detection Method Based on Parameter Optimization VMD-IWT Combined Noise Reduction
## 4 Simulation Signal Verification
### Noise Reduction Effect Analysis  
The signal-to-noise ratio (SNR) and root-mean-square error (RMSE) are chosen as the evaluation metrics for noise reduction. SNR quantifies the ratio of the overall signal to the noise components, with higher values indicating superior noise reduction efficacy. The RMSE, on the other hand, measures the average magnitude of the errors between detected and actual values, a smaller RMSE indicates a higher accuracy or precision of the detection. The calculation formulas for SNR and RMSE are as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}\sum\limits_{i=1}^{N}(x_{i}-y_{i})^{2}} \tag{15}\]  
\[\text{SNR}=10\times\lg\left(\frac{\sum\limits_{i=1}^{N}x_{i}^{2}}{\sum \limits_{i=1}^{N}\left(x_{i}-y_{i}\right)^{2}}\right) \tag{16}\]  
where \(N\) represents the data length, \(x\) denotes the signal after noise reduction, and \(y\) represents the original signal.  
In order to evaluate the noise reduction effect of the proposed method, the WT (Soft), WT (Hard), EMD-WT (Soft), VMD-WT (Soft), and the RIME-VMD-IWT proposed in this paper are used to reduce noise in simulation signals. The VMD parameter combination (\(K\),\(A\)) is set to (4, 2000), while the parameter combination (\(K\),\(A\)) optimized by RIME is (6, 1834). As can be seen from Table 2, for simulated signals, the noise reduction effect of the wavelet soft threshold is slightly better than that of the hard threshold, and all noise reduction evaluations indexes of the RIME-VMD-IWT proposed in this paper are optimal. The noise reduction effect of RIME-VMD-IWT (Soft) is 22.36% higher in terms of SNR compared to VMD-WT (SOFT) and the other four methods. RMSE decreased by 14.93%. The root-mean-square error of the denoising method is the smallest, indicating that the deviation of the component after denoising is smaller. A larger SNR indicates that more effective signals are extracted. Figure 7 shows a comparison of the waveforms obtained by different noise reduction methods. It can be seen that EMD-WT (Soft) and VMD-WT (Soft) have a certain effect on noise removal, but the signal waveform after noise reduction has a poor fitting effect on the original sequence. However, the waveform after noise reduction using the method proposed in this paper is closer to the simulation signal, and the curve fitting degree is higher. Most of the peak noise signal is removed, and the end  
Figure 6: Correlation coefficient of IMF.  
effect and mode aliasing problems that occur during EMD denoising are avoided. This makes the waveform smoother and more continuous while retaining most of the features of the original signal. Therefore, the simulation signal experiment results demonstrate that the proposed RIME-VMD-IWT method has a better noise processing effect and strong noise reduction ability.
## 4 Conclusions  
A waveguide structure for temperature buffering was successfully designed to solve the problem that ordinary piezoelectric ultrasonic testing is not suitable for the high-temperature environment. Therefore, the paper designed a waveguide rod, determined its material, length and thickness with thermal simulations, and validated the feasibility with an experiment. It shows that SS316L is a good choice as the waveguide rod, and an optimum size is given to ensure the ultrasonic testing even at a high temperature of 500 deg C. The length is 300 mm, width 20 mm, and thickness 1 mm.  
Ultrasonic propagation through the designed waveguide rod and specimen was calculated. Propagation, reflection, attenuation, and dissipation of the ultrasonic wave were revealed in the whole process. It shows that the closer to the center, the clearer the ultrasound signal, and serious ultrasound signal attenuation and dispersion occur as it gradually moves far away from the center. Therefore, the signal receiving point should be located at the centerline of the waveguide rod.  
An ultrasonic-guided wave testing device was developed based on the designed waveguide structure. It measured the thickness of two specimens at different temperatures. It shows the detected thickness basically increases linearly with the temperature if the wave  
Figure 13: Predicted thickness at different temperatures.  
velocity is regarded as a constant. It implies the measurement error also increases with temperature, which already reaches 6.9% and 5.0% at only 250 deg C for the two specimens. A temperature-dependent method was applied to achieve high precision detection at high temperatures, in which the ultrasonic wave velocity was related with temperature.  
Conceptualization, Methodology, Writing--review and editing, Project administration, Y.L.; Software, Writing--original draft preparation, Visualization, X.L.; Validation, R.L.; Data curation, Z.Y.; Investigation, Resources, J.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant number 52076216 and 51706246.  
Not applicable.  
Data Availability Statement: Data available on request from the authors.  
The authors declare no conflict of interest.
# _remote sensing_
## 3 Related Data and Correction Models in Cal/Val of Altimeters
### Mass
#### 3.4.1 Campaigns of Sea Surface Measurements
The standard deviations were 2.34 cm, 2.26 cm, and 2.39 cm, respectively (Figure 7). This indicates good measurement results and fully demonstrates the changes in the local sea surface. The measured sea surface was used for the Cal/Val of HY-2B altimeter data.