esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
This study shows how sodium molybate is used to suppress corrosion, and steel weldments have a different corrosion behavior. This is validated not just through the outcomes of potentiodynamic polarization and hydrogen collection examinations but also via the detection of bubbles employing the YOLOv4 deep learning model.  
* With varying efficacy for each weldment zone, sodium molybate boosts the weldment's corrosion resistance in 1 M HCl solution. The oxide layer's stability is increased by the sodium molybate's mixed-type inhibition, which also increases the weldment's corrosion resistance.
* The weld metal's maximal inhibitory efficiency of the SM solution reached 59% with 0.4 g/L, the heat-affected zone at 52% with 0.2 g/L, and the base metal at 37% with 0.2 g/L of extract.
* The results of training the deep learning model using 3755 bubble images on the mAP50 reached 97.11%, showing that the minimum average bubble detected for the WM was 0.353 /mm\({}^{2}\) at an SM concentration of 0.4 g/L, while the HAZ was 0.612 /mm\({}^{2}\) at 0.2 g /L, and the BM was 1.055 /mm\({}^{2}\) at 0.2 g/L.
* The results from the three conducted tests--potentiodynamic polarization, hydrogen collection examinations, and bubble detection examinations (using deep learning)--display a consistent pattern regarding corrosion behavior. This suggests the potential application of deep learning techniques to analyze corrosion behavior in different scenarios.  
This investigation confirms the efficacy of sodium molybate as a corrosion inhibitor for steel weldments in acidic environments. Achieving optimal corrosion protection involves utilizing different inhibitor doses for distinct weldment zones. Employing machine learning techniques to analyze bubble generation on the weld surface allows for the identification of corrosion behavior.  
Conceptualization, F.A.A.; Methodology, F.A.A. and F.G.; Formal Analysis, F.A.A., C.-C.C. and F.G.; Investigation, F.A.A.; Data Curation, F.A.A. and F.G.; Writing--Original Draft, F.A.A.; Writing--Review and Editing, F.A.A., C.-C.C. and F.G.; Visualization, F.A.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The authors declare no conflicts of interest.
## 1 Introduction
In fact, in certain, specific domains, comparable detection accuracy can be achieved by these algorithms to that of two-stage algorithms. At present, a significant amount of research is conducted by many scholars on coal mine underground targets using target detection technology, leading to the achievement of certain research results [11; 12; 13]. However, targets such as miners, safety equipment, and coal blocks are mainly detected in the literature [11; 12; 13], and the drilling rig targets in the gas drainage environment are not studied. In view of this, later scholars conducted research on drilling rig targets. [PERSON] et al. [14] combined YOLOv5 with the DeepSort algorithm, and the detection speed of the drilling rig in the drilling field environment reached 64 FPS, but the accuracy is only 90.5%, and there is still room for improvement. [PERSON] et al. [15] used computer vision technology to achieve accurate counting of drill rods and calculated the corresponding drilling depth, and to track the drilling rig head to form a trajectory to quantify the number of drilling holes, but the detection speed is slow and does not meet the real-time requirements. Considering the aforementioned challenges, a novel rig detection algorithm YOLOv7-GFCA, is introduced for drill pipe counting. By capturing real-time location information of the drilling rig, the determination of the number of drill pipes is enabled, leading to accurate measurement of drilling depth and enhanced gas drainage efficiency. The key contributions of this research are summarized as follows:  
1. GhostNetV2 is used as the backbone network to reduce the amount of parameters brought about by redundant feature calculation and improve the detection speed;
2. The Lightweight FasterNet and the improved coordinate attention(CA) are integrated as a feature fusion network (FCA) to reduce the interference caused by coal dust and light;3. The Normalized Gaussian Wasserstein Distance(NWD) loss function is introduced to improve the positioning accuracy of the drilling rig target.
# Assessing the Capacity and Coverage of Satellite IoT for Developing Countries Using a CubeSat
## 3 Coverage and Capacity Evaluation
### Communication Time between the Satellite and GST(\(\tau\)(i))
#### 3.1.3 Link Budget Calculation  
The measured sensitivity is accounted to calculate the link budget in orbit, considering the satellite orbit as 400 km and FSPL at elevation angles of 10\({}^{\circ}\), 20\({}^{\circ}\), 30\({}^{\circ}\), 40\({}^{\circ}\), 60\({}^{\circ}\), 80\({}^{\circ}\) and 90\({}^{\circ}\). The transmit power of 20 mW is used since in most countries with this power, a license is not required to transmit with 433 MHz frequency an Industrial, Scientific, and Medical (ISM) band. The maximum permissible transmit power is determined by the radio communications regulation of each country. Using this sensitivity, the link budget is calculated considering the losses detailed in Table 4, both on the satellite side and the GSTs.  
Figure 12: Uplink success rate for receiver sensitivity of (a) 400 MHz and (b) 433 MHz.  
The link margins for varying elevation angles in the 400 km orbit are detailed in Table 5. The antenna gain variation of the GST for each elevation angle of the satellite is considered for the link margin calculations.  
According to the link margin calculations, a successful uplink of sensor data from the GST to the satellite can be achieved for elevation angles greater than 33 degrees. The communication time between the satellite and the GST can hence be determined for satellite passes with elevations greater than 33 degrees with Equation (3).  
\[\tau(i)\text{ in minutes}=10-\tau<33 \tag{3}\]  
\(\tau<33\) is the time in minutes when the elevations are less than 33 degrees. Satellites in LEO have an orbital period of approximately 90 min with an average of 10 min ground pass for at least four times in a day. To derive actual communication time between KITSUNE and the GSTs, the simulation for satellite orbital path is performed in Section 3.2.
## 3 Experimental Demonstration
### Experimental Setup  
Figure 2 gives the experimental setup of a 40 Gb/s non-return-to-zero (NRZ) system. The transmitter is comprised of a 40 Gb/s pattern generator that generates a length \(2^{31}-1\) pseudo random bit sequence, an external cavity laser source with 100 kHz linewidth. The 40 Gb/s LiNbO\({}_{3}\) Mach-Zehnder external modulator generates NRZ-OOK and NRZ-DPSK data by changing the bias point and the data voltage. The optical bandpass filter (Filter\({}_{5}\), 0.7 nm) is used to reduce amplified spontaneous emission (ASE) noise.  
The output spectrum of the QD-MLL is split by a \(1\times 2\) coupler, amplified and then filtered (Filter\({}_{1}\), Filter\({}_{2}\), 0.25 nm) to select two modes of the QD-MLL, forming pumps P1 and P2. ASE is suppressed by Filter\({}_{3}\) and Filter\({}_{4}\) (0.7 nm). The two pumps are polarization aligned with the 40 Gb/s modulated data and all three signals are injected into the XN-SOA using a \(14\) coupler.  
The XN-SOA used for FWM has a small signal gain of 33 dB, gain variation \(<\)1 dB with input polarization, and 15 dBm output saturation power at 550 mA bias current. At the output of the XN-SOA, the converted signal is selected using a manually tuned optical bandpass filter (Filter\({}_{6}\), 0.25 nm). The 3-dB bandwidth of the filter is narrower than the signal bandwidth, but as the filter has slow roll-off, performance is little impacted. Optical isolators are used to prevent reflections.  
Figure 2: Setup for wavelength conversion of 40 Gbit/s on off keying (OOK) and differential phase-shift keying (DPSK) signals.  
Our receiver is composed of an erbium doped fiber amplifier (EDFA) to amplify the selected signal, an optical bandpass filter to reduce ASE noise and a variable optical attenuator to adjust the received optical power. A delay-interferometer (DLI) converts the phase modulation into intensity modulation when using DPSK. A 1:4 electronic demultiplexing stage after photodetection is used to demultiplex the 40 Gb/s bit stream down to a rate compatible with our BER analyzer.  
We measure OSNR, conversion efficiency, and BER over a wide tuning range by fixing the input signal and first pump wavelengths, and sweeping the second pump over the 16 available wavelengths. We measure the conversion efficiencies for three values of input signal and first pump wavelengths while again sweeping the second pump over the 16 available wavelengths. This gives us the possibility to vary the pump detuning value from 0.8 nm to 12 nm for OOK, and 1.6 nm to 12.8 nm for DPSK.
# Low-Latency, Three-Phase PMU Algorithms: Review and Performance Comparison
## 3 Simulation Results
### Tests under Dynamic Conditions
#### 3.3.2 Frequency Ramp  
This test requires applying a three-phase positive sequence sinusoidal waveform whose frequency varies from 48 Hz to 52 Hz (between the instants \(t_{start}=3\) s and \(t_{end}=7\) s) with constant ROCOF equal to 1 Hs/s, starting from and reaching steady-state conditions. Performance is evaluated in terms of TVE, \(\mid\)FE\(\mid\) and \(\mid\)RFE\(\mid\), but measurements whose reporting instants are before \(t_{start}+2/f_{0}\) (thus two nominal cycles after the start of the ramp) or after \(t_{end}-2/f_{0}\) (that is two nominal cycles before the end of the ramp) are excluded from the analysis, as prescribed by [7]. Under this condition, the TVE values resulting from the algorithms IEC-P, SV-F and SV-IpDFT, which are not intrinsically based on a dynamic synchrophasor model, are significantly degraded with respect to off-nominal frequency conditions, as it can be noticed from Figure 20. On the contrary, the SV-TF method, whose filters are derived from a Taylor expansion of the synchrophasor, shows almost the same performance as that reported in Figure 5. In fact, the dynamics corresponds to a slowly-varying fundamental frequency which can be embedded by the TF synchrophasor model. The SV-TF method reaches the highest \(\mid\)FE\(\mid\) values, which are very close to those obtained under off-nominal frequency conditions (see Section 3.2.1); also the corresponding maximum \(\mid\)RFE\(\mid\) is not negligible (always \(<\)0.06 Hz/s), since the Taylor expansion truncation and the linearization used for ROCOF computation (see (37)) become more significant when higher-order derivatives are involved. It is interesting to notice that thanks to the normalization of filter \(R\) coefficients, SV-F has an ideal behaviour in the presence of constant ROCOF, and thus leading to virtually zero RFE in the presence of a frequency ramp. ROCOF measurement provided by the SV-IpDFT algorithm has a small spike (resulting in 0.025 Hz/s RFE) when frequency reaches 50 Hz. The reason is that the frequency estimation error changes sign exactly at the nominal frequency, and this affects the ROCOF measurement, which is obtained by numerical differentiation.  
Figure 19: Maximum \(\mid\)RFE\(\mid\) results under phase-angle modulations with modulation frequency \(f_{m}\).
## 3 Results
### Statistical Tests
#### 3.4.3 10-Fold Cross-Validation Paired \(t\)-Test
Evaluation under the ROC-AUC metric indicated that CB, XGB, and ANN each occupied the top tier without significant differences among them. These methods were uniformly more effective than RF, LR, and SVM in multiple comparisons. LGBM performed acceptably but lagged behind the leading group, suggesting that CB and XGB offered the strongest separation capacity. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **RF** & **ANN** & **LR** & **SVM** & **LGBM** & **CB** & **XGB** \\ \hline
**RF** & & PrC & arfc & c & C & APFC & APC \\ \hline
**ANN** & 3 & aprfc & pRc & pR & ARF & R \\ \hline
**LR** & 4 & 5 & & ARFC & ARFC & APRFC & APRFC \\ \hline
**SVM** & 1 & 3 & 4 & & rC & APRFC & PrC \\ \hline
**LGBM** & 1 & 2 & 4 & 2 & & APFC & PC \\ \hline
**CB** & 4 & 3 & 5 & 4 & 4 & & \\ \hline
**XGB** & 3 & 1 & 5 & 3 & 2 & & \\ \hline \hline \end{tabular}  
* A: Accuracy, P: Precision, R: Recall, F: F1-score, C: ROC-AUC. The colors represent the intensity of the statistically significant differences. Dark green indicates differences identified across all performance metrics (regardless of direction), while no color indicates differences identified on one or no metrics. The yellow diagonal separates the lower triangle (number of significant differences) from the upper triangle (metrics where significant differences is identified).  
\end{table} 
Table 8: Aggregate pairwise performance table for 10-fold CV paired \(t\)-test.
## 4 Conclusions  
In summary, a novel magnetic nanomaterial of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was prepared. And C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) was used as an adsorbent of Mag-dSPE and then combined with high-performance liquid chromatography. This method was used to detect 11 food additives in environmental water samples. Under the optimum conditions, the recovery rate of 11 food additives in environmental water samples was between 86.53 and 106.32%, and the LODs were below 3.1 ug/L. This study shows that this material can effectively enrich trace food additives from environmental water samples and has potential application value. However, C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) still needs improvement in some areas, such as the particle uniformity not being good enough. After that, the research direction can adjust the particle size of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) and prepare uniform nanoparticles. Then, it will be used as a substitute for silicon-based materials for the filling of chromatographic columns.  
**Supplementary Materials:** The following supporting information can be downloaded at [[https://www.mdpi.com/article/10.3390/molecules29153675/s1](https://www.mdpi.com/article/10.3390/molecules29153675/s1)]([https://www.mdpi.com/article/10.3390/molecules29153675/s1](https://www.mdpi.com/article/10.3390/molecules29153675/s1)), Figure S1: Nitrogen adsorption-desorption isotherms of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) (a), The corresponding pore size distribution curves of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) (b). Figure S2: Nitrogen adsorption-desorption isotherms of PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\). Figure S3: Reusability of C18\(-\)PS\(-\)DVB\(-\)Fe\({}_{3}\)O\({}_{4}\) for extraction of synthetic food additives (\(n=6\)), Table S1: Mobile phase gradient elution condition.  
**Author Contributions:** Writing--original draft preparation, C.L. and W.-X.L.; supervision, S.Z.; writing--review and editing, Y.-G.Z. and M.-L.Y.; funding acquisition, Y.-G.Z. All authors have read and agreed to the published version of the manuscript.  
**Funding:** We would like to thank the Zhejiang Provincial Key Research and Development Project (No. 2024C03231), Talents program of Zhejiang Shuren University (KXJ0522109). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Details are available from authors.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# WSN Nodes Placement Optimization Based on a Weighted Centroid Artificial Fish Swarm Algorithm
## 4 WCA-AFSA Performance Test  
Five benchmark functions, which include the Sphere function, Ackley function, Rastrigin function, Griewank function, and Rosenbrock function, are applied to verify the performance of the WCA-AFSA. These functions all have the features of multiple peaks and high dimension.  
(1) Sphere function:  
\[f_{1}(x)=\sum_{i=1}^{n}x_{i}^{2},\ x_{i}\in[-100,100],\ f_{\min}=0\]  
(2) [PERSON]'s function:  
\[f_{2}(x)=-20\exp(-0.2\sqrt{\frac{1}{n}\sum_{i=1}^{n}x_{i}^{2}})-\exp(\frac{1} {n}\sum_{i=1}^{n}\cos(2\pi x_{i}))+20+\exp(1),x_{i}\in[-32.768,32.768],f_{\min}=0\]  
(3) Generalized [PERSON]'s function:  
\[f_{3}(x)=\sum_{i=1}^{n}\left(x_{i}^{2}-10\cos(2\pi x_{i})+10\right),\ x_{i}\in[ -5.12,5.12],f_{\min}=0\]  
Figure 5: Flow chart of WCA-AFSA.  
(4) Generalized Griewank's function:  
\[f_{4}(x)=\sum_{i=1}^{n}\frac{x_{i}^{2}}{4000}-\prod_{i=1}^{n}\cos(\frac{x_{i}}{ \sqrt{i}})+1,\ x_{i}\in[-600,600],\ f_{\min}=0\]
* (5) Generalized Rosenbrock's function: \[f_{5}(x)=\sum_{i=1}^{n}\left[100(x_{i+1}-x_{i}^{2})^{2}+(1-x_{i})^{2}\right]\] \(f_{1}(x)\) and \(f_{2}(x)\) used for testing convergence speed and precision are single modal functions with single extreme values. \(f_{3}(x)\) and \(f_{4}(x)\) used for testing the global search ability are multiple modal functions with nonlinear multiple extreme values. \(f_{5}(x)\) is an abnormal function, which is applied for testing the algorithm performance.  
Set the size of the artificial fish swarm \(N=20\), initial _step_ = 0.5, and the number of maximum iterations \(N_{\max}=120\). The average value is listed in Table 1.  
From Table 1, the optimal actual average value by WCA-AFSA is smaller than that by AFSA. It demonstrates WCA-AFSA has higher convergence accuracy. The average time of WCA-AFSA is shorter than that by AFSA. It indicates WCA-AFSA is more effective. Figure 6 shows the comparison of the results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Function**} & \multicolumn{2}{c}{**Optimal**} & \multicolumn{2}{c}{**Optimal Actual Average Value**} & \multicolumn{2}{c}{**Average Time/s**} \\ \cline{3-6}  & **Theoretical Value** & \(f_{WCA-AFSA}\) & \(f_{AFSA}\) & \(T_{WCA-AFSA}\) & \(T_{AFSA}\) \\ \hline \(f_{1}(x)\) & 0 & 0.0035 & 0.0099 & 49.203655 & 50.245561 \\ \(f_{2}(x)\) & 0 & 0.0012 & 0.0040 & 48.945043 & 50.062352 \\ \(f_{3}(x)\) & 0 & 0.0427 & 0.1498 & 49.671079 & 50.784103 \\ \(f_{4}(x)\) & 0 & \(4.0878\times 10^{-5}\) & \(5.9343\times 110^{-5}\) & 49.660634 & 49.887099 \\ \(f_{5}(x)\) & 0 & 0.0052 & 0.0123 & 49.686631 & 50.670954 \\ \hline \end{tabular}
\end{table}
Table 1: Test results.  
Figure 6: _Cont._  
WCA-AFSA has better convergence precision and speed than AFSA. This means WCA-AFSA has better search ability.
# ILRS Reference Point Determination Using Close Range Photogrammetry
###### Abstract
Satellite laser ranging is a space geodetic technique organized under the International Laser Ranging Service (ILRS), which contributes to the origin and the scale of the GGRF. This investigation focuses on the analysis procedure of the ILRS reference point determination in a local frame. By convention, this reference point is defined by the orthogonal projection of the elevation axis onto the azimuth axis [12]. Since close range photogrammetry has been verified to be a suitable method to detect changes of the reflector geometries of VLBI radio telescopes with submillimeter accuracy [13; 14; 15], close range photogrammetry is transferred to the ILRS reference point determination of an SLR telescope, in this contribution. To the best of our knowledge, this is the first time close range photogrammetry has been used to determine the ILRS reference point. For that reason, the possible benefit w.r.t. the achievable accuracy and the measurement effort is investigated. Moreover, the influence of the stochastic model onto the estimates is studied by means of various dispersion matrices of the observations. Since most of the functional relations of the analysis process are nonlinear, we also address the bias of the estimates, which arises if the nonlinear function is replaced and is solved by its linear substitute problem. Section 2 concerns the mathematical approaches that are used for data analysis in Section 3. The bundle adjustment and the reference point determination model are described in detail in Sections 2.1 and 2.2, respectively. Moreover, the spherical simplex unscented transformation (SSUT) is introduced in Section 2.3. The SSUT is designed to efficiently obtain an approximated second-order accuracy for the estimates and allows investigation of the bias. A measurement campaign was carried out at the Geodetic Observatory Wettzell in September 2020. Section 3 describes the measurements and the analysis procedure. In Section 3.1, the chosen measurement instrument and the measurement configuration at the SLR telescope, the Satellite Observing System Wettzell, is presented. The data analysis and the obtained results are discussed in Section 3.2. Finally, Section 4 concludes this investigation.
# A Semantically Aware Multi-View 3D Reconstruction Method for Urban Applications
## 3 Results and Discussion
### SfM Based on Dynamic and Static Separation
In the color transitions from blue to green to red, blue indicates smaller errors and red indicates larger errors. The assignment of colors was based on relative error values. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Old Gate**} & \multicolumn{2}{c}{**Life Sciences Building**} \\ \hline  & **SfM** & **Sem-SfM** & **SfM** & **Sem-SfM** \\ \hline Alignment accuracy (RMS) & 0.088 & 0.15 & 0.12 & 0.11 \\ \hline Max. distance & 3.188 & 1.739 & 377.846 & 463 \\ \hline Average distance & 0.163856 & 0.09 & 0.4365 & 0.1995 \\ \hline Sigma & 0.300248 & 0.12 & 3.3472 & 3.146 \\ \hline Max. error & 0.0494042 & 0.048 & 1.611 & 2.07 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sparse reconstruction accuracy comparison. that remain stationary for short periods or move slowly within the scene are more likely to cause errors. We compared the number of landmarks generated by the semantic SfM and the original method. Among these, landmarks belonging to dynamic objects and those with inconsistent semantic attributes accounted for approximately 6.5% of the total. To intuitively demonstrate the positive value of adding semantic information into the feature matching process, we selected two images from the Tsinghua University's Old Gate dataset for detailed analysis and demonstration. The results are shown in Figure 9.  
Figure 9a shows the error of semantic inconsistency in feature point matching. These points may be matched together due to similar grayscale features, but they are actually mismatches. The result after semantic segmentation is no longer displayed in pixels but is divided into area blocks based on the essential attributes in the scene. This kind of high-dimensional information that goes beyond two dimensions is no longer affected by the gray value of a single pixel, so it is easy to find semantically  
Figure 9: Semantic-based feature matching: (**a**) semantically inconsistent mismatched point pairs; (**b**) feature point matching pairs of dynamic targets in the scene; and (**c**) feature point matching results after semantic optimization. inconsistent matching errors. Figure 9b shows the impact of dynamic targets on feature matching. It can be seen that slow-moving objects in the scene or people who are stationary for a short period of time are more likely to form false matches. These people or objects are not completely static, and this mismatch has a negative impact on the SfM. Since our data were not collected synchronously, this mismatch would be more likely to be encountered in adjacent frames. Figure 9c shows the matching results after eliminating the first two error-introduction items. It can be seen that although there are some individual mismatches, all feature point pairs matched, as a whole, are basically correct.