esa-sceva/satcom-chunk-collection huggingface dataset:
# A Robust Framework for Real-Time Iris Landmarks Detection Using Deep Learning
## 4 Results and Discussion
### Discussion  
The performances of all the models are good for different evaluation parameters such as MAE, Model Size, Response time, and mIoU (Mean Intersection over Union). The graph in Figure 6 shows that the performance of ResNet50 is better than other models in terms of Mean Absolute Error metrics. The response time of MobileNet version 2 is significantly less compared to the other models, as given in Table 3. The output size of MobileNet is more minimal than others. The most critical performance metric is the Mean Intersection Over Union (mIoU), which demonstrates that all the models' performances are good, but the MobileNet model performs outstandingly. After analyzing all the models on all performance metrics, we can say that the performance of MobileNet version 2 is exceptional over all the parameters as compared to ResNet50, VGG16, and VGG19.
# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 3 Experimental Results and Analysis
### Robustness  
A robust watermarking system must be able to counter attacks encountered in real-world scenarios. In this paper, our focus is on pre-processing attacks, which aim to manipulate the marked image to hinder watermark extraction. To enhance the robustness of our system against these attacks, a common strategy involves including pre-processed images in the training set. Due to limited computational resources, our study focuses on four common attacks: filtering, resizing, cropping, and flipping.  
In the filtering attack, an adversary with prior knowledge of the frequency domain may attempt to eliminate the embedded watermark by filtering out high-frequency components from the marked image. To simulate this attack, we filter out high-frequency components from marked images. Specifically, for each channel of a marked image, we calculate the FFT coefficients. We preserve the central region, spanning size \([128^{2},256^{2}]\), while zeroing out the other coefficients. For the resizing attack, we randomly alter the input images to smaller or larger resolutions within the range \([128^{2},512^{2}]\) and then restore these manipulated images to their original size to extract the watermark. In the cropping attack, we retain only certain pixels within the range \([224^{2},256^{2}]\), setting the rest to zero to mimic cropping. In the flipping attack, the marked image undergoes a horizontal or vertical inversion. It is important to note that, while our paper tests a limited number of common attacks, there is potential for further exploration, given sufficient computational resources.  
Figure 6 present examples of pre-processed images and the corresponding extracted watermarks. These examples illustrate that the embedded watermark can still be extracted with satisfactory quality despite various pre-processing operations. This underscores the effectiveness of adversarial training in enhancing robustness against pre-processing attacks.  
Figure 5: Frequency domain invisible performance (FP) of different methods on different tasks. Before—denotes the corresponding task, after—denotes the corresponding watermark. DE denotes “de-raining”, PT denotes “paint-transfer”. Watermarks include Baboon and IEEE [17; 19].  
is provided in Table 6, with a focus on resilience against pre-processing attacks and a watermark's imperceptibility in both spatial and frequency domains. Within Table 6, \"Yes\" denotes that the method exhibits robustness against the corresponding attack or achieves imperceptibility in the relevant domain, while \"Partially\" indicates that some processed images demonstrate good imperceptibility, while others may exhibit noticeable visual artifacts. Our analysis reveals that, in contrast to previous methods, which only partially satisfy these criteria, our proposed method successfully fulfills all the established objectives. This encompasses resistance against pre-processing attacks and ensuring that distortions caused by concealed watermarks remain imperceptible in both spatial and frequency domains.
## 3 Results and Analysis
### Effects of the Input Parameter on the Flatness Degree of the Weld Bead  
Figure 10a displays the effects of the wire feeding rate on the flatness degree of the weld bead surface. When the wire feeding rate is between 8.7 m/min and 9.9 m/min, the concave degree of the weld-bead surface increases with the increase of the wire feed rate. As the wire feed rate further increases, the concave degree of the weld-bead surface reduces significantly. This is mainly because the welding current has a direct correlation with the wire-feed rate and the electromagnetic force of the welding arc. Meanwhile, as the welding torch oscillates, more molten metal is squeezed into the groove and solidifies on both sides, making a concaved weld bead. As the wire feeding rate increases continuously, the welding height on the unit length increases continuously, and the concave degree of the weld-bead surface decreases.  
Figure 10b shows the influence of the welding speed rate on the concave degree of the weld-bead surface. It is observed that as the welding speed increases, the concave degree increases first and then decreases. In actual welding processes, the gravity force surges the molten metal to the front of the arc, so the volume of the molten metal behind the arc reduces significantly, and the molten pool solidifies with a concave shape. As the welding speed increases, the speed of the molten metal flow does not change as fast as the welding  
Figure 9: Effect of (**a**) Welding speed, (**b**) Oscillation width, and (**c,d**) welding position and Oscillation width on welding height.  
speed, so the volume of the solidified molten pool behind the arc increases, and the concave degree of the weld-bead surface improves.  
The influence of welding position \(\alpha\) on the flatness degree of the surface is especially apparent. As shown in Figure 10c, before 80\({}^{\circ}\), gravity promotes the weld pool to fit into the weld groove; with the increase of the welding position \(\alpha\), the gravity gradually makes the molten pool detach from the weld groove, which makes the weld bead surface change from concave to convex.
# Accuracy of Assessing Weight Status in Adults by Structured Observation
## 2 Materials and Methods
### Procedures for Data Collection
#### 2.3.1 Measures Estimated by Observation  
Height and weight were estimated in categories, as follows:  
* Height: (1) \(\leq\)144 cm, (2) 145-154 cm, (3) 155-164 cm, (4) 165-174 cm, (5) 175-184 cm, (6) \(\geq\)185 cm;
* Weight: (1) \(\leq\)44 kg, (2) 45-54 kg, (3) 55-64 kg, (4) 65-74 kg, (5) 75-84 kg, (6) 85-94 kg, (7) \(\geq\)95 kg.  
Due to the low percentage of participants in some categories, for data analysis, the variables were recoded into new categories, as follows:  
* Height: (1) \(\leq\)154 cm, (2) 155-164, (3) \(\geq\)165 cm;
* Weight: (1) \(\leq\)54 kg, (2) 55-74 kg, (3) \(\geq\)75 kg.  
BMI category estimates were given by the number of the figures of Stunkard's scale selected by the observer (from 1 to 9), each one identified with its corresponding BMI according to [PERSON] et al. [18], for females and males, respectively: Figure 1--18.3/19.8 kg/m\({}^{2}\); Figure 2--19.3/21.1 kg/m\({}^{2}\); Figure 3--20.9/22.2 kg/m\({}^{2}\); Figure 4--23.1/23.6 kg/m\({}^{2}\); Figure 5--26.2/25.8 kg/m\({}^{2}\); Figure 6--29.9/28.1 kg/m\({}^{2}\); Figure 7--34.3/31.5 kg/m\({}^{2}\); Figure 8--38.6/35.2 kg/m\({}^{2}\); Figure 9--45.4/41.5 kg/m\({}^{2}\). These were then classified according to the World Health Organization (WHO) cut-offs [18] into four classes: (1) underweight (BMI \(<\) 18.5 kg/m\({}^{2}\)); (2) normal weight (BMI 18.5-24.9 kg/m\({}^{2}\)); (3) overweight (BMI 25.0-29.9 kg/m\({}^{2}\)); and (4) obese (BMI \(\geq\) 30.0 kg/m\({}^{2}\)).
## 4 Classification of Energy Efficient MAC Protocols for UWSNs
### Carrier sense multiple access (CSMA)  
The CSMA protocol is an analogous class of random-access protocols in which all nodes first sense the channel's attributes before using it for communication [103]. This prevents the user from wasting the limited communication resources available in UWSNs. A brief description of the aforementioned technique can be found in [13]. Although RTS/CTS-based protocols such as CSMA have outperformed ALOHA protocols in terrestrial networks. Their efficiency in UWSNs could be quite low due to the significant propagation delay discussed in [104], CSMA based on RTS/CTS and procedure of sending and receiving data packets are depicted in Figures 3 and 4[104].
# S-Band Doppler Wave Radar System
###### Abstract  
In this paper, a novel shore-based S-band microwave Doppler coherent wave radar (Microwave Ocean Remote Sensor (MORSE)) is designed to improve wave measurements. Marine radars, which operate in the X band, have been widely used for ocean monitoring because of their low cost, small size and flexibility. However, because of the non-coherent measurements and strong absorption of X-band radio waves by rain, these radar systems suffer considerable performance loss in moist weather. Furthermore, frequent calibrations to modify the modulation transfer function are required. To overcome these shortcomings, MORSE, which operates in the S band, was developed by Wuhan University. Because of the coherent measurements of this sensor, it is able to measure the radial velocity of water particles via the Doppler effect. Then the relation between the velocity spectrum and wave height spectrum can be used to obtain the wave height spectra. Finally, wave parameters are estimated from the wave height spectra by the spectrum moment method. Comparisons between MORSE and Waverider MKIII are conducted in this study, and the results, including the non-directional wave height spectra, significant wave height and average wave period, are calculated and displayed. The correlation coefficient of the significant wave height is larger than 0.9, whereas that of the average wave period is approximately 0.4, demonstrating the effectiveness of MORSE for the continuous monitoring of ocean areas with high accuracy.  
S-band radar; MORSE; microwave ocean remote sensor; coherent method; wave measuring; significant wave height; directional wave height spectrum +
Footnote †: journal: Remote Sensing  
1
###### Abstract  
To verify the reliability of a typical Pb-free circuit board applied for space exploration, five circuits were put into low temperature and shock test. However, after the test, memories on all five circuits were out of function. To investigate the cause of the failure, a series of methods for failure analysis was carried out, including X-ray detection, cross-section analysis, Scanning Electron Microscope (SEM) analysis, and contrast test. Through failure analysis, the failure was located in the Pb-free (Sn-3.0 Ag-0.5 Cu) solder joint, and we confirmed that the failure occurred because of the low temperature and change of fracture characteristic of Sn-3.0 Ag-0.5 Cu (SAC305). A verification test was conducted to verify the failure mechanism. Through analyzing data and fracture surface morphology, the cause of failure was ascertained. At low temperature, the fracture characteristic of SAC305 changed from ductileness to brittleness. The crack occurred at solder joints because of stress loaded by shock test. When the crack reached a specific length, the failure occurred. The temperature of the material's characteristic change was \(-\)70\(-\)80 \({}^{\circ}\)C. It could be a reference for Pb-free circuit board use in a space environment.  
SAC305; BGA; low temperature; fracture failure
## 2 Related Work
By using this additional information, wDBFD improves RED's fairness. However, this scheme also increases the complexity. In CSFQ [4], an edge router has to maintain per-flow state and is in charge of state (flow arrive rate) insertion into packet headers. Whenever a core router receives a packet, CSFQ has to estimate the fair share rate and uses a simple probabilistic model to accept or discard the new arrival. CSFQ achieves reasonable fairness; moreover, it pushes complexity toward the edge routers, which simplifies the sophisticated implementation in the core routers. In general, the number of active flows in core routers is relatively larger than in edge routers. Therefore, CSFQ can be deployed in network environmentsconsisting of high-speed core routers and moderate-speed edge routers. The architecture of RFQ is similar to CSFQ, which mainly consists of packet coloring and buffer management [5]. RFQ transfers the flow arriving rate into a set of layers, with a globally consistent color per layer. Next, the edge routers insert the color into packet headers. When a packet arrives at a core router, the arrival will be discarded only if its color level is over the color threshold. The color threshold dynamically changes in accordance with traffic variations. Compared with CSFQ, RFQ has approximate fairness, but it only carries a simple color level rather than an explicit flow arriving rate. Furthermore, it wards off exponential averaging estimation when a packet is generated. In summary, both CSFQ and RFQ schemes classify the routers as edge or core routers, and only the edge routers maintain per-flow state. The fairness of RPQ tends to approach that of DRR, and it outperforms several schemes, such as CSFQ, DDE, CHOKe and FIFO [6]. In addition, RPQ has a complexity of O(1) and is simple to implement in high-speed networks. However, RPQ has an expensive implementation cost and a large queue delay. Currently, several TCP variants are adopted by end users, and heterogeneous congestion control schemes have thus become a characteristic of newly emerging networks. In contrast to pure TCP connections, the fairness of several well-known AQM schemes, such as RED and CHOKe, among heterogeneous TCP connections is discussed [19]. We do not consider the effect of TCP variants here, but it is an interesting topic as an extension of AFQ applications.
## 2 Materials and Methods
### FF-OCT System Configuration  
For the experiments, as shown in Figure 1, a halogen lamp (KI-100W, Korea Instech, Daegu, Korea), which had a center wavelength of 650 nm and a spectral bandwidth of 150 nm, was used as a broadband light source. Kohler illuminating optics were utilized to illuminate a sample with a spatially uniform light. The beam was divided by a beam splitter (BS), directed to the reference and sample arm, and then focused using a pair of microscope objective lenses (UMPLFL N 10 XW, 0.3 NA, Olympus, MO, Tokyo, Japan) placed on both arms. To match the interferometric visibility, a neutral density filter (NDF) was applied in the reference arm and, to match the optical path length changed by the NDF, a window glass (WG) was used in the sample arm. A series of interferograms were captured using a charge-coupled device (CCD-1020, VDS, CCD, Osnabruck, Germany). In addition, the position of the reference mirror (RM) was sinusoidally modulated with a piezoelectric transducer (P-841, PI, PZT, Karlsruhe, Germany). The CCD was operated at an acquisition frequency of 20 Hz and the PZT was driven at 5 Hz. The depth scan--to obtain a series of en face images along the depth direction of a sample--was conducted with 1 \(\upmu\)m steps using a high-precision linear motorized stage. The axial and lateral resolutions of the FF-OCT system were 1.5 and 2.7 \(\upmu\)m, respectively. To verify the proposed post-processing method, the smallest two letters of a Philippine one peso coin were imaged. A series of en face images of the coin were reconstructed for a 50 \(\upmu\)m depth with 1 \(\upmu\)m steps.
## 3 Double-Beam System Traversed by a Moving Force
### Verification of the Analytical Solutions in Section 3.1  
An example of the double-beam system with two identical beams (\(s_{\rm m}=1,\ s_{\rm k}=1\)) subjected to a moving force is used firstly to verify the analytical solutions in Section 3.1. To be concise, only the formulae for fixed-fixed and pinned-pinned boundary conditions are verified.
## 3 GNSS Data and Results
### Lefkada Island
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline \multirow{2}{*}{**Site**} & **Latitude** & **Longitude** & \multirow{2}{*}{**Period**} & **V\({}_{\text{East}}\)** & **V\({}_{\text{North}}\)** & **V\({}_{\text{Up}}\)** \\  & **(\({}^{\circ}\))** & **(\({}^{\circ}\))** & & **(mm/Year)** & **(mm/Year)** & **(mm/Year)** \\ \hline \multirow{2}{*}{AGRI} & \multirow{2}{*}{38.6240} & \multirow{2}{*}{21.4090} & 5 January 2011–12 February 2019 & \multirow{2}{*}{12.54 \(\pm\) 0.01 years} & \multirow{2}{*}{6.96 \(\pm\) 0.01 days} & \multirow{2}{*}{\(-\)0.30 \(\pm\) 0.04 days} \\  & & & 8.11 years & & & \\ \hline \multirow{2}{*}{KCTH} & \multirow{2}{*}{38.4116} & \multirow{2}{*}{21.2469} & 20 November 2013–31 October 2021 & \multirow{2}{*}{14.12 \(\pm\) 0.01 days} & \multirow{2}{*}{\(-\)3.94 \(\pm\) 0.01 days} & \multirow{2}{*}{\(-\)1.25 \(\pm\) 0.03 days} \\  & & & 7.95 years & & & \\ \hline \multirow{2}{*}{PAT0} & \multirow{2}{*}{38.2837} & \multirow{2}{*}{21.7868} & 27 January 2009–31 October 2021 & \multirow{2}{*}{7.20 \(\pm\) 0.01 days} & \multirow{2}{*}{\(-\)6.82 \(\pm\) 0.01 days} & \multirow{2}{*}{\(-\)0.46 \(\pm\) 0.02 days} \\  & & & 12.77 years & & & \\ \hline \multirow{2}{*}{RLSO} & \multirow{2}{*}{38.0558} & \multirow{2}{*}{21.4647} & 3 June 2011–26 February 2017 & \multirow{2}{*}{8.45 \(\pm\) 0.03 days} & \multirow{2}{*}{\(-\)7.53 \(\pm\) 0.03 days} & \multirow{2}{*}{0.34 \(\pm\) 0.07 days} \\  & & & 5.74 years & & & \\  & & & 1 March 2019–31 October 2021 & \multirow{2}{*}{10.03 \(\pm\) 0.08 days} & \multirow{2}{*}{\(-\)10.95 \(\pm\) 0.06 days} & \multirow{2}{*}{\(-\)2.27 \(\pm\) 0.16 days} \\  & & & 2.67 years & & & \\ \hline \multirow{6}{*}{PYRG} & \multirow{6}{*}{37.6788} & \multirow{6}{*}{21.4622} & 2 December 2013–25 October 2018 & \multirow{2}{*}{5.03 \(\pm\) 0.02 days} & \multirow{2}{*}{\(-\)12.51 \(\pm\) 0.02 days} & \multirow{2}{*}{\(-\)0.40 \(\pm\) 0.06 days} \\  & & & October 2018 & & & \\ \cline{1-1} \cline{5-5}  & & & 4.90 years & & & \\ \cline{1-1} \cline{5-5}  & & & 27 October 2018–12 July 2019 & \multirow{2}{*}{\(-\)6.47 \(\pm\) 0.29 days} & \multirow{2}{*}{\(-\)17.74 \(\pm\) 0.29 days} & \multirow{2}{*}{\(-\)0.91 \(\pm\) 1.03 days} \\  & & & 2019 & & & \\ \cline{1-1} \cline{5-5}  & & & 13 July 2019–31 October 2021 & \multirow{2}{*}{3.86 \(\pm\) 0.06 days} & \multirow{2}{*}{\(-\)13.79 \(\pm\) 0.08 days} & \multirow{2}{*}{\(-\)0.94 \(\pm\) 0.23 days} \\  & & & 2.30 years & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Velocities of GNSS stations in western Greece and the NW Peloponnese (ITRF 2014). series of the two stations showed an analogous motion to the pre-seismic period, but with slightly smaller horizontal amplitudes (Figure 8). Similar velocity vectors for the post-seismic period were also estimated for the EXAN and DRAN GNSS sites. Calculation of the baseline changes between SPAN and PONT sites after August 2016 shows that the shortening of the distance between these two sites remains, as was the case for the pre-seismic period, but with a slightly decreased amplitude (\(-1.81\,\pm\,0.05\,\mathrm{mm}/\mathrm{year}\)).
## 3 Results and Discussion
### Infrared Spectral Split Peak Fitting of Extracts
#### 3.2.3 Results of Peak Splitting Fits for Extracts in the 2800-3000 cm\({}^{-1}\) Interval  
Figure 4 shows the fitted IR peak splitting curves for the three solvent extracts in the 2800-3000 cm\({}^{-1}\) interval, and the corresponding fitted data results are shown in Table 4. The peaks in this interval are attributed to the stretching vibration of the aliphatic hydrocarbon CHx, and this dictates that six to eight peaks are appropriate for the deconvolution of this section of the spectrum. From the fitted parameters it is clear that both extracts are  
Figure 3: Infrared spectra of extracts in the 1000–1800 cm\({}^{-1}\) range. ((**a**) is the carbon disulphide fit, (**b**) is the acetone fit and (**c**) is the mixed solvent fit. The red line is the fitted curve and the blue line is the sub peak).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Peak \#**} & \multirow{2}{*}{**Position \(\sigma\) (cm\({}^{-1}\))**} & \multirow{2}{*}{**Assignment**} & \multicolumn{2}{c}{**Area Percentage (\%)**} \\ \cline{3-5}  & & & **ACE** & **CS\({}_{2}\)** & **ACE/CS\({}_{2}\)** \\ \hline
1 & 1600 & Aromatic C=C & 8.41 & 9.68 & 8.35 \\
2 & 1470 & CH\({}_{3}\)-,CH\({}_{2}\)- & 4.36 & 0 & 15.95 \\
3 & 1440 & Asymmetric -CH\({}_{3}\)-,CH\({}_{2}\) & 22.80 & 8.69 & 7.38 \\
4 & 1380 & Symmetric -CH\({}_{3}\)-,CH\({}_{2}\) & 6.10 & 28.27 & 13.73 \\
5 & 1320 & Ar-O-C & 18.56 & 24.65 & 9.05 \\
6 & 1150 & C-O-C & 17.47 & 5.99 & 33.85 \\
7 & 1110 & S=O & 0.68 & 5.32 & 1.23 \\
8 & 1050 & Si-O-C & 21.62 & 17.39 & 10.46 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Infrared spectra of three solvent extracts 1000–1800 cm\({}^{-1}\) split peak fitting ratio of each absorption peak area [14].  
dominated by aliphatic -CH except for the acetone extract, which is dominated by the symmetric -CH\({}_{2}\) stretching vibration.
## 2 Materials and Experimental Procedure
testing of notched specimens listed in Table 2. Similarly to the case of variable strain rate data, in the case of changing the notch size and radius, good agreement is observed between similar cases. Smooth specimens were the most ductile, and the fracture strain progressively reduced as the minimum specimen radius and notch radius decreased. Figure 2: Geometrical details of specimens for tensile tests at (**a**) smooth-type quasi-static and dynamic strain rates, (**b**) notched R0.4 type, (**c**) notched R20 type, (**d**) notched R50 type and (**e**) detailed representation of notched region; (**f**) typical manufactured specimen. Figure 4: Servo-hydraulic MTS 244.11 technical specifications. Figure 3: Experimental setup. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Specimen** & **Testing Speed** & **Strain Rate during** & **Socimen Type** & **Radius R** & **Minimum** & \\
**Nonenclature** & **(mm/s)** & **Tensile Test (s\({}^{-1}\))** & **Specimen Type** & **(mm)** & **Radius A (mm)** & \(\eta^{*}\) \\ \hline C1\_0.001\_NR & \(0.1\) & \(0.001\) & Smooth & - & - & 0.333 \\ C2\_0.001\_NR & \(0.1\) & \(0.001\) & Smooth & - & - & 0.333 \\ C3\_0.001\_NR & \(0.1\) & \(0.001\) & Smooth & - & - & 0.033 \\ D1\_0.5 NR & \(50\) & \(0.5\) & Smooth & - & - & 0.333 \\ D2\_0.5 NR & \(50\) & \(0.5\) & Smooth & - & - & 0.333 \\ D3\_0.5 NR & \(50\) & \(0.5\) & Smooth & - & - & 0.033 \\ D1\_1 NR & \(100\) & \(1\) & Smooth & - & - & 0.333 \\ D2\_1 NR & \(100\) & \(1\) & Smooth & - & - & 0.333 \\ D3\_1 NR & \(100\) & \(1\) & Smooth & - & - & 0.033 \\ D1\_2 NR & \(200\) & \(2\) & Smooth & - & - & 0.333 \\ D2\_2 NR & \(200\) & \(2\) & Smooth & - & - & 0.033 \\ C4\_0.001R20 & \(0.1\) & \(0.001\) & Notched & \(20\) & \(4.5\) & 0.484 \\ C5\_0.001R20 & \(0.1\) & \(0.001\) & Notched & \(20\) & \(4.48\) & 0.484 \\ C6\_0.001R50 & \(0.1\) & \(0.001\) & Notched & \(50\) & \(4.5\) & 0.395 \\ C7\_0.001R50 & \(0.1\) & \(0.001\) & Notched & \(50\) & \(4.48\) & 0.394 \\ C8\_0.001R0.4 & \(0.1\) & \(0.001\) & Notched & \(0.4\) & \(4\) & 2.867 \\ C9\_0.001R0.4 & \(0.1\) & \(0.001\) & Notched & \(0.4\) & \(3.98\) & 2.961 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Manufactured specimen specifications and testing parameters (C—static; D—dynamic _strain rate_notch radius). Figure 5: Detailed specimen fixture mechanism. Notable reduction in both ultimate stress and failure strain for the C7_0.001R0.4 and C8_0.001R0.4, was observed, resulting in a considerably brittle fracture behavior in comparison to the smooth cases.