esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Analysis of Photoluminescence Spectra  
[PERSON] et al. [26] performed low-temperature (10 K) PL and absorption measurements on several InP\({}_{1-x}\)Sb\({}_{x}\) epilayers grown by OMVPE using InP, InAs, and InSb substrates. Large miscibility gaps were predicted [26] from x = 0.02 to 0.97 from the second derivative of [PERSON]'s free energy [32]. We have estimated the energy bandgaps E\({}^{\rm PL}\) at 13 K on GS-MBE-grown InP\({}_{1-x}\)Sb\({}_{x}\)/InAs samples having different compositions x by using a 532 nm DP-SSL excitation source. In Table 3, the results of our PL study are included (see Figure 6) for 0.10 \(\leq\) x \(\leq\) 0.48 using solid black inverted triangles, green-filled circles, and light-blue-filled squares. Comparison is made with the absorption (open blue circles) and PL data (red open square) of [PERSON] et al. [26]. Again, the x-dependent energy bandgaps (Figure 6, magenta solid line) of ternary alloys E\({}^{\rm InP}_{\rm g}(\mathrm{x})\) are calculated by adopting a second-order polynomial equation [18]:  
\[\mathrm{E}^{\rm InP}_{\rm g}(\mathrm{x})=\mathrm{E}^{\rm InP}_{\rm g}(1- \mathrm{x})+\mathrm{E}^{\rm InSb}_{\rm g}\mathrm{x}-\mathrm{C}_{\rm T}\mathrm{ x}(1-\mathrm{x}), \tag{17}\]  
where E\({}^{\rm InP}_{\rm g}\), E\({}^{\rm InSb}_{\rm g}\) are the direct bandgaps of the bulk InP, InSb materials, and the term C\({}_{\rm T}\) (\(\equiv\)1.9) is used as a bowing parameter.  
The perusal of Figure 6 has revealed two E\({}^{\rm PL}\) energy phases (small red bracket) with values displayed by black inverted triangles, green solid spheres, and a nearly constant value of InAs (substrate) shown by sky-blue-colored squares. For x \(\geq\) 0.30, our PL study has provided only one E\({}^{\rm PL}\) energy phase (large red bracket) indicated by black triangles. Earlier studies by [PERSON] et al. [26] on InP\({}_{1-x}\)Sb\({}_{x}\) epilayers have suggested large miscibility gaps between x = 0.02 and 0.97. Our results indicated that high-quality single-phase epilayers can be obtained around x \(\sim\) 0.3 in the center of the miscibility gap. The GS-MBE-grown epitaxial layer with composition x = 0.31 is lattice-matched to the InAs substrate. The strain energy caused by lattice mismatch has locally changed the range of miscibility, making the growth of a single crystal possible. Obviously, the OMVPE results cannot be fully applied to GS-MBE-prepared InP\({}_{1-x}\)Sb\({}_{x}\)/n-InAs samples, as the growth situation may differ from equilibrium conditions. Moreover, our MDF simulation for InP\({}_{0.67}\)Sb\({}_{0.33}\) alloy provided the lowest energy gap E\({}_{0}\) \(\sim\) 0.46 eV, in good agreement with the existing first-principles calculation and the PL measurements.
# Global GNSS-RO Electron Density in the Lower Ionosphere
## 2 Data and Methods
### \(N_{e}\) Retrievals
#### 2.3.4 TEC and Vertical Gradient of \(hTEC(h_{t})\)
In summary, the \(TEC\) retrieval method demonstrated in Figure 7c is practically useful for the high-rate RO data with the top above 220 km. On the other hand, the method in Figure 7d is applicable to the Spire near-real-time (NRT) data when the top of RO profiles reaches \(h_{t}\sim 175\) km. For accurate \(TEC\) retrievals, these high-\(h_{t}\) RO observations (close to the \(F2\) peak) are more desirable. Thus, it is recommended in future GNSS-RO operations to extend the RO acquisition up to at least \(h_{t}=220\) km.
# Study on Classification Method of Soil Liquefaction Potential Based on Decision Tree
## 6 Criteria Table of Liquefaction Potential Grade
### Organized Results in Tabular Form  
By integrating the results of the above two trees, the criteria for the liquefaction potential were obtained, as shown in Table 6. These criteria divided the liquefaction potential into four grades, and the liquefaction probabilities from low to high were 1 (low), 2 (medium-low), 3 (medium-high), and 4 (high).  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline \(a_{\text{max}}\) (\(g\)) & (0, 120) & (120, 140) & (140, 160) & (160, 180) & (180, 200) & (200, 316) \\ \hline (0, 0.075) & 2 & 2 & 2 & 1 & 1 & 1 \\ (0.075, 0.18) & 3 & 3 & 2 & 1 & 1 & 1 \\ (0.18, 0.37) & 4 & 3 & 2 & 2 & 2 & 1 \\ (0.37, 0.4) & 4 & 3 & 3 & 3 & 2 & 1 \\ (0.4, 0.76) & 4 & 4 & 4 & 3 & 2 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Criteria table of liquefaction potential grade.  
Figure 5: Tree for shear wave velocity liquefaction potential for medium grade (20% \(<\)\(P_{L}\)\(<\) 80%).  
Considering the test error of the shear wave velocity, the shear wave velocity values in the first column were rounded to the nearest multiple of ten; that is, 142 m/s was rounded to 140 m/s and 182 m/s was rounded to 180 m/s. Since 317 m/s was the maximum shear wave velocity in the sample set, it was not processed as the upper limit value. The sites with shear wave velocities greater than 317 m/s were assessed as having low liquefaction potentials. After this sorting, the final criterion table of the liquefaction potential grades was obtained, as shown in Table 6.  
In the process of using the above liquefaction potential criteria, the shear wave velocity of saturated soil and the site ground motion intensity were used. For example, if the predicted ground motion intensity was 0.1 g and the shear wave velocity of the liquefiable layer was 130 m/s, then the liquefaction potential was identified as grade 1; that is, the liquefaction probability was in the range of 0-20%, and the liquefaction potential was low.  
In the liquefaction potential criteria, the variation trends of the \(V_{s}\) of the saturated soil layer and \(a_{\text{max}}\) of the sites for different liquefaction grades were intuitive; that is, with the increase in \(V_{s}\) or the increase in \(a_{\text{max}}\), the liquefaction potential became stronger. This is consistent with the current understanding of liquefaction.  
The judgment criteria of the liquefaction potential were applied to the data in the fitting database, and the performance was evaluated based on the accuracy of the liquefaction probability ranges of 0-20%, 20-50%, 50-80%, and 80-100%. The confusion matrix was obtained, as shown in Table 7.  
The accuracy was between 60% and 93% at the four grades of liquefaction. After statistical analysis, it was found that 74% of the misjudgment cases were sites with low liquefaction potential grades that were predicted to be the sites with high liquefaction potential grades.
## 1 Introduction
* Investments in network wiring are skewed towards backbone cables and the center of a network, with little attention paid to the needs of users at the edge of a network. In the international arena, although 90% of buildings in a city are within 1.5 km from a backbone cable, most of them cannot be connected, not only because of the high cost of USD 100,000-200,000 per kilometer of fiber optic cable laying costs but also due to the fact that many cities, in order to avoid traffic congestion, air pollution, tree transplantation, and the destruction of historical sites, have been gradually restricting the digging of trench laying for fiber optic cables so as to make it more end-user-oriented. The last kilometer is quite difficult, so FSO is undoubtedly an important technical choice to solve this contradiction. Since the 1970s, the U.S. Air Force Research Laboratory (AFRL) has been exploring airborne laser communication technology. The AFRL has successfully developed several laser communication terminals while confirming the feasibility of airborne laser communication. In addition, the Jet Propulsion Laboratory (JPL) [1], Lincoln Laboratory, German Aerospace Agency (DLR), and U.S. Naval Laboratory (NRL) have conducted in-depth research on exploring UAV-borne laser communication technology. With the growing maturity of UAV optical communication technology, expanding its application areas and scope is becoming increasingly important. Within this domain, long-distance and high-speed transmission technology has been receiving increasing attention. Compared with space and ground platforms, UAV platforms have the characteristics of a high moving speed,high levels of vibration, a harsh environment, and severe atmospheric influence and have strict requirements on loaded equipment's size, weight, and energy consumption. In contrast, higher transmission rates are often required in practical applications. Therefore, the development of airborne laser communication systems needs to comprehensively consider a variety of influencing factors at present in this field to cope with the impact of the above technology research direction: optical communication modulation and demodulation technology; laser communication terminal lightweight and miniaturization technology; wireless visual communication capture, tracking, and targeting technology; all-weather technology, and so on. This paper outlines the international and domestic research progress and development history of wireless optical communication high-speed transmission technology, summarizes the elemental composition of its system, discusses the critical issues of lightweight and small-sized unmanned aerial vehicle (UAV) visual communication based on the existing theories, and looks forward to the future prospects and research directions of UAV visual communication.
# Smart Black Box 2.0: Efficient High-Bandwidth Driving Data Collection Based on Video Anomalies
## 2 Related Work
### Real-World Driving Datasets  
The increasing popularity of deep-learning methods for self-driving perception tasks has created a demand for high-quality high-bandwidth datasets. Naturalistic field operation test projects such as [28; 29] have been used in the past to gather large amounts of driving data. One such dataset [28] uses 100 cars to log nearly 43,000 h of video and vehicle performance data over a distance of 2,000,000 miles. The more recent Safety Pilot Model Deployment dataset [29] contains roughly 17,000,000 miles of data collected over almost 64,400 h, including 17 TB of video. However, these datasets primarily focus on the capture of low-bandwidth data; the video streams of both datasets are compressed and downsampled to low frame rates.  
Recently, high-quality computer vision-oriented datasets have been published. These include general driving datasets like Cityscapes [30], KITTI [31], and BDD100K [32] and traffic anomaly datasets like A3D [14], DADA [33], and Detection of Traffic Anomaly (DoTA) [15]. Cityscapes contains 24,999 labeled images at 55 GB, while KITTI includes 7481 images at 12 GB, in addition to 29 GB of point clouds and GPS and IMU data. BDD100K is one of the largest public driving datasets, having 100,000 HD video clips (1.8 TB) for over 1100 driving hours in a variety of conditions. A3D, DADA, and DoTA focus specifically on traffic anomalies. A3D contains 1500 on-road accident clips with accident start and end times labeled. DADA released 1000 video clips with simulated driver eye-gaze. DoTA is comprised of 4677 videos with spatial, temporal, and anomaly category annotations. Table 2 summarizes some major driving video datasets.  
Datasets like BDD100K and DoTA have significantly extended publicly available data access for deep-learning methods to use. However, anomaly-focused datasets are still relatively small; larger datasets like BDD100K contain very few EOIs with which to test self-driving algorithms. As a result, the evaluation of the SBB required the creation of a combined dataset using BDD100K and DoTA video clips in order to have sufficient quantities of both normal and anomalous driving data. The SBB aims to address this problem by providing a method to collect high-value video data across an entire fleet of vehicles.
## 4 Fluid Inclusion Study  
### Fluid Inclusion Petrography  
Based on careful petographical observation, microthermometry at room temperature and Raman spectroscopy, four types of fluid inclusions trapped in the quartz grains related to the ore-forming event were identified, according to their phases. These types of fluid inclusions were type 1 aqueous, type 2 H\({}_{2}\)O-CO\({}_{2}\), type 3 daughter mineral-bearing, and type 4 CO\({}_{2}\) fluid inclusions, according to their abundance from high to low, respectively. Type 1 aqueous inclusions, generally 2-15 \(\upmu\)m in diameter, consist of two phases (liquid H\({}_{2}\)O + vapor H\({}_{2}\)O), having a variable degree of fill (10-60 vol.% gas) (Figure 5d). They normally develop as clusters or along pseudo-secondary trails, possessing negative crystal, ellipsoid, irregular, or elongated shapes (Figure 5d,h,i). Type 2 H\({}_{2}\)O-CO\({}_{2}\) inclusions contain three phases of liquid H\({}_{2}\)O + liquid CO\({}_{2}\) + CO\({}_{2}\)-rich vapor (Figure 5e), with a CO\({}_{2}\) volumetric proportion of 20-60% (Figure 5e). They are usually 3-15 \(\upmu\)m in diameter, and occur in isolation, with ellipsoid, elongated, or irregular shapes (Figure 5e). Type 3 daughter mineral-bearing inclusions are composed of one salt daughter mineral, which has a variable volume ratio of 20-60%, aqueous solution, and a vapor bubble (Figure 5f). They are generally isolated and have ellipsoid shapes, ranging from 9 to 15 \(\upmu\)m in diameter (Figure 5f). Type 4 CO\({}_{2}\) inclusions comprise liquid CO\({}_{2}\) and vapor CO\({}_{2}\), usually showing a constant degree of fill (30-50 vol.% gas) (Figure 5g) and are typically 2 to 15 \(\upmu\)m in diameter (Figure 5g). They occur in isolation, with round, ellipsoid, elongated, or irregular shapes (Figure 5g).
## 2 Experiment and Computing CZM Parameters  
### Cohesive Zone Model  
In this paper, a bilinear CZM was applied to analyze the interface damage between CCBP and ACWL. The relationship between stress and displacement for the bilinear traction-separation law is plotted in Figure 1. During the interface damage evolution process, the interface stress \(t\) increases gradually to the maximum value \(t^{0}\), and then interface damage initiates. After interface damage initiation, the interface stress decreases gradually. The interface cohesive element completely fails when the maximum displacement \(\delta^{f}\) is attained. The area under the stress-displacement curve is critical fracture energy \(G^{C}\).  
Initiation of interface damage is judged by the quadratic nominal stress criterion. As defined in Equation (1), the interfacial initiates when the quadratic interaction function reaches 1.  
\[\left\{\frac{\left\langle t_{n}\right\rangle}{t_{n}^{0}}\right\}^{2}+\left\{ \frac{\left\langle t_{s}\right\rangle}{t_{s}^{0}}\right\}^{2}+\left\{\frac{ \left\langle t_{t}\right\rangle}{t_{t}^{0}}\right\}^{2}=1 \tag{1}\]  
where \(t_{n},t_{s},t_{t}\) represent the normal stress, shear stress in the first and second direction, separately, \(t_{n}^{0}\) is the tensile strength, \(t_{s}^{0},t_{t}^{0}\) are the shear strength in the first and second direction, respectively, and \(\left\langle\cdot\right\rangle\) is the Macaulay bracket, which means that no damage will initiate in the pure compression state.  
The fracture toughness, which is the area under the stress-displacement curve, is used as the crack propagation criterion of the cohesive element. The failure criterion adopted in this paper can be expressed as following:  
\[\left\{\frac{G_{n}}{G_{n}^{C}}\right\}^{2}+\left\{\frac{G_{s}}{G_{s}^{C}} \right\}^{2}+\left\{\frac{G_{t}}{G_{t}^{C}}\right\}^{2}=1 \tag{2}\]  
where \(G_{n},G_{s},G_{t}\) are energy produced in the normal, the first, and the second shear directions, respectively, and \(G_{n}^{C},G_{s}^{C},G_{t}^{C}\) are the critical fracture energy required to cause debond failure in the normal, the first, and the second shear directions, respectively. Two damage variables in ABAQUS used to describe the failure process of interface cohesive element are QUADSCRT and SDEG [25]. QUADSCRT and SDEG are dimensionless variables and the in between ranges are 0-1. When QUADSCRT reaches 1, the microcracks expand into macroscopic cracks. When the value of SDEG reaches 1, the interface completely delaminates.  
Figure 1: Stress-displacement curve of bilinear traction-separation law.
###### Abstract  
This study investigated the impact of six high-intensity interval training (HIIT) running sessions on 1% or 10% slopes on various physiological and performance parameters in 25 men. The participants underwent assessments of VO\({}_{\text{2 max}}\), time to exhaustion on 1% slope (TLim1%), and time to exhaustion on 10% slope (TLim10%) in the initial three visits. They were then randomly assigned to control (CON), HIIT on 1% slope (GT1%), or HIIT on 10% slope (GT10%) groups. Over three weeks, participants performed six HIIT sessions with equalized workload based on their individual maximal oxygen uptake (\(\text{VVO}_{\text{2 max}}\)). The sessions comprised 50% of TLim, with a 1:1 ratio of exercise to recovery at 50% \(\text{VVO}_{\text{2 max}}\). The results indicated significant improvements in \(\text{VO}_{\text{2 max}}\) and peak velocity (VPeak) after HIIT on both slopes. Heart rate (HR) differed between sessions for GT1%, while no significant differences were observed for GT10%. Ratings of perceived exertion (RPE) were significantly reduced for GT1% after the third session, with a similar trend for GT10%. In summary, six HIIT sessions on a 1% or 10% slope effectively enhanced \(\text{VO}_{\text{2 max}}\) and VPeak, but there was no improvement in TLim performance, suggesting no adaptive transfer between training groups.  
a +
Footnote †: journal: Applied  
_Acticle_  
Impact of High-Intensity Interval Training on Different Slopes on Aerobic Performance: A Randomized Controlled Trial
# Accelerated Correction of Reflection Artifacts by Deep Neural Networks in Photo-Acoustic Tomography
## 1 Introduction
It is worth pointing out that the reference [25] considers another type of reflection artifact with different focuses and approaches. The reflection in [25] is caused by point-like targets inside the tissue, while ours by planar reflectors outside the tissue. The network in [25] is mostly based on convolutional neural networks (CNNs) while ours on the U-net structure. The two networks target different problems with associated unique networks and therefore are of independent values. We remark that besides in PAT, DNNs have been successfully applied to other imaging modalities as well, see [29; 30; 31; 32; 33; 34; 35] for its applications in CT.
## 3 Manufacturing Difficulties
### Mounting Apertures  
The laminations could be kept together at the back by weld lines, but a similar method could not be used to provide the required strength for the lateral forces, especially in view of the large vibrations that could be expected with fast transit of the moving trolley, with the forces for one module exceeding 8 kN for the longitudinal force and even more for the lateral (depending on the air gap). Instead, it was decided to use additional mounting apertures, positioned towards the back of the stator core (20 mm from the back edge, as visible in Figure 3c,d). These apertures were used for mounting with M16 bolts (16 mm diameter), but through isolating tubes so that eddy currents would not be generated due to short-circuited turns, neither through the remainder of the metallic structure (clamping plates and the supporting members) nor between the laminations themselves (due to the welds present at the back).
## 2 Materials and Methods  
### Study Area and Data Source  
#### 2.1.1 Study Area  
Qiaotou Town, located in the northwest of Hainan Island (19\({}^{\circ}\)23\({}^{\prime}\)-20\({}^{\circ}\)01\({}^{\prime}\) N, 109\({}^{\circ}\)45\({}^{\prime}\)-110\({}^{\circ}\)15\({}^{\prime}\) E), is part of Chengmai County (Figure 1). The town benefits from a favorable geographical location, bordered by the Qiongzhou Strait to the north and a coastline approximately 26 km long. The region is rich in land resources, with selenium-rich soils containing up to 60 micrograms of selenium per kilogram, earning itself the good reputation of the nickname \"Selenium-rich Town of Hainan\" [48]. This area is a key agricultural production base for Chengmai County [49]. Qiaotou Town experiences a tropical monsoon climate, with an average annual temperature ranging from 22.5 \({}^{\circ}\)C to 25.6 \({}^{\circ}\)C [50]. Annual sunshine hours range from 1780 to 2600, while annual precipitation varies between 1500 mm and 2500 mm, with around 1000 mm recorded in the western coastal area [51]. The town covers an area of 70.3 km\({}^{2}\), with 32,500 acres (13,147 hectares) of farmland. It administratively oversees seven village committees and one neighborhood committee, comprising 67 natural villages and a total population of 22,500. Agriculture in the town primarily focuses on rice, selenium-rich sweet potatoes, and bananas [52]. However, the abandonment of farmland has become an increasingly pressing issue due to factors such as changes in the economic structure, urbanization, and the rising number of residents working outside the town [53]. A survey of abandoned farmland in this area is of practical importance.
# Improving DFT-Based Image Watermarking Using Particle Swarm Optimization Algorithm
## 5 Experimental Results and Discussion
### Watermark Robustness  
To evaluate the watermark robustness of the proposed method, we used several signal processing, geometrical attacks, and combined attacks. Assuming ergodicity, the BCR is defined as the ratio between the number of correctly decoded bits and the total number of embedded bits. According to [6,33], a threshold value \(Th\) must be defined to determine the presence or absence of the watermark \(W\). To formalize the computation of \(Th\), we consider a binomial distribution with success probability equal to 0.5, and then the false alarm probability \(P_{fa}\) for \(L\) bits is given by (16), and a threshold value \(T\) in (16) must be provided to \(P_{fa}\) becomes smaller than a predetermined value [6,33].  
Figure 7: (**a**) Original image. (**b**) Watermarked image using conventional discrete Fourier transform (DFT), \(r_{1}=50\), \(r_{2}=150\), \(\alpha=1.5\), \(L=64\), PSNR = 39.65 dB, VIF = 0.78. (**c**) Watermarked image using PSO-DFT, \(r_{1}=79\), \(r_{2}=250\), \(\alpha=0.5\), \(L=64\), PSNR = 42.07 dB, VIF = 0.86.  
\[P_{fa}=\sum_{z=T}^{L}(0.5)^{L}\cdot\left(\frac{L!}{z!(L-z)!}\right) \tag{16}\]  
where \(L\) is the watermark length. Based on the Bernoulli trials assumption, \(z\) is independent random variable with binomial distribution [33]. In this way, considering \(L\) = 32 and \(T\) = 25, the \(P_{fa}\) = 0.0011 and \(T\)h = \(T/L\) = 25/32 = 0.78. Recall by the inclusion-exclusion probability principle that the addition of the bit error rate (BER) and BCR must equal 1. Note that the values of \(T\), \(P_{fa}\) and \(T\)h are adjusted according to the value assigned to the watermark length \(L\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Image** & \multicolumn{4}{c}{**PSO-DFT**} & \multicolumn{4}{c}{**Conventional DFT**} \\ \hline  & \(r_{1}\) & \(r_{2}\) & \(\alpha\) & **PSNR (dB)** & **VIF** & \(r_{1}\) & \(r_{2}\) & \(\alpha\) & **PSNR (dB)** & **VIF** \\ \hline  & 86 & 110 & 2.62 & 43.17 & 0.90 & & & 42.14 & 0.87 \\ \hline  & 61 & 143 & 0.65 & 44.96 & 0.94 & & & 42.33 & 0.81 \\ \hline  & 71 & 105 & 1.97 & 43.82 & 0.92 & 50 & 150 & 1.5 & 42.21 & 0.87 \\ \hline  & 56 & 153 & 0.67 & 44.70 & 0.93 & & & 42.26 & 0.83 \\ \hline  & 76 & 108 & 1.51 & 44.30 & 0.95 & & & 42.24 & 0.88 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Performance comparison in terms of PSNR and VIF between the optimized values of \(r_{1}\), \(r_{2}\) and \(\alpha\) using PSO algorithm vs. fixed values in conventional DFT watermarking.  
Figure 8: The PSO convergence history of the proposed method using an image with predominant texturized content.