esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Pohang Earthquake-Induced Liquefaction at Youngil-Man Port Using the UBCSAND2 Model
## 3 Results and Discussion
### Excess Pore Water Pressure  
Figure 18 presents the maximum of the excess pore water pressure ratio \(R_{\rm u,seis}\) contours at the end of the earthquake. This value is the ratio of the pore water pressure seismically generated and the initial vertical effective stress, which differs from the commonly used ratio of the current pore pressure and total vertical effective stress (\(r_{\rm u}=u/\sigma_{\rm y}\)). [PERSON] and [PERSON] (2017) suggested that the value of \(R_{\rm u,seis}\) can be determined as follows:  
\[R_{u,seis}\ =\ 1.0-\frac{\sigma^{\prime}y}{\sigma^{\prime}y,i}\ =\ \frac{\Delta u_{ cycleic}}{\sigma^{\prime}y,i} \tag{20}\]  
where \(\sigma^{\prime}y\) is the current vertical effective stress, \(\sigma^{\prime}y,i\) is the initial (pre-earthquake) vertical effective stress, and \(\Delta u_{\rm cyclic}\) is the cyclically induced change in pore pressure.  
Based on the contours of \(R_{u,seis}\) in Figure 18, the soils are mainly liquefied in the second silty sand layer, which is more significant in the far field and near the quay wall.  
Figure 19 presents the excess pore water pressure generation during the earthquake of observed elements in Figure 15. Elements E1, E2, and E3 are defined as the liquefied second silty sand layer, and they are assigned by the UBCSAND2 model. Element E4 is considered as non-liquefied soil and is assigned by the Mohr-Coulomb model. It is clear that elements E1 and E2 reach a peak value of \(\rm R_{u,seis}\)\(\approx\) 1.0, in which liquefaction occurs in these elements. Although element E3 firstly generated to the peak value of \(\rm R_{u,seis}\) = 0.9, however, it decreased slightly to nearly 0.6 at the end of the shaking. In contrast, the peak \(\rm R_{u,seis}\) of element E4 is approximately 0.4, in which no liquefaction occurs in this element.  
Figure 17: Initial stress state at the start of the earthquake. (**a**) Initial horizontal stress ratio (\(\rm K_{0}=\sigma^{\prime}x/\sigma^{\prime}y\)) contours (dimension unit in meters); (**b**) initial static shear stress ratio (\(\rm\alpha=\tau_{xy}/\sigma^{\prime}y\)) contours (dimension unit in meters).
# Conjugate Heat Transfer Analysis and Heat Dissipation Design of Nucleic Acid Detector Instrument
## 2 Geometry and Experimental Method
### Experimental Method  
As shown in Figure 1c,d, the DNA amplification module used a thermoelectric cooler (TEC) to increase and decrease the temperature of the biochip; therefore, the TEC was considered to be a heat source. A test platform, shown in Figure 2, was constructed to identify other heat sources. An infrared thermal imager was used to determine the positions of the heat sources (FLUKE T401 PRO, resolution: 640 \(\times\) 480, range: 253.15-923.15 K, accuracy: \(\pm\)2 K) and a thermocouple was used to measure their temperatures (thermocouple: KAIPUSEN K, range: 253.15-473.15 K, accuracy: \(\pm\)0.1 K). Imaging was conducted at room temperature (approximately 299.15 K) and the results are shown in Figure 3. The other heat sources (called primary heat sources) in the instrument were located on the printed circuit boards (PCBs). Thus, there were three components acting as heat sources in the instrument: TEC and two PCBs.  
The temperature measured at the surface of a heat source was assumed to be the temperature of the heat source. The temperatures on each side of the TEC and the outer surface temperatures of the primary heat sources on the PCBs were recorded with a data acquisition interval of 2 s. The temperatures of the heat sources throughout the DNA amplification process are shown in Figure 4. As shown in Figure 4a, a maximum temperature of 368.15 K was recorded on TEC side1 during the amplification cycling stage. Figure 4b shows the temperatures of the primary heat sources in the PCBs, which increased initially before stabilizing.  
Figure 3: Imaging showing locations of the heat sources: (**a**) Location of heat source inside the instrument; (**b**) Location of heat source on PCB.  
Figure 2: Overview of test platform.
## 2 Materials and Methods  
### Study Identification  
The current systematic review and meta-analysis were performed consistent with suggestions by the Preferred Reporting Items for the Systematic Reviews and Meta-Analysis (PRISMA) statement. We reported all checklist items (Supplementary Table S1) and the PRISMA flow diagram [26]. Given that we already completed data extraction and analyses, the protocol registration for this study failed. According to the PRISMA statement [27], we established the inclusion criteria following the PICOS format [28]: (1) Population: healthy adults including athletes, high, moderately, or lightly trained adults, and no trained adults: (2) Intervention: compression garments; (3) Comparison: control group without wearing compression garments; (4) Outcome: exercise performances (i.e., endurance, speed, power and strength, functional motor performances, and sport-related performances); (5) Study design: randomized control trials (RCT) including either crossover or parallel design. Further, literature review studies, case studies, and studies without sufficient information for effect size calculation were removed. Two authors ([PERSON] and RK) completed an independent literature identification process using two search engines (i.e., PubMed and Web of Science) from 12 September to 30 October 2023. We used the following keywords: (compression OR compressive OR elastic OR spandex OR nylon OR neoprene OR latex) AND (garment OR sportswear OR stocking OR sleeves OR underwear OR braces OR swimwear) AND (physical function OR physical performance OR fitness OR exercise performance).  
Initially, we found 1692 potential studies (i.e., 1073 studies from PubMed and 619 studies from the Web of Science), and a further 279 duplicated studies were excluded. Then, we additionally eliminated 1371 studies (i.e., 119 review studies, 20 case studies, 13 animal studies, and 1219 studies that did not meet our inclusion criteria). Accordingly, we included 42 studies in data synthesis for the meta-analysis [6; 7; 9; 11; 17; 18; 19; 21; 20; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62]. Figure 1 showsthe overall procedures of literature search and identification. Additionally, we added bibliometric data for the included studies in Supplementary Table S2.
# Automatic Classification of Foot Thermograms Using Machine Learning Techniques
## 2 Materials and Methods  
### Thermographic Image Dataset  
In our study, we used a public dataset of thermal images (thermograms) of the plantar region, available online.  
In their work, [PERSON] et al. [15] described the acquisition protocol to collect the thermograms of both feet of 167 individuals (122 diabetics and 45 healthy) recruited from four medical institutions located in the city of Puebla, Mexico, over a period of 3 years. Before the image acquisition procedure, each participant had a resting period of 15 min, in a room with controlled temperature, in order to reach a state of thermodynamic equilibrium and to improve the accuracy of temperature variation detection. Two different infrared cameras were used for thermogram acquisition (FLIR E60 and FLIR E6). The emissivity parameter was set to 0.98, which is the recommended emissivity for measurement of human skin with an IR camera.  
The camera was fixed by a vertical tripod placed one meter away from the feet. The raw thermogram dimensions are 320 \(\times\) 240 pixels, and each one has a corresponding temperature file of equal dimensions, in which the temperature value of each point (pixel in the thermogram) is stored.  
After acquisition, the raw images were processed to separate the left and right foot in two different sub-images. These sub-images are segmented and spatial transformed to position the foot in a vertical alignment. The publicly available dataset is composed of individual thermograms of each foot, stored in a _csv_ format.
## 4 Computing Requirements  
When considering the practical implementation of the described algorithms, it is critical to evaluate the number of floating operations (Flops) required. Figure 10 shows the number of floating operations required to perform the described RFI cancellation algorithm for \(\mathit{INR}=100\), as a function of the wavelet type (Haar and optimum ones in Figure 7) and the sequence length, both for decomposition levels equal to 6 (Figure 10a) and 12 (Figure 10b), for which the algorithm performance is very similar except for PRN like signals (Figure 5). Differences in the number of Flops with other \(\mathit{INR}\) values down to 0 (no RFI) are \(\leq\)4% and are not shown not to overload the figure.  
As expected, the simplest wavelet type (Haar) is the one that requires the smallest number of Flops. On the other hand, the discrete Meyer (dmey) is the one that requires the largest number of Flops, about 10-20 times more than the Haar wavelet. For the other wavelet types (reverse biorthogonal 1.3 and 1.5 -rbio1.3 and rbio1.5-, and symlets 3 -sym3-) the number of required floating operations is about 2-3 times larger than the Haar one, which may justify its use in large RFI scenarios when the highest RFI rejection may be required (Figures 6 and 7). The improved performance of the discrete Meyer transform may be questioned when balanced with the much larger computation requirements.  
Below 10-20 samples length there is a saturation effect due to the computational overhead, which suggests using sequences longer than \(\geq\)20 samples. The differences between Figures 10a and 10b are just a factor \(\leq 2.5\) for the shortest sequence lengths, being nearly identical for the sequences longer than \(\sim\)10-100 samples per period.  
Assuming a system operating at L-band (1.400-1.427 MHz) that acquires 20 samples per period to warrant enough RFI rejection (Figure 4), the sampling rate is 20 samples/period \(\cdot\) 27\(\cdot\)10\({}^{6}\) periods /s = 540 Msamples/s. Unfortunately, this high data rate cannot be processed with today's spaceborne processors (e.g. 4 MFlops/s with Leon 3 [27]) or even ground-based FPGAs [28]. However, the proposed algorithm can be applied either at intermediate frequency or for the in-phase and quadrature components of the demodulated signal separately, to reduce the signal bandwidth by a factor of 2 and accordingly the computing requirements per processor, at the expense of doubling the number of processors. Further reductions can be achieved by sub-banding (dividing the original band into smaller ones) and applying the proposed algorithm to each of the sub-bands.
# Comparison of Three Biological Control Models of _Pycnoporus__sanguineus_ on Phytopathogenic Fungi
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model’s**} & \multirow{2}{*}{**Treatment**} & \multicolumn{2}{c}{_Botrytis cinerea_} & \multicolumn{2}{c}{_Fusarium oxysporum_} \\ \cline{3-6}  & & **GR (mm/day)** & \(\mathbf{p\leq 0.05}\) & **GR (mm/day)** & \(\mathbf{p\leq 0.05}\) \\ \hline \multirow{2}{*}{Model 1} & Control (PDA) & (\(\pm\)1.5) 7.84 & a & (\(\pm\)0.4) 3.47 & a \\  & _Pyosporus sanguineus_ & (\(\pm\)1.4) 7.02 & a & (\(\pm\)0.5) 3.18 & a \\ \hline \multirow{4}{*}{Model 2} & Control (PDA) & (\(\pm\)1.3) 7.77 & a & (\(\pm\)0.8) 4.25 & a \\  & Cinnabarin (CH\({}_{2}\)OH-COOH-NH\({}_{2}\)) & (\(\pm\)1.6) 6.60 & ab & (\(\pm\)0.6) 3.88 & a \\  & Captain (C\({}_{3}\)HgCl\({}_{3}\)NO\({}_{2}\)S) & (\(\pm\)0.8) 1.94 & ce & (\(\pm\)0.4) 3.16 & a \\  & Benomyl (C\({}_{14}\)Hg\({}_{18}\)N\({}_{4}\)O\({}_{3}\)) & (\(\pm\)0.6) 1.46 & ce & (\(\pm\)0.4) 3.48 & a \\ \hline \multirow{4}{*}{Model 3} & Control (PDA) & (\(\pm\)1.5) 7.61 & a & (\(\pm\)1.2) 4.28 & a \\  & PDA + _Pyosporus sanguineus extract_ & (\(\pm\)0.8) 3.85 & bc & (\(\pm\)0.3) 2.99 & a \\ \cline{1-1}  & PDA + Captain (C\({}_{3}\)Hg\({}_{8}\)Cl\({}_{3}\)NO\({}_{2}\)S) & (\(\pm\)0.02) 0.02 & d & (\(\pm\)0.2) 1.22 & b \\ \cline{1-1}  & PDA + Benomyl (C\({}_{14}\)Hg\({}_{18}\)N\({}_{4}\)O\({}_{3}\)) & (\(\pm\)0.2) 0.93 & e & (\(\pm\)0.2) 0.50 & c \\ \hline \hline \end{tabular} Mean values followed by the same letter do not present statistically significant differences (_p_ \(\leq\) 0.05) according to [PERSON]’s test.  
\end{table} 
Table 2: Mean values with standard error and ANOVA results for the growth rate among the different treatments evaluated within each model. Figure 2: Percentage of inhibition of radial growth of _B. cinerea_ (gray) and _F. oxysporum_ (white) within treatments; dual tests of antagonistic activity with _P. sanguineus_ (M1); antifungal effectiveness tests with cinnabarin, captan, and benomyl (M2); and antifungal effectiveness tests of culture media supplemented with _P. sanguineus_ extract, captan, and benomyl (M3). Marks at the top indicate significant differences between _B. cinerea_ and _F. oxysporum_ within each treatment (_t_-test). Photographs in rows below each treatment (**a**) _B. cinerea_ and (**b**) _F. oxysporum_ are provided as visual reference of their effect. * The correlation is significant at the 0.05 level. The GR values among the different treatments evaluated against _F. oxysporum_ using ANOVA indicate that there were no significant differences in growth presented by Models 1 and 2, nor by the PDA control and PDA with _P. sanguineus_ extract in Model 3, according to the mean comparison tests conducted (_p_ < 0.05). The only decrease observed was due to the action of the PDA with captan and benomyl in Model 3 (1.22 mm/day and 0.05 mm/day, respectively, \(p\) < 0.05; Table 2).
# Experimental Study on Dynamic Response Characteristics of RPC and RC Micro Piles in SAJBs
## 3 Experimental Results
### Deformation of Piles
#### 3.4.1 The Influence of Frequencies  
Figure 11 shows the horizontal deformations of RPC and RC micro piles under sinusoidal wave loads with different frequencies (from case 1 to case 5). As seen in Figure 11, the horizontal deformation distribution laws of the pile along its buried depth are mainly consistent for each pile. The maximum deformations of piles under different frequency loads are observed at the pile head. It can be seen that the horizontal deformations of piles above the soil surface decrease linearly from the pile head to the soil surface under different sinusoidal wave loads. The horizontal deformations of the pile body decrease significantly with the increase of buried depth. Then, the bending moments of piles gradually diminish to 0 mm or even become a slightly negative value as the buried depth continuously increases.  
To be noted, it can be clearly seen from Figure 11 that the maximum horizontal deformations of piles under different frequency sine waves are observed, which vary from 1 mm to 5 mm at the pile head. The horizontal deformation of the RPC pile increases gradually under sinusoidal wave loads from 1 Hz to 4 Hz, but decreases significantly under sinusoidal wave loads from 8 Hz to 16 Hz. The deformations of the pile are more significant when the RPC pile is subjected to a sinusoidal wave of  
Figure 11: Horizontal deformations of piles under different frequencies loads. (**a**) RPC micro pile.  
Figure 10: Displacement time history curves of the reactive powder concrete (RPC) pile at the pile head.  
4 Hz and 8 Hz. However, the deformations of pile are more significant under the frequencies of 2 Hz and 4 Hz for the RC pile. It was because the natural frequencies of RPC and RC piles are inconsistent, that the first natural frequency of RPC ranges from 4 Hz to 8 Hz, and that of the RC pile is between 2 Hz and 4 Hz. In addition, the horizontal deformations of RPC and RC piles are 4.5 mm and 5.0 mm, respectively, at the pile head. Consequently, it concluded that the deformation of the RC pile is larger than that of the RPC pile, but the increment is slight.
## 1 Introduction  
Aesthetics represents an essential aspect of orthodontics, being the primary motivation behind patients seeking treatment [1, 2]. Patients' growing demand for the most invisible appliance possible has brought about an increasing use of alternative solutions to traditional metal labial brackets, such as aesthetic brackets [3]. Indeed, while stainless steel brackets (SS) are widely employed in labial orthodontic treatment [3], they have poor aesthetic proprieties. Ceramic aesthetic brackets, on the other hand, have superior optical properties but several mechanical disadvantages compared to metal ones, among which are an increased risk of enamel damage during debonding [4, 5, 6, 7], lack of plastic deformation [4, 5, 6, 7], a major risk of wing fracture [4, 5, 6, 7] and greater frictional resistance than SS brackets [8, 9, 10, 11, 12, 13]. This latter fact was confirmed by [PERSON] et al. [14], who conducted a comprehensive review of the literature [15, 16, 17, 18, 19, 20] and reported that aesthetic brackets display higher frictional resistance values than various other commercially available brackets. In fact, it has been observed that the frictional behaviour of aesthetic brackets deters many orthodontists from using them [21]. Indeed, increased frictional resistance can cause the orthodontist to apply excessive force during treatment, which can lead to patient discomfort and anchorage loss, as well as to irreversible damage to the tooth-supporting tissues [16].  
A potential alternative could be in-house 3D-printed resin brackets, thanks to the currently increasing range of three-dimensional printing applications in dentistry [22]. Computer-aided design/computer-aided manufacturing (CAD/CAM) technology has a relatively long history in dentistry, and combined with advanced 3D-imaging and modelling technologies, such as intraoral scanning and cone-beam computed tomography (CBCT), 3D-printing technology has found numerous applications. Research has demonstrated that stereolithography (SLA) and digital light processing (DLP) are the most accurate 3D-printing techniques [23], but CAD/CAM technology allows biocompatible, dimensionally accurate [24] and cheap custom resin brackets to be manufactured in-house. In-house 3D-printed resin brackets have better physical properties than ceramic ones, being in some cases equivalent to metal brackets [25; 26; 27], and [PERSON] et al. found that 3D brackets have better sliding properties than commercial ones [27].  
Due to the importance of the friction values during sliding mechanics in fixed appliance treatment, more studies are needed to compare the sliding resistance of in-house 3D-printed resin brackets and conventional brackets [25; 26; 27]. Hence, this study was designed to evaluate the differences in frictional resistance generated by in-house 3D-printed resin brackets (IH3 DBs) versus commercially available brackets in combination with three different archwires. The null hypothesis was that there would be no difference in the friction values among the different brackets in the bracket/archwire configurations analysed.
# Holographic Lens Array for Solar Collector with Large Angle and Expanded Spectral Width
## 2 Principle  
### Working Principle of HLASC  
The working principle of the proposed HLASC is shown in Figure 1. The HLASC contains many periodically arranged off-axis holographic lenses (OHL), which diffract incident light from a large angle range to the solar cell located below the side. In the recording process of the HLASC, a lens array with a large relative aperture (the ratio of effective aperture to focal length) provides a recording light with a large convergence angle, allowing each OHL to achieve large angle reconstruction and thus have a large collection angle. It should be pointed out that the recording light is divergent spherical light, and the reconstructing sunlight is approximately parallel light. Due to the mismatch of Bragg conditions, the diffraction efficiency of the recording wavelength at a certain angle will not be high. However, the decrease in diffraction efficiency at the recording wavelength leads to an improvement in the average diffraction efficiency of a wider spectrum (see Section 2.2 for details), so sunlight can also be effectively collected within the whole convergence angle.  
Therefore, we can define the convergence angle of the recording light as the collection angle. Specifically, the relationship between the collection angle \(\varphi\) and the lens array parameters is as follows:  
\[\varphi=2\arctan\left(\frac{D}{2f}\right) \tag{1}\]  
where \(D\) is the effective aperture of each small OHL, and \(f\) is the focal length of OHL.  
The two-dimensional array structure enables each small area of the HLASC to have a large collection angle, which is a significant advantage compared to a single holographic lens structure. Different from traditional holographic solar collectors that typically utilize the focusing properties of holographic lenses to reduce the area of the solar cell, it is the off-axis layout in our system that greatly reduces the area of solar cell. In other words, the HLASC achieves the concentration of solar energy by deflecting sunlight.
## 2 Background  
### Problem Formulation  
The Dynamic Job-shop Scheduling (DJSS) issue is an example of an optimization problem, and it operates as follows: the job-shop problem is characterized by a set of \(n\) jobs, denoted as \(J=\{I_{1},I_{2},\cdots,I_{n}\}\), and \(m\) machines, denoted as \(M=\{M_{1},M_{2},\cdots,M_{m}\}\), at every decision point. Each job comprises a set of \(n_{i}\) operations that must be processed in a specified sequence. Each machine can perform no more than one operation simultaneously. Each operation must be performed using a specific machine. The task involves determining an appropriate sequence of operations across all machines in order to establish an appropriate schedule at every point of decision-making. The scheduling effectiveness is evaluated using the Makespan (MS), Mean Flow Time (MFT), and Mean Weighted Tardiness (MWT). The proposed mathematical model is as follows:  
_Objective functions_  
\[\min\,MS=max\{C_{i}|i=1,2,\cdots,n\} \tag{1}\]  
\[\min\,MFT=\frac{\sum_{i=1}^{n}(C_{i}-AT_{i})}{|\mathbb{C}|} \tag{2}\]  
\[\min\,MWT=\frac{\sum_{i\in\mathbb{T}}w_{i}(C_{i}-D_{i})}{|\mathbb{T}|} \tag{3}\]  
S.T.  
\[C_{i,1}-p_{i,1}\geq 0,\forall i \tag{4}\]  
\[C_{i,j}-p_{i,j}\geq C_{i,j-1},\forall i,j \tag{5}\]  
\[(C_{i,j}-p_{i,j}-C_{i,j^{\prime}})\cdot Y_{i,j^{\prime},j^{\prime}}\geq 0, \forall i,i^{\prime},j,j^{\prime} \tag{6}\]where, \(p_{i,j}\) is the processing time of operation \(O_{i,j}\), \(C_{i,j}\) is the completion time of operation \(O_{i,j}\), \(AT_{i}\) is the arrival time of job \(i\), \(D_{i}\) is the due date of job \(i\), \(w_{i}\) is the weight of job \(i\), \(|\mathbb{C}|\) denotes the completed jobs, and \(\mathbb{T}=\{i\in\mathbb{C}:C_{i}-d_{i}>0\}\) denotes the tardy jobs. If operation \(O_{i,j}\) precedes operation \(O_{i,j^{\prime}}\), \(Y_{i,j^{\prime},j^{\prime}}=1\); otherwise \(Y_{i,j,j^{\prime},j^{\prime}}=0\). The primary objectives (1), (2), and (3) are to minimize the makespan, mean flow time, and mean weighted tardiness, respectively. Equation (4) denotes that the starting time of the first operations of jobs must possess a non-negative value. Equation (5) guarantees that every job is executed by a single machine, exclusively. According to Equation (6), there exists a constraint that limits each machine to the processing of a single job at any given time.