esa-sceva/satcom-chunk-collection huggingface dataset:
# AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks
## 5 Performance Analysis
### Testing Framework
The Tagged Stream Mux combines the header and payload blocks and forwards them to the constellation modulator, followed by processing through a low-pass filter, fractional resampler, and gain controller. The modulation type and gain parameters are determined based on the previous choices, according to the classification results. The modulated complex samples are then transmitted across the Stojanovic underwater channel model [15] and received at the receiver block. After passing through the resampler, automatic gain control (AGC), and filter blocks, clock synchronization is performed by the Symbol Sync block, and, after equalization (In our system, we prefer a simplified design; thus, we choose to consider a linear equalizer. However, different choices are possible, e.g., a decision feedback equalizer (DFE), which in any case, does not imply any change into the AI-driven controller behavior), phase correction is achieved using the Costas loop. Demodulation is carried out by the constellation decoder, which passes the decoded bytes to the differential decoder. The Correlate Access Code block searches for the specific 64-bit access key pattern, and the Repack Bits block reassembles the bits in most significant bit (MSB) style before forwarding them to the CRC Check block. The CRC Check block checks the accuracy of the received bits. Upon successful detection  
Figure 3: Components of the system. of the access key and verification of the CRC, the bits are repacked and the information is saved.
# Impact of Ferment Processing Parameters on the Quality of White Pan Bread
## 3 Results and Discussion
### Fermentation Temperature
#### 3.4.2 Effect of Fermentation Temperature on Bread Processing
Overall, a fermentation temperature of 35 \({}^{\circ}\)C resulted in the most noticeable improvements to the dough handling properties without exhibiting stickiness at ferment inclusion levels of 30% and 50%.
# Lower-Limb Flexibility Profile in 142 Competitive Female Football Players: A Cross-Sectional Study
## 2 Materials and Methods
### Procedures  
All measurements were conducted in the medical room of the sports facilities under standard conditions (24 \({}^{\circ}\)C) and performed by the same two Sports Science experts, each with over 15 years of experience and developers of the ROM-SPORT assessment method [1]. Lower-limb flexibility (11 ROM tests) was analyzed using the ROM-SPORT battery. For a detailed description of the assessment procedure and ROM tests, refer to article [1].  
The criterion validity (gold standard) of the ROM tests was based on earlier studies by [PERSON] et al. [21] and [PERSON] et al. [22] who consider the radiological measurement to be the gold standard, as muscle origin and insertion can be observed and thus muscle extensibility can be determined by ROM. In addition, the ROM tests were selected due to their operational validity, as recognized by certain American Medical Organizations [23; 24], and their inclusion in leading Sports Medicine manuals [17; 23], which are grounded in anatomical principles and supported by extensive clinical and athletic experience. Furthermore, previous studies conducted by our research group [1; 25] have reported moderate to high intra-tester reliability for all ROM procedures used by the evaluators responsible for the testing sessions. The coefficients of variation (CV) ranged from 0.2% to 9.1%, specifically: 0.4%, 1.7%, 9.1%, 3.5%, 3.7%, 3.5%, 3.4%, 1.0%, 0.2%, and 1.2% for hip flexion with knee flexed and extended, hip extension, hip abduction with hip flexed, hip abduction, hip adduction with hip flexed, hip internal rotation, hip external rotation, knee flexion and ankle dorsiflexion with knee flexed and extended, respectively. Moreover, recent study involving football players has shown no significant differences in reliability metrics for various ROM values and strategy indicators across four separate testing sessions conducted during the first week of preseason [1]. In like manner, previous systematic review has demonstrated that this method of ROM assessment offers more advantages than disadvantages compared to other flexibility assessment protocols [26].  
Two weeks prior to the assessment session, testers and participants engaged in practice sessions to promote familiarization with the theoretical foundations and practical execution of the testing protocol. The tests were administered in a randomized circuit format due to time constraints. Maximal passive ROM was determined either by the sensation of a moderate stretch or the identification of a synergistic movement that increased ROM (false negative) [1]. Each test was performed three times per player and the mean of the two closest measurements was used for subsequent statistical analysis.  
Before the ROM assessment, a standardized dynamic warm-up of 20 min was carried out, including aerobic exercises and dynamic flexibility drills targeting the whole body [27].
# Fuzzy Supplier Selection Method Based on Smaller-The-Better Quality Characteristic
## 3 Research Design and Methods
### Developing a Fuzzy Supplier Selection Model
This study calculates the total area \(a_{Ti}\) of \(\eta_{i}(x)\) as follows:  
\[a_{Ti}=\int\limits_{Q_{Li}}^{Q_{Bi}}\eta_{i}(x)\ d_{x}. \tag{23}\]  
Let \(x=c_{ij}\), such that \(\eta_{i}\big{(}c_{ij}\big{)}=\eta_{j}\big{(}c_{ij}\big{)}\). Then, this study calculates the slashed area \(a_{ij}\) of \(\eta_{i}(x)\) in the following:  
\[a_{ij}=\int\limits_{c}^{Q_{Bi}}\eta_{i}(x)\ d_{x}. \tag{24}\]  
According to [PERSON] [45], this study used \(a_{ij}\) as the numerator and \(a_{Ti}\) as the denominator and then performed a fuzzy testing method using \(a_{ij}/a_{Ti}\). [PERSON] et al. [12] felt that calculating this ratio was difficult and thus replaced it with the ratio quotient of their base lengths. First, let \(d_{ij}=Q_{Ri}-c_{ij}\), so \(d_{ij}\) is the base length of the slashed area of \(\eta_{i}(x)\). Based on Equation (19), \(d_{ij}\) is shown as follows:  
\[d_{ij}=\big{(}Q_{PLi0}^{*}-1.5\big{)}\times\sqrt{\frac{\chi_{0.9975;n-1}^{2}}{ n}}+\frac{Z_{0.0025}}{\sqrt{n}}-\big{(}c_{ij}-1.5\big{)}. \tag{25}\]  
Correspondingly, the base length of \(\eta_{i}(x)\) can be exhibited as \(d_{Ti}=Q_{Ri}-Q_{Li}\). Based on Equations (17) and (19), \(d_{Ti}\) can be shown as follows:  
\[d_{Ti}=\big{(}Q_{PLi0}^{*}-1.5\big{)}\times\left(\sqrt{\frac{\chi_{0.9975;n-1} ^{2}}{n}}-\sqrt{\frac{\chi_{0.0025;n-1}^{2}}{n}}\right)+2\frac{Z_{0.0025}}{ \sqrt{n}}. \tag{26}\]For the sake of convenience, we followed [PERSON] et al. [12] and replaced \(a_{ij}/a_{Ti}\) with \(d_{ij}/d_{Ti}\) for fuzzy testing. The fuzzy testing ratio \(d_{ij}/d_{Ti}\) is defined as  
\[d_{ij}/d_{Ti}=\frac{\left(Q_{PUIi0}^{*}-1.5\right)\times\sqrt{\frac{\chi_{0.99 75_{\text{sn-1}}}^{2}}{n}+\frac{Z_{0.005}}{\sqrt{n}}-\left(c_{ij}-1.5\right)}}{ \left(Q_{PUIi0}^{*}-1.5\right)\left(\sqrt{\frac{\chi_{0.9975_{\text{sn-1}}}^{2} }{n}}-\sqrt{\frac{\chi_{0.0025_{\text{sn-1}}}^{2}}{n}}\right)+2\times\frac{Z_{0.0025}}{\sqrt{n}}}. \tag{27}\]  
If we let \(0<\phi_{1}<\phi_{2}<0.5\), in accordance with [PERSON] [45] and [PERSON] et al. [12], where \(\phi_{1}\) and \(\phi_{2}\) are two expert coefficients, will be taken into consideration in the following rules:  
(1) If \(d_{ij}/d_{Ti}\leq\phi_{1}\), then \(H_{0}\) is rejected, and \(Q_{PUIi}<Q_{PUIj}\) is concluded. (2) If \(\phi_{1}<d_{ij}/d_{Ti}<\phi_{2}\), then the decision of whether to reject/not reject cannot be made. (3) If \(\phi_{2}\leq d_{ij}/d_{Ti}\), then \(H_{0}\) is not rejected, and \(Q_{PUIi}=Q_{PUIj}\) is concluded.
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 3 Result
### Surface Roughness  
The average surface roughness (\(Ra\)) of the off-axis parabolic surface after rough grinding and finish grinding was detected, as shown in Figure 5. After rough grinding, the surface roughness following step machining along the meridian direction is 1.234 um, and the surface roughness following step machining along the arc vector direction is 1.661 um. After finishing, the surface roughness following step machining along the meridian direction is 0.550 um, and the surface roughness following step machining along the arc vector direction is 0.475 um. For both rough grinding and finish grinding, there are large peaks on both sides of the wheel path.  
Figure 3: Surface shape after grinding: (**a**) rough grinding surface: step along the meridian direction; (**b**) rough grinding surface: step along the arc vector direction; (**c**) finish grinding surface: step along the meridian direction; (**d**) finish grinding surface: step along the meridian direction.  
Figure 4: The actual contour of scallop height: (**a**) step along the meridian direction; (**b**) step along the arc vector direction.  
Figure 5: Surface roughness after grinding: (**a**) rough grinding surface morphology: step along the meridian direction; (**b**) rough grinding surface morphology: step along the arc vector direction; (**c**) finish grinding surface morphology: step along the meridian direction; (**d**) finish grinding surface morphology: step along the meridian direction.
## 3 Data and Methods
### GOSAT TANSO Data  
The GOSAT was launched on January 23, 2009 from Tangeashima Island, Japan, and is a joint project of the Japan Aerospace Exploration Agency (JAXA), the Ministry of the Environment of Japan (MOE) and the National Institute for Environmental Studies (NIES) of Japan. The primary purpose of the GOSAT project is to accurately estimate the emissions and absorptions of GHG on a subcontinental scale to assist environmental administrations in evaluating the carbon balance of land-based ecosystems and to provide assessments of regional emissions and absorptions [66]. The GOSAT main sensor (TANSO) is a nadir-looking Fourier Transform Spectrometer (FTS) with four spectral bands, from the visible to thermal infrared. GOSAT is the World's first spacecraft designed to measure XCO\({}_{2}\) and XCH\({}_{4}\), the two major GHG concentrations, from SWIR bands with global coverage every three days [67]. GOSAT is a sun-synchronous orbit with a local overpass time of 13:00 [68, 69]. It derivesthe CO\({}_{2}\) and CH\({}_{4}\) column amounts by observing ground-surface scattered solar spectra in the 1.6 and 2.0 \(\upmu\)m regions, which contain suitable CO\({}_{2}\) and CH\({}_{4}\) absorption bands, and the satellite also measures vertical profiles higher than approximately 2 km by measuring thermal emission spectra from the atmosphere [8].  
TANSO FTS SWIR Level 2 Version 02.00 and 02.11 (stored column abundances of CO\({}_{2}\) and CH\({}_{4}\) retrieved from the radiance spectra in bands 1 through 3 of FTS) data from June 2009 to November 2011 were used in this study. The relative accuracy of TANSO FTS Level 2 data is 0.3-1% (1\(\sim\)4 ppm) for CO\({}_{2}\)[70]. The GOSAT FTS SWIR Level 2 data are point data and the footprint is a circle with 10.5 km diameter at nadir [71]; thus, we obtained the monthly CO\({}_{2}\) concentration easily. Moreover, in this study, we assumed that the TANSO XCO\({}_{2}\) were the true value without bias. TANSO XCO\({}_{2}\) data were also used to check the accuracy of the modeled CO\({}_{2}\) concentration from MODIS derived products.
## 4 Experimental Results and Analyses
### Experiment on Shot Boundary Detection
#### 4.2.3 TRECVID 2001 Dataset  
The TRECVID 2001 Dataset [69] is mostly used for shot boundary detection. The reference data of the transitions are assigned to four different categories: cut, dissolve, fade-in/out, and other. In the  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Name** & **Shots** & **Detected** & **Correct** & **False** & **Miss** & **Precision** & **Recall** \\ \hline Big Buck Bunny & 91* & 99 & 85 & 14 & 6 & 0.86 & 0.93 \\ Cosmos Laundromat & 94 & 104 & 85 & 19 & 9 & 0.82 & 0.90 \\ Elephants Dream & 130 & 146 & 113 & 33 & 17 & 0.77 & 0.87 \\ Tears of Steel & 136 & 156 & 125 & 31 & 11 & 0.80 & 0.92 \\ Sintel & 198 & 221 & 174 & 47 & 24 & 0.79 & 0.88 \\ Valkaama & 85 & 93 & 79 & 14 & 6 & 0.85 & 0.93 \\ Total & 734 & 819 & 661 & 158 & 73 & 0.81 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The results of SBD for the OVSD dataset of our method.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Id-Name** & **Total** & **Shots in** & **Detected** & **Correct** & **False** & **Miss** & **Precision** & **Recall** & **F1-Score** \\  & **Shots** & **10 Min** & **Shots** & **Shots** & **Shots** & **Shots** & & **Precision** & **Recall** & **F1-Score** \\ \hline
01\_From\_Pole\_to\_Pole & 445 & 71 & 86 & 66 & 20 & 5 & 0.767 & 0.929 & 0.8402 \\
02\_Mountains & 383 & 111 & 117 & 103 & 14 & 8 & 0.963 & 0.928 & 0.9452 \\
03\_Leworlds & 421 & 84 & 98 & 77 & 11 & 7 & 0.786 & 0.917 & 0.8464 \\
04\_Great Plains & 472 & 86 & 94 & 78 & 16 & 8 & 0.830 & 0.907 & 0.8668 \\
05\_Jungles & 460 & 63 & 77 & 57 & 20 & 6 & 0.740 & 0.905 & 0.8142 \\
06\_Seasonal Forests & 526 & 88 & 108 & 74 & 34 & 14 & 0.685 & 0.841 & 0.7550 \\
07\_Fresh\_Water & 531 & 99 & 104 & 90 & 14 & 9 & 0.865 & 0.909 & 0.8864 \\
08\_Ocean\_Deep & 410 & 65 & 76 & 57 & 19 & 8 & 0.750 & 0.877 & 0.8086 \\
09\_Sshlow\_Sos & 366 & 65 & 68 & 58 & 10 & 7 & 0.853 & 0.892 & 0.8720 \\
10\_Caves & 374 & 71 & 72 & 67 & 15 & 4 & 0.931 & 0.944 & 0.9374 \\
11\_Deserts & 467 & 72 & 71 & 65 & 6 & 7 & 0.915 & 0.903 & 0.9090 \\ Total & 4855 & 875 & 971 & 792 & 179 & 83 & 0.816 & 0.905 & 0.8580 \\ \hline \hline \end{tabular}
\end{table}
Table 12: The results for the BBC Planet Earth Dataset.  
Figure 9: The representative frames of the BBC Planet Earth dataset.
## 2 Subsurface Crack Forms and Their Distribution in the UVAG Process  
### Experimental Setup  
In order to unveil the variety of subsurface crack forms in the actual UVAG process, 40 sets of experiments on BK7 optical glass samples were carried out on a five-axis ultrasonic vibration precision machining center (DMG Ultrasonic 70-5 Linear). The grinding wheel was a hollow diamond grinding wheel produced by Schott Diamantwerkzeuge GmbH. Both internal and external coolant were used in the experiments. The experimental setup is shown in Figure 1. The molar composition and material properties of the BK7 optical glass are listed in Table 1.  
In order to comprehensively investigate the varying effects of process parameters on the subsurface crack quantity distribution and MSSCD, a single-factor experiment design was used in this study. The diameter of the grinding wheel (\(D_{g}\)), average diameter of abrasive grains (\(d_{a}\)), spindle rotational speed (\(n\)), feed rate (\(V_{f}\)), grinding depth (\(a_{p}\)), and ultrasonic vibration amplitude (\(A\)) were selected as the key experimental process parameters. Supplementary Materials Table S1 summarizes the grinding and ultrasonic vibration process specifications used in the experiments. After experiment completion, the cross-sections of specimens were ultra-precision polished, followed by hydrofluoric acid etching. In order to prevent excessive crack corrosion, hydrofluoric acid (40%) was diluted to 1%, and the polished specimens were submerged in the diluted hydrofluoric acid solution for 10 min. The polishing method and the subsurface crack morphologies after corroding are shown in Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Optical Glass**} & \begin{tabular}{c} **SiO\({}_{2}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **B\({}_{2}\)O\({}_{3}\)** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Na\({}_{2}\)O** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **K\({}_{2}\)O** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **BaO** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Elastic Modulus** \\ **(E/Gpa)** \\ \end{tabular} & **Poisson Ratio (v)** \\ \hline BK7 & 74 & 10 & 10 & 5 & 1 & 80.9 & 0.21 \\ \hline \end{tabular}
\end{table}
Table 1: Molar composition, elastic modulus, and Poisson’s ratio of BK7 optical glass.  
Figure 1: Experimental setup.  
Figure 2: Subsurface cracks with different corrosion extents.  
After the polishing and corroding, the optical glass specimens were cleaned several times by using an ultrasonic vibration cleaner. A scanning electron microscope was used to examine the morphologies of the subsurface cracks. By scanning all over the cross-section of each sample, the sub-surface crack with maximum depth along grinding depth direction was found using ImageJ software. The highest and lowest points were selected at the top of the machined surface and the bottom of propagation position of the crack, respectively. Then, the value of _MSSCD_ was obtained using the software. An example of profile measurement is shown in Figure 3.
## 6 Abstracted Services and Middleware of WSN Virtualization
### Delay Tolerance  
Delay Tolerant Network (DTN) is aimed at enabling reliable communication over links that do not offer high quality transmission. Therefore, these links provide packet loss and long delays. DTN provides a solution to these problems by applying buffers at each hop and using feedback from the destination node. Buffers keep the messages in memory when link quality is unsatisfactory, and transmit when link quality is good.  
Middleware for virtualization provides the solution due to its dynamic nature. Furthermore, middleware for DTN can provide virtual links and buffers on the overlay network to improve high latency/low data rate. Furthermore, it provides solution for disconnection and transmission errors due to the knowledge of the whole network. VITRO middleware framework provides solution the DTN [44, 45]by virtualization of the network. DSAM [138] is a middleware that provides a communication layer between the service and different applications for DTN. Middleware proposed in [139] dynamically adapts the connections. Moreover, it switches between the connections according to the network applications and conditions.
# Speckle Noise Detection and Removal for Laser Speech Measurement Systems
###### Abstract
Most of the people listening to laser detection signals agreed that due to the appearance of speckle noise, they were psychologically afraid to listen to the voice, and easily tired when listening to this voice, resulting in low speech recognition. In addition, because the background noise is usually removed based on the noise estimation, the irregular appearance of speckle noise affects the accuracy of this estimation. Therefore, removing the speckle noise first is also conducive to the subsequent background noise removal. In previous works, we used a decorrelation method based on linear prediction (LP) model to detect the location of the speckle noise by improving the Noise-to-Signal Ratio (NSR) of the detection signal, and designed an interpolator to replace the speckle noise [4]. However, the previous method used the direct threshold to judge the noise position. For very weak signals, this method has limitations and the noise location accuracy is not high. In this paper, we present a simple yet efficient technique that can restore laser measured speech signals that are corrupted by speckle noise. The speckle noise detection method, combining decorrelation preprocessing, average short\(-\)term energy and kurtosis to extract the signal and locate the noise according to the threshold which involves relatively little calculation, thereby increasing the computing speed. The decorrelation preprocessing and the double threshold criterion highly increase the noise positioning accuracy. The method of replacing contaminated samples with linear coded samples is also efficient in restoring the signal and reducing the distortion. The results show that the proposed automatic noise detection and removal method outperforms other related methods across a wide range of degraded audio signals.
## 3 GidA/MnmE-Associated Virulence of Gram-Positive Bacteria  
The role of GidA and MnmE in the pathogenesis of Gram-positive bacteria has not been as extensively studied as in Gram-negative bacteria, but there is evidence of an association with pathogenesis, particularly in _Streptococcus_. In _Streptococcus pyogenes_, transposon mutagenesis of _gidA_ resulted in reduced expression of SpeB, an important pyrogenic and cardiotoxic virulence factor in group A _Streptococcus_[37]. Further analysis showed _speB_ transcription was delayed in a _gidA_ mutant via the transcriptional activator RopB. Overall, a _gidA_ deletion mutant displayed a nearly normal transcriptional profile of _S. pyogenes_ virulence factors, but showed reduced expression of several virulence proteins including mitogenic factor (MF), streptolysin O (SLO), NAD-glycohydrolase (SPN), and surface M protein [37]. The reduced expression of these virulence proteins correlated to a _gidA_ deletion mutant being highly attenuated in a murine subcutaneous ulcer model of soft tissue infection. Furthermore, their study showed that a \(mmmE\) deletion mutant displayed the same phenotypes displayed by a _gidA_ deletion mutant [37].  
GidA has also been associated with virulence of other _Streptococcus_ bacteria as well. In _Streptococcus mutans_, random-insertion mutagenesis identified a _gidA_ mutant with reduced ability to withstand stressful conditions, such as low pH, high osmotic pressure, high temperature, and bacitracin exposure [44]. Additionally, loss of GidA and/or MnmE resulted in a 50% decrease in glucose-dependent biofilm formation, a key virulence property of _S. mutans_. Interestingly, loss of GidA did not result in reduced expression of the most common secreted and cell surface virulence protein of _S. mutans_[44]. Another study in _Streptococcus suis_ serotype 2 (SS2), the most prevalent and virulent serotype, showed _gidA_ was up-regulated _in vivo_ during experimental infection in pigs suggesting GidA is an important virulence factor in SS2 infection [45].  
To date, the only other study of Gram-positive bacteria involving GidA was in _Lactococcus garviaeae_, an emerging aquaculture pathogen. In this study, signature-tagged mutagenesis identified a deletion mutant homologous to _gidA_ that displayed a reduced ability to grow in rainbow trout [46]. This preliminary genome-wide scan for virulence factors of _L. garviaeae_ could provide targets for future therapeutic strategies due to its association with mastitis in domestic animals and endocarditis and septicemia in humans [46].