esa-sceva/satcom-chunk-collection huggingface dataset:
# Policy Compression for Intelligent Continuous Control on Low-Power Edge Devices
## 6 Results and Discussion
### Runtime Performance
Measuring the direct impact of policy distillation on power consumption improvements is less straightforward, as this is more a property of the hardware than the individual model. You can force the device to periodically switch to a lower power state by artificially limiting the frame rate, but this difference is usually negligible compared to a switch in hardware class [33]. Instead, to optimize for this, we suggest searching for the hardware with the lowest power consumption that can still run the compressed model at an acceptable speed. For example, with a target of 600 steps per second, the Raspberry Pi 3B consumes around 4.2 W [33] and student 3 is a valid option. It will consume the same power when running the original model but at half the inference speed. If the target is 800 steps per second, however, a jump to an Nvidia Jetson TX2 running at 15 W [34] becomes necessary. \begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**Device** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline AMD Ryzen 3900X (CPU) & 11,284 & 11,246 & 10,051 & 9112 & 9993 & 9038 & 7631 & 8794 & 9369 & 8154 \\ Nvidia RTX 2080 Ti (GPU) & 3753 & 3726 & 3322 & 3022 & 3340 & 3050 & 2559 & 3019 & 3332 & 3049 \\ Nvidia A100 (GPU) & 3544 & 3552 & 3247 & 2516 & 3246 & 2998 & 2609 & 2992 & 3237 & 2994 \\ Nvidia GTX 1080 Ti (GPU) & 1804 & 1813 & 1612 & 1452 & 1612 & 1453 & 1213 & 1450 & 1605 & 1448 \\ Nvidia Jetson TX2 (CPU) & 946 & 947 & 840 & 755 & 825 & 736 & 603 & 680 & 679 & 486 \\ Nvidia Jetson TX2 (GPU) & 285 & 298 & 271 & 247 & 270 & 247 & 210 & 247 & 270 & 246 \\ Raspberry Pi 3B (CPU) & 702 & 700 & 620 & 562 & 603 & 540 & 452 & 478 & 428 & 335 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average steps per second for the student sizes and various low and high-power devices. We conclude this section by arguing the importance of carefully designing the architecture of the model with your target device in mind, performing benchmarks to evaluate the best option that meets your runtime requirements, and applying our proposed distillation method based on the KL-divergence to achieve the best model for your use case. Optionally, a trade-off can be made between the average return and steps per second to achieve the best result.
## 4 Discussion  
### Conclusions  
In this paper, we created a dedicated dataset for sandstorms that may lead to the blurring of license plate recognition, reduction in contrast, and loss of some information, and used this enhanced CSCL dataset to train the AlexNetBN network. Our method proves its effectiveness through mathematical principles and model training and testing. We compared the AlexNetBN model after using the optimization method with the AlexNet model without the optimization method; the former performed extremely well on the test set, with a recognition accuracy of more than 91%, while the latter did not perform well on the test set, with a recognition accuracy of only about 80%, which shows that our method can handle the problem of license plate recognition in areas with frequent sandstorms and large variations in light intensity more effectively.
## 2 Materials and Methods
### Grid-Insensitive Solutions and Model Validation
#### 2.3.2 Model Validation  
Considering the employment of a simplified ideal street canyon model in this study, the validation method encompassed the comparison of simulated data with wind tunnel experiments, following a methodology derived from established studies [7; 16; 19; 20; 21; 22; 35; 36; 37]. The wind tunnel experiment data were obtained from CODASC ([[http://www.codasc.de](http://www.codasc.de)]([http://www.codasc.de](http://www.codasc.de)) (accessed on 23 June 2023)).  
The experimental data for WT cases with a street canyon aspect ratio (W/H) of 2 and length-to-height (L/H) of 10, conducted under \(\theta\) = 0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\), were obtained to validate the present numerical model. During the WT experiments, sulfur hexafluoride (SF6) gas was continuously released from line sources. The mean concentrations of SF6 were measured on the canyon walls and presented in terms of the dimensionless pollutant concentration, denoted as \(C^{+}\). In Figure 4, the dimensionless pollutant concentration on both the leeward wall (LW) and the windward wall (WW) is depicted. Notably, these walls represent the investigated area where outdoor vehicle particles can easily penetrate indoors through doors and windows [38]. The presentation covers parallel, oblique, and perpendicular wind directions. Given the symmetry of airflow and canyon geometry in parallel wind direction, the profiles are only presented on one wall.  
Figure 4: Normalized concentration distribution of simulated data (CFD) and wind tunnel (WT) on LW and WW. (**a**) \(\theta\) = 0\({}^{\circ}\), (**b**) \(\theta\) = 45\({}^{\circ}\) (**c**) \(\theta\) = 90\({}^{\circ}\).  
Statistical analysis was conducted to evaluate the overall model performance according to the instruction [39]. Several metrics were adopted, including the fractional deviation (FB), the normalized mean square error (NMSE), the fraction of predictions within a factor of two of observations (FAC2), and the correlation coefficient (R). The requirements for valid data were as follows: \(-0.3<\) FB \(<0.3\), NMSE \(<1.5\), FAC2 \(>0.5\), and R \(>0.8\)[40]. In Table 2, all of the above metrics were within acceptable criteria. Although when \(\theta=45^{\circ}\), the relative difference of WW was relatively large, all metrics were within an acceptable range, so this set of data could be accepted. The reason for the significant relative difference may be due to unstable monitoring of the wind tunnel experiment, resulting in some negative values in the wind tunnel database.  
Overall, the numerical simulations successfully replicated the distribution of pollutants observed in wind tunnel experiments. Furthermore, the results from the four statistical analysis metrics employed to assess the model's reliability consistently fell within an acceptable range. As a result, the model demonstrated its suitability for predicting air flow and PM\({}_{2.5}\) dispersion within street canyons.
# A Review of Methods and Applications for a Heart Rate Variability Analysis
## 5 Dimensionality Reduction Techniques
### Independent Component Analysis (ICA)
#### 5.3.4 Fast ICA Algorithm  
Fast ICA is an algorithm designed by [PERSON] at the Helsinki University of Technology to implement ICA effectively [62]. The Fast ICA algorithm is simple and requires minimal memory space. This algorithm assumes that the data (say \(x\)) are already subjected to preprocessing like centering and whitening. The algorithm aims to find a direction (in terms of a unit vector \(w\)) so that the non-Gaussianity of the projection \(w^{T}x\) is maximized, where \(w^{T}x\) represents an independent component. The non-Gaussianity is computed in negentropy, as discussed in [74]. A fixed-point repetition system is used to discover the highest non-Gaussianity of the \(w^{T}x\) projection. Since several independent components (like \(w_{1}^{T}x,\ldots,w_{n}^{T}x\)) need to be calculated for the input data, the decorrelation between them is crucial. The decorrelation is generally achieved using a deflation scheme developed using Gram-Schmidt decorrelation [74]. The estimation of independent components is performed one after another. This is similar to a statistical technique called a projection pursuit, which involves finding the possible number of projections in multi-dimensional data. The Fast ICA algorithm possesses the advantage of being parallel and distributed, and possesses computational ease and less of a memory requirement.
## 4 Optimization Problem Modeling
### System Cache Queue Modeling  
As the computational tasks for IoT devices appear unpredictably and are sent to relay satellites in successive time slots, it is necessary to model the computation task cache queue at the devices. Given that relay satellites might handle data transmission services, accept and transfer computational tasks, and perform onboard tasks in each time slot, it is important to model a queue at the relay satellites to illustrate the caching scenario. Inspired by the analysis and modeling in [47; 48; 49], we model the queueing as follows.
# The Potential Therapeutic Role of Bruton Tyrosine Kinase Inhibition in Neurodegenerative Diseases
## 3 Bruton Tyrosine Kinase's Inhibitors in Neurodegenerative Disease
### Multiple Sclerosis
For instance, fenebrutinib is currently under evaluation in different MS trials, including studies on primary progressive MS (FENtrepid, ID: NCT04544449) and relapsing MS (FENhance 1 and 2, IDs: NCT04586010 and NCT04586023; FENupta, ID: NCT05119569), comparing it to correlizumab and terifulunomide. In particular, in FENupta, fenebrutinib showed maintenance of low disease activity in relapsing MS with a favorable safety profile, supporting phase III clinical trials [73]. Regarding FENtrepid and FENhance 1 and 2, the first data from these studies are expected at the end of 2025 and will be pivotal in characterizing fenebrutinib's effects on disease progression across the MS spectrum. Orelabrutinib is in phase 2 testing for relapsing-remitting MS (ID: NCT04711148), where it caused significant lesion reduction in all active treatment groups compared to placebo, while remibrutinib is being compared to terifulunomide in a large phase 3 relapsing MS study (ID: NCT05147220). Lastly, tolebrutinib showed efficacy in reducing brain lesions in RMS but had all trials paused due to liver toxicity concerns [74]. Unlike existing MS therapies that broadly suppress immune function and increase infection risk, BTK inhibitors offer a more targeted approach by modulating pathogenic immune pathways while sparing protective immunity. This mechanism may be particularly valuable in progressive forms of MS, where inflammation is compartmentalized within the CNS and resistant to peripheral immunosuppression [75]. In summary, BTK plays a critical role in MS pathogenesis through its effects on B cells and myeloid cells. BTK inhibition offers a rational, targeted therapeutic strategy with the potential to ameliorate disease activity, prevent progression, and reduce treatment-related toxicity in MS patients.
## 3 Results  
Descriptive statistics are reported in Table 1. Due to the non-normal distribution of most of the variables, a non-parametric test was used for all the comparisons except for the minimum HR. The present data show how, using a MAD device, respiratory parameters have been significantly reduced, decreasing apnea/hypopnea and desaturation events considerably. In fact, the mean AHI decreased by 21 and the ODI decreased by 13.8.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Mean**} & \multicolumn{2}{c}{**Standard**} & \multicolumn{2}{c}{**Mean**} & \multicolumn{2}{c}{**Normality**} & \multirow{2}{*}{**Test**} & \multirow{2}{*}{**\(p\)Value***} \\  & & **Deviation** & & & & & \\ \hline \multirow{2}{*}{AHI} & T0 & 25.34 & 10.6 & \multirow{2}{*}{\(-\)21.25} & * & [PERSON] & \\  & T1 & 5.53 & 4.1 & & & signed rank & \\ \hline \multirow{2}{*}{ODI} & T0 & 17.71 & 8.1 & \multirow{2}{*}{\(-\)13.87} & ns & [PERSON] & \\  & T1 & 3.84 & 3.0 & & * & signed rank & \\ \hline \multirow{2}{*}{Mean HR} & T0 & 59.92 & 7.1 & \multirow{2}{*}{\(-\)2.12} & * & [PERSON] & \\  & T1 & 57.80 & 6.9 & & ns & signed rank & ns \\ \hline \multirow{2}{*}{HR min} & T0 & 42.25 & 10.0 & \multirow{2}{*}{\(+\)4.29} & ns & Paired samples & \\  & T1 & 46.54 & 8.1 & & ns & T test & \\ \hline \multirow{2}{*}{HR max} & T0 & 136.10 & 44.6 & \multirow{2}{*}{\(-\)20.4} & * & [PERSON] & \\  & T1 & 115.70 & 34.8 & & ns & signed rank & \\ \hline \hline \end{tabular}  
* Statistically significant with \(p<0.05\); ** statistically significant with \(p<0.01\); ns, non-significant. Mandibular advancement device (MAD), apnea/hypopnea index (AHI), oxygen desaturation index (ODI), heart rate (HR) in beats per minute.  
\end{table}
Table 1: Descriptive statistics for the studied variables and statistical comparison before and after mandibular advancement device (MAD) therapy (\(n=50\)).  
Figure 1: Intraoral view of the customizable mandibular advancement device.  
Heart parameters are also significantly modified as a result of the use of a MAD. In particular, the maximum HR value decreased by 20 bpm and the minimum HR value increased by 4 bpm after MAD therapy. On the other hand, the value of the mean HR decreased by \(-\)2.12, which was statistically insignificant.
## 3 Image Acquisition and Methodology
### Image Characteristics Analysis
#### 3.2.1 Characteristics Analysis of the Lesion Leaf Images in the RGB Color Space  
In order to segment the lesions in the collected images, the color information should be used to analyze the variation rules of the different color components in different color spaces. In the RGB color space, through the analysis of the color components of R, G, and B in a large number of diseased leaves, it was found that the red component changes the most during the transition from a normal leaf to a diseased leaf. The color changes of R, G, and B in a1, b1, c1, and d1 in Figure 2 correspond to the values of R, G, and B of the white line pixels in a, b, c, and d in Figure 2, respectively. Since the white line of the mark includes both the normal leaf pixels and the diseased leaf pixels, the color with the most obvious change was found by analyzing the three-color change curve. As can be seen from Figure 2, the red R-value of the normal area is small, but the red R-value of the diseased area increased suddenly and even reached the maximum value among the three colors, indicating that the difference between the red R-value of the pixel of the normal leaf area and the pixel of the diseased leaf area is the largest, which could be used as the basis for the segmentation of the diseased image.  
Figure 2: Three-color change curves of the diseased leaves in the RGB color space.
## 3 Constitutive Model of Rock Mass Under THMC Coupling
### Constitutive Model Under THMC Coupling with Governing Equations
#### 3.1.3 Thermal Behavior (T)  
Temperature changes cause thermal expansion or contraction in the rock matrix and influence other fields, such as permeability (hydraulic field) and reaction rates (chemical field). The heat conduction through rocks is governed by Fourier's law.  
Governing Equation:  
\[qT=-k_{T}\
abla\mathrm{T} \tag{7}\]  
In Equation (7), \(qT\) is the heat flux, \(k_{T}\) is the thermal conductivity, and \(\
abla\mathrm{T}\) is the temperature gradient.  
The energy conservation equation, accounting for heat conduction and generation, is as follows:  
\[\rho c\frac{\partial T}{\partial t}=\
abla.(k_{T}\
abla\mathrm{T})+Q_{T} \tag{8}\]  
where Equation (8) contains \(\rho\) as the rock density, \(c\) as the specific heat capacity, \(T\) as the temperature, and \(Q_{T}\) as the heat source term (e.g., due to radioactive decay or chemical reactions).  
Thermal expansion is integrated into the mechanical strain equation:  
\[\epsilon T=\alpha_{T}(T-T_{0}) \tag{9}\]  
where \(\epsilon T\) is the thermal strain, \(\alpha_{T}\) is the coefficient of thermal expansion, and \(T_{0}\) is the reference temperature in Equation (9).
# MPEG and DA-AD Resilient DCT-Based Video Watermarking Using Adaptive Frame Selection
## 3 Proposed Method
### Adaptive Frame Selection  
With the proposed watermark embedding, pseudo-random patterns as a watermark are inserted into video frames that are robust against MPEG compression. The details of the adaptive frame selection process are summarized below.  
To observe the compression effect of H.264 on a watermark extractor, we conducted a simple experiment. Using the algorithm introduced in Section 3.4, a sample pseudo-random pattern was repeatedly embedded into each frame. The modified frames were  
Figure 1: Overall process of the proposed watermarking scheme: (**a**) Watermarking embedding step, and (**b**) extraction step.  
then compressed using the MPEG-4 part 10 codec algorithm. Subsequently, the correlation responses in each video frame image were computed by adopting the extraction process described in Section 3.5. Figure 2 presents a sample of the H.264 compression effect on the correlation values as an extractor response. The watermark detector correlations are decreased in the middle frame section, which includes several video motions. Moreover, a degradation of the extractor responses becomes quite severe in a compressed video. In the case of a compressed video, the extractor responses are entirely degraded, and periodic peaks occur in the sequence of the extractor responses. These peaks generally appear in the intra-coded picture (I-frame) in a group of pictures (GOP) [28]. I-frames in GOP are more robust against lossy compression than other of video frames such as P and B-frames because they are less affected by intra-frame encoding with a motion estimation.  
In our research, watermarks were efficiently embedded using this property. For example, the embedding of watermark patterns into video frames that are not robust to MPEG compression is inefficient. Thus, as shown in Figure 3, 1-s intervals with the highest correlation responses were selected from a 7.5-s interval. The selected video frames were watermarked during the embedding step, and the other frames remained unchanged to achieve a higher imperceptibility score.  
A watermarked frame exists in any 15-s frame intervals in the video after the adaptive frame selection because the maximum interval of the time between two consecutive watermarked frame intervals is 13 s. Therefore, the proposed watermarking system ensures that 16 bits of secret data can be extracted from any 15-s interval in the compressed video frames.
## 2 Materials and Methods
### Methods
#### 2.3.2 Descriptions of GPP Models
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Biomes and Other LULC** & **Ratio (\(\sigma\))** & **LUE (\(\varepsilon\)) Max** & **Tmin** & **Tmax** & **Topt** \\ \hline Evergreen needleleaf (EVGNL) & 0.5853 & 0.985 & 10 & 40 & 20 \\ Evergreen broadleaf (EVGB) & 0.4125 & 0.485 & 10 & 48 & 28 \\ Deciduous needleleaf (DecNL) & 0.5488 & 0.692 & 10 & 40 & 20 \\ Deciduous broadleaf (DECB) & 0.5488 & 0.542 & 10 & 40 & 20 \\ Grass and crop (Grass) & 0.5523 & 0.542 & 10 & 48 & 30 \\ Nonvegetated & na & 0.542 & 10 & 48 & 27 \\ Urban & na & 0.542 & 10 & 48 & 27 \\ Water bodies & na & 0.389 & 10 & 40 & 27 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The specific look-up table of the optimized \(\sigma\) ratio, temperature parameters (in \({}^{\circ}\)C) and maximum light-use efficiency (LUE (\(\varepsilon\))max) for different dominated biomes and other land covers in the GLR-SSA. where \(APAR_{Chl}\) and \(APAR_{Camopy}\) were calculated from photosynthetically active radiation (PAR) and the fraction of PAR absorbed by chlorophyll and canopy, respectively [17], as shown in Equations (8) and (10). \[APAR_{Chl}=PAR\times fPAR_{Chl} \tag{8}\]  
where \(fPAR_{Chl}\) is calculated as a linear function of the enhance vegetation index (EVI), which was modified from a previous model framework [6; 37]. \[fAPAR_{Chl}=(EVI-0.1)\times 1.25 \tag{9}\]  
The coefficients 0.1 and 1.25 were used to adjust for sparse vegetation and nonvegetated area. The \(fPAR_{Camopy}\) in this study was estimated to be equivalent to the leaf area index (LAI) or NDVI of the vegetation types in the GLR-SSA's terrestrial ecosystem, as suggested by [PERSON] [44]. \[APAR_{Camopy}=PAR\times LAI_{canopy} \tag{10}\]  
The \(\varepsilon_{g}\) is a function of temperature limitation (\(T_{solar}\)) and water stress (\(W_{scalar}\)) from its maximum value \(\varepsilon_{max}\), which was derived from different biomes (Table 2); the following equation (Equation (11)) is \(\varepsilon_{g}\) estimation formula:  
\[\varepsilon_{g}=\varepsilon_{max}\times T_{scalar}\times W_{scalar} \tag{11}\]  
Both \(T_{scalar}\) and \(W_{scalar}\) range from 0 to 1, and they can be calculated as follows:  
\[T_{scalar}=\frac{(T-T_{max})\times(T-T_{min})}{(T-T_{max})\times(T-T_{min})- \left(T-T_{opt}\right)^{2}} \tag{12}\]  
\[W_{scalar}=\frac{1+LSWI}{1+LSWI_{max}} \tag{13}\]  
\(T\) is air temperature (\({}^{\circ}\)C); \(T_{min}\), \(T_{max}\) and \(T_{opt}\) are minimum, maximum and optimal temperature (\({}^{\circ}\)C) parameters for photosynthesis of each biome, respectively [16]. Their values are illustrated in the different biomes look-up table (Table 2). \(LSWI_{max}\) is the maximum land surface water index at snow-free at each pixel and each timescale [37].
# DynseNet: A Dynamic Dense-Connection Neural Network for Land-Sea Classification of Radar Targets
## 2 The Proposed Methodology  
The proposed DynseNet architecture is shown in Figure 2 and primarily consists of a dynamic convolution module, a dense network, and an attention fusion module. First, we use the dynamic convolution module to dynamically adjust the convolution operations based on the input data, allowing the model to better adapt to input data. The input data is obtained after the X-band radar receives the echo information, which is then processed through Cell-Averaging Constant-False-Alarm-Rate (CA-CFAR) detection [20], clustering, etc. The detailed processing procedure is described in Section 2.1. Next, we employ a dense network to extract echo features. Through the dense connectivity mechanism, each layer receives feature maps from all preceding layers, enabling feature reuse. Finally, we utilize a dual-domain attention fusion module to fuse information from both global and local domains to obtain relevant features. These relevant features are then fed into a classifier to achieve classification of land and vessel targets.  
Figure 2: The overall structure of our proposed method.
# The Role of Microorganisms and Their Antibacterial Compounds in Food Biopreservation
## 4 Antimicrobial Compounds Produced by Yeasts  
Yeasts are a diverse group of microorganisms that can thrive in a variety of challenging environments and subsequently spread throughout both human and environmental ecosystems. Scientists are paying more and more attention to the competitive characteristics against other microbes, and they have suggested that these qualities could be successfully applied in the food biopreservation process [92].  
Competitiveness for nutrients and available space, the synthesis and secretion of antibacterial compounds, mycoparasitism and the secretion of lytic enzymes, the formation of biofilms, quorum sensing, the induction of systemic resistance in the host, and the generation of reactive oxygen species are the primary mechanisms of the action of bioprotective yeasts. By preadapting yeasts to abiotic stressors such as sublethal oxidative stress and cold acclimation, one can increase the efficacy of antagonistic yeasts and their ability to perform biocontrol functions under a variety of environmental conditions, hence lowering financial losses [93].  
Because of their broad-spectrum antibacterial activity, genetic stability, minimal nutritional needs, and capacity to flourish at low temperatures, yeasts are appealing candidatesfor biological control. Moreover, yeasts are resistant to oxidative damage and low pH. These characteristics provide credence to the use of yeast as a biological antagonist [94].  
There have been a lot of studies carried out on the use of yeasts as biocontrol agents in processed foods like coffee, juices, cheese, dry-cured meat products, and fermented goods, but not much on their antagonistic activity in managing undesired yeasts in the brewing and wine industries or the biological control of postharvest diseases in fruits. Table 3 shows selected examples of the usage of yeasts in food biopreservation [95].