esa-sceva/satcom-chunk-collection huggingface dataset:
# Glucose Isomerase: Functions, Structures, and Applications
## 2 Function  
### Substrate Specificity  
Glucose/xylose isomerase (GI/XI) is known to catalyze the reversible isomerization of D-glucose and D-xylose to D-fructose and D-xylulose, respectively (Figure 1).  
In addition to its natural substrates, GI can isomerize various pentoses, hexoses, sugar alcohols, and sugar phosphates. It exhibits activity against a broad spectrum of sugar  
Figure 1: Catalytic reaction of glucose isomerase. Reversible isomerization between (**a**) D-glucose and D-fructose, and (**b**) D-xylose and D-xylulose.  
substrates, including D-ribose [5], L-rhamulose [6], L-arabinose [7], and D-allose [8], although the substrate specificity of GIs can vary depending on the source strain or organism [1]. For example, GI from _Streptomyces griseofuscus_ S-41 shows isomerase activity on D-glucose (activity 100%) and D-xylose (287%) as well as on D-ribose (20%) [5]. The GI from _Streptomyces rubiginos_ exhibits activity on L-arabinose, in addition to D-glucose and D-xylose [7]. L-arabinose is widely used in food products with a low glycemic index, and in pharmaceutical and chemical industries [9]. The commercially available immobilized GI from _Streptomyces murinus_ (SmGI) has previously been used to produce L-rhamulose (6-deoxy-L-sorbose) from 300 E \({}^{-1}\) L-rhamnose in a packed-bed reactor, with a 45% conversion yield [6]. L-rhamulose plays an important role in sugar metabolism and has wide applications in the flavor industry [6; 10]. Furthermore, SmGI has been employed to produce D-allose from D-allulose in a packed bed reactor [11]. At the optimal conditions of pH 8.0 and 60 \({}^{\circ}\)C, SmGI produced an average of 150 g/L D-allose over 20 days from 500 g/L D-allulose as the substrate (productivity of 36 g/L/h). D-allose is a rare sugar used as a non-caloric and non-toxic sweetener [8]; it has attracted increasing research interest owing to its beneficial medical properties, including anti-cancer, anti-oxidant, anti-inflammatory, and anti-hypertensive effects [11]. Moreover, SmGI can isomerize D-glucose, D-xylose, D-ribose, and L-rhamnose to D-fructose, D-xylulose, D-ribulose, and L-rhamnulose, respectively [11]. Thus, besides their normal ability to interconvert D-glucose/D-fructose and D-xylose/D-xylulose, GIs can also produce rare sugars that are used in food or medicinal products. GIs from various species could be developed for industrial applications by exploiting their intrinsic substrate specificity for the isomerization for rare sugars.
###### Abstract  
In the presence of modeling uncertainties and input saturation, this paper proposes a practical adaptive sliding mode control scheme for an underactuated unmanned surface vehicle (USV) using neural network, auxiliary dynamic system, sliding mode control and backstepping technique. First, the radial basis function neural network with minimum learning parameter method (MLP) is constructed to online approximate the uncertain system dynamics, which uses single parameter instead of all weights online learning, leading to a reduction in the computational burdens. Then a hyperbolic tangent function is adopted to reduce the chattering phenomenon due to the sliding mode surface. Meanwhile, the auxiliary dynamic system and the adaptive technology are employed to handle input saturation and unknown disturbances, respectively. In addition, a neural shunting model is introduced to eliminate the \"explosion of complexity\" problem caused by the backstepping method for virtual control derivation. The stability of the closed-loop system is guaranteed by the Lyapunov stability theory. Finally, simulations are provided to validate the effectiveness of the proposed control scheme.  
Keywords:underactuated USV; trajectory tracking; neural network; adaptive technology; sliding mode control; input saturation +
Footnote †: journal: Applied Sciences
## 2 Laser Control of Ring Currents in a Molecule Having a Single Aromatic Ring
### Laser-Polarization Effects on Ring Currents within the Frozen-Nuclei Approximation
#### 2.2.1 General Form of Polarized Laser Pulses  
Before entering into the analysis of optical excitation processes in aromatic molecules, let us describe the mathematical form of a classical laser field of arbitrary polarization. It is given in the dipole approximation by:  
\[\mathbf{E}(t)=\frac{E^{0}}{2}f(t)\Big{[}e^{-i(\omega t+\varphi)}\mathbf{e}+e^{i( \omega t+\varphi)}\mathbf{e}^{*}\Big{]}, \tag{4}\]  
where \(t\) is time, \(E^{0}\) is the peak field strength, \(\omega\) is the central frequency, \(\varphi\) is the optical phase, and \(\mathbf{e}\) is the complex polarization unit vector. The envelope function \(f(t)\) varies between zero and unity for \([0,\,t_{f}]\) with \(t_{f}\) being the pulse duration; otherwise \(f(t)=0\). The polarization vector \(\mathbf{e}\) for an arbitrarily polarized light is:  
\[\mathbf{e}=e^{-i\delta}(\cos\beta)\mathbf{e}_{+1}+e^{i\delta}(\sin\beta) \mathbf{e}_{-1}, \tag{5}\]  
where \(\mathbf{e}_{+1}\) and \(\mathbf{e}_{-1}\) are the spherical unit vectors corresponding to positive and negative helicities (spins), respectively. When the laser field propagates in the \(z\) direction and thus oscillates in the \(xy\) plane, the spherical unit vectors can be defined as follows:  
\[\mathbf{e}_{\pm 1}\equiv 2^{-1/2}\big{(}\mathbf{e}_{x}\pm i\mathbf{e}_{y} \big{)}, \tag{6}\]  
where \(\mathbf{e}_{x}\) and \(\mathbf{e}_{y}\) are the unit vectors in the \(x\) and \(y\) directions, respectively (in some references the signs \(\mp\) are placed before \(2^{-1/2}\)). Following the convention in optics, we refer to \(\mathbf{e}_{+1}\) and \(\mathbf{e}_{-1}\) as left and right circular polarizations, respectively. In Equation (5), \(\delta\) denotes the orientation angle of the major axis of the polarization ellipse with respect to the \(x\) axis and \(\beta\) is termed the ellipticity angle in this article (Figure 1). The range of \(\beta\) is \([0,\,\pi/2]\) and the minor-to-major axial ratio of the polarization ellipse is \(\mid\)tan \((\pi/4\,-\,\beta)\mid\); for example, \(\beta=0,\,\pi/4\), and \(\pi/2\) designate left circular, linear, and right circular polarizations, respectively. The general form of \(\mathbf{E}(t)\) in Equation (4) will be utilized in the model analysis presented in Section 2.2.2.
## 1 Introduction
Furthermore, the model demonstrates enhanced robustness by focusing on discriminative regions and reducing the impact of irrelevant information. In summary, this paper builds upon existing technologies and introduces significant improvements to overcome the limitations of traditional methods in detecting low-quality forged images. The proposed YOLOv9-ARC algorithm offers a more efficient and adaptable solution, advancing the state-of-the-art in forgery detection under challenging conditions. The key contributions of this paper are outlined below:  
1. We propose YOLOv9-ARC, which is an improved detection framework specifically designed for low-quality deepfake images characterized by noise, blur, and low resolution. 2. We introduce Adaptive Kernel Convolution (AKConv) to enhance the model's ability to extract meaningful features under degraded visual conditions. 3. We incorporate the Convolutional Block Attention Module (CBAM) to emphasize critical facial regions and suppress irrelevant noise, improving detection accuracy. 4. Extensive experiments demonstrate that YOLOv9-ARC achieves superior performance and generalization on low-quality datasets compared to baseline methods. Compared to existing techniques, the innovations of this paper include the following:  
1. Combining adaptive convolution and attention mechanisms to improve detection performance: Unlike methods that rely only on super-resolution restoration (e.g., [PERSON] et al. [16]) or feature extraction (e.g., [PERSON] et al. [14]), YOLOv9-ARC integratesboth adaptive convolution and attention mechanisms, achieving more robust low-quality forgery detection. 2. Better generalization and adaptability: While the DIFLD method [12] mainly relies on high-dimensional feature distribution learning, AKConv enables the model to optimize feature extraction across varying resolutions and noise levels, improving its adaptability to complex datasets. 3. Optimized computational efficiency: Compared to computationally expensive super-resolution methods (e.g., [PERSON] et al. [20], who used a pix2 pix network for low-resolution face enhancement), YOLOv9-ARC reduces computational complexity through its lightweight AKConv design, making it more suitable for real-time applications. This paper is structured as follows: Section 1 reviews related works on deepfake detection with a focus on challenges posed by low-quality synthetic images. Section 2 introduces the proposed YOLOv9-ARC model in detail, including the AKConv and CBAM modules. Section 3 presents the experimental setup and evaluation results on the DFDC dataset. Section 4 discusses the implications of the findings, current limitations, and future research directions. Finally, Section 5 concludes the paper by summarizing the key contributions and outcomes.
## 5 Results and Analysis
### Key Performance Indicators  
Power, energy and equivalent level of storage are discussed in this section. Table 2 summarizes the main indicators of the system components.  
\begin{table}
\begin{tabular}{l l c c c c} \hline \hline  & **Performance Indicator** & **Spring Day** & **Summer Day** & **Autumn Day** & **Winter Day** \\ \hline \multirow{4}{*}{Energy (\&Wh)} & Electricity from wind energy & 9.99 & 13.6 & 14.4 & 13.2 \\  & Electricity from fuel cells & 6.25 & 5.66 & 6.65 & 5.11 \\  & Electricity for hydrogen generation & 6.36 & 7.87 & 5.58 & 6.60 \\  & Load consumption & 9.86 & 10.40 & 14.65 & 11.75 \\  & Fed-in energy to the grid & 0 & 1.26 & 1.53 & 6.06 \\ \hline \multirow{4}{*}{Power (\&Wh)} & Load power & 0–5.81 & 0–6.1 & 0–9.63 & 0–8.91 \\  & Power from wind & 0–1.4 & 0–4 & 0–1.2 & 0–0.9 \\ \cline{1-1}  & Power from fuel cells & 0–5.71 & 0–5.82 & 0–8.68 & 0–8.29 \\ \cline{1-1}  & Fed-in power to the grid & 0 & 0–3.75 & 0–0.95 & 0.82 \\ \hline \multirow{4}{*}{Duration (h)} & Fuel cell duty time & 14.6 & 10.3 & 9.2 & 7.3 \\  & Hydrogen production & 9.4 & 12.5 & 13.6 & 16.7 \\ \cline{1-1}  & Peak hours & 0.09 & 0.03 & 0.05 & 0.25 \\ \hline \multirow{4}{*}{Percentage (\%)} & \(ESOC_{S}\) & 50–73.2 & 18.6–90 & 16–90 & 50–90 \\ \cline{1-1}  & Local consumption of the wind energy & 100 & 90.7 & 89.4 & 51.7 \\ \cline{1-1}  & Fed-in energy to the grid & 0 & 9.3 & 10.6 & 48.3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Key performance indicators of the proposed system.  
Figure 12: Smart grid fed-in power in the four days. (**a**) spring day; (**b**) summer day; (**c**) autumn day; (**d**) winter day.
# A Study on Identification of Development Status of MASS Technologies and Directions of Improvement
## 3 Decision-Making System for MASS Operation
[PERSON] et al. [35] also proposed a decision system that determines the optimal ship speed by considering weather data. Recently, [PERSON] et al. [36] analyzed research related to decision support systems and classified nine categories (collision avoidance; weather conditions; routing; stability and cargo; engine damage; ship maneuvering; hull loads and damage; ice navigation routing; miscellaneous), with consideration for the technology readiness level related to MASS operation. This paper also analyzed the area of application, potential end-users, features, and gaps and limitations. Issues identified and items to be considered for decision support technology for MASS are presented in Table 2.  
It will be difficult to say which of the technology will evolve into a major technology. However, there is no doubt that decision support or intelligent decision systems to respond to collision avoidance, emergency situations, optimal route determination, and optimal ship speed determination should be applied to the MASS.
###### Abstract  
This study aimed to assess the penetration of AH-Plus (AHP) and AH-Plus Bioceramic (AHPB) sealers into dentimal tubules subsequent to different irrigation activation protocols. One hundred fifty teeth were divided into five groups according to the final irrigation protocol: conventional syringe irrigation (CSI), EndoActivator (EA), passive ultrasonic irrigation (PUI), Er.Cr.YSGG laser, and SWEEPS. Then, the teeth were divided into two subgroups (\(n\) = 15) according to the canal sealer type. The maximum penetration depth (MPD) point and percentage of the sealer were evaluated using confocal laser scanning microscopy. Then, the data were statistically analyzed. The highest MPD values were observed in the SWEEPS groups, while the lowest value was in the CSI groups. Considering the effect of the sealer type and irrigation protocol interaction, there was a significant difference between the Er,Cr:YSGG laser, and EA groups for the AHPB sealer (\(p\) < 0.05), while it was insignificant for the AHP sealer (\(p\) > 0.05). The AHP and AHPB sealers statistically presented similar penetration properties. Laser-activated irrigation presented a higher sealer penetration compared to the other techniques.  
bioceramic; Er,Cr:YSSG; irrigation activation; sealer penetration; SWEEPS Article
## 2 Three-Component Decomposition  
According to Freeman-Durden three-component decomposition [21], for a multi-look polarimetric SAR image, the \(3\times 3\) polarimetric coherency matrix \(\mathbf{T}\) can be decomposed into surface, double-bounce, and volume scattering components,  
\[\mathbf{T}=P_{s}\mathbf{T}_{surface}+P_{d}\mathbf{T}_{double}+P_{v}\mathbf{T}_{volume}, \tag{1}\]  
where \(P_{s}\), \(P_{d}\), and \(P_{v}\) are the powers of the surface, double-bounce, and volume scattering, and \(\mathbf{T}_{surface}\), \(\mathbf{T}_{double}\), and \(\mathbf{T}_{volume}\) are the models of coherency matrices of the surface, double-bounce, and volume scattering, respectively.  
\(\mathbf{T}_{surface}\) is the scattering model of a first-order Bragg surface scatterer. \(\mathbf{T}_{double}\) is the scattering model of a dihedral corner reflector scatterer. \(\mathbf{T}_{volume}\) is the scattering model of the sum of a cloud of randomly oriented cylinder-like scatterers, i.e.,  
\[\mathbf{T}_{volume}=\frac{1}{4}\begin{bmatrix}2&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix}. \tag{2}\]  
Assuming the scattering is reflection symmetry, [PERSON]'s three-component decomposition can be successfully used to discriminate the scatterer with different surface, double-bounce, and volume scattering components. However, there will exist some pixels that the symmetry assumption does not hold. As a result, the power of some decomposition components will be negative. To solve the negative power problem, the author in [28] proposed an improved three-component decomposition method. He found that the cause of the problem is that the volume scattering component is modeled by a cloud of randomly oriented cylinder-like scatterers, which will cause the volume scattering power to be overestimated sometimes. Thus, he proposed processing the coherency matrix using a deorientation step and modeling the volume scattering \(\mathbf{T}_{volume}\) by a \(3\times 3\) unit diagonal matrix \(\mathbf{T}_{vol}\), i.e.,  
\[\mathbf{T}_{vol}=\frac{1}{3}\begin{bmatrix}1&0&0\\ 0&1&0\\ 0&0&1\end{bmatrix}. \tag{3}\]  
In the deorientation process, the polarization orientation angle (POA) is firstly estimated by [PERSON]'s method [29] or the method in [28]. The coherency matrix is then rotated by the POA. If the estimated POA is \(\theta\), then the deorientation coherency matrix \(\mathbf{T}^{\prime}\) is  
\[\mathbf{T}^{\prime}=deorientation(\mathbf{T})=\mathbf{U}\mathbf{T}\mathbf{U}^{H}, \tag{4}\]where \(\mathbf{U}^{H}\) denotes the Hermite transpose of \(\mathbf{U}\), and \(\mathbf{U}\) is given by  
\[\mathbf{U}=\begin{bmatrix}1&0&0\\ 0&cos(2\theta)&sin(2\theta)\\ 0&-sin(2\theta)&cos(2\theta)\end{bmatrix}. \tag{5}\]  
The improved three-component decomposition can then be represented as follows.  
\[\mathbf{T}^{\prime}=P_{s}^{\prime}\mathbf{T}_{surface}+P_{d}^{\prime}\mathbf{T}_{double} +P_{v}^{\prime}\mathbf{T}_{vol}, \tag{6}\]  
where \(P_{s}^{\prime}\), \(P_{d}^{\prime}\), and \(P_{v}^{\prime}\) are the new powers of the surface, double-bounce, and volume scattering, and \(\mathbf{T}_{vol}\) is the new model of coherency matrices of volume scattering.
# Evaluation Method for Rock Burst Hazards in Strip Filling of Working Faces in Deep Coal Mines
## 4 Case Investigations
### Impact Hazard Evaluation
#### 4.2.2 Local Impact Risk
 \tag{20}\]  
According to the geological conditions of the C8301 working face, taking \(W_{\text{ET}}=3.52\) and putting it into Equation (11), the influence coefficient of coal elastic energy index on local impact risk can be obtained as follows:  
\[\varphi_{W_{\text{ET}}}=0.1667W_{\text{ET}}-0.0834\quad(0,424) \tag{21}\]  
By putting Equations (19)-(21) into Equations (12) and (13), the C8301 working face's belt entry, track entry, and working face are divided into six impact risk areas, including two weak impact risk areas, two medium impact risk areas, and two strong impact risk areas, as shown in Table 3 and Figure 7.  
The comprehensive index method is to determine the impact risk level of the mining area by comprehensively analyzing the geological factors and mining technical conditions of the mining area. It is a commonly used impact risk evaluation method in the field of coal mines. The comprehensive index method is used to evaluate the impact risk of the C8301 working face in the Yunhe coal mine, and the evaluation results are shown in Figure 8. Compared with the evaluation results obtained in this paper, the evaluation results obtained by the comprehensive index method have a larger range of strong impact risk areas and medium impact risk areas. This is because filling mining does not consider the significance of filling mining for the prevention and control of rock bursts. For the \begin{table}
\begin{tabular}{c c c} \hline \hline
**Influencing Factors** & **Stress Increment Concentration** & **Influence Range/m** \\ \hline Folds & 0.1 & 0, 424 \\ Initial fracture & 0.1 & 60, 90 \\ Working face square & 0.2 & 90, 160 \\ Two working face square & 0.5 & 160, 300 \\ Three working face square & 0.7 & 300, 380 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Stress increment concentration factors of impact risk in strip working face. The commonly used pressure relief measures include hydraulic fracturing, large-diameter borehole pressure relief, deep blasting, and so on. Based on the evaluation results of the impact risk of the C8301 working face in the Yunhe coal mine, for the medium impact risk area and the strong impact risk area, it is suggested that the large-diameter borehole pressure relief and the strike blasting roof cutting technology of the coal body can be used to relieve the pressure of the high-stress coal body and the 8.4 m thick hard and thick sandstone above, thereby reducing the stress concentration of the surrounding rock of the roadway. At the same time, it can strengthen the advanced support of the roadway to effectively prevent the occurrence of rock bursts.
# Generic Methodology for Field Calibration of Nacelle-Based Wind Lidars
## 6 Uncertainties of Reconstructed Wind Characteristics
### Wind Field Reconstruction Techniques  
To reconstruct wind characteristics from single Doppler wind lidar data, hypotheses on the flow field must be made, i.e., a flow model is assumed.  
Examples of spatial assumptions that may be employed in wind field reconstruction (WFR) are: horizontal homogeneity (e.g., VAD techniques classically used by ground-based lidars); two- or three-dimensional wind vector; vertical and/or longitudinal shear profile (linear, power law, log-law, etc.). These assumptions are sufficient for static WFR (see [8], suitable for power performance applications), which disregards the propagation of the wind field over time. In the dynamic case (see [19; 20], suitable for turbine control applications) flow models may additionally assume specific velocity and turbulence field structures (coherence) and [PERSON]'s frozen turbulence hypothesis.  
Once the flow model is defined, a least squares problem can be formulated: lidar measurements may be fitted to the model by projecting wind characteristics onto multiple LOS, thus minimising errors between'simulated' and measured LOS velocities.  
When the least squares problem is linear (such as in certain cases of flow homogeneity), analytical expressions of wind characteristics can be derived by matrix inversion [8].  
The usable methods to propagate uncertainties on \(V_{LOS}\) and other inputs to wind characteristics depend on the complexity of the WFR. Numerical techniques such as Monte-Carlo, bootstrap or Polynomial Chaos Expansion may be implemented. Although they might be computationally expensive, they are particularly relevant for non-linear flow models where advanced WFR fitting techniques are employed. The aforementioned uncertainty propagation methods are additionally appropriate to handle correlation between uncertainty distributions. Details on advanced WFR techniques and the corresponding uncertainty propagation methods are outside the scope of this paper.  
For the two-beam lidar case hereafter, we chose to apply the GUM methodology as simple analytical expressions of wind characteristics are available.
## 1 Introduction  
Plants emit a series of characteristic volatile blends, such as terpen and green leaf volatiles, when they are damaged by insect feeding. These herbivore-induced plant volatiles are known to either attract natural enemies of the herbivores or induce defense responses of other plants in the vicinity, and thus volatiles play an important role in plant defense against herbivores [1; 2; 3]. Plant volatile emissions are activated by not only elicitors found in herbivore oral secretions [4; 5; 6; 7], but also treatment with a phytohormone, jasmonic acid (JA) [8], and its related compound, methyl jasmonate (MeJA) [3; 9; 10]. It has been shown that JA signaling lies downstream of insect feeding [11] and that exogenous MeJA is converted into jasmonoyl isoleucine (JA-Ile) via JA _in planta_, and the resulting active endogenous JA-Ile activates plant indirect defenses [12].  
The Japanese beetle, _Popillia japonica_ (Coleoptera: Scarabaeidae), is known to be a destructive pest in North America that damages leaves, flowers or fruits of more than 300 plant species [13]. Both female and male beetles are attracted by floral or fruit-like odors, such as geraniol, eugenol and phenethyl propionate, which enhance the attraction of males when treated with a sex pheromone, japonicum [14, 15]. During our field research around Akita City in Japan, the beetles are often found on the leaves of the giant knotweed, _Fallopia sachalinensis_ (Polygonaceae), and infested plants in the field smell like sweet grapes (our personal observation). The same grape-like odor is reproduced under laboratory conditions where the beetles are fed on \(F\). _sachalinensis_ leaves. In this context, we speculate that the odor will function for beetle attraction, and plan to investigate the volatile composition. Here we report the identification of volatiles from the leaves of \(F\). _sachalinensis_ infested by \(P\). _japonica_ and that an uncommon type of herbivore-induced volatile, phenylacetonitrile (benzyl cyanide), from \(F\). _sachalinensis_ is also induced by treatment with exogenous airborne MeJA.