esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 BIT* Algorithm Procedure
### Improved BIT* + Energy Consumption Equation + TD3 Hybrid Path Planning Strategy Model
#### 3.3.6 Path Reward Mechanism Design  
The path reward mechanism of the BIT* algorithm comprehensively considers path length, turning cost, and energy consumption to ensure that the Unmanned Surface Vehicle (USV) avoids collisions while minimizing energy consumption as much as possible. The calculation formula for the reward value is as follows:  
\[R=P_{length}+C_{turning}+C_{emerging}, \tag{8}\]  
where \(\text{P}_{\text{length}}\) represents the path length, \(\text{C}_{\text{turning}}\) denotes the turning cost, and \(\text{C}_{\text{emerging}}\) represents the energy consumption. A shorter path length, lower turning cost, and lower energy consumption result in a higher reward value. The path length is calculated by summing the Euclidean distances between consecutive nodes, the turning cost is determined by accumulating the weighted sum of all turning angles in the path, and the energy consumption is derived from a combination of path length and turning cost, with weighting coefficients used to balance their respective impacts on energy expenditure. This reward mechanism not only guides the USV towards selecting the shortest path but also discourages the generation of excessively tortuous trajectories, thus enabling efficient and energy-conscious path planning in complex environments. For instance, between two paths, a longer path might be favored due to its fewer turns and smaller turning angles, resulting in a higher reward. Through this mechanism, the BIT* algorithm achieves a balance between path length, turning cost, and energy consumption, generating paths that are both efficient and energy-conscious, suitable for USV navigation tasks in complex environments.  
For safety considerations, Unmanned Surface Vehicles (USVs) must avoid collisions and successfully complete navigation tasks. Consequently, we employ the following safety reward to prevent collisions:  
\[R_{crack}=-10\;(\text{Black}). \tag{9}\]
# Anti-Galling Cold, Dry Forging of Pure Titanium by Plasma-Carburized AISI420J2 Dies
## 3 Experimental Results
### Plasma Carburizing of AISI420J2 Dies  
XRD analysis was employed to describe the carbon supersaturation into the martensitic stainless steel matrix at 673 K. After the first-principle calculation of this carbon supersaturation [12], the Fe-Cr lattice expands itself by the carbon solute atom occupation of its octahedral vacancy site. Similar to nitrogen supersaturation [13; 14], this lattice expansion is detected by the peak shift of the original \(\alpha\)'-lattice to the lower \(2\theta\) direction. Figure 3 depicts the narrow-scanned XRD diagram in the range of 43\({}^{\circ}\) \(<\) 2\(\theta\) \(<\) 46\({}^{\circ}\). The original peak of \(\alpha\)' (110) at 2\(\theta\) = 44.5\({}^{\circ}\) shifts to two peaks at 2\(\theta\) = 43.6\({}^{\circ}\) and 43.7\({}^{\circ}\), respectively. As reported for the nitrogen supersaturation of AISI420J2 specimens at 673 K, these peaks correspond to the formation of carbon-supersaturated austenitic and martensitic phases (\(\gamma_{\rm C}\) and \(\alpha\)'C) of AISI420J2, respectively. The SEM-EDX image of the cross section of the carburized punch reveals that the carburized layer thickness at 673 K for 14.4 ks reaches 50 \(\mu\)m.  
Figure 3: XRD diagram of the surface of the plasma-carburized AISI420J2 punch.  
Figure 4 depicts the SEM-EDX image of the surface of the plasma-carburized AISI420J2 punch with its element mapping. The case-hardened AISI420J2 punch substrate consists of an iron-chromium binary alloy matrix and chromium carbides. For these precipitates, little carbon agglomerate was detected on the binary alloy punch surface. That is, no free or unbound carbon dots were present on the carburized iron-chromium binary alloy matrix surface.
# Design of a Computed Tomography Automation Architecture
## 2 Computed Tomography Setup and Initial Methods
### Source Types  
Industrial radiography sources vary in geometry, energy, strength, and radiation type. Discussion of the different beam geometries was discussed earlier--parallel, fan, or cone beam. Energies for X-ray industrial radiography vary widely, usually from \(\sim\)30 keV to several MeV [13]. X-ray tubes normally are used for the low and medium source energies up to 450 keV. Radioactive isotopes can also be used in radiography, which require no electrical power supply. Some of the radioisotopes (and average energy levels) used include Tm-170 (72 keV), Yb-169 (200 keV), Ir-192 (450 keV), Se-75 (320 keV), Co-60 (1250 keV), and Cs-137 (660 keV). MeV-level radiography normally requires a linear accelerator to be used as a source. Neutron radiography utilizes a separate radiation source type and is used because materials have different attenuating properties for neutrons and photons. Neutron radiography can be performed in beamline facilities at many nuclear research reactors, spallation source facilities, or independent from large facilities with fusion neutron generators [14].  
Beamline facilities at nuclear research reactors can also be used for photon radiography. For example, a MeV-level photon imaging facility was built and demonstrated at the University of Texas at Austin's \"Training Research and Isotope Production General Atomics\" (TRIGA) MARK-II research reactor [15]. This illustrates the flexibility in source type that can be used for industrial radiography, although the strength and geometric resolution capabilities of the reactor source fall short of those that can be achieved using a linear accelerator. An X-ray and gamma-ray radiography facility was set up and the 7 DOF SIA5D robotic manipulator has since been used to perform automated CT acquisition with a scintillator and digital charge-coupled device (CCD) camera.
## 3 Results
### In-Situ X-ray Diffraction of LiCl-PDMS Foam during Delhydration Reaction  
We report, in Figure 3, the crystal structure evolution of LiCl-PDMS foam while dehydrating through in situ XRD measurements. The XRD patterns code, the experimental conditions, the refined lattice parameters, and the volume and phase fraction are reported in Table 1.  
It is well known from literature that LiCl, besides anhydrous, exists in another four solid hydrate forms with one, two, three, and five water molecules, and that these salts are extremely hygroscopic and soluble in water. As inferred from XRD analysis in Figure 2 (pattern (a)), at the initial conditions, namely r.T. and when exposed to air, the LiCl in the PDMS foam is completely amorphous (Figure 3 (pattern [a])), thus indicating that the salt is overhydrated.  
After 30 min under a dry N2 flow, lithium chloride was monohydrated (PDF 04-013-8884) and exhibited a tetragonal structure (P42/nmc; Figure 3 (pattern [b])). After 1 h under the same conditions, the material began to dehydrate (Figure 3 (pattern [c])) and the cubic (Fm-3m) anhydrous phase (PDF 00-004-0664) was present in \(\sim\)3.7% of the material fraction, as calculated from the Rietveld refinement. As the temperature increased to 50 \({}^{\circ}\)C, the most intense reflections indicative of the cubic structure (indexed (111) and (200)) began to be more clearly distinguishable (Figure 3 (pattern [d])), while the ones indicative of the tetragonal structure (indexed (202) and (220)) significantly decreased in intensity. From the phase fraction evolution in Table 1, it is clear that most of the dehydration reaction occurs during the isothermal holding step at 50 \({}^{\circ}\)C (Figure 3 (pattern [e])). Indeed, after  
Figure 3: In situ XRD analysis of LiCl-PDMS foam under dry inert atmosphere while heating with measurement temperature as indicated (\({}^{\circ}\)C).  
Figure 2: Three-dimensional optical image of (**a**) cross-section area of LiCl-PDMS foam at 50\(\times\) magnification. (**b**) Higher magnification of a portion of the foam.  
reaching this temperature, the fraction of the anhydrous phase increased by about 60%. At 80 \({}^{\circ}\)C, the material was fully dehydrated (Figure 3 (pattern [f])), and a slight increase in the lattice parameters was observed after the isothermal hold at 80 \({}^{\circ}\)C for 30 min (Figure 3 (pattern [g]), Table 1). As lithium chloride transformed from monohydrate to anhydrous, it underwent a significant volume contraction, reduced by ~70% of its initial volume.  
After the complete dehydration reaction, the temperature in the XRD chamber was decreased to r.T. and the collected XRD pattern is reported in Figure 4. The analysis after cooling at r.T. (Figure 4, patterns (h) and (i)) showed the persistence of the anhydrous phase obviously due to the lack of humidity in the chamber. In order to rehydrate the material, the LiCl-PDMS foam was exposed to air at r.T. for 1 h. The collected XRD pattern (Figure 4, pattern (j)) showed a completely amorphous structure due to the overhydration of the LiCl salt. This, as expected, is indicative of the complete reaction reversibility.
# Estimation of Thermal Diffusivity for Greenhouse Soil Temperature Simulation
## 2 Materials and Methods  
### Theory of Soil Temperature Simulation  
Soil heat transfer occurs mainly via molecular transmission. In this study, the soil was assumed to be infinitely deep, and heat transfer occurred in a one-dimensional isotropic medium. With uniform thermal properties, the open-field soil surface temperature can be calculated by [14]  
\[\mathrm{T}(0,t)=T_{ave}(0)+A(0)\sin[\omega t-\varphi]\] (1a) where \[\mathrm{T}(0,t)\] is the temperature at the soil surface at time t, \[{}^{\circ}\mathrm{C}\] \[t\] is the time of day, h; \[T_{ave}(0)\] is the average temperature at the soil surface, \[{}^{\circ}\mathrm{C}\] ; \[A(0)\] is the amplitude of the soil surface temperature fluctuation, \[{}^{\circ}\mathrm{C}\] ; \[\varphi\] is the phase shift, the delay of soil surface temperature change at soil surface, h; \[\omega\] is the radial frequency and is expressed as \[\omega=\frac{2\pi}{\tau}\] (1b) where \[\tau\] is the period of temperature fluctuation and is set to 24 h.  
From Equation (1a), the temperature at any depth \[z\] and time \[t\] is given by: \[\mathrm{T}(z,t)=T_{ave}(z)+A(z)\sin[\omega t-\varphi-\varphi_{z}]\] (2) where \[A(z)\] is the amplitude of the soil temperature fluctuation at depth \[z\], \[{}^{\circ}\mathrm{C}\] \[T_{ave}(z)\] is the average temperature at the soil depth \[z\], \[{}^{\circ}\mathrm{C}\] ; \[\varphi_{z}\] is the soil thermal transfer phase shift, the delay between the soil surface and depth \[z\] ; \[A(z)\] and \[\varphi_{z}\] are defined as [8] \[A(z)=A(0)\exp(-z\mathrm{B})\] (3)\[\varphi_{z}=-zB \tag{4}\]  
where B is the inverse of the damping depth, and it is calculated by:  
\[B=\sqrt{\omega/2k} \tag{5}\]  
where \(k\) is the soil thermal diffusivity, m\({}^{2}\) s\({}^{-1}\), and it is calculated by:  
\[k=\lambda/\rho c \tag{6a}\]  
where \(\lambda\) is the soil thermal conductivity (w m\({}^{-1}\) K\({}^{-1}\))  
\(\rho\) is the density of the soil, kg m\({}^{-3}\)  
c is the soil specific heat, J kg\({}^{1}\) K\({}^{-1}\)  
\(\rho c\) is the volumetric heat capacity, J m\({}^{-3}\) K\({}^{-1}\)  
Similarly, the soil temperature at depth \(z_{2}\) can be calculated from the soil temperature at depth z\({}_{1}\) as follows:  
\[T(z_{2},t)=T_{ave}(z_{2})+A(z_{2})\sin\left[\omega t-\varphi-\varphi_{z_{1}}- \varphi_{z_{2}-z_{1}}\right] \tag{6b}\]  
With the boundary conditions at the surface (\(z=0\)) and substituting of Equations (3) and (4) into Equation (2), the soil temperature at any depth \(z\) and time \(t\) can be calculated by:  
\[T(z,t)=T_{ave}(z)+A(0)\exp(-zB)\sin[\omega t-\varphi-zB] \tag{7}\]  
In addition, the soil temperature at depth \(z_{2}\) predicted from the soil temperature at depth \(z_{1}\) can be calculated by:  
\[T(z_{2},t)=T_{ave}(z_{2})+A(z_{1})\exp(-z_{2}B)\sin\left[\omega t-\varphi- \varphi_{z_{1}}-(z_{2}-z_{1})B\right] \tag{8}\]  
The soil thermal diffusivity needed to calculate the inverse of damping depth (B) can be determined using one of the three methods discussed in the following section.
## 3 Results and Discussion
### Other Effects (Surface and Thermal Convection Effect)  
When the probe light and pump laser light were focused on the solvent/glass surface, we did not observe an amplification of the absorbance change. Though an air/solvent surface effect on the laser-induced crystallization was reported in reference [32], no indication of a contribution from the surface effect was found in our experiment. The phenomena, which we observed as absorbance change, are most likely to occur in bulk solvent circumstances.  
When the temperature gradient occurs in a solvent, the thermal convection should affect the molecular motion. The volume force by convection is described as follows [63]:  
\[F=g\rho\beta T \tag{4}\]  
where \(g\) is the gravitational acceleration, \(\rho\) is the density of water, \(\beta\) is the thermal expansion coefficient, and \(T\) is the temperature difference from room temperature. As discussed in the former report [48], the TPPS molecular volume (\(\sum_{i}\frac{4}{3}nr_{i}\)\({}^{3}\); \(r_{i}\) is van der Waals radius of TPPS atom i) and temperature rise for laser irradiation were estimated to be 1.46 nm\({}^{2}\) and 20 degrees, respectively. For these parameters and estimation, the force on the TPPS molecule by convection was \(6.0\times 10^{-26}\) N. The light-gradient force was \(1.0\times 10^{-24}\) N with a laser irradiation power of 265 W/cm\({}^{2}\), radii of the irradiation spot of 120 \(\upmu\)m, and TPPS polarizability of \(3.9\times 10^{-38}\) cm\({}^{2}\)/_V_, as estimated with the following formula [64]:  
\[\alpha=3\varepsilon_{0}v \tag{5}\]  
where \(\varepsilon_{0}\) is the vacuum permittivity and \(v\) is the TPPS volume mentioned above. Thus, the thermal convection effect was even smaller than the effect of the light-gradient force.
# Study on the Influence of Rock Shape on Rolling Distance
## 4 Theoretical Analysis
### Model Derivation
This ratio, which quantifies the rolling resistance dependent on the stone's shape, is referred to as the rolling resistance coefficient \(\psi\):  
\[\psi=\frac{u_{1}-u_{2}}{u_{1}} \tag{7}\]  
where  
\(u_{1}\)--the area of the smallest circumscribed circle among the maximum two-dimensional cross-sectional shapes of the stone perpendicular to the slope;  
\(u_{2}\)--the area of the maximum two-dimensional cross-sectional shape of the stone perpendicular to the slope. As mentioned earlier, the rolling resistance of the stone is influenced by both its shape and the deformation that transpires on the rolling surface as the stone moves. To calculate the overall rolling friction coefficient, we must take into account rolling resistance coefficients from two perspectives: the properties of the rolling surface in contact with the stone and the geometric shape of the stone. The rolling friction coefficient \(\mu\) for the rolling surface material employed in this experiment is determined through experimentation. Meanwhile, the rolling friction coefficient \(\psi\), stemming from the geometric shape of the stone, is computed using Equation (7). The rolling resistance \(f\) of the test block during rolling is subsequently expressed as:  
\[f=(\psi+\mu)N \tag{8}\]  
where  
\(\mu\)--rolling friction coefficient originating from ground characteristics, determined experimentally in this paper. When the stone is in motion on the ground, a kinematic analysis of the block depicted in Figure 24 produces the following equations:  
\[mg\sin\theta-f=ma \tag{9}\]  
\[a=g[\sin\theta-(\psi+\mu)\cos\theta] \tag{10}\]  
\[v^{2}-v_{0}^{2}=2 as \tag{11}\]  
Figure 20:0  
Figure 20:1 Schematic of three categories of scales for rock blocks. \(S\)--total rolling distance;  
\(S_{n}\)--rolling distance on different ground features. For test blocks with an aspect ratio below two, when defining the rolling range, you can initially compute the deviation angle \(\beta\) using [PERSON]'s method [50]. Subsequently, guided by the calculated deviation angle, you can ascertain the maximum rolling distance free from deviations, and the maximum rolling distance with deviations, for rolling along both the long and short axes. Connecting the determined endpoints forms a sector-shaped rolling range, as depicted in Figure 25a. For test blocks with an aspect ratio below two, it is sufficient to calculate the rolling stopping distance when rolling along the axis with lower rolling resistance and subsequently connect the computed endpoints, as illustrated in Figure 25b.
# Advancing GAN Deepfake Detection: Mixed Datasets and Comprehensive Artifact Analysis
## 2 Related Work
### Deepfake Creation Methods  
In this section, we explore deepfake creation methods using Generative Adversarial Networks (GANs), acknowledging that other techniques also exist for generating forgeries.  
1. _Creation of forgeries with GANs_  
The Generative Adversarial Network (GAN) [2] is a machine learning framework that consists of two competing neural networks: the generator (G) and the discriminator (D). These networks engage in a zero-sum game [14; 15; 16].  
The generator produces outputs without learning the features of the input training dataset, meaning it does not grasp the semantics of the data. Meanwhile, the discriminator evaluates both the training samples and the generated samples, training to maximize the probability of correctly classifying real training samples as genuine and generated samples as fake.  
During training, the generator continually refines its outputs to create realistic images that can deceive the discriminator, while the discriminator works to accurately distinguish between real and generated images. Both networks are optimized simultaneously usingcross-entropy loss functions in a two-player minimax game, with \(\Lambda(D;G)\) serving as the value function [17].  
\[\min_{G}\max_{D}\Gamma(G,D)=E_{x\in p_{data}}[log(D(x))]+E_{z\in p_{gan}}[log(1- D(G(z)))] \tag{1}\]  
It is thus inevitable to obtain better results in a model where two different deep learning networks compete against each other.  
The generator is also sometimes known as the decoder, while the discriminator is alternatively known as the encoder or parser.  
Various types of GANs have been developed for face and body alteration and forgery. RSGAN [18] and StarGAN [19] perform face replacement using facial and hair features, and facial features and facial expressions, respectively. GANimation [20] performs synthesis while transferring the contours of a face without distorting the transferred identity. PGGAN [9] produces hyper-realistic fakes while BiGAN [4] produces fakes that are difficult to detect due to its use of a random noise variable received by the encoder in addition to the GAN structure. CBiGAN [21] was developed to minimize BiGAN's generalization losses and complete the learning process at lower power consumption. It has been stated that WGAN [22] provides better quality output due to improved stability in the optimization process and the correlation between the convergence of the generator and the loss function compared to the standard GAN model [14].  
Figure 1 shows examples of faces generated by InterfaceGAN, ProGAN, StyleGAN3, and a real image from the CelebA dataset.
## 4 Simulations
### Simulation Parameters and Simulation Process
Overhead on average: This parameter represents the network overhead of a successful message delivery between a pair of nodes. Overhead on average \(O_{node}\) can be calculated as \(O_{node}=\left(T_{tr}-T_{s}\right)/T_{tr}\), where \(T_{tr}\) is the total time of message forwarding and \(T_{s}\) is the total time of successful data transmission between nodes. Average end-to-end delay: This parameter refers to the delay of routing selections, of relay nodes waiting for messages, and of message forwarding. Average end-to-end delay can be shown as \(\theta_{del}=\theta_{sum}/\chi_{node}\), where \(\theta_{sum}\) is the total delay that is accumulated by each node and \(\chi_{node}\) is the number of nodes that successfully obtain the message.
###### Abstract  
The uncertainty and complexity of the geological parameters of coalbed methane (CBM) reservoirs may result in the assignment of atypical exact values to a whole block. Therefore, the influence of different resources and productivity characterization indices on the development of CBM blocks was analyzed systematically in this research. Furthermore, this research reported an interval-number index evaluation system and a gray relational analysis optimization model for CBM development. The weights of the evaluation indices were determined by combining the entropy method and the best worst method (BWM) with the objective deviation law of the data and the subjective experience of experts. Based on the gray system theory, a multi-index gray relational analysis optimization model was established to evaluate the interval values and optimize the development potential of CBM blocks with complex geological conditions. The results show that the order of importance of the eight resources and reservoir evaluation indices, from highest to lowest, is permeability, reservoir pressure gradient, gas content, reservoir thickness, reservoir depth, critical reservoir ratio, Young's modulus, and reservoir temperature. The order of the target blocks, from most to least optimal, is the Sanjiao block, Fanzhuang-Zhengzhuang block, Daning-Jixian block, Gujiao block, and Fengrun block. The evaluation results are consistent with actual conditions. Thus, the multi-index gray relation analysis optimization model, on account of the gray system theory, is feasible for the development evaluation of CBM blocks under complex conditions. This model is suitable for the CBM blocks in the range of 547 km\({}^{2}\)-675 km\({}^{2}\). When using this model, the index values must be representative and accurate.  
Keywords: CBM block evaluation; combination weight; gray relational analysis; interval-number index +
Footnote †: journal: Applied Sciences
# Advancing Smart Zero-Carbon Cities: High-Resolution Wind Energy Forecasting to 36 Hours Ahead
### Research Gap  
As illustrated in Table 2, reference [38] demonstrated a robust approach to both, wind speed and power forecasting, using models without a comparative analysis of AI models and considering varying time horizons. Similarly, reference [39] focused on short-term combined forecasting for wind power, without extending to computational time and cost evaluations or diverse time horizons. In reference [31], the study on offshore wind turbine power prediction models is limited solely to power prediction without addressing wind speed prediction or the broader range of AI model comparisons. Reference [40] explored enhanced wind speed prediction techniques and has dropped the integration of these predictions with power outputs or examined computational implications. Reference [41] covered ensemble learning methods for wind turbine power forecasting and missed out on the AI model comparisons and long-term forecasting horizons. Reference [42] provided a data augmentation approach to wind power forecasting, offering insights into model  
Figure 2: Wind speed and power forecasting time scales and applications [38; 39].  
performance improvements without expanding on the computational aspects or different time frames.