esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyclic Consistent Image Style Transformation: From Model to System
## 3 Image Style Transfer Technology Based on GAN
### Image Style Transfer Based on CycleGAN  
Despite their effectiveness, GAN-based image style transfer techniques have several limitations, including the need for extensive data, significant computational resources, and lengthy training times. During training, the generator and discriminator can become unstable, complicating convergence to an optimal solution. Additionally, the generator may overfit to fine details and noise in the target style image, resulting in generated images that lack clarity and accuracy.  
Traditional image-to-image translation methods often require separate models for different tasks. For example, converting a black-and-white photo to color demands one model, while transforming a horse image into a zebra requires another. The CycleGAN architecture addresses these issues by introducing a cyclic consistency loss function, which enables image translation between domains without paired training data [31]. This innovation significantly reduces the cost and time required for training. Further advancements have enhanced the CycleGAN model by improving the cyclic consistency loss function to learn many-to-many mappings without paired data, thereby boosting GAN performance in unsupervised settings [32]. Moreover, some studies have refined CycleGAN's image translation capabilities in complex scenes by incorporating a patch-based discriminator [33].  
Other research has explored the integration of network architectures like U-Net and ResNet into GANs to achieve more effective style transfer. For instance, DU-GAN [34] incorporates a U-Net-based discriminator within the GAN framework. This U-Net-based discriminator offers dual benefits: it provides pixel-level feedback to the denoising network through its output while also considering global structure at the semantic level via its intermediate layers. In another study [35], a signal-to-image conversion method was developed to transform time-domain fault signals into RGB image format, which serves as the input datatype for ResNet-50. This led to the proposal of a new TCNN (ResNet-50) structure, which has been tested on multiple datasets, including the bearing damage dataset from KAT datacenter, the motor bearing dataset from Case Western Reserve University (CWRU), and a self-priming centrifugal pump dataset.  
[PERSON] et al. [36] introduced an iterative architecture for Restorable Arbitrary Style Transfer (RAST) to address content leak issues in arbitrary style transfer. RAST achieves the transmission of both content and style information through multi-restorations, controlling the content-style balance in stylized images through the precision of image restoration. To ensure the effectiveness of this architecture, two novel loss functions--multi-restoration loss and style difference loss--were designed. Additionally, a new quantitative evaluation method was proposed to assess content preservation and style embedding performance.
## 4 Review of Literature
### Supply and Delivery in Routing Optimisation Problems
#### 4.1.1 Machine Learning Methods in Supply and Delivery VRPs  
Among all of the specified VRP solution approaches classified, the adoption of the ML solution approach is very limited, due to the curse of dimensionality that is inherited by ML. This is observed in Tables 14-16. However, some papers did employ the ML solution approach on a realistic problem instance focusing on a specific case study. For instance, ref. [29] incorporated an assessment on sight within a distribution problem by introducing corporation between three humanitarian agents: Relief Assessment Team (RAT), Emergency Response Team (ERT), and Decision Making Agent (DMA) in order to deliver relief supply more efficiently. The DMA learns to make a routing decision for both RAT and ERT through reinforcement learning, which incorporates both the dynamic and stochastic nature of the demand and route access. The initial assessment is made by RAT at a location that is decided by DMA via route also computed by the DMA. RAT, in return, would update the demand and route information back to the DMA, which could be used in order to decide on demand distribution via ERT, which communicates in the same manner as RAT. The highlight of this methodology is the ability of DMA to learn continuously in both online and offline modes. In the offline mode, simulation is performed to generate a scenario in allowing the DMA to learn. Meanwhile, in the online mode, real information is updated and included in the learning process of DMA. Thus, this particular work highlights the strength of the ML solution approach in addressing complex coordination while addressing both dynamic and stochastic elements of the problem in a flexible manner.
###### Abstract  
This study focused on the experimental drying of Bo Chinh ginseng by the ultrasound-assisted heat pump drying method, in which the effects of drying air temperature (T), ultrasonic power (P), intermittency ratio of ultrasound generator (A) on the moisture effective diffusion coefficient (Deff), saponin content (Sp) and color change (dE) during the drying process were determined. The results showed that the drying time was reduced from 12.5 h at 35 \({}^{\circ}\)C to 8.9 h at 40 \({}^{\circ}\)C and greatly reduced to 5.8 h at 55 \({}^{\circ}\)C. The moisture diffusion coefficient Deff and color change index increased with the increase in drying temperature. Meanwhile, from 35 \({}^{\circ}\)C to 45 \({}^{\circ}\)C, the saponin content after drying tended to increase gradually, but when the temperature continued to rise from 45 \({}^{\circ}\)C to 55 \({}^{\circ}\)C, the saponin content decreased. The effect of ultrasonic power in the drying process was in particular as follows. The drying time of 8.8 h at 40 W ultrasonic power reduced to 7.8 h at 80 W ultrasonic power, and reduced to 7 h at 160 W ultrasonic power. The remaining saponin content after drying tended to increase gradually at the power level of 40-120 W (from 89.2% to 95.2%) and decrease when the power increased from 120 to 160 W (from 95.2% to 90.5%). The moisture diffusion coefficient Deff also increased with the increase in ultrasonic power. Meanwhile, the color change of dried products decreased as the ultrasonic power increased in the range of 40-120 W but increased with the ultrasonic power range from 120 to 160 W. Additionally, the experimental method and Box-Behnken design were used for optimizing the drying process with the optimal drying conditions such as drying air temperature of 45.2 \({}^{\circ}\)C, ultrasonic power of 127.7 W and intermittency ratio of ultrasound generator of 0.18.  
heat pump; drying temperature; saponin; Bo Chinh ginseng; ultrasound; drying time; moisture effective diffusion coefficient; color change Article
# In-Vehicle Data for Predicting Road Conditions and Driving Style Using Machine Learning
## 8 Discussion
Another solution is to use oversampling techniques to increase the positive classes' samples. The permutation feature importance technique was used to rank the input features based on its impact on the detection results. It was noticed that decision trees and random forests have almost the same rank for the features, while SVM showed different ranking. If we have to develop a voting system to choose between many ML detection, it is important to choose ML algorithms that build different classification models and think differently. Feature ranking showed that some features did not have high impact to the detection results. For example, engine load and manifold absolute pressure data have a very low impact on the driving style detection. Altitude variation and vehicle speed variation have a very low impact on driving style detection in SVM. Therefore, eliminating low-ranked features can improve the ML system performance and helps avoiding model over fitting; it also helps in the practical implementation of the system. Figure 3: Accuracy, precision, recall and F-score for road surface conditions results. More work can be added in the future to this research. Collecting more data from other vehicle systems, such as suspension, brake and gear, can help improve the results. Extracting some statistics from the data, such as mean, standard deviation and median, can add more value to the input features. Ranking the features and eliminating the low impact features is also a good practice to reduce system complexity. Fusing data from many resources provides a better understanding about the vehicle surrounding area and then yield to a better prediction system. Therefore, fusing in-vehicle data with connected vehicle data should boost the performance of the ML system. Adding data from sensors such as camera, radar and Lidar will improve the detection results. A deep learning algorithm, such as neural network, can be suggested as a future work. Neural networks may have a better performance than the conventional ML algorithms, such as random forests and SVM. However, deep learning techniques require more computation  
Figure 4: Accuracy, precision, recall and F-score for road traffic conditions results. Figure 5: Accuracy, precision, recall and F-score for driving style results. and then a more expensive system. Therefore, choosing between deep learning and the traditional ML algorithms is a trade off between system accuracy and system cost.
# Effect of Initial Granular Structure on the Evolution of Contact Force Chains
###### Abstract  
The effect of initial granular structural conditions on load transmission patterns was experimentally investigated. Two types of granular structures were prepared by laminating cylindrical model particles of different diameters, to which photoelastic sheets were attached. Two-dimensional, reflective photoelasticity tests were performed under two granular conditions: (1) a uniform structure without initial defects and (2) with initial local imperfections at the bottom of the granular assembly. Two granular assemblies were tested for uniaxial compressive loading and shallow foundation loading conditions. For macroscopic analyses of the load-displacement relationship, the photoelastic response of individual particles was measured to microscopically observe the distribution of the main contact force chains within each granular assembly. Furthermore, the effect of initial local defects on the bearing capacity of granular assemblies was examined by confirming particle movement and the expansion of initial local defects in the granular assembly via particle image velocimetry (PIV). As a result, a completely different form of internal contact force chain was developed from the beginning of loading to the final failure stage, depending upon whether or not initial local instability existed in the granular assembly. In particular, a significant effect on the bearing capacity was found under shallow foundation loading conditions.  
bearing capacity; contact force chain; granular assembly; granular instability; shallow foundation; photoelasticity; particle image velocimetry
## 4 Discussion
### Mechanisms of Microstructure Evolution Using Beam Oscillation  
The stirring effect of beam oscillation on the molten pool is the main reason for the grain refinement, which has been studied in detail in oscillating laser welding [33] and oscillating laser cladding [34]. The fast and periodic motion of the oscillation beam generates an oscillatory shear force on the melt pool. Further, the oscillatory shear force is increased sharply as the oscillation frequency increases, driving the melt to form vortices along the oscillation trajectory. Under the effect of the rotating vortex, the initial solidified grain structure is disrupted to provide more nucleation sites for subsequent grain growth, resulting in grain refinement [35]. Therefore, the higher the oscillation frequency, the better the grain refinement effect.  
In addition, according to the experimental results, the precipitated phases in the thin-walled components are Al\({}_{8}\)Mn\({}_{5}\) and Mg\({}_{17}\)Al\({}_{12}\), the ratio of which is affected by the Al/Mn atomic content. From the above discussion, it is clear that the stirring effect of the oscillating laser on the melt pool disrupts the crystal structure of the initial solidification to produce more nucleation sites. The nucleation sites increase with the growth of oscillation frequency, making the Al/Mn atoms easier to crystallize and, thus, improving the precipitated phase content. Meanwhile, the precipitated phase produces collision and polymerization to increase the size as a result of the accelerated flow rate of the melt pool. Further, part of the precipitated phase will be re-dissolved and diffused due to the accumulation of heat at the middle and top so that the solidified content is slightly lower.  
Therefore, as shown in Figure 15, the mechanism of reducing the grain size and increasing the proportion of the precipitated phase using an oscillating laser can be summarized as follows.  
Figure 14: Relation curve between scanning speed and oscillating frequency.  
When the laser does not oscillate, there is no stirring melt flow in the melt pool, and grain growth and precipitation phase precipitation are only influenced by the temperature gradient melt flow. The probability of collision and aggregation of Al/Mn atoms is reduced, and most of them are diffusely distributed in the \(\alpha\)-Al matrix without forming precipitation phases. Eventually, after melt pool solidification, coarse-sized grains, as well as a low percentage of precipitated phases, are obtained in the components.  
When the beam oscillates, the stirred melt flow generated with the oscillating laser breaks up the grains and accelerates the melt flow. The Al/Mn atoms in the fast-moving melt flow have a higher probability of colliding and coalescing together to form precipitation phases. Because these precipitation phases are precipitated from the liquid phase, they are also able to provide nucleation sites for grain growth. Eventually, after melt pool solidification, the components produced with beam oscillation have refined grains and more precipitated phases.
## 3 Results
### Expression of KRAS and HRAS Genes in NSCLC  
Statistical analysis in the TNMplot database revealed that _KRAS_ was highly expressed in lung adenocarcinoma in comparison with the normal samples from non-cancerous patients (tumor n = 524, normal n = 486) and the difference was statistically significant (\(p<0.05\)), as presented in Figure 2a. The _KRAS_ gene was also overexpressed in lung squamous cell carcinoma on the basis of analysis 476 normal and 501 tumor samples (\(p<0.05\)) (Figure 2b).  
As it was shown in Figure 2c, the _HRAS_ gene expression level in LUAD samples (n = 524) was lower than in normal tissue (n = 486), with a statistical significance of \(p<0.05\). On the contrary, _HRAS_ in LUSC was overexpressed compared to non-cancerous patients (tumor n = 501, normal n = 476) and this difference was statistically significant (\(p<0.05\)) (Figure 2d). Detailed statistical values of the above-mentioned analyses are contained in Supplementary Table S1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Gene Name** &
\begin{tabular}{c} **GeneBank Accession** \\ **Number** \\ \end{tabular} & **Sequence** & **Product Length (bp)** \\ \hline _GAPDH_ & NM\_001289745 & Reverse: 5′-ATGCCAGTGAGCTTCCGTTCAGC-3′ & 145 \\  & & Forward: 5′-TGGTATCGTGAAAGGACTCATGAC-3′ & \\ _HRAS_ & NM\_176795 & Reverse: 5′-GCCCTGCCCCACCTG-3′ & 276 \\  & & Forward: 5′-CACGGAAGGTCCTGAGGGG-3′ & \\ _KRAS_ & NM\_004985 & Reverse: 5′-TCCTGAGAATTCCTATTG-3′ & 134 \\  & & Forward: 5′-GCCCTGAAAATGATG-3′ & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Sequences of primers used in the study.  
Figure 1: _KRAS_ (**a**) and _HRAS_ (**b**) pan-cancer expression (significant differences are marked with red).  
Figure 2: A comparison of _KRAS_ and _HRAS_ expression between normal and tumor tissues of NSCLC patients in the TNMplot database ([[https://www.tnmplot.com/](https://www.tnmplot.com/)]([https://www.tnmplot.com/](https://www.tnmplot.com/)) (accessed on 15 June 2022)). _KRAS_ gene in LUAD and LUSC samples was significantly overexpressed in tumor compared to normal (**a**,**b**). _HRAS_ mRNA expression was lower in LUAD than in normal tissue (**c**), but higher in LUSC (**d**).
## 3 Regression Analysis of Price (USD/MWh) Data for West, North, and Houston Regions in ERCOT
### Hourly Models for West Region Price Data  
Hour 3 AM Model:  
\[LN(Price)=b_{0}+b_{1}WSapp18+b_{2}Wprice1+b_{3}Wprice32+\] \[b_{4}LN(NGprice)+b_{5}LN(Wgen)+b_{6}LN(Ngen)+b_{7}(Wload)+\] \[b_{8}LN(REload)\]  
Hour 11 AM Model:  
\[LN(Price)=b_{0} +b_{1}WSapp18+b_{2}Wprice50+b_{3}LN(NGprice)\] \[+b_{4}LN(Sgen)+b_{5}LN(Wgen)+b_{6}LN(Ngen)+b_{7}(Wload)\] \[+b_{8}LN(REload)\]Hour 4 PM Model: \[\begin{array}{ll}LN(Price)=b_{0}&+b_{1}WSapp18+b_{2}Wprice90+b_{3}LN(NGprice)\\ &+b_{4}LN(Sgen)+b_{5}LN(Wgen)+b_{6}LN(Ngen)+b_{7}(Wload)\\ &+b_{8}LN(REload)\end{array}\] Variable Definitions: For convenience, the dummy variable definitions from above follow: * WSapp18: Active wind and solar applications equal 18 or more is coded 1, else 0; * Wprice1: In the 3 AM model, if price is at most USD 1, Wprice1 is coded 1, else 0; * Wprice32: In the 3 AM model, if price is at least USD 32, Wprice32 is coded 1, else 0; * Wprice50: In the 11 AM model, if price is at least USD 50, Wprice50 is coded 1, else 0; * Wprice90: In the 4 PM model, if price is at least USD 90, Wprice90 is coded 1, else 0; * NGprice: Natural Gas Price; * Wgen: Wind Generation; * Ngen: Nuclear Generation; * Wload: West Load; * REload: Rest of ERCOT Load is always defined as the ERCOT Load minus the specific region's load; hence, here it is the ERCOT Load minus West Load, and similarly for the other regions in the models in Sections 3.3 and 3.4.
# Estimation of the Measurement Accuracy of Wireless Passive Resonance Sensors
## 2 Signal-to-Noise and Interference Ratio by a High-Q Resonator as Cooperative Target
### Interference Due to Environmental Echoes
\(\left(\frac{\lambda}{4\pi R}\right)^{4}\) gives the geometrical spread of the readout signal and the response signal, \(e^{-2\alpha_{m}R}\) a possible viscose damping of the signal along the distance \(R\) in the transmission medium, \(e^{-2\alpha t_{gate}}\) the decaying of the resonator signal due to the waiting time \(t_{gate}\) until all strong environmental echoes have died out, and \(g_{t}\) and \(g_{r}\) the gain of the transducers to couple into the transmission medium. If a bi-static setup is applied, \(g_{t}{}^{2}\) or \(g_{r}{}^{2}\) has to be replaced by \(g_{t1}\cdot g_{t2}\) or \(g_{r1}\cdot g_{r2}\), respectively. The decay of the ambient echoes usually can be characterized by a quality factor, \(Q_{tc}\). The above equation can be rewritten with the help of \(Q_{tc}\) as  
\[P_{gated}=e^{\left(-2\pi\frac{Q_{tc}}{Q}\right)}\cdot P_{in}. \tag{14}\]  
Figure 5 shows the start of the received power \(P_{gated}\), which is still present in the reader after the time gate in order to eliminate ambient echoes before the start of sampling. This power level then decreases according to the quality factor of the resonator. The excitation setup and the colors for the frequency bands were selected as in Figure 3. The remaining power for resonators with a quality factor of 10 is shown in dotted lines, for resonators with a quality factor of 100 in dashed lines, and for resonators with a quality factor of 1000 in solid lines. The quality factor of the transmission channel \(Q_{tc}\) was set to 15. The signals from the resonators with a quality factor of 10 are strongly attenuated by the time gate, while the signals from the resonators with a quality of 100 or 1000 were hardly reduced. Figure 5: Remaining initial power level \(P_{gated}\) in the reader when reading passive resonant transponders with a quality factor of 10, 100 and 1000 after a time gate to eliminate ambient echoes. The quality factor of the transmission channel was set to 15. There are three curves for each of the frequencies 433 MHz (red line), 868 MHz (green line), 915 MHz (blue line), 2.45 GHz (magenta line) and 5.8 GHz (black line) for the resonator quality factors of 1000 (solid line), 100 (dashed line) and 10 (dotted line). All other parameters correspond to those in Figure 3.  
The signal with power \(P_{g\!g\!d}\) in Equation (13) usually is boosted with a low-noise amplifier and then mixed down to baseband with the actual local oscillator signal and sampled. Depending on the distance between the stimulating frequency \(\omega\) and the natural frequency of the resonator \(\omega_{d}\), the down mixed signal only shows a few oscillations before it disappears into noise.
## 2 Generalized Longitudinal Equivalent Continuous Model
### Longitudinal Equivalent Bending Stiffness  
Under the longitudinal bending, when the tensions of the bolts are all smaller than the elastic limit \(P_{y}\), the whole segment is in the elastic state. Figures 1 and 2 show the longitudinal deformation and stress distribution of the segment inside and outside the influence scope of annular gaps. \(a\) and \(b\) are the long half axis and short half axis of the ellipse respectively, the ellipse is the cross-section of the segment under surrounding rock pressure, \(\phi\) shows the position of neutral axis, \(c\) is the distance between neutral axis and y-axis, \(ds\) is the length of the micro-segmentation, \(d\beta\) is the corresponding angle and \(x\) is the distance between neutral axis and the micro-segmentation, \(\varepsilon_{t}\) is the tension strain of the segment and \(\varepsilon_{c}\) is the compressive strain of the segment, \(M\) is the bending moment, \(E_{c}\) is the elastic modulus of tunnel reinforced concrete segment and \(\delta\) is the displacement; \(ls\) is the segment length between the centerline of the two-segment rings and \(\theta\) is the rotation angle.  
Based on the basic assumptions, the equilibrium equation, the deformation compatibility equation, and the tunnel equivalent bending stiffness (\(EI\))\({}_{eq}\) can be obtained [31].  
\[{(EI)}_{eq}=\frac{M\!I_{s}}{\theta}=\frac{M\!I_{s}}{\frac{\frac{M}{E_{c}I_{s}} +\frac{M(\!I_{s}\!-\!\lambda\!l)}{\frac{E_{c}I_{s}}{\lambda l}+\frac{M(\!I_{s} \!-\!\lambda\!l)}{E_{c}I_{c}}}}+\frac{M(\!I_{s}\!-\!\lambda\!l)}{E_{c}I_{c}} =E_{c}I_{c}\frac{I_{s}}{\frac{E_{c}I_{s}}{\lambda l}+\frac{E_{c}I_{s}}{\lambda \left(E_{c}+k_{j}\right)}}+\left(I_{s}-\lambda l\right). \tag{2}\]  
The longitudinal stiffness effective rate \(\eta\) is [32],  
\[\eta=\frac{\left(EI\right)_{eq}}{E_{c}I_{c}}, \tag{3}\]  
where [16],  
\[\begin{array}{l}Z_{1}=\frac{(a+b)b^{2}t\left(\frac{a}{2}-\varphi\right)}{2} +\frac{(a+b)^{3}t\sin^{2}\varphi\left(\frac{a}{2}-\varphi\right)}{4}+\frac{(a +b)b^{2}t\sin 2\varphi}{4}-\frac{(a+b)^{2}t\sin 2\varphi}{2}\\ Z_{2}=\frac{(a+b)b^{2}t\left(\frac{a}{2}+\varphi\right)}{2}+\frac{(a+b)^{3}t \sin^{2}\varphi\left(\frac{a}{2}+\varphi\right)}{4}-\frac{(a+b)b^{2}t\sin 2 \varphi}{4}+\frac{(a+b)^{2}t\sin 2\varphi}{2}\end{array}\]  
\(I_{c}\) is the inertia moment of tunnel segment cross-section; \(t\) is the thickness of lining ring.  
Figure 1: Diagram of stress and deformation of the lining within the influence range of annular gaps [16].  
Figure 2: Diagram of stress and deformation of the lining outside the influence range of annular gaps [16].