esa-sceva/satcom-chunk-collection huggingface dataset:
# Patch-Wise-Based Diffusion Model with Uncertainty Guidance for Low-Light Image Enhancement
## 3 Methods
### Implementation Details  
The proposed low-light image enhancement (LLIE) framework leverages a U-Net-based diffusion model with uncertainty guidance and patch-wise regularization. The U-Net consists of an encoder, bottleneck, and decoder with skip connections to extract hierarchical features and reconstruct the enhanced image, the structure detail shown in Table 1. The framework incorporates a parallel uncertainty estimation network to generatepixel-wise uncertainty maps, guiding the diffusion process to focus on challenging regions. During the forward diffusion, Gaussian noise is added iteratively, and the reverse diffusion process uses the U-Net to progressively refine the image. Patch-wise regularization ensures structural consistency by aligning the singular value matrices of clustered patches between the generated and ground truth images. The total loss combines an uncertainty-guided regression loss and a structural regularization loss, weighted dynamically. The model is trained using the Adam optimizer with a decaying learning rate and evaluated on multiple datasets (e.g., LOLv1, LOLv2-real, LOLv2-synthetic) with metrics such as PSNR, SSIM, and LPIPS, ensuring strong performance and replicability.
# Holistic Utility Satisfaction in Cloud Data Centre Network Using Reinforcement Learning
## 3 Modelling of the CDCN  
By definition, a CDC is composed of three essential elements, which are the cloud manager, virtualised data centres, and cloud users, as depicted in Figure 1. As shown in Figure 1, a cloud data centre network is composed of multiple physical data centres, which are under the control of a cloud manager/provider. Each CDC consists of a resource pool, which is comprised of VMs associated with the CPU cores, storage capacity, and RAM.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Strategy** & **References** \\ \hline
**Multi-disciplinary** & [2; 19; 20; 21; 22; 24; 28; 29; 30] \\
**Multi-disciplinary** & [36; 38; 40; 44; 65; 52; 57] \\ \hline
**ML-based** & [39; 41; 53; 54; 55; 56; 43; 53; 51; 52; 53; 54; 55; 56] \\ \hline
**Game-theory-based** & [26; 32; 48] \\ \hline
**Evolutionary/multi-objective-based** & [3; 37; 47; 49; 50] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of different cloud data centre resource allocation strategies.  
Figure 1: VM resource pools (CPU, RAM, etc.) in a typical cloud data centre network managed by a cloud provider.  
For a clearer demonstration of the parameters that are used in this section, we include the system parameters in Table 2.  
We assumed that time is slotted/framed and can be described by \(t=0,1,2,\cdots\). The time slot duration/period is considered to be \(\tau\). We also assumed that, in the cloud data centre environment, the number of active CDCN users is denoted by \(M(t)\), and the CDCN is comprised of \(N(t)\) physical and distinct CDCs at time frame \(t\).
## 5 Experimental Results and Analysis
### Over-the-Horizon PTV Perception Algorithm
#### 5.4.2 Result Analysis
Since the vehicle speed characteristics will be affected by the traffic flow in the section and the geographical environment, half an hour later, when the vehicle passes through a road section with expressway toll booths, especially the ETC channel, the owner tends to forget to reduce the speed to a reasonable range. The speed of the vehicle is within 30 km/h or 20 km/h, which is potentially threatening compared with normal driving vehicles. However, the traffic flow from the ramp to the main road is generally relatively small, and many car owners will involuntarily speed up. Therefore, on ramps and toll stations, a small number of passenger cars will appear to be relatively speeding. The safety distance in Figure 15b is 4 km. Due to the increase of the safety distance, the number of PTVs around the truck has increased, and the number of PTVs tracked and counted every 600 s is about four; while the speed of passenger cars fluctuates greatly, in  
Figure 15: Statistics of PTVs behind the safety distance. (**a**) safety warning distance: 2 km. (**b**) safety warning distance: 4 km. (**c**) safety warning distance: 6 km. the first half hour, there were approximately four PTVs around, and after half an hour, due to the influence of the geographical environment, the number of PTVs reached nine. The safety distance in Figure 15c is 6 km, and the PTVs around the truck have also increased due to the increase in the safety distance. The number of PTVs tracked every 600 s is approximately 8; while the bus has approximately 5 PTVs around the first half hour, half an hour later, the PTVs reach 14 due to the influence of the geographical environment. In order to capture the changing characteristics of PTV on the expressway in the dimension of safety distance, and to analyze the distribution law of PTV, the experiment conducts statistical analysis on trucks and passenger vehicles according to different safety distances in the current time. The experimental results are shown in Figure 16. Observing the experimental results, it can be seen that the safety distance dimension of PTV around passenger cars and trucks is an overall growth curve. With the increase of the safety distance, the PTV also increases to varying degrees, showing the objective law of expressway vehicle driving.
## 7 Results
### Sensor Comparison
#### 7.3.2 Field of View (FOV)  
Figure 13 shows segments of the Pisces constellation as captured by subpayloads 1 and 2 near the end of their operational phase in flight. The \(2048\times 2048\) resolution images were processed for celestial coordinates using astrometry.net [39] and labeled using the Tycho-2 catalog. Due to the optical sensors' similar FOV and placement within the payload, there was an expected overlap in starfield observations within regions of starfield imaging. Furthermore, Table 4 presents the calibration metrics alongside the right ascension and declination values from images presented in Figure 13.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Characteristic** & **PCO** & **IDS** \\ \hline Pixel Scale (arcsec/pixel) & 52.3 & 72.1 \\ Central Right Ascension (degrees) & 1.012 & 1.170 \\ Central Declination (degrees) & 9.816 & 10.757 \\ Orientation (East of North in degrees) & \(-\)153.6 & 153.4 \\ Radius (degrees) & 21.056 & 29.004 \\ \hline \hline \end{tabular}
\end{table}
Table 4: A detailed comparison of two cameras, PCO and IDS, based on various characteristics relevant to stellar observations from \(2048\times 2048\) images.  
Figure 12: The histogram displays the frequency of stars detected at different magnitudes (brightness levels) in the Johnson V (visual) band, with two datasets represented: subpayload 1 in blue and subpayload 2 in red. The x-axis represents the magnitude (Johnson V), a logarithmic scale used to measure the brightness of stars, while the y-axis indicates the frequency of stars at the detection’s magnitude ranges.
# Early Predictability of Grasping Movements by Neurofunctional Representations: A Feasibility Study
## 3 Results
### Grasping Posture Categorizations  
On inspecting the remaining classifiers to compare the grasping posture categorizations, the significantly highest accuracies were achieved using the C5 categorization according to the Cutkosky taxonomy (_p_ < 0.008) [30]. On excluding scenario B again, these average accuracies were BA = 0.88 \(\pm\) 0.07 for the TS_LR algorithm, and BA = 0.90 \(\pm\) 0.06 for the TS_SVM algorithm for the combined delta and gamma frequency band and the long epoch (Figure 6). In this case, a trend towards maximum accuracy values was found for the TS_LR algorithm with BA = 0.90 \(\pm\) 0.06 in scenario C, and with BA = 0.91 \(\pm\) 0.05 for the TS_SVM algorithm in scenario A. Across all scenarios, lower accuracies were observed in the G6 categorization with the TS_LR algorithm with mean BA = 0.73 \(\pm\) 0.06, and for the TS_SVM algorithm with mean BA = 0.78 \(\pm\) 0.07 (averaged over all scenarios). On excluding scenario D (fine-tuning with pre-training using S12/S13 data and hyper-parameter adjustments for the target subject), a comparably high accuracy result for S12 and the TS_SVM algorithm was found for the F4 categorization. Due to the general dominance of the C5 categorization, the G6 and the F4 categorization results are omitted in the following. However, it is important to note that a different number of grasps fall into the categorizations and a direct comparison between categorizations has to be considered with caution.  
Figure 5: Balanced discrimination accuracies (mean \(\pm\) sd over all grasps) as a function of the grasping posture categorization G6 of the applied classifiers linear regression (LR), linear discriminant analysis (LDA), support vector machine (SVM), LR in tangent space (TS_LR), SVM in tangent space (TS_SVM), Riemann minimum distance to mean (MDM), MDM extended by geodesic filtering (FgMDM), and scenarios A–D from Figure 4 using bandpass-filtered (_f_s-\(\gamma\) = 0.5–100 Hz) and epoched (\(-\)1000 ms \(\leq E_{L}\leq\) 500 ms) EEG signals.  
Figure 6: Balanced discrimination accuracies (mean \(\pm\) sd over all grasps) of the classifiers LR in tangent space (TS_LR) and SVM in tangent space (TS_SVM), and scenarios A–D from Figure 4 as a function of grasping posture categorizations G6, C5, and F4 from Table 1 using bandpass-filtered (_f_s-\(\gamma\) = 0.5–100 Hz) and epoched (\(-\)1000 ms \(\leq E_{L}\leq\) 500 ms) EEG signals. The C5 categorization yields significantly better results (_p_ < 0.008).
# An Integrated DQN and RF Packet Routing Framework for the V2X Network
## 3 Design of the IDRF System
### Offline Phase
#### 3.1.5 Deep Reinforcement Learning Training Unit
This selection process is represented by Equation (6). The expected packet transmission path is depicted in Figure 16.  
\[Reward=\left\{ \begin{aligned} \frac{1-DR_{i}}{\sum_{j=1}^{i}1-DR_{j}} \text{, }packet\text{ }transmission\text{ is complete;}\\ 0\text{, }v_{i}\text{ is not chosen as a relay node;}\\ -1\text{, }packet\text{ transmission }fall;\end{aligned} \right. \tag{5}\]  
\[a_{t}=argmax_{a}Q(w(s_{t}),a;w) \tag{6}\]  
Figure 16: The expected packet transmission path is based on the DQN decision during the offline phase.
## 4 Results and Discussion
### Permeability Test  
A total of 78 cylindrical samples are placed in a permeability gage and subjected to 5 bar water pressure for 24 h. Then, the splitting test is conducted, and the depth of water penetration is measured. The results of the permeability test on the 78 cylindrical samples are demonstrated in Figure 10 for different water-to-binder ratios. As seen in Figure 10, the positive effect of nano-MgO on permeability is available in all samples. The most optimal result (minimum amount of permeability) belonged to the water-to-binder ratio equal to 0.44 for samples with 1% nano-MgO. As expected, the addition of nano-powder filled the nanosized pores and reduced water penetration. In addition, the expansive feature of nano-MgO plays an effective role in the reduction of concrete permeability. By the completion of the hydration process of nano-MgO, the product of hydration needs more space, pores of the concrete are occupied, and as a result, permeability comes down. As seen in Figure 10a, 1% of nano-MgO is the optimum amount of this nanomaterial for the 0.44 water-to-binder ratio. It reduces the permeability of concrete up to 63%. In addition, 1.5% of nano-MgO has reduced permeability, but the reduction is not as strong as that of 1% nano MgO. As observed in Figure 10, increasing the water-to-binder ratio causes the optimum amount of nano-MgO to differ. Regarding Figure 10b,c, the best results are gained with 1.5% nano-MgO for samples cured for 56 days. Regarding the high standard deviation of these samples (STD = 1.2) in comparison with other samples, it seems that the least permeability for this group (w/c = 0.62, nano-MgO = 1.5% and samples cured for 56 days) is not reliable; as a result, 0.5% of nano-MgO plays the most efficient role in decreasing the permeability, which is 26% less than that of plain samples.  
Figure 9: Tensile strength vs. age for water-to-binder ratios of (**a**) 0.44, (**b**) 0.49, and (**c**) 0.62.  
Figure 10: Permeability vs. age for water-to-binder ratios of (**a**) 0.44, (**b**) 0.49, and (**c**) 0.62.
## 4 Results  
### The GPS-calibrated InSAR Result from April 2016 to April 2017  
First, we presented the removal of the systematic errors in the initial InSAR result using the GPS measurements. Figure 6 shows the differences between the initial InSAR velocities and the GPS velocities along the LOS directions at the 14 GPS stations. A positive value shows that the velocity derived from InSAR was smaller than that from GPS. The differences in Figure 6 indicate that the InSAR velocities are larger in the far range toward Eastern Yunlin, but smaller in the near range towards the Taiwan Strait, suggesting a linear trend of systematic errors in the initial InSAR velocities. In this case, the linear trend of InSAR velocities was 0.38 mm/a per km compared to the GPS velocities at the 14 GPS stations, and the offset value was -5.50 mm/a compared to the velocity at GPS station LNJS, which was located at a stable place with the smallest vertical and horizontal movements (mm/a level) in Yunlin.  
Using the calibration described in Section 3, we obtained the final (calibrated) InSAR velocities (Figure 7). The RMS difference between the initial InSAR and GPS velocities was 15.0 mm/a and decreased to 8.5 mm/a after the GPS calibration. The differences between the GPS-calibrated InSAR velocities and the GPS velocities are mostly below 10 mm/a (Figure 7). In Figure 7, warm colors show land subsidence (negative LOS velocities) and cold colors show land uplift. The region with the largest land subsidence rate was in Southern-central Yunlin, near Tuku Township, where the subsidence rate reached 64.6 mm/a.  
Figure 6: Differences between the initial InSAR and GPS velocities in the LOS directions, showing a tilting surface due to the systematic errors in the initial result.
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
## 2 System Model
### Performance Metric  
Define the bit error rate (BER) corresponding to the \(i\)-th tag at the reader as \(p_{i}^{e}\). According to [25], \(p_{i}^{e}\) is given as  
\[p_{i}^{e}=\frac{1}{2}\text{erfc}\left(\sqrt{\frac{p_{i}^{b}s_{i}}{\sigma^{2}} }\right)=\frac{1}{2}\text{erfc}\Big{(}\sqrt{a_{i}n_{i}P_{i}}\Big{)},\]  
where \(a_{i}=\frac{n_{i}S_{i}}{\sigma^{2}}\) and \(\sigma^{2}\) is the noise power at the reader. To make the system more practical, the quality of service (QoS) is considered. Denote the maximum BER that the system can tolerate as \(\varepsilon\) (i.e., \(p_{i}^{e}\leq\varepsilon\)).  
The successful transmission bits of each symbol in the active state--named as _goodput_--can be given as  
\[G_{i}=G_{i}(P_{i},t_{i}^{a},n_{i})=t_{i}^{a}(1-p_{i}^{e})=t_{i}^{a}\left[ \frac{1}{2}+\frac{1}{2}\text{erf}\Big{(}\sqrt{a_{i}n_{i}P_{i}}\Big{)}\right],\]  
where function \(\text{erfc}(x)=1-\text{erf}(x)\). The performance metric, _total goodput_, is given as \(\sum_{i=1}^{N}G_{i}\).
# A Game Theoretic Approach for Balancing Energy Consumption in Clustered Wireless Sensor Networks
## 5 The Protocol Details  
In this section, the details of our protocol will be described and we call it a game theoretic approach for balancing energy consumption in clustered WSNs (GTAB). The procedure [30; 32] of GTAB is composed of a one-time initialization phase and lots of repeated rounds which can further be divided into topology formation and steady-state phases, as shown in Figure 3.  
In initialization phase, a \"Start\" message will be broadcasted by BS within the whole network, so that the distances to BS can be estimated by sensor nodes in the network by utilizing received signal strengths. Then, each node broadcasts a \"Hello\" message which contains its node ID within the communication radius \(R\). Each node can know all its neighbor nodes as it will receive other nodes' messages. Followed the initialization phase is topology formation phase, where CHs will be selected firstly. Then, an appropriate CH will be chosen by each node for cluster joining. Based on this clustered network topology, each node begins to collect data from the surrounding environment, and transmits the data to BS at steady-state phase. To avoid the communication interference during wireless communication, each CH will create an individual Time Division Multiple Access (TDMA) schedule for all its member nodes. Then a sensor node goes dormant during most of the time. Only when its own time slot arrives, it will be awakened for data transmission. After a CH has received all member nodes' data, it will transmit the data to BS according to a randomly selected Code Division Multiple Access (CDMA) code. In the following of this section, we will emphatically introduce the topology formation phase that can be divided into CHs election and clusters formation phases.
## 3 Case Study
### OSP Considering the Evolution of Structural States and Material Properties
#### 3.4.2 Select the Number of Sensors  
Based on the selected modal orders for 18 cases, the corresponding MAC-MAX versus the number of sensors is plotted, and the results are shown in Figure 19.  
To determine the optimal number of sensors (ONS), considering the preliminary selection principles for the number of sensors in previous sections, the initially selected sensor quantity ranges for 18 cases are shown in Table 9. Use IAHA to calculate the MAC-MAX, fitness value, and S for all ranges, and plot the curves and histograms as shown in Figure 20. Each calculation is performed 10 times, and the average values are taken. From Figure 20, it can be observed that the number of sensors corresponding to the minimum MAC-MAX and minimum fitness value for each case are as follows: 27, 22, 30, 20, 20, 20, 32, 20, 20, 32, 20, 22, 24, 29, 31, 32, 33, 34, 36, and 36. The number of sensors selected through the S criterion is 27, 22, 30, 20, 20, 20, 32, 20, 22, 24, 29, 31, 32, 33, 34, 36, and 36, respectively,  
Figure 19: Variation curve of maximum value of off-diagonal elements of MAC matrix with the number of sensors.  
Figure 18: Modal order shapes selected under typical cases: (**a**) Case 3, (**b**) Case 4, (**c**) Case 7, (**d**) Case 13, and (**e**) Case 18.  
Figure 20: _Cont._  
Figure 20 shows that the selected number of sensors for 18 cases are 27, 22, 30, 20, 20, 20, 20, 32, 20, 22, 24, 29, 31, 32, 33, 34, 36, and 36, respectively. In most cases, the number of sensors has increased compared to the seamless condition, with only no changes when considering contraction joints, dam material zoning, and the dam's elastic modulus zoning degradation for 10 years, separately. In terms of the number of sensors, the influence of cracks is greater than that of contraction joints, especially when affected by two long-distance horizontal cracks at elevations of 105 m and 111.5 m in the downstream surface. Considering the elastic modulus zoning degradation of the dam body, as the degradation period increases, the number of sensors also increases.
# Case Study of Underground Shield Tunnels in Interchange Piles Foundation Underpinning Construction
## 1 Introduction
### Engineering Geological Conditions  
As the engineering geological investigation report shows, the stratum from ground surface to the depth of 28.4 m is divided into 4 main layers in terms of soil characteristics:  
1 miscellaneous backfilling layer containing a small amount of construction waste, stone debris and other impurities ranging from 1.0 m\(\sim\)2.3 m in thickness; 2 silty clay layer with a few gravel fragments ranging from 2.85 m\(\sim\)4.2 m; 3 cobblestone soil layer with an average thickness of 7.5 m ranging from 4.8 m\(\sim\)12.3 m; 4 medium to slightly weathered rock layer. According to the site geological survey results, the rock layer is relatively hard and presented blocky structure. There is no weak interlayer, void or fractures found in the rock layer. And the 4 th rock layer is the bearing layer for the original bridge piles foundation in this construction project. The main soil mechanical properties are listed in Table 1.
## 3 Results
### Comparison with the Previously Published Methods  
The LLOQ values of the proposed method is less than the other previously methods [2,12,18], suggesting that this method is more sensitive than the other methods. Moreover, the proposed method is simpler, economic and fast Table 5.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Analytical Method** & **Linearity Range** & **MRM Transition** & **LLOQ Volume \(\mu\)L** & **Sample Volume \(\mu\)L** & **Sample Preparation** & **Separation Retention Time (min)** & **References** \\ \hline HPLC-UV & 50–2000 & — & 50 & 100.0 & \begin{tabular}{c} Sep-pack column \\ \end{tabular} & 7.7 & [18] \\ \hline \begin{tabular}{c} UPLC- \\ MS/MS \\ \end{tabular} & 1.0–500 & 447.0 \(\rightarrow\) 361.9 & 1.0 & 100.0 & \begin{tabular}{c} LLE \\ (Ethyl acetate \\ +Na OH) \\ \end{tabular} & 0.92 & [12] \\ \hline LC-MS/MS & 3.0–800 & \begin{tabular}{c} 447.10 \\ 362.0 \\ \end{tabular} & 3.0 & 150.0 & SPE & 2.6 & [20] \\ \hline \
\begin{tabular}{c} UPLC- \\ MS/MS \\ \end{tabular} & 0.5–1000 & 447.0 \(\rightarrow\) 86.0 & 0.5 & 100 & PPT +LLE & 1.2 &
\begin{tabular}{c} Current method \\ \end{tabular} \\ \hline \hline \end{tabular}  
\begin{tabular}{c c c c c c c} \hline \hline \end{tabular}  
\begin{tabular}{c} LLE: Liquid–liquid extraction; SPE: Solid phase extraction; PPT: Protein precipitation. \\ \hline \hline \end{tabular}  
\end{table}
Table 5: Comparison of the proposed method with previous reported methods for the determination of ERDA in plasma samples.  
Figure 5: AGREE pictogram for the greenness assessment of the proposed method.
## Effect of _Phyllanthus emblica_ Linn. on Tensile Strength of Virgin and Bleached Hairs
###### Abstract  
In Ayurveda medicine, _Phyllanthus emblica_ Linn. (emblica) has been used as a hair nourisher for more than a decade by soaking it overnight, but no study has proved the effect of emblica on hair. This research aims to determine the effect of emblica solution on the tensile strength of three types of hair: virgin hair (VH), bleached hair (BH), and twice bleached hair (TH). The investigated active substances were deionized water (DI water) as a control, 3% emblica extract solution (3% EXS), 6% emblica extract solution (6% EXS), and 9% emblica extract solution (9% EXS). Black Virgin Asian hair was measured and analyzed before bleaching with a 12% bleaching agent once (BH) and twice (TH). Three treatments and the control were applied to each type of hair under a controlled condition. The tensile characteristics and surface morphology of all treated hairs were measured and analyzed by texture analysis (TA-XT Plus Texture Analyzer, Stable Micro Systems), Aramo (Aramo-SG Skin & Hair analysis system), and scanning electron microscopy (SEM model JSM-5410 LV JEOL). The results of the nanoindentation test proved that the tensile strength and extensibility of all three types of hair increased in all concentrations (DI water, 3% EXS, 6% EXS, and 9% EXS). High magnification SEM images were taken from the cuticle surfaces and cross-sections. Emblica extracted solution (EXS) formed a coating around the hair, especially cuticle damaged by bleaching (BH and TH). The breaking pattern from the cross-section images showed that emblica extract solution reinforced all hair types. In conclusion, the emblica extract solution has a significant positive effect on the tensile strength and extensibility of VH, BH, and TH. The result has proved the ancient intelligence that the emblica nourishes the hair. In addition, our results show additional benefit by strengthening virgin hair and bleached hair.  
emblica extract solution; tensile strength; extensibility; virgin hair; bleached hair 2020 10 6305; doi:10.3390/app10186305