esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Permeability Coefficient  
Table 8 summarizes the permeability coefficients of soil samples in the three different stress paths. The three soils had permeability coefficients ranging from large to small: silty sand, silt, and mucky silty clay. In the unloading path, silt and silty sand have higher permeability coefficients than that of the conventional path, whereas the permeability coefficient of mucky silty clay was lower than that of the conventional path, and gradually increased with increasing unloading intensity. In the unloading-reloading path, silt had a higher  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Properties**} & \multirow{2}{*}{**Soils**} & \multicolumn{8}{c}{**Stress Path**} \\ \cline{3-10}  & & \(\mathbf{A-0}\) & \(\mathbf{B-1}\) & \(\mathbf{B-2}\) & \(\mathbf{B-3}\) & \(\mathbf{C-1}\) & \(\mathbf{C-2}\) & \(\mathbf{C-3}\) \\ \hline \multirow{3}{*}{Shear wave velocity (m/s)} & Silt & 241.5 & 209.9 & 207 & 204.3 & 240.1 & 238.1 & 235.3 \\  & Silty sand & 250.3 & 221.5 & 217.6 & 215.5 & 250 & 249.1 & 248.4 \\  & Mucky silty clay & 228.1 & 204.3 & 189.8 & 185.4 & 227.8 & 215.5 & 209.9 \\ \hline \multirow{3}{*}{Shear modulus (MPa)} & Silt & 103.81 & 78.42 & 76.27 & 74.29 & 102.61 & 100.91 & 98.55 \\  & Silty sand & 111.52 & 87.33 & 84.28 & 82.66 & 111.25 & 110.45 & 109.83 \\ \cline{1-1}  & Mucky silty clay & 92.61 & 78.42 & 64.12 & 61.18 & 92.37 & 82.66 & 78.42 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Experimental results of shear wave velocity and shear modulus under different stress paths.  
Figure 10: Normalized small-strain shear modulus–unloading intensity relationship of three soils under different stress paths.  
permeability coefficient than that of the conventional path, while silty sand and mucky silty clay had a lower permeability coefficient than that of the conventional path. The variation curve of the normalized permeability coefficient with unloading intensity for three soils is shown in Figure 11. The permeability coefficient increased as unloading intensity increased. Unloading strength had the greatest effect on the permeability of mucky silty clay, but the overall permeability coefficient was small. In comparison to silty sand, the permeability of silt was more dependent on unloading intensity. The permeability coefficient increased by 11% and 16% in the unloading and unloading-reloading paths, respectively.
## 6 Limitations and Future Research Directions
### Justification for Participant Selection  
Participants with normal vision were chosen to optimize the audio interface and identify any usability issues before conducting targeted studies with users with visual impairments. By first ensuring the system's effectiveness and ease of use with sighted participants, we can make necessary adjustments and improvements to better cater to the specific needs of users with visual impairments in subsequent studies. The current study was a preliminary investigation to establish the feasibility of the proposed interfaces. We will conduct a follow-up study specifically involving participants with visual impairments. Additionally, we will provide a clear justification for the initial use of participants without visual impairments, explaining that this phase was necessary to identify and resolve potential usability issues before extending the study to the intended user group.
## 2 Materials and Methods
### Research Methods  
#### 2.2.1 Technical Process  
Taking the walnut planting area in Ganquan Town as the research object and the Sentinel-2 image data provided by the GEE cloud platform as the data source, this thesis classifies land types in the research area by combining spectral features, terrain features, textural features, and the vegetation index. It then extracts the walnut planting area with the use of a random forest classification algorithm. This was combined with MODIS image data, and the walnut growth in the research area from April to mid-September 2021 was analyzed with the use of the NDVI difference model. The specific research process is as follows: (1) first, the obtained Sentinel-2 image data set is preprocessed by using methods such as image cloud removal, clipping, and fusion; (2) the feature set is constructed via spectral features, terrain features, textural features, and vegetation index features; (3) ground objects are classified, and the planting area of walnut is extracted; (4) the extracted area is compared with the Statistical Yearbook data to verify its accuracy; (5) NDVI with MODIS image data are calculated during the monitoring period, the NDVI maximum value for every ten-day image is synthesized, and then the difference value is calculated; (6) walnut growth in the same month of 2020 is monitored with the method of contemporary comparisons. The flow chart is shown in Figure 2.
## Nomenclature  
CAE Computer Aided Engineering  
DLL Dynamic Link Library  
DOF Degree of Freedom  
FAST Fatigue, Aerodynamics, Structures, and Turbulence  
FEA Finite Element Analysis  
FWT Floating Wind Turbine  
FPSO Floating Production, Storage and Offloading unit  
LC Load Case  
MAP++ Mooring Analysis Program  
MPM Multiple-Point Mooring  
MSQS Multi-Segmented, Quasi-Static  
NREL National Renewable Energy Laboratory  
OC4 Offshore Code Comparison Collaboration Continuation  
OWE Offshore Wind Energy  
PSD Power Spectral Density  
RAO Response Amplitude Operator  
SPM Single-Point Mooring  
TLS Tension Leg Spar  
WEC Wave Energy Converter
## 1 Introduction
As the elastomer loading increases, the slurry viscosity increases, which increases equivalent circulating density (ECD) and will ultimately require slower pumping rates to reduce the risk of unnecessarily fracturing the formation [12]. Self-healing cement has emerged as an innovative approach to improving the durability of wellbore cement by autonomously repairing cracks and restoring mechanical properties. By incorporating materials that react to environmental triggers--such as water, CO2, or temperature fluctuations--self-healing cement can mitigate the risks associated with wellbore deterioration. Figure 1 shows the Mechanical properties of various self-healing cement materials, including compressive strength, Young's modulus, and Poisson's ratio obtained from the literature that present significant support to the wells. Self-healing cement has emerged as a proactive solution capable of autonomously repairing microcracks, mitigating permeability increase, and restoring mechanical strength [11; 12; 13]. To build upon these advancements, this study aims to develop and optimize a novel self-healing cement formulation tailored for CO2 injection and geothermal wells. The primary research objectives are as follows:  
1. Design a cement system incorporating Barite, Pozzolan, and Chalcedony/Zeolite that enhances long-term chemical and mechanical stability under CO2-rich and high-temperature conditions. 2. Employ a Design of Experiment (DOE) framework to systematically determine the most effective compositions for minimizing porosity and enhancing self-sealing performance. 3. Use PHREEQC 3-based geochemical simulations, integrated with Python 3.12 scripting, to evaluate porosity evolution, mineral transformation, and phase volume changes over time. 4. Benchmark the performance of the proposed self-healing formulations against conventional cement to demonstrate improvements in durability and crack resistance. These objectives aim to establish a validated, simulation-based methodology for designing resilient cement systems that improve wellbore integrity in harsh subsurface environments.
# Functionally Graded AISI 316L and AISI H13 Manufactured by L-DED for Die and Mould Applications
###### Abstract  
Tooling in the die and mould industry is subjected to high-wear and high-temperature environments, which often leads to the premature failure of this high-added-value tooling. When severe damage occurs, an alternative to replacing the whole component consists of the repair by laser-directed energy deposition (L-DED). For that end, intermediate layers are commonly employed as buffer material, where introducing a functionally graded material (FGM) might be beneficial to avoid material incompatibilities and improve the overall performance of the tooling. In the present work, an FGM composed of gradient AISI 316L to AISI H13 has been manufactured, and its microstructure and hardness analysed. Firstly, cracking owing to the formation of brittle intermediate phases has been detected. Secondly, an increase of the hardness and a decrease of the corrosion resistance has been observed when transitioning from AISI 316L to AISI H13. Thirdly, despite the FGM composition evolving linearly, nonlinear material properties such as hardness and corrosion have been observed, which are conditioned by the microstructure formed during the L-DED process and the nonlinear influence of the composition of steel on such properties. Consequently, nonlinear compositional gradients are recommended if linear mechanical properties are to be obtained in the case of steel FGMs.  
functionally graded materials; stainless steel; hot-work tool steel; Laser DED; repair; coatings; die and mould industry; tooling Article
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 4 Experiments  
### Datasets  
The DVD dataset [36] consists of 71 video sequences captured using a high-speed camera at 240 fps. To generate realistic motion blur, blurred frames at 30 fps were synthesized by averaging consecutive sharp frames. The dataset provides paired blurred-sharp frame sequences covering diverse scenarios, including indoor and outdoor scenes, camera shake, object motion, and varying lighting conditions. The training set includes 61 sequences, with the remaining 10 sequences reserved for testing.  
The GoPro dataset [28] contains 33 video sequences captured using a GoPro camera at 240 fps. Similar to the DVD dataset, motion blur at 30 fps was simulated by averaging multiple consecutive sharp frames, resulting in 3214 blurred-sharp frame pairs. These sequences primarily feature outdoor scenes with significant camera motion and dynamic objects, making them particularly challenging for deblurring tasks. The dataset is divided into 22 training sequences and 11 test sequences. The GoPro dataset is widely used due to its effective simulation of real motion blur characteristics encountered in consumer-grade cameras.  
The BSD dataset [39] is a real-world video deblurring dataset collected using a beam-splitter acquisition system with two synchronized cameras. It contains three different blur-intensity configurations (1 ms-8 ms, 2 ms-16 ms, and 3 ms-24 ms exposure pairs), with each containing 100 video sequences. The sequences cover diverse real-world scenarios featuring various motion patterns, including camera shake and object motion in both indoor and outdoor environments. The BSD dataset has demonstrated superior generalization capability compared to synthetic datasets, making it particularly valuable for developing and evaluating video deblurring algorithms intended for real-world applications.
# Optimal Resource Allocation Policies for Multi-User Backscatter Communication Systems
## 2 System Model
### Tag Configuration  
Since batteries are only available for the semi-passive tags, the configurations of passive and semi-passive tags are quite different. Assuming the reader transmission power for the \(i\)-th tag as \(P_{i}\), the receive power at this tag is given as \(P_{i}^{\text{rec}}=P_{i}h_{i}\). Considering the efficiency of both backscattered and harvested power, the signals are modulated by binary phase shift keying (BPSK) [24].
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Mimo
#### 3.4.3 Joint Spatial and Temporal ANN Equalizer  
Conventionally, MIMO decoding algorithm and compensator-like decision feedback equalizer (DFE) are adopted in MIMO-VLC receivers to compensate the spatial crosstalkand remove the ISI step by step. The proposal of the vertical Bell Labs layered space time (V-BLAST) paves the way for joint spatial and temporal equalization in MIMO systems [74].  
Considering the inherent nonlinear feature of MIMO-VLC systems, researchers have proposed the joint spatial and temporal ANN equalizer in [75] for both imaging and nonimaging MIMO-VLC links. The structure of the joint spatial and temporal ANN equalizer is similar to a matrix DFE structure. The data structure being fed to ANN contains the received signal vector with a feedforward delay line and the estimated signal vector with a feedback delay line. As for the input layer of the ANN, it contains two dimensions where one is spatial and the other is a temporal dimension. The number of input nodes is slightly adjusted to the oversampling factor of the fractionally spaced equalization scheme. According to the experimental results in [75], the proposed ANN-based joint spatial and temporal equalization scheme could outperform the traditional DFE and is able to compensate for the nonlinear channel distortion and cross-talks. Additionally, the proposed method could have better performance when the channel is ill-conditioned.
## 3 Results and Discussion
### Plackett-Burman Design (PBD)
#### 3.1.1 Investigation of Statistically Significant Variables  
A Pareto chart of the standardized effects was used to decide upon the statistically significant variables. As shown in Figure 2, the left panel shows that pH (A) was the most statistically significant variable affecting the %R compared to dye concentration (DC) (C) in the case of \(q_{e}\) (right panel). Analysis of variance (ANOVA) data are shown in Table 4. The data shown further confirm the findings of the Pareto chart with normal and half-normal probability plots (not shown). Statistical significance of the tested variables at 95.0 confidence intervals (95.0 CI) was estimated by F-test and \(p\)-value \(<\) 0.05. It is noteworthy to mention that a significant improvement in data quality was observed following response transformation by employing Box-Cox transformation [Equation (3)] together with stepwise analysis in the case of %R and forward selection in the case of \(q_{e}\)[38; 39; 40].  
\[\mathrm{Y}^{\prime}=(\mathrm{Y}_{\lambda}\ \text{-1})/\lambda \tag{3}\]  
where \(\mathrm{Y}^{\prime}\) is the transformed response and \(\lambda\  
To test the applicability of the proposed models, the experimental values of the two responses--%R and \(q_{\rm e}\)--were compared to the model predicted values (Table 2). Results show a good agreement between both. The values of the coefficient of determination (R\({}^{2}\)) were high enough (0.9477 and 0.9658 for %R and \(q_{\rm e}\), respectively) to indicate the model linearity. The potential of the suggested models to foresee new responses was spotted from the high values of R\({}^{2}\) (predicted) observed following response transformation (0.8463 and 0.8996 for %R and \(q_{\rm e}\), respectively). As shown from the two regression equations, while the adsorbent dose (AD) was positively impacting the %R, it negatively impacted the \(q_{\rm e}\). The direction of the rest of the effects was the same for both responses.
# Data Compression with a Time Limit
## 4 Lossy Compression
### Test 2  
The dataset \(D\) to be compressed consisted of 1000 images, for a total size of 692.2 MB (691,215,000 bytes). Single runs of the compression tools with the indicated quality setups yielded the following results shown in Table 9:  
From Figure 12 it is possible to observe that the fastest tool was JPG1 with a quality setup of 95, while the slowest was JPEG_NO_OPTIMIZE14 with a quality setup of 20. The best tool for output size was WEBP11 with a quality setup of 3, while the worst was JPEG_NO_OPTIMIZE3 with a quality setup of 95.  
Figure 11: Optimal mix.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Tool** & **Size of Output in Bytes** & **Time in ms.** &
\begin{tabular}{c} **Quality** \\ **Parameter** \\ \end{tabular} & **SSIM** \\ \hline JPG1 & 66,567,945 & 3140 & 95 & 0.984874 \\ WEBP2 & 45,131,526 & 4321 & 95 & 0.985433 \\ JPEG-NO OPTIMIZE3 & 70,390,195 & 5305 & 95 & 0.984874 \\ JPEG4 & 21,543,285 & 5680 & 48 & 0.944169 \\ JPEG5 & 15,001,444 & 5613 & 25 & 0.907689 \\ JPEG6 & 13,186,571 & 5683 & 20 & 0.889513 \\ JPEG7 & 12,510,155 & 5667 & 18 & 0.882732 \\ WEBP8 & 13,685,922 & 3482 & 48 & 0.95248 \\ WEBB9 & 10,041,668 & 3283 & 25 & 0.93276 \\ WEBP10 & 8,200,328 & 3384 & 14 & 0.91429 \\ WEBP11 & 5,839,634 & 4032 & 3 & 0.874497 \\ JPEG-NO OPTIMIZE12 & 22,619,623 & 5732 & 48 & 0.944170 \\ JPEG-NO OPTIMIZE13 & 16,340,793 & 5938 & 25 & 0.907669 \\ JPEG-NO OPTIMIZE14 & 14,648,964 & 6368 & 20 & 0.889482 \\ JPEG-NO OPTIMIZE15 & 14,017,389 & 5984 & 18 & 0.882703 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Test 2.  
Figure 13 shows the useful setups.  
Figure 14 shows the optimal mix.  
Figure 12: Setups.  
Figure 13: Useful setups.  
In the case where the time limit is 3300 ms. and the SSIM quality limit is set to 0.874144, ms., the optimal mix is given by the mix of the WEBP9 and WEBP10 setups.  
The number of images to compress with WEBP9 was found to be 832 with a quality of 25, while for WEBP11 it was 168 with a quality of 14.  
Compressing \(D\) as suggested by the algorithm resulted in a compression time of 3768 ms.  
The output size was 9,751,208 bytes with an SSIM quality index of 0.927159.  
The time budget was practically used entirely.  
Also, in this case there was a small margin of error of about 468 ms.
## 5 Numerical Results and Comparison
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multicolumn{1}{c}{**Order**} & \multicolumn{1}{c}{**1 BW**} & \multicolumn{1}{c}{**1 FW**} & \multicolumn{1}{c}{**2 BW**} & \multicolumn{1}{c}{**2 FW**} & \multicolumn{1}{c}{**3 BW**} & \multicolumn{1}{c}{**3 FW**} & \multicolumn{1}{c}{**4 BW**} & \multicolumn{1}{c}{**4 FW**} \\ \hline
22 & 0.00440 & 0.0167 & 0.0375 & 0.0616 & 0.0928 & 0.120 & 0.151 & 0.172 \\
33 & 0.00427 & 0.0163 & 0.0363 & 0.0602 & 0.0898 & 0.117 & 0.146 & 0.167 \\
44 & 0.0042  
The spatial discretization method provides an efficient technique to treat irregular structures. In Tables 9-16, the natural frequencies are listed for different angular momentum to show the convergence with increasing element numbers. The power of the spatial discretization has been demonstrated by satisfactory results. With increasing element numbers, the computation time will increase. However, lower order discretization may provide data with sufficient accuracy. Compared to modal discretization, more computational cost is required. Such a drawback opens the chance to deal with structures of irregular shapes. For both methods, the higher gyroscope momentum requires higher order discretization to ensure accuracy. In Table 17, the results of the modal discretization and spatial discretization were compared with the gyroscope momentum up to 2000 Nms, where the 240 order discretization was used. The deviations between the natural frequencies of the two methods were less than 5%, which validates the accuracy of both methods.
## 3 Results and Discussion
### Hardness  
The hardness is a mechanical property that is closely related to the wear resistance of the films. Figure 6 shows the hardness of the films obtained by nanoindentation tests. The results of hardness for the DLC films were increased from almost 19 to 24 GPa by increasing the Ne percentage to 50% in the discharge gas, which was due to the deposition of a higher amount of sp\({}^{3}\) carbon when Ar was partially replaced by Ne in the discharge gas [14]. This showed that the 50% Ne-DLC film not only had the lowest COF in the Stribeck curves, but it also had the highest hardness; hence, it was expected that this film also had a better wear  
Figure 4: Drops of PAO 8 oil snapshots at 0.06 s on the surfaces: (**a**) CrN, (**b**) Ar-DLC, (**c**) 25% Ne-DLC and (**d**) 50% Ne-DLC.  
Figure 5: Contact angle measurements and surface free energy of the coating films.  
resistance in comparison with the other films. On the other hand, the hardness of CrN was also high (20 GPa), but it had much less wear resistance in comparison with the DLC films, as was reported in the previous work [17]. This can be due to the high COF of the CrN film that resulted in higher wear rates in comparison to the DLC films, despite having a similar hardness value [29].
### Tunnel Overview  
The left line and the right line of the Yongfutun tunnel are located in Siding Town, Rong'an County, Liuzhou City, Guangxi. Their lengths are 5640 m and 5647 m, respectively. Their longitudinal slopes are \(-2.37\%\) one-way slopes. As a separated extra-long tunnel, the Yongfutun tunnel zone belongs to the geomorphic area of tectonic dissolution with a peak-cluster depression and valleys, with a maximum buried depth of approximately 301 m. According to the drilling and geological mapping results, at the tunnel site, with a relatively thin overburden, the lithology of the underlying bedrock is dolomitic limestone of upper Liujiang formation (D3) and middle Donggangling formation (D2D) of the Devonian system. The dominating tunnel surrounding rocks are of Grade III and Grade IV. The unfavorable geology in the tunnel area is mainly karst. Several sinkholes were found in the tunnel during the survey, while geophysical exploration showed that the tunnel may have karst fissures, karst caves, and underground rivers, etc.  
The water inflow section is located at the tunnel exit. The rock mass joints and fissures around the tunnel site are developed. There are two groups of main joint fissures at the exit. The occurrence of L1 is 250\({}^{\circ}\)\(\angle\) 86\({}^{\circ}\), closed, with an extension length of 2-3 m and a density of 1-2 pieces/m; L2 occurrence is 320\({}^{\circ}\)\(\angle\) 80\({}^{\circ}\), closed, with an extension length of 3-4 m and a density of 2-3 strips/m. Dissolution depression L42 is found at 175 m to the right of K66+450 and dissolution depression L43 is found at 210 m to the left of ZK66+643.  
The geostructure of the tunnel site is located in the first Indosinian substructure (Devonian system). There is no tertiary stratum in the area. According to the development of quaternary river terraces and karst caves, the Himalayan movement involves mainly upward movement, which is manifested in terraces and karst cave layers of different heights at all levels. The underground water in the tunnel site area is mainly supplied by atmospheric rainfall and discharged in vein and linear form to the low-lying depression along with the pores, bedrock fissures, and dissolution fissures. The amount of karst water is controlled by the degree of karst development and supply source, and the seasonality is obvious. In particular, the surface flow formed in the rainy season easily infiltrates rapidly along the dissolution fissure and water drop tunnel, and the groundwater flow increases sharply, which has an important impact on tunnel construction.  
On 17 June 2019, a mud outburst and water inrush disaster occurred at the K66+490 palm surface on the right line of the tunnel. This disaster was caused by a hidden cavern triggered by blasting construction. As of 30 June 2019, the cumulative amount of water and mud inrush reached 10,000 cubic meters (see Figures 1 and 2).  
Figure 1: Water pushing and mud outburst in tunnel. (**a**) Facilities at the entrance of cave destroyed by water inrush. (**b**) Water inrush and sludge inside cave.