esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Key Parameters and Engineering Experiments of Coal Gangue Slurry Filling Technology
## 2 Gagneue Grouting Filling Technology in Goafs  
### Principle and System Composition of Gagneue Grouting Filling Technology in Goafs  
Based on the dual needs of not affecting the average production of tens of millions of tons of coal from mines and achieving the large-scale underground disposal of coal gangue, the concept of \"fluidized filling\" is proposed, which means that the coal gangue is made into a coal gangue slurry and filled into the gaps and fissure spaces in the collapse zone, fissure zone, and separation zone of goafs.  
Gagne grouting filling technology in a goaf involves breaking or grinding coal gangue into particle aggregates with a specific particle size distribution, mixing them with water in a proportion to produce a particular concentration of gangue slurry, and then using a grouting pump to fill the gangue slurry into the overlying rock fissure space in the goaf through pipeline transportation. Ultimately, the large-scale underground disposal of coal gangue is achieved without affecting the average production of the coal mine, as shown in Figure 1. Gagneue grouting filling technology in a goaf includes a slurry preparation system, a slurry pumping system, and a slurry filling system. The slurry preparation system uses technical means such as crushing, screening, or grinding to form the raw coal gangue into a particle aggregate with a particular particle size distribution and then forms a specific concentration of gangue slurry in a mixer with water in a certain proportion; the slurry pumping system is used to pressurize the gangue slurry using a grouting pump or gravity flow, and the slurry is then transported to the target area underground or on the ground through pipeline transportation; the grouting filling system is used to fill the gangue slurry in the target area into the gaps and fissure spaces in the collapse zone, fissure zone, or separation zone using one or more methods of low-level grouting, adjacent-level grouting, or high-level grouting.
## 3 Results  
### Biodegradability of Diesel and Its Main Constituents  
The Pa and Kp strains showed a significant difference in their PH degradation efficacy, such that the degradation efficacy of the Pa strain was 15% higher than that of the Kp strain (Figure 1a). The degradation efficacy of these two strains increased with cultivation time. Maximum values of 58% were observed after 10 days cultivation for the Pa strain, and of 45% for the Kp strain, although little change was observed until Day 14. There was a significant difference between the two strains in degrading the diesel constituents phenanthrene, C\({}_{16}\), C\({}_{20}\) and C\({}_{24}\) alkanes (Figure 1b-f). Specifically, the Pa strain showed a higher degradation efficacy than the Kp strain throughout the cultivation time. This difference was greater for phenanthrene, C\({}_{20}\) and C\({}_{24}\), than for C\({}_{16}\). For another diesel constituent -C\({}_{12}\), the Kp strain had a significantly better degrading capability than the Pa strain (Figure 1c); the Kp strain could degrade 90% of C\({}_{12}\)-containing organics within six days cultivation, while the Pa strain could remove less than 15% of the compounds throughout the culture period.  
In summary, degradation efficacy increased with culture time and the Pa strain performed much better than the Kp strain in reducing phenanthrene and C\({}_{16}\), C\({}_{20}\) and C\({}_{24}\) alkanes, while Kp was superior in degrading C\({}_{12}\) alkanes.  
Figure 1: Degradation efficacy of Kp and Pa strains for: (**a**) diesel, (**b**) phenanthrene, (**c**) n-dodecane (C\({}_{12}\)), (**d**) n-hexadecane (C\({}_{16}\)), (**e**) n-eicosane (C\({}_{20}\)), and (**f**) n-tetracosane (C\({}_{24}\)). The corresponding hydrocarbon degradation efficacy values represent the mean \(\pm\) SD of three independent replicates (n = 3). In each data sample, \(P_{\mathrm{k}}\), \(P_{\mathrm{s}}\), and \(P_{\mathrm{t}\mathrm{t}\mathrm{s}}\) represent the \(P\) value of time, strain and interaction between the strains, respectively. Different letters above the columns represent significant differences according to [PERSON]’s HSD test (\(p<0.05\)).
## 4 Conclusions  
In this paper, the ultimate strength and strain of cylindrical concrete specimens confined with FRP composites were studied using four soft computational methods. The optimal RBNN, ANFIS-SC, ANFIS-FCM and M5 Tree models obtained by trying different control parameters were compared with respect to RMSE, MARE and \(R^{2}\) statistics. RBNN and ANFIS-FCM provided almost the same level of accuracy, and they performed better than the other models in estimating strength of FRP-confined concrete cylindrical specimens by using the inputs of strain ratio and the confinement stiffness ratio. In estimating strain of FRPs, however, the ANFIS-SC model performed slightly better than the RBNN and ANFIS-FCM models. Among the applied models, the M5 Tree model was the least accurate at estimating strength and strain of FRPs. The effects of strain ratio and the confinement stiffness ratio inputs on strength and strain were also examined using ANFIS-SC, and the confinement stiffness ratio was found to be more significant than strain ratio in strength and strain.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), which is funded by the Ministry of Science, ICT & Future Planning of the Republic of Korea (2017R1A2B2010120).  
**Author Contributions:** [PERSON] conceived and designed the database; [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. [PERSON] also analyzed the data and handled the final proofreading.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
Figure 7: Variation of strain versus strain ratio and confinement stiffness ratio for the optimal ANFIS-SC model.
# Analytical and Graphical Profiling of Thread-Milling Cutters for Forming Internal Threads
## 5 Research Methodology and Experimental Results  
Deviations Inherent in the Thread-Milling Scheme with Thread-Milling Cutters Determined by the Analytical Method  
Through the developed analytical method for profiling, the deviations of the profile angle of the milling part were determined as \(\Delta\alpha_{0}\). The influence of the change in the diameter of the internal thread D on the profile angle \(\alpha_{0}\) at different ratios of the diameters of the tool and the thread d\({}_{0}\)/D, as well as at a certain pitch of the thread P, was studied. The rake angle of the front surface is \(\gamma_{0}\) = 0\({}^{\circ}\). The forming of internal threads of different diameters with thread-milling cutters M10 \(\times\) 1, M10 \(\times\) 1.5 and M10 \(\times\) 2 was studied. Threads M10, M12, M14, M16, M18, M20, M22 and M24 are formed with steps P1, P1.5 and P2. The results obtained are given in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**D, mm** & **d\({}_{0}\), mm** & **A\({}_{0}\), mm** & **d\({}_{0}\)/D** & **r\({}_{0}\), mm** & **r\({}_{20}\), mm** & **r\({}_{10}\), mm** & **\(\alpha_{0_{o}}\)\({}^{\circ}\)** & \(\Delta\alpha_{0_{o}}\)\({}^{\circ}\) \\ \hline \hline \multicolumn{8}{c}{Thread-Milling Cutter M10 \(\times\) 1} \\ \hline M10 \(\times\) 1 & & 0.792 & 0.842 & 4.2506 & 3.9253 & 3.7083 & 59.9125 & 0.0875 \\ \hline M12 \(\times\) 1 & & 1.792 & 0.701 & 4.2237 & 3.8986 & 3.6818 & 59.9419 & 0.0581 \\ \hline M14 \(\times\) 1 & & 2.792 & 0.601 & 4.2169 & 3.8919 & 3.6752 & 59.9581 & 0.0419 \\ \hline M16 \(\times\) 1 & & 3.792 & 0.526 & 4.2140 & 3.8891 & 3.6724 & 59.9683 Figures 21 and 22 present a graphical interpretation of the results obtained in Table 3. Based on the calculations made, the following conclusions can be drawn:  
* It was found that with an increase in the diameter of the thread D, at a constant pitch of the thread P and a constant outer diameter of the tool d\({}_{0}\), the deviation of the profile angle of the tool surface \(\Delta\alpha_{0}\) decreases;
* It was found that increasing the pitch of the internal thread P leads to an increase in the deviation of the profile angle of the tool surface \(\Delta\alpha_{0}\);
* It was found that with an increase in the ratio of the diameters of the tool and the thread d\({}_{0}\)/D at a constant pitch of the thread P, the deviation of the profile angle of the tool surface \(\Delta\alpha_{0}\) increases. Figure 21: Deviation of the profile angle \(\Delta\alpha_{0}\) depending on the change in the diameter of the internal thread D at a certain thread pitch P.
# A Refined Approach to Segmenting and Quantifying Inter-Fracture Spaces in Facial Bone CT Imaging
## 2 Materials and Methods
### Refinement of Ground Truth Segmentation Dataset  
We performed a systematic refinement process using the nnU-Net framework, as illustrated in Figure 3. The workflow starts with a medical doctor (MD) annotating the fracture regions with bounding boxes. Subsequently, two labelers utilized these annotations to generate 1766 initial annotations (unrefined GT). To generate the refined GT, we partitioned the data using a 5 fold cross-validation strategy. In each fold, 80% of the data was used as the training set, while 20% was used as the validation set. We then performed preprocessing and normalization. Specifically, the input CT volume for each patient, denoted as \(x_{i}\), was transformed into a preprocessed input \(\hat{x}_{i}\) through a function \(\Phi\):  
\[\hat{x}_{i}=\Phi(x_{i}), \tag{1}\]  
where \(\Phi\) represents the preprocessing and normalization function of nnU-Net. The segmentation model \(f_{\theta}\) was applied to the preprocessed CT volume \(\hat{x}_{i}\) to generate the predicted segmentation mask \(m_{i}\):  
\[m_{i}=f_{\theta}(\hat{x}_{i}), \tag{2}\]  
where \(m_{i}\) is the predicted segmentation mask for the \(i\)th CT volume. To improve the segmentation accuracy, we optimized a combined loss function during the training process. The combined loss function \(L_{\text{combined}}\) balances the pixel-wise classification accuracy and spatial overlap by integrating the cross-entropy loss \(L_{\text{CE}}\) and the Dice loss \(L_{\text{Dice}}\). The cross-entropy loss is defined as follows:  
\[L_{\text{CE}}=-\frac{1}{N}\sum_{i=1}^{N}[y_{i}\log(p_{i})+(1-y_{i})\log(1-p_{ i})], \tag{3}\]  
Figure 2: Initial ground truth segmentation performed using ITK-SNAP.  
where \(N\) is the number of voxels, \(y_{i}\) is the ground truth label for voxel \(i\), and \(p_{i}\) is the predicted probability of voxel \(i\) belonging to the fracture region. The Dice loss is defined as follows:  
\[L_{\text{Dice}}=1-\frac{2\sum_{i=1}^{N}y_{i}p_{i}}{\sum_{i=1}^{N}y_{i}+\sum_{i=1 }^{N}p_{i}}. \tag{4}\]  
The combined loss function is expressed by  
\[L_{\text{combined}}=\alpha L_{\text{CE}}+\beta L_{\text{Dice}}, \tag{5}\]  
where \(\alpha\) and \(\beta\) are weighting coefficients which balance the contributions of the two loss functions.In our experiments, we set \(\alpha=1\) and \(\beta=1\) to assign equal importance to both loss functions. Using the models trained on each fold, we generated prediction masks for the respective fold. These prediction masks were ensembled to create the refined GT by averaging the masks across all folds:  
\[m_{\text{ensemble}}(x)=\frac{1}{K}\sum_{k=1}^{K}m_{k}(x), \tag{6}\]  
where \(m_{k}(x)\) is the predicted mask from the \(k\)th fold, \(K\) is the total number of folds (i.e., 5), and \(m_{\text{ensemble}}(x)\) is the final ensemble mask.
# Induction Heating-Assisted Compaction in Porous Asphalt Pavements: A Computational Study
## 1 Introduction  
### Development of Low Temperature Asphalt  
Asphalt concrete mix is a combination of asphalt binder and mineral particles of various sizes (i.e., filler and aggregates). The minerals and the binder act as the structural skeleton and glue of the asphalt mix, respectively. Conventional asphalt mixes are produced and compacted at temperatures higher than 150 \({}^{\circ}\)C because the viscosity of asphalt binder impedes its workability at ambient temperatures. However, working with mixes at such high temperatures, greenhouse gas emissions and fumes are produced. For this reason, the asphalt industry is constantly looking for solutions to lower energy demands and reduce emissions, and the utilization of technologies that can reduce the in-plant mixing and in-field compaction temperatures of asphalt pavements are under evaluation. These technologies are named low temperature asphalt (LTA) [1; 2; 3] technologies, and the asphalt materials developed incorporating these technologies are produced at temperatures 30 \({}^{\circ}\)C lower (or more) than temperatures typically used in the production of hot-mixed asphalt (HMA).  
There are important environmental and health benefits associated with lower production temperatures, including lower fuel consumption and greenhouse gas emissions and reduced exposure of workers to asphalt fumes. Lower production temperatures can also potentially improve pavement performance by providing added time to mix compaction and allowing improved compaction during cold weather paving conditions. Porous Asphalt (PA) concrete is taken as case material in this research to study the construction process of low temperature porous asphalt pavement, and thus named LTPA hereinafter.
## 3 Results
### End-to-End Models with Gaussian Noise  
With the noisy dataset, performance of all end-to-end models significantly degraded (Figure 7, Table 6). FCN and ResNet achieved mean F\({}_{1}\)-scores of 0.75 (standard deviation, 0.24) and 0.74 (0.22). Noteworthy changes were that ResNet outperformed FCN in the \(SNR=0.4\) case, but was overtaken again at higher noise levels (lower \(SNR\)) (Figure 6). At higher noise levels, all models underperform random guess for the binary case (i.e., F\({}_{1}\)-score of 0.5).  
Figure 6: Violin plot of mean F1-score performances for feature-based models aggregated across the full range of signal-to-noise ratios from 0.001 to 0.6.  
Figure 7: F1-score performance (mean lines with 5% CI) of end-to-end models at signal-to-noise ratios (\(SNR\)) ranging from 0.01 (in blue) to 0.40 (in red).
## 3 The Synchronous Machine
### Mechanical and Electrical Angles  
Many machines have more than two magnetic poles on the rotor. If this is the case, it is convenient to concentrate on the electric and magnetic quantities of a single pair of poles, and to recognize that quantities associated with other pole pairs are identical. For this reason, it is convenient to express the rotor angle in electrical degrees or electrical radians rather than in mechanical units.  
One pair of poles is mapped to 360 electrical degrees or 2\(\pi\) electrical radians. Since there are poles/2 electric cycles in one mechanical cycle, it follows that  
\[\theta=\frac{\text{poles}}{2}\theta_{m}, \tag{71}\]  
where  
* \(\theta\) is the rotor electrical angle, with respect to a fixed point on the stator.
* \(\theta_{m}\) is the rotor mechanical angle, with respect to a fixed point on the stator.
* \"poles\" is the number of magnetic poles on the rotor (must be even).  
In addition, \(\omega=\text{d}\theta/\text{d}t\) is the rotor electrical frequency, and \(\omega_{m}=\text{d}\theta_{m}/\text{d}t\) is the rotor mechanical frequency. Following (71),  
\[\omega=\frac{\text{poles}}{2}\omega_{m}. \tag{72}\]  
Both frequencies are measured in rad/s. We also define \(\omega_{s}\) to be the nominal grid frequency. For instance, \(\omega_{s}=2\pi\)50 or 2\(\pi\)60 rad/s.  
Figure 9: Schematic diagram of a three-phase synchronous machine with 2 poles.
## 4 Creep Model of Surrounding Rock and Parameter Determination
### Inversion of Creep Parameters of Surrounding Rocks
#### 4.2.4 Creep Parameter Inversion Analysis Based on Monitored Data  
The deformation data obtained from the field tests are used as the input for training the inversion model, and the creep parameters listed in Table 7 are obtained. The long-term deformation is calculated using the FLAC\({}^{\text{3D}}\) model shown in Figure 8 to verify the feasibility of the determined creep parameters. Figure 12 compares the monitoring and simulation results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Creep Parameter** & \(E_{\text{M}}\) **(GPa)** & \(E_{\text{K}}\) **(GPa)** & \(\eta_{\text{M}}\) **(Pa-s)** & \(\eta_{\text{K}}\) **(Pa-s)** \\ \hline XZ43+090 & 5.12 & 2.1 & \(1.7\times 10^{15}\) & \(0.95\times 10^{15}\) \\ \hline XZ67+070 & 5.01 & 2.0 & \(1.5\times 10^{15}\) & \(1.0\times 10^{15}\) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Creep parameter inversion values of surrounding rocks.  
Figure 12: Simulation and experimental results: (**a**) XZ43+090, (**b**) XZ67+070.  
For XZ43+090 and XZ67+070, the long-term deformation and deformation trend of the rock mass determined using the creep parameters of the rock mass that are obtained from the GA-BP neural network inversion model are consistent with the monitored data. This indicates that the creep parameters obtained using inversion can reflect the creep deformation characteristics of surrounding rocks.
# A Security-Oriented Data-Sharing Scheme Based on Blockchain
## 4 Experiment and Analysis
### Security Analysis of Off-Chain Data  
The storage architecture proposed in this paper separates data from data ownership proof. During the process of data sharing, the data owner \(u_{i}\) will store data off-chain in various forms, such as video, audio, image, and text. In order to analyze the security of data in the off-chain part of this architecture, text file data will be taken as an example and analyzed from the perspectives of data tampering prevention, data reliability, and data trustworthiness.  
Data Consistency: Since the digest of the data and the data are stored on-chain and off-chain, respectively, data consistency is a critical issue that needs attention. In the scheme designed in this paper, after data demanders obtain the corresponding NFT, it includes the storage address of the requested data in IPFS and the hash value of the data. Data demanders can obtain the data through the storage address and then calculate the hash value of the obtained data to compare it with the hash value stored in the NFT. If they match, it indicates that the integrity of the data has not been compromised. If they do not match, the transaction would be rolled back, and the data provider would be informed that the data was corrupted during the storage process.  
Data reliability: In the design of this paper, the off-chain data exhibits higher reliability and availability. Each node in the IPFS network can act as a storage and transmission node for the files, providing higher reliability and availability.  
Data trustworthiness: The security feature of data trust is presented by the off-chain data in the scheme proposed in this paper. The hash values returned by IPFS are encrypted with lightweight algorithms based on ECC and ECDHE in this paper. The unencrypted hash value of the shared data storage address has the risk of data leakage. Unauthorized data demander \(u_{j}\) can directly use the hash value to download the ciphertext of the data stored in IPFS. However, the hash value of the shared data storage address, encrypted and processed by the lightweight algorithm proposed in this paper, can only be decrypted by a legitimate user holding the shared key. This ensures access to the data in IPFS and maintains the trustworthiness of the data under the chain.  
Potential centralization risks: In our scheme, IPFS is adopted as a centralized storage for data, serving as an alternative to traditional cloud storage methods. However, the difference lies in the fact that IPFS, based on its stronger robustness, offers enhanced security. Since IPFS is inherently decentralized, it is difficult for attackers to launch central attacks, such as DDoS. Even if some blocks of data are compromised, IPFS can still recover it through identical data blocks available on other nodes. Therefore, although the data are stored centrally, the risks associated with data centralization are greatly mitigated with the characteristics of IPFS.
## 5 Empirical Analysis and Results
### Assessment of Measurement Model  
By using the PLS-SEM, the conceptual research model was evaluated in two steps, the evaluation of the structural (the inner model) and measurement models (the outer model) [55]. The assessment of the measurement model, also called the outer model, determines individual item reliability, internal consistency reliability, content validity, convergent validity, and discriminant validity [53; 57; 58] as shown in Figure 2. The assessment of the structural model is described in Section 5.3  
Figure 2: The Measurement Model, \"\" means moderating between IV and DV  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline
**Latent** & **CCA** & **COM** & **CP** & **CUL** & **RA** & **REL** & **RP** & **SEC** & **TMS** & **TR** \\
**Variables** & & & & & & & & & & & \\ \hline CCA & 1.000 & & & & & & & & & & \\ COM & 0.785 & 0.851 & & & & & & & & & \\ CP & 0.639 & 0.576 & 0.831 & & & & & & & \\ CUL & \(-\)0.805 & \(-\)0.684 & \(-\)0.538 & 0.813 & & & & & & \\ RA & 0.564 & 0.520 & 0.402 & \(-\)0.458 & 0.797 & & & & & \\ REL & 0.763 & 0.820 & 0.536 & \(-\)0.661 & 0.450 & 0.838 & & & & \\ RP & 0.734 & 0.629 & 0.510 & \(-\)0.929 & 0.432 & 0.612 & 0.896 & & & \\ SEC & 0.754 & 0.627 & 0.522 & \(-\)0.787 & 0.381 & 0.610 & 0.747 & 0.901 & & \\ TMS & 0.621 & 0.478 & 0.414 & \(-\)0.610 & 0.239 & 0.474 & 0.570 & 0.556 & 0.717 & \\ TR & 0.771 & 0.680 & 0.491 & \(-\)0.722 & 0.467 & 0.722 & 0.663 & 0.685 & 0.510 & 0.849 \\ \hline \end{tabular} Note. CCA = Cloud Computing Adoption, COM = Compatibility, CP = Competitive Pressure, CUL = Culture, RA = Relative Advantage, REL = Reliability, RP = Regulatory Policy, SEC = Security, TMS = Top Management System, TR = Technology Readiness.  
\end{table}
Table 4: Latent Variable Correlations and Square Roots of Average Variance Extracted.