esa-sceva/satcom-chunk-collection huggingface dataset:
# Mechanical Properties of Disconnectable Coupling Joints for Steel Bracing under Eccentric Load
## 5 Flexural Performance under Eccentric Load
### Parameters Analysis on Flexural Performance
#### 5.2.6 Influence of Channel Steel Thickness  
Figure 14(a) shows the moment-rotation curves for different channel thickness. In the early stage of loading, the influence of varying channel thicknesses on flexural performance is slight when the bending moment is less than 150 kN\(\cdot\)m. When the bending moment is greater than 150 kN\(\cdot\)m, the effect of channel thickness is significant. According to the relationship curves in Figure 14(b), the initial rotational stiffness and moment resistance increase with channel thickness, and the increase rate gradually decreases. The increase rate of initial rotational stiffness decreases from 7.1% to 4.0%, and the increase rate of moment resistance decreases from 10.9% to 5.4%, when the channel thickness increases from 7 mm to 19 mm.
# Performance of Payement Temperature Prediction Models
## 2 Materials and Methods
### Analysis
#### 2.4.4 Statistical Analysis  
Statistical analysis methods, including the determination coefficient (\(R^{2}\)), Variance Accounted For (_VAF_), Maximum Relative Error (_MRE_) and Root Mean Square Error (_RMSE_) were used to evaluate model performance for the different materials, for both calibrated and uncalibrated data, as well as model performance at the Akumadan and Sogakope test sites in Ghana.  
* \(R^{2}\) measures goodness of fit, i.e., how well a linear regression model fits the measured data, where \(R^{2}=\left[\frac{\sum_{i=1}^{N}\left(x-\overline{x}\right)\left(y-\overline{ y}\right)}{\sum_{i=1}^{N}\left(x-\overline{x}\right)^{2}\sum_{i=1}^{N} \left(y-\overline{y}\right)^{2}}\right]^{2}\). \(R^{2}\) varies between 0 and 1; the closer it is to 1, the better the model, i.e., 100% of the variation in the model can be explained by the predictor variables;
* _VAF_ measures the variance accounted for between measured values and predicted values, where \(VAF=100\left[1-\frac{v\pi\pi\left(x-y\right)}{v\pi\left(x\right)}\right]\). The closer the _VAF_ is to 100, the better the model;
* _MRE_ is defined as the ratio of the absolute error of the predicted value to the measured value, where \(MRE=max\left(100\frac{\left|x-y\right|}{x}\right)\). This provides a measure of how large the error is relative to measured values, so that the closer the value is to zero, the better the model;
* _RMSE_ measures the extent to which predicted values deviate from measured/actual values, on average, where \(RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(x-y\right)^{2}}\). _RMSE_ decreases as the error in the predicted values decreases, so that the closer the value is to zero, the better the model.  
Here, \(x\) and \(y\) are measured values and predicted values, respectively; \(\overline{x}\) and \(\overline{y}\) are average measured values and average predicted values, respectively; _var_(.) is the variance.  
The statistical results for \(R^{2}\) were expressed as a percentage and plotted on graphs alongside the _VAF_ (%) and _MRE_ (%). The _RMSE_ was not converted to percentage to avoid vertical scale exaggeration. The plots are presented in Figures 1-3. The closer the \(R^{2}\) (%) and _VAF_ (%) are to 100, the more accurate the model predictions, while the closer the _MRE_ (%) and _RMSE_ are to zero, the more accurate the model predictions.  
Figure 1: Comparison of statistical analysis results for uncalibrated and calibrated predicted maximum temperatures.  
Figure 2: Comparison of statistical analysis results for uncalibrated and calibrated predicted minimum temperatures.
# A Blockchain-Assisted Security Protocol for Group Handover of MTC Devices in 5G Wireless Networks
## 8 Conclusions  
In this paper, we have proposed the blockchain-assisted group handover authentication protocol for MTC communication in 5G wireless networks. The BSPGH protocol  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Protocol**} & \multicolumn{10}{c}{**Security Features**} \\ \cline{2-11}  & **MA** & **PP** & **PFS** & **RA** & **IA** & **DoS** & **DDoS** & **SKL** & **SA** & **MITM** \\ \hline BSPGH & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline
5G-AKA & & \(\surd\) & & \(\surd\) & \(\surd\) & & & & \(\surd\) & \(\surd\) \\ \hline PPHAP & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline NASS & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & & \(\surd\) & \(\surd\) & \(\surd\) \\ \hline \hline \end{tabular}  
* MA = mutual authentication; PP = privacy protection; PFS = perfect forward secrecy; RA = replay attack prevention; IA = imperson attack prevention; DoS = DoS attack prevention; DDoS = DDoS attack prevention; SKL = session key leakage; SA = sybil attack prevention; MITM = man-in-the-middle attack prevention.  
\end{table}
Table 4: Security analysis of protocols.  
aims to reduce the authentication time when multiple MTCDs undergo frequent handovers between gNBs by adopting group handover authentication. The proposed BSPGH scheme requires no modification to the existing 5G network architecture specified by the 3 GPP standard, making it easy to deploy. By formal verification using Scyther Tool and BAN-logic, we have analyzed the protocol's security properties, demonstrating that it can provide perfect forward secrecy and resist impersonation attacks, DoS/DDoS attacks, and some other attacks. It also ensures the anonymity of the MTCDs. Moreover, the performance analysis has shown that the BSPGH scheme can meet the requirements of various application scenarios to confirm its efficiency.  
Conceptualization, R.M., J.Z. and M.M.; methodology, R.M.; software, R.M.; validation, J.Z. and M.M.; formal analysis, R.M.; investigation, R.M.; resources, J.Z. and M.M.; data curation, R.M.; writing--original draft preparation, R.M.; writing--review and editing, J.Z. and M.M.; visualization, R.M.; supervision, J.Z. and M.M.; project administration, J.Z. and M.M.; funding acquisition, J.Z. All authors have read and agreed to the published version of the manuscript.  
The work of this paper was funded by the National Natural Science Foundation of China (No. 62171387), and the China Postdoctoral Science Foundation (No. 2019M663475).  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
# Practical Methodology for a Three-Dimensional-Printed Hybrid Desalination System
###### Abstract  
In response to the growing demand for potable water, this study presents a practical methodology for designing and fabricating a hybrid desalination system that integrates reverse electrodialysis and electrodialysis using 3D-printing technology. The hybrid system combines the energy generation potential of RED with the salt removal capabilities of ED, reducing energy consumption. Customized reactors were designed to enhance flow distribution and ion exchange, with computational fluid dynamics simulations validating the hydrodynamic performance. The reactors were fabricated using 3D printing, allowing rapid, cost-effective production, with functional reactors constructed in under 24 h. The system achieved a 15% reduction in salt concentration within one hour, with a specific energy consumption of 0.1388 Wh/m\({}^{3}\) and a water recovery rate of 50%. These results demonstrate the functionality of the RED-ED hybrid system for achieving energy savings and performing water desalination. This methodology provides a scalable and replicable solution for water treatment applications, especially in regions with abundant salinity gradients and limited freshwater resources, while offering a multidisciplinary approach that integrates physicochemical and engineering principles for effective device development.  
3D printing; electrochemical flow reactor; desalination; salinity gradient energy; electrodialysis; reverse electrodialysis; CFD modeling
# Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems
## 4 The Proposed Scheduling  
In this section, we introduce the CEM-RM algorithm for the multipath retransmission scheme based on graph routing in WirelessHART. We also present the proposed approaches to make the scheduling more efficient. The symbols and related functions used in the algorithms are listed in Table 1.
# Channel Code-Book (CCB): Semantic Image-Adaptive Transmission in Satellite-Ground Scenario
## 5 Simulation Results and Analysis
### Simulation Parameters  
Table 1 presents the model structure and the output size for each layer. In the entire model, we employ the Gaussian Error Linear Unit (GELU) as the activation function. GELU is a nonlinear activation function that integrates the properties of the Gaussian distribution with a linear function. This combination enables the function to capture complex data patterns, enhancing the model's ability to understand intricate details within images and effectively capture the nonlinear characteristics of the data. For the CIFAR10 dataset, we use two stages: \([N1,N2]=[2,4]\), \([C1,C2]=[128,256]\), with the window size set to 2. For the DIV2K dataset, we utilize four stages: \([N1,N2,N3,N4]=[2,2,6,2]\), \([C1,C2,C3,C4]=[128,192,256,320]\), with the window size set to 8. The model structure of baseline is shown in Table 2.  
We employ the Adam optimizer with a learning rate of 0.0001. The batch sizes of the CIFAR10 dataset and the DIV2K dataset are set to 128 and 4, respectively. The CCB model is trained in the SR channel with SNR from \(-5\) dB to 20 dB, and the channel parameters are shown in Table 3. To initialize the codebook entries, we first utilize the Gaussian distribution to randomly generate initial values. After completing the training of a channel model, the pre-trained model parameters are subsequently employed to initialize and train other channel models. All these parameters are implemented in the PyTorch architecture, utilizing the NVIDIA A10 GPU.  
\begin{table}
\begin{tabular}{l l l} \hline \hline  & **Layers** & **Output Size** \\ \hline Encoder & PatchEmbed & 256 \\  & SwinTransformerBlock \(\times\) 2 & 128 \\  & SwinTransformerBlock \(\times\) 4 & 256 \\  & LayerNorm & 256 \\  & Linear & 96 \\ \hline Denoiser & Encoder1 & (1, 128, 64, 96) \\  & Middle & (4, 64, 32, 48) \\  & Decoder1 & (2, 128, 64, 96) \\  & Conv\_last & (1, 128, 64, 96) \\ \hline Decoder & SwinTransformerBlock \(\times\) 4 & 256 \\  & SwinTransformerBlock \(\times\) 2 & 128 \\  & PatchReverseMerging & 128 \\  & Linear & 256 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model structure.
## 2 Materials and Methods
### Preparation Techniques  
KTP, due to its low water solubility, requires a pre-dispersion step. A KTP-containing nanosuspension was prepared following our previously established method [16]. Briefly, the drug particles were dispersed in a water-based stabilizer solution composed of polyvinyl alcohol (1% \(w/v\)) and sodium dodecyl sulfate (0.1% \(w/v\)). Wet media milling was then conducted using a planetary ball mill (Retsch Planetary Ball Mill PM 100 MA, Retsch GmbH, Haan, Germany) with 20.00 g of 0.3 mm zirconium dioxide (ZrO\({}_{2}\)) beads. Milling took place in a 50 mL chamber at 400 rpm for 60 min.  
Inhalable dry powders were produced using a Mini Spray Dryer (Buchi B-191, Flawil, Switzerland) with parameters optimized based on our previous study [16]: inlet temperature 90 \({}^{\circ}\)C, outlet temperature 60 \({}^{\circ}\)C, aspirator at 80%, pump rate at 2%, and a flow rate of 600 L/min. MNT, widely used as a mucolytic agent in cystic fibrosis therapy, was combined with KTP in a 1:1 mass ratio to evaluate its therapeutic potential. To improve dispersibility, leucine was also incorporated alongside MNT, resulting in a formulation designated as F1. For comparison, a reference formulation without MNT or leucine was prepared and labeled as F0.
# Multi-Source Image Fusion Based on BEMD and Region Sharpness Guidance Region Overlapping Algorithm
## 4 Multi-Source Image Fusion Algorithm
### Selection Strategy by Sharpness Comparison
The commonly used DFT method, the Tenengrad method, and the Laplacian function method all take into account the change of gray value in both horizontal and vertical directions when calculating sharpness, so it is easy to be affected by fringe noise, and the calculation results fluctuate greatly and the accuracy is poor. Aiming at this problem and considering the configuration of calculating image sharpness, an operator with eight directions, as shown in Figure 4, is designed for calculation. In Figure 4, the expressions of the eight directional operators are, respectively, as follows:  
\[\left\{\begin{array}{l}\mathrm{G}_{1}(\mathrm{x},\mathrm{y})=\mathrm{f}( \mathrm{x}-1,\mathrm{y}+1)+\  
Through experimental comparison, it is found that the operator takes into account the change trend of sharpness in eight directions, and uses the above eight operators to convolve GBIMF images, respectively. The maximum value of the absolute value of the calculated result of each pixel is selected as the result, and the corresponding direction operator of the result is selected as the final sharpness value as the key for evaluation. The resulting fusion image quality is greatly improved. Figure 5 is the final fusion effect diagram of the DFT operator, the Laplacian operator [11], and the improved Sobel operator. It can be seen that the improved Sobel operator has better clarity in the final fusion image and more obvious details in the fusion image. As shown in the red box in the figure, the resulting image has uniform brightness and less noise. The quality of the final fusion image is improved.
## 2 Background
### Structural Health Monitoring
As one of the most representative low-cost SHM implementations, the Jindo Bridge is a 484-m cable-stayed structure located in Jindo, South Korea, monitored by a smart WSN since 2009. This monitoring system consists of two base stations and 70 measuring nodes; every measuring node has a high-efficiency computational device, Imote2 (Imote2 IBB2400 AC. Moog Inc., New York, NY, USA); multi-scale sensor boards; and a battery pack (7.5 V supply voltage). The multi-scale sensor boards include analog accelerometers with a measurement range of \(\pm\)2.0 g, a 16-bit analog-to-digital converter (ADC) signal digitizer, 3-D ultra-sonic anemometers, and sensors of temperature, humidity, and light [15]. The Torre Aquila tower is a 31-m tall medieval structure settled in Trento, Italy, where a WSN was installed for health monitoring in 2008. The configuration of the WSN involves a base station and 16 measuring nodes, each of which comprises a 12-bit ADC processor module 3 MATE! (3 MATE!. TRETEC S.R.L., Trento, Italy), a portal battery (6.0 V supply voltage), and a sensor categorized by type of analysis: an ambient conditions sensor (temperature, relative moisture, and luminosity), a micro-electromechanical systems (MEMS) accelerometer with a measurement range of \(\pm\)1.5 g, or a strain gauge [16]. Likewise, the Golden Gate Bridge is a suspension structure of a 1280-m main span located in San Francisco Bay, United States, examined by an SHM WSN since 2007. This wireless monitoring system is composed of a single base station and 64 nodes arranged throughout the main span and tower of the bridge. Every deployed node, powered by 8.0 V supply voltage, includes a MICAz Mote (MICAz Mote. Moog Inc., New York, NY, USA) along with a sensor board: two MEMS accelerometers with a 16-bit antialiasing filter and a measurement range of \(\pm\)2.0 g, and a thermometer [17]. The primary motivation for implementing WSNs is their extraordinary cost-effectiveness. Table 1 presents the economic investments required for some relevant infrastructure projects adopting both types of SHM systems.
# A Multiscale CNN-Based Intrinsic Permeability Prediction in Deformable Porous Media
###### Abstract  
This work introduces a novel application for predicting the macroscopic intrinsic permeability tensor in deformable porous media, using a limited set of \(\mu\)-CT images of real microgeometries. The primary goal is to develop an efficient, machine learning (ML)-based method that overcomes the limitations of traditional permeability estimation techniques, which often rely on time-consuming experiments or computationally expensive fluid dynamics simulations. The novelty of this work lies in leveraging convolutional neural networks (CNNs) to predict pore-fluid flow behavior under deformation and anisotropic flow conditions. The approach utilizes binarized CT images of porous microstructures to predict the permeability tensor, a crucial parameter in continuum porous media flow modeling. The methodology involves four steps: (1) constructing a dataset of CT images from Bentheim sandstone at varying volumetric strain levels; (2) conducting pore-scale flow simulations using the lattice Boltzmann method (LBM) to obtain permeability data; (3) training the CNN model with processed CT images as inputs and permeability tensors as outputs; and (4) employing techniques like data augmentation to enhance model generalization. Examples demonstrate the CNN's ability to accurately predict the permeability tensor in connection with the deformation state through the porosity parameter. A source code has been made available as open access.  
machine learning; convolutional neural networks; permeability tensor; porous media flow; multiscale modeling; porous microstructure; Bentheim sandstone; data augmentation
## Appendix C Protection Formulation Notation  
Appendix C documents the notation required to define the fundamentals of the mathematical formulations. The notation discussed here is used to model protection.  
**Sets:**  
\(\mathcal{Z}\)--set of protection zones, indexed by \(\zeta\)  
\(\mathcal{P}\)--set of protective devices, indexed by \(p\)  
\(\mathcal{P}_{\zeta}\)--set of protective devices in zone \(\zeta\), indexed by \(p\)  
\(\mathcal{E}_{\zeta}\)--set of branches with protective devices, indexed by \(p\)  
**Parameters:**  
\(\mathcal{Z}^{\{g,op\}}_{ijk}\)--operating area for ground fault admittance relay \((i,j,k)\)  
\(\mathcal{Z}^{\{l,op\}}_{ijk}\)--operating area for line fault admittance relay \((i,j,k)\)  
\(A=0.14\)--time-overcurrent relay time-dial scaling  
\(B=0.02\)--time-overcurrent relay curve shape  
\(T_{c}=0.5\) s--coordination time interval for time-overcurrent relays  
\(\hat{\mathbf{t}}^{p}=7\) s--maximum allowable trip time for time-overcurrent relays  
**Variables:**  
\(T_{ijk}\)--time-overcurrent relay time-dial setting, indexed by \((i,j,k)\)  
\(I^{p}_{ijk}\)--time-overcurrent relay pickup current, indexed by \((i,j,k)\)  
\(t^{p}_{ijk}\)--time-overcurrent relay trip time, indexed by \((i,j,k)\)  
\(t^{p}_{(1)}\)--1 st order statistic of time-overcurrent relay trip times  
\(\hat{Z}^{\{g,k\}}_{ijkf}\)--Measured positive-sequence impedance between relay \((i,j,k)\) and fault for line-ground faults  
\(\hat{Z}^{\{l,+\}}_{ijkf}\)--Measured positive-sequence impedance between relay \((i,j,k)\) and fault for line-line faults  
\(\hat{Z}^{+}_{ijkf}\)--Measured positive-sequence impedance at relay \((i,j,k)\)  
\(\hat{Z}^{0}_{ijkf}\)--Measured zero-sequence impedance at relay \((i,j,k)\)  
\(k_{ijkf}\)--Residual compensation factor for ground-fault relay \((i,j,k)\)
# Converting Concurrent Range Index Structure to Range Index Structure for Disaggregated Memory
## 5 Related Work
### Conversion Techniques for Emerging Hardware  
Emerging persistent memory opens up new challenges as it has unique characteristics. However, legacy storage systems still depend on volatile index structures. Moreover, newly developed persistent indexes are less stable than mature volatile indexes [26; 27; 28; 29; 30]. Hence, the researchers focus on leveraging the existing concurrent indexes for persistent memory.  
**Recipe**[26] proposes the protocols to convert volatile concurrent data structure to persistent indexes by adding persistence instructions, such as _clwb and sfence, after_ store instruction. Recipe supports both lock-based and lock-free data structures.  
**NVTraverse**[31] and **Log-Free Concurrent Data Structure**[30] propose approaches to convert the volatile lock-free data structure to a persistent lock-free data structure. NVTraverse adds persistence instructions after every _store_ and _load_ instruction to guarantee the persistence of the data structure. Log-Free Concurrent Data Structure proposes the _link and persist_ concept to guarantee persistence using pointer marking. In link and persist, not only does the writer persist the data structure but the reader also persists the data structure when it detects unpersisted data.  
Previous studies require in-depth knowledge of the volatile indexes as they have to add persistence instructions manually. There have been research efforts to develop the framework to convert the volatile indexes to persistent ones systemically.  
**Pronto**[28] is a framework to convert a legacy volatile index structure to a persistent index structure. In Pronto, the volatile index structures are stored in both volatile DRAM and persistent memory. The foreground threads access the index structure in the DRAM. If the threads update the index structure, the update will be written to the operation log. A background thread updates the index structure in the persistent memory using the operation log.  
**TIPS**[29] is another framework to make volatile index structures persistent. TIPS stores the index structure on persistent memory. In DRAM, TIPS keeps a hash-table cache to provide high performance. TIPS also uses an operation log to guarantee persistence and leverages background threads to update the index structures on persistent memory asynchronously.
## 3 Results and Discussion
### Time-Based SARIMA Statistical Control Model
Figure 8 shows the modeled and actual _P3_/_P2_ values. For ease of visualization, only the month of October is shown. The RMSE is also calculated between the real values measured at the Noja facility between 1 October 2021 and 31 December 2021 and the predictions made for the same dates, obtaining a value of 0.018. Figure 7: Representation of model-derived values and real values of _P3_/_P2_: (**a**) month of April, (**b**) month of August. Figure 9 shows the fit of the prediction with the values sampled at the installation from 1 October to 31 December. The value of the coefficient of determination, 0.9648, confirms the good correlation between the actual values and the predictions obtained with the model [55]. The new SARIMA model allows to predict the headloss between the PRV and the critical point of the area at any given day/time, based on previous historic pressure data of the area. As an advantage to the model built in [5], flow measurement is not needed. Unlike the model built by [PERSON] et al. [27], the SARIMA model will permit commissioning a CPC PM strategy without connecting the PRV with the critical point of the WDN. Figure 8: Representation of actual and derived values of the _P3/P2_ model in October (Validation). Figure 9: Coefficient of determination between actual and predicted values.