esa-sceva/satcom-chunk-collection huggingface dataset:
# Implementation of an Attribute-Based Encryption Scheme Based on SM9
## 3 Implementation and Evaluation of SM9 Identity-Based Encryption
### KEM-DEM Algorithms
#### 3.3.1 KEM-DEM-Encrypt (\(\text{M}_{\text{pk}}\), \(\text{ID}_{\text{A}}\), \(\text{m}\))  
Given an identity string \(ID_{A}\), plain text \(m\) (of bit length \(mlen\)), and master public key \(M_{pk}\), the operation runs, as shown in Algorithm 5.  
The encryption algorithm is a combination of KEM and DEM. It first generates and encapsulates a random key, and then uses that key to encrypt the message while using different types of DEM. Finally, a message authentication code is created to ensure the integrity and authenticity of the ciphertext. The encapsulated key, encrypted message, and authentication code make up the ciphertext. Figure 2 shows the flow chart of the encryption algorithm.  
Figure 2: Encryption algorithm flow.  
3.2. KEM-DEM-Decrypt (\(\text{M}_{\text{pk}}\), \(\text{ID}_{\text{A}}\), \(\text{DE}_{\text{A}}\), \((\text{C}_{1}\), \(\text{C}_{2}\), \(\text{C}_{3}\)))  
Given the master public key \(M_{pk}\), an identity string \(ID_{A}\), the corresponding private key \(DE_{A}\), and cipher text \((\text{C}_{1}\), \(\text{C}_{2}\), \(\text{C}_{3})\), and the operation runs, as shown in Algorithm 6.  
```
1 IF \(\text{C}_{1}\
ot\in\text{C}_{1}^{*}\)
2 output \(\bot\) and terminate
3 ELSE
4\(t\gets e(\text{C}_{1},DE_{A})\)
5 IF DEM is Stream cipher based on KDF
6\(klen\gets\text{C}_{2}\_len+v\)
7\(K_{1}\parallel K_{2}\leftarrow\textbf{KDF}(H_{v},\text{C}_{1}\parallel t \big{\|}ID_{A},klen)\)
8 IF \(K_{1}\) is full zero
9 output \(\bot\) and terminate
10 ELSE
11\(m\gets\text{C}_{2}\oplus K_{1}\)
12 ELSE IF DEM is Block cipher combined with KDF
13\(klen\gets\text{K}_{1}\_len+v\)
14\(K_{1}\parallel K_{2}\leftarrow\textbf{KDF}(H_{v},\text{C}_{1}\parallel t \big{\|}ID_{A},klen)\)
15 IF \(K_{1}\) is full zero
16 output\(\bot\) and terminate
17 ELSE
18\(m\gets\text{D}ec\)\((K_{1},\text{C}_{2})\)
19\(\text{C}_{3}\prime\gets H_{v}(\text{C}_{2}\|K_{2})\)
20 IF \(\text{C}_{3}\prime\
ot\in\text{C}_{3}\)
21 output\(\bot\) and terminate
22 ELSE
23 output \(m\)
```  
**Algorithm 6**\(KEM-DEM-Decrypt\)\((M_{pk},ID_{A},DE_{A},(\text{C}_{1},\text{C}_{2},\text{C}_{3}))\)  
Upon receiving the ciphertext, the decryption algorithm decapsulates the secret key, then decrypts the message using the same type of DEM, as used by the encryption. Finally, it verifies the authentication code. Only when the integrity and authenticity of the ciphertext are confirmed is the plaintext output. Based on the algorithm, the flow chart of this process is shown in Figure 3.  
Figure 3: Decryption algorithm flow.  
We can see that the performances of using two types of DEM are very close when comparing Figures 4 and 5. This indicates that the encryption and decryption processes of the SM4 block cipher have approximately the same speed as the stream cipher. The execution time of decryption is approximately three times as much as that of the encryption. Through further comparison and analysis, we found that the pairing operation leads to such difference in the execution time.
# Experimental Study on High-Energy Gas Fracturing Artificial Coal
## 2 Gas Generation Principle of Fracturing Agent and Fracturing Mechanism of High-Energy Gas  
### Gas Generation Principle of Fracturing Agent  
The fracturing agent mainly consists of a heating agent, a gas-producing agent, and an oxygen-supplying agent. The oxygen supply agent provides oxygen to the heating agent, causing it to oxidize and emit a large amount of heat, and then the gas production agent is decomposed by heat to produce a large amount of gas. The authors' team chose aluminum powder as the heating agent, ammonium perchlorate as the oxygen-supplying agent, and the main gas-producing agent to make the fracturing agent. Under high-temperature conditions, ammonium perchlorate decomposes to generate oxygen, and then the aluminum powder is oxidized to give off a lot of heat, heat to promote the thermal decomposition of ammonium perchlorate to produce a large amount of high-temperature and high-pressure gas; the main reaction equation is shown in (1-3).  
\[4\mathrm{Al}+3\mathrm{O}_{2}=2\mathrm{Al}_{2}\mathrm{O}_{3} \tag{1}\]  
\[2\mathrm{NH}_{4}\mathrm{ClO}_{4}=\mathrm{N}_{2}+4\mathrm{H}_{2}\mathrm{O}+ \mathrm{Cl}_{2}+2\mathrm{O}_{2} \tag{2}\]  
\[(\mathrm{NH}_{4})_{2}\mathrm{C}_{2}\mathrm{O}_{4}=2\mathrm{NH}_{3}+\mathrm{CO}+ \mathrm{CO}_{2}+\mathrm{H}_{2}\mathrm{O} \tag{3}\]
## 3 Results
### Main Topics in Laboratory-Scale Experimentation of UHS
#### 3.2.1 Main Rock Types
Other rock types are also considered for hydrogen storage as alternatives in areas where porous rock formations and saline structures are not available. Cavernous structures, such as engineered or natural caves in rocks of other rock types of predominantly silicate compositions (i.e., banded gneiss, granitic gneiss, mignatite), are also being studied for their suitability in safely storing hydrogen, due to their low reactivity with H\({}_{2}\) and experiences from other gas storage projects [55]. Both lined and unlined rock caverns are possible solutions for H\({}_{2}\) storage. In the case of lined rock caverns, Sweden has been at the forefront of research into cavernous structures for hydrogen storage through the \"HyBRIT\" [56]. The Swedish HyBRIT project is developing a 100 m\({}^{3}\) hard rock cavern for hydrogen to be used in the decarbonization of steel making [56], using a concrete lining. The International Energy Agency (IEA) has compiled several UHS projects; Table 2 presents some examples of different storage structures and reservoir rocks. The table shows the project name, operator, status, country, year, and storage capacity. In order to reliably assess the storage capacity and understand the migration of the fluids during and after injection and extraction, it is fundamental to know the physical properties of the reservoir and caprocks (together with the properties of the fluids present in the reservoir system). The effective porosity of the reservoir rock (i.e., the relative volume of interconnected pores) will determine the storage capacity in porous media, while the absolute permeability (i.e., the ease of flow of a single fluid through the rock) and the relationships between capillary pressure, relative permeability, and water saturation are parameters that govern fluid migration [58]. While capillary pressure and relative permeability are dependent on both rock and fluid properties and must be assessed under site-specific conditions (see Section 3.2.2), effective porosity and absolute permeability are intrinsic properties of the rocks that are routinely measured in the laboratory by means of instrumental analytical techniques (e.g., Hg-intrusion porosimetry, gas permeability, etc.). Porous geological media is usually characterized by relatively high values of porosity (>10%) and permeability (>100 mD) (e.g., [50]), and effective caprocks, such as mudstones, marls, or salts, have extremely low porosity and permeability values. Several studies addressed the variability of the physical properties (e.g., porosity and permeability) of salt formations under different loading conditions and with different impurity contents, as this is a critical parameter for UHS in salt caverns (e.g., [59; 60; 61]).
## 6 Case Studies
### Experimental Design  
The modified IEEE-33 and IEEE-69 node distribution systems [36,37] are employed as the test systems in this study. The modified IEEE-33 node distribution system comprises 33 nodes, including 5 generation nodes and 28 load nodes, interconnected by 32 lines. These lines comprise 28 branch lines and 4 boundary lines, as illustrated in Figure 2. Similarly, the modified IEEE-69 node distribution system consists of 69 nodes, which include 6 generation nodes and 63 load nodes, interconnected by 68 lines. These lines compass 63 branch lines and 5 boundary lines, as depicted in Figure 3. In this study, it is assumed that only the boundary lines are equipped with remote control switches, which are initially set to a default state of disconnection. The distribution lines are set to a default state of closed. The reactance (\(r_{ij}\)) of lines and power demand (\(P_{i}\)) at the nodes are adopted from the original data of the systems [36,37]. Further details regarding other data settings are provided in the following subsections.
# Open Radio Access Networks For Smart IoT Systems: State of Art and Future Directions
## 4 Challenges and Future Research Directions
### Challenges  
Based on the investigation conducted in this study, it is clear that O-RAN can support IoT systems in several ways. However, these systems comprise diverse devices used by different application domains with different communication protocols to achieve their respective goals. This could lead to several challenges that need to be addressed before  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference, Domain** & **Task** & **Gain** \\ \hline Ref. [86], IoT devices work & Computation offloading from IoT devices to the network edge & 20 \% reduction in energy consumption among for IoT devices \\ \hline Ref. [64], Transportation cation latency & Age of processing offloading for minimizing communication latency & More than 90 \% reduction in computation cost leading to an effective communication for vehicular networks \\ \hline Ref. [61], Energy & Digital beamforming for IoE & Efficient beamforming network parameters required by the IoE \\ \hline Ref. [62], Healthcare & Healthcare remedies covering rural communities & Cost effective remote healthcare services \\ \hline Ref. [34], Manufacturing & Network slicing for IIoT & Robust network for an effective monitoring and control in IIoT \\ \hline \hline \end{tabular}
\end{table}
Table 6: State-of-the-art benefits of O-RAN-based IoT systems.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference, Year** & **Objective** & **Gap** & **Contribution** & **Remarks** \\ \hline Ref. [61], 2022 & \(X_{3},Y_{1}\) & Beamforming based on massive multiple-input and multiple-output (MIMO) in O-RAN is an open problem & Implemented zero-forcing in O-RAN to prove digital beamforming reduces fronthaul traffic and minimizes interference in IoE’s communication channels \\ \hline Ref. [85], 2022 & \(X_{3},Y_{2}\) & Model-based state estimation methods are inefficient nowadays & O-RAN for intelligent data-driven distributed state estimation of phasor measurement units (PMUs) & Effective PMU monitoring requires solutions that consider real-time performance, data quality and integrity, and privacy and security \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of O-RAN in smart energy systems.  
these systems can be widely deployed. We have summarized the challenges based on the dimensions below.
# Performance Modeling Framework for IoT-over-Satellite Using Shared Radio Spectrum
## 3 IoT-over-Satellite Performance Modeling
### Terrestrial Interference Model
#### 3.3.1 Use Case Scenarios and Interference Population Thinning  
To simulate a real-world situation, we assume IoT devices are about \(i\) times to the human population, and we presume \(i=3\) according to Reference [31]. Also, in order to create an accurate interference model for IoT-over-Satellite communication, we deploy the Remoteness Index of Australia (ARIA) to classify all interference sources into the following classes [32]:  
* Class 1 (sea): Interference sources on the sea level and are at least 100 km away from the urban and suburban areas. Class 1 is having the lowest interference since the smallest amount of IoT devices are located in Class 1. Since the focus of the paper is on estimating the terrestrial interference from the land, we assume there are no IoT devices on the sea level.
* Class 2 (remote): Interference sources that score more than 10 in the ARIA (ARIA G5) representing a remote area, which is at least 141 km away from the nearest remote center, 264 km away from the nearest rural center, and 614 km away from the nearest suburban center. Class 2 is likely to have lower interference than Class 3.
* Class 3 (rural): Interference sources that score between 5-10 in the ARIA (ARIA G4) representing a rural area, which is around 217 km away from the nearest suburban center, 134 km away from the nearest rural center. Class 3 is expected to have the highest interference among these classes.  
We also apply the thinning to estimate the average active IoT devices and the number of active interference sources can be calculated as  
\[\overline{\rm N}_{\rm active}=\frac{D\%\times N}{N_{\rm C}}, \tag{14}\]  
in which the active IoT devices are equal to the total IoT devices\((N)\) times the regulated duty cycle (\(D\%\)) divides by the number of channel (\(N_{\rm C}\)). We assume 1% of duty cycle, \(N\) is equal to 3 times of the human population as the worst-case scenario based on the radio regulation [26; 27], so that we can find the lowest bound on SINR in the evaluation framework.
# Three Steps towards Better Forecasting for Streamflow Deep Learning
## 3 Results and Discussion
### Performance of Step 1  
In Step 1, the LSTM and ANN algorithms were developed and compared. It was found that LSTM performed much better than ANN. Several literature reviews also supported this by identifying the LSTM as the best deep learning model for time series data due to its ability to keep selective memory. LSTM algorithm could also filter the hydrological noise and retrieve the intrinsic characteristics of the hydrological series for simulation and future forecasting purposes.  
Table 1 indicated that the ANN model had a regression of 0.4520, MSE 78.4215, MAE 3.7135 m\({}^{3}\)/s, NSE 0.1994 and RMSE 8.8556 m\({}^{3}\)/s. Furthermore, the best LSTM had regression 0.9055, MSE 17,8532, MAE 1.4365 m\({}^{3}\)/s, NSE 0.8190 and RMSE 5.3695 m\({}^{3}\)/s. Generally, it had shown a double improvement in overall results.  
When comparing the graphs between Figures 7 and 8 on peak-to-peak values between the observed and forecast flows, it was evident that LSTM was much better than ANN models. Therefore, LSTM was chosen as the primary research model for this study.
# Walking-Age Estimator Based on Gait Parameters Using Kernel Regression
## 3 Results
### Results of Multiple Regression Analysis  
Figure 5a shows the relationship between the ages estimated by multiple regression analysis and the actual ages of the walkers. The adjusted \(R^{2}\) value was 0.415. Table 3 presents the partial regression coefficients of the significant explanatory variables with \(p<0.05\). Since all explanatory variables were standardized as \(z\)-scores, the coefficients reflect their relative contributions to age estimation. The selected variables included height, weight, mediolateral velocity of the CoM, step width, and stride length. The estimated ages were moderately correlated with the actual ages in the dataset, with a correlation coefficient of 0.65. The mean absolute error was 12.9 \(\pm\) 0.40 years. Estimated ages ranged from 12 to 93 years, whereas the actual ages ranged from 20 to 75 years. The maximum absolute error was 38.3 years.
# Bio-Inspired Sleep Control for Improving the Energy Efficiency of a MEC System
## 4 Bio-Inspired MECS Sleep Control Method
### MECS Activation Process  
The workload imposed on active MECSs increases with the increase in the number of sleeping MECSs, which increases the task queue of the active MECS. Therefore, if an active MECS is likely to violate the constraint associated with the task service delay, the MECS must wake up the neighboring MECSs that are in the sleep state. The task arrival process is stochastic, the dynamics of which are unknown in advance. Therefore, to reduce unnecessary wake-up, we assign a threshold value \(q_{th}^{on}\) to an active MECS to determine whether to wake up sleeping MECSs. Let us denote the maximum task queue size of a MECS \(i\) as \(Q_{i}^{max}\). Then, if \(Q_{i}(t)/Q_{i}^{max}>q_{th}^{on}\) at the start of time slot \(t\), an active MECS \(i\) considers that it is highly likely to violate the task service latency during a time slot. Thus, an active MECS starts the wake-up process when the following condition is satisfied at the beginning of a time slot.  
\[\frac{Q_{i}(t)}{Q_{i}^{max}}>q_{th}^{on}. \tag{10}\]  
The threshold value \(q_{th}^{on}\) is an operational parameter that can be configured by a MEC system operator. This value results in a trade-off between the amount of energy saved and the possibility of quality of service (QoS) violation. As \(q_{th}^{on}\) increases, the sleeping time of a MECS increases because the time at which an active MECS wakes up a sleeping MECS is suspended. Therefore, the amount of energy saved increases with the increase in \(q_{th}^{on}\) while the possibility of QoS violation increases as \(q_{th}^{on}\) increases.  
In this study, we adopt a conservative approach to the MECS activation process. We assume that a MEC system operator satisfying the QoS constraint (i.e., the length of the task queue) is more important than the amount of saved energy. Therefore, when an active MECS decides to begin the activation process, it wakes up all the sleeping neighbors and distributes the tasks in its task queue fairly to all the MECSs that were woken up to reduce the risk of QoS violation. Specifically, if a MECS \(i\) wakes up \(n_{i}^{w}=|N_{i}-N_{i}^{ON}(t)|\) sleeping neighbors, the MEC transfers \(Q_{i}(t)/(n_{i}^{w}+1)\) number of tasks to the MECSs that were woken-up.
# Review of the AI-Based Analysis of Abdominal Organs from Routine CT Scans
## 2 Historical Evolution
### Deep Learning Revolution (2018-2024)
#### 2.3.7 Multiscale DL and CART  
In 2020, [PERSON] et al. presented the findings of a retrospective study focused on the automatic detection of blunt hepatic injuries. They utilized a multiscale deep learning algorithm, trained on manually labeled data with an 80/20% cross-validation split, to derive voxel-wise measurements of liver lacerations. Liver volume was determined using a pre-trained liver segmentation model. The Liver Parenchymal Disruption Index (LPDI) was automatically calculated for each patient. To enhance diagnostic accuracy, a classification and regression tree (CART) analysis incorporated both automated LPDI measurements and manually segmented contrast extravasation (CE) volumes. They achieved a Dice score of 0.61 for blunt hepatic injury [18].
## 3 Results and Discussion
### Dynamic Mechanical Analysis  
The results of the DMA analysis of composite samples are shown in Figure 5. As can be seen from Figure 5, the dynamic elastic moduli \(E^{\prime}\) of all the samples were almost within the same range as the static moduli obtained in three-point bending tests (\(E_{f}=3.4\)-\(4.4\) GPa). The introduction of spherical (fullerenes) and anisometric (nanotubes) nanoparticles resulted in some decrease of the modulus. The most significant decrease was observed in composites containing 5 wt% of fullerenes. The layered filler (graphene) caused some decrease of the modulus at lower concentrations, while at 5 wt% it rose in 0.4 GPa, in 10% over 4 GPa in pure PS. The frequency dependencies of the loss modulus in all composite samples were allocated below those for pure PS. The values of loss modulus were in the range of 0.17-0.33 GPa. The frequency dependencies of the loss modulus for composites filled with spherical and layered fillers had a parabolic shape similar to that of pure PS, which can be associated with segmental mobility in the polymer matrix. The maximum was observed at the frequency of 25-30 Hz. At higher concentrations of anisometric particles, the shape of the frequency dependencies of the loss modulus changed from parabolic to linear. The frequency dependencies of the mechanical loss tangent had a similar shape as those for the loss modulus, and the values were in the range of \(\tan\delta=(40-65)\cdot 10^{-3}\). The increase in filler concentration of all allotropic forms of carbon caused the transition from parabolic to the linear shape of the dependencies.  
[PERSON] et al. [52] reported a 50% rise in the storage modulus of PS-based composites with functionalized graphene obtained by polymerization in situ. Some increase in the storage modulus was observed also in PS composites with 1 wt% of single- or multiwalled carbon nanotubes, both pristine and modified [53]. Samples were fabricated by compression molding.
## 4 Experimental Verification  
### Experimental Setup  
A great impact on production can be caused in actual operation when failure occurs in rolling bearing. The recognition of bearing failure mostly depends on bearing fault characteristic frequency extraction. In order to verify the effectiveness of the proposed method in the experiment, the signal of bearing test rig is employed to perform analysis. The structural of bearing test rig is shown in Figure 3 and the whole experiment device is driven by a 220 V (550 W-50 Hz) AC motor. The position of the yellow arrow in the figure is replaceable bearing. The electrical discharge machining (EDM) method was used to pitting the replaceable bearing outer ring to simulate the bearing outer ring failure. The vertical direction experiment acceleration signals of fault bearing on the right side of test bench were collected by US CSI2130 Data Analyzers. The parameters of the bearing test rig failure frequency are shown in Table 2. It is worthy of mention that the rotation frequency is \(f_{r}=24.17\) Hz and fault frequency of the outer race is \(f_{o}=87.01\) Hz.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**The Variance of Noise**} & \multicolumn{2}{c}{**The SNR after Denoising by Different Methods**} \\ \cline{2-4}  & **The Proposed Method** & **TV Denoising** & **Wavelet Denoising** \\ \hline
0.1 & 18.66 & 9.06 & 15.59 \\
0.2 & 16.52 & 8.41 & 12.44 \\
0.3 & 15.15 & 7.07 & 11.57 \\
0.4 & 12.13 & 4.69 & 10.28 \\
0.5 & 11.31 & 3.31 & 9.20 \\
0.6 & 10.28 & 2.17 & 8.31 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The performance of different denoising methods. SNR: signal-to-noise ratio; TV: total variation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rotation Speed r/min** & **Rotation Frequency/Hz** & **Sampling Rate/Hz** & **Sampling Time/s** & **Outer Fault/Hz** \\ \hline
1450 & 24.17 & 16384 & 1 & 87.01 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The experiment parameter and failure frequency of bearing.  
Figure 3: Bearing test rig.