esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Review: Slant Links Rain Fade Models
### Statistical Models
#### 4.2.5 Das Model  
The concept of the Das model [93; 115] is based on considering the Gaussian distribution of the conditional occurrence of rain attenuation with a specific value of attenuation occurring before. The validation demonstrates a good agreement between measured and predicted attenuation using this model. However, it has not been compared with other relevant models. The working procedure of this model is shown in Algorithm 14.  
```
/* Check attenuation trend */
1 if\(|a(kT_{s})-a((k-1)T_{s})|\leq 1\)then
2 x+-x /* No change */
3 if\(|a(kT_{s})-a((k-1)T_{s})|<-1\)then
4 x+-x 1 /* Down */
5 if\(|a(kT_{s})-a((k-1)T_{s})|>1\)then
6 x+-x+1/* Up */
7 Compute \(\mu(x)\gets a_{0}+a_{1}x+a_{2}x^{2}/*\) x: attenuation */
8 Compute \(\sigma(x)\gets b_{0}+b_{1}x+b_{2}x^{2}\)
9 Compute \(G_{i}\)\(\leftarrow\)Generate GRV (\(\mu\), \(\sigma\)) /* CWR: Gaussian Random Variable, \(i\in R\) */
1 if\(G_{i}\) falls within \(\mu\pm 2\sigma\)then
10\(RV_{i}\gets G_{i}/*\) RV: Random variable */
11 else
12\(G_{i}\)\(\leftarrow\)Generate GRV (\(\mu\), \(\sigma\)) /* Generate again the \(\mu\), \(\sigma\) */
13 Compute \(C_{\text{correction}}\)\(\leftarrow\)Compare (\(A_{i}\), \(M_{i}\))
14 Compute \(A_{i}\gets RV_{i}+C_{\text{correction}}\)
15 Return \(A_{i}\)
```  
**Algorithm 14**Das Model [93]  
Advantages  
The model showed good performance for predicting the time series (of rain attenuation) at Ku bands over the Earth-space link. Another exemplary aspect of the Das model is that it can also be applied if there are some missing measured data.  
Limitations  
The Gaussian distribution was used in this model for predicting the attenuation time series, and the distribution was not fitted well with measured data. Another drawback is that if more than a 1 dB change in input power is detected, the procedure simply changes the current value by \(\pm 1\), which fails to track more than a 1 dB change in the previous input.
## 1 Introduction
### Continuity Mechanism of Anchoring State  
The anchoring system employs a phased progressive anchoring strategy, sequentially achieving synergistic support through head-end expansion anchoring, expansion segment anchoring, and grouting body anchoring. First, the head-end expansion anchoring establishes initial constraints at the borehole collar via radial expansion. The controlled micro-fracturing effect optimizes local stress distribution and provides mechanical boundaries for subsequent phases. Subsequently, the distal end of the expansion segment undergoes directional expansion under constrained conditions, with a grout isolation sleeve physically separating the anchoring zone from the grouting area to prevent stress interference caused by multi-stage load superposition. Finally, grout is injected into the pre-compacted enclosed space, where it fully permeates through the fracture network of the rock-soil mass. This process not only repairs micro-damage induced during earlier construction stages but also forms a continuous reinforcement zone through filling-cementation effects, significantly enhancing the integrity and durability of the anchoring interface. This sequential design effectively balances dual requirements of construction disturbance control and anchoring performance enhancement.  
Mechanical Analysis of a Hollow Grouting Anchor with an Expanded Umbrella Head and an Expanded Tube
# MSmart: Smart Contract Vulnerability Analysis and Improved Strategies Based on Smartcheck
## 4 MSmart's Detection Method  
### Integer Overflow  
The integer overflow vulnerability satisfies two basic conditions: (1) an arithmetic operation occurs, and (2) the arithmetic operation has an uint data type on one side. If both conditions are met and there is no integer overflow protection, it can be concluded that there is an integer overflow vulnerability. In the experiment, the integer overflow protection  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Differences** & **MSmart** & **Smartcheck** \\ \hline integer overflow & ✓ & x \\ timestamp dependence & ✓ & ✓ \\ self-destruct & ✓ & ✓ \\ delegatecall & ✓ & x \\ DOS & ✓ & x \\ batch detection & ✓ & x \\ \hline \hline \end{tabular}
\end{table}
Table 1: Differences between MSmart and Smartcheck.  
Figure 9: uint256 (cnt) * _value code analysis tree.  
is taken into account in order to reduce false positives. Integer overflow vulnerabilities may presently be protected against in two ways: (1) by using the standard SafeMath [36] library, and (2) by using the \"if\" or \"require\" judgment statements. To guard against integer overflow vulnerabilities, for instance, use the line \"if (uint256 (cnt) *_value 2'256 - 1)\" as a judgment statement.  
Based on the above analysis, the basic ideas for improvement are shown in Algorithm 1:  
The integer overflow detection algorithm is shown in Algorithm 1.  
```
Input: a smart contract P Output: integer overflow vulnerabilities
1: Convert the source code to the corresponding path diagram
2: Match according to the path expression pattern
3:if pattern matching found arithmetic operation then
4:if the data type involved in the arithmetic operation is the uint type then
5:if not using the standard library function SafeMath then
6:if without using if or require statement guards then
7:return integer overflow vulnerability
8:else
9:return 0
10:endif
11:else
12:return 0
13:endif
14:else
15:return 0
16:endif
17:else
18:return 0
19:endif
20:Integrate all discovered vulnerabilities
```  
**Algorithm 1**The integer overflow detection algorithm.  
In the integer overflow detection algorithm, we mainly check the contract in the following way: (1) The data are examined to determine whether one of the types is uint, if the contract contains an arithmetic operation. (2) MSmart determines if the contract makes use of the SafeMath standard library. (3) The presence or absence of an \"if\" or \"require\" judgment statement before an arithmetic operation occurs. MSmart will identify an integer overflow vulnerability if there is an arithmetic computation that does not utilize the SafeMath library and an \"if\" or \"require\" judgment statement.
# Palm Date Leaf Clipping: A New Method to Reduce PAPR in OFDM Systems
## 5 Simulation Results
### Comparative Study
#### 5.3.1 PAPR Comparison with PTS Variants  
In this subsection, we compare the proposed method with PTS and some of its variants: Hybrid Genetic Algorithm PTS (HGA-PTS) [15], Genetic Algorithm PTS (GA-PTS) [34], Random Search PTS (RS) [38], Iterative flipping PTS (IPTS) [38], PSO-PTS [16], and Optimal PTS (OPTS) with an exhaustive search [15]. The parameters of PTS are as follows:  
* \(w=4\) (Phase rotation factors {+1, -1, -j, +j});
* \(V=8\) (Sub-blocks number);
* \(P=30\) (Genetic algorithm: population size);
* \(I=40\) (Genetic algorithms: number of generations or iterations).  
The performance in terms of the PAPR is depicted in Figure 19. Table 4 summarizes the CCDF values and the gain in terms of the PAPR at CCDF \(=10^{-3}\) of the proposed Palm Clipping method compared with PTS and its variants.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Modulation** & \multicolumn{2}{c}{**4-QAM**} & \multicolumn{2}{c}{**16-QAM**} & \multicolumn{2}{c}{**64-QAM**} \\ \hline
**CR [dB]** & **PAPR Gain** & **BER Loss** & **PAPR Gain** & **BER Loss** & **PAPR Gain** & **BER Loss** \\ \hline
1 & 8.29 & 3.99 & 7.87 & \(>\)4 & 6.50 & \(>\)5 \\
2 & 7.89 & 2.584 & 7.46 & \(>\)4 & 6.52 & \(>\)5 \\
3 & 7.40 & 1.52 & 6.96 & \(>\)4 & 6.15 & \(>\)5 \\
4 & 6.83 & 0.892 & 6.40 & \(>\)3.9 & 5.78 & \(>\)5 \\
5 & 6.2 & 0.454 & 5.86 & 3.84 & 5.33 & \(>\)5 \\
6 & 5.46 & 0 According to the result depicted in Figure 19 and the gain presented in Table 4, we note that the PC method gives the best results compared with IPTS, PSO-PTS, and GA-PTS when CR = 7 dB. Hybrid HGA-PTS is better than the PC method when CR = 7 dB. When CR = 6 dB, the proposed PC method presents about 0.3 dB less than the HGA-PTS. In addition, we note that the PC method achieves a good PAPR reduction of about 5.67 dB when CR = 5 dB, which is the same value as when HGA-PTS is run with \(V=32\). The obtained value is greater than an exhaustive search OPTS. From this study, we conclude that the Palm Clipping method is not complex (without iterations) and does not require side information compared with PTS and their variants.
# Three-Dimensional Geological Modeling Method Based on Potential Vector Fields
## 2 Materials and Methods
### Generalized Marching Cubes Algorithm
(b) Borehole stratification information: This dataset records the depth information of the strata within each borehole, including the starting depth and the ending depth of each layer. Figure 4: Modeling flowchart. Figure 5: Borehole distribution map of the study area. As shown in Figure 6, the data points located at the stratigraphic interface are control points that can be directly obtained from borehole data, represented by red points. The data points located within the strata are inserted between the control points, represented by blue points. The coordinates of the control points, \(\mathbf{x}_{r}=(x_{r},y_{r},z_{r})\), can be expressed as  
\[ \begin{cases}x_{r}&=X\\ y_{r}&=Y\\ z_{r}&=Z-D\end{cases} , \tag{21}\]  
where \(X\) and \(Y\) represent the geographic coordinates of the borehole, \(Z\) denotes the elevation at the borehole mouth, and \(D\) refers to the depth of the control point, expressed using either the starting or reaching depth of the stratum. A series of data points with equal spacing are inserted between adjacent control points, and the specific number of these data points, denoted as \(n_{a}\), is  
\[n_{\text{a}}=\max\left\{\left\lceil\frac{d_{\text{a}}}{d_{\text{max  
marching cubes algorithm, and the vertex normal vectors of the triangles are computed using Equation (20). This process achieves illumination smoothing, and the collection of all triangular facets forms the final geological surface model. 4 GPU Parallel Processing  
Traditional methods for solving linear systems of equations have a time complexity of \(O(n^{3})\). When using a CPU to solve Equation (8), the computation time increases rapidly as the number of data points grows. For example, when the number of data points reaches several thousand, the solution process may take several hours. The GPU is a hardware platform designed for performing large-scale parallel computations, with hundreds or even thousands of processing cores. Based on the single instruction, multiple threads (SIMT) architecture, it enables efficient parallel computation using thread blocks. Many researchers have studied the parallel solution of large linear systems using GPUs [30; 31; 32]. In this paper, the LU decomposition API provided by the CUBLAS library is utilized to accelerate the solution of the linear system, thereby improving modeling efficiency.
_Applied_  
_sciences_  
_Article_  
**Potential Applications in Relation to the Various Physicochemical Characteristics of Al-Hasa Oasis**  
**Clays in Saudi Arabia**  
**[PERSON] 1, [PERSON] 2 and [PERSON] 2,*  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Received: 28 November 2020; Accepted: 14 December 2020; Published: 17 December 2020  
**Featured Application:** **P**algyorskite and **montmorillonite are the dominant phyllosilicate minerals in Al-Hasa oasis clays, although their coexistence is usually rare. Al-Hasa oasis clays can be used as alternative cheap materials for removal of heavy metals as they have the largest adsorption capacities toward lead (74.07 mg/g), compared with the other Saudi clays--namely, Tabuk clay (30 mg/g), Baha clay, (ca. 24 mg/g) and Khaiber clay (ca. 10 mg/g). In addition, Al-Hasa oasis clays have strong acidic properties, narrow pores and high thermal stabilities (up to 470 \({}^{\circ}\)C) and can therefore be used as catalysts in alcohol dehydration and hydrocarbon isomerization.**  
**Abstract:** In this work, various physicochemical characteristics, e.g., surface properties and mineralogical compositions, of five clays collected from different sites in the Al-Hasa oasis in Saudi Arabia have been investigated. Analysis of the mineralogical compositions of the clays in the study by X-ray diffraction indicated the coexistence of palygorskite, montmorillonite, illite, kaolinite, chlorite, calcite and quartz in different percentages. Thermogravimetric analysis indicated that all studied clays exhibited dehydroxylation temperatures higher than 470 \({}^{\circ}\)C. On the other hand, pore size distribution analysis of clays from N\({}_{2}\) adsorption indicated the presence of micro- and narrow mesopores (of 1.3-2.8 nm). Furthermore, the capability of the different clays for removal of Pb (II) from aqueous solution has been studied. The adsorption process was described through the Langmuir, Freundlich, Temkin and Dubinin-Radushkevich models. The Langmuir model was the most suitable compared to the other models in the case of palygorskite- and montmorillonite-rich clays. However, the Temkin model better represented the adsorption process of Pb (II) on calcite-rich clay. The clay sample with 61.0 wt% of palygorskite was found to be the most effective at removing Pb (II), with a maximum removal capacity of 74.07 mg/g at pH 6, with a contact time of 6 h and at 25 \({}^{\circ}\)C. Generally, the adsorption mechanism of lead over all the studied clays followed the pseudo-second-order kinetics. On the other hand, the catalytic activity of clays in the study has been tested in methanol conversion. The acidic clays, those containing high amounts of montmorillonite, showed higher selectivity to ethylene, viz., 78.9%, with a methanol conversion of 39.1% at 350 \({}^{\circ}\) C and 0.1 MPa.  
**Keywords:** Al-Hasa clay; palygorskite; montmorillonite; calcite; lead; methanol; kinetics; adsorption models
## 1 Introduction
Currency transactions, as certificates that cannot be tampered with, ensure the reliability of authentication information. 2. According to the network topology, multiple areas are divided, and nodes in the area collaborate to generate blocks. After a node receives a transaction, the transaction is temporarily stored in the local cache pool, and the node independently verifies the local transactions. This scheme not only reduces the network load of intersatellite transmission but also reduces the computational overhead of a single node in verifying transactions. 3. Computational overhead of BAPC is compared with other protocols, and the security of BAPC is analyzed. The asymmetric encryption operation, signature operation and verification operation adopted in BAPC are at the average computation level, and there are fewer communication times. In addition, in terms of security, it not only ensures the security of the key but also effectively resists replay attacks, denial of service attacks and impersonation attacks. 4. BAPC is simulated on the NS2 network simulation platform. Simulation results show that BAPC greatly reduces switching authentication time compared with other protocols. In addition, compared with no regional partitioning, regional partitioning significantly reduces the block generation time and the network throughput. Moreover, the computation time of the signature and validation operations are tested on Python. The remainder of this paper is organized as follows. Section 2 reviews the relevant work in satellite access authentication. Section 3 introduces the blockchain and cryptocurrency technology and describes the system model. In Section 4, the three stages of the proposed authentication protocol are introduced. Section 5 describes the method and analysis of area division. Section 6 compares the computational overhead with other protocolsand analyzes the security. In Section 7, the simulation results on the NS2 platform are presented. Section 8 concludes this paper.
## 4 Empirical Analysis
### Evaluation of Services
#### 4.3.2 Comparison of Analysis Results between Developers and Local Governments  
Here, we compare the priority analysis results between the thermography image analysis of the developer (n = 13), and local governments' operator groups (hereinafter, local governments, n = 12) (Table 12). First, the two groups had different number one and two service models. Pedestrian detection at night (A-1-1) and nighttime mobility detection (A-1-2) were ranked first and second by local governments, but third and fourth by developers. Detection service of precursor fire events within buildings (C-2-1, first priority) and within facilities (C-2-2, second priority) were ranked the highest by developers, but relatively lower (eighth and fifth, respectively) by local governments. Thus, developers highly evaluated the importance and practical application of service models regarding temperature changes in heat, which are the core principles of thermography cameras, especially pre-fire phenomenon detection. In contrast, local governments highly evaluated the importance of control over citizen safety within the thermography camera service area. An additional example is that intrusion status detection service at night (A-2-3) was ranked as the third most important by local governments but as the 20 th by developers. Second, developers evaluated the importance of road weather risk detection services more highly than local governments (e.g., road ice detection (B-1-3) and road surface temperature detection (B-1-1)). This is also a service model that clearly reflects the temperature change detection features of a thermography camera.
## 4 Artificial Intelligence (AI) in Healthcare
Similarly, the affordability and availability of both wearable sensors and smartphones appear to be veritable devices to quickly drive the accumulation of human medical data, whereas an emerging subfield of AI (ML techniques) is also available to map those medical data to provide clinical predictions [97]. The proof-of-concept for an accurate, automatic, patient-specific system that is tunable to a person's needs presented by [98; 99; 100] is an example of a seizure-prediction system that can increase an epileptic patient's independence and allow for preventative treatment. A robust classification algorithm in ML (a DL classifier) was used to train a model to distinguish between interictal and preictal signals. DL is an ML technique and is a great computational tool that supports attributes to be automatically learnt from training data [102]. Generally, the use of DL is for training a class of algorithms termed deep neural networks to accomplish specific tasks. The use of neuromorphic hardware in combination with DL models can offer a basis for an always-on, real-time, patient-specific, wearable-sensor seizure early warning system with marginal power consumption with reliable and durable performance. Furthermore, a different study by [103] aimed at automatic scoring Parkinsonian tremors. The study proposed ML algorithms to predict the Unified Parkinson's Disease Rating Scale (UPDRS). In their study, a wristwatch-type wearable sensor device fitted with an accelerometer and a gyroscope was used to measure the tremor signals of eighty-five Parkinson's disease patients. The number of features initially extracted from each signal was nineteen, but eventually, the dimension of the features was strategically abridged using  
Figure 8: Classification of ML techniques into various sub-categories. a pairwise correlation approach. Five commonly used ML algorithms, such as the DT, DA, kNN, RF and SVM, were applied to the selected features to explore the automatic scoring of the severity of Parkinsonian disease tremors. Accuracy, precision, and recall were the metrics used to gauge the performance of the classifiers and compare the findings with similar studies. These use-cases of successful implementations and applications of AI and wearable sensors has made it critical and necessary to complement conventional healthcare system with these disruptive technologies. The technologies among many things can assist in informed clinical decision-making processes since imprecise results can misinform both clinicians and patients. Hence, an AI-based sensor healthcare system can assist in diagnosing disease symptoms and obtaining timely aid from clinicians and caregivers. The system will equally be of assistance to elderly or young people for regular in-home-based health monitoring and diagnosis.
## 1 Introduction
### Background  
The concept of oxidizing fuels with oxygen provided by solid oxygen carrier materials rather than with oxygen from air have several potential applications [1]. The one that has received the widest attention is Chemical-Looping Combustion (CLC) [2], which is a combustion technology that would enable carbon dioxide capture without significant costs for gas separation. In CLC, fuel is oxidized with oxygen from a solid oxygen carrier material in particle form, exemplified below by oxidation of methane with manganese (II,III) oxide, see Reaction (1).  
\[\mathrm{CH_{4}(g)}+4\mathrm{Mn_{3}O_{4}(s)}{\rightarrow}12\mathrm{MnO(s)}+ \mathrm{CO_{2}(g)}+2\mathrm{H_{2}O(g)}\quad\Delta\mathrm{H^{\circ}}_{298}=125 \mathrm{ kJ}/\mathrm{mol_{CH4}} \tag{1}\]  
The products are carbon dioxide, steam and manganese (II) oxide, of which the latter is oxidized to its initial state with air in a separate reactor vessel, see Reaction (2).  
\[2\mathrm{O_{2}(g)}+12\mathrm{MnO(s)}{\rightarrow}4\mathrm{Mn_{3}O_{4}(s)} \quad\Delta\mathrm{H^{\circ}}_{298}=-927\mathrm{ kJ}/\mathrm{mol_{CH4}} \tag{2}\]  
The oxidized material can then be returned to the first reactor vessel and oxidize more fuel. This way fuel and air is never mixed and the flue gas will be undiluted by \(\mathrm{N_{2}}\). The sum of Reactions (1) and (2) is Reaction (3), i.e., ordinary combustion of the fuel with oxygen.  
\[\mathrm{CH_{4}(g)}+2\mathrm{O_{2}(g)}{\rightarrow}\mathrm{CO_{2}(g)}+2\mathrm{ H_{2}O(g)}\quad\Delta\mathrm{H^{\circ}}_{298}=-802\mathrm{ kJ}/\mathrm{mol_{CH4}} \tag{3}\]  
Some oxygen carriers such as for example manganese (III) oxide are capable of releasing gas phase oxygen directly into an atmosphere with low oxygen fugacity, see Reaction (4).  
\[6\mathrm{Mn_{2}O_{3}(s)}{\rightarrow}\mathrm{O_{2}(g)}+4\mathrm{Mn_{3}O_{4}( s)}\quad\Delta\mathrm{H^{\circ}}_{298}=195\mathrm{ kJ}/\mathrm{mol_{CH4}} \tag{4}\]  
The mechanism described in Reaction (4) is referred to as Chemical-Looping with Oxygen Uncoupling (CLOU) [3]. Oxygen released can react directly with gaseous or solid fuel in accordance with Reaction (3). A chemical-looping process could very well involve fuel oxidation both via Reaction (1) and via Reactions (4) and (3).
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 5 Main Implementation and Results  
This section presents the initial results of the implementation. The first tests were conducted entirely in AEInnova's laboratory, a spin-off of the Microelectronics Department at the Autonomous University of Barcelona, where the authors of this paper are principal founders. Subsequently, the device will be tested under real-world conditions at a pharmaceutical facility located in Barcelona. The Spanish company Hipra, a COVID-19 vaccine manufacturing facility, granted access to perform the first real-world tests on a mechanical (ball float) steam trap.