esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials
### Sentinel-1 SAR Dataset  
The _Sentinel-1_ mission is part of the European Copernicus programme. The satellite acquires radar data with a spatial resolution between 5-40 m depending on the acquisition mode. The temporal resolution is 5 days due to two twin satellites, S1A and S1B.  
The radar data were retrieved from the NASA Alaska Satellite Facility (ASF, [[https://asf.alaska.edu/](https://asf.alaska.edu/)]([https://asf.alaska.edu/](https://asf.alaska.edu/)), last accessed 7 November 2022) and processed in the SNAP v.8.0.0 [34] and the Google Earth Engine (GEE) [1; 35].  
The Sentinel-1 (hereinafter called S1) mission provides data from a dual-polarized C-band SAR (Synthetic Aperture Radar) instrument. The Google Earth Engine provided only the Sentinel-1 ground range detected (GRD) collection. Each scene was preprocessed with the Sentinel-1 Toolbox in the SNAP using the following steps: (1) thermal and other noise removal, (2) Speckle-Lee filter application, (3) radiometric calibration, (4) ground correction using DTM 10m VDA (normally SRTM 30m worldwide) and (5), the final corrected values for the ground were converted into decibels via log scaling (10 \(\times\) log10 (x)).  
The level-1 data were processed into either single look complex (SLC) and/or ground range detected (GRD) products. The SLC products preserved the phase information and were processed at the natural pixel spacing whereas the GRD products contained the detected amplitude and were multi-looked to reduce the impact of speckle. In particular, the level-1 SLC (IW) interferometric wide products (IW) were adopted [36].  
The IW swath mode was the main acquisition mode over land and satisfied the majority of the service requirements ([PERSON] 2009 [37]). As mentioned before, the SLC IW data were adopted by creating two separate datasets with the same orbit, frame and path of the scene in the study area. The two time series stacks, including all scenes ranging from 1 January 2020 to 31 December 2020 in ascending and descending mode, were considered. Those characteristics are reported in Table 2. As reported by [38], the main distortion in SAR data was the elevation displacement. In a radar image, the displacement was toward the sensor and became quite large when the sensor was nearly overhead. The displacement increased with a decreasing incidence angle. The characteristics resulting from the geometric relationship between the sensor and the terrain that were unique to radar imagery were foreshortening, layover and shadowing. The topographic features such as mountains and artificial targets such as tall buildings were displaced from their desired orthographic position. The effect was removed from an image through independent knowledge of the terrain profile.  
The ascending and descending values were both processed in the SNAP v.8.0.0 and then imported into the GEE to create a mosaicked-median composite to reduce the geometric distortions in the slopes where, normally, a given acquisition mode occurs.
## 7 Security and Privacy Challenges and Limitations in AMRSys
### Access Control
#### 7.6.2 Authentication  
In order to achieve the goal of identification, a technique and a process need to be adopted by the robot to detect and validate the authenticity and the identity of the user. The different techniques available to authenticate a user are compared in Table 3, and, thus, Table 3 elaborates and considers the viability and feasibility of studies of the adoption of authentication mechanisms among potential AMRSys users; while, different types of authentication mechanism are also described below [63; 64]:  
* Password-Based Authentication: This form of authentication is one of the most common and most popular forms of authentication. However, this technique invites multiple forms of attack e.g., phishing attacks, man-in-middle attacks, brute force attacks, dictionary attacks, credential stuffing attacks, keylogger attacks, etc. * Multi-Factor Based Authentication: This technique is more secure than password-based authentication. This is because in this approach multiple independent methods or combinations of different platforms and techniques are used; e.g., a combination of password and authentication session keys, generated using an authenticator application or through a SMS mobile phone. * Certificate-Based Authentication: This authentication technique uses digital certificates with keys (public and private) to authenticate the user or the system that has this certificate. These certificates are presented as a proof of authenticity of the user to the server, and the server confirms the genuineness of the certificate and the certificate-issuing authority through the association of the keys with its certificate. * Token-Based Authentication: This technique allows users to enter their credentials to the server, and the server provides a unique encrypted random string (token) that the system recognizes. In the future, without using the credentials, this token generated with the credential of the user by the server is used for authenticating. However, whoever has this token can compromise the system. This method is safer than directly using a password; however, this method is also prone to different kinds of password-based attacks, as highlighted earlier under password-based authentication. * Biometric Based Authentication: This form of authentication uses the biometric data of an individual to uniquely identify the user. This form of authentication has gained popularity because the biometric data is associated and stays with the individual and generally does not change (facial, fingerprint, voice, retina, gait, heart signal). Moreover, it does not involve memory recall or require technical skills from the user, so it can be used by any user to identify the individual to the system. However, one of its biggest drawbacks is that it can also be easily extracted, unlike a password, token, or certificate that can be memorized or stored (locally or remotely). Moreover, biometric data, such as fingerprint, voice, and facial data, can be extracted and replicated easily in this machine learning- and AI-powered 21 st century.
## 2 Experimental Setup and Material  
In this paper, a nanosecond laser source (Pulse 532-50-LP) was selected for LWJ machining according to the research findings of [PERSON] and [PERSON] [15]. The main parameters are shown in Table 1. The schematic diagram of the LWJ machining system is demonstrated in Figure 1 which is similar to that employed by [PERSON] et al. [16]. By means of beam expansion, reflection, and spatial shaping, the laser beam is finally focused onto the upper surface of the ruby nozzle. With the assistance of the coaxial observing system, the focused laser spot is delivered into the nozzle with a diameter of 60 \(\upmu\)m. Hence, a coupling jet is generated which can be utilized for CMCs machining. For the purpose of increasing the coupling efficiency, i.e., reducing the loss of laser energy in the machining process, ultrapure water was employed in the hydraulic system.  
The CMCs substrates applied in this study were supplied by AVIC Manufacturing Technology Institute. The detailed parameters are listed in Table 2. As shown in Figure 2a-d, the anisotropic woven SiC fibers are demonstrated as a schematic diagram of three-dimensional CMCs and their actual morphology. The average diameter of the fiber is approximately 11 \(\upmu\)m with a 1\(\upmu\)m PyC coating which is similar to that employed by [PERSON] et al. [16].  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameter** & **Wavelength** & **Pulse Duration** & **Repetition Rate** & **Average Power** & **Pulse Energy** & **Beam Diameter** \\ \hline Value & 532 nm & 86 ns & 50 KHz & 50 W (max) & 1 mJ (max) & 2 mm (1/e\({}^{2}\)) \\ \hline \end{tabular}
\end{table}
Table 1: Main output parameters of laser source.  
Figure 1: Schematic of LWJ machining system.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Density & 2060 kg/m\({}^{3}\) \\ Diameter of SiC fiber & 8–15 \(\upmu\)m \\ Thickness of PyC coating & 1 \(\upmu\)m \\ Thickness of CMCs material & 3 mm \\ Thermal conductivity & 5.843 W/m\(\cdot\)K \\ Specific heat capacity & 2767 J/kg\(\cdot\)K \\ Ablation temperature & 2973.15 K \\ Heat of sublimation & 7.471 \(\times\) 10\({}^{6}\) J/kg \\ \hline
**3D CMCs** & **3D Prefab** & **2D SiC** \\
**structure** & **Structure** & **fibers net** \\ \hline
**Figure 2: _Cont._** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Detailed parameters of CMCs.
# Optimization of Cellular Automata Model for Moving Bottlenecks in Urban Roads
## 2 A Cellular Automaton Model for Urban Roads Considering Vehicle Heterogeneity  
The model established in this paper is a two-lane cellular automaton model based on the NaSch model, which takes into account vehicle safety distance, the speed of adjacent vehicles, the acceleration and deceleration performance of different types of vehicles, and driver reaction time. It establishes a car-following rule suitable for the mixed traffic flow of small cars and heavy vehicles in intermittent flow on urban roads.
## 4 Timing and Carrier Frequency Synchronization through Preamble Detection
### Computational Complexity Analysis  
Let us consider the computational complexity of the proposed synchronization method and compare it with that of the conventional [PERSON]'s method. Note that the comparison is conducted under the same preamble length for both methods, i.e., \(2N\) according to Section 3. For both methods, the main consumption of computational complexity goes to the part of the calculation of SAC. Usually, the computational complexity can be evaluated by the quantity of additions and multiplications, given in detail as follows.  
For the conventional [PERSON]'s method [17], there are two SAC windows for the positive preamble part and negative preamble part, respectively, with each being of \(N\) in length, as illustrated in Figure 1b. The calculation begins when the first nonzero entry of the preamble falls into the previous positive SAC window, and ends when the last nonzero entry of the preamble leaves the latter negative SAC window. Hence, with the total preamble length being \(2N\), the SAC operation during the synchronization of the [PERSON]'s method requires \(T_{\text{mul}}^{(\text{M})}=2.5N\) times of multiplications and \(T_{\text{add}}^{(\text{M})}=10N\) times of additions. Thus the total computational complexity of [PERSON]'s method is in the order of \(\mathcal{O}(N)\).  
For the proposed method, as described in Section 4.1, there are three SAC windows \(R_{1,n}\), \(R_{2,n}\), and \(R_{3,n}\), with each being of length \(N\). The calculation of SAC begins when the first nonzero entry of the preamble falls into the \(R_{3,n}\) SAC window, and ends when the last nonzero entry of the preamble leaves the \(R_{1,n}\) SAC window. Hence, with the total preamble length also being \(2N\), the SAC operation during the synchronization of the proposed method requires \(T_{\text{mul}}^{(\text{P})}=9N\) times of multiplications and \(T_{\text{add}}^{(\text{P})}=18N\) times of additions in total. Thus the total computational complexity of the proposed method is also in the order of \(\mathcal{O}(N)\).  
Although there is a moderate complexity increase to the proposed method due to more correlation and multiplication operations, both the proposed and conventional methods have a computational complexity in the order of \(\mathcal{O}(N)\). Hence, the cost of calculation resource is in the same order, and the proposed method is applicable for practical system implementation.
# Resistance of Concretes to External Chlorides in the Presence and Absence of Sulfphates: A Review
## 4 Concluding Remarks  
* The rate of ingress of external chlorides into concrete is dependent on several factors, such as the pore structure of the concrete matrix, curing conditions, w/b ratio, use of supplementary cementitious materials (SCMs) and the chloride binding ability of the cementitious materials in the concrete matrix.
* Chloride binding is beneficial to the durability of a concrete structure in that it removes chloride ions from the pore solution that would have been available to initiate chloride-induced corrosion of the steel reinforcements. Hence, higher chloride binding capacity  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  
* Notes: PC—Portland cement; LS—Limestone; NP—Natural pozzolana; FA—Fly ash; MK—Metakaolin; GGBS—Ground granulated blast-furnace slag; SF—Silica fume; APS—Activated paper sludge; HSR—High-sulphate-resistant cement; CAC—Calcium aluminate cement; TCC—Total chloride content; DME—Dynamic modulus of elasticity; FTIR—Fourier-Transform Infrared Spectroscopy; MIP—Mercury Intrusion Porosimetry; XRD—X-ray diffraction; SEM-EDX—Scanning Electron Microscopy-Energy Dispersive X-ray Spectroscopy; TGA—Thermogravitem analysis.  
\end{table}
Table 5: Selected recent studies to highlight differences in binder compositions and methodologies.  
of cement leads to reduced risk of chloride-induced reinforcement corrosion. Chloride binding also improves freeze-thaw resistance of concrete in cold weather.
* Chloride binding is influenced by several factors, such as the cement type, alkalinity of the pore solution, cation type of the salt, concentration of chloride solution, temperature, presence of other anions, e.g., sulphates and carbonates, and presence of SCMs in the mix. The increased aluminate contents of SCMs are beneficial to chloride binding. Similarly, increasing C\({}_{3}\)A contents in cements improve chloride binding.
* Environmental factors also affect chloride binding, it being reduced at higher pH, in the presence of sodium ions or other anions. Meanwhile, chloride binding increases with the concentration of the chloride solution. The influence of temperature on chloride binding was not clear.
* In exposure to combined chloride-sulphate solutions, the sulphates reduce chloride binding, thus, ultimately leaving reinforcement more susceptible to corrosion. However, in the short term, sulphate ions can hinder chloride penetration due to ettrinigite formation. Conversely, the presence of sodium chloride tends to mitigate sodium sulphate attack. However, the presence of chloride may worsen MgSO\({}_{4}\) attack, especially at low temperatures (around 5 \({}^{\circ}\)C).
# Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review
## 3 Overview of the Application of Machine Learning in Construction Productivity Publications
### Data Analysis and Evaluation
#### 4.2.1 Model Selection  
Experts and estimators in the construction sector may assess construction productivity rates under certain circumstances using their mental models. In actuality, estimators assess these variables by mentally modeling the production process in order to get an estimate [6]. However, relying only on one's judgment is constrained by the expertise and experience of the estimator in question and may not always yield accurate and consistent estimates. Therefore, the formalization of this modeling process is needed via an integrated, cohesive, and methodical approach for productivity measurement and analysis. Currently, there are various ML models used to predict construction productivity. Nevertheless, there is no set benchmark for choosing the best algorithm. Consequently, experimenting with multiple algorithms applied to the same dataset to determine the best model for a particular case is one way to deal with this [4; 5; 66; 86]. Upon analyzing the existing literature, as summarized in Table 5, it has become apparent that among the prediction models ANNs have been extensively employed for both labor and equipment since then and now. Despite ANNs showing promising accuracies, [PERSON] and [PERSON] [87] has suggested a modified neural network-driven fuzzy reasoning framework to improve productivity estimation accuracy even further. Besides, the exploration of meta-ensemble ML models via different variation types was created to demonstrate the innovation of other potential algorithms instead of an ANN [66]. Generally, selecting CPM techniques is mostly based on the amount and characteristics of the influencing factors, the intricacy of the mapping connection, the capacity of a certain modeling method, and the researcher's preferences. Evidently, references [4; 86] adopted the same dataset but in terms of technicality different techniques, such as single point estimate and prediction interval, were adopted. A straightforward approach was carried out by [PERSON] et al. [4], comparing the results of the four types of ANN models to obtain the most accurate model, which is BNN. However, [PERSON] et al. [86] proposed an ANN-based PIs to increase the model robustness and with the inclusion of uncertainty, the predicted variable will fall within the lower and upper range with a defined confidence level. This will result in having a more reliable prediction. Considering both scenarios, ANN-based PIs can be considered a hybrid approach compared to the deterministic model.
## 3 Results and Discussion
### Elevating Vegetation Monitoring with Drones: Real-Time Insights and Species Dominance Mapping
Nonetheless, it is important to acknowledge the limitations of this technology. Unlike general satellite imaging analysis of vegetation changes, the operation of drones in adverse weather conditions such as strong winds and heavy rains presents challenges [39; 40]. Consequently, suitable weather conditions for drone flights are required, and with further advancements, restrictions imposed by climate conditions must be minimized. Future research endeavors should focus on discussing the management of spatial and vegetation information for the establishment of a GIS database to facilitate systematic data management. To achieve precise and extensive management, investigations should involve a wide range of drone photography and images encompassing various spectra.
# Exploiting Blockchain Technology for Enhancing Digital Twins' Security and Transparency
## 3 Proposed Approach
### Implementation
#### 3.1.3 Smart Contracts
It also incorporates access control mechanisms to ensure that only authorized entities, such as a manager proxy, can interact with the contract's critical functions. The pseudocode highlights the logical flow of these operations and the modifiers that enforce security. ```
1:procedurenewCorresponding(_sender,_receiver)
2:ifMappingExists(_sender) is true then
3: revert
4: Setcorrespondings[_sender].to\(\leftarrow\)_receiver
5: Setcorrespondings[_sender].exists\(\leftarrow\) true
6:proceduredeleteCorresponding(_sender)
7:ifMappingExists(_sender) is false then
8: revert
9: Setcorrespondings[_sender].exists\(\leftarrow\) false
10:procedureupdateEntity(_sender,_receiver)
11:ifMappingExists(_sender) is false then
12: revert
13: Updatecorrespondings[_sender].to\(\leftarrow\)_receiver
```  
**Algorithm 2** Manager contract administrative functionalities  
```
1:procedureInitialize
2: Setowner\(\leftarrow\)msg.sender
3:procedureIsCaller
4: require(ManagerProxyAddress == msg.sender)
5: Continue
6:procedureIsOwner
7: require(owner == msg.sender)
8: Continue
9:procedureAuthorizeUpgrade(newImplementation) override
10:ifIsOwner condition is not met then
11: revert
12:procedureStoreVal(Hash, Storer)
13:ifIsCaller condition is not met then
14: revert
15:ifManagerProxyAddress == msg.senderthen
16: revert
17: SetCheckSum[Storer]\(\leftarrow\)Hash
18:procedureRetrieveVal(fromPK)
19:ifIsCaller condition is not met then
20: revert
21:ifthenManagerProxyAddress == msg.sender
22: revert
23: ReturnCheckSum[fromPK]
24:procedureSetCaller(CallerAddress)
25:ifIsOwner condition is not met then
26: revert
27: SetManagerProxyAddress \(\leftarrow\)CallerAddress
```  
**Algorithm 3** Storage Contract Operations  
The complete architecture regarding the developed smart contracts is provided in the form of a class diagram in Figure 4. This representation highlights the symbiotic relationship established between the contracts and the integration, in terms of pattern utilization, of the UUPS.
## 6 Simulations Results and Discussions
### Network Lifetime  
The network lifetime is a crucial performance metric for evaluating the effectiveness of any WSN routing protocol. Network lifetime refers to the duration between the deployment of the WSN and the time when all the SNs die due to energy depletion. Figure 16 depicts the number of rounds versus the percentage of SNs dying for both the proposed EEUCH protocol and the benchmark protocols. WSNs utilizing the EEUCH protocol exhibit a longer lifetime compared to the benchmark protocols. This is due to the fact that the proposed protocol consumes less energy, resulting in the efficient use of the available energy source (i.e., battery) of the SNs. Furthermore, Table 3 presents the lifetime performance of the benchmark protocols and the proposed protocol.
## 3 Models Overview
### Traditional Machine Learning Model
#### 3.1.2 Partial Least Squares Regression (PLSR)  
PLSR [20] is a multivariate regression analysis method based on principal component analysis. The fundamental idea of PLSR is to project the independent and dependent variables into separate new spaces in a way that maximizes the correlation between them in this new space. PLSR can effectively address the issue of multicollinearity in spectral data, simplify the complexity of the data, and can also improve the stability and modeling efficiency of the model. When modeling soil spectral data using PLSR, it is crucial to select an appropriate number of latent variables. Cross-validation is commonly used as an evaluation metric, and the visualization method is employed to determine the optimal number of latent variables. As a general rule, the cross-validation error tends to decrease initially and then increase as the number of latent variables increases. The optimal number of latent variables is usually the point at which the cross-validation error is minimized.
## 4 Analysis of Experimental Results
### Analysis of Fracture Opening on Slurry Diffusion Law  
Figure (a)a,b presents the pressure variation curves of slurry with different fracture apertures as a function of diffusion distance. It can be observed from the graph that, under different fracture aperture conditions, the change in slurry pressure follows a nearly identical pattern. As the diffusion distance increases, slurry pressure decreases, displaying a non-linear distribution. When the fracture aperture is larger, the measured slurry pressure is lower, and the differences in slurry pressure at the early stage, middle stage, and final stage of the diffusion path are 0.0005 MPa, 0.0079 MPa, and 0.00085 MPa, respectively. The slurry pressure differences exhibit the same \"initial increase and subsequent decrease\" trend over the diffusion distance. This \"initial increase and subsequent decrease\" primarily pertains to the variations in the rate of slurry pressure decay. In the early stages, the slurry pressure decays more rapidly, while in the later stages, the rate of decay gradually decreases. Here, the \"first increase and then reduce\" mainly refers to the slurry pressure decay rate changes; the slurry pressure decay rate is larger in the early part of the slurry pressure, while the later part of the decay rate gradually decreases. This is because when the fracture aperture is larger, there is more space for slurry flow within the fracture, resulting in a more significant release of slurry pressure within the fracture and, subsequently, lower slurry pressure. Conversely, when the fracture aperture is smaller, the slurry experiences more  
Figure 11: Plot of slurry pressure variations with diffusion distance for different fracture roughnesses.  
concentrated forces within the fracture, leading to a lower degree of slurry pressure decay and, consequently, higher slurry pressure.