esa-sceva/satcom-chunk-collection huggingface dataset:
# [UOM - Free5 gc]
#### 3.3.6 Bidirectional Pipeline  
Once the NDT infrastructure has been deployed and provisioned, it is necessary to establish the different pipelines to be used in the complete system. Depending on the type of elements interconnected and their communication necessities, two different pipelines are distinguished: V2V (virtual to virtual) and P2V (physical to virtual). The former is performed within the NDT, interconnecting each of the DTs that compose it. These pipelines allow for reflecting of the behavior of the communication that takes place in the physical world, without the time limitation present in the physical world, which allows the communication of large data flows in reduced times, and facilitates the emulation task, accelerating the obtention of results. The second supervises the interconnection between the virtual world of the NDT and the physical world, allowing the continuous feedback loop between the DT and its physical counterpart, providing the system with continuous co-evolution and cooperation capabilities.  
Focusing on the type of P2V communication, it is necessary to approach it from a cybersecurity perspective due to the risks and threats associated with an environment of this type, such as risks associated with data, risk to physical devices modeled through DTs, to networks through those that are interconnected or to the network applications that operate on them. As described by [PERSON] et al. in [29], P2V communication in NDT architectures must comply with the requirements of data confidentiality and integrity and the stability of the authenticity of policies, as well as the unforgeability based on the authenticity of both parties, physical and digital.
## 5 Performance Evaluation
### Results and Analysis
#### 5.3.2 Performance under Oversized Tasks  
In a mesh topology with good connectivity, we designed two sets of large-scale tasks. In the first group, we set the size of the long flows to be sent to 40 MB, and in the second group, we sent a long flow of 20 MB for medium tasks. We observed the performance of the algorithm from the four perspectives of average throughput, the average delay of ping, average delay, and global real-time load.  
It can be observed that the average throughput of MBLFS is significantly higher than the other two algorithms in a congested network environment. Compared with the other two algorithms in Figures 8(a) and 8(a), it is improved by nearly 12% and 58%, respectively. In Figures 8(b) and 8(b), the average time delay of small flow can also be significantly reduced, shortening by 53% and 68%, respectively. It can be seen that the MFLBS algorithm under the mesh topology network model is less sensitive to small flows than the one-hop DLBS and FCFS in the case of network congestion. The MFLBS algorithm not only reduces the delay of small flows but also effectively reduces the average delay. Observing the detailed information of the global real-time load in Figures 8(c) and 8(c), MFLBS has obviously maintained a higher utilization rate than the other two algorithms in Figure 5. Although this feature is not obvious in Figure 8(c), the overall load of MFLBS can maintain a relatively stable state in both cases of 40 MB and 20 MB. The turbulence of the broken line is smaller than that of the one-hop DLBS and FCFS algorithms, and it can end the scheduling faster. Comparing the overall results of Figures 9 and 10, we found that the overall performance of MFLBS is better when the task is heavier, and the blockage is more serious.  
Figure 9: Cont.  
In summary, we found that our MFLBS algorithm has a higher throughput when the network task load is relatively large, and it can take into account the transmission of small flows, effectively reducing the average delay of the data flows.
## 1 Introduction
Therefore, based on different SAR configurations and available surface parameters, various empirical, semi-empirical, and theoretical models have been proposed to retrieve soil moisture at regional scales over the last few decades [25; 26; 27]. Developed by ESA to ensure continuity of C-band SAR data, the Sentinel-1 constellation features a temporal resolution of six days and spatial resolution of \(5\times 20\) m over land [28], which are appropriate for soil moisture estimation over Poyang Lake ungauged zone. Currently, several algorithms have been proposed to retrieve soil moisture operationally from Sentinel-1 data, using change detection techniques [29; 30], Bayesian approach [31], and Artificial Neural Network (ANN) [32], respectively. Both change detection and Bayesian methods exploit the short revisit time of Sentinel-1 data and assume that the backscatter cross section of soil surfaces changes over short timescales, mainly due to variations in soil moisture, while the average characteristics of surface roughness remain almost unaltered [31]. This assumption might be invalidated by the pronounced heterogeneity of soil surfaces when estimating soil moisture from Sentinel-1 data at full spatial resolution. The ANN algorithm trained with theoretical model can retrieve soil moisture accurately from single SAR acquisition, but a robust and extensive reference dataset for the training of the ANN technique is essential, which is lacking for most areas [32]. In this paper, a linear mixed effects model is developed to estimate surface soil moisture from multi-temporal Sentinel-1 images. It is assumed in the first place that a linear relationship exists between surface soil moisture and the backscattering coefficient in logarithmic units for bare and vegetated surfaces. This linear relationship might be affected by several time and space varying parameters, such as terrain, vegetation, and soil properties. It is assumed then that variability in the SM-\(\sigma^{0}\) relationship can be accounted for by introducing the LME model, which has been successfully applied in the prediction of particulate matter with aerodynamic diameter \(\leq\)2.5 \(\upmu\)m (PM\({}_{2.5}\)) using satellite aerosol optical depth (AOD) products [33; 34; 35; 36]. Under these assumptions, time series Sentinel-1 backscatter images and ground soil moisture measurements over Poyang Lake ungauged zone are used to calibrate the LME model, in which the SM-\(\sigma^{0}\) relationship varies both by time and space. The paper is organized as follows. In Section 2, the study area and the datasets of radar and ground measurements are briefly introduced. Section 3 describes both the linear mixed and fixed effects models, and then proposes a soil moisture index. Performance comparisons between different models and validation of the model predicted soil moisture indices are shown in Section 4. Section 5 discusses the results, and Section 6 draws the main conclusions.
# Effect of Nanofluids on Boiling Heat Transfer Performance
## 2 Nanofluid Application in Engineering
### Nanofluid in the Collector  
[PERSON] et al. [52] reviewed the importance of nanofluids for thermal performance for six types solar collectors. In addition, an improved quantification of the performance of solar collectors by different types of nanofluids is compiled. [PERSON] et al. [53] studied in detail the use of nanofluids in concentrating solar collectors, including heating, cooling, power, and triads. Emphasis is placed on the enhancement of the thermal efficiency of the collector using nanofluids. Figure 4 shows the types of nanoparticles in solar collectors and the range of applications of solar collectors. As can be seen from the figure, there are many types of nanofluids used in solar collectors. The types of solar collectors include Flat plate, evacuated tube, direct absorber, parabolic trough, solar dish, and thermal photovoltaic. Solar collector is a special kind of heat exchanger which convert solar radiation into two types of energy such as thermal energy in solar thermal applications and electrical energy directly in photovoltaic applications. In the case of thermal application, solar devices absorb the solar radiant energy incident on its surface; transform it into heat and transfer the heat to the working fluid flowing through them.  
Nanoparticle shape and type have an effect on the heat transfer of direct absorption solar collectors [54]. At room temperature, the thermal conductivity of nanofluids is 0.4% higher than that of pure water. At the surface of the photothermal conversion test of simulated solar irradiation with an intensity of 1 kW/m\({}^{2}\), after irradiation for 3000 s, the temperature of all spherical metal nanofluids was increased by 5 K compared with pure water, and the efficiency was improved by 35%. Nonspherical silver and graphene/silver are more efficient and increase by 35%. Water-based single-walled carbon nanotube nanofluids with a volume concentration of 0.2% can increase the efficiency of vacuum tube solar collectors by 66% [55]. Water-based CuO and Al\({}_{2}\)O\({}_{3}\) nanofluids can improve the thermal efficiency of parabolic trough collectors [56]. [PERSON] [57] evaluated the performance of water-based  
Figure 4: Application of nanofluids in solar collectors.  
CuO/MWCNT mixture nanofluids and water-based MgO/MWCNT mixture nanofluids for flat plate collectors. The results show that the optimal operating conditions of the plate collector occur at a mass flow rate of 0.025-0.03 kg/s, and the nanoparticle concentration range is 0.75 to 1.0%. Compared with the base fluid, the energy efficiency of the MgO/MWCNT mixture nanofluid is improved, 25.1%, and compared with water-based MgO nanofluid energy efficiency increased by 16.28%. The performance of the MgO/MWCNT mixture nanofluid is closer to that of the water-based MWCNT nanofluid than the CuO/MWCNT mixture nanofluid.  
It can be seen that the application of nanofluids in solar collectors can effectively increase the temperature of the base fluid and have a positive impact on the efficiency of the collector.
# A Study on the Feasibility of Natural Frequency-Based Crack Detection
## 2 Methodology
Secondly, to appropriately describe the stress singularities at the crack tip and show the discontinuities of displacement and slope across the crack, corner functions [30] are added to the well-known admissible functions consisting of regular polynomials. Thirdly, when deriving the stiffness matrix in the RRM, a mesh of triangles is used to perform Gaussian integration. The Gaussian quadrature rule of degree 19, developed by [PERSON] [31], is applied to the triangles to retrieve integration points and weights. After the initial stress distribution is obtained via the XFEM, initial stresses at Gaussian integration points are interpolated using triangulation-based natural neighbour interpolation [32]. More details about the second stage are given in Section 3. In the third stage, the measurability of the natural frequency change \(\Delta\omega=\omega_{0}-\omega_{s}\) is analysed. It should be noted that whether \(\Delta\omega\) is measurable largely depends on the equipment being used. Based on some experimental studies featuring either contact or non-contact vibration measurement in the literature [33; 34; 35; 36; 37], one can safely say that in the frequency range of 0-1000 Hz, a frequency change of 0.1 Hz can be measured confidently using a currently available device. Therefore, in this study, if \(\Delta\omega\) is large enough to be captured (i.e., \(\Delta\omega\geq 0.1\) Hz), early crack detection based on natural frequency measurement is considered possible. If \(\Delta\omega\) is too subtle to be measured (i.e., \(\Delta\omega<0.1\) Hz), then natural frequency-based early crack detection may not be useful. This is further discussed through numerical examples in Section 5 considering different metals.
# DEEDP: Document-Level Event Extraction Model Incorporating Dependency Paths
## 2 Related Works
### Document-Level Event Extraction  
In many real scenarios, several event records may be described in a document, and arguments for a record may be scattered. Consequently, DEE has attracted increasing attention, and many promising techniques have been developed. To evaluate their performance, two benchmark datasets, MUC-4 [16] and ChFinAnn [8], were constructed. MUC-4 is a document-level template-filling task that aims at identifying event arguments for filling the corresponding pre-defined roles with associated role types from a document. Researchers on EE have proposed a variety of advanced approaches to improve performance on this task. Recent studies have explored the extraction of role fillers utilizing manually designed linguistic features [25] and neural-based contextual representations [26; 27; 28].  
For arguments-scattering and multi-events, the real challenges for DEE, previous studies focus mainly on the ChFinAnn task. DCFEE [9] proposed a key event detection model for DEE that extracts events from the central sentence and searches for missing arguments from the surrounding ones. Doc2 EDAG [8] treated DEE as a table-filling task and utilizes multiple Transformer encoders to capture sentence and document features to obtain document-aware entities and sentence representations, and then performs triggered event records construction with a tracker. GIT [11] proposed a heterogeneous graph interaction framework to model global information interactions among sentences and entities, and adopted an improved tracker to construct detected event records. DE-PPN [12] proposed an encoder-decoder model to obtain document-aware representations and generate events in parallel. MMR [16] transforms DEE into a machine reading comprehension (MRC) task with a multi-granularity reader framework. Although these methods have improved the performance of DEE, they ignore the feature loss problem caused by mean pooling and maxpooling operations or neglect the interdependency between local semantic information and global semantic information, which limits performance improvement.
# An Improved Orthogonal Matching Pursuit Algorithm for CS-Based Channel Estimation
## 2 Materials and Methods
### Wireless Communication System Model Based on CS Channel Estimation
#### 2.1.2 SC-FDE System Based on CS Channel Estimation
For wireless broadband channels, the non-zero elements of \(H_{i}\) are mainly concentrated in a few positions, which are approximated as follows:  
\[H_{i}(n)=\sum_{k=0}^{K-1}\alpha_{i,k}\delta(n-\tau_{i,k})\ n\in[0,L] \tag{15}\]  
The path delays are assumed to be integers in this research. The \(k\)th path gain is \(\alpha_{i,K}\), and the delay is \(\tau_{i,K}\). As shown in Equation (15), the element value of \(H_{i}\) is \(\alpha_{i,K}\) at \(\tau_{i,K}\), the value of the other elements is zero, and there are \(K\) non-zero elements. The path delay set is recorded as \(\Omega_{i}=[\tau_{i,0},\tau_{i,1},\cdots,\tau_{i,K-1}]\), and the path gain is set as \(A_{i}=[\alpha_{i,0},\alpha_{i,1},\cdots,\alpha_{i,K-1}]\). Without a loss of generality, \(0\leq\tau_{i,0}\leq\tau_{i,1}\leq\cdots\leq\tau_{i,K-1}\leq L-1\) can be supposed. In general, we have that \(K\) is much less than \(L\), denoted by \(K<<L\); that is, the channel has a significant sparse characteristic. Therefore, channel estimation can be performed using CS methods. In CS algorithms, \(R_{i}\) is considered the observation vector, \(\Phi_{i}\) is the observation matrix, \(M\) is the number of observations, and \(K\) is the sparsity of \(H_{i}\). It has been proven that \(\Phi_{i}\) satisfies the RIP condition required in CS theory [26]. The idea of CS is to turn the problem of solving \(L\) unknowns \(H_{i}\) into the problem of estimating the \(2K\) unknowns \(\Omega_{i}\) and \(A_{i}\), the key of which is the correct estimation of \(\Omega_{i}\). When \(M<L+1\), the problem of solving \(H_{i}\) in Equation (13) turns into an underdetermined equation solution problem. Since \(H_{i}\) is a sparse variable, according to CS theory, the solution of this underdetermined equation can be obtained through the optimization problem below to obtain the estimation of channel parameter \(H_{i}\):  
\[\hat{H}_{i}=\underset{H_{i}}{\text{arg min}}\|H_{i}\|_{0}\ \text{s.t.}\ \|R_{i}-\Phi_{i}H_{i}\|_{2}\leq\varepsilon \tag{16}\]  
where \(\varepsilon\) is a noise-related non-negative constant. In recent years, the development of CS theory has provided corresponding algorithms for solving the above problem, which have mainly included relaxed convex optimization algorithms, basis pursuit (BP) algorithms, matching pursuit (MP) algorithms based on a greedy search, etc.
# A Context-Aware Edge-Based VANET Communication Scheme for ITS +
## 1 Introduction
This paper improves [22] by introducing a multi-hop data virtualization approach for connection-independent communications, and also presents new simulation results to show the performance advantage of the proposed approach over existing studies in a more comprehensive manner. The main contributions of this paper are as follows. * We propose a context-aware general edge-based scheme that is possible to handle different types of communications with different requirements. * The proposed scheme employs a fuzzy logic-based edge node selection protocol to find the best edge nodes in a decentralized network, which can achieve an efficient use of wireless resources by conducting packet forwarding through edges. * The proposed scheme also uses a reinforcement learning algorithm to learn the best route for the last two-hop communications. * We launch extensive simulations to evaluate the proposed scheme by comparing with other baselines.
# A Fast Calibration Method for Sensors of Atmospheric Detection System
## 2 System Physics Modeling
### Dynamic Heat Transfer Model of System
#### 2.2.1 Dynamic Heat Transfer Model of Ethanol Bath Thermostat  
The heating and cooling of the ethanol bath are carried out by a heating resistor and refrigeration compressor, and the whole process is carried out at constant cooling and heating power. The compressor, heating resistor, and ethanol directly exchange heat, and the exchanged heat \(Q_{1}\) can be expressed as  
\[Q_{1}=C_{e}\cdot m_{e}\cdot\Delta T_{1}, \tag{1}\]  
where \(C_{e}\) is the specific heat capacity of ethanol, \(m_{e}\) is the mass of ethanol in the thermostat, and \(\Delta T_{1}\) is the temperature difference between the ethanol temperature and the set temperature.  
Figure 1: (**a**) Temperature sensor calibration system for ADS, mainly including ADS, thermostat, glass tube, fixed fixture, and standard thermometer. (**b**) The physical photo of the temperature sensor calibration system for ADS.  
Through many calibration experiments, the temperature control process of an ethanol bath thermostat is a typical first-order system. The temperature change \(\Delta T_{1}\) is used as the input of the thermostat. After adjusting time \(t_{a}\), the system reaches a stable state with a margin of error of 2% and can be expressed as [41]  
\[G_{1}(s)=\frac{T_{1}(s)}{Q_{1}(s)}=\frac{1}{0.25t_{a}\cdot s+1}, \tag{2}\]  
where \(T_{1}\) is the temperature of ethanol.  
The temperature calibration system uses computer control, the frequency of data sampling frequency is \(f\), and the sampling interval is \(\Delta t=1/f\), and the transfer function of the ethanol thermostat is discrete. Before the transfer function of the thermostat, the discrete transfer function is obtained by the serial Zero-Order Holder (ZOH),  
\[G_{1}(z)=Z\left[\frac{1-e^{-\Delta ts}}{s}G_{1}(s)\right]=\frac{z-1}{z}Z\left[ \frac{1}{s(0.25t_{a}\cdot s+1)}\right]=\frac{1-e^{-\frac{\Delta t}{\Delta t}}}{ z-e^{-\frac{\Delta t}{\Delta t}}}. \tag{3}\]
# HCL Control Strategy for an Adaptive Roadway Lighting Distribution
## 2 Materials and Methods
### Establish the Freeform Surface Formula for a Roadway Lighting Lens
#### 2.4.1 Incident Ray Surface  
After the LED light enters the lens, it travels according to the rule of refraction. Since the incident surface is a freeform surface, as shown in Figure 7, the location of each point \(C_{c}(x_{c},y_{c})\) will be obtained through calculations. The calculation is roughly the same as that of a total internal reflection lens regarding the central refractive lens. First, we need to build the linear equation of the incident light:  
\[y=x\tan(90-\theta_{i})=m_{1}x \tag{13}\]  
where \(\theta_{i}\) is the incident angle of the LED light and \(m_{1}\) is the slope of the LED incident light.  
The relation between the incident lens and the geometric relation of \(\theta_{1}\) and \(\theta_{2}\) is shown in Figure 7 and is based on [PERSON]'s law, with \(\theta_{2}=\theta_{1}+(\beta-\theta_{i})\). After the light passes through the freeform surface, \(\beta\) is the included angle between the light and the optical axis, the calculation formula is as shown in Formula (14):  
\[\theta_{1}=\cot^{-1}[\frac{n_{1}-n_{2}\cos(\beta-\theta_{i})}{n_{2}\sin(\beta -\theta_{i})}] \tag{14}\]  
Regarding the freeform surface control point \(C_{c}(x_{c},y_{c})\) of the intermediate lens, the tangent equation and the slope equation \(m_{2}\) passing through the control point are as follows:  
\[y-y_{c}=m_{2}(x-x_{c}) \tag{15}\]  
\[m_{2}=-\tan(\theta_{i}-\theta_{1}) \tag{16}\]We solve the simultaneous equations of (13) and (15) by using an iterative method to obtain the coordinates of the control points of the central refractive lens area and connect the points to form the curved surface of the lens.  
\[x=\frac{m_{2}x_{c}^{(0)}-y_{c}^{(0)}}{m_{2}-m_{1}}=x_{c}^{(1)} \tag{17}\]  
\[y=m_{1}(\frac{m_{2}x_{c}^{(0)}-y_{c}^{(0)}}{m_{2}-m_{1}})=y_{c}^{(1)} \tag{18}\]
## 3 UniChain Architecture
### Software Components  
The software components of the UniChain system are proposed in this section (see Figure 1).  
Generally, UniChain is an Ethereum-based management system to enable permissions management and accessing EARs. UniChain is decentralized solution that utilizes extensible smart contracts to encode access rights. The UniChain system consists of two types of nodes: student nodes and university nodes. A student node consists of five main components: EARs Interface, Backend Library, Ethereum client, Cipher/Decipher Manager and Data Base (DB) Manager. In addition to these mentioned components, a university node has a Records Evaluation Manager (REM) component that is responsible for calculating the degree of nodes. The next sections describe these components in detail. As shownin Figure 1, a user requests a service (add, update, read, etc.) from the UniChain system via a web page (1). The request will be transferred to the Ethereum client via the Backend library (2 and 3). The backend library first performs low level formatting and parsing to communicate with the Ethereum client (2). The Ethereum client in turns communicates with the smart contracts to query the \"access rights\" regarding the received request (4). Upon receiving the \"approval\" from the blockchain network, the Ethereum client will transfer it to the Cipher/Decipher Manager through the backend library (5-7). The Cipher/Decipher Manager performs the encryption/deception schema and transfers the query to the DB Manager that is responsible for accessing the records DB (8 and 9). More details regarding the interaction between the nodes and the smart contracts in different scenarios are presented in Section 4. Furthermore, a university node evaluates the EARs stored in its database upon receiving a notification by the use of the REM component. This component communicates with the database through the DB Manager (a-c).