esa-sceva/satcom-chunk-collection huggingface dataset:
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
###### Abstract  
GaoFen-5 (02) (GF5-02) is a new Chinese operational satellite that was launched on 7 September 2021. The Directional Polarimetric Camera (DPC) is one of the main payloads and is mainly used for the remote sensing monitoring of atmospheric components such as aerosols and water vapor. At present, the DPC is in the stage of on-orbit testing, and no public DPC precipitable water vapor (PWV) data are available. In this study, a PWV retrieval algorithm based on the spectral characteristics of DPC data is developed. The algorithm consists of three parts: (1) the construction of the lookup table, (2) the calculation of water vapor absorption transmittance (WVAT) in the band at 910 nm, and (3) DPC PWV retrieval. The global PWV results derived from DPC data are spatially continuous, which can illustrate the global distribution of water vapor content well. The validation based on the Aerosol Robotic Network (AERONET) PWV data shows that the DPC PWV data have accuracy similar to that of Moderate-resolution Imaging Spectroradiometer (MODIS) PWV data, with coefficient correlation of determination (\(R^{2}\)), mean absolute error (MAE), and relative error (RE) of 0.32, 0.30, and 0.93 using the DPC and 0.23, 0.36, and 0.96 using the MODIS, respectively. The results show that our proposed DPC PWV retrieval algorithm is feasible and has high accuracy. By analyzing the errors, we found that the calibration coefficients of the DPC in the 865 nm and 910 nm bands need to be updated.  
GF5-02 satellite; Directional Polarimetric Camera (DPC); precipitable water vapor (PWV); remote sensing retrieval 20231594. [[https://doi.org/10.3390/rs15010094](https://doi.org/10.3390/rs15010094)]([https://doi.org/10.3390/rs15010094](https://doi.org/10.3390/rs15010094))  
1
# Crossover Operator Inspired by the Selection Operator
## 3 Experiment and Results Discussion  
The main aim of the research was to assess the impact of the use of a new crossover method on the effectiveness of the evolutionary algorithm for the problem of task scheduling compared with crossover methods described in the literature. The experiments were conducted for a modified symmetric traveling salesman problem, in which each graph node (task) is described by many parameters. Thanks to this, we are dealing with a reduction in the cost of the path in the high-dimensional space. This is a model equivalent to the popular issue of sequencing production tasks, but it should be emphasized that it is still only an abstract TSP in which the set of parameters and the distances between the sequenced nodes are not related to any real production tasks and setup costs. This research was carried out for a synthetically generated sequence of 250, 500, and 1000 tasks described by a set of ten continuous parameters. Random parameter values were normalized and were in the range <0;1> (uniform distribution). The minimized objective function was the sum of the transition costs between all tasks in the sequence, excluding the cost of the most expensive connection. As mentioned in Section 2.2, after the optimization is performed, the circular graph of the best individual is broken at the place where the connection between neighboring tasks is the most expensive. For the purposes of the study, the cost of transition between tasks was defined based on the Euclidean distance (5), the square of this value, and the Manhattan distance (6):  
\[c_{i}^{(E)}(\overline{\textbf{P}}_{i},\overline{\textbf{P}}_{i+1})=\sqrt{ \sum_{k=1}^{K}(p_{i,k}-p_{i+1,k})^{2}} \tag{5}\]  
\[c_{i}^{(M)}(\overline{\textbf{P}}_{i},\overline{\textbf{P}}_{i+1})=\sum_{k=1}^ {K}|p_{i,k}-p_{i+1,k}| \tag{6}\]  
Figure 7: Flow diagram of the proposed crossover operator. This crossover subalgorithm is implemented in each generation of the evolutionary algorithm.  
where \(K\)--dimension of the parameter space describing the sequenced tasks; \(p_{i,k}\)--the \(k\)-th parameter of the \(i\)-th task.  
Although completely synthetic, these three distances (costs of transitions between tasks) have varying scales and degrees of non-linearity, reflecting the complexity of real-world costs, e.g., in production task sequencing. Consequently, they offer some level of representativeness for comparative analysis. The total cost of executing a sequence (the fitness function for the optimization) is defined by the formula:  
\[C_{T}=\sum_{i=1}^{N}c_{i}^{(D)}-c_{max}^{(D)} \tag{7}\]  
where \(N\)--the number of tasks in the optimized sequence, \(c_{i}^{(D)}\)--the cost of the \(i\)-th connection for the chosen distance \(D\) (either Euclidean or Manhattan), \(c_{max}^{(D)}\)--the cost of the most expensive connection in the sequence ring. Please note that after optimization, the cost of the most expensive connection (transition between tasks) is subtracted because at this point the cyclic chromosome graph will always be split at this point.
# Toward QoS Monitoring in IoT Edge Devices Driven Healthcare--A Systematic Literature Review
## 5 Related Work
### Comparison  
The authors of the aforementioned papers have tried to include as many models, frameworks, and technologies as they can in their publications. However, their literature evaluations are only concerned with general characteristics of enabling technology in smart healthcare. It was discovered that just one review article [41] was comparable to our review paper, but QoS was not stressed, and the same coverage of too many technologies was also noted.  
The scope of QoS optimization has increased in current smart healthcare applications to cover several important areas, such as monitoring and control in real-time: Smart healthcare applications use a range of sensors and equipment to keep tabs on patients. For these devices to operate consistently and properly, as well as for the timely and accurate transmission of the data they collect, QoS optimization is crucial [43]. Patients can receive care remotely through telemedicine and other forms of remote monitoring thanks to smart healthcare applications. QoS optimization is crucial for ensuring that patient data are transferred swiftly and accurately and that video and audio communications are clear and have low latency. Predictive analytics and machine learning: smart healthcare applications evaluate patient data and spot potential health hazards using predictive analytics and machine learning. The speedy and accurate completion of these studies and the accessibility of the data for healthcare practitioners are both dependent on QoS optimization [44]. Smart healthcare applications must be able to communicate with a variety of tools, platforms, and systems. This is known as interoperability. The effective communication of these systems and the accuracy and dependability of the data they exchange depend on QoS improvement. Security and privacy: reliable healthcare applications must ensure that patient information is not accessed by unauthorized individuals and is only utilized for the purposes for which it was collected. QoS optimization is essential for the security of the systems and the data [45]. In general, QoS optimization is a crucial component of smart healthcare applications because it guarantees that patients receive the best care possible and that healthcare professionals have the information, they need to make wise decisions. The analysis of the survey articles and SLRs mentioned above is summarized in Table 3.
# Efficient Layered Parallel Architecture and Application for Large Matrix LDPC Decoder
## 3 Design and Implementation
### Partially Block-Parallel
During the update, the 1 st is read out from VM, the old 2 nd is read out from SM, and then the 1 st read out from VM is written into the position of the 2 nd in SM. After the update is completed, the 2 nd is obtained and written into VM. The later update is similar. It can be observed that for the same column, since SM only has space to store \(n-1\) signs, the \(n\) signs are cyclically stored in \(n-1\) position in SM; so, the read and write addresses of SM are not fixed and will change continuously during decoding.
## 4 Hardware Architecture  
In this section, we describe the detailed architecture of our prototype hardware design. Figure 5 shows the block diagram of our proposed satellite IP route lookup engine. Inthe lookup engine, there are four main modules: CBHF, Bitmap Lookup, PR-Trie Lookup, and NHI Lookup. As shown, the input IP address is first fed into the CBHF module, and then the CBHF module checks for the existence of subtries for the input IP address in the four levels. We assume that the matching subtrie of the highest priority is designated as a PR-Trie, the CBHF module will output the PR-Trie address, and forward it to the Bitmap Lookup module to query on-chip memory, where the P-Trie and R-Trie bitmaps are stored. The PR-Trie Lookup module reads the P-Trie and R-Trie data from the preceding module, computing the OHT bitmap according to the PR-Trie algorithm, and generating the addresses of the target next-hop information (NHI) and imaginary root information, which are, respectively, stored in two first-in-first-out (FIFO) buffers for pipelining to accelerate the processing speed. Finally, the NHI Lookup module obtains output port data using the above addresses, then picks out the correct NHI based on Algorithm A5.
# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 3 Methodology
### Power Optimization Process  
As mentioned in the previous section, all the PV parameters (e.g., \(I_{ph}\), \(V_{oc}\), \(I_{sc}\), \(R_{p}\), \(R_{s}\)) are associated with temperature and irradiance and are calculated based on PV single- diode modeling. A genetic algorithm is implemented and developed to estimate the expected (MPP) PV values according to irradiance and temperature. In essence, the genetic optimization algorithm, as detailed below, heuristically finds the MPP as well as the electric qualities at this point with regard to monitored outdoor values.
## 2 Related Work  
### Collaboration in Metaverse  
The idea of collaborating inside mixed reality applications is not new. Studies on this subject spread over the last three decades. One of the initial articles that treat this idea shows that two issues can be solved thanks to the particularities of extended environments: the seamlessness of the activities and the enhancement of reality [16]. Initially, due to the technological limitations, researchers used to focus on creating system architectures that would support real-time collaboration--tools that would allow interaction and equipment that would increase immersion. This research would translate into the overall improvement of presence, the main focus of most virtual and augmented reality scenarios [17].  
Meanwhile, things have improved, both technologically and acceptance-wise. Besides virtual reality (VR), augmented/extended/mixed reality also gained popularity. Technology evolved and became cheaper. We now have commercial mixed reality equipment at our disposal, such as Microsoft HoloLens 2, Vuzix Blade 2 [18], Magic Leap 2 [19], and many others. However, unlike VR headsets such as Oculus Quest 2, mixed reality headsets are more expensive.  
Remote collaboration can be enhanced by the use of extended reality technologies [20], which provide tools that can be used to create virtual environments in which users can view data from multiple sources, including 3D virtual avatars, and interact with each other in real time [21].  
Nevertheless, what traits are offered by the mixed reality, and how do they empower the field workforce and allow it to increase its efficiency? As presented in several studies [22; 23], mixed reality offers time-sensitive solutions to critical issues. Information is quickly accessible during on-site operations in ways that were not possible before. Mixed reality connects workers in challenging conditions with remote experts [24] to solve problems faster and safer. Thanks to the development of communication technologies (5G), workers can now hear real-time audio and see video documentation directly where they need it. Besides architecture, engineering, construction, and operation (AECO) [25], mixed reality has also been used in mining operations [26], food production, and consumption [27], healthcare [28], and, more recently, in socializing and entertainment. The idea of mixing VR objects with the real environment is so appealing that one of the biggest tech companies, Facebook, has rebranded itself as Meta and has a long-term purpose of creating the means and technology to make the Metaverse a reality [29].
## 8 Discussion
### Motivations
#### 8.3.2 The Internet of Things (IoT)  
Carries with it a vast volume of data that must be carried via the network. As a result of the addition of sophisticated sensors and controllers to their devices, several device manufacturers had already included the capacity of collecting data from their gadgets. Within this decade, it became obvious that reporting on and analysing data gathered from sensors and control devices were becoming increasingly popular among people.  
Management of a smart city. In a smart city, information is available on a variety of topics, including the environment, tourism, traffic, social life, mobility, energy, and so on. A complete picture of the city is provided by the data collection and organisation provided by heterogeneous sensors, which allows for the implementation of early warning solutions and the development of predictive models, thereby increasing the city's resilience  
Figure 8: Categories of recommendations according to audience.  
and limiting the negative effects on citizens caused by unexpected events. The environmental monitoring and structural monitoring applications are the ones that stand out in this scenario.  
Management of public and private metering systems. To make the development of smart metering systems economically viable, the goal in this scenario is to integrate smart metering services into a single transportation network. For example, the citizen or company can remotely monitor the status of their house or office, allowing the system to adapt quickly to various occurrences in order to provide the desired comfort while conserving energy, as seen in the image below. In this context, data privacy and security are critical considerations: it is vital to ensure that the information gathered will not be used to violate the privacy of the user or be utilised by malevolent parties in order to do this.  
Management of industrial processes as part of a whole. The requirement for integrated industrial management is a crucial component of innovation processes in the Fourth Industrial Revolution (Industrie 4.0). The establishment of dedicated network infrastructure, on the other hand, can be a considerable financial burden. It is anticipated that the 5G network would provide an infrastructure that will meet the demanding specifications required for industrial automation applications while still being cost-effective. In this scenario, the ability to meet the severe URLLC requirements will be critical in determining the outcome.
## 3 Results and Discussion  
The effects of laser application on the plant height parameters of maize are shown in Table 2. Laser treatment significantly affected plant height of maize, along with different exposure time and power density. Generally, laser treatment increased plant height, as compared to those seeds without treatment (control). The pre-sowing irradiation of blue laser for 85 s at 4 mW/cm\({}^{2}\) significantly increased the plant height (211 cm) with respect to the seedlings from the control seeds (201 cm). A similar trend was observed for the red laser (Table 2), and the highest plant height (207 cm) resulted from 105 s of exposure in 2 mW/cm\({}^{2}\) as compared to the control seeds. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Treatments** & **No.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 3 Trade-offs in Wireless Network Design
### Spectrum Regulations  
Another tackling design choice is the one between unlicensed Industrial, Scientific and Medical (ISM) and licensed bands. On the one hand, worldwide permitted unlicensed operation reduces the runtime costs but has no regulatory protection against interference by other wireless networks operating in the same frequency band. On the other hand, even though licensed bands prevent interference, they typically depend on an external operator. Therefore, network issues cannot be immediately resolved on site, the external operator needs to resolve them. This introduces administrative delays which are unaffordable in time-critical industrial applications. Communication technologies operating in the unlicensed spectrum are maintained and managed locally. However, several co-located or overlapping wireless networks operating in the same frequency band will interfere with each other and can experience decreased QoS and extensive packet loss [14]. In an effort to alleviate coexistence issues in unlicensed spectrum, regulatory bodies have issued a number of norms such as a Clear Channel Assessment (CCA) check before each transmission by all devices, that is, a device has to sense if the channel is free by energy detection or other types of Detect And Avoid (DAA) mechanisms [14]. Although the DAA mechanisms improve the coexistence between the contending wireless nodes and networks, collisions can still occur. Apart from collisions, medium sensing adds to the latency and introduces non-determinism due to the medium congestion. Aforementioned facts significantly limit the use of wireless solutions in closed loop control applications in automation industry. A limiting regulation is present in unlicensed sub-GHz spectrum as well. Devices with an operating range of 863-868 MHz in Europe, 916.5-927.5 MHz in Japan and 902-928 MHz in the US must comply with the maximum duty cycle limit of 2.8% and 10% for the. Access Point (AP) provided that they support LBT and Adaptive Frequency Agility (AFA) features, 1% otherwise [11].
# Fragility Analysis of Transmission Towers Subjected to Downburst Winds
## 3 Typical Engineering Case Study
### Fragility Analysis
#### 3.5.1 Downburst Wind Load Case
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Damage States**} & \multicolumn{4}{c}{**Wind Attack Angle**} \\ \cline{2-5}  & **0\({}^{\circ}\)** & **45\({}^{\circ}\)** & **60\({}^{\circ}\)** & **90\({}^{\circ}\)** \\ \hline \multicolumn{1}{c}{Slight damage} & 0.8–2.3 & 0.6–2.6 & 0.8–2.8 & 0.9–2.2 \\ \hline \multicolumn{1}{c}{Severe damage} & 1.0–2.1 & 0.9–2.4 & 1.0–2.6 & 1.1–2.0 \\ \hline \multicolumn{1}{c}{Collapse} & 1.1–2.0 & 1.0–2.3 & 1.1–2.5 & 1.2–1.9 \\ \hline \multicolumn{1}{c}{Critical radial distance} & 1.6 & 1.6 & 1.6 & 1.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Dimensionless radial distance (R/D) with different angles in different damage states (35 KV). finite element analysis, and each numerical analysis is much more time-consuming. To reduce the time consumption, the Latin hypercube sampling method, a kind of stratified sampling method, is adopted in this paper. The samples extracted from the high-dimension random space by this method are relatively uniform and do not produce obvious clustering phenomena. Compared with Monte Carlo sampling, the method of Latin hypercube sampling can achieve the same results using fewer samples. The probability of the capacity of the samples of the tower under the downburst is fitted by a lognormal distribution.
## 2 RF Features
### Time Difference of Arrival  
Time Difference Of Arrival (TDOA) is another time-based measurement which illustrates the difference between the time signal travelling from two transmitters to the same receiver. In the conventional approach, cross-correlation is used to extract this value, computing the delay that maximizes the cross-correlation function. One advantage of TDOA compared to TOA is that synchronisation is only required among the transmitters. TDOA suffers from the same issues as TOA, i.e., imperfection hardware and LOS blockage.
## 3 Solution of the Problem  
We introduce the relation for \(u\), \(v\) and \(T\) as:  
\[u=\frac{\partial\psi}{\partial y},\qquad\qquad v=-\frac{\partial\psi}{ \partial x},\qquad\qquad\theta(\eta)=\frac{T-T_{\infty}}{T_{w}-T_{\infty}}, \tag{9}\]  
where the stream function \(\psi(\eta)\) and dimensionless similarity variable \(\eta\) are defined by (see, for example, [PERSON] et al. [43])  
\[\psi=v_{f}R_{\varepsilon_{x}}^{\frac{1}{2}}f(\eta),\qquad\qquad\eta=\frac{y}{ x}R_{\varepsilon_{x}}^{\frac{1}{2}}. \tag{10}\]  
Equations (9) and (10) together with Equations (6)-(8) reduce the boundary value problem (2)-(5) to:  
\[(a+A-A\theta)f^{\prime\prime\prime}+(1-\phi)^{2.5}(1-\phi+\phi\frac{\rho_{s}} {\rho_{f}})(\frac{1}{2}ff^{\prime\prime})-A\theta^{\prime}f^{\prime\prime}-k^ {*}(a+A-A\theta)(f^{\prime}-1)=0, \tag{11}\]  
\[(1+\epsilon\theta)\theta^{\prime\prime}+\epsilon\theta^{\prime 2}+P_{r}(\frac{k_{f}} {k_{n_{f}}})\left(1-\phi+\phi\frac{(\rho C_{p})_{s}}{(\rho C_{p})_{f}}\right) \left(\frac{1}{2}f\theta^{\prime}\right)=0, \tag{12}\]  
\[f(\eta)=S,\quad f^{\prime}(\eta)=\delta f^{\prime\prime}(\eta)\quad\text{at} \quad\eta=0;\quad f^{\prime}(\eta)\to 1\quad\text{as}\quad\eta\to\infty \tag{13}\]\[\theta(\eta)=1+\Delta\theta^{\prime}(\eta)\quad\text{at}\quad\eta=0;\quad\theta( \eta)\to 0\quad\text{as}\quad\eta\to\infty, \tag{14}\]  
where \(A=b(T_{w}-T_{\infty})\) is the viscosity parameter, \(k^{*}=\frac{1}{D_{xx}R_{ex}}\) is the permeability parameter, \(D_{a_{x}}=\ \frac{k}{x^{2}}\) is the local Darcy number, \(P_{r}=\frac{v_{f}}{a_{f}}\) is the Prandtl number, \(\alpha_{f}=\frac{k_{f}}{(\rho C_{p})_{f}}\) is the diffusivity parameter, \(\epsilon\) is the thermal conductivity parameter, \(\delta=\frac{Lu_{\infty}}{v_{f}}\) is the velocity slip parameter and \(\Delta=\ \frac{Du_{\infty}}{v_{f}}\) is the thermal slip parameter.  
The important physical quantities of interest are the skin friction coefficient \(C_{F}\) (rate of shear stress) and the local Nusselt number \(N_{u_{x}}\) (rate of heat transfer at the surface). The skin friction coefficient and the Nusselt number are defined as:  
\[C_{F}=\frac{\tau_{w}}{\rho U_{w}^{2}},\qquad N_{u_{x}}=\frac{xq_{w}}{k_{f}(T_{ w}-T_{\infty})}, \tag{15}\]  
where the local wall shear stress \(\tau_{w}\) and the heat transfer from the plate \(q_{w}\) are given by:  
\[\tau_{w}=-\mu_{n_{f}}(\frac{\partial u}{\partial y})_{y=0},\qquad q_{w}=\kappa _{n_{f}}(\frac{\partial T}{\partial y})_{y=0} \tag{16}\]  
with \(u\) the flow velocity parallel to the porous plate and \(y\) the distance to the plate. Using Equation (16), the dimensionless forms of Equation (15) become:  
\[C_{f}R_{ex}^{1/2}(1-\phi)^{2.5}=-f^{\prime\prime}(0),\qquad N_{u_{x}}R_{ex}^{- 1/2}(\frac{k_{f}}{k_{n_{f}}})=-\theta^{\prime}(0). \tag{17}\]