esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 The Conveyor to Data Acquisition
The 3D aerial surveys using LIDAR technology surveys were carried out in order to generate digital terrain 3D models which can be used for various applications such as urban and transport planning, environmental impact assessments, modeling of runoff water, monitoring of and enforcement of land use activities. The terrain 3D models and the sea bottom 3D models from the ERDF project are being merged to form an innovative integrated 3D perspective for the Maltese Islands. All data from this project was also planned to be made viewable from a web portal, known as a Shared Environmental Information System (SEIS) [19]. The SEIS system serves as the dissemination portal for the domains of air, water, noise, radiation and soil, as well as digital elevation models, digital surface models and the aerial imagery. Interestingly, this project is innovative in the fact that it also acted as a main facilitator for the implementation of the data component of a number of international directives and it ensured the delivery of one factor that has hindered virtuality research; that related to the free delivery of all project data to the general public. The latter has brought together the requirements as outlined by the Commission's Communication COM (2008) 46 Final \"Towards a Shared Environmental Information System\", the INSPIRE Directive (Directive 2007/2/EC) and the Aarhus Convention. This completed, the next step was to identify the potential to use this output as the launching pad for the dissemination of data that allows for the increase in spatial awareness-the scope of this project.
To the best of our knowledge, this is the first study that not only examines the performance of different big data processing platforms for different types of applications in resource-constrained environments but also comprehensively explores their deployment in computing layers, namely edge, fog, and cloud computing layers, using real-world applications. Figure 1: Overall organization of data processing platform deployment. This study primarily focuses on conducting a thorough comparative and analytical analysis of big data platform deployment across edge, fog, and cloud computing layers rather than introducing new methods or technologies. We emphasize the evaluation of edge/fog computing big data processing platforms and aim to offer practical recommendations derived from experimental findings. While this work may not present new methods, its significance lies in providing valuable insights into the comparative efficiency and applicability of big data processing platforms across different computing layers. The comprehensive nature of this study, covering three computing paradigms across various layers with real-world applications and detailed experimental findings, contributes practical guidance for decision making in big data platform deployment. In summary, we make the following contributions in this paper. * We conduct an extensive performance comparison of the edge-/fog-based processing of big data management frameworks against other cloud-based solutions. Our analysis covers the deployment of three different big data platforms based on three data processing applications. * We present interesting findings that suggest that an edge- and fog-based big data management platform is a viable and promising option for applications where local processing power of local data is desired. * We further explore the impact of the deployment of edge- and fog-based big data management frameworks and present recommendations regarding how to better use big data management frameworks for data science applications. In addition, we discuss the research challenges and opportunities in building a more practical edge-/fog-based big data management system. The remainder of this paper is structured as follows. Section 2 summarizes some related studies. Section 3 explains the details of our methodology for the evaluation of the performance of big data management platforms. Section 4 describes the experiments that we carried out and the results of the experiments. Section 5 presents the discussion and recommendations based on the implications and results of our experiments. Finally, our conclusions and suggestions for future work are included in Section 6.
###### Abstract
Second, small power capacity solar panels are installed on roofs of houses, hotels, and commercial establishments for personal use [17]. In this, it is connected to the electrical connection of the power company and operates; On the other hand, there are two types of independent operation without connection to the mains. There are many difficulties operating without a proper electrical connection [18]. First, it needs a battery. Battery maintenance costs are also a problem. When electricity is produced more than we need, the electricity from sunlight goes to waste when the battery is full of capacity [19]. Apart from this, 15 to 20 percent of energy is wasted due to the storage and reuse of electricity in batteries. Solar panels or photovoltaic systems using panels (SPV panels) are placed on rooftops or solar farms arranged so that solar radiation falls on the solar photovoltaic panels to facilitate the reaction of converting sunlight into electricity [20]. A solar photovoltaic system converts solar energy into electrical energy. A battery converts chemical energy into electrical energy, an automobile engine converts chemical energy into mechanical energy, or an electric motor (in an electric vehicle, EV) converts electrical into mechanical energy [21]. An SPV cell converts solar energy into electrical energy. A solar cell does not use the sun's heat to generate electricity, but light rays interact with semiconductor materials to generate electricity. This current flows from the semiconductor to the output leads. These leads are connected to batteries or grids through some electronic circuits and inverters to control and generate alternating currents [22]. Solar energy is used for a home, industrial unit, or small community. A disadvantage of this system is that when the power goes out, the system also shuts down. It is for safety reasons, as grid-tied inverters should automatically disconnect when they do not sense the grid. It means that during a blackout or emergency, It cannot supply power and store energy for later use [23; 24]. The following are the key contributions of the manuscript:The main contribution of this research is to develop autonomous forecasting models with a larger range of features that accurately recognizes occurrences. Here, an AUTO-encoded-based Neural Network (AUTO-NN) forecast model combines a Restricted Boltzmann Machines (RBM) for image retrieval and a Back Propagation Neural Network (BPNN) for component categorization. The organization of this article is given as the following Section 1 illustrates the locale of photovoltaic plants, energy generation, and the role of deep learning in the energy prediction field. In Section 2, the literature on energy prediction is reviewed. Section 3 explains the projected energy forecasting process by means of constructing an arrangement. In Section 4, the investigational study is specified among graphs by evaluating three customary techniques. Finally, Section 5 presents the conclusion and future research direction.
## 3 Results
### Effect of Aluminum Sulfate on Performance of Machine-Made Sand Concrete
#### 3.2.3 Effect of Aluminum Sulfate Content on the Permeability of Machine-Made Sand Concrete  
Figure 7 shows that, when the amount of aluminum sulfate was increased, the concrete water permeability and chloride permeability coefficients firstly decreased and then increased. When the content was 1%, the two permeability coefficients of the concrete reached the minimum values. When the content was 1%, the water and chloride permeability coefficients were reduced by 22.5% and 19.5% compared to those of the sample with no aluminum sulfate. This shows that the aluminum sulfate could improve the impermeability of the concrete when the content was within the optimum content. This is because the aluminate sulfate accelerated and increased the formation of ettringit, which filled the pores and compensated for any microcracks caused by volume shrinkage during cement hydration, thereby improving the rigidity and waterproof ability of the concrete. Too much aluminum sulfate could increase the pore size and the microcracks as reported in literature [56], and increase the permeability of the concrete.  
Figure 5: Effect of aluminum sulfate on slump of fresh concrete.  
Figure 6: Effect of aluminum sulfate on the compressive strength of concrete.  
In summary, when the aluminum sulfate content was less than 1%, the impermeability of concrete was improved. In addition, the concrete had the best impermeability when the content was 1%. At the same time, aluminum sulfate also promoted the early strength of concrete; however, the slump of fresh concrete was slightly decreased. It was the ettringite formed by the reaction of aluminum sulfate with Ca(OH)\({}_{2}\) which decreased the pores in the concrete, thereby improving the impermeability of the concrete. On the other hand, aluminum sulfate simultaneously introduced some free Al\({}^{3+}\) ions, which could restrain the movement of Cl\({}^{-}\) in the pores of the concrete.
## 6 Simulation Results
### Experimental Environments  
As illustrated in Figure 10, three different traffic scenarios and two special cases are considered to examine the system performance: (1) Scenario 1, a random walk; (2) Scenario 2, a straight-line moving; (3) Scenario 3, moving through an intersection; (4) Scenario 4, a backward propagation; and (5) Scenario 5, the sink and the source vehicles traveling in the opposite direction. The arrows represent the moving directions. For Scenario 1 (Figure 10 (top left)), associated with each moving node is a random position. It is assumed that each node moves at its own uniform rate and direction. The source node stars from (510, 150) to move up; the sink node stars from (200, 490) to move right. For Scenario 2, Figure 10 (top right) shows a straight-line moving model on a straight 40 meter-wide road. For Scenario 3, Figure 10 (middle left) shows a moving model, where the nodes are traveling through an intersection. The source node and the sink node move up and move forward to the right, respectively. Compared with Scenario 2, two special cases (Scenarios 4 (Figure 10 (middle right)) and 5 (Figure 10 (bottom))) have similar system performances. That is, for a backward propagation case (_i.e._, the sink vehicle is behind the source vehicle) and for the case that the sink and the source vehicles are traveling  
\begin{table}
\begin{tabular}{c c c c} \hline  & \(\theta\) & **Transmission Range** & **Average Speed** \\ \hline Default & \(75^{\circ}\) & 150 m & 21 m/s \\ Angle variation & 30\({}^{\circ}\)\(\sim\)75\({}^{\circ}\) & 150 m & 21 m/s \\ Distance variation & \(75^{\circ}\) & 110\(\sim\)200 m & 21 m/s \\ Speed variation & \(75^{\circ}\) & 150 m & 13\(\sim\)26 m/s \\ \hline \end{tabular}
\end{table}
Table 2: The values of simulation parameters.  
in the opposite direction, the network behaviors are close to those of Scenario 2. In the following subsections, we will focus on the performance discussion of Scenarios 1\(\sim\)3.
# Dispersion and Pressure Sensitivity of Carbon Nanofiber-Reinforced Polyurethane Cement
## 4 Conclusions  
In this study, CNF was added to a newly developed PUC structurally reinforced composite material, making it a self-sensing, self-monitoring intelligent reinforcement material. The dispersion of CNF was evaluated according to the coefficient of variation of the hexahedron resistance. At the same time, the SEM observation method was used to verify the sensitivity of the CNFPUC mixture as an intelligent induction material. The following conclusions were drawn:  
1. Seepage curves of CNFPUC composites under different poly-ash ratios were established and the percolation interval of the CNF content as the concentration of conductive filler for CNFPUC composites was determined.
2. Under the condition of equal CNF content in a percolation interval, the same amount of samples were obtained from different parts of the CNFPUC mixture in the upper, middle and lower parts to construct a hexahedron test block. The coefficient of variation of the hexahedron resistance can be used to judge the dispersion effect of the CNF in the PU matrix with different poly-ash ratios and silica fume contents.  
Figure 10: Relationship curve for CNF content and resistance coefficient of variation in CNFPUC hexahedron.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Proportion of Silica Fume**} & \multicolumn{5}{c}{**Poly-Ash Ratio**} \\ \cline{2-7}  & **1:1:1.6** & **1:1:1.5** & **1:1:1.4** & **1:1:1.3** & **1:1:1.2** \\ \hline
0 & 3–3.5\% & 3.5–4\% & 4–4.5\% & 4.5–5\% & 5–5.5\% \\
5 & — & 3–3.5\% & 3.5–4\% & 4–4.5\% & 4.5–5\% \\
8 & — & — & 3–3.5\% & 3.5–4\% & 4–4.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Optimal blending ratio of CNF.  
3. The dispersion effect of CNF in the PU matrix determines the varistor characteristics of CNFPUC as a smart material. The addition of the silica fume can effectively reduce the variation coefficient of hexahedron CNFPUC, increase its sensitivity coefficient and make the piezoresistive effect of CNFPUC more pronounced in the elastic stage.
4. Using sensitivity of 500 as the reference index for varistor characteristics, a quadratic curve between the concentration of CNF conductive filler and the coefficient of variation of the hexahedron resistance of CNFPUC composite was established. The dispersion effect of CNFPUC composite CNF can be evaluated and the sensitivity of CNFPUC can be determined. This can provide a new and simple method for determining the sensitivity of a CNFPUC mixture.  
N.Y. and Q.S. conceived the study; N.Y. and K.Z. performed the experiments; N.Y. analyzed the data; N.Y. and Q.S. wrote the main manuscript text; and all authors approved the final manuscript.  
This research was funded by the Social Development Project of Qiqihar Science and Technology Bureau (SFGG-201701) and Scientific Research Project of Qiqihar University, No. 135309122.  
The authors declare no conflict of interest.
# Parameter Extraction of Solar Photovoltaic Model Based on Nutracker Optimization Algorithm
## 2 Solar Cell Model  
### Photovoltaic (PV) Cell Models  
The models of photovoltaic (PV) cells mainly include a series of mathematical models, such as the single diode model (SDM), double diode model (DDM), triple diode model (TDM), diode models with reverse bias characteristics [29], and the improved double diode model (MDDSCM) [30; 31], and so on. In this study, we adopted the most commonly used models: SDM, DDM, and TDM. Figure 1 and Table 1 show the circuit structure and equations (including the circuit equation of the PV component) of the SDM, DDM, and TDM. In the equations, \(I_{L}\) represents the output current, \(I_{ph}\) represents the photocurrent of the PV component, \(I_{SD1}\), \(I_{SD2}\), and \(I_{SD3}\) represent the reverse saturation current, \(N\)s represents the number of cells connected in series, \(Np\) represents the number of cells connected in parallel, \(V_{T}\) (\(m\)=\(n\)*\(k\)*\(T/q\)) is the thermal voltage of the PV component, \(q\) is the electron charge (1.60217646 \(\times\) 10\({}^{-19}\) C), \(k\) is the Boltzmann constant (1.3806503 \(\times\) 10\({}^{-23}\) J/k), \(T\) is the temperature of the PN junction (in K), \(n\) is the ideality factor of the diode, \(Rp\) is the parallel resistance, \(R\)s is the series resistance, and \(R_{L}\) is the external load resistance.
To construct a generalized MR image standardization model that could transform MR images acquired from different MR scanners and/or using different parameters, while multi-modality MR images are not needed for training and spatial intensity nonuniformities might be eliminated with the fusion of regional and global information, we propose a universal intensity standardization method based on cycle generative adversarial network [21]. This method applies a many-to-one framework with jump connections in the generators and weak-pair data augmentation strategy. This method is able to produce proper intensity standardization results. However, the original CycleGAN is mainly used in image segmentation as well as low-resolution image generation, while the cycle consistency is an indirect structural similarity indicator which is easily affected by other factors and unable to remove image blurring introduced by convolutional neural networks. Meanwhile, the weak-pair method used in the original method does not guarantee a consistent balance of randomness and structural similarity when the axial density of the dataset varies. These all result in a reduced resolution of the standardized images. To achieve better preservation of MR image spatial resolution and generate precision standardization results, in this paper, we propose a resolution-oriented MR image intensity standardization method to generate MR image datasets with strong intensity and spatial uniformity. This method is based on the cycle generative adversarial network (CycleGAN) [22] under the extended many-to-one framework. Each generator in this framework, in particular, applies cascading residual blocks to enhance super resolution performance. The generators also apply normalized mutual information (NMI) as a part of the loss function, which is able to reduce and even eliminate image blurring by directly measuring structural errors. Moreover, an advanced weak-pair data augmentation method is applied to adapt to the varying MR image axial density. The paper is organized as follows. Section 2 expatiates on the proposed method. The dataset and data preprocessing steps are shown in Section 3. Section 4 describes the experiments and the results. The discussion is presented in Section 5 and the conclusion is made in Section 6.
## 4 Self-Healing Approaches
### Machine-Learning Algorithms
#### 4.3.6 Artificial Immune System  
Artificial immune system (AIS) is a class of computational models inspired by the biological immune system. In AIS, the immune system is modelled to perform specific tasks such as pattern recognition, anomaly detection, and fault diagnosis. AIS can detect and respond to faults or anomalies in a system. The AIS consists of two main components: the detector and the effector. The detector component of the AIS works by comparing the system's current state to a reference model. If the current state deviates from the reference model, the detector identifies it as an anomaly. This process is like how the biological immune system detects foreign agents such as viruses or bacteria. According to [PERSON] and [PERSON], AIS mimics how biological mechanisms fight unknown threats, like how an organism resists a new virus and mimics the process in a computer system [31]. The effector component of the AIS then takes action to correct the anomaly. Depending on the monitored system, the effector can take various forms, including triggering a backup system, adjusting settings, or rerouting traffic. The effector can also learn from previous actions to improve its effectiveness in responding to future anomalies. An advantage of AIS is its ability to adapt and learn from new information, like the biological immune system. AIS can learn from past experiences to improve its accuracy in detecting and responding to faults.  
Additionally, AIS can be designed to be fault tolerant, allowing the system to continue to function even if some components fail. However, a potential limitation of AIS is its complexity, which can make it challenging to implement and optimise. Additionally, the effectiveness of AIS depends on the availability of a suitable reference model and the ability to tune the system's parameters.  
Existing research in this area of developing AIS proposes several approaches, but the most common are:  
* Negative selection: When an anomaly is detected based on the classifying entities being part of the \"non-self\" originating system;
* Positive selection: When an anomaly is detected based on the classification of the \"self\" originating system;
* Danger theory: This approach raises the alarm if a harmful signal is detected, regardless of whether the entity is of \"self\" or of \"non-self\" of the originating system.
## 3 Finite Element Model
### Calculation of RMS Magnetic Flux Density  
According to the usual evaluation procedures applied in magnetic field exposure studies, we are mainly interested in computing lateral profiles of the RMS magnetic flux density at various heights above the ground, particularly at the standard height of 1 m. Such profiles of \(B_{RMS}\) are calculated in a separate Microsoft Excel worksheet, where a sufficiently large number of instantaneous magnetic flux density profiles (generated over a 20-ms period) are imported and then \"summed\" together with the formula [18; 27]:  
\[B_{RMS}(i)=\sqrt{\frac{1}{N}\sum_{n=1}^{N}B_{n}^{2}(i)}, \tag{3}\]  
where \(B_{1}(i)\),..., \(B_{N}(i)\) are the instantaneous values of the magnetic flux density corresponding to the point \(i\) of the profile and \(N\) stands for the total number of values (profiles). Here, we use \(N=73\).  
On this basis, for \(I=319\) A, Figure 6a shows lateral profiles of the instantaneous magnetic flux density up to 10 m from the central axis of the three-phase power cable in trefoil formation, at the height of 1 m above the ground, as well as the correspondent \(B_{RMS}\) profile (blue thick line). A 2D distribution of the instantaneous magnetic flux density around the three-phase cable in trefoil formation (at the time \(t=16.94\) ms, at which the peak magnetic flux density in Figure 6a is obtained) is presented in Figure 6b. Similarly, by adopting \(I=352\) A, Figure 7a shows simulation results for the three-phase power cable in flat formation. A 2D distribution of the instantaneous magnetic flux density around the three-phase cable in flat formation (at the time \(t=15.56\) ms, at which the peak magnetic flux density in Figure 7a is obtained) is depicted in Figure 7b. More details on the magnetic fields and induced shield currents from both configurations are discussed in Section 5.
# Transmitter Diversity Gain Technique Aided Irregular Channel Coding for Mobile Video Transmission
## 4 Proposed System Setup
### Source Decoding and Video Quality Evaluation:  
Afterwards, the profitable number of iterations of iterative decoding comes to an end and the round is off; the soft-bit output sequence \((L_{D,p}(\hat{x}_{1}))\) of the correspondence scheme is deconcatenated into type \(A(\hat{x}_{a})\), type \(B(\hat{x}_{b})\), and type \(C(\hat{x}_{c})\) partitions and is presented to the _MUX_ block. The _MUX_ block converted the partitions \((A,B,\) and \(C)\) into their resultant output stream of \(\hat{x}_{k}\), which is finally presented to the employed AVC decoder in order to generate the final decoded video sequence.