esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Feasibility Study Through System Simulations
### Mechanisms to Optimize the Models and Minimize the Error Distance  
We benchmark different pattern-matching methods using different kinds of input data arrangements and using different NN structures (also referred to as NN architectures by some authors), namely feedforward and convolutional. The challenge is to find a suitable NN that generalizes for each pattern-matching method. Simply making the NN bigger is not necessarily a solution, as the NN tends to overfit.  
When training an NN model, there is no guarantee of obtaining the best fit. An NN may look unsuitable just because there were not enough training epochs or the optimization algorithm became stuck in local minima. In this latter case, the model may have been trained with a set of initial weights that produced a solution worse than that which could have been obtained with another set of initial weights. This is a problem when benchmarking NNs for selecting the best model. The challenge is how to select the most suitable NN structure for each pattern-matching method so that the fit is optimized to return a minimum error distance, minimizing the probability of bad fits.  
Given the non-convex problem to be solved and the infinite possibilities in terms of how to construct an NN, we cannot guarantee that we are delivering the best result that could be obtained for each one of the proposed pattern-matching methods. However, when selecting the NN structure for each pattern-matching method and when calculating the results, we do our best to aim for a solution that minimizes the positioning error with the approaches discussed below.  
We use the word _mechanism_ to give a name to the different approaches that are used to optimize the model adopted in a pattern-matching method so it can deliver results with a minimum positioning error. Three mechanisms are considered, acting at different levels, ranging from the selection of the NN structure to the repetition of the training with different initial weights. The mechanisms considered are as follows:  
1. Selection of the NN structure. * The goal is to select a suitable NN structure such that the model can deliver results with a minimum positioning error when compared to models implementing other NN structures.
2. Selection of the best model weights among training epochs. * The goal is to select the model weights that minimize the positioning error, generalizing the model for fixed tiles.
3. Selection of the best weights among model training iterations with different initial random weights. * The goal is to select the model weights that minimize the positioning error, generalizing the model for moving tiles.
# Risk-Based Cybersecurity Compliance Assessment System (RC2 AS)
## 4 Risk-Based Cybersecurity Compliance Assessment System (RC2 AS)
### Proposed Risk-Based Cybersecurity Compliance Assessment System (RC2 AS)
#### 4.2.2 RC2 AS Supporting Tool  
To facilitate the assessment process, the RC2 AS supporting tool uses a questionnaire approach. Accordingly, each control will have one or more questions to assess and measure the organization's compliance with a specific control. Table 6 highlights the RC2 AS with subdomain structure.  
The questions on a particular control could depend on each other. An example of questions' dependency is shown in Table 7. Only the first question on control (1-2-1) will be presented. Only if the answer is \"Yes\" or \"Partially Implemented\" will Q2 and Q3 be displayed. The following question will not appear if the answer to Q1 is \"No\". This will expedite the assessment process and make it more convenient. On the other hand, there might be no dependency between the questions, as shown in control (1-2-2), where each question does not depend on the others.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Ref. No. of** & & & \\
**Subdomain** & & & \\ \hline \multirow{3}{*}{Control Ref. No.} & & Question(1) & RC2 AS compliance answer(1) & \\  & & Question(2) & RC2 AS compliance answer(2) & Compliance \\ \cline{1-1}  & & &... & Status \\ \cline{1-1}  & & Question(n) 1  & RC2 AS compliance answer(n) 1  & \\ \hline \hline \end{tabular}
\end{table}
Table 6: RC2 AS supporting tool with subdomain structure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{RC2 AS High-Level Questionnaire} \\ \hline \hline \multicolumn{4}{c}{General Question} \\ \hline
1. Does the entity use cloud computing? & & Yes & & No \\
2. Does the entity have a third party? & & Yes & & No \\
3. Does the entity use industrial control systems and & & & No \\ operational technology (ICS/OT)? & & Yes & & No \\ \hline \multicolumn{4}{c}{Calculation Compliance Mode} \\ \hline \hline \multicolumn{4}{c}{Strict compliance} \\ \hline \hline \multicolumn{4}{c}{Semi-strict compliance} \\ \hline \hline \multicolumn{4}{c}{Weighted compliance} \\ \hline \hline \multicolumn{4}{c}{RC2 AS weighted compliance} \\ \hline \end{tabular}
\end{table}
Table 5: RC2 AS High-level questionnaire.  
Figure 5: The proposed risk-based cybersecurity compliance assessment system (RC2 AS) workflow.  
Accordingly, the organization answers each question to measure its compliance level. The answer could be one of the following options. (a) If the organization accomplished all of the requirements of an associated question, then the chosen answer is 'Yes'. (b) If the organization partially implements the requirements, then the organization selects the percentage of this implementation (>0% to \(\leq\) 35%, >35% to \(\leq\) 85%, or >85% to <100%). Otherwise, (c) the answer selection will be 'No'. Lastly, (d) if it is not applicable, the answer 'NA' will be selected. These RC2 AS compliance answers are derived and inspired by the current compliance status of the ECC assessment tool shown in Table 3. The different RC2 AS answer options are shown in Table 8.
## 7 Conclusions and future perspectives  
In summary, there is conclusive evidence that the production of infectious virus from HCV-infected cells relies on several aspects of lipid metabolism (summarized in Figure 2). However, further studies are necessary to improve our understanding at the molecular level of the underlying mechanisms that engage metabolic pathways with both HCV RNA replication and virion assembly. In the case of RNA replication, the dependence on both fatty acid and cholesterol metabolism may indicate the need for a particular lipophilic environment to support genome synthesis. Creating such an environment may rely not only on lipid biosynthetic pathways but also factors involved in membrane organization such as class III phosphatidylitol 4-kinase alpha (PI4K-III\(\alpha\)), which plays a critical role in HCV RNA replication [142-144]. Moreover, additional studies into the biology of lipid droplets, and more in-depth analysis of the molecular requirements for the latter stages in the HCV life cycle, will help delineate the exact role for these organelles in the generation of HCV virions. Finally, the reliance of HCV upon both lipid biosynthesis and storage raises the distinct possibility that these topics could be important for development of new therapies for pharmacological inhibition of HCV morphogenesis.  
Figure 2: Inhibition of HCV RNA replication and virion assembly by blocking lipid biosynthesis and VLDL assembly. HCV RNA replication occurs at the ER membrane in specialized sites (the membranous web) that contain replication complexes. HCV core associates with cytosolic LDs and its interaction with NS5A at HCV RNA replication sites enables engagement of viral genomes with core to initiate virus assembly, possibly creating nucleocapsids. Assembly is then thought to proceed through unknown processes in which the viral envelope glycoproteins (E1 and E2) are added to nucleocapsids and combine also with the VLDL assembly pathway to yield lipoviroparticles [121] that are released from the cell. Production of virus particles is thought to occur through interaction with the VLDL assembly pathway, which is a two-stage process. Firstly, there is initial lipidation of translocated apoB by MTP to create a pre-VLDL particle. Bulk triacylglycerglycerol from cytosolic LDs is added to pre-VLDL particles through a process that is considered to produce lumenal LDs. Additional lipoprotein components are added also at this stage (including apoE) to generate mature VLDL. Steps in fatty acid and cholesterol biosynthesis and the VLDL assembly pathway that can be impaired thereby blocking HCV RNA replication and virion assembly are boxed. Abbreviations are as follows: ACC, acetyl-CoA carboxylase; FAS, fatty acid synthase; GGTase 1, geranylgeranyltransferase 1; MTP, microsomal transfer protein; PUFAs, polyunsaturated fatty acids.
## 4 Design and Optimization of Hardware Prototype System  
Based on the system modeling and error analysis in the previous sections, a scalar-based deeply-coupled system on an embedded platform is developed. The design and optimization methods of the hardware prototype are described in this section. The INS-aided PLLs in the deeply-coupled system will be designed based on the proposed steady-state error model on the embedded hardware platform.
# A Wireless Monitoring System for Cracks on the Surface of Reactor Containment Buildings
## 2 Background and Motivation  
The generation of electricity through nuclear energy has many advantages such as lowering greenhouse gas emission, being reliable, and being independent from natural aspects. However, the safety of nuclear power plants are most important, as any slight mistakes could lead to severe disasters. Chernobyl and Fukushima nuclear accidents are just two good examples. Reactor containment building (RCB), as the fourth and final defense layer for a nuclear reactor, is designed to keep radioactive substances from getting into the environment in operational states and in accident conditions like loss of coolant accidents (LOCAs), and protect the nuclear power plant against external natural and human induced events [16]. Then, usually RCBs have to undergo pressure tests to verify that they comply with the highest safety standards. Pressure tests are conducted during commissioning and in-service tests, which are also called initial and periodic tests. They comprise several measurement phases at different pressure levels to verify that the structural behavior of the RCB is consistent with the design and demonstrate that the leak rate of the RCB does not exceed the specified maximum leak rate. Commissioning tests for the RCB are carried out prior to the first criticality of the reactor and in-service tests are usually carried out every ten years. Pressure tests could last as long as two weeks and one very important task during tests is to inspect the surface of the RCB and measure the changes of crack width on the concrete structure in real-time.  
Considering that cracks to be concerned about may appear anywhere of the RCB, on the dome or on the perimeter wall, it is impractical to carry out the monitoring task with wired systems. Currently,used battery powered devices that store data locally and gather them after pressure tests also have drawbacks. The monitoring data cannot be obtained by users in real-time for further analysis and users are not informed if some devices stop working during the test, thus leading to incomplete data. The advantages of WSNs and the successful cases of using them to monitor other structures motivate us to introduce this technology for real-time crack width changes monitoring on the surface of RCBs. As far as we know, this is the first time to use wireless sensor networks technology for RCB monitoring. We develop customized platforms and corresponding software. The deployment in Daya Bay Nuclear Power Station shows the effectiveness of the system.
# Hyperlipidemic Rabbit Models for Anti-Atherosclerotic Drug Development
###### Abstract  
Hyperlipidemia or dyslipidemia is a major risk factor for atherosclerotic diseases. Experimental animals play an important role in elucidating the molecular mechanisms of the pathophysiology of hyperlipidemia as well as in drug development. Rabbits are one of the most suitable models to study human hyperlipidemia because many features of the lipoprotein metabolism of rabbits are similar to those of humans such as LDL-rich lipoproteins in plasma, apolipoprotein B mRNA editing, and cholesteryl ester transfer protein. Currently, three types of rabbit models are commonly used for studying hyperlipidemia: (1) diet-induced hyperlipidemic rabbits, (2) spontaneous hyperlipidemic rabbits, and (3) gene-manipulated rabbits (transgenic and knockout rabbits). In this review, we give an overview of the features of hyperlipidemic rabbits and discuss the usefulness of rabbits for the development of anti-atherogenic drugs.  
rabbit; hyperlipidemia; lipoprotein; apolipoprotein; drug development +
Footnote †: journal:  
susceptibility to a cholesterol diet, it is easy to induce hyperlipidemia and atherosclerosis in wild-type rabbits [5], which is different from most strains of wild-type mice. The hyperlipidemic models of mice have been generated by the targeting of genes, such as apolipoprotein (apo) E and LDL receptor (LDLR) [6; 7]. Nevertheless, there are a number of features that make rabbits an appropriate model to study human hyperlipidemia. Unlike wild-type mice and rats (rodents) in which HDL is a major lipoprotein in plasma, \(\approx\)40% of plasma cholesterol in wild-type rabbits and \(>\)90% in cholesterol-fed rabbits are contained in the apo B-containing particles such as VLDL and LDL [8]. Humans and rabbits have abundant plasma cholesterol ester transfer protein (CETP), an important regulator of HDL and cholesterol metabolism, whereas rodents do not have CETP [9]. Given that the restricted editing of apo B mRNA only occurs in the intestine in humans and rabbits, apo B-48 is only present in intesthally derived CM and CM remnants in humans and rabbits. However, in rodents, apo B mRNA editing occurs in both the intestine and liver [10]; therefore, apo B-48 is contained in both CM and VLDL particles. Furthermore, hepatic LDL in both humans and rabbits is highly down-regulated according to the level of cholesterol uptake in the liver [8]. In addition, the appropriate size of rabbits enables researchers to obtain large amounts of plasma samples for both in vitro and in vivo studies.
## 2 Overview of the Power Ramp-Rate Control Methods
### Simple Moving Average (SMA) Method  
The SMA method is known for its ease of implementation and because it allows for adjusting PRR fluctuations. The mathematical expression for the SMA method is given by [14; 15]:  
\[\mathrm{P_{ess}[k]=\frac{\sum_{j=0}^{W-1}P_{pv}[k-j]}{W}-P_{pv}[k]. \tag{4}\]  
where W is the window size for calculating the average. For this method, as the window size W becomes larger, the power fluctuations at the output become softer. However, this implies more DoD and more computational cost.
## 3 Results and Discussion
### Performance Comparison  
We compared the performance of the proposed approach with that of supervised deep CNNs [18; 31; 35; 36; 37]. This is because we found use of the latter in recent geology literature focusing on rock porosity classification. In addition, we also compared them with industry-standard object detection models such as YOLO5 [34; 38] and Faster RCNN [37]. All experimental verifications were performed using an HP-Blade server with 64-bit Ubuntu 16:04, kernel 4.4, Intel Xeon(R) CPU E5-2690 2.6 GHz (64 cores), RAM 128 GB, 5.4 TB SSD [32; 33; 37; 39]. Detailed comparative criticisms are presented in the following subsections.
## 1 Introduction
In doing so, we seek to address the question: How much of the variance in GPP can be explained with satellite-derived quantities using a simple observation-based approach, particularly in comparison with more sophisticated approaches? To answer this question, we use GPP estimates derived at eddy covariance flux tower stations from the FLUXNET 2015 database to both calibrate and evaluate our satellite-based models. The results are examined at various temporal and spatial resolutions. We also compare with other data-driven GPP estimates and proxies as benchmarks. This comparison provides some insights into which parameters may be important for machine learning (ML) GPP estimates and ultimately may be used to improve the ML results.
## 4 Discussion
### WTD-Based Model  
The differences between sites are visible in the yearly cumulative GPP values. Lower GPP fluxes in the areas with higher presence of mosses are consistently reported for peatland [11], and CR presents the highest abundance of _Sphagnum_ spp. Despite the greater presence of mosses, the WTD-based model was still not selected as the best overall model for the CR site. This approach was developed for Sphagnum-dominated peatlands,  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Period** & **RH** & **TRH** & **TRH\_exp** & **NRH** & **EXP** & **WTD** & **Multi-Model** & **SD** \\ \hline \multirow{2}{*}{CL\_1} & Total & \(-1109.0\) & \(-1072.4\) & \(-1109.5\) & \(-1163.7\) & \(-1017.8\) & \(-1063.7\) & \(-1058.1\) & \(\  
assuming that water saturation of _Sphagnum_ spp. governs its photosynthetic capacity [22]. However, other authors have indicated that, with stable moisture in _Sphagnum_ spp., the GPP is governed by PAR [28]. Vascular plants, the most abundant in the study area, also present a GPP driven mainly by PAR when conditions are not extreme, which is generally the case in the study area. Additionally, although mosses are more abundant in CR, the site is situated in a floating mat area. As a result, the oscillations of WTD are accompanied by surface oscillations, which reduce the impact of WTD changes on vegetation, making less relevant to incorporate WTD into GPP modelling.  
The inclusion of WTD in the [PERSON] et al. [22] approach has shown some unusual drops with the deepest WTDs. Usually, including environmental parameters increases the complexity of the model and its performance with more accurate results, but they can also induce outliers when abrupt changes occur [4]. Instead of the short-term WTD values, this article proposes using the average WTD of the fifteen previous days to avoid sudden drops in the GPP trend. With this simplification of the values, we can account for the legacy effect of WTD, and it has solved most of the drops present when the model was used for thirty minutes and daily WTDs. The number of campaigns during the 2021 year (10) can induce the remaining outliers from the WTD-based model. Possibly, the estimated optimum and tolerated WTD levels cannot adequately reflect the vegetation thresholds during the periods between campaigns and induce the worst performance of this model and this was further amplified during 2020 with even less data. Similar limitations have been observed in seasonal approaches with other environmental variables [4].
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 3 Achievable Rates, Outage Probabilities and Bit-Error-Rates Analysis
### Bit-Error-Rates (BER) Analysis  
We give the Bit-error analysis for the proposed CR-NOMA system in accordance with the guidelines in [111, 112]. The average error probability (\(P_{e}^{\rm UE_{1}}\)) of the UE\({}_{1}\) when its symbols are identified properly and erroneously via SIC processing is shown in [111] as  
\[P_{e}^{\rm UE_{1}}=\frac{1}{2}\Bigg{(}1-\sqrt{\frac{\gamma_{\mathcal{B}_{1}}}{ 2+\gamma_{\mathcal{B}_{1}}}}\,\Bigg{)}+\frac{1}{8}\Bigg{[}\sqrt{\frac{\gamma_ {\mathcal{B}_{2}}}{2+\gamma_{\mathcal{B}_{2}}}}-\sqrt{\frac{\gamma_{\mathcal{B} _{3}}}{2+\gamma_{\mathcal{B}_{3}}}}+\sqrt{\frac{\gamma_{\mathcal{B}_{4}}}{2+ \gamma_{\mathcal{B}_{4}}}}-\sqrt{\frac{\gamma_{\mathcal{B}_{5}}}{2+\gamma_{ \mathcal{B}_{5}}}}\,\Bigg{]} \tag{32}\]  
and  
\[\gamma_{B_{4}} =\frac{\big{(}2\sqrt{2\varepsilon_{2}}+\sqrt{\varepsilon_{1}}\ \big{)}^{2}}{N_{0}}\mathbb{E}\Big{[}|\mathbf{h}_{R,1}|^{2} \Big{]} \tag{34}\] \[\gamma_{B_{5}} =\frac{\big{(}2\sqrt{2\varepsilon_{2}}-\sqrt{\varepsilon_{1}}\ \big{)}^{2}}{N_{0}}\mathbb{E}\Big{[}|\mathbf{h}_{R,1}|^{2} \Big{]}\]  
Equation (33) represents the condition that UE\({}_{1}\) is able to detect its signals correctly, whereas (34) represents the condition that the signals are incorrectly detected.  
Similarly, the total average BER (\(P_{e}^{\text{UE}_{2}}\)) performance of UE\({}_{2}\) from [111] is  
\[P_{e}^{\text{UE}_{2}}=\frac{1}{4}\Bigg{[}\Bigg{(}1-\sqrt{\frac{\gamma_{A_{1}} }{2+\gamma_{A_{1}}}}\ \Bigg{)}+\Bigg{(}1-\sqrt{\frac{\gamma_{A_{2}}}{2+\gamma_{A_{2}}}}\ \Bigg{)}\Bigg{]} \tag{35}\]  
where \(\gamma_{A_{1}}\) and \(\gamma_{A_{2}}\) are the SNRs of different signal constellation points expressed as  
\[\gamma_{A_{1}} =\frac{\big{(}\sqrt{2\varepsilon_{2}}+\sqrt{\varepsilon_{1}}\ \big{)}^{2}}{N_{0}}\mathbb{E}\Big{[}|\mathbf{h}_{R,2}|^{2} \Big{]} \tag{36}\] \[\gamma_{A_{2}} =\frac{\big{(}\sqrt{2\varepsilon_{2}}-\sqrt{\varepsilon_{1}}\ \big{)}^{2}}{N_{0}}\mathbb{E}\Big{[}|\mathbf{h}_{R,2}|^{2} \Big{]}\]
# GPS-Induced Disparity Correction for Accurate Object Placement in Augmented Reality
## 2 Materials and Methods
### Coordinate Mapping for 3D Object Rendering in AR  
A separate coordinate system is used to facilitate the rendering of various 3D objects. This allows easy adjustment of the 3D object size and position, making the objects more readily displayable on AR devices. The Digital Twin Server maintains both the 3D object  
Figure 1: Workflow diagram of the AR system, showcasing the data flow between key components, such as UAV data acquisition, building data processing, server synchronization, and user interaction.  
coordinate system and the latitude, longitude, and elevation coordinates of the building. Critical processes include 3D object creation and three-point matching execution on AR devices. Three-point matching refers to the task of mapping the object coordinate system to the GPS coordinate system. Once the mapping is complete, the app can retrieve and display 3D object information from the server using the building information in the object coordinate system to properly overlay the objects in the AR environment. As illustrated in Figure 2, once three-point matching is completed, objects denoted as OBJ can be rendered using the acquired building coordinates. However, errors in the GPS position, such as with points A, B, and C, can lead to location inaccuracies. Therefore, it is imperative to correct the device location and ensure perfect image rendering through semantic segmentation.