esa-sceva/satcom-chunk-collection huggingface dataset:
# Compression-Assisted Adaptive ECC and RAID Scattering for NAND Flash Storage Devices
###### Abstract  
NAND flash memory-based storage devices are vulnerable to errors induced by NAND flash memory cells. Error-correction codes (ECCs) are integrated into the flash memory controller to correct errors in flash memory. However, since ECCs show inherent limits in checking the excessive increase in errors, a complementary method should be considered for the reliability of flash storage devices. In this paper, we propose a scheme based on lossless data compression that enhances the error recovery ability of flash storage devices, which applies to improve recovery capability both of inside and outside the page. Within a page, ECC encoding is realized on compressed data by the adaptive ECC module, which results in a reduced code rate. From the perspective of outside the page, the compressed data are not placed at the beginning of the page, but rather is placed at a specific location within the page, which makes it possible to skip certain pages during the recovery phase. As a result, the proposed scheme improves the uncorrectable bit error rate (UBER) of the legacy system.  
NAND flash memory; P/E cycle; compression; adaptive ECC; RAID scattering; stripe log; parity
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
###### Abstract  
In satellite positioning systems, optimizing navigation satellite constellation and reducing the observation residuals are usually adopted to improve positioning precision and accuracy of the receiver. This paper presents a method to improve positioning precision by using multi-frequency navigation signals. The observation data of CAPS and GPS system are used to simulate the experiment. When the number of downlink frequencies is different, the root mean square of positioning error, improvement percentage, and standard deviation are calculated, respectively. When the number of descending frequencies is k, the root mean square of positioning error in three-dimensional space is \(1/\sqrt{\text{k}}\) of that in single frequency. The theoretical derivation and experiment show that the precision of satellite positioning can be effectively improved by using multi-frequency navigation signals. The research work can provide theoretical support and data reference for the future research of satellite positioning.  
multi-frequency navigation; satellite positioning; positioning precision; PDOP +
Footnote †: journal: Sensors  
**Citation:** [PERSON]; [PERSON]; [PERSON] Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals.  
_Sensors_**2022**, 22, 4210. [[https://doi.org/10.3390/s22114210](https://doi.org/10.3390/s22114210)]([https://doi.org/10.3390/s22114210](https://doi.org/10.3390/s22114210))
## 3 Results  
### Mean Navigation Device Usage Time  
The log files of the navigation devices contain recordings from the start of 2014 to the end of 2016. A total of 130 operations can clearly be identified for the analysis. Frequency distribution of the period of use, with a class width of 3 min, is generated.  
The following bar chart (Figure 3) shows a non-symmetrical distribution and is right-skewed. Nearly 50% of the navigation uses lasted less than 6 min. The most frequent class, with 46 entries, is the 3-6-min range, followed by the third class of 6-9 min, with 30 entries.  
Table 5 shows a mean duration of 7.5 min with a standard deviation of 5 min. In view of the high standard deviation and the strongly right-skewed frequency distribution, the median of 6.4 min should be regarded as a more informative value.  
The outliers, particularly the maximum of 29.7 min and the high dispersion, can firstly be explained using a navigated suction instrument; secondly, the pointer instrument might have been located in the magnetic field of the transmitter so that activity was still being recorded. Other potential factors that can lead to a longer accumulated time are faulty intraoperative registration processes or repetitions of registration. Successful registration takes less than 1 min, according to the log files. Longer usage times should be regarded as unrealistic because the pointer is only used briefly for checking the position. The minimum of 0.6 min indicates a registration process but no further use of the pointer and, hence, of the navigation device.  
The periods of use are not assigned to the respective doctors in the analysis, which means that it is not possible to classify usage time according to the surgeon's level of experience; therefore, no statement can be made as to whether an experienced surgeon uses the device for a shorter time than an inexperienced one.
# Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM)
## 2 Method
### BIM Exporting Formats
#### 2.1.1 Green Building XML (gbXML)  
The Green Building XML (gbXML) schema, developed by Green Building Studio Inc., is focused on environmental data. It was originally developed to enable interoperability between the building design in CAD softwares and energy analysis tools [34]. However, gbXML also attends to exchange data among diverse simulation instruments. In its format, the geometric and weather data, user profiles, and energy information are exchanged. Nevertheless, some parameters about building HVAC components are discarded [42].  
At the moment of exporting the gbXML file from Revit, some decisions must be made. First, it is necessary to create an analytical model, and to check, in the Revit export gbXML interface, if the spaces and the geometry are defined precisely. If there is any error with both characteristics, it would not be able to conclude the gbXML export. Secondly, in order to achieve good interoperability, some parameters must also be activated in Revit export gbXML, such as rooms and detailed elements; otherwise, in the gbXML, there will be a lack of information needed for the creation of the BEM, in reference to the geometry and the envelope materials.  
It is possible to see, from Figure 3, that all the thermal zones of the residential building were generated correctly in the gbXML file. Contrarily, the warehouse study presented a particularity in its analytical model and in the gbXML export. Its analytical spaces' 3D view did not show all the thermal zones that were created. However, at the point of the gbXML export, it can be seen that the zones were generated properly.
# Solving Partial Differential Equations Using Deep Learning and Physical Constraints
## 2 Methodology
### Physics-Informed Neural Networks
Therefore, many researchers have commonly used automatic differentiation in their studies of PINNs [29]. In this study, for the surrogate network \(\hat{u}\), we derive the neural network by the automatic differentiation according to the chain rule. Moreover, since the network \(f\) has the same parameters as the network \(\hat{u}\), both networks are trained by minimizing a loss function. Specifically, Figure 2 shows a schematic diagram of a physics-informed neural network. The next main task is to find the best neural network parameters that minimize the defined loss function. In a physics-informed neural network, the loss function is defined, as follows  
\[J(\mathbf{\theta})=MSE_{u}+MSE_{f} \tag{4}\]  
The calculation of the mean square error (MSE) is given by the following formula:  
\[MSE_{u}=\frac{1}{N_{u}}\sum_{i=1}^{N_{u}}\left|\dot{u}^{i}-u\left(x_{u}^{i},t_ {u}^{i}\right)\right|^{2} \tag{5}\]  
\[MSE_{f}=\frac{1}{N_{f}}\sum_{i=1}^{N_{f}}\left|f\left(x_{f}^{i},t_{f}^{i} \right)\right|^{2} \tag{6}\]  
Here, \(u\left(x_{u}^{i},t_{u}^{i}\right)\) denotes training data from initial and boundary conditions and \(u\left(x_{f}^{i},t_{f}^{i}\right)\) denotes the training data in the space-time domain. Equation (5) requires the neural network to satisfy the initial and boundary conditions, while Equation (6) requires the neural network to satisfy the constraints of the partial differential equation, which corresponds to the physical information part of the neural network. Next, the optimization problem for Equation (4) is addressed by optimizing the parameters in order to find the minimum value of the loss function, i.e., we seek the following parameters. \[w^{*}=\underset{w\in\theta}{\arg\min}(J(w)) \tag{7}\]  
\[b^{*}=\underset{b\in\theta}{\arg\min}(J(b)) \tag{8}\]  
In the last step, we use gradient-based optimizers to minimize the loss function, such as SGD, RMSprop, Adam, and L-BFGS [46; 47; 48]. It is found that, for smooth PDE solutions, L-BFGS can find a good solution faster than Adam, using fewer iterations. This is because Adam optimizer relies only on the first order derivative, whereas L-BFGS uses the second order derivative of the loss function [49]. However, one problem with L-BFGS is that it is more likely to get stuck on a bad local minimum. Considering their respective advantages, in this study we end up using a combination of L-BFGS and Adam optimizer to minimize the loss function. Besides, we also use a residual-based adaptive refinement (RAR) [50] method to improve the training effect by increasing the number of residual points in regions with large residuals of partial differential equations until the residuals are less than the threshold. By the above method, we will obtain trained neural networks that can be used to approximate the solutions of partial differential equations. In the next part, we will use the above method to study three important partial differential equations: the one-dimensional wave equation, the KdV-Burgers equation and the KdV equation.
# Improved JPS Path Optimization for Mobile Robots Based on Angle-Propagation Theta* Algorithm
## 3 JPS Path Optimization Based on Angle-Propagation Theta* Algorithm
### JPS Path Optimization Process with Viewable Angle Based on AP Theta *  
In this paper, we improve the path optimization strategy for the JPS algorithm based on the idea of using angles to find the shortest path in the AP Theta* algorithm. The complete optimization path process is shown in the following example.  
1. First, the starting point (f,2) is taken as the first search point, and we judge whether there is an LOS reachable between the search point and the end point. If the LOS is reachable, the algorithm ends; if the LOS is not reachable, the path is discretized and loaded into the \(Rout(<s,1,2,\ldots,e>)\) list. Set the child node (e,3) of the start point to the current node \(x\). Then, set the viewable range of \(x\) to \([-\infty,+\infty]\). Next, the constraint calculation of the viewable angle of \(x\) is needed to obtain its viewable angle as [0,45]. Set the (d,4) node to \(x^{\prime}\) in the order of the nodes in the \(Rout(<s,1,2,\ldots,e>)\) list. The LOS reachability between all discrete points and the search point is checked in turn until the first node (a,8) is found that does not have LOS reachability, as shown in Figure 8 (**1**).
2. Set (a,8) to flag \(R_{m}\) and (a,7) to flag \(R_{m-1}\). The obstacle vertices within the triangular region of the field of view consisting of \(R_{m}\), \(R_{m-1}\) and the search points are searched. The optimal turning angle (e,3) is found by calculating and comparing the cost of each vertex to the search point, as identified by the triangle in Figure 8 (**1**).
3. Set the node (e,3) as the next search point and its path sub-node (d,4) as the current node \(x\). Continue the search by the method provided in step 1 until the next search point is found, as shown in Figure 8 (**2**).  
If the current search point is found to have LOS reachable with the end of the path by detection, the search is ended, as shown in Figure 8 (**3**). The final generated path is (f,2), (e,3), (b,7), and (a,11).
# [PERSON] on Korean Block Ciphers
## 2 Related Works
### Quantum Implementations and Algorithms
#### 2.3.3 Previous Quantum Implementations  
Advanced Encryption Standard (AES) is the international block cipher standard [19] and the block cipher is widely used in practice, such as database encryption and network security. For this reason, a number of works focused on quantum resource estimation for the AES block cipher.  
[PERSON] et al. have presented first quantum circuits to implement an exhaustive key search for the AES and analyzed the quantum resources required to carry out such attacks [6]. All three variants of AES (key size 128, 192, and 256 bit) are implemented for the [PERSON]'s quantum algorithm with optimal operations for each layer.  
[PERSON] et al. presented new quantum circuits for all three AES key lengths. For AES-128, the number of Toffoli gates can be reduced by more than 88% compared to [PERSON] et al.'s estimates [6], while simultaneously reducing the number of qubits [20].  
Previous works of AES have derived the full gate cost by analyzing quantum circuits for the cipher but focused on minimizing the number of qubits. In [8], they have studied the cost of quantum key search attacks under a depth restriction and introduced techniques that reduce the oracle depth, even if it requires more qubits. As part of this work, they released Q# implementations of the full Grover oracle for AES-128, AES-192, and AES-256, including unit tests and code to reproduce their quantum resource estimates.  
In [9], the reversible implementation of SIMON block cipher was presented. This block cipher is hardware-oriented suggested by NSA. They have successfully implemented the Grover oracle and Grover diffusion for key search. They have estimated the resources in terms of CNOT, Toffoli, and X gates, which are required to carry out the quantum attack on the block cipher.
## 4 Conclusions  
This study examines the techno-economic aspects of recovering waste heat from an industrial AF that is emitted into the environment via the use of several integrated systems. Additionally, heat recovery's environmental impact was discussed. Utilizing four distinct integrated systems, the waste heat at 1093.15 K was assessed. To achieve maximum power generation, a parametric investigation was conducted on the SRC input temperature of the suggested system. The study's findings led to an evaluation of the systems' net output power and thermal efficiency. Consequently,  
* When the SRC is investigated, the maximum net thermal efficiency of the combined system is calculated as 0.34 at 150 bar and 1146 K, respectively. In addition, maximum net power was observed as 429.83 kW.
* One of the main contributions of this study is the reduction of CO2 emissions. In summary, the use of these combined systems is directly influential in reducing global warming.
* When the ORC is investigated, the maximum net thermal efficiency is calculated as 0.14 at 90 bar and 632.5 K, respectively. In addition, maximum net power was seen as 127.6 kW.
* The optimum value for the SRC inlet temperature difference is 221.6 K. At this point, the thermal efficiency and total power of the proposed system are 0.19 and 596.6 kW, respectively. In addition, the minimum electricity generation cost value for the proposed system is 50.1972 per kWh.  
Conceptualization, B.B. and E.K.; Methodology, B.B. and E.K.; Software, B.B. and E.K.; Validation, B.B., J.K. and E.K.; Formal analysis, W.T. and A.A. ([PERSON]); Resources, A.A. ([PERSON]) and Y.H.A.; Data curation, M.Y.N. and M.B.; Writing--original draft, B.B.; Writing--review and editing, E.K. and A.A. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not Applicable.  
The data presented in this study are available on request for confidentiality reasons from the corresponding author. The data are not publicly available due to participants of this study did not agree for their data to be shared publicly.  
All the authors are grateful to Karabuk University for providing its software and hardware infrastructure to realize the current study.  
The authors declare no conflicts of interest.
## 1 Introduction
### Review Approach and Methodology, Original Contribution, and Paper Structure
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Database**} & \multirow{2}{*}{**Publication Type**} & \multicolumn{2}{c}{**Search Term (for All of the Fields in Database)**} \\  & & **BIM** & **DT** & **BIM + DT** \\ \hline \multirow{2}{*}{Web of Science} & Articles & 111,477 & 10,433 & 642 \\  & Review articles & 5094 & 4672 & 96 \\ \hline \multirow{2}{*}{Scopus} & Articles & 17,779 & 10,940 & 8388 \\  & Review articles & 1158 & 1084 & 2106 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Results of the literature review in bibliometric databases. * Distributed control systems (e.g., embedded systems, fieldbus networks, Internet of Things, mesh networks, edge and fog computing, multiaccess edge computing, fog-based architectures, frameworks, networking platform);
* Design (e.g., control systems design, service-oriented architecture, control as a service, servitization, automated control system design, network architecture and topology, smart buildings design, optimal sensor placement, smart retrofitting, sustainable building design, model-based design, digital twin design verification);
* Communication and data processing (e.g., semantic specification, semantic model, data model, ontology, data integration, reference data model, interoperability, workflow, cloud computing, communication standards, fieldbus standards, cyber security, data privacy);
* Energy management (e.g., energy management systems, home energy management system, load balancing and shifting, smart grid, microgrid, energy efficiency, energy flexibility, demand response, demand-side management, energy performance of buildings, EPBD). Following the final selection and filtering of publications, a set of 75 items was identified as being relevant to the subject of building information modeling (BIM) and digital twins (DTs), as well as a range of detailed issues related to building automation, energy efficiency, and facility management. In this review, the majority of the selected publications were included, along with the additional literature pertaining to the technical and functional organization of BACS and BMS systems. Considering them, this paper makes a significant contribution to the field of BACS by examining the potential of using BIM and DT tools in the design and effective use of BACS with IoT elements in buildings. The authors identify three areas of original contribution and formulate the following theses:  
1. The list of BACS functions defined in the EN ISO 52120 standard and the SRI service guidelines should be used as a framework for the selection and organization of key BIM functions supporting technical and functional BACS design. Furthermore, these guidelines can be used to optimize DT structures in buildings, particularly as a tool for dynamic and efficient energy management in buildings. 2. It is possible to use DT structures in the implementation of the detailed method of calculating SRI and the precise selection of BACS functions with the analysis of the
## 2 Push-Pull Converter: Operating Principle and Non-Ideal Electrical Model
### Non-Ideal Push-Pull Converter Circuit  
The electrical circuit in Figure 1 allows modelling the ideal behaviour of the push-pull converter, which is a simple approximation of the actual converter behaviour. To develop an accurate model of the converter, non-idealities associated with the transformer, the power switching devices and the converter output low-pass filter must be considered. With this in mind, the authors developed and validated in [17] the non-ideal model shown in Figure 2, which the authors demonstrated exhibits behaviour very close to the actual circuit of a push-pull converter.  
The non-idealities in Figure 2 are as follows (please see list of nomenclature and symbols in this document): for the transformer, the series resistance and the leakage inductances and capacitances of the primary and secondary windings (\(R_{L_{P}}\), \(R_{L_{S}}\), \(L_{P_{1,2}}\), \(L_{S_{12}}\), \(C_{P_{1,2}}\) and \(C_{S_{1,2}}\), respectively) as well as the core losses associated with the magnetisation current, eddy currents and parasitic fluxes (\(L_{M}\) and \(R_{Nu}\)) have been considered.  
For power transistors (Mosfets), series drain-sink saturation resistance (\(R_{DS}\)) and parallel output capacitances (\(C_{OSS_{1,2}}\)) are considered. For rectifier diodes, the series resistance (\(R_{D}\)) and threshold voltage (\(V_{\gamma}\)) are considered.  
Finally, for the converter output \(LC\) filter, series resistance of both the inductance (\(R_{L_{F}}\)) and the filter capacitor (\(R_{C_{F}}\)) are considered.  
As stated in [17], because they are considered non-idealities of negligible effect, mainly because they can be avoided with a good design and manufacture of the converter, the following is assumed: As for the transformer, both windings will be identical. In terms of operation, the transformer shall be considered to be fully magnetised and demagnetised in each operating cycle. Likewise, the transformer core will always operate within the linear region of the magnetisation curve, so no core saturation effects will be considered.  
Similarly, in the case of power switching devices, it will be considered that the switching times will be negligible with respect to the duty cycle corresponding to the operating frequency. It is also assumed that the power switching devices will be of adequate quality, so their leakage currents will be neglected.  
Based on the above, for the complete definition of the non-ideal behaviour of the push-pull converter, the following 14 non-idealities will be considered: \(R_{L_{P}},R_{L_{S}},L_{P_{x}},L_{S_{v}},C_{P_{x}},\ C_{S_{x}},\)\(L_{M},\ R_{Nu},\ R_{DS},C_{OSS_{x}},R_{D},\ V_{\gamma},\ R_{L_{F}}\) and \(R_{C_{F}}\).
# Wet Snow Flashover Characteristics of 500-kV AC Insulator Strings with Different Arrangements
## 2 Experimental Setup, Specimens, and Test Procedure  
### Experimental Setup  
The wet snow flashover tests were carried out in a large artificial climate chamber of China Electric Power Research Institute (CEPRI) in Beijing, as shown in Figure 1. The chamber has a diameter of 22 m and a height of 34 m. It consists of a power system, cooling system, water spraying system, and ice melting system. Various conditions can be achieved such as high altitude, low air temperature, heavy fog, ice, rain, and snow. An altitude from 0 m to 5500 m and an ambient temperature from 0 \({}^{\circ}\)C to \(-\)19 \({}^{\circ}\)C can be simulated in the chamber. The power supplier for the flashover test was an AC test transformer with capacity of 800 kV/6 A.  
Figure 1: _Cont._  
To simulate snowstorms, a snow cannon as displayed in Figure 2 was used to produce the wet snow. The technical parameters of the snow canon are listed in Table 1.