esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methods  
### General Idea  
The general idea of this paper is shown in Figure 1 and consists of two main parts. The first part is about the analysis of evaluation indicators and influencing factors. Through a literature review, it was found that there are many evaluation indicators for the operational effect of green wave with the most commonly used representative indicators being the average number of stops, average travel time, and average delay [6; 17; 18; 19]. Therefore, we selected these three indicators for analysis. There are also many factors influencing the operational effect of the green wave. Physical factors such as the geometric design of intersections and spacing between intersections may prevent implementation of green wave schemes at intersections of arterial roads. However, the focus of this study was on arterial road intersections where green wave schemes have been implemented. Therefore, using a relatively reasonable geometric design and spacing of intersections, we selected non-physical influencing factors related to the operational effect of the green wave for further research. Based on the literature review, we selected four representative influencing factors for analysis: design speed, signal timing, pedestrian crossing, and heavy vehicle travel [20; 21; 22; 23; 24].  
The second part is about the correlation analysis between evaluation indicators and influencing factors. To evaluate the operational effect of the green wave objectively, traffic simulation software such as Vissim was used to output the values of evaluation indicators under different conditions. To facilitate analysis, the value of influence corresponding to multiple evaluation indicators was further calculated so that the relationship between the value of influence and the influencing factors could be analyzed. Because the factors and the value of influence lack a data sequence directly used for association analysis, and the relationship between them is unclear, sensitivity analysis and grey relational analysis were combined to analyze the influencing factors and value of influence. Sensitivity analysis is  
Figure 1: General idea.  
mainly used to obtain a data sequence of the factors and value of influence. Grey relational analysis is mainly used to evaluate the ranking of the factors in their degree of correlation.
## 1 Introduction  
The maintenance costs of building components could be as much as two or three times the cost of design and construction [1]. Building facility managers are responsible for managing such costs associated with the facilities. In particular, managing the replacement cost is especially important as replacement tasks generally involve a significant amount of maintenance costs. For replacing a building component, a facility manager must investigate the details of the components to determine the costs associated with each component. This type of information will differ between facilities. As these properties can vary widely, it can be difficult to resolve issues such as determining the cost of replacing a window. One of the difficulties encountered in estimating the replacement costs of building components is the retrieval of relevant cost data. Generally, the cost data of building components is stored separately in the form of spreadsheets, plans/drawings, and construction documents. As drawings and spreadsheets are stored independently, it is difficult to use/analyze them. Exchanging data has been challenging in facility management.  
Meanwhile, the construction industry is experiencing explosive growth in its ability to both generate and collect data. Advances in scientific data collection and computerization have generateda flood of data [2]. Furthermore, the recent development of a new technology, building information modeling (BIM), has provided facility managers with a large database consisting of three-dimensional (3D) digital data in the form of geometries as well as management data comprising the quantities and properties of building components. In particular, Industry Foundation Class (IFC) defined by International BuildingSMART has been applied in many studies as it provides extensive and diverse information regarding building components.  
Therefore, the aim of this study is to develop a model to estimate the replacement cost of building components by retrieving cost information from IFC. The process to export IFC from BIM is not elaborated in this study because it is already well established by International BuildingSMART and has been used for a long time. Case-based reasoning (CBR) is applied to identify similar cases from among various alternatives. The CBR model in this study is focused on the replacement of windows for office buildings, which are a building component within a facility, and the costs associated with that replacement. The model was developed using two computer programs: (1) factor selection and weight assignment were accomplished using Weka 3, which is a data mining software that uses machine learning algorithms to define factor weights, and (2) myCBR is an open-source program used for CBR applications.
## 3 Methodology
The global cost function is composed of four parts: (1) the cost of tie points; (2) the cost of GNSS measurements; (3) the cost of GCPs; and (4) the cost of image measurements of GCPs. Three strategies were implied here to determine the observation weight. The first strategy weights observations by their true standard deviation known a priori (similar to Gauss-Markov model). The second strategy controlled observation weights of each category, which avoids overweighting one single category only due to its abundance (e.g., tie point observations are abundant compared to GCP observations). The third strategy handled robustness; a higher weight was given to observations having small residuals during BBA procedure. The minimization problem was solved with Levenberg-Marquardt (L-M) method. The L-M is in essence the Gauss-Newton method enriched with a damping factor to handle rank-deficient Jacobian matrices [27]. Typically, a single lever-arm was estimated per image block. Here, to deal with an image block of multiple flight configurations (i.e., nadir and oblique camera-looking flights), the cost function was extended to take into account multiple lever-arms in one image block.
## 4 Results and Analysis of Impact Tests
### Failure Features of Samples under Impact Loading
In short, the failure mode could be categorized as tensile-shear failure. From the above description, we found that the failure process of the specimens is quite fast. There are three kinds of cracks that occurred around the holes, namely spalling cracks, tensile cracks and shear cracks. Concerning the failure modes of these kinds of specimens, they can be summarized as follows: the samples of group G1 exhibit split tensile failure, the samples of groups G2 and G4 present shear failure, and the samples of groups G3, G5, G6 and G7 all suffered from tensile-shear failure. Consequently, the failure behaviors of the specimens highly relied on the opening number and opening layout. When the impact direction was horizontal, tensile cracks appeared between two horizontally arranged holes, shear cracks appeared between two inclined holes, but no direct cracks occurred between two vertical holes. With regard to the violent occurrence of spalling cracks, it is very similar to rock burst in deep hard-rock tunnel. Therefore, the fracturing characteristics of the specimens with different layouts could provide some useful guidance for the understanding of tunnel instability mechanisms and the prevention and control of rock dynamic disasters.
# Reliability Prediction of Mixed-Signal Module Based on Multi-Stress Field Failure Mechanisms
## 3 Failure Modes and Mechanism Identification of Key Components in the Communication Module
### Analysis of Communication Module Operation and Key Component Classification
Historical experimental data for various circuits confirm that under nominal operating conditions, these components exhibit inherent ro \begin{table}
\begin{tabular}{c c c c} \hline
**Component** & **Function** & **Primary Failure Mode** & **Failure Impact** \\ \hline Dide & Circuit protection & Short circuit & No significant impact \\ Capacitor & Filtering & Short circuit & No significant impact \\ Resistor & RS422 signal transmission & Parameter drift & No significant impact \\  & & Delay increase & Increased signal transmission time \\ FPGA & Controller & Loss of control functionality & Communication module failure \\  & & Solder joint fracture & Open circuit at pins \\ SRAM & Data storage & Data read errors & Communication errors at device \\  & & Solder joint fracture & Open circuit at device \\ \hline \end{tabular}
\end{table} 
Table 1: Failure modes and their impact on key components of the communication module. Figure 3: Workflow of the communication module. bustness, and mission-profile-based derating imposes a minimal impact on overall system functionality [25; 26]. Consequently, they can be disregarded in reliability-critical scenarios. The FPGA serves as the core control unit of the communication module, and its primary failure modes include increased signal delay due to electrical and thermal stress, the loss of control functionality, and solder joint fatigue failure caused by thermal stress. An increase in delay extends the signal transmission time, the loss of control functionality prevents the communication module from operating properly, and solder joint fracture results in an open circuit at the device pins. SRAM is responsible for data storage in the communication module. Its primary failure modes include data read errors and solder joint fractures. Data read errors may introduce communication failures, while solder joint fractures can cause an open circuit at the device pins. FPGA and SRAM are identified as the critical components due to their relatively significant influence based on the analysis of failure modes and their impact on key components within the communication module.
# An Architecture to Manage Incoming Traffic of Inter-Domain Routing Using OpenFlow Networks
## 5 Proof of Concept
### Prototype Environment  
To set up the prototype environment, two physical machines were required to implement the topology adopted (see Figure 5). One physical machine executed the following components of the prototype environment: the _OpenFlow Switch_, the _SDN Controller_ of AS 100, and the _50 Networks_. Each _50 Networks_ propagated its prefix and had only one virtual machine to generate traffic. For convenience, those prefixes ranged from 192.168.1.0/24 to 192.168.50.0/24. Furthermore, the configuration of the physical machine was an 8 CPU core with a maximum clock rate of 2.20 GHz, 8 GB of RAM, and a network card of 1 Gbps. For the AS 100 configuration, each virtual machine in the _50 Networks_ was emulated using a container (lightweight virtualization). For the OpenFlow Switch, the Open vSwitch [31] was used. The protocol enabled in the Open vSwitch was OpenFlow version 1.3. The proposed architecture was executed in the modules of the Ryu SDN controller [19].  
The other machine had a 3 GB of RAM, CPU with a maximum clock rate of 2.4 GHz, and a network card of 1 Gbps. This machine was used to create AS 300, AS 200, and AS 400. Those ASes were an instance of the full virtual machines executed in Virtualbox [32] with Quagga [18]. The terminal of the virtual machine of AS 400 was used to send and receive JSON messages between the OpenFlow Controller in the AS 100 and the AS 400.  
A crossover Ethernet cable was used to connect the physical machines. However, to avoid interference between the OpenFlow Switch and the AS 300 and AS 200, it divided the 1 Gbps bandwidth between the two machines with the Linux _tc_ tool (traffic control), allocating two virtual interfaces of 100 Mbps. Thus, the connection between AS 300 and the OpenFlow Switch had an available 100 Mbps as well as the connection between AS 200 and the OpenFlow Switch. Hence, the network traffic from AS 100 to AS 400 is limited to the 100 Mbps passing through path AS 200 and AS 400, as well as the path AS 300 and AS 400.  
Regarding the network traffic, the perform network throughput tests tool _ipref_[33] was used. Each of the virtual machines in _50 Networks_ used the _ipref_ to generate UDP packets in a controllable way. Furthermore, for the _Server_ component, it was executed in the _ipref_[33] in UDP server mode, which was started in the AS 400 network. The server was set up with the IP address of 172.16.0.1/24, and connected to the BGP router in the AS 400 infrastructure.
# Survey of Cybersecurity Governance, Threats, and Countermeasures for the Power Grid +
## 5 Potential Countermeasures to Cybersecurity Threats
### Potential Countermeasures for Integrity and Confidentiality Threats  
Cybersecurity threats affecting integrity of ICS communication are often targeted at specific protocols. The Modbus and DNP3 protocols that are compatible with legacy serial devices are especially vulnerable to eavesdropping and alteration. The major cybersecurity threats that impact confidentiality in the smart grid are primarily focused on the Advanced Metering Infrastructure (AMI). The AMI is a system of smart meters, communication networks, and data management systems that enables two-way communication between customers and utilities. This two-way communication enables better monitoring and more accurate billing for utilities and more accurate consumption behavior for customers. However, since more customers use this model, there are increased access points for security attacks.  
In both of these cases, encryption is an effective countermeasure for data integrity and confidentiality. The IEEE Secure SCADA Communications Protocol (SSCP) [155] is  
Figure 4: CISA Recommended Secure Architecture.  
targeted at employing encryption on serial implementation of protocols. In [156], [PERSON] et al. employed TLS to the Modbus protocol to produce a significant improvement in secrecy of data. The combination of these countermeasures removes the gap between security of legacy devices to their modern IED counterparts.
## 3 Results
### Evaluation of Encryption Algorithm  
The evaluation of the encryption procedure is based on the transformation of PPG signals, with the calculated values shown in Table 5. The table shows the determined values of PRD and SNR for 12 PPG signals before encryption and after decryption. The calculated values can be used to estimate the magnitude of the differences between the values of the studied signals. The studied parameters show that there are no large differences between the original and the decrypted signals, and therefore the considered procedures are suitable for application in cardiac information, as they retain their diagnostic capabilities. The Signal to Ratio varies from 31.83 to 46.37, which are good parameter values showing a sufficient degree of preservation of the quality of the converted signal.  
\begin{table}
\begin{tabular}{c c c} \hline
**Cardio Record \(\boldsymbol{\mathscr{N}}\)** & **PRD [\%]** & **PSNR [dB]** \\ \hline
1 & 0.2014 & 53.11 \\
2 & 0.1911 & 52.03 \\
3 & 0.1805 & 46.38 \\
4 & 0.2502 & 48.44 \\
5 & 0.1611 & 51.08 \\
6 & 0.1173 & 44.37 \\
7 & 0.1239 & 48.55 \\
8 & 0.1386 & 44.07 \\
9 & 0.1604 & 49.75 \\
10 & 0.2091 & 54.98 \\
11 & 0.1035 & 44.09 \\
12 & 0.1357 & 52.47 \\ \hline \end{tabular}
\end{table}
Table 4: Values of PRD and PSNR for PPG signals.  
\begin{table}
\begin{tabular}{c c c} \hline
**Cardio Record \(\boldsymbol{\mathscr{N}}\)** & **PRD [\%]** & **SNR [dB]** \\ \hline
1 & 0.0372 & 34.89 \\
2 & 0.1497 & 46.37 \\
3 & 0.1023 & 31.83 \\
4 & 0.1132 & 36.06 \\
5 & 0.0656 & 32.88 \\
6 & 0.0944 & 32.14 \\
7 & 0.1633 & 38.56 \\
8 & 0.1948 & 32.58 \\
9 & 0.1408 & 36.37 \\
10 & 0.1012 & 41.07 \\
11 & 0.1167 & 41.28 \\
12 & 0.0683 & 32.64 \\ \hline \end{tabular}
\end{table}
Table 5: Values of PRD and SNR for PPG signals.
# Angle-Only Cooperative Orbit Determination Considering Attitude Uncertainty
## 5 Conclusions  
In this paper, the autonomous attitude and orbit determination problem of a two-spacecraft system using angle-only measurements is studied. The observability of the system is analyzed based on the theory of observability matrix. Five cases are analyzed and the observability analysis results are as follows:  
* When the orbits of both observer and target are known, and the attitude of the target is unknown and unchanged, the navigation system is observable.
* When the orbit of observer is known, and the attitude and orbit of the target are unknown, the navigation system is observable.
* When the orbits of observer and target and the attitude of the target are unknown, the navigation system is unobservable in the two-body dynamics. The navigation system becomes observable when considering high-order perturbations.
* When the orbits of observer and target and the attitude of the target are unknown, and the attitude of the target is changed, the navigation system is observable.  
In addition, the observability analysis and the filter results both verify that compared to the perturbation acceleration, a suitable attitude maneuver is more likely to attach obvious improvement to the observability of the two-spacecraft system.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & **I** & **II** & **III** & **IV** & **V** \\ \hline Perturbation & _none_ & _none_ & _none_ & \(J_{2}\) & \(J_{2}+J_{3}+J_{4}\) & _none_ & _none_ \\ Dynamics & Equation (3) & Equation (3) & Equation (4) & Equation (4) & Equation (3) & Equation (3) \\ Quaternion of \(S_{1}\) & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ Orbit state of \(S_{1}\) & ✓ & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ \\ Orbit state of \(S_{2}\) & ✓ & ✗ & ✗ & ✗ & ✗ & ✗ & ✗ \\ Angular velocity of \(S_{1}\) & _none_ & _none_ & _none_ & _none_ & _none_ & ✓ & ✗ \\ Observability & Y & Y & N & N & Y & Y & Y \\ \hline \hline \end{tabular}
\end{table}
Table 7: Observability under different situations.  
Figure 21: The estimation errors of angular velocity of case V (nonperturbation).  
**Author Contributions:** Conceptualization, J.W., C.L., Y.W. and Q.X.; methodology and software, X.Z. and Y.S. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Basic Scientific Research Project (Grant No. JCKY2020903B002), the National Natural Science Foundation of China (Grant No. 51827806), the Space Preresearch Project (Grant No. TJ20203A020159) and the Shared Technical Preresearch Project (Grant No. 41412050601).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 Home Area Network
### Home Communication General Architecture
#### 3.1.2 HAN Oriented Cloud  
Cloud computing has just emerged as a new paradigm of ubiquitous computing and has been one of the utmost significant improvements in computer technologies and industry in recent years. The development of cloud computing expands the capacity of computer calculation and the convenience for the users [40]. Though home automation technologies already are commercially available, they are essentially intended for signal-family smart homes with a high cost, and are alongside the continuous development of digital appliances in smart homes.  
There are many benefits of introducing two-way communication of the smart home HAN with a cloud based system. In the first place, information of the house's expected electricity usage comportment is concentrated and made available to a utility, load serving entity or an aggregator. Therefore, these entities are able to execute their optimization processes by ensuring precise information of their consumers. Another significant feature is that the end-user could remotely access with a smartphone, data regarding electricity consumption or even set parameters to the HAN in real-time.  
Moreover, the computational resources part of HAN in a domestic environment are systems that might have reduced capacities of storage and computational power and in the case of data being storedin a local database or a Personal Computer (PC), the risk of losing the data or it being corrupted by third parties.
## 3 Results and Discussion
### Basic Indicators of PPA/SBS/SBR Composite-Modified Asphalt Cold Rehydration Solution  
To conveniently record and analyze the fundamental indicators of PPA/SBS/SBR composite modified asphalt, this article refers to the high-temperature type PPA/SBS/SBR composite modified asphalt cold replenishment liquid as high-temperature type asphalt cold replenishment liquid (H-ACRL), the low-temperature type PPA/SBS/SBR composite modified asphalt cold replenishment liquid as low-temperature type asphalt cold replenishment liquid (L-ACRL), and the asphalt cold replenishment liquid as (ACRL). The basic indicators of base asphalt and different ACRLs after short-term aging are shown in Figure 4. After adding modifiers, surfactants, diluents, and reinforcing agent to the base asphalt, the penetration of PPA/SB/SB-modified asphalt increased compared to that of the base asphalt; the softening point increased; and the ductility at 5 \({}^{\circ}\)C increased. After adding modifiers, surfactants, diluents, and reinforcing agent, the increase in asphalt penetration was greater than 70%, mainly due to the improved flow properties of PPA/SB/SB-modified asphalt. The addition of modifiers, surfactants, diluents, and reinforcing agent to the base asphalt increased the softening point by more than 25%, indicating that modifiers, surfactants, diluents, and reinforcing agent also improved the high-temperature performance of the asphalt. The modifiers, surfactants, diluents, and reinforcing agent caused the 5 \({}^{\circ}\)C ductility of the asphalt to exceed 50%, indicating that modifiers, surfactants, diluents, and reinforcing agent improved the low-temperature performance of the asphalt.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Asphalt Cold**} & \multicolumn{6}{c}{**Composition Dosage (\%)**} \\ \cline{2-7}  & **SBS** & **SBR** & **Ercamide** & **Polyphosphate** & **Diesel Oil** & **Acrylate** \\ \hline L-ACRL & 4 & 4 & 1.5 & 0.9 & 18 & 7 \\ H-ACRL & 5 & 3 & 0.9 & 1.2 & 15 & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Composition ratios of different asphalt cold replenishment fluids.