esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result and Validation  
### Posterior Unit Weight Variance and Statistics  
In this section, we first analyzed the change of iterations with the posterior unit weight variance changes as well as the statistics based on the optimal weighting method proposed in Section 2.2. Figure 2 (left) gives the change of posterior unit weight variance for six types of equations with iterations for Scheme 2 at date UTC 12:00 p.m. day of year (Doy) 303, 2015, while the Figure 2 (right) is an enlargement of Figure 2 (left) for the iterations from 2 to 20. As it is presented that the posterior unit weight variances tune to very similar values after two iterations and meet the requirement stated in Section 2.2 after five iterations, five iterations is therefore selected in this resolution. It also can be seen that the posterior unit weight variances of three types of GNSS observations (GPS/GLONASS/BDS) are actually different and converged to the same value gradually, and the value is about 1.15 at UTC 12:00 p.m., Doy 303, 2015 in our experiment after 10 iterations, which manifest the point that the observation equation established using different satellite systems should be regarded as the various input information. Figure 3 shows the relationship between the statistics of improved Bartlett test and iterations over the experimental period of Doy 303 to 317, 2015 with the tomography interval of one hour under the conditions of 10\({}^{\circ}\) and 15\({}^{\circ}\) elevation angle mask, respectively. It can be clearly seen from Figure 3 that most statistics reach the given criterion after five to seven iterations with the average iterations of 5.5 and 5.7, respectively, which expresses the features of quick convergence of the proposed method mentioned above.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Satellite System** & **GPS: L1/L2; GLONASS: G1/G2; BDS: B1/B2** \\ \hline \hline \multirow{6}{*}{Parameters to be estimated} & Satellite antenna Phase center correction: FCO (Phase Center Offset) and PCV \\  & (Phase Center Variations) values for GPS /GLONASS/G Galileo from jgs.atv were \\  & used; Corrections for BDS adopted the values provided by WHU [46] \\ \cline{2-3}  & Receiver antenna Phase center correction: FCO and PCV values for GPS and \\  & GLONASS from jgs.atv were used; Corrections for Galileo/BDS were assumed \\  & the same as that of GPS; \\ \hline \hline \multirow{6}{*}{Parameters to be estimated} & Station coordinate: Static \\ \cline{2-3}  & Receiver clock: White noise \\ \cline{2-3}  & Zenith wet delay: Random walk with a power density of 10\({}^{8}\) m\({}^{2}\)/s \\ \cline{2-3}  & BDS: inter-system bias (ISB) (whole BDS constellation) \\ \cline{2-3}  & GLONASS: inner-frequency bias (IFB) (each satellite) \\ \cline{2-3}  & Float ambiguities \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 2: The change of posterior unit weight variances for six types of equations with iteration for Scheme 2 at date UTC 12:00 p.m. Doy 303, UTC 12:00 p.m., 2015.
## 3 Algorithm Design
### Nash Equilibrium Solution  
The main task of the static game is to obtain the best response spectrum for each user. When all the users fail to take other strategies to increase their own revenues, the Nash equilibrium is reached. Equation (9) is derived to obtain the optimal profit function:  
\[\frac{\partial U_{i}}{\partial b_{i}}=\frac{\sigma_{i}r_{i}k_{i}}{1+r_{i}k_{i} b_{i}}-\frac{4}{1+e^{-\alpha(\frac{1}{r_{i}}-1)}}b_{i}-\sum\limits_{j=1,j\
eq i }^{N}b_{j}-v(b_{i}+\sum\limits_{j=1,j\
eq i}^{N}b_{j}) \tag{10}\]  
Let the value of Equation (10) be zero and the following is obtained:  
\[\begin{split}& b_{i}=\frac{-[v+\frac{4}{(\frac{1}{r_{i}}-1)}+(1+v )r_{i}k\sum\limits_{j=1,j\
eq i}^{N}b_{j}]}{2*(2r_{i}k\frac{2}{1+e^{-\alpha( \frac{1}{r_{i}}-1)}}+r_{i}k_{i}v)}+\\ &\frac{\frac{1+e^{-\alpha(\frac{1}{r_{i}}-1)}}{1+e^{-\alpha( \frac{1}{r_{i}}-1)}}+(1+v)r_{i}k\sum\limits_{j=1,j\
eq i}^{N}b_{j}]^{2}-4*(2r _{i}k\frac{2}{1+e^{-\alpha(\frac{1}{r_{i}}-1)}}+r_{i}k_{i}v)(1+v)\sum\limits_ {j=1,j\
eq i}^{N}b_{j}-\sigma_{i}r_{i}k_{i}]}{2*(2r_{i}k\frac{2}{1+e^{-\alpha( \frac{1}{r_{i}}-1)}}+r_{k}k_{i}v)}}\end{split} \tag{11}\]  
The spectrum allocation curve of each SU can be obtained and the intersection points are the Nash equilibrium of the static game.  
At the start of the dynamic game, each SU tentatively changes the size of the allocated bandwidth at the moment \(t\). At the same time, the DC constantly adjusts the spectral unit price. We used \(b_{i}[t]\) to represent the allocated bandwidth for the \(SU_{i}\) at moment \(t\); and \(U_{i}[t]\) to represent the SUs' revenue at moment \(t\). The SU adjusts its strategy through the changes in revenue and bandwidth. The allocated bandwidth for the SU at the moment \(t+1\) is:  
\[b_{i}[t+1]=b_{i}[t]+\beta_{i}b_{i}[t]\frac{\partial U_{i}[t]}{\partial b_{i}[ t]}. \tag{12}\]  
In Equation (12), \(\beta_{i}\) represents the learning factor of \(SU_{i}\), which reflects the capability of SU in observing bandwidth and leasing bandwidth changes [8]. By constantly iterating, \(b_{i}[t+1]=b_{i}[t]\) is reached and the game reaches the Nash equilibrium. Furthermore, the SU profit is the largest in this game.  
The stability of the game theory is discussed and the Jacobian matrix is established:  
\[J=\begin{bmatrix}\frac{\partial b_{1}[t+1]}{\partial b_{1}[t]}&\frac{\partial b_{ 1}[t+1]}{\partial b_{2}[t]}&\dots&\frac{\partial b_{1}[t+1]}{\partial b_{N}[t]} \\ \frac{\partial b_{2}[t+1]}{\partial b_{1}[t]}&\frac{\partial b_{2}[t+1]}{ \partial b_{2}[t]}&\dots&\frac{\partial b_{2}[t+1]}{\partial b_{N}[t]}\\ \vdots&\vdots&\ddots&\vdots\\ \frac{\partial b_{N}[t+1]}{\partial b_{1}[t]}&\frac{\partial b_{N}[t+1]}{ \partial b_{2}[t]}&\dots&\frac{\partial b_{N}[t+1]}{\partial b_{N}[t]}\\ \end{bmatrix} \tag{13}\]  
The absolute value of all eigenvalues of Jacobian matrix is less than 1 and the relation of the learning factor is obtained. This mutual relation can guarantee the stability of the game process [9].
## 3 Results and Discussion
### Optimization Process Proposed
Figure 9 provides a flowchart for which the proposed optimization algorithm had been used to find the optimal location of the CoG. Figure 10 shows the node grids that both stages of the optimization used. Shown on the left is the first 500 \(\times\) 500 node grid. It covers the entire stability triangle. The first stage of the optimization provided the best node. It can be considered as a first estimation of the optimal CoG location. It is the center of the 10 \(\times\) 10 node grid and appears at the right of the same figure. This second grid will be used to find the position of the CoG of the load with a precision of 10 \(\times\) 10 mm. Figure 9: Flowchart of the optimization process. The optimization process that was used to validate the proposed methodology appears. It is based on the optimal conditions for a load to be carried by a trailer that has two 4- and 6-axle modules. The trailer properties that were considered in this study are those of the Intercombi model of Scheuerle company [38]. In this case, the proposed load was a cylindrical tank of 108.5 tons with five large pipes on its left side (Figure 11). The figure shows the hydraulic configuration of the trailer, which consists of group 1 (red) with six cylinders and groups 2 and 3 (purple and red) with seven cylinders. This configuration traces a stability triangle that also appears in Figure 10.  
Table 5 shows the data of the proposed load (weight, dimensions, and position of its CoG). It also shows the optimal number of modular trailers and axles, coordinates to locate the CoG of the load, and oil pressures of the suspension system, as determined by the optimization algorithm. Figure 11: Optimized transport: 108.5 Tn cylindrical tank on 10 axle trailer (Intercombi model of Scheuerle company [38]). Figure 10: Graphical representation of search grids. \begin{table}
\begin{tabular}{c c} \hline \hline
**Characteristics** & **Value** \\ \hline Load & Cylindrical tank \\ Dimensions & 19.5 \(\times\) 3.2 \(\times\) 2.5 m \\ Load weight & 108,500 kg \\ Trailer weight & 33,000 kg \\ Initial CoG coordinates & 0.0 \(\times\) 0.2 \(\times\) 2.1 m \\ Optimum CoG coordinates & 0.3 \(\times\) 0.0 \(\times\) 2.1 m \\ Number of trailers & 2 \\ Number of axles & 10 (4 + 6) \\ Tipping angle (on even road) & 7.2\({}^{\circ}\) \\ Maximum axle load & 21,600 kg \\ Oil pressures & 8.1 \(\times\) 8.1 \(\times\) 5.9 bar \\ Hydraulic configuration & 6,7,7 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Key values of the optimized transport.
# Evaluating Prediction Models for Airport Passenger Throughput Using a Hybrid Method
## 3 Data and Hypothesis  
For a comprehensive assessment of models for airports under different development conditions, 203 airports in China are studied, which covers various influence factors such as conditions of economic development, conditions of industrial structure and conditions of infrastructure. All the 203 airports have been under operation for at least 5 years, which avoids the impact of APT fluctuation during earlier times. Due to the breaking out of COVID-19, the APT of each airport decreased sharply in 2020 and 2021 [34]. The APT of all the airports in China in 2020 was only 63.3% in comparison to that in 2019, and such conditions will inevitably lead to the failures of all the models. Since the completion times of each airport are diverse from each other, the observations of APT before 2014 are used to fit the initial models. For each airport, the number of observation data for fitting is at least 15, which ensures fitting accuracy. The remaining observations, 2015-2019, are used to evaluate the forecasting performance of the models.  
The data for the research can be divided into two types. One is the historical APT observations, which can be obtained from the Civil Aviation Administration of China (CAAC, [[http://www.caac.gov.cn/en/SY/](http://www.caac.gov.cn/en/SY/)]([http://www.caac.gov.cn/en/SY/](http://www.caac.gov.cn/en/SY/)) (accessed on 22 August 2022)). The other one is the indicators of economic development, which contains gross domestic product (GDP), GDP  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Level** & **Number of Airports** & **Percentage** \\ \hline APT less than 2 million & 39 & 19\% \\ APT between 2 million and 10 million & 34 & 17\% \\ APT more than 10 million & 130 & 64\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: The distribution of 203 studied airports.  
of primary industry, GDP of secondary industry, GDP of tertiary industry, resident population, year-end population, urbanization rate, imports and exports, disposable income, total retail sales of consumer goods, tourist visits and tourism income. The data can be obtained from the National Bureau of Statistics of China (NBSC, [[http://www.stats.gov.cn/english/](http://www.stats.gov.cn/english/)]([http://www.stats.gov.cn/english/](http://www.stats.gov.cn/english/)) (accessed on 22 August 2022)).
## 3 Attack Surface Description
### Gateway to Internet Service Provider  
To be connected to the Internet, almost everyone relies on ISPs. The amount of control an ISP has over the security and privacy policies of users varies from country to country. However, since the connected device sends Domain Name Service (DNS) requests to the default ISP DNS servers, they can monitor the Internet activities that are being performed on the device. ISPs can also track a user through their devices when the user logs into their network [29; 30]. The amount of access the ISP has compared to other organizations is much higher as they are the only means of connection to the network between a user and the Internet [31]. When the user connects to the Internet, all their data pass through the ISP architecture before reaching the intended destination.  
Most ISPs collect data about users, devices, personal information, Internet activity, and location, which is mentioned in their privacy policy. The ISP collects and stores user personal information through registration forms. They also collect data automatically regarding the MAC address, IP address, number of devices, types of devices, status of connected devices, network traffic data, device configurations, and many other key pieces of information that can be used to learn about a user and their online behavior [32; 33]. ISPs can also see passwords set by the user if the password is entered in HTTP sites or sites with expired Secure Sockets Layer (SSL) certificates [35].  
In many cases, modems and routers are provided by the ISP and cannot be manually configured by the user. The routers need to be configured by ISP applications, which reduces the amount of control the user has over the security settings. The security control of routers is very important as data flow through them when connected to the Internet, and hackers can exploit a router to retrieve crucial personal data for misuse [30].  
ISPs can share the collected data for various scenarios. User data can be obtained from the government through the ISP for monitoring. The privacy policies set by the ISP also include a clause that states that user data can be shared with affiliated organizations [32]. These factors can be considered as a breach of privacy as the user has no control over the sharing of their data by the ISP. As the Internet has become a vital part of everyone's life, data on Internet activity can be used to understand the personality and behavior of users [36], to predict user online behavior [37], and to influence user decision and behavior without them realizing it.  
There are two crucial network positions where security issues can arise--one is at the user end and the other is at the ISP architecture. As we want to conduct non-intrusive network probing, we will conduct our study at the user end, which will be further elaborated on in the next section. We will analyze the network traffic going through the router when multiple devices are connected to it via Wi-Fi for security checks both when the devices are browsing the Internet and when the devices are idle.
# Protein Dynamics in F-like Bacterial Conjugation
## 5 The Structures of Conjugative DNA Transfer Proteins  
As stated previously, bacterial conjugation is a contact dependent mechanism of transferring genetic material. Conjugative plasmids can provide their host bacterium novel virulence genes that enhance the survivability of species in their microenvironments or to inhabit new biomes. The MOB genes of conjugative plasmids code for DNA transfer proteins, which are critical components for the effective replication and assembly of the relaxosome, allowing for the rapid spread of plasmids through bacterial colonies. Presented below are the five proteins that intricately function to ensure plasmid DNA transfer occurs quickly and efficiently. Disrupting any of these proteins implicates the effectiveness of DNA transfer, and many have solved structures at high resolution, making them enticing targets for drug discovery.
## Appendix A View Factors and Operative Temperature Formulas
The operative temperature evaluated (ideally) on the surface of a clothed user is thus a function of air and mean radiant temperatures, and of the heat transfer coefficients measured at that same point. As \(\bar{t}_{r}=\bar{t}_{r}(a,b,c)\), also the radiation coefficient \(h_{r}\) in (A4) is a function of the radiator dimensions \(a\) and \(b\), together with the distance radiator-person \(c\). Comparison of the operative temperature as computed with (4), namely with \(h_{r}\) and \(h_{c}\) instead of the arithmetic average (3) for \(t_{cl}=25\ ^{\circ}\)C is illustrated in Figure A2. It can be easily verified that for underfloor and ceiling heater the \(t_{op}\) difference is even more negligible (of the order \(\sim\)0.01 \({}^{\circ}\)C). Figure A3 also shows the precision of the interpolations used in this paper for a type-21 panel radiator. One might also wonder about the effect of (4) on the cross-check with IDA ICE, namely when computing the mean radiant temperature without considering the heater projections on the perpendicular surfaces. The result is given in Figure A1. Quite interestingly, as a last remark, we note that by shifting the point for the calculation of view factors on the surfaces perpendicular to the radiator, one can cancel their effect and recover the IDA ICE result with accuracy close to \(\sim\)0.01. This curious coincidence perhaps recalls to mind the role of inertial observers in Newtonian mechanics. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Height (m)**} & 0.30 & \(-0.0046w^{2}+0.0357w+19.833\) \\  & 0.45 & \(-0.0066w^{2}+0.0494w+19.839\) \\  & 0.60 & \(-0.0069w^{2}+0.053w+19.851\) \\  & 0.90 & \(-0.0111w^{2}+0.0786w+19.859\) \\ \hline \multirow{2}{*}{**Width (m)**} & 0.60 & - \\  & 0.80 & - \\  & 1.20 & \(0.1079h+19.84\) \\ \multirow{2}{*}{**Width (m)**} & 1.60 & \(0.1268h+19.842\) \\  & 2.00 & \(0.1404h+19.846\) \\ \multirow{2}{*}{**Width (m)**} & 2.60 & \(0.1489h+19.853\) \\  & 3.00 & \(0.1403h+19.86\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Operative temperature \(t_{op}\) (°C) in function of panel width and height, 21-type.
# Customer-Oriented Quality of Service Management Method for the Future Intent-Based Networking
###### Abstract  
The rapid development and spread of communication technologies is now becoming a global information revolution. Customers have a need for communication services, which could be flexibly configured in accordance with their Quality of Experience (QoE) requirements. Realizing the close connection between customer experience and profitability, the service provider has been placing more and more attention on customer experience and QoE. The traditional quality of service management method based on SLA (Service Level Agreement) is not sufficient as a means to provide QoE-related contracts between service providers and customers. The current SLA method is mostly limited and focused on technical aspects of QoS (Quality of Service). Furthermore, they do not follow on the network the principles and semantic approach to the QoS specification for a communication service using QoE parameters. In this paper, we propose a customer-oriented quality of service management method for future IBN (Intent-Based Networking). It is based on a new QoE metric on a scale from 1 to 5, which allows one to take into account the commercial value of e-services for customers. Based on this approach, the network configuration and functionality of network equipment automatically changes depending on customer requirements. To implement the new method of service quality management, an algorithm for routing data packets in the network was developed, taking into account the current load of the forecast path. The algorithm of billing system functioning in conditions of customer-oriented quality management in telecommunication networks has been created. To investigate the effectiveness of the proposed method of service quality management with the traditional SLA method, we developed a simulation network model with the implementation of two approaches. By conducting a simulation, it was determined that the proposed method gives an average gain of 2-5 times for the criterion of the number of customers who require high quality of experience of the service.  
quality of experience (QoE); quality of service (QoS); intent-based networking (IBN); electronic services (e-services); service level agreement (SLA); controller +
Footnote †: journal: Applied Sciences
## 3 Data Source and Data Preprocessing  
### Data Source  
The satellite data used in the present paper include: (1) CHAMP vector observation data from 2008.1\(\sim\)2010.9. The data in this period correspond to low solar activity years with a corresponding satellite orbital altitude of 250-340 km, as shown in Figure 2. Therefore, the data are less affected by the external magnetic field. This condition is conducive to the exploration of middle and small-scale magnetic anomaly signals. (2) Given that the Swarm C satellite orbit is excessively high to be sensitive to middle and small-scale magnetic anomaly signals, the vector observation data of Swarm A and B satellites from 2014.1-2015.12 with an orbital altitude of 450-510 km are selected. Satellite circular half-orbit separation and data are subsampled every 5s before data processing. Track separation is conducted along-track filtering corrections of observations. At satellite orbit altitude, sampling with 5 s intervals corresponds to a spacing of approximately 35 km. The sampling can weaken the reciprocal mixing of small-scale magnetic field signals and effectively control the anisotropic error to be introduced in the final model.
# Numerical Analysis of Seismic Pounding between Adjacent Buildings Accounting for SSI
## 2 Theoretical Model Framework
### Approximate Normal Modes of Adjacent Buildings
The damping ratio \(\big{(}\overline{\xi}\big{)}\) in relation to the coefficient of restitution \((e)\) provides the dissipation of energy during impact [53]. \(D\) is the distance between the buildings. \(F^{ap}_{\theta ijn}\) can be calculated as \(F^{ap}_{xijn}\) using the related \(\delta_{ij\theta n}(t)\) and \(\dot{\delta}_{ij\theta n}(t)\) in Equation (9). \(F^{ap}_{n}(t)=T^{a}_{n}T^{ap}_{ijn}\) in Equation (7) is an \(8\times 1\) vector with bounding forces. As a result, the approximate method substantially reduces the size of the matrices from \(3\ N+5\) to \(8\) for each building. The modal responses with regard to the displacement of each building, \(D^{a}_{n}(t)\) and \(D^{b}_{n}(t)\), are derived using the method of direct integration in Equation (7). The following equation is derived to obtain all the responses of the two nonproportionally damped asymmetric buildings on the top of an elastic half-space:  
\[U^{a}(t)=\sum_{n=1}^{3N+5}U^{a}_{n}(t)\approx\sum_{n=1}^{3N+5}T^{a}_{n}D^{a}_ {n}(t);U^{b}(t)=\sum_{n=1}^{3S+5}U^{b}_{n}(t)\approx\sum_{n=1}^{3S+5}T^{b}_{n} D^{b}_{n}(t) \tag{10}\]  
The first few modal responses in Equation (10), to acquire a satisfying result, should be summed similarly to the displacement analysis.