esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Improving Satellite Positioning Precision Based on Multi-Frequency Navigation Signals
## 2 Methodology and Equations
### Virtual Clock Technology  
The virtual clock has a pseudo-range differential effect; in the implementation of virtual clock technology, the integrated baseband of the ground station of the CAPS system is synchronized with the atomic clock of CAPS; the navigation signals are sent from the integrated baseband of the ground station and transmitted to the satellites through the radio frequency transmission channel [21]. The satellite transponder forwards the signals and broadcasts them to the CAPS coverage area, and the user receiver receives the navigation signals to achieve positioning, and the ground station itself also receives the navigation signals at the same time, as shown in Figure 4.  
Figure 2: Layout of the six satellites with the smallest GDOP value.  
The observed pseudo-range of the user receiver is:  
\[\rho=c\cdot(t_{c}-t_{v}) \tag{1}\]  
where c is the speed of light; \(t_{c}\) is the total delay of the signal sent from the integrated baseband transmitting terminal of the ground station, through the radio frequency transmission channel, space uplink, satellite forwarding, and space downlink to subscriber receiver; \(t_{v}\) is the time correction of the virtual clock.  
The time correction of the virtual clock is obtained through the virtual clock model, as follows [24]:  
\[t_{v}=a_{4}(t-t_{0})^{4}+a_{3}(t-t_{0})^{3}+a_{2}(t-t_{0})^{2}+a_{1}(t-t_{0})+a _{0} \tag{2}\]  
where t is the time when the subscriber receiver receives the signals; \(t_{0}\) is the start time of the virtual clock model; \(a_{0}\), \(a_{1}\), \(a_{2}\), \(a_{3}\), and \(a_{4}\) are detected by the ground monitoring station.  
Figure 4: The realization process of the ground station transmitting and receiving the navigation signals.  
Figure 3: Comparison of downlink single-frequency and multi-frequency signals from in-orbit satellites. (**a**) Each satellite sends down one navigation signal; (**b**) each satellite sends down multiple navigation signals with multi-frequencies.  
The ground station forwards the model parameters to the subscrib receiver through the navigation message, and the CAPS navigation information is generated by the ground station, including time information, synchronization information, satellite orbit parameters, virtual clock model parameters, wide area differential information, local pseudo-range differential information, local position difference data, etc.  
Regarding the virtual clock deviation caused by the satellite position deviation, [PERSON] et al. also made theoretical analysis and actual measurement [21]. When there is 1 km difference between the actual position of the satellite and the predicted position of the satellite by ephemeris data, the receiver uses virtual clock technology to correct the ephemeris error, and the result of the positioning solution is almost the same as the result calculated by using the actual position of the satellite, which shows that the virtual clock technology can cancel or greatly weaken the ephemeris error.
# Inter-Story Drift Ratio Detection of High-Rise Buildings Based on Ambient Noise Recordings
## 4 Results and Analysis
### Results and Analysis of the Calculated IDRs  
The known floor height data and the deflection function were used to obtain each floor's drift and calculate the D-value of the drift and height data of adjacent floors to obtain the calculated IDRs. Then, the calculated IDRs were compared with the IDR limit in the Technical Specification for Concrete Structures of Tall Building of China (JGJ3-2010) [4] to analyze the condition of the Zhonghe Building.  
The calculated IDRs are shown in Figure 8.  
In Figure 8, the calculated IDRs increased at floors 1-14 and decreased at floors 15-21; the maximum IDR occurring at the 14 th floor, 62 m, was \(1.140\times 10^{-3}\).  
The maximum IDR was \(1.140\times 10^{-3}\) at 62 m, which is less than the IDR limit of \(1/550\), which is approximately \(1.818\times 10^{-3}\), from the Technical Specification for Concrete Structures of Tall Building of China (JGJ3-2010) [4], and larger than the total IDR of \(6.122\times 10^{-4}\) obtained from Section 4.3.  
This shows that the maximum IDR does not equal the total IDR from the top floor to the ground, although the difference between the calculated maximum IDR and total IDR is acceptable. If the total IDR from the top floor to the ground is widely used to replace the maximum IDR, this would obviously lead to a decline in accuracy.  
After checking the IDRs, it can be determined that the structural stiffness of the Zhonghe Building of Tongji University meets the requirements and remains stable under loads at the highest wind level of 9.
# Area Division Using Affinity Propagation for Multi-Robot Coverage Path Planning
## 4 The Proposed Algorithm  
### Data Initialization  
To develop a strong solution for the multi-robot CPP problem, we introduced a new algorithm based on AP [27] (Figure 2). AP is a powerful clustering algorithm commonly used for dividing and assigning areas. In our approach, we rely on AP to group data points  
Figure 1: Image (**a**) depicts a cluster that consists of a single cell (blue cell). Green cells represent the cells that have 4-neighbor connectivity with the blue cell. Image (**b**) depicts a larger environment along with its 4-neighbors. Essentially, 4-neighbor connectivity between cells prevents a robot from moving diagonally within the environment.  
into meaningful clusters. The algorithm operates by exchanging messages containing real values among the data points. These messages help the algorithm determine the best number of clusters and how to assign the data points to those clusters. The algorithm continues this message exchange process until a consensus is reached on the optimal cluster configuration. This methodology allows us to bypass the necessity to predetermine the number of robots or the dimensions of the respective sub-areas arbitrarily. As opposed to conventional applications, we employed a meticulous transformation process to adapt our grid-like environment, A, into a dataset suitable for AP (Figure 3).
# Analysis of University Students' Behavior Based on a Fusion K-Means Clustering Algorithm
## 4 Discussion
This does not take into account the diversity of student behavior and the knowledge contained in the data itself, and the evaluation criteria for labels are often subjective. For example, in accordance with the traditional supervised machine learning algorithm (such as decision trees, random forests, etc. ), students with a score greater than 60 can be labeled as \"good students\", and those with scores less than 60 can be labeled as \"bad students\". Student behavior data and label data are input into a supervised machine learning model for training and analysis. After the training is completed, when the behavior data of a new student is input, the model will output which category the student belongs to. Obviously, this method has two problems. First, it is difficult to quantify the evaluation criteria of the label. People can question: \"Why is the label threshold of good students 60 instead of 50 or 70?\" Therefore, the judgment of labels is often vague, and the classification results of students are not objective. Second, the environmental conditions of different schools are different. It is possible that the difficulty of the exam of school B is greater than that of school A, and a student with a score of 50 may be defined as a \"good student\". Therefore, the judgment of student labels is often more complicated and the model is difficult to adjust flexibly. The method proposed in this paper does not need to determine in advance how many types of university students there are, nor does it need to determine in advance which type a student belongs to. This method uses unsupervised clustering to automatically classify students' behavior data based on the similarity, so the result is more objective and accurate, and it can reflect the impact of the university's own characteristics on students' behavior. 2. The traditional K-Means clustering algorithm can select the number of clusters according to the SSE value, but the number of possible classes of the data needs to be estimated in advance. This is unrealistic for unfamiliar large data sets because it is impossible to determine the number of student behavior categories in advance. Additionally, the K-Means clustering algorithm may not find the cluster center. As for CFSFDP, its computing time is relatively long, which cannot be applied to large-scale campus data. The method proposed in this paper combines the advantages of the two algorithms, which can accurately determine the number of student behavior categories and cluster centers, and can also process large-scale university student behavior data at a reasonable speed.
# Feature Importances: A Tool to Explain Radio Propagation and Reduce Model Complexity
###### Abstract  
Machine learning models have been widely deployed to tackle the problem of radio propagation. In addition to helping in the estimation of path loss, they can also be used to better understand the details of various propagation scenarios. Our current work exploits the inherent ranking of feature importances provided by XGBoost and Random Forest as a means of indicating the contribution of the underlying propagation mechanisms. A comparison between two different transmitter antenna heights, revealing the associated propagation profiles, is made. Feature selection is then implemented, leading to models with reduced complexity, and consequently reduced training and response times, based on the previously calculated importances.  
radio propagation; path loss; machine learning; feature importances; feature selection; XGBoost; Random Forest +
Footnote †: journal: Pattern Recognition  
18 June 202017 August 202017  
a means to produce models with fewer inputs, describing the trade-off between prediction performance and model complexity.  
The contributions of our work can be summarized as follows:  
* Insight into the model's behavior is gained through the association between the changes in feature importances and the emergence of different radio propagation mechanisms.
* Simpler and faster models are deployed through a feature selection procedure based on the ranked importances.  
The remainder of this paper is structured as follows: Section 2 describes the physical mechanisms that emerge according to the transmitter's height, and Section 3 analyzes the details of the machine learning models. The results are presented in Section 4 and discussed in Section 5.
## 3 Methodology
### Platoon Formation and Optimization Control Strategy
The factors that need to be considered in the preliminary vehicle formation are the difference between the actual distance between the vehicles (\(S_{i-1}(t)-S_{i}(t)\)) and the desired distance (\(d_{s}\)) and the limitations of the vehicle's motion attributes (the value of the vehicle velocity (\(v_{i}(t)\)) cannot be greater than the value of the desired velocity (\(v_{0}\)), and the acceleration (\(a_{i}(t)\)) and deceleration (\(b_{i}(t)\)) of the vehicle cannot exceed the values of the maximum acceleration (\(a_{max}\)) and maximum deceleration (\(b_{max}\)) respectively). The specific expression is shown in Equation (16). \[\left\{ \begin{array}{l}S_{i-1}(t)-S_{i}(t)-d_{s}=0\\ |v_{i}(t)|\leq|v_{0}|\\ |a_{i}(t)|\leq|a_{\max}|\\ |b_{i}(t)|\leq|b_{\max}|\end{array} \right. \tag{16}\]  
In addition, it is necessary to combine the information flow topology of the platoon, which means that not only the front and rear vehicles but also the vehicles with information interaction with the platoon leader need to be calibrated in the platoon. Therefore, it is necessary to combine the corresponding graph-based control protocol of Equation (11) to control the vehicle and finally establish a preliminary platoon configuration. Note that if the vehicle's movement state does not change (the vehicle maintains a constant velocity in real time) and the traffic environment does not change (the road velocity limit value does not change, the road geometry does not change, etc. ), the driving strategy of the platoon does not need to change. However, once there is a motion change of any vehicle, it is necessary to perform Step 4 to optimize the design of the driving strategy. Step 4: The optimal driving strategy for a platoon  
Consistency in vehicle movement behavior was considered as the objective function to improve the traffic efficiency of the platoon in many previous studies (fixed space headway between each adjacent vehicles), which ignores the impact of dynamic changes in the motion status on optimization results. After the formation of the platoon, the control method shown in Equation (12) in Section 3.1.2 is applied to find the optimal driving strategy. The optimization is mainly reflected in the car-following performance in the lateral direction and lane-keeping performance in the longitudinal direction, and this method can adjust the movement behavior of the vehicles in the platoon through the dynamic change in the distribution of the safety potential field. Under such a control method, both driving safety and traffic efficiency can be improved for the platoon.
# Adaptive Impedance-Based Fault Location Algorithm for Active Distribution Networks
## 3 Adaptive Fault Location Algorithm for Active Distribution Systems
### Step 2: Processing of DER Information
#### 3.2.1 Using the Information Provided by IEDs Installed at the DER  
First, fault voltage and current phasors in DER are estimated. Then, the system parameters, line currents, and nodal voltages are estimated. Consider the power distribution system illustrated in Figure 4.  
With the DER fault voltage and current phasors, a backward-sweep process from DER to substation can be performed, using Equations (11) and (12).  
\[\left[\begin{array}{c}\Psi_{DER_{k}}^{p,f}\\ I_{DER_{k}}^{p,f}\end{array}\right]=\left[\begin{array}{cc}\mathbf{d}_{l}&-\mathbf{b }_{l}\\ -\mathbf{c}_{l}&\mathbf{a}_{l}\end{array}\right]\cdot\left[\begin{array}{c}\Psi_{DER _{k+1}}^{p,f}\\ I_{DER_{k+1}}^{p,f}\end{array}\right] \tag{11}\]  
\[\mathbf{I}_{DER_{k-1}k}^{p,f}=\mathbf{I}_{DER_{k}}^{p,f}-\mathbf{I}_{L_{k}}^{p,f} \tag{12}\]  
where,  
\[\mathbf{a}_{l}=\mathbf{d}_{l}=\mathbf{I}+0,5\cdot\mathbf{Z}_{k,k+1}\cdot\mathbf{Y}_{k} \tag{13}\]  
\[\mathbf{b}_{l}=\mathbf{Z}_{k,k+1} \tag{14}\]  
\[\mathbf{c}_{l}=\mathbf{Y}_{k}+0.25\cdot\mathbf{Y}_{k}\cdot\mathbf{Z}_{k,k+1}\cdot\mathbf{Y}_{k} \tag{15}\]  
Superscripts \((p)\) and \((f)\) indicate that prefault or fault period measurements are used. Moreover, for each line section under study between the substation and the DER illustrated in Figure 4, the current \(\mathbf{I}_{F,k+1}^{f}\) is assumed to be equal to \(\mathbf{I}_{DER_{k-1,k}}^{f}\). This is in order to estimate the fault current, as it will be shown in step 3.  
Figure 4: Estimation of the fault voltage and current phasor in all nodes of the Distributed Energy Resources’ (DER) radial.
## 2 Materials and Methods
### Data Processing
#### 2.6.2 Apparent Resistivity Dispersion--Apparent Resistivity Curve  
The \(\rho_{a}\) parameter (\(\rho_{a}\)), using Equations (2) and (3), will result in several dispersion points, as observed in Figure 3. Note how the figure is plotted as a function of the period (\(T=1/f\)) for ease of analysis (the longer the period, the greater the _skin depth_). After obtaining all the associated \(\rho_{a}\), a statistical data treatment is carried out for values of frequencies (periods) chosen in such a way that a reliable resistivity curve is obtained.  
To choose the period points to be evaluated, we consider the conditions stipulated in [6]:  
* The chosen periods (or frequencies) must be equally spaced on a logarithmic scale;
* Ideally, it would help to have between 6-10 frequencies per magnitude. More frequencies are unnecessary, and fewer may result in unreliable values. Points are selected as follows: \[\begin{array}{c}f_{1}=f_{max},\\ f_{2}=f_{max}/\sqrt{1.8},\\ f_{3}=f_{max}/1.8,\\ \vdots\\ f_{k}=f_{max}/1.8^{\sqrt{(k-1)}},\end{array}\] (5) where \(f_{max}=10\) Hz (sample rate = 20 Hz).  
After obtaining the desired frequencies, we observe that the dispersion of the data for each point will have a Gaussian behavior, as seen in Figure 4. This allows us to find a single \(\rho_{a}\) value associated with a single frequency/period and the corresponding standard deviation. Figure 3 shows an example of a curve obtained from fitting our dispersion data.  
Figure 3: Apparent resistivity \(\rho_{a}\) dispersion as a function of the period (\(T\)). The gray dots represent the raw data computed from Equation (3). The red dots represent the fitted curve used for the apparent resistivities of the partial time window (‘xy’ means HQE-HFN).  
Now that we have the \(\rho_{a}\) curve for each hour, an \(\rho_{a}\) map is made in the total window (days), thus allowing precise observation of the space-time changes where the target seismic event occurred.
## 5 Service Discovery Mechanism
### SDM_TYPE TLV Forecasting and Service Table  
Each node has a local service table where it stores its own services and another service table (remote service table) where it stores locally services announced by other nodes in the network. The local service table has the information shown in Figure 9.  
The _Service Name_ parameter indicates the name of the service that will be announced; the _Lifetime_ parameter indicates the time to live of the service in the network; and the _Service Description_ gives a detailed description of the service. There will be as many entries in the local service table as services announced by this node. The remote service table of each node will be filled with the information received in HELLO and TC messages that have a TLV structure with service announcements. The remote service table of each node has the information shown in Figure 10.  
It is possible for a node to receive a message with a service announcement that it already has in its remote service table. In this case, if the time to live of the service announced is greater than the time to live of the service stored in its remote service table, the node will update the information of the  
Figure 9: Local service table of each node.  
service table with the new values. In the case the service announced matches one of the services that this node offers, the service is not stored in its remote services table. Local services always prevail over remote services.  
Whenever a node has a service to announce or a service update to send, it will use HELLO messages through the previously defined TLV structure. HELLO messages have a periodicity defined in the INTERVAL_TIME attribute. This attribute is a user-configurable parameter. When the _Lifetime_ parameter of a service in the remote service table of each node reaches zero, the node waits the following INTERVAL_TIME to know if the service is no longer available in the network. If after that time, the service is not re-announced, the node deletes this entry from its remote service table. During this time, if a node wants to access the service, a false service discovery can happen.
# _remote sensing_
## 3 Related Data and Correction Models in Cal/Val of Altimeters
### Datum of WSCS by the PGSs  
PGS data of Wai'ling'ding (WLDD) and Zhi'wan (ZWAN) islands was collected from 24 August 2019 to 30 June 2023 ([[ftp://1.203.103.214](ftp://1.203.103.214)]([ftp://1.203.103.214](ftp://1.203.103.214)), accessed on 28 August 2023). These data were processed with a combined solution of 61 GNSS stations around this area, which were downloaded from the GNSS Research Center (GRC), Wuhan University. The GAMIT/GLOBK software was used to process the daily data of the stations, and then the high-precision baseline and coordinate adjustments were obtained. The total number of GNSS data was 60,479, with 1217 data for ZWAN and 1203 data for WLDD. After that, the noise analysis was performed using the Hector software [19; 20]. The Generalized Gauss-Markov noise (GGM) and white noise + generalized Gauss-Markov noise (WN + GGM) models were used to analyze the noise of the PGS daily solution data. Details of the PGS processing can be found in [16].  
The velocities of the N, E, and U components of ZWAN and WLDD were derived in ITRF 2014 (Table 1). Additional results derived from the Jet Propulsion Laboratory (JPL), Scripps Orbit and Permanent Array Center (SOPAC), and the combined results (COMB) of HKWS, which was only about 30 km from WSCS ([[ftp://garner.ucsd.edu](ftp://garner.ucsd.edu)]([ftp://garner.ucsd.edu](ftp://garner.ucsd.edu)), accessed on 15 January 2024, Figure 2), was also used to make comparisons with the trends of WLDDand ZWAN. There was a bit of a difference in the three components (Table 1). This may be caused by the time series of the solution. The SOPAC, JPL, and COMB solutions ranged from June 2010 to July 2024, and the measurements for WSCS take more than three years. This requires longer time series measurements to construct a model of the coordinates. The accurate coordinates of the PGSs will set the datum for the campaign of Cal/Val of the altimeters. The coordinates of the X, Y, and Z components of ZWAN and WLDD were derived in ITRF 2014. Time series of the two PGSs were shown in Figure 3.
_Applied_  
_sciences_  
_Article_  
**Low-Loss and Broadband Silicon Photonic 3-dB**  
**Power Splitter with Enhanced Coupling of**  
**Shallow-Etched Rib Waveguides**  
**[PERSON], [PERSON] and [PERSON] *(c)**  
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Gwangju 61005, Korea; [EMAIL_ADDRESS] (V.H.N.); [EMAIL_ADDRESS] (I.K.K.)  
* Correspondence: [EMAIL_ADDRESS]  
Received: 10 June 2020; Accepted: 25 June 2020; Published: 29 June 2020  
**Featured Application: Silicon photonics, High-density integrated photonic devices, Photonic switches, 1 x N power splitters.**  
**Abstract: A silicon photonic 3-dB power splitter is one of the essential components to demonstrate large-scale silicon photonic integrated circuits (PICs), and can be utilized to implement modulators, 1 x 2 switches, and 1 x N power splitters for various PIC applications. In this paper, we reported the design and experimental demonstration of low-loss and broadband silicon photonic 3-dB power splitters. The power splitter was realized by adiabatically tapered rib waveguides with 60-nm shallow etches. The shallow-etched rib waveguides offered strong coupling and relaxed critical dimensions (a taper tip width of 200 nm and gap spacing of 300 nm). The fabricated device exhibited an excess loss as low as 0.06 dB at a 1550-nm wavelength and a broad operating wavelength range from 1470 nm to 1570 nm. The relaxed critical dimensions (\(\geq\)200 nm) make the power splitter compatible with standard fabrication processes of existing silicon photonics foundries.**  
**Keywords: silicon photonics; 3-dB power splitter; low-loss; broadband; shallow-etched rib waveguides**