esa-sceva/satcom-chunk-collection huggingface dataset:
# Study on Structural Robustness of Isolated Structure Based on Seismic Response
## 3 Model Analysis of Vertical Irregular Structure
### Base Isolated Structure Model Design  
Because the model is the base isolation structure, special seismic isolation design is needed to select the appropriate type of isolation bearing, and to determine the rational form of isolation bearing layout. The layout of the seismic isolation bearings is shown in Figure 2. The rubber isolation bearing pressure stress limit is 12 MPa, the specific parameters of the bearing are summarized in Table 1.  
When the stiffness center of the isolation layer does not coincide with that of the upper structure, it is necessary to consider the torsional coupling effect of the structure. The severe torsional effect can also cause damage to the bearing away from the center and even endanger the safety of the whole structure. The results show that, regardless of whether the center of the upper structure deviates from the center of mass, as long as the isolation center coincides with the center of gravity of the upper structure, it can no longer pay much attention to the torsion effect of the isolation layer. Therefore, controlling the eccentricity of isolation structure is also an important link in the design of isolation structure. The eccentricity of three kinds of vertical irregularly isolated structures is calculated respectively, as shown in Table 2.  
Figure 3: Isolation bearing layout. (**a**) Model A; (**b**) Model B; (**c**) Model C.  
Figure 2: Structure plane layout and distribution of cases. (**a**) Model A; (**b**) Model B; (**c**) Model C.  
It can be seen from Table 2 that the eccentricity ratios of the three models are small in both directions. It is indicated that the center of the isolation structure coincides with the center of the upper structure, and the structure will not show significant tensional coupling effects under the horizontal earthquake. Therefore, the bearing layout is reasonable.
# Securing Construction Workers' Data Security and Privacy with Blockchain Technology
## 2 Literature Review
### Blockchain Technology as an Information Management System  
Blockchain technology has emerged as a transformative solution for information and data management, and it offers unparalleled security, transparency, and efficiency. With its decentralized and distributed nature, blockchain ensures data integrity and authenticity through consensus mechanisms, thus eliminating the need for intermediaries [65]. The immutability of blockchain records provides a tamper-proof system that is particularly valuable for industries like construction, where maintaining an accurate and tamper-proof record of information is critical [69]. Enhanced data privacy and control are achieved through cryptographic keys, thus allowing individuals to manage access permissions [70].  
Blockchain also streamlines data management by eliminating the need for reconciling multiple copies of data across different systems [71]. Its distributed nature ensures synchronized data for all participants, thereby reducing errors and duplication [72; 73]. And, its decentralization enhances security by preventing single points of failure and promoting trust and transparency among stakeholders. Overall, blockchain technology offers unparalleled security, transparency, and efficiency in information management [60; 74; 75].
# Improvement in Signal Phase Detection Using Deep Learning with Parallel Fully Connected Layers
## 3 Results and Discussions
The phase-output time is normalized by the output time of the minimum-finding algorithm, which is represented by the red line in Figure 6.  
The result shows that the phase-output time decreases with the increase in the number of simultaneous output signal pixels. Whereas the phase-output time for the single-pixel classification is approximately 18 times longer than the conventional minimum-finding algorithm, it is less than 1.7 times when 32 pixels are simultaneously classified. Therefore, the phase-output time can be significantly reduced by increasing the number of pixels to be simultaneously classified. The phase-output time can be further improved by increasing the number of pixels to be simultaneously classified. In fact, when the number of simultaneous output signal pixels is increased to 64 pixels, the normalized o  
Figure 5: Experimental results of the phase determination with deep learning. “Conventional” in the figure represents the minimum-finding algorithm mentioned in Section 2.1. However, in this case, PxER increases to some extent. For example, at RNyq = 1.4, Log\({}_{10}\)PxER increases to \(-3.4\) at 64 pixels. This is due to the reduction in the number of training data extracted from one detected image, but it is not a critical defect of our method. If the number of training data can be made large enough, PxER can maintain a small value even at 64 pixels. Therefore, we confirm that PxER in our proposed method can be significantly reduced compared with that in the conventional minimum-finding algorithm. Simultaneously, the phase-output time can be suppressed to almost the same level by employing parallel fully connected layers.
## 4 Results and Discussion
### Second Application: Control of Tilt, Sidelobes and Nulls
#### 4.3.1 Operation Conditions
Hence, to analyze the effectiveness of GA-MMC in the region of interest of the E-plane, tests for main operating conditions were chosen, as shown in Table 3. The adopted criteria for considering the simulation results approved were (i) maximum error of \(\pm 0.5\,^{\circ}\)for the \(\theta_{\text{0}}\), due to the resolution available (\(\Delta\theta=0.573\,^{\circ}\)or 0.01 rad), (ii) signal-to-interference ratio, _SIR_, between main lobe (\(A_{M}\)) and interference (maximum between \(A_{\text{II}}\) and \(A_{\text{12}}\)) greater than or equal to 50 dB, and (iii) \(\text{RSLL}\geq 15\) dB in all different directions from those of the main lobe and of the nulls. This RSLL range was adopted because it is at a typical value in PRRs for an RTS and due to the additional objective of the _SIR_. \begin{table}
\begin{tabular}{c c} \hline \hline
**Test** & \(\mathbf{0_{0}}\)**, \(\mathbf{0_{11}}\) e \(\mathbf{0_{12}}\) \\ \hline
**01** & \(90\,^{\circ}\), \(10\,^{\circ}\)e \(170\,^{\circ}\) \\
**02** & \(45\,^{\circ}\), \(10\,^{\circ}\)e \(170\,^{\circ}\) \\
**03** & \(90\,^{\circ}\), \(130\,^{\circ}\)e \(140\,^{\circ}\) \\
**04** & \(45\,^{\circ}\), \(130\,^{\circ}\)e \(140\,^{\circ}\) \\
**05** & \(90\,^{\circ}\), \(100\,^{\circ}\)e \(80\,^{\circ}\) \\
**06** & \(45\,^{\circ}\), \(55\,^{\circ}\)e \(35\,^{\circ}\) \\
**07** & \(90\,^{\circ}\), \(135\,^{\circ}\)e \(10\,^{\circ}\) \\
**08** & \(45\,^{\circ}\), \(10\,^{\circ}\)e \(135\,^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Tests of the second application of GA-MMC [13].
## 2 Electromagnetic Interference Characteristics of Converter Station to the Surrounding Environment  
To understand the electromagnetic interference characteristics of the converter station equipment and the impact on the UAV, it needs first to identify the electromagnetic emission generated by the power converter station equipment.
# MDPN: Multilevel Distribution Propagation Network for Few-Shot Image Classification
## 4 Experiment
### Experiment Results  
**Main Results.** We contrast MDPN's performance with that of the well-known ProtoNet and the current top models, such as DeepEMD [39], FEAT [40], and FRN [41]. We tested the approach with the same dataset, CUB-200-2011, and backbone, ResNet12, for an accurate comparison. Table 1 demonstrates that MDPN outperforms the baseline (DPGN [26]) as well as the majority of the existing methods.
In this approach, the PS vector of mmWave TX is adjusted in the first MAB stage, and based on it, the PS vector of the RIS is modified in the next stage and vice versa over the time horizon. Thus, at each trial, the player needs to only explore one set of the PS vectors, either that belonging to the mmWave TX or that belonging to the RIS, which reduces the computational complexity of the constructed MAB game. Two common MAB algorithms, namely Thompson sampling (TS) [16] and upper confidence bound (UCB) [17], are used to implement the proposed nested two-stage MAB and compare their performances under the mmWave-RIS environment. * Numerical analyses are conducted to prove the effectiveness of the proposed mmWave-RIS communication system over benchmarks against the optimal performance under different simulation scenarios. The rest of this paper is constructed as follows: Section 2 summarizes the related works, and Section 3 discusses the system model and the problem formulation. Section 4 proposes the antenna codebook design and the nested two-stage MAB approach. Section 5 delivers the numerical analysis, followed by the concluding remarks in Section 6.
## 2 Data and Methods
### Methodology  
To observe tropical and subtropical water vapor distribution and variability and assess the quality of the new C2 dataset, C2 GNSS RO specific humidity observations, along with ERA5 and MERRA-2 reanalysis profiles, were obtained for September-December 2019 within 40\({}^{\circ}\)N-40\({}^{\circ}\)S. The gridded reanalysis data were collocated to times and locations of RO profiles, including the tangent drift of the profiles. This was done by finding the minimization of two functions: the first being the linear fit to the RO profile latitudes and longitudes dependent on the geopotential height, and the second being the bilinear fit to the model geopotential height on a given model level at a given time step dependent on latitude and longitude [34]. The minimization of these two functions returned the latitude, longitude, and geopotential heights at which the RO profile crossed the given model level at each model time step. Bilinear interpolation in space and linear interpolation in time to these crossing points gave profiles of model data on model levels that followed the path of each RO profile. The moisture profiles from all three datasets were interpolated to the same uniform 50 m vertical grid for ease of comparison.  
Profile pairs (C2/ERA5 and C2/MERRA-2) were then compared and differences were recorded. Individual profile differences were displayed in scatterplots and two-dimensional histograms and were also displayed by gridding both zonally (2.5\({}^{\circ}\)) and by latitude/longitude (5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\)). We focused our study on two tropospheric altitudes: 3 km was chosen as it is in the moist lower troposphere but is generally just above the boundary layer and thus avoids the negative bias commonly observed within the boundary layer in RO profiles, and 7 km was also chosen as it is within the mid-troposphere. Specific humidity differences were compared using absolute value (in g/kg) and by percentages, such that:  
\[\text{SH Difference}=((\text{C2}_{\text{SH}}\text{-REANALYSIS}_{\text{SH}})/ \text{REANALYSIS}_{\text{SH}})*100 \tag{1}\]  
Moisture differences were also studied in smaller regions where large moisture differences and weaker correlations are observed in order to better understand why large SH differences can occur between profile pairs, including in regions that see frequent deep  
Figure 1: C2 sampling using 5\({}^{\circ}\)\(\times\) 5\({}^{\circ}\) grids (color contours) along with penetration percentage to at least 1 km above the surface (dashed contours) from September–December 2019.  
convection (e.g., central Africa) or strong moisture gradients (e.g., over the Northern Pacific Warm Pool in winter).
## 5 Conclusions  
In this paper, a unidirectional orbit-determination method for non-atomic clock extended users using navigation Ka-band inter-satellite links is innovatively proposed. After the extended user completes angle pointing calculation and unidirectional capture tracking, under the condition of limited navigation inter-satellite link time slot resources, it collects unidirectional ranging values between the extended user and navigation satellites per minute. The clock offset model is used to calculate and compensate for the clock offset, improving the time precision by three orders of magnitude and the frequency precision by two orders of magnitude, achieving high-precision time-frequency synchronization and bidirectional communication. After the establishment of the bidirectional link, with three to four inter-satellite link time slots per minute and one hour of observation per day, two days of unidirectional ranging values are collected to establish a mechanical model and state transition matrix for real-time orbit determination and calculation.  
For the first time in China, this technology has enabled extended users to access navigation inter-satellite links and achieve hundred-meter-level orbit determination. Compared with traditional space-ground control links, it can provide 24 h timing for spacecraft without geographical restrictions and, compared with conventional methods of accessing navigation inter-satellite links, it occupies fewer navigation inter-satellite link resources and does not affect the normal functions of the navigation constellation. This technology solves the problem of overseas extended users utilizing limited time slot resources for emergency measurement and orbit determination, greatly enhancing the robustness of extended users. It provides strong technical support for various applications of navigation Ka-band inter-satellite links in China.  
Conceptualization, Y.S. and B.L.; methodology, Y.S.; software, R.M.; validation, Y.S., B.L. and R.M.; writing--original draft preparation, Y.S.; writing--review and editing, H.L.; supervision, H.Z. and W.W.; project administration, Y.Y. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The author, [PERSON], is employed by China Aerospace Science and Technology Corporation Limited and is also a student at Southeast University. Professor [PERSON] and [PERSON], my supervisors, gave technical guidance in the project. The remaining authors declare that the research was conducted without any commercial or financial relationship that could be interpreted as a potential conflict of interest.  
The following abbreviations are used in this manuscript:  
Global Navigation Satellite System  
Tf&C  Tracking, Telemetry and Command  
GPS  Global Positioning System  
GEO  GEostationary Orbit  
IGSO  Inclined GeoSynchronous Orbit  
MEO  Medium Earth Orbit  
ECI  Earth-Centered Inertial  
ms  milliseconds  
kHz  kilohertz  
us  microseconds
# Analyzing the Economic Performance of a TLS-Based Structural Safety Diagnosis Process
## 3 Results
### Economic Performance Analysis
#### 3.4.2 Total Cost (TC)  
By combining the average number of customers in the system (_L_) derived from the queueing model, the number of servers, and the daily service cost (_SC_) and waiting cost (_WC_), the daily total cost (_TC_) was calculated. The results indicate significant reductions in survey duration, labor costs, and waiting costs when adopting the TLS-based 3D scanning reverse modeling approach. Specifically, applying Equation (12) to each project yields the following outcomes.  
In Project A, the total daily cost of the existing method is KRW 3,127,437, and a total of KRW 125,097,493 must be spent over 40 days. On the other hand, the total daily cost of the TLS-based 3D scanning reverse modeling method is reduced to KRW 959,485, and a total of KRW 13,432,793 is spent over 14 days of investigation. This represents a significant cost reduction of 89% compared to the existing method.  
Project B has a total daily cost of KRW 7,640,877 under the existing method, and a total of KRW 611,270,187 is required to perform it for 80 days. However, the TLS-based 3D scanning reverse modeling method is significantly reduced to KRW 2,217,135 per day, which means that it will only cost KRW 121,942,416 in total when performed for 55 days. This represents an 80% cost reduction compared to the existing method.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Classification**} & \multicolumn{2}{c}{**Project A**} & \multicolumn{2}{c}{**Project B**} & \multicolumn{2}{c}{**Project C**} \\ \cline{2-7}  & **As-Is** & **To-Be** & **As-Is** & **To-Be** & **As-Is** & **To-Be** \\ \hline \(C_{s}\) & 1,172,789 & 890,244 & 2,865,329 & 1,735,149 & 2,865,329 & 1,452,604 \\ Server & 1 & 1 & 1 & 1 & 1 & 1 \\ \(C_{w}\) & 195,465 & 148,374 & 477,555 & 289,192 & 477,555 & 242,101 \\ \(L\) & 10 & 0.47 & 10 & 1.67 & 7 & 0.6 \\ _SC_ & 1,172,789 & 890,244 & 2,865,329 & 1,735,149 & 2,865,329 & 1,452,604 \\ _WC_ & 1,954,648 & 69,241 & 4,775,548 & 481,986 & 3,342,884 & 145,260 \\ \hline \end{tabular}
\end{table}
Table 6: Service cost and waiting cost comparison for visual inspection and TLS-based methods.  
In the case of Project C, the total daily cost of the existing method is KRW 6,208,213, and a total of KRW 956,064,776 must be spent over 154 days. In comparison, the TLS-based 3D scanning reverse modeling method is significantly reduced to KRW 1,597,864 per day, which amounts to a total of KRW 105,459,050 over 66 days, representing an 89% cost reduction.  
Detailed comparisons of cost-saving effects for each project are summarized in Table 7.  
In other words, TLS inspection-based diagnosis methods can be used to ensure high data reliability and complete coverage in large-scale investigations, such as fire damage or aging structure diagnoses, while also greatly reducing the actual manpower and investigation time, thereby reducing the investigation period and total cost.
# Matched Filtering for MIMO Coherent Optical Communications with Mode-Dependent Loss Channels
## 5 Numerical Simulation of Linear MIMO FSE Receiver for MDL-Impaired Optical Channel
### Performance Loss Metric for FSE-Based MIMO Receiver  
We define the performance loss metric (in dB) of the FSE-based MIMO receiver in MDL-impaired channels for certain mode \(i\) as:  
\[L(i)=SNR_{in}(i)-SNDR_{out}(i) \tag{42}\]  
where \(SNR_{(in)}(i)=10\cdot\log_{10}\left(\frac{S}{N}\right)_{in}(i)\), and \(\underline{SNDR}_{out}=[SNDR_{out}(1),SNDR_{out}(2),\ldots,\)\(SNDR_{out}(D)]^{T}\) is calculated as defined in [42] and Equation (28) in [43] for MIMO implementation of the FSE. Given a set of system model parameters, the numerical simulation will generate a total of \(D\cdot N_{ch}\) values of \(L(i)\). The average loss \(AL\) is calculated for each channel realization of among the available \(N_{ch}\) as:  
\[AL=\frac{1}{D}\sum_{i=1}^{D}L(i). \tag{43}\]  
Two FSE-based MIMO receiver performance metrics can be extracted from the \(D\cdot N_{ch}\) calculated values of \(L(i)\):  
* \(ML_{95}\) is defined as the 95 th percentile of the \(L(i)\) distribution obtained for any optical channel realization and mode;\(AML_{95}\) is defined as the 95 th percentile of the \(AL\) distribution obtained for any optical channel realization.  
The values for the parameters used in the simulation are summarized in Table 1.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## 2 Materials and Methods
### The Bottoms-Up Correlated Error Model  
The CYGNSS Level 1 correlated error model is constructed to represent the major error sources based on the physical and operational characteristics of the CYGNSS observatories. These errors are correlated over both space and time, and depend on several variables relating to the operation of the CYGNSS constellation.  
The construction of the error model is intended to be realistic enough to represent measurable and plausible correlated errors, yet flexible enough to allow for tuning and parameterization. The general modeled structure of correlated error takes the form:  
\[R_{mod}(i,j)=\frac{1}{\mathcal{N}}\sum_{n}K_{n}(i,j), \tag{5}\]  
where \(R_{mod}(i,j)\) is the normalized correlated error between any two samples \(i\) and \(j\) with a domain \(-1\leq R_{mod}\leq 1\). There are \(n\) component terms that are added together, and each of the \(n\)th term corresponds to a unique source of error. \(K_{n}(i,j)\) represents the error covariance between any two samples \(i\) and \(j\) for a specific component \(n\). \(\mathcal{N}\) is a normalization constant to ensure that the diagonal terms of the \(R_{mod}\) matrix is 1.  
Each of the \(K_{n}\) terms can be further generalized as follows:  
\[K_{n}(i,j)=(E(n))^{2}\cdot Ecorr_{n}(i,j)\, \tag{6}\]  
where \(E(n)\) represents the magnitude of each error component \(n\) as calculated in [33; 34; 36] and can be thought of as the variance of the error, and \(Ecorr_{n}(i,j)\) is a derived function with a domain \(-1\leq Ecorr\leq 1\) that represents the correlation in error between samples \(i\) and \(j\) for each component \(n\).  
Each \(Ecorr\) function depends on different arguments depending on the nature of the error source. The individual errors are explored in depth in the following sections. Further, several tuning parameters have been added to assist with validation. The initial values of the tuning parameters are 1 and essentially leave the model output unmodified. However, this process assumes that the relative magnitude of the error components could be incorrectly specified, and by varying the tuning parameters, the model can be shaped to match a validation dataset.  
\begin{table}
\begin{tabular}{c c} \hline
**Error Term** & **Error Magnitude [dB]** \\ \hline \(E(G^{R})\) & 0.43 [25] \\ \(E(P^{S})\) & 0.23 [35] \\ \(E(G^{Z})\) & 0.20 [33] \\ \(E(P^{Z})\) & 0.18 [33] \\ \(E(\zeta^{E}\ )\) & 0.15 [33] \\ \(E(\overline{A})\) & 0.05 [34] \\ \(E(L^{tnm})\) & 0.04 [34] \\ \(E(R^{tot})\) & \textless{}0.01 [34] \\ \(E(L^{Z})\) & \textless{}0.01 (assumed \(\sim E\big{(}R^{tot}\big{)}\)) \\ \hline \end{tabular}
\end{table}
Table 1: The magnitudes of 1-sigma errors for each term in Equation (4). The shaded rows indicate that the absolute magnitudes of these terms are negligible compared to the dominant error terms and are neglected in the construction of the error model in this work.
# A Comprehensive Review of GNSS/INS Integration Techniques for Land and Air Vehicle Applications
###### Abstract  
Navigation systems are of interest for applications in both civilian and military vehicles. Satellite navigation systems and inertial navigation systems are the most applied in this area. They have complementary properties, which has led to a trend of integrating these systems. At present, there are several approaches to GNSS/INS integration: loosely coupled, tightly coupled and deeply coupled and many approaches to their modifications in dependence of application and arising problems with measurements, such as lack of GNSS measurements or poor quality of GNSS and INS measurements. This article presents an extensive review of the available modern approaches and their modifications for integrating INS and GNSS measurements, arranging them and highlights the main problems arising for the considered type of integration approach. The article includes a review of various integration tools based on the Kalman filter and intelligent systems, INS mechanization and features of development of an INS measurement error model that is necessary for integration, the main problems of GNSS/INS integration and a comparative description of the solutions proposed by the authors for solving these problems. The findings of this work are useful for further research in the field of inertial and satellite navigation, as well as for engineers involved in the practical implementation of integrated GNSS/INS systems.  
inertial and satellite navigation; GNSS/INS integration; tools and approaches to GNSS/INS measurement integration