esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Measurement Environments and Methods
### Time Domain Measurement Method  
The data collecting mode of the spectrum analyzer was used in the time domain measurement. We recorded the time domain waveform of electromagnetic noise at 315 MHz, 779 MHz, and 916 MHz. These three frequencies and the 200 kHz bandwidth selected by the time domain measurement are the frequency points and bandwidth adopted by low power wireless sensor chips on the market. The sampling rate was set to 2 MHz and the measurement duration was set to 134.2 s. As in frequency domain measurement, the antenna was measured by horizontal polarization and vertical polarization, respectively. The parameters of time domain measurements are listed in Table 2.
# Study on Abnormal Pattern Detection Method for In-Service Bridge Based on Lasso Regression
###### Abstract  
The real-time operational safety of in-service bridges has received wide attention in recent years. By fully utilizing the health monitoring data of bridges, a structural abnormal pattern detection method based on data mining can be established to effectively ensure the safety of in-service bridges. This paper takes a large-span arch bridge as the research object, analyzes the time-based variation of the main monitoring data of the structure, establishes Lasso regression models for load characteristic indicators and vertical bending fundamental frequency of the structure under different time scales, and uses the residuals of the Lasso model to indicate the structural state and identify abnormal patterns. Firstly, the monitoring data of bridge structural temperature, girder end displacement, and girder acceleration were analyzed, and the interrelationships were studied to extract characteristic parameters of structural load characteristics and structural frequency. Then, the time-varying patterns of structural response were analyzed, and Lasso regression models and their regression variables were discussed based on monitoring data under two different time scales: daily cycle and annual cycle. The abnormal pattern detection method for bridge structures was developed. Finally, the effectiveness of this method was verified by taking the bridge deck pavement replacement as the abnormal pattern. The research results indicate that the proposed bridge structure abnormal pattern detection method based on Lasso regression can effectively monitor changes in the state of the bridge, and the residual dispersion of the model established on the annual cycle scale is relatively smaller than that on the daily cycle scale, resulting in better abnormal detection performance.  
bridge engineering structural health monitoring; Lasso regression; data analysis; abnormal pattern detection; deck pavement +
Footnote †: journal: Applied Soft. 2024, 14, 2829. [[https://doi.org/10.3309/app1407289](https://doi.org/10.3309/app1407289)]([https://doi.org/10.3309/app1407289](https://doi.org/10.3309/app1407289))
## 5 Future Work Discussions
### Flying Base Station for Security Opportunities  
While this paper reviews the issues and gaps that challenge the security of flying base stations, flying base stations can provide unique opportunities to aid the security of base station operation. Such opportunities include advancing channel reliability and jamming resiliency by using the mobility of the flying base station, using the line-of-sight path securing to provide additional security properties (which can especially be important for mmWave communications as it gets block on the physical barriers), improving reliability by jointly processing the communications with line-of-sight sensing, and creating unique physical-layer signatures based on the mobility and the corresponding channel variations.
## 3 Model Problems and Numerical Results
### Model Problem 1
#### 3.1.5 Quantity of Interest  
The quantity of interest (QoI) we consider for all Monte Carlo simulations is the vertical displacement of the bottom layer of nodes of the beam, as indicated by arrows in Figure 14. The computed solution on each level is interpolated as if it was computed on the finest level. The MLQMC algorithm automatically chooses the node which has the biggest variance as the determining node for the computation of the required number of samples. By doing so, it is assured that the variance constraint is satisfied for all the other nodes.
# An Efficient Imaging Method for Medium-Earth-Orbit Multichannel SAR-GMTI Systems
## 6 Discussion
### Link-Budget Determination  
The high orbital altitude gives MEO SAR the advantages of short revisit time and wide coverage. However, it also requires radar having a high transmit power and large antenna area, because the signal energy decays rapidly with the distance. In the following, we will briefly discuss the relationships between the target's SNR and radar's transmit power and antenna area.  
Figure 9: Flowchart of moving target detection and refocusing.  
Since the MEO SAR considered in this paper is of relatively low resolution, the size of the ground moving target can be considered to be smaller than the area of a resolution cell. The SNR of the focused target can then be calculated as [1]  
\[SNR=\frac{P_{ave}G_{\mathrm{r}}G_{\mathrm{r}}\lambda^{3}\sigma_{t}c}{256\pi^{3}{R_ {0}}^{3}{K}TB_{\mathrm{r}}F_{n}L_{s}V_{\mathrm{e}}\rho_{r}\rho_{a}}, \tag{53}\]  
where \(P_{ave}\) is the average transmit power, \(G_{t}\) is the gain of the transmitting antenna, \(G_{r}\) is the gain of the receiving antenna, \(\sigma_{t}\) is the target's radar cross section (RCS), \(R_{0}\) is the distance from the target to the radar, \(K\) is the Boltzmann's constant, \(T\) is the temperature of receiver, \(B_{T}\) is the bandwidth of the transmitted signal, \(F_{n}\) is the receiver noise figure, \(L_{s}\) is the system losses, \(V_{e}\) is the effective velocity of satellite, \(\rho_{r}\) and \(\rho_{a}\) are the range and azimuth resolution, respectively.  
We assume that the multichannel system has three channels in azimuth, and the whole antenna is used for transmission. The SNR per channel can then be expressed as  
\[\begin{array}{ll}SNR&=\frac{P_{ave}\lambda^{3}\sigma_{t}c}{256\pi^{3}R^{3}K \lambda TB_{T}E_{n}L_{s}V_{\mathrm{e}}\rho_{r}\rho_{a}}\cdot 4\pi\eta\frac{A}{ \lambda^{2}}\cdot 4\pi\eta\frac{A_{r}}{\lambda^{2}}\\ &=\frac{P_{ave}\sigma_{t}\gamma_{r}\gamma^{2}A^{2}}{48\pi R^{3}K\lambda TB_{T}E _{n}L_{s}V_{\mathrm{e}}\rho_{a}\lambda^{\prime}},\end{array} \tag{54}\]  
where \(\eta\) is the antenna efficiency, \(A\) is the antenna area, and \(A_{r}=A/3\) is the area of the receiving antenna per channel.  
Based on (54), numerical experiments are conducted to investigate the relationship between the average transmit power, antenna area, and SNR. A target with an RCS of 2 m  
Figure 11: Refocusing results of target 2. (**a**) SAR image of target 2; (**b**) Contour plot of target 2.  
Figure 10: Refocusing results of target 1. (**a**) SAR image of target 1; (**b**) Contour plot of target 1.
## 4 Privacy-Preserving Smart Metering with Authentication Capability  
In this section, we propose a privacy-preserving smart metering scheme. The proposed scheme consists of aggregation path generation and path key establishment followed by data encryption (using symmetric homomorphic encryption [37]) and signature generation (using an identity-based sequential aggregate signature algorithm [29]). The proposed scheme allows the concentrator to aggregate metering data from smart meters on the routing path and verify the authenticity of the aggregated result. Thus, both user privacy and data integrity are achieved during the reporting of metering data.
# Recent Advances in Femtosecond Laser-Induced Surface Structuring for Oil-Water Separation
## 2 Femtosecond Laser-Induced Self-Organized Periodic Surface Structures (LIPSS)  
Apart from the complex, patterned surfaces attained by FS laser micromachining under ablation conditions, surface processing by FS lasers can also give rise to various submicron- and micron-sized, self-organized laser-induced periodic surface structures (LIPSS) created under practically non-ablative conditions [27; 35]. Figure 3 represents an example of the different LIPSS structures attained on silicon upon irradiation with different numbers of FS pulses at a constant incident fluence. With a low number of pulses, the formation of laser-induced surface ripples perpendicular to the laser beam polarization occurs. Upon increasing the number of pulses, the formation of supra-wavelength laser-induced surface grooves (LISG), oriented parallel to the beam polarization, takes place. Further increasing the number of incident pulses gives rise to the creation of a quasi-periodic array of FS laser-induced surface micro-spikes (LISS) [2].  
Figure 2: Typical setup used for femtosecond laser surface structuring and microfabrication.
## 3 Results and Discussion  
### Compressive Strength  
Figure 1 shows the compressive strength of the SAR specimens in different proportions at 1, 4, 8, and 28 days [14]. The compressive strength of the specimens showed an upward trend over the duration of the curing time, which conforms to the strength-change laws of concrete. With each increase in the proportion of silt in the mixture, the compressive strength decreased.  
After curing for 28 days, the M2 specimen had the highest compressive strength, about 15.92 MPa, and the M8 specimen had the lowest compressive strength of about 10.62 MPa. According to the Chinese standard GB 50010-2010 [28], the compressive strength of each mixture specimen was between C15 and C20. In general, the compressive strength of an artificial reef should be above C30 [3; 29] to ensure that it will not break when dropped to the seafloor. To analyze the feasibility of seabed silt as an artificial reef material, the SAR specimens were made only of cement, seabed silt, and pure water, so as to avoid the influence of other materials on the test results. Unlike ordinary concrete, there were no other aggregates in the raw material. As conventional artificial reefs are made of concrete that includes gravel, sand, and cement, their strength can be adjusted by changing the particle size of the aggregate or the strength grade of the cement. Therefore, although incorporating seabed silt reduced the strength of the SAR specimens, the SAR strength could be similarly adjusted to meet the strength demands for artificial reefs. In addition, any shells were removed from the seabed silt during screening. If they were instead crushed and mixed into the reef, they could improve the SAR strength to some extent. They could also be evenly distributed on the surface of the artificial reef to provide a natural substrate for oyster adhesion [18; 19].
# Synergistic Potential of Optical and Radar Remote Sensing for Snow Cover Monitoring
## 3 Data and Methods  
### Methods  
In this research, we propose a methodology for the analysis of SCA characteristics in the main snow-dominated montane regions of the Iberian Peninsula. Furthermore, we assess the potential advantage of the combination of SAR and optical remote sensing for SCA monitoring in the Iberian mountains (Figure 2). The proposed methodology includes the following steps: (1) SCA characterization in terms of PSCPI and snow season length, as well as the determination of the degree of relationship between the latitude and snowline elevation in the Iberian mountains; (2); the assessment of the recent climate change impacts on snow cover (3); the analysis of the potential of SAR for spatial gap-filling optical remote sensing SCA data; and (4) the evaluation of the potential limitations of optical and SAR remote sensing for SCA monitoring.
## 4 Results and Performance Evaluation
### Performance Metrics  
The performance metrics used are summarized as follows:  
* The number of alive nodes per cluster round.
* Network Lifetime Rate: This gives the alive node rate of the nodes present over the cluster round indirectly. It can also be called the possible remaining lifetime in the form of cluster rounds with changing levels of energy.
* Data Packet Received: This is the network throughput, which refers to the data packets or messages delivered successfully.
* Energy Consumption: The energy consumption in the network for cluster rounds is calculated by the difference between the initial energy and the residual energy.
* Fairness and Effectiveness: The fairness and effectiveness of the experiment can be ensured by analyzing the network lifetime, data packet received, and energy consumption.  
The performance analysis of the different parameters was calculated, and we compared EEHC [17], EECT [32], and S-EECP [33] with the TIGSO-EDS method. The comparison of the alive nodes of the TIGSO-EDS method with EEHC, EECT, and S-EECP was performed, and the analysis shows that the proposed TIGSO-EDS method improved the alive node rate by 10.12%, 6.79%, and 4.53% when compared with EEHC, EECT, and S-EECP, respectively.  
A comparison of the alive nodes of TIGSO-EDS is shown in Figure 4. A method with EEHC, EECT, and S-EECP was implemented, and the analysis shows that the proposed TIGSO-EDS method improved the alive node rate by 10.12%, 6.79%, and 4.53% when compared with EEHC, EECT, and S-EECP, respectively. There were three methods, namely energy-efficient heterogeneous clustered, energy-efficient clustering technique, and single-hop energy-efficient clustering protocol, for calculating the network lifetime, which were proposed by TIGSO-EDS. Figure 5 shows that TIGSO-EDS improved the network lifetime rate by 19.77%, 9.25%, and 6.37% when compared with EEHC, EECT, and S-EECP, respectively.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Description** & **Symbol** & **Value** \\ \hline The proportion of normal nodes and advanced nodes & M & 0.3 \\ The proportion of advanced nodes and super nodes & \(m_{0}\) & 0.5 \\ Advanced nodes energy factor & \(\beta\) & 2 \\ Supernodes energy factor & \(\alpha\) & 3 \\ Primary energy level & \(E_{o}\) & 0.5 J \\ Size of the data packet & L & 4000 bytes \\ The constraint of transmit amplifier & \(\varepsilon_{fs}\) & 10 pJ/bit/m\({}^{2}\) \\ Constraint transmit amplifier if \(d_{BS}\leq d_{0}\) & \(\varepsilon_{mp}\) & 0.0013 pJ/bit/m\({}^{4}\) \\ Dissipated energy per bit \(d_{BS}\geq d_{0}\) & \(E_{elec}\) & 50/bit \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters used for TIGSO-EDS.
# Application of the Supercapacitor for Energy Storage in China: Role and Strategy
## 3 Development of China's Supercapacitor Field
### Application Status
#### 3.2.2 Wind Power System  
The energy storage device installed in a wind farm is equivalent to constructing a power station with suitable capacity in a wind farm. As the proportion of the installed wind capacity in the total installed capacity increases gradually, when the voltage of the grid connection node in the wind farm drops as a result of power grid failure or disturbance, it has a strong impact on the system stability if cut off the wind farm. For the range of grid voltage drop, the supercapacitor can conveniently provide reactive power for the power grid to avoid the wind turbines being damaged by over-current and thus achieve grid-connected operation, uninterrupted. [PERSON] et al. studied the large capacity energy storage system and its application based on supercapacitor, and they provided the low voltage ride-through solution for wind farms [39]. GCN new energy issued a notice that the group completed the generating capacity of 928.6 gigawatt-hours according to the consolidated statement in January 2020, 12.2% less than the same period in 2019. According to the 2020 National Economic Statistic Bulletin released by the National Bureau of Statistics, the grid-connected wind power installed capacity reached 281.53 million kW, up 34.6% from the same period last year.  
The wind turbine adjusts the blade angle through a pitch control system to maintain a constant engine speed. The pitch control system must equip backup batteries to shut off safety when a major accident happens. The characteristics of the supercapacitor are intermediate, between those of the battery and capacitance. Not only will it reach the capacity of the battery, but it also can operate without maintenance during its service life. Thus, it is very suitable for the environment of the pitch control system. The MW-level supercapacitor solution in the pitch control system is already commercialized in China. The downstream industries of pitch-used supercapacitors have entered the mature stage [40].
## 3 The Proposed Algorithm
### Notations  
\(n\)--the number of product types.  
\(M\)--the number of machines required to meet the demands of the \(n\) product types.  
\(O\)--number of operators required to operate the \(M\) machines.  
\(\psi_{j}\)--the periodical production rate of product \(j\) (\(j=1,2,\ldots,n\)) on any machine.  
\(\lambda_{j}\)--the periodical demand rate for product \(j\) (\(j=1,2,\ldots,n\)). According to our assumptions, \(\psi_{j}>\lambda_{j}\).  
\(T_{\psi_{j}}\)--the production time for producing of product \(j\) in one cycle.  
\(T_{\lambda_{j}}\)--the time in one cycle that the machine produces other products (and does not produce products \(j\)) or is idle.  
\(T_{S_{j}}\)--the setup time for producing products \(j\). According to our assumptions, the setup times are identical and fixed and do not depend on the schedule.  
\(Q_{j}\)--the batch size of product \(j\) produced in each cycle (equal to demand for product \(j\). In other words, \(Q_{j}=\lambda_{j}\)).  
\(T_{j}\)--the cycle time of product \(j\) (\(T_{j}=T_{\psi_{j}}+T_{\lambda_{j}}\)). The cycle time is equal to 1 for each product \(j\).  
\(M^{*}\)--the optimal (minimum) number of machines required to meet the demands of the \(n\) product types.  
\(C\)--the number of machines that is determined by a heuristic method, which is a \"good\" feasible solution to \(M^{*}\), \(C\geq M^{*}\).
# Graph Layer Security: Encrypting Information via Common Networked Physics
## 3 GLS Encryption Using Physical Networked Dynamic
### Relay Selection & Weight Computation
#### 3.2.3 Overall Relay Selection Algorithm  
After the elaboration of the GFT-based surrogate and the weight computation, we provide the relay selection algorithm in Algorithm 1. The input is the training data of the physical networked dynamic for GFT surrogate \(\mathbf{\Gamma}\) computation. Step 1 is to compute the GFT surrogate that will be used for the following weight computation. Step 2 is the initialisation for the successive convex optimisation method of weight computation. Steps 3-7 are to pursue the successive convex optimisation, in which the weight vector \(\mathbf{\alpha}^{(\text{Tx},\text{Rx})}\) is successively computed by solving the convex problem in Equations (22) and (23). Then, the output is the computed weight \(\mathbf{\alpha}^{(\text{Tx},\text{Rx})}\).  
It is noteworthy that the proposed relay selection algorithm is in the offline manner. To be specific, the relay is selected without any knowledge of the real networked dynamics that will be used for communication encryption, but the dynamic training data from the simulator that is able to characterise the dependencies of the physical networked dynamics. In this view, two benefits are given in the following. First, the relays and their weights can be computed and saved in advance, and do not need to be re-evaluated if the hidden governing dynamic equations in Equation (1) and the network topology are fixed. As such, the computational complexity of the relay selection algorithm is trivial, and no delay will be caused by the algorithm in the securing of wireless communications. Second, the computations of the relays and their weights do not depend on the real networked dynamics that will be used for information encryption, which suggests the communication security even if the selected relays and their weights are revealed by an active or a passive attacker. We will discuss this in the following parts.  
```
0:\(D\) groups of training data, i.e., \(\mathbf{Y}\) of networked dynamics.
1: Compute GFT-based dynamic surrogate using Equations (14) and (15).
2: Assign \(k=0\). Find am initial feasible solution \(\mathbf{\alpha}^{\left(\mathrm{Tx,Rx}\right)}(0)\) and an initial slack variable \(\beta(0)\). Assign \(\Delta(0)=f(\mathbf{\alpha}^{\left(\mathrm{Tx,Rx}\right)}(0))\).
3:while\(\Delta(k)>\epsilon\)do
4: Assign \(k=k+1\).
5: Update \(\mathbf{\alpha}^{\left(\mathrm{Tx,Rx}\right)}(k)\) and \(\beta(k)\) by solving convex problem in Equations (22) and (23) with initial variables \(\mathbf{\alpha}^{\left(\mathrm{Tx,Rx}\right)}(k-1)\) and \(\beta(k-1)\).
6: Compute \(\Delta(k)=f(\mathbf{\alpha}^{\left(\mathrm{Tx,Rx}\right)}(k))-f(\mathbf{\alpha}^{ \left(\mathrm{Tx,Rx}\right)}(k-1))\).
7:endwhile
8: Assign \(\mathbf{\alpha}^{\left(\mathrm{Tx,Rx}\right)}(k)\) as optimal \(\mathbf{\alpha}^{\left(\mathrm{Tx,Rx}\right)}\).
9: Return \(\mathbf{\alpha}^{\left(\mathrm{Tx,Rx}\right)}\).
```  
**Algorithm 1** Offline relay selection algorithm
# HARE: Supporting Efficient Uplink Multi-Hop Communications in Self-Organizing LPWANs
## 5 Protocol Stack
### Transport Layer  
Reliable end-to-end communications from network STAs to the GW, where retransmissions are only executed when needed and by the minimum number of involved devices, are achieved in HARE by using the mechanisms described below.
## 3 Model and Algorithm Flow
### Mimu/GNSS Loose Integration Model  
When the train is turning and the GNSS signal is good, the motion constraints are unavailable. In this case, the 15-dimensional MIMU/GNSS loose combination EKF filter is used for train navigation. The state error vector of the system is defined as  
\[\mathbf{X}_{1}=\left[\begin{array}{cc}(\mathbf{\phi}^{n})^{T}&(\delta p^{n})^{T}&( \delta P^{n})^{T}&\left(\delta e^{b}\right)^{T}&\left(\delta\
abla^{b}\right)^ {T}\end{array}\right]^{T} \tag{15}\]  
where \(\mathbf{\phi}^{n}\) denotes the attitude errors, \(\delta p^{n}\) denotes the velocity errors, \(\delta P^{n}\) denotes the positioning errors, \(\delta e^{b}\) denotes the bias errors of the gyroscopes in the \(b\) frame, and \(\delta\
abla^{b}\) denotes the bias errors of accelerometers in the \(b\) frame.  
The state equation can be written as  
\[\dot{\mathbf{X}}_{1}=\mathbf{F}_{INS}\mathbf{X}_{1}+\mathbf{G}_{1}W_{1} \tag{16}\]  
Figure 6: Navigation Algorithm Flowchart.  
where \(\mathbf{F}_{INS}\) is the state transfer matrix, \(\mathbf{G}_{1}\) is the noise transferring matrix, and \(\mathbf{W}_{1}\) is the state model noise matrix.\(\mathbf{G}_{1}\) and \(\mathbf{W}_{1}\) are defined as  
\[\mathbf{G}_{1}=\left[\begin{array}{cc}\mathbf{C}_{b}^{n}&0_{3\times 3}\\ 0_{3\times 3}&\mathbf{C}_{b}^{n}\\ 0_{3\times 3}&0_{3\times 3}\\ 0_{3\times 3}&0_{3\times 3}\\ 0_{3\times 3}&0_{3\times 3}\end{array}\right] \tag{17}\]  
\[\mathbf{W}_{1}=\left[\begin{array}{cc}W_{gx}^{b}&W_{gy}^{b}&W_{gz}^{b}&W_{ax}^{ b}&W_{ay}^{b}&W_{ax}^{b}\end{array}\right]^{T} \tag{18}\]  
where \(\mathbf{C}_{b}^{n}\) represents the rotation matrix from the \(b\) frame to the \(n\) frame, \(W_{gi}^{b}\) represents the \(i\)-axis gyroscope noise where \(i=x,y,z\), and \(W_{gi}^{b}\) represents the \(i\)-axis accelerometer noise.  
The measurement model of GNSS/MIU integration can be expressed as  
\[\mathbf{Z}_{1}=\left[\begin{array}{cc}\mathbf{v}_{INS}^{n}-\mathbf{v}_{GNSS}^{n}\\ \mathbf{P}_{INS}^{n}-\mathbf{P}_{GNSS}^{n}\end{array}\right]=\mathbf{H}_{1}\mathbf{X}_{1}+\bm {V}_{GNSS} \tag{19}\]  
where \(\mathbf{Z}_{1}\) denotes the measurement matrix, \(\mathbf{v}_{INS}^{n}\) and \(\mathbf{P}_{INS}^{n}\) are the speed and position of the inertial navigation system in the \(n\) frame, \(\mathbf{v}_{GNSS}^{n}\) and \(\mathbf{P}_{GNSS}^{n}\) are the speed and position of the receiver in the \(n\) frame, \(\mathbf{H}_{1}\) denotes the observation matrix, and \(\mathbf{V}_{GNSS}\) denotes the GNSS measurement noise.  
The detailed description of \(\mathbf{H}_{1}\) is  
\[\mathbf{H}_{1}=\left[\begin{array}{cc}0_{3\times 3}&\mathbf{I}_{3\times 3}&0_{3\times 3 }&0_{3\times 3}\\ 0_{3\times 3}&0_{3\times 3}&\mathbf{I}_{3\times 3}&0_{3\times 3}\end{array} \right]^{T} \tag{20}\]