esa-sceva/satcom-chunk-collection huggingface dataset:
# Hypernetwork Representation Learning with the Set Constraint
## 6 Experiments
### Link Prediction
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Training Ratios**} \\ \cline{2-9}  & **60\%** & **65\%** & **70\%** & **75\%** & **80\%** & **85\%** & **90\%** \\ \hline Deepwalk & 0.4852 & 0.4954 & 0.4934 & 0.4580 & 0.4901 & 0.4638 & 0.4713 \\ node2 vec & 0.4500 & 0.4525 & 0.4490 & 0.4525 & 0.4329 & 0.4712 & 0.4345 \\ LINE & 0.4750 & 0.4672 & 0.4636 & 0.4625 & 0.4741 & 0.4523 & 0.4768 \\ GraRep & 0.5025 & 0.5089 & 0.4867 & 0.5051 & 0.5557 & 0.5835 & 0.5362 \\ HOPE & 0.5055 & 0.5269 & 0.4933 & 0.4690 & 0.4941 & 0.4668 & 0.4271 \\ SDNE & 0.2948 & 0.4310 & 0.4454 & 0.5050 & 0.5196 & 0.3536 & 0.3836 \\ HRSC & 0.7871 & 0.7774 & 0.7822 & 0.7920 & 0.7747 & 0.7983 & 0.8056 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: AUC values on drug. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{6}{c}{**Training Ratios**} \\ \cline{2-9}  & **60\%** & **65\%** & **70\%** & **75\%** & **80\%** & **85\%** & **90\%** \\ \hline Deepwalk & 0.4308 & 0.4278 & 0.4205 & 0.4583 & 0.4418 & 0.4914 & 0.4831 \\ node2 vec & 0.3660 & 0.3614 & 0.3808 & 0.3939 & 0.3834 & 0.3958 & 0.3649 \\ LINE & 0.4575 & 0.4829 & 0.4761 & 0.4562 & 0.4429 & 0.4663 & 0.4574 \\ GraRep & 0.3873 & 0.3805 & 0.3882 & 0.3765 & 0.3820 & 0.3857 & 0.3874 \\ HOPE & 0.3805 & 0.3676 & 0.3416 & 0.2971 & 0.2794 & 0.2518 & 0.2334 \\ SDNE & 0.3262 & 0.4371 & 0.4319 & 0.3157 & 0.4379 & 0.3527 & 0.4540 \\ HRSC & 0.7516 & 0.7562 & 0.7488 & 0.7449 & 0.7236 & 0.7325 & 0.7279 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AUC values on GPS. ratios, and worse than DeepWalk and GraRep at larger training ratios, which indicates the effectiveness of HRSC, and verifies that HRSC has the ability to accurately estimate the relations among the nodes. * The link prediction performance of DeepWalk is second only to that of HRSC, which indicates that node representation vectors obtained by directly training DeepWalk on the hypernetwork datasets contains the hyperedge information to a certain extent. It is worth noting that SDNE is the worst performing baseline method overall and has unstable performance at different training ratios, which verifies that SDNE is not universal. In contrast, HRSC performs consistently at different training ratios, which demonstrates its feasibility and robustness. * By introducing the hyperedges, the node representation vectors learned by HRSC are better than that of other baseline methods without considering the hyperedges, which verifies our assumption that it is beneficial to link prediction task to incorporate the hyperedges into the process of hypernetwork representation learning. In short, the above observations show that HRSC can learn high-quality node representation vectors, which help to accurately estimate the relations among the nodes. In addition, the experimental results of the link prediction task demonstrate the effectiveness and robustness of HRSC.
# Characterization of the Evolution of Energy Loss Rate in Cyclic Load Testing of Marine Soft Soil
###### Abstract  
The study of energy evolution laws has been widely applied in the geotechnical analysis of soft rocks and coal seams. However, research on marine soft soil has primarily focused on consolidation behavior, shear properties, and microstructural evolution, with limited exploration of systematic methods based on energy evolution laws. This paper addresses this gap by analyzing dynamic triaxial test data to explore the relationship between energy loss rate and the number of cyclic loadings in marine soft soil. Through energy loss calculations and derivations, a mathematical expression for the energy loss rate is established using curve fitting. Additionally, an envelope diagram depicting the energy dissipation rate of marine soft soil under varying perimeter pressures and water contents is presented. This diagram serves as a criterion for assessing the stability of marine soft soil, which is further validated through engineering applications. The findings offer novel approaches and insights for developing theoretical models and rapid strength assessment techniques for marine soft soil in engineering practices.  
marine soft soil; dynamic triaxial test; energy dissipation rate; envelope surfaces Article
## 3 The Receiving Process of the Composite Signal
### The Fast Switching of the Composite Signal  
The switching at different devices will cause a frequent loss of locked communication links. When the change rate of the Doppler Frequency is small, the out-of-lock process has little effect on data demodulation. However, if the Doppler frequency changes drastically, the communication link cannot be locked, and the data cannot be demodulated.  
To solve this problem, the novel composite signal using the parameter of the BOC signal assists the retracking of the BPSK signal, which only costs one clock cycle.  
There are two preconditions:  
1. The BOC signal is continuously transmitted; and
2. The transmission of uplink BPSK signals is controlled by the master device. The steps of the fast-switching algorithm are as follows. In Figure 7, we propose the control flow of the master device to the slave device. First, master control device A transmits the BPSK signal; then, before switching, it controls device A to stop transmitting the BPSK signal using the transmission stop frame. Then, master device control device B transmits the BPSK signal.  
1. The master device starts communication with device A. The links contain the BOC signal and BPSK signal.
2. Before switching, device C transmits one frame of data to control device A to stop transmitting the BPSK signal. In addition, the frame can control device B to start transmitting the BPSK signal to device C.
3. Master device C uses the code offset and Doppler frequency in the BOC signal of device B to demodulate the BPSK signal from device B.
4. If master device C needs to communicate with device A, the process is the same as steps 2 and step 3.
# A Large Capacity Histogram-Based Watermarking Algorithm for Three Consecutive Bins
## 3 The Proposed Method  
In this section, a novel high-capacity octal histogram-based watermarking algorithm for three consecutive bins is proposed. The proposed watermarking algorithm comprises three parts: the selection of the embedding range, the position selection of the pixel to be modified, and the embedding and extraction processes of the watermark. Next, we will present them in detail.
# Performance Aspects of Retrodirective RFID Tags +
## 2 Materials and Methods
Apart from the use of a 'full' \(\mathbf{S}\) matrix to model the tag's response in the case of the retrodirective characteristic, additional issues need to be considered. Usually, in the relative literature, in the channel shown in (1), the signals at the tag's antenna(s) are assumed uncorrelated. Nonetheless, for a set of radiators to be collectively denoted as array antennas, they should be correlated. The solution of reducing the correlation by increasing the interelement distance is not available here due to widely accepted footprint constraints accompanying the RF tag design. Due to the correlation of the received signal at each antenna, the system should be studied as a phased array one. In this case, it is necessary to incorporate the direction of arrivals (DOAs) and the directions of departures (DODs) into the model (finite scatterer model). Then it is, [15]:  
\[ \begin{split}&\mathbf{H}_{f}=\Psi_{r}^{tag}\cdot h_{f}\cdot( \Psi_{T})^{\mathsf{T}};\\ &\mathbf{H}_{b}=\Psi_{R}\cdot h_{b}\cdot\left(\Psi_{t}^{tag} \right)^{\mathsf{T}},\end{split}  \tag{3}\]  
where \(\Psi_{T},\Psi_{r}^{tag},\Psi_{R},\Psi_{t}^{tag}\) are the transmitter's transmitting array, the tag's receiving array, the receiver's receiving array, and the tag's transmitting array response column vectors. \(h_{f},h_{b}\) are the complex channel gain vectors for the forward and the backscattered links. Here, we focus on the narrowband case (we neglect delays), and we assume flat fading. Inserting (3) into (1)a, with spatial signature equipped, pinhole channel model for RF tags is provided.
## 5 Problems and Development Trends
### Future Prospects and Trends
#### 5.2.2 Popularization of Intelligent Technologies and Precision Aquaculture  
The continuous development of artificial intelligence (AI), the Internet of Things (IoT), and big data technologies is driving aquaculture into an era of intelligence and precision. In large yellow croaker fry selection, the integration of smart sensors and algorithmic models enables real-time monitoring and dynamic management of fry health, growth dynamics, and aquaculture environment parameters. Additionally, the widespread application of intelligent feeding systems, water quality control devices, and automatic early warning mechanisms will further enhance the efficiency and decision-making accuracy of the selection process. AI-driven multi-dimensional data analysis also provides key support for scientific breeding, driving the shift from experience-driven to data-driven farming models.
# A Transplantatable Frequency Selective Metasurface for High-Order Harmonic Suppression
## 3 Experimental Verification  
In this section, a prototype of the patch-grid FSS integrating with HS-FSMSE has been fabricated, as shown in Figure 12. The fabricated prototype has five layers of metal structure printed on four substrates with dielectric constant \(\varepsilon_{r}=2.65\) and the loss tangent of 0.003. The layers are separated by air with plastic screws. The total size of the fabricated FSS is 235 \(\times\) 235 \(\times\) 7 mm. The air spacing is 3 and 2, respectively. Since the dimension of the fabricated FSS is relatively small especially in the far-field environment which will make the diffraction becomes strong, we use the two-port method in the near-field region to measure the transmission feature of FSS. In the measurement, we measured the transmission and reflection characteristics of the two horns measurement system without FSMSE for calibration. The near field coupling effect can be eliminated. For frequency selective surfaces, the features do not vary with different kinds of electromagnetic wave. The plane wave setup in the simulation is mean to model the far-field environment. Since we have removed the near-field coupling effect, the measurement results could be equal to the theoretical model when the plane wave is incident on the FSS.  
Experiments were performed by using three pairs of transmitting and receiving horn antennas that can cover different frequency ranges from 2 to 30 GHz. In these measurements, the broadband horns (2-18 GHz), K-band (18-26.5 GHz) horns and Ka-band (26.5-30 GHz) horns were used to measure the transmission coefficients of the fabricated FSMS. The system configuration for the measurement is shown in Figure 13, where Tx and Rx stand for the transmitting and receiving antennas, respectively, and RUT represents the fabricated FSS which was tested in the microwave chamber. The distance from Rx to FSS is 100 mm and from Tx to RUT is also 100 mm. It is worth noting that the center of RUT should be in direct line with that of Rx and Tx. The Anritsu MS46322A (E, Anritsu, Atsugi-shi, Kanagawa, Japan, 2015) 40 GHz vector network analyzer is used to measure the transmission characteristics. And we also measured the transmission and reflection characteristics of only two horns system without the FSS for calibration. Figure 14 shows the comparison of measured transmission coefficients between the original patch-grid FSS with and without HS-FSMSE. It can be seen that the designed HS-FSMSE effectively removes the undesired spurious transmission windows of the original patch-grid FSS. Even if the oblique incident angle is as large as 60 degree, the HS-FSMSE still works well. The measured frequency responses after calibration agree well with the simulated ones. The contribution of the proposed HS-FSMSE for the complementary resonant loop FSS is almost the same.  
Figure 11: Transmission coefficient for oblique incidences with different angles on the complementary loop FSS with HF-FSMSE, (**a**) TE polarization and (**b**) TM polarization (The green shadow represents suppressed harmonic transmission bands).
## Proposed Approach
### The Data Set and the Simulation Setup  
The data used for calibration and validations of the model were obtained during a comprehensive traffic study in Szczecin, Poland, during which the city area was divided into 255 communication areas. The boundaries of communication divisions were along major roads, rivers and other traffic barriers. Traffic surveys were conducted. Interviews were conducted with truck drivers and traffic volume and traffic pattern were measured. The following information was collected: the generic structure of vehicles (passenger cars, vans, minibuses, trucks without trailers, trucks with trailers and semi-trailers), and number of people in the vehicle. For external areas of the city, the country of origin of the vehicle, the source and destination of the journey, the frequency of the journey and the motives of the journey were also ascertained. The survey was conducted on weekdays from 6 a.m. to 10 p.m.  
The field data provided information on the average number of vehicles passing through the roundabout (Table 3).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Name of Roundabout** & **Number of** & **Average Number** & **Average Number** \\  & & **Lanes** & **of Vehicles/day** & **of Vehicles/h** \\ \hline
1. & Herman Haken Roundabout & 3 & 6346 & 397 \\
2. & University Roundabout & 3 & 5579 & 349 \\
3. & Gierosa Roundabout & 2 & 5893 & 368 \\
4. & Giedroycin Roundabout & 2 & 8174 & 511 \\
5. & Grunwaldzki Square & 3 & 12,132 & 758 \\
6. & Odrodzemia Square & 2 & 10,972 & 686 \\
7. & Szarych Szeregow Square & 3 & 11,526 & 720 \\
8. & Ulanów Podolskich Roundabout & 2 & 8538 & 533 \\
9. & Ronald Reagan Roundabout & 2 & 4712 & 295 \\
10. & Lupaszka Roundabout & 1 & 3171 & 198 \\ \hline \hline \end{tabular}
\end{table}
Table 3: List of roundabouts (Szczecin, Poland) that were under observation.  
Figure 1: Average tendency to use the individual lanes at (**a**) 2-lane; (**b**) 3-lane roundabouts in Poland. Source: own research.  
Based on the data obtained, the number of vehicles in the model was set at 500 vehicles per hour (average value resulting from the table is 481.5).
## 2 An ESNet of Real-Time Semantic Segmentation Based on DCNN
### Analysis of the Principle of Atrous Convolution
In recent years, dilated convolutions have been widely used in semantic segmentation systems, which have effectively improved the accuracy of semantic segmentation [33; 34].
###### Abstract  
Wireless sensor network (WSN) with energy-saving capabilities have drawn considerable attention in recent years, as they are the key for long-term monitoring and embedded applications. To improve the power efficiency of wireless sensor nodes, a wake-up technology was introduced in the research community. Such a device reduces the system's energy consumption without affecting the latency. Thereby, the introduction of wake-up receiver (WuRx)-based technology has grown in several sectors. The use of WuRx in a real environment without consideration of physical environmental conditions, such as the reflection, refraction, and diffraction caused by different materials, that affect the reliability of the whole network. Indeed, the simulation of different protocols and scenarios under such circumstances is a success key for a reliable WSN. Simulating different scenarios is required to evaluate the proposed architecture before its deployment in a real-world environment. The contribution of this study emerges in the modeling of different link quality metrics, both hardware and software metrics that will be integrated into an objective modular network testbed in C++ (OMNeT++) discrete event simulator afterward are discussed, with the received signal strength indicator (RSSI) for the hardware metric case and the packet error rate (PER) for the software metric study case using WuRx based on a wake-up matcher and SPIRIT1 transceiver. The different behaviors of the two chips are modeled using machine learning (ML) regression to define parameters such as sensitivity and transition interval for the PER for both radio modules. The generated module was able to detect the variation in the PER distribution as a response in the real experiment output by implementing different analytical functions in the simulator.  
wake-up receiver; wireless sensor network; received signal strength; packet error rate; OMNeT++ +
Footnote †: journal: Sensors  
0023  
3
## 3 Results
### Rockfall Mapping  
Rockfall detection utilized Sentinel-1 SLC radar images. Interferometric processing of co-registered images identified surface changes indicative of rockfalls, with accurate spatial representation ensured by geocoding ([PERSON] et al., 2013 [16]; [PERSON] et al., 2019 [4]). The rockfall map (Figure 5) shows locations and incidents along National Road 66 and slopes for the following dates: 11 January, 22 March, 31 August, 16 September, 3 October, 28 November, and 2 October 2022.  
Figure 4: Flood map of the Jiului Gorge region, illustrating the extent and severity of flood events based on Sentinel-1 GRD images.  
Figure 5 presents a spatial analysis of rockfall incidents along National Road 66 and surrounding slopes within the Jiului Gorge. Leveraging Sentinel-I radar imagery and interferometric processing techniques, the map pinpoints rockfall locations across specific dates ([PERSON] et al., 2022 [17]; [PERSON] 2019 [13], [PERSON] et al., 2014 [18]). This visualization serves as a critical tool for understanding the spatial distribution of rockfall hazards in the region, aiding in risk assessment and potential mitigation strategies for the protection of transportation infrastructure.
## 2 Materials and Methods
### Data
#### 2.1.5 Nested Air Quality Prediction Modeling System (NAQPMS) Data  
The simulated PM\({}_{2.5}\) data used in this study were obtained from the Nested Air Quality Prediction Modeling System (NAQPMS); the NAQPMS is a multiscale air-quality model developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences. The NAQPMS uses a new generation of mesoscale Weather Research and Forecast (WRF) models with a resolution of 1-30 km developed by the National Center for Environmental Prediction (NCEP), the National Center for Atmospheric Research (NCAR), and other research institutions and universities [45; 46]. The NAQPMS can simulate atmospheric trace gases (e.g., O\({}_{3}\), NOx, SO\({}_{2}\), CO) and atmospheric aerosol components (e.g., sand, sea salt, sulfate, nitrate, ammonium, and carbon-containing aerosols). It has been widely used in the study of the development mechanism and transport deposition process of sand, dust, acid rain, ozone, and particulate pollution, as well as the forecast and prediction of urban air quality [47]. To be consistent with the AHI AOD resolution, the NAQPMS was resampled to 1 km by the nearest neighbor method.
# Modelling High-Resolution Actual Evapotranspiration through Sentinel-2 and Sentinel-3 Data Fusion
## 2 Materials and Methods  
In this section we first describe the evaluated ET models (Section 2.1), before presenting the validation sites (Section 2.2) and the input data sources (Section 2.3).
# Surface Plasmon Resonance-Based Fiber Optic Sensors Utilizing Molecular Imprinting
## 5 Molecular Imprinting
### Elements of Molecular Imprinting
#### 5.3.3 Cross-Linkers  
In polymerization process, cross-linkers are used for fixing the functional monomer around the template which leads to the construction of rigid and highly cross-linked MIPs, even after the binding/unbinding of the template. Thus, it can be said that the type and amount of cross-linking agent used for the MIP synthesis strongly affect the rigidity and flexibility of the MIPs. It has been shown by [PERSON] and [PERSON] in 1972 that the degree of cross-linking for the synthesis of MIPs strongly affects the selectivity and binding capability of MIPs [73]. In cases of lower amounts of cross-linker, the stiffness of the MIPs would be poor, which leads to their instability, hence it causes poor selectivity. It was observed that for below 10% of cross-linking, the MIP loses the selective nature [112], while for higher degrees of cross-linking, MIPs show kinetic hindrance towards the reversible binding of the template. This decreases the number of recognition sites per unit mass of MIPs [113]. Thus, to achieve the optimum flexibility/rigidity of the MIPs, the amount and type of cross-linker should be optimized. Figure 6 shows the cross-linkers most commonly used for MIP synthesis.  
Figure 5: Chemical structures of common functional monomers used for MIP synthesis: (**a**) 4-vinyl aniline; (**b**) acrylic acid (AA); (**c**) methacrylic acid (MAA); (**d**) methyl methacrylate (MMA); (**e**) _p_-vinylbenzoic acid; (**f**) styrene; (**g**) acrylamide (AM); (**h**) methacrylamide; (i) 2-hydroxymetnacrylate (HEMA).
# An Optimized Fractional-Order PID Horizontal Vibration Control Approach for a High-Speed Elevator
## 2 Dynamic Model of Horizontal Vibration of High-Speed Elevator
### Fractional Order PID Controller  
There are two main definitions of fractional calculus: Riemann-Liouville and Grunwald-Letnikov definitions.  
Riemann-Liouville definition:  
\[aD_{t}^{\alpha}f(t)=\frac{1}{\Gamma(m-\alpha)}\left(\frac{d}{dt}\right)^{m} \int_{a}^{t}\frac{f(\tau)}{(t-\tau)^{\alpha-m+1}}d\tau \tag{13}\]  
In Equation (13), \(\alpha\) is the order of the calculus, and a and t are the upper and lower limits of the integration. For \(m-1<\alpha<m\), m\(\in\) N, where \(\Gamma(\cdot)\) is [PERSON]'s gamma function.  
The Laplace transform of the RL fractional derivative is expressed as follows:  
\[L\{aD_{t}^{\alpha}\}=\int_{0}^{\infty}e^{-st}D_{t}^{\alpha}f(t)dt=s^{\alpha}F( s)-\sum_{k=0}^{m-1}s^{\alpha-k-1}D^{k}f(0) \tag{14}\]  
When the initial condition is 0, Equation (12) can be expressed as:  
\[L\{0D_{t}^{\alpha}\}=s^{\alpha}F(s) \tag{15}\]  
FOPID's transfer function is as follows:  
\[G(s)=K_{p}+\frac{K_{i}}{s^{\lambda}}+K_{d}s^{u} \tag{16}\]  
The integral order \(\lambda\) and differential order \(\mu\) can take any value from 0 to 1. Conventional PID controllers are special cases where both integral and differential orders are taken as 1. The two order parameters bring more freedom to the controller's adjustment. The parameter plane of the FOPID controller is shown in Figure 3.
## 4 Experimental Verification and Analysis
### Evaluation Index  
To comprehensively evaluate the model performance, the experimental relevant indicators included Precision (P), Recall (R), mean Average Precision (mAP), and F1 value. Among them, P is used to measure the accuracy of model detection of pressed rigging defects; R is used to evaluate the comprehensiveness of model detection of pressed rigging defects; and mAP is used to evaluate the overall performance of the model in the task of detecting defects in pressed rigging. F1 values are often used to evaluate the performance of a test model.