esa-sceva/satcom-chunk-collection huggingface dataset:
# Offline-Online Change Detection for Sentinel-1 InSAR Time Series
## 2 Methods
### Changes in Displacement Time Series  
For most geophysical applications there are two types of changes in deformation time series that are of interest: offsets and changes in displacement gradients. Note that our aim is to detect displacement changes in both scenarios, and so regions moving at a constant displacement rate are not of interest for this study unless there is a change from steady-state.  
In the sections below we describe the two components of the algorithm that detect each of these changes.
###### Abstract  
With the continuous and large-scale development of renewable energy, there is a prominent decrease in the level of inertia in new power systems. This decrease leads to the weakening of the system's capability to provide inertia support and frequency regulation during disturbance events. The wind turbines (WT), as the main representatives of renewable energy generation, should be more efficiently involved in the power system frequency regulation dynamics. However, optimal frequency regulation is difficult to achieve through the combined inertial control strategy of wind turbines because it greatly depends on control parameters and fluctuates in different scenarios. To cope with disturbance efficiently and quickly in different scenarios and obtain the optimal frequency regulation results, this paper presents an improved combined inertial intelligent control strategy of WT based on contractive autoencoder (CAE) and deep neural network (DNN). This method obtains the optimal parameters for combined inertial control using the particle swarm optimization (PSO) algorithm, then effectively extracts features from actual data using CAE followed by building a network model to predict the optimal combined inertial control parameters online. To verify and test the proposed method, it is applied in the IEEE 9-bus test system. The simulation results show that the method can obtain optimal control parameters with a faster computational time, good prediction accuracy, and generalization capability.  
primary frequency regulation; combined inertial control; particle swarm optimization; contractive autoencoder; deep neural network
## 5 Research Trends in Ge Photodetectors
### Avalanche Photodetectors  
For Ge photodetectors' application in Si photonics IC, a next level pre-amplifier is necessary to further transform the current signal into a voltage signal for later CMOS IC processing. Avalanche photodetectors offers much lower signal-to-noise ratio compared to PIN or MSM structures. Therefore, more and more interest is being focused on Ge-based avalanche photodetectors. Recently, Intel [53], IBM [55] and IME [54] have all reported successful fabrication of such devices.  
The first Ge-based APD was demonstrated by [PERSON] _et al._[53]. For the reported device, a separate-absorption-charge-multiplication (SACM) configuration (Figure 8) is used to take advantage of both Si's low noise property and Ge's strong absorption near 1.55 \(\upmu\)m wavelength. The device exhibits low excess noise with low k\({}_{\mathrm{eff}}\) of \(\sim\)0.09. The reported sensitivity of \(-\)28 dBm at 10 Gb/s is equivalent to a commercial III-V APD and the bandwidth-efficiency product of 340 GHz is even higher than its III-V counterpart, thanks to much lower k value of Si compare to InP material.  
Figure 8: Schematic of Ge/Si APD with a typical SACM configuration. From [53].  
Figure 7: Normalized frequency response at a wavelength of 1,552 nm for PIN detectors with diameters varying from 10 to 30 \(\upmu\)m. From [14].  
Another Ge APD configuration is a conventional MSM structure with nano-engineered metal-to-metal spacing as small as 200 nm reported by [PERSON] _et al_. [55]. With a low bias voltage of \(\sim\)1.5 V, the electric field in the immediate vicinity of the metal contact is already high enough to initiate avalanche amplification. Although the whole APD structure is built on Ge, whose properties are not optimized for APD, the device exhibits a excess noise factor with k\({}_{\mathrm{eff}}\)\(\sim\) 0.2, high speed of \(\sim\)40 GHz, and bandwidth-efficiency product of 350 GHz at the wavelength of 1.3 \(\upmu\)m. Although the high dark current due to the small metal spacing requires more optimization, the Ge MSM APD shows great potential for the application of Ge in avalanche photodetection.  
The APD devices reported above are working at 1.3 \(\upmu\)m due to the incorporation of Si into Ge which gives rise to undesired reduction of the absorption efficiency at 1,550 nm. Using two-step Ge growth with a SiGe buffer layer method, the first waveguide-base Ge APD working at 1.55 \(\upmu\)m was reported by [PERSON] _et al_. [54]. The device is fabricated based on a SACM structure. A waveguide was used to increase device efficiency and facilitate future Si photonics integration. The reported high responsivity at unity gain was as high as \(\sim\)0.8 A/W and a product bandwidth-efficiency of 105 GHz was achieved.
## 2 Main Concepts  
### Single-Frame Reconstruction  
The technique of reconstructing HDR images from a single image, or single frame, aims to recover information lost in areas of high and low exposure, using a low dynamic range image as input and generating a high dynamic range image as output. The scope of this technique can vary [8; 39; 40], resulting in a 16-bit depth image [41; 42] or tone mapping [23; 43].  
The challenges of this technique mainly involve the generation of artifacts, i.e., noticeable visual errors [14]. To overcome these problems, a common strategy is to use image restoration networks with blocks that create attention maps capable of identifying over-exposed and underexposed regions and mitigating their influence on the result. In this scenario, the most widespread approach is the use of architectures based on convolution blocks [44; 45], although there are recent proposals that apply other strategies [46], as well as the use of specific sensors [47].
# Estimation of Resonance Frequency for Systems with Contact Using Linear Dynamics Methods
## 5 Experimental Verification
### Tests on Satellite Antenna Structure  
An additional structure was selected as another experimental object. Non-destructive tests were performed on the ESEO satellite antenna panel. The panel is made out of two-layered CFRP composite, bonded in lightweight foam structures to support electronics and stiffen the setup. The cross section with the description is shown in Figure 7. The contact of this case is based on the fixation method.  
Figure 6: Manufactured test bench for experimental verification of the presented formula.  
The component is tightened to the satellite structure with three bolts in cut-out holes in the main structure. The upper half cycle is when the panel is supported only on the bolt heads, the lower half cycle is when it is supported on the edge of the cut-out hole in the main structure (Figure 8).  
The numerical model for the modal analysis of the antenna was prepared with layered shell elements for CFRP panels and solid elements for foam parts. The shell and solid elements were glued together in the interface areas. The electrical connector was modeled as a mass point attached to its fixation points in the CFRP panel. The mechanical properties of the materials are presented in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **CFRP** & & & **Rohacell 51 IG-F** & & **PCB** & \\ \hline \(E_{1}\) & 60 & GPa & \(E\) & 70 & MPa & \(E\) & 70 & GPa \\ \(E_{2}\) & 60 & GPa & \(\
u\) & 0.3 & - \\ \(\
u_{12}\) & 0.07 & - & \(\rho\) & 52 & kg/m\({}^{3}\) & \(\rho\) & 9000 & kg/m\({}^{3}\) \\ G\({}_{12}\) & 4 & GPa & & & & & \\ G\({}_{23}\) & 3 & GPa & & & & & \\ G\({}_{13}\) & 3 & GPa & & & & & \\ \(\rho\) & 1500 & kg/m\({}^{3}\) & & & & & \\ \hline \end{tabular}
\end{table}
Table 3: Mechanical properties used for antenna simulation.  
Figure 8: Boundary conditions—fixation of bolt heads in upper half-cycle (**left**) and support contact edge in bottom half-cycle (**right**).  
Figure 7: Cross section of the satellite antenna panel used for experimental tests.  
The experimental tests of the component were completed with the use of Dongling Technologies vibration machine model with GT600M table. Vibrations were measured with the Polytec vibrometer model: PSV-500. The software used for the control was Siemens NV LMS Test.Lab. The component was screwed with three bolts to the fixation tool, mounted on the table of the vibration machine. The test stand is shown in Figure 9. The test was performed in accordance with the ESA requirements [26]. The test method was sine sweep of equal enforced acceleration of the base. The tested frequency range was 20-2600 Hz with an acceleration amplitude of 1 g on the \(Z\) axis.
# Recent Advancement of Data-Driven Models in Wireless Sensor Networks: A Survey
###### Abstract  
Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN's energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation.  
wireless sensor network; application types; data-driven models; event-driven; query-driven; time-driven; hybrid-driven; data collection; energy consumption  
1 Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia; [EMAIL_ADDRESS]  
2 Department of Computer Sciences, Karakoram International University, Gilgit Baltistan 15100, Pakistan; [EMAIL_ADDRESS]  
3
## 2 Materials and Methods
### Laboratory Analysis  
#### 2.2.1 Petrographic Studies  
In this study, beachrock types were grouped according to [PERSON]'s classification of carbonate rocks ([PERSON], 1962, as cited in [21]) using five properties: grain size, chemically precipitated cements, textural maturity, miscellaneous transported constituents, and clan designation. Folk's fivefold name must be in the following format: (Grain size): (chemically precipitated cements) (textural maturity) (miscellaneous transported constituents) (clan designation). Samples of beachrock that were brittle and poorly sorted were easy to break into pieces. Resin was added to such samples to solidify them, and they were cut to make thin sections for microscope slides. The collected samples were then examined in the laboratory using an optical petrographic microscope equipped for polarized light microscopy. The petrographic analysis was carried out at the Optical Geology Laboratory to assess the biostratigraphy of the samples.  
2.2.2 Inductively Coupled Plasma-Atomic Emission Spectroscopy and Mass Spectrometry (ICP-AES and MS)  
Each prepared beachrock sample (10 g) was added to lithium metaborate/lithium tetraborate flux, well mixed, and fused in a furnace at 1000 \({}^{\circ}\)C. Then, each sample was cooled and dissolved in 100 mL of 4% nitric acid and 2% hydrochloric acid for preparation and reduce of organic compound of samples. This solution was then analyzed using ICP-AES in the laboratory of ALS Canada Ltd. (North Vancouver, Canada). Major and trace elements were analyzed by ALS Canada Ltd. (North Vancouver, BC) using the ICP-AES and ICP-MS (method code ME-MS81L, iCAP 6000 Thermo Fischer Scientific) methods after aqua regia digestion (ARD) for combined whole rock analysis, trace elements by fusion and aqua regia digestion for the volatile trace elements, carbon and sulfur for combustion analysis, and several detection limit options for base metals. Four certified reference materials, ME-ICP06, OA-GRA05, ME-MS81, and TOT-ICP06, were measured using the same procedure as the samples for data calibration [18]. The loss on ignition (LOI) was determined using the gravimetric method. Each prepared beachrock sample (10 g) was placed in an oven at 1000 \({}^{\circ}\)C for one hour, cooled, and then weighed. The resulting material was then cooled and dissolved in an acid mixture containing nitric, hydrochloric, and hydrofluoric acids. Beachrock sample consists of 90% carbonate compound, therefore it was sensitive to the acid solution during the chromatography analysis.The normalization values for plotting the trace element concentration of the limestone samples are from the Average Phanerozoic Limestone [19], while Post-Archean Australian Shale (PAAS) normalization [22] was used for plotting major oxide and rare earth element (REE) concentrations in the samples [23; 24].
# Optimization Environment Definition for Beam Steering Reflectarray Antenna Design
## 3 Evolutionary Algorithms
### Algorithm Comparison
Figure 8 shows the complete convergence curves of mBBO, nBBO, DE, and SNO: each thin line corresponds to a single optimization trial, while the thick red line is the average one. These curves clearly show the differences between DE and SNO: for the first algorithm after half of the optimization process all of the trials have already reached convergence, while for SNO they continue to improve until the end of the available time. For what concerns the reliability, the DE trials have a higher dispersion of the results than SNO ones. Therefore, it is clear that SNO, in this specific electromagnetic problem, is able to reach the same performance of DE, but its reduced standard deviation makes it reliable for this computationally intensive problem.
# Optimizing Rule Weights to Improve FRBS Clustering in Wireless Sensor Networks
## 4 Rule Weights Interpretation
### Scenario Comparison  
After analyzing the weights obtained for maximizing FND, HND, or LND, in this section, we present some examples of rules that could improve the result for one metric in some scenarios, but not for all, while there were other rules that maximized one metric for all the scenarios.  
In Table 13, all the rules that improved the results for FND only for Scenario 1 are enumerated, whereas they were completely useless for the other two.  
Moreover, we can find examples of rules that maximized the result of FND for two scenarios (see Table 14) or for all, as can be seen in Table 15. In this last case, it can be observed that, for example, for most rules, the node was at an \"adequate\" distance from the BS, which means that nodes placed at an intermediate distance help the performance of the network to achieve good metrics. In contrast, in Table 16, the detailed rules were completely useless to maximize FND. We could make similar observations about the other two metrics, HND and LND, but we have summarized the results for FND because the whole set can be found in the link at the beginning of Section 4.1. We should remark that, after the optimization process, there were no rules whose weight was 0 for the three scenarios, consequently, the application of the three stages of KB had no rules without importance for this clustering method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**dBS** & **C** & **ND** & **RE** & **CHR** & **Output** \\ \hline F & M & M & H & L & lm \\ A & M & L & L & M & llm \\ A & L & H & L & H & m \\ C & M & H & M & H & s \\ A & M & H & M & H & ls \\ \hline \hline \end{tabular}
\end{table}
Table 13: Rules that optimized FND for Scenario 1 but not for 2 and 3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**dBS** & **C** & **ND** & **RE** & **CHR** & **Output** \\ \hline F & M & H & L & L & lm \\ C & L & M & M & L & lm \\ C & L & L & H & L & lm \\ A & L & H & L & M & lm \\ F & M & M & M & M & lm \\ A & L & H & M & M & m \\ C & H & M & H & M & s \\ F & L & L & M & H & llm \\ A & M & L & H & H & hlm \\ C & M & M & H & H & s \\ \hline \hline \end{tabular}
\end{table}
Table 12: Rules whose weights were null for the three KBs for Scenario 1.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**dBS** & **C** & **ND** & **RE** & **CHR** & **Output** \\ \hline F & M & H & L & L & lm \\ C & L & M & M & L & lm \\ C & L & L & H & L & lm \\ A & L & H & L & M & lm \\ F & M & M & M & M & lm \\ A & L & H & M & M & m \\ C & H & M & H & M & s \\ F & L & L & M & H & llm \\ A & M & L & H & H & hlm \\ C & M & M & H & H & s \\ \hline \hline \end{tabular}
\end{table}
Table 14: Rules that optimized FND for Scenario 1 and 2 but not for 3.
# The Full-Field Path Tracking of Agricultural Machinery Based on PSO-Enhanced Fuzzy Standley Model
## 2 Fuzzy-Based Stanley Model
### Parameter Self-Adaptation Based on Fuzzy Algorithm
The typical defuzzification methods include maximum subordination principle, center of gravity method and weighted average method [30]. The center of gravity method is widely used in industry application because of its smooth output, so the center of gravity method is chosen in our work for defuzzification, which is formulated as  
\[u_{0}=\frac{\sum_{k=1}^{m}u_{k}\mu_{k}(u_{k})}{\sum_{k=1}^{m}\mu_{k}(u_{k})} \tag{6}\]  
where \(\mu_{0}\) is the output coefficient; \(m\) is the number of levels; \(\mu_{k}\) is the coefficients for each level; and \(\mu_{k}(u_{k})\) is the degree of membership.
# Are Strong Baselines Enough? False News Detection with Machine Learning
## 2 Materials and Methods
### Datasets
#### 2.2.2 FakeNewsNet Data Repository  
The FakeNewsNet [35] is a data repository that contains two datasets. The datasets were collected from PolitiFact.com and GossipCop. The datasets include the collected news articles, social context, and information about users who interacted with the article on social media. The inclusion of user interaction information makes this data repository useful when detecting false news from social media. As shown by [PERSON] [33] and [PERSON] [34] the inclusion of user analysis is an excellent way to detect false news that is spread in social media. The datasets contain source URLs to the news articles, the title of the news, and the tweet IDs of users who interacted with the article on Twitter.  
The datasets are different in size, where the dataset collected from GossipCop is considerably larger than the dataset collected from PolitiFact.com. The distribution of false and true news in the datasets is imbalanced, especially in the dataset collected from GossipCop. The dataset collected from PolitiFact.com contains 432 news articles labeled as false and 624 news articles labeled as real. The dataset collected from GossipCop contains 6048 news articles labeled as false and 16,817 labeled as real.  
The articles in the PolitiFact.com dataset focus on political issues, whereas the articles in the GossipCop dataset contain news about celebrities. GossipCop used to be a website to fact-check articles and stories related to the entertainment industry. As GossipCop mainly focused on false stories, it provides a reason why the imbalance between real and false articles is large in the dataset.  
The average title length in the PolitiFact.com dataset is 10.74 tokens, and in the GossipCop dataset, it is 10.067 tokens. In the PolitiFact.com dataset, a news article was interacted with by 1.329 different users on average, and in the GossipCop dataset, the same average was 1.064. Table 6 shows an example of statements found in the FakeNewsNet [35] data repository.
## 1 Introduction
[40] set up an approximation formula for predicting the wind loads acting on vehicles traveling at longitudinal and vertical wind turbulences in the wind-vehicle-bridge system, and [PERSON] and [PERSON] [4] further considered the effect of aerodynamic admittance functions on unsteady aerodynamic forces. [PERSON] et al. [41] also reproduced the time-space distribution of the turbulent wind and introduced a stochastic methodology of aerodynamic loads on trains to evaluate the crosswind stability of trains by means of the characteristic wind curves. However, it should be noted that the complete turbulence field is not considered in the simulation of unsteady crosswind forces on trains in the aforementioned studies, which may result in an underestimation of the force fluctuation and peak caused by the wind turbulence. As a matter of fact, a train traveling through crosswinds will experience three-dimensional stochastic wind turbulence with longitudinal, lateral, and vertical components. [PERSON] et al. [36] pointed out that lateral turbulence cannot be ignored in the analysis of train operational safety if cross wind is not normal to the car body. As a result, the chief work of this study is to propose a formula for predicting unsteady crosswind forces on a moving train in the presence of a complete turbulence field, which is a further development and improvement for previous investigations by [PERSON] [34], [PERSON] et al. [36] and [PERSON] et al. [40]. Specifically, it establishes a prediction model for train force coefficients using CFD simulated data and details how wind turbulence with longitudinal, lateral, and vertical components at various crosswind directions produce fluctuating forces of a moving train through a simple mathematical derivation. The findings of this study are useful for precisely calculating dynamic responses and accurately assessing the running safety of trains caused by crosswinds in future investigations. This paper is organized as follows. In Section 2, the computational algorithm and brief details of the CFD model are presented and the numerical accuracy is validated by a scaled wind test. Section 3 describes the flow characteristics of a moving train traveling on a viaduct and proposes a model for predicting the aerodynamic coefficient varying with the resultant yaw angle. Section 4 introduces an interpolating method and a moving point approach for generating the fluctuating wind velocity time series at the position of the train. In Section 5, the calculation formula of unsteady crosswind forces on a moving train that takes into account the complete turbulence field is developed, and the effects of the weighting function and crosswind direction on unsteady forces are explored using numerical examples. Finally, some broad conclusions are drawn and further works are discussed in Section 6.
# Cross-Layer Protocol Based on Directional Reception in Underwater Acoustic Wireless Sensor Networks
## 4 Proposed CLIC Protocol
### Collision-Tolerant MAC Protocol with CT Priority
The interference determination scheme is given in Figure 4. As shown in Figure 4, Nodes 1, 2, 3, and 6 have sent CT packets, and Node 1 has the highest priority. Since the interference power of packets sent from Node 2 and received by Node R is greater than MeanP, Node 2 is silent. The interference power of the packets received by Node R from Node 3 and Node 6 is less than MeanP. When Node 6 transmits data to Node 7, since Node 7 is outside the transmission range of Node 1, the packet transmission of Node 1 will not affect the packet reception of Node 7, so Node 6 can transmit packets in the next slot. When Node 3 transmits data to Node 4, Node 4 is located within the transmission range of Node 1, so Node 3 cannot transmit data to Node 4, and Node 3 remains silent. 3. After the CT slot, the nodes that are not silent will transmit the DATA and wait for the ACK in the next slot. Figure 5 is an overview of DMAC. At the beginning of the CT slot, nodes send CT contention channels. Nodes with high CT priority send packets in the next slot and wait for ACK in the ACK slot.