esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Evaluation of Structural Safety for Aged Onshore Wind Foundation to Extend Service Life
## 1 Introduction  
Wind energy is drawing attention worldwide as a representative renewable energy source. Wind energy can be easily obtained from natural wind resources, but creating a new wind farm is never an easy task. A long period of time, from site selection to planning, civil affairs, licensing, financing, and construction, requires a very difficult and complicated process for many people to create a new wind farm. Wind farms constructed in such a difficult way are not able to operate indefinitely (power generation). In general, the service life of a wind turbine is about 20 years, and wind turbines that have reached the service life can no longer be operated and should be decommissioned. As a result, it is common sense that the wind farm composed of many wind turbines should also be decommissioned after a certain period of time, about 20 years [1].  
However, it is very nationally and socially wasteful to decommission of a wind farm after the wind turbine has reached the end of its service life. Currently, the wind farms in operation were built in the early days of wind power generation, and are mostly located in areas with excellent wind resources. Therefore, not having wind power generation again in these regions is also a national waste of energy resources. Namely, in order to successfully achieve the renewable energy plan promoted by the governments of each country, it is important to continue the wind power generation without destroying the wind farms currently in operation, as well as to construct a new wind farm [2], as presented in Figure 1[3, 4]. This is also because it is very difficult to find new candidates for wind farms because regions with abundant wind resources are limited [1, 2].  
In this paper, we discuss the method of structural safety evaluation and re-use of the onshore wind foundation in the case of the use of wind turbines after the service life and in the case of unexpected damage to the foundation during the service life.
# Region-Based CNN Method with Deformable Modules for Visually Classifying Concrete Cracks
## 3 Implementation
### Workstation and Training Settings  
The equipment used for calculation in the study is a desktop workstation with CUDA 10.0, cudnn 7.1, and MXnet deep learning framework installed on the Ubuntu 16.04 system. The hardware configuration of the workstation is as follows: a Core i7-9400 [EMAIL_ADDRESS] GHz CPU, 16 GB DDR4 memory, and a colorful GeForce RTX 2080 ti graphics processing unit (GPU) with 11 GB memory.  
Determining the hyper-parameters is necessary to train a deep learning model. Previous studies have proven that three key parameters, including the anchor size, batch size, and learning rate, have a significant impact on the performance of a deep learning model [43]. In the present study, two groups of anchor sizes (8, 16, 32 and 32, 64, 128), two batch sizes (64, 256), and three learning rates (0.001, 0.0001, and 0.0005) were adopted to train these models under consideration. Therefore, there are 12 combinations of these parameters for each model training. Each case was trained by an end-to-end training strategy. During the training process, the average loss for every 100 batches was recorded and the model for each training epoch was saved. It should be noted that if the crack area matches the crack type of the ground truth with the intersection over union (IoU) at least 50% and the calculated probability of softmax greater than 0.7, the area is counted as a true positive.
# Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming
## 2 Model Analysis of LEO Constellation Navigation Resource Allocation  
### Network Model  
The navigation resource allocation for the LEO constellation needs to consider two primary types of LEO navigation enhancement functions: navigation signal transmission and SBM. The system architecture is shown in Figure 1.  
Figure 1: LEO constellation navigation enhanced function architecture diagram.  
As depicted in Figure 1, individual LEO satellites are dedicated to a single function--either navigation signal transmission or SBM--but not both simultaneously. This segregation is essential because the power of navigation signals significantly exceeds that of the signals received for SBM. Consequently, if the frequencies of SBM and navigation signal transmission are similar or overlap, substantial interference ensues. To mitigate this, satellites are configured with transceiver isolation, whereby different satellites within the constellation are assigned to either navigation signal transmission or SBM. This ensures that each satellite is optimized for its specific function without cross-interference.
## 2 Materials and Methods
### Sc-CO\({}_{2}\) Extraction  
The Sc-CO\({}_{2}\) extraction of coals were conducted via HA Supercritical Extractor/Reactor produced by Nantong Haian Supercritical Extraction Instrument Co. LTD (Nantong, China). The equipment can bear a maximum pressure of 50 MPa and a maximum temperature of 300 \({}^{\circ}\)C. The flow chart of Sc-CO\({}_{2}\) extraction is shown in Figure 2. The operations are as follows:  
(1) The extraction cell was opened and \(\sim\)100 g dried sample was put into the supercritical extraction cell, which was then covered with the cauldron and the screws were tightened to ensure a good seal.  
(2) The CO\({}_{2}\) gas cylinder valve was opened to allow the CO\({}_{2}\) gas flow through the purifier and the cooling system, after which it was eventually compressed into the extraction cell. After the pressure and temperature of the cell were adjusted to \(\sim\)10 MPa and 40 \({}^{\circ}\)C, the CO\({}_{2}\) gas cylinder valve was closed to ensure that the extraction cell remained under the designed condition. The temperature and pressure were monitored throughout the process.  
(3) After 24 h of Sc-CO\({}_{2}\) extraction, the supercritical equipment was shut down, the gas was released to bring the pressure of the extraction cell to the atmospheric pressure, the coal sample was collected after the extraction cell was cooled to room temperature, the separation cells were washed by a solvent, and the waste liquid was collected for testing.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Sample** & **Qiyi Coal** & **Liulin Coal** & **Dengjiazhuang Coal** & **Shuangliu Coal** & **Maozequ Coal** \\ \hline \(R_{\mathrm{o,max}}\) (\%) & 0.79 & 1.06 & 1.33 & 1.58 & 1.80 \\ Vitrinite (\%) & 78.40 & 78.48 & 67.41 & 56.84 & 63.37 \\ Internitine (\%) & 14.87 & 19.16 & 25.18 & 37.71 & 30.43 \\ Exinite (\%) & 5.48 & 2.36 & 4.94 & 3.13 & 4.72 \\ Mineral (\%) & 1.25 & 10.0 & 2.47 & 2.32 & 1.48 \\ M\({}_{\mathrm{ad}}\) (\%) & 4.34 & 1.94 & 1.09 & 0.83 & 0.98 \\ A\({}_{\mathrm{d}}\) (\%) & 6.57 & 10.51 & 8.47 & 11.36 & 7.69 \\ V\({}_{\mathrm{ad}}\) (\%) & 31.05 & 27.59 & 26.85 & 23.52 & 20.31 \\ \hline \end{tabular}
\end{table}
Table 1: Vitrinite reflectance, macroals, and proximate analysis of the coals.  
Figure 1: Location of coal samples collected.
## 4 State of Art
### Magnetic Force Sensors
#### 4.3.1 Magnetocristive Sensors
The fiber optic sensor employed the intrinsic magnetostriction of the ferromagnetic core of a standard current transformer and was interrogated with a Mach-Zehnder interferometer. The sensor avoided certain defects of Faraday based optical current sensors reported, such as, optical power source fluctuations, phase drifts and polarization dependency. Figure 13 represents the experimental configuration used by [PERSON] _et al_. **Figure 13.** Experimental arrangement used for measuring electric current on high voltage systems. More recently, [PERSON] _et al_[64] presented results of measurement of AC and DC magnetic fields by using a fiber optic interferometric sensor for structural health monitoring. The principle of operation was based on changes in the optical path length of the cavity between a magnetostrictive wire and a fiber optic tip. The sensing configuration consisted of a Michelson fiber interferometer using a \(3\times 3\) single mode fiber coupler. They were able to detect the instant separation between the wire and fiber end with an accuracy of 50 nm corresponding to a magnetic field in a range of 50 nT to 800 \(\upmu\)T.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 4 WSN Systems in Different Applications: Aerospace, Bridge Structures, and Wind Turbines
### WSN in Wind Turbines
#### 4.3.1 Large Wind Turbines and Wind Turbine Farms
Communication Mode--transmitting signals/data between units by a cable, wireless media, satellite, etc. Software, which works as an interface between the operator and the units, is an important element that allows the operator to visualize and control the functions of the process [87].
## 3 Software Platform and Simulation Tools  
In the present study, we use the latest version of Contiki 3.0. As mentioned above, Contiki is an open-source operating system for the Internet of Things that supports tiny low-cost, low-power microcontrollers connected to the Internet. We focus on Contiki available features such as platforms, network protocols, and radio duty cycles to provide an optimized way to reduce energy consumption in IoT nodes [30]. In this research, we use the Cooja simulator available at Contiki Os to simulate the network scenario and run the proposed method on the nodes. The collect-view data gathering software is used to evaluate the status of network nodes in terms of different parameters. One of the strengths of Cooja is the ability to simulate radio medium activity, that the researchers can graphically analyze the radio states [31; 32].
# Transmission Removal from a Single Glass Scene and Its Application in Photographer Identification
## 4 Results
### Transmission Removal Results for General Scenarios

Table 2: Quantitative evaluations for transmission removal using three different error metrics of LPIPS, SSIM, and PSNR. (The best and second-best results are shown in bold and underlined, respectively.) Figure 9: LPIPS value comparisons of the transmission removal results on all 50 real-world mixture images. Lei2021: [13], Li2014: [14], Chang2021: [16], Li2020: [17]. each transmission removal image for ease of observation, and the equalized version was placed below the corresponding original image of the recovery results. In fact, a visually better DL contrast enhancement method, such as Zero-DCE [42], LE-GAN [43], can be used here to enhance the visual effect further. It would even be possible to directly adopt the enhancement network from the literature [20], but from the concern of simply measuring the effect of transmission removal, this is not in our scope for this paper. As shown in the figure, the four reflection layer images generated by our method are visually closest to the ground truth reflections among all the methods. Specifically, the results of [14; 17] are obscure and lack sufficient information, while the results of [13; 16] contain relatively clearer reflection contextual information than [14; 17], although they also contain some transmission information. Our results are clearer than [13; 16] in general, especially in terms of retaining texture information, and our method also has a better separation effect in terms of transmission layer removal. It is worth pointing out that because the training code and dataset are not released in [10; 20], we did not directly compare with them. \begin{table}
\begin{tabular}{c c} \hline \hline  & **Training Time (hour)** \\ \hline [PERSON] et al. [13] & 17 \({}^{\dagger}\) \\ [PERSON] et al. [16] & 21 \({}^{\dagger}\) \\ [PERSON] et al. [17] & 20 \({}^{\dagger}\) \\ Ours(b) & 7 \\ \hline \hline \end{tabular} Note: “\(+\)” stands for the retraining time for our training dataset.  
\end{table} 
Table 3: Training time of the proposed network and other networks  
Figure 10: Qualitative comparison of the transmission removal results on real-world mixture images. (i)–(iv) correspond to four different test images. For each test image, the first row and second row are the direct outputs of all methods and their corresponding histogram equalized versions. Lei2021: [13], Li2014: [14], Chang2021: [16], Li2020: [17]. Meanwhile, we also recorded the training time of [13; 16; 17], and our network in Table 3 when the epoch was set as 50. From the table, our network has the least training time in the same training set, which is more conducive to rapid comparison. In the end, we also tested our method on some synthetic mixture images. Figure 11 shows three examples of transmission removal results of our method and comparison methods. From the results of the synthetic images, we can still conclude that our method has better visual effects and reflection layer details.
## 5 Results and Discussion  
### Simulation Scenarios  
In this section, two experimental scenarios based on real wireless Ethernet bridge connections are analyzed from point to point, as shown in Figure 2. The height of the TX antenna is 25 m at Aghia Paraskevi (Access Point), and the RX antenna is 55 m high on the transmission tower of Mount Hymettus (station). Both terrestrial microwave points are part of the Hellenic Broadcast Corporation, which is the state-owned public radio and television broadcaster of Greece. The microwave transmission parameters are provided in Table 1, where both the transmitter and receiver in the link have the same types of antennas.  
Figure 1: (**a**) Flowchart and (**b**) pseudocode of the project.  
For these experiments, the equipment of the transmitter and the receiver operates in the C and X bands. They are both supplied by the company Ubiquiti Networks. The names of the models are PowerBeam and AirFiber AF-11, and their specifications are described in the relevant datasheets [14,15]. The two microwave systems use the Time Division Duplex (TDD) method to send and receive data using a single radio channel. Figure 3 displays the path profile between the transmitter (Access point) and receiver (station) sites.
# A Review of Sharp-Switching Band-Modulation Devices
## 1 Introduction
### Device Variants  
The architecture of the Z\({}^{2}\)-FET can be further enriched in several ways.  
_Dual ground-planes_ (Figure 3a): The ground-plane (GP-P) located underneath the ungated portion of the body is negatively biased and generates the necessary \(P^{*}\) electrostatic doping. The sister ground-plane, located beneath the gated region, is positively biased and assists the front gate in forming a strong energy barrier [17]. This architecture eliminates the inconvenience of a single ground plane, which tends to counteract the front-gate action (Figure 2a).  
_Physically-doped ungated section_ (Figure 3b): The idea here is to eliminate the back-gate action. Boron implantation replaces the \(P^{*}\) electrostatic doping, without affecting the sharpness of the device characteristics [18]. This solution is well suited to FinFETs and nanowires, although it does sacrifice some of the reconfigurability.  
\(Z^{3}\)-_FET_ (Figure 3c): This Zero top-gate variant has the partitioning of the body doping controlled by the twin ground-planes [19]. The omission of the gate stack enables operation at higher voltage, without dielectric reliability issues. Furthermore, the free surface can be used for superior detection of light, biochemical species, or radiation.
# A Lightweight Convolutional Neural Network Method for Two-Dimensional PhotoPlethysmoGraphy Signals
## 3 Experiment
### Dataset  
The PPG signals used in this study were sourced from the BIDMC PPG and Respiration Dataset [20] available on Physionet. In order to conduct a more accurate experimental analysis, the PPG signals of 16 individuals were screened as research samples, and the sampling frequency was 125 Hz. These individuals included a total of eight men and eight women. The oldest was 88 years old, the youngest was 19 years old, and the average age was 41 years old, ensuring the representativeness and validity of the data. After preprocessing the dataset and eliminating any aberrant values, 4439 two-dimensional images were generated, each contributing between 200 and 350 images. For model training and learning, 70% of the overall dataset for each individual was allocated as the training set. The remaining 30% was the test set to evaluate the model's performance and accuracy.
# Bonded-Particle Model with Nonlinear Elastic Tensile Stiffness for Rock-Like Materials
###### Abstract  
The bonded-particle model (BPM) is a very efficient numerical method in dealing with initiation and propagation of cracks in rocks and can model the fracture processes and most of macro parameters of rocks well. However, typical discrete element method (DEM) underestimates the ratio of the uniaxial compressive strength to the tensile strength (UCS/TS). In this paper, a new DEM method with a nonlinear elastic tensile model embedded in BPM is proposed, which is named as nonlinear elastic tensile bonded particle model (NET-BPM). The relationships between micro parameters in NET-BPM and macro parameters of specimens are investigated by simulating uniaxial compression tests and direct tension tests. The results show that both the shape coefficient of the nonlinear elastic model and the bond width coefficient are important in predicting the value of UCS/TS, whose value ranging from 5 to 45 was obtained in our simulations. It is shown that the NET-BPM model is able to reproduce the nonlinear behavior of hard rocks such as Lac du Bonnet (LDB) granite and the quartzite under tension and the ratio of compressive Young's modulus to tensile Young's modulus higher than 1.0. Furthermore, the stress-strain curves in the simulations of LDB granite and the quartzite with NET-BPM model are in good agreement with the experimental results. NET-BPM is proved to be a very suitable method for modelling the deformation and fracture of rock-like materials.  
discrete element model; nonlinear elastic model; UCS/TS; Young's modulus; calibration Article
###### Abstract  
Device-to-device (D2D) cooperative relay can improve network coverage and throughput by assisting users with inferior channel conditions to implement multi-hop transmissions. Due to the limited battery capacity of handheld equipment, energy efficiency is an important issue to be optimized. Considering the two-hop D2D relay communication scenario, this paper focuses on how to maximize the energy efficiency while guaranteeing the quality of service (QoS) requirements of both cellular and D2D links by jointly optimizing relay selection, spectrum allocation and power control. Since the four-dimensional matching involved in the joint optimization problem is NP-hard, a pricing-based two-stage matching algorithm is proposed to reduce dimensionality and provide a tractable solution. In the first stage, the spectrum resources reused by relay-to-receiver links are determined by a two-dimensional matching. Then, a three-dimensional matching is conducted to match users, relays and the spectrum resources reused by transmitter-to-relay links. In the process of preference establishment of the second stage, the optimal transmit power is solved to guarantee that the D2D link has the maximized energy efficiency. Simulation results show that the proposed algorithm not only has a good performance on energy efficiency, but also enhances the average number of served users compared to the case without any relay.  
D2D; energy efficiency; resource management; cooperative relay; matching theory +
Footnote †: journal: Applied Sciences
# Improving Named Entity Recognition for Social Media with Data Augmentation
## 3 Method  
The social media NER task is commonly approached as a sequence tagging task, where the input sequence \(X=x_{1},x_{2},\cdots,x_{n}\), which consists of \(n\) tokens, is annotated with corresponding named entity (NE) labels \(Y=y_{1},y_{2},\cdots,y_{n}\) of the same length. In line with this approach, we propose a neural model with data augmentation for social media NER. First, we pre-trained a BERT language model to extract semantic vectors of each word in the training data. Then, we used the GloVe model to obtain similar entities via data augmentation methods. The GloVe model helped us to obtain similar entities for each named entity in the training data, which were then subjected to replacement or semantic transformation. Finally, we fed the augmented data, along with the original data, into the Bi-LSTM-CRF model for training and used a self-attention layer to capture the underlying information of the features and reduce the dependence on external information. By fine-tuning the model parameters, we obtained the optimal labels for each named entity in the test data. The architecture of our model is depicted in Figure 1.
## 4 Results and Discussion
### Forced Vibration under Different Load Positions
The monitoring point's axial relative position is 0.8, keeping other parameters  
Figure 11: Influence of various relative load positions on the displacement response of the monitoring point located at an axial relative position of 0.65 and a circumferential angle of 0\({}^{\circ}\): (**a**) radial direction, (**b**) axial direction. Figure 10: Influence of a partly circumferential crack on the displacement response of the monitoring points (circumferential angle 0\({}^{\circ}\)) with a relative load position of 0.45: (**a**) radial direction, (**b**) axial direction. consistent with those in Figure 11. The general trend in the variation of displacement amplitude difference depicted in Figures 11 and 12 is similar. Otherwise, there are some minor differences. In Figure 12, the upward trend of the radial and axial amplitude differences does not weaken when the load moves from the axial relative position of 0.4 to 0.45. In addition, the ranges of amplitude difference change for both radial and axial directions in Figure 12 and are smaller than those of corresponding directions in Figure 11.  
In summary, this section studies the influence of various point load positions on the displacement response around the crack. First, as the load point nears the crack, the amplitude differences of the monitoring points exhibit an initial increase followed by a decrease as the distance between the monitoring point and the crack grows. This phenomenon vanishes gradually with the increasing distance between the load position and the crack, ultimately becoming a consistent downward trend in the amplitude difference. According to prior results, the subsequent analysis chooses monitoring points with regular amplitude difference variations. Then, the amplitude differences are analyzed under different load positions for the monitoring points. The results indicate that the corresponding amplitude differences experience an upward trend as the load point approaches the crack. In addition, the rise in crack depth intensifies this trend. These results can provide a basis for determining the optimal locations of excitation and monitoring points when identifying crack circumferential position.