esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Introduction  
In the seminiferous tubules of the testis, spermatogonia differentiate into spermatocytes and then spermatids during spermatogenesis, leading to the formation of spermatatozoa. Spermat are intimately associated with the supporting Sertoli cells. The communication between these cells involves the formation of gap junctions, of which Gja1 (Connexin43) is the main unit. Each cell participating in the formation of a gap junction harbors a connexon, made of six connexin protein units, on its plasma membrane. The gap junction is made by the interaction between two connexons from adjacent cells and allows the exchange of different second messengers and small molecules (<1 kDa), including the cyclic nucleotides cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP), inositol trisphosphate, glutathione, and calcium ions.  
The expression of Gja1 in spermatatogonia has been confirmed in several studies [1, 2]. Absence of Gja1 in mice results in an important depletion of primordial germ cells in fetal testes [3]. Sertoli cells' specific inactivation of _Gja1_ results in an abnormal formation of the blood-tests barrier, infertility and/or Sertoli cells only syndrome [4, 5, 6, 7]. However, mice with germ cells with specific disruption of _Gja1_ expression are fertile and do not have apparent changes in the assembly of the blood-testis barrier [8], most likely due to the compensatory expression of other connexins in germ cells.  
Regulation of the abundance of Gja1 in spermatatogonia involves various processes, including gene transcription, mRNA maturation, protein synthesis, post-translationalmodifications, connexon assembly, cytoskeletal anchoring, plasma membrane integration and protein degradation [9]. Gene expression of _Gja1_ is abnormally decreased in most testicular germ cell tumors (TGCT) [10]. In fact, various types of human testicular tumors exhibit reduced or even absent _Gja1_ expression in Sertoli cells and germ/tumor cells [11; 12]. Hence, a better understanding of the mechanisms of transcriptional regulation of _Gja1_ in spermatozoa will provide a better understanding of how this connexin is deregulated during the development of testicular cancer. Previously, we have shown that members of the activator protein-1 (AP-1) family of transcription factors, Fos and Jun, cooperate to activate the _Gja1_ promoter in TM3 Leydig and TM4 Sertoli cells [13]. Moreover, Jun also cooperates with Sox8/Sox9 to increase _Gja1_ expression in Sertoli cell lines [14]. Different signaling pathways and kinases, including the cAMP/protein kinase A (PKA) and Ca\({}^{2+}\)/c calmodulin-dependent protein kinases (CaMK), can modulate the activity of AP-1 members [15].  
Here, we propose that the Ca\({}^{2+}\)/calmodulin-dependent signaling and/or cAMP/PKA pathways may be involved in the regulation of _Gja1_ in GC-1 spermatogional cells. Such regulation may involve the activation of AP-1 members, followed by their recruitment to the proximal region of the _Gja1_ promoter.
# Evaluation of Bol Corrosion Degree Based on Non-Destructive Testing and Neural Network
## 2 Corrosion Mechanism and Experimental Study of Anchor Bolts
### Corrosion Theoretical Model of Anchor Bolts
#### 2.1.2 Corrosion Current Calculation  
The constant-current-accelerated corrosion method was employed in this study to accelerate the corrosion rate of the anchor bolt by applying a constant electric current to its corroded surface. Specifically, this current can be calculated using the following equation:  
\[I\ =\ is\ =i\pi dl \tag{7}\]  
where \(i\) represents the current density on the corroded surface of the anchor bolt, \(s\) represents the area of that surface, \(d\) represents the diameter of the anchor bolt, and \(l\) represents the length of the anchor bolt. In this experiment, the constant current method was employed with a current magnitude of 2.5 A and a corrosion contact area of 5942 mm\({}^{2}\). The calculation yields an average corrosion current density of 0.04 A/cm\({}^{2}\).
# A Dynamic, Cost-Aware, Optimized Maintenance Policy for Interactive Exploration of Linked Data
## 4 Experimental Study
### Performance Evaluation
#### 4.2.1 Maintenance Cost  
We define the maintenance cost as the time taken to perform maintenance operations. Figure 3 shows the maintenance cost for each policy on each dataset. Our proposed approach, CAMP, performed the maintenance job in the background when the system was idle and enough resources were available. Therefore, the overhead of our proposed approach was completely hidden from the user and measured the required system time in order to perform the offline maintenance task. The maintenance cost was measured as a sum of the response time and total time. Our approach produced a lower elapsed time of 5 s as the query does not pay for the cost of maintaining the view. Compared with _Eager Maintenance_ where the query has to wait until the maintenance job is completed, the average response time was 15 s. However, PageRank, Size and TTL did not consider maintenance cost while updating the local views and these strategies performed worst as the query had to wait until the maintenance jobs were completed. Similarly, in the case of the ChangeRatio and ChangeRate, these policies use the periodic update function that keeps on tracking the changed occurred in the Linked Data cloud. As these policies often run in the background, they produced high latency.
## 2 Eigenvalue Analysis of Dielectric Resonator  
The electromagnetic analysis, aiming at determining resonant frequency of SPDR test-fixture, without and with dielectric sample inserted, has been conducted for the structure following [9]. The SPDR is composed of two ceramic disks that are separated with a gap, supported with dielectric rods and surrounded with a metal enclosure. The benchmark is depicted in Figure 1 and considered with relative permittivity values \(\varepsilon_{1}=2.06,\ \varepsilon_{2}=4.43,\ \varepsilon_{3}=30.15\) and dimensions \(r_{1}=1\ \text{mm},\ r_{2}=4\ \text{mm},\ r_{3}=8.41\text{mm},\ r_{4}=13\ \text{mm},\ r_{5}=23\ \text{mm},\ h1=4.26\text{mm},\ h_{2}=2\ \text{mm},\ h_{3}=6.36\ \text{mm},\ t_{gap}=1.6\ \text{mm}\). Such a resonator configuration has a nominal frequency of ca. 4.8 GHz. The conducted electromagnetic analysis benefits from axial symmetry of SPDR; therefore, the modelling scenario is reduced to the computational 2D problem. In the considered problem, the sample thickness is denoted by \(t_{sample}\) and has relative electric permittivity \(\varepsilon_{sample}\). Metallic enclosure outside the resonator is taken as perfect electric conductor (PEC) for the numerical purposes.  
Firstly, a resource efficient vector 2D (V2D) formulation of the FDTD method for axisymmetric structures has been employed to extract the fundamental resonant frequency of the resonator without a sample (i.e., \(\varepsilon_{sample}=1\)). The aforementioned formulation allows for a problem size reduction by three orders of magnitude compared to standard 3D modelling, from 19,000,000 of FDTD cells and 1.8 GB of RAM memory occupation required for 3D model, to 17,000 of FDTD cells and 2 MB of RAM occupation for V2D. The computation of the resonant frequency takes ca. 30 s when run on nVidia GeForce GTX Titan.  
For the same problem, an analogous axisymmetric formulation of frequency domain FEM (FD-FEM) [5, 16] is also used to determine the fundamental frequency. Since it is physically known that the fundamental mode will only have \(\phi\) independent \(E_{\phi}\) component, the discretization is also done only for the \(\phi\)-component of E-field using scalar nodal shape functions. An in-house developed C ++ solver is used with a mesh consisting of nearly 100,000 triangular elements, and it takes just under 2 s to determine the resonant frequency on an Intel-i7 processor, and it occupies 150 MB of RAM.  
As expected, for a deterministic resonator problem, a frequency domain approach is capable of providing faster solutions than a time-domain approach. The obtained resonant frequencies with varying sample thicknesses and permittivities are given in the following section and verified with respect to own measurements performed for the purpose of this paper.
# A Study on Technology Competition of Graphene Biomedical Technology Based on Patent Analysis
###### Abstract  
Graphene, with high biocompatibility, physiological solubility and stability, has been reported as an emerging material for biomedical applications such as biosensors, drug delivery, and tissue engineering. Recently, identifying the technological competition (TC) of graphene biomedical technology has received worldwide attention from stakeholders. However, few studies have attached great importance to review the TC of this field by the analysis of patents. The main objective of this study is to develop a new and comprehensive method to investigate TC in a given technology field by conducting a patent review and then employing a patent roadmap to dig out the technology opportunity. The effectiveness of the approach is verified with the case study on graphene biomedical technology. Compared to previous research, this study makes the following important contributions. First, this study provides a new and systematic framework for the dynamic analysis of TC in a given technology field. It also extends the research perspectives of TC for industry, assignees, and technology, employs a patent roadmap to dig out technology opportunities, and enables stakeholders to understand TC from a dynamic perspective. Second, this study integrates patent analysis with a patent roadmap that has not appeared in existing methodologies of patent review. Third, it first introduces indicators (e.g., high value patent and competition position of top assignees) to the previous patent roadmap and provides a new methodology for patent roadmaps from a country level and assignee level. Finally, this study provides useful information for stakeholders interested in graphene biomedical technology, helps them to find new technology opportunities in this field, encourages them to determine the direction of future research, and has important significance for its application to diverse other emerging technologies.  
technology competition; graphene; biomedical; patent analysis; patent roadmap; technology opportunity +
Footnote †: journal: Applied Sciences
###### Abstract  
Recently, increasing interest in managing pedestrian and bicycle flows has been demonstrated by cities and transportation professionals aiming to reach community goals related to health, safety, and the environment. Precise forecasting of pedestrian and bicycle traffic flow is crucial for identifying the potential use of bicycle and pedestrian infrastructure and improving bicyclists' safety and comfort. Advances in sensory technology enable collecting massive traffic flow data, including road traffic, bicycle, and pedestrian traffic flow. This paper introduces a novel deep hybrid learning model with a fully guided-attention mechanism to improve bicycles and pedestrians' traffic flow forecasting. Notably, the proposed approach extends the modeling capability of the Variational Autoencoder (VAE) by merging a long short-term memory (LSTM) model with the VAE's decoder and using a self-attention mechanism at multi-stage of the VAE model (i.e., decoder and before data resampling). Specifically, LSTM improves the VAE decoder's capacity in learning temporal dependencies, and the guided-attention units enable selecting relevant features based on the self-attention mechanism. This proposed deep hybrid learning model with a multi-stage guided-attention mechanism is called GAHD-VAE. Proposed methods were validated with traffic measurements from six publicly available pedestrian and bicycle traffic flow datasets. The proposed method provides promising forecasting results but requires no assumptions that the data are drawn from a given distribution. Results revealed that the GAHD-VAE methodology can efficiently enhance the traffic forecasting accuracy and achieved better performance than the deep learning methods VAE, LSTM, gated recurrent units (GRUs), bidirectional LSTM, bidirectional GRU, convolutional neural network (CNN), and convolutional LSTM (ConvLSTM), and four shallow methods, linear regression, lasso regression, ridge regression, and support vector regression.  
variational autoencoder; self-attention; hybrid deep learning; traffic flow forecasting.
## 2 Materials and Methods
### Experiments
During the training process, three techniques for regularization were adopted: early stopping, dropout, and batch normalization. Early stopping was implemented to avoid overfitting and to stop the training process in case no further improvements were seen in the validation loss. A patience of 5 epochs was used with minimum improvements of \(1\times 10^{-2}\) on the validation loss. At the end of each training process, the model's best weights determined by the early stopping mechanism were restored. Dropout layers were enabled during the training process before each transposed convolution with a probability of 25%. Moreover, after each convolutional layer, batch normalization was performed. Moreover, to guarantee the reproducibility of the tests, in each training of the cross-validation process the networks were initialized with the same weights generated from the same seed number, using a normal distribution and the Glorot initialization [55]. The two subtasks introduced in Section 2 are evaluated as follows. * The _binary classification between burned and unburned_ areas is evaluated with Precision, Recall, and F1-Score metrics [56]. Precision considers the purity of the predictions: among the pixels predicted as belonging to a certain class, e.g., belonging to a burned region, it indicates the percentage of matches with the GT. Recall verifies the ability of the estimator to recognize all the pixels belonging to a certain class, specified in the GT. Therefore, given the whole set of pixels belonging to a certain class (referring to the GT), the recall is the percentage of correctly predicted pixels among the whole set. The F1-Score is the harmonic mean between Precision and Recall. It is as a measure of accuracy with the property to consider the class imbalance. \begin{table}
\begin{tabular}{l c c} \hline \hline
**Transformation** & **Probability** & **Parameters** \\ \hline Random rotation & 50\% & Angle: [\(-50^{o}\), \(+50^{o}\)] \\ Random horizontal flip & 50\% & - \\ Random vertical flip & 50\% & - \\ Random shear & 50\% & Angle: [\(-20^{o}\), \(+20^{o}\)] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Data augmentation parameters. * The _estimation of damage severity_, concerning the distinction between 5 severity levels, is evaluated with Root Mean Squared Error (RMSE) metric. Given the ordinal relationship between damage severity levels, the RMSE gives a measure of distance between the prediction and the ground truth.
# GNSS Spoofing Identification and Smoothing Localization Method for GNSS/Visual SLAM System
## 5 Experimental Analysis under Forwarding Spoofing Attack
### Rural  
For the rural scene shown in Figure 4(a), the segment from 100 s to 150 s of the data set is exposed to a GNSS spoofing with 20 m spoofing distance. Figure 10 shows the absolute position errors of GNSS, VINS-Fusion, and PTC-SLM after GNSS forwarding spoofing attacks. Figure 11 shows the trajectory of the three methods. Their maximum value (Max), average value (Mean), median value (Median), minimum value (Min), rmse and std of the absolute position errors of the three methods are shown in Table 5 below.  
It can be seen that when the GNSS method fails to detect GNSS spoofing attacks, the GNSS localization results are directly affected by GNSS spoofing, and the absolute position error of GNSS method grows immediately as the GNSS spoofing attacks occur as shown in the blue curve in Figure 10. The error on trajectory is shown in Figure 11. The rmse of GNSS increased to 6.459 m.  
The VINS-Fusion method uses GNSS and visual SLAM data to obtain the localization. With the injection of GNSS spoofing, the localization result of VINS-Fusion will gradually deviate from the normal track. When the GNSS attack is lifted, the localization result of VINS-Fusion will return to the normal track. The absolute position error is shown in the brown curve in Figure 10, and the error on the trajectory is shown in Figure 11. The rmse of VINS-Fusion is 6.644 m.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(E_{s}\) & Max/m & Mean/m & Median/m & Min/m & Rmse/m & Std/m \\ \hline
8.2 m & 36.18 & 17.18 & 21.56 & 0.40 & 21.40 & 12.75 \\
11.5 m & 16.55 & 1.57 & 1.22 & 0.06 & 2.33 & 1.73 \\
14.7 m & 16.54 & 1.54 & 1.21 & 0.06 & 2.31 & 1.72 \\
26.2 m & 23.36 & 1.6  
The PTC-SLM method can detect GNSS forwarding spoofing attacks in time by inverting GNSS signals, and adopt a smoothing localization strategy to suppress the impact of GNSS spoofing. The absolute position error is shown in the green curve in Figure 10, and the error on the trajectory is shown in Figure 11c. It can be seen that the localization trajectory result basically coincides with the ground-truth (reference), and the rmse of PTC-SLM is 1.342 m, with an improvement of 79.223% compared with the GNSS method.
###### Abstract  
The operational environment of offshore wind turbines is much more complex than that of onshore wind turbines. Facing the persistent wind and wave forces, offshore wind turbines are prone to vibration problems, which are not conducive to their long-term operation. Under this background, first, how the wave affects the vibration characteristics of offshore wind turbines is analyzed. Based on the existing wave and wave load models, we analytically show that there exist fluctuating components related to the hydrodynamic frequency in the aerodynamic load and aerodynamic torque of offshore wind turbines. Simulation results based on a GH Bladed platform further validates the analysis. Second, in order to reduce the joint impacts of the wave, wind shear and tower shadow on the wind turbine, a variable pitch control method is proposed. The integrated tower top vibration acceleration signal is superimposed on the collective pitch reference signal, then the triple frequency (3P) fluctuating component of the wind turbine output power and the azimuth angle of each blade are converted into the pitch angle adjustment signal of each blade, which is superimposed on the collective pitch signal for individual pitch control. The simulation results show that the proposed pitch control strategy can effectively smooth the fluctuation of blade root flap-wise load caused by wind and wave, and significantly reduce the fluctuation of aerodynamic torque and output power of offshore wind turbines.  
wind force; wave force; aerodynamic load; aerodynamic torque; pitch control; vibration reduction
## 2 Basic Theory
### Basic Theory of Quantile Regression  
The essence of quantile regression (QR) [30] is to estimate the different conditional quantiles of response variables by taking different values of \(\tau\), so as to obtain the regression prediction model under all quantiles. The calculation formula is:  
\[Q_{Y}(\tau|X)=\alpha_{0}(\tau)+\alpha_{0}(\tau)X_{1}+\alpha_{0}(\tau)X_{2}+ \cdots+\alpha_{k}(\tau)X_{k}=X\cdot\alpha(\tau) \tag{8}\]  
where, \(\alpha(\tau)=[\alpha_{0}(\tau),\alpha_{1}(\tau),\ldots,\alpha_{k}(\tau)]^{\prime}\) is the model parameter related to quantile \(\tau\), the appropriate \(\alpha(\tau)\) needs to be solved by optimizing the following formula :  
\[min\sum_{i=1}^{N}\rho_{\tau}(Y_{i}-X_{i}\alpha(\tau))=min\sum_{i|Y_{i}\geq X_ {i}\alpha(\tau)}\tau(Y_{i}-X_{i}\alpha(\tau))+min\sum_{i|Y_{i}<X_{i}\alpha( \tau)}(\tau-1)(Y_{i}-X_{i}\alpha(\tau)) \tag{9}\]  
where, \(N\) is the number of samples, \(\rho\) is the optimization function.  
\[\rho_{\tau}(u)=\begin{cases}\tau u,u\geq 0,\\ (\tau-1)u,u<0.\end{cases} \tag{10}\]  
After obtaining the estimated value of \(\alpha(\tau)\), the estimated value of the response variable under \(\tau\) quantile can be obtained according to Equation (8).
## 3 Proposed MISO Converter
### Voltage Conversion Ratio  
To facilitate the derivation of the voltage conversion ratio and voltages across the energy-transferring capacitors \(C_{1}\), \(C_{2}\), and \(C_{3}\), only states 1, 4, and 8 are considered. In addition, the blanking times of the switches, the primary-side leakage inductances \(L_{lbp1}\) and \(L_{lbp2}\), and the secondary-side leakage inductance \(L_{lbs1}\) and \(L_{lbs2}\) are ignored.  
\[n=\frac{N_{s1}}{N_{p1}}=\frac{N_{s2}}{N_{p2}} \tag{4}\]  
From state 1 shown in Figure 7, the expressions for the voltages on \(L_{m1}\) and \(L_{m2}\) are as follows:  
\[v_{Lm1}=V_{d1} \tag{5}\]  
Figure 16: Circuit operation for state 11.  
Figure 17: Circuit operation for state 12.  
\[v_{Lm2}=V_{d2} \tag{6}\]  
From state 4 shown in Figure 10, the expression for the voltage on \(L_{m1}\) is the same as that state 1 but the voltage on \(L_{m2}\) is as follows:  
\[v_{Lm2}=V_{d2}-V_{C5} \tag{7}\]  
Additionally, the expressions for the voltages \(V_{C1}\), \(V_{C5}\), and \(V_{o}\) can be expressed as  
\[V_{C1}=V_{C5}-n(V_{d2}-V_{C5})+V_{C2}+nV_{d1} \tag{8}\]  
\[V_{o}=V_{C5}-n(V_{d2}-V_{C5})+V_{C2}+V_{C3} \tag{9}\]  
\[V_{C5}=\frac{V_{d2}}{1-D_{b}} \tag{10}\]  
On the other hand, from state 9 shown in Figure 14, the expression for the voltage on \(L_{m2}\) is the same as that for state 1 but the voltage on \(L_{m1}\) is as below:  
\[v_{Lm1}=V_{d1}-V_{C4} \tag{11}\]  
At the same time, the voltages on C2, \(C_{1}\), and \(C_{4}\) are given by  
\[V_{C2}=V_{C4}-n(V_{d1}-V_{C4})+nV_{d2} \tag{12}\]  
\[V_{C1}=V_{C3} \tag{13}\]  
\[V_{C4}=\frac{V_{d1}}{1-D_{a}} \tag{14}\]  
By substituting (14) into (12), the expression for \(V_{C2}\) is given by  
\[V_{C2}=\frac{(n+1)V_{d1}}{1-D_{a}}+n(V_{d2}-V_{d1}) \tag{15}\]  
By substituting (10) and (12) into (8), the expression for \(V_{C1}\) can be obtained as  
\[V_{C1}=\frac{(n+1)}{1-D_{a}}V_{d1}+\frac{(n+1)}{1-D_{b}}V_{d2} \tag{16}\]  
By substituting Equations (10), (12)-(14), and (16) into Equation (9), the output voltage \(V_{o}\) is given by  
\[V_{o}=\frac{2+n(1+D_{a})}{1-D_{a}}V_{d1}+\frac{2(1+n)}{1-D_{b}}V_{d2} \tag{17}\]
# Improved Mask R-CNN Combined with Otsu Preprocessing for Rice Panicle Detection and Segmentation
## 2 Dataset and Method
### Method  
The principle of the original Mask R-CNN algorithm is shown in Figure 2. Mask R-CNN is an algorithm for the detection and segmentation of targets in complex backgrounds. Feature pyramid networks (FPN) and anchor technologies are used to optimize the detection effect of targets at different scales, and the fully convolutional networks (FCN) are combined to achieve accurate segmentation of the objective [35]. Here, we propose a method combining an improved Mask R-CNN with Otsu preprocessing, called Panicle-Mask, to further improve detection accuracy. The flowchart is shown in Figure 3. The original Mask R-CNN algorithm was first improved and optimized by adjusting the anchor and detection box accuracy for its dataset, labelled as B, C, and D, and the rice images are preprocessed by the Otsu algorithm, labelled as A, and then input into the trained model for detection and segmentation.  
Figure 1: Distribution diagram of 12 rice experimental fields.  
Figure 2: Mask R-CNN model structure.