esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
One of the main issues with the distribution line's tiny resistance grounding system is the identification and handling of single-phase grounding faults. Current detection methods for low impedance faults include threshold, artificial intelligence, and the conventional zero-sequence overcurrent protection mechanism. An efficient method of grounding short-circuit protection is conventional zero-sequence overcurrent protection. Nevertheless, with this technique it is equally challenging to identify high-impedance problems, and noise can readily skew its accuracy [19]. By extracting the grounding fault current's change rate and waveform curvature, respectively, and establishing suitable thresholds for both, the threshold approach completes fault detection [20]. In a similar manner, the threshold value is employed for judgment after fault characteristics including the wavelet detail coefficient, wavelet energy, and wavelet entropy of the fault current are extracted, respectively [21]. An increasing number of artificial intelligence techniques are being used in the field of defect diagnostics as a result of the technology's ongoing development. To achieve the detection of online data, the non-negative matrix decomposition technique defines the detection index and trains the data during normal operation [22]. Machine learning was utilized to categorize fault and non-fault states after fault features were identified using techniques such as the Hilbert-Huang transform (HHT) [23]. By creating a deep learning model, the error is found. Nevertheless, in distribution networks, these detection techniques are not effective for problems with poor waveform response. This paper suggests a grounding fault detection method based on support vector machine (SVM) for distribution networks, aiming to address the fact that when a grounding fault occurs in the network, the fault current is typically small and the conventional protection measures are difficult to effectively identify and it is difficult to quickly remove the fault. The particle swarm optimization (PSO) technique optimizes important SVMparameters, enabling the quick and accurate identification of single-phase grounding fault states. The single-phase grounding fault feature extraction, detection model building and training, and model detection accuracy validation are all covered in detail in this study. The advantages and disadvantages of different fault detection methods are shown in Table 1.  
Single-Phase Grounding Fault Detection Model Based on Particle Swarm Optimization Support Vector Machine Algorithm
## 1 Introduction  
Cobots operate in shared workspaces with humans, necessitating seamless interaction with their human counterparts [1]. This interaction goes beyond mere task execution; it involves a dynamic exchange of information, coordination of efforts, and mutual understanding to optimize the synergies between human skills and robotic precision [2].  
Effective communication and collaboration between human workers and cobots are pivotal for maximizing the benefits of cobot integration. The need arises from the intricacies of shared tasks, where human operators and cobots must coordinate their actions to achieve optimal results [3]. Moreover, as cobots are designed to be more adaptable and responsive to changes in the manufacturing environment, clear communication becomes essential for successful task allocation, problem-solving, and overall operational efficiency [4]. Effective communication fosters a sense of cooperation, trust, and shared purpose, thereby enhancing the overall productivity and safety of the collaborative work environment [5].  
This paper focuses on human-cobot communication related to cobots' assistive functions. Vocal communication can free a worker's hands and eyes, allowing them to perform tasks while receiving information or instructions through audio support [6]. This approach is preferred over augmented reality (AR) or head-mounted display-based support in many application areas today [7]. So, for assisting a worker performing manufacturing or assembly tasks, vocal and audio communication are preferred over other communication forms.  
This paper delves into strategies for optimizing vocal communication and collaboration between human workers and cobots. It shows that these strategies enhance productivity and safety in assistive work. This is its contribution to the transformative role of collaborative robotics in modern manufacturing. By addressing this critical nexus, we aim to guide future research endeavors and industry practices toward a better collaboration of humans and cobots on the factory floor.  
This paper continues as follows: Section 2 presents a literature review. Section 3 defines the main cobot assistive tasks in industrial settings. Section 4 defines proposed strategies for optimizing vocal communication, Section 5 presents a case study, Section 6 discusses an illustrative example, and Section 7 concludes the paper.
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
###### Abstract  
Transparent antennas have been continuously developed for integration with solar cells, vehicular communications, and ultra-high-speed communications such as 5G in recent years. A transparent antenna takes advantage of spatial extensibility more so than all other antennas in terms of wide range of usable area. In addition, the production price of transparent antennas is steadily decreasing due to the development of nano-process technology. This paper reviews published studies of transparent antennas classified by various materials in terms of optical transmittance and electrical, sheet resistance. The transparent electrodes for the transparent antenna are logically classified and the transparent antennas are described according to the characteristics of each electrode. Finally, the contributions transparent antennas can make toward next-generation 5G high-speed communication are discussed.  
transparent antenna; nano carbon; transparent conductive oxides; conductive polymer; metallic nanostructure; Antenna-on-Display; AoD 2018
# Learning an Efficient Convolution Neural Network for Pansharpening
## 3 Proposed Model
### Dilated Convolution  
It has been commonly acknowledged that the context information facilitates the reconstruction of corrupted pixels in image processing tasks. To efficiently capture the context information, the receptive field of the CNN model is supposed to be enlarged during the training procedure. Specifically, the receptive field can be enlarged by stacking more convolutional layers or increasing the filter size; however, both of the two approaches significantly increase the computational burden and there is the risk of overfitting. As a trade-off between receptive field and network complexity, we adopt dilated convolution [19; 20] as a substitute for traditional convolutional layer.  
Dilated convolution is noted for its expansion capacity of the receptive field without introducing extra computational complexity. For the basic \(3\times 3\) convolution, a dilated filter with dilation factor s (s-DConv) can be interpreted as a sparse filter of size \((2s+1)\times(2s+1)\). The receptive field of the dilated filter is equivalent to \(2s+1\), while only 9 entries of fixed positions are non-zeros. Figure 2a  
Figure 1: Architecture of the proposed dilated multilevel network.  
Figure 2: (**a**) Dilated filters with dilation factor s = 1, 2, 3. (**b**) Comparison of different block architecture.  
provides visualization of the dilated filter with dilation factors set as 1, 2, and 3. The complexity of our dilated multilevel block is calculated using:  
\[O((I+1)\times 3\times 3\times C_{out}+C_{in}\times 3\times 3\times C_{out}+C_{in} \times 3\times 3\times C_{out}) \tag{5}\]  
where \(C_{in}\) and \(C_{out}\) are the number of input and output channels of convolutional layers, which are set as 64 and the number of spectral bands (1) is 8. Without the dilated kernel, the computational complexity should be calculated using:  
\[O((I+1)\times 3\times 3\times C_{out}+C_{in}\times 5\times 5\times C_{out}+C_{in} \times 7\times 7\times C_{out}) \tag{6}\]  
It can be seen from Equations (5) and (6) that our method greatly reduces the cost of calculation by nearly 74%.
# Experimental Assessment of the Spatial and Temporal Distribution of Non-Contact Tonometer Airflows
## 2 Materials and Methods
### Experimental Set Up
#### 2.1.3 Pressure Recording  
The set-up required a digital oscilloscope (NI myDAQ) connected to a laptop (Toshiba LT, Intel Core i5-2520M CPU 2.5 GH, RAM 8.00 GB; Windows 7 Enterprise 64-bit) within LabVIEW. The pressure sensor was connected to the oscilloscope. The sensor was housed in a custom designed holder which allowed precise horizontal, vertical and angular movement  
Figure 2: The spatial distribution of the CorvisST airflow pressure was investigated using mapping grids (**a**) which were based on the dimension of the CorvisST screen capturing the sensor tube opening, (**b**) resulting in the development of two mapping grids with a resolution of 1 mm (m = 137 measurement points) and of 600 \(\upmu\)m (m = 382 measurement points). Circles highlighted in yellow represent the image of the sensor tube opening on the CorvisST screen (**a**) and on the two mapping grids (**b**).  
in 50 \(\upmu\)m increments, Figure 4(3). The distance between the nozzle and the sensor was adjusted to 11.0 mm. To ensure adequate positioning spirit levels, digital rulers and two digital camera systems were used to line up the nozzle and the sensor position in real-time.  
Figure 4: Final set-up to record the air puff of the CorvisST and the ORA: the pressure sensor (1), the rulers (2) and the movement apparatus (3) to align the tube opening to the respective device nozzle.  
Figure 3: Set-up for the investigation of the ORA airflow: (**a**) schematic view from above and (**b**) view from the front (**c**) from the side and (**d**) from above (1: pressure sensor; 2: ORA; 3 and 4: ruler; 5: movement apparatus; 6: camera).  
For the CorvisST measurements only, the mainboard of the CorvisST was connected to the oscilloscope to record the piston chamber pressure. The main board signal that initiated the cylinder movement was used as a trigger to commence the oscilloscope recording.  
For the ORA, the air-puff and the oscilloscope recordings were initiated manually.  
A programme was coded in LabVIEW 2015 (V15.0f2, 32-bit) to record and export pressure readings. The program consists of a loop (n = 30), and the input signal range was set to 0 to 5 V, 10 kHz and 20,000 samples. For each measurement, a time frame of 60 s and 1.2 million time points was recorded. The LabVIEW programme used to process the readings for the spatial and temporal assessment of the airflow provided a temporal resolution of 100 \(\upmu\)s. Output signals were saved as binary TDMS files containing the raw readings. Subsequently, the binary TDMS files were imported into Microsoft Excel 2016 for Mac (Microsoft Inc., Redmond, WA, USA).  
For the CorvisST, the programme was able to capture the pressure input signals simultaneously: signals from both the sensor and CorvisST mainboard.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes
### Bionic Graphene ECG Electrodes
#### 2.1.4 Self-Healing ECG Electrode  
If contact ECG electrodes can completely mimic human skin, which is stretchable, compliant, and self-healing, long-term ECG monitoring will comfortable and the electrodes will be more durable. To realize a self-healing ECG electrode with excellent stretchability and flexibility beyond that of human skin, [PERSON] et al. realized a bionic electrode, based on conductive hydrogels, constructed of a nerve-like nanonetwork [43]. Through incorporating proanthocyanins/reduced graphene oxide (PC/rGO) with a nerve-like nanonetwork into a glycerol-plasticized polyvinyl alcohol-borax (PVA-borax) hydrogel system, a PC/rGO/PVA hydrogel with ultrahigh stretchability (\(>\)5000%), excellent compliance (1 mm), and fast self-healing ability (3 s, 95.73%) was realized (Figure 1e). The excellent self-healing ability of the hydrogel electrode is mainly due to the glycerol, which provides many free borax-chelating sites for self-repair. Like the regeneration process of subcutaneous neural networks, the catechol groups on the PC/rGO surface form many crosslinked bonds with the borax, which makes the hydrogel electrode heal quickly.  
When attached to human skin, the electrode had a high signal-to-noise ratio, and the characteristic peaks of the ECG signals were clearly defined (Figure 1f), without a wandering baseline. Besides, the hydrogel electrode fits well with human skin, the testers-felt comfortable, and it did not affect their motion. Moreover, its self-healing ability ensures it can heal itself quickly when damaged, which greatly improves the service life of the electrode, making it suitable for long-term ECG monitoring.
## 3 Mutual Coupling Suppression using Frequency-Selective Surface
### Frequency-Selective Surface for Mutual Coupling Reduction  
The proposed FSS can be used to reduce mutual coupling between the two monopole antennas. For that purpose, six FSS unit cells are combined into a wall and placed between the closely spaced elements. Figure 7a-d shows the wall geometry. Its total size is \(12.70\) mm \(\times\) 19.08 mm. Figures 8 and 9 show the effect of the FSS wall for the case of Design 1. The isolation is improved from \(-12\) dB to \(-20\) dB in the desired frequency band. Similarly, the FSS wall applied to Design 2 improves the isolation from \(-15\) dB to \(-21\) dB.  
Figure 8: Transmission coefficient of Design 1 with FSS unit cells only.  
Figure 7: FSS-based isolation wall: (**a**) arrangement of FSS unit cells, (**b**) metallic strip, (**c**) FSS and metallic wall, (**d**) ground plane slot.  
Figure 9: Transmission coefficient of Design 2 with FSS unit cells only.
## 1 _Article_ Tissue Response to a Heat Resistant Silicate-Based and an  
Epoxy Resin-Based Endodontic Sealer Implanted in Rat [PERSON] 1,2, [PERSON] 3,*(c), [PERSON] 1,2 and [PERSON] 4  
The effect of high temperatures on the properties of endodontic sealers during warm compaction of gutta-percha may be a matter of concern. This study aimed to evaluate the effect of heat on the biocompatibility and bioactivity of EndoSequence BC Sealer HiFlow (ESHF; Brasseler, Savannah, GA, USA) and AH Plus (AHPS; Dentsply, De Trey, Konstanz, Germany) when implanted into the bone tissue of rat tibias. **Methods:** Medical-grade silicone tubes containing freshly prepared ESHF or AHPS were heated to 100 \({}^{\circ}\)C and then cooled down to 65 \({}^{\circ}\)C before being implanted in the tibias of 24 Wistar rats. The outer walls of the tubes served as controls. After 10, 30 and 90 days, the animals were euthanized and the implants and their surrounding tissues were dissected, fixed in formalin, and processed for microscopic evaluation. **Results:** After 10 days postoperatively, a severe inflammatory reaction without reactionary bone formation was observed in contact with ESHF and AHPS. The severity of the reaction had decreased at the 30-day observation period for both sealers but only ESHF samples showed new bone formation adjacent to the sealer. After 90 days, no inflammatory cells were found in contact with ESHF, while a thin fibrous tissue capsule and complete bone separation of the surrounding areas were observed in contact with the material. For AH Plus, a fibrous connective tissue containing scarce remaining inflammatory cells could be observed in most of the samples, however, in the absence of new bone formation. No significant differences (\(p>0.05\)) between ESHF and AHPS were found at the 10-day observation period. At the 30 and 90-day, significant differences (\(p<0.05\)) between both materials were observed. The reaction to the controls showed significant differences with ESHF and AHPS for all experimental periods. Significant differences (\(p<0.05\)) for the total effect of time were also found between both sealers (\(p<0.05\)). **Conclusions:** At the end of the experiment, heated ESHF reacted as a biocompatible/bioactive material and stimulated continued development of new healthy bone. Although AHPS was tolerated well by the surrounding tissues, the sealer did not promote new reparative bone formation.  
AH Plus; biocompatibility; bioactivity; endodontics; sealers; warm vertical compaction
# An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks
## 1 Introduction
The experiment shows that the proposed protocol has significant advantages in prolonging network lifetime and reliable data transmission. Figure 1: Architecture of wireless body area network communication. Furthermore, the related work is introduced in Section 2; Section 3 presents the system mode; Section 4 gives a detailed description of the protocol proposed; and the experimental results of our proposed protocol are provided in Section 5. Finally, we summarize this paper in Section 6.
# CRSAtt: By Capturing Relational Span and Using Attention for Relation Classification
## 3 Methodology
### Focal Loss Function  
In traditional multi-classification problems, the most commonly used loss function is the cross-entropy loss function. Its binary classification formulation form is as follows:  
\[L_{ce}=-y\log\hat{y}-(1-y)\log(1-\hat{y})=\left\{\begin{array}{ll}-\log(\hat {y}),&y=1\\ -\log(1-\hat{y}),&y=0\end{array}\right. \tag{14}\]  
where \(y\in\{0,1\}\) is the true label and \(\hat{y}\) is the predicted value.  
Although the cross-entropy loss function has the advantages of fast convergence and good results in applications, it does not cope with the problem of imbalance between hard and easy samples. Meanwhile, for the classification of hard and easy samples, the cross-entropy loss function is treated consistently; so, when the loss of a large number of easy samples is accumulated, the loss contribution of hard samples is almost completely swamped, which makes it difficult for the network model to learn the classification of hard samples. To cope with the problem of imbalance between difficult and easy samples in relation classification data, we adopt focal loss [31; 32; 33] instead of the traditional cross-entropy loss function. Focal loss, as its name suggests, can focus on the problem of classification of difficult samples and the problem of imbalance between samples of different categories. Its binary classification formula has the following form:  
\[L_{fl}=\left\{\begin{array}{ll}-(1-\hat{y})^{\gamma}\log\hat{y},&y=1\\ -\hat{y}^{\gamma}\log(1-\hat{y}),&y=0\end{array}\right. \tag{15}\]  
The focal loss balances the imbalance of samples by the parameter \(\gamma\). For example, if the negative samples are much more than the positive samples, the model will definitely favor the negative class with a larger number; then, the \(\hat{y}^{\gamma}\) of the negative class will be very small, while the \((1-\hat{y}^{\gamma})\) of the positive class will be very large, and the model will then start to focus on the positive samples. After \((1-\hat{y}^{\gamma})\) and \(\hat{y}^{\gamma}\) are adjusted, the status of the positive and negative samples may be reversed. Therefore, it is necessary to downscale the positive samples by adding an \(\alpha\) parameter to Equation (15), which is used to balance the weights of the whole formula. The final focal loss function formula is shown below:  
\[L_{fl}=\left\{\begin{array}{ll}-\alpha(1-\hat{y})^{\gamma}\log\hat{y},&y=1 \\ -(1-\alpha)\hat{y}^{\gamma}\log(1-\hat{y}),&y=0\end{array}\right. \tag{16}\]where \(\alpha\in[0,1]\) is the weighting factor; \(\gamma\) is the focusing parameter, where y \(\epsilon\{0,1\}\) is the real label; and \(\hat{y}\) is the predicted value. The final focal loss function is extended to multi-classification by the following equation:  
\[L_{fl}=-\alpha_{t}(\mathbf{1}-\hat{y}_{t})^{\gamma}\log\hat{y}_{t} \tag{17}\]  
where \(\alpha_{t}\) is the weighting factor for each category and \(\hat{y}_{t}\) is the predicted value of the target.
## 3 Uncertainty Quantification with the gPC
In the iPC method, the degrees of freedom are written as (refer to Equation (17)),  
\[\mathbf{u}(\xi)=\sum_{i=0}^{q}\mathbf{u}^{i}\Psi_{i}(\xi) \tag{22}\]  
and this expansion is inserted into the system equations as in  
\[\frac{d}{dt}(\sum_{i=0}^{q}\mathbf{u}^{i}\Psi_{i}(\xi))=\mathbf{f}(\sum_{i=0} ^{q}\mathbf{u}^{i}\Psi_{i}(\xi),s). \tag{23}\]  
To find the spectral coefficients of the expansion, a new set of equations, called the iPCE equations are derived by applying Galerkin projection to Equation (23),  
\[\frac{d}{dt}\int_{\mathcal{E}}(\sum_{i=0}^{q}\mathbf{u}^{i}\Psi_{i}(\xi))\Psi_ {i}d\xi=\int_{\mathcal{E}}\mathbf{f}(\sum_{i=0}^{q}\mathbf{u}^{i}\Psi_{i}(\xi ),s)\Psi_{i}d\xi. \tag{24}\]We now apply iPCE to the Lorenz attractor, a chaotic system commonly used as a test case,  
\[\frac{dx}{dt}=\sigma(y-x),\,\frac{dy}{dt}=x(\rho-z)-y,\,\frac{dz}{dt}=xy-\beta z, \tag{25}\]  
where \(\mathbf{s}=[\sigma,\rho,\beta]\) denotes the parameters vector (typical values are \(\sigma=10\), \(\rho=28\) and \(\beta=\frac{8}{3}\)). The system variables are written in their spectral form  
\[[x(t),y(t),z(t)]=[\sum_{i=0}^{q}x^{i}\Psi_{i}(\xi),\sum_{i=0}^{q}y^{i}\Psi_{i}( \xi),\sum_{i=0}^{q}z^{i}\Psi_{i}(\xi)]. \tag{26}\]  
For demonstration purposes, we take \(m=1\) and \(c=1\), and Equation (18) gives \(q=1\). Uncertainty is introduced through \(\beta=\beta_{0}+\beta_{1}\xi\). Substituting into the Lorenz system and applying Galerkin projections, we eventually get the system  
\[ \begin{split}\frac{dx^{0}}{dt}&=\sigma(y^{0}-x^{0}) \frac{dx^{1}}{dt}=&\sigma(y^{1}-x^{1})\\ \frac{dy^{0}}{dt}&=x^{0}\rho-x^{0}z^{0}-x^{1}z^{1}- y^{0}\frac{dy^{1}}{dt}=& x^{1}\rho-x^{0}z^{1}-x^{1}z^{0}-y^{1}\\ \frac{dz^{0}}{dt}&=x^{0}y^{0}+x^{1}y^{1}-\beta^{0}z^ {0}-\beta^{1}z^{1}\frac{dz^{1}}{dt}=& x^{0}y^{1}+x^{1}y^{0}-\beta^{0}z^ {1}-\beta^{1}z^{0}.\end{split}  \tag{27}\]  
This coupled system is integrated explicitly with a variable time-step Runge Kutta and unit initial conditions. We also performed MC simulations with \(N=5000\) samples over a time-frame \(T=40\), which ensures the solution has become chaotic. The mean value (expectation) of the \(x(t)\) coordinate of the trajectory obtained from iPCE and Monte-Carlo (MC) are compared in Figure 1.  
The MC result is obtained as follows. The Lorenz equations in (25) are integrated using \(N\) sample values of \(\beta\) that follow the given distribution. At each time instant \(t\), the expectation of \(x(t)\) is computed from the \(N\) available samples and plotted as a function of time. Note that the expectation tends to \(0\) as \(t\) increases; this is due to the symmetry of the attractor. The iPC solution, however, starts to deviate substantially from the MC at around \(t=15\). Increasing the chaos order \(c\) will not alleviate this deviation; it will simply delay it at later time. Figure 1: Comparison of the mean value of \(x\) coordinate of the trajectory, as computed by intrusive Polynomial Chaos (iPC) and Monte–Carlo (MC) with \(N=5000\) samples. Uncertainty is introduced through \(\beta=\beta_{0}+\beta_{1}\xi\), where \(\xi\sim\mathcal{N}(0,1)\).
# Big Data Collection in Large-Scale Wireless Sensor Networks
## 3 Overview of Big Data
### Areas of Utility of Big Data
#### 3.4.1 Scientific Research  
In the scientific and technical fields, scientists and engineers particularly face Big Data generated automatically by sensors or measuring instruments. In medical research, technologies associated with Big Data have led to spectacular advances in the analysis of the human genome. This knowledge of the genome, coupled with others, makes it possible to better understand the evolution of pathologies, and to improve preventive measures and even care protocols.