esa-sceva/satcom-chunk-collection huggingface dataset:
# Wideband Circularly Polarized Antenna Based on a Non-Uniform Metasurface
## Antenna Design
### Antenna with Uniform MS
Meanwhile, the higher CP resonance is almost stable with the variation of \(W_{p}\). The data confirm that the patch is critical for the lower band operation. A similar behavior with higher CP resonance when tuning \(W_{i}\) can be observed in Figure 4b. However, as the MS is an effective radiating aperture of the proposed antenna, the variation of \(W_{i}\) also has a significant effect on the lower frequency, which shifts downwards as \(W_{i}\) increases.
## 3 Data Source and Analysis
### Using a Neural Network to Build an Early Warning Model
#### 3.2.6 Model Accuracy Test  
Following the completion of the model training, the questionnaire survey was conducted again, and resulted in the acquisition of 214 samples of data. The 214 samples of data were predicted, and the predicted output was compared with the expected output of the sample to test the validity of the model. The comparison results of the selected samples are shown in Table 11.  
The experimental results indicate that with 180 test samples, accurate predictions were made with an accuracy rate of 84.11%. The expected level and the actual level were found to be in close alignment, indicating the reliability of the experimental outcomes. This indicates that the early warning model can accurately predict and further research can be conducted.  
Figure 3: Neural network model.
# Name Disambiguation Scheme Based on Heterogeneous Academic Sites
## 2 Related Work
### Limitations of Previous Studies  
Traditional name disambiguation schemes use pre-constructed structured datasets. Name disambiguation on actual research materials from heterogeneous academic search services in operation faces an issue: if the presence or absence of metadata in the name disambiguation is not considered, direct application of the name disambiguation scheme becomes unfeasible. Furthermore, as academic search services vary in the type of research materials they offer based on their purpose, different academic services must be searched to review the works of a researcher published in various formats. Even if the research materials are of the same type, the absence of shared metadata between different academic search services can make name disambiguation exceedingly difficult without separate preprocessing. For instance, while some research materials may list affiliations as general as \"Chungbuk National University\", others might provide detailed affiliations such as \"Chungbuk National University, Information and Communication Engineering\", necessitating data preprocessing.  
Additionally, as academic search services such as Scopus, Web of Science, and google scholar provide research materials specific to their own purposes, finding all the works of an author in one academic search site can be challenging if they have published different types of research materials. For example, if an author publishes a paper based on a particular research project and produces reports or patents as research outcomes, searching for these different types of research materials within a single academic search service becomes very difficult. Ultimately, users have to search for research materials on multiple academic search services. Hence, in this study, we collected research materials from various operational academic search services and performed name disambiguation analysis on the collected material. We also considered the metadata from various academic search services to apply a uniform preprocessing approach. This led to the advantage of identifying potential attributes to consider in the name disambiguation scheme. The main motivations and significances of this paper are as follows:  
1. Academic search sites are diverse. There is a need for a name disambiguation scheme that considers various metadata of papers registered in various academic search sites.
2. Researchers' achievements are diverse such as papers, conference papers, technical reports, projects, and patents. Therefore, we need a name disambiguation scheme that considers various researcher's achievements.
3. Some name disambiguation schemes are only suitable for specific metadata. It is difficult to create a name disambiguation scheme that is suitable for all languages or metadata. Therefore, it is necessary to provide a name disambiguation scheme that is suitable for the inputted metadata. In this paper, we propose a multi-classifier that executes a different scheme to disambiguate names depending on the inputted metadata.
## 3 Results
### Training and Interference  
As a validation scenario, we trained the network with a set of 100 images depicting four 12 \(\times\) 12 pixel, black and white decimal digits ranging from 5 to 8 (Figure 8a). The images were inserted to the network sequentially and the pixel values were mapped to the bias of the VCSELs at the CDL. It is of utmost importance to point out that no data-labeling was performed, and weight adaptation occurred through an unsupervised version of STDP. The first convolutional layer aimed for 33 target patterns. Typical examples are shown in Figure 8b. In the CL2 the network was trained to detect combinations of patterns from the CL1 layer (Figure 8c). The shift of the CW\({}_{2}\) was equal to the number of pixels per row (6 pixels horizontally). After the horizontal scanning of the image was completed, the CW\({}_{2}\) was shifted 6 pixels downwards and the same procedure was repeated until all of the training images were scanned. The detailed scanning process of the CL1 alleviates the need for more complex training in the CL2 and CL3 and no spatial overlap is needed in them. The reason is that the CL1 identified most of the basic patterns of the images. Based on this, CL2 patterns are a combination of simpler patterns which are identified in an unsupervised manner in the CL1 (Figure 8c). The same applies to the CL3 Figure 8d. Following these basic training rules and without labeling the data, the Classification Layer of the network self-constructed an abstract version of the original images and classified the input images based on their similarity with the abstracted versions (Figure 8e). In order to validate our method, we repeated the training process but in this case we changed the training set by using images illustrating the digits 1-4. The network adapted to new features and patterns, recalibrating the weights. Furthermore, it is worth mentioning that we have not used a data set comprising of all the digits (0-9) due to the fact that training even for such a small dataset was time-consuming. This stems from the fact that we realized a full-scale simulation, incorporating multiple layers and dozens of physically accurate VCSELs, instead of simplistic spiking models. Towards this direction, we adapted our PSCNN model to be compatible with parallel processing through 4352 GPU cores (Nvidia RTX 2080 Ti) so as to speed-up computation [42]. Although, in principle, the whole network could be computed in parallel, we managed to evaluate only the first two layers (CDL and CL1). This stemmed from the need to keep long time-traces (temporal information) among layers that in turn lead to an extensive memory demand that could not be handled by our GPU system. Fortunately, CDL and CL1 have the vast majority of network's neurons; thus, even with these restrictions, the overall speed enhancement achieved was \(\times\)30. This enhancement resulted in a training time of 25 min/image, which is acceptable considering the number and complexity of the simulated model. On the other hand, this execution speed increase, resulted in negligible inference time, even when using the whole dataset.
## 6 Types and Challenges of NCS
### Types of NCS
#### 6.1.2 Networked Control  
Delays caused by the network and the packet losses are still difficult issues for NCSs. Reckoning on managing the network delays and the packet dropouts, different ways to measure stability, and managing styles for NCSs have been developed over the past decade. A brief review of a few of them is discussed below.
## 2 Operational Strategy of BESS and Prediction System
### Operational Strategy of BESS
(1) If the difference between the distributed power generation and load demand is positive and the battery is not fully charged, the control system will invoke the charging operation. The remaining power will be distributed among all batteries until the battery is charged to the SOC upper limit. When the battery is charged to the upper limit of SOC, if the microgrid is in the grid \(-\)connected mode, the remaining electricity will be sold to the distribution network. If the microgrid is in the islanded operation mode, the control system will invoke the generation power limiting operation. (2) If the distributed power supply and demand on the load side are equal, there is no need for the BESS to operate, and it may work in standby mode to provide ancillary services such as dynamics frequency and voltage support. (3) If the difference between the distributed power generation and load demand is negative and the battery is not fully discharged to the lower limit of SOC, the control system will invoke the discharge operation. When the battery is fully discharged to the lower limit of SOC, if the microgrid is in the mode which is connected to the grid, and the remaining required power can be supplied by the distribution grid. If the microgrid is in island operation mode, the load shedding operation will be invoked by the control system. For battery charge state detection using the neural network method, a large number of corresponding outside factors such as current and voltage, as well as battery charge state data can be applied using training datasheets. The neural network is repeatedly trained and trialed by forwarding the propagation of input information and backpropagation of error transmission. When the predicted charge state is within the error range required by the design, the predicted value of the battery's charge state could be obtained by inputting new data. The neural network algorithm has a strong nonlinear fitting ability, without considering the internal structure of the battery, for external excitation, the relationship between the input and output can be obtained by training a large number of input and output samples, so it can be that the dynamic characteristics of the battery are well fitted to realize the SOC estimation of the battery. Additionally, it has high estimation accuracy. When there are enough battery data samples, high accuracy can be obtained, and the neural network estimates the SOC. It is suitable for a variety of power batteries and can effectively solve the problems in background technology.
## 3 AI/ML in the Standards and Industry
### Open RAN  
We have seen in the previous section that the disaggregation between hardware and software along with programmability using the principles of SDN/NFV are crucial for adopting a service-based architecture (SBA) in the 5G core network, and subsequently integrating big data analytics and intelligent control. Open RAN aims at bringing intelligence also into the RAN and is mainly driven by the combined efforts of two organizations: the O-RAN Alliance, founded in 2018, focuses on the development of open and standardised interfaces in the RAN [97], while a project group inside the Telecom Infra Group (TIP) focuses on the design of a software-based RAN using commercial off-the-shelf (COTS) hardware and disaggregated software [98].  
Using the principles of SDN/NFV, commodity hardware is used in the Open RAN (the O-RAN Alliance uses the term white box hardware), and the various functions are implemented in software, possibly by different vendors, due to openness. For instance, a remote radio unit (RRU) by vendor A can communicate via an open interface to a virtual PHY or MAC layer function developed by vendor B running on an Intel x86-based COTS server. Avoiding vendor lock-in can reduce the cost of deploying and upgrading the network, and can also lower the entry barriers for small vendors, bringing more innovation  
Figure 7: The ML intent is provided to the Machine Learning Function Orchestrator (MLFO), which specifies the associated SRC and SINK interfaces. In this illustration, the SRC is located at the core network and collects measurements, e.g., for access and mobility management (AMF) for a UE, which are subsequently provided to the ML sandbox. The sandbox retrieves suitable ML models from the marketplace, e.g., for mobility prediction, and assess their performance using a digital twin of the underlay network. AFs-t, NFs-t and RAN-t stand for the digital twin application functions, network functions and RAN implementation. Finally, the SINK which, in this illustration, is located at the RAN may for instance request the gNB to allocate more resource blocks to the UE as the latter is about to travel across an area with poor line-of-sight wireless propagation conditions.  
into the market. Currently, MNOs are in different stages of adopting O-RAN, with more than 30 MNOs participating in the Open RAN project group in TIP. Open RAN is an evolution of virtualized RAN, as illustrated in Figure 8.
## 2 Materials and Methods
### Defect Detection Using Convolutional Neural Network (CNN)
#### 2.3.3 Training and Validation of the CNN  
In this study, RGB C-scan images, encompassing both defect-free and defective cases, each with dimensions of 313 \(\times\) 125 pixels, were used for training and validation. The model comprised 88,131,394 trainable parameters. The architecture of the VGG-16 network used in this study is illustrated in Figure 7.  
The sequential VGG-16 architecture used in this study consisted of the following layers:  
* Two convolutional layers with 64 channels, 3 \(\times\) 3 kernel size, and ReLU activation;
* One max-pooling layer with 2 \(\times\) 2 pool size and 2 \(\times\) 2 stride;
* Two convolutional layers with 128 channels, 3 \(\times\) 3 kernel size, and ReLU activation;
* One max-pooling layer with 2 \(\times\) 2 pool size and 2 \(\times\) 2 stride;
* Three convolutional layers with 256 channels, 3 \(\times\) 3 kernel size, and ReLU activation;
* One max-pooling layer with 2 \(\times\) 2 pool size and 2 \(\times\) 2 stride;
* Three convolutional layers with 512 channels, 3 \(\times\) 3 kernel size, and ReLU activation;
* One max-pooling layer with 2 \(\times\) 2 pool size and 2 \(\times\) 2 stride;
* Two fully connected (Dense) layers with 4096 units each;
* One Dense Softmax layer with two units for binary classification (defect/no-defect).  
The model was trained using the Adam optimizer with a learning rate of 0.0000015, and categorical cross-entropy was selected as the loss function to evaluate probabilistic  
Figure 7: Architecture of the VGG-16 neural network used for classification of defects in C-scan images.  
outputs. The model was set to be trained for 50 epochs with 30 steps per epoch and 10 validation steps. Convergence was observed after 22 epochs, as the validation accuracy plateaued in the last 10 epochs without further improvement. The obtained model accuracy and validation losses are presented in Figure 8.  
The model's training accuracy and losses, as shown in Figure 8, demonstrate a clear progression throughout the training process. At the initial epoch (epoch 0), the model achieved an accuracy of 0.75, with a validation loss of 0.55. By the 5 th epoch, validation loss had improved to 0.33, while accuracy increased to 0.94. This upward trend continued, and by the 22 nd epoch, the model reached a validation loss of 0.02 and an accuracy of 0.99.
# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 4 Algorithm Design
### Link Duration Factor  
Since the vehicle is moving quickly, it is important to evaluate the duration of the link \(l=(v,w)\). The link duration depends on how long the two nodes can communicate stably. For routing, a longer duration link can undoubtedly ensure stable communication services. After a node obtains the location information of itself and its neighbors, it can estimate the duration of a link.  
Figure 6 shows a scenario for calculating the link duration, and \(R\) is the node-stable radio transmission range. \(\Delta D(l)\) is the trend of the relative distance between the two nodes, and a positive value \(\Delta D(l)\) represents the distance motion between the two nodes. The relative speed between the two nodes is as follows:  
\[RV_{\Delta t}(l)=\frac{\Delta D(l)}{\Delta t}. \tag{6}\]  
The link duration can be calculated as follows:  
\[LD(l)=\begin{cases}\frac{|R-DIST^{t_{i+1}}(v,w)|}{RV_{\Delta t}(l)},&if\ \Delta D(l)>0\\ \frac{DIST^{t_{i}+1}(v,w)}{|RV_{\Delta t}(l)|},&if\ \Delta D(l)<0\cdot\\ \frac{R}{2\cdot sp_{max}},&if\ \Delta D(l)=0\end{cases} \tag{7}\]  
When \(\Delta D(l)>0\), the expected time for both nodes to exceed the transmission range is \(\frac{|R-DIST^{t_{i+1}}(v,w)|}{RV_{\Delta t}(l)}\). When \(\Delta D(l)<0\), the time when two nodes are close to each other is calculated. The closer the two nodes are, the shorter the expected time value. Therefore, the two nodes are not suitable for data transmission. If \(\Delta D(l)=0\), \(LD(l)\) is set to a fixed value.  
The link duration factor can be calculated as follows:  
\[LDF(l)=\begin{cases}\frac{LD(l)}{n\cdot T_{B}},&LD(l)<n\cdot T_{B}\\ 1,&\text{otherwise}.\end{cases}, \tag{8}\]  
where \(T_{B}\) indicates the beacon interval. \(n\) is an adjustable value. If the \(LD(l)\) calculated by Equation (7) is less than the link-duration threshold \(n\cdot T_{B}\), the linear formula is used to normalize the link duration. Proper \(n\) values can help the selection of forwarding nodes.
# A Point Crack Source Location Method without Velocity Information in Anisotropic Plates
## 6 Conclusions  
In this paper, a new acoustic source localization method was proposed for anisotropic plates. The proposed method can be regarded as a variation of the previous methods [16; 17; 19; 21; 22; 23]. We modified the ellipse equation representing wave propagation to a different form in order to remove the unknown wave speed, while the methods in [16; 17] can be applied only when the wave velocity is known and those in [19; 21; 22; 23] required special sensor placement (L-shape, Z-shape, or squared shape) to remove the unknown wave velocity. This paper provides a new formula for finding the center of the ellipse, considered an acoustic source, from the three arbitrary sensor positions. We also took the time-of-arrival (TOA) errors into account and applied them to the new formula. Therefore, the new formula presented here is a modified form of the elliptic equation including the TOA error.  
In order to verify the robustness of the presented method according to the noise and TOA thresholds, numerical experiments were performed. Furthermore, pencil-lead breakage (PLB) experiments were conducted on a carbon-fiber-reinforced polymers (CFRP) plate to demonstrate the effectiveness of the presented method. If there was noise in the TOA, then the proposed method robustly found the source position, since there was a term for correcting TOA errors in the proposed equation. Even when experimenting with changing the TOA threshold values, the proposed method finds the source location within an error of 10%. At the end, as a result of applying the speed deviation minimization method (SDMM) to various PLB positions, the PLB positions were found with an error within 10%.  
Future studies may focus on considering an ellipse equation with an unknown rotational angle (e.g., [22]). In addition, since the proposed method was applied to a CFRP plate in this study, one may study the effects of conducting experimental investigations on objects made of CFRP, such as hydrogen storage containers.  
Data curation, W.G.L. and J.K.; Formal analysis, D.G.L. and H.K.; Methodology, H.K.; Software, D.G.L.; Writing--original draft, D.G.L. and H.K.; Writing--review & editing, W.G.L. and J.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (20202910100070, Development of in-operation diagnostic system and safety inspection criteria based on AET for safety inspection of high-pressure storage containers for hydrogen stations).  
Not applicable.  
The authors declare no conflict of interest.
# Effects of High Toughness and Welding Residual Stress for Unstable Fracture Prevention
## 2 Experimental Procedure
### Brittle Crack Propagation Test Procedure  
The large-scale ESSO test was performed with actual structural welded joints to evaluate the possibility of preventing an unstable fracture under the design temperature (\(-10\)\({}^{\circ}\)C) and design stress loading condition of 460 MPa-yield-strength-class steel (289 MPa). Initially, the test region was isothermally controlled by using liquid nitrogen and the temperature was maintained within \(\pm 2\)\({}^{\circ}\)C every 50 mm along the entire crack line. The specimen contained a 150 mm long window frame-type notch through the thickness of the plate. The specimen was installed on the 3000-ton tensile test machine by attaching it to the supplemental sample holder, with total dimensions of approximately 5000 mm in length, 1800 mm in height, and 150 mm in thickness. The experiment is often called the ESSO test, designed for the evaluation of brittle crack-arrestability in large-scale structures by impacting an instant load into a window frame-type notch tip. In general ship design, the design stress is applied with consideration of the material yield strength and material factor in each steel grade [25; 26; 27]; for 460-MPa-yield-strength-class steel, the design stress becomes 289 MPa, according to the classification society rule [2; 3; 4; 28]. When the tensile load and target temperature were stabilized, an impact energy of 2.7 kJ was applied to the full-thickness penetrated notch tip.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Current, (A)** & **Voltage, (V)** & **Speed, (mm/min)** & **Heat input, (kJ/mm)** \\ \hline
400 & 28 & 400 & 1.68 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Welding conditions.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Element (wt. \%)** & **C** & **Si** & **Mn** & **Ni** & **Cr** & **S** & **P** & **Fe** \\ \hline \multirow{8}{*}{**Welding consumables**} & A & 0.06 & 0.29 & 1.23 & - & - & 0.008 & 0.007 & Bal. \\ \cline{2-8}  & B & 0.06 & 0.26 & 1.15 & - & - & 0.007 & 0.008 & Bal. \\ \cline{2-8}  & C & 0.03 & 0.4 & 0.3 & 61.0 & 21.5 & 0.007 & 0.008 & Bal. \\ \cline{2-8}  & D & 0.05 & 0.48 & 1.33 & 0.52 & - & 0.006 & 0.016 & Bal. \\ \cline{2-8}  & E & 0.04 & 0.85 & 1.95 & 0.01 & 0.20 & 0.010 & 0.007 & Bal. \\ \cline{2-8}  & F & 0.02 & 0.21 & 2.75 & 62.1 & - & 0.002 & 0.003 & Bal. \\ \cline{2-8}  & G & 0.28 & 0.6 & 5.8 & 32.7 & 1.6 & 0.003 & 0.005 & Bal. \\ \cline{2-8}  & H & 0.05 & 0.4 & 1.55 & 10.2 & 9.6 & - & - & Bal. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Chemical composition of each welding consumable.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Items** & \multicolumn{2}{c}{**Yield Strength,**} & **Tensile Strength,** & **Elongation,** & **CVN,** \\  & & **MPa** & **MPa** & **\%** & **J** \\ \hline \multirow{8}{*}{**Welding consumables**} & A & \(\geq\)540 & 620–760 & \(\geq\)17 & \(-60\)\({}^{\circ}\)C, \(\geq\)27 \\ \cline{2-6}  & B & \(\geq\)470 & 550–690 & \(\geq\)22 & \(-60\)\({}^{\circ}\)C, \(\geq\)27 \\ \cline{2-6}  & C & \(\geq\)706 &