esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
Hence, this processing technique is able to mitigate the negative effect of impurity defects and subsurface cracks on the laser damage resistance of fused silica. However, there are some shortcomings that need to be urgently solved as well. For example, the temperature gradient in laser heated zone can produce high residual thermal stress and the surface figure error caused by laser preprocessing may induce wavefront distortion, which greatly limits the capacity and advancement of this technique in improving the laser damage resistance [32]. For the plasma etching process, carbon tetrafluoride (CF\({}_{4}\)) gas is necessary for effectively etching the silica materials. However, the CF\({}_{4}\) gas and its residual etching reaction product may become new sources of impurity defects, polluting the processed surface and consequently influencing the effect of improving the laser damage resistance [30]. However, high-quality optical surfaces with low roughness and few SSD defects can be achieved with the MRF method, and the magnetic Fe-ion defects from the magnetorheological fluid may be deposited inside the finished optical surfaces, resulting in low LIDT. However, for the HF acid etching technique, polished fused silica optics are immerged in various HF-based etchants (HF or NH\({}_{4}\)F:HF mixed solutions with various ratios) to remove the re-deposition layer and partial SSD defects. With the HF acid etching, the crack and scratch defects hidden in deep subsurface of fused silica can be blunted and no new impurity defects would be introduced as well [26]. Besides, under the assistance of megahertz-frequency agitation, the scouring effect of micro jet on the cleaned optical surfaces could be enhanced, causing the micro-particles and impurity defects adhering to the optical surface/subsurface to be further removed [28; 33]. As a result, the laser damage resistance of finished fused silica would be greatly enhanced. In the latter part of this work, the HF acid etching technique is employed to remove the SSD defects on fused silica. Then, the etching rate, impurity contents, surface quality and laser damage resistance of etched silica samples are thoroughly investigated and analyzed to validate the role of the HF acid etching technique in removing the subsurface impurity defects and promoting the laser energy capacity of fused silica.
## 2 Materials and Methods
### Evaluation of Thermal Cycles  
The recorded thermal cycles were used for different purposes. Firstly, the cooling rates based on the cooling times between 1200 and 800 \({}^{\circ}\)C (t\({}_{12/8}\)) were calculated. Secondly, the time that each weld run was exposed to critical temperature ranges for the formation of secondary phases was calculated.  
In the selection of critical temperature ranges for the formation of secondary phases, data available in the literature and extracted from computational thermodynamics (JMatPro, Sente Software, Surrey, UK) were used. For intragranular secondary austenite (v\({}_{2}\)), formation was assumed to happen at temperatures higher than 500 \({}^{\circ}\)C [2; 18] and dissolution at temperatures around 1375 \({}^{\circ}\)C [19]. For chromium nitrides, the temperature range for most rapid formation was from 700 to 900 \({}^{\circ}\)C [2] and dissolution around 1070 \({}^{\circ}\)C according to JMatPro. The upper temperature for \(\sigma\)-phase formation was calculated based on the experimental chemical composition of the welds using JMatPro. The lower temperature for relatively rapid formation was set to 750 \({}^{\circ}\)C as TTT diagrams by JMatPro showed that around 100 s at that temperature would be required to have 0.1% \(\sigma\)-phase precipitated. The upper temperature was found to be between 1055 and 1073 \({}^{\circ}\)C, depending on the specific weld metal composition considered. A temperature of 1073 \({}^{\circ}\)C was selected to be the upper temperature for \(\sigma\)-phase in the analysis of the thermal cycles.  
The following approach was used to calculate the time that each weld run was exposed to critical temperature ranges in the formation of secondary phases: In those weld passes where the liquidus temperature was reached, only the cooling part of the thermal cycle curve was considered. However, in the weld passes where the liquidus temperature was not reached, the heating and cooling branches of the thermal cycle were considered. The thermal cycles were also used to evaluate a possible dissolution of nitrides and \(\sigma\)-phase at temperatures above their upper temperature for thermodynamic stability; hence by calculating the time at which the weld passes were exposed at temperatures higher than those upper temperatures.
## 3 Algorithm for Power Control and Power Management  
The algorithm for the power control operating simultaneously with the power management is presented in Figures 2-5. The available power block is given in Figure 3. The power management is introduced with the power control flowchart to provide a high running rate for real-time power management, and complex computation is avoided due to the computation time. Therefore, the power of the described full DC microgrid can be balanced in real-time by running the proposed algorithm. In order to present optimal load demand management, this algorithm for power control and power management integrates a load optimization algorithm, which can run in real-time to decide the optimal load management. In order to forbid the BS from directly selling power to the public grid, the power management algorithm is designed respecting the following rules: the public grid can sell power to support the load demand, the BS, and the SC; however, the public grid can only buy the PV-generated power.  
In Figure 2, Figure 4, and Figure 5, the inputs of the algorithm are the power measurement value and real-time estimated value; the output is the controller reference values for the PV sources, the BS, the public grid, the DG, and the SC. In Figure 5, there are two sub-flowcharts, a and b, which can operate at different periods. The load optimization runs in real-time according to Equation (14). The available power is calculated according to the BS available power \(p_{BS\_AVALL}\), the DG available power \(p_{DG\_AVALL}\), the public grid available power, and the common DC compensated power in Figure 3. The public grid available power equals \(p_{G\_MAX}\). The proposed power management consists of 11 cases, which are detailed in the following sections of this paper.  
Figure 2: Flowchart of operational power control and management algorithm.
## Contributions  
The contributions of this article are summarized as follows:  
* To minimize the network energy consumption, EERD scheme is designed for U-WSNs, in which the choice of nodes for information forwarding is achieved using certain weight function parameters (highest residual energy, lowest BER, and shortest distance). The scheme chooses a node among the neighbor nodes as a DSN, which has the highest function value. The selection parameter of highest residual energy helps to choose the robust node as a DSN, which prolongs the network lifetime. The shortest distance decreases the path length, which also decreases energy consumption. The scheme reduces the adverse channel impairments, by checking the BER and selecting the best link for further packet forwarding. Due to single-path routing and robust function attribution in the scheme, it decreases the energy consumption, maximizes the network life, and minimizes delay.
* Single-path routing in EERD decreases the network reliability. To improve the reliability, the CoEERD scheme is introduced, which uses the cooperation of a single RLN with the DSN. The best forwarder nodes (RLN and DSN) are selected by considering the above discussed function parameters. The SRN advances the information packet towards the RLN and DSN using broadcast nature. The DSN checks the BER, if it is above the threshold, it forwards a request (REQ) to the RLN for sending the same packet. The RLN retransmits the packet along with the ACK to DSN. For diversity, the MRC technique is used, which merges packets and advances them further to the final DSN.
* For quick packet delivery, the shortest Euclidian distance is used as a selection parameter for the best forwarder nodes. The calculation of the shortest distance between the source, DSN, and RLN reduces the path length. The latency during the packet advancement decreases and the ratio of packet delivery increases in both the schemes.
* Energy consumption is low in the EERD scheme. The inclusion of cooperation to EERD to make the CoEERD scheme uses one more node as a relay node. This increases energy usage of CoEERD. However, it has still lower energy consumption than some other existing schemes involving cooperation.
## 3 Analysis and Results
### Quantitative Analysis
#### 3.1.1 Comparative Analysis of Investment Cost
For clarity in the following discussion, \"BIM\" will represent the scenario where professional consulting services are involved, while \"Non-BIM\" will represent situations where construction companies use BIM on their own. During data collection, professional BIM consulting companies and construction companies selected similar-scale work content from Project 1 and Project 2 for measurement. On the other hand, some literature indicates that BIM technology has not been widely disseminated and applied in the South Korean construction industry, particularly in construction projects [24]. On-site construction management personnel still exhibit hesitation toward adopting BIM technology [25]. This observation was confirmed through in-depth interviews with BIM consulting companies and management personnel from construction companies. Presently, the practical application of BIM in South Korean construction projects is primarily limited to the design phase. Due to technological imperfections and a lack of experience, a significant gap exists in the actual application of BIM for most construction companies. Therefore, the focus of this quantitative analysis is primarily on (e) labor costs; (f) costs of resolving clashes and preventing delay; and (g) rework costs incurred due to design changes and errors. These aspects are considered critical areas that require special attention in the application of BIM during the construction phase. In the case of rework resulting from design changes and errors, it involves modifying existing models and adding detailed models. Labor costs are composed of the quantity of labor and the corresponding work details on the construction site, reviewed by on-site construction management. Regarding the costs associated with resolving clashes and preventing construction delays, this includes measures such as proactively addressing clash issues and preventing delays in the construction schedule. These measures involve creating and simulating safety-integrated models, updating data, and similar actions. According to Table 4, in the investigation of labor costs, it was found that, with the participation of professional BIM consulting, the labor costs in Project 1 were USD 12,395, and in Project 2, they were USD 17,600. Without BIM consulting, the labor costs were USD 61,976 and USD 51,646, respectively. Regarding the cost of resolving collision issues and preventing delays, during the BIM consulting period, the construction cost for Project 1 was USD 30,643, and for Project 2, it was USD 15,838. The costs without BIM consulting were USD 45,965 and USD 24,445, respectively, which was significantly higher than the working environment with the participation of professional BIM consulting. In terms of rework when design changes and errors occur, the rework costs for projects 1 and 2 with BIM consulting were USD 24,790 and USD 44,415, respectively. The costs without BIM consulting were USD 37,185 and USD 66,623, significantly higher than the construction costs for contractors using BIM. For performance measurement analysis, in
# Knowledge Graph Double Interaction Graph Neural Network for Recommendation Algorithm
## 5 Experiments
### Results and Analysis
#### 5.3.3 Hyper-Parameters Sensitivity Analysis  
The influence of the value of each hyper-parameter on the recommendation results of KGDI was studied. In the hyper-parameters tuning experiments, the remaining hyper-parameters remained the same as the table except that the selected hyper-parameters were changed.  
When the embedding dimension \(d\) took values of 4, 8, 16, 32, 64, and 128 the corresponding AUC values in the MoviesLens-1M and Book-Crossing datasets are shown in Table 5.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{5}{c}{\(r\)} \\ \cline{2-6}  & **20\%** & **40\%** & **60\%** & **80\%** & **100\%** \\ \hline RippleNet & 0.832 & 0.883 & 0.901 & 0.913 & 0.915 \\ KGCN & 0.781 & 0.840 & 0.869 & 0.877 & 0.880 \\ KGDI & 0.841 & 0.860 & 0.882 & 0.896 & 0.905 \\ \hline \hline \end{tabular}
\end{table}
Table 4: AUC results of MoviesLens-1M in CTR prediction with different training set r ratios.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(d\) & **4** & **8** & **16** & **32** & **64** & **128** \\ \hline MoviesLens-1M & 0.877 & 0.880 & 0.891 & 0.898 & 0.905 & 0.902 \\ Book-Crossing & 0.656 & 0.693 & 0.696 & 0.698 & 0.694 & 0.681 \\ \hline \hline \end{tabular}
\end{table}
Table 5: AUC result of KGDI with different dimension of embedding.  
Figure 9: Embeddings t-SNE visualization. (**a**) MoviesLens-1M. (**b**) Book-Crossing.  
It can be clearly seen from the results in the figure that with the increase in the dimension, the performance of the recommender system can be improved initially because the larger dimension can learn more information contained in the dataset and knowledge graph, which encodes more information about the users and entities. If the dimension is too large, the performance of the recommender system begins to decline. This is due to the excessive dimension and will lead to the adverse effect of over-fitting during training.  
When the number of neighbor sampling entities \(K\) was 2, 4, 8, 16, 32, and 64 in item feature extraction the corresponding AUC values in MoviesLens-1M and Book-Crossing datasets are shown in Table 6. It can be seen from the results in the figure that when the number of neighbor sampling entities is too large, more noise will be introduced, which is not conducive to the performance of the model, thereby reducing the recommendation quality.
# You Only Hear Once: A YOLO-like Algorithm for Audio Segmentation and Sound Event Detection
## 5 Discussion  
The results in Section 4 show that YOHO has multiple advantages over the state-of-the-art CRNN architecture. We examined the model for two different tasks--music-speech detection and environmental sound event detection. Music-speech detection is relatively a simpler task because of a larger and diverse training set. Additionally, there are only two acoustic classes to predict. On the other hand, environmental sound event detection was harder because of smaller and lower quality training sets. In addition, the number of acoustic classes was greater. However, in both scenarios, YOHO generalised better than CRNN and CNN. YOHO obtained state-of-the-art performance for music-speech detection on the MIREX 2018 competition dataset. We understand that YOHO has not obtained state-of-the-art performance on TUT Sound Events and Urban-SED datasets. However, it is important to note that the purpose of this study is to shift the paradigm from frame-based classification to regression for audio segmentation and sound event detection. There is a vast body of research involving CNN and CRNN architectures. It is not within the capacity of this study to incorporate all these optimisations for YOHO. As this is the first study that explores this paradigm, we believe that optimisations such as weak label learning [11] and envelope estimation [53] will improve YOHO's performance.  
We also explored the idea of creating a regression-based CRNN that adopts the YOHO paradigm. We replaced the Conv1D layer with a B-GRU block. However, this slightly worsened the performance of the algorithm. This is because the YOHO network has many convolutional layers that reduces the temporal resolution from 801 to 26. Hence, the B-GRU blocks may not be effective on such a small number of time steps. However, alternative structures such as CNN-transformers [55] may be a promising avenue to explore.  
YOHO was significantly quicker than the CNN and CRNN models because it had to predict fewer outputs and computationally cheaper post-processing. As explained in the paper, the output produced by YOHO is more end-to-end. For example, the output dimensions in music-speech detection is 26 \(\times\) 6 for YOHO versus 801 \(\times\) 2 for the CRNN. This corresponds to 156 output neurons for YOHO and 1602 for the CRNN. Furthermore, the CRNN needs to convert frame-based classifications to time boundaries. However, YOHO directly outputs the time boundaries. Due to the above reasons, YOHO is significantly quicker. Due to faster inference, YOHO is more suitable for real-time applications such as surveillance, self-driving automobiles, bioacoustic monitoring, and real-time remixing.
# Propagation Characteristics of a Twisted Cosine-Gaussian Correlated Radially Polarized Beam
## 1 Introduction
The results yield a useful guideline for manipulation of novel vector structure beams by using twist phases and customized correlation functions, and promote important potential applications, ranging from beam shaping, optical tweezers, optical imaging, and free space optical communications.
## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Evaluation Metrics for the Model  
The model was evaluated using overall accuracy (OA), precision, recall and F1 scores based on the validation dataset. Moreover, these metrics were used in the overlay analysis of the segmented results and all annotated ground truth data to quantify the model performance for the entire dataset. OA measures the correctness of predictions made by the model across all classes. It is defined as the ratio of the number of pixels that match between the predicted class label and the ground truth label and the total number of pixels in the dataset. Precision is a measure of the accuracy of the positive predictions made by the model. It answers the question: \"How many of the pixels predicted as positive are truly positive?\". Recall is a measure of the ability of the model to capture all positive instances. It answers the question: \"How many of the truly positive pixels did the model correctly predict?\". The F1 score is the harmonic mean of precision and recall and provides a balance between these two metrics. Together, these metrics provide a comprehensive understanding of the model's performance, considering aspects such as accuracy, reliability of predictions, ability to detect the target class, and the trade-off between false positives and false negatives (SPOT INTELLIGENCE, [[https://spotintelligence.com/2023/05/08/f1-score/](https://spotintelligence.com/2023/05/08/f1-score/)]([https://spotintelligence.com/2023/05/08/f1-score/](https://spotintelligence.com/2023/05/08/f1-score/)); accessed on 14 December 2023). OA, precision, recall, and F1 scores were calculated using the following formulas:  
\[\text{Precision}=\text{TP}/(\text{TP}+\text{FP}) \tag{2}\]  
\[\text{Recall}=\text{TP}/(\text{TP}+\text{FN}) \tag{3}\]  
\[\text{F1}=2\times\{(\text{Precision}\times\text{Recall})/(\text{Precision}+ \text{Recall})\} \tag{4}\]  
where  
TP (True Positives): Pixels correctly predicted as positive (belonging to the target class).  
FP (False Positives): Pixels incorrectly predicted as positive (model predicts the class, but it is not actually present).  
FN (False Negatives): Pixels incorrectly predicted as negative (model fails to predict the class when it should have).  
The intersection over union (IoU), which measures the ratio of the intersection of the predicted and ground truth regions to their union, was also determined. The IoU quantifies how well the predicted segmentation aligns with the actual segmentation and is particularly useful for evaluating the spatial accuracy of the segmentation. The formula for IoU is as follows:  
\[\text{IoU}=(\text{Area of Intersection})/(\text{Area of Union}) \tag{5}\]  
In addition, confusion matrices were constructed as heat maps to visualize the performance of machine learning models in predicting Japanese oak crown.
# Adaptive Antenna Array Control Algorithm in Radiocommunication Systems
## 2 Adaptive Antennas in Radio Communications
The directivity characteristic of a given system can be written as [18]:  
\[E(\theta)=E_{e}(\theta){\sum_{m=1}^{M}w_{m}A_{x}e^{i[(m-1)k\text{s}sin(\theta )-\beta_{n}]}} \tag{1}\]  
where:  
\(E_{e}(\theta)\)--radiation characteristics of a single element of the antenna array (we assume that these elements have the same characteristics);  
\(w_{m}\)--complex weight of the adaptive system;  
\(A_{x}\)--signal amplitude;  
\(k\)--propagation vector;  
\(d\)--distance between array elements;  
\(\beta_{n}\)--phase shift between array elements. Several useful signals \(d_{ni}=A_{di}e^{-j(-n\varphi_{di})}\) may fall on the antenna system from different directions \(\theta_{1}\), \(\ldots\), \(\theta_{K}\) and interfering signals from the directions \(\varphi_{1}\), \(\ldots\), \(\varphi_{L}\). The signal received by each element will be the sum of all signals, both desired and interfering, reaching the antenna array and thermal noise. \[ \begin{split} s_{n}=\sum_{i=1}^{K}s_{sys}e^{i[(m-1)k\text{s}sin( \theta_{i})]}+\sum_{i=1}^{L}z_{i}e^{i[(m-1)k\text{s}sin(\varphi_{i})]}+n_{m}\\ (\text{received signal})=(\text{useful signals})+(\text{ interfering signals})+(\text{noise})\end{split}  \tag{2}\]  
Using the calculated  
In general, these are complex quantities, and they affect both the change in the amplitude and phase of each element of the antenna array.