esa-sceva/satcom-chunk-collection huggingface dataset:
# A Feature-Oriented Reconstruction Method for Surface-Defect Detection on Aluminum Profiles
## 3 Proposed Methods
### Aluminum Profiles Surface-Defect Detection  
#### 3.3.1 Defect-Detection Process  
The flow of the unsupervised surface-defect inspection method for aluminum profiles is shown in Figure 10.  
The aluminum profile surface-defect detection method process is as follows:  
1. Area-feature image acquisition. The main image of the aluminum profile obtained by the aluminum profile image preprocessing method is cropped to an image of uniform specification of 224 \(\times\) 224, i.e., an area image, and then image restoration operations are performed one by one on the area image to remove its randomly distributed complex texture, and obtain an area-feature image that effectively maintains the essential feature information of the aluminum profile surface.
2. Mask image production. The entire area image obtained from one aluminum profile image is sequentially masked with two fixed masks each with a removal rate of 75%, respectively, to obtain two mask images, as shown in Figure 11.  
Figure 10: Flowchart of the surface-defect detection method.  
For mask image 1, the mask region is denoted by \(\left\{\left\{\left\{M_{4i+j}\right\}\right\}_{i=0}^{48}\right\}_{j=0}^{2}\). For mask image 2, the mask region is denoted by \(\left\{\left\{M_{4i+j}\right\}_{i=0}^{48}\right\}_{j=1}^{3}\).
3. Feature Reconstruction: The mask image is fed into the detection model, and the feature-reconstructed image of the corresponding area-feature image is reconstructed sequentially.
4. Defective or non-defective judgment: The MSSIM comparison is performed on the feature-reconstructed image and the regional feature image, and the regional image whose obtained MSSIM value is less than the judgment threshold is judged to be a defective image, and the regional image whose obtained MSSIM value is greater than the judgment threshold is judged to be a non-defective image.
5. Aluminum profile surface-defect detection results are classified: If all the regional images cropped out of an aluminum profile image are non-defective images, the aluminum profile is judged to be a non-defective product, otherwise it is judged to be a defective product.
## 2 Materials and Methods
### Experimental Dataset  
In our study, we utilized the SEED (SJTU Emotion EEG Dataset), an openly available dataset for thorough analysis. This dataset encompasses data from 15 Chinese subjects with a gender distribution of 7 males and 8 females and an average age of 23.27 years (standard deviation: 2.37). The emotional stimuli for the participants were derived from 15 Chinese film clips, each designed to elicit positive, neutral, or negative emotions, and each film lasted approximately 4 min. To execute our experiments, each participant engaged in 15 trials, resulting in a total of 45 trials (15 trials for each of the three emotional categories: positive, neutral, and negative). The experimental design comprised three distinct groups of experiments [19]. In each trial, the subjects were exposed to emotional stimuli through designated film clips, inducing the specified emotion (positive, neutral, or negative). This rigorous experimental setup aimed to comprehensively capture the varied responses to emotional stimuli across the different emotional categories.
## 4 Experimental Results and Discussion
### Discussion  
The second channel of the embedded system that is used to connect the stations to the WMN backhaul connections can be further modified to suit the needs of various unique situations. For example, sensors can be used to connect to the WMN backhaul connection using ZigBee, Bluetooth or Wi-Fi, depending on the application's needs. These connections make it possible to implement the network that incorporates the advantages of two or more wireless technologies: the hybrid version of WMN and WSN. The advantage of Wi-Fi is the ability to transmit at a high data rate and the long communication range is suitable for the backhaul connection while the advantage of ZigBee, Bluetooth or the power efficient 802.11 is much lower consumption which can be used for the WSN [5,6].
## 2 Results and Discussion
### Evaluation of the Effect of Independent Variables on the Extraction of VOCs
#### 2.1.3 Sample Weight  
Sample weight in the experiments ranged from 0.5 to 2.0 g of _E. klotzschiana_ pulp. The volume contained in the headspace flask must be sufficient for the isolation of volatiles and their adsorption and/or absorption by the sorbent to occur [9].  
It was found that sample weight did not influence volatile extraction yield when using DVB/CAR/PDMS and PDMS/DVB fibers. However, it is noteworthy that, due to the larger sample volume, it was possible to detect an increase in yield in PDMS/DVB assays that used more than 2.0 g of pulp with extraction times shorter than 16 min. This result portrays that a greater quantity of analytes were eluted into the vapor phase (headspace) due to the more significant amount of pulp, and the fiber could adsorb this greater fraction, maximizing the extracted area.  
When analyzing results with DVB/CAR/PDMS fibers, the most considerable areas were obtained only from 1.4 g of pulp, regardless of the temperature or time employed.  
As for PA fibers, the variation in pulp amount positively affected the yield of extraction of volatiles, increasing the area extracted when the amount of pulp was more significant than 2.0 g. However, it is essential to emphasize that 0.5 g was enough to obtain the same volatile areas. What happens when using 2.0 g of pulp or more is the greater accumulation of analytes in the headspace due to the more significant amount of substrate, so that the content was able to be absorbed by the polyacrylate sorbent, for the volatiles present in the pulp of _E. klotzschiana_ at the levels and parameters studied [34].
## 5 Proposed Model Architecture
### Proposed U-IntentCapsNet  
In the proposed model of intent detection, the U-IntentCapsNet, described in Figure 3, two types of capsules, namely, the WordCaps and IntentCaps inspired by [9; 10], are used. For a given input query, the query is represented by an input embedding module. The WordCaps encodes this representation into an embedding sequence with forward and backward contexts, using a bidirectional recurrent neural network model. These context-aware sentence representations are fed into the IntentCaps. The IntentCaps learns to detect intents via dynamic routing that explicitly models the hierarchical relationship between the capsules. The IntentCaps construct respective intent class representation by aggregating like intent sequences. For the intent detection, the max-margin loss is used [7], that considers a maximum margin loss on each labeled query and the intent of the query is determined by choosing the activation vector with the largest norm. All module of the proposed model are described in further detail in the following sections.
## 3 Results and Discussion  
### IDEAL Test  
#### 3.1.1 Effect of Basalt Fiber on the IDEAL Test Result  
As it can be seen from Table 4, there are 10 kinds of different mixtures and the mixtures can be further divided into five kinds of different mixtures using a larger classification method (SMA-13 using SBS modified asphalt, SUP-13 using SBS modified asphalt, SUP-20 using pure asphalt, SUP-20 using SBS modified asphalt and SUP-25 using pure asphalt), and each kind of these mixtures have two types (with and without basalt fiber). IDEAL cracking test results and standard deviation are shown in Figures 6 and 7. It can be concluded from Figure 6 that the \(G_{\text{initial}}\) index increased after adding basalt fibers. The increasing extent of the sample 1, 3, 5, 7, and 9 are 15.4, 11.9, 19.6, 9.3, and 13.6%, respectively. Thus, the basalt fiber can well enhance the initial-cracking resistance of asphalt mixtures. This is mainly because basalt fiber has a very good mechanical property and it could help disperse the stress inside the asphalt mixtures during the crack forming process [20]. The added energy appeared in the test result is caused by the adhesion between basalt fiber and asphalt and the basalt fiber itself. It can also be seen from Figure 7 that the basalt fiber could help slow down the crack propagation rate. Figure 8 illustrates the cracks of asphalt mixtures with and without fiber, respectively, as shown in Figure 8, basalt fiber could act as a bridge to bond the crack and slow down the development of the crack when there was a minor crack occurred. Basalt fiber could bear some stress [20] inside the asphalt mixtures and could enhance the anti-cracking ability of asphalt mixtures. The bonding between the basalt fiber and the asphalt also contribute to the delaying of the propagation rate. Because in the crack propagation process, the viscous asphalt could bond with the basalt fiber, and it could also transfer the stress to the fiber.  
Figure 5: SCB test: (**a**) Testing curve; (**b**) Test photo.  
Figure 8: Cracks of mixtures: (**a**) without basalt fiber; (**b**) with basalt fiber.  
Figure 6: Effect of basalt fiber on \(G_{\text{initial}}\).  
Figure 7: Effect of basalt fiber on \(CT_{\text{index}}\).
# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 3 Numerical Analysis and Results  
Currently, location estimation using the HAPS is not yet available and is considered to be operated as a base station. Because of this situation, no demonstration experiment of location estimation using the HAPS has been realized. In this section, two methods of direction of arrival estimation will be applied for the HAPS for the location estimation of ground emitters. Since the phase difference method is just a primitive version of the Beamformer one, only the Beamformer and MUSIC methods are introduced in this section for evaluation of the localization accuracy.
## 1 Introduction
The array adopts a 14-layer PCB, where Layers 1-6 and 7-14 are first fabricated separately, and then the two parts are sintered together by copper paste. The top layers of the multilayer PCB are designed for antenna elements, the bottom layer is attached to the beamformer chips, and the middle layers include RF transmission lines, power lines and control lines of the chips. In order to reduce the complexity of its fabrication process, the overall array of 2048 (64 \(\times\) 32) dual-polarized antenna elements was divided into 16 identical subarrays, each containing 128 (8 \(\times\) 16) elements. Following calibration in a far-field anechoic chamber, this array was able to achieve a scanning range of \(\pm\)70\({}^{\circ}\) and a gain-to-noise temperature (G/T) value greater than 12 dB/K at boresight. The array's polarization could also be adjusted by controlling the amplitude and phase of its beamformer chips. The rest of this report is organized as follows. In Section 2, the effect of a copper stub on the performance of the vertical interconnection structure in the multilayer PCB is analyzed, and the use of the copper paste sintering technology in the PCB fabrication process is introduced. Section 3 presents a 2048-element \(Ku\)-band receive phased array designed with the novel PCB process for SATCOM applications. In Section 4, calibration and measurement of the fabricated receive phased array is presented. Final conclusions are then presented in Section 5.
# Total Internal Reflection Lens for Optical Wireless Receivers
## 5 Conclusions  
This article considers the design and experimental characterization of a freeform TIR lens as an optical concentrator for OWC receivers. The experimental characterization of the lens prototype provides practical performance merits for the first time in the context of OWC and proves the general feasibility for data transmission. The proposed TIR lens is a compact concentrator with an entrance aperture of \(\mathcal{O}\)8.8 mm and a height of only \(h\) = 6.9 mm. It fits on a cuboidal SMD package and requires no index-matching gel or glue within the optical path. Therefore, it simplifies the assembly compared to a corresponding CPC. The acceptance angle \(\theta_{\text{a}}\) of the prototype is \(\pm 5^{\circ}\). It achieves a concentration ratio of \(g_{\text{o}}\) = 44.7 (16.5 dB) within the FOV center. Small concentrators such as the proposed TIR lens require precise positioning. The investigations suggest that more precise mounting structures can improve the assembly. One drawback of the TIR lens is the complex lens structure that makes the characterization of the shape accuracy difficult, especially within the lens cavity.  
**Author Contributions:** Conceptualization, R.K. and T.S.; data curation, R.K.; formal analysis, R.K.; investigation, R.K. and T.S.; methodology, R.K.; project administration, A.N. and F.D.; resources, R.K.; software, R.K.; supervision, A.N. and F.D.; validation, R.K. and T.S.; visualization, R.K.; writing--original draft, R.K.; writing--review and editing, R.K., T.S., M.S., A.N., M.F. and F.D. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Acoustic-Based Engine Fault Diagnosis Using WPT, PCA and Bayesian Optimization
## 3 Methodology
### Model Evaluation  
The extracted feature matrix is then fed into the three models described in Figure 4, and the performance of each model is evaluated using the metrics listed in Table 7. True Positive (TP) are the data points which are accurately classified as faulty data and False Positive (FP) are the data points which are incorrectly classified as faulty data. True Negative (TN) are the data points which are accurately classified as healthy data and False Negative (FN) are the data points which are incorrectly classified as healthy data. Specificity indicates the proportion of negatives that are correctly identified, whereas precision indicates the proportion of positive that are correctly identified. These metrics along with the most commonly used accuracy measure are all very important in examining a model's performance.
## 7 Experimental Evaluation
### Streaming ML Scalability and Adaptability
Across the entire trace, the average MAE for the batch-based model is 0.29. On the contrary, the streaming ML model, with its per-record retraining process, achieves a low MAE consistently, with an average value of 0.027, i.e., an order of magnitude improvement, showcasing its great adaptability. The observations regarding model size (Figure 16b) and runtime (Figure 16c) are the same as with the classification case discussed above. Figure 16: _Cont._  
**Key takeaways:** Streaming ML demonstrates robust and stable model performance despite sudden shifts in workload access patterns (moving from one trace to another). On the other hand, batch-based approaches occasionally suffer from significant performance drops (e.g., up to 10% F1 score for _Next Offset Class_) or erratic behavior (e.g., MAE oscillations between 0.03 and 0.92 for _Next Offset_), even when retraining incrementally (3 rd scenario). The streaming ML model size and runtime remain bounded and low, even after processing millions of training instances.
# Applications of GNSS-RO to Numerical Weather Prediction and Tropical Cyclone Forecast
## 4 Outlook
### Trends Toward Expanding New Features of RO  
#### 4.2.1 Polarimetric RO Technique  
A new feature of the RO is the detection of heavy precipitation via polarimetric RO (PRO). Although the RO profiles have a high vertical resolution and are minimally affected by clouds and precipitation, they have a low horizontal resolution of approximately 100 km. If the horizontal resolution can be improved, it will benefit short-/medium-range weather forecasts such as rainfall and TC forecasts. The PRO technique aims to address the low horizontal resolution of RO. The approach involves deploying a closely spaced RO satellite train for a denser sampling. This strategy provides near-simultaneous observations of the water vapor profiles surrounding the target heavy-precipitation region.  
The PRO receivers launched in February 2018 are onboard the Spanish PAZ (peace in Spanish) spacecraft, and a concept-of-proof experiment has been carried out [84]. The authors of [84; 85; 86] studied the probability of the train constellation in encountering heavy-precipitation regions, and possible orbital strategies for implementing such a constellation. If the concept can be proven, the precipitation forecast can be improved. This will be conducive to the precipitation forecasts in NWP and TC monitoring.
## 3 Results and Discussion
### Effects of PSA Emulsions on the Heat of the Hydration of Modified Mortar  
Since the added PSA emulsion is wrapped on the surface of cement particles, it usually hinders the hydration of cement mortar. Here, we tested the effects of adding different ratios and different dosages of PSA emulsion on the exothermic course of the hydration of cement mortar. The specific results are shown in Figure 4.  
Figure 4a shows that all PSA emulsions can delay cement hydration. While reducing the peak value of the hydration heat release rate, they also postpone the time at which the peak value occurs; however, the differences between different PSA emulsions are very small. This may be because the types and ratios of emulsifiers used in the PSA emulsions are the same, resulting in very small differences in the zeta potentials of PSA emulsions. The differences in the adsorption properties of these emulsions on cement are not significant, which in turn leads to very small differences in the effects of different PSA emulsions on the hydration heat release rate.  
Figure 4b shows the hydration heat test results of the neat pastes modified with SA4 emulsion at different dosages. As the P/C ratio increases, the peak value of the hydration heat release rate continuously decreases, and the time when the peak value occurs is continuously postponed. When the P/C ratio is 15%, the time when the peak value of the heat release rate occurs exceeds 48 h, which is approximately four times that of the blank mortar. The peak value of the heat release decreases significantly, being less than half that of the blank mortar. When the dosage of the emulsion is relatively high, its inhibitory effect on the hydration of cement is more obvious, which is bound to impact the early mechanical properties and toughness of the series of PSA emulsion-modified mortars.
## 2 Related Works
### Software and Tool Analysis  
Table 1 provides a comprehensive comparative analysis of the testing systems. It reveals that a fully functional and sophisticated TS is quite complex, and its performance heavily relies on the support of implementation tools.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Testing System** & **Communication Standard** & **Network Simulator** & **Traffic Simulator** & **Features** & **Compared to Proposed** \\ \hline Resource Selection [9] & LTE-V2X PC5 & NS-3 & SUMO & Resource selection & \(\times\) \\ \hline Multi-stack [10] & IEEE 802.11p, C-V2X Mode 4 and LTE & NS-3 & SUMO & Multi-stack & \(\times\) \\ \hline INTEGRATION & & & & & \\ Large-Scale Modeling Framework [11] & Direct C-V2X & Analytical Model & INTEGRATION & Large-scale (downtown LA. area, 145,000 vehicles) & \(\times\) \\ \hline Eclipse MOSAIC-based & & IEEE 802.11p & Simple Network Simulator & SUMO & Flexible architecture for new add-in components & \(\checkmark\) Layered software architecture \\ \hline Commercial V2I RSUs Deployment [13] & C-V2X & / & / & \(\checkmark\) GUI, COTS RSU, DOT & \(\checkmark\) Devices, Vehicles \\ \hline SEP4 CAM [14] & C-V2X & Ns-3 & / & \(\checkmark\) cross-border and cross-domain environments, vehicle tracks from real-world scenarios [20] \\ \hline VDT Method for C-V2X & C-V2X & Channel Simulator & / & Real-world channel parameters & \(\checkmark\) Previous work uses the same method to build communication channel [4] \\ \hline On-road Features based In-chamber Testing [15] & C-V2X & / & SUMO & GNSS control & \(\checkmark\)Previous work [15] \\ \hline MCity [2] & / & / & \(\checkmark\) Remote operating system & / \\ \hline AstaZero [3] & / & / & \(\checkmark\) Digital twin simulation platform & / \\ \hline Smart Highway [16; 17] & ITS-G5/C-V2X & / & / & User custom & \(\checkmark\) User custom \\ \hline Large-scale LTE-V2X Test [18] & LTE-V2X & SimulLTE & SUMO & Large-scale OBUs & \(\checkmark\) Scalable Density instead of vehicles & Node Array \\ \hline Proposed & C-V2X & NS-3 & SUMO & Software architecture & / \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparative analysis of the aforementioned testing systems.  
Testing systems use co-simulation techniques that combine network and traffic simulators with other functional components or architectures to achieve automated testing. Most software or tools are more suited to research or development purposes, needing more flexible architectures or user-defined functions, and are, therefore, inadequate for standardized commercial testing requirements.  
The last column of the Table 1 compares the proposed software architecture with other testing systems. Based on the characteristics of existing testing systems and our previous work, we designed a universal testing and evaluation system for V2X applications and proposed a software architecture paradigm.