esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Thermal Degradation (Weight Loss)  
The weight loss (%) of the pure epoxy and reinforced composites is shown in Figure 8. Thermogravimetric analysis (TGA) can determine a material's thermal stability and thermal decomposition. The weight loss (%) of epoxy, and the EQ-20, EQ-25, EQ-30, and EQ-35 composites were evaluated by TGA with rising temperatures. The initial decomposition of the epoxy composite started from the softening of the polymer composite at 215 \({}^{\circ}\)C, followed by a major degradation at 230 \({}^{\circ}\)C; then, 70% of the weight was lost at 326 \({}^{\circ}\)C, and its further decomposition continued with the rising temperature until its complete degradation.  
Apart from the epoxy composite, the quartz fiber/epoxy composites (EQ-20, EQ-25, EQ-30, and EQ-35) decomposed differently with a smaller amount of weight loss. The initial decomposition of sample EQ-20 (0.20 quartz fiber) started at 245 \({}^{\circ}\)C; for EQ-25 (0.25 quartz fiber), it started at 280 \({}^{\circ}\)C; for sample EQ-30 (0.30 quartz fiber), it started at 290 \({}^{\circ}\)C; and for sample EQ-35 (0.35 quartz fiber), it started at 308 \({}^{\circ}\)C. Significant degradation in these composites was observed within the temperature range of 285 \({}^{\circ}\)C and 650 \({}^{\circ}\)C. The major weight loss was observed due to the decomposition of the epoxy resin which is a polymeric component. It was seen that increasing the fraction of quartz fibers provided better thermal stability to the composite compared to the pure epoxy composite. This was evident from the decreasing weight loss of 74.25%, 71.3%, 70.3%, and 64.15%, respectively. Increasing the fraction of quartz fibers reduced the epoxy faction in the reinforced composite.  
Figure 7: Bending strength of quartz fiber/epoxy composites.  
Moreover, the lower quantity of epoxy resin was efficiently cured by limiting the molecular chain's thermal vibration up to some extent. In this manner, heat conduction becomes lower and thus, the quartz fiber/epoxy composite exhibited better thermal stability than the pure epoxy composite. Embedding a small quantity of quartz fibers into the epoxy resin increases the manufacturing cost roughly by 15 to 20% when compared to producing pure epoxy composite, all in pursuit of achieving enhanced properties.
## 2 Literature Review
### Ensemble Learning
#### 2.1.1 Bagging  
Bagging, also known as bootstrap aggregating [19], is the method of randomly creating samples of data out of a single dataset with replacement. The bagging method provides the different random subsets of the training data to each learner. Bagging is to secure diversity in terms of data. Bagging is suitable for models with a high model complexity, such as decision tree (DT) algorithms. DT predicts by iteratively dividing the space of input variables into non-overlapping multi-dimensional rectangles, and the result is similar to the shape of a tree. The result of the DT is tree-like; DT is simple and easy to apply but may have a lower predictive performance than more complex models. This problem with DT can be improved with a bagging decision tree (BDT) model.  
AdaBoost [20], an abbreviation of Adaptive Boost, is a classification-based machine learning model. After setting the initial model as a weak learner, AdaBoost sequentially compensates for the weaknesses of the previous model by weighting the data that the previous model did not predict well. Large errors made by earlier models can be compensated by the subsequent models. Finally, a strong learner is generated by linearly combining these weak learners.  
Random forest (RF) [21] is a specialized bagging for decision tree algorithms using two methods to increase the diversity of ensemble-bagging and randomly selected predictor variables. In terms of datasets, sampling with replacement is introduced, and in terms of variables, randomly selected variables from the p variables are used. In other words, RF introduces diversity not only in terms of data but also in terms of variables.  
In general, the prediction error of a model can be considered to include bias and variance. Creating a model that accurately predicts the construction cost is ultimately finding a model that minimizes bias and variance. Bias indicates how well the model captures the underlying relationship between features and target outputs, whereas variance indicates how much the range of predictions fluctuates in the training data [22]. Bagging tends to reduce variance more than bias and does not work well with a relatively simple model.
# A Defect Detection Method Based on YOLOv7 for Automated Remanufacturing
## 3 Training and Results
### Evaluation Metrics  
In this work, standard evaluation metrics are adopted, the same as in YOLOv7, to study and compare the performance of the different variants of the YOLOv7 models on different metal defect datasets. Traditionally, the mean average precision (mAP), which is the area under the precision and recall curve calculated using (1), is calculated at a 0.5 threshold of the intersection over union (IoU) (mAP_0.5). However, a recent trend in the research field is to compute mAP over multiple IoU values following the COCO interpretation from 0.5 to 0.95 with a step of 0.05 (mAP_0.5:0.95). This has proven to affect the model with a better localization reward. However, some of the recent studies in the defect detection field still use the mAP_0.5; thus, both metrics will be reported in this study. In addition, inference speed is also observed for each dataset.  
\[AP=\int_{0}^{1}\left(precision\times recall\right)\,d(recall) \tag{1}\]  
\[Precision\,=\frac{TP}{TP+FP} \tag{2}\]  
\[Recall=\frac{TP}{TP+FN} \tag{3}\]  
where \(TP\), \(FP\), and \(FN\) are the true positive, false positive, and false negative of the bounding box predictions, respectively.  
\begin{table}
\begin{tabular}{c c} \hline
**Property** & **Value** \\ \hline CPU & AMD Ryzen Threadripper 3970X 32-Core \\ GPU & NVIDIA GeForce RTX 3090/24 GB \\ CUDA cores/version & 10,496/11.8 \\ Operating system & Windows Server 2019 \\ RAM & 128 GB \\ PyTorch & 1.10.1 \\ \hline \end{tabular}
\end{table}
Table 2: Training machine specifications.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Learning rate & 0.001 & Batch Size & 8–32 \\ Momentum & 0.937 & Image Size & Depends on dataset \\ Weight decay & 0.0005 & Epochs & 100–300 \\ \hline \end{tabular}
\end{table}
Table 3: Training parameters.
# Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification +
## 3 Results
### Comparison with a Standard Note Detection Algorithm  
We have compared our note detection system with Tony, a state-of-the-art algorithm that uses HMMs with note onset, stable and offset states to detect notes in multiple pitch tracks calculated emphasising different frequency ranges. As our speech/singing discrimination algorithm only uses the 'note/no note' decision, we have compared precisely this aspect in the two algorithms. With this purpose, we have used the Bertsolaritza database explained in Section 2.1. We have labelled 10 ms spaced frames with a binary label (note/no note) with both algorithms and calculated the agreement between systems using the kappa score [35] in each audio file. The histogram of these scores is shown in Figure 8.  
We can observe that the agreement between both note detection systems is strong for most files in the database, with a mean kappa of 0.73 \(\pm\) 0.08.
# Signal Detection for Ambient Backscatter Communication with OFDM Carriers
## 4 Numerical Results
In both detector schemes, the BER remains unchanged despite of large SNR because of strong direct-link interference [8] or SNR wall problem [17]. Figure 5: BER versus SNR with \(N_{cp}\). As we predicted, the BER increases as \(N_{cp}\) decreases. Since \(N\) is defined to be proportional to \(N_{cp}\), while \(N_{cp}\) should be greater than the channel delay spread \(L\) to eliminating the interference, there exists tradeoffs between the CP length and the subcarrier spacing as well as the CP length and the tag rate. In order to assess the performance of the proposed detector, we plot the ROC curves in Figure 6, which shows the relationship between the probability of detection \(P_{d}\) and the probability of false alarm \(P_{f}\) for a given SNR \(\gamma\). We confirm that the proposed approach provides better ROC values when \(\gamma\) is small, i.e., the performance gain becomes much larger in the lower SNR environment. As the SNR increases, the detection threshold must be set higher to obtain a good ROC.
## 3. Experimental Results and Discussion
### Simulation Validation Experiments
#### 3.2.3 Seed Distribution within the Pressurized Tube  
The distribution of seeds within the pressurized tube is shown in Figure 8 under different inlet airflow velocities and seeding quantities. In the straight section, some seeds initially move along the outer side of the vane's wall. As the airflow acts on the seeds, they accelerate and gradually disperse throughout the entire pressurized tube. After passing through the corrugated section of the pressurized tube, the presence of corrugations causes an increase in seed velocity. The disturbance from the corrugations ensures that seeds are uniformly distributed in the middle part of the corrugated section, thus ensuring the uniformity of seed flow into the distribution head.  
Figure 7: Pressure distribution contour map.  
Under the same seeding quantity, with an increase in the inlet airflow velocity, the uniformity coefficient of seed distribution within the pressurized tube initially decreases and then increases. When the inlet airflow velocity reaches 35 m/s, seeds gradually move towards the center of the pressurized tube, and seed distribution becomes more dispersed. The distance over which seeds move along the vane's straight section wall is the shortest. However, when the inlet airflow velocity reaches 55 m/s, the distance over which seeds move along the straight section wall increases, leading to reduced uniformity of seed distribution within the corrugated section and a decrease in seeding system performance. Therefore, in the design of seeding systems and the selection of fans, it is advisable to minimize the inlet airflow velocity  
decreases, and the uniformity coefficient of seed distribution within the pressurized tube shows a trend of initially decreasing and then increasing.
## 3 Results and Discussion
### Flow Control Effect by AC-DBD Plasma Actuation
#### 3.2.4 Flow Vorticity  
The normalised z-vorticity contour (\(\omega\)H/U) downstream of the rear end of the trailer model is displayed in Figure 16. In the baseline trailer (Figure 16a), considerably high levels of positive z-vorticity appear in the recirculating bubble. In those cases with the linear AC-DBD plasma actuator being implemented (Figure 16b-f), the z-vorticity contours remain similar to that shown in the baseline trailer (Figure 16a). This suggests that the linear AC-DBD plasma actuation exerts no obvious effect in altering the flow vorticity along the wake region downstream of the trailer rear end regardless of the actuation frequency being used.  
Figure 16: Normalised z-vorticity downstream of the trailer rear end at various plasma actuation frequencies. (**a**) Baseline, (**b**) 7 kHz, (c) 8 kHz, (d) 9 kHz, (**e**) 10 kHz, and (**f**) 11 kHz.  
Figure 17 shows the normalised z-vorticity profiles measured at various normalised locations downstream of the trailer rear end. From Figure 17, it can be seen that compared to the baseline trailer, using the linear AC-DBD plasma actuation at the rear end of a square-back trailer shows no obvious effects in changing the z-vorticity levels along the wake region below the upper shear layer (i.e., the region \(-1<y/H<-0.3\)) at all the locations being studied (Figure 17a-f). In contrast, stronger vorticity fluctuation downstream of the trailer rear end occurred in those trailers with the linear AC-DBD plasma actuation being implemented. Interestingly, the only exception appears when the actuation frequency is at 11 kHz. At this actuation frequency, the z-vorticity profiles become similar to those shown in the baseline trailer at all the normalised x-locations being studied.
###### Abstract  
Rapid urbanisation and industrialisation have intensified the Urban Heat Island (UHI) effect, significantly increasing energy demand for thermal comfort. Urban buildings consume considerable energy throughout the year, which can be reduced by incorporating Phase Change Materials (PCMs) into building materials. PCMs effectively regulate temperature by storing and releasing heat as latent heat during phase transitions. However, to prevent leakage, PCMs can be encapsulated in co-axial polymeric Phase Change Fibres (PCFs), representing an innovative approach in scientific research. This study optimised the coagulation bath and produced PCFs using commercial cellulose acetate as the sheath and polyethylene glycol (PEG 600 and 1000) as the core via the wet-spinning method. The first part of this work investigated the coagulation bath using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR) analyses of the characteristic peak areas. In contrast, the second part examined the PCFs' morphological, chemical and thermal properties using Bright-field microscopy, ATR-FTIR, Differential Scanning Calorimetry (DSC) and Thermogravimetric Analysis (TGA) techniques. The results demonstrated the successful production of PCFs with an optimised coagulation bath. Bright-field microscopy and ATR-FTIR confirmed the well-defined morphology and the presence of PEG in the fibre core. TGA analysis showed high thermal stability in the PCFs, with mass loss observed at high degradation temperatures, ranging from \(\sim\)264 \({}^{\circ}\)C to 397 \({}^{\circ}\)C for the PCFs with PEG 600 and from \(\sim\)273 \({}^{\circ}\)C to 413 \({}^{\circ}\)C for the PCFs with PEG 1000. Meanwhile, DSC analysis revealed melting points of \(\sim\)12.64 \({}^{\circ}\)C and 11.04 \({}^{\circ}\)C, with endothermic enthalpy of \(\sim\)39.24 \({}^{\circ}\)C and 30.59 \({}^{\circ}\)C and exothermic enthalpy of \(\sim\)50.17 \({}^{\circ}\)C and 40.93 \({}^{\circ}\)C, respectively, for PCFs with PEG 600, and melting points of \(\sim\)40.32 \({}^{\circ}\)C and 41.13 \({}^{\circ}\)C, with endothermic enthalpy of \(\sim\)83.47 \({}^{\circ}\)C and 98.88 \({}^{\circ}\)C and exothermic enthalpy of \(\sim\)84.66 \({}^{\circ}\)C and 88.79 \({}^{\circ}\)C, respectively, for PCFs with PEG 1000. These results validate the potential of PCFs for applications in building materials for civil engineering, promoting thermal efficiency and structural stability.  
urban heat island (UHI); coagulation bath optimisation; smart materials; phase change fibres; cellulose acetate; polyethylene glycol; wet-spinning; thermal comfort
# Prediction of Traffic Volume Based on Deep Learning Model for AADT Correction
## 2 Review of Related Studies  
The introduction of advanced traffic volume survey equipment capable of automatically detecting traffic volumes has allowed for addressing the limitations of traditional traffic surveys by reducing outliers and missing values. However, the unpredictability of the traffic volume data due to equipment malfunctions and communication disruptions continues to pose significant challenges. Consequently, extensive research has been conducted to correct and predict the traffic volume data. These studies fall into three primary categories: (1) traffic volume correction studies utilizing historical data, (2) traffic volume prediction studies employing statistical learning algorithms, and (3) traffic-volume-related prediction studies using deep learning techniques.
## 3 Navigation
### Fusion Method
#### 3.2.1 Gps + IMU
[66] investigated the vision-based estimates with measurements from other sensors, GPS, and IMU using fuzzy adaptive multiple models, the results of which exhibited improved accuracy and faster convergence of the fusion filter.
# Improving Medical X-ray Report Generation by Using Knowledge Graph
## 3 Our Approach  
Our framework consists of a classification module, a generation module, and an interpretation module, as shown in Figure 1. The classification module consists of a multi-view image encoder, a text encoder, and a graph convolutional network based on a knowledge graph. We first build a knowledge graph in a data-driven manner (Section 3.1.1), then use a multi-view image encoder to read multiple chest X-ray images and extract the global visual feature representation, which is passed to a fully connected layer to decouple the global visual feature representation into multiple low-dimensional visual embedding (Section 3.1.2). At the same time, the text encoder reads the clinical documents and summarizes the content into text summary embedding, and then uses the \"Add &LayerNorm\" operation to wrap the visual embedding and text summary embedding together to obtain the image-text hybrid features, which are referred to as context-related embedding of the disease topic (Section 3.1.3). The episodic embedding is passed to a graph convolutional network (GCN) based on a knowledge graph that propagates semantic correlations between disease topics based on the knowledge graph to inject prior knowledge into concept representation learning (Section 3.1.4). The generation module takes as initial input the rich disease embedding that passes through the graph convolutional network to generate the text (Section 3.2). Finally, the generated text is sent to the interpreter for fine-tuning to align with the disease-related topics of the classification module (Section 3.3). In what follows, we elaborate on these three modules.
# Energy Management Scheme for an EV Smart
## 3 Component Modeling and Control
### Four-Quadrant Converter Controller  
The inverter controller receives the \(PQ\) references from the EMS. The control is in the \(dq\) frame with a conventional current loop. Synchronization of the inverter with the grid is achieved through the phase locked loop (PLL), which intakes the three-phase voltage signals from the PCC bus \(v_{pcc(abc)}\)  
Figure 7: Four-quadrant converter circuit topology with LCL filter.  
The PLL configuration is shown in Figure 8. Various output signals from the PLL are utilized in the current control.  
The EMS controls real and reactive power using a current-control strategy. The goal of the current control is to generate the three-phase duty ratios \(d_{abc}\) for the PWM and the switching pulses for the switches \(S_{1}\) to \(S_{6}\) of the inverter. These signals are derived from the active and reactive power references. The reference direct and quadrature currents \(i_{d}^{*}\) and \(i_{d}^{*}\) for the current control are calculated from the \(P_{(inv)}^{*}\) and \(Q_{(inv)}^{*}\) references established by the EMS as follows:  
\[i_{d}^{*}=\frac{2}{3(v_{d}^{2}+v_{q}^{2})}[P_{(inv)}^{*}v_{d}+Q_{(inv)}^{*}v_{q}] \tag{16}\]  
\[i_{q}^{*}=\frac{2}{3(v_{d}^{2}+v_{q}^{2})}[P_{(inv)}^{*}v_{q}-Q_{(inv)}^{*}v_{d}] \tag{17}\]  
where \(v_{d}\) and \(v_{q}\) are the direct and quadrature voltages from PLL. The instantaneous \(dq\) currents \(i_{d}\) and \(i_{q}\) are generated from the three-phase LCL filter current \(i_{f(abc)}\). The control process is shown in Figure 9.
## 1 Introduction
According to the 3 GPP Release 14 standards [11], LTE-vehicle (LTE-V)/C-V2X has demonstrated its ability to meet the low-latency requirements of vehicular networks. Cellular networks offer inherent advantages in delivering V2I communications due to their high data rates, widespread coverage, robust quality of service (QoS) support, and deep network penetration. The 3 GPP-based cellular technology, endorsed by 5 GAA, is believed to provide superior performance and a more future-proof radio access solution than DSRC. The study report from 5 GAA [12] shows a comparative performance analysis between LTE C-V2X and 802.11p and demonstrates that LTE C-V2X has better performance due to its superior physical layer compared to DSRC. However, for the more advanced V2X applications (e.g., semi- or fully automated driving), which have very low latency and very high bandwidth requirements, even LTE C-V2X falls short in such cases [13]. Hence, 5G new radio (NR) comes into play to become a complementary solution to the existing LTE C-V2X technology. For that, 3 GPP formulates the technical specifications for NR V2X in TR 38.885 [14]. Furthermore, 3 GPP Release 16 standardizes the V2X on top of 5G NR, which is standardized in Release 15 [15]. However, so far, very little work has been carried out on the 3 GPP NR V2X system and specifications [16; 17; 1]. In summary, there are a number of reasons that motivated us to conduct this research work. Firstly, although 5G NR is a contemporary and promising technology for V2X communication, there is a scarcity of open-source simulators designed for 5G V2X communications that align with the 3 GPP NR V2X Release 16 specifications. This posesa significant limitation for the research community. Secondly, the commercially available simulators are expensive and offer limited opportunities for source code modification to suit research needs. Thirdly, the substantial costs associated with deploying vehicular test beds and conducting pre/post experimental validation underscore the ongoing demand for a realistic simulation platform [18; 19; 20]. Therefore, it would be advantageous to allocate more resources to the development and enhancement of open-source simulators tailored to NR V2X communication in compliance with 3 GPP Release 16, enabling a comprehensive performance analysis in diverse network scenarios (an initial result from this work has been presented at the IEEE WAMICON'23 conference) [21]. The remainder of the paper is organized as follows. Section 2 describes the system model. The problem statement is presented in Section 3. A brief overview of DSRC, LTE C-V2X, and 5G NR V2X is presented in Section 4. The simulation setup and performance analysis are shown in Section 5. Finally, we conclude this paper in Section 6.
## 2 Materials and Methods
#### Statistical Analysis  
A total of 160 measurements were collected and recorded in an Excel spreadsheet (Microsoft Excel 2015; Microsoft Corp., Redmond, WA, USA). The control group consisted of a fixed reference value (205) with no variance and was, therefore, excluded from normality testing. The distribution of \(\Delta\)RGB values in the experimental groups was assessed using the Shapiro-Wilk test. All experimental datasets showed a normal distribution (\(p>0.05\)), allowing for the use of parametric statistical tests for further analysis. Paired \(t\)-tests were used to compare the color changes between T0 and T14 for both the 7 H and the 12 H wear protocols. Subsequently, paired \(t\)-tests were also conducted to directly compare the color change values between the two wear-time protocols. Additionally, a post hoc power analysis was conducted based on the mean difference and standard deviation of the paired \(\Delta\)RGB values observed between the 7 H and 12 H protocols. The effect size (Cohen's d) was calculated and used to estimate statistical power using a paired \(t\)-test model with \(\alpha=0.05\). To assess measurement repeatability, Pearson's correlation coefficient was calculated between the first and second measurement sets for each aligner, in order to demonstrate the consistency of the results (\(p<0.001\)).