esa-sceva/satcom-chunk-collection huggingface dataset:
# EER-DETR: An Improved Method for Detecting Defects on the Surface of Solar Panels Based on RT-DETR
## 2 Related Work
### Transformer-Based Object Detection Network  
In the realm of object detection, the use of Transformer models has gained significant attention. The Vision Transformer [21] demonstrated that Transformer architectures can be effectively applied to image processing by partitioning an image into patches. Its performance on image recognition tasks is competitive with state-of-the-art convolutional networks. A noteworthy advancement in object detection is DETR [22], which successfully leveraged Transformers for this purpose. DETR utilizes CNNs (such as ResNet50/101) for feature extraction, feeding the extracted features into a Transformer encoder. This encoder, in turn, generates object locations and category information via a decoder, and the model is trained end to end without requiring traditional post-processing techniques like NMS. While DETR showcases the potential of Transformer-based object detection, it faces challenges like slow convergence and difficulty detecting small objects. To address these, Deformable-DETR [23] introduced the deformable attention mechanism, which converts the dense attention into sparse, trainable attention, accelerating convergence. Despite these improvements, computational overhead in the decoder remains a bottleneck. Efficient DETR [24] enhances decoder query capability by selecting key positions from the dense predictions. However, these models still struggle with the computational complexity due to the deep stacking of encoders and decoders. To tackle this, RT-DETR [25] focused on utilizing the final layer of the CNN-extracted feature map, which contains most of the global information, and applied a single encoder layer for faster inference without sacrificing accuracy. By fusing the encoder feature map with shallow multi-scale features to create a feature pyramid, RT-DETR achieves faster computation while maintaining high performance. Consequently, RT-DETR serves as the baseline model for this study.  
However, RT-DETR also has the problem of excessive parameters and a heavy computational burden. Therefore, it is urgent to develop a lightweight model that can significantly reduce the number of parameters while maintaining high detection accuracy.
## 3 Results
### Terpenes
#### 3.2.4 Principal Component Analysis  
The biplots of the terpenes as well as their interactions with the yields for the respective flower positions showed that more than 70% of the variation can be displayed by two examined components (Figure 8).  
The PCA revealed that linalool and camphene were positively correlated (Figure 8a). In addition, there was a slightly positive correlation between camphene and linalool. On the other hand, a negative correlation was detected for these two terpenes with ocimene and \(\beta\)-myrcene. Furthermore, a positive correlation was found between \(\beta\)-pinene, humulene, limonene and cryophyllene. Each strain displayed its typical terpene profile. A4 was dominated by \(\alpha\)-pinene and \(\beta\)-pinene, whereas E19 accumulated more linalool, humulene and cryophyllene. On the other hand, KAN was associated with \(\beta\)-myrcene ocimene and \(\alpha\)-pinene.  
A further PCA was performed to analyze a possible dilution effect of the terpenes with increasing flower yield [5; 6]. This was carried out for each of the three flower positions. The PCA for the MTBs (Figure 8b) indicated the same interaction as the sole consideration for only the terpenes, without the influence of the specific yield (Figure 8a), i.e., no impact of MTB yield on the respective terpene profile. For the STBs, a negative interaction between flower yield and terpene concentration was found (Figure 8c), which became more pronounced for the SBs (Figure 8d). These negative correlations were found for all terpenes, except linalool.  
Figure 8: Biplot of the impact of terpene-by-strain period means on (**a**) terpene concentrations for three strains (A4, E19 and KAN), (**b**) terpene concentrations and yield of the main top buds (MTB’s) for three strains, (**c**) terpene concentrations and yield of the side top buds (STB’s) for three strains, and (**d**) terpene concentrations and yield of the side buds (SB ’s) for three strains. PC1 and PC2 are principal components 1 and 2.
## 2 Background
### Effects of Induced Vibration on Printed Parts  
Hybrid processes for additive manufacturing currently exist [33], i.e., combining additive manufacturing with a material removal or re-melting process. However, there is limited research on the benefits of adding vibration to additive manufacturing. One such study by [PERSON] et al. [34] explored the effect of induced ultrasonic vibrations on the mechanical properties of ABS samples created using the FDM process. The authors observed an improvement in the bending properties of the parts printed using vibration-assisted FDM. They observed that as the energy spent for vibrations went up, the defects started to vanish, providing a more uniform material distribution.  
[PERSON] et al. [35] studied the mechanical properties of ABS reinforced with carbon fiber printed with induced vibrations. In their work, vibrations were generated using a dc motor and an offset weight. The induced vibrations helped in reducing porosity by around 3% while increasing the Weibull modulus from 25 to 57.
## 5 RC-LARR: RSU-Assisted Cloud-Based Location-Aware Hybrid Routing
### Registration Phase  
When a vehicle enters the RSU range, it sends the join packet to RSU, and RSU makes the entry in its table. The newly entered vehicle sends the hello packet to all vehicles in its range and receives a reply from its neighbors. The vehicle makes an entry or updates its local routing table. If any change occurs in its local routing table, the node informs the RSU about the change. This process is repeated by all vehicles at certain intervals.  
The RSU maintains a table in which it keeps the entries of all neighboring nodes of the vehicles registered with it. The RSU updates the entry on the main server. The cloud-based control plane layer 2 also maintains the table in which all information sent by the RSU is kept. Hence, it maintains the global view of the topology. The registration phase uses Algorithm 1 for registration.  
```
Input: VID=Vehicle ID, Pos=Current Location Output: Vehicle registration
```
1. Begin
2. \(\textit{RSUID}\gets get\textit{RSUID}(\textit{VID},\textit{Pos})\)
3. If Ischanged(\(\textit{RSUID}\))==True then
4. \(\textit{List\_neighbour}\leftarrow\textit{Hello\_broadcast}(\textit{VID},\textit{Pos})\)
5. \(\textit{UpdateRSU}(\textit{VID},\textit{List\_neighbour})\)
6. Else
7. If\(\textit{NeighbourChanged}(\textit{List\_neighbour})\)then
8. \(\textit{updateListNeighbour}()\)
9. \(\textit{updateRSU}(\textit{VID},\textit{List\_neighbour},\textit{Pos})\)
10. End If
11. End If
12. End ```
**Algorithm 1**Registration
## 3 Materials and Methods
### Navigating to a Destination
#### 3.1.2 Navigation  
Tracking the exact location of the PVI indoors is impeded by many obstacles. While a GPS is used widely in outdoor tracking, it is inaccurate when used for tracking objects indoors. Using the accelerometer and the compass (or magnetometer) to track users proved to work better than GPS tracking, which requires a wireless connection to function. At first, the PVI search for a marker around them to use it as a starting point. Once a marker is detected, it tells them that the initial location is identified. After that, it asks PVI to specify the desired target location. Then, it estimates the shortest path from the start point to the target location and instructs the PVI to start walking in the appropriate direction. Several techniques may be implemented, but the authors have chosen the Dijkstra algorithm. When walking, it guides them to the destination using navigation commands. When the PVI reach the first location of the path and eventually scan the marker placed on the wall, our system guides them to the next maker on the graph. This process is repeated until the PVI arrive at their destination. The main feature of the navigation module is to give continuous navigation commands to reach the destination.
# A Review of Blast Loading in the Urban Environment
## 2 Current Knowledge
### Channelling and Shielding
The key factors governing this have yet to be identified, either numerically or experimentally. In a related study, [PERSON] et al. [40] investigated the shielding effects of houses. A terraced and detached layout was constructed with concrete beams and blocks. Results from both layouts suggested that _\"the presence of a building between the explosion and the \"target\" building produced a shielding effect and the blast resultants were reduced compared to the situation where there was no intervening building\"_. This generally agrees with the findings from [PERSON] et al. [37] as discussed above. However, [PERSON] et al. [40] showed that the most effective form of shielding occurred when the buildings were near each other, tall, and relatively \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Areal Density**} & \multicolumn{2}{c}{**Impulse Reduction**} \\ \cline{2-3}  & **Direct Line** & **No Direct Line** \\ \hline
28.6\% & 93.5\% & 87.5\% \\
22.0\% & 88.8\% & 88.2\% \\
17.9\% & 85.1\% & 86.6\% \\ Average & 89.1\% & 87.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reduced loading due to shielding on a target wall from blasts contained within random arrays of varying building areal density. Note: 93.5% shows that the average impulse along the target wall was 93.5% of the unshielded value—a 6.5% reduction, adapted from [PERSON] et al. [37]. Figure 2: Diagram showing plan views of the channelling (**left**) and shielding (**right**) type layouts used within the Air3D simulations. Red circles highlight the charge location. Adapted from [PERSON] et al. [37]. large compared to the explosive mass used. Whilst this conclusion can be taken from this study, it could be perceived as contradicting the findings from [PERSON] et al. (2017), where increasing the areal density resulted in less effective shielding. However, this discrepancy cannot be assured; the building height is not accounted for in [PERSON] et al. (2017), and the building layouts differ between the studies. As a minimum, it could be logical to assume that the areal density, specific layout, and building heights should all be considered in predicting urban blast loading. An additional conclusion from [PERSON] et al. (2017) was that the blast interaction with the first row of buildings was the most important factor regarding the shielding effect of the blast compared to other rows of buildings. This was later confirmed by the modelling work of [PERSON] et al. (2017), described in Section 3.3, who found that channelling dominated closest to the charge and hypothesised that channelling is a localised effect, whereas shielding is cumulative.
# Effect of Aging Time on the Composition of the Volatile Components of An-tea
## 3 Results
### Relative Compositions of Volatile Compounds in Differently Aged An-tea Samples
The relative contents of ketones and aldehydes in the CK samples were 18.06% and 8.30%, respectively. During the early stages of aging, the relative contents of these two types of compounds increased by 13.16% and 13.05%, respectively, significantly increasing their accumulation. The HAS, MAS, LAS, and CK samples had relative alcohol contents of 6.85%, 10.99%, 16.08%, and 19.68%, respectively, indicating a decreasing trend with increasing aging time, while, in contrast, the ester contents were 26.45%, 6.47%, 7.96%, and 8.22%, respectively, indicating an increasing trend with increasing aging time. The ester content of HAS was significantly higher, with those of MAS and LAS being similar to that of CK. The hydrocarbon contents of HAS, MAS, LAS, and CK samples were similar at 6.29%, 5.28%, 6.61%, and 4.22%, respectively, while the contents of nitrogen-containing compounds were 1.48%, 7.16%, 1.27%, and 4.69%, respectively. The contents of other compounds were 3.42%, 10.03%, 0.67%, and 1.23%, respectively, with the MAS samples showing the highest contents. Phenols were only present in LAS and CK with relative contents of 2.28% and 16.25%, respectively, indicating that phenols underwent a rapid transformation during the early stages of aging. The main volatile compounds and their relative contents also differed in samples with different degrees of aging (Figure 3). The compounds with the highest contents in HAS were phenyl acetate (15.28%), methyl salicate (6.88%), linalool (6.85%), \(\beta\)-ionone (5.27%), and dihydroactinidiolide (4.29%); those in MAS were benzaldehyde (6.81%), dimethoxybenzene (5.04%), \(\beta\)-ionone (4.92%), and linalool (4.24%); those in LAS were \(\beta\)-ionone (13.30%), linalool (8.80%), 3,7-dimethyl-l,5,7-octatriene-3-ol (6.86%), and benzaldehyde (5.04%); and those in CK were 4-amino-o-cresol (12.38%), 3,7-dimethyl-l,5,7-octatriene-3-ol (10.30%), linalool (9.38%), salicylic acid methyl ester (7.51%), and \(\beta\)-ionone (6.37%). In general, high contents of \(\beta\)-ionone and linalool were present in all four samples, with relative contents ranging from 4.92% to 13.30% and from 4.24% to 9.38%, respectively. Thus. these compounds were the key volatile components, so they would probably also provide the characteristics of the An-tea aroma.
## 6 Evaluation and Performance
### Computational Complexity  
Each stage of the scheme involves fundamental cryptographic operations, whose complexity is analyzed as a function of the size \(n\) of the cryptographic module and the key length \(k\).  
The results describe how the implementation of blinding and signing functions can operate and their degree of efficiency, as shown in Table 1, thus allowing the implementation of the scheme on various environments.
## 4 Experiments
### Synthetic Stress Combinations  
For the next set of experiments we decided to perform the modifications to the audio recordings of Set 2 that had achieved higher accuracy rates in Set 1. These were pitch [\(-3\%\), \(+3\%\)] and signal speed [\(-15\%\), \(-10\%\), \(-5\%\)] as mentioned. We joined Sets 1 and 2, transforming the problem in a 21-speaker SI task and combined all the synthetic stress data into one dataset, augmenting in a factor of 6 the original data size, 5 modifications plus the original dataset. The same analysis were done for Set 1 and Sets 1 + 2.  
The equivalence between each training set used and case number is shown in Figure 7. On this figure, we can appreciate the different combinations of configurations of the original and modified datasets used for training. These compositions were grouped forming different combinations in order to acknowledge the differences in accuracy for each particular setting used in the training stage. These experiments were repeated 20 times for reliability with the outcome shown in Table 4.  
In Table 4 we can appreciate two types of experiments, some in which we substitute data and others where we augment data on the training stage. As for substituting the original set by a synthetically stressed one, we have experiments 6 and 7 to be compared with experiments 1 and 2  
Figure 5: Results for synthetic datasets, Set 1 Pitch modifications.  
Figure 6: Results for synthetic datasets, Set 1 Speed modifications.  
Figure 7: Equivalence between training data used and case of experiment in Section 4.5.  
respectively. Data substitution achieves similar results to those with original data when using synthetic data converted out of neutral speech for training (case 1 vs. case 6) as well as better identification rates when using synthetic data obtained from stressed speech (case 2 vs. case 7).  
Data augmentation experiments are 3, 4, 5, 8, 9 and 10. The outcome is indeed positive, the best results are achieved in experiment 8 with a 99.45% of accuracy for Sets 1 + 2. These results show us that augmenting the data with synthetically stressed utterances of speech boosts the SI rate.  
One of the objectives of these experiments was to determine whether experiment 4 could outperform experiment 2. This would mean that we had accomplished the task of generating appropriate synthetically stressed speech out of neutral utterances. However, we can see that the procedure we employed was not enough to be used as a substitute. Nevertheless, in Table 4 one can see that case 4 performs better than case 6, which in turn outperforms case 1. This shows that stressing speech synthetically and using it as training data alongside with original stressed data increases the performance of the SI system.
# Overview on the Evolution of Laser Welding of Vascular and Nervous Tissues
## 2 Laser Welding of Vascular and Nervous Tissues  
### Contextualization  
Different repair techniques can be applied to nerves, arteries, and veins. The success of these surgeries depends on several factors, such as the severity of the injury, the surgical technique, the surgeon's experience, and the post-operative biological response [40]. In the most common surgeries (arteriotomies, venomies, arteriovenous fistulas, and nerve coaptations), the conventional repair procedure is based on sutures. However, as displayed in Table 1, besides the suture-based procedures, different reconstructive techniques, presenting improved operative and post-operative results, have been developed.  
Although the most conventional nonabsorbable sutures are reliable and relatively inexpensive, several studies have pointed to a direct relation between them and post-operative biological problems, such as foreign-body reactions, intimal hyperplasia, and anastomotic stenosis [40; 41; 42; 45]. In order to minimize these problems, [PERSON] et al. [41] compared the application of absorbable and  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Techniques for Nerve, Arteries, and Veins Repairs**} \\ \hline \hline \multirow{3}{*}{**Conventional techniques**} & \multirow{3}{*}{Suture} & Nonabsorbable suture [41; 42; 43] \\ \cline{3-3}  & & Absorbable suture [41] \\ \hline \hline \multirow{7}{*}{**Nonconventional techniques**} & \multirow{7}{*}{Laser welding} & Argon laser welding [41; 44] \\ \cline{3-3}  & & CO\({}_{2}\) laser welding [44; 45; 46; 47] \\ \cline{3-3}  & & Nd:YAG laser welding [44] \\ \cline{3-3}  & & Infrared diode laser welding [48; 49] \\ \cline{3-3}  & & Potassium titanyl phosphate laser welding [50] \\ \cline{3-3}  & & Laser welding with intraluminal light source [51] \\ \cline{3-3}  & & Photochemical tissue bonding [40; 52; 53; 54] \\ \cline{3-3}  & & Electrosurgical high-frequency welding [55] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Techniques for nerve, arteries, and veins repairs.  
nonabsorbable sutures. Although similar burst strengths were achieved, the absorbable sutures were found to present an excellent recovery of the blood vessel wall, with minimal inflammatory modifications. However, prior to this study, the medical evolution had already shown results towards a sutureless approach with the integration of laser in medical surgeries. Repair of blood vessels with Nd:YAG laser was achieved in 1979 [56] and, some years later, nerve coaptations by laser welding were successfully reported by [PERSON] et al. [44; 57]. The advantages of laser welding were evident: increased accuracy, decreased operative time, low foreign-body reactions, and a reduction in the formation of post-operative aneurysms. Despite these advantages, which have been corroborated by recent literature [58], many studies have emphasized the relevance of the surgical experience to achieve successful results [41; 45; 50].
## 3 Results and Discussion
### Statistical Analysis for Estimation of K Values  
Nonlinear statistical analysis was carried out for the prediction of the K values. Some experimental data from the work performed by [PERSON] et al. (2017) were also used in the multiple nonlinear regression analysis. Many correlations of K values were tried, but using the results of this study, the best coefficient of determination was obtained with R\({}^{2}\) = 0.96, while including the results of [PERSON] et al. (2017), the coefficient of determination was obtained with R\({}^{2}\) = 0.95 (Table 8).  
The correlation obtained as a result of the statistical analysis is given in Equation (3). The K values calculated from the experimental results and the predicted K values are compared in Figure 14.  
\[K=\ e^{(A.\ Dr+B.\ \gamma+C.\ \varnothing+D.\ \delta^{0.05}+E.\ \sigma_{v}^{ \prime}+F.\ R_{n}+G)+H} \tag{3}\]  
where Dr is the relative density of sand, \(\gamma\) is unit weight, \(\phi\) is the internal friction angle of sand, \(\delta\) is the skin friction angle, \(\sigma_{v}^{\prime}\) is the average vertical overburden pressure, R\({}_{n}\) is the normalized roughness coefficient, and A, B, C, D, E, F are coefficients of the equation, which are given in Table 9.  
Figure 13: The increase in the K value of model piles at different relative densities.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model Pile** & **D\({}_{\text{r}}\) (\%)** & **\(\gamma_{\text{n}}\) (kN/m\({}^{3}\))** & **Q\({}_{\text{f}}\) (kN)** & \(\sigma_{v}^{\prime}\) (kN/m\({}^{3}\))** & **(\(\delta\)) (\({}^{\circ}\))** & **K** \\ \hline  & 45 & 15.2 & 0.15 & 3.650 & 35.1 & 2.01 \\  & 70 & 16.4 & 0.76 & 3.875 & 36.2 & 3.26 \\  & 40 & 15.2 & 1.90 & 4.075 & 37.9 & 2.39 \\  & 70 & 16.4 & 3.20 & 4.325 & 35.3 & 4.33 \\ \hline \hline \end{tabular}
\end{table}
Table 8: In the research of [PERSON] (2017).  
Figure 14: (a,b) comparison between the calculated K values from experimental results and predicted K values from Equation (3).
# Numerical Analysis of the Cooling Performance in a 7.2 kW Integrated Bidirectional OBC/LDC Module
## 2 Development of the Numerical Model
### Simulation of the Operating Mode  
As shown in Figure 4, the bidirectional integrated circuit has four operating modes: OBC or LDC single operation, LDC operation, or simultaneous operation of the OBC and LDC in forward and backward directions.  
Figure 3: Resonant characteristic curve.  
In Operating Mode 1, the OBC operates independently and the vehicle remains stationary. The LDC does not operate in this mode because the low-voltage battery is sufficiently charged. When compared to a driving situation, the total load of the circuit in this mode is significantly lower.  
In Operating Mode 2, the LDC operates independently. In this mode, the low-voltage battery is charged by the high-voltage battery, while the vehicle is driving. Under driving conditions, the 12 V battery has 80% load. The OBC circuit does not operate without the AC charge cable connected, and the power flows from the high-voltage battery toward the LDC circuit. Consequently, the maximum power of the LDC in independent operation is 2.5 kW.  
Operating Mode 3 is defined as the simultaneous activation of the OBC and LDC circuits when the low- and high-voltage batteries are not sufficiently charged. This mode provides enough additional power to operate the controllers in the EV, such as the engine control unit (ECU) and battery management system (BMS). In total, an output power of 6.9 and 0.3 kW can be generated in the OBC and LDC circuits, respectively.  
Operating Mode 4 is defined as the time when power flows from the high-voltage battery to power the system and the LDC. This occurs when the EV is stationary. In this mode, output powers of 3 and 0.3 kW can be generated in the OBC and LDC circuits, respectively.  
Using these definitions of the operating modes, the heat loss in each mode was predicted by simulating the circuit. The heat loss over time for each of the major heating elements was derived, and the values were applied to the heat flow model to analyze the cooling characteristics in the integrated module.