esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method
### Improve the Loss Function
#### 3.5.1 NWD Loss Function  
The NWD loss function is a similarity metric method that does not rely on the overlap of the bounding boxes. Its main characteristics include the introduction of sample weights and normalization to better handle the differences in the importance of different samples and measure the similarity between samples. In the process of defect detection, the information of the target object and the background information are concentrated in the central part and boundary part of the bounding box, respectively. Firstly, the bounding box is modeled as a 2D Gaussian distribution, with the weight of the central pixel set to the maximum value and decreasing gradually from the center to the boundary. Secondly, a new metric called the Normalized Wasserstein Distance (NWD) is proposed, which helps to more accurately measure the similarity between two Gaussian distributions. Specifically, when detecting small targets, they occupy only a few pixels in size and usually lack significant information, making it difficult for traditional object detectors to achieve satisfactory results. Therefore, the introduction of the Wasserstein distance allows for the calculation of the similarity between their corresponding Gaussian distributions, thus reducing the sensitivity of IoU to positional deviations of small target defects. The NWD loss function is represented by Equation (8).  
Modeled as follows:  
\[L_{\mathit{NWD}}=1-NWD\big{(}N_{p},N_{g}\big{)} \tag{8}\]  
\(L_{\mathit{NWD}}\) is It is used to guide model training. The goal is to make the NWD between the predicted Gaussian distribution \(N_{p}\) and the real Gaussian distribution \(N_{g}\) as small as possible; that is, the closer they are, the smaller the loss and the better the model performance.
# Aortic Hemodynamics with Accelerated Dual-Venc 4D Flow MRI in Type B Aortic Dissection
## 2 Materials and Methods
### Image Processing and Segmentation
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Acquisition Type** & **Single-Venc 4D Flow MRI** & **Dual-Venc 4D Flow MRI** & **High-Venc 4D Flow MRI** \\ \hline
**Scanner** & 1.5T MRI & 1.5T MRI & 1.5T MRI \\ \hline
**Contrast** & No & No & No \\ \hline
**Scan Time (min)** & 11.12 +/ – 2.64 & 5.96 +/ – 1.33 & n/a \\ \hline
**Acceleration Factor (R)** & 2 & 7.7 & 7.7 \\ \hline
**Field of View (mm\({}^{2}\))** & 365-459 \(\times\) 459–499 & 306–399 \(\times\) 380–399 & 306–399 \(\times\) 380–399 \\ \hline
**Slice Thickness (mm)** & 2.8–3.5 & 2.5–2.8 & 2.5–2.8 \\ \hline
**Repetition Time (ms)** & 4.5–6.2 & 4.5–5.2 & 4.5–5.2 \\ \hline
**Echo Time (ms)** & 2.18 & 3 & 3 \\ \hline
**Spatial Resolution (mm\({}^{3}\))** & 2.6 \(\times\) 2.6 \(\times\) 2.8–3.5 & 2.1–2.2 \(\times\) 2.1–2.2 \(\times\) 2.5–2.8 & 2.1–2.2 \(\times\) 2.1–2.2 \(\times\) 2.5–2.8 \\ \hline
**Temporal Resolution (ms)** & 26.7–52.8 & 36.5–41.8 & 36.5–41.8 \\ \hline
**Flip Angle (\({}^{\circ}\))** & 7 & 7 & 7 \\ \hline
**Velocity Encoding (cm/s)** & 160 & 80 and 160 & 160 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: 4D flow magnetic resonance imaging (MRI) scan parameters and mean and standard deviation of the scan time for each acquisition type (scan time was not applicable to high-venc 4D flow MRI data as it was derived from the dual-venc 4D flow MRI acquisition). Figure 1: (**A**) Postprocessing of the 4D flow magnetic resonance imaging (MRI) data on MATLAB and manual true (TL) and false lumen (FL) 3D segmentations. (**B**) The volumetric maps of peak velocity and kinetic energy in TL and reverse flow and stasis in FL from the dual-venc (DV) acquisition and high-venc data extracted from the DV acquisition in one subject with de novo type B aortic dissection. Statistical Analysis  
The repeated measures analysis of variance (ANOVA) method and Pearson correlation coefficients (r) were used to perform groupwise comparisons between the acquisition types. The velocity-noise-ratio (VNR) in the TL in the patient group was calculated by dividing the mean velocity over the entire cardiac cycle in the TL by the velocity noise estimated by the standard deviation (SD) of measured velocities in the static spine. The obtained VNR values were compared pairwise among the acquisition types by the repeated measures ANOVA method. Intraclass correlation coefficients (ICC) were calculated between the baseline and follow-up scans for the interscan reliability assessment of each sequence type using the following reliability levels: \(<\)0.5 indicates poor reliability, between 0.5 and 0.75 indicates moderate reliability, between 0.75 and 0.9 indicates good reliability, and any value \(>\)0.9 is indicative of excellent reliability [34]. A \(p\)-value \(<\) 0.05 is considered statistically significant. The average scan time and their SDs of the entire cohort consisting of both baseline and follow-up scans were also reported for each acquisition type.
# Applying Deep Learning to Medical Imaging: A Review
## 4 Deep Learning for Specific Medical Imaging Modalities
### Ultrasound Imaging
#### 4.4.2 Challenges and Future Directions  
One of the challenges in applying DL techniques to ultrasound imaging is the limited availability of annotated datasets, particularly for rare diseases. Moreover, ultrasound images are prone to artifacts and noise, which can affect the performance of DL models. Therefore, developing robust DL models that can handle these challenges is an important future direction. Additionally, developing transferable models that can handle variations in imaging protocols and patient populations is also an important future direction.
# Robust Chromatic Adaptation Based Color Correction Technology for Underwater Images
## 6 Conclusions  
A simple chromatic adaptation-based color correction method for underwater images is proposed in this paper. The underwater RGB image is first linearized to make its pixel values proportional to the light intensities arrived at the pixels, ensuring the color correction operation is performed in a linear space. The illumination is estimated in the uniform chromatic space of CIE 1960 UCS using the brightest 10% pixels based on the WP hypothesis. The chromatic adaptation transform is implemented in the device-independent color space of CIE 1931 XYZ. Experiments show that the proposed method is quite robust and could produce visually pleasing results while the other methods often introduce artifacts or  
Figure 16: Enhanced underwater images obtained by combining the proposed color correction method with CLAHE: (**a**) Raw underwater images, (**b**) Results of the proposed color correction method, (**c**) Results by applying CLAHE to images shown in (**b**), (**d**) Reference images. Raw and reference images are provided with the UIEB dataset [8].  
Figure 15: Comparisons on results obtained by the proposed method with and without linearization: (**a**) Raw underwater images, (**b**) Results with linearization, (**c**) Results without linearization.  
over-enhancement. How to combine the color correction method with contrast improvement methods or model-based restoration methods to get the in-air images of underwater scenes will be the focus of follow-up work.  
Conceptualization, Y.L., Y.T. and X.Y.; methodology, X.Y. and H.F.; software, C.Y.; validation, C.Y. and Z.Z.; investigation, X.Y.; resources, X.Y. and Z.Z.; writing--original draft preparation, X.Y.; writing--review and editing, X.Y., H.F. and W.L.; visualization, D.W.; funding acquisition, Y.L., Y.T. and H.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (61991413, 61973224), the Key Research and Development Program of Liaoning (2019 JH2/10100014), the Natural Science Foundation of Liaoning Province (2019-ZD-0673, 2019-KF-01-15), the Key Laboratory of Road Construction Technology and Equipment (Chang'an University), MOE (300102259506).  
The authors declare no conflict of interest.
## 4 Discussion
### Heterogeneity  
Adapting the existing acoustic scattering theories to complicated DED microstructures will be challenging due to two reasons: (a) the spatial heterogeneity, and (b) grain size definition. The C-Scans in Figure 8 suggest that the spatial variability/heterogeneity is much more significant in the DED sample compared to the conventional sample. This variability calls into question the fundamental definition of attenuation and backscatter coefficients, especially for heterogeneous structures. The acoustic parameters which are used to calculate the attenuation and backscatter coefficients such as velocity, diffraction correction, etc., are dependent on homogeneous assumptions. These properties are typically averaged over the beam volume. The C-Scans suggest that we may not be able to use the standard definitions of attenuation and backscatter coefficients to characterize DED samples.  
Secondly, the definition of grain in these complex microstructures is not consistent in the literature. To correlate the results with scattering theories, we need to perform mean free path calculations [43; 56], which requires us to clearly define grain morphology. Unfortunately, there seems to be no clear consensus in the additive manufacturing/DED research community on the definition of a grain size for meta-stable microstructures [57; 58; 59; 60; 61; 62]. Specifically for IN718, the segregation of Laves phase at the grain boundaries results in a cellular microstructure. If we assume the cell is the grain, then based on average cell size, the grain size will 5-10 \(\upmu\)m, and if we define the grain to be the macrocolony of cells, then the average grain size will be \(\approx\)250-500 \(\upmu\)m. The macrograin definition seems to agree well with the results presented here. However, more research is needed from an acoustic perspective to determine the appropriate scattering functions for the corresponding grain size.
# A Large Bridge Traffic Operation Status Impact Assessment Model Based on AHP-Delphi-SVD Method
## 4 Theory and Method of Traffic Operation Status Evaluation  
According to the analysis of the traffic-influencing factors mentioned earlier, the dynamic evaluation of bridge traffic operations mainly includes the real-time operation status and the impact of the bridge itself on the operation status. In the long run, the working status of various components of the bridge is in a dynamic and changing state, which will generate a series of dynamic parameter data about the bridge; however, in a relatively short period of time, the state of various components of the bridge can be seen as unchanged to a certain extent. If there is a sudden change under certain conditions, it will have a significant impact on the traffic operation status of the bridge in a short period of time.  
The key to multi-objective system evaluation lies in how to scientifically and reasonably weight and comprehensively calculate various indicators, and integrate a multi-objectiveproblem into a single-indicator form [14]. This article improves the Analytic Hierarchy Process (AHP) and Delphi combined with Singular Value Decomposition (SVD) to obtain the weight values of each evaluation-influencing factor in the evaluation model. A partial pseudocode of the algorithm can be found in Appendix A.
# Vision-Based Damage Detection Method Using Multi-Scale Local Information Entropy and Data Fusion
## 3 Numerical Investigations of the Methodology  
It is worth noting that the proposed method is based on a combination of vision-based measurements and entropy-based modal singularity analysis, which is fundamentally not limited to specific structural forms. Therefore, considering the beam as the most common and fundamental structure in industrial scenarios, the beam structure will be selected as the object of study to validate the proposed method in the following.
## 3 Methods and Results  
Structural characteristics of the faults, fault-propagation folds, and fissures in terms of density, length, number, and position related to the fault plains were determined using merging topography terrain interpretation, satellite image analysis, and field surveys. We produced a digital elevation model (DEM) (accurately 0.03 m) using the real-time kinematic (RTK) technique in order to prepare the detailed topographic cross-sections of the area. We used the Mohr circle method for the cross-sections and applied two-dimensional strain types along with them (volume strain on planes XY and YZ). Based on the volume strain on planes XY, YZ, and XZ, we calculated the 3-D volume strain within the fault zone.
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 3 Results  
### Autonomous Vehicle Performance  
Autonomous water vehicles were deployed into multiple water bodies for a variety of weather conditions, spanning seasons from mid-summer to early winter. The majority of trials were conducted on Lake Raleigh, a 75-acre lake located on North Carolina State University's Centennial Campus, to evaluate the boats' manual and autonomous navigational capabilities and confirm the herbicide dispersal system functionality.  
Figure 15: GPS coordinate extraction with image preprocessing: (**a**) raw/unprocessed hydroacoustic image and (**b**) preprocessed image optimized for optical character recognition (OCR). Generated output: N035.46.0334W078.40.7344.
## 6 Conclusions  
This paper has provided a systematic study on M2M systems for mHealth applications from a wireless communication perspective. We have, then, focused on end-to-end connectivity in M2M systems. After discussing the integration challenges between diverse communication technologies, we have highlighted different design approaches for end-to-end connectivity through examples of practical testbed implementations for healthcare services. Finally, a list of recent research projects in the context of mHealth has been given, with emphasis on the different technical solutions adopted in each project. Based on the high-level ETSI M2M system architecture, we have first focused on the device domain, by presenting an overview of the recently proposed PHY layer technologies and MAC layer design approaches that tackle the specific challenges of mHealth applications for WBANs.  
Concluding, despite the considerable amount of work conducted in the rapidly growing area of M2M communications for mHealth, there are still many open issues to be addressed. Apart from the specific challenges in each aspect of the mHealth systems, efforts must be concentrated on standardization activities, that will enable the market exploitation of the scientific contributions in this field by paving the road for the development of interoperable M2M mHealth solutions.
# Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN
## 6 Results and Discussion
### Interference/Collision Rate  
The interference or collision rate, which represents the ratio of total received packets to packets lost due to interference, serves as a measure of network performance. In Figures 10 and 11, we examine the performance of several LoRaWAN ADR implementations relating to the interference ratio. The proposed SSFIR-ADR algorithm consistently demonstrates a lower interference ratio across different application data intervals and node densities except for FL-ADR. It is worth noting that longer application data intervals generally exhibit lower interference ratios compared to faster data intervals. Both graphs in Figures 10 and 11 illustrate the effectiveness of the proposed SSFIR-ADR models in reducing interference ratios across all scenarios. For instance, in Figure 11, the standard ADR algorithm shows an interference rate ranging from 0.19 to 0.36 for this network with up to 300 EDs. In contrast, SSFIR-ADR2 exhibits an interference rate between 0.07 and 0.26, while SSFIR-ADR1 ranges from 0.09 to 0.26. The stochastic approach employed in our proposed model for optimising spreading factor allocation contributes to a reduction of over 20% in the interference rate in comparison with the standard ADR algorithm and 14% improvement in comparison with the ns3-ADR algorithm. However, the FL-ADR algorithm outperforms the SSFIR-ADR models by approximately 12% in terms of interference reduction. Overall, the results establish the efficacy of the proposed SSFIR-ADR models in mitigating interference and improving network performance.
## 2 Analytical Method
### Equivalent Stress and Strain  
Usually, in the bending analysis of sheet metals, two kinds of loading conditions are assumed: uniaxial and plane strain-plane stress loading conditions. By applying these two loading conditions and considering the associated flow rule for plastic deformation, a relationship among stresses in three directions can be found, and the yield function for the specific loading condition can be written only as a function of bending stress, \(\sigma_{1}\) as described in Equation (3),  
\[f=\beta|\sigma_{1}|=\left\{\begin{array}{ll}\beta\sigma_{1},&\sigma_{1}>0\\ -\beta\sigma_{1},&\sigma_{1}<0\end{array}\right. \tag{3}\]  
where \(\beta\) is a function of loading condition. In fact, the yield function is assumed to be a homogeneous function of degree one with \(\sigma_{1}\) as the only independent variable. For asymmetric materials, \(\beta\) is a function of loading condition and the sign of the stress. Therefore, depending on the loading condition, it has different values in tension \(\beta_{t}\), and compression \(\beta_{c}\), as defined in Equation (4),  
\[f=\beta|\sigma_{1}|=\left\{\begin{array}{ll}\beta_{t}\sigma_{1},&\sigma_{1}> 0\\ -\beta_{c}\sigma_{1},&\sigma_{1}<0\end{array}\right. \tag{4}\]  
By using the associated flow rule and applying equivalent plastic strain increment as the plastic multiplier, we have:  
\[d\varepsilon_{i}^{p}=d\varepsilon_{ef}\frac{\partial f}{\partial\sigma_{i}}. \tag{5}\]  
From Equations (4) and (5), the bending plastic strain for an asymmetric material is given as:  
\[\varepsilon_{1}^{p}=\left\{\begin{array}{ll}\beta_{t}\varepsilon_{a\mu},& \varepsilon_{1}^{p}>0\\ -\beta_{c}\varepsilon_{a\mu},&\varepsilon_{1}^{p}<0\end{array}\right. \tag{6}\]  
Figure 1: Schematic diagram of stress distributions with the curvature through the sheet thickness.
## 4 Results and Discussion
### Relationship between Initial Chord and Static Elastic Modulus  
Table 12 shows the correction factors and MAPE for the \(Ei\) and \(Ec\) values extracted from Figure 4. For Mix 1 and Mix 2, by applying the correction factors of 0.91-0.95 and 0.89-0.90 for each age, respectively, the MAPE could be minimized. As a result of applying the average correction factor for each mix, the \(Ec\) could be predicted with an error of approximately 5%. In addition, it was found that the correction factor of 0.89 had the lowest MAPE for the entire data set (Mix 1 + Mix 2).  
The initial chord elastic modulus (\(Ei\)) values predicted by the four methods had errors of 3-6% from the actual \(Ei\), so the correction factors with the static elastic modulus (\(Ec\)) were calculated to be equal to 0.89. The \(Ec\) values were predicted by applying a correction factor (0.89) to the predicted \(Ei\), which is compared with the actual \(Ec\) in Figure 15. The LT mode has a higher correlation with the \(Ei\) than the TR mode, and the frequency of the LT mode is used, because the resonance frequency is clearly seen up to the higher-order mode.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Day 4** & **Day 7** & **Day 14** & **Day 28** & **Average** \\ \hline \multirow{2}{*}{Mix 1} & 0.95 & 0.89 & 0.87 & 0.91 & 0.91 \\  & (5.51\%) & (2.97\%) & (3.94\%) & (6.31\%) & (5.27\%) \\ \multirow{2}{*}{Mix 2} & 0.90 & 0.88 & 0.88 & 0.89 & 0.89 \\  & (4.15\%) & (2.97\%) & (4.78\%) & (4.88\%) & (4.14\%) \\ \hline \hline \end{tabular}
\end{table}
Table 12: Comparison of correction factors and MAPE of \(Ei\) and \(Ec\) according to mix and age.  
Figure 15: Comparison of Ec predicted with Ei and actual Ec.  
The predicted static elastic modulus (_Ec_) through the correction of _ASTMLT_ (_Ed_ measured by the first frequency in LT modes) and measured mean absolute percentage error (MAPE) of _Ec_ was 6.57% and had the largest error. It is difficult to overcome the nonlinearity of concrete, as the theoretical ASTM equation based on the resonance frequency test has a constant density and Poisson's ratio within a general range. The MAPEs between the _Ec_ values predicted by the ensemble, ANN, and MLR methods and the actual _Ec_ were determined to be 4.63%, 4.69%, and 5.53%, respectively. Two or more frequencies are used to compensate for the nonlinearity of concrete, and have a linear relationship with the actual _Ec_.
## 1 Introduction
In another addition, it has been proven that the TFBN has a low hop distance, which is an attractive feature of a HIN [26]. However, the detailed study on density parameters along with their trade-off analysis of the proposed TFBN has not been carried out yet. Therefore, the aim of this study is to assess the static performance of the TFBN in comparison with other networks by determining the static network performance parameters for the higher-levels of the TFBN network, density parameter analysis, and the trade-off between these density parameters. The remaining part of the paper is arranged as follows. Sections 2 and 3 describe the architecture of the TFBN network and the routing mechanism within the network, respectively. Section 4 analyzes the static network performance of the TFBN for both lower and higher levels. Next, the density parameters and their trade-off factor are analyzed in Section 5, along with the presentation of the novel parameter. Then, Section 6 delineates the main outcomes of this work and also provides the future research directions based on the outcome. Finally, the conclusion of this framework is disclosed in Section 7.
## 3 Experiments and Results
### Performance Evaluation Metrics of the Network Model  
To comprehensively evaluate the performance of the proposed detector, we adopted standard metrics covering accuracy, robustness, and computational efficiency. During inference, the model was assessed using precision, recall, F1-score, and mean Average Precision (mAP). These metrics are defined as follows.
## 3 Results & Discussion  
### Results Analysis  
#### 3.1.1 Torsional Stress Comparison  
In the test, the jack load, \(P=10\) kN, and the external torque, \(\widetilde{M}=8.5\)\(\mathrm{kN}\cdot\mathrm{m}\), the restrained torsional normal stress at measuring points \(\mathrm{S}_{1}\), \(\mathrm{S}_{2}\), and \(\mathrm{X}_{1}\), and the total shear stress at measuring points \(\mathrm{S}_{4}\), \(W_{2}\), and \(\mathrm{X}_{3}\) on the test section were selected as the research objects. The comparison of the measured values, \(T_{\mathrm{t}}\), theoretical values, \(T_{\mathrm{h}}\), and finite element values, \(T_{\mathrm{e}}\), of the torsional stress at each measuring point are shown in Table 1.  
It can be seen from Table 1 that the theoretical values and finite element values of each measuring point of the test section are in good agreement with the measured values, and the error in each measuring point is within 10%, which validates the calculation method provided in this paper.  
It can be seen from reference [18] that the error between the calculation result of the previous theoretical formula and the measured value is relatively large, while the error between the calculation results in this paper and the measured value is smaller, and the calculation accuracy is higher.  
Figure 8: Test-loading photos: (**a**) load application; (**b**) revolute joint.  
Figure 9: Test arrangement: (**a**) test section (unit: cm); (**b**) measuring point.