esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Data and Strategy Selection
### Processing Strategy  
In our research, the approaches for PPP time transfer are succinctly detailed in Table 2. We have incorporated and corrected known errors, such as relativistic effects and the Sagnac effect, by applying the IERS standard models [29]. The receiver clock offset is modeled as white noise. We have developed PPP time transfer strategies for Galileo, GPS, and their integrated Galileo/GPS systems, leveraging the Galileo HAS service. The time transfer from the IGS final clock products serve as our reference standard. To ensure the convergence of the PPP, we excluded the initial day's data from our precision calculations. In the Galileo/GPS PPP model, an ISB parameter is included. The receiver clock offset is set relative to Galileo observations, and for GPS, it comprises both the Galileo receiver clock offset and the ISB. Notably, in static PPP time transfer, the receiver's coordinates are treated as a constant, while in kinematic PPP time transfer, they are regarded as white noise. In addition, the Melbourne-Wubbena (MW) and geometry-free (GF) combination are used for cycle slip detection [32].  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Estimator** & **Kalman Filter** \\ \hline Signals & Galileo: E1/E5a \\  & GPS: L1/L2 \\ \hline PCV (Phase Center Variation) and PCO & \\ (Phase Center Ofset) & Corrected by “igs20.4\(\mathrm{\SIUnitSymbolMicro}\) \\ \hline Receiver clock offset & Estimated with a white noise model (\(10^{4}\) m\({}^{2}\)) \\ \hline Precise products & Broadcast ephemeris + HAS correction \\ \cline{2-3}  & IGS final products \\ \hline \multirow{3}{*}{Tropospheric delay} & ZHD (Zenith Hydrostatic Delay): corrected [33] \\ \cline{2-3}  & ZWD (Zenith Wet Delay): estimated with a random walk noise model (\(3\times 10^{-8}\) m\({}^{2}\)/s) \\ \hline Tidal displacement & Corrected [34] \\ \hline Phase ambiguities & Estimate as constant at each arc; when cycle-slip happened, estimated as white noise model (\(10^{4}\) m\({}^{2}\)) \\ \hline \multirow{3}{*}{Receiver coordinates} & Static model: Estimate as constant \\ \cline{2-3}  & Kinematic model: Estimate as white noise (\(10^{4}\) m\({}^{2}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Overview of the PPP approach utilized in this study.  
Figure 1: The selected GNSS station distribution map.
# Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model
###### Abstract  
The delayed surface subsidence caused by coal seam mining is a problem that cannot be ignored, while accurate prediction of the surface subsidence provides a guarantee of the safety and stability of the relevant areas. However, the traditional Knothe model has limitations in considering delayed surface subsidence. Because of this, the Knothe-n time function model is segmented and improved by using the data of the subsidence area obtained from a FLAC3D-based numerical model, and the maximum delayed surface subsidence in different periods is calculated. The analytical results are compared with the numerical results to validate the effectiveness of the improved segmented time function model in predicting delayed surface subsidence. The improved model is applied to predict the surface subsidence in the Yutianbao subsidence area. The root-mean-square error between the predicted and measured values for the maximum subsidence monitoring point is 1.12, and the root-mean-square error between the average predicted and measured values for the surface monitoring points is 0.37, which verifies the accuracy of the improved model. The prediction model provides a scientific basis for environmental protection and safety management after coal seam mining.  
coal seam mining; FLAC3D; Knothe-n time function; subsidence prediction +
Footnote †: journal: Applied Sciences
# Microalgal Biomass Generation via Electroflotation: A Cost-Effective Dewaring Technology
## 5 Electroflotation-Facilitated Dewatering for Microalgal Biomass Generation
### Factors Affecting the Performance of Electroflotation-Based Microalgal Dewatering Process  
Typically, in flotation systems, and by extension, electroflotation systems, smaller bubble sizes are preferred owing to the higher surface area-to-volume ratio [74]. As such, the success of electroflotation in generating microalgal biomass via efficient dewatering is largely dependent on the quality of microbubbles, such as size, quantity, and distribution, which is closely intertwined with factors such as the process conditions, characteristics of the electrodes, and condition of the microalgal broth and/or electrolyte [17; 75].
## 2 TLS Principle
### Comprehensive Model
#### 2.2.3 The Comprehensive Model  
Combining the conclusions of Sections 2.2.1 and 2.2.2, the relationship between the values measured in TLS technology and the experimental parameters is established. In constant voltage bias, the current variation is:  
\[\Delta I\approx\frac{C_{1}P_{0}}{R}\int S_{1-2}\
abla f(x,y,v,t)\cdot\mathrm{d} \vec{l}_{1-2}-\frac{C_{1}P_{0}V_{0}}{R^{2}}\int\frac{\rho_{0}\alpha_{\mathrm{ TCR}}}{A}f(x,y,v,t)\mathrm{d}I \tag{20}\]  
In constant voltage bias, the voltage variation is:  
\[\Delta V=-C_{1}P_{0}\int S_{1-2}\
abla f(x,y,v,t)\cdot\mathrm{d}\vec{l}_{1-2}+ C_{1}P_{0}I_{0}\int\frac{\rho_{0}\alpha_{\mathrm{TCR}}}{A}f(x,y,v,t)\mathrm{d}I \tag{21}\]  
where:  
\[C_{1}=\frac{1-R}{4\pi\rho c\gamma},\ f(x,y,v,t)=\int_{0}^{t}\frac{1}{\omega^{2 }+4k\left(t-t^{\prime}\right)}\exp\left[-\frac{(x-vt^{\prime})^{2}+y^{2}}{ \omega^{2}+4k\left(t-t^{\prime}\right)}\right]\mathrm{d}t^{\prime} \tag{22}\]  
The meaning of each consideration is already explained in the content above. Equations (20) and (21) entirely describe the principle of TLS technology. They correlate the measurable quantities (current variation \(\Delta I\) and voltage variation \(\Delta V\)) with adjustable quantities (laser power \(P_{0}\), scanning velocity \(v\), and bias conditions \(V_{0}/I_{0}\)).  
The structure and material composition of actual ICs are complex. Therefore, the evolution of the temperature field with time also varies with different materials and structures. However, the predictions of the comprehensive model are still in good agreement with the experimental results.
# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
###### Abstract  
The process of iris recognition can result in a decline in recognition performance when the resolution of the iris images is insufficient. In this study, a super-resolution model for iris images, namely SwinGIris, which combines the Swin Transformer and the Generative Adversarial Network (GAN), is introduced. SwinGIris performs quadruple super-resolution reconstruction for low-resolution iris images, aiming to improve the resolution of iris images and thereby improving the recognition accuracy of iris recognition systems. The model utilizes residual Swin Transformer blocks to extract depth global features, and the progressive upsampling method along with sub-pixel convolution is conducive to focusing on the high-frequency iris information in the presence of more non-iris information. In order to preserve high-frequency details, the discriminator employs a VGG-style relative classifier to guide the generator in generating super-resolution images. In experimental section, we enhance low-resolution (56 \(\times\) 56) iris images to high-resolution (224 \(\times\) 224) iris images. Experimental results indicate that the SwinGIris model achieves satisfactory outcomes in restoring low-resolution iris image textures while preserving identity information.  
Keywords: iris recognition; image reconstruction; super-resolution; transformer; Generative Adversarial Network (GAN) +
Footnote †: journal:
## 2 Materials and Methods
### Methodology  
This section delineates the Heliosat-2 algorithm, an advanced methodology for estimating solar irradiance from satellite imagery and ground observations. The approach integrates sophisticated image classification techniques, using deep learning, rigorous statistical analyses, and empirical calculations to ensure precision in solar irradiance values. Additionally, the validation of this method employs robust statistical indices which quantify the accuracy of the Heliosat-2 estimates in comparison to ground-based measurements. Due to the different spatial resolutions of the Himawari-8 and MSG images, the Himawari-8 images were first re-gridded to match the resolution of MSG (i.e., 3 km) before the implementation of Heliosat-2 method. Spatial re-gridding helps reduce systematic bias due to resolution mismatch and is a common practice which has been followed in several studies [36; 37; 38].
# Profiling Taste and Aroma Compound Metabolism during Apricot Fruit Development and Ripening
###### Abstract  
Sugars, organic acids and volatiles of apricot were determined by HPLC and GC-MS during fruit development and ripening, and the key taste and aroma components were identified by integrating flavor compound contents with consumers' evaluation. Sucrose and glucose were the major sugars in apricot fruit. The contents of all sugars increased rapidly, and the accumulation pattern of sugars converted from glucose-predominated to sucrose-predominated during fruit development and ripening. Sucrose synthase (SS), sorbitol oxidase (SO) and sorbitol dehydrogenase (SDH) are under tight developmental control and they might play important roles in sugar accumulation. Almost all organic acids identified increased during early development and then decrease rapidly. During early development, fruit mainly accumulated quinate and malate, with the increase of citrate after maturation, and quinate, malate and citrate were the predominant organic acids at the ripening stage. The odor activity values (OAV) of aroma volatiles showed that 18 aroma compounds were the characteristic components of apricot fruit. Aldehydes and terpenes decreased significantly during the whole development period, whereas lactones and apocarotenoids significantly increased with fruit ripening. The partial least squares regression (PLSR) results revealed that \(\beta\)-ionone, \(\gamma\)-decalactone, sucrose and citrate are the key characteristic flavor factors contributing to consumer acceptance. Carotenoid cleavage dioxygenases (CCD) may be involved in \(\beta\)-ionone formation in apricot fruit.  
apricot; sugars; organic acids; volatiles; flavor; metabolites; fruit quality +
Footnote †: journal: J. Mol. Sci. 2016, 17, 998
## 5 Error Analysis from a User-Centric Approach
### Untrue Result Retrieval Problems  
The issue of untrue result retrieval arises when the model generates responses based on incorrect information because the information on the retrieved site is outdated, fake, or simply untrue. This issue is independent of the Internet search engine or model's capabilities, as the generator may produce the appropriate search query for the context of a conversation and retrieve what appears to be an ideal site. The root cause of this problem is the presence of incorrect information on the Internet, which is a longstanding issue.  
In the second turn of Case 3, as illustrated in Figure 5, the query generator produces an appropriate query, _[PERSON] transfer_, concerning the current club of _[PERSON]_. However, the information in the retrieved article inaccurately suggests that _[PERSON]_ will be transferred to _Inter Miami_.  
Figure 4: An example of Case 2: search query generation problems.  
Figure 5: An example of Case 3: untrue result retrieval problems.  
Fortunately, this issue was infrequently encountered in our tests with BlenderBot 2.0. This may be mitigated by BlenderBot 2.0's reliance on Wikipedia ([[https://www.wikipedia.org/](https://www.wikipedia.org/)]([https://www.wikipedia.org/](https://www.wikipedia.org/)) (accessed on 1 May 2024)), one of the most actively updated sites with dynamic information correction.  
In the case of LLMs, \"untrue result retrieval problems\" are a significant concern, particularly regarding hallucinations. LLMs, while advanced in processing and generating language, can sometimes retrieve or generate information that is inaccurate or entirely fabricated. This phenomenon, often referred to as \"hallucination\", poses a major challenge in ensuring the reliability and trustworthiness of the outputs provided by these models. Hallucinations in LLMs can occur due to various factors, including biases in the training data, limitations in understanding context, or errors in interpreting the user's intent. Addressing these issues is crucial for improving the accuracy and credibility of LLMs, especially in applications where factual correctness is paramount.
## 4 Discussion
The results above show that in the early years, keywords related to airframe and equipment defects, such as Aircraft and Equipment Problems, were relatively high in frequency, but from the 2010s onward, the frequency of keywords related to human factors, such as Human Factors and Situational Awareness, increased. Also, we can see that the share of keywords related to human factors has increased since 2016. This suggests that while advances in UAS technology have improved aircraft reliability, pilot error and lack of situational awareness continue to pose a safety threat. However, it is worth noting that the absolute frequency of the Aircraft/Equipment Problem keyword has not decreased in recent years. This suggests that despite advances in UAS technology, there are still instances of defects, indicating the need for continued technological advancement and quality control. Given these results, it can be effective to prioritize by Technology Readiness Level (TRL), considering the rate of technological advancement and its impact. Applying this to a STAMP/STPA analysis can also help identify cost-effectiveness and prioritize safety measures. The STAMP/STPA analysis conducted in this study identified a total of 99 loss scenarios, 99 safety constraints, and 297 recommendations. Among the identified scenarios, 12 were categorized as extreme risk, 7 as very high risk, 13 as high risk, 56 as medium risk, and 12 as very low risk, based on the risk matrix approach. These quantitative results provide a clearer understanding of the distribution of risk levels associated with UAS integration into public safety systems. Furthermore, the analysis revealed that human factors \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Years** & **Aircraft** & **Equipment Problem** & **Human Factors** & **Situational Awareness** \\ \hline
1993–2000 & 4 & 3 & 2 & N/A \\ \hline
2001–2005 & 3 & 2 & 3 & N/A \\ \hline
2006–2010 & 5 & 4 & 7 & N/A \\ \hline
2011–2015 & 2 & 3 & 12 & 2 \\ \hline
2016–2020 & 6 & 8 & 27 & 5 \\ \hline
2021–2024: & 8 & 12 & 35 & 9 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Incident/Accident type statistics by year. Figure 8: Comparison of UAS incident/accident statistics. accounted for the highest proportion of causal factors at 57.3%, followed by hardware at 18.6%, procedures at 12.4%, environment at 6.5%, and software at 5.1%. These findings highlight the critical need to address human factor-related risks through targeted training, standardized protocols, and enhanced situational awareness.
# Applying Deep Learning Methods for Mammography Analysis and Breast Cancer Detection
## 2 Review of Medical Imaging Techniques for BC Detection
### Medical Imaging Techniques for BC Diagnosis
#### 2.1.3 Magnetic Resonance Imaging (MRI)  
MRI is a non-invasive medical imaging technique that uses a magnetic field and radio waves to produce detailed cross-sectional images of the organs and monitored tissues. Breast MRI is recognized as the technique with the greatest sensitivity when compared to other screening tests as it projects the shape, size, and position of the specific tumors. However, it is a time-consuming and expensive procedure.
## 7 Conclusions  
In this paper, we have formulated a multi-objective optimization problem for MEC-enabled UAV-assisted VANETs by jointly considering the task offloading, the resource allocation, and the security assurance with the aim to minimize the task processing delay. To deal with this hard problem, we have decoupled the multi-objective optimization problem as two subproblems. Exploiting the relax-and-rounding method and the Lagrangian method, we have proposed an iterative algorithm, which effectively solves the joint optimization problem. The simulation results have demonstrated that our proposed LBTO algorithm can achieve a significant performance gain in comparison with other algorithms in terms of the successful task processing ratio and the task processing delay. Our work can be widely applied to offer low-latency and high-reliable edge computing services to vehicle users in a complex network environment, such as the urban ITS and the smart city.  
Y.H.: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Data Curation, Writing--Original Draft Preparation, and Visualization. D.Z.: Conceptualization, Methodology, Software, Validation, Formal Analysis, Investigation, Resources, Data Curation, Writing--Review and Editing, Visualization, Supervision, Project Administration, and Funding Acquisition. F.H.: Conceptualization, Software, Validation, Formal Analysis, Investigation, Writing--Review and Editing, D.W.: Conceptualization, Methodology, Software, Formal Analysis, Investigation, Resources, Writing--Review and Editing, Supervision, Project Administration, and Funding Acquisition. X.T.: Conceptualization, Software, Formal Analysis, Writing--Review and Editing, Supervision, and Funding Acquisition. R.Z.: Conceptualization, Formal Analysis, Resources, Writing--Review and Editing, Supervision, Project Administration, and Funding Acquisition. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China under Grant 61901381, 61901379, and 61102078, in part by the Foundation of the State Key Laboratory of Integrated Services Networks of Xidian University under Grant ISN21-06, in part by the National Key Research and Development Program of China under Grants 2020 YFB1807004 and 2020 YFB1807003, in part by the open research fund of National Mobile Communications Research Laboratory, Southeast University under Grant 2020D04, in part by the Innovation Foundation for Doctor Dissertation of Northwestern Polytechnical University under Grant CX202037, and in part by the Program of China Scholarships Council under Grant 202006290166. Part of the journal hasbeen accepted by the 2021 IEEE International Conference on Computer Communications Workshops (INFOCOM Workshops).  
**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.
## 6 Description of the Round Robin Results
### PD Clustering Test  
The clustering test for insulation defects in AC grids and DC grids described in Section 2.3 was applied to the C1 and C2 clustering tools. The approved AI PD recognition tools ACR2 and DCR2 were used to check the requirements of AC and DC clustering tests. The summary of these results is shown in Table 11. The green colour and blue colour in Table 10 are used for successful recognitions with a confidence level higher and lower than 75% respectively and the red colour is for wrong recognitions. Percentage figures are the confidence level supplied by the PD recognition tool under qualification. Orange colour is used for no detected insulation defect.  
For the AC clustering test, 13/24 (54%) successful recognitions with a confidence level \(>75\%\) were achieved by the approved ACR2 recognition tool after the C1 clustering  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**AC Cases** & **Clustering Tool** & **Corona** & **Floating** & **Surface** & **Cavity** & **Noise \#1** & **Noise \#2** \\ \hline
1 & & 49\% & & 100\% & & & 100\% \\
2 & & 31\% & 99\% & & & 100\% \\
3 & & 46\% & & 63\% & & 97\% \\
4 & & 87\% & & 74\% & & 100\% \\
5 & & No detected & 99\% & & & 100\% \\
6 & & 48\% & & & & 100\% \\
7 & & & 100\% & & 100\% & & 100\% \\
8 & & & & 100\% & & 100\% \\
9 & & & 90\% & 90\% & & 100\% \\
10 & & & & No detected & 99\% & & 99\% \\
11 & & & & 99\% & & No detected & 99\% \\
12 & & & & 99\% & & 97\% & 99\% \\ \hline Mean & 68.7\% & 42.3\% & 97.7\% & 77.2\% & 13.0\% & 82.8\% & 99.3\% \\ \hline
1 & & No detected & 100\% & & & 100\% \\
2 & & 99\% & 100\% & & & 100\% \\
3 & & & 100\% & & & 100\% \\
4 & & No detected & & 61\% & & 100\% \\
5 & & & 100\% & 100\% & & 99\% \\
6 & & 99\% & & 98\% & & 100\% \\
7 & C2 & & 100\% & 100\% & & 100\% \\
8 & & & & 100\% & & 100\% \\
9 & & & & 100\% & & 97\% \\
10 & & & & 100\% & 100\% & 100\% \\
11 & & & & 100\% & 100\% & 95\% \\
12 & & & & 100\% & & 99\% \\ \hline Mean & 93.0\% & 66.3\% & 100.0\% & 93.5\% & 99.7\% & 99.2\% & 99.2\% \\ \hline
**DC Cases** & **Clustering Tool** & **Corona** & **Floating** & **Surface** & **Cavity** & **Noise \#1** & **Noise \#2** \\ \hline DC (+) & & 99\% & & 79\% & & 68\% & 70\% \\ DC (\(-\)) & C1 & & 84\% & & 88\% & 92\% & 73\% \\ \hline Mean & 83.8\% & 90.0\% & 84.0\% & 79.0\% & 98.0\% & 80.0\% & 71.5\% \\ \hline DC (+) & & 93\% & & 81\% & & 81\% & 96\% \\ \hline DC (\(-\)) & C2 & & 90\% & & 97\% & 100\% & 100\% \\ \hline Mean & 91.6\% & 93.0\% & 90.0\% & 81.0\% & 97.0\% & 90.5\% & 98.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 11: Results of AI PD clustering tools for AC.  
tool was applied, consequently, the C1 clustering tool cannot be approved using the ACR2 recognition tool. However, 21/24 (87.5%) successful recognitions with a confidence level > 75% were achieved by the approved ACR2 recognition tool after the C2 clustering tool was applied. Furthermore, the C2 clustering tool also passed the HVDC clustering test with 4/4 (100%) success recognitions and with a confidence level > 75%, using the DCR2 recognition tool. Therefore, C2 can be considered an approved clustering tool.