esa-sceva/satcom-chunk-collection huggingface dataset:
# Brain Extraction Methods in Neonatal Brain MRI and Their Effects on Intracranial Volumes
## Appendix A Appendix A.1. BET2 Parameters  
To define the parameters for the brain extraction (BE) of neonatal studies, BET2 from FSL version 6.0.6.4 [8; 14; 44] was tested with modified options from the BET2 program:  
* Fractional intensity threshold (<f> parameter varied from 0 to 1 in steps of 0.05; 0.50 is the default value), where smaller values results in larger brain outline estimates, and higher values result in smaller brain outline estimates.
* Vertical gradient in fractional intensity threshold (<g> parameter varied from \(-1\) to 1 in steps of 0.05; 0 is the default value), where positive values result in a larger brain outline at the bottom and a smaller one at the top of the scale, and negative values result in the opposite.
* Robust brain centre estimation (<R>), iterates BET2 several times with the purpose of improving the BE, when the input data contains a considerable amount of non-brain matter.  
* Eye and optic nerve cleanup (<S>) attempts to clean up the residual eye and optic nerve voxels that are remaining after running standard BE using BET2.
* Bias field and neck cleanup (<B>) attempts to reduce image bias and residual neck voxels after running standard BE using BET2.  
Only one of the options (<R>, <S>, and <B>) can be selected for each BET2 run, together with <f> and <g>.  
The extracted brain images were assessed for quality (i.e., an accurate brain outline, excluding voxels from the eyes, optic nerve, neck, arms, and hands in the neonatal MRI) and according to the dice coefficient (DC) score results, after comparing the results to those with manual segmentation.  
After testing all the options, the options (<f> from 0.50 to 0.60 and <g> from \(-\)0.10 to 0.20) that provided higher DC scores were pre-selected to perform a further analysis (Figure 11). The DC scores were tested for general differences with the Friedman test (\(\chi\)2 (20) = 376.364, \(p\) < 0.001). For post hoc comparisons, Bonferroni-adjusted significance tests were used for pairwise comparisons.  
The parameters <f> 0.6 and <g> 0.1 were used for all the neonates' MRI images, as they provided the best DC score (0.936 \(\pm\) 0.006) and showed no statistically significant differences when compared with other options that also provided high DC scores (>0.931). Including the option <R> also provided good results, without statistically significant differences (paired samples \(t\)-test: t(9) = 1.899; \(p\) = 0.071), but since this option generally runs slower (4.87 \(\pm\) 0.37 s) compared with the standard (2.20 \(\pm\) 0.15 s), it was not considered further.
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
## 6 Discussion and Analysis  
### Analysis of the Causes of Deformation  
Based on the time series results shown in Figure 8 and the deformation prediction data in Figure 9, combined with the historical data of precipitation and climate change in  
Figure 13: Comparison of InSAR-3D-CTPIM and Static-PIM vertical deformation results.  
Figure 14[35], and the conclusions of previous studies, an in-depth causal analysis of the temporal and spatial distribution characteristics of the deformation in the study area was as follows:  
1. Long-term coal mining activities in the Jiansheng coal mine area have formed obvious voids; due to the existence of these voids within the rock body, the overlying rock layer has deformed after being subjected to its own gravity and the role of the overlying rock layer. This deformation further affects the state of stress distribution in the surrounding rock mass [36]. When the mining activity reaches a certain level, the surface will show obvious signs of deformation, such as geological phenomena like collapses and cracks.
2. Considering that the Jiansheng coal mine is located on the southwest side of the Baisha Reservoir, and that the underground aquifers in the area of the Henan Jiansheng coal mine are relatively rich in water content, continuous coal mining activities have damaged the original geological stability, resulting in a gradual lowering of the groundwater level. As the groundwater level drops, the pressure on the rock and soil layers gradually increases. Particularly in the hydrophobic area, the increase in pressure has caused the overlying layers of rock and soil to subside, eventually triggering the phenomenon of land subsidence.
3. The study area is located at the northern end of the Qinling-Huaihe line in China, with a typical temperate monsoon climate. The climate is cold and dry in winter, and hot and rainy in summer. Especially during the hot and rainy season, the shallow aquifer is effectively recharged, which is the main reason for the small rise in the ground surface level between 2 October and 14 October 2016 [37].
4. During the annual Spring Festival, a large number of miners choose to return home due to the cold weather, resulting in a significant reduction in mining activity in the coal mines. The reduction in mining activity also effectively slows down the rate of deformation of the mine surface. This is also an important reason why the overall deformation of the mine surface remained relatively stable during the period from December 2015 to March 2016.
# Multidimensional Study on Users' Evaluation of the KRAKEN Personal Data Sharing Platform
## 3 Results
### Privacy Preserving Data Sharing Systems  
Participants to the health pilot were asked \"Are you interested to use the privacy preserving analytics of KRAKEN?\" In response, 11 participants expressed interest to access this type of service offered by the platform, while 1 participant did not provide any answer. In general, the privacy preserving analytics were considered as a service providing an added value to users of the platform, especially for those interested to access statistics for research purposes. One participant mentioned the importance of having access to customizable analytics, fitting the needs of each study. Another participant stressed the need for the platform to be kept updated with the latest crypto techniques and to provide state-of-the-art anonymization mechanisms for data sharing. We asked participants \"Would you be willing to use a web system that will secure your data, even if you will have to authenticate through a mobile app?\" Five participants replied that the double authentication modality is nowadays quite familiar to users, so they did not see any obstacle in using this method also to access the KRAKEN marketplace. Three participants expressed a preference for using already existing certified systems to authenticate (e.g., SPID), to lower the effort required by the user to download and install the mobile app. Four participants reported their frustration with installing and using the KRAKEN SSI mobile app, due to compatibility and usability problems experienced with the prototype release tested during the evaluation; therefore, they recommended to improve its usability to avoid future users from abandoning the platform after the first interaction.  
Participants to the education pilot were asked \"Are you interested to use the privacy preserving analytics of KRAKEN?\" In response, two participants replied they would require knowing or read more about this functionality to have an opinion on that. One participant said they might be interested in using such a service, once supported by the platform. When asked \"Would you be willing to use a web system that will secure your data, even if you will have to authenticate through a mobile app?\", all three participants agreed that they would be willing to use such an authentication modality, since it is quite common and secure nowadays. However, they all stressed the need of improving the usability of the SSIapp and its synchronization with the marketplace to avoid confusing the user in their first interaction with the KRAKEN solution.
# Urban Transportation Network Design for Fresh Fruit and Vegetables Using GIS-The Case of Bangkok
## 4 Methodology
### Step I: Collecting Data and Making Assumptions
#### 4.1.1. Wholesale Market, DC, and Retail Market  
The information and data collection for scenario analysis were performed based on the following set of assumptions:  
* Location of existing DC: Talaad Thai market is the biggest wholesale market in Thailand and Southeast Asia for trading of agriculture goods. This existing DC supplies fresh fruits and vegetables to all of the main retailers in Bangkok.
* Location of eight candidate distribution DCs (see Table 2 and Figure 4): one of the development agendas of Thailand's logistics development plan (2017-2021) was formulated for developing a fresh products distribution system to reduce cost and waste as well as flagship projects [33]. Moreover, Thailand's agricultural development plan from 2017 to 2021 aims to promote agricultural supply chain management from producer to end customer [34] by encouraging the establishment of service centers (facilities) in the community that provide storage, distribution, and transportation services for agricultural products. To control the quality of perishable products, a fresh products DC has to build, improve, and develop with the advanced technological system in order to meet customer requirements. In addition, the location of eight candidate DCs is easy to access from the outer ring road, highway, and available area in Bangkok metropolitan.
* Location of six major wholesale markets (see Table 3, Figure 4: fresh fruit and vegetable central markets located in each surrounding provinces, wholesalers in the central market supply their product to DC.
* Six major wholesale markets serve the same type of fresh fruits and vegetables.
* Location of 141 main retail markets in Bangkok (see Figure 3): the data refer to the department of city planning and urban development, Bangkok metropolitan administration studied about fresh retail market in 50 districts of Bangkok.
* Demand for each retail market based on the population in each district with an average daily consumption of fresh fruits and vegetables in Thailand.
# The Experiments and Stability Analysis of Hypersonic Boundary Layer Transition on a Flat Plate
## 2 Experiment Facility and Model
### Linear Stability Theory  
Linear stability theory (LST) is established under the assumption of small perturbations [4], and under this assumption, the perturbation can be written in the form of the sum of the basic flow and the perturbation quantity:  
\[p(x,y,z,t)=\overline{p}(x,y)+p^{\prime}(x,y,z,t) \tag{9}\]  
In hypersonic boundary layer, \(p\) (\(x\), \(y\), \(z\), \(t\)) represents \(u\), \(v\), \(w\), \(\rho\), and T. The basic flow field is obtained by solving the Blasius equation. It is assumed that the flow in the boundary layer is a parallel flow, and the small perturbation \(p^{\prime}\) can be written in the form of the traveling wave as follows:  
\[p^{\prime}(x,y,z,t)=p(y)e^{i(ax+\beta z-\omega t)}+c.c. \tag{10}\]  
where \(\alpha\), \(\beta\), and \(\omega\) are the streamwise wave number, the spanwise wave number, and the frequency, respectively. In addition, _c.c._ represents the conjugate complex number. The generalized eigenvalue problem can be obtained by putting Equation (2) into the wave equation and ignoring the second-order small quantities. The eigenvalue is the streamwise growth rate, and the eigenvector represents the disturbance shape along the wall normal. In spatial mode, \(\beta\) and \(\omega\) are real numbers, and \(\alpha\) is an imaginary number:  
\[\alpha=\alpha_{r}+i\alpha_{i} \tag{11}\]  
The real part represents the wave number of the disturbance, and the imaginary part represents the disturbance growth rate. When \(\alpha_{i}<0\), the disturbance is unstable along the flow direction.  
In the realm of laminar-turbulent transition (LST) analysis, a widely applied approach involves the computation of the \(N\)-factor using a semi-empirical method known as the \(e^{N}\) method. The determination of the \(N\)- factor is calculated by the following formula [23]:  
\[N=-\int\limits_{x_{0}}^{x}\alpha_{i}dx \tag{12}\]  
where \(x_{0}\) represents the flow direction position where the disturbance wave first begins to be unstable, and \(x_{0}\) is different at different frequencies. In general, the NT in a low-speed wind tunnel is generally 6-9, while in a hypersonic wind tunnel, the divergence of \(N_{\text{T}}\) is quite large. In the case of hypersonic flow, the \(N\)- factor as the transition criterion needs to be determined by wind tunnel experiments.
# Novel Deep Level Image State Ensemble Enhancement Method for M87 Imaging
###### Abstract  
Standard spatial domain filters fail to adequately denoise and enhance the contrast of an image. These filters have drawbacks like oversmoothing, diminished texture, and lack of generative capabilities. This paper proposes a new method of image reconstruction, Image State Ensemble Enhancement (ISEE), based on our previous work, Image State Ensemble Decomposition (ISED). Deep level ISEE and ISED have been developed to produce a class of filters that can address these issues. Full-reference and no-reference quality metrics are used to assess the image, and the full reference metrics showed a marked improvement, while the no-reference metrics were often better than the test image. The test image was taken from the Spitzer Space Telescope (SST), and ISEE reconstruction yielded improved structural detail over that of ISED and the original test image. Glare and noise were reduced in a narrow bandwidth, which led to the discovery of a vortex-shaped structure and an outburst in M87's dusty infrared core. The vortex is located over M87's visible core and black hole. This is verified with an SST and Hubble Space Telescope (HST) overlay, ISEE processed image. A counter-jet channel was also discovered, and it appears to be the path of the unobservable superluminal counter-jet.  
astronomy; black hole; decomposition; enhancement; filter; generative image; image reconstruction; image state ensemble decomposition method (ISED); image state ensemble enhancement method (ISEE); quantum imaging +
Footnote †: journal: Medical Image Analysis
# Intelligent Classification of Surrounding Rock of Tunnel Based on 10 Machine Learning Algorithms
## 3 Case Study
### Intelligent Surrounding Rock Classification in Test Tunnel  
Among the test section, drilling parameter information from 30 tunnel faces was collected by the intelligent surrounding rock classification system. The intelligent classification results of the surrounding rock on each tunnel face are shown in Table 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & **Mileage** &
\begin{tabular}{c} **Predicted** \\ **Results** \\ \end{tabular} & **Actual Results** & **Right/Error** \\ \hline  
I & III & Right \\ \multicolumn{4}{c}{Accuracy} & 86.7\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Intelligent classification of surrounding rock on each tunnel face.  
Figure 14: Tunnel face of this test section of the Gaojiaping Tunnel.  
In Table 8, the classification accuracy of the surrounding rock intelligent classification system on-site is 86.7%, which indicates that the system has good generalization.  
However, due to the single lithology and surrounding rocks (limestone, grade-III) in the field test section, its generalization performance must be further verified.
## 4 A Case Scenario in Smart Healthcare CPS
* Blockchain Security and Immutability: The blockchain's decentralized and tamper-resistant nature prevents the malicious actor from tampering with THE trust values stored in the BRGTL. Any attempt to manipulate the system is recorded in the Blockchain, maintaining the integrity of the trust assessment. In the above scenario, the GBG-RPL framework, with its blockchain-enabled Gini index components, successfully prevents malicious actors from exploiting vulnerabilities in a smart healthcare CPS. It establishes a secure, real-time, and centralized system for trust management, ensuring the integrity and confidentiality of patient records.
# Effect of Systemic Zoledronic Acid Dosing Regimens on Bone Regeneration in Osteoporotic Rats
###### Abstract  
The aim of this study was to evaluate the regeneration of bone defects created in the femoral condyle of osteoporoporotic rats, following intravenous (IV) zoledronate (ZA) treatment in three settings: pre-bone grafting (ZA-Pre), post-bone grafting (ZA-Post), and pre- plus post-bone grafting (ZA-Pre+Post). Twenty-four female Wistar rats were ovariectomized (OVX). After 12 weeks, bone defects were created in the left femoral condyle. All defects were grafted with a particulate inorganic cancellous bovine bone substitute. ZA (0.04 mg/kg, weekly) was administered to six rats 4 weeks pre-bone graft placement. To another six rats, ZA was given post-bone graft placement creation and continued for 6 weeks. Additional six rats received ZA treatment pre- and post-bone graft placement. Control animals received weekly saline intravenous injections. At 6 weeks post-bone graft placement, samples were retrieved for histological evaluation of the bone area percentage (BA%) and remaining bone graft percentage (RBG%). BA% for ZA-Pre (\(50.1\pm 3.5\%\)) and ZA-Post (\(49.2\pm 8.2\%\)) rats was significantly increased compared to that of the controls (\(35.4\pm 5.4\%\), \(p\)-value \(0.031\) and \(0.043\), respectively). In contrast, ZA-Pre+Post rats (\(40.7\pm 16.0\%\)) showed similar BA% compared to saline controls (\(p=0.663\)). For RBG%, all experimental groups showed similar results ranging from \(36.3\) to \(47.1\%\). Our data indicate that pre- or post-surgical systemic IV administration of ZA improves the regeneration of bone defects grafted with inorganic cancellous bovine-bone particles in osteoporoporotic bone conditions. However, no favorable effect on bone repair was seen for continued pre- plus post-surgical ZA treatment.  
osteoporosis; zoledronic acid; bone graft; bone regeneration; animal model +
Footnote †: journal: Applied Sciences
## 6 Implementation and Case Study
### Datasets
#### 6.3.2 OpenRAN Gym  
In addition to DAWN, we utilize the OpenRAN Gym dataset [19], which serves as an open-source experimental platform designed to bridge the gap between academic research and real-world Open RAN (Radio Access Network) deployments. OpenRAN Gym offers a rich set of emulated and real-world data, focusing on critical operational metrics that are vital for RAN optimization and orchestration. These metrics, summarized in Table 12, include CPU utilization, DU (Distributed Unit) energy consumption, and communication latency, each annotated with their respective ranges and units. CPU utilization is measured between 0% and 100%, reflecting the dynamic computational load on RAN nodes; DU energy consumption varies from 0 to 500 watts, capturing the power requirements of baseband processing; and latency, a fundamental performance indicator for wireless communications, spans 0 to 20 milliseconds. These metrics allow researchers to simulate diverse network conditions and resource constraints realistically. Moreover, OpenRAN Gym provides a flexible environment for training, testing, and validating ML models for network control, where reproducibility and scalability are critical. Its integration of standardized interfaces and the availability of various deployment scenarios make it an indispensable tool for advancing the research and practical implementation of intelligent RAN management strategies.
# Improvement of Power Converters Performance by an Efficient Use of Dead Time Compensation Technique
## 3 Proposed Dead Time Compensation Technique
### Improvement in Fundamental Voltage Magnitude and Phase Angle  
Dead-time induces certain drawbacks in the power electronic circuitry such as a decrease in fundamental voltage and distortion of other parameters. These effects can be effectively coped with through the incorporation of the proposed DTC. The fundamental voltage magnitude can be restored, and the harmonics minimized. Figure 11a for dead time 10 \(\upmu\)s presents the fundamental voltage  
\begin{table}
\begin{tabular}{c c c} \hline  & **Without DTC** & **With DTC 10 \(\upmu\)s** \\ \hline Fundamental Frequency (50 Hz) & 7.437 & 6.784 \\ \hline Total harmonics distortion THD (\%) & 16.26\% & 12.49\% \\ \hline \end{tabular}
\end{table}
Table 4: FFT analysis of the load current.  
Figure 10: Calculation of total harmonic distortion (THD) without and with DTC.  
magnitude in the absence of DTC and presents the same parameter after DTC, respectively. It is evident that the fundamental voltage has significantly improved as a result of the application of the proposed DTC technique.  
Phase angle distortion is also a downside of dead time. However, an efficient DTC strategy can handle this drawback to a certain degree. The proposed DTC model can achieve significant improvements in this domain as well. Figure 11b for dead time 10 \(\upmu\)s shows the phase angles distortion pre and posts using the DTC technique respectively. It is vividly evident from these figures that the phase angle distortion has been significantly reduced through the application of the proposed DTC technique.
## 3 Automatic Feature-Based Approach  
### Feature Detection  
#### Reference Image Coastline Extraction  
Starting with the image obtained after the geometric correction procedure (Keplerian model) and towards the goal of optimal feature matching of coastline satellite images, we extract the reference image coastline with the largest possible accuracy, while suppressing any unnecessary details. So, the coastline extraction is carried out in two steps. First, the reference image is convoluted with a Sobel operator so edges are estimated [22]. In the second step, a cloud overlay is obtained, _i.e.,_ for AVHRR data by a variation of a multi-band threshold method [23]. Reliable areas (non-occluded coastline), which are used as reference contours in the contour matching process, are obtained using this cloud overlay. Accepted contours are thinned up to one pixel width to allow precise matching. Figure 3(a) shows the reference image whereas the respective cloud overlay and extracted coastline (blue contours) are presented in Figures 3(c) and (e), respectively.  
_Sensed Image Gradients Energy Map Estimation_  
In this case, estimated edges are used to define a gradient energy map as follows,  
\[S(x,y)=-\sqrt{\
abla I(x,y)}|^{2} \tag{1}\]  
where \(\overline{\
abla I}\) is the sensed image gradient intensity normalized to [0,1].  
Once the sensed image cloud areas have been obtained, the reliable gradient energy map is computed by masking the gradients energy map, applying a morphological gradient, as described by [22], to the cloud-sea and the cloud-land contours (non-reliable areas). To facilitate the convergence of the contour-based algorithm, the estimated sensed image gradient energy map is smoothed by means of a Gaussian filter as,  
\[E_{\textit{edge}}=\{S(x,y)\cdot[M(x,y)\oplus B(x,y)]\}*G(x,y) \tag{2}\]  
where _G_(_x,y_) is a 2-D Gaussian function, _M_(_x,y_) is the binary cloud overlay and _B_(_x,y_) is a binary mask known as structuring element. The 2-D Gaussian function is defined by,  
\[G_{\sigma}(x,y)=e^{-\frac{\left(x^{2}+y^{2}\right)}{2\sigma^{2}}} \tag{3}\]  
and it is used as a filter to smooth the gradient map. The specific parameters of the Gaussian function are adjusted iteratively to improve the contour optimization approach.  
Figures 3(b) and (d) present the sensed image and the cloud overlay, respectively. Smoothed gradients energy map of reliable areas is presented in Figure 3(f).