esa-sceva/satcom-chunk-collection huggingface dataset:
# Region-of-Interest-Based Cardiac Image Segmentation with Deep Learning
## 4 Method
### Architectures  
Most semantic segmentation CNN architectures follow the same general pattern: using a contracting component which gradually shrinks the height and width of the image, obtaining a coarse representation which has strong semantic value. This is subsequently followed by an upsampling component that rebuilds the original size of the image with the help of higher resolution features previously computed in the contracting part.  
The U-Net we use differs from the standard version presented in [20] via the use of simple bilinear interpolation instead of transposed convolutions, the use of batch normalization [29] and the use of padding after each \(3\times 3\) convolution. Thus, the network outputs the same resolution as the input. The motivation behind opting to use plain interpolation is lowering complexity and memory restrictions, as no additional parameters are required for upsampling.  
The DeepLabV3+ [28] model also follows this general pattern, usually applying a standard classification CNN as a backbone, followed by a simple upsampling path consisting of consecutive convolutional layers and bilinear upsampling. The backbone used in this work is ResNeXt50 [30], a variation of the standard ResNet50 [30] that makes use of grouped convolutions to boost performance, while maintaining model complexity and parameter numbers. The ASPP [31] module is replaced by a single convolution to save memory and computation, as well as to reduce the chance of overfitting.  
Based on the ideas from [32], variations of these models were also tried, mainly reducing the width of the model in a bid to avoid overfitting, as well as trying multiple input resolutions in an attempt to find an optimal setup. Similar to [33], we use an extra parameter, the width multiplier, to control the width of the models. The number of filters in each layer is multiplied by the value of this parameter.
# Flutter Reliability Analysis of Xiangshan Harbor Highway Cable-Stayed Bridges in Service
## 2 The Flutter Analysis Framework for Flexible Bridge
### Influence of Main Beam Stiffiness on Flutter Stability  
One of the most important factors causing the flutter instability of bridges is the decrease in the stiffness of the bridge. With the increase of bridge span, the overall stiffness of bridges decreases, the flexibility increases, and the sensitivity to wind loads increases. In this research, the elastic modulus \(E\) of the steel were changed to represent the change of the main beam stiffness, which was adjusted to 0.5, 0.75, 1.0, 1.25 and 1.5 times of the design value, respectively. Then, the variation law of the critical flutter wind speed with the stiffness of the main beam could be obtained, while other parameters remain unchanged. The calculation results are shown in Figure 13.  
As can be seen from Figure 13, the critical flutter wind speed of Xiangshan Harbor Bridge presented an S-shaped change when the stiffness of the main beam was between 0.5 times and 1.5 times design value. With the increase of the stiffness of the main beam, the critical flutter wind speed increased from 58 m/s when the stiffness of the main beam was 0.5 times to 72 m/s when the stiffness of the main beam was 1.5 times, which is 24.14% higher than that of when the stiffness of the main beam is 0.5 times. The main reason for this phenomenon is that the increase of the stiffness of the main beam improves the torsion frequency of the bridge, which has a positive correlation with the flutter critical wind speed. In general, the change of main beam stiffness is directly proportional to the flutter critical wind speed of Xiangshan Harbor Bridge.
# Ionospheric Narrowband and Wideband HF Soundings for Communications Purposes: A Review
## 3 State of the Art of Sky Wave Sounding  
Signals can be transmitted beyond line-of-sight (BLOS) in HF communications through different mechanisms: (a) ground wave, (b) single sky-wave, (c) multi-hop skywave, (d) sporadic-E, (e) and soforth. These propagation modes and other ionospheric effects lead to the degradation of the signal, which suffers attenuation and time and frequency dispersion (Doppler spread and Doppler shift).  
In this section we review several projects whose shared goal has been the study of some or all of these impairments with the aim to improve HF communications and satellite navigation. First of all, we review a Radiocommunications section of the International Telecommunications Union (ITU-R) report on recommendations on HF field-strength measurement campaigns, we then go through a project on vertical ionospheric sounding and we finally address oblique sounding by means of three different projects focused on three different latitudes, that is, high, middle and trans-equatorial latitudes.
# Identification of Mode Shapes Based on Ambient Signals and the IA-VMD Method
## 3 The Iterative Adaptation VMD Method
### Intrinsic Mode Function  
The signal \(S(t)\) can be decomposed into several intrinsic mode functions with center frequency and limited bandwidth using the VMD method. Intrinsic mode functions are amplitude-modulated-frequency-modulated signals, written as (see [23])  
\[u_{k}(t)=A_{k}(t)\cos(\varphi_{k}(t)) \tag{4}\]  
where the envelope is non-negative \(A_{k}(t)\), \(\omega_{k}(t)=\varphi_{k}^{t}(t)=d\varphi(t)/dt\) is the instantaneous frequency of \(u_{k}(t)\), \(u_{k}(t)\) is a set of discrete subsignals, and the bandwidth sparsity of each subsignal is different in the spectral domain [22].  
Each modal component is close to the center frequency \(\omega_{k}\). The bandwidth is estimated through the \(H1\) Gaussian smoothness of the demodulated signal. Due to the sparsity of VMD, the resulting constrained variational problem is the following:  
\[\left\{\begin{array}{c}\min_{\{u_{k}\},\{\omega_{k}\}}\left\{\sum\limits_{ k}\|\partial_{t}\Big{[}\Big{(}\delta(t)+\frac{j}{\pi t}\Big{)}\otimes u_{k}(t) \Big{]}e^{-j\omega_{k}(t)}\|_{2}^{2}\right\}\\ s.t.\begin{array}{c}\sum\limits_{k=1}^{k}u_{k}=S(t)\end{array}\end{array}\right. \tag{5}\]  
where \(\{u_{k}\}\): = \(\{u_{1},\,\ldots,\,u_{k}\}\) represents the \(K\) components of signal \(S(t)\), and \(\{\omega_{k}\}\): = \(\{\omega_{1},\,\ldots,\,\omega_{k}\}\) represents the center frequencies of each component; \(\partial_{t}\) represents a partial derivative of \(t\); \(\otimes\) is convolution operation; \(\delta(t)\) is the unit impulse function; \(S(t)\) is the signal obtained after the original signal is filtered by bandpass filter.
## 4 DC Arc Fault Detection Based on the Proposed Model  
### Arc Fault Identification Method  
Just like the FFT-based method, we can use the PSD coefficients \(a_{t}(i)\)\((i=1,2,\ldots 12)\) deriving from Equation (6) to represent the corresponding time series of the measured current. According to Section 2.2, even if the arc current is observed in a short time interval, it can be regarded as a non-stationary random signal. In that case, an arc fault can be confirmed if there is a significant difference of the coefficients \(a_{12}(i)\) between the two adjacent \"observing time windows\".  
Figure 4: The AIC values of the proposed model for a given order from 1 to 20. (**a**) PV inverter #_A;_ (**b**) PV inverter #_B;_ (**c**) PV inverter #_C.  
Mathematically, such a difference can be evaluated by the correlation coefficient \(r_{i}\), which is defined as:  
\[r_{i}=\frac{Cov(a_{t},a_{t-1})}{\sqrt{Var[a_{t}]Var[a_{t-1}]}} \tag{10}\]  
where \(a_{t}\) and \(a_{t-1}\) are the PSD coefficients at observing time windows of \(t\) and \(t-1\), respectively. Equation (10) describes how similar the PSD between the two adjacent observing time windows is. In order to avoid the interference of the inverter noise, the difference of adjacent correlation coefficients \(r_{i}\) and \(r_{i-1}\) can be used for the arc fault identification. If \(|r_{i}-r_{i-1}|\) exceeds a predetermined threshold \(r_{th}\), the PV system is probably suffering from an arc fault. This protection logic can be illustrated using the flowchart as shown in Figure 5. The detailed procedures are:  
1. The DC bus current is being sampled in real-time until the data buffer is full for one observing time window. Then it goes to the next step;
2. The correlation coefficient \(r_{i}\) as well as the difference of \(|r_{i}-r_{i-1}|\) are calculated in this step;
3. If \(|r_{i}-r_{i-1}|>r_{th}\), a variable \(F\), indicating the possibility of arc fault, will increase by a predetermined number of \(a\). If not, the variable \(F\) will be reduced by a specific number of \(b\). It should be noted that the number of \(b\) should be less than the number of \(a\), so that the algorithm can deal with discontinuous arc faults. Empirically, the number of a can be 2, while the number of \(b\) can be 1;
4. If the value of the accumulator \(F\) is large enough (greater than a predetermined value \(\delta\), e.g., 12), an arc fault will be confirmed eventually.
## 2 Methods  
The methodology architecture includes three parts, as shown in Figure 1.  
1. The 3D real scene terrain is the basis of the geological recording and the 3D analysis. There are two ways to build a 3D real scene terrain model in the SuperMap platform. The first way is to use UAV photos to build the detailed 3D terrain. The other way is to use a digital elevation model (DEM) and scene images to build the large-scale terrain model. The two terrain models show geological information at different scales.
2. The geological recording can then be carried out on the 3D terrain. The geological objects and the 3D information extracted from images can be recorded according to the real coordinates.
3. Based on the recording, 3D analysis can be conducted, such as accumulation body estimation and block instability analysis. Multiple methods are integrated to be a hierarchical one to build the whole system.
# Deriving a Dilution of Precision Indicator for GNSS Factor Graph Optimization Solutions +
## 2 Materials and Methods
### DOP Computation for a Factor Graph Optimization Solution  
By definition, e.g., in [1], the DOP indicator is defined as the ratio between the uncertainty of the combined estimated states and the uncertainty of the GNSS observations, assuming that all GNSS observations are independent and affected by an error with the same variance \(\sigma_{obs}^{2}\):  
\[DOP_{k}=\frac{\sqrt{\sum_{j\in\mathcal{S}}\text{var}(\hat{x}_{j,k})}}{\sigma_ {obs}} \tag{14}\]  
where \(\text{var}(\hat{x}_{j,k})\) is the estimation variance of state \(x_{j,k}\), and \(\mathcal{S}\) is the set of indexes of the scalar states of interest. For example, if the state vector at a particular epoch is composed of \([east,north,up,clock\_bias]\), to compute the horizontal DOP, one has to combine the east and north position uncertainty and \(\mathcal{S}=[1,2]\).  
The previous section provided the estimation uncertainty computation for both snapshot and batch solutions, for a scalar element of the state vector. The computation can then be performed for the scalar states of interest before combining them into a DOP indicator.  
We define the DOP indicators coming from the snapshot solution, noted \(DOP_{k}^{(snap)}\), and the one coming from the batch solution including a motion factor, noted \(DOP_{k}^{(batch)}\)  
\[DOP_{k}^{(snap)} =\frac{\sqrt{\sum_{j\in\mathcal{S}}(\mathbf{P}_{k}^{(snap)})_{j,j}}}{\sigma_{obs}}=\sqrt{\sum_{j\in\mathcal{S}}(\mathbf{H}_{k}^{\text{T}} \mathbf{H}_{k})_{j,j}} \tag{15}\] \[DOP_{k}^{(batch)} =\frac{\sqrt{\sum_{j\in\mathcal{S}}(\mathbf{P}_{k}^{(batch)})_{j,j}}}{\sigma_{obs}}\approx\frac{\sqrt{\sum_{j\in\mathcal{S}}\frac{N_{j,k}( \sigma_{j,1},\ldots,\sigma_{j,k})}{D_{j}(\sigma_{j,1},\ldots,\sigma_{j,k})}}}{ \sigma_{obs}} \tag{16}\]  
Note that while the snapshot DOP does not depend on \(\sigma_{obs}\), the batch DOP depends on the ratio between the motion uncertainty and the observation uncertainty \(q/\sigma_{obs}\).
## 2 Materials and Methods  
A comprehensive and systematic literature review was carried out by searching three prominent scientific databases: PubMed, Scopus, and SpringerLink. For ensuring the methodological rigor and replicability for our narrative review, the SANRA checklist was used [7]. The objective of this search was to identify all peer-reviewed articles that discuss the potential complications and adverse effects associated with high-intensity focused ultrasound (HIFU) treatments when applied as an esthetic procedure. Eligible studies included randomized controlled trials, controlled trials, cohort studies, and case series. Objective and subjective outcomes were assessed. To ensure the most thorough collection of relevant studies, the search strategy was meticulously designed using a combination of specific keywords and phrases, including \"HIFU in esthetics\", \"rejuvenation\", \"facial tightening\",\"side effects\", \"complications\", \"risks\", and \"contraindications\". These keywords were strategically chosen to capture a broad range of studies discussing both the efficacy and safety concerns related to HIFU applications.  
In order to ensure inclusivity and the most extensive coverage of existing research, no restrictions were applied regarding language or the publication date of the studies. This approach facilitated the retrieval of the most up-to-date and historically significant research on HIFU technology in esthetic medicine. The review encompassed a wide spectrum of articles, including clinical trials, systematic reviews, case studies, and retrospective analyses that specifically focused on the complications, risks, and potential contraindications of HIFU procedures. Through this rigorous selection process, a total of twenty-two relevant studies published between 2010 and 2025 were identified for inclusion in the final analysis. These studies provided valuable insights into the side effects, complications, and limitations of HIFU in esthetic medicine. A notable observation was that all the selected studies involved female participants, highlighting a potential gender bias in the existing research. This finding suggests that additional studies involving male subjects may be necessary to gain a more comprehensive understanding of the effects and safety profile of HIFU across different populations.  
To maintain the highest level of accuracy and relevance, studies that did not explicitly report complications, side effects, or potential risks associated with HIFU treatment were systematically excluded from the analysis. This ensured that only scientifically rigorous and directly relevant data contributed to the overall findings of this review. The inclusion criteria prioritized studies that offered quantitative and qualitative assessments of post-treatment complications, as well as those that provided clear documentation of patient outcomes following HIFU procedures. Also, there were some limitations of our review, such as heterogeneity in study designs, small sample sizes, a lack of long-term follow-up data, and an all-female patient population across these twenty studies.
## 4 Results and Discussion
### Comparision of Observed and Simulated GWLF Potential  
To ensure the accuracy of the estimated GWLF results, the simulated results can be compared with the observed GWLF from 18 groundwater monitoring wells with 8 hydrological years of data. A scatter plot with a 45-degree reference line was used to verify the difference between the simulated and observed GWLF values. As shown in Figure 14, the value for the observed GWLF does tend to increase as the value of the simulated GWLF  
Figure 13: The estimated weighting coefficients in the wet and dry seasons for different years.  
increases for both types of seasons. The data points are randomly scattered around the reference line. This outcome indicates that this presented model is an unbiased model. In addition, the data points for the dry season are confined at approximately six meters of groundwater level fluctuation. The data points for the wet season are spread up to approximately 18 m of groundwater level fluctuation.  
In addition to the visual interpretation, Equation (5) can be used to compute the percentage error between the estimated and observed GWLF values for each data point. The average error between the estimated and observed GWLF values from 144 measurements for the dry season is approximately 33.2%. The average error between the estimated and observed GWLF values from 144 measurements for the wet season is approximately 19.2%. Based on [PERSON]'s [32] guidelines of error for calibration/verification tolerances to watershed model users, the percentage mean error value is within 20%, which indicates that the verified model is considered \"very good\"; the percentage mean error value is between 20% and 30%, which indicates that the verified model is considered \"good\"; the percentage mean error value is between 30% and 45%, which indicates that the verified model is considered \"fair\"; the percentage mean error value is over 45%, which indicates that the verified model is considered \"poor\". The above guidance is provided for the verification of sediment simulation in a watershed model. Although this guideline does not directly address the evaluation of GWLF simulation results, both GWLF and sediment parameters are considered challenging while performing simulations of hydrological models. Therefore, in the absence of a reference for the simulation of GWLF, this study refers to this guideline to evaluate the performance of the proposed model in simulating GWLF. Based on the guidance, the proposed model for predicting GWLF in the dry season can be considered \"fair\", but that in the wet season can be considered \"good\". Overall, the verification data demonstrate that the developed model can be expected to predict the GWLF for different seasons with a \"good\" level of accuracy.  
\[\text{\% error}=\left|\frac{\text{GWLF}_{estimated}-\text{GWLF}_{observed}}{ \text{GWLF}_{observed}}\right|\times 100 \tag{5}\]
# Design and Analysis of an Inductive Coupling System for the Early Detection of Heart Failure
## 2 Methodology and the Optimization Procedure  
The changes in the resonance frequency of the coils can be utilized to determine the distance between the coils and the size of the ventricular chamber. The size of the coils is determined by the dimensions of the heart. Ansys HFSS was used to optimize the coils with respect to turns, outer diameter, wire thickness, wire spacing, and pitch at an operational frequency of 13.56 MHz using the iterative optimization approach outlined in [13]. The transceiver (\(Tx\)) coil and receiver coil (\(Rx_{1}\) and \(Rx_{2}\)) locations are depicted in the schematic diagram in Section 1, which may be found in Figure 1. It is abundantly clear from the figure that the receiver coils are positioned on the surface of the ventricular chambers of the heart. These coils transmit information to the transceiver coil that is positioned above the chest whenever there is a change in the distance between them. A VNA is connected to both ends of the \(Tx\) coil so that the changes in the size of the heart can easily be monitored.
## 2 Related Work
[43] addressed the problem of low-illumination retinex images because dark regions were not properly enhanced, and these regions became overexposed. They used a retinex-based method to enhance the illumination map by using a brightness enhancement function (BEF) to estimate the illumination map by multiscale fusion. Their method worked well on natural images with varying contrast. In our research, the retinex method is used for medical images, and these are iterative methods. Medical images are different from natural images, because they have many problems such as low-varying contrast, center light reflex, and inconsistency of image details, as well as noise. The iterative retinex is tested for medical images, and it provides a better enhanced image and impacts segmentation performance, as explained in the results and discussion section. In this work, we analyze an algorithm to solve the low-varying contrast problem. We used publicly available medical image databases such as retinal image databases and MRI brain image databases. We worked on the McCann Retinex algorithm, an iterative machine learning algorithm, to estimate the best-illuminated image or normalize the contrast. The study is based on a detailed analysis of the McCann Retinex algorithm on MRI brain images and retinal fundus images. The main contribution of this research is that it implements the iteration-based McCann Retinex algorithm to handle the challenge of uneven illumination, low-varying contrast, and noise in MRI brain images and fundus retinal color images. The other main contribution of this research work is the validation of the impact of the image enhancement technique on the segmentation of retinal vessels and detection of brain tumors. The McCann Retinex algorithm was modified based on the iterative process, and the iterative process followed the ratio-product-reset-average operation. The most complicated parameter in the retinex algorithm is the adjustment of the optimal iterations, because each iteration impacts the details of the image. We analyzed the effect of each iteration of the McCann Retinex algorithm on the retinal fundus images from the HRF database and the Onsis brain MRI database. Section 3 contains the proposed method.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 3 Digital Twins for Solar Photovoltaics
### PV DT Technologies
#### 3.3.1 Weather Monitoring and IoT Sensors  
One of the major components of a PV DT system is its reliance on weather and environmental conditions (WECs) to determine the performance of the physical PV array.  
Figure 5: Functional sub-modules of a PV-DT with robotics integration.  
Therefore, high-accuracy sensors are required to sample the ambient WECs as fast as possible [68; 69; 70]. In addition to weather monitoring sensors, other sensors are required, such as energy yield and IoT sensors [71; 72]. Data collected from these sensors are used to model or forecast operating conditions in the DT using different ML algorithms. Some available sensors are listed in Figure 5 and [73]. High climate variability means that long-term WEC predictions and WEC predictions using the next available monitoring station sensors may be unreliable. Thus, the recent trends that provide reliable information rely on short-term or now-casting prediction data from WEC sensors installed on the same site at the PV installation [74; 75]. Climate change has increased research activities in the development of meteorology DTs [76; 77] as a way to sharpen insights on global, regional, and localized variations in WECs and is expected to impact energy yield modeling for renewable sources, especially PV and wind sources. In the context of PV DTs, the physical structure of the WEC sensors is less important than the accuracy and reliability of the data they supply.