esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Automatic and User-Independent Segmentation  
Deep learning enables automated identification and delineation of regions of interest in biomedical images, and consequently, it is of great interest in radiology. Nevertheless, it requires high computational power and long training times. In order to overcome this issue, ENet [22] and ERFNet [23] have been proposed as fast and lightweight networks capable of obtaining accurate segmentation with low training time and hardware requirements. Indeed, it was developed for fast inference and high accuracy in augmented reality and automotive scenarios where hardware availability is limited.  
Specifically, ENet is based on building blocks of residual networks, with each block consisting of 3 convolutional layers. These are a \(1\times 1\) projection that reduces dimensionality, a regular convolutional layer, and a \(1\times 1\) expansion with batch normalization. ENet has asymmetric convolutions characterized by separable convolutions with sequences of \(5\times 1\) and \(1\times 5\) convolutions. The \(5\times 5\) convolution has 25 parameters, while the corresponding asymmetric convolution has only 10 parameters to reduce the network size. Finally, ENet uses a single starting block in addition to several variations of the bottleneck layer. The bottleneck layer is used to force the network to learn the most salient features present in the input and, consequently, to ignore the irrelevant parts.  
ERFNet is optimized over ENet to improve accuracy and efficiency. The basic building block module is a non-bottleneck 1D layer comprised of two sets of factorized (separable or asymmetric) convolutions of a size of \(3\times 1\) followed by the \(1\times 3\) with rectified linear unit non-linearity. The input feature map of the main convolution path is added element-wise to the output of the convolution path, which represents the input of the next layer after applying the rectified linear unit non-linearity. The size of input is 512 \(\times\) 512, while the down-sampler block is similar to that of ENet architecture.  
Furthermore, both ENet and ERFNet can be used as an alternative to transfer learning, usually used to compensate for the lack of labeled biomedical images. ENET and ERFNet can learn a lot of information, even from small datasets, as demonstrated in several biomedical segmentation studies [24; 25; 26; 27], in which they outperformed other state-of-the-art deep learning approaches, such as UNet.
## 2 Materials and Methods
### Cell Segmentation  
This module performs the automated segmentation of the individual HEp-2 cells. Such a task is one of the most challenging of the automated IIF analysis, because the segmentation algorithm has to cope with a large heterogeneity of shapes and textures. Moreover, very often, inside the HEp-2 images the cells are partially overlapped with a consequent separation problem.  
In this work, the segmentation phase was carried out by developing the following three steps:  
Figure 3: The Computer Aided Diagnosis (CAD) working flow—the system aims to reproduce the operations flow made by the immunologist, by making a classification of fluorescence only for not-negative images, and by operating a cells classification which will then use the results of these classifications, to provide a final output.  
1 Pre-segmentation: Aimed at identifying regions of interest ROIs (Regions of Interest).
2 Randomized Hough transform for ellipse: Aimed at identifying the ellipse that best characterizes the generic cell.
3 Active contours model: Starting from an elliptic curve, evolve expanding towards the cellular contour
## 1 Introduction
### First Passage Time Analysis of System Response
 \tag{16}\]  
According to the bistable theory and the two-state model theory, the transition rate of particles between the two stable states can be expressed as follows:  
\[R_{1,2}(t)=\frac{1}{2\pi}\sqrt{\frac{\lambda_{s1}^{+}\lambda_{s1}^{-}\lambda_ {s1}^{+}}{|\lambda_{uu}^{-}|}}\exp\left(\frac{\widetilde{U}(x_{s1},0,t)- \widetilde{U}(x_{uu},0,t)}{D}\right) \tag{17}\]  
\[R_{2,1}(t)=\frac{1}{2\pi}\sqrt{\frac{\lambda_{s2}^{+}\lambda_{s2}^{-}\lambda_ {s1}^{+}}{|\lambda_{uu}^{-}|}}\exp\left(\frac{\widetilde{U}(x_{s2},0,t)- \widetilde{U}(x_{uu},0,t)}{D}\right) \tag{18}\]  
Finally, the expression for the average time of the system's first transition from the steady-state point \(x_{s1}\) to the steady-state point \(x_{s2}\), also known as the MFPT of the system, is derived from Equations (16)-(18). \[T_{12}=T(x_{s1}\to x_{s2})=2\pi\sqrt{\frac{|\lambda_{uu}^{-}|}{\lambda_{s1}^{ +}\lambda_{s1}^{-}\lambda_{uu}^{+}}}\exp\left(\frac{\widetilde{U}(x_{uu},0,0)- \widetilde{U}(x_{s2},0,0)}{D}\right) \tag{19}\]  
As can be seen from the graph in Figure 2 showing the relationship between the damping coefficient and the MFPT of the particle, when the noise intensity remains constant, as the damping coefficient increases, the MFPT of the particle also increases, indicating that the presence of the damping coefficient causes the particle to be disturbed by damping forces during the transition, hindering the particle's movement. Conversely, when the damping coefficient remains constant, the greater the noise intensity, the shorter the mean first passage time of the particle, suggesting that during the particle's motion, the noise energy is converted into kinetic energy required for the particle's transition, promoting the particle's transition.
# Carbazole Derivatives as STAT Inhibitors: An Overview
## 3 STATs Inhibitors
### Natural Compounds as STATs Inhibitors  
Several studies also identified many lead candidates such as STAT3 inhibitors from natural products. However, the mechanism of action is not completely defined. Curcumin, a phenolic compound derived from the perennial herb _Curcuma longa_, hampers JAK-STAT signaling at 15 uM, promotes cell cycle arrest, and impedes cell invasion in vitro in a small cell lung cancer model [92]. The treatment of mice bearing gastric cancer xenografts with curcumin suppressed IL-6 production by IL-1\(\beta\)-stimulated myeloid-derived suppressor cells, associated with decreased activation of STAT3 and nuclear factor-kB (NF-kB).  
Curcumin analogues with ameliorated bioavailability and stability, such as FLLL132, had improved efficacy (IC\({}_{50}\) values of 0.75-1.45 uM) in inhibiting both pSTAT3 and total STAT3. Furthermore, they caused STAT3 ubiquitylation and possible proteasomal degradation in canine and human osteosarcoma cells in vitro [93]. HO-3867 (Figure 5), curcumin analog, downregulated likewise STAT3 signaling in cisplatin-resistant human ovarian cancer cells, thus intensifying sensitivity to cisplatin [94]. HO-3867 also promoted apoptosis in BRCA1-mutated human ovarian cancer cells harboring aberrantly active STAT3 [95].  
Other findings estimated that LLL12 (Figure 5), another small-molecule inhibitor of STAT3 signaling based on curcumin [96], hampers STAT3 activation by impeding its recruitment to the receptor and avoiding phosphorylation by tyrosine kinases, and by preventing the dimerization [97]. LLL12 decreased cell viability, promoted the apoptotic process, and suppressed colony formation and migration in vitro in glioblastoma, osteosarcoma, and breast cancer cells. It also interfered with angiogenesis, tumor vasculature development, and tumor growth in vivo in osteosarcoma xenograft models [96; 97]. Reserstratrol (3,5,4\({}^{\prime}\)-trihydroxystilbene) (Figure 5), a polyphenic compound mainly found in red grapes, inhibits STAT3 signaling at high micromolar concentrations, thus affecting tumoral cell growth. Reserstratrol treatment also prevents constitutive and IL-6-induced STAT3 activation in multiple myeloma, leukemia, and other tumor cell types, reduces the expression of BCL-2 and other anti-apoptotic proteins, in vitro [98]. The resveratrol derivative LY871 arrested cell growth (IC\({}_{50}\) value of 20 uM) and decreased STAT3-mediated MMP9 expression [99]. Moreover, the inhibition of the JAK-STAT3 pathway by resveratrol or its analogue, piceatannol (3,3\({}^{\prime}\),A\({}^{\prime}\)-trantirhydroxystilbene), reduced BCL-XL and BCL-2 expression and sensitized lung carcinoma, multiple myeloma, prostate pancreatic  
Figure 5: Some inhibitors of STAT3 activity with no carbazole structure.  
cancer, and the glioblastoma multiforme patient-derived CD133-positive cells to radiation or chemotherapy in vitro.
## 6 Computational Results
### Lower Bounds for Datasets B and X
#### 6.5.1 Dual Bounds Computable in Less Than One Hour
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & [16].1 & [16].2 & LB(0) & LB(0) & LB(1) & LB(1) & LB(2) & LB(3) & LB(3) \\  & **Gap** & **Gap** & **t(s)** & **Gap** & **t(s)** & **Gap** & **t(s)** & **Gap** \\ \hline B6 & 56.44\% & 16.58\% & 82 & 18.12\% & 124 & 14.89\% & 356 & 11.67\% \\ B7 & 52.86\% & 15.58\% & 66 & 15.34\% & 181 & 13.39\% & 991 & 11.16\% \\ B8 & 65.32\% & 23.60\% & 221 & 23.12\% & 1215 & 20.20\% & 218k & 16.92\% \\ B9 & 63.30\% & 21.72\% & 282 & 21.73\% & 1776 & 18.71\% & 218k & 15.36\% \\ B10 & 60.92\% & 18.03\% & 224 & 18.64\% & 573 & 16.46\% & 1030 & 13.42\% \\ \hline X11 & 57.81\% & 15.40\% & 92 & 14.44\% & 436 & 12.84\% & 1267 & 11.07\% \\ X12 & 52.65\% & 14.22\% & 117 & 13.90\% & 496 & 12.27\% & 1137 & 10.28\% \\ X13 & 66.20\% & 18.53\% & 261 & 17.90\% & 424 & 15.86\% & 1701 & 14.11\% \\ X14 & 64.67\% & 17.21\% & 270 & 16.84\% & 861 & 14.87\% & 3887 & 13.11\% \\ X15 & 61.76\% & 16.83\% & 421 & 18.03\% & 1528 & 16.10\% & 6931 & 13.49\% \\ \hline Total & 60.15\% & 17.80\% & 2036 & 17.84\% & 13k & 15.58\% & 453k & 13.07\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparing with [16] the dual bounds using MILP computations of \(LB(k_{0})\) with \(k_{0}<3\). This induces results of Table 9, considering in our computational experiments only the results having a total computational time inferior to one hour, which corresponds to some LP and LP+cuts computations. We note that we did not optimize the results obtained in one hour. The results of Tables 7-9 show a graduation of lower bounds with an increasing quality when computation times increase. Having the computation times of the lower bounds [16] or other dual bounds, one can compare the quality of lower bounds in equivalent computational times using equivalent hardware. Our dual bounds are very scalable, and, if a total computation time is allowed, one can divide this total solving time by the number of clusters of scenarios, in order to define a time limit for each MILP sub-problem. In particular, the lower bounds for instances B8 and B9 should be significantly improved allowing one hour total computations and LP+cuts truncated computations.
## 2 Materials and Methods
### Numerical Method  
The optical properties of the metasurfaces were analyzed using a computational code combining rigorous coupled-wave analysis (RCWA) method [55] with a scattering-matrix (S-matrix) algorithm [56]. The RCWA method is based on Fourier representation of Maxwell equations, and is a rigorous method in electromagnetism. This method had been impractical for a long time because of the very slow convergence, and became practical for arbitrary periodic structures, including metals, by the finding of the inverse Fourier factorization algorithm in 1996 [57]. The RCWA-S-matrix code is particularly suitable for numerical calculations for optical spectra, such as reflection and transmission in arbitrary periodic structures. The code was numerically executed on supercomputers in a multi-parallel implementation (MPI) manner, and the \(R\) spectra were evaluated. Owing to the MPI parallelization, the calculation time for the \(R\) spectra was substantially reduced. Resonant EM fields were also evaluated using the RCWA-S-matrix code. The \(R\) spectra and EM fields in this article were thus obtained.  
To implement realistic computations, we used material parameters in the literature that compiled measured data or representative values. The complex refractive index of Si was taken from [58]. The refractive indices of air and SiO\({}_{2}\) were set to 1.00027 and 1.46, respectively, in the range of present interest.
###### Abstract  
The abandoned mercury (Hg) mine area in Puerto Princesa City, Palawan was included in the list of abandoned and inactive mines in the Philippines which pose a high risk to human health and the environment, and require rehabilitation. The mine site, operated by Palawan Quicksilver Mines, Inc. (PQMI) from 1953 to 1976, is located approximately 3 km inland from Honda Bay coast and within the catchment of the Tagburos River, which is a local fishery and recreational area. In this study, total Hg levels in soils and sediments were measured to assess the possible release of Hg from the site into the surrounding natural environment. Results showed that total Hg (THg) concentrations found in soils (0.04-67.5 mg kg\({}^{-1}\)), mine waste calcines (52.7-924.2 mg kg\({}^{-1}\)), river sediments (1.8-119 mg kg\({}^{-1}\)), and marine sediments (0.04-12.7 mg kg\({}^{-1}\)) were elevated compared to the global background of 0.045-0.16 mg kg\({}^{-1}\). The high concentrations of Hg in soils and river sediments were influenced by the different pathways for the release of Hg from its mine operations; while Hg in marine sediments was caused by the erosion of mine waste calcines near the pit lake, and calcines used to construct a wharf at the nearby Honda Bay. Mine wastes represent the largest source of Hg contamination in the area, due to the low efficiency of the recovery process during calcination. This work corresponds with the Minamata Convention on Mercury on the updated environmental assessments of abandoned Hg mines as potential source sites of mercury contamination.  
mercury; PQMI; Palawan; abandoned mines; mine wastes; sediments +
Footnote †: journal: Applied Sciences
## 2 Methodology
### Accuracy Assessment  
To evaluate the signal de-noising quality of the improved SOBI signal de-noising method, four objective evaluation indexes were used in this study. These include the noise rejection ratio (NRR) [35], the noise mode (NM) [36; 37], the signal energy ratio (SER) [36; 37], and the root mean square error (RMSE) [35].  
NRR can reflect the ability to suppress interference and improve SNR. NRR is defined as:  
\[NRR=10(\lg\delta_{1}{}^{2}-\lg\delta_{2}{}^{2}), \tag{5}\]  
where \(\delta_{1}\) and \(\delta_{2}\) are the quasi-deviations of the signals before and after signal de-noising, respectively.  
SER and NM are the quantitative evaluations of noise removal efficiency and noise quantity from the energy point of view, respectively. SER reflects the energy similarity between the de-noised signal and the original signal, and NM reflects the overall level of noise. The SER and NM are defined as:  
\[SER=\sqrt{\sum_{n=1}^{N}y^{2}(n)}/\sqrt{\sum_{n=1}^{N}s^{2}(n)}, \tag{6}\]  
\[NM=\sqrt{\sum_{n=1}^{N}\left(y(n)-s(n)\right)^{2}}, \tag{7}\]  
where \(s(n)\) represents the original observation signal; \(y(n)\) represents the signal after noise reduction; and \(n\) represents the number of sampling points.  
RMSE is used to measure the deviation between the observed value and the true value. The RMSE is defined as:  
\[RMSE=\sqrt{\frac{1}{N}\sum_{n=1}^{N}\left(y(n)-s(n)\right)^{2}}, \tag{8}\]where \(s(n)\) represents the original observation signal; \(y(n)\) represents the signal after noise reduction; and \(n\) represents the number of sampling points.  
Generally, the larger the NRR and NM values are, the closer the NM value is to one. Additionally, the smaller the RMSE value is, the more enhanced the signal de-noising is.
# Signal Consensus in TSP of the Same Grid in Road Network
## 5 Simulation Results
### Simulation Results of Consensus Algorithm with Input Constraints in TSP  
Figure 5 shows the dynamic processes of 5 weights of priority in Figure 2 by using consensus Algorithm (4), the state trajectories and the disagreement function \(\left\|\delta\right\|^{2}\) associated with consensus Algorithm (4) are shown in Figure 6.  
It is can be seen from the simulation results that the consensus is also reached for all weight of transit signal priority in the same grid by using consensus Algorithm (4).  
Compared Figure 6 with Figure 4, the rate of convergence of the consensus Algorithm (4) is slower and smoother than (3). Information interaction between the nodes is limited and more stable in consensus Algorithm (4), it is more suitable for the signal consensus in the TSP.
# 6G Enabled Industrial Internet of Everything: Towards a Theoretical Framework
## 11 Recommendations
### 6G Network Specification Requirements  
We suggest the following recommendations for 6G specification requirements for the connectivity (deep, intelligent, ubiquitous, holographic) vision as outlined below in Section 5 of this study:  
* 6G network application ought to be human centric.  
High security, and privacy should be key features of 6G.
* Application Types: (i) MTRLLC, (ii) mURLLC, (iii) HCS, and (iv) MPS
* Device Types: (i) Sensors and Devices related CIIW, (ii) Connected Robotics and Autonomous Systems (CRAS), (iii) XR and CBI Equipment, and (iv) Intelligent Plants.
* Architecture: (i) Cell free smart surfaces at High Frequency supported by \(\leq\)100 m tiny and dense mmWave cell access, (ii) Hotspots served by Drone carried Base Stations, (iii) Sub-6 GHZ cells, and (iv) 5-Layer Platform.
* Frequency Bands: (i)Sub--6 GHZ, (ii) mmWave for mobile access, (iii) Exploration of THz bands (above 140 GHz), and (iv) Non-RF (e.g., Optical, Visible Light Communication (VLC) etc.).
* End to End Delay: Less than 1 millisecond
* Processing and Radio only Delay: 10 Nano Seconds
* Rate Requirements: One (1) Tera Bit Per Second.
* Spectral and Energy Efficiency Gains: 1000\(\times\) in \(\text{bps}/\text{Hz}/\text{m}^{3}\) (volumetric)
* End to End Reliability: Seven 9 Seconds
## 2 Methodology
### Generation of the Virtual Degraded Image  
#### 2.1.1 Light Propagation Simulation Using the Angular Spectrum Method  
To comprehensively evaluate the propagation and diffraction of light through the UDC panel, we employed the angular spectrum method (ASM). The ASM is a powerful tool for understanding how light propagates from one plane to another, making it particularly effective for analyzing complex optical systems, such as UDC panels [20; 21; 22; 23; 24; 25]. This method simulates light propagation by decomposing the optical field into an infinite number of plane waves of the same frequency traveling in different directions using a two-dimensionalFourier transform. Mathematically, inverse Fourier and Fourier transformations are expressed as [26; 27]  
\[\begin{array}{l}u(x,y,0)=\int\int U\big{(}f_{x},f_{y}\big{)}e^{i2\pi(f_{x}x+f_{ y}y)}df_{x}df_{y},\\ U\big{(}f_{x},f_{y}\big{)}=\int\int u(x,y,0)e^{-i2\pi(f_{x}x+f_{y}y)}dxdy=\Re\{u(x, y,0)\}_{(f_{x},f_{y})}.\end{array} \tag{2}\]  
In Equation (2), \(u(x,\ y,\ 0)\) represents the intensity field when the initial distance \(z\) is zero, where \(x\) and \(y\) are spatial coordinates. The term \(U\big{(}f_{x},\ f_{y}\big{)}\) corresponds to the weight associated with the plane wave \(\exp\big{[}i2\pi\big{(}f_{x}x+f_{y}y\big{)}\big{]}\), which represents the angular spectrum of the optical field.  
Figure 2: Overall flowchart of the proposed method for image restoration in the UDC system.  
By analyzing individual plane waves and their propagation characteristics, the ASM enables the independent propagation of each Fourier component. This property allows us to analyze the behavior of light as it interacts with the various components and structures of a UDC panel separately. The propagation of each Fourier component is expressed mathematically as follows [28,29]:  
\[\begin{array}{c}e^{i\vec{k}\cdot\vec{\tau}}=e^{2\pi(f_{x}x+f_{yy}+z\sqrt{( \frac{1}{\lambda})^{2}-f_{x}^{2}-f_{y}^{2}})},\\ u(x,y,z)=\int\int U\big{(}f_{x},f_{y}\big{)}e^{i2\pi(f_{x}x+f_{yy}+z\sqrt{( \frac{1}{\lambda})^{2}-f_{x}^{2}-f_{y}^{2}})}df_{x}df_{y}\\ =\Re^{-1}\bigg{\{}\Re\{u(x,y,0)\}_{(f_{x},f_{y})}e^{i2\pi z\sqrt{(\frac{1}{ \lambda})^{2}-f_{x}^{2}-f_{y}^{2}}}\bigg{\}}_{(x,y)}.\end{array} \tag{3}\]  
In Equation (3), the exponential term describes the propagation of a plane wave over the distance \(z\), \(u(x,\,y,\,z)\) represents the intensity field at the distance \(z\) from the initial plane, and \(\lambda\) is the wavelength of light. To reconstruct the intensity field at the distance \(z\), an inverse Fourier transform is applied to combine the propagated Fourier components with their respective weights \(U\big{(}f_{x},\,f_{y}\big{)}\). By using Equation (3), we can analyze how light propagates through, is diffracted by, and interacts with the UDC panel.
## 2 Materials and Methods
### Study Design  
All measurements were conducted during morning hours by the same researcher, who was well-trained and experienced with both the equipment and the test protocol. Body mass (kg) and height (cm) were recorded prior to testing. Body height was measured using a stadiometer (Metrisis, Athens, Greece), while body mass was determined using an octopolar analyzer Tanita MC 780 MA (TANITA, Tokyo, Japan). In the study, a repeated-measures observational design was assessed to assess shoulder sensorimotor function during the sway test (ST) across two separate visits, with a one-week interval between them. Participants were instructed to avoid vigorous physical activity between the two visits.  
Visit 1: For the between-trial (intra-rater) reliability, two measurements were performed with 15 min of rest between them. The reliability was calculated between the 1 st and 2 nd measurements.  
Visit 2: For the between-day (test-retest) reliability, one measurement was performed one-week later, during the 2 nd visit in the laboratory. The reliability was calculated between the 1 st measurement from Visit 1 and the measurement from Visit 2.  
Each series of the test protocol comprised four parts of the ST: first with eyes open and then with eyes closed, separately for the left and right upper limbs (Figure 1).  
In the study, the AMTI force plate (Advanced Mechanical Technology Inc., Watertown, MA, USA) was utilized with an acquisition frequency of 1000 Hz, a rate recommended as suitable for body balance assessment. Calibration of the platform was conducted before data collection according to the manufacturer's guidelines.  
Figure 1: Flowchart of measurement procedure.  
Participants were instructed to maintain balance with minimal movement and to perform the task naturally to complete the test successfully. They started in a standard push-up position, with the contralateral arm placed behind their back. In this setup, participants assumed a plank position on the platform, aiming to remain as still as possible. The eyes-open test was conducted first with participants holding the plank position on each hand for 30 s, followed by a one-minute interval between trials on the right and on the left hand. After a two-minute rest period, the test was repeated with eyes closed (Figure 2).
# Information Criteria for Signal Extraction Using Singular Spectrum Analysis: White and Red Noise
## 2 Model Order Selection  
This section presents an overview of the existing methods for model order selection, including both general information criteria and specific criteria that employ signal subspace models.