esa-sceva/satcom-chunk-collection huggingface dataset:
# An Empirical Evaluation of Nuclei Segmentation from H&E Images in a Real Application Scenario
###### Abstract  
Cell nuclei segmentation is a challenging task, especially in real applications, when the target images significantly differ between them. This task is also challenging for methods based on convolutional neural networks (CNNs), which have recently boosted the performance of cell nuclei segmentation systems. However, when training data are scarce or not representative of deployment scenarios, they may suffer from overfitting to a different extent, and may hardly generalise to images that differ from the ones used for training. In this work, we focus on real-world, challenging application scenarios when no annotated images from a given dataset are available, or when few images (even unlabelled) of the same domain are available to perform domain adaptation. To simulate this scenario, we performed extensive cross-dataset experiments on several CNN-based state-of-the-art cell nuclei segmentation methods. Our results show that some of the existing CNN-based approaches are capable of generalising to target images which resemble the ones used for training. In contrast, their effectiveness considerably degrades when target and source significantly differ in colours and scale.  
Keywords:digital pathology; H&E staining; cell nuclei segmentation; cross-dataset +
Footnote †: journal: Applied Sciences
## 4 Results
### Model Evaluation  
As assumed in the conceptual model, the PLS-SEM model was specified to include two exogenous constructs (SL_FF and SI_PS) and five endogenous constructs (PEOU, PU, HM, PE, and BIOU). All constructs were modeled as reflective. Following the appropriate steps [54; 55; 56], the reliability and validity of the measurement model were fully evaluated. First, the outer loadings (see Table 3) were above the recommended value of 0.708 [55]. Moreover, construct internal consistency reliability was evaluated in terms of composite reliability (rho_a and rho_c) and Cronbach's Alpha coefficients, and all values were found to be greater than the 0.7 threshold [55]. Therefore, considering all the reliability of the above criteria, the measurement model was supported. Furthermore, the average variance extracted (AVE) values (Table 3) for all reflective constructs were greater than 0.5, supporting the convergent validity of the PLS-SEM measurement model [55]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **N** & **Min.** & **Max.** & **Mean** & **Std. Dev.** \\ \hline SI\_FF & 302 & 1.000 & 5.000 & 3.210 & 0.980 \\ \hline SI\_PS & 302 & 1.000 & 5.000 & 2.962 & 1.028 \\ \hline PEOU & 302 & 1.000 & 5.000 & 4.102 & 0.856 \\ \hline PU & 302 & 1.000 & 5.000 & 3.965 & 0.830 \\ \hline HM & 302 & 1.000 & 5.000 & 3.602 & 0.864 \\ \hline PE & 302 & 1.000 & 5.000 & 3.833 & 0.937 \\ \hline BIOU & 302 & 1.000 & 5.000 & 3.953 & 0.960 \\ \hline \hline \end{tabular} N—number of cases.  
\end{table} 
Table 2: Descriptive statistics of research variables. In terms of discriminant validity [54; 55; 56], all the indicator cross loading, constructs, and heterotrait-monotrait criterion (HTMT) [57] were considered. None of the indicators had any loading higher on the other constructs than on its own, and all HTMT values were lower than 0.9 threshold and (with one exception) lower than 0.85 (Table 4), thus supporting the discriminant validity of the constructs. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Constructs** & **BIOU** & **HM** & **PE** & **PEOU** & **PU** & **SI_FF** & **SI_PS** \\ \hline \hline \multicolumn{2}{c}{BIOU} & \multicolumn{8}{c}{} & \multicolumn{8}{c}{} & \multicolumn{8}{c}{} \\ \hline \hline \multicolumn{2}{c}{HM} & 0.776 & \multicolumn{8}{c}{} & \multicolumn{8}{c}{} & \multicolumn{8}{c}{} \\ \hline \hline \multicolumn{2}{c}{PE} & 0.887 & 0.750 & \multicolumn{8}{c}{} & \multicolumn{8}{c}{} \\ \hline \hline \multicolumn{2}{c}{PEOU} & 0.768 & 0.645 & 0.790 & \multicolumn{8}{c}{} \\ \hline \hline \multicolumn{2}{c}{PU} & 0.703 & 0.768 & 0.683 & 0.720 & \multicolumn{8}{c}{} \\ \hline \hline \multicolumn{2}{c}{SI\_FF} & 0.524 & 0.477 & 0.523 & 0.427 & 0.391 & \multicolumn{8}{c}{} \\ \hline \hline \multicolumn{2}{c}{SI\_PS} & 0.485 & 0.450 & 0.482 & 0.375 & 0.314 & 0.782 \\ \hline \end{tabular} Source: authors with SmartPLS 4 [50].  
\end{table} 
Table 4: Discriminant validity—HTMT ratio.
## 3 System Integration
### FPGA Implementation  
The generated files from MATLAB are stored into a read only memory (ROM), and the interface of the chain to the ROM is created. By using the debugging tools in VIVADO, virtual input output (VIO), and integrated logic analyzer (ILA), the output should be observed from the chain to make sure that the results are correct. Additionally, the mixed-mode clock manager (MCMM) in FPGA is used to generate deferent clock domains to make sure that the phases between them do not change as time passes.
## 2 Sliding Mode Control Design  
In this section, the mathematical procedure for the SMC design is detailed through a five-step procedure: The first step consists of obtaining the SBBC mathematical model. The second step corresponds to the description of the proposed sliding surface for DC voltage regulation and PFC. The third step consists of the validation of the transversality condition. The fourth step is the validation of existence conditions. The fifth step is the validation of the equivalent control condition to evaluate SBBC under ideal control conditions. According to the authors of [30,31], transversality, existence and equivalent control conditions are sufficient for guaranteeing stability and convergence of the SMC.  
The control objectives are as follows: (1) control the waveform of AC current, keeping it sinusoidal and in phase with AC voltage for PFC; (2) control the amplitude of the AC current ripple near zero crossing in order to reduce \(THD_{i}\); and (3) regulate the DC voltage according to load requirements.
# Retrospective Spectrum-Conversion Method Based on Time-Modulated Van Atta Array
## 5 Spectrum-Conversion Method  
Based on the previous analysis, the AFSS reflector is made to perform periodic modulation as a function of time, as shown in Figure 14. The duty cycle of the rectangular pulse train is \(\alpha\), the pulse width is \(\alpha T_{\rm s}\), and the switching period is \(T_{\rm s}\). In this case, the periodic time-domain modulated signal is  
\[p(t)=\left(1-x\right)\text{rect}\left(\frac{t}{\alpha T_{s}}\right)\otimes \sum_{-\infty}^{+\infty}\delta(t-nT_{s})+x \tag{4}\]  
Figure 14: The reflection coefficient of the switch-type active Van Atta array after periodic modulation.  
Figure 13: The measurement results of the RCS of the switch-type active Van Atta array.  
Among them, rect(\(\cdot\)) is a rectangular pulse signal: when \(1\,t/\alpha T_{\mathrm{s}}\,|<0.5\), its value is 1, otherwise it is equal to 0. \(\otimes\) represents the convolution operation, \(\delta(\cdot)\) is the impulse function, and n is a positive integer.  
According to the corresponding relationship of the Fourier transform, rect(\(t/\alpha T_{\mathrm{s}}\)) \(\leftrightarrow\)\(\alpha T_{\mathrm{s}}\)sinc(\(\alpha T_{\mathrm{s}}\)f), where sinc(y) = sin(\(\pi\)y)/\(\pi\)y. Its expanded Fourier series is  
\[p(t)=A_{0}+\sum\limits_{-\infty,n\
eq 0}^{+\infty}A_{n}\cos\bigl{(}2n\pi f_{ \mathrm{s}}\dot{t}\bigr{)} \tag{5}\]  
Among them, the amplitude coefficient A\({}_{0}\) = \((1-x)\alpha+x\), A\({}_{\mathrm{n}}\) = \((1/n\pi)(1-x)(\sin(n\pi\alpha))\), f\({}_{\mathrm{s}}\) = \(1/T_{\mathrm{s}}\) is the modulation frequency, and the signal spectrum is expressed as  
\[P(f)=\sum\limits_{-\infty,n\
eq 0}^{+\infty}A_{n}\delta(f-nf_{\mathrm{s}})+A_{ 0}\delta(f) \tag{6}\]  
In Equation (6), P(f) contains the impulse frequency component, which is generated by the time-domain DC component of the modulation function. Therefore, the spectrum contains many discrete sidebands, and the sideband envelopes obey the sinc function distribution.  
When the radar signal s(t) is incident on the time-modulated AFSS reflector and its spectrum falls within the adjustable region, the echo signal can be expressed as  
\[r(t)=s(t)\times p(t) \tag{7}\]  
According to the relationship of the Fourier transform pair, the Equations (6) and (7) are brought into the equation, and the spectrum of the echo signal is  
\[\begin{array}{ll}R(f)&=S(f)\otimes P(f)\\ &=\sum\limits_{-\infty,n\
eq 0}^
## 2 Materials and Methods
### Body Composition Assessment  
The body composition of elite athletes was measured in a certified laboratory at the Lithuanian Sports Medicine Centre. The analyses of height, body weight, and body composition were performed via X-scan (Jawon Medical Co., Jinyrang Industrial Complex, Gyeongsan, Republic of Korea, EN-ISO: 13488) using a multi-frequency bioelectrical impedance analysis (MFBIA) method with frequencies ranging from 1 kHz to 1000 kHz and clinically developed by an isotope dilution method called the 'golden method', capable of predicting both intracellular and extracellular fluid values [43; 44; 45; 46]. While applying a body composition analyzer, significant improvements were introduced for predicting extracellular fluid and total body water using MFBIA, as the swept multi-frequency facility was able to differentiate the proportions of intra- and extracellular fluid. At low frequencies (<50 kHz), the bioelectrical current was assumed to pass through the extracellular fluid. In contrast, at frequencies above 100 kHz and up 1000 kHz (100 kHz, 250 kHz, 550 kHz, and 1000 kHz), the current passed through all body fluids and tissues and consequently provided various ranges and more precise information about athletes' body composition.  
All athletes were informed of the necessary preparation procedures prior to the bioimpedanceulatory evaluation. The study participants were informed to make the following preparations: (1) to starve for 4 h, during that time no solid or liquid foods were permitted; (2) to completely avoid taking part in moderate-intensity physical activities and using caffeine as well as diuretic medications 24 h before the testing; (3) to urinate prior the evaluation [20; 44].  
The outcomes obtained via the MFBIA are referred to height (m), fat-free mass (kg and %), muscle mass (kg and %), and body fat mass (kg and %). The values of body fat percentages were classified into 'extremely low','very low', 'below normal', 'normal', 'above normal','very excessive', and 'extremely excessive' and were, respectively, assigned to the percentiles of 3 rd (P\({}_{3}\)), 10 th (P\({}_{10}\)), 25 th (P\({}_{25}\)), 50 th (P\({}_{50}\)), 75 th (P\({}_{75}\)), 90 th (P\({}_{90}\)), and 97 th (P\({}_{97}\)) [47]. Additionally, indicators such as body mass index (BMI) (kg/m\({}^{2}\)) [48] and muscle-to-fat ratio (MFR) were calculated. The BMI was calculated by dividing the body mass (kg) by height (m\({}^{2}\)). The MFR was calculated as body fat mass (kg) divided by muscle mass (kg) [9].
## 3 Results and Discussion  
### Statistical Analysis Results for the Two Soil Textures  
The descriptive statistical analysis (Table 2) shows that in loam soil, the highest mean SMC (16.19%) was found at 20 cm with the least variability (CV = 21.7%), while the deepest layer (80 cm) had the most variability (CV = 39%) ranging from 5.49% to 20.77%. In silt loam soil, SMC was generally higher at all depths, with the highest mean SMC at 20 cm (18.27%). However, the deepest layers (60 cm and 80 cm) demonstrated higher variations (CV = 42.6% and 47.8%, respectively) with SMC ranging from 7.92% to 26.58%, indicating less consistent moisture retention at depth.  
The repeated measures ANOVA revealed substantial variation in SMC across depths in both soil textures (\(p<0.05\)). The variation in SMC across depths (Figure 3) highlights the more stable behavior of silt loam soil in the upper layers and the higher variability observed in loam soil at deeper layers.  
The observed patterns confirmed the significant impact of depth on SMC variability, where deeper layers in both soil textures exhibited higher CV%, indicating less predictable moisture retention likely due to soil structural changes and lower infiltration rates. These findings highlight the impact of soil depth on SMC variability, emphasizing the importance of considering both soil texture and depth during monitoring [44; 45] and the need for considering soil texture and depth in irrigation planning and management to optimize water resources and increase water use efficiency.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Soil Texture** & **Depth (cm)** & **Mean SMC (\%)** & **SD (\%)** & **CV (\%)** \\ \hline Loam & 5 & 12.8 & 3.08 & 24 \\ Loam & 20 & 16.19 & 3.51 & 21.7 \\ Loam & 40 & 14.66 & 3.76 & 25.6 \\ Loam & 60 & 12.61 & 4.39 & 34.8 \\ Loam & 80 & 12.68 & 4.94 & 39 \\ Silt loam & 5 & 14.99 & 2.62 & 17.5 \\ Silt loam & 20 & 18.27 & 3.51 & 19.2 \\ Silt loam & 40 & 18.03 & 4.11 & 22.8 \\ Silt loam & 60 & 16.97 & 7.23 & 42.6 \\ Silt loam & 80 & 16.47 & 7.87 & 47.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Descriptive statistical analysis results for both soil textures at five depths.
## 3 Results  
### Spatial and Temporal Variations of Land Use Type in the Sanjiang Plain from 2000 to 2015  
#### 3.1.1 Temporal Variations  
According to the main features of the land cover in the Sanjiang Plain and the Level 1 type of land use in _Current Land Use Classification_ ([[http://www.gov.cn/](http://www.gov.cn/)]([http://www.gov.cn/](http://www.gov.cn/)) accessed on 11 May 2021), there were six kinds of land use types: cultivated land, water, grassland, forestland, residential land, and wetland. The cultivated land area in the Sanjiang Plain increased by 0.25 \(\times\) 10\({}^{4}\) km\({}^{2}\) from 2000 to 2015, and its area proportion increased from 48.29% in 2000 to 50.61% in 2015 (Table 2). However, both the forestland area and the wetland area exhibit decreasing trends. The area proportion of the forestland decreased by 0.95% from 2000 to 2015, which represents a decrease of 0.10 \(\times\) 10\({}^{4}\) km\({}^{2}\). The wetland area decreased by 0.15 \(\times\) 10\({}^{4}\) km\({}^{2}\) from 2000 to 2015, and the area proportion of wetland areas decreased from 8.07% in 2000 to 6.70% in 2015. Grassland, water, and residential land exhibited stable variations, with small fluctuations in the area proportion (<0.05%).
# Creating Automated Microsoft Bicep Application Infrastructure from GitHub in the Azure Cloud
## 3 Project Overview and Objectives
### Target Environments  
A crucial part of this project is designing the automated deployment to support multiple target environments, specifically development (dev), quality assurance (QA), and production (prod). Each environment serves a distinct purpose in the software delivery lifecycle, and the infrastructure automation must accommodate its unique requirements while ensuring consistency and repeatability.  
Development Environment (Dev):  
The dev environment is intended for active development work, frequent testing, and rapid iteration. Infrastructure in this environment should be lightweight and cost-efficient, while still closely mirroring production configurations to ensure meaningful testing. The automated deployment should support easy creation and teardown of dev environments, enabling developers to spin up isolated sandboxes on demand.  
Quality Assurance Environment (QA):  
The QA environment serves as the staging ground for more formal testing, including integration, regression, and user acceptance testing. It must replicate production configurations as closely as possible to identify and resolve issues before deployment to end users. Automated deployment ensures that QA environments can be refreshed reliably, maintaining alignment with the latest production infrastructure definitions.  
Production Environment (Prod):  
The production environment is the live system serving real users. It requires the highest level of stability, security, and compliance. Automated deployment pipelines for production must include additional safeguards such as manual approvals, policy enforcement, and monitoring integrations. The Bicep modules and GitHub Actions workflows must support these production-specific requirements while maintaining the same core definitions as dev and QA, ensuring true environment parity.  
By explicitly supporting these target environments, the project embodies a key DevOps principle: \"if it works in dev, it should work in prod.\" Consistent, automated deployment across environments reduces bugs caused by configuration drift, accelerates delivery cycles, and increases confidence in releases. It also supports practices such as blue-green deployments or canary releases, which rely on the ability to reproduce infrastructure consistently across multiple parallel environments [21].
# A Three-Dimensional Electrochemical Process for the Removal of Carbamazepine
## 3 Results and Discussion
### 3.3D Experiments  
After studying the 2D electrochemical and adsorption processes, their combination was also explored through the study of the 3D electrochemical process, for which the influences of some parameters were evaluated: interelectrode distance, particulate electrode, reaction time, energy consumption, current, and energy efficiencies.
# An Integrated Software for Computing Mechanical Properties of Crystalline Material by Means of XRD
## 3 Results and Discussion
### Evaluation of Hardness  
Many studies have found that the full width of the maximum X-ray diffraction line (half-width) has a relation to the dislocation or disordering of crystalline matrixes. This is a result of many changes in the crystalline matrix in various industrial processes such as alloying by other metallic elements, hardening by quenching, plastic deformation, fatigue stress, etc. In this study, the hardening levels of quenched steel specimens were nondestructively investigated using XRD. A previous study has determined a linear relation between the Rockwell hardness HRC and the half-width of the diffraction line of quenched and tempered carbon steel [8]:  
\[HRC=87.85B+20.34 \tag{27}\]  
where \(B=2\sqrt{2\ln 2\sigma}=0.35\sigma\) is the half-width of the diffraction line and \(\sigma\) is the standard deviation of the Gaussian curve. In the case of the parabola method,  
\[HRC=103.01 FHW+21.31 \tag{28}\]  
where \(FHW\) is the full width at half of the maximum X-ray intensity of the diffraction line.  
Ten JIS-type S50C samples with dimensions of 50 \(\times\) 50 \(\times\) 200 mm were quenched at 850 \({}^{\circ}\)C in water, and then nine of them were tempered from 250 \({}^{\circ}\)C to 650 \({}^{\circ}\)C, in steps of 50 \({}^{\circ}\)C, for 45 min to eliminate the inhomogeneous distribution. The sample surfaces were polished with every paper and then electrolytically polished to remove about 1 mm of the surface layer. The hardness of samples was measured using the Rockwell hardness testing method. Table 5 shows the experimental conditions for XRD measurement.  
Figure 10 compares the HRC hardness with the line half-width. A straight line is fitted to the experimental points to establish the relation between FHW and HRC using Equations (27) and (28). From this practical linear relation, the hardness of quenched carbon steel can be easily estimated from the measured half-width. The same procedure can be applied to determine the hardness of quenched copper, stainless steel, plastically deformed steel, nickel alloys, etc.  
\begin{table}
\begin{tabular}{c c c} \hline
**Phase** & **XRD** & **Chemical Analysis** \\ \hline WC & 77.86 \(\pm\) 1.30 & 78.63 \(\pm\) 0.59 \\ TiC & 14.96 \(\pm\) 1.65 & 14.17 \(\pm\) 0.52 \\ Co & 5.86 \(\pm\) 1.47 & 6.79 \(\pm\) 0.02 \\ \hline \end{tabular}
\end{table}
Table 4: Phase compositions of carbide T15K6 (%).  
\begin{table}
\begin{tabular}{c c} \hline
**Diffraction Type** & **\(\Omega\)-Type, Fixed-\(\eta\)** \\ \hline Diffraction plane & (211) \\ Scanning range & 80–85\({}^{\circ}\) \\ Step size & 0.04\({}^{\circ}\) \\ Preset time & 5 s \\ Voltage and current & 20 kV and 10 mA \\ \hline \end{tabular}
\end{table}
Table 5: XRD experimental conditions for JIS S50C steel.
## 4 Discussion  
In the realm of medical innovation, the application of photobiomodulation has emerged as a promising approach for treating nerve injuries in the oral district [17; 18; 19]. The quest to optimize this therapy hinges upon selecting the appropriate laser type and wavelengths. Researchers delve into the intricate interplay between laser characteristics and their compatibility with the unique anatomical and physiological aspects of the oral nerve network. An exploration of the optimal laser type, such as low-level laser therapy (PBMT), and the most effective wavelengths unfolds as a pivotal pursuit [18; 19]. As the medical community strives to unravel the potential of photobiomodulation, questions abound regarding the outcomes that it yields in terms of the recovery of nervous function. Preliminary findings hint at the tantalizing prospect of accelerated nerve regeneration, potentially offering a new ray of hope for patients grappling with nerve injuries in the oral district [20; 21; 22]. By examining the documented results, insights emerge into the extent of neural recovery, improvements in sensory and motor functions, and the overall quality of life enhancements experienced by individuals subjected to this innovative treatment paradigm. As research continues to illuminate the intricate mechanisms underlying photobiomodulation, a clearer picture emerges of its transformative potential in revitalizing nerve function and ushering in a new era of oral nerve injury rehabilitation [20; 21; 22]. The potential of laser light to impact the peripheral nervous system has been explored in both the neuromuscular and somatosensory systems. This investigation dates to 1978 when it was discovered that laser radiation directed at exposed nerve tissue had a direct and beneficial effect as a preventive measure and a therapeutic intervention [23]. Currently, the preferred treatment for peripheral nerve injuries is advanced microsurgical repair or autologous nerve grafting. However, functional recovery is often unsatisfactory, and there is a need for new therapeutic approaches. Schwann cells in the peripheral nervous system play a crucial role in nerve repair. Various strategies, like pharmacological and cell-based therapies, have been used to enhance recovery, but none provide a universal cure and may have drawbacks and side effects [24]  
Various treatment methods, including exercise, electrical stimulation (ES), magnetic stimulation, low-intensity ultrasound (LIU), and phototherapy, have been explored to enhance peripheral nerve regeneration. Exercise, especially aerobic activities like swimming and walking, promotes axonal growth and synaptic improvement, and when combined with ES, it shows better outcomes. Magnetic stimulation increases axon numbers and diameters, likely through stimulating NGF activity. A low-intensity ultrasound induces positive responses by promoting blood circulation and releasing neurotrophic factors. Phototherapy