esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of a Miniaturizable System for Optical Sensing Application to Human Cells
###### Abstract  
Conventional approaches to human intracellular optical sensing, generally, require dedicated laboratories with bulky detection systems. They are performed by cell labeling procedures based on the use of fluorophores that are, mostly, phototoxic, invasive, bleached in case of prolonged light exposures, which require carriers and/or structural modifications for the cellular uptake. These issues, together with the sensitivity of the eukaryotic cell model, could be problematic towards the development of a robust sensing system suitable for biomedical screening. In this work, we studied a sensing system resulting from the combination of the commercial tris(2,2'bipyridyl)ruthenium(II) fluorophore, for cell labeling, with a potentially miniaturizable optical system composed by a laser source and a photomultiplier tube, for the fluorescence analysis.  
We used the human dermal fibroblasts (HDF) as a cell model for the characterization of the proposed approach together with MTT assays, to validate the cells viability after treatments, and fluorescence and confocal laser scanning microscopy, to confirm the labeling. We investigated the versatility of this carrier-free labeling method with other cell models by using human colon adenocarcinoma (LoVo) cells, performing the cellular uptake and testing the cytotoxicity of Ru(bpy)3\({}^{2+}\), as a possible application in biomedical diagnostics.  
portable cell sensing system; human cell labeling; fluorophore; fluorescence detection; photomultiplier; confocal laser scanning microscopy +
Footnote †: journal: Applied Physics Letters  
_Article_
# Combining Dielectric and Hyperspectral Data for Apple Core Browning Detection
## 3 Method
### Canonical Correlation Analysis  
After concatenating photonic data, high-dimensional data often contain a significant amount of redundant information. This redundant information may not contribute to the performance of the model and can even lead to overfitting. By applying dimensionality reduction techniques, we can eliminate this redundancy and select the most relevant features, thereby improving the model's performance. Canonical correlation analysis (CCA) is a multivariate statistical method that can be considered as a generalization of principal component analysis (PCA) for multiple views. CCA is commonly used for dimensionality reduction in raw data. Specifically, CCA maps two sets of variables to a lower-dimensional space, maximizing the correlation between the two sets of variables in the new lower-dimensional space.  
Assuming we have two sets of variables, X and Y, each containing n samples and p1 and p2 features, respectively, represented as X = [x1, x2,..., xp1] and Y = [y1, y2,..., yp2], the CCA algorithm can be described in the following steps:  
1. Compute the covariance matrix Cov(X, Y) between the two sets of variables.
2. Perform singular value decomposition (SVD) on the covariance matrix to obtain eigenvalues and eigenvectors, i.e., Cov(X, Y) = U \(\times\) S \(\times\) VT, where U and V are orthogonal matrices, and S is a diagonal matrix.  
Figure 5: The modeling process for combining dielectric and hyperspectral data.  
Calculate the projection matrices Wx and Wy for the two sets of variables to maximize the correlation after projection. Specifically, X and Y can be projected onto the subspace spanned by the first k eigenvectors of U and V, resulting in the projected data X' and Y', i.e., X' = X \(\times\) Wx and Y' = Y \(\times\) Wy, where Wx and Wy are orthogonal matrices of dimensions \(p_{1}\times p_{1}\) and \(p_{2}\times p_{2}\), respectively.
4. Finally, compute the correlation coefficient r between the two sets of variables, given by r = maxi (sqrt(\(\lambda\_i\))), where \(\lambda_{i}\) represents the \(i\)-th eigenvalue.
## 5 Cybersecurity in MGs
### Vulnerability Assessment and Risk Analysis  
Vulnerability assessment and risk analysis are two related but distinct processes that are often used in the field of cybersecurity to identify potential threats and vulnerabilities in computer systems, networks, and other digital assets.  
Vulnerability assessment involves the systematic examination of a system or network to identify vulnerabilities that could be exploited by attackers. This can involve both automated and manual techniques, such as scanning for open ports, analyzing software configurations, and testing for known vulnerabilities in specific applications. Risk analysis, on the other hand, involves a more comprehensive examination of the potential impact of a security breach, including the likelihood of an attack occurring and the potential consequences for the organization. This can involve evaluating the value of assets that could be compromised, the cost of remediation, and the potential impact on reputation, financial stability, and legal liability [253; 254].  
Performing a vulnerability assessment in a microgrid involves several important steps to ensure that the microgrid system is secure and resilient. This process includes identifying the assets and infrastructure that need to be assessed, identifying potential threats, analyzing vulnerabilities, evaluating the likelihood and impact of an attack, developing and implementing mitigation strategies, and continuously monitoring and updating the microgrid system to ensure ongoing security and resilience. By following these steps, a vulnerability assessment can identify potential threats and vulnerabilities, assess the impact of an attack, and develop and implement effective mitigation strategies to protect the microgrid system. Figure 13 illustrates some of the main and crucial steps to perform a vulnerability assessment in a microgrid.  
Figure 13: Crucial steps to perform a vulnerability assessment in a microgrid.  
In addition to vulnerability assessment, to ensure the safe and reliable operation of MGs, it is essential to perform a comprehensive risk analysis that identifies potential hazards, assesses their likelihood and impact, prioritizes risks, develops appropriate risk mitigation strategies, and monitors the effectiveness of the risk management process over time [103; 255]. As illustrated in Figure 14, the main and crucial steps to perform a risk analysis in a microgrid includes hazard identification, likelihood assessment, impact assessment, risk prioritization, risk tolerance determination, evaluation of existing controls, development of risk mitigation strategies, implementation of risk mitigation measures, and monitoring and review. By following these steps, organizations can effectively manage risk in their MGs and ensure the continuity of critical services even in the face of unexpected events.
## 2 Second-Order Statistical Modeling of Current-Steering Electronic DACs (eDAC)
### Matrix-Based Modeling of Current-Steering eDACs: From Current Sources to Output Levels
\begin{split}\
abla J_{c}&\equiv J_{c+1}-J_{c},\ \mathrm{for}\ c=1,2,3,\ldots,C-1\\ \
abla J_{1}&\equiv J_{2}-J_{1},\ \
abla J_{2} \equiv J_{3}-J_{2},\ldots,\
abla J_{C-1}\equiv J_{C}-J_{C-1}\end{split}  \tag{21}\]  
_The sequence of constellation steps is arrayed into a \"steps vector\" \(\
abla\mathbf{J}\equiv\{\
abla J_{c}\}_{c=1}^{C-1}\)._  
_The steps-operator, \(\
abla\), alternatively called first-difference operator, is the linear mapping_  
\[\
abla:\ \mathbb{R}^{C}\rightarrow\mathbb{R}^{C-1},\ \ \ \mathbf{J}\mapsto \
abla\mathbf{J},\ \ \ l_{c}\mapsto\
abla J_{c}\equiv J_{c+1}-J_{c} \tag{22}\]  
**Lemma 1**. : _The \(c\)-th level, \(J_{c}\) of a constellation \(\mathbf{J}\equiv\{J_{c}\}_{c=1}^{C}\) is obtained by accumulating the first \(c\) - 1 steps of the given sequence, having them added to the initial element, \(J_{1}\):_  
\[J_{c}\ =J_{1}+\sum_{i=1}^{c-1}\
abla J_{i} \tag{23}\]  
Formula (23) for the \(c\)-th level of the output constellation, in terms of the steps, is formally obtained as the solution of the recursion,  
\[J_{c+1}\ =J_{c}+\
abla J_{c} \tag{24}\]  
The full scale of the constellation is then given by the accumulation of all \(C-1\) steps:  
\[\mathrm{FS}\equiv J_{C}-J_{1}=\sum_{i=1}^{C-1}\
abla J_{i} \tag{25}\]  
The (arithmetic) \"average step\" is obtained by scaling the FS (25) by the \(\frac{1}{C-1}\) factor:  
\[\overline{\
abla\mathbf{J}}\equiv\frac{1}{C-1}\sum_{i=1}^{C-1}\
abla J_{i}= \frac{1}{C-1}\mathrm{FS} \tag{26}\]  
Taking the (stochastic) mean of the average step yields the \"mean-average-step\":  
\[\langle\overline{\
abla\mathbf{J}}\rangle\equiv\frac{1}{C-1}\sum_{i=1}^{C-1} \langle\
abla J_{i}\rangle=\frac{1}{C-1}\langle\mathrm{FS}\rangle \tag{27}\]The mean-average-step \(\left\langle\overline{\
abla\mathbf{J}}\right\rangle\) coincides with the fixed mean step of a deterministic \"nominal\" uniform reference constellation \(\mathbf{J}^{\text{nom}}\equiv\{I_{c}^{\text{nom}}\}_{c=1}^{C}\) specified to have its LSL and MSL, respectively, coinciding with the mean LSL and mean MSL of the given constellation:  
\[ \begin{split}& J_{c}^{\text{nom}}=J_{1}+(c-1)\ \overline{\
abla\mathbf{J}}=J_{1}+(c-1)\ \tfrac{\text{PS}}{\text{C}-1}=J_{1}+\tfrac{\text{C}-1}{\text{C}-1}(J_{C}-J_{1} )=\tfrac{\text{C}-\text{C}}{\text{C}-1}\cdot J_{1}+\tfrac{\text{C}-1}{\text{C}- 1}\cdot J_{C}\\ & J_{1|C}^{\text{nom}}=J_{1|C}\end{split}  \tag{28}\]  
Note: The \"nominal\" (reference) constellation \(\mathbf{J}^{\text{nom}}\) is random whenever the original constellation \(\mathbf{J}\) is, since \(\mathbf{J}^{\text{nom}}\) is defined to satisfy the \"boundary conditions\" \(J_{1|C}^{\text{nom}}=J_{1|C}\), i.e., \(\mathbf{J}^{\text{nom}},\mathbf{J}\) have the same LSL and MSL. However, \(\mathbf{J}^{\text{nom}}\) is always a uniform constellation, consistent with the convex linear combination (28), specifying its inner levels.
## 3 Results and Discussion
### Femtosecond Time-Resolved Spectra  
From the experiments results presented above using CW light, it is found that the electron distribution changes near the Fermi surface due to plasmon, current and temperature modulation are mutually different (temperature difference spectra are equivalent to temperature modulation spectra). To understand the remarkable difference observed among these spectra, it is desirable to obtain temporally-resolved information of the spectral change before thermal relaxation and thus to isolate any direct contribution due to plasmon from that of the temperature rise.  
The result of the femtosecond pump-probe experiment is shown in Figure 10, where the reflection change spectra near the interband transition edge at different delay times are overlaid on each other. The change in the reflection spectrum rises for times over about 500 fs.  
Figure 11 shows the calculated reflectance change near the interband transition region after single particle excitation performed by [PERSON] et al. [14]. Our measured relative difference reflectance (\(\Delta\)R/R) spectra following plasmon excitation show a very similar variation with the maximum change around 500 fs as that predicted for single-particle excitation. From this comparison, the dynamic signal observed is attributed to the process in which the plasmon relaxes to individual (independent or uncoupled) electronic excitations, which evolve into the thermal equilibrium state of the electron system.  
Figure 11: Calculated \(\Delta\)R/R spectra after excitation of respective particles. (Reproduced with permission from Figure 12b in [14]).  
Figure 10: Experimental \(\Delta\)R/R spectra after excitation of plasmons in the Au film.
# Investigating the Effect of Motion Capture Suits on the Test-Retest Reliability of Gait Parameters
## 4 Discussion
### Summary of Results  
Ankle angle metric reliability was poor in the suited and moderate at best in the non-suited conditions when using the PiG model, but good to excellent in the suited trials and excellent in the non-suited trials when using the CGM2 model. Using the PiG model, reliability of knee angle metrics was moderate, and hip angle metrics good to excellent, in both suited and non-suited conditions. The CGM2 model, showed good to excellent reliability across both suited and non-suited conditions at the knee and in non-suited conditions at the hip, with the hip showing excellent reliability across all timepoints during the suited condition. The MDC of the ankle angle was significantly smaller in the non-suited condition than the suited using the PiG model. The CGM2 model showed no statistical difference between the suited and non-suited conditions with MDCs ranging from 0.46-0.76\({}^{\circ}\) in the non-suited and 0.91-1.49\({}^{\circ}\) in the suited conditions (PDifference = 0.083). There was little difference in PiG MDC of knee or hip angle metrics between non-suited and suited conditions. CGM2 showed similar trends, with little difference between the MDCs of both knee and hip angle metrics in suited and non-suited trials. The CGM2 model performed better than the PiG model with MDCs at the ankle being significantly lower in both suited (\(p\) = 0.03) and non-suited (\(p\) < 0.01) conditions and significantly lower in suited conditions at both the knee (\(p\) = 0.03) and hip (\(p\) = 0.02). An alternative foot segment definition for PiG improved reliability of the ankle angle in both conditions compared to the PiG, with good reliability in the non-suited conditions and moderate in the suited.
## 5 Detection in UWB Positioning Algorithms
### MI-Algorithms for UWB IPS
* **Naive Bayes(NB):** The Naive Bayesian approach is based on the Bayesian principle for conditional probabilities [100; 101]. The algorithm calculates the probability of each attribute value, then gives the values of each instance's attributes. All instance probabilities are from the training set, and then the maximum probability is used to predict the class of the new instance. Given a new dataset of the form \(<a_{1},a_{2},\ldots,a_{n}>\), the predicted class for this instance dataset \(l_{predicted}\) is \[l_{predicted}=\operatorname*{arg\,max}_{l\in L}P(l)\prod_{i=1}^{n}P(a_{i}\mid l).\] (15) where \(L\) is a vector of all attribute values, \(P(l)\) is the prior probability of \(l\), \(P(a|l)\) is the probability of \(l\) given condition \(a\), and \(P(l)\) is the prior probability of \(a\). * **Neural Network (NN):** In recent times, the neural network (NN), one type of deep learning (DL), has become relatively competitive for classification, clustering, pattern recognition and regression in various different areas [102]. It is an information management model that works in a similar way to the biological nervous systems function of the human brain [103]. The advantage of NN application is that it provides more accurate results due to complex natural systems with large numbers of inputs; thus, the network can generate the best possible result without the requirement of redesigning the output criteria [104]. In order to accomplish high-precision positioning, different NN models were proposed and evaluated for the implementation, such as the  
Figure 14: SVM algorithm. multi-layer perceptron (MLP) [105], radial basis function (RBF) [106] and generalized regression neural network (GRNN).
###### Abstract  
For analysis of engineering structural materials to withstand harsh environmental conditions, accurate knowledge of properties such as flow stress and failure over conditions of high strain rate and temperature plays an essential role. Such properties of additively manufactured Ti6 Al4V(ELI) are not adequately studied. This paper documents an investigation of the high strain rate and temperature properties of different forms of heat-treated Ti6 Al4V(ELI) samples produced by the direct metal laser sintering (DMLS). The microstructure and texture of the heat-treated samples were analysed using a scanning electron microscope (SEM) equipped with an electron backscatter diffraction detector for electron backscatter diffraction (EBSD) analysis. The split Hopkinson pressure bar (SHPB) equipment was used to carry out tests at strain rates of 750, 1500 and 2450 s\({}^{-1}\), and temperatures of 25, 200 and 500 \({}^{\circ}\)C. The heat-treated samples of DMLS Ti6 Al4V(ELI) alloys tested here were found to be sensitive to strain rate and temperature. At most strain rates and temperatures, the samples with finer microstructure exhibited higher dynamic strength and lower strain, while the dynamic strength and strain were lower and higher, respectively, for samples with coarse microstructure. The cut surfaces of the samples tested were characterised by a network of well-formed adiabatic shear bands (ASBs) with cracks propagating along them. The thickness of these ASBs varied with the strain rate, temperature, and various alloy forms.  
direct laser metal sintering; Ti6 Al4V(ELI); SHPB; high strain rate; flow stress; fracture strain; adiabatic shear band +
Footnote †: journal: Applied Sciences
## 1 Introduction
### Dating Vitrified Structures  
The initial attempts at dating the forts directly involved the utilization of the radiocarbon-bon method [17; 18; 19]. However, uncertainties and doubts afflicted these early efforts, such as calibrating the radiocarbon timeline and determining the amount of radiocarbon present in such ancient times. An attempt to date with archaeomagnetism has been trued on the vitrified wall at the Misericordia, Serpa, Portugal, Late Bronze Age site [10].  
Thermoluminescence, developed for personal and environmental dosimetry [20], and proposed for determining the age of ancient ceramics [21; 22], was subsequently applied to date various other types of heated archaeological materials, such as burnt flint and stones (see, for example, [23]). It was applied by [PERSON] at the end of the eighties to the vitrified forts [6; 24]. From his more recent paper [5], it appears that this dating technique is promising even if the application seems to be related to a particular firing temperature. In more recent years, studies focused on the luminescence properties and the TL dating feasibility of archaeological glass [25] have highlighted that the mosaic tesserae are particularly suitable for dosimetric analysis by means of TL. This paper delves into an intriguing archaeological-geological case that demands an absolute chronology for partial resolution. To address this, the implementation of an experimental protocol based on thermoluminescence dosimetry, the \"Pre-bleached with Blue LED\" protocol, is suggested. This method, originally developed for studying accidental dosimeters in mobile phone displays [26], has shown success when applied to Islamic mosaic tesserae [27].  
[PERSON] and [PERSON]'s (2013) protocol has a significant advantage in its ability to overcome the limitations of classical thermoluminescence methodology only. This is achieved by isolating a thermally more stable TL signal and establishing a fading curve. Although this methodology adequately addresses incidental dosimetry, its direct applicability to archaeological dating is limited. The distinction lies in the nature of the dosimetric events: incidental dosimetry involves instantaneous and intense ionizing events, with doses significantly higher than those from natural and clearly defined sources over time. Dating, on the other hand, involves an extended period of time. By simultaneously examining fading, information on signal loss and trap characteristics can be obtained, aiding reinterpretation.
## 2 Materials and Methods
### Relevant Features of Avionics and Test Benches
#### 2.1.3 Product-Side I/O Example for the ARINC 429  
With the use of digital systems in the 1960s and 1970s, it became necessary to exchange data between them. From the beginning, the numerical data in the network protocols--analogous to the processing systems--were binary coded, in order to transport them from system to system. An example of this is the ARINC 429, which is an inexpensive communication protocol to implement. It has resulted in being, by far the most widely used communication protocol for airborne equipment in civilian aviation. It was developed in the late 1970s and was first used in the early 1980s [25] (pp. 88-89).  
With the ARINC 429, an 8-bit label number is assigned per message. This label number is the identifier for a 32-bit message. The label number is used to determine how the message is to be decoded. Each message has a parity bit at the end, which is used to verify the correctness of the message. The bits in between--9 to 31--can be used for the user data [6] (p. 34-4) (see Figure 5), even if bits 9 and 10 are reserved for source/destination identifiers (SDE) and 30 and 31 are reserved for state identification (signed/status matrix--SSM). The ARINC 429 operates in 12,5 or 100 Kb/S.  
Due to the high prevalence of the ARINC 429 in avionics, the interface is supported by effectively every test bench in aviation. However, this protocol is of particular importance for the present work, due to its wide distribution within only one domain, as it is susceptible to obsolescence.
## 3 Results
### Evaluation of Mechanical Deformability and Practical Applicability of the ENMH  
The heating performance of the ENMH under mechanical stretching was evaluated by applying tensile strain from 0% to 100%, while monitoring surface temperature (Figure 5a). Under a constant voltage of 1 V, the ENMH withstood 100% elongation without physical damage. In the unstretched state, the surface temperature rapidly increased and stabilized at approximately 125 \({}^{\circ}\)C. Upon stretching, the temperature temporarily decreased to 93.6 \({}^{\circ}\)C but recovered to 94.07% of the initial value, maintaining a stable level of around 118 \({}^{\circ}\)C during deformation. After strain release, the temperature remained near the recovered value rather than returning to the initial maximum, indicating sustained thermal output. These results demonstrate that the ENMH retains reliable heating performance under high tensile strain. In contrast, the ENMH(-BSA step) fractured at 100% strain.  
The heating performance under repeated bending was assessed by cyclically bending the sample to a 120\({}^{\circ}\) angle every 5 s (Figure 5b). Under a constant voltage of 0.6 V, the ENMH maintained stable thermal output throughout the test. The average surface temperature decreased slightly by 6.5%, and no physical damage was observed after eight bending cycles. In contrast, the ENMH(-BSA step) exhibited a gradual temperature decline with each cycle, resulting in a 38.3% reduction in average temperature. Physical failure occurred after the seventh bending cycle, followed by a sharp drop in surface temperature.  
Dynamic flexibility on highly mobile areas was evaluated by attaching the ENMH to the knuckle region of a human index finger and measuring thermal response during repeated flexion and extension (Figure 5c). A constant voltage of 0.4 V was applied. When the finger was extended, the surface temperature remained stable at approximately 41.2 \({}^{\circ}\)C. During flexion, it decreased to approximately 36.2 \({}^{\circ}\)C due to increased electrical resistance from mechanical deformation--a drop of about 5 \({}^{\circ}\)C or 12.1%. Despite continuous motion, heating behavior remained consistent. The inset graph (185-215 s) shows repeatable temperature fluctuations corresponding to finger movement cycles.  
To assess suitability for wearable clothing integration, the ENMH was mounted on a piece of nonwoven fabric and placed on the inner wrist, a representative curved region of the body (Figure 5d). Under a constant voltage of 0.4 V, the surface temperature rapidly rose and stabilized within the range of 47-49 \({}^{\circ}\)C for the 5 min test duration. After the voltage was turned off, the temperature briefly remained elevated before rapidly decreasing. Infrared thermal imaging showed an initial surface temperature of approximately 26.4 \({}^{\circ}\)C, increasing to a maximum of 49.8 \({}^{\circ}\)C with localized heating. Throughout the 5 min operation, the ENMH maintained stable thermal output without visible physical damage, confirming its practical applicability in wearable systems.