esa-sceva/satcom-chunk-collection huggingface dataset:
# Cell Response to Oxidative Stress in Antarctic Filamentous Fungi
## 7 Cold Stress Response of Antarctic Fungi and Adaptation
### Morphological Modification Caused by Cold Stress
It has been observed that certain colonies exhibit multicellular meristematic clusters, in addition to formations characterized by concentric rings that display variations in density and thickness. Furthermore, some colonies present rings composed of sterile mycelium. In certain instances, the colony surface is marked by irregularities, featuring convoluted wrinkles and pronounced radial furrows. The mycelium of _Penicillium_ sp. 1-6-4 isolated at a temperature of 4 \({}^{\circ}\)C exhibited a significant presence of intercalary, swollen, and thick-walled cells that develop into chlamdospores, known as long-term survival structures. The significance of chlamdospores for survival at low temperatures has been evidenced in the case of the plant pathogen _Zymoseptoria tritici_[208]. In the microbial communities of soils found in the Antarctic, Arctic, and Alpine regions, fungi exhibiting dark septate hyphae are the most prevalent [6]. Considerable ultrastructural changes have been observed in the cells of _P. grisefulvum_ following a short-term temperature downshift to 6 \({}^{\circ}\)C [188]. Analysis via transmission electron microscopy (TEM) indicated an increase in vacuolar volume, focal damage to the cell wall, and structural changes in the mitochondria. Moreover, ultrastructural findings revealed evidence of plasma membrane turnover, characterized by frequent deep infoldings of the plasmalemma that penetrate the cytoplasm, extending toward the center of the hyphae. The study of two Antarctic fungal strains under extreme cold conditions revealed that the mesophilic _A. glaucus_ 363 exhibited more substantial ultrastructural changes in both its cells and conidia compared to the psychrotolerant _P. commune_ 161 [187]. Scanning electron microscopy (SEM) results suggest that the psychrotolerant strain has a significantly enhanced ability to adapt and thrive in such harsh environments. This assertion is further substantiated by the findings of [PERSON] et al. [209], who observed ultrastructural changes in the mitochondrial matrix and rupture of nuclear membranes in a temperature-sensitive mutant of _Schizosaccharomyces pombe_ following a temperature decrease from 20 \({}^{\circ}\)C to 4 \({}^{\circ}\)C. A related trend has been identified in various studies. Psychrophilic yeasts of the genera _Mrakia_ and _Mrakiella_, sourced from glacial regions in Antarctica and the Alps, reveal marked differences in their ultrastructure compared to mesophilic yeasts [186]. [PERSON] et al. [35] also observed changes in the morphological characteristics of Antarctic fungi subsequent to several freeze-thaw cycles.
# Characterization of the Aroma Profile of Commercial Prosecco Sparkling Wines
## Results and Discussion  
### General Composition of Prosecco Wines  
The data of the basic oenological parameters are reported in Table 2. Although the wines were from different producers and purchased in the same local supermarket, a certain homogeneity of the data was observed. Tartaric acid, acetic acid, lactic acid, residual sugars, free SO\({}_{2}\), NH\({}_{4}\) and amino nitrogen showed relative standard deviation variations greater than 20%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & **Min** & **Max** & **Mean** & **Standard Deviation** & **RSD 3 (\%)** \\ \hline Ethanol (\(v/v\)) & 11.06 & 12.13 & 11.38 & 0.27 & 2.41 \\ Free SO\({}_{2}\) (mg/L) & 8.0 & 41.8 & 20.4 & 8.1 & 40 \\ Total SO\({}_{2}\) (mg/L) & 80 & 158 & 124 & 22 & 18 \\ Tartaric acid (g/L) & 0.8 & 2.0 & 1.4 & 0.30 & 21 \\ Acetic acid (g/L) & 0.0 & 0.4 & 0.2 & 0.08 & 51 \\ Malic acid (g/L) & 1.3 & 2.8 & 2.2 & 0.37 & 17 \\ Lactic acid (g/L) & 0.0 & 0.6 & 0.2 & 0.21 & 122 \\ Total acidity (g/L) & 5.2 & 8.9 & 6.0 & 0.94 & 16 \\ Sugar 4 (g/L) & 8.4 & 19 & 13 & 2.9 & 22 \\ Acetaldehyde (mg/L) & 26 & 69 & 58 & 8.9 & 15 \\ Glycerol (g/L) & 3.8 & 5.2 & 4.6 & 0.43 & 9.3 \\ NH\({}_{4}\) (mg/L) & \textless{}14 & 301 & 48 & 71 & 147 \\ PAN 1 (mg/L) & 12 & 112 & 36 & 19 & 53 \\ YAN 2 (mg/L) & 12 & 352 & 85 & 81 & 96 \\ Total polyphenols (mg/L) & 142 & 242 & 185 & 25 & 14 \\ \hline \end{tabular}
\end{table}
Table 2: Base enological parameters.  
The wines were characterized by low concentrations, compared with their odor threshold, of acetaldehyde, lactic acid and acetic acid, the latter in all cases well below the legal limit, indicating that the fermentations in general were regular and without issue or microbiological spoilage. The low values or even absence of lactic acid indicated that the wines had not undergone malolactic fermentation. The wines showed different values of free SO\({}_{2}\), on average 20 mg/L. These values seemed quite low and suggested that the wines could undergo oxidation or the development of microorganisms.
# Lithium-Ion Battery State of Health Estimation Based on CNN-LSTM-Attention-FVIM Health Features
## 3 Algorithm Principle Explanation
### Convolutional Neural Networks  
A CNN (Convolutional Neural Network) is composed of a convolution layer, activation function, pooling layer, fully connected layer, and other auxiliary layers. The function of convolution layer is to extract local features and perform dot product operation on local area with input data through sliding window (filter). The purpose of activation activation function is to introduce nonlinearity and enhance the expressiveness of the model. The role of the pooling layer is to reduce spatial dimensions, enhance translation invariance, and prevent overfitting. The functions of the fully connected layer and other auxiliary layers are to map high-level features to the sample label space (such as classification output), standardize each layer input, accelerate training, and improve generalization ability. The core of the CNN is to deconstruct spatial hierarchical feature patterns from high-dimensional data through local perception and parameter sharing. Its essence is to perform local window sliding calculation on input data through the convolution kernel (filter) to generate a feature map. By connecting convolution kernels of different sizes in parallel (such as 1 \(\times\) 3, 3 \(\times\) 3, 5 \(\times\) 3), the CNN can capture fine-grained local features (such as instantaneous fluctuation of battery voltage) and coarse-grained global trends (such as overall capacity decay curve) at the same time. In lithium-ion battery SOH prediction, this capability enables the model to identify small distortions (early signs of degradation) and long-term trends (aging rates) in the charge and discharge curves [35]. In traditional CNNs, the weights of each channel feature are equal, while modern variants (such as SENet) introduce channel attention mechanisms to dynamically adjust the importance of each channel. For example, in battery data, the voltage channel may be more informative than the temperature channel at a specific cycle stage, and the model can adaptively enhance the contribution of key channels.
# A Secure Protocol against Selfish and Pollution Attacker Misbehavior in Clustered WSNs
Consequently, our solution ensures data availability and integrity with considering the confidentiality requirement already existing. Our proposition is a secure protocol against selfish and pollution attacker misbehavior in clustered _WSNs_, named _SSP_. It aims to shield against a coalition of misbehaving _CH_ nodes that initiate a _DoS_ attack by dropping received packets or a pollution attack by altering them. The main contributions of this paper are as follows:  
1. Preventing a _DoS_ attack by a group of _CH_ nodes and ensuring a reliable transfer of data between source and _BS_. 2. Ensuring data integrity and error correction despite the existence of a pollution attack performed by a group of _CH_ nodes. 3. Locating the misbehaving nodes among _CH_s and implementing a punishment mechanism against them to enhance the network availability and integrity. 4. Providing simulation results that prove the effectiveness of our protocol compared to a defenseless protocol, regarding the percentage of correct messages received and also considering the case when the number of misbehaving nodes is varying. 5. Proving the scalability of the protocol. The present paper is structured as follows. Section 2 gives feedback on the literature dealing with existing secure protocols for _DoS_ and pollution attacks prevention/detection. Section 3 presents the assumptions about the system and the threat model used in the paper. The design steps for the protocol and a theoretical discussion are given in Section 4. The simulation set-up and the results are presented in Section 5. Section 6 summarizes the paper and provides conclusions.
## 3 Results
### Multivariate Analyses  
The analysis of 35 components analyzed by PCA showed that more than 70% of the accumulated variance resided in seven components. When adjusting the analysis towards these seven components and plotting the distribution of sites, the first component accumulated 48.06% of the variance and the second 19.74%.  
The distribution of sites categorized by sector and seasons is shown in Figure 8. The factors that determine the distribution of these with the greatest weight in the first component were the organic compounds accumulated in sediments: nitrite, nitrate, ammonium and phosphate, followed by the electrical conductivity and REDOX of sediments, as well as the salinity-electrical conductivity of the water and to a lesser extent the abundance and richness of benthic organisms.  
In the second component, the variables with the highest incidence were the water pH, OD and parameters involved in the organic enrichment of the water, such as total nitrogen, ammonium, nitrate the ammonium, followed by the pH of sediments and suspended settleable solids.  
The distribution of sites and sectoral groups showed different situations between sampling periods. RVS sites concerning REM drift apart during the winter (Figure 8, lower graph). During the summer (Figure 8, upper graph) the PCA analysis showed a high similarity of conditions between Tambillo, Palma Real and Muisne.  
Figure 8: Principal components analysis. Sectors: C = Cojimies, M = Muisne; T = Tambillo, P = Palma Real. W = Winter (lower graph), S = Summer (upper graph).  
The analysis of canonical correspondences between sedimentary variables and benthic species did not show a distribution determined by sector when inputting the 56 collected species. This was attributed to the low relative abundance of 23 species (less than 1%) and nine species with single collections. By adjusting the analysis towards the 11 species shared between the four sectors in any seasonal period (Figure 9), the Polychaetes Nephthydae _Nephty_s p1, Maldanidae _Euclynene_ sp and the bivalve _Polymesoda inflata_ would decrease associated with the lower values of REDOX and the total nitrogen content in the sediments. Moreover, the polychaete Cossuridae _Cossura rostrata_ would increase with the accumulation of ammonium, nitrates, nitrites, phosphates and the increase in electrical conductivity and pH of sediments.
## 4 Result and Discussion
### Simulation Setup  
Our simulations environment is created in Python on a machine with an Intel Core i7 processor, 16 GB memory, NVIDIA GeForce GT 1030 graphics card, and 64 bit Linux (Ubuntu 18.04) operating system. The LSTM algorithm is programmed in the TensorFlow framework, and the Q-learning algorithm is programmed in Python. A Mininet emulator is utilized to design the communication network. Users may simulate actual network topologies with this tool. It also has built-in support for SDN architecture. For the experimentation, we utilized the publicly available 3 Degrees of Freedom (3 DoF) teleoperation system haptic dataset [33]. A human operator at the master side uses a Phantom Omni haptic device and interacts with the virtual environment, which serves as a controlled domain, in order to capture the haptic traces. The position, velocity control signals of the human operator at the master side, and force feedback from the salve side for the 3 DoF teleoperation dataset are depicted in Figure 6.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Settings Used** \\ \hline \multicolumn{3}{c}{**Q-Learning Algorithm**} \\ \hline Learning rate (\(\alpha\)) & 0.1, 0.5 \\ Discounted factor (\(\gamma\)) & 0.8 \\ Exploitation/Exploration coefficient (\(\varepsilon\)) & 0.5 \\ Episodes (\(T\)) & 40 \\ No. of Training Epochs & 1-5 \\ Set of action space (\(A\)) & 7 \\ Set of state space (\(S\)) & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 6: Dynamic interaction of the human operator with virtual application via haptic device. (**a**) positions of the human operator’s hand at master side device, (**b**) velocity traces of the operator (**c**) force data traces of the teleopertor x,y and z-axis.
# Laplacian Eigenmaps Dimensionality Reduction Based on Clustering-Adjusted Similarity
## 3 Experiment Setup  
### Datasets  
We employ experiments on ten publicly available UCI datasets to quantitatively evaluate the performance of LE-CAS. The statistics of these datasets are listed in Table 1. These datasets are with different numbers of features and of samples: Msplice represents dataset on molecular biology splice online learning, which is used for multiclass clustering. W1a data set records information from a TV series called W1A. Soccer-sub1 stores the information on players registered in FIFA. Madelon is an artificial dataset, which was part of the NIPS 2003 feature selection challenge. This is a two-class classification problem with continuous input variables. FG-NET is a dataset used for face recognition. ORL contains 400 images of 40 different people, was created by the Olivetti research laboratory in Cambridge, England, between April 1992 and April 1994. Musk describes a set of 102 molecules of which 39 are judged by human experts to be musks and the remaining 63 molecules are judged to be non-musks. CNAE-9 is a data set containing 1080 documents of free text business descriptions of Brazilian companies categorized into a subset of 9 categories cataloged in a table called National Classification of Economic Activities. SECOM is about data from a semi-conductor manufacturing process. DrivFace contains images sequences of subjects while driving in real scenarios. All data sets are available from the UCI Machine Learning Repository ([[http://archive.ics.uci.edu/ml/index.php](http://archive.ics.uci.edu/ml/index.php)]([http://archive.ics.uci.edu/ml/index.php](http://archive.ics.uci.edu/ml/index.php))).
# A Comprehensive Signal Quality Assessment for BDS/Galileo/GPS Satellites and Signals
## 2 Data and Methods  
In order to study the performances of signals and satellites of BDS-3, quality evaluations on C/N0, code noise, and multipath were conducted using data from the MGEX stations that can process the BDS-3 signals.
###### Abstract
Considering the significant social value of these technologies in reducing road fatalities and enhancing mobility experiences for vulnerable groups, we followed the PRISMA 2020 guidelines [14] for systematic reviews and utilized CiteSpace software (version 6.4.R1 Advanced, Drexel University, Philadelphia, PA, USA) as our primary visualization tool [15] to examine publication trends, international collaboration networks, research hotspots, and knowledge evolution trajectories from 2000 to 2025. This multidimensional analysis not only presents how VRU safety wearable technologies have evolved across different user groups, technological approaches, and application scenarios but also identifies key trends and opportunities for future research. To guide our systematic investigation, we formulated three specific research questions:  
RQ1: What are the overall publication trends in wearable technology research focused on vulnerable road user safety from 2000 to 2025? RQ2: How have research topics and technological focuses in the field of wearable technologies for enhancing vulnerable road user safety evolved in distinct phases over time? RQ3: What are the future research trends in the field of wearable technologies for enhancing vulnerable road user safety? The remainder of this paper is structured as follows: Section 2 introduces the systematic literature retrieval strategy, inclusion criteria, and application of the CiteSpace bibliometric analysis tool. Section 3 presents the publication trends of wearable technology research for VRU safety (addressing RQ1), international collaboration networks, and knowledge domain cluster analysis. Section 4 reviews the evolutionary characteristics and application trends of wearable safety technologies for different vulnerable road user groups (pedestrians, two-wheeler users, and visually impaired persons). Section 5 explores the phase-wise evolution of research themes and technological focus (addressing RQ2) as well as future directions and opportunities (addressing RQ3). Finally, the Section 6 summarizes the paradigm shift from passive sensing to active prediction systems in this field and the key research implications.
## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Al** & **As** & **Ca** & **Cd** & **Cr** & **Cu** & **K** \\ \hline \multicolumn{10}{c}{**mg 100 g\({}^{-1}\)**} \\ \hline \multicolumn{10}{c}{**x \(\pm\) SD**} \\ \hline Control sample & 2.26 \({}^{\mathrm{h}}\)\(\pm\) 0.06 & \(<\)0.01 & 32.7 \({}^{\mathrm{h}}\)\(\pm\) 0.64 & \(<\)0.01 & \(<\)0.01 & 0.17 \({}^{\mathrm{h}}\)\(\pm\) 0.01 & 303.57 \({}^{\mathrm{c}}\)\(\pm\) 3.75 \\ Conditioning & 0.14 \({}^{\mathrm{a}}\)\(\pm\) 0.06 & \(<\)0.01 & 30.84 \({}^{\mathrm{a}}\)\(\pm\) 0.70 & \(<\)0.01 & \(<\)0.01 & 0.18 \({}^{\mathrm{h}}\)\(\pm\) 0.01 & 264.34 \({}^{\mathrm{h}}\)\(\pm\) 4.83 \\ Conditioning with biochar & \(<\)0.01 & \(<\)0.01 & 29.90 \({}^{\mathrm{a}}\)\(\pm\) 0.70 & \(<\)0.01 & \(<\)0.01 & 0.07 \({}^{\mathrm{a}}\)\(\pm\) 0.01 & 208.20 \({}^{\mathrm{a}}\)\(\pm\) 3.56 \\ \hline \multicolumn{10}{c}{**Mg**} & **Mn** & **Na** & **Ni** & **P** & **Pb** & **S** \\ \cline{2-6}  & \multicolumn{10}{c}{**mg 100 g\({}^{-1}\)**} \\ \hline Control sample & 38.52 \({}^{\mathrm{c}}\)\(\pm\) 0.56 & 0.21 \({}^{\mathrm{h}}\)\(\pm\) 0.02 & 35.80 \({}^{\mathrm{c}}\)\(\pm\) 0.29 & \(<\)0.01 & 66.00 \({}^{\mathrm{h}}\)\(\pm\) 0.34 & \(<\)0.01 & 33.41 \({}^{\mathrm{c}}\)\(\pm\) 0.21 \\ Conditioning & 25.36 \({}^{\mathrm{b}}\)\(\pm\) 0.55 & 0.12 \({}^{\mathrm{a}}\)\(\pm\) 0.03 & 32.07 \({}^{\mathrm{b}}\)\(\pm\) 0.83 & \(<\)0.01 & 72.24 \({}^{\mathrm{c}}\)\(\pm\) 0.48 & \(<\)0.01 & 30.21 \({}^{\mathrm{b}}\)\(\pm\) 0.18 \\ Conditioning with biochar & 18.27 \({}^{\mathrm{a}}\)\(\pm\) 0.34 & 0.11 \({}^{\mathrm{a}}\)\(\pm\) 0.01 & 7.71 \({}^{\mathrm{a}}\)\(\pm\) 0.29 & \(<\)0.01 & 55.73 \({}^{\mathrm{a}}\)\(\pm\) 0.57 & \(<\)0.01 & 23.77 \({}^{\mathrm{a}}\)\(\pm\) 0.24 \\ \hline \end{tabular}
\end{table} 
Table 5: Contents of selected macro- and micro-elements in overground parts of Virginiaallow plants in the control sample, in the hydro-conditioned sample, and the sample hydroconditioned with biochar added. X-mean, SD–standard deviation. According to [PERSON]’s test, means marked with the same letter are not significantly different at \(\leq\) 0.05.
## 3 Types of Scanning Patterns
### Spiral Scanning
#### 3.1.2 Mean Acquisition Time for Spiral Scan  
If the uncertainty area spans from \(-u\) to \(+u\) in both the horizontal (\(x\)) and vertical (\(y\)) directions, where \(u\) is the maximum radial distance, then the total length \(L_{\mathrm{s}}\) of the spiral path, as defined by Equation (11), is given by  
\[L_{\mathrm{s}}=\frac{n}{2\pi}\int_{0}^{\frac{2\pi u}{n}}\sqrt{1+\theta^{2}}\,d\theta. \tag{13}\]  
Since a constant linear velocity is a prerequisite in laser space communication, the scanning speed is given by [40]  
\[v_{c}=\frac{n}{\Delta t}, \tag{14}\]  
where \(\Delta t\) is the dwell time at each scan spot. It is related to the bandwidth of the actuator and can be calculated using the system response time \(t_{R}\) and the time taken to travel the optical link distance \(l_{\mathrm{link}}\) between the transmitter and receiver. The dwell time is expressed as [46]  
\[\Delta t=t_{R}+2\frac{l_{\mathrm{link}}}{c}. \tag{15}\]  
Therefore, using Equations (13) and (14), the total scan time \(T_{\mathrm{s}}\) to cover the spiral path can be expressed as  
\[T_{\mathrm{s}}(u)=\frac{\Delta t}{2\pi}\int_{0}^{\frac{2\pi u}{\pi}}\sqrt{1+ \theta^{2}}\,d\theta. \tag{16}\]  
To find the mean acquisition time, Equation (16) needs to be weighted by the PDF of the receiver satellite position, which follows a Rayleigh distribution given by  
\[f(u)=\frac{u}{\sigma^{2}}\exp\left(-\frac{u^{2}}{2\sigma^{2}}\right), \tag{17}\]  
where \(u\) is the radial distance and \(\sigma\) is the standard deviation.  
The expected value of the acquisition time \(T_{\mathrm{s}}(u)\), denoted as \(E[T_{\mathrm{s}}]\), is given by [46]  
\[E[T_{\mathrm{s}}]=\int_{0}^{u}T_{\mathrm{s}}(u)\times f(u)\,du. \tag{18}\]  
Figure 1: Spiral scan path covering the uncertainty area. (**a**) Spiral scan path with overlap factor = 0. (**b**) Spiral scan path with overlap factor = 0.2.  
Substituting \(T_{\rm s}(u)=\frac{\Delta t}{2\pi}\int_{0}^{\frac{2\pi u}{n}}\sqrt{1+\theta^{2}}\,d\theta\) and the Rayleigh PDF into Equation (18), we obtain  
\[E[T_{\rm s}]=\int_{0}^{u}\!\left(\frac{\Delta t}{2\pi}\int_{0}^{\frac{2\pi u}{n} }\sqrt{1+\theta^{2}}\,d\theta\right)\times\frac{u}{\sigma^{2}}\exp\!\left(- \frac{u^{2}}{2\sigma^{2}}\right)\,du. \tag{19}\]  
The complete expression for the expected value of \(T(u)\) is, therefore, as follows:  
\[E[T_{\rm s}]=\frac{\Delta t}{2\pi\sigma^{2}}\int_{0}^{u}\left(u\int_{0}^{\frac {2\pi u}{n}}\sqrt{1+\theta^{2}}\,d\theta\right)\times\exp\!\left(-\frac{u^{2} }{2\sigma^{2}}\right)\,du. \tag{20}\]  
This equation determines the mean acquisition time required to scan the uncertainty area, incorporating the Rayleigh distribution for \(u\). Its value can be computed numerically.
## 2 Materials and Methods
### Western Blots  
To assess Spike expression by western blot (WB), 1 \(\times\) 10\({}^{6}\) cells were seeded and cultured in 6-well plates. THP-1 (human monocytes), iDC and Hs 729T cells were transduced with CoroVaxG.5, CoroVaxG.3 or Ad.C (MOI of 1000 for THP-1 and 500 for Hs 729T). The cells were washed twice with ice-cold PBS and lysed in a Laemmli sample buffer 2X. Protein extracts were separated by SDS-PAGE with a 10% gel and transferred to nitrocellulose membranes (Bio-Rad Laboratories Hercules, CA, USA). The membranes were probed with anti-spike Ab (40150-T62, Sino Biological Wayne, PA, USA) and anti-beta-actin Ab (A4700; Sigma, St. Louis, MO, USA). After incubation with HRP-AffiniPure Goat Anti-Rabbit IgG (Jackson ImmunoResearch), chemiluminescence was detected with ECL following the manufacturer's instructions (Amersham, Marlborough, MA, USA) and digitized by Image Quant LAS 4000 (GE-Cytiva Marlborough, MA, USA). Semi-quantifications of WB assays were performed by densitometry using the ImageJ software 1.53 and normalizing by \(\beta\)-actin expression.