esa-sceva/satcom-chunk-collection huggingface dataset:
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 3 Discussion
### Other Wine Variables  
In our study, the effect of the UV received by grapes on the color of the resulting wines was not significant, probably reflecting the weak response of anthocyanins [16]. Previous results obtained on this subject are diverse. Ambient UV increased both anthocyanins and color in Pinot noir wines [22], and anthocyanins but not color in Tempranillo wines [23], while high UV-B did not change either anthocyanins or color in Tempranillo [16] and Malbec [11] wines. The effect of UV on the wine antioxidant capacity was inconsistent, despite the increase in several dihydroxylated (quercetins) and trihydroxylated (myricetins) flavonoids, whose antioxidant capacity is higher than that of monohydroylated kaempferols or isorhamnetins. This lack of clear response is contrasted with the increase in antioxidant capacity of wines made from grapes exposed to ambient UV and high UV-B [16; 23]. Given the commercial importance of wine color and the health implications of wine antioxidant activity, these discrepancies should be better studied.
## 2 Material and Methods
### Methods  
The bibliographic databases Web of Science (WoS), Scopus, and PubMed were used in this study, selected for their wide research coverage and rich data that support in-depth bibliometric analysis. To focus on relevant literature, we applied specific filters, narrowing the scope to original articles and reviews in English. After filtering, we manually reviewed each article to ensure that it complied with the criteria of our study, which determined the final sample size. Descriptive statistics were then used to analyze the main features of the data set, including authors and research groups, topic clusters, and emerging trends. This allowed us to map the evolution of key terminology and major research achievements in the field of VR-BCI systems. We identified temporal trends to monitor changes in research coverage over time and grouped publications into topic clusters, revealing relationships between different research areas. This process highlights relevant topics and research fields (Figure 1).  
The study followed specific elements of the PRISMA 2020 guidelines for bibliographic reviews, focusing on aspects such as the rationale (item 3), objectives (item 4), eligibility criteria (item 5), information sources (item 6), search strategy (item 7), selection process (item 8), data collection process (item 9), synthesis methods (item 13a), synthesis results (item 20b), and discussion (item 23a). For bibliometric analysis, we used tools embedded in the Web of Science (WoS), Scopus, and PubMed databases, as well as the Biblioshiny tool from the Bibliometric Rv.4.1.3 package (GNU GPL). This methodology supports bibliometric and scientometric studies, often allowing for refined categorization by conceptual structures, research areas, authors, documents, and sources. The results are presented using graphs and tables that allow for flexible analysis and visualization options. Given the interdisciplinary scope and complexity of the topic, we have collected the most important results of the review later in this article.  
To refine our search to suit our research objectives, we used advanced filtered queries, limiting results to English-language articles. In WoS, searches were performed using the \"Subject\" field (consisting of title, abstract, keyword plus, and other keywords); in Scopus, using article title, abstract, and keywords; and in PubMed, using manual keyword sets. The databases were searched for articles using keywords such as \"virtual reality\", \"brain-computer interface\", and \"system\" or similar. The selected publications were then further refined (see Figure 2), by manually reviewing articles, removing irrelevant items and duplicates, which resulted in our final sample size. The number of selected articles decreased from 98 (published 2005-2024) to 3.
## 2 Results and Discussion  
### Analysis of Cucumber Leaf Volatiles by GC-MS and Variation in Total Volatile Content  
In the present study, we conducted a detailed volatile investigation of the metabolic response of cucumber leaves to N-sufficient and N-deficient treatments. The qualitative compounds were quantified and are listed in Table S1. A total of 24 volatile compounds were identified and quantified including 15 aldehydes, two ketones, two alkenes and five other volatile compounds. The amount of total volatiles accounted for 1914.71 to 4097.54 ng/g FW (fresh weight) in N-sufficient treatments and 411.73 to 5523.84 ng/g FW in N-deficient treatments. The lowest amount was detected at Day 7 during both treatments, and the highest content was detected at Days 6 and 5. At Days 1, 3, 6, and 7, the total amount of volatiles in N-deficient nutrient solution was significantly lower than in plants which were cultivated in N-sufficient nutrient solution. The total volatile content in the N-deficient treatment clearly increased at Day 5 (Figure 1).  
A wide variation in leaf volatile compounds was observed among the different nitrogen treatments (Table S1). During N-deficient treatments, all the volatile compounds displayed a significant difference compared to the N-sufficient treatments from Days 1 to 7. Among the 24 volatile compounds, most showed lower level of volatile compounds in N-deficient treatments from Day 1 to Day 7, with the exception of hexanal, (_E_)-2-hexenal, and (_E_,_E_)-2,4-hexadienal which belong to the C6 aldehydes and displayed higher levels at Days 5, 2 and 3, respectively (Figure S1). Interestingly, \(\beta\)-caryophyllene and \(\alpha\)-curcumene, which were detected in all N-sufficient leaves, were not found in any of the leaves from the N-deficient treatments.
# Collusion Attack Resilient 3D Mesh Watermarking Based on Anti-Collusion Fingerprint Code
## 4 Experimental Results
### Robustness Test for Combination of Noise Addition and Collusion Attack  
Figure 7 shows robustness to the combination of noise addition and collusion attack. We added Gaussian noise \(\mathcal{N}(0,\sigma)\) to the averaged model, where \(\sigma=0.1,0.01,0.001\), or \(0.0001\), and traced the attackers using the proposed detector. The performance of the proposed method for the noise strength \(\sigma<0.001\) was as good as the result for \(\sigma=0\), and the results in cases with \(\sigma=0.001\) was also acceptable. On the other hand, in the experiments with strong noise (\(\sigma\geq 0.01\)), the accuracy of our method decreased. Therefore, the result shows that the proposed method was robust to the combination of a small amount of added noise and collusion attack.  
Figure 6: Comparison test results with collusion attack.
## 4 Practical Experiments
### Laboratory Applications with Three-Phase Voltage Source
\[S_{l}^{*}=\sqrt{\left(S_{pcc}^{r}\right)^{2}+S_{nl}^{2}-S_{n,pcc}^{2}}\text{, and }S_{s}^{*}=\sqrt{\left(S_{pcc}^{r}\right)^{2}+S_{ns}^{2}-S_{n,pcc}^{2}}.\]  
Indeed, as an example that illustrates how the values of apparent powers of the source and load could approximately be obtained from the powers measured using the grounding point (\(G\)) as the voltage reference point (fifth column in Table 5), the apparent power of the source (\(S_{s}^{*}\)) in the test circuit of Figure 4 would have the following value, using the second above equation in which \(S_{pcc}^{r}=384.12\) VA is the apparent power measured by the SIMPELEC analyzer at the PCC (third column, last file in Table 5) and the neutral-displacement powers of the source (\(S_{ns}=65.406\) VA) and PCC (\(S_{n,pcc}=0.343\) VA) are obtained from the fourth column in Table 5:  
Figure 10: [PERSON]’s power screenshots of the Fluke 437 under unbalanced voltage supply using as voltage reference the neutral point of the (**a**) source, and (**b**) load. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference Point** & **Analyzer** & \begin{tabular}{c} **Apparent** \\ **(VA)** \\ \end{tabular} & \
\begin{tabular}{c} **Neutral (\(S_{n}\))** \\ **(VA)** \\ \end{tabular} &
\begin{tabular}{c} **Apparent \({}^{*}\)** \\ **(VA)** \\ \end{tabular} \\ \hline
**Source** & Fluke 437 & 389.5 & 65.406 & 389.2 \\
**(ref. pt. N)** & SIMPELEC & 389.65 & 65.406 & 389.648 \\ \hline
**Load** & Fluke 437 & 386.4 & 47.562 & 386.49 \\
**(ref. pt. n)** & SIMPELEC & 386.57 & 47.562 & 387.05 \\ \hline
**PCC** & Fluke 437 & 383.6 & 0.343 & - \\
**(ref. pt. G)** & SIMPELEC & 384.12 & 0.343 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Apparent and neutral-displacement powers measured by the Fluke 437 and SIMPELEC power analyzers for the PCC of the tested circuit with respect to the voltage reference point (N, n, or G)). (*) Calculated from the powers measured at source and load using the grounding point (G) as voltage reference point (ref. pt.). \[S_{\text{s}}^{*}=\sqrt{\left(S_{PC}^{r}\right)^{2}+S_{\text{ms}}^{2}-S_{n,PC}^{2}}= \sqrt{384.12^{2}+65.406^{2}-0.343^{2}}=389.648\text{ VA}.\]  
This result would agree well with the real value of the apparent power measured by the SIMPELEC power analyzer using the source neutral point (N) as the reference point (389.65 VA).
## 3 Management of Electrical Grids by Means of WSANs
### Application of WSANs to Manage the Whole Electrical Grid  
WSANs offer significant advantages compared to traditional wired monitoring solutions and, more precisely, in the context of electrical grids that include MGs at the distribution network level, and SGs [27]. Nowadays, typical wired sensors present high cost and low flexibility compared with wireless solutions. In addition to their low-cost and easiness of deployment, WSANs are adapted to the management of electrical grids because they can be deployed over a large area, covering the different feeders and buses of the network. In addition, the embedded intelligence of the nodes allows implementing fault tolerant control algorithms [28]. These capabilities make WSANs a promising solution to gather operation information not only from the distributed energy production units, but also from the different equipment, including those placed at the consumer sites. The collected data allow the network operator to rapidly establish the necessary mechanisms to face different critical events, such as sudden variations in power generation or equipment failures, in an autonomous and appropriate way [29].
## 2 Near Real-Time Remote Sensing Architecture
### Workflow of NRRS  
The traditional remote sensing architecture can be formulated as follows [32]:  
* The OC&M system coordinates the in-orbit satellite resources, TT&C resources, and data-receiving resources according to the remote sensing needs and task priorities, forms satellite TT&C plans, control parameters, and data-receiving plans, and then sends them to the TT&C system and data receiving stations, respectively.
* The TT&C system uploads the commands to the satellite after receiving the control commands and TT&C plan.
* The remote sensing satellite sends the original image data to the data receiving station.
* The data-receiving station sends the data to the data-processing system, generates corresponding data and information products, and sends them to users of remote sensing.  
Under the presented NRRS architecture based on satellite Internet, users of remote sensing, OC&M system, and remote sensing satellites are seamlessly connected to the satellite Internet, facilitating a streamlined and efficient workflow:  
* Users of remote sensing can access the satellite Internet through broadband communication terminals from anywhere and submit requirements for remote sensing to the OC&M system.
* The OC&M system uploads remote sensing tasks to the remote sensing satellites in real time via satellite Internet and inter-satellite links.
* After performing the shooting task, the remote sensing satellite with the capability of in-orbit processing accesses the satellite Internet, and transmits the original data to the users in near real time, enabling them to be free of constraints of gateway stations and network resources.  
Figure 2 demonstrates the workflow of NRRS and traditional remote sensing. Compared with the workflow of traditional remote sensing, the number of NRRS tasks is  
reduced from five steps to three steps, which improves the efficiency of information flow and reduces the difficulty of resource coordination.
# A Realistic, Flexible and Extendible Network Emulation Platform for Space Networks
## 4 Designs
### Network Topology
#### 4.3.2 Upper Logical Network Topology  
The upper logical network topology remains consistent with the SN emulation scenario. With the scene information, including time-varying connection relationships, dynamic link characteristics, emulation node parameters, etc., the main controller generates corresponding commands, such as flow entities. Then the data plane is driven by these commands. In this way, the proposed platform emulates a network scenario with the arbitrary logical topology described in logical plane.
###### Abstract  
Electroabsorption in metallic nanoparticles within transparent dielectric media has been measured. In particular, gold nanoparticles in glass and subnanometer-size metallic domains in iodine doped nonconjugated conductive polymer have been studied. Measurements have been made for applied ac fields at 4 kHz, at a wavelength close to the onset of the surface plasmon resonance. The measured electroabsorption (imaginary part of \(\chi^{(3)}\) or Kerr coefficient) has a quadratic dependence on electric field. Its magnitudes were compared for different sizes of the metallic nanoparticles down to the subnanometer-size particles in iodine-doped nonconjugated conductive polymer. As in the case of quadratic electro-optic effect reported earlier, electroabsorption has approximately a 1/d\({}^{3}\) dependence, d being the diameter of nanoparticle. This is consistent with existing theories on confined metallic systems.  
electroabsorption; metallic nano- and subnanometer particles; nonlinear optics; Kerr coefficient; nonconjugated conductive polymers +
Footnote †: journal: Applied Physics Letters
# Performance Evaluation of Machine Learning Methods for Anomaly Detection in CubeSat Solar Panels
## 3 Methods
### Anomaly Definition
#### 3.1.2 Type 2: Solar Cell Failure  
Solar cell failure (or type 2) characterizes the anomaly of a compromised cell in a panel (of any panels). This specific failure is only noticeable during the illumination period. Based on the actual telemetry data of the BIRDS-3 satellite (Uguisu), the voltage sensor reading shows consecutive values (at least three samples) within 2000 to 3000 mV with no expected electrical current reading (<50 mA). Analytically, one solar cell in the Uguisu satellite is subject to an anomaly condition. However, this particular condition can happen instantly in the transition period of entering and exiting the eclipse. Therefore, the labeling method considers the previous and the following data points in a sequence. We define such anomaly as  
\[2000\;\mathrm{mV}\;\leq\;V_{i}(t-1,\;t,\;t+1)\leq 3000\;\mathrm{mV}\;\wedge I _{i}(t)<50\;\mathrm{mA}\;, \tag{2}\]  
with \(V_{i}(t)\) and \(I_{i}(t)\) represent the value of voltage and current sensor readings in a sample of \(t\) and for panel \(i\), respectively. Accordingly, Figure 7 depicts the voltage and current sensor readings in both normal (black) and anomalous (red) sections for type 2 anomaly by applying (2). It covers approximately 2.5 orbital periods. Type 2 anomalies in the dataset contaminate 7.22% of the samples within the dataset.  
Figure 6: Type 1 anomalies in (a) voltage and (b) current data.
## 3 Proposed Power Losses Model
### Proposed Conduction Losses Model
#### 3.1.3 The Proposed Equivalent Three-Order Harmonic Model of the Duty Cycle  
Noticing that \(D(\alpha)\) is a piecewise function with a period of \(2\pi\) and it is continuous over a period of \([-\pi,\pi]\), it meets the Dirichlet criterion, which means that \(D(\alpha)\) can be expanded as a Fourier series.  
Equation (15) can be also expressed as:  
\[d(\alpha)=\left(2D(\alpha)-1\right)/m=\left\{\begin{array}{ll}\frac{\sqrt{3 }}{2}\cos(\alpha-\frac{\pi}{6})&0\leq\alpha\leq\frac{\pi}{3}\\ \frac{3}{2}\cos\alpha&\frac{\pi}{3}\leq\alpha\leq\frac{\pi}{3}\\ \frac{\sqrt{3}}{2}\cos(\alpha+\frac{\pi}{6})&\frac{2\pi}{3}\leq\alpha\leq \pi\\ \frac{\sqrt{3}}{2}\cos(\alpha-\frac{\pi}{6})&-\pi\leq\alpha\leq-\frac{\pi}{3} \\ \frac{3}{2}\cos\alpha&-\frac{2\pi}{3}\leq\alpha\leq-\frac{\pi}{3}\\ \frac{\sqrt{3}}{2}\cos(\alpha+\frac{\pi}{6})&-\frac{\pi}{3}\leq\alpha\leq 0\\ \end{array}\right. \tag{19}\]  
Similarly, it can be proved that \(d(\alpha)\) can be expanded as a Fourier series. The expansion form of the Fourier series can be expressed as:  
\[\overline{d(\alpha)}=\frac{a_{0}}{2}+\sum\
olimits_{n=1}^{\infty}\left(a_{n} \cos(n\alpha)\right)+b_{n}\sin(n\alpha)) \tag{20}\]  
where:  
\[a_{0}=0;a_{1}=1;a_{n}=\frac{2\sqrt{3}(\sin(\pi\times\frac{\pi}{6}))^{2}-4\sqrt {3}(\sin(\pi\times\frac{\pi}{6}))^{2}+4\sqrt{3}(\sin(\pi\times\frac{\pi}{6})) ^{2}}{2\pi-2\pi m^{2}},n=2,3,4\ldots\]  
\[b_{n}=0,n=1,2,3\ldots\]Further analysis of the coefficient \(a_{n}\) can be obtained as:  
\[a_{n}=\left\{\begin{array}{cc}\frac{3\sqrt{3}}{(1-3^{2k})\times\pi}&n=3^{k}\\ 0&n\
eq 3^{k}\end{array}\right.\]  
When the fundamental wave and the three-order harmonic component are considered, \(\overline{d(\alpha)}\) can be expressed as \(d_{3}(\alpha)\) in Equation (21). The waveform of \(d_{3}(\alpha)\) is drawn and it is compared with the waveform of \(d(\alpha)\) shown in Figure 3a. The difference between \(d_{3}(\alpha)\) and \(d(\alpha)\) is small with the peak error of 0.04325.  
\[d_{3}(\alpha)=\cos\alpha-\frac{3\sqrt{3}}{8\pi}\cos(3\alpha) \tag{21}\]  
When the fundamental wave, the three- and five-order harmonic components are considered, \(\overline{d(\alpha)}\) can be expressed as \(d_{5}(\alpha)\) in Equation (22). The waveform of \(d_{5}(\alpha)\) is drawn and it is compared with the waveform of \(d(\alpha)\) shown in Figure 3b. The difference between \(d_{5}(\alpha)\) and \(d(\alpha)\) is smaller with the peak error of 0.02258.  
\[d_{5}(\alpha)=\cos\alpha-\frac{3\sqrt{3}}{8\pi}\cos(3\alpha)-\frac{3\sqrt{3}}{8 0\pi}\cos(9\alpha) \tag{22}\]  
Figure 3c shows the error comparison for \(d_{3}(\alpha)\) and \(d_{5}(\alpha)\). It shows that these two errors are very close, which means that the addition of the five-order harmonic component has little impact on improvement of fitting accuracy. Furthermore, considering that the calculation of \(d_{3}(\alpha)\) has lower computation requirements than \(d_{5}(\alpha)\), \(d_{3}(\alpha)\) is selected to represent the \(d(\alpha)\).
# Future Perspectives for Gamma-ray Burst Detection from Space
## 6 LEAP
### Expected Performance  
As a wide-FoV instrument, LEAP maintains some level of polarization sensitivity out to at least 75\({}^{\circ}\) off-axis, providing an effective FoV of \(\sim\)1.5\(\pi\) sr. Minimal obstructions from the ISS within the FoV maximize sky exposure and minimize photon absorption and scattering effects. However, scattered flux from ISS structures (such as solar panels) must always be considered in the analysis of both the spectrum and the polarization. Response simulations spanning a range in energy, spectral shape, and incidence direction have been used in estimates of instrument performance. Figure 10 shows, for a three-year mission, the number of expected GRBs as a function of MDP. These estimates show that LEAP will attain its requirement of 65 GRBs with \(<\)30% MDP, with a significant margin.  
Figure 9: The LEAP payload includes seven independent polarimeter modules. When mounted on the outside of the ISS, the array of modules points toward the local zenith and scans the sky for GRBs.
## 5 Conclusions  
This paper examines byproducts (magnesite tailings and fly ash) to evaluate their suitability as cement additives, with the ultimate goal of the production of environmentally friendly cements from now on. The main conclusions of this study are given in the below remarks:  
* Byproducts from different regions of Greece, such as magnesite tailings from Evoia and fly ash from Kardia (Ptolemais), which have been combined together for first time, are suitable reinforcement additives.
* The effect of nano MgO content is the most critical parameter for the physicochemical performance of produced cement, compared to that of the fly ash content.  
Figure 13: The AFM results of cement made by nano MgO and fly ash additives: (**a**), group I, (**b**) group II, (**c**) group III.  
* More satisfactory results in the physicochemical properties of the produced cement were from samples of group II, containing 3-4% of nano MgO. Nano MgO content above 4% seems to have a negative influence on the compressive strength of the produced cement, simultaneously reducing its durability. Therefore, the amount of nano MgO determines the physicochemical performance of the produced cement.
* The increased microroughness of the cements is in accordance with the mechanical behavior of the produced cements.  
Future scope: Our research team, following the basic principles of the circular economy, aims to produce a green concrete in the future using exclusively industrial byproducts for both cement and aggregates in mixtures capable of withstanding uniaxial loads and temperature changes.  
Conceptualization, P.P.G. and P.P.; methodology, P.P.G., P.P., A.R., P.L. and M.K.; software, M.K. and P.P.; investigation, P.P.G., P.P., A.R. and P.L.; resources, P.P.G., P.P. and P.L.; data curation, P.P.G., P.P., A.R. and P.L.; writing--original draft preparation, P.P.G. and P.P.; writing--review and editing, P.P.; visualization, P.P.; supervision, P.P. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 4 People Segmentation
### People Detection in Thermal Infrared Based on Frame Subtraction
#### 4.1.1 Frame Subtraction Analysis  
The previous image \(I_{IR}(x,y,t-\Delta t)\) and the current one \(I_{IR}(x,y,t)\) are used. Image subtraction and thresholding are performed on these frames, as shown in Equation (6), where \(\theta_{sub}\) is experimentally fixed as \(16\%\) of the maximum value of a \(256\) grey level image. This binarized image is combined with the image \(I_{c}\) (obtained during the stage based on a single frame after an initial thresholding and morphological filter of \(I_{IR}\)) by an \"AND\" operation, obtaining binary image \(I_{sc}\). This way, false positives due to small illumination changes are discarded, by ensuring that the zones with motion have also warm heat concentrations similar to humans. Regions-of-interest (ROIs) with an area superior to \(A_{min}\) (calculated as shown in Equation (5)) and with a percentage of pixels set to \(MAX\) greater than a rate threshold \(\psi\) (experimentally fixed at a \(5\%\) of the area of the ROI) are extracted from \(I_{sc}\) in the list of blobs \(L_{MOV}\). Notice that \(A_{I_{c}}\) is the area of image \(I_{c}\).  
\[A_{min}=\frac{A_{I_{c}}}{400} \tag{5}\]  
\[I_{s}(x,y)=\left\{\begin{array}{ll}\mbox{max, if }|I_{IR}(x,y,t)-I_{IR}(x,y,t- \Delta t)|>\theta_{sub}\\ \mbox{min, otherwise}\end{array}\right. \tag{6}\]
# Railway Track Monitoring Using Train Measurements: An Experimental Case Study
## 5 Fault Detection on Dublin-Belfast Route  
### Fault Detection Method  
Figure 10 gives an overview of the proposed fault detection algorithm. The raw acceleration data are first processed using the Hilbert Huang Transform (HHT) method, as explained in Section 3.1, which results in Hilbert amplitudes. Subsequently, the Hilbert amplitudes are decomposed while using the PBD method explained in Section 3.2. The selected PF is then scaled using the scaling process that is explained in Section 4.2. Finally, the scaled PF is used to detect faults on the railway track. The procedure is employed in an experimental case study in the following sections.