esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Reducing the Activity of Fe\({}^{3+}\) to Fe\({}^{2+}\)  
First, we prepared a calibration curve for Fe\({}^{2+}\) ions using aqueous solutions of VI iron II sulphate (FeSO\({}_{4}\)). For this purpose, the following were introduced into 100 mL volumetric flasks: appropriate amounts of FeSO\({}_{4}\) (so that the resulting concentrations of Fe\({}^{2+}\) ions range from 0.5 to 5 mg/L) and 1 mg of ascorbic acid in order to reduce any Fe\({}^{3+}\) ions in the sample present in the test sample. We fill the flasks to the mark with distilled water, close them snugly with a stopper, and agitate them to produce uniform solutions.  
In the following step of the test, 1000 \(\upmu\)L of the aqueous FeSO\({}_{4}\) solution with ascorbic acid and 1000 \(\upmu\)L of the aqueous ferrozine solution (with a concentration of 1 g/L) were added into glass test tubes. The tubes were closed tightly with a stopper and incubated at room temperature for 10 min, and then the absorbance of the test solutions was measured using a spectrophotometer at a wavelength of \(\uplambda=562\) nm. Spectrophotometric analyses were carried out in triplicate using a Thermo Scientific GENESYS 50 instrument, obtaining a calibration curve for Fe\({}^{2+}\) ions (y = 0.4888x + 0.0064; R\({}^{2}\) = 0.999).  
The capacity to reduce iron III ions to iron II using the ferrozine technique was assessed as follows: 1000 \(\upmu\)L of aqueous FeCl\({}_{3}\) solution (0.5 g/L Fe\({}^{3+}\) concentration), 1 \(\upmu\)L of FSE, and 1000 \(\upmu\)L of aqueous ferrozine solution (1 g/L concentration) were put into glass tubes. The tubes are closed tightly with a stopper and incubated at room temperature for 10 min, and then the absorbance of the test solutions (\(\uplambda=562\) nm) is measured using a spectrophotometer. The instrument was zeroed by adding 1000 \(\upmu\)L of aqueous FeCl3 solution, 1 \(\upmu\)L of distilled water, and 1000 \(\upmu\)L of aqueous ferrozine solution as a reference.  
We estimated the reduction activity (\(RA\)) of Fe\({}^{3+}\) to Fe\({}^{2+}\) using the following formula shown in Equation (2) [3]:  
\[RA=\frac{\left[(C_{F2+b.s.}-C_{F2+t.s.})\cdot V_{s}\right]}{V_{FSE}}.100\% \tag{10}\]  
where:  
\(RA\)--reduction activity of Fe\({}^{3+}\) [mg/L],  
\(C_{F2+b.s.}\)--concentration of Fe\({}^{2+}\) ions in the blank sample [mg/L],  
\(C_{F2+t.s.}\)--concentration of Fe\({}^{2+}\) ions in the tested sample [mg/L],  
\(V_{s}\)--total volume of solution introduced into volumetric flasks [L],  
\(V_{FSE}\)--volume of FSE introduced into volumetric flasks [L].
## 2 Novel Technologies of 6G for Smart Grid Applications
### Massive MIMO (mMIMO)  
The fundamental principle of massive MIMO is to create multiple spatial channels between the BS and user devices by exploiting the spatial domain. Each antenna at the BS transmits signals simultaneously, and the user devices receive a combination of these signals, which are then isolated, as well as decoded at the receiver end by sophisticated techniques. In the traditional MIMO systems, both the user devices and BS have multiple antennas for throughput and reliability improvements. However, traditional MIMO-based wireless communication has a limited number of antennas at the user devices and BS.  
In contrast, mMIMO significantly increases the number of BS antennas (i.e., in the order of tens or hundreds). Furthermore, mMIMO is a key performer in the case of 6G, which provides ultra-high speed data rates, along with low-latency and ultra-reliable communication. Moreover, MIMO enhances spectral efficiency, as well as energy efficiency [79; 80]. The spectral efficiency is significantly improved due to the development of MIMO. Thus, mMIMO is incorporated with 6G wireless communication for spectral efficiency, capacity, and energy-efficient improvements [81].
# Efficient Online Engagement Analytics Algorithm Toolkit That Can Run on Edge
## 4 Experimental Results
### Runtime Performance  
The authors evaluated the performance of the example solution shown in Figure 2 using a sample video from [74] at two different resolutions, \(1280\times 720\) (HD) and \(1920\times 1080\) (FHD). The experiments were conducted on a Raspberry Pi 4 Model B with 1 GB RAM. The solution uses Mediapipe's \"FlowLimiterCalculator\" component to achieve real-time low latency performance in practice, but the author disabled it during experimentation. The results, which can be found in Figure 8, show that the solution runs at an average of 30 frames per second on HD video and approximately 13 frames per second on FHD video.
# Analysis of Circuit Simulation Considering Total Ionizing Dose Effects on FinFET and Nanowire FET
## 2 Methodology of TID Simulation  
### Device Structure Design  
The TID effects of an NMOS FinFET and NW-FET have been analyzed [22]. In this study, we analyze the TID effect of PMOS devices. The PMOS NW-FET was stacked in five stages to match the current and device size to those in the NMOS device study. The FinFET and NW-FET were similarly constructed by the three-dimensional (3D) technology computer-aided design (TCAD) simulation tool. The PMOS FinFET was designed as an equal NMOS considering the International Technology Roadmap for Semiconductors (ITRS) [23]. Figure 1 shows the TCAD structures of the FinFET and NW-FET.  
The devices were made with a silicon body and the n- and p-types dopant was phosphorus and boron. The device channel insulator materials were deposited SiO\({}_{2}\) and HfO\({}_{2}\). The \(I_{\text{D}}\)-\(V_{G}\) characteristics were calibrated for an accurate comparison of the TID effects. Tables 1 and 2 show each design's parameters and the initial \(V_{T}\) characteristics of the FinFET and NW-FET, respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Design Factor** & **FinFET** & **NW-FET** \\ \hline Channel length & 12 nm & 12 nm \\ FinFET’s width and & 6 nm & 6 nm \\ NW-FET’s diameter & 42 nm & 30 nm \\ Height (NW-FET total height) & 489.5 nm\({}^{2}\) & 474.9 nm\({}^{2}\) \\ Total area & \(1\times 10^{21}\) cm\({}^{-3}\) & \(1\times 10^{21}\) cm\({}^{-3}\) \\ S/D doping & \(1\times 10^{16}\) cm\({}^{-3}\) & \(1\times 10^{16}\) cm\({}^{-3}\) \\ Oxide area & 237.5 nm\({}^{2}\) & 333.6 nm\({}^{2}\) \\ EOT & 0.812 nm & 0.812 nm \\ \hline \hline \end{tabular}
\end{table}
Table 1: Device’s parameters.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**\(V_{DD}\) = 0.65 V** & **FinFET (\(V_{T}\))** & **NW-FET (\(V_{T}\))** \\ \hline NMOS & 0.33 V & 0.4 V \\ PMOS & 0.3 V & 0.38 V \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(V_{T}\) data of each device.
## 3 Results  
### Abiotic Parameters  
Monthly measurements of surface water T, S, pH, and O\({}_{2}\) [%] were recorded at two stations, Station P (within the marsh) and Station S (coastal sea). The data reveal seasonal and spatial variability, with notably higher salinity and temperature extremes at Station P, particularly during summer months, accompanied by lower O\({}_{2}\) [%] levels and pH compared to the control site (Table S1). The hydrographic data show that the swamp became progressively warmer over the investigated period, with average temperatures rising from 21.6 \(\pm\) 7.2 \({}^{\circ}\)C in 2022 to 22.1 \(\pm\) 7.8 \({}^{\circ}\)C in 2023 and reaching 24.2 \(\pm\) 8.8 \({}^{\circ}\)C in 2024. The adjacent sea also exhibited a warming trend, though more modestly, increasing from 20.1 \(\pm\) 4.7 \({}^{\circ}\)C in 2022 to 21.3 \(\pm\) 5.1 \({}^{\circ}\)C in 2024. Swamp salinity remained similar in 2022 (38.0 \(\pm\) 8.4 %) and 2023 (39.0 \(\pm\) 6.8 %), but surpassed 40% in 2024 (40.4 \(\pm\) 9.5 %), with peaks of 56.7% in July. In contrast, the sea salinity slightly increased from 36.5 \(\pm\) 0.8 % in 2022 to 36.8 \(\pm\) 0.7 % in 2023 before dropping to 35.4 \(\pm\) 2.4 % in 2024. Interestingly, a marked drop in swamp salinity was recorded in May 2024 (24.9%), possibly indicating freshwater input. Average swamp oxygen saturation remained around 72.2 \(\pm\) 22.4% in 2022 and 72.8 \(\pm\) 17.5% in 2023 but dropped significantly to 62.6 \(\pm\) 25.4% in 2024, with extreme lows (~23-27%) in the summer. Meanwhile, the sea maintained high oxygen saturation, ranging between 98.8 \(\pm\) 5.6% and 99.6 \(\pm\) 3.9%. Swamp pH values were stable at around 7.90 \(\pm\) 0.1 in 2022 and 7.90 \(\pm\) 0.1 in 2023 but declined to 7.76 \(\pm\) 0.3 in 2024. In contrast, sea pH showed a slight increase from 8.19 \(\pm\) 0.0 to 8.23 \(\pm\) 0.0. Overall, the swamp showed increasingly extreme summer conditions, with elevated temperatures, salinity, and oxygen depletion, highlighting its growing environmental stress and sensitivity compared to the adjacent sea.
## 4 Discussion
The review included studies evaluating the effect of autologous platelet preparations on infrabony periodontal defects and concluded that most of them failed to prove that the difference between the control and experimental groups was statistically significant, given the great differences in the shape and size of the defects, primarily in terms of the number of bony walls that exist in the defect [61]. Research has shown that changes in the number of revolutions of the centrifuge (centrifugal force intensity) and the length of time of centrifugation affect the biological characteristics of the preparations obtained, such as the amount and type of growth factors they release and the dynamics of their release [32; 62]. To minimize individual differences in their characteristics, it is necessary to standardize clinical protocols for their production. It is also necessary to determine the benefit from combining these preparations with other types of bone grafts [26] and allografts [4] in the treatment of bone defects. It can be concluded that the use of some grafting material and autologous blood preparations can, to a certain extent, reduce the resorption of bone tissue in the post-extraction period and prove to be an optimal procedure in therapy after tooth extraction [19]. Although it is evident that, today, the positive effect of the application of growth factor concentrates on tissue healing, and therefore on extraction wounds, the necessary biocompatibility, safety of their application, clinical reproducibility of the preparation methodology and \"cost-effect\" relationship must still be kept in mind during its administration. The use of a clearly defined, safe and controlled technique can be a powerful tool to achieve better tissue regeneration in many clinical applications. Many studies evaluated the formation of bone tissue by analyzing two-dimensional X-ray images (periapical or OPT) [63; 64], but in the present study, 3D CBCT images were used to obtain more accurate information, i.e., surface 3D models of cavities, due to higher spatial resolution. Moreover, application of the presented methodology for extraction and analysis on surface 3D models for the density, volume and periodontal pocket presents a new way to acquire reliable results on the basis of the reconstructed 3D geometry of the cavity. This study has certain limitations, primarily in terms of the sample size and the fact that it was only conducted in the mandibular third molar region. The region of the third molar in the lower jaw shows certain peculiarities in relation to the healing of extraction wounds in other parts of the oral cavity. Further, the preoperative platelet and leukocyte count was not taken into consideration.
# Validation Analysis of Drought Monitoring Based on FY-4 Satellite
## 3 Results  
### Analysis of Spatial and Temporal Correlations  
The spatial distribution of the correlation between site MCI and TVDI is shown in Figure 4. In the Huanghuai region, 119 (32%) sites show a positive correlation, 253 sites (68%) show a negative correlation, with 15.1% (_p_ < 0.05), and mainly distributed in Shandong and Henan Provinces (Figure 4a). In Yunnan Province, 44 (35.8%) sites show a positive correlation and 79 sites show a negative correlation with 6.5% (_p_ <0.05), mainly in the central part of Yunnan Province. In Guangdong Province, 20 (25.3%) sites show a positive correlation and 59 stations show a negative correlation with 51.9% (_p_ <0.05), mainly in the eastern and northern Guangdong Province.  
During these drought events, the sites of significantly negative correlation were 50 (13.4%), 5 (4.1%), and 40 (50.6%) in the Huanghuai region, Yunnan Province, and Guangdong Province, respectively. These sites were mainly concentrated in moderate and severe drought areas (drought with more than 30 occurrences), and the negative correlation is not significant in the areas with less severe drought (Figure 4d-f). The results indicate that TVDI has a better monitoring effect in the moderate and severe droughts area (TVDI above moderate drought).  
The spatial distribution of the correlation between SRHI and TVDI on pixels is shown in Figure 5. The percentage of significantly negative correlations was 24.8%, 28.7%, and 40.7% in the Huanghuai region, Yunnan Province, and Guangdong Province, while the  
Figure 4: TVDI and MCI site correlation and drought count statistics on TVDI pixels ((**a,d**) Huanghuai region; (**b,e**) Yunnan Province; (**c,f**) Guangdong Province).  
insignificant pixels accounted for 75.2%, 71.3%, and 59.3%, respectively. The pixels in the gray show no significantly negative correlation between TVDI and SRHI (_p_ > 0.05) because there are too many null-value pixels due to the cloud. Additionally, the pixels passing the significance test (_p_ < 0.05) in the three regions are distributed in the area of severe drought, the same with the site MCI and TVDI.
## 2 State of the Art
### Impact of Multiple Data Streams on Performance in a Topology with Bottlenecks  
Based on utilized services and scenarios in medium-voltage linear topology, it is necessary to consider bottlenecks where multiple sources communicate with a concentrator. A link with the lowest throughput defines the total network throughput [54; 55]. Nowadays, high automation is considered for use of periodic data transfers from distribution substations. A common case is that, during the data transfer, another request for different, possibly higher-priority data arrives and must be served as soon as possible. These reasons were the motivation for our simulations shown in Sections 4.2 and 4.3. The main goal is to compare simulations to field measurements.
# FPGA-Based Manchester Decoder for IEEE 802.15.7 Visible Light Communications
## 4 FPGA Implementation
### I/Q Demodulator  
The data-bursts coming from the circular buffer, composed by 100-sample, reach two parallel multipliers in the I/Q demodulator, designed based on [40], (see Figure 10). The bursts are supported by the data-valid flag. The aim of this block is calculating the I and Q values according to Equation (6). A look-up dual port memory holds 100-sample of a  
Figure 9: Acquired data are temporarily stored in the circular buffer. They are written from the 10 MHz side, and read from the 100 MHz domain in blocks of 100 samples that represent the data covering a \(T_{b}\).  
period of cosine signal, with 12-bit resolution. The address logic generates two parallel read addresses so that a cosine and sine signal feed the corresponding multipliers. The cosine signal is obtained by reading the look-up from address 0, while the sine signal is obtained by starting the reading from address 75, i.e., with a 270\({}^{\circ}\) phase-delay. The full 24-bit dynamics of the multipliers output is maintained. These signals feed two parallel accumulators working at 31-bit. The data-valid signal is used to reset the address logics and to zero the I/Q accumulators before the beginning of each data burst.
###### Abstract  
Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, this paper proposes a method for applying knowledge graph technology with integrated feature data in spacecraft anomaly detection. First, the ontology concepts of the spacecraft equipment knowledge graph are designed according to expert knowledge, and then feature data are extracted from the historical operation data of the spacecraft in various states to build a rich spacecraft equipment knowledge graph. Next, spacecraft anomaly event knowledge graphs are constructed based on various types of anomaly features. During spacecraft operation, telemetry data are matched with the feature data in the knowledge graph, enabling anomaly device location and anomaly cause judgment. Experimental results show that this method, which utilizes spacecraft anomaly prior knowledge for anomaly detection and causes interpretation, has high practicality and efficiency. This research demonstrates the promising application prospects of knowledge graph technology in the field of spacecraft anomaly detection.  
spacecraft anomaly detection; knowledge graph; feature data integration Article
## 4 Robustness Analysis of the ADN against the CCF
### Relationship Analysis of Robustness and Tolerance Parameters
In order to avoid the subsequent nodes failure after the failure of a node, it is required that the redistributed load should not exceed the margin of the remaining nodes in the PN. After simplification, the condition is \((1-\Theta_{P})\times f_{P}(1-\Theta_{P})\times<\)_Thres\(>\geq<\)load\({}_{P}>\)_. Accordingly, the condition in the CN is \((1-\Theta_{I})\times f_{I}(1-\Theta_{I})\times<\)_Thres\(>\geq<\)load\({}_{I}>\)_. **Proposition 1**. : _When the tolerance parameters increase to infinity, that are \(\alpha_{P}\)\(\rightarrow\)\(\infty\) and \(\alpha_{I}\)\(\rightarrow\)\(\infty\), the expected proportions \(R_{\text{PF}}(2N+1)\) and \(R_{\text{IF}}(2N)\) of the remaining normal operational nodes reach their upper limits, which are the results of a pure interdependence model without considering the load redistribution. (The proof of Proposition 1 is given in Appendix E.)_  
Therefore, according to Proposition 1, the fifth conclusion of our analysis is that the robustness of the ADN is greater, when the tolerance parameters increase. If the redistributed load is equal to its margin of the remaining nodes, then the expected tolerance parameters are shown in Equations (16) and (17). \[\langle\alpha_{P}\rangle\ =\ \frac{\sum\limits_{d_{P}\ =\ 1}^{\infty}F_{P}(d_{P}) \times Prob_{P}(d_{P})+g}{\theta_{P}\times f_{P}(\theta_{P})\times\sum\limits_ {d_{P}\ =\ 1}^{\infty}F_{P}(d_{P})\times Prob_{P}(d_{P})}\ =\ \frac{1+\left(\frac{g}{\sum\limits_{d_{P}\ =\ 1}^{\infty}F_{P}(d_{P})\times Prob_{P}(d_{P})} \right)}{\theta_{P}\times f_{P}(\theta_{P})} \tag{16}\]  
\[\langle\alpha_{I}\rangle\ =\ \frac{\sum\limits_{d_{I}\ =\ 1}^{\infty}F_{I}(d_{P}) \times Prob_{I}(d_{I})}{\theta_{I}\times f_{I}(\theta_{I})\times\sum\limits_ {d_{I}\ =\ 1}^{\infty}F_{I}(d_{I})\times Prob_{I}(d_{I})}\ =\ \frac{1}{\theta_{I}\times f_{I}(\theta_{I})} \tag{17}\]  
Furthermore, the expected critical tolerance parameters \(<\)\(\alpha_{P\text{-}Critical}\)\(>\) and \(<\)\(\alpha_{I\text{-}Critical}\)\(>\) can be obtained in Equations (18) and (19). \[\langle load_{P}\rangle\ =\ \sum_{d_{P}\ =\ 1}^{\infty}F_{P}(d_{P})\times Prob _{P}(d_{P})+g \tag{10}\]0  
\[\langle load_{P}\rangle\ =\ \sum_{d_{P}\ =\ 1}^{\infty}F_{P}(d_{P})\times Prob _{P}(d_{P})+g \tag{10}\]1  
If the tolerance parameters \(\alpha_{P}\) and \(\alpha_{I}\) are greater than these critical ones respectively, the CCF caused by the load redistribution will not occur, which improves the robustness of the ADN against the CCF.
# Nonconvex Nonlinear Transformation of Low-Rank Approximation for Tensor Completion
## 5 Numerical Experiments
### MRI Image Completion  
In order to further verify the robustness of our model across different datasets, we continued, conducting experiments on the MRI dataset ([[https://adni.loni.usc.edu/](https://adni.loni.usc.edu/)]([https://adni.loni.usc.edu/](https://adni.loni.usc.edu/)), accessed on 25 January 2024). One slice of the dataset is shown in Figure 5. The size of the dataset is 197 \(\times\) 233 \(\times\) 189. We conducted tests at various sampling rates: 2.5%, 5%, 10%, and 15%. Table 3 reports the PSNR and SSIM of the results obtained by different methods, with the best results marked in bold. It can be seen from Table 3 that our method achieves the best metrics across all sampling rates. For visual comparison, Figure 5 displays the corresponding completion slices of all the tested models at different sampling rates.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{**SR = 2.5\%**} & \multicolumn{2}{c}{**SR = 5\%**} & \multicolumn{2}{c}{**SR = 10\%**} & \multicolumn{2}{c}{**SR = 15\%**} \\ \cline{3-10}  & & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** & **PSNR** & **SSIM** \\ \hline \multirow{4}{*}{color images} & Observed & 5.776 & 0.0058 & 5.885 & 0.0126 & 6.124 & 0.0301 & 6.465 & 0.0489 \\  & TNN & 6.853 & 0.0460 & 14.808 & 0.5885 & 20.737 & 0.8407 & 23.789 & 0.9067 \\ \cline{1-1}  & UTNN & 7.532 & 0.0807 & 14.078 & 0.5432 & 20.359 & 0.8326 & 23.816 & 0.8966 \\ \cline{1-1}  & FTNN & 11.018 & 0.3150 & 15.178 & 0.6178 & 20.088 & 0.8293 & 23.214 & 0.9129 \\ \cline{1-1}  & NTTNN & 15.585 & 0.5920 & 17.666 & 0.7374 & 20.809 & 0.8596 & 23.509 & **0.9239** \\ \cline{1-1}  & OUR & **15.818** & **0.6375** & **18.648** & **0.7453** & **21.090** & **0.8613** & **23.638** & 0.8795 \\ \hline \hline \end{tabular}
\end{table}
Table 2: TNN, UTNN, FTNN, NTTNN, and our model’s PSNR and SSIM results for the completion of color images at different sampling rates, with the optimal values highlighted in bold.  
Figure 4: The results of color images data using different methods at different sampling rates (from top to bottom: SR = 2.5%, 5%, 10%, 15%).  
According to the results, it can be seen that our model better preserves the local details of the image, with smoother edge contours and clearer internal structures at the cortical edges in brain anatomy compared to other methods.
## 2 Materials and Methods
### Field Experiments on Vineyards  
To evaluate the efficiency of Nano-MeJA and Ap-MeJA in the field, four treatments were carried out on Monastrell (_Vitis vinifera_ L.) grapevines from experimental vineyards in Jumilla (Murcia, Spain), which were grafted onto 1103-Paulsen rootstocks and trained in a vertical trellis system. Vine rows were arranged N-NW to S-SE with a between-rowand within-row spacing of 3 \(\times\) 1.25 m in a completely randomised block design, with 30 vines for each treatment and 10 vines for each replication. The treatments applied were as follows: (1) an aqueous solution of MeJA at a concentration of 10 mM (MeJA), (2) an aqueous suspension of 3.6 g L\({}^{-1}\) Nano-MeJA (resulting in a total concentration of 1 mM MeJA), (3) an aqueous suspension of 7.8 g L\({}^{-1}\) Ap-MeJA (resulting in a total concentration of 1 mM MeJA) and (4) an aqueous solution of Tween 80 only (control) used as a wetting agent (0.1 \(v/v\)) in all treatments. Foliar applications were made at veraison and one week later and approximately 200 mL per plant was sprayed. This assay was conducted during the 2020 season and climatological data are shown in work by [PERSON] et al. [28].