esa-sceva/satcom-chunk-collection huggingface dataset:
# Automated MRI Video Analysis for Pediatric Neuro-Oncology: An Experimental Approach
## 2 Materials and Methods  
The study was conducted under the scientific activities of the Polish Mother's Memorial Hospital Research Institute. The study utilized video files generated from a single-phase contrast-enhanced head MRI in two planes (frontal and sagittal) of a child diagnosed with a posterior fossa tumor, specifically a medulloblastoma, which was confirmed by histopathological examination. The video images were obtained from a 9-year-old child and dated 24 April 2024. The bioethics committee determined that the study did not require ethical approval as it did not affect the therapeutic and diagnostic procedures for the patient. All recordings used in the study were anonymized, and the guardians provided written consent for the use of the MRI recordings for research purposes.  
Inclusion Criteria: MRI images of a child's head, enhanced with single-phase contrast, showing a brain tumor located in the posterior cranial fossa. The images must consist of at least 200 slices to produce a video file approximately 9-10 s long. Only images free from motion, technical issues, or other artifacts that could hinder interpretation are included. The images must be of sufficient quality to clearly visualize the tumor and surrounding brain structures. Additionally, the images should not show any other central nervous system (CNS) pathologies.  
Exclusion Criteria: MRI images other than those of the brain that show tumors located outside the posterior cranial fossa. MRI images that show additional CNS pathologies, such as vascular malformations, strokes, demyelinating changes, etc.  
MRI images of low quality make interpretation difficult. Images with too few slices generate a video that is too short. MRI images with motion, technical, or other artifacts could impede interpretation.
# Bio-Inspired Algorithms for Efficient Clustering and Routing in Flying Ad Hoc Networks
## 4 HMAO Algorithms
### Cluster Maintenance Phase
If the LCF exceeds a predefined threshold, indicating congestion, the CH searches for neighboring clusters within a specified distance that have lower LCF values and available capacities. For each eligible neighboring cluster, the CH calculates the number of UAVs that can be accepted based on the LCF threshold. Next, the CH selects the neighboring cluster, computes the quantity of UAVs to be transferred to that target using the difference in the LCF value at congested and the scaling factor, and proceeds to move them, updating their respective LCF values for both source and destination. This process ensures optimal UAV distribution, reduces congestion, and maintains an efficient network performance.
# Design and Simulation of Low-Orbit Satellite Broadcast Signal Receiving and Processing Terminal
###### Abstract  
As an important space-based intelligence acquisition and combat command receiving component, the low-orbit satellite broadcast signal receiving terminal is an important guarantee for realizing the full-dimensional joint operations of our military's multi-services. This design is based on the SoC platform. Compared to the traditional low-orbit reconnaissance satellite reception and processing terminal business process, a narrowband anti-interference module is added and the interference-to-signal ratio reaches 57 dB and 47 dB when resisting interference from a single frequency band and three frequency bands, respectively. Digital beamforming is used for signal processing and beamforming gain of the whole machine reaches 14-15 dBic; compared to traditional capture and tracking modules, this terminal uses a time domain parallel frequency domain FFT fast acquisition method and a bit synchronization loop is added to the tracking loop to aiming at the problem that the frequency signal acquisition speed is not fast enough and addressing the issue that information symbol rate is not an integer multiple of the pseudo-code, thus complete bit synchronization within 60 ms, with a bit error rate of 0; the coding gain can reach 7 dB. Combined with the solution of decoding and positioning solution algorithms, this scheme used the JFM/K325T chip to complete the design simulation of the complete receiving and processing terminal.  
Keywords:time-frequency domain interference suppression; beam synthesis; acquisition tracking; bit synchronization loop; simulation design +
Footnote †: journal: Electronics
# WRTU-16T: Write-Enhanced Low-Power Radiation-Tolerant SRAM for Space Applications
## 3 Implementation Results and Analysis
### Word Line Write Trip Voltage  
The WWTV means the voltage difference between the supply voltage and the word line voltage at the point where the voltages of the two storage nodes cross during a write operation. This metric is the minimum WL driving condition required to stably invert data, and is used as a quantitative measure to evaluate the write stability of SRAMs. Traditionally, the write static noise margin (WSNM) has been used to evaluate the write robustness, but recent studies have emphasized that the WWTV is a more practical metric because it directly reflects the dynamic write conditions [11]. The WWTV is closely correlated with the WAT. In general, SRAMs with a short WAT have a faster voltage inversion of the storage node, so they can write stably even under a lower WL voltage, and thus show a high WWTV. On the other hand, SRAM with a long WAT generally shows a low WWTV because they need to maintain a higher WL voltage for sufficient time. As shown in Figure 10, WRTU-16T achieves the highest WWTV (366.13 mV) among SRAMs. This suggests that the WRTU-16T can perform stable write operations even under low-WL voltage conditions, providing a wider operating margin and improved write stability.  
Figure 10: Comparison of WWTV with other SRAMs: (**a**) at VDD = 1 V, (**b**) at various VDD levels from 0.9 V to 1.1 V.  
Figure 9: Comparison of WAT with other SRAMs: (**a**) at VDD = 1 V, (**b**) at various VDD levels from 0.9 V to 1.1 V.
## 4 Conclusions  
In this study, a three-dimensional porous HA/WS skeleton was incorporated into simulated body fluids to investigate the process of apatite nanocrystal formation through biomineralization and its microstructural diversity. Furthermore, mouse C57 BL/J6 preosteoclast cells were employed for in vitro cell adhesion experiments. We observed the adherence of C57 BL/J6 osteoclasts to the three-dimensional porous HA/WS skeleton and explored the influence of biomineralization-induced microstructure variations in apatite nanocrystals on cell adhesion, leading to the following conclusions:  
1. The biomineralization process was successfully achieved after a 21 day immersion in 1.5 \(\times\) SBF, resulting in the formation of densely laminar apatite nanocrystals. These flake-shaped nanocrystals were deposited within the three-dimensional porous HA/WS skeleton pores, each with an outer diameter of approximately 2 \(\upmu\)m, an inner layer thickness of about 1 \(\upmu\)m, and a single thickness ranging in tens of nanometers. This morphology is attributed to the concentration and distribution of Ca ions present in simulated body fluids.  
Figure 19: SEM images showing more details of osteoclast cell antenna adhesion on apatite nanocrystals of porous three-dimensional HA/WS skeleton. (**a–c**) Different views of cells attached tightly on walls and holes inside, at different magnifications, after 3 days of seeding.  
2. Apatite nanocrystals exhibit exceptional interfacial properties with a three-dimensional porous HA/WS skeleton substrate. The sedimentary environment within the three-dimensional porous HA/WS skeleton is complex and diverse, resulting in diverse apatite nanocrystals being formed through biomineralization in SBF solution. These nanocrystals can enhance pore wall thickness, reduce original pore size, and promote densification. This highlights the practical significance of channel structure design in bio-porous materials. Furthermore, variations in microscopic morphology and particle size in apatite nanocrystals also significantly impact subsequent cell experiments.  
Conceptualization, B.J. and X.H.; methodology, M.F.; software, X.L.; validation, B.J., B.Y. and S.Y.; formal analysis, X.C.; investigation, X.M.; resources, X.H.; data curation, Z.H.; writing--original draft preparation, X.H.; writing--review and editing, B.J.; visualization, J.C.; supervision, Z.H.; project administration, X.M.; funding acquisition, M.F. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Australian Research Council (ARC) Discovery Project Scheme, grant number DP110105296.  
Not applicable.  
The authors declare that there are no conflict of interest regarding the publication of this paper.
## 3 Methods
### Estimation of Net Primary Productivity  
Based on the MODIS NDVI monthly synthetic products and meteorological observation data during the same period, this study used the TEC flux model provided by the National Meteorological Center to estimate the vegetation NPP of the Kokyar Project Area. The spatial resolution of the \(NPP\) in this study was 1000 m, and the temporal units are annual (sum of monthly data).  
\[NPP=GPP-R_{a} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Name** & **Format** & \begin{tabular}{c} **Spatial** \\ **Resolution** \\ \end{tabular} & \
\begin{tabular}{c} **Time** \\ **Resolution** \\ \end{tabular} &
\begin{tabular}{c} **Source** \\ **Source** \\ \end{tabular} \\ \hline
2019 QZKK0603-zygjsl & NETCDF & 1000 m & Monthly & NTPDC \\
2019 QZKK0603–zygjpv & NETCDF & 1000 m & Monthly & NTPDC \\ MOD13A3 & HDF & 250 m & Monthly & NASA \\ SRTM DEM & Tiff & 250 m & — & USGS \\ Landsat 5, 7, 8 & HDF & 1000 m & Seasonal & NASA \\ \hline \hline \end{tabular}  
* NTPDC: National Tibetan Plateau Data Center ([[https://data.tpdc.ac.cn/en/news](https://data.tpdc.ac.cn/en/news)]([https://data.tpdc.ac.cn/en/news](https://data.tpdc.ac.cn/en/news)), accessed on 12 May 2022). NASA: National Aeronautics and Space Administration ([[https://www.nasa.gov/](https://www.nasa.gov/)]([https://www.nasa.gov/](https://www.nasa.gov/)), accessed on 12 May 2022). USGS: United States Geological Survey ([[https://www.usgs.gov/](https://www.usgs.gov/)]([https://www.usgs.gov/](https://www.usgs.gov/)), accessed on 12 May 2022).  
\end{table}
Table 1: Detailed description of the data.  
Figure 1: Sketch map of the research area.  
where \(R_{a}\) is the assimilation product consumed by the respiration of the trophoblast, expressed as:  
\[R_{a}=1-(7.825+1.145*Ta)/100 \tag{2}\]  
where \(Ta\) is the monthly average temperature.  
In the TEC, the \(GPP\) is calculated using:  
\[GPP=\varepsilon^{*}\times T_{\varepsilon}\times W_{\varepsilon}\times FPAR \times PAR \tag{3}\]  
where \(\varepsilon^{*}\) is the maximum rate of light energy utilization, \(T_{\varepsilon}\) is the temperature stress factor, \(W_{\varepsilon}\) is the water stress factor, and \(FPAR\) is the ratio of the photosynthetic effective radiation absorbed by vegetation photosynthesis.
## 2 The ELD Problem Formulation
### Considered Constraints in the ELD Problem  
#### 2.3.1 Power Balance Requirement  
The active power generated by power sources has to be equal to the sum of the total load demand (\(p_{d}\)) and transmission loss (\(p_{l}\)). It can be seen by the function in the following formula.  
\[\sum_{i=1}^{n}p_{i}=p_{d}+p_{l} \tag{6}\]  
where total real power loss is calculated by using the [PERSON]'s formula below:  
\[p_{l}=\sum_{i=1}^{n}\sum_{j=1}^{n}p_{i}B_{ij}p_{j}+\sum_{i=1}^{n}B_{00}p_{i}+B _{00} \tag{7}\]  
where \(B_{ij}\), \(B_{0i}\), and \(B_{00}\) are transmission loss coefficients.  
Power output limits requirement: Each TGU has serious limits within the upper level and lower level of generation. They must not exceed this segment as shown in the following rule.  
\[p_{i,\min}\leq p_{i}\leq p_{i,\max} \tag{8}\]  
where \(p_{i,max}\) is the highest acceptable working power of the \(i\)th TGU.
# Experimental and Numerical Evaluation of Calcium-Silicate-Based Mineral Foam for Blast Mitigation
## 2 Air-Blast Experiments
### Results from Experiments
#### 2.4.3 Response of the Aluminum Backplate with the Protective Foam  
This section analyzes the mineral foam's influence on the backplate's response. Figure 9 depicts a comparison of the OPD profiles when the protective foam is added.  
The OPD profiles exhibit similarities to those observed without foam protection, though there is a slight delay due to the increased time required for the shock wave to travel through the mineral foam. When the protective foam is added, the reflected overpressure and impulse increase by 123 and 66%, respectively. However, the deflection at the backplate's center is reduced by approximately 50%, measuring around 11.6, 10.9 and 10.6 mm for configurations A, B, and C, respectively. The SC is expected to mitigate the blast load through pulverization of the mineral foam, as illustrated in Figure 10.  
Figure 10: Mitigation of the blast load through pulverization of the mineral foam.  
Figure 9: Experimental deflection profiles for tests 1: (**a**) Without the protective foam, (**b**) in Configuration A, (**c**) in Configuration B, and (**d**) in Configuration C.  
However, a post-mortem visual inspection of the tested foams in configuration A reveals suboptimal use of the mineral foam, characterized by the presence of numerous large uncrushed lumps, as illustrated in Figure 11.  
Figure 10 illustrates a case where the foam is crushed optimally. It corresponds to a \(\zeta\) value of 50 m\({}^{-1}\), while for configuration A, \(\zeta\) value is 13.3 m\({}^{-1}\). To increase the foam's crushing effectiveness, \(\zeta\) should be increased as well. Therefore, configurations B and C have been implemented, where \(\zeta\) values are increased to 27.1 and 34.3 m\({}^{-1}\), respectively. [PERSON] et al. [19] also demonstrated that increasing the effectiveness of the SC by playing on the core configuration can be achieved.  
Figure 12 shows the OPD of the center of the plate, the mean permanent deformation \(\delta\) after the tests and summarizes the results of the different configurations for comparison. The results for both configurations, B and C, are very similar in terms of the maximum deflection of the plate compared to that of configuration A, i.e., a reduction of 50% compared to the maximum deflection of the unprotected plate. Configuration C, also the lighter solution, is the most effective in reducing the OPD at the center of the plate, as shown in Table 1.  
Figure 11: Post-mortem visual inspection of the fractured foam layer for configuration A.  
Figure 12: Mid-span deflection of the backplate with and without the protective foam. Tests 1 are shown with a solid line, Tests 2 with a dotted line and Tests 3 with dash-dot line.
# Leveraging IoT Devices for Atrial Fibrillation Detection: A Comprehensive Study of AI Techniques
## 2 Materials and Methods
### Classification
#### 2.3.1 ML Models Applied to Extracted Features  
We employed three distinct feature extraction methods for the PPG data: signal-based Features, wavelet spectrogram-based features, and CNN-based features derived from wavelet spectrogram images. For each method, we applied a consistent set of machine learning models, including Support Vector Machines (SVM), Random Forests (RF), and Multi-Layer Perceptrons (MLP) for classification. These models were selected due to their effectiveness in handling classification tasks, their ability to generalize well to unseen data, as well as their complementary strengths.  
SVM is particularly effective in handling binary classification problems while excelling in high-dimensional spaces and managing non-linearly separable data, making it ideal for capturing complex patterns. RF, a robust ensemble method, is well-suited to handling a large number of features and avoiding overfitting by averaging the predictions of multiple decision trees. MLP, a neural network approach, is capable of capturing complex patterns and non-linear relationships in the data, making it adaptable to the binary classification of PPG signals.  
To further enhance predictive performance, we implemented a stacking model that combined the predictions of multiple promising models using logistic regression as the final estimator. This approach aimed to leverage the strengths of multiple models to improve overall predictive performance.. A standardized pipeline was established for each feature extraction and ML model combination in order to ensure consistent and fair evaluation. This encompassed data scaling, dimensionality reduction via Principal Component Analysis (PCA), hyperparameter optimization through grid search, and performance evaluation using accuracy metrics based on stratified K-fold cross-validation.
## 3 Results and Discussion
### Substrate Coating
#### 3.4.2 Coating Characterization  
Two and three-dimensional surface images of the modified substrate membranes are displayed in Figure 5, where bright and dark areas correspond to the peak and valleys, respectively. The surface roughness parameters measured for the surface coating are listed in Table 3. Measurements were carried out following the same protocol as those for the uncoated substrate. The arithmetic mean roughness (Ra) was taken as the most representative parameter of the roughness of the surface.  
Figure 5 confirms that the different dissolutions were uniformly extended by spin coating on the substrate surface.  
The results obtained, by digital image processing (Figure. 5), and the roughness quantitative data (Table 3), show: (i) the unmodified PVA/GA coating (Figure 5a) to have an average roughness of 0.95 \(\upmu\)m, which is close to the roughness obtained for the as received substrate, which was 0.46 \(\upmu\)m, demonstrating the uniformity of the coating; (ii) a uniform distribution of peak-to-valley morphology can be observed for the coatings of PVA/GA/TiO\({}_{2}\)-NPs, PVA/GA/Ag-NPs and PVA/GA/Ag-TiO\({}_{2}\)-NPs, average roughness of 2.1, 5.0 and 1.46 \(\upmu\)m respectively; (iii) PVA/GA/ZnO-NPs coating showed the roughest surface. Its average roughness value, obtained as an average of three coated specimens was  
Figure 4: FTIR spectra from: (**a**) pure PVA and (**b**) PVA crosslinked by glutaraldehyde (GA).  
9.8 \(\upmu\)m, closely followed by PVA/GA/CuO-NPs coating, average roughness 9.28 \(\upmu\)m. These data will be taken into account in the discussion of the antibiofouling coating effect.  
Figure 5: Three- and two-dimensional images of all coating studied: (**a**) original substrate; (**b**) PVA/GA coating; (**c**) PVA/GA/AgNPs; (**d**) PVA/GA/TiO\({}_{2}\)-NPs; (**e**) PVA/GA/ZnONPs; (**f**) PVA/GA/Ag-TiO\({}_{2}\)NPs and (**g**) PVA/GA/CuONPs.
# A Dynamic Urban Mobility Index from Clustering of Vehicle Speeds in a Tourist-Heavy City
## 2 Materials and Methods
### GPS Dataset  
Data were obtained from 500 GPS tracker units installed on a mix of vehicles in cooperation with city authorities. Table 1 lists the technical and operational specifications of the GPS trackers, while Table 2 lists the vehicles with installed trackers by type.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Property** & **Measure** \\ \hline Position uncertainty & 5 m \\ Interval of data logging & 30 s (stationary)/100 m or 45\({}^{\circ}\) heading change (in motion) \\ Speed resolution & 0.1 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: GPS Technical and Operational Specifications.  
Figure 1: Map of the city of Baguio’s location within The Philippines. Zones corresponding to _barangays_ are denoted by dashed lines, while the solid lines show the city boundaries. There are 129 zones spread throughout a land area of 57.51 km\({}^{2}\).  
Public transportation within city limits is dominated by taxi services, which, due to the uneven terrain and resulting variations in road grade, commonly use sport utility vehicles seating up to four passengers. Taxi operators range from private individuals operating one or two units to companies that field more. As part of the collaboration with the local authorities, 399 taxi units were fitted with GPS trackers.  
The next category consists of passenger jeepneys, with 61 vehicles fitted with GPS trackers. Analogous to minibuses in other countries, jeepneys are paratransit vehicles [37] that typically seat 12 to 22 passengers and are a common mode of public transport across The Philippines. Like taxi operators in the city, jeepney operators can be companies or private individuals. All operators of public transport vehicles, whether taxis or jeepneys, are required to register with the local government and receive a business permit to operate.  
Finally, the remaining 40 GPS trackers were installed on vehicles owned by the local government (37 units) or by private volunteers (3 units). The 500 vehicles are thus used as probe vehicles for obtaining information on mobility conditions within the city.  
The GPS data collection used in this study ran from October 2022 to July 2023. Collected data include coordinates in longitude and latitude, vehicle speed, time, and bearing; geolocation, speed, and time were extracted for further use.  
Figure 2 shows a sample GPS trace from a randomly chosen vehicle (GPS tracker id: 869731050330141) on 17 July 2023, from 8 a.m. to 8 p.m. Notice that the vehicle exhibited a mix of slow speeds (red) and fast speeds (blue).
# A Bibliometric Analysis and Visualization of In-Vehicle Communication Protocols
## 3 Results
### Current Status of In-Vehicle Communication Protocol  
#### 3.1.1 Annual Trends in Publication  
The \"Recent Development of Communication Protocols in Vehicles\" graph showcases publication tendencies concerned with in-vehicle communication protocols from 2018 during the first quarter of 2025. Increasing scientific output from 2018 to 2023 depicts/surging interest in research and development activities focusing on the subject area. Publication numbers started slightly below 300 in 2018 and increased steadily to just above 650 in 2023. This phenomenon, also referred to as a surge, can be attributed to the swift improvements in the field of automotive technologies, which include autonomous systems, vehicular ad hoc network (VANET) systems, Automotive Ethernet systems, and IoT as a constituent of smart transport systems [12]. Nonetheless, a steep decline in numbers from 2024, especially in the early months of 2025, is highly observable. This sharp drop from the norm is most likely not the result of an actual cutback in research activity but signifies an unfinished dataset for 2025, which is still pending. These indicators are commonplace in bibliometric studies where data capture for the current year's data remains incomplete. From a high-level perspective, this trend captures the sustained and intense increase in activity within the research domain for the last couple of years, further underlining the increasing importance of the research attention of the field. Figure 1, which shows annual trends in publication, was generated using MS Excel.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Type of Document** & **Frequency** & **Proportion** \\ \hline Article & 1324 & 45.36\% \\ \hline Book chapter & 86 & 2.95\% \\ \hline Conference paper & 1465 & 50.19\% \\ \hline Review & 44 & 1.51\% \\ \hline Grand Total & 2919 & 100.00\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Type of document retrieved.  
Figure 1: Annual trends in scholarly publications on in-vehicle communication protocols from 2018 to 2025. Note: Data for 2025 only reflect publications indexed from January to April because of the timing of data extraction. This partial-year dataset may cause an apparent decline that does not represent a true reduction in research activity.