esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
The data compilation shown in Figure 9 allowed us to suggest a mechanism of cellular adaptation to low glycerol: in this case, the metabolic flow is toward alternative carbon sources' utilization, such as glycerolipid and \(\beta\)-glucan biosynthesis, solute transport across the plasma membrane, growth arrest, and an oxidative stress response under cationic stress and glycerol depletion. Also, a group of putative Zinc-Cluster transcription factors have been associated with glycerol deprivation, suggesting they act on fatty acid metabolism and peroxisome proliferation to adapt yeast cells to cationic and oxidative stress. However, how these transcription factors act in _C. neoformans_ is not known at this point. Still, this work has certainly opened an avenue for further investigation on a very important topic for pathogenesis and virulence, since the stress response is at the center of pathogen adaptation in the host. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/jof10080531/s1](https://www.mdpi.com/article/10.3390/jof10080531/s1)]([https://www.mdpi.com/article/10.3390/jof10080531/s1](https://www.mdpi.com/article/10.3390/jof10080531/s1)), Figures S1-S9: western blot original images; Figures S10-S19: microscopy original images; Table S1: plasmid list; Table S2: strain list; Table S3: primer list. **Author Contributions:** Conceptualization, A.T.R.V., M.A.V. and R.C.P. ; data curation, R.S.S., O.J.B. and A.T.R.V. ; formal analysis, R.S.S., G.M.-S., A.A.A.P., M.P., S.D.D. and O.J.B.B; funding acquisition, A.T.R.V., M.A.V. and R.C.P. ; methodology, R.S.S., G.M.-S., A.A.A.P., M.P. and S.D.D. ; project administration, R.C.P. ; supervision, R.C.P. ; writing--original draft, R.S.S., G.M.-S., A.T.R.V., M.A.V. and R.C.P. ; writing--review and editing, O.J.B.B. and R.C.P. All authors have read and agreed to the published version of the manuscript. **Funding:** Fundacao de Amparo a Pesquisa do Estado de Sao Paulo supported this work through funding to RCP (2020/01000-8 and 2023/11339-0) and MAV (2015/04400-9); RSS (2021/06290-7), GMS (2022/03307-9 and 2023/08675-9), and MP (2021/14632-5) were supported by Fundacao de Amparo a Pesquisa do Estado de Sao Paulo scholarship grants. ATRV was supported by FAPERJ (E-26/201.046/2022 and E-26/210.012/2020) and CNPq 307145/2021-2. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** All the relevant data in the paper are reported in Supplementary Information files. RNA-seq raw data is available at NCBI SRA database under accession number PRJNA1120105. **Conflicts of Interest:** The authors declare no conflict of interest.
###### Abstract  
The Ganges-Brahmaputra-Meghna (GBM) delta, characterized by complex topography and hydrological conditions, is highly susceptible to recurrent flooding, particularly in its coastal regions where tidal dynamics hinder floodwater discharge. This study integrates Synthetic Aperture Radar (SAR) imagery with machine learning (ML) techniques to assess near real-time flood inund patterns associated with extreme weather events, including recent cyclones between 2017 to 2024 (namely, Mora, Ttili, Fani, Amphan, Yaas, Sittang, Midhili, and Remal) as well as intense monsoonal rainfall during the same period, across a large spatial scale, to support disaster risk management efforts. Three machine learning algorithms, namely, random forest (RF), support vector machine (SVM), and K-nearest neighbors (KNN), were applied to flood extent data derived from SAR imagery to enhance flood detection accuracy. Among these, the SVM algorithm demonstrated the highest classification accuracy (75%) and exhibited superior robustness in delineating flood-affected areas. The analysis reveals that both cyclone intensity and rainfall magnitude significantly influence flood extent, with the western coastal zone (e.g., Morrelganj and Kaliganj) being most consistently affected. The peak inundation extent was observed during the 2023 monsoon (10,333 sq. km), while interannual variability in rainfall intensity directly influenced the spatial extent of flood-affected zones. In parallel, eight major cyclones, including Amphan (2020) and Remal (2024), triggered substantial flooding, with the most severe inundation recorded during Cyclone Remal with an area of 9243 sq. km. Morrelganj and Chakaria were consistently identified as flood hotspots during both monsoonal and cyclonic events. Comparative analysis indicates that cyclones result in larger areas with low-level inundation (19,085 sq. km) compared to monsoons (13,829 sq. km). However, monsoon events result in a larger area impacted by frequent inundation, underscoring the critical role of rainfall intensity. These findings underscore the utility of SAR-ML integration in operational flood monitoring and highlight the urgent need for localized, event-specific flood risk management strategies to enhance flood resilience in the GBM delta.  
cyclone; inundation; monsoon rainfall; machine learning (ML); SVM; RF; KNN; Synthetic Aperture Radar (SAR)
## 1 Introduction
The selectivity of SPME for various analytes depends on the type of absorbent, to a great extent. In recent years, various nanomaterials (NMs) have been used to improve performance of analytical methods. The selectivity and sensitivity of analytical techniques have been remarkably improved by using NMs as the sorbents [14]. High porosity, large surface area, chemical stability, physical durability, and specific affinity toward target analytes are the properties that make the NMs more beneficial than the traditional sorbent materials. These features caused NMs to attract a great attention in the analytical and bioanalytical applications of SPME [15]. In this regard, silica-based nanocomposites, different carbon-based nanosorbents, metal-organic frameworks (MOFs), and covalent organic frameworks (COFs) have been recently employed for sampling and separation of organic and inorganic analytes in different matrices [16]. MOFs or metal-organic coordination polymers (MOCPs) are microspore advanced polymeric crystalline materials that are made from hybrid inorganic-organic molecules [17]. MOFs have some unique properties, such as permanent nanoscale porosity, high surface area, costly diversity in their structures, tunable pore size, structure flexibility, and high loading capacity [18]. These compounds have been employed in various research fields, especially in separation science, gas storage, catalysis, sensor technology, drug delivery, and biomedical imaging [19]. In comparison with typical adsorbents like polyacrylate (PA), polydimethylsiloxane (PDMS), activated carbon, and zeolites (with a surface area of about 150, 450, 1500 and 300 m\({}^{2}\)-g\({}^{-1}\), respectively), MOFs have much larger surface areas, even up to 7000 m\({}^{2}\)-g\({}^{-1}\)[20]. Recently, the Materialof Institute Lavoisier (MIL) has introduced a novel class of organic metal frameworks called MILs as super-clusters that are promising adsorbents for the analytical separation purposes [21]. MIL-101(Cr) is a porous MOF, based on chromium and terephthalate atoms, that has been used to absorb and store hydrogen gas. It has been shown that the absorption properties and the hydrothermal stability of the MOFs can be improved by preparing their composites with other materials, especially nanostructured compounds [16]. However, few MOFs nanocomposites with conductive polymers have been used as microextraction sorbents [22]. This research was aimed to develop a different type of MIL-101(Cr)/PPy nanocomposite structure on surface of a stainless-steel fiber, using an electrophoretic deposition method, for microextraction sampling purposes. The MIL-101(Cr)/PPy-coated fiber was then applied for sampling MTBE in contaminated soil samples through a HS-SPME strategy, followed by GC/FID measurement, for the first time. The results showed the superiority of the developed PPy@MIL-101(Cr)-coated fiber in comparison with PA, PPY, PDMS, and DVB/CAR/PDMS fibers for the analysis of solid samples. The developed method was successfully employed for the analysis of MTBE in different soil samples contaminated by oil products.
# Orifice versus Converging-Nozzle Grid Turbulence: A Wavelet Perspective
## 4 Results
### Fourier Transform  
Fast Fourier transform was applied to the streamwise velocity fluctuation signals to inspect the turbulent energy spectrum along the mean flow direction. Thus, power spectra of fluctuation velocity signals were invoked to reveal information regarding the turbulence kinetic energy associated with the cascade of large to small eddies.  
A popular assumption considered while investigating grid-generated turbulence is that the flow field over a cross-section plane normal to the mean flow can be treated as uniform. Based on this assumption, the parameters investigated simply along the central axis can effectively reflect the turbulence characteristics of the full cross-section. However, the extent of lateral flow uniformity may not be the same for all cases and depends on grid geometry and configuration [3]. On a similar note, the Fourier transform is evaluated at the central measuring point to represent the characteristics of the entire measurement grid. Figure 5 shows the results of the fast Fourier transform (FFT) of the fluctuating streamwise velocity signal at the central measuring location. The initial, somewhat noisy, signal was filtered after transformation into the frequency domain. A Savitzky-Golay finite impulse filter (FIR) of the first order and a frame length of 999 Hz was used for this purpose [33].  
It is clear from Figure 5 that most of the turbulent energy lies predominantly in the lower frequency range, indicating the presence of large vortical structures in the flow. This suggests that large eddies possess most of the energy of the turbulence, as frequency is inversely related to the size of the eddying motion. It can be observed that the amount of energy drops sharply with increasing frequency. At higher frequencies, the spectrum diminishes to virtually a zero value, signalling a huge dissipation of small eddies at the location. The power spectrum shifts to a higher value over the entire frequency range as the wind speed increases. Furthermore, the power spectrum for the OPP is significantly higher in comparison to the CNPP for frequencies \(f\lesssim 50.2\) Hz at the lower \(\text{Re}_{\text{D}}\) and \(f\lesssim 104.6\) Hz at the higher \(\text{Re}_{\text{D}}\). At higher frequencies, this difference diminishes with plummeting kinetic energy content. Also worth noting is the occasional higher energy values for the CNPP in the moderate-frequency range \(f\approx 140\)-\(800\) Hz at the higher studied \(\text{Re}_{\text{D}}\). This behaviour of the spectrum substantiates the unique deviation of the velocity profile and turbulence intensity value in case of the CNPP at the higher \(\text{Re}_{\text{D}}\), discussed previously in Sections 4.1 and 4.2.  
Figure 5: Fourier transform at the centre point of the measurement grid at X/D = 10 D for the two perforated plates.
# Analysis of Craniocervical Abnormalities in Osteogenesis Imperfecta during Growth
###### Abstract  
Osteogenesis Imperfecta (OI) is a genetic disease characterized by osteopenia and bone fragility in which the craniocervical junction is also affected. This is of special relevance due to the high prevalence in anomalies described in the literature as follows: basilar invagination, basilar impression, and platybasia. **Methods.** We analyzed 19 lateral skull radiographs and 14 magnetic resonance images (MRIs) of 28 patients with OI in which eight linear and five angular measurements were plotted to determine the existence of craniocervical junction anomalies and compare them with 38 lateral skull radiographs and 28 MRIs performed on age-matched healthy controls. **Results.** From the reference values obtained from the control sample for each age group, we established the limit value at which pathology could be suspected. Some of the variables studied showed a clear trend associated with growth. More than half of the patients (60.71%) presented an anomaly in the skull base. **Conclusions.** According to the diagnostic criteria used and taking +2.5 SD as the limit value, 10.71% of the patients had basilar invagination, 35.71% had basilar impression, and 39.29% had platybasia, the latter being the most common finding.  
osteogenesis imperfeta; craniocervical junction; platybasia; basilar impression; basilar invagination; magnetic resonance imaging Article
## 10 Conclusions and Future Work  
### Conclusions  
1. The fault-tolerance capability of an IoT network is critical, especially when mission-critical systems are built using IoT technologies.
2. The fault-tolerance capability of an IoT network can be enhanced by making suitable changes in each of the layers of the IoT. This paper focuses on enhancing the fault-tolerance capability considering the controller layer.
3. A single base station-based IoT is risky and unsuitable for implementing mission-critical systems.
4. The fault-tolerance capability of the IoT network improves when a redundant base station is added, and the same is connected via an intelligent-relay-based network. The success rate of the revised IoT network increased from 0.827 to 0.948, which is a 14.23% improvement.
5. Considering both parts of the network, the combined success rate, which includes the path from cluster heads to the base station and the path from the base station to the controller, improved from 0.45 to 0.64, a 42% improvement. The latency of communication using such a network is minimum.
6. The path-finding algorithm implemented in the intelligent relays requires fewer operations than any other algorithm presented in the literature.
## 4 Conclusions and Loose Ends
### Security-Proofing--Concerns on IoT Network Reliance
#### 4.1.1 Vulnerabilities and Long-Term Patching  
Communication relays, by their function, could be targeted by attackers seeking to intercept or manipulate data. These nodes become critical points of security, especially considering they are exposed two-fold, as they need to interact with two different networks. Therefore, deep attention with robust protection measures are necessary. Encryption protocols, such as TLS/SSL, are vital in securing data in transit, ensuring that data passing through the relay remains confidential and tamper proof. Nevertheless, one must not ignore up-and-coming quantum computing, which may be used to break classic cryptographic algorithms in a matter of seconds.  
Fortunately, so-called Post-Quantum Cryptography (PQC) is being actively developed, and has already proven to be reliable. PQC algorithms provide protection against both classical and quantum attacks, and they can be implemented on classical, binary computers [47; 48; 49; 50].  
This is a vital requisite to guarantee long-term security and integrity within IoT networks, as these devices are expected to continue relying on classic computing CPUs for years to come, even when quantum computing starts to settle in other fields. However, it is mandatory for vendors, developers, and the like to ensure current IoT devices receive future firmware updates, as failing to do so may result in significant vulnerable points within larger networks.  
Keeping the Relay and its connected devices updated with the latest security patches is fundamental in protecting against known vulnerabilities. Automated update mechanisms can facilitate this process, ensuring that the system is resilient against emerging threats.
# Quasi-All-Passive Thermal Control System Design and On-Orbit Validation of Luojia 1-01 Satellite
## 5 Experimental Verification
### Test Results  
The temperatures of the instruments of the satellite are obtained during the thermal balance test in both high temperature conditions and low temperature conditions. There are two measuring points on both camera and battery which are considered to be critical instruments to the satellite system. It can be concluded from the Table 13 that all the temperatures of instruments are well controlled. The highest temperature, which is 29.3 \({}^{\circ}\)C, appears on the navigation unit during high temperature  
Figure 14: Testing site of Luojia 1-01.  
conditions and the lowest temperature, which is \(-0.5^{\circ}\)C, appears on the star tracker A during low temperature conditions.  
The duty cycle and power consumption of the heating regions of active heat control are worth paying attention to. The parameters are shown in Table 14. It can be seen that the power consumption is 1.28 W and 2.72 W, respectively, under high and low temperature conditions. Because of the extremeness of the high temperature conditions, the power consumption at low temperature is a little bit over the maximum satellite power consumption.
## 2 Velocity Estimation  
In this section, the two considered techniques for GNSS-based velocity estimation are described: Doppler-based velocity estimation in Section 2.1 and TDCP-based velocity estimation in Section 2.2. For both algorithms, the measurements are differently weighted according to a suitable criterion, which is explained in Section 2.3. The adopted RAIM-FDE technique, that is, the subset testing, is described in Section 2.4.  
The following nomenclature is adopted hereinafter:  
Receiver position: \(\underline{r}_{u}=[x_{u},y_{u},z_{u}]\).  
Satellite position: \(\underline{r}_{S}=[x_{S},y_{S},z_{S}]\).  
Receiver velocity: \(\underline{v}_{u}=\left[v_{ux},v_{uy},v_{uz}\right]\).  
Satellite velocity: \(\underline{v}_{S}=\left[v_{Sx},v_{Sy},v_{Sz}\right]\).  
Unit vector of the direction from receiver to satellite: \(\hat{\varepsilon}=\left[e_{x},e_{y},e_{z}\right]\).  
Geometric distance between receiver and satellite: \(d\).  
Positions and velocities are expressed in the Earth-centered, Earth-fixed (ECEF) frame.
## 2 Materials and Methods
### SiC Sensors: Tecnology Features
#### 2.1.2 Monte Carlo Simulations of the SiC Response
The presence of the bulk substrate traversed by the particles causes a non-negligible angular dispersion and increasing divergence of the incident beam as it is shown in Figure 3b. A 10 \(\upmu\)m SiC active layer (simulating the free-standing membrane) and a detector with a total thickness of 500 \(\upmu\)m, including the substrate, is simulated considering an incident beam with a mean energy of 9 MeV and input angular distribution shown in Figure 3b (blue points). As can be seen, the angular distribution after the 10 \(\upmu\)m-thick SiC detector is very well overlapped with the one of the incident beam, indicating that unsignificant perturbation is provided by the sensor. On the other hand, after the 500 \(\upmu\)m-thick detector, the angular distribution results are much wider with a divergence that reaches 20\({}^{\circ}\). This clearly demonstrates that free-standing membranes are fundamental for a practical use of such detectors for beam monitoring under UHDR beams, especially at lower incident  
Figure 2: Produced charge within a 1 \(\times\) 1 cm\({}^{2}\) (**a**) and 2 \(\times\) 2 mm\({}^{2}\) (**b**) SiC detector for electron beams of 7 MeV, 9 MeV, 100 MeV and 200 MeV delivering an equivalent dose in water of 2 Gy. Results are obtained by means of Monte Carlo Geant4 simulations for SiC active thicknesses of 2 \(\upmu\)m, 5 \(\upmu\)m, 10 \(\upmu\)m, 15 \(\upmu\)m and 20 \(\upmu\)m.  
energies. Differently, when dealing with VHEE, the presence of the substrate does not heavily affect the angular distribution of the incident electron beam, as expected.
## 2 Benchmarking Mobile Laser Scanning Systems
### Methods for Accuracy Evaluation  
Firstly, the received point clouds were checked by comparing them with the reference data (Section 2.1.2) to detect any gross errors either in elevation or plane. If there was a larger systematic shift than one of a few centimeters, this was compensated to ensure validity in the comparison; especially a large systematic shift in plane can lead to distorted elevation accuracy results and it is a common practice to use some ground control points in laser scanning surveys to eliminate the bias.  
Comparison between the elevation reference points and the received MLS point clouds was carried out using the _Output control report_ tool in TerraScan-software [40]. It reads in the reference points and loads every laser point within a given search radius from the individual reference points. Then a small triangulated surface model is created from the laser points and laser elevation is computed for each of the reference points' easting-northing locations from the triangulated model surface. This effectively interpolates the laser elevation from the three laser points closest to the reference point to be compared. The search radius used for the densest point clouds (RIEGL, Streetmapper and Optech Lynx) was 20 cm, for the others it was 50 cm. The maximum permitted slope in the triangulated model was set to 20\({}^{\circ}\).  
The planimetric accuracy was evaluated by measuring the reference targets in the received MLS point clouds and then computing the differences in easting and northing.  
The most deviating values were checked against the ground truth and removed from the analysis if there was any doubt that the error was due to the target, not due to the system. These errors were mainly detected in the analysis of elevation accuracy and they were due to parked cars or changes in vegetation. Following this 'gross error filtering', the systematic errors were removed, in plane  
Figure 14: TerraTec AS mapping the Espoonlahti test field with an Optech Lynx Mobile Mapper.  
separately for easting and northing, and the accuracy values were computed. The minimum, maximum and standard deviation values were computed for both elevation and planimetric accuracy. The mean and root mean squared errors (abbreviated to RMSE, Equation (1)) were determined for the description of the planimetric accuracy:  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{n}\left(d_{i}\right)^{2}}{n}} \tag{1}\]  
where \(d_{i}\) is the distance between the reference target and the point cloud target, and \(n\) is the number of samples.