esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Three-Dimensional Electrical Impedance Tomography System for Rock Samples
## 5 Control System  
To meet the requirements of real-time performance and multifunctionality, this design incorporates a lightweight real-time operating system uCOS-III [28] on the STM32 platform. Compared with a bare computer system, RTOS provides an efficient solution for meeting stringent real-time requirements [29; 30]. uCOS-III is an operating system specifically designed for controllers with limited onboard hardware resources. It enables function trimming to achieve simplicity by retaining only the necessary features required by the instrument, thereby minimizing resource utilization on the control board.  
Figure 5: Short-circuit noise curve of six channels.  
The software design of the embedded system primarily consists of three parts: a hardware driver for low-level operations, the porting of the operating system, and the application-level task programs running on the operating system.  
In the application layer development, the various functions that the embedded system needs to implement are encapsulated into specific tasks. The designed task modules mainly include:  
1. Initialization Task: This task primarily performs hardware initialization, operating system initialization, and the creation of other tasks. It then starts the task scheduling of the operating system.
2. Command Parsing Task: The embedded system receives commands from the Android App through the Bluetooth module. Simultaneously, upon receiving a command, it provides feedback to the Android App to indicate that the command has been received, ensuring stable information transmission. The command information consists of a 64-bit binary stream.
3. Relay Control Task: The main functionality of this task is to control the relay switches. As the accurate opening and closing of relays in each measurement directly impact the measurement accuracy of the instrument, a validation mechanism for relays has been designed in the software program.
4. Data Storage Task: The embedded system needs to store the measured raw data in an SD memory card to create data backups. Direct Memory Access (DMA) technology is used for data storage. DMA enables high-speed data transfer between peripherals and memory or between memories. Without CPU intervention, data can be moved quickly using DMA, saving CPU resources for other operations.
5. Data Transmission Task: Data transmission is conducted through a Wi-Fi module, establishing a stable Transmission Control Protocol (TCP) communication with the Android App.
## 3 Experimental and Simulation Setup
### Simulation Setup  
The simulation experiment was run on NS-3 version 3.33. NS-3 stands out as an open-source discrete-event network simulator, providing the capability to trace internal events, a flexible configuration system, and diverse modules for simulating other technologies such as ethernet, Long Term Evolution (LTE), and WiFi in multi-technology scenarios. This versatility allows different segments of the same network modeled and different systems to be interconnected when required. The widely adopted NS-3 simulator is well recognized in research and academia, diligently maintained by an active community, and often supported by the Google Summer of Code as a flagship open-source project [41].  
Our simulations aimed to replicate a 5G-enabled vehicular communication scenario featuring an OBU and an RSU within a virtual urban environment. The OBU was modeled as a mobile node that generates messages and establishes 5G connections with a strategically  
Figure 3: Experiment architecture.  
placed stationary RSU. The experiment focused on assessing the energy consumption induced by the impact of 5G communication, with variable transmission range settings on the edge node (RSU) to simulate distances of 30 m, 50 m, 70 m, and 80 m. The simulation was executed for multiple scenarios and the resulting data were analyzed to understand how the communication distance influenced the energy consumption of the edge node in the 5G vehicular communication system.
# The Evolution and Development Trends of LNG Loading and Unloading Arms
## 2 Development of Marine LNG Loading and Unloading Arms  
In the mid-19 th century, [PERSON] designed the world's first flexible loading and unloading arm, which could effectively prevent oil and gas leakage [9]. In 1956, FMC Corporation manufactured the world's first marine fluid loading and unloading equipment, and in the 1970s, it successfully developed the world's first 600 mm-caliber fluid loading and unloading arm. Internationally, there are mainly three structures of fluid loading and unloading equipment, namely the self-supporting mechanism of FMC Corporation, the independent supporting structure of SVT in Germany, and the new four-bar linkage transmission mechanism of Kanon in the Netherlands [10]. The Dutch company Kanon combined these three structures to develop a new type of fluid loading and unloading arm with a self-supporting and independent supporting mechanism, which can greatly improve the actual working performance of the fluid loading and unloading arm. In addition, the British company Woodfield has conducted in-depth research on special loading and unloading arms. Based on the traditional fluid loading and unloading arms, it has developed a new type of special fluid loading and unloading arm with low-temperature resistance and corrosion resistance, which can transport corrosive products at a low temperature of minus 150 degrees. The development history of LNG loading and unloading arms is shown in Figure 1.  
The marine LNG loading and unloading arm is an important facility installed at the dock for unloading LNG from LNG carriers [11]. When an LNG carrier arrives at the receiving terminal, LNG is transported to the terminal storage tanks through a series of devices such as liquid-phase loading and unloading arms and unloading pipelines [12]. Internationally, there are only a handful of manufacturers that can independently produce marine LNG loading and unloading arms. Typical structures of marine LNG loading and unloading arms include the products of FMC Technologies, Connex SVT in Germany, and Niigata in Japan. For example, the ARCTIC-type LNG loading and unloading arm produced by SVT in Germany can complete the loading task in an environment of \(-\)196 \({}^{\circ}\)C and 45 bar.
## 2 Materials and Methods  
### ERA5 Dataset  
ERA5 data are produced by the Copernicus Climate Change Service (C3S) at ECMWF and is the fifth-generation ECMWF atmospheric reanalysis of the global climate covering the period from January 1940 to the present [33]. Compared to the discontinued ERA-Interim reanalysis model, ERA5 assimilates a larger number of observations, rising from an average of 0.75 million per day in 1979 to about 24 million in 2018, boosted mainly due to the increase in satellite radiances over time, as well as recent additions like GNSS-RO, scatterometer ocean vector wind and altimeter wave height data, ozone products, and ground-based radar observations [38]. It is freely available online by the C3S at [[https://cds.climate.opernicus.eu/cdsapp#](https://cds.climate.opernicus.eu/cdsapp#)]([https://cds.climate.opernicus.eu/cdsapp#](https://cds.climate.opernicus.eu/cdsapp#))!/dataset/reanalysis-era5-complete?tab=overview (accessed on 1 May 2024) at a horizontal resolution of 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) on 37 pressure levels from 1000 hPa to 1 hPa. Given the available ERA5 variables, the vertical water vapor density (\(\rho_{v}\)) profiles, can be calculated by  
\[\rho_{v}=\frac{P\cdot q}{287.055\cdot T_{v}} \tag{1}\]  
where \(P\) is the atmospheric pressure (Pa), \(T_{v}\) is the virtual temperature (K), and \(q\) is the specific humidity (kg/kg) [39]. Hundreds of studies involving the ERA5 model have established it as one of the best reanalyses ever produced, providing an unparalleled dataset for validation in the absence of in situ observations.
# Two-Stage Fusion-Based Audiovisual Remote Sensing Scene Classification
## 4 Experiments
### Fusion Strategy Experiments  
This section chooses various \(\varphi(\cdot)\) and \(\phi(\cdot)\) functions to find the best function settings for feature fusion and decision fusion. On the three metrics (precision, recall, and F1-score), as shown in Table 6, we used the average, max, and sum functions to show the quantitative results of various fusion strategies.  
The following observations can be made. First of all, the baseline (ADVANCE scheme) uses feature splicing in the feature-level fusion method to combine audio features and image features. This straightforward fusion approach is not superior to the single-mode image method. When using a two-stage hybrid fusion strategy, the fusion's performance will increase and its constituent features will become more abstract. Second, in feature-level fusion and decision-level fusion, the performance of the \(max\) and \(sum\) functions for the selection of the \(\varphi(\cdot)\) and \(\phi(\cdot)\) functions was nearly identical, and both were superior to the _mean_ function. The most-ideal strategy is when the \(max+sum\) function setting was used. Last but not least, the proposed strategy was actually a result of feature enhancement performed in two steps. The weights of the audio and the image that determine the results of classification were not equal under the two stages' weighting; instead, the more-significant party was given a greater weight.  
Figure 10: The log-Mel spectrogram of two audio samples under the previous conditions. The category label appears at the top of the spectrogram, and the file name within the dataset appears at the bottom.
## 3 Extension to Nonlinear Observations  
In a real radar multi-target tracking system, the observation model is usually nonlinear:  
\[z_{k}=\begin{bmatrix}\theta\\ r\end{bmatrix}=h(\mathbf{x}_{k})+\mathbf{v}_{k} \tag{51}\]  
where \(\theta\) and \(r\) are azimuth and range, and \(h(\mathbf{x})\) is as follows:  
\[h(\mathbf{x})=\begin{bmatrix}\arccos\left(\frac{\eta_{x}-s_{x}}{\sqrt{\left(\eta_{ x}-s_{x}\right)^{2}+\left(\eta_{y}-s_{y}\right)^{2}}}\right)\\ \sqrt{\left(\eta_{x}-s_{x}\right)^{2}+\left(\eta_{y}-s_{y}\right)^{2}}\end{bmatrix} \tag{52}\]  
where \(\mathbf{x}=\begin{bmatrix}\eta_{x}&\dot{\eta}_{x}&\eta_{y}&\dot{\eta}_{y}\end{bmatrix}^ {\mathrm{T}}\) and \(\begin{bmatrix}s_{x}&s_{y}\end{bmatrix}^{\mathrm{T}}\) denote the state vector and position of a sensor, respectively. In this case, a conversion of the observation is required. The converted measurement is given by:  
\[\mathbf{z}_{xy}=\begin{bmatrix}&s_{x}+r\cos\theta\\ &s_{y}+r\sin\theta\end{bmatrix} \tag{53}\]  
The error covariance of \(\mathbf{z}_{xy}\) is:  
\[\mathbf{R}_{xy}=\mathbf{G}\begin{bmatrix}\sigma_{\theta}^{2}&0\\ 0&\sigma_{r}^{2}\end{bmatrix}\mathbf{G}^{\mathrm{T}}=\mathbf{G}\mathbf{R}_{k}\mathbf{G}^{ \mathrm{T}} \tag{54}\]  
where \(\sigma_{\theta}\) and \(\sigma_{r}\) are the standard deviations of angle and range noises, and matrix \(\mathbf{G}\) can be given by:  
\[\mathbf{G}=\begin{bmatrix}-r\sin\theta&\cos\theta\\ r\cos\theta&\sin\theta\end{bmatrix} \tag{55}\]  
In the case of a nonlinear observation, the measurements in (11), (16), (26), (31), (36) and (39)-(41) are the converted measurement, and the error covariance \(\mathbf{R}_{k}\) in (18), (26), (33) and (43) should be replaced by converted error covariance \(\mathbf{R}_{xy}\).
# Radiomics in Gynaecological Imaging: A State-of-the-Art Review
## 4 Cervical Cancer  
Cervical cancer (CC) is ranked fourth among malignancies in terms of incidence, prevalence, and mortality in women worldwide; however, when early diagnosis is made, CC is one of the most successfully treatable forms of cancer [60]. Patients with CC are staged according to the TNM classification, and the clinical staging is based on the FIGO staging system [61]. The initial workup for assessment of pelvic tumour extent and to guide treatment options is based on pelvic MRI according to consensus recommendations by the ESMO, ESTRO, and European Society of Pathology (ESP) guidelines [62].  
Radiological assessment of cervical malignancy for the initial staging, response monitoring, and evaluation of disease recurrence is performed on pelvic MRI according to the ESUR guidelines [63]. The standard pelvic MRI protocol includes T1 WI without and with saturation, T2 WI with a slice thickness of 4 mm or less, and DWI sequences while contrast-enhanced MRI remains optional. However, pelvic MRI has some limitations in the assessment of CC, mainly related to the limited accuracy for nodes status, largely based on the size criteria (i.e., \(\geq\)1.0 cm in short axis) which yields a low pooled sensitivity of 56-61% [64], in the assessment of parametrial invasion with a pooled sensitivity of 76% and specificity of 94% [65], as well as in the evaluation of residual disease after chemoradiation therapy with sensitivity and specificity of 80% and 55%, respectively [66]. Given the current limitations of standard imaging techniques, over the last decade, there has been an increasing number of studies investigating if radiomics applied to CT or MRI may fill the current gaps in patients with CC.  
Most of these radiomics studies proved a moderate to high performance of radiomics or combined clinical-radiomics models suggesting that radiomics might be used as a prognostic biomarker and helpful in tailoring therapeutic management. In addition, some papers addressed technical issues about radiomics, including the development of a fully automatic whole-volume tumour segmentation tool [67], evaluation of robustness, stability, and reproducibility of radiomic features in pelvic MRI, suggesting the application of normalization prior to features extraction [68; 69; 70; 71] and the definition of the best volume of interest to achieve a specific outcome [72].  
Multicentric and prospective radiomic studies applied to CC are summarised in Table 2.
## 4 Conclusions  
High-moment FeCo nanoparticles with tunable compositions can be easily obtained by topochemical H\({}_{2}\) reduction (\(\sim\)350 \({}^{\circ}\)C) of Co-ferrite nanoparticles with different Fe/Co ratios. Both structural and magnetic characterizations confirmed the formation of metallic FeCo alloy nanoparticles with an average crystallite size of \(\sim\)30 nm featured by a high magnetization (\(\sim\)265 Am\({}^{2}\)/kg at 0 K and 0.5 T), significantly larger than the values of the FeCo bulk alloys of similar compositions, which could be ascribed to the presence of C atoms in the crystal structure coming from residual carbon present on the surface of the oxide particles. Temperature-dependent magnetization measurements indicate that the reduction of Co-ferrite to FeCo nanoparticles is a one-step process whose efficiency in terms of reduction of the processing temperature can be significantly enhanced through the application of a moderate external field (0.5 T), thus paving the way for the development of sustainable syntheses of high-moment metal nanoparticles.  
Conceptualization, A.O. and D.P.; methodology, F.L., P.C., N.P., F.C. and D.P.; validation C.S., V.R., F.L., M.F., N.P., F.C. and D.P.; formal analysis, C.S., N.P., F.C. and D.P.; investigation, A.O., G.V., P.M., J.-P.M.M., P.C., F.C. and D.P.; resources, N.P., F.C. and D.P.; data curation, A.O. and D.P.; writing--original draft preparation, A.O.; writing--review and editing, G.V., P.M., C.S., N.P., F.C. and D.P.; visualization, A.O.; supervision, V.R., M.F., N.P., F.C. and D.P.; project administration, D.P.; funding acquisition, V.R. and D.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Russian Science Foundation, grant no. 21-72-30032.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
Figure 5: Temperature dependence of the magnetization measured during cooling under a magnetic field of 0.5 T for as-reduced Fe\({}_{66}\)Co\({}_{34}\) and Fe\({}_{50}\)Co\({}_{50}\) MNPs. (left axis) M is the magnetic moment divided by the mass of initial oxide powder (m\({}_{\text{oxide}}\)). (right axis) M is the magnetic moment divided by the mass of derived metal m\({}_{\text{metal}}\) = m\({}_{\text{oxide}}\)\(\times\) (M\({}_{\text{w}}\)oxide/M\({}_{\text{w}}\)metal).
# Ice Production in Ross Ice Shelf Polynyas during 2017-2018 from Sentinel-1 SAR Images
###### Abstract  
High sea ice production (SIP) generates high-salinity water, thus, influencing the global thermohaline circulation. Estimation from passive microwave data and heat flux models have indicated that the Ross Ice Shelf polynya (RISP) may be the highest SIP region in the Southern Oceans. However, the coarse spatial resolution of passive microwave data limited the accuracy of these estimates. The Sentinel-1 Synthetic Aperture Radar dataset with high spatial and temporal resolution provides an unprecedented opportunity to more accurately distinguish both polynya area/extent and occurrence. In this study, the SIPs of RISP and McMurdo Sound polynya (MSP) from 1 March-30 November 2017 and 2018 are calculated based on Sentinel-1 SAR data (for area/extent) and AMSR2 data (for ice thickness). The results show that the wind-driven polynyas in these two years occurred from the middle of March to the middle of November, and the occurrence frequency in 2017 was 90, less than 114 in 2018. However, the annual mean cumulative SIP area and volume in 2017 were similar to (or slightly larger than) those in 2018. The average annual cumulative polynya area and ice volume of these two years were 1,040,213 km\({}^{2}\) and 184 km\({}^{3}\) for the RISP, and 90,505 km\({}^{2}\) and 16 km\({}^{3}\) for the MSP, respectively. This annual cumulative SIP (volume) is only 1/3-2/3 of those obtained using the previous methods, implying that ice production in the Ross Sea might have been significantly overestimated in the past and deserves further investigations.  
polynya ice production; SAR; Sentinel-1; sea ice; AMSR-2 2020212 12 1484 10.3390/rs12091484 12.350.-y 12020.- 5 May 2020
## 3 Results
### Secondary Outcomes  
The secondary outcomes, assessed using the VAS scale, are presented in Figure 2. At enrollment, the two study groups had comparable VAS scores, indicating similar perceptions of nasal obstruction. After 5 days of treatment (T1), a significant reduction in VAS scores was observed in the active group, reflecting a notable improvement in the subjective sensation of nasal obstruction. At T1, the mean VAS score was 64.3 (95% CI: 58.4-70.3) in the placebo group and 40.0 (95% CI: 33.7-46.3) in the active treatment group, further supporting the efficacy of the active product in alleviating nasal symptoms. Statistically significant differences were noted between the groups at T1 and between T0 and T1 within the active group.  
Figure 1: Total score of the I-NOSE questionnaire in the two study groups during this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **PLACEBO** & **ACTIVE** \\  & **N = 30** & **N = 30** \\ \hline Severity of nasal obstruction at T0 & & \\ Mild (5–25) & 0 & 3 (10\%) \\ Moderate (30–50) & 1 (3.3\%) & 6 (20\%) \\ Severe (55–75) & 24 (80\%) & 13 (43.3\%) \\ Extreme (80–100) & 5 (16.7\%) & 8 (26.7\%) \\ \hline Severity of nasal obstruction at T1 & & \\ Mild (5–25) & 0 & 17 (56.7\%) \\ Moderate (30–50) & 1 (3.3\%) & 12 (40\%) \\ Severe (55–75) & 22 (73.3\%) & 1 (3.3\%) \\ Extreme (80–100) & 7 (23.3\%) & 0 \\ \hline \hline \end{tabular} Discrete variables are reported as the number and proportion of subjects with the characteristic of interest.  
\end{table}
Table 3: Distribution of severity of nasal obstruction symptoms during the study period in the two groups.  
After 10 days from enrollment (T2), 60% of subjects in the active group achieved complete resolution of AR symptoms, compared to only 5.3% in the placebo group. This difference was statistically significant, underscoring the superior efficacy of the active product in resolving symptoms.  
To explore potential confounding factors influencing the treatment response, a multi-variable linear regression analysis was performed using the change in NOSE score (\(\Delta\)NOSE score) as the dependent variable. The independent variables included exposure to passive smoking, exposure to pets, urban setting, damp housing, breastfeeding, and the number of respiratory infections per year. The analysis revealed that none of these variables were significantly associated with the change in NOSE score, neither in the overall study population nor when stratified by treatment group. These findings suggest that the observed improvement in nasal obstruction symptoms was not influenced by these baseline characteristics.
###### Abstract  
With the rapid expansion of electronic information technology and rising material safety needs, the creation of composite materials that perform both electromagnetic microwave absorption (EMA) and flame retardancy has arisen as a materials science research hotspot. Metal-organic frameworks (MOFs) have great potential for developing novel multifunctional composite materials due to their unique structural characteristics and customizable functions. This work presents a comprehensive assessment of the most recent research findings on MOF-based EMA-flame retardant dual-functional composites. The fundamental mechanisms of EMA and flame retardancy are covered, including dielectric loss, magnetic loss, and both condensed-phase and gas-phase flame retardancy mechanisms. The development of composites based on Fe-MOF, Co-MOF, Ni-MOF, and polymetallic MOF in terms of EMA and flame retardancy is highlighted. These materials offer exceptional EMA performance and strong flame retardancy effects thanks to their unique structural designs and component regulations. In addition, some materials have great infrared stealth, thermal insulation, hydrophobic, and mechanical qualities. Ultimately, the problems of MOF-based dual-functional composites and their development possibilities are reviewed, giving valuable references for the development of new multifunctional composite materials.  
metal-organic framework; microwave absorption; flame retardant; composite materials +
Footnote †: journal: Composites Science  
**2025**, 9, 121