esa-sceva/satcom-chunk-collection huggingface dataset:
# Untargeted Urinary Volatilomics Reveals Hexadecanal as a Potential Biomarker for Preeclampsia
## 4 Materials and Methods
### Data Processing  
After GC-MS analysis, data were normalized to minimize systematic bias and reduce technical variation to ensure accurate identification of metabolomic changes. All raw mass spectrometry data were processed using MS-DIAL software ver. 4.9., which enabled deconvolution, denoising, smoothing, peak identification, peak alignment and peak filtering [31; 32].  
Unknown metabolites were identified by comparison with the NIST library, and further validation and biological interpretation were performed using the Human Metabolome Database (HMDB). After identification, the data matrix was uploaded to the MetaboAnalyst 6.0 platform for multivariate statistical analysis [32]. The data matrix contained 91 samples with 107 peaks (\(m\)/z and retention time). The two groups were defined as follows: the preeclampsia (PE) group, consisting of 45 samples, and the control group, consisting of 46 samples.  
Before statistical analyses, the data were standardized to the median. Three univariate statistical tests were performed to assess the differences between the groups: fold-change analysis, \(t\)-tests and volcano plots. The significance threshold for the \(t\)-test was set at \(p<0.05\) and the fold change (FC) threshold was set at 2.  
Following the initial univariate analyses, ROC curve analysis was employed to evaluate the diagnostic potential of the identified biomarker.  
In addition, the identified potential biomarkers were subjected to enrichment and pathway analysis using the MetaboAnalyst 6.0 platform. Pathways with an impact score of more than 0.10 were classified as significant and potentially relevant to the pathophysiology of PE.
# A Multi-Channel Uncertainty-Aware Multi-Resolution Network for MR to CT Synthesis
## 2 Methods
### Data  
The whole-body dataset used for training and validation consisted of 32 pairs of \(T_{1}\)- and \(T_{2}\)-weighted MR images (voxel size \(0.67\times 0.67\times 5\) mm\({}^{3}\)) and CT images (voxel size \(1.37\times 1.37\times 3.27\) mm\({}^{3}\)). Both MR and CT images were acquired in consecutive sessions on the same day. Whole-body MR images were acquired in four stages, thus requiring pre-processing to generate a uniform MR image. Firstly, the bias-field within each MR image was corrected. Secondly, the four distinct stages were fused using a percentile-based intensity harmonisation approach. All images were then resampled to CT resolution. In order to align MR and CT images, a two-step registration approach was performed. In the first step, MR and CT images were aligned using a rigid registration algorithm followed by a very-low-degree-of-freedom non-rigid deformation. The second step included a non-linear registration, which used a cubic B-spline with normalised mutual information to correct for soft tissue shift [22; 23].  
An additional head dataset was used for training and validation on a different region of the body. The dataset consisted of pairs of \(T_{1}\)- and \(T_{2}\)-weighted MR (voxel size \(1.1\times 1.1\times 1.1\) mm\({}^{3}\)) and CT (voxel size \(0.586\times 0.586\times 2.5\) mm\({}^{3}\)) images of 20 patients. The data was co-registered following the same series of rigid and non-rigid registration operations as for the whole-body data. All images were then resampled to approximately 1 mm-isotropic resolution.  
All images within both datasets were rescaled to be between 0 and 1 as it has been found to increase stability during training.
## 3 Data and Method  
### Data  
#### Data
before the disaster. Subsequently, 15 Sentinel-1 images from descending Track 48, collected between October 2023 and March 2024, were applied to discover the surface deformation after the disaster. We employed the Shuttle Radar Topography Mission (SRTM) DEM with a 30 m resolution as the reference DEM to remove the topographic phase.
# Sensors and Communication Simulation for Unmanned Traffic Management
## 4 Agent-Based Simulation in a Distributed Architecture
### Simulation Execution
Then, it submits the different flight plans to the authorization process and sets those approved (or affected by a contingency) to be simulated. After that, the simulation starts at user request and an event is generated with a start order that is sent to all the simulation servicesthrough the actions bus. From this moment on, all the agents are activated, and their internal processes of status update started. At the same time, the actions bus and message bus are also activated. When the agents start working, the exchange of messages in the form of events between the different agents through the message bus begins. At the same time, the periodic sending of information from the simulation agents towards the UTM system also starts. This information is also compiled into messages that are transmitted across the simulated network (mobile communications simulation service and network simulation service) using the message bus. Then, it is captured by the interface with evaluated UTM service microservice, translated to UTM data formats, and finally forwarded to the UTM platform. In parallel, the action planner sends the actions in the planned time instant to the actions bus that makes them arrive to the corresponding simulation service, so that the agents act accordingly modifying their behavior. As the UTM system receives the different input messages, it generates outputs that are available for the analysis of the evaluation services. At the same time, current agents' state can be retrieved at any time through the pre-flight and orchestration service that has access to it through a REST interface with the different simulation services. With this information, the evaluation services can produce their evaluation metrics, achieving the ultimate goal of our platform. This simulation process is depicted in Figure 4.  
Finally, the environment on top of which the simulation is developed is defined by the meteorological service, which is in charge of providing weather information (either real by accessing openweathermap or user-defined), the airspace definition service that provides information of non-flight zones, and the ground service that allows access to a digital terrain model (using TanDEM-X 90m data [27]). Additionally, the drone registration service manages the UTM identifiers allowing mapping them together with the drones in simulation to retrieve their information. To do so, it implements a functionality that mimics the registration functionality of a UTM system. These microservices model and share the simulation environment with the UTM platform, so it may adapt UTM operation to the rules, airspace, terrain and weather. Figure 4: Simulation services interaction in simulation time.
## 1 Introduction
In general, the GNC aspects of ADR pose a significant challenge in the development of the mission, especially when considering a non-cooperative target, and hence the technical descriptions in industrial publications often do not provide a fine level of detail [15, 21, 24]. Nonetheless, there are some key elements that are commonly assumed in ADR operations. For mission design and guidance, the use of passively safe trajectories to approach the target is widely employed [24, 27]. For navigation, angles-only navigation [28] is gaining increasing attention, especially for far-mid range phases, while visual-based navigation [21] is commonly used when approaching the target. Finally, for the control, a particular focus is put on the use of six Degrees-of-Freedom (6 DOF) control and forced motion in the close-proximity phase [29]. This paper addresses the topic of close-proximity operation for non-cooperative targets, using, as reference, mission ClearSpace-1 (CS-1), whose importance is widely recognized, and focuses on the areas of design and validation of Guidance, Navigation, and Control(GNC), and Mission Analysis (MA). The main contributions of the paper are the consolidated elaboration of Close-Proximity concepts and guidelines, stemming from [11], their application to the GNC/FDIR solutions considered for CS-1, and a positive demonstration of the V&V compliance by design, analysis, and testing. The GNC solutions and V&V approaches described in the paper have an application to ADR and IOS solutions in general, and can be standardized in aspects such as the following: vision-based navigation for close-range rendezvous with illumination constraints, robust control for performance and stability robustness of high-precision robotic capture, passively safe approach and target inspection through Relative Orbital Elements (ROE) guidance with vector collinearity, active safety with the Cancel and Collision Avoidance Maneuver (CAM), an efficient V&V of solutions through a combination of fast and of high-fidelity simulators, allowing us to identify properly the worst case conditions in the former, and a detailed analysis and confirmation of the results in the latter. Whereas the requirements elaborated in [11] have a formal and self-contained formulation, in this paper they are embedded in the body of the text, to provide for context and improve readability. Likewise, the V&V, by design and by analysis, are addressed explicitly in [11], whereas in this paper these are inherently associated with the description of the design options and their analysis, respectively. Finally, the V&V by testing corresponds to the Monte Carlo campaign, as described in a dedicated section. This paper is organized as follows: Section 2 outlines the CS-1 case study; Section 3 defines the CPO concepts and demonstrates their validity by design and analysis; Section 4 addresses the GNC/FDIR system validity, also by design and analysis; and Section 5 presents the validation by Model-in-the-Loop testing. Finally, Section 6 discusses the conclusions and the way forward of this work.
# Bulk Grain Cargo Hold Condensation Based on Computational Fluid Dynamics
## 3 Numerical Simulation
### Initial Simulation Parameter Setting
#### 3.3.2 Boundary Condition  
(1) Solar Radiation Boundary  
For the solar radiation during the ship's voyage, because the radiation to the hatch enclosure at the hatch is only present in the morning and evening, the angle of incidence of the sun on the hatch coating is small, so the radiation of the sun on the hatch coaming is also small. Therefore, to shorten the calculation time and improve the accuracy of the calculation results, assuming that solar radiation only affects the hatch enclosure during ship navigation, the absorption coefficient of the cargo hold hatch cover is set to 0.8.  
(2) Sea Convective Heat Exchange Boundary  
1. The shipside and the bottom of the cargo hold are set as the convective wall. The heat transfer coefficient of the wall in contact with seawater is set to 7616.36 W/m\({}^{2}\cdot\)K [40] and the heat transfer coefficient of the wall in contact with air is set to 5.62 W/m\({}^{2}\cdot\)K.  
(3) Ventilation Boundary  
2. According to the ventilation record, the ventilation time from 2 July to 16 August is shown in Table 3.  
The voyage started on 2 July 2019, and the navigation to Longkou was completed on 16 August 2019. Since the ventilation form was natural ventilation with small speed, the air inlet wind speed was set to 1 m/s, and the outlet was set to pressure outlet according to the feedback data from the ship. In this paper, it is assumed that the grain surface is fully ventilated.  
In summary, the simulation boundary conditions for the ship's cargo hold storage and transportation process are set as in Table 4:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Name** & **Boundary Type** & **Parameter** & **Value** \\ \hline \hline Ventilation inlet & Speed Inlet & Wind velocity, \(v_{in}\) & 1 m/s \\ \hline Ventilation outlet & Pressure outlet & Gauge pressure,\(P_{\text{out}}\) & 0 Pa \\ \hline  & & Soybean density, \(\rho_{g}\) & 769.7 kg/m\({}^{3}\) \\  & & Specific heat capacity, C\({}_{\text{g}}\) & 1870 kJ/(kg\(\cdot\)K) \\ Grain & Porous media & Thermal conductivity, \(\lambda_{g}\) & 0.13 W/(m\(\cdot\)K) \\  & & Porosity, \(\varphi\) & 0.5 \\  & & Viscosity coefficient, \(\gamma\) & 1.2 \(\times\) 10\({}^{9}\) m\({}^{2}\)/s \\  & & Inertial resistance, C\({}_{2}\) & 2800 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Setting of boundary conditions and parameters.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Date** & **Ventilation** & **Ventilation Record** \\ \hline
2 July–12 July & No & Fumigation \\
13 July–19 July & Yes & Ventilation \\
20 July–25 July & No & Wind and waves \\
26 July–7 August & Yes & Ventilation \\
8 August & No & Rainfall \\
9 August–16 August & Yes & Ventilation \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ventilation recording data during the period from July 2 to August 16.
## 3 Results and Discussion
### Characterization of the DP Composite
#### 3.1.4 Thermal Cycle Test  
Figure 7 and Table 3 show that the changes in the phase change temperature and enthalpy of DP after 300 cycles and 600 cycles are not significant. It can be seen that the melting temperature and solidification temperature of DP were basically the same before and after the thermal cycles, and that the average thermal latent heat value decreased by 0.79 J/g only. This indicates that the newly prepared DP is thermally stable and reliable over a long period of time, and is considered a good potential candidate for practical application.  
Figure 6: FT-IR spectra: (**a**) paraffin, (**b**) diatomite, and (**c**) diatomite/paraffin.
# Secure and Dynamic Memory Management Architecture for Virtualization Technologies in IoT Devices
## 6 Operational Changes in the Proposed Architecture
#### Memory Allocation:  
Segments are allotted to virtual machines by storing the segment ID and virtual machine ID in the MPT table, and the assigned segment information is passed to the hypervisor. The hypervisor passes this information to the corresponding domain/virtual machine through _start_info_pages_ and _shared_info_pages_. This information is used by the guest operating system to update the _free_pages_ list. The memory allocation function in the guest OS assigns these _free_pages_ to virtual address space and updates the page tables and TLB.
## 2 Theoretical Foundations  
### The Technology Acceptance Model  
The technology acceptance model (TAM) [10] provides this study's conceptual framework. TAM is based on the theory of rational behavior (TRA) [12] and the theory of planned behavior [13] in social psychology. TAM builds a logical chain to interpret and predict potential users' acceptance of emerging technologies based on perception-attitude-intention-behavior. As shown in Figure 1, the core of the model is to identify and emphasize perceived usefulness (PU) and perceived ease of use (PEU) as the decisive factors affecting users' technology adoption intentions [14]. Specifically, from an individual subjective perspective, PU refers to the extent to which the use of a certain technology can improve work performance or achieve goals. On the other hand, PEU focuses on the simplicity of technology, which means the user's perceived ease of using the technology. TAM further pointed out that PEU significantly affects PU, which can explain the differences in PU levels between users.  
In previous research, the effectiveness of TAM has been widely verified when it is used as a framework for understanding technology adoption behavior in diverse contexts [15]. Given the innovative application of AR technology in the retail field of cultural heritage products, it represents an innovation in the retail format and provides consumers with an unprecedented shopping experience. Therefore, TAM has become a compelling theoretical foundation for studying consumers' acceptance of AR technology to promote the purchase behavior of cultural heritage products. Through the TAM framework, this study can systematically analyze how AR technology affects PU and PEU consumers of cultural heritage products and verify its impact on purchase intention.
# Two-Channel Information Fusion Weak Signal Detection Based on Correntropy Method +
###### Abstract  
In recent years, as a simple and effective method of noise reduction, singular value decomposition (SVD) has been widely concerned and applied. The idea of SVD for denoising is mainly to remove singular components (SCs) with small singular value (SV), which ignores the weak signals buried in strong noise. Aiming to extract the weak signals in strong noise, this paper proposed a method of selecting SCs by the correntropy-induced metric (CIM). Then, the frequency components of characteristic signals can be found through cyclic correntropy spectrum (CCES) which is the extension of the correntropy (CE). The proposed method firstly merges the signals collected by the two channels, secondly uses the principal components analysis (PCA) method to reduce the dimensionality, thirdly uses the singular value decomposition method to decompose the signal, fourthly calculates the CIM value to determine the selected singular components for construction, and finally uses the cyclic correntropy spectrum displaying the characteristics of the reconstructed signal. The experimental results show that the proposed method has a good effect on feature extraction.  
singular value decomposition; correntropy induced metric; two-channel information fusion; weak feature extraction; cyclic correntropy spectrum +
Footnote †: Copyright © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
# Promising Approaches for Heat Utilization in Agricultural Machinery Engines
## 2 Presentation of the Main Material  
### Research Methodology  
This study utilized several technical and economic indicators. The technical indicators included the exhaust gas energy utilization factor (\(\psi_{g}\)), the coolant energy utilization factor (\(\psi_{c}\)), and the specific power of the heat recovery device. The economic indicators comprised return, investment, and the simple payback period.  
The exhaust gas energy utilization factor is calculated as follows:  
\[\psi_{g}=\frac{T_{g}-T_{b}}{T_{g}-T_{a}}, \tag{1}\]  
where \(T_{g}\) is the exhaust gas temperature, K, \(T_{a}\) is the ambient temperature, K, and \(T_{b}\) is the exhaust gas temperature after passing through the heat recovery system, K.  
The coolant energy from an engine can be applied for heating purposes, such as space heating or fuel preheating. The coolant energy utilization factor is determined by the following:  
\[\psi_{c}=\frac{T_{P_{1}}-T_{h}}{T_{P_{1}}-T_{P_{2}}}, \tag{2}\]  
where \(T_{P_{1}}\) is the coolant temperature at the engine outlet, K, \(T_{P_{2}}\) is the coolant temperature at the engine inlet, K, and \(T_{h}\) is the coolant temperature at the outlet of the waste heat recovery exchanger, K.  
The exhaust gas temperature must exceed the dew point temperature of sulfuric acid vapors. The minimum permissible exhaust gas outlet temperature is calculated by the formula:  
\[T_{b}=T_{S}+98.5\times\sqrt[3]{S}+\Delta T_{S},\ \text{K}, \tag{3}\]  
where \(T_{S}\) is the steam dew point temperature, K, \(S\) is the sulfur content in diesel fuel (%), and \(\Delta T_{S}\) is a temperature margin set at 25 K.  
We propose using the total waste heat utilization factor to evaluate the energy efficiency of a heat recovery system. This factor represents the ratio of the system's power to the total input energy from fuel, calculated as:  
\[\xi=\frac{P_{hr}\times\eta_{e}}{P_{e}\times(1-\eta_{e})},\ \text{kW}/\text{kW}, \tag{4}\]  
where \(P_{hr}\) is the power of the waste heat recovery unit, kW, \(\eta_{e}\) is the efficiency of the diesel engine, and \(P_{e}\) is the rated power of the engine, kW.  
The capacity of the waste heat absorption chiller can be calculated using the formula:  
\[P_{a}=\varepsilon\times G_{cg}\times G_{pg}\times\big{(}T_{g}-T_{b}\big{)},\ \text{kW}, \tag{5}\]  
where \(\varepsilon\) is the power coefficient, \(G_{cg}\) is the exhaust gas mass flow rate, kg/s, and \(G_{pg}\) is the specific heat capacity of the exhaust gases, kJ/(kg\(\cdot\)K).  
Standard methods were used to evaluate engine efficiency at different intake temperatures. An economic analysis was performed by calculating the simple payback period and determining the maximum acceptable investment cost.
# Using Long-Range Wireless Sensor Network to Track the Illegal Cutting Log
## 2 Research Methodology and Proposed System
### Flowchart of Proposed System  
The occurrence of illegal cutting is detected by a sound sensor and an accelerometer sensor while the sensor nodes are in active. The data that were sensed by those sensors proceeded by the Arduino Uno. The Raspberry Pi and LoRa GPS/HAT proceed with the information of GPS data such as RSSI data, latitude, and longitude. The data that was collected from each sensor node was then sent to the Outdoor LoRa OLG01 Gateway if the illegal cutting has occurred. Illegal logging occurred when the value measured by the sound sensor exceeded a predetermined threshold. In addition, the changes of accelerometer data sensor must be in accordance with the changes in the determined threshold value, which indicates a change of tree position from the previous position. When both sensors are stated in a true logical condition, the illegal logging is indicated as happened.  
All the aggregation data at the gateway will be sent to LoRa Network Server through the IP connection. The data were computed in the cloud server and displayed the result on a monitor or mobile device. The aggregation data sent are the data translated by LoRa in the form of RSSI data, sound sensor, vibration sensor, and GPS data like latitude and longitude. All the data recorded in the database will be displayed and informed to the user through telegrams and web applications. The system components can be seen in the flowchart shown in Figure 6.  
Figure 6: Flowchart of the Proposed System.  
In the term of the user, the flowchart of the application of illegal logging can be seen in Figure 7. When the telegram message was received by the user (e.g., forest ranger), the user opened the illegal logging application and saw the information about the activity. The tree position can be tracked by clicking on the google map application, and the user can chase the perpetrator of the illegal log theft.