esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of the Dihedral Corner Reflector's RCS Features in Multi-Resource SAR
## 2 The RCS Calculation Model for Dihedral CR
### A Simplified RCS Model for a Dihedral CR Considering the Incident Angle Changes  
Regardless of the type of corner reflector, the RCS of the CR is directly related to its effective scattering area, which changes with the CR's size and the direction of the SAR incident beam. In this paper, we analyze the reduction of the effective scattering area \(S_{e}\) with a deviation angle \(\beta\) between the SAR incident angle and the CR axis. Figure 1a illustrates the cross section of a dihedral CR with a square panel, in which \(\eta\) and \(\alpha\) represent the SAR local incident angle and the CR's tilt angle, respectively. When the deviation angle \(\beta\) exists, the effective scattering area \(S^{\prime}_{e}\) (shown in Figure 1b) is smaller than the case when the CR axis is aligned to the SAR incident angle.  
According to the geometry, \(S^{\prime}_{e}\) can be derived by \(S_{e}\) with a deviation angle \(\beta\):  
\[S^{\prime}_{e}=\sqrt{2}\text{sin}(45-|\beta|)S_{e} \tag{3}\]  
Combining Equations (1) and (3), this paper proposes a simplified estimation model of RCS reduction of the dihedral CR that takes into account the deviation angle. When there is a deviation angle \(\beta\), the RCS reduction of a dihedral CR with a square panel is as follows:  
\[\sigma^{q}_{D}=\frac{16\pi L^{4}\text{sin}^{2}(45-|\beta|)}{\lambda^{2}} \tag{4}\]  
In the same way, the RCS reduction of a dihedral CR with a semicircular panel is as follows:  
\[\sigma^{c}_{D}=\frac{\pi^{3}R^{4}\text{sin}^{2}(45-|\beta|)}{4\lambda^{2}} \tag{5}\]  
Figure 1: (**a**) The schematic diagram of the cross-section of the effective scattering area for the dihedral reflector. (**b**) The 3D schematic diagram of the effective scattering area of the dihedral reflector.  
where \(R\) represents the diameter of the semicircular pane.
## 3 Case Study: Purse Seiner in the Adriatic Sea
### LCA Models for Different Power Systems
#### 3.2.9 The LCA of an Ammonia-Powered Ship  
Ammonia has been widely investigated as a hydrogen carrier. In this paper, its use onboard in a fuel cell and ICE is analysed. As for the fuel cell, the SOFC is considered because it is fuel-flexible and allows for the fast decomposition of ammonia on hydrogen and nitrogen (due to high operating temperatures). If the PEMFC is used instead of the SOFC, the ammonia would need to go through a cracker and a purifier to ensure the provision of pure hydrogen to the fuel cell. That option results in higher costs and fuel consumption [86].  
The electricity consumption, average power, and energy consumption of a ship powered by a SOFC correspond to those of the hydrogen-powered ship described in Section 3.2.8. Ammonia consumption is calculated with the following equation:  
\[FC_{SOFC-A}=\frac{EC_{SOFC}}{\eta_{SOFC-A}\cdot LHV_{A}}, \tag{16}\]  
where \(\eta_{SOFC-A}\) refers to the system efficiency of SOFC fuelled with ammonia (55%) [99] and \(LHV_{A}\) represents lower heating value ammonia (kWh/kg) (Table 1) [89].  
Figure 14: Processes included in the LCA of a hydrogen-powered ship (FC-H).  
The processes included in the LCA of an ammonia-powered ship with the fuel cell (A-H) are presented in Figure 15. During fishing activities, there are no tailpipe emissions, i.e., TTW emissions are equal to zero.  
The manufacturing of the SOFC and the WTT phase of electricity correspond to the ones from Section 3.2.8. The WTT phase of ammonia includes natural gas extraction, production of ammonia, and its distribution via tank trucks for 1000 km.  
Currently, there are no commercially available ammonia ICEs, but MAN promises their availability in 2024 [100]. A dual-fuel marine engine powered by ammonia and diesel has been investigated in many studies. A pilot fuel (MDO or MGO) is used to initiate combustion in a share of 5-10% [101]. In this paper, the pilot use of 5% is assumed, while specific fuel consumptions are obtained from a study by [PERSON] et al. [66], and they equal 358.6 g/kWh for ammonia and 190 g/kWh for the pilot fuel, respectively. Fuel consumptions can be calculated with Equations (11) and (12).  
The processes included in the LCA of an ammonia-powered ship (ICE-A) are presented in Figure 16.  
The environmental impact of the engine is the same as that of the diesel engine in Section 3.2.1, while TTW emissions are calculated based on Equation (13), with emission factors for diesel presented in Table 3. The WTT phase of ammonia corresponds to the WTT phase of ammonia used in a fuel cell.
## 12 Results
### Quantitative Evaluation
This hybrid framework opens avenues for incorporating self-supervised and unsupervised learning strategies to further reduce dependence on fully sampled ground truth data, expanding applicability to diverse and rare clinical scenarios. Moreover, future work will investigate the extension of this approach beyond neuroimaging to other anatomical regions and multi-tracer PET studies, leveraging the modular nature of the pipeline. The potential to integrate real-time reconstruction capabilities with advanced motion correction will pave the way for dynamic imaging applications, such as functional MR-PET studies, where rapid temporal resolution and spatial fidelity are paramount. In conclusion, this study presents a comprehensive, statistically validated, and clinically relevant advancement in MR-PET super-resolution imaging. By judiciously combining compressed sensing, adversarial generative modeling, and robust motion compensation, the proposed framework transcends existing reconstruction methodologies, delivering faster acquisitions, superior image quality, and enhanced anatomical accuracy. These attributes collectively promise to improve diagnostic confidence and patient care, establishing a new benchmark for hybrid imaging reconstruction that aligns with the demands of modern clinical practice and future technological innovation. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Motion Compensation Procedure** & **Mean TRE [Voxels]** & **Std DEV** & _p-Value_ \\ \hline Not applied & 4.90 & 2.60 & \textless{}0.002 \\ [PERSON] et al. [67] & 2.72 & 0.78 & \textless{}0.005 \\ [PERSON] et al. [30] & 2.41 & 0.27 & \textless{}0.005 \\ [PERSON] et al. [68] & 3.55 & 0.37 & \textless{}0.004 \\ [PERSON] et al. [69] & 2.01 & 0.37 & \textless{}0.004 \\ [PERSON] et al. [70] & 3.01 & 0.29 & \textless{}0.006 \\ [PERSON] et al. [71] & 1.66 & 0.31 & \textless{}0.003 \\ MIND [72] & 1.82 & 0.19 & \textless{}0.004 \\ [PERSON] et al. [73] & 1.73 & 0.16 & \textless{}0.009 \\ VoxelMorph [26] & 1.52 & 0.15 & \textless{}0.002 \\ TransReg [74] & 1.45 & 0.14 & \textless{}0.002 \\ WGAN Deformable MC (proposed) & 1.40 & 0.17 & \textless{}0.002 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Statistical parameters of various motion compensation (registration) methods evaluated with respect to the proposed technique. TRE = Target Registration Error (voxels). Lower values indicate better registration accuracy.
## 4 Experiments
### Discussion
#### 4.3.3 Expert Evaluation on Ontology Quality  
Beyond quantitative evaluation metrics, expert assessment plays a critical role in validating the logical coherence and practical applicability of generated ontologies. To further understand the strengths and weaknesses of the models, domain experts were consulted to assess the conceptual accuracy of class definitions and relationships, the logical consistency of generated axioms, and the overall utility of the ontology for real-world applications.  
Experts noted that GPT-4 generally produced well-structured ontologies, but some inconsistencies appeared in domain-specific adaptations, requiring manual corrections. For example, in a generated ontology for Search and Rescue (SAR) operations, GPT-4 correctly classified IncidentCommander as a subclass of EmergencyResponder but mistakenly included it under both OperationalUnit and CommandUnit without clear distinction. This overlap had to be manually refined to maintain logical consistency.  
Mistral 7B, on the other hand, was found to be more computationally efficient but required substantial post-processing due to missing or misclassified relationships. In some cases, it omitted necessary transitive relationships. For example, the model failed to infer that if a Firetruck isAssignedTo Station1 and Station1 isPartOf ResponseZoneA, then the Firetruck should be implicitly associated with ResponseZoneA. These inference gaps were commonly identified in expert reviews.  
To ensure the reliability of expert assessments, multiple evaluators reviewed the ontologies independently. While formal inter-rater reliability calculations were not conducted, the consistency of independent expert feedback suggests a high level of agreement in assessing logical coherence and applicability. Experts particularly emphasized that while fine-tuned LLMs significantly aid ontology engineering, human validation remains essential to refine structure, resolve ambiguities, and correct logical inconsistencies.
## 2 Methodology
### Short-Term Voltage Stability Assessment  
Figure 2 depicts the structure for implementing the deep learning-based method for STVS assessment. It consists of three stages: database generation, offline training, and online implementation. To represent the possible operating condition of the practical power system, different simulation conditions are run, and the data set is generated. The input are the post-disturbance bus voltage trajectories obtained for the particular operating condition obtained within the OTW. The output are the labeled samples generated with the bus voltage trajectories within the simulation period obtained using TDS. Here, the deep learning model (TempCNN-LSTM) is trained to map the input-output relationship between bus voltage trajectories and the corresponding STVS state of the power system subjected to disturbance. The overall process of the proposed method for the real-time STVS is shown in Figure 3. Finally, at the online implementation stage, the trained deep learning model at the control station predicts the STVS (stable, stable-with FIDVR phenomenon, and unstable) state based on the available samples of post-fault bus voltage trajectories from PMUs without the prior knowledge of which fault occurred in the power system. As a result, preventive measures can be implemented quickly while maintaining the system's overall stability.  
Figure 3: Proposed method for real-time STVS.  
Figure 2: Proposed framework for STVS assessment.  
In this study, apart from the TempCNN-LSTM model, other deep learning algorithms such as TempCNN and LSTM and machine learning algorithms such as support vector machine (SVM) and decision tree (DT) are used for model formation. In addition, for the N-1 contingency case, a few labeled data samples are generated to update the trained model in case of a network topology change condition using the transfer learning approach as shown in Figure 2. Finally, the re-trained model is then implemented at the control center to predict the STVS status of the power system.
# Graphene-Based Flexible Electrode for Electrocardiogram Signal Monitoring
## 2 Research on Graphene-Based Flexible ECG Electrodes  
Human skin is an irregular and flexible surface. Thus, compared to flexible electrodes, the use of rigid electrodes may cause many problems [30, 31]. For instance: (i) Rigid electrodes cannot adhere to the skin with high compatibility, which means the electrodes cannot maintain good electrical contact with the skin, resulting in high electrode-skin impedance. (ii) When people are going about their daily activities, rigid electrodes are easy to move relative to the skin and will introduce movement noise during long-term monitoring. (iii) Rigid electrodes are uncomfortable and may cause skin damage during long-term wearing. Based on the disadvantages of rigid ECG electrodes, many flexible ECG electrodes have been developed [32; 33]. Flexible ECG electrodes are a kind of ECG electrode that can be uniformly attached to the human skin (a few of which are invasive) for ECG monitoring. With high flexibility, flexible ECG electrodes can follow skin movement and reduce motion noise interference. Besides, good contact between the flexible electrode and skin can reduce electrode-skin contact impedance and ensure the quality of the collected ECG signal. Moreover, except for a few for implantable monitoring devices, flexible ECG electrodes do not damage the skin, which improves comfort and is suitable for long-term wear.  
When combined with the excellent properties of graphene, a variety of graphene-based flexible ECG electrodes have been developed. According to their different design methods, structures, materials, etc., we divide them into bionic, fabric-based, biodegradable, laser-induced/scribed, modified-graphene, sponge-like, invasive, and other flexible electrodes, and introduce their respective characteristics.
# Channel Estimation and Iterative Decoding for Underwater Acoustic OTFS Communication Systems
## 4 Underwater Acoustic OTFS System Design
### Frame Design
#### 4.1.1 Design of \(M\) and \(N\)  
In this study, the channel DD response is assumed to be constant, that is, the parameters \(h_{i}\), \(\tau_{i}\), and \(v_{i}\) in Equation (3) are constant within one frame period [23]. Based on this assumption, the OTFS frame length \(T_{f}\) cannot exceed the channel coherence time, which is typically short for a UWA channel. Consequently, \(N\) and \(M\) should not be excessively large simultaneously. Thus, we must carefully select \(N\) and \(M\) to ensure the robustness of UWA communication.  
Typically, in conventional OTFS systems, a large \(M\) is chosen to meet the delay resolution requirements, resulting in a smaller \(N\) with bandwidth \(B=M\Delta f\) and frame length \(T_{f}=NT\). This often leads to \(M>>N\). However, in UWA communication, the system's bandwidth is limited and the Doppler spread \(v_{\max}\) is often significant. Consequently, a small \(N\) cannot satisfy the Doppler resolution requirement for an underspread condition [23].  
Accordingly, we must set a suitable value of \(M\) value to avoid significant fractional delays, and appropriately increase \(N\) to enhance the system's Doppler tolerance. Simultaneously, we must ensure that the frame duration \(T_{f}\) remains within the channel coherence time to maintain reliable communication.
## 5 Heat Transfer Analysis of Internal Meshing Screw Mixer
### Heat Transfer Mechanism in Two Models
Displayed in Figure 9, the evolving trends of fluid average temperature, temperature uniformity, and instantaneous heat transfer comprehensive indicators are analyzed within the time frame of \(t=1\)-\(4\) s for both operational modes. Figure 9a provides a comparative view of the average fluid temperature and temperature uniformity in these two modes. The illustration reveals a consistent increase in the overall average fluid temperature over the specified time range. This phenomenon is attributed to the elevated temperature of the heat transfer wall in relation to the fluid. Notably, Mode 2 consistently maintains a higher average temperature than Mode 1. This is primarily due to the smaller heat transfer wall area of the rotor in Mode 2 compared to the stator in Mode 2, resulting in the dissipation of the same amount of heat over a reduced surface area and, consequently, leading to a higher average fluid temperature. In Figure 9b, the temperature uniformity curve provides valuable insights. It reveals an initial decrease, followed by a periodic pattern of increase and decrease in both operational modes. This distinctive behavior arises from the uneven temperature distribution between the inner surface of the rotor and the outer stator wall. The process begins with either the inner or outer surfaces heating and transferring heat. As the heat transfer continues, the rotor rotates along its own axis within the inner stator wall while moving along the stator. Both rotations are synchronized and in opposite directions, leading to periodic variations in fluid temperature and, consequently, a corresponding cyclic dynamic stability. Notably, the magnitude and amplitude of fluctuations in Mode 1 are greater than those in Mode 2. This disparity indicates that Mode 1 maintains a more uniform system temperature distribution, resulting in enhanced heat transfer efficiency. The even temperature distribution reduces heat accumulation and losses, ultimately improving the overall heat transfer efficiency. Referring to the instantaneous heat transfer comprehensive index diagram depicted in Figure 9b, we can observe that the trends of these indicators under both operational modes exhibit remarkable similarities. Furthermore, the variations in Mode 1 exhibit a steeper ascent and descent pattern, accentuating the contrast in heat transfer performance between the two modes. This differentiation is aptly illustrated through the temperature uniformity  
Figure 8: Temperature variation cloud maps in two modes: (**a**) Mode 1 temperature change cloud chart; and (**b**) Mode 2 temperature change cloud chart. and heat transfer comprehensive index curves. Notably, Mode 1 demonstrates superior convective heat transfer capabilities compared to Mode 2.
# Review on Fast Pyrolysis of Biomass for Biofuel Production from [PERSON]
## 3 Biomass Generalities
### Influence of the Parameters on the Products Used during Fast Pyrolysis
#### 3.5.2 Influence of the Heating Rate  
As the heating rate increases at a low temperature of about 773 K, the yield of biofuels produced in condensable gases increases, and that of biofuels produced in carbon decreases [27].  
When the heating rate increases at high temperatures, the efficiency of biofuel production in gases decreases. When the heating rate is high, it leads to the fast formation of gas, and thus a fast elevation of the pressure inside the particle with a sudden ejection of the gases produced. Meanwhile, the efficiency of biofuel production in condensable gases and coal decreases with an increasing heating rate [27].  
According to a study that was conducted by [PERSON] et al. [39], the yield is low when the fast pyrolysis is performed at a temperature that varies between 773 K and 1073 K, while a high yield of H\({}_{2}\) rich gas is obtained at a high temperature (1073 K).  
Another hypothesis was provided by [PERSON] et al. [40]. Pyrolysis of oil palm husks was studied by using thermogravimetric analysis. The influence of heating rate on kinetic parameters (activation energy, frequency factor and reaction order) was investigated. Pyrolysis of oil palm shells was carried out in an inert atmosphere, using nitrogen as the medium gas. Experience shows that the kinetic behavior of the samples can be divided into three zones. The first zone lies within the temperature range 300-380 \({}^{\circ}\)C, the second between 380 and 450 \({}^{\circ}\)C and the third between 450 and 850 \({}^{\circ}\)C. The experimental results also showed that the activation energy was relatively constant despite the variation in heating rate. The different pyrolyzed biomasses and coal in a drop furnace showed that the heating rate had a significant effect on the distribution of pyrolysis products. The difference observed in the amounts of char formed by the biomass and coal is mainly attributed to the cellulose contained in the biomass. Indeed, the dehydration of cellulose into anhydrous cellulose, the intermediate compound responsible for the high char yields, takes place at temperatures below 573 K. Above this temperature, it is the volatile products that are formed in the majority. When the heating rate is very high, the residence time of the biomass at temperatures below 573 K is very low, and the dehydration of the cellulose does not take place. Little anhydrous cellulose is formed, and consequently, little char is formed. On the other hand, these conditions of high heating rate favor the formation of char with high porosity and high reactivity [40].
# Activity Recommendation Model Using Rank Correlation for Chronic Stress Management
## 2 Materials and Methods  
### Stress Management Service Using the Perceived Stress Scale  
The Perceived Stress Scale (PSS) developed by [PERSON] is a psychological tool to measure one's stress perception [6]. PSS is the big scale used in the world to evaluate one's subjective stress degree in everyday life. A user answers within five minutes ten questions about the stress often felt for one month. The weighting value of the points for each question ranges from 0 to 4, in which '0' means feel no stress, '1' feel almost no stress', '2' sometimes feel stress, '3' feel stress often, and '4' feel stress very often.  
The Validation of the PSS developed by [PERSON] was carried out through three sample groups of students. Test results showed a high correlation between the PSS and the stress scale. The higher the total score of PSS, the more stress [7]. The PSS index of different stress levels identifies the cut-off criteria for PSS scores and stress levels. If the total score is 13 points and less, it means the normal stress status. In other words, a person's perceives its current stress as Eustress and has no serious stress factors, and therefore the stress can bring about a positive effect. Scores ranging from 14 to 16 mean mild stress which starts to be influential. If this stress lasts, it is possible to have distress. Scores ranging from 17 to 18 mean moderate stress which requires careful attention. Scores of 19 and higher mean incredibly severe stress. In this case, this stress is highly likely to develop into a mental disease so that it is required to seek professional help [8]. Figure 1 illustrates the PSS based stress management service process.  
Figure 1 shows appropriate services depending on the range of results based on the user's PSS results obtained from the survey. For example, normal stress below 13 points and mild stress between 14 and 16 points are continuously tracked through the smart device. It periodically performs the PSS test to observe the user's stress changes and provide appropriate chronic stress management. However, if the user is in a moderate stress state of 17 to 18 points or a very stressful state of more than 19 points, continuous stress management is not enough, and the state is considered to be very dangerous. Therefore, it is highly recommended to get help with stress management from experts through their diagnosis.
## 2 Materials and Methods
### RNA Isolation, cDNA Synthesis and RT-qPCR Analysis  
Total RNA from cancer cell cultures was isolated with the use of Total RNA Mini Plus Kit (A&A Biotechnology, Gdynia, Poland), following the manufacturer's instruction. Reverse transcription of total RNA to the cDNA was performed using iScript Reverse Transcription Supermix for RT-qPCR (Bio-Rad, Hercules, CA, USA). A quantitative verification of genes was performed using CFX96 TouchTM Real-Time PCR Detection System instrument (Bio-Rad). The PCR reaction mixture contained cDNA samples, RNase-free water, SYBR Green Supermix (Bio-Rad), and primers for following genes: Apoptosis-inducing factor mitochondria associated 1 (_Alfmi_), AKT serine/threonine kinase 1 (_AKT1_), apoptotic peptidase activating factor 1 (_Apaf-1_), Bcl-associated agonist of cell death (_BAD_), Bcl2-binding component 3 (_BBC3_), caspase-3 (_CASP-3_), caspase-7 (_CASP-7_), caspase-8 (_CASP-8_), diablo IAP-binding mitochondrial protein (_DIABLO_), Fas-associated death domain (_FADD_),Fas cell surface death receptor (_FAS_), and tumor protein p53 (_TP53_). The conditions of individual PCR reactions were optimized for the given pair of oligonucleotide primers (Table S1). Basic conditions were as follows: 95 \({}^{\circ}\)C for 2 min (initial step), 45 PCR cycles at 95 \({}^{\circ}\)C for 15 s (denaturation) and 60 \({}^{\circ}\)C for 30 s (annealing), and 75 \({}^{\circ}\)C for 1 min (extension). Results were normalized using reference gene \(\beta\)-actin (_ACTB_) and were determined using the \(2-\Delta\)ACT method as previously reported by [PERSON] and [PERSON] (1999).