esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### DPPH Radical Scavenging Test  
Rice wine is known to contain antioxidants such as ferulic acid [19]. In addition, \({}^{1}\)H NMR measurements showed differences in the chemical shifts and integration ratios in the aromatic region among the samples, which may also affect their antioxidant capacities. Therefore, a DPPH radical scavenging test was conducted to confirm the antioxidant capacities of Samples A, B, and C. Ascorbic acid (ASC) was tested in the same manner for comparison. The IC\({}_{50}\) inhibitory concentrations were A: 2364.7 \(\pm\) 185.3 \(\upmu\)g/mL, B: 3041.9 \(\pm\) 355.1 \(\upmu\)g/mL, C: 3842.7 \(\pm\) 228.1 \(\upmu\)g/mL, and ASC: 0.98 \(\pm\) 0.04 \(\upmu\)g/mL (Figure 5). The IC\({}_{50}\) of Sample A was significantly lower than those of Samples B and C (p \(<\) 0.01), and the IC\({}_{50}\) of Sample B was significantly lower than that of Sample C (p \(<\) 0.01). Although the IC\({}_{50}\) values of Samples A, B, and C were very high compared to the IC\({}_{50}\) value of ASC, differences in the comprehensive antioxidant capacity were observed. Because sake rice is reported to contain ferulic acid, an antioxidant, and a substance related to beauty [19], this indicates differences in the antioxidant capacities of the samples due to the presence of diverse antioxidants. [PERSON] et al. reported that ethylglucoside suppressed barrier breakdown by promoting keratinocyte differentiation [37]. [PERSON] et al. were the first to investigate the formation of n-propanol, isobutanol, isoamyl alcohol, and phenylethanol during rice wine fermentation. Their group reported differences in higher alcohol content depending on the type of rice used [38]. [PERSON] et al. reported differences in the amino acid content of different rice varieties through rice wine production. They also suggested that the type of rice, i.e., the choice of variety used in production, is more correlated with the quality of rice wine [39]. In this study, substances exhibiting antioxidant activity have not been analyzed in detail. However, the results of the \({}^{1}\)H NMR and IR measurements and DPPH tests were used to verify the comprehensive antioxidant capacity. Further detailed verification of the components responsible for antioxidant activity should be conducted in the future. In this study, samples of rice wine (Japanese sake) from a sake brewery in Saitama Prefecture, Japan, were used as a case study. Sake has a unique flavor owing to the complex combination of rice, yeast, preparation water, and local temperature. We will continue to evaluate the physical properties of sake through instrumental analysis to provide greater information to people worldwide on what components give rice wine its taste.  
Figure 5: IC\({}_{50}\) of Samples A, B, C and Ascorbic acid in a DPPH radical scavenging test. Values are the mean \(\pm\) SD (\(n\) = 3), **\(p<\) 0.01 (Tukey test).
## 1 Introduction
This integration of techniques whilst in its infancy has begun to emerge with some contemporary studies [5; 37; 39; 40; 41; 42; 43] starting to extend various algorithms by integrating geo-statistical methods to enhance prediction accuracy, whilst controlling for Spatial dependency. However, to date, fewer studies have explored the application of Eigenvector Spatial Filters (ESF) within supervised forms of Penalized regression algorithms to account for spatial effects for mass appraisal purposes to investigate whether this provides a more suitable methodology for AVM practice and the reduction of inequity. Therefore the purpose and objective of this study is to apply a methodological approach which provides a simple alternative for including location within traditional regression and supervised Penalized regression approaches while offering a potentially more readily usable approach for property tax assessment jurisdictions [44]. In doing so, this study extends mass appraisal modeling by incorporating eigenvectors generated from a contiguity-based spatial weights matrix to capture unexplained SA within Regularized regression to investigate whether this enhances prediction accuracy in a more adoptable and explainable way. The application of Regularized regression is investigated as these type of'supervised' models are more 'front-facing' and transparent for mass appraisal explainability, defensibility and accounting for complex functional forms, model complexity and overfitting [45; 46]. The estimated models are developed in training samples and using cross-validation applied to predict market values within the validation sample. The modelling outcomes are further subject to different valuation error measures, and discrimination between the models is determined based on their relative performance.
## 2 Materials and Methods
### Catalyst Characterisation  
XPS  
The XPS measurements were recorded using a thermo Scientific K-alpha+ spectrometer. The sample analysis was achieved using a monochromatic Al X-ray source running at 72 W, with the signal recorded over an oval-shape area of 600 \(\times\) 400 \(\upmu\). The data were recorded at 150 eV for survey scans and 40 eV for high resolution (HR) scans with 1 eV and 0.1 eV step sizes, respectively. The analysis of the data was performed using CASAXPS (v2.3.17 PR1.1), using Scofield sensitivity factors and an energy exponent of \(-\)0.6.  
TEM  
The transmission electron microscopy (TEM) experiments were completed using a double Cs aberration-corrected FEI Titan3 Themis 60-300 microscope. The microscope was equipped with a monochromator, an X-FEG gun, and a high-efficiency XEDS ChemiSTEM. This comprised four windowless SDD detectors. HR-STEM imaging was completed at 200 kV, applying a high-angle annular dark-field (HAADF) detector with a camera length of 11.5 cm. The HAADF-STEM procedure is responsive to the atomic number of the elements, in which the intensity is roughly proportional to the square of the atomic number (Z2), and it provides the opportunity to differentiate the small nanoparticles carried on light supports. The XEDS mappings were achieved using a beam current of 200 pA and a dwell time per pixel of 128 \(\upmu\)s. The elemental maps were filtered using a Gaussian blur of 0.8 (Velox software) to enhance the quality of the images. Due to the STEM-HAADF images of the catalysts, the diameters of more than 200 randomly chosen metal particles were measured and the resultant metal particle size distributions (PSD) were defined. As a result, the average particle diameter (d) was computed corresponding to the following expression: \(\mathrm{d}=\mathrm{\Sigma_{n}d_{i}}/\mathrm{\Sigma_{n}}\), where \(\mathrm{n_{i}}\geq 200\). Equally, the total metal dispersion was evaluated according to D = Ns/Nt, where Ns is the total number of surface-metal atoms and Nt is total number of atoms in the metal particle. For the particle size calculations, the ImageJ software was used.
## 2 Materials and Methods
### Observed Variables  
Eight variables were measured during the test (see Table 3). Five of them were metrics for evaluating the changes in physical strain on body, workload, human-exoskeleton interaction force, and subjective perception of the support force. The other three variables were assisting metrics, which helped to enable a determination of the different phases of motion not only by using motion data.  
Figure 4: Test procedure.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Variable**} & \multicolumn{2}{c}{**Measurement System**} \\ \hline \multirow{5}{*}{Method} & Muscle activity (objective physical strain) & Surface EMG (Myon 320, 1.000 Hz, Myon AG, Schwarzenberg, Schweiz) \\ \cline{2-3}  & Perceived exertion (subjective physical strain) & Questionnaire with body map and Borg Scale CR-10 [19] \\ \cline{2-3}  & Push force (workload) & \multicolumn{2}{c}{NASA-TLX questionnaire evaluated in six subscales [20]} \\ \cline{2-3}  & Human-exoskeleton interaction force & One force plate (AMTI OPT, 464 \(\times\) 508 mm, Advanced Mechanical Technology Inc., Watertown, NY, USA) \\ \cline{2-3}  & Human-exoskeleton interaction force & One pressure sensing mat (X3 Pro System, 127 \(\times\) 254 mm, Xsensor Technology Corporation, Calgary, AB, Canada) \\ \cline{2-3}  & Subjective perception of the support force & Questionnaire for user experience \\ \cline{2-3}  & Electric current and voltage screwdriver & \multicolumn{2}{c}{Datalogger on screwdriver [17]} \\ \cline{2-3}  & Elevation angle exoskeleton & \multicolumn{2}{c}{Datalogger on Lucy} \\ \cline{2-3}  & Human motion & Vicon (Vicon Bonita, Oxford Metrics Ltd., Oxford, UK) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Variables and measurement systems.  
The activities of eight muscles were recorded with surface EMG: M. flexor carpi ulnaris (FCU), M. biceps brachii (BB), M. deltoideus anterior (DA), M. latissimus dorsi (LD), on both the dominant and non-dominant side. The physical exertion was also recorded with subjective questionnaires (NASA-TLX, body map) in order to compare the user's evaluation with the measured EMG data. The push force (\(F_{pu}\)) of the user was measured to ensure that the same workload was applied to the participants for the different support levels. The push force was indirectly measured from the vertical component (\(F_{GRF,\,v}\)) of the 3-axis ground reaction force \(F_{GRF}\). This provided a good approximation of the push force during the screw phase, since the user was in a static posture and no inertial forces occurred. The pressure sensing mat was placed wrinkle-free between the dominant upper arm and the armrest of the exoskeleton. It measured the pressure on the complete surface of the armrest. The normal interaction force between the dominant upper arm and the armrest was calculated by the measurement software. A questionnaire was used to compare the measured interaction force with the subjective evaluation of the support level.
## 2 Materials and Methods
### Regional Precipitation Data  
Regional precipitation isotope data was needed for this study to identify any possible deviation from the Global Meteoric Water Line (GMWL) to characterise local meteoric conditions better. The closest Global Network of Isotope in Precipitation (GNIP) data stations to the study location in the eastern Dahomey Basin are Cotonou (Republic of Benin) to the west and Doula (Cameroon) to the east. The only station in Nigeria is situated in Kano, at the northern savannah region of Nigeria. Meanwhile, Cotonou, and Doula, located along the coast of West Africa, share similar climate conditions with the study area (Figure 4). In light of this, a total of 134 regional and annual precipitation isotope data of \(\delta^{18}\)O and \(\delta^{2}\)H were collected from the Doula, Cameroon (50), Cotonou, Republic of Benin (50) and Kano, Nigeria (33) meteorological stations during 2009-2018. These isotope data were downloaded from the GNIP and are presented later in this study. The results are expressed as \(\delta\)-values relative to V-SMOW (Vienna Standard Mean Ocean Water), and the measurement precision is 0.01%0 and 0.2%0 for \(\delta^{18}\)O and \(\delta^{2}\)H, respectively.
## 4 Discussion
### Trainees' Interaction with the AR Application  
Main features of the AR application were the presentation of the flight guidance cues, and the interactive feedback with elements of gamification. Trainees' subjective experience in interaction with the AR-supported training concept was analyzed with regard to performance in post-test, when the experimental group did not use AR. Results show that higher AR presence was associated with lower objective deviation scores in post-test. Presence is considered to be an essential contributor to total immersion [19; 47] and is facilitated by multimedia features and game-based challenges [48]. Higher comfort with AR during training was also associated with lower deviation scores and a better approach quality in post-test. Thus, these results indicate that there is an interdependence between the trainees' learning performance, subjective experience, and interaction with the AR-supported training application. Interestingly, better self-assessment in post-test was associated with less focused attention and lower flow scores during the AR training. Although focus of attention was assessed as an element of AR immersion [19], these findings need to be interpreted with care because the approach task required attention sharing among multiple control parameters such as descend rate, speed, and heading.  
There were no significant differences between women and men in the AR immersion and their assessment of AR features. Thus, the findings of previous research [13] were not replicated, indicating that gender preferences may be rather application-specific and cannot be generalized to the interaction with other AR applications. Nevertheless, more research with larger numbers of participants is needed to gain clarity on gender preferences related to AR-supported training concepts.
# Engine Fault Detection by Sound Analysis and Machine Learning
## 4 Results and Discussion  
In this section, the vehicle fault diagnosis framework was presented and discussed. The performance metrics and analysis are provided for models utilizing MFCC-based features and DWT-based features. Additionally, a detailed frequency analysis to identify the most relevant frequency components for each fault type, was given. Obtained results are also compared with existing studies to highlight the effectiveness and improvements achieved by the employed methodology.
# A Vehicle-Infrastructure Cooperative Perception Network Based on Multi-Scale Dynamic Feature Fusion
###### Abstract  
Vehicle-infrastructure cooperative perception enhances the perception capabilities of autonomous vehicles by facilitating the exchange of complementary information between vehicles and infrastructure. However, real-world environments often present challenges such as differences in sensor resolution and installation angles, which create a domain gap that complicates the integration of features from these two sources. This domain gap can hinder the overall performance of the perception system. To tackle this issue, we propose a novel vehicle-infrastructure cooperative perception network designed to effectively bridge the feature integration between vehicle and infrastructure sensors. Our approach includes a Multi-Scale Dynamic Feature Fusion Module designed to comprehensively integrate features from both vehicle and infrastructure across spatial and semantic dimensions. For feature fusion at each scale, we introduce the Multi-Source Dynamic Interaction Module (MSDI) and the Per-Point Self-Attention Module (PPSA). The MSDI dynamically adjusts the interaction between vehicle and infrastructure features based on environmental changes, generating enhanced interacting features. Subsequently, the PPSA aggregates these interacted features with the original vehicle-infrastructure features at the same spatial location. Additionally, we have constructed a real-world vehicle-infrastructure cooperative perception dataset, DZGSet, which includes multi-category annotations. Extensive experiments conducted on the DAIR-V2X and our self-collected DZGSet datasets demonstrate that our proposed method achieves Average Precision (AP) scores at IoU 0.5 of 0.780 and 0.652, and AP scores at IoU 0.7 of 0.632 and 0.493, respectively. These results indicate that our proposed method outperforms existing cooperative perception methods. Consequently, the proposed approach significantly improves the performance of cooperative perception, enabling more accurate and reliable autonomous vehicle operation.  
vehicle-infrastructure cooperative perception; vehicle-infrastructure features fusion; Multi-Scale Dynamic Feature Fusion; cooperative perception dataset +
Footnote †: journal: Applied sciences  
0000-0001-5000-0001-5000][PERSON]
## 3 Results
### Feature Wavelength Selection  
Feature wavelength selection is pivotal in diminishing model complexity and enhancing model performance for NIR spectra data from rapeseed plant seeds. A rigorous comparative analysis was undertaken across six distinct wavelength selection algorithms, including SiPLS, BiPLS, RF, IRIV, VCPA, and CARS, as well as their combinations. The intent was to screen the characteristic wavelengths inherent to the modeling data. Subsequently, the modeling outcomes produced via different variable selection methodologies were contrasted and evaluated.  
Figure 3: SVR model predictions of rapeseed plant seed germination rates after SG convolution smoothing preprocessing. (**a**) Training set. (**b**) Testing set.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Spectral Preprocessing Method**} & \multicolumn{2}{c}{**Modeling Parameters**} & \multicolumn{2}{c}{**Training Set**} & \multicolumn{2}{c}{**Test Set**} \\ \cline{2-6}  & **c** & **g** & **RMSEC(\%)** & **R2c** & **RMSEP/(\%)** & **R2p** \\ \hline MC & 181.0193 & 0.0009765 & 27.13 & 0.6760 & 26.99 & 0.4188 \\ SNV & 11.3137 & 0.0027621 & 28.29 & 0.6491 & 28.96 & 0.3232 \\ MSC & 4 & 0.0039063 & 29.35 & 0.6212 & 29.29 & 0.2837 \\ FD & 1.4142 & 0.0009765 & 24.81 & 0.7452 & 27.71 & 0.3777 \\ SG Convolutional smoothing & 256 & 0.0009765 & 26.76 & 0.6839 & 26.87 & 0.4276 \\ SG Convolutional smoothing + MC & 181.0193 & 0.0009765 & 27.17 & 0.6750 & 26.98 & 0.4187 \\ SG Convolutional smoothing + SNV & 0.35355 & 0.03125 & 29.35 & 0.6479 & 29.43 & 0.2457 \\ SG Convolutional smoothing + MSC & 4 & 0.0039063 & 29.39 & 0.6199 & 29.29 & 0.2835 \\ SG Convolution Smooth + FD & 1.4142 & 0.0009765 & 24.92 & 0.7423 & 27.67 & 0.3745 \\ \hline \hline \end{tabular}
\end{table}
Table 3: SVR modeling results with different preprocessing methods.
# Comparative Study of Different Approaches for Islanding Detection of Distributed Generation Systems
## 2 Literature Review
### Sandia Voltage Shift:
#### 2.2.4 Machine Learning Method  
This method deals with the studies of characteristics of events from the data set (main data) and uses that knowledge to find the islanding condition in test data. The decision tree approach can make a complex decision-making process into a simpler decision with a solution easier to interpret [63].  
1. C4.5 Decision Tree-Based Islanding Detection [64]:Based on the C4.5 decision tree model, this method of detection first gathers sampled data obtained from the simulation result. These data are then used to construct a C4.5 classification model. Other data are then used to verify the working of the C4.5 decision tree. Each cycle of data processing is then used for islanding detection, and the result is used to improve the C4.5 decision tree itself--thus resulting in a circular loop of island detection and improvement of the detection scheme.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 5 Discussion--Open Issues

Table 2: _Cont._* In the cases of ML model training, in the majority of the studied works, such as in [79; 84; 106; 111], DRL approaches have been considered, as they can adapt more effectively to various network deployments and reconfigurations. The alternative approaches of supervised or unsupervised learning would involve data collection from scratch and retraining that can increase the computational load and deteriorate the system's latency. * The full deployment of highly demanding latency applications, such as autonomous driving or zero touch smart manufacturing, could be leveraged by private IEC infrastructures to avoid an imbalance in the computational load of edge or cloud servers from other applications and public networks' latency. In this case, private 5G infrastructures can be deployed within the premises of a manufacturing unit along with dedicated edge servers that process data and train ML models. In cases of dispersed units, these data can be then sent to private cloud domains for further analysis and macroscopic KPI optimization. * There is a fundamental tradeoff between the enforcement of strict security policies in every data transfer procedure between IoT nodes and edge-cloud/servers and computational load. In these cases, the concept of trusted nodes can be applied: IoT nodes enter the IEC only after their robustness against specific security attacks has been verified. Even so, this does not rule out the possibility of future attacks. Therefore, anomaly detection algorithms in the edge or cloud domain can detect unusual data patterns. * Serverless computing along with blockchain technology can be quite effective towards the concept of PaaS in IEC systems, due to the elimination of third-party involvement. By decomposing service computations as a set of microservices, service providers could make the most out of cloud and edge computing by exploiting their elasticity. During its lifetime, the service could scale-in or scale-down to adjust the number of instances of each microservice to the workload, thus reducing the operational expenditures of the service. In the same context, as derived from the presented works, there are still open issues to be addressed prior to the full deployment of the IEC continuum. * Resource optimization is important in the IEC continuum, due to the magnitude of involved infrastructures and applications. Therefore, the employed ML models should
# Drone SAR Imaging for Monitoring an Active Landslide Adjacent to the M25 at Flint Hall Farm
## 3 Materials and Methods
### Hardware Development
#### 3.2.1 Drone  
The Shenzhen DJI Sciences and Technologies Ltd. (DJI) Matrice 600 Pro is an industrial grade hexacopter from Shenzhen, China. Compared to a typical quadcopter, the large aircraft frame size allows for a larger payload to be attached, with a potential hovering time of 18 min with a maximum payload weight of 5.5 kg. The two additional frame arms provide better flight stability by spreading the additional payload weight more evenly across the yaw axis. Hexacopters are also considered to be safer than quadcopters since the additional frame arms, motors, and batteries provide redundancies in the event of system or component failure. The majority of drone radar systems in the literature utilise this specific drone model [15; 16; 17; 18; 19; 21; 22], thus demonstrating its suitability for this type of custom payload application.
# CSNOMA: Carrier Sense Non-Orthogonal Multiple Access
###### Abstract  
In this paper, we investigate the possibility of the cross-layer design of a distributed random access scheme with considering physical (PHY) and multiple access control (MAC) layers, which utilizes the interference cancellation technique. In this regard, we propose a new multiple access protocol, named carrier sense non-orthogonal multiple access (CSNOMA). We consider the spatially randomly distributed interferers to realistically capture the effect of interference. The proposed protocol shows better area spectral efficiency than carrier sense multiple access (CSMA), as the node density increases. We also present a practical signaling design compatible with IEEE 802.11 DCF mode.  
random access; interference cancellation; distributed MAC; area spectral efficiency