esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Specim Preparation and Testing Process
### Testing Process  
Rock mechanics experiments are fundamental methods for determining the basic mechanical properties of rock materials and are important means of revealing the strength and deformation characteristics of rock-like materials [1]. In order to obtain the basic mechanical parameters of coal-rock masses, this section employs a uniaxial loading method to load coal samples, rock samples, primary coal-rock combinations, and artificial coal-rock combinations separately, with a loading rate of 100 N/s, until the samples are fractured and the loading is stopped. During the experiment, acoustic emission signals generated during the sample's damage and fracture process are synchronously collected. Strain gauges  
Figure 1: Schematic diagram of the specimens.  
are used to measure the axial and radial strains of the samples. For primary coal-rock combinations and artificial coal-rock combinations, the measured axial deformation value represents the sum of the axial deformations of the coal body and rock body, while the measured radial deformation value represents the radial deformation of the coal body proportion. See Table 1 for detailed sample information.  
This study utilized the RLW-500G coal-rock triaxial testing system to conduct uniaxial loading tests on samples. The maximum axial loading pressure of the equipment is 500 KN. The acoustic emission testing equipment used was the PCI-2 acoustic emission test analysis system manufactured by Physical Acoustics Corporation of the United States, with a sampling frequency of 5000 KHz and a threshold of 46 dB (Figure 2). Six groups of samples were prepared. Combined with the acoustic emission system, uniaxial compression tests on samples were conducted to reveal the deformation and failure characteristics, as well as the response laws, of coal-rock composite samples with different coal-rock ratios. This is aimed at providing a certain research basis for the prevention and control of dynamic disasters in deep coal mining. The samples (raw coal-C, rock-R, artificial coal-rock combination-CR, primary coal-rock combination-CSR) were set with five different coal-rock ratios, namely, 10:0, 0:10, 5:5, 3:7, and 9:1.
# A Review of Magnetoelectric Composites Based on ZnO Nanostructures
## 4 Conclusions  
In this review, we highlighted the research and development on ZnO-based magnetoelectric composites in various forms, including thin films, micro- and nanorods and nano composites. Although several materials are utilized for exploiting ME phenomena, ZnO presents significant interest due to its unique properties, including biocompatibility, non-toxicity, low-cost fabrication and potential applicability in large area electronics. A significant portion of the research effort focuses on the integration of ZnO micro- and nanostructures with advanced magnetostrictive materials for the realization of novel ME heterostructures. ZnO thin films were utilized extensively in the fabrication of ME composites as the piezoelectric constituent, in diverse device architectures ranging from cantilevers, surface acoustic waves (SAW) and bulk acoustic waves (BAW) devices, to MEMS FBAR and microcantilever sensors. In addition, thin-film characterization methods such as XRD and GIXRD have enabled us to assess magnetostrictive strain at the atomic scale, something that can be leveraged extensively in ME research beyond ZnO. Furthermore, the doping of pure ZnO films can induce ferromagnetic properties with an enhanced magnetoelectric response, while suitable co-doping results in ferroelectric and ferromagnetic phenomena that can simultaneously exist in ZnO thin films. ZnO micro- and nanorod composites, on the other hand, constitute the most promising route for ME composites based on ZnO. This is supported by the fact that there is already substantial research around the basic electronic and structural properties of these structures, and by combining them with advanced magnetostrictive materials, this can lead to novel architectures suitable for sensing, biomedical applications and energy harvesting, in conjunction with the introduction of new effects stemming from the interaction between ferromagnetism, photoexcitation, piezoelectricity and semiconducting phenomena.  
Further research into the combination of magnetostrictive materials with ZnO nanostructures, especially micro- and nanorods, will pave the way for a new generation of devices able to detect extremely low magnetics fields suitable for portable devices and personal health care applications.  
Conceptualization, A.B. and C.T.; methodology, A.B. and C.T.; writing--original draft preparation, A.B.; writing--review and editing, A.B., A.K. and C.T.; visualization, A.B.; funding acquisition, C.T. All authors have read and agreed to the published version of the manuscript.  
This research is co-financed by Greece and the European Union (European Social Fund-ESF) through the Operational Program <<Human Resources Development, Education and Lifelong Learning>> in the context of the project <<Strengthening Human Resources Research Potential via Doctorate Research-2 nd Cycle>> (MIS-5000432), implemented by the State Scholarships Foundation (IKY).  
Not applicable.  
The authors acknowledge [PERSON] for a thorough reading of the manuscript.  
The authors declare no conflict of interest.
# Networked Roadside Perception Units for Autonomous Driving
## 4 Networked RSPUs
### Distance Priority Algorithm  
Under a broader cooperation perception, the more efficient and safer ITS application does get supported, albeit with an increase in the number of messages. In addition to lowering PDR, it also exacerbates the element of delay. To illustrate, it is not possible for a transmitted to broadcast all messages amidst heavy traffic due to the saturation of its wire link's capacity; 3\(\sim\)27 Mbps is the data rate associated with IEEE802.11p. If the \(CPM_{CAM}\)s exceeds the wireless link's sending rate of messages sent to the transmitter, the queue received further messages, thus resulting in increased delay and waiting time and rendering the received information redundant.  
To overcome these challenges, our approach entails the prioritization of messages on the basis of their contribution to safety. Based on the volume of road traffic, there is variation in the total number of objects that are detected (number of messages). In case there is adequate capacity for broadcasting more \(CPM_{CAM}\)s, the ones with more information on the distant object join the queue.  
\(d_{max}\), is the peak distance of the transmitter, whereas the \(r_{q0}\) denotes the constantly monitored ratio of queue occupancy. After \(CPM_{CAM}\), is received, the distance \(d\) is calculated between the transmitter position and that of the detected object within the aforementioned message. This is done with a view to ascertaining whether the packet must join the queue or dropped in the following manner:  
\[r_{q0}<1-\frac{d}{d_{max}}. \tag{2}\]  
If \"true\" is the value returned by the equation, the queue witnesses the addition of \(CPM_{CAM}\); Accordingly, \(CPM_{CAM}\)s containing additional information on distant objects gets dropped when the value of \(r_{q0}\) is adequately large; its value is also reduced by the algorithm to reduce end-to-end  
Figure 5: Overview of the networked RSPUs.  
deformations. Notably, the transmitter begins dropping \(CPM_{CAM8}\) with additional information (distance related) prior to the queue getting full. However, it is able to maintain sufficient space for information related to future usage.
# A Lightweight Small Target Detection Algorithm for UAV Platforms
## 2 Preliminary Work
### Attention Mechanisms  
The attention mechanism [17] is a technique commonly used in computer science and machine learning to enhance the accuracy and efficiency of models in processing sequential data. In traditional neural networks, the output of each neuron is determined solely by the outputs of all neurons in the previous layer. However, in the attention mechanism, the output of each neuron is not only dependent on the outputs of all neurons in the previous layer, but can also be weighted according to different parts of the input data, meaning that different weights are assigned to different parts. This allows the model to pay more attention to key information in the input sequence, thus improving the model's accuracy and efficiency. The attention mechanism is widely applied in the field of computer vision and plays an important role in tasks such as target detection and target tracking, allowing the model to focus on more important target features while ignoring unimportant background information, thereby enhancing the accuracy and robustness of the model.
## 2 Data Acquisition
### Clinical Procedures  
In this study, we utilized data obtained from a random subset (n = 8) of participants enrolled in an observational cohort study involving 33 extremely preterm infants born at \(\leq\)28 weeks estimated gestation at Starship Children's Hospital, Auckland, New Zealand, spanning the years 2014 to 2017. All infants in the cohort received standard clinical and nursing care. The initiation of EEG recordings occurred as soon as possible after birth and extended for a period of 3 days, with a median starting age of 4:36 h (range: 2:45 to 6:32 h). EEG activity was monitored using leads symmetrically placed on the left and right sides of the infant's skull. Recordings were made on the reBRM2 monitor, a research version of the BRM2 manufactured by BrainZ Instruments, Auckland, New Zealand. The EEG signals from both sides were amplified by a factor of 5000 and subjected to filtering using a first-order high-pass filter with a \(-\)3 dB frequency at 1 Hz and a fourth-order low-pass Butterworth filter with a \(-\)3 dB frequency at 50 Hz. The signal was digitized by the computer at a sampling rate of 256 Hz, and data were continuously collected for 48 h from initiation, with exclusive analysis conducted on recordings obtained during the first 6-7 h after birth.  
In this study, only the initial 6 h of EEG data from each neonate were employed for sharp wave analysis. Data from each neonate were selected from either the left or right EEG channel, prioritizing channels with minimal signal corruption. Initially, an expert (HA) manually labeled HI sharp waves in the entire dataset to establish a reliable benchmark for comparison with automatically detected sharp waves.  
To ensure the integrity of both clinical care and research activities, experienced neonatal intensive care unit (NICU) nurses, specifically assigned for this project and available in addition to the standard clinical team, placed the EEG electrodes. Instances of physiological instability, drug administration, blood sampling, and routine infant care were documented. Details of antenatal and postnatal treatments, including the administration of sedatives and steroids, were recorded. Infants with congenital or genetic abnormalities, or those with scalp injury/infection, were excluded from the analysis. EEG leads were sited once the infant was stabilised and the first 6 h only of EEG recordings utilised for the current analysis so the exposure to medication was quite limited. Specifically, the majority had received antenatal corticosteroids as per routine practice, antenatal magnesium sulphate was also commonly given and the majority received routine antibiotics with amoxicillin and gentamicin. Surfactant, if required, had typically been administered prior to the start of EEG monitoring. Due to the real world aspects of providing appropriate clinical care to extremely premature infants in the first few hours of life, it is not possible to collect EEG data without prior exposure to drugs such as antenatal steroids and surfactant.
## 3 Results and Discussion
### Characterization of AuNPs and ITZ-Coated AuNPs
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sample** & **SEM Images** & **EDS Mapping** \\ \hline Au synthesis 1 + ITZ & & \\ Au synthesis 2 + ITZ & & \\ Au synthesis 3 + ITZ &  
The X-ray diffraction method was employed to ascertain the crystalline nature of the active substance, which was confirmed in all samples. The data are presented in the Supplementary Material (Figure S7). In the subsequent phase of the investigation, the apparent viscosity of the resultant carbopol gels (Gels 1-4) was evaluated. The findings are documented in Table 4.  
It was demonstrated that an increase in particle size (AuNPs coated by ITZ) within the hydrogel resulted in a corresponding rise in viscosity. This observation follows the findings of [PERSON] et al. [42], which also demonstrated that larger AuNPs exhibited a greater propensity to enhance the viscosity of the gels in comparison to smaller AuNPs. Additionally, [PERSON] et al. [43] demonstrated that the incorporation of larger nanoparticles into the formulation resulted in a notable increase in gel viscosity. \begin{table}
\begin{tabular}{c c c c c} \hline
**Gel Sample** & **Gel 1 (AuSyn1 + ITZ)** & **Gel 2 (AuSyn2 + ITZ)** & **Gel 3 (AuSyn3 + ITZ)** & **Gel 4 (ITZ)** \\ \hline \multicolumn{5}{c}{50 rpm/min} \\ \hline Measurement 1 [mPa*s] & 1574 & 1851 & 2030 & 2020 \\ \hline Measurement 2 [mPa*s] & 1610 & 1848 & 2022 & 1984 \\ \hline Measurement 3 [mPa*s] & 1580 & 1843 & 2015 & 2030 \\ \hline Measurement 4 [mPa*s] & 1582 & 1841 & 2012 & 2022 \\ \hline Measurement 5 [mPa*s] & 1585 & 1841 & 2012 & 2017 \\ \hline Mean & 1586.20 & 1844.80 & 2018.20 & 2014.60 \\ \hline SD & 13.90 & 4.49 & 7.76 & 17.77 \\ \hline RSD & 0.88 & 0.24 & 0.38 & 0.88 \\ \hline \end{tabular} SD—standard deviation; RSD—relative standard deviation.  
\end{table} 
Table 4: The results of the apparent viscosity test of the obtained carbopol hydrogels. Figure 1: The FTIR spectra of the studied materials.
## 4 The Proposed Framework
### The Methodological Framework
#### 4.1.6 UHI Selection by Reliability Analysis  
In order to evaluate the reliability of an urban hotspot and identify it as a UHI, a reliability index is measured that is connected to the uncertainty of the membership of the pixels that compose the hotspot, where the uncertainty of the membership of a pixel to a cluster is given by its membership degree to that cluster.  
This measure is obtained using a zonal statistical functionality that provides, for each hotspot polygon, the average value of the membership degrees to its own clusters of the pixels that compose it.  
Formally, if the hth hotspot is obtained by aggregating \(\mathrm{N_{h}}\) pixels and \(\mathrm{u_{j}}\) is the membership degree of the jth pixel to the cluster to which it is assigned, the reliability of the hotspot is given by the following:  
\[\mathrm{rel_{h}}=\frac{1}{\mathrm{N_{h}}}\mathrm{\sum_{j=1}^{N_{h}}}\mathrm{ u_{j}} \tag{17}\]  
This reliability index allows us to analyze the uncertainty and reliability of hotspots.  
Finally, each urban hotspot is identified as a UHI and is assigned a reliability level. The reliability measure provides an estimate of how reliable the hotspot identification is as a UHI. Hotspots with reliability values higher than 75% are UHIs with a high degree of certainty; hotspots with reliability levels lower than 50% may represent areas with different urban and morphological characteristics that influence the surface behavior; they may be potential UHIs, but an in-depth urban scale analysis is needed to understand the behavior of the urban surface during heatwaves in these areas.
# Impact and Control of Reflected Noise from an Overpass Bottom
## 3 Results and Discussion
### Reflected Noise Analysis and Improvement Results with Different Overpass Heights
Since traffic flow at this site was very high, reflected noise was considerable. In addition, the level of reflected noise decreased as distance from the overpass increased. When the distance between the simulation location and overpass increased from 15 m to 30, 45, 60, and 75 m, reflected noise was reduced by 2.6, 3.5, 4.0, and 4.5 dB(A), respectively. By simulation, when the distance from the overpass side exceeded 30 m, the level of reflected noise decreased significantly. [PERSON] et al. [43] reported that under the same exposure of traffic noise, an urban area with green space had a lower level of noise annoyance than that without green space. Therefore, a proper green space buffer zone that increases the distance between the source of reflected noise and an affected area can effectively reduce reflected noise and noise annoyance of residents. When sound absorbing material had sound absorption rates of 0.36 and 0.85, the decrease in reflected noise was 0.2-1.1 and 0.6-3.6 dB(A), respecti  
Figure 6: Simulation drawing of reflected noise change at different overpass heights (low overpass section at Site B). absorption rate of the material on the overpass bottom increased, the noise reduction effect increased. The effect of reflection noise reduction was best when the material had a sound absorption rate of 0.85.
## 3 Results
For the DSA product, the wide Raman peak at 1014 cm\({}^{-1}\) indicates that it is formed by a mixture of sulfate and carbonate. Magnesium sulfates present Raman peaks that depend on the degree of hydration, going from a peak at 983.6 cm\({}^{-1}\), for hexahydrite, and changing to higher wavenumbers as the degree of hydration decreases, up to 1046.1 cm\({}^{-1}\) for the monohydrate (kieserite, MgSO\({}_{4}\)-H\({}_{2}\)O) [27]. The observed peak is centered between those corresponding to the tetrahydrate (MgSO\({}_{4}\)-4H\({}_{2}\)O, Starkeyite, 1000.3 cm\({}^{-1}\)) and the trihydrate (1023.8 cm\({}^{-1}\)), suggesting that these two components are present in the DSA product, in addition to hexahydrate. The sulfate does not show in the corresponding diffractogram (Figure 3) but we suspect that low intensity sulfate peaks may be among the several broad peaks observed. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Phase** & **scCO\({}_{2}\)-od** & **scCO\({}_{2}\)-hpd** & **DSA** \\ \hline Magnesite & 0\% & 20\% & 0\% \\ Nesquehonite & 65\% & 0\% & 54\% \\ Lansfordite & 27\% & 60\% & 40\% \\ Hydromagnesite & 8\% & 20\% & 6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Magnesium carbonates phase composition determined by Rietveld refinement for products from scCO\({}_{2}\) synthesis (oven dried and hot plate dried), and dumbbell-shaped structures from aqueous synthesis. Figure 4: X-ray diffraction patterns (blue lines) of (**a**) magnesium carbonate, from aqueous route synthesis with nitrate as precursor, showing a mixture of dypingite and hydromagnesite, according to Rietveld refinement. (**b**) Magnesium carbonate, from aqueous route synthesis with sulfate as precursor, showing a mixture of nesuehouite and hydromagnesite, according to Rietveld refinement. We should note that usequehonite has been reported to form in a matter of minutes [30], suggesting that our supercritical synthesis route may not require reaction times as long as 12 h, and we intend to explore what is the shortest time for full precipitation.
## 3 Results
### Integration between Social Vulnerability, Single-Hazard, and Multi-Hazard Classes
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Municipality** & **Level** & **SVI x LH** & **LH Code** & **SVI x FH** & **FH Code** & **SVI x MH** & **MH Code** \\ \hline \multirow{4}{*}{Bernalda} & \multirow{4}{*}{L} & \multirow{4}{*}{24} & LH2, LH5, & \multirow{4}{*}{86} & LH2, LH5, & \multirow{4}{*}{9} & LH2, FH3 & \multirow{4}{*}{2} & MH5, MH8 \\  & & & LH10, LH11 & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{4}{*}{Colobraro} & \multirow{4}{*}{M} & \multirow{4}{*}{1} & \multirow{4}{*}{12} & LH12 & \multirow{4}{*}{1} & LH2, LH3, & \multirow{4}{*}{7} & HH3 & \multirow{4}{*}{4} & MH5, MH9, \\  & & & LH10, LH12, & LH13 & & & & \\ \cline{1-1} \cline{5-7}  & & & & LH12 & & & & \\ \cline{1-1} \cline{5-7}  & & & & LH12 & & & & \\ \hline \multirow{4}{*}{Craco} & \multirow{4}{*}{L} & \multirow{4}{*}{18} & LH2, LH3, & \multirow{4}{*}{7} & LH3 & \multirow{4}{*}{4} & MH5, MH9, \\  & & & LH10, LH12, & LH13 & & & & MH11 \\ \cline{1-1} \cline{5-7}  & & & LH13 & & & & & \\ \cline{1-1} \cline{5-7}  & & & 4 & LH9, LH13 & & & & \\ \cline{1-1} \cline{5-7}  & & & 1 & **LH4** & & & & \\ \hline \multirow{4}{*}{Montelbano Jonico} & \multirow{4}{*}{M} & \multirow{4}{*}{4} & LH2, LH4, & \multirow{4}{*}{11} & LH2, FH3 & \multirow{4}{*}{3} & MH5, MH8, \\  & & & & LH12 & & & & \\ \cline{1-1} \cline{5-7}  & & & & LH12 & & & & \\ \cline{1-1} \cline{5-7}  & & & & & & & \\ \cline{1-1} \cline{5-7}  & & & & LH3, LH4, & \multirow{4}{*}{26} & LH2, FH3 & \multirow{4}{*}{3} & MH4, MH7, \\ \cline{1-1} \cline{5-7}  & & & LH9, LH12 & & & & & MH12 \\ \cline{1-1} \cline{5-7}  & & & 4 & LH4, LH8 & & & & 2 \\ \cline{1-1} \cline{5-7}  & & & 8 & LH4, LH9 & & & & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Final outcome of the integration of the SVI and FH, LH, and MH, with an indication of the number of census tracts in the different overlap classes for each municipality under investigation. The columns related to “LH Code”, “FH Code”, and “MH Code” show the type of hazard derived from the qualitative investigation (Table 2) for each semi-quantitative level. Each color represents the type of hazard considered in the integration process, whereas the shade represents the integration level. L = low; M = medium; H = high. Lastly, in order to present a detailed description of the overall results at the local level, Figures 12-14 depict a magnified view of the different study area sectors by integrating the outcomes derived from the overall quantitative analysis (Figure 11) with those from the hazard qualitative investigation (Figure 8). The codes represent the specific hazard (Table 1) to which the different levels of social vulnerability are exposed.