esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimised Twin Fluid Atomiser Design for High-Viscosity, Shear-Thinning Fluids
## 2 Materials and Methods
### Setup
Ten nozzle blanks and ten nozzle cages were available for this study, resulting in ten different nozzle assemblies, each varying in the number and diameter of gas holes \(d_{G}\) as well as in apex angle \(\theta\). Gas nozzle holes were drilled in a discrete and equidistant arrangement with respect to each nozzle configuration (NC). Figure 2 shows a nozzle  
Figure 2: (1) nozzle blank; (2) welded nozzle blank and cage; (3) complete nozzle assembly; (4) bottom view of complete nozzle assembly. assembly consisting of six gas nozzle holes, each with a diameter of 6 mm (\(d_{G}\)), and \(\theta=90^{\circ}\). With regards to Figure 2, a nozzle insert was placed inside the nozzle assembly to enable atomisation. An O-ring in the nozzle insert ensured that the liquid flowed through the liquid hole with diameter \(d_{L}\) of the nozzle insert, while it also secured the nozzle insert in its position. The investigated nozzle configurations were operated in either confined or free-fall mode, depending on the presence and geometry of the nozzle insert. In setups with a nozzle insert, atomisation was initiated directly at the insert tip, consistent with confined atomisers. In the absence of the nozzle insert, the liquid flowed in a free-fall manner before being impinged by the atomising agent, resembling a free-fall atomiser [36]. The geometric impingement distance (IDG) was defined as the axial distance between the exit of the gas nozzle and the first point of contact with the liquid at the liquid nozzle tip, considering the unwidened gas jet, as shown in Figure 3. Regarding the investigated atomiser design, IDG increases as the apex angle of the atomising agent decreases. A preliminary sizing of six \(6\times 10^{-3}\) m diameter holes was selected, based on the assumption of air flowing at standard conditions with a normal volume flow rate of \(5.56\times 10^{-2}\) std m\({}^{3}\)s\({}^{-1}\). This specific design flow rate was selected to ensure that the air exits the nozzle at sonic speed, under over-pressure conditions, for a maximum flow rate of \(1.11\times 10^{-1}\) std m\({}^{3}\)s\({}^{-1}\). Standard thermodynamic law for isentropic gas flow was applied, confirming sonic outflow under over-pressure conditions at the gas nozzle exits..  
Building upon the baseline design shown in Figure 2, additional nozzle configurations, listed in Table 3, were manufactured with the objective of maintaining a nearly constant area ratio \(A_{G}/A_{L}\), constrained by the available drill bit increments of \(5\times 10^{-4}\) m. The ratio \(A_{G}/A_{L}\) represents the relationship between the cross-sectional area of all gas nozzle holes and the liquid pouring hole with its corresponding diameter \(d_{L}\). The nozzle inserts had an inner diameter \(d_{L}\) of \(8\times 10^{-3}\) m. The ratio \(s/d_{G}\) represents the circumferential distance \(s\) relative to the diameter of the gas nozzle holes \(d_{G}\), as described in Figure 2.  
Figure 3: Visualisation of IDG (**a**) and IDL (**b**).
## 2 Methodology
### Area Level  
The area level of the D-LSE will be utilized only if bad data detection is flagged in the substation level of the D-LSE. It is important to note that substation-level bad data detection is flagged only for breaker current measurements. Thus, there is a possibility that bad voltage measurement data can be available at the area level. The area level of the proposed D-LSE is shown in Figure 4b.  
Figure 3 shows how the area level is fitted in the D-LSE. The \(T_{MF}\) is the computational time for matrix formation, and \(T_{MS}\) is the computational time for voltage estimation. When there is bad data detection at the substation level, a bad data detection flag is sent to the area level. The area level will be based on the LSE formulation explained under Section 2.1. LSE will be formulated by intentionally neglecting bad measurements in the detected substation, thus improving the estimation accuracy. The area's topology can be derived from the substation BBMs in the considered area, as shown in Figure 3 for the Level 2-Area in the H-TNTP.
## 3 Case Validation: Typhoon Haikui
### Impact Awareness
#### 3.2.1 Social Impact  
Flooding was the most reported disaster type on social media during Typhoon Haikui, with 119 posts containing specific location information. An hourly and daily analysis of flood reports was conducted and compared with precipitation data for the same periods.  
As of 6 September, there were over 80 areas with flooding in urban districts, including 12 underpasses. The maximum road surface water depth reached 1.4 m, while the deepest underpass flooding approached 4 m. To date, 147 towns have been affected, with a total of 51,272 people impacted, including 36,026 who have been urgently relocated. There are 63 heavily-flooded residential communities, 44 underground parking garages that have been flooded, and 16 flooded urban villages. Additionally, 4194.65 hectares of farmland has been submerged, 170 roads and bridges have been damaged, 919 houses have been damaged, 67 houses have collapsed, 125 power lines have been affected, 85.4 km of communication lines have been damaged, and 21 communication base stations have been impacted. The direct economic losses are estimated at approximately CNY 552.1 million. Severe flooding occurred on several urban roads, with 81 identified flooded areas; by 5:00 p.m. on the 6 th, 60 of these had been cleared, while work was underway to address the remaining 21.  
As shown in Figure 11, Flooding disaster points in Fuzhou are primarily concentrated in the city's core districts, including Gulou District, Taijiang District, Jin'an District, Cangshan District, Mawei District, and Changle District. These urban core areas account for 68.91% of the total number of flooding disaster points. Fuzhou's topography is characteristic of an estuarine basin, with mid- and low-altitude mountains in the west and alternating hills and plains in the east. Surface runoff primarily flows through the low-lying areas along the Minjiang River and its tributaries, where the city's central districts are located. Therefore, the high concentration of flooding points in the lower-elevation central areas is reasonable. In this study, we conducted a spatial overlay analysis by combining the geographic location data of stranded individuals with Fuzhou's population density data. The results showed that stranded individuals were mainly distributed in densely-populated areas, particularly in and around Taijiang District and Gulou District, exhibiting a clear spatial correlation. This finding highlights the relationship between the distribution of stranded individuals and residential patterns during disaster events, providing important insights for optimizing the allocation of rescue resources and improving the efficiency of emergency responses.  
Figure 11: Total social impact map.
# Lightweight and Security-Enhanced Key Agreement Protocol Using PUF for IoD Environments
## 5 Cryptanalysis of [PERSON]'s Scheme
### Mobile Device Impersonation Attack  
An attacker must compute the authentication request message (\(\{A_{M},C_{M},G_{M},PK_{M},T_{1}\}\)) and response message (\(\{Auth_{GCS},PK_{D}^{*},T_{7}\}\)) to impersonate a legitimate mobile device.  
**Step 1:**: An attacker can eavesdrop on public-channel messages \(\{I_{M},PK_{M},A_{D},C_{M},H_{DM},T_{3}\}\) to obtain these values.
**Step 2:**: An attacker can compute \(D_{M}\) by performing an XOR operation on \(H_{DM}\) and the drone's public key, \(PK_{D}\).
**Step 3:**: Using \(C_{M}\) and \(D_{M}\), the attacker can calculate \(G_{M}\).
**Step 4:**: The attacker can create the \(PK_{AM}\) by selecting a random value.
**Step 5:**: Using the drone's identity, \(D_{M}\), and \(C_{M}\) value, the attacker can calculate \(SK_{D}\), allowing the attacker to compute \(Auth_{GCS}\). The \(PK_{D}^{*}\) value is not used, so it does not need to be computed.  
Therefore, [PERSON]'s protocol is vulnerable to mobile device impersonation attacks.
# Electro-Thermal Properties of Carbon-Fiber-Reinforced Mortar Prisms
## 5 Conclusions  
Electric current can flow through fiber-reinforced prisms due to the carbon fibers. Carbon fiber is a conductive material, and the amount of current flowing through the prisms depends on the voltage applied on them and the resistances introduced by the amount of fibers. When a constant power of 30 W is applied to each of the prisms reinforced by fibers of four different percentages, namely, 0.5%, 1.0%, 1.5% and 2.0%, the average temperature increment of the observed prism surface is greater for the higher percentage prisms. The heating efficiency of these prisms is higher for those of higher percentage, but the differences between them reduce as the heating time increases. The efficiency is in the range of 0.67 to 0.77 for the 5 min heating. The temperature increments for (1, 3) 5 min heating are (9.75\({}^{\circ}\), 24.4\({}^{\circ}\)) 33.3\({}^{\circ}\), (11.9\({}^{\circ}\), 26.75\({}^{\circ}\)) 37.35\({}^{\circ}\), (14.95\({}^{\circ}\), 30.45\({}^{\circ}\)) 43.5\({}^{\circ}\) and (15.0\({}^{\circ}\), 34.45\({}^{\circ}\)) 47.65\({}^{\circ}\) for the 0.5%, 1.0%, 1.5% and 2.0% prisms, respectively, as shown in Figure 10.  
Figure 10: Comparisons of the surfaces’ average temperature increments of four different percentage prisms: (a) Directly measured, (b) Compensated.  
These values indicate that the highest increments are recorded at the first 1 min, and the increments are decreasing as the heating time length increases for all percentage prisms. The first 1 min also records the highest temperature increment for the lines representing the lowest and the highest temperature zones, and one of the normal lines to two electrodes, on the surfaces. In these cases, the increments at the first 1 min are higher than those for 1 to 3 min and 3 to 5 min heating. The physical reasons behind these facts are unclear as of now. The unchanging temperature ratio of two points on the surface during the 5 min heating also requires explanation.  
Conceptualization, J.-Y.S. and J.P.; methodology, J.-Y.S. and J.K.; software, J.K. and G.H.; validation, J.-Y.S. and T.V.; formal analysis, J.-Y.S., J.K. and G.H.; investigation, J.-Y.S., J.K., J.P. and G.H.; resources, J.K. and J.P.; data curation, J.K. and J.P.; writing--original draft preparation, J.-Y.S.; writing--review and editing, J.-Y.S. and T.V.; visualization, J.K.; supervision, G.H. and J.P.; project administration, G.H.; funding acquisition, G.H. and J.P. All authors have read and agreed to the published version of the manuscript.  
Priority Reseach Centers Program through the National Reseach Foundation of Korea (NRF) funded by the Ministry of Education (Grant Number: NRF-2018R1A6A1A03025542).  
Not applicable.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Effects of Telekia speciosa Essential Oils Melanoma Cells
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Time of Treatment**} & \multirow{2}{*}{**Cell Line**} & \multicolumn{2}{c}{**IC\({}_{50}\) (\(\mathrm{\SIUnitSymbolMicro m}\)/L)**} \\ \cline{3-4}  & & **Flower Essential Oil** & **Cisplatin** \\ \hline \multirow{4}{*}{24 h} & A375 & 7.2 \(\pm\) 1.6 & 7.4 \(\pm\) 1.9 \\  & C32 & 14.2 \(\pm\) 2.8 & 10.6 \(\pm\) 1.6 \\  & HaCaT & 10.2 \(\pm\) 1.9 & 13.8 \(\pm\) 1.5 \\  & Fibroblasts & \(>\)25 & \(>\)25 \\ \hline \multirow{4}{*}{48 h} & A375 & 5.1 \(\pm\) 1.4 & 3.7 \(\pm\) 1.2 \\  & C32 & 11.3 \(\pm\) 1.7 & 2.9 \(\pm\) 0.8 \\  & HaCaT & 8.1 \(\pm\) 1.2 & 2.8 \(\pm\) 1.0 \\  & Fibroblasts & 17.1 \(\pm\) 1.5 & \(>\)25 \\ \hline \multirow{4}{*}{72 h} & A375 & 3.4 \(\pm\) 1.1 & 2.0 \(\pm\) 0.8 \\  & C32 & 6.3 \(\pm\) 1.5 & 1.7 \(\pm\) 0.6 \\ \cline{1-1}  & HaCaT & 4.3 \(\pm\) 1.1 & 1.4 \(\pm\) 0.5 \\ \cline{1-1}  & Fibroblasts & 11.8 \(\pm\) 1.6 & 14.5 \(\pm\) 1.4 \\ \hline \end{tabular}
\end{table} 
Table 2: Half-maximum inhibitory concentration (IC\({}_{50}\)) values (\(\mathrm{\SIUnitSymbolMicro m}\)/mL) calculated for the essential oil from _Telekia speciosa_ flowers and cisplatin towards the melanoma cell lines (A375, C32), keratinocytes (HaCaT), and human skin fibroblasts.
## 4 Cases of Study and Validation  
### Case Studies: Application and Evaluation  
To validate the generalizability of the proposed UHI models, each of the four linear models developed (Section 3.3) was applied to an external Spanish city not included in the calibration phase, but representative of each SCS climate zone. The selected cities were:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SCS and Cities** & **MAE** & **MAPE** & **RMSE** \\ \hline SCS 1 & & & \\ \hline Avila & 1.20 & 6.50 & 1.60 \\ Gijon & 1.14 & 6.48 & 1.42 \\ Leon & 1.39 & 6.66 & 1.73 \\ Lugo & 1.64 & 9.80 & 1.94 \\ Vigo & 0.97 & 5.51 & 1.24 \\ Mean & 1.27 & 6.99 & 1.59 \\ \hline SCS 2 & & & \\ \hline Badalona & 0.84 & 3.46 & 1.09 \\ Cuenca & 1.36 & 6.02 & 1.56 \\ Orense & 1.19 & 5.08 & 1.50 \\ Salamanca & 0.92 & 4.32 & 1.27 \\ Segovia & 1.62 & 8.93 & 2.19 \\ Mean & 1.19 & 5.56 & 1.52 \\ \hline SCS 3 & & & \\ \hline Albacete & 0.68 & 2.81 & 0.90 \\ Ciudad Real & 1.11 & 4.69 & 1.48 \\ Lerida & 0.90 & 3.63 & 1.17 \\ Malaga & 1.18 & 4.89 & 1.51 \\ Murcia & 1.94 & 6.91 & 2.17 \\ Mean & 1.16 & 4.59 & 1.45 \\ \hline SCS 4 & & & \\ \hline Alicante & 0.63 & 2.40 & 0.79 \\ Almeria & 0.72 & 2.81 & 0.97 \\ Caceres & 1.00 & 4.14 & 1.34 \\ Cordoba & 1.53 & 5.41 & 1.72 \\ Huelva & 1.82 & 7.66 & 2.18 \\ Mean & 1.14 & 4.48 & 1.40 \\ \hline \hline \end{tabular}
\end{table}
Table 7: MAE, MAPE, and RMSE of the four linear models generated for each SHS climate zone.  
* Burgos for SCS 1 (cooler inland zone),
* Teruel for SCS 2 (moderate continental zone),
* Guadalajara for SCS 3 (transition zone),
* Jaen for SCS 4 (hotter southern zone).  
The performance of the models was evaluated in terms of MAE, MAPE, and RMSE. The results, presented in Table 8, are compared with the average performance obtained during the calibration phase for each corresponding SCS model (Table 7).  
In all cases, the error metrics for the external cities fall within or below the expected range observed during model calibration. Notably, the SCS 3 and SCS 2 models perform particularly well in Guadalajara and Teruel, respectively, with RMSE values under 1.1 \({}^{\circ}\)C, suggesting good generalization capacity. Jaen, in SCS 4, shows slightly higher error than the training average, which may reflect the more complex thermal behavior in southern climates with higher urban heterogeneity.  
Additionally, compared to previous urban LST modeling studies, the MAE values obtained here (0.7-1.5 \({}^{\circ}\)C) are within the typical range reported in the literature. For example, ref. [51] reported an acceptable MAE of \(\pm\)3 \({}^{\circ}\)C for LST in similar large-scale urban areas.
## 4 Conclusions  
In this paper, we investigate synchronization between two FHN neurons by using the energy method to estimate the influence of time delay in signal transmission on synchronization conditions and synchronous dynamics. According to the concept of the energy method, we can calculate the change rate of energy of the synchronization error system from the original system. If the rate of change of the energy varies periodically, the energy will remain unchanged in one period. Since the energy change rate of the error system can be divided into two parts (periodic and non-periodic), we only need to consider the non-periodic part to determine the evolution tendency of the error system's energy. The error system's energy will eventually disappear if the non-periodic part is less than zero. According to this line of thinking, we first derive the synchronization criterion for two coupled FHN neurons without time delay in signal transmission. Analytical and numerical results indicate that it is a feasible approach, to derive synchronization conditions by calculating the rate of change of energy of synchronization error system. Our analysis shows that if time delay in signal transmission is ignored in two coupled FHN neurons, synchronous spikes do not occur for any given coupling strength.  
Further investigation shows that the energy method is also valid for the delay differential system. A synchronization condition depending on time delay can be derived if the neural system has an unstable trivial equilibrium. Our research suggests that the existence of the time delay in signal transmission can not only slightly change the synchronization range of coupling strength, but also affect the synchronous dynamics. The two FHN neurons can easily control the occurrence of synchronous spikes or neural rest by only adjusting the time delay in signal transmission. The research result helps us to understand the regulatory mechanisms of synchronization and the synchronous dynamics of neural networks.  
In addition, it is worth pointing out that the method of analysis used in this paper can be easily developed to judge synchronization of N coupled FHN neurons. For N coupled FHN neurons, we need to calculate the rate of change of the energy of the error system, consisting of N-1 second-order differential equations with or without time delay. This is feasible for small calculation quantities.  
**Author Contributions:** [PERSON] performed some theoretical analysis and wrote the paper; [PERSON] carried numerical simulations; [PERSON] performed other theoretical analysis.  
**Funding:** This work was supported by the National Natural Science Foundation of China (Grant Nos. 11672185, 11672177).  
**Conflicts of Interest:** The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript and in the decision to publish the results.
# Low Temperature Chemoselective Hydrogenation of Aldehydes over a Magnetic Pd Catalyst
## 3 Results and Discussion
### Effect of the Hydrogen Pressure on the Hydrogenation of Benzaldehyde  
The effect of hydrogen pressure on the reduction of benzaldehyde was also studied. As shown in Figure 1, the conversion of benzaldehyde increased with the increasing of the hydrogen pressure. As the hydrogen pressure increasing from 1 bar to 10 bar, the conversion of benzaldehyde increased from 36.8% to 100% after reacted for 1 h at room temperature. The increasing hydrogen pressure might result in the increasing hydrogen concentration in the reaction solution, which accelerated the reaction. However, it seemed that the selectivity of benzyl alcohol wasinsensitive to the hydrogen pressure, which remained \(>\)95% under different hydrogen pressures. Considered the reaction efficiency, we chose 10 bar as the optimizedhydrogen pressure.
# Improving Passband Characteristics in Chebyshev Sharpened Comb Decimation Filters
###### Abstract  
This work presents the design of optimal and multiplierless compensators for Chebyshev sharpened comb decimation filters. The narrowband and wideband compensators are proposed. For the narrowband, the compensator with a magnitude response is proposed in a sinusoidal form, while for the wideband, two compensators with magnitude responses of two sinusoidal functions are introduced. The optimum design is performed using particle swarm optimization (PSO), while the multiplierless design is realized by presenting optimum parameters in a signed-power-of-two (SPT) form. Unlike the methods in the literature, this approach presents flexibility in design, allowing for an exchange between the quality of optimization and the complexity. Comparisons with the compensators from the literature demonstrated that the proposed method provides much better compensation while requiring fewer or slightly increased number of adders. Possible practical applications and potential future research work are also included.  
decimation; comb; sharpening; Chebyshev sharpening; compensator; narrowband; wideband +
Footnote †: journal: Applied sciences
## 3 Results  
This section reports on the forest cover loss detection results from Sentinel-2 for a region in the Huehuetenango Department, Guatem, and another region in the State of Mato Grosso, Brazil. The presented forest cover loss maps were generated from a baseline image composite for the reference year 2019 compared to change detection images from Sentinel-2 acquired from January to August 2022. Multiple such change detections are aggregated into a raster file (the analyst report) incorporating a number of'report layers' of spatial information about each pixel in the raster. These reports are designed to be easily understandable by non-experts and are intended to be used to demonstrate due diligence by importers and exporters to show that their trade does not inadvertently cause deforestation in the source regions of their produce.
# Micro-Gear Point Cloud Segmentation Based on Multi-Scale Point Transformer
## 5 Conclusions  
This study addresses the segmentation of point clouds for industrial small precision components by constructing a custom dataset containing 1101 small gear models. This dataset was obtained through a complete Metal Injection Molding (MIM) process and orthogonal experiments, ensuring it adequately reflects the characteristics of the MIM process.  
Based on this dataset, a multiscale feature fusion Point Transformer network was proposed, which enhances the modeling capabilities for complex structured point clouds by incorporating global feature extraction modules and an upsampling module. Experimental results indicate that the proposed method achieves significant improvements over the original network, with evaluation metrics confirming its effectiveness and superiority.  
The dataset and algorithm developed in this study are not only technical achievements but also lay a foundation for future research using deep learning point cloud data processing techniques in industrial production. However, both the dataset and the point cloud segmentation algorithm have limitations. The dataset should include more types of gears, and the segmentation algorithm should be further optimized to improve training efficiency and adapt to a broader range of gear point cloud data.  
Looking forward, we plan to expand the dataset to accommodate all common types of gears and upgrade the segmentation algorithm by optimizing its modules to ensure more efficient training. Additionally, by fully utilizing the dataset, networks such as point cloud completion and registration will be designed, aiming to further promote the application of point cloud data in industrial production.  
Figure 18: Multiple mIoU comparisons of PRA-Net, PointTransformer, and ours.  
In summary, our dataset and segmentation algorithm have achieved significant progress, overcoming longstanding challenges and opening new avenues for the application of point cloud data. We will continue to advance research in this field.  
Conceptualization, Y.S. and G.Q.; methodology, Y.S. and X.W.; software, Y.S. and G.Q.; writing--original draft preparation, Y.S. and G.Q.; writing--review and editing, Y.S. and B.L.; supervision, X.W.; funding acquisition, B.L. All authors have read and agreed to the published version of the manuscript.  
This paper is supported by a grant from the collaborative innovation project of Chaoyang District, Beijing (CYXC2102); is Supported by the Academic Research Projects of Beijing Union University (No. JZ10202003); is Supported by the National Natural Science Foundation of China (No. 5177050098).  
Not applicable.  
Restrictions apply to the availability of these data. Data were obtained from other research projects and are available from the corresponding authors with the permission of the respective owner.  
The authors declare no conflicts of interest.