esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation and Crashworthiness Testing
### Results Comparison  
The comparisons between testing and simulation in terms of acceleration and velocity at the marker point are shown in Figure 7. The marker point is located at the pelvis. The margin of error between the results of the simulation and the testing is no more than 10%.  
With the time on the horizontal axis and the spine load on the vertical axis, the line graph illustrates the trend of the spine load of the ATD as the change of time during the crash. The orange curve indicates the simulation result and the blue curve indicates the test result. As is shown in Figure 8, the initial motion conditions are applied to the seat at the moment \(t=0\), and the collision between the seat and the ATD begins after 65 ms. After approximately 28 ms, the spine load rapidly increases to a peak  
Figure 5: Curve of the acceleration applied to the seat over time.  
Figure 6: A frame during the process of the test.  
smaller value which is nearly zero. During the simulation, when \(t=88.3\) ms, the peak spine load (i.e., 8578 N) appears. In the test, the maximum value of the spine load (i.e., 8387 N) occurs at \(t=93.1\) ms. Like the trend of the acceleration applied to the human-seat coupling system with time, the region enclosed by the spine load and time was also approximated using an isosceles triangle. With the restraint of the seat belt, the spine load fluctuates in a small range from \(t=130\) ms to \(t=330\) ms. After 130 ms, the seat is no longer pushed and there is no excessive spine load, so the process after that has no reference value.  
Overall, the margin of error between the results of the simulation and the testing is no more than 10%. Thus, the simulation result is considered to be basically in accordance with the actual situation and acceptable.
# Variability of Interpolation Errors and Mutual Enhancement of Different Interpolation Methods
## 3 Results
### Accuracy Evaluation After Fusion of Interpolation Results
The MEAN after fusion was \(-\)0.001 m\({}^{3}\), 0.008 m\({}^{3}\) better than before fusion for RBF, and 0.007 m\({}^{3}\) better for UK. The STD after fusion was 0.376 m\({}^{3}\), a reduction of 0.135 m\({}^{3}\) for RBF and 0.151 m\({}^{3}\) for UK. These results demonstrate some improvement in key metrics. As shown in Tables 4 and 5, the interpolation density variations 0.009 points/km\({}^{2}\) for PM\({}_{2.5}\) concentration data and 756,409.05 points/km\({}^{2}\) for height data--result in spatial discrepancies between interpolation methods. After weighted fusion, the RMSE for PM\({}_{2.5}\) concentration data with a sparse interpolation density (0.009 points/km\({}^{2}\)) showed a two-order of magnitude reduction compared to IDW and UK interpolations before fusion. This indicates that weighted fusion performs exceptionally well for PM\({}_{2.5}\) concentration data. For MIN, MAX, MEAN, and STD metrics, the global weighted fusion also demonstrated strong performance. In contrast, the height data, with a dense interpolation density of 756,409.05 points/km\({}^{2}\), showed a slight improvement in the RMSE after global weighted fusion, but the effect was not significant. The MAX value post-fusion increased compared to pre-fusion. Analysis of two sample areas with different point densities revealed that interpolation methods yield better results with sparse, uniform point data, where fusion between methods is effective. Conversely, with dense point data, the fusion effect of different methods tends to be average or even less effective.
## 4 Results
### Wet Snow and Streamflow Interaction
This pattern was also verified when analyzing the time when the peak in baseflow was achieved for each cycle (red triangles over each curve in Figure 10). A lower number of wet snow pixels generating runoff were needed to achieve the maximum baseflow peak. Finally, to verify our findings, we analyzed a totally independent melting cycle from a fourth hydrological year, 2019-2020. This cycle had a duration of 78 days, from 19 March 2020 to 5 June 2020, and a maximum SWE of 50 mm. The shape followed a similar pattern than previous melting cycles. Moreover, it had similar timing and a maximum SWE and hence, it was placed close to the purple curve, which reached a maximum SWE of 52 mm. The linear relationship found for long-lasting melting cycles was explored deeply to analyze the potential of S-1 SAR imagery to foresee streamflow dynamics. Previous studies in the area highlighted that despite the absence of aquifers in a strict sense, the highly fractured nature of the lithology results in a highly permeable surface structure that delays snowmelt water inflow into the river [103]. To analyze this delay, we used two variables that can be computed in near real-time: the number of wet snow pixels, which represented the melting dynamics, and the baseflow at the outlet of the catchment which represented  
Figure 9: Depletion curves. The x-axis represents the dimensionless cumulative number of pixels contributing to melting runoff in each cycle (\(\Sigma\)LMo*). The y-axis represents the dimensionless cumulative baseflow* during each cycle. The 2016–2017 event is represented by the green lines. The 2017–2018 event is represented by the blue lines. The 2018–2019 events are represented by the purple line, the orange, and brown line. The validation 2019–2020 event is represented by the red line. SWE max value for each cycle is also included in the legend. Red triangles represent the peak in baseflow during each event. Black dots represent the 25% (\(\bullet\)) of the total time of each cycle, black crosses represent 50% (\(\times\)) of the total time of each cycle, and black plusses represent 75% (+) of the total time of each cycle. the streamflow response. Then, for each melting cycle, we calculated this delay as the number of days between the date when the number of wet snow pixels was maximum and the date when maximum baseflow was achieved. A delay of 21, 13, and 30 days for each of the three cycles was found, respectively. After applying this delay, correlation between wet snow pixels and baseflow was computed using the determination coefficient (R\({}^{2}\)) (Figure 10). R\({}^{2}\) ranged between 0.62 and 0.83. Therefore, a linear relationship was hypothesized between the variables (Figure 10). The slope of the line (baseflow/number of wet snow pixels) indicates the speed of wet snow pixels transformed into baseflow. Shorter melting cycles implies a lower slope, that is, a lower speed. This finding is in line with previous results, where the maximum baseflow peak (red triangles in Figure 9) was observed earlier in cycles with higher maximum of SWE than in those with a lower maximum SWE.
## 6 Conclusions  
In summary, by comparing the different results of the CL device and SN device under three different installation conditions, the influence of the flow field distortion generated by the header on the USM measurement results was studied. Based on the experimental results, the flow field distribution was further investigated by CFD simulation. The main conclusions were as follows:  
* The accurate experimental results showed that the measurement results of SN device were consistent very well with the CL device, while the measurement error of the SN device without FC was about 1% higher than the reference. The flow field distortion effect generated by the header had a significant influence on the measurement results of USM due to the nonconforming Profile factor, while the difference of Symmetry and Cross-flow could be obviously eliminated by the double-cross-section designing: The simulation results showed that it was at about 25D without FC in the SN device that the velocity profile restored to a fully developed state and all characteristic parameters could meet the requirements, while it was at 10D with FC. The installation of FC could improve the accuracy of measurement results, because the FC could improve the velocity profile, and it also could effectively eliminate vortices and cross-flow.  
The characteristic parameter of USM could be determined when the meter was calibrated in the laboratory. When the USM was used in the field with header, these values should be checked to ensure that the field installation was satisfactory in terms of flow field distribution. Once installed, they can be checked again periodically. Any significant change in these parameters that does not correlate with a change in Re or upstream pipework should be investigated further.  
Figure 20: Characteristic parameters at 19D corresponding to the experimental condition.  
**Author Contributions:** Conceptualization, J.W. and C.L.; methodology, W.C.; software, W.C.; validation, J.W. and C.L.; formal analysis, W.C.; investigation, W.C.; resources, W.C.; data curation, W.C.; writing--original draft preparation, W.C.; writing--review and editing, J.W. and C.L.; funding acquisition, C.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by The National Key Research and Development Program of China \"Research and application of national quality infrastructure\" [grant number: 2017 YFF0205305].  
Not applicable.  
**Data Availability Statement:** All data generated or analyzed during this study are included in this manuscript.  
The authors sincerely thank the Gas Flow Laboratory of National Institute of Metrology of China for their great support during the experiments.  
The authors declare no conflict of interest.
# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
###### Abstract  
Spliced optical satellite cameras suffering from low stitching accuracy are influenced by various factors which can greatly restrict their applications. Most previous studies have focused on the geometric precision of stitched images, which is influenced by the stitching consistency and the relationships between different inner CCD (Charge-Coupled Device) images. Therefore, the stitching accuracy is of great significance in multiple CCD image production. Traditionally, the line-time normalization method has been applied for inner CCD image stitching based on designed line-times with the assumption of uniform sampling during imaging. However, the misalignment of the designed and actual line-time affected by various factors can lead to image distortion. Therefore, this paper investigates the performance of different normalization methods to produce stitched images with higher geometric performance using the actual line-time. First, the geometric distortions caused by misalignments between the designed and actual line-time are analyzed to show the differences in sampling rate and step-points. To overcome the distortions introduced by the fitting error of the designed line-time, three fine normalization methods based on the actual line-time, respectively called scene-based, block-based, and line-based line-time normalization methods, are introduced and compared with the traditional method. The scene-based and block-based line-time normalization methods fit the actual line-time section-by-section, while the line-based method builds the relationships between adjacent inner CCD images line-by-line. Images obtained from the Sustainable Development Goals Satellite 1 (SDGSAT-1) satellite are used for verification of different methods. The performance of the designed line-time normalization method and three fine actual line-time normalization methods is compared; the stitching accuracy can reach about 0.8, 0.56, 0.5, and 0.45 pixels, respectively. The time consumption of these four compared methods is about 5.5 s, 4.9 s, 5.4 s, and 58.9 s, respectively. Therefore, the block-based actual line-time normalization method utilized in practice can provide a good balance between running time and accuracy. In the future, we intend to find a new way to improve the efficiency of line-based line-time normalization methods to produce stitched images with higher geometric consistency and accuracy.  
multiple CCDs; actual line-time; fine line-time normalization; geometric performance
# Effects of Driving Frequency and Voltage on the Performances of Vibrating Mesh Nebulizers
## 3 Results  
### Nebulization Characteristics by Driving Frequency and Voltage  
As shown in Figure 5, output rates and power consumption increased significantly as driving voltage increased. In Figure 5a, output rates for driving frequencies not equal to resonance frequencies were significantly reduced. Output rates were reduced by 11.0-30.1%, 17.5-45.2%, and 24.0-46.4% at driving frequency discrepancies of F\({}_{\text{r}}\)\(\pm\) 1%, 2%, and 3%, respectively. Percentage output rates versus output rates at maximum voltage 70 V\({}_{\text{PP}}\) suggested by the manufacturer were \(-\)45.4-19.0%, \(-\)42.8-21.2%, \(-\)45.0-22.9%, \(-\)35.0-19.4%, \(-\)40.8-32.1%, 47.5-41.2%, and \(-\)64.0-35.4% at these driving frequency discrepancies, respectively. In Figure 5b, power consumptions decreased significantly at non-resonance driving frequencies and reduced by 6.6-13.6%, 9.5-19.2%, and 11.0-22.5% for driving frequency discrepancies of F\({}_{\text{r}}\)\(\pm\) 1%, 2%, and 3%, respectively. Power consumption changes were \(-\)36.4-39.5%, \(-\)39.4-41.2%, \(-\)35.6-44.0%, \(-\)33.4-44.0%, \(-\)35.7-44.9%, \(-\)38.1-42.2%, and \(-\)34.9-39.5% for 70 V\({}_{\text{PP}}\) at these driving frequency discrepancies, respectively. Particle sizes were significantly greater at non-resonance driving frequencies at constant driving voltage (Figure 5c). Particle size increases by frequencies changes from the resonance frequency were 1.6-7.7%, 1.8-11.3%, 4.0-15.0% at F\({}_{\text{r}}\)\(\pm\) 1%, 2%, and 3%, respectively.
## 2 Materials and Methods
### Experimental Methods  
The experiments were performed in a flow loop system that has a horizontal, vertical, and inclined section. However, in this paper, we are only considering the 4-m horizontal section as our test section. The experimental setup is a 60-m-long closed cycle system for water and open cycle system for air. The liquid is pumped by a 5 HP pump that creates the required large volume water flow through DN80 or 2.9 ID PVC clear pipes. The airline of the flow loop comprises of DN15 and DN25 mild steel pipes, which supply air from the lab air supply at 670 kPa (100 psi) shut-in pressure. It also includes a DN 25 ball check valve just before the air and the liquid mixing zone to prevent any liquid from entering the air pipeline. There are two Omega PX603-100G5V pressure transducers with a range of 0 to 100 psi in the 2-m long horizontal test section. There were some specific experimental conditions used for this setup. The airflow range was approximately 85 L/min to 3300 L/min, and the water flow ranged from almost 250 L/min to 850 L/min. Within this range, the experimental setup mostly provides slug flow for two-phase flow, as well as providing bubble flow and wavy flow at some intervals. Figure 1 presents a schematic representation of the experimental setup. For this study, both gas/Newtonian and gas/non-Newtonian fluid flow cases were considered. A Universal Data Acquisition System from National Instruments was used to collect all types of data from the flowmeter and sensors, which consisted of four NI 9219 universal modules with four channels, each providing 100 samples per second. The experimental conditions are reported in Table 1, including brief details of the rheological model used for xanthan gum solutions.  
\begin{table}
\begin{tabular}{c c} \hline
**Newtonian Fluid** & **Water** \\ \hline Non-Newtonian Fluid & 0.1\% xanthan gum solution (Power Law Index, n = 0.81) and (Power Law Index, m or k = 0.009344) \\ Liquid velocity range & 1.5 to 2.5 m/s \\ Air velocity & 2.9 to 6.4 m/s \\ \hline \end{tabular}
\end{table}
Table 1: The chemicals and experimental parameters used in this study.  
Slug flow is the most frequent two-phase flow phenomenon experienced in horizontal or near horizontal pipelines in practical applications. Slug flow in pipes is encountered in different fields, such as the production and transportation of oil and gas, food industry, chemical industry, and other industrial applications. Slug frequency, in addition to water hammering, leads to various operational problems, such as pipeline network instability, equipment damage, pressure fluctuations, and vibration of the system. In the oil and gas production industries, slug flow also influences the internal corrosion rate increase of carbon steel pipelines. Slug flow creates high turbulence, which can break the pipe wall inhibitor's protection layer [37].
## 3 Proposed Method
### The Use of Multiple Link Metrics  
The performance of routing protocols is dependent on the packet size and packet generation rate of the traffic being transmitted. Therefore, there are tendencies that different link metrics will perform differently when used to transmit variable application traffic types. Some of the link metrics that have already been implemented with OLSR are the ETX, MD and ML. The ETX measures the number of transmissions, including re-transmissions, needed to send a unicast packet over a wireless link. The expected number of transmissions necessary to deliver the data packet considering both its transmission and successful acknowledgement as required by the IEEE 802.11 protocol is evaluated as [24]:  
\[ETX=\frac{1}{D_{f}\times D_{r}} \tag{1}\]  
where \(D_{f}\) and \(D_{r}\) are the forward and reverse delivery ratio of broadcast probe packets of a fixed size transmitted periodically.  
The ETX link metric used in OLSR is deficient in certain aspects, these include: (i) inability to model the transmission interference and channel errors [25]; (ii) inability to model accurately the delivery rate of a network link since it does not consider the forwarding behaviour of nodes that have established such links [24]; (iii) inability to distinguish between links with different capacities; and (iv) the loss probability of small probes differ from that of the data packets [26]. The OLSR-ML (Minimum Loss) [PERSON] et al. [27], as the name implies, selects paths with minimum loss rates using the probability of successful transmission of forward and reverse link delivery ratios. Unlike ETX, the route probability in ML is given by the product of the link probabilities, instead of the sum of their inverse probabilities. ML has the advantage of eliminating high loss rate routes, but the disadvantage is that some low quality links may still be considered in choosing a given route. It is given as:  
\[ML=D_{f}\times D_{r} \tag{2}\]  
where \((D_{f}\times D_{r})\) is the probability of the successful delivery of probe packets from the source to the destination and from the destination to the source (reverse direction).  
OLSR-MD (Minimum Delay) [28] proposed by [PERSON] et al. selects paths between nodes with the lowest sum of transmission delays to the destination. The transmission delay measurement comes from the ad hoc probe, which is a great advantage, because it takes into account the differences in clock synchronization. The different link metrics being studied trade off between different QoS parameters. For example, a routing protocol using a particular link metric can achieve high throughput values at the cost of increased end-to-end delay in the case of static networks like SG [29].  
Considering the problems with the single metric and the fact that AMI functionality is dependent on the ability of different application traffic to meet certain performance requirements, this work explores multiple metrics with OLSR to improve WMN's ability to provide QoS guarantees to a variety of traffic types in the NAN.
## 4 Sizing Optimization
### Constraints  
* Power balance constraint without FDR implementation: The total power generated should satisfy the energy demand at any given time. \[PV_{p}(t)+WT_{p}(t)+P_{b}^{ds}(t)-P_{b}^{ch}(t)=PL(t)\quad\text{for case 1}\] (15) where \(PL\) is the load demand, \(P_{b}^{ds}(t)\) and \(P_{b}^{ch}(t)\) is the BESS discharging and charging power.
* Power balance constraint with the consideration of the FDR rescheduling program can be expressed as: \[PV_{p}(t)+WT_{p}(t)+P_{b}^{ds}(t)-P_{b}(t)^{ch}=PL^{\prime}(t)\quad\text{for case 2}\] (16)
* BESS constraints: the energy stored in the BESS must be bounded within the minimum (\(SOC^{min}\)) and the maximum state of charge (\(SOC^{max}\)) limit. \[SOC^{min}\leq SOC(t)\leq SOC^{max}\] (17)* BESS maximum charging and discharging power is bounded by the maximum charging (\(P_{b}^{ch\_max}\)) and discharging power capacity (\(P_{b}^{ds\_max}\)). \[P_{b}^{ch}(t) \leq P_{b}^{ch\_max} \text{ charging phase}\] (18) \[P_{b}^{ds}(t) \leq P_{b}^{ds\_max} \text{ discharging phase}\] (19)
* Charging and discharging of the BESS cannot occur simultaneously. \[U_{b}^{ch}(t)=[0]\text{ or }[1]\] (20) \[U_{b}^{ds}(t)=[0]\text{ or }[1]\] (21) \[U_{b}^{ch}(t)+U_{b}^{ds}(t)=[0]\text{ or }[1]\] (22) where \(U_{b}^{ch}(t)\) and \(U_{b}^{ds}(t)\) are the integer variables denoting the charging and discharging operation state of the BESS, respectively.
## 3 Results and Discussion
### Huanglongbing Effect on Fruit Size, Color, SSC, and TA
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multicolumn{3}{c}{**2020**} & \multicolumn{3}{c}{**2021**} \\ \cline{2-7}  & **HLB\(-\)** & **HLB+** & **_p-Value_** & **HLB\(-\)** & **HLB+** & **_p-Value_** \\ \hline Fruit weight (g) & 482.8 a & 230.8 b & \textless{}0.001 *** & 500.6 a & 228.8 b & \textless{}0.001 *** \\ Fruit length (mm) & 89.3 a & 74.0 b & \textless{}0.001 *** & 96.7a & 74.8 b & \textless{}0.001 *** \\ Fruit diameter (mm) & 104.8 a & 79.9 b & \textless{}0.001 *** & 99.9 a & 81.2 b & \textless{}0.001 *** \\ Peel a\({}^{\star}\) & 85.0 a & \textless{}7.25 b & \textless{}0.001 *** & 8.56 & \textless{}-0.19 & 0.058 \\ Peel b\({}^{\star}\) & 42.62 & 46.54 & 0.053 & 45.13 & 52.89 & 0.083 \\ Peel a\({}^{\star}\)/b\({}^{\star}\) ratio & 0.199 a & \textless{}0.156 b & \textless{}0.001 *** & 0.190 a & \textless{}0.036 b & \textless{}0.001 *** \\ Hue angle (\({}^{\circ}\)) & 78.8 b & 98.8 a & \textless{}0.001 *** & 79.3 b & 92.3 a & \textless{}0.001 *** \\ SSC (\%) & 10.77 a & 9.50 b & 0.009 *** & 9.63 a & 8.35 b & 0.008 *** \\ TA (\%) & 1.10 a & 1.15 a & 0.408 & 0.89 a & 0.99 a & 0.347 \\ SSC/TA ratio & 9.79 a & 8.28 b & 0.029 * & 10.93 a & 8.61 b & 0.042 * \\ \hline \hline \end{tabular}  
* and *** indicate a significant difference between HLB\(-\) and HLB+ according to the Tukey test at _p_\(\leq\) 0.05 and 0.001, respectively. Different lowercase letters indicate a significant difference between HLB\(-\) and HLB+ (p \(\leq\) 0.05).  
\end{table} 
Table 1: Quality attributes of ‘Ray Ruby’ grapefruit and juice of healthy (HLB\(-\)) and HLB-affected (HLB+) fruits of two harvest seasons, 2020 and 2021.  
had a much smaller tree canopy and fewer leaves [4; 22], which could be one of the reasons HLB+ fruits were greener. HLB delays fruit development; thus, TA remains at high levels for a longer time. This high TA content is often reported for the juice of HLB+ oranges [4]. However, in this research, HLB did not significantly affect juice TA content in grapefruit juice (Table 1).