esa-sceva/satcom-chunk-collection huggingface dataset:
# Experimental Measurements of a Joint 5G-VLC Communication for Future Vehicular Networks
## V Advanced Viability Experimental Activity
### Experimental Results
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Technology** & **Latency: Best Fitting Distribution** & **Distribution Parameters [\(\mu\), \(\sigma\), \(\
u\)]** \\ \hline
5G & t-location scale & [0.0088, 7.43 \(\times\) 10\({}^{-4}\), 1.09] \\ VLC & t-location scale & [0.0119, 0.001, 1.253] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Best fitting distribution for 5G and VLC technologies. Figure 10: CDF and CDF error of the latency for empirical data and fitting distributions. Overall system (5G link and VLC link). Figure 9: CDF and CDF error of the latency for empirical data and fitting distributions. 5G link only. The VLC latency time could even be decreased by reducing the packet length, i.e., by optimizing the message length.
## 2 Materials and Methods
### GC-MS Analysis of Volatile Compounds  
The method of sample pretreatment referred to a previous study [4], with the difference that each headspace vial in this study contained three grams of sample. A fiber (65 \(\upmu\)m, PDMS/DVB, Supelco, Bellefonte, PA, USA) was inserted into the headspace of a vial to extract the volatile compounds at 40 \({}^{\circ}\)C for 30 min. At the end of extraction, the fiber was desorbed into the injection port of the GC for 5 min. The time of solvent delay was 3 min. The samples were analyzed using the Agilent gas chromatography mass spectrometry (GC-MS) instrument (7890-5975) with a DB-WAX (60 m \(\times\) 250 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m, Agilent  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Cultivar Name** & **Abbreviation Code** & **Origin** & **Flesh Color** & **Fruit Texture** & **Brix (\%)** & **Acid** & **Harvest Date** \\ \hline
1 & Xinfengmilu & XFML & Shanghai, China & White & Soft-melting & 14.9 & Sub-acid & Mid-July \\
2 & Hujingmilu & HJML & Jiangsu, China & White & Soft-melting & 13.2 & Sub-acid & Mid-July \\
3 & Datuamilu & DTML & Shanghai, China & White & Soft -melting & 14.3 & Sub-acid & Early-July \\
4 & Hikawa & HH & Japan & White & Soft-melting & 10.2 & Sub-acid & Mid-June \\
5 & Jinxiu & JXiU & Shanghai, China & Yellow & Hard-melting & 18.5 & Sub-acid & Mid-August \\
6 & Jinxiang & JXIANG & Shanghai, China & Yellow & Hard-melting & 10.1 & Sub-acid & Early-July \\
7 & Jinchun & JC & Shanghai, China & Yellow & Hard-melting & 11.0 & Sub-acid & Early-June \\
8 & Jinguan & JG & Shanghai, China & Yellow & Hard-melting & 14.6 & Sub-acid & Mid-July \\ \hline \hline \end{tabular}
\end{table}
Table 1: The characteristic information of peach materials.  
Figure 1: Representative images of eight peach cultivars used in this study. The codes under each image are the abbreviation names for each cultivar.  
122-7062) under splitless injection mode. Helium (99.999%) was used as a carrier gas at a constant flow (1 mL/min).  
The GC oven temperature was initially held at 40 \({}^{\circ}\)C for 2 min, increased to 100 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min and then increased to 230 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and held for 5 min. The mass detector was used in an electron impact mode at 70 eV, an ion source temperature of 230 \({}^{\circ}\)C and a mass spectrometry scan range of \(m/z\) 30-350. According to the NIST 08 (National Institute of Standards and Technology) mass spectrometry database, the volatile compound was identified by matching degree, retention time, retention index (RI) and standard chemicals. Compounds with matching degrees greater than 80% were selected as effective aroma components. In order to quantify the components, semi-quantification was performed using an internal standard method to calculate the relative concentration of volatile compounds in the samples. Measurements were repeated three times for each sample.
# SARDIM: High-Speed Near-Memory Processing Architecture for Synthetic Aperture Radar Imaging
## 5 Conclusions  
In this study, we introduced the SARDIMM, an NMP architecture based on a single-channel dual-rank DDR4 DIMM. The SARDIMM offers optional support for FFT, reference signal multiplication, and IFFT to accelerate the RDA for SAR image generation. By inte  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module** & **CLB LUTs** & **CLB Registers** & **DSPs** & **BRAMs** \\ \hline Buffers & 384 & 15 & - & 144 \\ FPBF module & 9474 & 2051 & 40 & - \\ NMP controller & 635 & 158 & - & - \\ \hline Total & 10,493 & 2224 & 40 & 144 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Implementation results of the proposed NMP module.  
Figure 11: Normalized DRAM total energy of the range compression, azimuth FFT, azimuth reference signal multiplication followed by azimuth IFFT, and end-to-end RDA on the baseline system and SARDIMM; MUL denotes reference signal multiplication.  
grating the NMP module into a buffer device positioned between the memory and memory controller, the SARDIMM operates seamlessly without requiring additional modifications to the DRAM chip. This design allows the NMP behavior to be controlled via standard memory write requests, ensuring compatibility with existing commercial memory systems. The SARDIMM supports the FP16 data type, and its NMP module is equipped with a floating-point adder, multiplier, and divider. We implemented and validated the NMP module of SARDIMM on an FPGA board, utilizing 10,493 LUTs, 2224 FFs, 40 DSPs, and 144 BRAMs. The performance evaluation conducted on images of varying sizes using customized gem5 and DRAMSim3 demonstrated an average speedup of 6.67\(\times\) in end-to-end RDA, along with overall DRAM energy savings of 46.21% compared to the baseline system. In addition to the RDA, which includes FFT, reference signal multiplication, and IFFT operations, we plan to conduct future work exploring other algorithms for SAR image generation. For example, we intend to investigate the effectiveness of SARDIMM in accelerating the CSA and omega-k algorithm. These experiments will further demonstrate the versatility and performance benefits of SARDIMM in a broader range of SAR image processing applications.  
[PERSON] designed the SARDIMM and implemented the NMP module, performed the simulation and evaluation, and wrote the paper. [PERSON] and [PERSON] evaluated the proposed SARDIMM and revised this manuscript. [PERSON] conceived of and led the research, analyzed the experimental results, and wrote the paper. All authors read and agreed to the published version of the manuscript.  
This work was supported by the Technology Innovation Program (No. 00144288, 00144290), funded by the Ministry of Trade, Industry, and Energy (MOTIE, Korea), and CAD tools were supported by the IDEC.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Strategies for Deploying a Sensor Network to Explore Planetary Lava Tubes
## 6 Power Transfer  
The robots laid down along the cave can also be used for wireless power transfer by beaming a high-power laser from the lander/rover to the robots inside the cave through a multi-hop channel. Wireless power transfer through laser is modeled in three phases: (a) Electricity to laser conversion where electrical power input is converted into a laser beam; (b) Laser transmission where laser power is attenuated while traveling through a medium; and (c) Laser to electricity conversion where the laser beam is converted into electrical power by photovoltaic panels.
## 3 Results and Discussion
### Precision  
Inter-assay precision (Table 3) in food was between 0.44 (genistein) and 11.21% (matairesinol), and intra-assay precision between 1.97 (biochanin A) and 13.16% (kaempfer). Serum inter-assay precision was between 1.46 (kaempfer at 40 ng/mL) and 14.87% (quercetin at 16 ng/mL). The intra-assay precision for serum did not exceed 15% and was found between 0.85 and 13.32%, with the highest values for daidzein (11.31% at 16 ng/mL and 13.32% at 40 ng/mL). While urine inter-assay precision was within acceptable levels (1.32-13%), except for resveratrol with 15.71%. Intra-assay precision for urine was below 7%. Acceptable values have been previously reported (Supplementary Tables S1-S3) for food [12, 28], serum [32, 36, 37, 44] and urine [17, 32, 37, 41, 44].  
Accuracy and precision values indicate that the HPLC-DAD-ESI-MS method is useful for the analysis of the 16 phytoestrogens and proved to be exact, reproducible and reliable at the evaluated levels. As only one quality control level for urine and food, and two levels for serum were included in the analysis, this could be considered as a limitation of the study. Nonetheless, analytical controls such as glassware blank, reagent blank, matrix blank, and phytoestrogens solution were included in every run to ensure the quality of the analytical test.
# Simulation Analysis and Experiment of Piezoelectric Pump with Tapered Cross-Section Vibrator
## 5 Simulation Analysis of Pumping Performance Based on Fluid-Solid Coupling
### Coupling Simulation Model of the Piezoelectric Pump  
A simulation model of the piezoelectric pump was established using a software COMSOL Multiphysics 6.1. The size of the pump chamber (Length \(\times\) Width \(\times\) Height) was set as 85 \(\times\) 20 \(\times\) 10 (mm). Both diameters of the inlet and outlet tubes were 5 mm, the fluid medium was water with a temperature of 20 \({}^{\circ}\)C under standard atmospheric pressure, density = 998.2 kg/m\({}^{3}\), and the dynamic viscosity was 1.003 \(\times\) 10\({}^{-3}\) kg/m\(\cdot\)s, the inlet of the pump chamber was set as the speed inlet, and the outlet was set as the 0 pressure outlet.  
Simulating this fluid-solid coupling in COMSOL Multiphysics 6.1 is to reveal the interactions between the vibrator powered by electricity and the fluid. These interactions include electrostatic field, solid mechanics field, and laminar flow physical field in the current model, which correspond to the piezoelectric ceramic sheet, vibrator substrate, and fluid domain (water). Specifically, there is a piezoelectric coupling between the electrostatic field (piezoelectric ceramic sheet) and the solid mechanics field (vibrator substrate) and a fluid-solid coupling between the vibrator and the fluid domain (water). The electrostatic module of the piezoelectric ceramic sheet is considered as charge conservation. Thus, the external surface potential is \(V=V_{0}sin(2\pi ft)\), where \(V_{0}\) and \(f\) are the amplitude and frequency of the driving voltage, respectively. The contact surface between the ceramic sheet and the vibrator substrate is set as the ground boundary, and the contact surface between the piezoelectric vibrator and the fluid is the fluid-solid coupling boundary. The fixed end of the vibrator is a fixed constraint. The coupling interface of the piezoelectric effect is treated as the solid mechanical field and the electrostatic field; the fluid-solid coupling interface is considered as the laminar flow physical field and the solid mechanical field. The fluid domain is set as the dynamic mesh deformation domain. The mesh element type is selected as free tetrahedral mesh with a size range of 0.5-2 mm. The maximum element growth rate is 1.5, and the curvature factor is 0.4. The mesh model is shown in Figure 5.
## 2 Research Conditions and Methods
### Analysis of Pavement Performance Development Law  
#### 2.2.1 Performance Decay Analysis  
According to statistical analysis of performance testing data during the life cycle of rural roads in China and relevant literature surveys, road performance shows different attenuation trends when different maintenance opportunities and measures are selected ([PERSON]. [17] et al. 2015, [PERSON] [18] et al. 2018). Figure 3 summarizes and presents decay curves of road performance under three different treatment strategies: preventive maintenance, functional repair priority, and structural repair priority. The bold curve (a) represents the early use of preventive maintenance techniques such as slurry sealing layer when small pavement damage occurs, resulting in slow performance decay to t1 and corresponding repairs to maintain high road conditions. However, as road age increases, overall pavement performance continues to decline in later stages until the road loses its intended service function. The bold curve (b) represents regular maintenance using techniques like crushed stone regeneration, milling, and repaving one or two layers when the pavement structure is damaged to a certain ex-tent and performance decays to t2 after the early adoption of preventive maintenance strategies to restore it to original technical condition. The bold curve (c) only starts selecting the necessary structural repair measures like milling and repaving three layers for maintenance when pavement performance continues to decline until the severe loss of use function at t3, restoring pavement performance quickly to its original level. This curve has a relatively long degradation period with average overall road performance.  
Based on the above analysis, to address the characteristics of road condition degradation during the life cycle of rural roads, selecting a suitable functional repair priority strategy for curve (b) can maintain the overall performance degradation of road conditions within a reasonable range and achieve long-term stability and preservation of rural road pavement structure.  
Figure 3: Performance decay curve of rural road pavement.
# The Impact of Cloud Versus Local Infrastructure on Automatic IoT-Driven Hydroponic Systems
## 4 Discussion
### Interpretation of the Results  
Beyond the technical aspects highlighted by the tests, this study's results clearly show the necessary trade-offs between performance and costs. AFCI, thanks to its scalability capabilities, uses the Azure platform to process and analyze data efficiently. However, the significant costs associated with the entire infrastructure offset this advantage.  
On the other hand, LSI, through a local server and a customized software solution, offers a higher degree of independence but raises issues related to maintenance and scalability. The data show that the local infrastructure can quickly become a limitation as the volume of processed data increases. Consequently, it becomes necessary to make additional investments in hardware equipment and qualified human resources.  
An important point to note is the different ways in which the two infrastructures manage data security. AFCI uses a highly developed authentication system based on Azure infrastructure, reducing the risk of unauthorized access. However, dependency on an external service raises privacy concerns that are not problematic in hydroponic systems. These systems do not handle sensitive data involving temperature, humidity, electrical conductivity, and liquid levels in containers. Being completely autonomous, LSI eliminates this risk but is vulnerable to attacks and potential hardware failures that could lead to data loss.  
Summarizing the research results, the answers to the RQs are:  
* RQ1: AFCI uses Microsoft Azure for all processes involving data from the IoT infrastructure. This underscores the need for a permanent internet connection. LSI manages data locally, using its own servers and dedicated scripts. Therefore, in the case of LSI, there is no dependency on the internet;
* RQ2: The performance indicators studied in the results show that LSI has a response time of 200 ms compared to 300 ms for AFCI. Additionally, LSI is superior to AFCI in terms of reliability and costs. AFCI offers scalability, global accessibility, and advanced security, but with recurring costs of USD 82.57/month;
* RQ3: LSI is ideal for small, autonomous farms where cost is a priority. AFCI is more suitable for large, distributed farms where accessibility and interoperability are central elements.  
The results obtained have implications for how infrastructure solutions are chosen based on the typology of each application. This study demonstrates that there is no universally optimal solution; the choice must be made according to each project's priorities. LSI is a viable solution for a small business with a low volume of data and limited financial resources. On the other hand, for a project with high scalability and global accessibility requirements, AFCI is a more suitable choice despite the higher costs.
## 2 Method and Material  
### Agro-IBIS Model Description  
Agro-IBIS is an improved version of the IBIS DEM [29, 31], with the capability to represent both natural and managed ecosystems [30]. The model was developed to simulate the rapid biophysical processes and long-term ecosystem dynamics in response to environmental drivers. It has been evaluated within forests at local and regional scales [34, 35], and has been used for many applications such as the quantification of trends in net primary productivity in the 20 th century [36] and climate regulation services of ecosystems throughout the Western Hemisphere [37]. The model is designed with a hierarchical conceptual framework, and includes several sub-models (e.g., land surface module, vegetation dynamic module, soil biogeochemistry module) that are capable of simulating vegetation canopy physics, vegetation phenology, soil physics and hydrology, and ecosystem biogeochemistry.  
Agro-IBIS has two critical phenophases for natural vegetation--leaf onset and leaf offset. For temperate deciduous trees, leaf onset and offset are defined as the date when LAI starts to increase from a minimum value, and the date when LAI starts to decrease from the peak value, respectively. The model originally used a simple scheme in which leaf onset and offset were both triggered by a critical temperature threshold [29]. Currently, the phenology model is modified from the algorithm developed in literature [19], which is based on GDD for leaf onset and the combination of photoperiod and temperature threshold for leaf offset. An evaluation study at three AmeriFlux sites showed that both schemes had poor performance in representing the phenology at the individual site level; simulated leaf onset dates were generally earlier than the observations with biases up to seven weeks, which led to large errors in canopy structure, such as canopy height and maximum LAI, and in turn the carbon and water exchange [34]. Evaluation at the regional scale showed relatively good performance in capturing the LAI evolution in the northern portion of U.S. eastern deciduous forest; however, earlier onsets were also found in the southern portion, which might be a result of the single threshold of GDD used in the model [35]. While the regional evaluation supports the argument that parameters of phenological models may vary with geographic location [14], the local evaluation implies that the applicability of parameters may change with spatial scale (e.g., from continental scale to site scale).
## 4 Discussion  
We begin the discussions of our experimental analysis stating that the exact approach is only able to cope well with the smaller instances. CPLEX finds the proven optimal solutions for the first 3 instances, but then its performance worsens dramatically. HLS and NNRA obtain the optimal solution for these 3 instances as well. None of the heuristic algorithms stands out from the other for all instances. Taking as reference the set of instances with 10-100 customers, HLS performs much better than NNRA, obtaining best solutions for 12 (out of 14) instances relatively quickly, while NNRA (\(\rho=0.99\)) obtains best solutions for 5 (out of 14). Note that, for the first 3 instances of set 1, the algorithms achieved the proven optimal solutions. NNRA (\(\rho=1.00\)), however, obtains best solutions only for 2 instances of set 1, suggesting that being too greedy with regard to the nearest neighbor strategy, in general, can lead to poor solutions. We would like to stress, however, that the solutions are not too bad if we consider the average percentage difference \(\Delta\) between the solutions of HLS and NNRA for these instances (\(\Delta_{0.99}=1.98\%\) and \(\Delta_{1.00}=2.59\%\) on average).  
Figure 4: Plot of solution cost versus instance.  
Figure 3: Plot of computation time versus instance.  
Concerning set 2 with larger size instances with 150-550 customers, NNRA outperforms HLS. In fact, NNRA (\(\rho=0.99\)) achieves best solutions for 9 (out of 14) instances, while NNRA (\(\rho=1.00\)) obtains best solutions for 10 (out of 14) instances. Moreover, the computation time used by HLS increases significantly, while NNRA uses the same time limit of 60 s without compromising the comparative quality of its solutions. In addition, the percentage difference \(\Delta\) between the solutions for this set of larger instances is very expressive, now favoring NNRA (\(\Delta_{0.99}=-9.05\%\) and \(\Delta_{1.00}=-10.33\%\) on average). The overall average percentage difference also favors our approach (\(\Delta_{0.99}=-3.53\%\) and \(\Delta_{1.00}=-3.87\%\) on average).  
As a final remark, although the comparative results have been expressive, we would like to point out that due to the simplicity of our algorithm and the relatively poor results it obtained for small instances, we believe that the optimality gap of the solutions may still be considerable for the larger-size instances. In fact, since our algorithm is very fast, it could be used to provide initial solutions to more sophisticated search methods. For instance, our solution could be used to boost the search method proposed by [PERSON] and [PERSON] (2019).