esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variable** & **N** & **Mean** & **Median** & **Minimum** & **Maximum** & **Q1** & **Q4** & **Q Range** & **SD** \\ \hline VH BHB & 53 & 0.38 & 0.40 & 0.13 & 0.57 & 0.28 & 0.47 & 0.44 & 0.12 \\ CSF occ BHB & 53 & 0.34 & 0.33 & 0.09 & 0.55 & 0.24 & 0.45 & 0.45 & 0.13 \\ \cline{1-1} (mmol/L) & 24 & 0.19 & 0.19 & 0.11 & 0.30 & 0.14 & 0.25 & 0.19 & 0.06 \\ Peripheral blood & 35 & 2.50 & 1.90 & 0.30 & 7.30 & 0.80 & 2.80 & 7.00 & 2.25 \\ \hline \multirow{8}{*}{Peripheral blood} & \multicolumn{5}{c}{N = number of cases, SD = standard deviation; VH, CSF occ, CSF L were analyzed by method B, peripheral blood using method A.} \\  & & & & & & & & \\ \cline{1-1}  & **(1) VH BHB (53 Cases)** & \multicolumn{5}{c}{**(2) CSF occ BHB**} & \multicolumn{5}{c}{**(3) CSF L BHB (24 Cases)**} & \multicolumn{5}{c}{**(4) Peripheral Blood**} \\ \cline{1-1}  & **Values** & **N** & **\%** & **N** & **\%** & **N** & **\%** \\ \hline Normal & 0 & 0 & 1 & 1.89 & 0 & 4.17 & 2 & 5.71 \\ \hline Increased & 44 & 83.02 & 43 & 81.13 & 24 & 100 & 22 & 62.86 \\ \hline Ketoadicosis & 9 & 16.98 & 9 & 16.98 & 0 & 0 & 11 & 31.43 \\ \hline Total & 53 & 100 & 53 & 100 & 24 & 100 & 35 & 100 \\ \hline \end{tabular}
\end{table} 
Table 2: Statistical description of BHB values. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Variable** & **PMI** & **No. of Cases** & **Mean** & **Std. Deviation** & **Minimum** & **Maximum** & \(p\) \\ \hline \multirow{8}{*}{BHB V VH} & 24 h & 36 & 0.39 & 0.12 & 0.13 & 0.57 & \multirow{8}{*}{0.86} \\  & 36 h & 7 & 0.39 & 0.14 & 0.23 & 0.57 & \multirow{8}{*}{0.86} \\  & 48 h & 8 & 0.36 & 0.09 & 0.23 & 0.46 & \multirow{8}{*}{0.86} \\  & 72 h & 2 & 0.35 & 0.07 & 0.30 & 0.40 & \\  & Total & 53 & 0.39 & 0.12 & 0.13 & 0.57 & \\ \hline \multirow{8}{*}{BHB CSF Occ} & 24 h & 36 & 0.35 & 0.13 & 0.10 & 0.55 & \multirow{8}{*}{0.71} \\  & 36 h & 7 & 0.36 & 0.15 & 0.17 & 0.55 & \\ \cline{1-1}  & 48 h & 8 & 0.29 & 0.13 & 0.15 & 0.45 & 0.71 \\ \cline{1-1}  & 72 h & 2 & 0.31 & 0.06 & 0.27 & 0.35 & \\ \cline{1-1}  & Total & 53 & 0.34 & 0.13 & 0.10 & 0.55 & \\ \hline \multirow{8}{*}{BHB CSF L} & 24 h & 15 & 0.19 & 0.06 & 0.11 & 0.30 & \multirow{8}{*}{0.58} \\  & 36 h & 3 & 0.22 & 0.08 & 0.13 & 0.30 & \\ \cline{1-1}  & 48 h & 4 & 0.18 & 0.04 & 0.15 & 0.24 & 0.58 \\ \cline{1-1}  & 72 h & 2 & 0.24 & 0.02 & 0.22 & 0.26 & \multirow{8}{*}{0.71} \\ \cline{1-1}  & Total & 24 & 0.19 & 0.06 & 0.11 & 0.30 & \\ \hline \multirow{8}{*}{BHB peripheral blood} & 24 h & 25 & 2.66 & 2.22 & 0.30 & 7.10 & \multirow{8}{*}{0.38} \\ \cline{1-1}  & 36 h & 4 & 3.58 & 3.49 & 0.60 & 7.30 & \\ \cline{1-1} peripheral & 48 h & 4 & 1.33 & 0.63 & 0.70 & 2.20 & 0.38 \\ \cline{1-1} blood & 72 h & 2 & 0.85 & 0.07 & 0.80 & 0.90 & \\ \cline{1-1}  & Total & 35 & 2.51 & 2.25 & 0.30 & 7.30 & \\ \hline \end{tabular}
\end{table} 
Table 3: Frequency of BHB values according to threshold values.
## 2 Materials and Methods
### Evaluation Model Construction and Sensitivity Analysis
#### 2.3.2 Multi-Attribute Evaluation Index Sensitivity Analysis
The corresponding weight changes in indexes play a very important role in multi-attribute evaluation and decision analysis. When the weight change is \(\Delta W_{i}\) and the evaluation value change is \(\Delta B\), the sensitivity \(\gamma_{i}\) of the weight of evaluation index \(U_{i}\) can be expressed as  
\[\gamma_{i}=\left|\frac{\Delta B/B}{\left(\Delta W_{i}/W_{i}\right)^{2}}\right| \tag{14}\]  
The research used the eigenvalues of the judgment matrix to calculate the weight of each index. The weight change depended on the changes in the judgment matrix, mainly in the following situations:  
1. Through the specific discussion of the AHP, once the judgment matrix in the criterion layer changes, the corresponding index layer weights and evaluation results change, that is, the indexes under the same criterion change in the same direction and proportion;2 Once the judgment matrix of the index layer changes, the evaluation results will also change accordingly;
3 Once the judgment matrix of the criterion layer and the index layer is changed at the same time, the evaluation results will also change accordingly. The research took the single-factor sensitivity of the weight change in a single index as an example for analysis. If the weight of an index \(U_{i}\) starts from 0 and changes to 1 with a certain step size \(\sigma=0.01\), if the weight of the index \(U_{j}\) changes and is inversely proportional to the proportion of the index weight in indexes other than \(U_{i}\), and if the number of indexes is \(n\), and the sum of the index weights is 1, then the weight of the index \(U_{j}\) after the \(k\)th change is:  
\[W_{j}^{k}=W_{j}-\frac{W_{j}k\sigma}{\sum_{i=1}^{n}W_{i}-W_{i}} \tag{15}\]
## 1 Introduction
[7] showed that uncertainty is related to the spatial variability and proposed using this approach for model parametrization. 2-D footprints have been also combined with coarse spatial resolution data [13]. In this paper, we analyze the impact of spatial heterogeneity on RS-based _GPP_ models in a Mediterranean TGE using high spatial airborne hyperspectral images. A 2-D probability distribution modeling approach is used to match the EC footprint and the remotely sensed data. This matched dataset of RS and flux data is used to parameterize different predictive models in order to address the following research questions:  
1. What is the impact of the spatial mismatch between EC and remote sensor footprints on the estimation of Half-hourly _GPP_ (_GPP_\({}_{\text{hh}}\))? Moreover, what is the role of spatial heterogeneity in is matter? 2. What is the impact of the temporal mismatch between RS data and fluxes on the estimation of _GPP_\({}_{\text{hh}}\)?To understand which factors drive uncertainties when spatio-temporal mismatches occur (e.g., differences in meteorological conditions, in vegetation properties, etc. ), we use four _GPP_ models. These omit or differently describe light absorption and/or use efficiency and therefore their performance is expected to offer some information on the source of the uncertainties in the prediction of _GPP_.
# Terahertz-Wave Absorption Gas Sensing for Dimethyl Sulfoxide
## 3 Results and Discussion
The repeating features observed in the rotational spectrum for DMSO exist because for each \(J^{\prime\prime}\) (lower state \(J\)), there is a series of \(K_{c}\)\" (lower state \(K_{c}\)), described by \(K_{c}\)\" = \(J^{\prime\prime}\), \(J^{\prime\prime}-1\), \(J^{\prime\prime}-2\),... 0, that are separated by approximately (A + B)/C = -5.5 GHz, according to the energy level states participating in _a_-type _R_-branch transitions [34]. Each band is separated by approximately half of this separation value (-2.75 GHz) because each band begins with a different \(J^{\prime\prime}\). For example, as shown in Figure 7, the repeating bands between 270 and 279 GHz have the strongest features that alternate between \(J^{\prime\prime}\) = 31 and \(J^{\prime\prime}\) = 32. Within each repeating feature, the transitions are more tightly spaced, at around \(\sim\)0.35 GHz, and the transitions exist in a sequence where the transitions are weaker at lower frequencies and weaker transitions are lower for \(J^{\prime\prime}\) and \(K_{c}\)\". The spacing of transitions within a repeating band is a function of the rotational energy levels that results in neighboring transitions having \(J^{\prime\prime}\) that differ by two and \(K_{c}\)\" that differ by five, i.e., these transitions exist for lower states that have quite different lower-state rotational energies, resulting in different transition intensities due to the differences in thermal populations. The DMSO spectrum in the 220-330 GHz region exhibited spectral fingerprints, with the distinctive repeating patterns, that allow for the selective measurement of DMSO in the presence of other species, as there are many DMSO transitions to choose from. Most importantly, water vapor interference in the present frequency region is negligible and VOCs, that may be simultaneously present, have sparser spectra than DMSO [9], making it possible to find regions where DMSO is isolated. Given the proximity of DMSO lines to one another, low-pressure conditions are preferred for DMSO gas sensing, where line blending  
Figure 7: Comparison of measured absorbance (bottom) with line positions and intensities for the ground vibrational state simulated using PGOPHER [33] and the rotational constants of [PERSON] et al. [27] in the 270–279 GHz frequency range. due to pressure broadening is minimized. Here, we observed peak absorbance in each band that ranged from 0.005 to 0.01 for 0.4 Torr (53 Pa) of DMSO at 297 K and a 21.6 cm pathlength. Therefore, for a noise floor of \(\pm\)0.001 in absorbance, the DMSO detection limit is 1.3-2.6 \(\times\) 10\({}^{15}\) molecules/cm\({}^{3}\) per meter of absorption pathlength (50-100 ppm) for a 1.1 s acquisition time. It is possible, with slower frequency scan rates (\(\sim\)100 s acquisition time) and averaging/filtering techniques, to lower the noise floor by an order-of-magnitude or more, suggesting that detection limits of 10\({}^{14}\) molecules/cm\({}^{3}\) per meter pathlength are possible for DMSO in the current frequency range.
# Accurate Estimation of Air Pollution in Outdoor Routes for Citizens and Decision Making
###### Abstract  
There is clear evidence of the effects of air pollution on health. In this paper, we present an innovative application designed to assess Air Quality (AQ) exposure based on the World Health Organization's AQ Guidelines, analysing pollutants and their concentrations independently. Our aim is to provide this information to citizens based on their health profile (medical history or requirements) before and during outdoor trips of their choice, both walking and cycling, empowering them to proactively make informed personal decisions about their route choices and identifying potentially unhealthy travel environments. For this purpose, we have access to official data from AQ monitoring stations that are updated periodically every 10 min. Then, by using spatial interpolation techniques (with Ordinary Kriging), we estimate each pollutant over a grid superimposed on the city map. Once the pollutants have been mapped on each route, they are analysed in order to consider the different alternatives for deciding and planning changes in speed or trajectory. We evaluated the application in the city of Valencia (Spain) as a use case under different scenarios, and showed the results to assess exposure to pollution on the routes of citizens.  
air quality; pollution; official AQ monitoring stations; open-data; pollution exposure +
Footnote †: journal: Applied  
1
Footnote 1: email: {[EMAIL_ADDRESS]; Tel: +34-96-354-3563}
# Solving Multidimensional Partial Differential Equations Using Efficient Quantum Circuits
## 3 Related Work  
In our review of the existing work related to solving PDEs using quantum computing, the existing techniques can be broadly classified as those based on HHL [14] and VQA [36]. The HHL algorithm [14], designed to solve linear systems, offers exponential speedup for sparse coefficient matrices and can be applied to PDEs after discretization using FDM [15]. An improved version of the HHL algorithm [37] improves efficiency by reducing computation time while maintaining accuracy. However, both methods require fault-tolerant quantum operations, making them unsuitable for NISQ devices. To overcome these limitations, VQA-based approaches have been explored. The authors in [17] develop a minimum potential energy-based cost function to solve the Poisson equation, similar to VQE-based PDE solvers. The authors in [18] use VQLS with discrete finite element methods, achieving scalability up to 10 qubits, while the authors in [19] apply VQLS to the Poisson equation, resulting in good performance on quantum simulations but poor performance on real quantum hardware. Furthermore, the authors in [20] propose a VQA approach that minimizes quantum measurements and resource usage. Our study of PDE solvers using VQA shows that existing techniques for solving PDEs have low accuracy and long execution times compared to analytical solutions, as well as limited scalability with qubits. The results of our proposed PDE solver algorithm, which utilizes FDM, C2Q encoding, and numerical instantiation in Variant 1, and FDM, C2Q encoding, and CCD in Variant 2, outperform VQA-based PDE solvers in accuracy, scalability, and execution time, and generates favorable results on noise-free and noisy simulators, as well as on hardware emulators and real quantum hardware.
# Exploring the Potential of Microextraction in the Survey of Food Fruits and Vegetable Safety
## 2 Extraction Approaches Used to Assess Food Safety  
Different analytical approaches are employed to assess food safety. The experimental layout most frequently used is briefly represented in Figure 2. As can be depicted, sample treatment is a very important step of the procedure. An inappropriate sample preparation or target analytes extraction will prevent the obtainment of good analytical performance for those analytes independent of the analytical power and sophistication of the instruments involved in the following steps. Regardless of the extraction procedure, high-performance liquid chromatography (HPLC), the use of ultraviolet (UV) or mass spectrometry (MS) as detection modes, or gas chromatography (GC) coupled with MS are the analytical separation techniques most often reported (Figure 2).  
Depending on the nature of the target analytes, specific extraction techniques should be chosen for their determination. Considering solid samples such as fresh fruits and vegetables, if we are interested in volatile analytes, lyophilization or grinding of the sample for homogenization may be sufficient to obtain the analytes in the headspace. The volatile analytes can be then collected using an appropriate extraction procedure, such as solid-phase microextraction (SPME), followed by GC analysis [73]. On the other hand, if the analytes are not volatile, a solid-liquid extraction (solid-liquid extraction (SLE), ultrasound-assisted extraction (UAE) or supercritical-fluid extraction (SFE)) should be performed in advance to transfer the selected analytes from the fruits and vegetables matrices to a suitable organic phase. A second extraction step, using, for instance, liquid-liquid extraction (LLE), can be then employed before eluting the target analytes in a solvent compatible with the succeeding chromatographic separation [74; 75; 76; 77]. Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) is particularly tailored for these applications, just involving an  
Figure 2: Scheme showing the steps to follow for the determination of pesticides, antibiotics, or preservatives in fruit and vegetable samples.  
initial salting-out extraction step followed by a dispersive solid-phase extraction (dSPE) clean-up [78].  
In the last few decades, alternatives to these conventional techniques have been developed. Most of them are known as green extraction techniques (GreETs) and have very attractive features such as simplifying or automating the extraction procedure, reducing extraction times as well as the volumes of samples and solvents used, and making them more environmentally friendly [12]. These alternatives are often reported as microextraction techniques and most of them employ the same separation principles of conventional extraction downscaled to a microextraction size. It is therefore very relevant to discuss the advantages and bottlenecks of migrating from conventional extraction procedures to microextraction techniques or GreETs in the survey of food safety. Accordingly, the next sections will discuss works found in the literature.
# Synchronization of Analog-Discrete Chaotic Systems for Wireless Sensor Network Design
## 2 Chaos Oscillators, Discrete Models and Chaotic Synchronization  
This section is devoted to chaos oscillators used in the study. The section presents the circuits of chaos oscillators with their respective mathematical models describing the dynamics; these models are used to derive the discrete representation of the circuit. The given chaos oscillators are also implemented in LTSpice to simulate the work of the analog circuit; the hardware circuit is developed as well. The work uses three different realizations of the oscillators; hence, the phase portraits (attractor projections) are compared to demonstrate the similarity of behavior. Finally, the section elaborates on the Pecora-Carroll synchronization technique applied to achieve analog-discrete and discrete-analog synchronization of the oscillators. These specific oscillators were chosen due to the similarity of behavior in different realizations.
# Progress and Prospect of Saline-Alkaline Soil Management Technology: A Review
## 3 Advanced Technology of Saline-Alkaline Soil Management
### Information Technology  
The application of information technology, such as remote sensing and intelligent irrigation systems, offers more accurate and efficient tools for managing saline-alkaline land. By continuously monitoring soil salt levels and moisture conditions, these technologies optimize irrigation and drainage schemes leading to improved management outcomes [75].  
Numerous scholars have explored the foundational theories governing soil water and solute transport, such as [PERSON]'s law and Fick's law, while introducing innovative perspectives to analyze the complex interactions of factors like soil texture, groundwater depth, and the dynamic processes of soil freezing and thawing on water and salt movement [75; 76]. The advantages and disadvantages of these models have also been discussed, including the challenges of the Hydrus model in addressing field-scale soil spatial variability [77; 78].  
Currently, there are many successful cases of integrating information technology with saline-alkaline and alkaline land management. For instance, [PERSON] et al. [14] used enhanced remote sensing imaging technology to analyze the spatiotemporal changes of saline-alkaline farmland in Huanghua City between 1992 and 2011. [PERSON] et al. [79] developed an ecological monitoring support system for saline-alkaline land, leveraging Web GIS technology. Using the Yellow River Delta as a case study, they refined machine learning algorithms models to achieve high-precision forecasting of soil salinity levels, integrated multiple ecological monitoring functions, and effectively supportedthe ecological health assessment and protection of saline-alkaline land. [PERSON] and colleagues [80] investigated the polarization optical properties of saline-alkaline land, integrating semi-empirical frameworks with machine learning techniques to accurately predict the surface reflectance of saline-alkaline land.
## 4 Experiment and Results
### Benchmarking  
The experiments were run on the created VM running Debian GNU/Linux 10 (64 bit). During the execution of the developed SAT-Hadoop-Processor software, we monitored some benchmarking using the **CMD 6:** time-f \"%e_%P_%M_%S  
Figure 6 shows that in parallel mode, the percent of CPU, the maximum reside memory, and the CPU/s increase significantly when the VM size grows because the software executes as many scripts simultaneously. Still, in standard mode, the software does not yield totally from the hardware capacity available in the cluster. Accordingly, we assumed that the parallel execution maximizes the employment of the hardware capacities to improve the execution time.  
Figure 7 shows the mean temperature of the CPU during all the pre-processing steps. Thus, the temperature ranges from 40 to 75 \({}^{\circ}\)C. It depends on the number of inputs, output files, the algorithm's complexity, and the operation's nature (reading, writing, calculating, networking, and so on). We note that the CPU's temperature is moderate, about 55 \({}^{\circ}\)C, during the decompression and the orbit filter steps because these two operations manage files in the HDD (moving, deleting, etc.). However, the CPU's temperature is high, around 65 \({}^{\circ}\)C, during the conversion, subset, and extraction because these scripts include many loops and computation instructions, so they consume further CPU.
Biophysical and Economic Analysis of Black Spruce Regeneration in Eastern Canada Using Global Climate Model Productivity Outputs  
[PERSON]  
1 School of Geography & Earth Sciences and McMaster Centre for Climate Change, McMaster University, Hamilton, ON L8S 4K1, Canada; [EMAIL_ADDRESS]  
2  
[PERSON]. [PERSON]  
2  
[PERSON]  
2  
[PERSON]  
1 School of Geography & Earth Sciences and McMaster Centre for Climate Change, McMaster University, Hamilton, ON L8S 4K1, Canada; [EMAIL_ADDRESS]  
2  
Footnote 1: email: {[EMAIL_ADDRESS]}  
Several studies have evaluated the economic feasibility of afforestation and reforestation in North America, but assessments that attempt to account for carbon sequestration through direct linkages to growth processes and their dependence on seasonal and inter-annual climate variations are lacking. Past studies (e.g., [6; 7; 8; 9; 10]) tend to utilize generalized forest growth and yield curves, which do not account for climate variability. Excluding climate variability could be problematic because seasonal and inter-annual variations, as well as long-term trends due to climate change, are expected to impact future forest productivity [11].  
Several studies have examined the effect of carbon revenues on harvest timing and stand values, when both timber and carbon sequestration benefits are considered [6; 12; 13; 14; 15]. These studies show that additional revenue flows (when carbon has an economic value) generally result in slightly delayed forest harvests, thus increasing the optimal harvest age [16; 17; 18]. The permanence of carbon sequestered (or lack thereof) ultimately affects the price a forest owner would receive for sequestered carbon. Given that carbon markets are not well established in many jurisdictions across the world including Canada, there is significant interest in better understanding how price levels may influence carbon-related forest investment decisions. However, there is also large uncertainty in future forest productivity, hence there is a need for tools and/or models to integrate biophysical potentials and economic values.  
In this study, we evaluate the economic feasibility of afforestation and reforestation projects under a changing climate. We focus on black spruce (_Pica mariana_) regeneration in the province of Ontario, Canada. Black spruce is a dominant species in the boreal region of this province (and in Canada as a whole)--covering approximately 41 percent of the province's forested area and accounting for up to 80% of the annual allowable forest cut [19]. The specific objectives of this study are to (i) generate future merchantable timber yield and carbon sequestration estimates for black spruce, utilizing GCM-based NPP and net ecosystem productivity (NEP) estimates that account for projections of climate variability; (ii) examine the economic potential of black spruce regeneration, estimated using net present value (NPV) analyses both inclusive and exclusive of carbon sequestration benefits; and (iii) conduct sensitivity analyses to determine how uncertainty in the economic inputs affects economic attractiveness of black spruce forests.