esa-sceva/satcom-chunk-collection huggingface dataset:
# Sentence Representation Method Based on Multi-Layer Semantic Network
## 5 Results and Discussion
### Results
#### 5.1.3 Experimental Results on Yelp Dataset  
The performance of each model on the Yelp dataset is shown in Table 5. The baseline model adopts the neural network structure (BiLSTM/CNN) and pooling layer (maximum pooling/self-attention pooling). Among them, the model of BiLSTM combined with self-attention pooling proposed by [PERSON] [29] performs best, and the accuracy rate on the Yelp test set reaches 64.2%. In contrast, the accuracy rate of the multi-layer semantic representation  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Matched Set (\%)** & **Unmatched Set (\%)** \\ \hline CBOW ([PERSON] et al., 2017) & 64.8 & 64.5 \\ BiLSTM ([PERSON] et al., 2017) & 66.9 & 66.9 \\ Shorted stacked BiLSTM ([PERSON] et al., 2017) & 74.6 & 73.6 \\ Model in this paper & 73.6 & 73.8 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of each model on Multi-NLI dataset.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **The Training Set (\%)** & **The Test Set (\%)** \\ \hline
300D LSTM ([PERSON] et al., 2016) & 83.9 & 80.6 \\
600D BiLSTM Intra-attention ([PERSON] et al., 2016) & 84.5 & 84.2 \\
600D BiLSTM Deep-Gated-attention ([PERSON] et al., 2017) & 90.5 & 85.5 \\ BiLSTM ([PERSON] et al., 2013) & 90.4 & 85.0 \\ Multi-head ([PERSON] et al., 2017) & 89.6 & 84.2 \\
300D DiSAN ([PERSON] et al., 2017) & 91.1 & 85.6 \\ Model in this paper & 91.7 & 86.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Accuracy of various models in SNLI dataset.  
network proposed in this paper is 63.8% on the Yelp test set. However, it does not exceed [PERSON]'s model, which also has relatively high accuracy.  
The reason for this situation is that, compared with the general classification problem, emotional analysis needs to be divided into several types according to the author's attitude. The various types cannot be completely distinguished. For example, \"very good\" and \"good\" have an inclusive relationship and cannot be completely separated. Therefore, this requires more in-depth mining of potential emotions in sentence representation sensory level information. Although the model proposed in this paper can obtain more information and make up for the lack of single-layer attention, the model's depth is not enough to capture deeper emotional analysis tasks. The accuracy rate on emotional tasks has not been improved.
# Modeling and Experimental Study of the Dual Cylinder Fluid Inerter
## 1 Introduction  
In 2002, [PERSON] of Cambridge University proposed the concept of an inerter [1] and constructed a new vibration-reduction theoretical system of \"inerter-spring-damping\". Inerters can help solve common problems in the field of vibration reduction, improving the performance of vehicle suspensions [2], enhancing the cross-country mobility of multi-axle vehicles [3], improving the safety of high-rise buildings, and greatly attenuating vibrations from earthquakes [4] and typhoons [5]; inerters also extend to other technical fields, such as resonance-suppression of ships [6], long-span cables [7] and bridges [8], high-speed rail suspensions [9], and aerospace [10].  
[PERSON] [11] first proposed the concept of a fluid inerter. The main source of inertia force is the fluid in the slender spiral tube or spiral groove, and the generation device of inertia force is a dual-rod single cylinder. It has the advantages of a large transmission ratio, small back gap, long lifespan, simple structure, and so on.  
[PERSON] and [PERSON] [12] established the first mechanical model of single-cylinder fluid inerters. Through the quantitative analysis of inertia force and parasitic damping force, it was proven that when fluid reciprocates through a slender spiral tube or spiral slot at high speed, a huge parasitic damping force will be generated, and the proportion of inertia force is relatively small. To control the parasitic damping force, researchers from the University of Sheffield [13, 14] and the University of Bristol [15] used magnetorheological fluid as a fluid medium to adjust the size of the parasitic damping force through an external magnetic field. [PERSON] and [PERSON] [16] adopted the method of fluid mechanics analysis to build a mechanical model of single-cylinder fluid inerters, proving that the parasitic damping force mainly comes from the secondary turbulence of the helical pipeline section. The magnitude and source of parasitic damping force have been fully studied, and reducing the resistance of fluid media has become a new hotspot in this field, but with little effect.  
Because of this situation, this paper draws on the dual-cylinder transmission mechanism of a hydraulic piston mass inerter [17] and proposes a new structural scheme, a dual-cylinder fluid inerter [18]. Where a single-cylinder fluid inerter has a spiral tube or groove, we instead it with a straight tube in order to eliminate the secondary flow of the spiral tube section, and the use of two double-acting single-rod cylinders instead of the original double-rod cylinder greatly reduces the parasitic damping force, while increasing the inertia force.
# Adipolin, Chemerin, Neprilysin and Metabolic Disorders Associated with Obesity
## 3 Results
### Adipokines and Anthropometric Parameters  
Adipolin concentrations were associated with age only among non-obese subjects (rs = 0.464; \(p\) = 0.004). In the obese group (rs = \(-\)0.072; \(p\) = 0.612) and the entire population (rs = 0.110; \(p\) = 0.300), there were non-statistically significant correlations. Blood adipolin concentration correlated positively with body weight (rs = 0.484; \(p\) = 0.031), BMI (rs = 0.471; \(p\) = 0.036), waist circumference (rs = 0.517; \(p\) = 0.019), WHR (rs = 0.529; \(p\) = 0.016) and body fat percentage (rs = 0.545; \(p\) = 0.013) in the non-obese women's group and with body weight (rs = 0.434; \(p\) = 0.024) and waist circumference (rs = 0.393; \(p\) = 0.043) among obese women. Among lean men, blood adipolin levels correlated positively with body fat percentage (rs = 0.577; \(p\) = 0.015). In addition, there was a positive correlation between adjolin concentration and WHR in subjects with normal glucose tolerance (rs = 0.543; \(p\) = 0.006).  
Neprilysin and chemerin concentrations were associated with anthropometric parameters only among non-obese subjects. The strongest association with waist circumference was observed for neprilysin (rs = \(-\)0.777; \(p\) < 0.001) in the non-obese male group. In this population, neprilysin concentration was also shown to correlate negatively with body weight (rs = \(-\)0.540; \(p\) = 0.025), BMI (rs = \(-\)0.532; \(p\) = 0.028), and WHR (rs = \(-\)0.636; \(p\) < 0.001). Similar correlations were not observed among women. For chemerin, a negative association was found between its concentration and BMI (rs = \(-\)0.348; \(p\) = 0.035) in the control group.  
\begin{table}
\begin{tabular}{l c c c} \hline
**Parameters** & **High CRP Group** & **Low CRP Group** & \(p\) \\ \hline Glycemia in 120\({}^{\prime}\) OGGT [mmol/L] & & & \\ AGT group & 40.00\% & 60.00\% & 0.561 \\ NGT group & 43.76\% & 56.14\% & \\ \hline Fast insulinemia [μU/L] & & & \\ \hline \(\geq\)12 & 50.00\% & 50.00\% & 0.437 \\ \(<\)12 & 33.33\% & 66.67\% & \\ \hline Triglycerides [mmol/L] & & & \\ \(\geq\)1.7 & 41.18\% & 58.82\% & 0.924 \\ \(<\)1.7 & 40.00\% & 60.00\% & \\ \hline HDL cholesterol [mmol/L] & & & \\ \(\geq\)1.0 (m) and \(\geq\)1.3 (w) & 41.18\% & 58.82\% & 0.924 \\ \(<\)1.0 (m) and \(<\)1.3 (w) & 40.00\% & 60.00\% & \\ \hline Blood pressure (BP) [mmHg] & & & \\ systolic BP \(\geq\) 130 and /or diastolic BP \(\geq\) 85 & 39.29\% & 60.71\% & 0.376 \\ systolic BP \(<\) 130 and /or diastolic BP \(<\) 85 & 66.67\% & 33.33\% & \\ \hline \end{tabular}
\end{table}
Table 3: Characteristics of selected metabolic disorders in two groups (with high or low CRP values) of obesity populations (\(n\) = 51).
## 6 Technoeconomic Challenges  
The technologies behind the development of this new generation of nanoplasmonic thin film sensors are vacuum deposition techniques, which are recognized as clean and environmentally sound technologies, since no dangerous byproducts or waste are produced.  
Although the initial cost of vacuum deposition techniques may be somewhat high when it comes to industrial upscaling, the production of these thin film sensors by magnetron sputtering, an efficient physical vapour deposition (PVD) technique, reveals it to be an economically viable process. To produce one thin film sensor (8 \(\times\) 8 mm\({}^{2}\)) in a R&D vacuum deposition chamber (where approximately 300 thin film sensors can be deposited simultaneously), the estimated production cost is around EUR 0.20. This value also includes the thermal treatment required to promote the growth of nanoparticles (see Section 4.3) and plasma treatments to enhance the sensitivity (see Section 4.4).  
If the thermal treatment and the working temperature do not exceed 450 \({}^{\circ}\)C [168], one may consider an inexpensive substrate, such as glass. This type of substrate may increase the final cost by 3% to 5%, which is a marginal difference. Nevertheless, if a temperature above 450 \({}^{\circ}\)C is needed [169; 234], more expensive substrates are required. For example, if a fused silica substrate is used, the final cost of the thin film sensor can increase up to EUR 0.75.  
Regarding the use of the GLAD technique, the substrate space limitation inside the vacuum system makes it an expensive technique, since only around 30 thin film sensors can be deposited in a similar R&D system, increasing their production cost by a factor of 10. Moreover, upscaling the production to an industrial system is not revealed to be a simple task [235], and further research is needed.
# Resource Allocation in Downlink VLC-NOMA Systems for Factory Automation Scenario
## 3 Proposed Resource Allocation Schemes
### Proposed User Pairing Scheme  
In this subsection, PBUP is proposed for user-pairing considering priorities in the downlink VLC-NOMA system. We assume two users in a user-pair.  
Algorithm 1 explains how the proposed PBUP works. If there are more than two released nodes, the node with the earliest deadline will have the highest priority, and its channel gain will be used to find another node to be user-paired. The other node to be user-paired will be decided by the difference between the channel gains. Every released node is compared with the node with the highest priority by channel gains. Then, the one with the largest different channel gain from the node with the highest priority will be user-paired with the node with the highest priority. Then, the node with a higher channel gain will be considered NU. Using DBPA, the power ratio between NU and FU will be decided. After deciding on the user-pair and power allocation, the transmission will proceed in one timeslot.  
Figure 6: Dataset generation.  
```
Input: Released nodes' information \(N\) Output: Scheduled Frame \(F\) for\(j\) = 0:Frame Sizedo
2\(L\) = Length(\(N\)) \(n1\) = \(N[0]\) for\(i\)=1:\(L\)do
3 if\(n1.deadline>N[i].deadline\)then
4\(n1\) = \(N[i]\)
5
6\(n2\) = \(N[0]\) \(h\) = abs(\(n1.h-n2.h\)) for\(i\) = 1:\(L\)do
7 if\(abs(n1.h-N[i].h)>h\)then
8\(h\) = abs(\(n1.h-N[i].h\)) \(n2\) = \(N[i]\)
9
10 if\(n1.h>=n2.h\)then
11\(F[j]\) = \(DNN\_PA(n1,\,n2)\)
12
13 else
14\(F[j]\) = \(DNN\_PA(n2,\,n1)\)
15
16 n1.totalTimeslot = n1.totalTimeslot - 1 timeslot
17 n2.totalTimeslot = n2.totalTimeslot - 1 timeslot
18 if\(n1.totalTimeslot==0\)then
19\(N\) = \(N\) - \(n1\)
20 if\(n2.totalTimeslot==0\)then
21\(N\) = \(N\) - \(n2\)
22\(F[j]\) = Allocating(\(n1,\,n2\))
```  
**Algorithm 1**Proposed user pairing scheme
## 3 Results and Discussion
### Comparative Analysis of the Investigated Scenarios
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Receptor** & **Scenario** & **MRT (\({}^{\circ}\)C)** & **\(\Delta\)MRT (\({}^{\circ}\)C)** & **\(\Delta\)MRT (\%)** & **UTCI (\({}^{\circ}\)C)** & **\(\Delta\)UTCI (\({}^{\circ}\)C)** & **\(\Delta\)UTCI (\%)** \\ \hline \(1\) & Baseline & 55.480 & - & - & 27.880 & - & - \\ \(1\) & Scenario 1 & 54.390 & 1.09 & 2.0 & 26.500 & 1.38 & 4.9 \\ \(1\) & Scenario 2 & 54.210 & 1.27 & 2.3 & 26.577 & 1.30 & 4.7 \\ \hline \(2\) & Baseline & 54.830 & - & - & 28.270 & - & - \\ \(2\) & Scenario 1 & 52.810 & 2.02 & 3.7 & 26.110 & 2.16 & 7.6 \\ \(2\) & Scenario 2 & 53.790 & 1.04 & 1.9 & 26.554 & 1.72 & 6.1 \\ \hline \(3\) & Baseline & 54.340 & - & - & 27.860 & - & - \\ \(3\) & Scenario 1 & 53.170 & 1.17 & 2.2 & 26.600 & 1.26 & 4.5 \\ \(3\) & Scenario 2 & 53.590 & 0.75 & 1.4 & 26.978 & 0.88 & 3.2 \\ \hline \(4\) & Baseline & 52.840 & - & - & 32.190 & - & - \\ \(4\) & Scenario 1 & 51.650 & 1.19 & 2.3 & 31.810 & 0.38 & 1.2 \\ \(4\) & Scenario 2 & 51.380 & 1.46 & 2.8 & 31.869 & 0.32 & 1.0 \\ \hline \(5\) & Baseline & 55.440 & - & - & 29.690 & - & - \\ \(5\) & Scenario 1 & 52.920 & 2.52 & 4.5 & 28.570 & 1.12 & 3.8 \\ \(5\) & Scenario 2 & 53.770 & 1.67 & 3.0 & 28.887 & 0.80 & 2.7 \\ \hline \(6\) & Baseline & 56.380 & - & - & 27.560 & - & - \\ \(6\) & Scenario 1 & 55.570 & 0.81 & 1.4 & 26.080 & 1.48 & 5.4 \\ \(6\) & Scenario 2 & 56.010 & 0.37 & 0.7 & 26.159 & 1.40 & 5.1 \\ \hline \hline \end{tabular}
\end{table} 
Table 13: Simulated values of MRT and UTCI across scenarios and selected receptors. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Receptor** & **Scenario** & \begin{tabular}{c} **PM10** \\ **(\#g/m3)** \\ \end{tabular}  & **\(\Delta\)PM10** & **\(\Delta\)PM10** \\ **(\#g/m3)** & **(\#g/m3)** & **\(\Delta\)PM10** (\%)** & **PM2.5** & **\(\Delta\)PM2.5** & **\(\Delta\)PM2.5** & **\(\Delta\)PM2.5** (\%)** \\ \hline \(1\) & Baseline & 1.065 & - & - & 1.185 & - & - \\ \(1\) & Scenario 1 & 1.027 & 0.038 & 3.6 & 1.080 & 0.105 & 8.9 \\ \(1\) & Scenario 2 & 1.059 & 0.006 & 0.6 & 1.130 & 0.055 & 4.6 \\ \hline \(2\) & Baseline & 1.120 & - & - & 0.975 & - & - \\ \(2\) & Scenario 1 & 1.082 & 0.038 & 3.4 & 0.950 & 0.025 & 2.6 The detailed discussion of these results is structured into three subsections. Section 3.2.1 analyzes changes in air quality, focusing on the reduction of coarse and fine particulate matter. Section 3.2.2 evaluates improvements in thermal comfort based on MRT and UTCI indicators. Finally, Section 3.2.3 integrates both dimensions to assess the overall effectiveness of the mitigation strategies and identify synergies between traffic reduction, green infrastructure, and material interventions. This structured approach allows for both localized and systemic assessments of environmental improvement, providing practical insights for sustainable urban and campus planning.
## 2 Materials and Methods
### Model Parameter Obtaining Process
#### 2.2.3 Computation Complexity  
Since genetic algorithm is a stochastic approach to solve a problem, it is, by definition, impossible to exactly determine its computation complexity. However, with restraints imposed on some of its parameters, the maximal computation complexity (in terms of time) can be determined.  
In the proposed method, the main restraint is the number of populations. After reaching this number, the algorithm stops, and the best score obtained by it is given as a result. Preliminary research pointed out that for the defined fitness function (MSE of the power spectra), the algorithm convergences in less than 150 iterations. Therefore, this number was chosen as a maximal number of populations.  
Knowing that every population is composed of a constant number of 256 individuals, it can be determined that for every genetic algorithm execution there will be maximally 38,400 (150 populations, each made from 256 individuals) fitness function evaluations. To determine the time needed for single fitness function evaluation, the function was executed for 100,000 times with different, random inputs. The mean time of those executions was used to determine maximal computation complexity of a proposed method.  
The genetic algorithm itself can be sped up by various means; for example, saving value for each individual and just loading the value instead of evaluating it, or imposing additional restraints, like, for example, maximum number of stall generations after which the algorithm stops. The value determined in this study is the maximum time needed for the complete GA execution.  
The study of time needed for the minimized function execution was conducted on the computer with Intel Core i7-3740 QM CPU (four 2.7 GHz cores, although function was evaluated without parallelization, i.e., using only one core), Crucial MX500 SSD drive (560 MB/s read speed), and 16 GB RAM (4 \(\times\) 4 GB DDR3, 1600 MHz), in the Matlab R2020a environment.
## 3 Results and Discussion
### Reasons behind the Failures of the Contemporary Approaches
#### 3.2.4 Detecting Objects of Irregular Shape  
This problem could be a major difficulty for the deep CNN approaches and [18] the tiny size of inferior quality (grayscale, resolution) images containing micro-pores and micro-fractures (visually small, in the current case). As discussed above, thin boundaries of pores and fractures are frequently observed along with a negligible level of intensity differences among the pixels lying inside or out of any tiny (small) \(\mathcal{H}_{i}\), \(\forall\)i \(\in\)\(\mathbb{Z}^{+}\) in any image sample in the present dataset (see Section 2.3).  
As a result, the pores and fractures are objects of extremely unusual shapes, and accurately retrieving their shape-related relevant information (for their future identification) using the present deep CNN implementation becomes quite imperfect, thus increasing misclassification. This result could be due to the layers of deep CNN extract better and specific features related to identifying pores and fractures. Hence, to identify unusual-shaped objects such as pores and fractures (in the current situation), even at the micro-level, their features must be firmly defined before classification. This task is quite difficult in the current case because of the unusual shapes of the pores and fractures, thus the number  
Figure 11: (**a**) New sample training set with the average similarity between the images calculated as \(\mathcal{D}_{\text{KL}}(\mathcal{E}_{i}||\mathcal{E}_{r})\approx 4.799\). Here, the regions containing pores and fractures are marked with green boxes. (**b**) The sample resulting images show the regions identified as pores and fractures by the deep CNN model. The identification accuracy was higher than that presented in Figure 7.  
of misclassification events increases. When using deep CNN and [15], the number of misclassifications increases even more if the training samples are few. In contrast, the proposed approach can detect unusually shaped pores and fractures with higher accuracy. This ability is a benefit of the pixel-wise exhaustive search (by analysing the intensity) approach in every image sample and the flexible choosing of the I -value (depending on the image quality and problem requirements) adopted by the proposed procedure, which is documented in Table 1. In contrast, as mentioned before, the proposed method does not require training and hence can work well with a few datasets.
# What's in Your Fruit Juice?--Rapid Quality Screening Based on Infrared (FT-IR) Spectroscopy
## 4 Conclusions  
In this study, a field-deployable portable FT-IR sensor, requiring no sample preparation, was utilized to simultaneously predict multiple quality traits in 68 fruit juice samples from the USA markets. Employing partial least square regression (PLSR) models, our results demonstrated a robust correlation (R\({}_{\text{CV}}\geq\) 0.93) between FT-IR predictions and reference values, accompanied by a low standard error of cross-validation (SECV \(\leq\) 59.42 mg/100 g). Notably, our findings revealed discrepancies of 21% and 37% in fruit juices deviating from the nutritional label values for sugars and ascorbic acid, respectively. Utilizing portable FT-IR devices offers a non-destructive, simultaneous, straightforward, and high-throughput method for the chemical profiling and real-time prediction of sugars and acid levels in fruit juices. The handiness and ruggedness of the FT-IR sensor make it a crucial \"out-of-the-laboratory\" analytical tool, particularly beneficial for fruit juice processors seeking efficient quality control measures. Conceptualization, D.P.A. and L.R.-S.; methodology, D.P.A. and L.R.-S.; validation, D.P.A. and L.R.-S.; formal analysis, D.P.A. ; investigation, D.P.A. ; resources, L.R.-S.; data curation, D.P.A. ; writing--original draft preparation, D.P.A. ; writing--review and editing, L.R.-S.; visualization, D.P.A. ; supervision, L.R.-S.; project administration, L.R.-S. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The data presented in this study are available on request from the corresponding author. The authors thank [PERSON] (The Ohio State University) for her technical support rendered during this study. Figure 3: PLS regression vector plots for soluble solids, glucose, fructose, sucrose, total sugars (**a**), tittatable acidity, citric acid, and ascorbic acid (**b**). a.u. *: arbitrary units.
## 2 Materials and Methods
### Data Collection  
PML-V2 ET data product, MOD16A2 ET data product, meteorological data, ESA CCI land use, and water level data were used in this study (Table 1). PML-V2 first used a water carbon coupled canopy conductance model to estimate ET and GPP [38]. [PERSON] et al. [39] Further improved the PML-V2 by incorporating the vapor pressure deficit constraint to GPP that was then used to constrain canopy conductance and ET. PML-V2 ET products used MODIS data and GLDAS meteorological data as the model input. MODIS data included leaf area index (MCD15A3H), albedo (MCD43A3), surface specific emissivity (MOD11A2), and land use (MCD12Q1) data, and GLDAS meteorological forcing data included precipitation, short-wave radiation, long-wave radiation, water vapor pressure, air temperature, and wind speed. The PML-V2 ET product was well calibrated against 8-day measurements at 95 widely-distributed flux towers globally, with the root mean  
Figure 1: The overall framework, study area, and location of meteorological stations.  
square error (RMSE) and Bias of 0.69 mm/day and\(-\)1.8%, respectively [39]. It was also verified across China [39; 40; 41]. In this study, 500 m and 8-day resolution of a PML-V2 ET product from 2000-2020 covering TGRA were downloaded from the Google Earth Engine.  
The MOD16A2 ET product (MOD16 ET) with the same spatial and temporal resolution to the PML-V2 ET product was used to validate PML-V2. This dataset was developed by [PERSON] et al. [42], based on an improved Penman-Monteith algorithm. It included evapotranspiration (ET), latent heat flux (LE), potential evapotranspiration (PET), and potential latent heat flux (PLE).  
The land use data in 2015 (Figure 1) was derived from the European Space Agency Climate Change Initiative (ESA CCI) epoch maps based on MERIS (300 m resolution) [43]. Qualitative evaluation showed that ESA CCI maps were in agreement with other satellite land cover products [44].  
Meteorological data, including precipitation (PT), air temperature (Ta), wind speed (WS), relative humidity (RH), and sunshine hours (SSD) were collected from the China Meteorological Administration at eight meteorological stations in TGRA during the period 2000-2020. The distribution of meteorological stations are shown in Figure 1 and detailed information of these stations are listed in Table 2. Water vapor pressure difference (VPD) was calculated from the daily mean air temperature and relative humidity.  
The water level data of the upstream of the Three Gorges Dam (TGD) were provided by the Three Gorges Corporation.