esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Evaluation
### Labeling Time  
Figure 2 depicts the labeling time for each dataset. Because a substantial amount of time was spent by CRT on smaller datasets, we omit its results for larger datasets. In all cases, REP+, and REP, outperformed PRM and CRT. Notable results were observed for large datasets, such as Treebank_e, SwissProt, and psd7003. As the time expended by PRM increased exponentially, the time expended by REP+ increased gradually. This occurred because PRM assigned larger prime numbers, and thus, additional time was required to determine the next prime number. REP+ uses much smaller prime  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**ID** & **Name** & **Nodes** & **Avg. Fanout (max)** & **Avg. Depth (max)** \\ \hline
1 & SigmoidRecord & 20,334 & 1.8 (89) & 5.4 (6) \\
2 & mondial-3.0 & 64,994 & 5.2 (1913) & 3.1 (5) \\
3 & partsupp & 96,488 & 2.0 (16,001) & 2.7 (3) \\
4 & nvm & 192,421 & 3.4 (4225) & 3.4 (5) \\
5 & orders & 300,616 & 2.0 (30,001) & 2.8 (3) \\
6 & nasa & 1,598,934 & 3.4 (4871) & 5.3 (8) \\
7 & lineitem & 2,048,247 & 2.0 (120,351) & 2.9 (3) \\
8 & dblp & 6,504,386 & 2.0 (328,858) & 3.0 (6) \\
9 & Treebank\_e & 7,337,533 & 3.0 (112,804) & 7.3 (36) \\
11 & psd7003 & 39,118,126 & 1.3 (262,526) & 5.8 (7) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Experimental datasets.  
numbers because it reuses them. CRT also reuses prime numbers; however, it considers every self label of ancestor nodes, which significantly reduces its speed.  
The performance results for dblp, which are poorer than the results for the larger dataset Treebank_e, are noteworthy. We observe that dblp has the largest max fanout, which means that there exists a node with a large number of child nodes. In REP+, the existence of a large number of sibling nodes is likely to increase the size of the prime numbers.
# Quantitative Analysis of Influencing Factors on Changzhou Ship Lock Capacity
## 4 Results and Discussion  
### Analysis of Hydrological Influencing  
To accurately extract the time periods that meet the normal navigable conditions of ships for the ship lock, a concurrent comparison was made between the daily hydrological data and the daily cargo volume data for 2020. The analysis results of the non-navigable time intervals are presented in Figure 5, which reveals the following observations:  
1. During the low-water period, the downstream water level persistently remained below the minimum navigable level, and the water level difference between upstream and downstream exceeded the maximum lock water head, leading to a sharp decrease in cargo volume. Particularly due to the low downstream water level at the end of January, the ship lock nearly came to a standstill.
2. The Xijiang River entered the low-water period from November, with the downstream water level and the water level difference approaching navigational design limits, placing significant pressure on navigational safety. In mid-November, the water level was too low, severely affecting cargo volume. As conditions improved slightly in December, coupled with strong demand for water transportation, cargo volume saw a rapid rebound, resulting in an overall increase for the month.
3. A flood occurred in mid-June, with the downstream water level nearly equal to the upstream water level, making it unsuitable for navigation. Additionally, due to reservoir water control, the downstream water level frequently fell below the minimum navigable level, impacting ship navigation.  
The analysis above validates the effectiveness of the method for eliminating navigability data redundancy, and also reveals that there is still room for improvement in the Changzhou ship lock capacity. Firstly, because there is a strong demand in December, it is necessary to further optimize ship lock scheduling and ensure a sufficient base flow for the channel to enhance ship navigation efficiency; secondly, during the flood period, water levels are ample and conditions are favorable, and ship passing remains generally stable, but there is a significant gap between the actual load and the approved load of ships, indicating that shipping potential is not being fully unleashed.
## 2 Results  
### Characteristics of Raw Materials--The Key Aroma Compounds of Coriander Seeds and Hops  
Odor-active components were determined in the raw materials used to produce wheat beer-rushed coriander seeds and hop. The Tettnanger variety of hops with an \(\alpha\)-acid content of 5.5%, an aromatic variety typical for wheat beers, was used. The olfactometric analysis showed the presence of 11 odor-active aroma components in coriander seeds and 8 in the hops used (Table 1 and Figure 1). All the detected components were terpen. \(\gamma\)-Terpinene, camphor, and carvacrol were the key aromas of coriander seeds that were not present in the hops used. Their content was 1232, 5646, and 65 \(\mu\)g/g, respectively. The olfactometric analysis (GC-O) also showed that the highest aroma intensity in the analyzed coriander seeds was found in linalool, carvacrol, and camphor, and their concentrations were above the detection thresholds. Particularly noteworthy was the 16 times higher concentration of linalool in the coriander seeds than in the hops. This accounted for over 70% of the content of all aroma components in the analyzed coriander seeds. A significantly higher geraniol content was also observed in the analyzed coriander seeds (2713 \(\mu\)g/g) compared to the analyzed hops (163 \(\mu\)g/g). In the case of hops, \(\beta\)-pinene and \(\beta\)-myrcene were characterized by the highest aroma intensity. These compounds are characterized by floral aromas, mainly rose. In the case of \(\beta\)-myrcene, a significantly higher amount was found in the analyzed hops (2394 \(\mu\)g/g) compared to coriander seeds (206 \(\mu\)g/g). This is also confirmed by the data presented by [PERSON] et al. [16], which describe that hops, regardless of their variety, are characterized by a higher content of this compound compared to coriander seeds.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline (\(\mu\)g/g) & \(m\mu\)z & **LRI 2** & **Threshold 3** & **Coriander Seeds** & **Hops (Tettnanger Seeds 5.5 \% Alpha Acids)** & **Sig 1** & **GC-O Descriptors 4** \\ \hline \multicolumn{8}{c}{**Terpenes**} \\ \hline \(\beta\)-pinene & 41, 69, and 93 & 969 & 4 & 85\({}^{\text{a}}\) & 205\({}^{\text{b}}\) & **...** & Pine and coriander [H] \\ \(\beta\)-myrcene & 41, 69, and 93 & 981 & 350 & 206\({}^{\text{a}}\) & 2394\({}^{\text{b}}\) & *** & Floral [FL] \\ p-cymene & 91, 119, and 134 & 1013 & 55.7 & 2 & 4 & ns & Citrus [FR] \\ \hline \end{tabular}
\end{table}
Table 1: Odor-active compounds of coriander seeds and hops.
_Appl. Sci._**2012**, \(2\), 566-583; doi:10.3390/app2030566  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Determination of Morphological Parameters of Supported Gold Nanoparticles: Comparison of AFM Combined with Optical Spectroscopy and Theoretical Modeling _versus_ TEM**  
**[PERSON] *, [PERSON] and [PERSON]**  
Institut fur Physik and Center for Interdisciplinary Nanostructure Science and Technology-CINSaT, Universitat Kassel, Heinrich-Plett-Str. 40, Kassel 34132, Germany  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +49-0-561-804-4501; Fax: +49-0-561-804-4518.  
_Received: 25 April 2012; in revised form: 11 June 2012 / Accepted: 15 June 2012 / Published: 2 July 2012_  
**Abstract:** The morphology of small gold particles prepared by Volmer-Weber growth on sapphire substrates have been investigated by two different characterization techniques. First, by non-extensive atomic force microscopy (AFM) in combination with optical spectroscopy and modeling of the optical properties using a theoretical model, recently developed in our group. Second, by extensive transmission electron microscopy (TEM). Comparing the results obtained with both techniques demonstrate that for small gold nanoparticles within the quasistatic limit, the morphological properties can be precisely determined by an appropriate theoretical modeling of the optical properties in combination with simple AFM measurements. The apparent mean axial ratio of the nanoparticles, _i.e._, the axial ratio that corresponds to the center frequency of the ensemble plasmon resonance, is obtained easily from the extinction spectrum. The mean size is determined by the nanoparticle number density and the amount of deposited material, measured by AFM and a quartz micro balance, respectively. To extract the most probable axial ratio of the nanoparticle ensemble, _i.e._, the axial ratio that corresponds to the most probable nanoparticle size in the ensemble, we apply the new theoretical model, which allows to extract the functional dependence of the nanoparticle shape on its size. The morphological parameters obtained with this procedure will be afterwards compared to extensive TEM measurements. The results obtained with both techniques yield excellent agreement. For example, the lateral dimensions of the nanoparticles after deposition of \(15.2\times 10^{15}\) atoms/cm\({}^{2}\) of gold has been compared. While a mean lateral diameter of \((13\pm 2)\) nm has been extracted from AFM, optical spectroscopy and modeling, a value of \((12\pm 2)\) nm is derived from TEM. The consistency of the resultsdemonstrate the precision of our new model. Moreover, since our theoretical model allows to extract the functional dependence of the nanoparticle size and shape, a relatively simple analysis is sufficient for a full characterization of small noble metal nanoparticles.  
keywords: gold nanoparticles; plasmon; quasistatic approximation; AFM; TEM; morphological characterization; theoretical modeling +
Footnote †: journal: Scientific Reports
# Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model
## 2 Related Work
further proposed HadoopViz [18], a MapReduce-based framework for large-scale spatial data visualization, in which six examples are implemented in HadoopViz through five abstract functions: scatterplots, road networks, frequency heat maps, satellite heat maps, vectorized maps, and country boundaries. Experiments have shown that HadoopViz can support the billion-scale visualization of spatial point data. [PERSON] et al. proposed and further refined GeoSparkViz [19; 20], a distributed spatial data management and visualization framework, which encapsulates the main steps of the map visualization process into a set of massively parallel map building operations and provides native support for spatial data map visualization. Experiments indicate that GeoSparkViz performs substantially better than HadoopViz. [PERSON] et al. proposed a display-driven BGVD visualization method, HiVision [21], which takes pixels as the computational unit and generates pixel values by querying the geographic vector elements affecting the pixel. Experiments have shown that the tile-generation performance of this method is stronger than that of HadoopViz and GeoSparkViz, and can be used to support the real-time visualization of BGVD. To sum up, the current mainstream visualization methods all take vector features as computing units. In order to obtain visualization images of BGVD, it is necessary to convert the geographical coordinates of vector features into pixel coordinates one by one to calculate pixel values. When the number of vector features increases sharply, the calculation cost of the traditional methods increases significantly. On the contrary, from the perspective of directly calculating pixel values, it is more advantageous to generate visualization effects by taking image pixels as computing units and directly calculating pixel values. In the field of geographic time series data visualization, some experts have also proposed using data aggregation and generalization strategies to compress information and reduce the number of variables that need to be displayed [5]. Based on the analysis of the above research ideas, in our previous work [22; 23; 24], we designed a series of methods including the computing framework, data index, and visualization tool for BGVD visualization, which achieved a greater performance improvement compared with traditional mainstream methods. Furthermore, in this paper, we carry out theoretical abstraction and modeling analysis on the research ideas and processing flow, and obtain the viewport generalization visualization model, which provides a stronger theoretical support for the method.
# Deep Learning- and IoT-Based Framework for Rock-Fall Early Warning
###### Abstract
The primary purpose of this framework is to augment prediction accuracy by eliminating the uncertainties and confusion that plague the prediction model. In order to achieve prediction accuracy augmentation, four different techniques were integrated, namely, detection model-based (computer vision and micro-seismic wave), prediction-based (deep learning model), an Internet of Things network (IoT), and a decision-making algorithm. Finally, the following are the key contributions of this study:  
* We propose an IoT-based framework for rock-fall early warning. * We created a deep learning model to predict the likelihood of rock-fall events. * We created a detection model-based micro-seismic wave and computer vision. * We have augmented the accuracy of a prediction model by fusing the detection model with a prediction model. * We developed a decision-making algorithm. * We provide a baseline methodology and a prediction accuracy benchmark for future related work. This study's remaining sections are structured as follows. In Section 2, the study area and issues are presented. Section 3 introduces data acquisition. Section 4 presents the methodology. Section 5 presents the results and discussion. The evaluation of the study is presented in Section 6.
## 2 Influence of Sensor Intrusion on Vocal Fold Contact Pressure Accuracy
### Methods  
To enable investigation of these behaviors, a custom facility, shown in Figure 3, was fabricated to enable characterization of the pressure transducer with a ground truth measure of contact pressure. The facility consisted of an Omega LCFD-1 Kg (2.2 lb) (Omega Engineering Inc., Norwalk, CT) force transducer, with \(\pm\)0.15% FSO accuracy, which acted as a ground truth measure of static contact force. The force transducer has a 5.08 cm diameter cylindrical aluminum plate affixed to it that acted as the primary contact surface. A silicone disk that measures 1.88 cm in diameter and 0.94 cm thick was fabricated from Smooth-On(r)Ecoflex 0030 (EF) (Smooth-On Inc., Macungie, PA, USA) silicone and placed on top of the aluminum plate. The elastic modulus of the silicone disk could be varied over a wide range based on the ratio of silicone to thinner that was used. The modulus of elasticity of each sample was measured using dynamic mechanical analysis on a TA Instruments Q800 at a frequency of 1 Hz and 5 % strain.  
By placing the intraglottal pressure transducer between the aluminum plate and the silicone disk, and applying a uniformally-distributed static load to the silicone disk, the static pressure was acquired. The area of contact between the silicone disk and the aluminum plate was calculated by dyeing the surface of the silicone disk. For each discrete static loading that was applied to the silicone disk, the area of contact was obtained by measuring the diameter of the colored dye that was transferred to the aluminum plate during loading. The ground truth contact pressure was then computed as the output of the force sensor divided by the contact area of the silicone disk. Each measurement was repeated three times.  
Individual aluminum contact plates were fabricated with different groove depths, such that the height of the transducer intrusion, \(H\), (see Figure 3) could be varied. Specifically, \(H\) prescribes the  
Figure 2: **(a)** Photograph of the Millar Mikro-Cath(tm) pressure transducer from a top view. **(b)** Schematic of the top and side views of the pressure transducer geometry and dimensions. Dimensions are shown in mm.  
distance between the surface of the sensing element of the pressure transducer and the surface of the aluminum plate against which the silicone disk is pressed.
# Aerial Scene Classification through Fine-Tuning with Adaptive Learning Rates and Label Smoothing
## 2 Methods
### Datasets  
We evaluate our proposed method on two common large-scale remote sensing image datasets, the Aerial Image Dataset (AID) [45] and the NWPU-RESISC45 dataset [46]. A detailed description of the two datasets is given in the following subsections.  
AID consists of about 10,000 remote sensing images with dimensions 600 \(\times\) 600 pixels, assigned to 30 classes [45]. Images are gathered from Google Earth imagery. They are selected from different continents and countries at different times of the year and weather conditions: mostly from China, Japan, Europe (Germany, England, Italy, and France), and the United States. Images from the AID dataset have a pixel resolution of half a meter. Figure 7 presents sample images of each class.  
The NWPU-RESISC45 dataset contains images collected from Google Earth imagery. The name of the dataset comes from its creator Northwestern Polytechnical University (NWPU). It consists of 31,500 aerial images split into 45 classes. Each class has 700 images with dimensions 256 \(\times\) 256 pixels. Except for four classes (island, lake, mountain, and snowberg), which exhibit a smaller spatial resolution, the other classes have spatial resolutions that vary in the range of 30 m-0.2 m. Figure 8 presents sample images of each class.  
Figure 7: Image classes in the AID dataset: (**a**) airport; (**b**) bare land; (**c**) baseball field; (**d**) beach; (**e**) bridge; (**f**) centre; (**g**) church; (**h**) commercial; (**i**) dense residential; (**j**) desert; (**k**) farmland; (**l**) forest; (**m**) industrial; (**n**) meadow; (**o**) medium residential; (**p**) mountain; (**q**) park; (**r**) parking; (**s**) playground; (**t**) pond; (**u**) port; (**v**) railway station; (**w**) resort; (**x**) river; (**y**) school; (**z**) sparse residential; (**aa**) square; (**ab**) stadium; (**ac**) storage tanks; (**ad**) viaduct.  
Figure 8: Image classes in the NWPU-RESISC45 dataset: (**a**) airplane; (**b**) airport; (**c**) baseball diamond; (**d**) baseball court (**e**) beach; (**f**) bridge; (**g**) chaparral; (**h**) church; (**i**) circular farmland; (**j**) cloud; (**k**) commercial area; (**l**) dense residential; (**m**) desert; (**n**) forest; (**o**) freeway; (**p**) golf course; (**q**) ground track field; (**r**) harbour; (**s**) industrial area; (**t**) intersection; (**u**) island; (**v**) lake; (**w**) meadow; (**x**) medium residential; (**y**) mobile home park; (**z**) mountain; (**aa**) overpass; (**ab**) palace; (**ac**) parking lot; (**ad**) railway; (**ae**) railway station; (**af**) rectangular farmland; (**ag**) river; (**ah**) roundabout; (**ai**) runway; (**aj**) sea ice; (**ak**) ship; (**al**) snowberg; (**am**) sparse residential; (**an**) stadium; (**ao**) storage tank; (**ap**) tennis court; (**aq**) terrace; (**ar**) thermal power station; (**as**) wetland.
## 3 Results and Discussion
### Cell Line Measurements
#### 3.1.4 Immunocytochemistry  
Morphological changes in the cell-cell junctions of A549 alveolar and CFBE bronchial epithelial cell layers after permeability experiments were assessed by immunostaining for \(\beta\)-catenin on A549 cells (Figure 4A) and zonula occludens protein-1 (ZO-1) on CFBE cells (Figure 4B). The results confirmed that KTP raw, F0, and F1 formulations did not impair barrier integrity, as evidenced by the continuous, belt-like immunostaining of \(\beta\)-catenin and ZO-1 at the epithelial cell borders, which was similar across all groups (Figure 4).  
Figure 4: Validation of barrier integrity in A549 alveolar epithelial and CFBE bronchial epithelial cell layers after permeability assays with KTP raw, F0, and F1. Immunostaining for (**A**) \(\beta\)-catenin and (**B**) zonula occludens protein-1. Cyan: cell nuclei; red: junctional proteins; scale bar: 20 \(\upmu\)m. C, control; KTP, ketoprofen raw.  
Figure 3: Evaluation of the tightness of the A549 alveolar and the CFBE bronchial epithelial co-culture models after the KTP raw, F0, and F1 DPIs permeability experiment by (**A,C**) transepithelial electrical resistance (TEER) measurement and the (**B,D**) permeability of fluorescein and albumin marker molecules. Values are presented as means \(\pm\) SD, \(n=4\). Statistical analysis: ANOVA followed by Dunett’s test.
## 4 Performance Analysis
### Simulation Results
#### 4.3.6 Network Lifetime Prediction  
In Section 4.2, lifetime prediction is the function of initial energy, average energy consumption and energy standard deviation. Figure 7 reports a comparison of normalised network lifetime prediction of the network running FSACO, EEABR, IACO and SensorAnt. Due to a lower average energy consumed in FSACO (Figure 4) and a significantly improved performance in the standard deviation (Figure 5), hence, FSACO had a higher expectation in terms of network lifetime. The result obtained from Figure 7 indicated that FSACO could provide longer network lifetime prediction than that of EEABR, IACO and SensorAnt, respectively.  
Figure 6: Comparison of the energy efficiency of FSACO, EEABR, IACO and SensorAnt under a static network scenario with 100 sensor nodes.  
Figure 7: Comparison of network lifetime predication of FSACO, EEABR, IACO and SensorAnt under a static network scenario with 100 sensor nodes.
# UV-Curable Silicone-Modified Polyurethane Acrylates for Food Freshness Monitoring
## 2 Materials and Methods
### Preparation of NR Covalent-Grafted Polyurethane Acrylates (NR-PUAs)  
Several polyurethane acrylates (PUAs) (Scheme 1) were prepared according to reference [19] by the formula shown in Table S1. For example, 0.3704 g DMBA, 28.5 g PEG-600, 0.4025 g TMP, 0.2168 g DBTDL, and 54.2 g acetone were mixed homogeneously, then 24.937 g IPDI was dropped in at 40 \({}^{\circ}\)C for 1 h, and the reaction was carried out at 60 \({}^{\circ}\)C for about 2 h. After that, 14.185 g HPA was added, and the reaction was conducted for another 2 h at 60 \({}^{\circ}\)C. Finally, a transparent sticky liquid of PUA-1 was prepared after being distilled at 130 mmHg/50 \({}^{\circ}\)C for about 1 h to remove acetone.  
Consequently, several NR-PUAs were fabricated according to reference [18], following the procedure shown in Scheme 2 and the formula shown in Table S2. For example, 0.0690 g EDC-HCl in 0.0276 g THF and 0.1104 g ethanol was added into a mixture of 10.0 g PUA-1, 4.0 g THF, and 16.0 g ethanol at 55 \({}^{\circ}\)C for 1 h, and the reaction was carried out for another 2 h. Then, 0.0420 g NHS in 0.0168 g THF and 0.0672 g ethanol was added at 0-5 \({}^{\circ}\)C for 0.5 h. The reaction was conducted for another 1 h. Later, 0.1050 g NR in 0.0420 g THF and 0.1680 g ethanol was added, and the reaction was conducted for about 24 h. Before the NR-PUA-1 product was obtained, the mixture was filtered with medium-speed filter paper to remove residual solid NR and distilled at 130 mmHg/60 \({}^{\circ}\)C for about 2 h to remove solvents.  
Fourier transform infrared (FT-IR) spectrum analysis of NR-PUA-3 was adopted to characterize the NR-PUAs (Figure 1). The sharp, moderate, single absorption peak at about 3335 cm\({}^{-1}\), rather than a double absorption peak, is the characteristic stretching vibration of N-H groups of NR [25]. The absorption peak at about 1703 cm\({}^{-1}\) is attributed to the amide bond resulting from the interaction between the carboxyl groups and the amino groups of NR. The peaks at about 2932 cm\({}^{-1}\) and 1621 cm\({}^{-1}\) are the characteristic absorption of C=C in acrylate groups. So, it can be concluded that NR was grafted onto the PUAs.  
**Scheme 2.** Procedure for the preparation of NR-PUAs.