esa-sceva/satcom-chunk-collection huggingface dataset:
# Postbuckling and Free Vibration of Multilayer Imperfect Nanobeams under a Pre-Stress Load
## 5 Conclusions  
An investigation into the postbuckling and free vibration response of size-dependent, geometrically imperfect multilayer nanobeams was introduced. The small-size effect was modeled according to the nonlocal elasticity differential model of [PERSON] within the nonlinear Bernoulli-Euler beam theory. It was found that the nonlocal effect reduces the buckling load and increases the postbuckling static response. The presence of initial imperfection changes the nature of the point at the onset of buckling from a bifurcation point to a limit point. The initial imperfection was found to be a key factor to enhance the buckling load; however, the optimum value of the initial imperfection that results in the maximum buckling load was found to be size dependent. The buckling load at different values of initial imperfection, a size parameter for a variety of lamination coefficients, was presented. Numerical results that show the buckling load and the load-deflection curves in the postbuckling domain for a variety of parameters were presented. It is shown that the buckling load with the presence of imperfection and a small-size effect can be less than or greater than the Euler buckling load. The linear free vibration problem has been solved for the vibration mode shapes and natural frequencies around the static postbuckling position. The fundamental natural frequency was found to be sensitive to the size effect, which has a significant qualitative and quantitative contribution. Moreover, the size effect was more prominent for higher modes of vibration where the buckling loads are significantly decreased. This study can be extended and supported by experimental testing to help design small-scale devices incorporating multilayered, initially curved beams at a nanoscale.  
[PERSON] and [PERSON] formulated the problem and generated the data. [PERSON] wrote the manuscript and presented the results, [PERSON] and [PERSON] reviewed the manuscript.  
Figure 11: The variation of the first four natural frequencies with the applied axial load for \(\alpha=1\) of the imperfect nanobeam, \(a=1\), and \(\overline{\mu}=(0,\ 0.02)\). (**a**) The first natural frequency; (**b**) The second natural frequency; (**c**) The third natural frequency; (**d**) The fourth natural frequency.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The third author acknowledges KFUPM for their support.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
The normalized difference vegetation index (NDVI) is a useful and frequently used tool for analyzing the distribution and photosynthetic capacity of vegetation [34; 35; 36]. Likewise, the enhanced vegetation index (EVI) is strongly associated with the leaf area index (LAI), canopy cover, biomass, and the fraction of absorbed photosynthetically active radiation [37; 38; 39]. Thus, using the combination of the NDVI, EVI, and ST-cube may facilitate a better representation of the changes in forest ecosystems. In this study, we used Landsat time series data to provide insight into the spatiotemporal variability in forests in Hunan, China. The aims of this study were as follows: (i) to analyze changes in the forest ecosystem that reveal high-frequency disturbance backgrounds, (ii) develop the IST-cube by combining the ST-cube with vegetation indices (NDVI, EVI), and (iii) extract spatiotemporal variation features using the IST-cube model in the study area from 1987 to 2020.
## 1 Nanomaterials and Devices  
### Thin-Piezo on Silicon Resonators Using a Novel Microfabrication Process  
[PERSON], [PERSON] and [PERSON]  
University of South Florida, Tampa, FL 33620, USA  
Radio frequency microelectromechanical systems (RF MEMS) are widely viewed as a potential enabling technology for the multi-standard monolithic transceivers on a single chip with high reliability, high performance, and very low (virtually zero) DC power consumption. Among various RF MEMS device components, resonators offer unique benefits because of their very high quality factor, which enables the implementation of advanced functions such as low insertion loss filters, mixer-filters, ultra-low phase noise oscillators, and even RF front-end channel selection. Thin-piezo on silicon (TPoS) resonators, in particular, are of interest in building very high performance resonators. A new technique to build a TPoS resonator using a silicon single crystal wafer is developed. The thin-film piezoelectric on single-crystal silicon reactive etched (TPoSCRE) technique presented allows batch fabrication of TPoS resonators without using silicon-on-insulator (SOI) wafers, while also retaining the same number of photolithography steps as a typical SOI based MEMS TPoS resonators. Typically, building TPoS resonators requires expensive SOI wafers. This method helps to reduce the cost of the thin-film piezoelectric on silicon MEMS devices. The isotropic release process in this method utilized a thin film of aluminum oxide deposited by atomic layer deposition (ALD). The ALD film protects the sidewall of the Si device structure from being etched. Figure 1 shows scan electron microscopy (SEM) images of the fabricated devices. The results of these devices are shown in Figure 2.  
Figure 1: SEM view of TPoSCRE fabricated TPoS RF MEMS resonator devices.
## 3 Principles of the Electromagnetic Radiation Testing Method
Through this investigation of indirect relationships, we can adjust wireless network parameters more effectively, enhancing the quality of wireless communication services. According to standards [9; 10], the objective of throughput measurement is to establish a relationship between wireless terminal throughput and downlink power. The MIMO wireless communication schematic and the definition of downlink power are illustrated in Figure 10, where the signal transmitted by the base station reaches the terminal antenna front through a channel and is coupled into the receiver, defining the strength of the signal reaching the terminal antenna front as downlink power. Figure 11 presents a typical throughput measurement result, specifically the relationship curve between throughput and downlink power, where the downlink power P corresponding to the throughput R is taken as a benchmark. It can be observed that the throughput increases with the downlink power until it reaches the maximum throughput. Following the analysis of the aforementioned preconditions, it is apparent that the magnitude of S-parameters significantly affects the MCL, which is defined as per 3 GPP specifications. In the MCL's definition formula, there exists a proportional relationship between the MCL and transmit power, provided that the terminal receiver sensitivity remains constant. Moreover, during testing in anechoic environments, the variability from external environments is relatively low, ensuring greater stability of the terminal receiver sensitivity. These conditions ensure that we can accurately assess signal transmission performance. As depicted in Figure 11, a positive proportional relationship exists between the downlink power and throughput until the maximum throughput is reached. This phenomenon further substantiates the proportional relationship between S-parameters and throughput, indirectly demonstrating the effectiveness and reliability of the electromagnetic compatibility radiation testing method. Through research on this relationship, we systematically understand the principles of the electromagnetic radiation testing method. This foundation allowed us to conduct detailed simulation analyses focused on S-parameters in subsequent research. During the simulation process, we utilized CADFEKO simulation software to generate S-parameter data associated with electromagnetic radiation. These simulated data served as important references for evaluating communication system performance. Subsequently, we compared these simulation results to the dB values in relevant standards for comparative analysis. Through this comparison, we delved into the specific influences of S-parameters on system performance. This process provides an important reference for quality assessment and optimization of communication in practical applications.
## 3 Results
### Smoking Status Classification  
Our SPPMI-based keyword extraction method can also be applied to training a smoking status classifier from EHR data. Several previous works have applied machine learning algorithms or statistical analysis to classify smoking status from EHR [28; 29; 30; 31; 32; 33]. Among these works, a linear support vector machine (SVM) trained from unigram and bigram bag of words has consistently shown the highest classification accuracy [7; 34; 35; 36].  
However, this smoking status classification accuracy can be further improved by preprocessing unigram and bigram bag of words by the keywords extracted from SPPMI. Instead of representing each EHR record by the frequencies of every word in the dataset, we represent it as a bag of keywords. For all non-keywords within the record, we simply treat them as an identical word. For example, if we decide to represent each record with five keywords extracted from SPPMI, each record becomes a vector with six dimensions (five for keywords and one for other non-keywords).  
To test the impact of our SPPMI-based extracted keywords on the smoking status classification, we have fixed the classifier to be a linear SVM. Subsequently, we compare  
\begin{table}
\begin{tabular}{c c c} \hline
**Never Smoker** & **Past Smoker** & **Current Smoker** \\ \hline \(\
exists\) negative 1 & \(\
exists\)\  
+++ ==WARNING: Truncated because of repetitions==
(\  
++  
the classification accuracy resulting from different clinical note vector representations. For all classification methods, we have trained the classifiers from 80% of our SNUH clinical notes data and used the rest as test data. As the entire EHR records were initially annotated with each patient's actual smoking status, accuracy can be measured in terms of F1 score to compare the impact of different vector representations on the classifier's performance (Table 5).  
Compared to the unigram and bigram-based Bag of Words approach used in [7; 34; 35; 36], classifying smoking status solely with the keywords extracted with our SPPMI-based approach improves the overall accuracy up to 1.8% (Table 4). This improvement in accuracy becomes more evident when we observe the classification accuracy of each smoking status (Tables S4-S7 in Supplementary Materials). For classifying smokers, our approach improves the F1 score by as much as 9.04%. Furthermore, the improved classification result also signifies that our method is capable of expanding meaningful keywords from our seed word.  
In terms of machine learning, preprocessing clinical note vectors by our keywords serves as a form of dimension reduction or feature engineering. However, our approach outperforms other conventional dimension reduction techniques in document vectors, such as Latent Semantic Analysis (LSA) [37; 38] and Latent Dirichlet Allocation (LDA) [39]. This superior smoking status classification result once again emphasizes the capability of our approach to expand keywords that are truly relevant to each smoking status.
# Plant Organic Acids as Natural Inhibitors of Foodborne Pathogens
## 3 Results
### Antibacterial and Antifungal Activity
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Organic Acid** & _Staphylococcus_ & _Escherichia_ & _Listeria_ & _Salmonella_ & **Mean MIC \(\pm\) SD for** \\  & _aureus_ & _coli_ & _monocytogenes_ & Typhimuriumurium_ & **All Bacteria** \\ \hline Tartaric acid & 5 & 1.25–2.5 & 2.5 & 2.5 & \(2.97\pm 1.33\) \\ \hline Valeric acid & 5 & 1.25–2.5 & 2.5 & 2.5 & \(2.97\pm 1.33\) \\ \hline Octendidine & 0.00004–0.00008 & 0.00008–0.00016 & 0.00008–0.00016 & 0.00008–0.00016 & 0.00016–0.00005 \\ dihydrochloride & (0.04–0.08 \(\upmu\)g/mL) & (0.08–0.16 \(\upmu\)g/mL) & (0.08–0.16 \(\upmu\)g/mL) & (0.08–0.16 \(\upmu\)g/mL) & (0.11 \(\pm\) 0.05 \(\upmu\)g/mL) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Organic Acid** & _Geotrichum candidium_ & _Penicillium candidium_ &
\begin{tabular}{c} **Mean MIC \(\pm\) SD** \\ **for Both Fungi** \\ \end{tabular} \\ \hline Acetic acid & 5 & 10 & \(7.5\pm 2.74\) \\ \hline Aminoacetic acid & \(>\)100 & \(>\)100 & \(>\)100 \\ \hline Ascorbic acid & 50–100 & 100 & \(83.33\pm 25.82\) \\ \hline Benzoic acid & 2.5–5 & 10 & \(7.08\pm 3.32\) \\ \hline Caproic acid & 5 & 10 & \(7.5\pm 2.74\) \\ \hline Chlorogenic acid & 1.25–5 & 5–10 & \(5.63\pm 3.69\) \\ \hline Citric acid & 5–10 & 10 & \(8.33\pm 2.58\) \\ \hline Formic acid & 5 & 10 & \(7.5\pm 2.74\) \\ \hline Fumaric acid & 5–10 & 20 & \(14.17\pm 6.65\) \\ \hline Glutamic acid & \(>\)100 & \(>\)100 & \(>\)100 \\ \hline Malic acid & 20–50 & 50 & \(45\pm 12.25\) \\ \hline Nicotinic acid & 10 & 20 & \(15\pm 5.48\) \\ \hline Oleic acid & 50 & 50 & \(50\pm 0.0\) \\ \hline Oxalic acid & 10–20 & 20 & \(16.67\pm 5.16\) \\ \hline Palmitic acid & 20–50 & 20–50 & \(30\pm 15.49\) \\ \hline Rosmarinic acid & 1.25–5 & 10 & \(6.46\pm 4.06\) \\ \hline Salicylic acid & 2.5–5 & 10 & \(7.08\pm 3.32\) \\ \hline Succinic acid & 50–100 & 100 & \(83.33\pm 25.82\) \\ \hline Tannic acid & 10–20 & 5–20 & \(14.17\pm 6.65\) \\ \hline Tartaric acid & 20–50 & 100 & \(70\pm 34.64\) \\ \hline Valeric acid & 5 & 5 & \(5\pm 0.0\) \\ \hline \multirow{2}{*}{Octenidine dihydro} & 0.00008–0.00016 & 0.00008–0.00032 & 0.00016 \(\pm\) 0.00009 \\  & (0.08–0.16 \(\upmu\)g/mL) & (0.08–0.32 \(\upmu\)g/mL) & (0.16 \(\pm\) 0.09 \(\upmu\)g/mL) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The minimal inhibitory concentrations (MIC) of the organic acids and octenidine against foodborne fungi. MIC values are presented in mg/mL. The last column shows the mean values and standard deviation (SD) for all readings for a given acid.
## 4 Conclusions and Future Work  
Constellation size and irregular configuration in complex NOMA systems are very challenging tasks in the design of communication systems. Introducing a simple-to-implement link-level model that carries the benefits of engineering intuition and the ability to demonstrate the relationships between the different implementation parameters in the system is critically important to facilitate the development of practical NOMA systems. Furthermore, such a model can be utilized as a form of domain knowledge, engaging model-based machine learning systems to address complex communication tasks. In this work, we proposed a new approach for designing and implementing link-level simulations.  
Figure 15: An example of neural network for prediction and optimization of physical layer link, trained on synthetic generated data obtained by symbolic model. The input features are represented by the squares, intermediate neurons by orange circles and output neurons by the blue circles.  
This new approach differs from previous approaches, e.g., [11; 12], as it defines the signal path element symbolically instead of coding them explicitly.
## 3 Materials and Methods  
This section presents the participants of this study, the experimental design describing research context and procedures, the digital navigation platforms used for the experiment, the data sources, and the methods used for data analysis in this wayfinding study.  
Wayfinding is important for students, faculty, staff, visitors, and anyone else on a university campus to understand where they are and where they want to go to attend a meeting or a class. Navigation systems are ubiquitous tools to assist wayfinders with various navigational tasks, thus reducing human effort for navigating. In this study, we conducted a field experiment using control and experiment groups to evaluate users' wayfinding performance and workload in an indoor-outdoor campus navigation when using Google Maps (an outdoor-only navigation app) in comparison with MazeMap (an indoor-outdoor navigation app) for wayfinding on a university campus. In addition to understanding the role of navigation aids, this study also explores the effects of spatial ability skills on perceived workload and wayfinding performance in campus wayfinding tasks and identifies challenges for designing efficient indoor-outdoor navigation systems for wayfinding in complex environments such as large university campuses. This study uses both quantitative and qualitative methods to address the three research questions presented in the introduction. The following hypotheses were formulated for the first two research questions:  
**Hypothesis (H1).**_Indoor-outdoor navigation app group will show better wayfinding performance as compared to the outdoor-only navigation app group in the university campus wayfinding task._  
**Hypothesis (H2).**_Indoor-outdoor navigation app group will experience less workload as compared to the outdoor-only navigation app group in the university campus wayfinding task._  
**Hypothesis (H3).**_Participants who have better spatial ability skills will experience less workload in the university campus wayfinding task._  
**Hypothesis (H4).**_Participants who have better spatial ability skills will have better wayfinding performance in the university campus wayfinding task._
## 4 Materials and Methods
### Project VI  
The goal of the project was to design and implement a system for prediction of the final state (error/non-error), based on input parameters of a production process in real time. Input and output numerical values of two parameters served as a training set. The data-mining part could be approached as both classification and regression problem.  
Data sources:  
* Input and output values of two parameters of a production process.  
The dataset consisted of measurements from several points on the surface of the products, recorded for 2 months. Each point had been saved in an individual.csv file and contained approximately 19,000 records. Out of the points, 26 contained enough measurements for both numerical attributes on input and output to performs further analysis.  
Before applying any data-mining models, we constructed correlation matrices for each point as to see whether there are any meaningful correlations. There was at least one moderate correlation between inputs and outputs in 18 out of 26 points. We also searched for correlations between points of the groups. As a results, there was at least moderate correlation found in 7 out of 8.  
We decided to use three different approaches which we would compare to each other in the end. Two of them dealt with individual points: the first one used models built via Rapid Miner, and the second one was an experimental setting of boundaries via Python. The third approach was similar to the first one, but instead of individual points, groups were used. We chose classification over regression for the task (though both methods would be possible due to the nature of the data) and we used sensitivity as an evaluation criterion.  
Though the initial correlation matrix looked more in favor of groups (87.50% at least moderately correlated vs. 69.23% for individual points), the results of validation proved different: Approach 3 (data-mining methods used specifically for groups) was successfully trained for only 37.50% of cases, which was less than for any other case; and furthermore, no groups were validated successfully.  
This left us with the first two Approaches, from which the first one was the very best (53.85% validated successfully on both cross and split validation), followed by the second Approach (46.15% successfully validated). In the last step of the 'Modelling' phase we combined the two Approaches in an ensemble model with results calculated via voting.  
The monitoring was set up in near-real time in the 'Deployment' phase. Before visualizing, the data were bulked from Qdb database ('Data Sources') into Kibana/Elasticsearch ('Data Storage Layer') via a Python parser ('Data Acquisition Layer'). On frontend, the data from Input and their predicted Output were displayed in form of 'traffic lights'. The table contained ID of a product, timestamp of recording, point on the surface of the product, values from two Input attributes, and predicted Output--green for OK, red for NOK, intuitively.
## Materials and Methods
### Data and Processing  
In this study, CUE at the monthly and seasonal scale derived from MODIS data products and the ancillary data were used to explore the spatial-temporal variations of CUE of natural ecosystems and their responses to climate and LSP changes. The main steps are as follows: (1) estimating NPP at monthly scale by the CASA (Carnegie-Ames-Stanford approach) model; (2) calculating monthly CUE of different ecosystems and performing the trend analysis; (3) extracting LSP metrics and analyzing the effects of phenology and climatic factors on the variations of ecosystem CUE by the correlation and partial correlation analysis methods.  
Figure 2 illustrates the technical approach of this study.  
MODIS and Meteorological Data  
The MOD17A2H version 6 GPP product is a cumulative 8-day composite of values based on the radiation use efficiency concept, which can be potentially used as an input to data models to compute energy, carbon, water cycle processes and biogeochemistry of vegetation [12]. Monthly Normalized Difference Vegetation Index (NDVI) spatial distribution data set was obtained from the MOD13A2. For the calculation of NPP, the Maximum Value Composite (MVC) was used to synthesize the monthly NDVI data. The phenological parameter extraction was based on 16-day data. Each time period of the MODIS data included 6 images.  
The MODIS land cover type dataset (MCD12Q1) was downloaded from the Land Processes Distributed Active Archive Center (LP DAAC) ([[https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/)]([https://lpdaac.usgs.gov/](https://lpdaac.usgs.gov/))) [13]. In this study, land cover data from 2001 to 2015 were adopted. According to the International Geosphere-Biosphere Program  
Figure 2: The flow chart of the research approach.  
(IGBP) class scheme and the regional characteristics of the SNP, natural ecosystem included the following four types, deciduous broad-leaved forest (DBF), mixed forest (MF), grassland (GRA) and wetland (WET).  
The total monthly radiation was obtained from 7 radiation stations (Figure 1). The remaining meteorological data of 29 meteorological stations within and near the SNP from 2001 to 2015 were acquired from the National Meteorological Information Center ([[http://data.cma.cn/](http://data.cma.cn/)]([http://data.cma.cn/](http://data.cma.cn/))). Most studies suggest that precipitation and temperature be the main meteorological factors affecting CUE [3; 13]. In this study, monthly precipitation (mm) and temperature (\({}^{\circ}\)C) were computed from May to November in order to explore the relationship between CUE and climate factors in growing seasons. MODIS, meteorological and other data were all resampled at a resolution of 1 km. Table 1 summarizes and describes the characteristics of the data and sources.