esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Determination of VEGFA and VEGFB Gene Expression  
All 42 samples of periapical inflamed tissues underwent qualitative gene expression analysis. In the first step of the analysis, the PCR product from the reference gene _ACTB_ was detected in all specimens. Therefore, all samples were included in the qualitative analysis of _VEGFA_ and _VEGFB_ gene expression.  
To detect gene expression, 1 mL of Fenozol (chaotropic salts and phenol; A&A Biotechnology, Poland) was added to the granulomatous tissue to isolate RNA using a Total RNA Mini Plus kit (A&A Biotechnology, Poland). The isolated RNA had an A260/280 ratio of 1.8-1.9. RNA samples were stored at \(-76\)\({}^{\circ}\)C before analysis. Reverse transcriptase (RT) reaction was performed using an Enhanced Avian HS RT-PCR kit (Sigma Aldrich, Munich, Germany) according to the manufacturer's two-step protocol. For the first step it contained 0.1 ug/uL of total RNA, 1 uL of anchored oligo(dT) primer (final concentration: 3.5 uM), 1 uL of dNTPs mix (final concentration: 500 uM each dNTP) and deionized, nuclease-free water. The mixture was centrifuged and incubated at 70 \({}^{\circ}\)C for 10 min. After that, it was chilled on ice, and the following components for the second step were added: 2 uL of 10X buffer for AMV-RT, 1 uL of RNase inhibitor (1 U/uL) and 1 uL of Enhanced Avian RT (1 U/uL). It was then mixed, centrifuged and incubated at 42 \({}^{\circ}\)C for 50 min. Total volume was 20 uL. cDNA was used immediately or stored at \(-20\)\({}^{\circ}\)C. Number of cycles was 35, and annealing temperature was 57 \({}^{\circ}\)C for VEGF-A and 56 \({}^{\circ}\)C for VEGF-B.  
Gene expression was detected using qualitative polymerase chain reaction (PCR). The mixture for PCR amplification consisted of a cDNA template, 0.7 uL of each primer (target genes: _VEGFA_ F 5\({}^{\prime}\)-GACCAAAGAGATAGAGCAAG-3\({}^{\prime}\), R 5\({}^{\prime}\)-ATACGCTCCAGGACTATTAC-3\({}^{\prime}\); _VEGFB_ F 5\({}^{\prime}\)-GAAAAGTGTGTCATGATG-3\({}^{\prime}\), R 5\({}^{\prime}\)-ATGCTCCACAG-3\({}^{\prime}\); reference gene: _ACTB_ F 5\({}^{\prime}\)-GTGGCGCCCCAGGCCA-3\({}^{\prime}\); R 5\({}^{\prime}\)-CTCCTTAATGTCACGCAGATTTC-3\({}^{\prime}\)), 10 uL of JumpStart RedTaq ReadyMix PCR Reaction Mix (Sigma Aldrich, Munich, Germany) and water to a final volume of 20 uL. Negative and positivecontrols were included in each PCR run. The negative control was a sample without a cDNA template. The positive control contained cDNA obtained from human umbilical vein endothelial cells (HUVEC) that express _VEGFA_ and _VEGFB_. PCR products were analyzed by electrophoresis on 2% agarose gel. PCR product size for qualitative analysis was 466 bp for _ACTB_, 105 bp for _VEGFA_ and 84 bp for _VEGFB_. Some examples of electrophoretic separation of PCR products are shown for the _VEGFA_ and _VEGFB_ genes in Figures 1 and 2.
## 4 Experiments
### Ensemble Learning
\begin{table}
\begin{tabular}{c c c c} \multicolumn{3}{c}{**(a)** CIFAR-10} \\ \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{2}{c}{**Accuracy (\%)**} & \multirow{2}{*}{**Epochs**} \\ \cline{2-3} \cline{5-5}  & \multicolumn{1}{c}{**Train**} & \multicolumn{1}{c}{**Test**} & \\ \hline \multicolumn{3}{c}{_Model 1_} \\ \hline \multirow{2}{*}{NAG} & 100.00 & **83.68** & 350 \\  & 100.00 & 83.54 & 144 \\ \hline \multirow{2}{*}{BatchNormNorm} & 100.00 & 86.45 & 350 \\  & 100.00 & **86.92** & 63 \\ \hline \multirow{2}{*}{Dropout} & 97.31 & 86.54 & 350 \\  & 96.89 & 86.57 & 237 \\ \hline \multirow{2}{*}{DataAugm} & 98.48 & **87.73** & 350 \\  & 97.10 & 87.51 & 152 \\ \hline \multirow{2}{*}{DataAugm + BatchNorm} & 99.49 & **89.54** & 350 \\  & 98.42 & 87.89 & 127 \\ \hline \multicolumn{3}{c}{_Model 2_} \\ \hline \multirow{2}{*}{Adam} & 100.00 & **79.84** & 350 \\  & 99.49 & 78.50 & 60 \\ \hline \multirow{2}{*}{BatchNormNorm} & 100.00 & **82.89** & 350 \\  & 100.00 & 82.62 & 72 \\ \hline \multirow{2}{*}{Dropout} & 98.89 & **83.64** & 350 \\  & 98.26 & 83.08 & 150 \\ \hline \multirow{2}{*}{DataAugm} & 97.33 & 86.99 & 350 \\  & 95.74 & **87.37** & 172 \\ \hline \multirow{2}{*}{DataAugm + BatchNorm} & 98.74 & **88.54** & 350 \\  & 94.56 & 86.36 & 127 \\ \hline \multicolumn{3}{c}{_Model 3_} \\ \hline \multirow{2}{*}{NAG} & 100.00 & **80.40** & 350 \\  & 100.00 & 80.16 & 190 \\ \hline \multirow{2}{*}{BatchNormNorm} & 100.00 & **81.21** & 350 \\  & 100.00 & 80.33 & 175 \\ \hline \multirow{2}{*}{Dropout} & 99.63 & 80.50 & 350 \\  & 98.69 & **80.96** & 135 \\ \hline \multirow{2}{*}{DataAugm} & 99.30 & **86.98** & 350 \\  & 98.04 & 86.62 & 157 \\ \hline \multirow{2}{*}{DataAugm + BatchNorm} & 99.41 & 85.00 & 350 \\  & 98.92 & **87.38** & 265 \\ \hline \hline \end{tabular}
\begin{tabular}{c c c} \multicolumn{3}{c}{_Model 1_} \\ \hline \hline \multirow{2}{*}{_BL1_} & 75.29 & 75.29 & _EstS1_ \\  & 73.55 & 77.69 & _EstS2_ \\  & 74.89 & 79.44 & _EstS3_ \\  & _BL4_ & 76.79 & 79.80 & _EstS4_ \\  & _BL5_ & 76.41 & **79.99** & _EstS5_ \\  & _BL5_ & 76.41 & **79.99** & _EstS5_ \\  & _BL5_ & 79.84 & & \\ \hline \multirow{2}{*}{Baseline} & 79.84 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Comparison of the accuracy results obtained with and without Early Stopping.
# Pido: Predictive Delay Optimization for Intertidal Wireless Sensor Networks
## 1 Introduction
After analyzing the delays in real IT-WSNs, we find extreme dynamic link quality, occupied buffer condition of nodes and fierce sea wave in the intertidal zone are the causes of delay problem. Combining delays from above sources and quantifying them accurately are crucial yet challenging. Second, it is challenging to predict future status of nodes and links accurately, even if obtaining the current status of nodes. While tidal predictions offer some prospect of predicting how long the delay will last based on shore elevation, the presence of waves and winds can make these predictions complicated. The disconnection and reconnection between nodes show imprecise statistical patterns, and therefore cannot be utilized for accurate prediction. To overcome the above challenges, we first model the delay in consistence IT-WSN. We divide delays into (a) queuing delay caused by intermittent connections, (b) queuing delay caused by buffer occupancy and (c) transmission delay caused by poor link quality, and we further summarize them into node delay and link delay. For prediction of node status, we utilize three implicit indicators instead of directly forecasting the future status of nodes. With the indirect indicators, we train a lightweight classifier to achieve accurate node status prediction. We extensively verify the feasibility of predicting node future status with field test results we collected in the IT-WSN deployed in Zhoushan, China. We further evaluate the performance of Pido in a large-scale simulation, and results indicate that Pido can decrease system end-to-end delay effectively with limited overhead on energy consumption. The remainder of the paper is organized as follows. We first discuss the related works and summarize our contributions in Section 2. We then explain the motivation and the application scenario of our work, present the design of Pido in details and evaluate its performance in Section 3. We then provide an overall assessment of Pido in Section 4.4 and conclude this paper in Section 6.
## 5 Performance Evaluation  
To achieve an appropriate evaluation for the proposed MAC protocol, we have evaluated it with two approaches both numerical analysis and experiments. The aSymMAC has not only shown a reasonable superframe structure to allow periodically reporting normal data measured by on-body nodes such as Vital Sign and ECG, but also outperforming the method that can concurrently achieve minimized delivery delay for emergency data transmissions during normal data handling, as described in previous section. From here on, we would describe and prove the performance of our proposed scheme in terms of transmission delay and energy efficiency, for both normal and emergency data transmissions.
# Portable Smart Spectrometer Integrated with Blockchain and Big Data Technology
## 7 Conclusions  
The PSS-1.0 has a spectral response over the range 400 nm to 1100 nm. Different spectral response ranges can be supported by changing its spectroscopic and photoelectric detection modules. After downloading the appropriate spectral analysis model from the spectral analysis system, the PSS-1.0 enables rapid on-site spectral analysis, and the results can be presented to the user through the smart device. The big data management system is used to save measured data per the users' preferences. This improves data reuse and provide resources for data analysis of spectral data. To prevent data from being tampered with, blockchain technology is used to provide each PPS-1.0 with a specific pair of private and public keys. Different spectral data record peers establish a consensus mechanism to record transaction data together. Peers can call the spectral data from each other by a transaction in the blockchain network. Combined with Internet technologies, this spectrometer provides a new way to utilize data and the credit of spectral data is increased. The design of this spectrometer and related systems can be used for the fields of production, monitoring and safety testing that  
Figure 14: Related data generated by the portable smart spectrometer: (**a**) private key, public key, and machine code, (**b**) generated hash and digital signature.  
Figure 13: Spectrum of a white LED measured by the two different spectrometers.  
require spectroscopic detection. The intelligence of spectroscopic detection facilitates automation and intelligence in production and monitoring. In a future work, we can focus on the radiometric calibration, and more accurate spectral calibration of the PSS-1.0, the optimization of spectrometer and user interaction, the analysis and processing of spectral big data, and the optimization of the blockchain system.  
Conceptualization, J.Y., W.H., and X.Y.; methodology, J.Y., W.H., and X.Y.; software, J.Y.; validation, J.Y. and Y.Y.; writing--original draft preparation, J.Y.; writing--review and editing, Y.Y.; supervision, Y.C.; project administration, X.Y.  
This research was funded by National Natural Science Foundation of China NSFC (grant number 11274398) and Fundamental Research Funds for the Central Universities (grant number 17 lgjc39).  
The authors declare no conflict of interest.
## 1 Introduction
caudatus_) as both raw flour and the extruded product has been evaluated in monogastric animals [18]. Its value for poultry was studied quite extensively in the 1980s [19; 20; 21; 22; 23; 24; 25]. More recently, different species of amaranth grain (_A. cruentus_ and _A. hypochondriacus_) as untreated and heat-treated (autoclaved and extruded) forms have been used in growth, feeding and metabolism trials in broilers, laying hens, quails and turkeys [18; 26; 27; 28; 29; 30; 31; 32]. The results of these studies demonstrate that inclusion of amaranth grain in broiler diets at low levels (\(\leq\)200 g/kg) results in comparable performance to diets based on a corn-soybean meal mixture. Higher levels of inclusion of amaranth, up to 400 g/kg, have had no adverse effects on production or carcase traits of laying hens or broilers provided that the amaranth was autoclaved or extruded [20; 22; 23; 25; 28; 32]. The AME\({}_{n}\) values for raw and autoclaved grain of _A. edulis_ have been determined as 2780 and 3072 kcal/kg on an as-fed basis [33]. The AME\({}_{n}\) value of extruded grain of _A. cruentus_, determined by regression in the study of [PERSON] and [PERSON] [21], was 3267 kcal/kg feed. [PERSON] et al. [25] reported 2832 and 3133 kcal/kg of AME for untreated and autoclaved _A. hypochondriacus_ grain, respectively. Different TME\({}_{n}\) values of amaranth grain have been detected for chickens and turkeys [26]. Enzyme preparations are used by the poultry feed production industry to improve the available energy content of diets containing cereal grains and plant protein meals [34; 35; 36; 37]. Different enzymes, for example \(\alpha\)-amylase or amyloglucidase, have variable effects on the physicochemical properties of amaranth starch, but generally improve its resistance to crystallisation and broaden the range of gelatinisation temperatures [38]. The \(\alpha\)-amylases from a proprietary thermostable bacteria product were more effective than those from _Bacillus subtilis_, which were in turn more effective than those from the fungus _Aspergillus oryzae_. However, the effects of enzymes on amaranth grain's available energy content have not been evaluated for poultry. Therefore, the objective of the present study was to determine the chemical composition and AMEn content of raw and autoclaved forms of _Amaranthus hybridus chorostachys_ with and without multienzyme addition in adult male broilers.
# [PERSON] Glacier Status Analysis Based on Historical Data and Moving Detection Using RPAS
## 6 Data Processing
### Processing of Historical Image Data
#### 6.2.3 Archive Sentinel Satellite Data  
As a new type of free satellite data, the Copernicus system was used, especially the Sentinel 2a satellite (Figure 21). Unfortunately, in the archive, only data from last two year are on-line. Older data must be ordered. Most of the satellite scenes are cloudy and cannot be used for this work, the images taken during summertime can be used without processing problems, other can be affected by the shadows, which are in the Greenland due to short day time very long (Figure 21b). We processed scenes from summertime and wintertime between 2019 and 2020 for prolonging of our study and research of usability of different image data. For comparison with other image data, only the one multispectral  
Figure 19: The Knud Rasmussen Glacier in a historical photo (detail); unfortunately, from this photo, it is not possible to get 3D information (only the glacier delimitation).  
Figure 20: Landsat data from Google Earth (**a**) from 1984, (**b**) from 2002, (**c**) from 2016.  
channel (Red) with a pixel size of 10 m was used. These images were used for delimitation of the Knud Rasmussen Glacier front. As it is visible on (Figure 21a,b), the glacier lost a front part after summertime in 2020.
## 3 Results
### Results at Room Level
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**S**} & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{t}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** \\  & **(Heated Room)** & **(Cooled Room)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **T\({}_{\mathrm{a}\mathrm{i}\mathrm{c}}\)** & **[W/m\({}^{2}\)]** & **[W/m\({}^{2}\) K]** & **[W/m\({}^{2}\) K]** & **[W/m\({}^{2}\) K]** \\ \hline a100 & 21.6 & 9.6 & 14.3 & 12.5 & 32.8 & 4.47 & 4.73 & 2.74 \\ a50 & 21.6 & 9.7 & 15.0 & 13.0 & 37.3 & 5.64 & 5.75 & 3.14 \\ a0 & 21.6 & 9.9 & 15.8 & 13.6 & 40.4 & 6.93 & 7.25 & 3.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of the results obtained from the heat flux meter and the thermocouple measurements. \(\mathrm{T}_{\mathrm{a}\mathrm{i}\mathrm{t}\mathrm{t}\mathrm{t}\mathrm{t}}\) is the internal air temperature (heated room), \(\mathrm{T}_{\mathrm{a}\mathrm{i}\mathrm{c}}\) is the external air temperature (cooled room), \(\mathrm{T}_{\mathrm{a}\mathrm{i}\mathrm{c}}\) is the internal surface temperature (heated wall side), and \(\mathrm{T}_{\mathrm{a}\mathrm{i}\mathrm{c}}\) is the external surface temperature (cooled wall side). of the indoor comfort conditions. The results of the measurements with the indoor climate analyser for each measurement point are reported in Table A1 (Appendix A). Moreover, Table 6 summarises the average results. As illustrated, the results show that the application of low emissivity pains a100 and a50 determines:  
* an increase of the average mean radiant temperature of up to \(\sim\)0.9 (\(\varepsilon=0.41\)) and up to \(\sim\)0.5 \({}^{\circ}\)C (\(\varepsilon=0.6\)) respect to the high emissivity paint (\(\varepsilon=0.88\));
* a slight increase in the average operative temperature of the room of \(\sim\)0.6 and \(\sim\)0.3 \({}^{\circ}\)C respectively with the a100 and the a50 paint;
* an improvement of the indoor comfort condition. The PMV change from \(-0.56\) with a0 paint (comfort category C [25]) to \(-0.40\) and \(-0.47\) (comfort category B [25]) with the low emissivity pains a100 and a50 respectively;
* linear increase of the \(\overline{T_{mr}}\), the \(\overline{T_{op}}\) and the PMV according to the decrease of the surface emissivity (Figure 13); and,
* a decrease of the average radiant planar asymmetry along the X-axis from 2.90 \({}^{\circ}\)C (\(\varepsilon=0.88\)) to 1.63 \({}^{\circ}\)C (\(\varepsilon=0.41\)).
# A Model-Driven Approach for Conducting Simulation Experiments
###### Abstract  
With the increasing complexity of simulation studies, and thus increasing complexity of simulation experiments, there is a high demand for better support for them to be conducted. Recently, model-driven approaches have been explored for facilitating the specification, execution, and reproducibility of simulation experiments. However, a more general approach that is suited for a variety of modeling and simulation areas, experiment types, and tools, which also allows for further automation, is still missing. Therefore, we present a novel model-driven engineering (MDE) framework for simulation studies that extends the state-of-the-art of conducting simulation experiments in the following ways: (a) Providing a structured representation of the various ingredients of simulation experiments in the form of meta models and collecting them in a repository improves knowledge sharing across application domains and simulation approaches. (b) Specifying simulation experiments in the quasi-standardized form of the meta models (e.g., via a GUI) and, subsequently, performing the automatic generation of experiment specifications in a language of choice increases both the productivity and quality of complex simulation experiments. (c) Automatic code transformation between specification languages via the meta models enables the reusability of simulation experiments. (d) Integrating the framework using a command-line interface allows for further automation of subprocesses within a simulation study. We demonstrate the advantages and practicality of our approach using real simulation studies from three different fields of simulation (stochastic discrete-event simulation of a cell signaling pathway, virtual prototyping of a neurostimulator, and finite element analysis of electric fields) and various experiment types (global sensitivity analysis, time course analysis, and convergence testing). The proposed framework can be the starting point for further automation of simulation experiments and, therefore, can assist in conducting simulation studies in a more systematic and effective manner. For example, based on this MDE framework, approaches for automatically selecting and parametrizing experimentation methods, or for planning follow-up activities depending on the context of the simulation study, could be developed.  
simulation; modeling; model-driven engineering; reproducibility; reusability; design of experiments; simulation experiments; experiment specification; discrete event simulation; virtual prototyping; finite element analysis +
Footnote †: journal: Applied Sci. 2022, 12, 7977. [[https://doi.org/10.3390/app1216797](https://doi.org/10.3390/app1216797)]([https://doi.org/10.3390/app1216797](https://doi.org/10.3390/app1216797))
## 3 System Parameter Analysis  
The input signal must strictly satisfy an amplitude of less than 1 and a frequency of less than 1 Hz because of the adiabatic approximation theory of SR. Therefore, it is necessary to understand the relationship between system parameters and SR output by simulating small parameter signals. Let the signal be  
\[s(t)=A\sin(2\pi f_{0}t)+n(t) \tag{13}\]  
where \(n(t)\) is the Gaussian white noise, the signal amplitude is \(A\) = 0.15, \(f_{0}\)= 0.025, and the noise intensity is \(D\) = 0.4.  
The time domain waveform and amplitude spectrum of noiseless signal are shown in Figure 3(a), and Figure 3(b) displays them with the noisy signal. In Figure 3(b), the periodicity of signal and \(f_{0}\) = 0.025 Hz cannot be resolved. The \(SNR\) calculated by Equation (12) is \(-22.9\) dB. In order to better analyze the relationship between the CMSR output signal and system parameters, the system parameters are fixed, \(a\) = 0.5, \(r\) = \(-0.8\), the interval of \(b\) is set to [0, 10], the step size is  
Figure 4: Simulation signal.  
Figure 5: Relationship between system parameters and output SNR.  
Figure 5 fully expresses that the relationship between the output _SNR_ and system parameters is not linear, but has random uncertainty. The three parameters together determine the output signal of the CMSR system. When \(a\) = 0.5, \(b\) = 6.5, \(r\) = \(-\)0.8, the output results of the SR system are as shown in Figure 5. Here, the periodicity of sinusoidal signals and \(f_{0}\) = 0.025 Hz can be clearly identified.
## 2 Materials and Methods
### Data Acquisition  
#### 2.3.1 ASD TerraSpec Halo Handheld Spectrometer Mineral Identifier  
The TerraSpec Halo handheld spectrometer Mineral Identifier [21] is a portable near-infrared spectrometer with a visible near-infrared (VNIR: 350-1000 nm) and near-infrared (SWIR: 1001-2500 nm) range [20]. It has an onboard GPS with a circular error probability of 1 m\({}^{2}\)[35]. The Halo handheld spectrometer captures spectral, mineral, and scalar results. Scalar values provide information on the sample's crystallinity and/or composition, which can serve as supplementary vectors to potential mineralization [20]. The Halo handheld spectrometer is able to capture Fe(III) iron mineral intensity as a scalar. This scalar depends on Fe(III) iron mineral surface abundance, and operates by calculating the ratio of the reflectance values found at a 742 nm to 500 nm wavelength. The higher the Fe\({}^{3+}i\) value recorded by the Halo handheld spectrometer, the more intense the Fe\({}^{3+}\) absorption. The Halo handheld spectrometer was used to help with ground-to-air and space linear regression and correlation testing.  
The Halo handheld spectrometer was deployed on 30 July 2020, taking samples at a 10 \(\times\) 10 m grid along the Wheal Maid valley. The Halo handheld spectrometer's window was wiped with a hint-free, nonabrasive cloth. Calibration was performed using the white reference disk provided. Fe\({}^{3+}i\) values were exported via the Halo handheld spectrometer Manager as ASD binary and ASCII files for the Fe(III) iron pixel distribution maps.
## 4 Testing of the Solution
### Testing Activities with NI RoboRIO
The results shown in Figure 11, which refer to the same testing activities made to obtain the other graphs, show that the maximum RTT in each delivery was measured as 250 ms, which is acceptable for the kind of system that is being built, since it is an almost imperceptible time for a human end user and data can still be retrieved in an almost-real time fashion so as not to delay any mission where data transmissions are required. Calculation of the RTT is done by Wireshark considering the maximum RTT of the packets that have been delivered in a tic of time, being 10 s long in this case. It has to be noted that in all these graphs the X axis contains the 30 min used to make the tests, whereas the Y axis is used for each of the variable results.