esa-sceva/satcom-chunk-collection huggingface dataset:
# A Concise Review of Silver Diamine Fluoride on Oral Biofilm
## 3 Results
### Application of Silver Diamine Fluoride on Bacteria
#### 3.1.2. Microbiota Change in Community Diversity and/or Composition within Biofilm  
A total of four studies reported on the microbiota diversity before and after SDF treatment. In terms of intervention with SDF solution, only a 38% concentration was adopted in all four studies (Table 2). The majority of the included studies that explored bactericidal properties were operated in vivo, with one study conducted in vitro; it used extracted caries teeth in an artificial mouth model [29]. Both saliva and plaque were collected in two studies [29; 30], whereas only plaque was collected in the other two studies [31; 32]. Two studies were conducted on preschool children, whereas one was conducted on primary school students [29], and another was conducted on adults with a mean age of 47 years [32]. The shortest study duration was 24 h, whereas the longest was 12 weeks. Among the included studies, three out of the four used the polymerase chain reaction amplification of 16S ribosomal ribonucleleic acid genes and MiSeq sequencing in diversity analyses. One  
Figure 1: Flowchart of the study selection process: identification, selection, eligibility, included.  
study used ribosomal ribonucleic acid sequencing and quantitative polymerase chain reaction quantification [31]. The Shannon index was the most frequently used index for diversity assessment.  
All four studies reported no significant change in diversity. However, one study found a significant decrease in the diversity of oral bacteria plaque within active caries, but not in arrested caries, 12 weeks after SDF treatment [30]. Furthermore, the same paper reported an alteration in the species of the bacteria both before and after the SDF treatment. _Streptococcus mutans_ and _Streptococcus sobrinus_ increased significantly after two and 12 weeks of the SDF treatment in the plaque within active caries. Meanwhile, _Lactobacillus_ sp. and _Rothia_ sp. increased significantly after two weeks. Another study reported that the carbohydrate transportation and metabolic functions in plaque were significantly reduced at 24 h and one week post-intervention [29].
## 2 Data and Methods
### Datasets and Study Sites
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Datasets/Cruises** & **Time** & **Size (N)** & **Measurements** & **Chl-a (mg\(\cdot\)m\({}^{-3}\))** & **Usage** \\ \hline SeaBASS & 2001–2012 & 1619 & \(a_{\rm ph}(\lambda)\) & NA & Gaussian curves \\ \cline{2-6}  & 1991–2007 & 430 & \(a_{\rm ph}(\lambda)\), HPLC & 0.02–13.2 & \(a_{\rm Gau}(\lambda)\) vs. Cpigs relationships \\ \hline IOCCG & NA & 500 & \(R_{\rm rs}(\lambda)\), \(a_{\rm ph}(\lambda)\), \(a_{\rm dg}(\lambda)\), \(b_{\rm bp}(\lambda)\) & 0.03–30 & \\ \hline Tara Oceans & 2010–2012 & 23 & \(R_{\rm rs}(\lambda)\), \(a_{\rm ph}(\lambda)\), \(HPLC\) & 0.02–0.95 & \\ \hline VIIRS cal/val & 2014–2015 & 21 & \(R_{\rm rs}(\lambda)\), \(a_{\rm ph}(\lambda)\), \(HPLC\) & 0.15–1.5 & \\ \hline BIOSOPE & 2004 & 31 & \(R_{\rm rs}(\lambda)\), \(a_{\rm ph}(\lambda)\), \(HPLC\) & 0.00036-3.06 & \\ \hline BATS & 2002–2012 & 148 & HPLC & 0.002–0.486 & \\ \hline MERIS & 2002–2012 & 148 & \(R_{\rm rs}(\lambda)\) & 0.037–0.325 & \\ \hline \end{tabular}
\end{table} 
Table 1: Datasets, time, variables, size and their usages in this study. Cpigs: pigment concentrations, N: the number of samples, NA: not applicable.
## 3 Non-Thermal Technologies in Food Processing
### UV Radiation  
Commonly, ultraviolet (UV) light irradiation has been utilized as a disinfectant for surface, water, and air but has also gained attention in the food industry as a fast and inexpensive method for hygenization of the surfaces of solid foods and liquid foods. Being a non-thermal technology, the advantages of it use are multiple: minimal loss of nutrients and sensorial quality of foods, no toxic residues, and low energy consumption compared with other thermal treatment commonly used for food decontamination. The latter is the main purpose of this technique due to its effectiveness in the inactivation of pathogenic microorganisms and spoilages [61; 113]. UV light is the part of the electromagnetic spectrum in the range from 200 to 400 nm, and it can be divided into three regions: from 200 to 280 nm, UV short wave (UV-C); from 280 to 320 nm, UV medium wave (UV-B); and from 320 to 400 nm, UV long wave (UV-A). Light is emitted from a gas discharge at wavelengths characteristic of the elements that compose the gas itself as well as the excitation, ionization, and kinetic energy of them. A gas discharge is a mixture of excited atoms, nonexcited atoms, cations, and electrons generated through a high voltage applied across a volume of gas. A schematization of UV lamp is shown in Figure 4.  
Figure 4: UV lamp. \begin{table}
\begin{tabular}{c c c c c} \hline
**Category** & **Type** & **Process Parameters** & **Use** & **Effects** & **Reference** \\  & & **(Type/Power/Frequency/Intensity/Time)** & & & \\ \hline \multirow{4}{*}{Melon} & \multirow{4}{*}{Water bath/25 kHz/4870 W/m\({}^{2}\)/10, 20, and 30 min} & \multicolumn{4}{c}{Samples immersed in distilled water present negative water loss values, in sucrose solution, values are positive and are higher when the sonication time increased.} \\  & &  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Category** & **Type** & \begin{tabular}{c} **Process Parameters** \\ **(Type/Power/frequency/Intensity/Time)** \\ \end{tabular}  & **Use** & **Effects** & **Reference** \\ \hline \multirow{4}{*}{ \begin{tabular}{c} Garlic cloves \\ Grape seeds \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} Solvent bath/35 kHz/30 min \\ 40 kHz/16-34 min \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} Aroma extraction \\ \end{tabular}  } & - & [84] \\ \cline{2-5}  & & & & \\ \cline{1-1} \cline
## 4 Experimental Validation
### Validation of the Proposed Power Loss Model  
In order to verify the accuracy of the proposed losses model, two different comparison validation strategies are adopted and the principle diagram is shown in Figure 10. One is the comparison of losses results between the traditional losses model and the proposed losses model. The other is the comparison between the chips losses results measured from the test-bench and the results calculated from the proposed losses model in real-time.  
The foster thermal model is built to measure the junction temperature of IGBTs and FWDs and the temperature is used as a feedback input to the fitting formulas of the temperature-dependent parameters of IGBTs and FWDs. Since the focus of this paper is not the building of the Foster thermal model, no detail introduction is presented about the thermal model. Figure 11 shows the principle of the electro-thermal interaction for the losses calculation.  
Figure 10: Two experimental validation strategies.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline stator resistance & 0.012(\(\Omega\)) \\ d-axis inductance & 0.00105(H) \\ q-axis inductance & 0.00252(H) \\ flux & 0.012(Wb) \\ pole pairs & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameters of the tested motor.
# HYPSO-1 CubeSat: First Images and In-Orbit Characterization
## 1 Introduction
The overall radiometric accuracy is investigated by comparing HYPSO-1 data collected over RadCalNet sites [22], and the relative radiometric accuracy between pixels is calculated over a uniform ocean scene. Finally, the SNR per wavelength is modeled and compared to what is achieved by other similar instruments. The results are discussed in light of the original mission objectives, and the way forward is outlined. An overview of mission parameters and resulting payload characteristics related to HYPSO-1 are presented in this, and in earlier publications are summarized in Table 1.
# UVCS: Unit Virtual Coordinate System for UAV Intra-Swarm Routing in GPS-Denied Environment
## 6 Numerical Results
### Metrics of Interest and Scenario  
As the primary indicator of the overall similarity between the network topology and the virtual one, we utilize the so-called topology similarity index. This index is defined as the Pearson correlation coefficient [37] between pairwise distances in the virtual and physical topology. For the routing performance, we provide a set of different metrics that, when considered together, propose deep insights into the developed approach's performance by assessing how close it is to the perfect case with full knowledge based on the Dijkstra algorithm. These metrics include (a) path similarity index that shows how close are chosen routes to those detected by the Dijkstra algorithm, (b) total number of discovered routes, and (c) average length of discovered routes, reflecting the ability of the routing protocol to discover paths in a network. Finally, we also compare the proposed approach to the Gradient Assisted Routing (GAR) algorithm specified in [19].  
To perform the comparison, we consider a wireless stationary network with a maximum of 50 randomly located nodes. Nodes appear one by one with a small delay (approximately five time steps) until all 50 nodes are online. At every given time instant, all currently online nodes organize a fully connected graph. Once all nodes are online, some additional time is given for the UVCS algorithm to converge.
## 2 Results and Discussion
### Effect of Nanoparticle on Gene Expression
#### 2.5.2 GPPS1 Gene Expression Analysis Result
In addition to FPPS1, the RT-qPCR results revealed notable fold changes of 18.13 and 14.57 for the expression of the GPPS1 gene in _S. alba_ and _P. gravelens_, respectively. While these levels were lower compared to FPPS1, they remain significant, highlighting the importance of GPPS1 in the biochemical pathways of both plant species. Variations in the expression patterns of GPPS1 between _S. alba_ and _P. gravelens_ may suggest species-specific regulatory mechanisms or environmental influences on gene expression [58]. Conversely, the study documented relatively high expression levels of the FPPS1 gene in both _S. alba_ and _P. gravelens_, with fold changes of 13.45 and 12.16, respectively. These findings suggest a critical role for FPPS1 in biological processes, potentially contributing to the production of significant secondary metabolites or engaging in essential physiological functions [59]. In contrast, the GPPS1 gene exhibited lower expression levels, with fold changes of 11.28 and 6.48 in _S. alba_ and _P. gravelens_, respectively. These lower levels may indicate a relatively lesser significance of GPPS1 in the examined biological processes or metabolic pathways compared to FPPS1 [60]. Despite the essential role of terpenoids in essential oils, comprising over 80% of their composition, the characterization of genes associated with terpenoid biosynthesis in rose-scented geranium _Pelargonium gravelens_ remains limited. Monoterpenes, a class of terpen with a chemical formula of C10H16, are particularly significant and are synthesized from the precursor geranyl pyrophosphate (GPP) by the enzyme geranyl pyrophosphate synthase (GPPS). These compounds can exhibit acyclic or cyclic structures [61; 62]. In plants, terpenoids are synthesized through two primary pathways: the methyleryth-thiritol phosphate (MEP) pathway in plastids and the mexolonate (MVA) pathway in the cytoplasm [63; 64]. terpenoid biosynthesis typically progresses through three stages. Initially, two intermediates, isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP), are formed, serving as common precursors for terpenoid synthesis and are interchangeable in both cytoplasm and plastids [65]. Subsequently, enzymatic modifications, including methylation, hydroxylation, and glycosylation, contribute to the diverse structures and functions of terpenoids, enriching the array of these compounds found in plants. Figure 12: Heatmaps and hierarchical clustering illustrate the results obtained from data of two significantly differential expressed GPPS1 and FPPS1 genes in _P. gravelens_ and _S. alba_ plants growing under (0.5 g/L) GW NP and Mg NP/GW application.
# Automated Cart with VIS/NIR Hyperspectral Reflectance and Fluorescence Imaging Capabilities
###### Abstract  
A system to take high-resolution Visible/Near Infra-Red (VIS/NIR) hyperspectral reflectance and fluorescence images in outdoor fields using ambient lighting or a pulsed laser (355 nm), respectively, for illumination purposes was designed, built, and tested. Components of the system include a semi-autonomous cart, a gated-intensified camera, a spectral adapter, a frequency-triple Nd:YAG (Neodymium-doped Yttrium Aluminium Garnet) laser, and optics to convert the Gaussian laser beam into a line-illumination source. The front wheels of the cart are independently powered by stepper motors that support stepping or continuous motion. When stepping, a spreadsheet is used to program parameters of image sets to be acquired at each step. For example, the spreadsheet can be used to set delays before the start of image acquisitions, acquisition times, and laser attenuation. One possible use of this functionality would be to establish acquisition parameters to facilitate the measurement of fluorescence decay-curve characteristics. The laser and camera are mounted on an aluminum plate that allows the optics to be calibrated in a laboratory setting and then moved to the cart. The system was validated by acquiring images of fluorescence responses of spinach leaves and dairy manure.  
automated cart; laser; imaging; hyperspectral imaging; fluorescence imaging; food safety; produce +
Footnote †: journal: Applied Sciences
## 3 Proposed Method
### Multi-Head Attention Gating Unit
#### 3.2.2 Gating Operation  
The gating mechanism regulates the flow of information by introducing control signals. The gating weights are generated by the sigmoid function, and the input and the transformed results are weighted and summed to achieve feature screening. Since the sigmoid function has a smooth curve characteristic, it can perform a nonlinear transformation on the input and help the model adapt to nonlinear relationships to learn more complex features.  
The gating operation consists of a linear layer and a sigmoid activation function. Its execution process is to first receive the input from the MHA. Then, the input is linearly transformed through the linear layer. Subsequently, the gating weight is generated through the sigmoid activation function. Finally, the gating weight is passed to the next layer of the network.  
The output after the gating operation can be expressed as  
\[g_{i}=\sigma\big{(}W_{g}H_{i}+b_{g}\big{)} \tag{16}\]  
where \(W_{g}\) denotes the weight matrix of the gating operation, \(b_{g}\) is the bias vector of the gating unit, \(H_{i}\) expresses the output of the MHA in the corresponding layer, and \(\sigma\) represents the sigmoid activation function.  
The sigmoid activation function [35] is  
\[f(x)=\frac{1}{1+e^{-x}} \tag{17}\]  
where \(e\) means the basis of the natural logarithm. The sigmoid function can map the input \(x\) into a continuous value between 0 and 1.  
Since the activation function in the gating operation is differentiable, this facilitates the use of the backpropagation algorithm to calculate the gradient and adopt the optimizer to update the model parameters during training to minimize the loss function. The gating operation can promote the stable propagation of gradients, which can alleviate the problem of gradient vanishing or gradient exploding to a certain extent.  
The MHA helps the model focus on important features. The gating operation can further filter the features. The adoption of MHA and gating operation enables the MLP model to effectively capture long-term dependencies in the input data, thereby improving the performance of the model.
_Appl. Sci._**2015**, \(5\), 682-694; doi:10.3390/app5040682  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Analysis of Losses in Open Circuit Voltage for an 18-\(\upmu\)m Silicon Solar Cell**  
**[PERSON] 1,*, [PERSON] 2, [PERSON] 3, [PERSON] 4 and [PERSON] 2,*
\begin{tabular}{} \end{tabular}**  
+++ ==WARNING: Truncated because of repetitions==  
++  
practical cells, which is caused by optical losses, bulk recombination, surface recombination, contact recombination, and resistance losses in a manner similar to that observed by [PERSON] [4].  
In a previous paper, an 18 \(\upmu\)m thin crystalline silicon solar cell on steel has achieved a best efficiency of 16.8% and a best open circuit voltage (\(V_{oc}\)) of 642.3 mV [5]. The structure of the ultrathin silicon (UTSi) solar cell is shown in Figure 1. The ultrathin silicon is 20 \(\upmu\)m thick, which is attached onto 125 \(\upmu\)m steel substrate, and includes three layers: top front surface field (FSF) layer which is n\({}^{+}\) and 1 \(\upmu\)m, middle base layer, which is n type and 18 \(\upmu\)m, and bottom emitter which is p\({}^{+}\) 1 \(\upmu\)m. Both surfaces of this thin silicon are well passivated and optically designed.  
However, according to our analysis the theoretical maximum \(V_{oc}\) of this thin silicon solar cell can be as high as 767 mV [5]. The \(V_{oc}\) difference between an ideal case and a practical case is due to the introduced recombination. This paper discusses these recombination sources that include non-ideal passivated front and rear surfaces, laser-introduced damage on the front surface, contacted regions on both front and rear surfaces and edge recombination. Based on our previous analysis, bulk recombination is negligible for such thin crystalline silicon solar cells [5]; thus, it is not discussed in this paper.  
This paper explores the \(V_{oc}\) losses through the first principle calculation and the measurement results on test samples and the UTSi solar cells. The \(V_{oc}\) loss at each step is figured out and a roadmap of \(V_{oc}\) reduction from the no loss (or modeled) case of 767 mV to the best measured value of 642.3 mV is established.
# Performance Analysis for COVID-19 Diagnosis Using Custom and State-of-the-Art Deep Learning Models
## 4 Results
### Confusion Matrix, ROC Curves and Area under Curve  
The confusion matrices for the three classes are presented in Figures 11-13.  
Figure 11: Confusion matrix for Custom-Model.  
Figure 10: F1 score plot for different models in terms of percentages.  
Figure 12: Confusion matrix for MobileNetV2 model.  
Figure 13: Confusion matrix for Xception model.  
From the confusion matrix, it can be seen that the MobileNetV2 model was able to predict two more COVID-19 cases than Xception and Custom-Model. It is better than the Xception model in this regard. The total number of correctly predicted cases by the MobileNetV2 model is 1027, while in the case of Xception, it is 1025.  
Moreover, the Xception model predicted the smallest number of false positive cases compared with Custom-Model and MobileNetV2 which performed least accurately in this regard. The total number of false positive cases for the Xception model is 12, for Custom-Model it is 25, and for MobileNetV2 it is 60. The number of false negative cases for the MobileNetV2 model is slightly lower than that of the Xception model, their values being 57 and 59, respectively.  
The ROC curves shown below are plotted for Class 0 (COVID-19), Class 1 (lung opacity) and Class 2 (normal). The ROC curve for custom-model is shown in the Figure 14.  
The ROC curve for MobileNetV2 is shown in the Figure 15.  
The ROC curve for Xception model is shown in the Figure 16.  
Figure 14: ROC curve for Custom-Model.  
Figure 15: ROC curve for MobileNetV2 model.  
The curves are similar for all three models; however, the AUC for the MobileNetV2 model and the Xception model are equivalent, having a value of 1.00, while Custom-Model scored 0.99 for the COVID-19 class.  
Further, performance differentiation can be seen in Table 2, which shows that the Xception model performed better than the other models in terms of macro and weighted averages for the COVID-19 class.
## 5 Results
### Baseline Models  
Our 3 DCNN(MBSTALRU) is compared with nine baselines as follows.  
**Prediction Approaches concerning Classical Time-series Models:**  
* HA: Historical average, we predict the average from historical values in the simulation process. This is a very basic model in the baseline steps [20].
* ARIMA: In the time series forecasting problem, the ARIMA model is a well-known model to predict about the future vales of time series [19].  
**Prediction Approaches concerning Classical Statistical Models:**  
* XGBoost [10]: XGBoost is a famous tree method, boosts the performance.
* LinUOTD [59]: This method is based on the linear regression method. This linear regression method works with spatial-temporal regularization.  
**Prediction Approaches concerning Deep Learning Models:**  
* Multilayer Perception (MLP): MLP is a neural network; the implementation of this method uses four fully connected layers [15; 16].
* ConvLSTM: In this method, add convolutional layers with LSTM [14].
* ST-ResNet: This method uses 2D images to models the citywide traffic flow at a different time interval. The ST-ResNet extracts the spatial features to utilize 2D CNN. The ResNet model utilizes the closeness, period, and trend information to predict the traffic flow [6]. In the ResNet model, aggregate the relevant features together to utilize the function tanh. The length trend we set for period and closeness as 4, 4, and 4, respectively.
* STDN: In this method, to predict traffic flow, combine attention mechanism, LSTM, and local 2D CNN. This method we utilize to make the model novel. This method gives us basics knowledge of the implementation of our strategy [51]. We modify the implementation source code of STDN to predict the traffic flow while maintaining the network structure of STDN.  
* MGSTC: The framework multi-gated spatial-temporal convolutional. In this work, we adopt the spatial-temporal gated mechanism with the spatial-temporal CNN blocks [21].  
We propose our method 3 DCNN(MBSTALRU) with three variants. The variants are listed as follows. Our proposed method consists of convolutional, feature embedding, and external branches.  
* **3 DCNN(MBSTRU):** In this variant, we removed the attention-based LSTM, we used the three layers of 3D convolutional with 4 to 8 layers of residual units. We stacked the feature embedding branch into the tensor. The tensor was taken as the input of the residual layers unit.
* **3 DCNN(MBSTAL):** In our proposed work, we utilize the 3D convolutional branches with the attention-based LSTM layers. The result of the 3D CNN is stacked and fed into the feature embedding branch to extract the features with attention-based LSTM to predict the citywide traffic flow.
* **3 DCNN(MBSTALRU):** In this variant is the proposed spatial-temporal CNN block to utilize with feature embedding and the external branch to predicting the citywide traffic flow simultaneously.