esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
_Mentha canadensis_ L. has important economic value for its abundance in essential oils. Menthol is the main component of _M. canadensis_ essential oils, which is certainly the best-known monoterpene for its simple structure and wide applications. However, the regulation of menthol biosynthesis remains elusive in _M. canadensis_. In this study, transcriptome sequencing of _M. canadensis_ with MeJA treatment was applied to illustrate the transcriptional regulation of plant secondary metabolites, especially menthol biosynthesis. Six sequencing libraries were constructed including three replicates for both control check (CK) and methyl jasmonate (MeJA) treatment and at least 8 Gb clean bases was produced for each library. After assembly, a total of 81,843 unigenes were obtained with an average length of 724 bp. Functional annotation indicated that 64.55% of unigenes could be annotated in at least one database. Additionally, 4430 differentially expressed genes (DEGs) with 2383 up-regulated and 2047 down-regulated transcripts were identified under MeJA treatment. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment indicated that \"Monoterpenoid biosynthesis\" was one of the most significantly enriched pathways in metabolism. Subsequently, DEGs involved in JA signal transduction, transcription factors, and monoterbens biosynthesis were analyzed. 9 orthologous genes involved in menthol biosynthesis were also identified. This is the first report of a transcriptome study of _M. canadensis_ and will facilitate the studies of monoterpene biosynthesis in the genus _Mentha_.  
Mentha canadensis L.; transcriptome sequencing; JA signaling; transcription factors; menthol biosynthesis 29 June 2018; 19 2364; doi:10.3390/ijms19082364
# Clinical Applications of In Vivo and Ex Vivo Confocal Microscopy
## 3 Ex Vivo Confocal Laser Scanning Microscopy
### Applications in Organs Other than the Skin  
The first study applying ex vivo FCM on different surgical specimens other than skin, was published by [PERSON] et al. in the 2014. In this pilot study the principal aim was to generate images from the tissue explored, to compare them with the histological ones [89].  
[PERSON] et al. and [PERSON] et al. evaluated and reviewed the application of ex vivo CLSM for breast, genitourinary tract, gastrointestinal tract and liver, thyroid and parathyroid, lymph nodes and larynx and lungs [7; 88; 112].  
Additionally, a promising, simple and quick (less than 5 min for specimen) application is represented by prostate cancer. Prostatic acinar adenocarcinoma is typified with FCM by nuclear atypia, irregular morphology of the glands, prominent nucleolus and absence of basal cell layers [113; 114].  
A substantial level of agreement between FCM and histology has been proven on 89 specimens collected from 13 patients undergoing radical laparoscopic prostatectomy for malignancy, with FCM showing a sensitivity of 83.3%, a specificity reaching 93.5% and 91% diagnostic accuracy, as compared to conventional histology [113].
## 3 Results
### Tests in Ocean Basin with GNSS Simulator  
The main objective to use the GNSS signal simulator was to validate the receiver performance under conditions similar to those that can be found on the deep sea, generating GNSS RF signals such as the ones obtained with a GNSS antenna in real operations. Figure 11 presents the different solutions achieved employing the GNSS simulator for 20-min captures, where solutions with different turbulences and wind speeds are compared. As indicated in Table 1, three conditions were emulated, and the output generated was post-processed to obtain the RTK solution.  
Figure 10: Real-time solution with different orbits and clock corrections in kinematic mode.  
* 1 HNMG: this simulation presents extreme model turbulence, high wave height (10.5 m), and high wind speed (25 m/s). Due to these conditions, the up component presents tall height variations, and the displacement in the horizontal domain (east and north components) are not especially relevant.
* 2 HNMG: in this case, a typical situation is emulated with the lower wind speed (4 m/s), normal turbulence, and low wave height (1 m). In this solution, the displacement is the lowest and in the up component varies slowly.
* 3 HNMG: this simulation presents high wind speed (20 m/s), normal turbulence, and low wave height (3.8 m). Due to the high wind speed and the lower turbulence, the displacement is higher in the horizontal domain (east and north).  
To compute the position error, this final solution has been compared with the files with the movement provided to the GNSS signal generator. Figure 12 presents the error achieved when using the GNSS simulator and post-processing technique. As expected, the RTK positioning error is below \(\pm\)10 cm once the solution has converged.  
Figure 11: Different solutions with the GNSS simulator.  
Figure 12: Solution error achieved in the GNSS simulator.
## 2 Landsat Overview
### Landsat Archive and Data Characteristics
Therefore, algorithms with dense time series and especially  
Figure 3: The total number of available Landsat images in the USGS archive from different Landsat instruments or sensors, which totaled 9,177,028 images as of 31 December 2020. (Source: USGS). those over large areas [59; 60] typically require the use of surface reflectance data, which has become the lowest level of analysis-ready data standard (e.g., as delivered by Landsat Collection 1 or 2).
## 3 Results
### Scoping Review
#### 3.2.2 Monitoring by Radio  
Positioning technologies commonly used in research include the Global Positioning System (GPS), wireless local area networks (WLANs), radio frequency identification (RFID), Bluetooth Low Energy (BLE), and ultra-wideband (UWB) systems. These technologies are often used to track objects in the field, such as construction workers [9] and mobile equipment [36]. Their use is critical to the advancement of safety management automation in construction.  
Radio localisation is used to identify hazards by assessing the proximity of workers to hazardous areas. In some cases, RFID technology is used to monitor workers and equipment on site in real time by attaching RFID tags to the workers and equipment to be monitored [37]. Each tag contains a communication system that transmits data to a central server and alerts workers if they are in danger.  
Numerous studies have used GPS and real-time location systems (RTLS) to track the location of workers on construction sites [38; 39; 40; 41; 42]. One study used 3D software to map workers' locations, visualise their positions, and process the data to generate alerts when a worker is at risk [39]. An alternative use of GPS is to map the positions of both workers and equipment in the field, making it possible to identify workers' proximity to hazards, facilitate real-time safety risk assessment, support dynamic evaluation of workers' safety conditions [38; 40], and assist safety engineers in decision making.  
UWB technology is used to monitor workers and construction sites by equipping workers with wearable tags and installing receivers in designated hazardous areas. This setup, when combined with predictive software, enables the detection of hazardous situations and supports real-time risk management [43].  
WLAN technology monitors worker behaviour by integrating a camera into the worker's helmet or goggles, which is connected to a smartphone that transmits the information via a wireless network to the information processing server and issues an alert in the event of a hazardous situation [44].  
BLE technology is also being used to monitor workers by installing sensors in safety harnesses [45; 46] and in fall-hazard environments [19; 47]. These sensors communicate via BLE with a receiver that transmits the data and uses Internet of things (IoT) systems to assess the actual risk and generate alerts for workers in case of non-compliance [45; 47].
###### Abstract  
Surface acoustic wave (SAW) devices based on multi-layer structures have been widely used in filters and sensors. The electromechanical coupling factor (\(K^{2}\)), which reflects energy-conversion efficiency, directly determines the bandwidth of the filter and the sensitivity of sensor. In this work, a new configuration of dual-mode (quasi-Rayleigh and quasi-Sezawa) SAW devices on a ZnO/SiC layered structure exhibiting significantly enhanced \(K^{2}\) was studied using the finite element method (FEM), which features in the partial etching of the piezoelectric film between the adjacent interdigitated electrodes (IDTs). The influences of piezoelectric film thickness, etching ratio, top electrodes, bottom electrodes, and the metallization ratio on the \(K^{2}\) were systematically investigated. The optimum \(K^{2}\) for the quasi-Rayleigh mode and quasi-Sezawa mode can exceed 12% and 8%, respectively, which increases by nearly 12 times and 2 times that of the conventional ZnO/SiC structure. Such significantly promoted \(K^{2}\) is of great benefit for better comprehensive performance of SAW devices. More specifically, a quasi-Rayleigh mode with relatively low acoustic velocity (\(V_{\text{p}}\)) can be applied into the miniaturization of SAW devices, while a quasi-Sezawa mode exhibiting a \(V_{\text{p}}\) value higher than 5000 m/s is suitable for fabricating SAW devices requiring high frequency and large bandwidth. This novel structure has proposed a viable route for fabricating SAW devices with excellent overall performance.  
surface acoustic wave; ZnO; SiC; multilayer; coupling factor +
Footnote †: journal: Applied Sciences
## 3 Results
### Pre-Post Indirect Cardiac Parameters after 30 min of WBPA-V-160 of Part 2  
The indirect cardiac parameters, such as LV ejection time and stroke volume, were similar between pre-evaluation and post-evaluation (Table 3). The data of cardiac output at pre-evaluation versus post-evaluation are shown in Figure 5. Cardiac output is the product of the heart rate (HR) and stroke volume (SV). Due to the reduction in heart rate after 30 min of WBPA-V-160, cardiac output was decreased after 30 min of WBPA-V-160.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Pre-Evaluation** & **Post-Evaluation** & **\(p\) Value** \\ \hline \multicolumn{4}{c}{**BP Parameters:**} \\ Systolic BP (mmHg) & 117.4 \(\pm\) 15.2 & 112.6 \(\pm\) 15.5 & 0.010 * \\ Diastolic BP (mmHg) & 70.9 \(\pm\) 10.3 & 68.4 \(\pm\) 9.7 & 0.028 * \\ Heart Rate (beats/min) & 72 \(\pm\) 12 & 68.9 \(\pm\) 10.9 & 0.001 * \\ \hline \multicolumn{4}{c}{**Central BP Parameters:**} \\ End Systolic Pressure (mmHg) & 126.6 \(\pm\) 17.8 & 120.5 \(\pm\) 16.8 & 0.016 * \\ End diastolic Pressure (mmHg) & 70.9 \(\pm\) 10.3 & 68.6 \(\pm\) 10.2 & 0.049 * \\ Mean Arterial BP (mmHg) & 88.8 \(\pm\) 13.4 & 85.2 \(\pm\) 11.9 & 0.031 * \\ Pulse Pressure (mmHg) & 55.5 \(\pm\) 9.8 & 51.4 \(\pm\) 9.4 & 0.036 * \\ \hline \multicolumn{4}{c}{**Indirect Cardiac Parameters:**} \\ LV Ejection Time (sec) & 0.289 \(\pm\) 0.040 & 0.288 \(\pm\) 0.04 & 0.953 \\ Stroke Volume (mL) & 49.6 \(\pm\) 3.4 & 49.2 \(\pm\) 3.1 & 0.501 \\ Cardiac Output (L/min) & 3.63 \(\pm\) 0.61 & 3.45 \(\pm\) 0.52 & 0.001 * \\ \hline \multicolumn{4}{c}{**Systemic Vascular (SV) Parameters:**} \\ SV Compliance (mL/mmHg) & 0.91 \(\pm\) 0.14 & 0.98 \(\pm\) 0.18 & 0.012 * \\ SV Resistance (dynes*sec/cm\({}^{5}\)) & 1983 \(\pm\) 343 & 2015 \(\pm\) 391 & 0.354 \\ \hline \multicolumn{4}{c}{**Brachial Artery (BA) Parameters:**} \\ BA Compliance (mL/mmHg) & 0.06 \(\pm\) 0.01 & 0.07 \(\pm\) 0.01 & 0.097 \\ BA Distensibility (\(\%\)/mmHg) & 5.82 \(\pm\) 1.01 & 6.30 \(\pm\) 1.22 & 0.031 * \\ BA Resistance (dynes*sec/cm\({}^{5}\)) & 213 \(\pm\) 57 & 214 \(\pm\) 48 & 0.883 \\ \hline \hline \end{tabular} Values are expressed as the mean \(\pm\) SD. LV: left ventricular. * Significant differences between pre-evaluation and post-evaluation of 160-degree V-shape Whole-Body Periodic Acceleration (WBPA-V-160) by using paired \(t\)-test.  
\end{table}
Table 3: Pre–post blood pressure (BP) and hemodynamic profiles after 30 min of WBPA-V-160.
## 3 System Model and Problem Formulation
### Problem Formulation
#### 3.2.5 Communication Performance Influenced by Extrapolation Accuracy  
High-speed relative motion causes a serious DFO in the LEO-SIN channel. By predicting the satellite's position and velocity using ephemeris extrapolation algorithms, the DFO can be calculated and compensated for [26,27].  
The DFO can be calculated using the traditional method as follows:  
\[f_{d}=f_{c}\frac{\left|\left(v_{s\to u}\right)_{ecf}\right|}{v_{c}} \tag{20}\]  
where \(\left(v_{s\to u}\right)_{ecf}\) denotes the velocity in the direction of the line connecting \(R_{u}\) and \(R_{s}\). It can be calculated that  
\[\left(v_{s\to u}\right)_{ecf}=\frac{R_{s}\cdot R_{u}}{\|R_{s}\|\cdot\|R_{u}\| }\cdot S_{v} \tag{21}\]  
where \(S_{v}\) denotes the satellite's velocity vector and \(\left(R_{u}\cdot R_{s}\right)/\left(|R_{u}||R_{s}|\right)\) represents the cosine of the angle between the satellite and the UE.  
According to Equation (4), the satellite's position and velocity are obtained in the ECEF coordinate system over a given period. Let the position vector of the UE be \(R_{u}=\left[X_{U},Y_{U},Z_{U}\right]^{T}\), and the position vector of the satellite be \(R_{s}=\left[X,Y,Z\right]^{T}\).  
Assume that the orthogonal frequency division multiplexing (OFDM) symbols transmitted from the satellite are received at the UE with a DFO \(\varepsilon_{f}\). The received signal \(y[n]\) can be expressed as  
\[y[n]=x[n]\cdot e^{-\frac{\beta\pi\varepsilon_{f}n}{N}} \tag{22}\]  
where \(x[n]\) is the transmitted signal, \(n\) ranges from 0 to \(N-1\), and \(N\) represents the number of subcarriers in one OFDM symbol.  
Applying the FFT to the signal \(y[n]\) converting to the frequency-domain signal \(Y[k]\),  
\[\begin{split}& Y[k]=FFT\{y[n]\}\\ &=\sum\limits_{n=0}^{N-1}y[n]e^{-\frac{j\pi kn}{N}}\\ &=\frac{\sin(\pi\epsilon_{f})}{N\sin(\frac{\pi\epsilon_{f}}{N})} e^{-\frac{j\pi\epsilon_{f}(N-1)}{N}}X[k]+I[k]\end{split} \tag{23}\]  
where \(X[k]\) is the frequency-domain signal obtained from the FFT of the transmitted signal \(x[n]\), and \(I[k]\) is the inter-carrier interference (ICI) caused by the DFS.  
When the signal \(Y[k]\) is affected by a frequency offset, the amplitude of the transmitted signal is reduced and its phase is rotated. When the frequency offset is an integer multiple of the subcarrier spacing, \(I[k]\) equals zero, indicating that there is no inter-carrier interference. However, when the frequency offset is a fractional multiple of the subcarrier spacing, ICI from other subcarriers occurs.
# A Methodology Based on Deep Learning for Contact Detection in Radar Images
## 4 Experiments and Analysis of Results
### Experimental Metrics
#### 4.2.3 Confusion Matrix  
The confusion matrix will allow the evaluation of the experiment results, considering the classes identified by the model. The confusion matrix used is shown in Table 4.  
Where:  
* _TP_: True actual and true predicted It is the object classified by the expert as a \"plot\" and predicted as positive, it is a ship.
* _FP_: False actual and true predicted The expert classified it as \"no\" and the algorithm predicted it as \"plot\" It is a false alarm.
* _FN_: True actual and false predicted. The expert classified it as a \"plot\" and has been predicted as \"no\". FN represents false negatives, this is missed contacts.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{**Real Values (Expert)**} \\ \cline{3-4}  & & **Positive “Plot”** & **Negative “No”** \\ \hline \multirow{3}{*}{Predicted values (algorithm)} & Positive & _TP_ & _FP_ \\  & Negative & _FN_ & _TN_ \\ \cline{1-1}  & Unclassified & _IP_ & _IN_ \\ \hline \hline \end{tabular}
\end{table}
Table 4: Confusion matrix.  
* _T_N: False actual and false predicted. It is a \"no\" class predicted; they are hits of the \"no\" class.
* NI: Unclassified negative It is a \"no\" object and has not been classified by the algorithm.
* PI: Unclassified positive It is a \"plot\" object and the algorithm has not classified it as a ship object.
# Experimental Investigation of Task Performance and Human Vigilance in Different Noise Environments
## 2 Materials and Methods
### Statistical Analysis  
Generalized additive mixed effect model (GAMM) analyses were performed using the open-source statistical package R, version 3.6.1 (R Project for Statistical Computing, Vienna, Austria), to test the fixed effects estimates of potential influencing factors on human task performance and vigilance, treating the subject as a random effect. The parameters analyzed in this study included the overall mental workload score of NASA-TLX, weighted task performance (overall performance, SYS performance, TRA performance, and RES performance), mean SampEn of HRV, and PVT metrics (performance, response time, fastest  
Figure 3: Flowchart of the formal experimental procedure.  
\(10\%\) response time, and slowest \(10\%\) response time). Among them, the GAMMs of the parameters such as subjective mental workload, task performance, and mean SampEn of HRV are shown in Equations (8) and (9), whereas the GAMMs of task performance under different mental workload and PVT performance are shown in Equations (10) and (11). Differences were considered statistically significant when \(p<0.05\).  
\[y=\beta_{1}+\beta_{2}(N85\text{-}S1)+\beta_{3}(N80\text{-}S1)+\beta_{4}(LMW)+ \beta_{5}(MMW)+b+e \tag{8}\]  
\[y=\beta_{1}^{*}+\beta_{2}^{*}(N85\text{-}S1)+(-\beta_{3}(N75\text{-}S2))+\beta _{4}^{*}(LMW)+(-\beta_{5}(HMW))+b^{*}+e^{*} \tag{9}\]  
\[y_{1}=\beta_{1}+\beta_{2}(N85\text{-}S1)+\beta_{3}(N80\text{-}S1)+b+e \tag{10}\]  
\[y_{1}=\beta_{1}^{*}+\beta_{2}^{*}(N85\text{-}S1)+(-\beta_{3}(N75\text{-}S2))+b ^{*}+e^{*} \tag{11}\]  
where \(y\) is the index of subjective mental workload, task performance, and mean SampEn of HRV, including overall mental workload score of NASA-TLX, overall performance, SYS performance, TRA performance, RES performance, and mean SampEn of HRV; \(y_{1}\) is the index of task performance and PVT performance, including overall performance, SYS performance, TRA performance, RES performance, PVT performance, PVT response time, PVT fastest \(10\%\) response time, and PVT slowest \(10\%\) response time; \(\beta_{1}\) and \(\beta_{1}^{*}\) are the fixed intercepts; \(\beta_{2}\) and \(\beta_{3}\) are the fixed effects of N85-S1 and N80-S1 compared with N75-S2, respectively; \(\beta_{2}^{*}\) is the fixed effects of comparison between N85-S1 and N80-S1; \(\beta_{4}\) and \(\beta_{5}\) are the fixed effects of LMW and MMW compared with HMW, respectively; \(\beta_{4}^{*}\) is the fixed effects of comparison between LMW and MMW; \(b\) and \(b^{*}\) are the random effects of the intercept for subjects; and \(e\) and \(e^{*}\) are the residuals.
# A Novel RBFNN-CNN Model for Speaker Identification in Stressful Talking Environments
## 4 Model Architecture  
Generally, the speaker identification process may be categorized into two crucial parts: feature extraction and classification. Figure 1 shows the speaker identification process. Speakers may be distinguished on the basis of some exceptional characteristics that are typical to a particular speaker by using the extracted features. Feature extraction is an important part of speaker identification; hence, features call for additional fine-tuning and use of appropriate approaches to come up with the ultimate identity of a speaker. Individual speaker models are formulated for each and every speaker by making use of these feature sets. All of the developed speaker models are stored. The features of the speech uttered by the unidentified speaker are extracted and compared with the developed speaker models using a speaker identification classifier, which checks if the features of the unknown speaker match with those in the developed speaker models. This leads to the identification of the unknown speaker. The speaker identification process explained in the current study uses a machine learning approach, whereby speakers are identified by considering the features extracted from the speaker's recorded speech. The features out of Mel-frequency cepstrum coefficients (MFCC) were used for classifier training. A 96-dimension feature of MFCCs was used to determine the observation vectors in all deep learning and classical techniques. A \"continuous mixture observation density\" was selected in each classifier with six states. MFCC determines the change in the straight cosine in the range of log control related to the direct Mel recurrence size. The human voice can be represented with high precision in Mel recurrence, due to uniform or similar dispersion of recurrence groups therein. The models are fed by an array of MFCCs of each time frame. Closed-set speaker identification is presented as follows: The words spoken by the given speaker are recorded, and the recording is compared with the developed speaker models (a finite set). The developed model that shows the most resemblance with the recording is considered. The recorded speech signals of the given speaker are used for extraction of MFCC. Overall, the MFCC technique will generate features from the inserted audio signal samples that are used as input for the speech recognition model. This is followed by classifier training with these features. The feature extraction process is also followed for the new speech signals we want to classify. The speaker with the closest resemblance is predicted by the trained classifiers. Figure 1 depicts the speaker identification approach employed in the current study.  
Figure 1: Speaker identification process.
# Hybrid Modeling for Stream Flow Estimation: Integrating Machine Learning and Federated Learning
## 4 Statistical Results
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Date**} & **Average Flow Rate** & **Average Air Temperature** & **Total Precipitation** \\  & & **(Favg, mm\({}^{3}\))** & **(Tavg, \({}^{\circ}\)C)** & **(Psum, mm)** \\ \hline Besdegirmen (E12A003) & 1 January 2023 & 432 & 6.7 & 3.5 \\ Besdegirmen (E12A003) & 2 January 2023 & 420 & 5.9 & 2.8 \\  & & & & \\ Melekbahce (E21A033) & 1 January 2023 & 532 & 3.8 & 1.9 \\ Melekbahce (E21A033) & 2 January 2023 & 511 & 4.2 & 2.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data overview for Besdegirmen (E12A003) and Melekbahce (E21A033) stations. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Cycle**} & \multirow{2}{*}{**Station**} & \multirow{2}{*}{**Local Data Samples**} & \multirow{2}{*}{**Local Model Training Loss**} & **Model Update** \\  & & & & **(Gradients)** \\ \hline
1 & Besdegirmen (E12A003) & 3000 & 0.235 & [0.016, \(-\)0.024, 0.008] \\
1 & Melekbahce (E21A033) & 2800 & 0.278 & [\(-\)0.009, 0.022, \(-\)0.013] \\
2 & Besdegirmen (E12A003) & 3000 & 0.198 & [0.012, \(-\)0.021, 0.009] \\
2 & Melekbahce (E21A033) & 2800 & 0.221 & [\(-\)0.008, 0.018, \(-\)0.012] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Federated learning process. robustness of the stream flow rate estimation model, ultimately leading to more reliable predictions for both stations. In Table 3, the statistical values of the two datasets, Besdegirmen and Melekbahce, are presented together for easy comparison. Each row corresponds to a specific data type (e.g., Daily average flow, Daily total rainfall, Daily average temperature), and the columns represent the statistical metrics (Mean, Minimum, Maximum, Standard Deviation, Distortion, Change, Mode, Median) for each dataset.