esa-sceva/satcom-chunk-collection huggingface dataset:
# Realtime Tsunami Prediction System Using Ocean Floor Network for Local Regions
## Real-Time Tsunami Prediction Technique
### Improvement of the Prediction
To avoid such cases, we introduced the composition of the crustal uplift into the pressure gauge data of the ocean floor network system (Figure 6). Our system normally calculates a short-term  
Figure 4: An example of a correlation map between observed data on the seafloor observatories and predicted points on the coastal areas. The left and right sides indicate the map just after triggering and that of 430 s later. Fault models are narrowed by the judgment of the source directory and the dispersion becomes small. window average (STA), a long-term window average (LTA), and a very long-term window average (VLTA), and it produces a graph of the distribution of the crustal uplift on the ocean floor network system in real time using VLTA. The default models are narrowed further using the graphed distribution of crustal uplift after extraction in the temporal order of the tsunami triggering. It takes several minutes for the distribution values to stabilize, and the length of the VLTA should be determined after taking into consideration the tsunami arrival time at the predicted points. Another issue that should be considered in the prediction system is that of linked events. Linked events have occurred around the Nankai Trough area: the 1946 Nankai earthquake occurred after the 1944 Showa Tonankai earthquake with a time interval of two years, but the time interval between the 1854 Ansei Tonankai and Nankai earthquakes was 30 h in the Edo era [11]. Although no one can know the time interval between a pair of  
Figure 5: Schematic figure of the extraction from the tsunami database (cited from [12]). In the local government we installed this system, the maximum model is extracted, however, how to extract different models is selectable. Figure 6: Checking detection of the crustal uplift using STA, LTA, and VLTA. We adopted one hour as VLTA in this case, and the window width can distinguish tsunami signals and components of crustal uplift. earthquakes, the prognostic system should predict the occurrence of earthquakes within a short time interval. It is difficult to distinguish between a tsunami component and a crustal uplift in real time. Our system, therefore, judges crustal uplift in real time using the ratio of the VLTA to the STA (Figure 7). After detection of the first tsunami, the arrival time of the tsunami at each ocean floor observatory is evaluated. If tsunami convergence is detected, the trigger flag is down, and checking of the tsunami trigger is initiated. The time of tsunami arrival depends on the VLTA. The tsunami height observed on the pressure gauge is a value relative to the height at the time of tsunami detection. The base of the pressure gauge is, therefore, reset after detection of the arrival of the tsunami.
_Applied_  
_sciences_  
_Article_  
**Evaluation of Copper-Contaminated Marginal**  
**Land for the Cultivation of Vetiver Grass**  
**(_Chrysopogon zizanioides_) as a Lignocellularic**  
**Feedstock and its Impact on Downstream**  
**Bioethanol Production**  
[PERSON] 1, [PERSON] 2 and [PERSON] 1,*  
* Department of Biological Sciences, Michigan Technological University, 1400 Townsend Dr., Houghton, MI 49931, USA
* Department of Civil, Environmental and Ocean Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA
* Correspondence: [EMAIL_ADDRESS]; Tel.: +906-487-1783; Fax: +906-487-3167  
Received: 5 May 2019; Accepted: 26 June 2019; Published: 1 July 2019  
**Featured Application: Vetiver grass is a suitable candidate for phytoremediation of copper-contaminated soil. The harvested biomass has the potential to be used as feedstock for bioethanol production.**  
**Abstract: Metal-contaminated soil could be sustainably used for biofuel feedstock production if the harvested biomass is amenable to bioethanol production. A 60-day greenhouse experiment was performed to evaluate (1) the potential of vetiver grass to phytostabilize soil contaminated with copper (Cu), and (2) the impact of Cu exposure on its lignocellulosic composition and downstream bioethanol production. Dilute acid pretreatment, enzymatic hydrolysis, and fermentation parameters were optimized sequentially for vetiver grass using response surface methodology (RSM). Results indicate that the lignocellulosic composition of vetiver grown on Cu-rich soil was favorably altered with a significant decrease in lignin and increase in hemicellulose and cellulose content. Hydrolysates produced from Cu exposed biomass achieved a significantly greater ethanol yield and volumetric productivity compared to those of the control biomass. Upon pretreatment, the hemicellulose hydrolysate showed an increase in total sugars per liter by 204.7% of the predicted yield. After fermentation, 110% of the predicted ethanol yield was obtained for the vetiver grown on Cu-contaminated soil. By contrast, for vetiver grown on uncontaminated soil a 62.3% of theoretical ethanol yield was achieved, indicating that vetiver has the potential to serve the dual purpose of phytoremediation and biofuel feedstock generation on contaminated sites.**  
**Keywords: phytoremediation; third-generation biofuels; lignocellulosic biomass; response surface methodology; optimization**
# Quadrotor UAV Dynamic Visual Servoing Based on Differential Flatness Theory
## 3 Tracking Strategy
### Loop 2: Trajectory Generation  
By using a vertical camera stabilizer, the image plane will always be parallel to the \((X_{w},Y_{w})\) plane of the Cartesian world coordinate system, as shown in Figure 2.  
We denote \((x,y,z,\theta,\phi,\psi)\) as quadrotor coordinates and \((x_{c},y_{c},z_{c},\theta_{c},\phi_{c},\psi_{c})\) as camera coordinates. So, we have \(x_{c}=x\); \(y_{c}=y\); \(z_{c}=z\); \(\psi_{c}=\psi\); \(\theta_{c}=0\); and \(\varnothing_{c}=0\). We assume that the quadrotor flies at a given constant altitude, \(z_{d}\), and we impose that the orientation of the quadrotor is the same as that of the vehicle to be tracked (the orientation is managed by the Yaw angle \(\psi_{c}=\psi\)). The translation movements along the \(X_{w}\)- and the \(Y_{w}\)-axes as well as the rotation along the \(Z_{w}\)-axis of the quadrotor are independent. In other words, to go from an initial situation to a final situation, there is an infinity of possible trajectories. We are therefore faced with a problem that is undersized: we have three variables to determine, \((X_{d},Y_{d},\psi_{d})\) using only two equations (coordinates of the point, \(P\), in the image plane). To remedy this problem, we choose a trajectory that connects the two situations in a way similar to that carried out by a differential mobile robot. This choice has legitimacy since we aim to track a car-type vehicle. We can therefore assimilate the behavior of the camera on board the quadrotor to that of a differential mobile robot that moves in the plane parallel to the plane \((X_{w},Y_{w})\) located at a distance, \(z_{d}\), from this plane and rotating according to the axis \(Z_{w}\) by an angle, \(\psi\), according to the following dynamics:  
\[\left\{\begin{array}{l}\dot{x}_{r}=v_{r}cos(\psi)\\ \dot{y}_{r}=v_{r}sin(\psi)\\ \psi=\omega_{r}\end{array}\right., \tag{6}\]  
Figure 2: Quadrotor coordinate system vs. Cartesian world coordinate system.  
where \(\dot{x}_{r}\) and \(\dot{y}_{r}\) are the translation velocities along the \(X_{w}\)- and \(Y_{w}\)-axes of the robot. \(v_{r}\) and \(\omega_{r}\) are, respectively, the linear speed and the angular speed of the robot.
## 8 Introduction to Proprietary and Standards-based Solutions
### Standards-Based Technologies
#### 8.2.5 Dash7  
DASH7, also known as DASH 7 Alliance Protocol (D7 AP), was developed for wireless sensor and actuator communications and has originated from ISO 18000-7 standard. An extension of activeradio frequency identification (RFID) technology, DASH7 [34] is a low power long range ISM band technology primarily operating at 433 MHz. However, it also supports communication with other bands at 868 MHz and 915 MHz [22]. It uses two-level GFSK modulation with a channel bandwidth of 25 kHz or 200 kHz along with data whitening and forward error correction features. It has a tiny open-source protocol stack supporting multi-year battery life, low latency, and more flexibility. It is used for low rate bursty data traffic up to 167 kbps. DASH7 supports multi-hop communication and mobility of nodes up to a range of 2 km [35]. The architecture comprises of endpoints, sub-controllers, and gateways. Endpoint nodes follow a strict duty cycle schedule, while sub-controllers collect the data packets from the endpoint nodes with some sleep cycles and low power restrictions. Gateways are continuously active to collect the packets from sub-controllers and endpoints and then send it to the server. DASH7 supports tree topology in the presence of sub-controllers or star in the presence of the endpoints.
## 1 Introduction
Their efficiency and reliability determine the future design, manufacturing, and business systems' effectiveness in many cases. Hence, research is continuously being conducted on IT systems' efficiency [22; 23] and their industrial applications [21; 23; 25]. This area is extensive, but the authors of this publication focused on selected programming languages such as Go, Java, and Python, which have great potential for their application, among other areas, in data analysis. Therefore, this paper aims to analyze the languages mentioned above in the context of data analysis efficiency based on decision trees: for example, application in industry, especially for the remote realization of whole design and production process such as rapid prototyping. The results obtained can guide the future use of the analyzed programming languages, including Golang in the area of Industry 4.0 and IoT elements. The rest of the paper is organized as follows. Section 2 is a reference to works on the performance of programming and the methods and means of data analysis. Section 3 refers to implementing the decision tree mechanism for the study of the customer's selection and further details of the design and production process on the example of a commercial system supporting rapid remote prototyping and measures used in decision trees that are the basis of the presented algorithm. These assumptions and the data obtained in this way formed the foundation for research work on the validation of capabilities, and the effectiveness of the selected programming languages based on the implementation of the decision tree algorithm in their use in decision-making processes dedicated to use in Industry 4.0. The work ends with a summary of the obtained research results and conclusions.
## 2 Resource Model
### Resource Description
The template does not have real-time values, so when the resources are consumed in the system, the influenced attributes will be inherited by the RIN from all attributes described in the RSDT. Thus, the system can dynamically determine the resource supply situation by monitoring the RINs. (5) The section \"cost\" is used for stating the price for a unit of resource. (6) The section \"availability\" states the constraint related to the availability of this type of resources. As an example \"this resource can only be provided on weekdays\" restricts the resource availability for weekdays. (7) The section \"legal rules for supply\" enumerates the legal aspects binding the availability and use of the resource. (8) The section \"other resource in the context of use\" declares the types of other resources required to use this resources. \begin{table}
\begin{tabular}{c c} \hline \hline
**Template Holder** & XYZ Wind Power Plant \\ \hline
**Template Unify ID** & 2001:0 db8:85a3:08d3:1319:8a2e:0370:7344 \\ \hline
**Resource Name** & Alternating Current \\ \hline  & continuous resource \\  & discretized by second \\
**Characters** & passively get resource \\  & estimate \\  & predicate \\ \hline  & Inherited: Voltage \\  & Inherited: Current \\
**Attributes** & Inherited: Frequency \\  & Inherited: Phase \\  & Inherited: Power \\ \hline
**Cost** & 5 U.S. cents/kWh \\ \hline
**Availability** & Location: YYY (longitude, latitude) \\  & Time: 80\% supply/per year \\ \hline
**Legal Rules for Supply** & URI to www.XYZWindPowerPlant.com \\ \hline  & electricity transformers \\
**Other Resource in the Context of Use** & electricity transmission group \\  & electricity protection group \\ \hline \hline \end{tabular}
\end{table} 
Table 2: RSDT published by a wind power plant in a power grid. In Table 2, we give the RSDT model for the \"alternating current\" resource supported by a wind power plant in a power grid. In a dynamic resource supporting environment, the attributes of resources usually change at any moment. Therefore, the template published by the resource provider is not suitable for expressing the changing attributes and supporting the related operations. The RIN uses a simple key-value method to indicate the values at run-time. In order to make RIN mechanism as simple as possible, we have designed a resource operating decision machine (RODM). RODM will manage context information, and other associations, and carry out computations for related decision making. To improve the efficiency of dynamic resource support, we let the RIN be initialized by the resource itself at run-time. Thus, the latency time is decreased. Table 3 illustrates an instance of a resource generated from the RSDT shown in Table 2.  
In this RIN sample, the resource has a unique ID, and five other attributes are inherited from the RSDT.
# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 3 Arabic Dialect Identification Model
### The Feature Extractor  
The feature extractor module matches input text to a set of features to provide better discriminant representation for the target classes in the mapped dimension space. We investigated two types of features; the first one is based on the TFIDF features [31]. TFIDF measures the relevancy of a word to a specific dialect represented by a group of documents for each dialect. Usually, the regional dialects are identified with compound expressions that consist of more than one word. So, for our feature set, we investigated using the unigram, bigrams, and trigrams expressions. We also investigated using different sizes of relevancy lists ranging from the most important 1000 keywords up to the most important 20,000 words.  
The second sort of feature relies on word embedding [32]. Word embedding maps the identities of the words from the discrete domain to the continuous domain, represented by word vectors that can be used as a lexical and semantic representation for words. Different embedding models were investigated. Such models have been developed using different datasets with different characteristics and are described in Section 4.
# A Radiation-Hardened Low-Power SRAM with Enhanced Write Capability for Space Applications
## 2 Proposed EWS-16T SRAM Cell
### SEU Recovery Analysis
#### 2.2.1 SEU at Node Q  
When an SEU causes node Q, initially storing logic '1' to flip to '0', transistor P5 turns on and N3 turns off. Transistors P1 and N1 remain off, while P5 remains active. As a result, the states of nodes QB and S1 remain unchanged, and node S0 is unaffected. Consequently, P4 and N2 remain on, while N6 remains off. Therefore, node Q is successfully restored to its original state through the feedback loop, demonstrating self-recovery. This self-recovery behavior of node Q is further illustrated in the transient response shown in Figure 5a, where  
Figure 4: SEU recovery waveforms of the EWS-16T cell at nodes Q and S0.  
the voltage level of Q initially drops due to the SEU but is successfully restored through the feedback loop.
# Speckle Noise Removal in OCT Images via Wavelet Transform and DnCNN
###### Abstract  
(1) Background: Due to its imaging principle, OCT generates images laden with significant speckle noise. The quality of OCT images is a crucial factor influencing diagnostic effectiveness, highlighting the importance of OCT image denoising. (2) Methods: The OCT image undergoes a Discrete Wavelet Transform (DWT) to decompose it into multiple scales, isolating high-frequency wavelet coefficients that encapsulate fine texture details. These high-frequency coefficients are further processed using a Shift-Invariant Wavelet Transform (SWT) to generate an additional set of coefficients, ensuring an enhanced feature preservation and reduced artifacts. Both the original DWT high-frequency coefficients and their SWT-transformed counterparts are independently denoised using a Deep Neural Convolutional Network (DnCNN). This dual-pathway approach leverages the complementary strengths of both transform domains to suppress noise effectively. The denoised outputs from the two pathways are fused using a correlation-based strategy. This step ensures the optimal integration of texture features by weighting the contributions of each pathway according to their correlation with the original image, preserving critical diagnostic information. Finally, the Inverse Wavelet Transform is applied to the fused coefficients to reconstruct the denoised OCT image in the spatial domain. This reconstruction step maintains structural integrity and enhances diagnostic clarity by preserving essential spatial features. (3) Results: The MSE, PSNR, and SSIM indices of the proposed algorithm in this paper were 4.9052, 44.8603, and 0.9514, respectively, achieving commendable results compared to other algorithms. The Sobel, Prewitt, and Canny operators were utilized for edge detection on images, which validated the enhancement effect of the proposed algorithm on image edges. (4) Conclusions: The proposed algorithm in this paper exhibits an exceptional performance in noise suppression and detail preservation, demonstrating its potential application in OCT image denoising. Future research can further explore the adaptability and optimization directions of this algorithm in complex noise environments, aiming to provide more theoretical support and practical evidence for enhancing OCT image quality.  
Octr image; image denoising; discrete wavelet transform; stationary wavelet transform; denoising convolutional neural network
# Topical Use of _Camabis sativa_ L. Biochemical
## 5 Cannabinoids
CBD and CBG are transcriptional repressors that can control cell differentiation and proliferation in the skin [96]. CBD induces nuclear export and degradation of BACH1, reducing stress oxidation and skin aging [61; 97]. CBG is an agonist of \(\alpha\)2-adrenooreceptor [73; 98], it inhibits the endocannabinoid membrane transporter [50; 79; 82]. CBC is the most potent agonist of the TRPA1 channel [81; 82; 99]. CBDV is a partial agonist of dopamine D2-like receptors [100]. It has been shown recently that D2-like receptor agonism in the skin, promotes the recovery of the skin barrier and wound healing [101; 102; 103]. Some of these phytocannabinoids (mainly THC and CBD) have been tested as a treatment against various skin conditions (see Table 3). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Disease** & **Phytocannabinoid** & **Model** & **Outcome** & **Reference** \\ \hline \multirow{4}{*}{Acne} & THC and CBD & In vivo (mouse) & Inhibition of cyclooxygenase and & [104] \\  & CBD & In vitro (human & lipoxygenase & \\  & CBD & In vitro (human & Inhibition of pro-inflammatory p65 NF-kB & [70] \\  & & sebocytes) & THCV suppresses sebocytes proliferation, & \\  & & & arachidonic acid-induced seborphoea & \\  & & lipogenesis, and has a strong & \\  & THCV & & anti-inflammatory action. & \\  & CBC, CBDV & CBC, CBDV suppresses arachidonic & \\  & CBG, CBGV & & acid-induced seborphoea & \\  & & & CBG and CBGV increase the sebaceous & \\  & & & lipid production (pro-lipogenic and & \\  & & & pro-acne effect). & \\  & & & & pro-acne effect). & \\  & & & ended (5 dose groups, & \\  & & & 368 subjects) & 40\% reduction of lesions after 12 weeks & \\ \hline \multirow{4}{*}{Allergic Contact Dermatitis} & CBD & In vitro (HaCaT cells) & Suppress inflammatory reaction by the & \\  & & & inhibition of TCP2 2, IL-6 3, IL-8 3, and & \\  & & & & TNF-\(\alpha\) 4  & \\ \hline \multirow{4}{*}{Melanoma} & THC & In vitro (melanoma & \multirow{4}{*}{Reduction in tumor size with THC+CBD} \\  & THC and CBD (Sativex) & cells) and in vivo & more effective than THC alone & \\  & THC & In vivo (mouse) & Inhibition of melanoma growth & [112] \\  & CBD & In vivo (mouse) & Reduction in tumor size & [113] \\ \hline \multirow{4}{*}{Psoriasis} & THC and CBD & In vitro (human-skin & \multirow{4}{*}{Inhibition of keratinocytes proliferation} \\  & & keratinocytes) & \\  & CBD and CBG & Human trial & \multirow{4}{*}{16–33\% reduction of lesions after 6 weeks} & \\ \cline{1-1}  & & (2 subjects) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Clinical evidence of the therapeutical potential of various cannabinoids. Because of its wide range of effects, formulation technologies are being developed to ensure better topical delivery of CBD for medical and cosmetic use [124; 125; 126; 127; 128].
# Application of Machine Learning Techniques to
## 4 Results and Discussion
### Validation of Selected GCMs  
The selected GCMs and simple mean of MME (21 GCMs) daily precipitation data at every grid station were resampled for the seasonal cycles. The KS test was applied to every grid station and the data distribution was compared with the APHRODITE data distribution. \(p\)-values \(<\) 0.05 demarcate between the null and alternative hypotheses. When the \(p\)-value \(<\) 0.05, the similar distribution hypothesis is rejected and vice versa. Figure 10 shows the spatial distribution of the \(p\)-value for the KS test. The result shows that the selected GCMs present a higher degree of similarity in the daily seasonal distribution to the APHRODITE distribution as compared with the simple mean of MME distribution. The results show that 45% of the time-series distribution of the selected GCMs' datasets did not fall in the rejection zone (when \(\alpha\) = 5%), on the other hand, when using the datasets of simple mean MME, 28% of the datasets did not fall in the rejection zone for the warm-wet season. 76% of the time-series distribution of the datasets of the selected GCMs did not fall in the rejection zone (when \(\alpha\) = 5%), whereas only traces of 10% of the datasets were not in the rejection zone when the  
Figure 8: Climate zones in the Jhelum and Chenab river basins in the (**a**) warm-wet, (**b**) cold-dry, (**c**) cold-wet and (**d**) warm-dry seasons.  
simple mean MME was tested. The results validated the GCM selection procedure when assessed against past performance.  
Figure 10: The \(p\)-values for the K-S Test comparing the APHRODITE data with selected GCMs data and conventional composite mean data of 21 GCMs for warm-wet, cold-dry, cold-wet and warm-dry season (**a**) Using Selected GCMs (**b**) Simple mean Multimodel Ensemble. The green color bands are presenting the zones where the null hypothesis of “similar distribution” is not rejected.  
Figure 9: The spatial distribution of the highly correlated general circulation models (GCMs) with the observed data in each respective climate zone for the (**a**) warm-wet, (**b**) cold-dry, (**c**) cold-wet and (**d**) warm-dry seasons.