esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization and Modelling of Potential Seaborne Disasters, in the ANA Region
## 3 Literature Review  
### Accidental Causation Risk Assessment Models  
Prevention of accidents and mitigation against risk of failures in maritime transportation is of crucial importance. There are over 100 risk assessment models in the literature [13]. Accident causation models are crucial in both risk assessment and post-accident analysis. This is because of the impact on lives of stakeholders involved such as passengers, members of crew, impact on the environment, and impact on the economy. The risk of failures in maritime accidents has been attributed to many combined factors. Accident causation risk assessment models help identify the underlying factors that contribute to accidents, which can then be addressed to prevent future incidents [14]. Among the most commonly used are Fault Tree Analysis (FTA), Reliability Block Diagrams (RBD), and Failure Mode Effect Analysis (FMEA). This has led to a growing trend in the use of hybrid techniques to model risk [15; 16]. An overview of accident causation models is provided by [14].  
Other prevailing risk assessment techniques tend to be related to human error [17], which is estimated to be responsible for nearly 70% of maritime accidents, and hence the need for human error analysis. Human Factor Analysis and Classification System (HFACS) is a tool designed to categorize and evaluate human errors that contribute to accident occurrences [14]. HFACS has been successfully applied in research on failure.  
However, the role of such assessment techniques in shaping incident reports is still not sufficiently significant in terms of safety improvement. For example, although safety legislations in the sites of major oil and gas industries have been driven primarily by major hazardous incidents [15], it can also be argued that in the aftermath of many oil and gas industrial incidents, despite the fact that changes do occur often, unfortunately these tend not to be in a standardised unified manner across the industry [18]. This may be attributed to two main reasons. Firstly, it takes a long time and much effort to reach the so-called'regulatory convergence'--a term that means new policy regulations are adopted by industries at both national and international levels [19]. Secondly, there is reported evidence from some industries, such as aviation [20], that the introduction of new regulations or other changes in safety routines may themselves be the cause of new near misses and eventually incidents with severe consequences.
## 3 Agricultural Residue Valorisation for Wastewater Treatment  
It has been estimated that approximately 998 million tons of agricultural wastes are generated each year. In the search for economic and eco-friendly alternatives to management, agricultural residues have been proposed as biosorption materials for the removal of water pollutants [5].  
[PERSON] et al. [6] reviewed the literature published on the potential use of biochar, a carbon-rich adsorbent produced from different organic biomass, in faecal sludge management in developing countries. This work determined that biochar is a viable option for faecal sludge management due to its capacity to bind different inorganic and organic pollutants. Incorporating biochar as a low-cost adsorbent in pit latrine sludge management could lead an improvement in the quality of water resources and, in addition, biochar-amended sludge could be repurposed as a useful economical by-product.  
[PERSON] et al. [7] obtained activated carbon from organ nutshells by means of chemical activation. Once this material was characterized, it was assayed as an adsorbent for the removal of two emerging contaminants employed as model pollutants, a stimulant (caffeine) and an anti-inflammatory drug (diclofenac). The highest adsorption capacity was about 126 mg and 210 mg per gram of activated carbon, for diclofenac and caffeine, respectively. The adsorption process was described by a pseudo-second-order kinetic model and the thermodynamic parameters indicated that this process was spontaneous and exothermic for diclofenac and endothermic in the case of caffeine.  
[PERSON] et al. [8] employed date stems as a precursor to prepare activated carbon (AC) by calcination. The AC was modified by a hydroxylation strategy to increase the hydroxyl groups over the surface, resulting in AC-OH. In addition, to ensure that photodegradation took place, AC was impregnated into TiO\({}_{2}\) solution to produce AC-TiO\({}_{2}\). The obtained materials were evaluated to remove pharmaceutical contaminants based on atenol (AT) and propranolol (PR). Results showed that the removal of AT and PR reached 92% by adsorption, while 94% was obtained by photodegradation. Comparing both processes, adsorption proved to be more suitable for removing pollutants from water, since it presented low energy consumption, which revealed AC-OH as a low-cost and environmentally friendly material suitable for wastewater treatment on an industrial scale.
Remote Sens._**2015**, \(7\), 12041-12056; doi:10.3390/rs70912041  
_remote sensing_  
**ISSN 2072-4292**  
www.mdpi.com/journal/remotesensing  
_Article_  
Temporal Monitoring of the Soil Freeze-Thaw Cycles over a Snow-Covered Surface by Using Air-Launched Ground-Penetrating Radar  
**[PERSON] 1,*, [PERSON] 2, [PERSON] [PERSON] 1, [PERSON] 3, [PERSON] 2 and [PERSON] 4**  
\({}^{1}\) Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, 23955-6900 Thuwal, Saudi Arabia; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Agrosphere (IBG-3), Forschungszentrum Jlich GmbH, 52425 Juelich, Germany;  
E-Mails: [EMAIL_ADDRESS] (L.W.); [EMAIL_ADDRESS] (H.V.)  
\({}^{3}\) Federal Institute for Geosciences and Natural Resources (BGR), Stilleweg 2, 30655 Hannover, Germany; E-Mail: [PERSON]DRESS]  
\({}^{4}\) Earth and Life Institute, Universite Catholique de Louvain, Croix du Sud, 2 box L7.05.02, Louvain-la-Neuve B-1348, Belgium; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +966-2808-2197.  
Academic Editors: [PERSON], [PERSON] and [PERSON]  
_Received: 30 April 2015 / Accepted: 6 July 2015 / Published: 18 September 2015_  
system permits non-invasive time-lapse observation of the soil freeze-thaw dynamics without disturbing the structure of the snow cover. The proposed method shows promise for the real-time mapping and monitoring of the shallow frozen layer at the field scale.  
ground-penetrating radar; freeze-thaw cycles; seasonal snow cover; inversion
Additional monitoring of phenological shifts using remote sensing techniques can help managers assess system-wide trends. A landscape-scale vegetation assessment of the Triple Crown and a broad-perspective multidecadal analysis of the vegetation patterns provide a valuable comparative framework to analyze the trail network. Threats from the changing climate, the shifting wildfire regimes, and the spread of invasive species warrant holistic monitoring of the Triple Crown National Scenic Trail system. To better understand the environmental trends along each trail corridor, our objectives for this research are to (1) assess the vegetation health in terms of the land cover change and vegetation productivity; (2) evaluate the forest loss and recovery rates after significant disturbance events such as wildfires; and (3) assess the phenological change across the trail system to better understand the impacts of shifting climate regimes.
# Hardware-Based Architecture for DNN Wireless Communication Models
###### Abstract  
Multiple Input Multiple Output Orthogonal Frequency Division Multiplexing (MIMO OFDM) is a key technology for wireless communication systems. However, because of the problem of a high peak-to-average power ratio (PAPR), OFDM symbols can be distorted at the MIMO OFDM transmitter. It degrades the signal detection and channel estimation performance at the MIMO OFDM receiver. In this paper, three deep neural network (DNN) models are proposed to solve the problem of non-linear distortions introduced by the power amplifier (PA) of the transmitters and replace the conventional digital signal processing (DSP) modules at the receivers in 2 \(\times\) 2 MIMO OFDM and 4 \(\times\) 4 MIMO OFDM systems. Proposed model type I uses the DNN model to de-map the signals at the receiver. Proposed model type II uses the DNN model to learn and filter out the channel noises at the receiver. Proposed model type III uses the DNN model to de-map and detect the signals at the receiver. All three model types attempt to solve the non-linear problem. The robust bit error rate (BER) performances of the proposed receivers are achieved through the software and hardware implementation results. In addition, we have also implemented appropriate hardware architectures for the proposed DNN models using special techniques, such as quantization and pipeline to check the feasibility in practice, which recent studies have not done. Our hardware architectures are successfully designed and implemented on the Virtea 7 vc709 FPGA board.  
artificial intelligence; deep learning; hardware design; MIMO; OFDM +
Footnote †: journal:  
1 Computer Engineering Department, University of Information Technology,  
Ho Chi Minh City 70000, Vietnam 12 Vietnam National University, Ho Chi Minh City 70000, Vietnam 2  
Footnote 1: [[https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors)]([https://www.mdpi.com/journal/sensors](https://www.mdpi.com/journal/sensors))
# A Bimodal Lognormal Distribution Model for the Prediction of COVID-19 Deaths
## 6 Discussion  
### Parameter Identifiability  
A relevant issue of epidemiological models is the identifiability of their parameters. In particular, structural identifiability concerns the well-posedeness of the mathematical problem, while practical identifiability is related to the possibility of estimating the parameter values based on the available data [22]. Several methods have been developed to assess the parameter identifiability in models formulated in terms of differential equations to be solved numerically [23]. The present model is formulated instead in terms of assumed analytical expressions, namely Equations (1) and (2), which include six parameters (\(A\), \(\mu_{1}\), \(\sigma_{1}\), \(\mu_{2}\), \(\sigma_{2}\), and \(\alpha\)) for each country. Assuming, without loss of generality, that \(\mu_{1}<\mu_{2}\) (and excluding the degenerate case, where the two lognormal distributions overlap onto a single distribution), any set of parameter values can be uniquely mapped to a model output. Thus, the present model is structurally identifiable [24]. The model is also practically identifiable, as proven by the successful computation of parameters through the implemented data fitting procedure.
## 4 Experimental Results and Discussion  
### EVM Penalty in Terms of SFO Error  
To investigate the effects of SFO estimation accuracy on EVM performance, we artificially set the SFO from \(-10\) to 10 ppm to simulate the SFO error, and the corresponding experiments are carried out in a 1 m tap water channel. Figure 3 shows the measured EVM performance as a function of the SFO error, where the 16-QAM constellations are inserted when the SFO errors are \(\pm 3\) ppm and \(\pm 10\) ppm, respectively. As shown in Figure 3, with the increase in the SFO error, the EVM performance gradually declines. For the SFOs with opposite values, the EVM penalties are essentially the same due to the adding of CP and CS in each OFDM symbol. When the SFO error is up to \(\pm 10\) ppm, the EVM performance has deteriorated significantly. Compared with the error-free case, the corresponding EVM penalties are approximately 2.5 dB. When the SFO errors are dipped to \(\pm 3\) ppm, the EVM penalties are only 0.3 dB, and the phase rotations of the constellations are very slight, which has a negligible effect on the system performance. This is also supported by our previous work in real-time optical OFDM systems [32]. Therefore, we can take the estimated offset \(<\pm 3\) ppm as our accuracy target because it has little impact on the system performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Physical dimensions of the tank & \(1\) m \(\times\) 0.5 m \(\times\) 0.5 m \\ Laser wavelength & 450 nm \\ LD output power & 30 mW \\ The divergence angles in the parallel/vertical & \(6.5^{\circ}/22.5^{\circ}\) \\ \hline DAC/ADC resolutions & 8/10 bits \\ DAC/ADC sampling rate & 10/50 MS/s \\ Underwater transmission distance & 0.5/1 m \\ IFFT/FFT size & 64 \\ CP and CS length & 16 \\ Data-carrying subcarriers & From 3 to 30 \\ Number of TS per OFDM frame & 2 \\ Number of OFDM symbol & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Key parameters of the UWOC system.
## 1 Introduction
(2024) combined deep learning with 3D point cloud data to achieve high-precision crack detection in complex environments [10]. The evolution of bridge crack identification methods, from traditional image processing to the integration of 3D point cloud and deep learning techniques, has continuously improved detection accuracy. Therefore, future research should explore the integration of multi-source data and intelligent technologies to enhance the efficiency and precision of crack detection, which is the primary goal of this study. Previous studies on crack detections on concrete bridges focus on developing methods using computer vision techniques and image processing algorithms based on high-resolution 2D images. The detection results highly rely on the quality of the collected data and are not always computationally efficient. The scientific contribution of this study is to propose a spatiotemporal change detection and model updating tool for trustworthy bridge structural health assessment. This study proposed a novel crack detection method using 3D point cloud data for detecting cracks and deformations that occur on aging bridges. In addition, the proposed method resolves the challenge of model updating using the collected 3D point cloud data. Hence, by analyzing the above detection results, the finite element parameters of bridge structures can be realized.
# 2D Winograd CNN Chip for COVID-19 and Pneumonia Detection
## 4 Discussion
### Limitations and Future Directions  
Based on the above results, it can be found that the structure of the hardware circuit will directly affect the performance, area, and speed of the circuit. Although the outputs of the two circuits are the same, the operation methods of the circuits are also very different. For example, the Winograd acceleration circuit greatly reduces the number of multiplications in traditional CNN operations and replaces them with additions, which are much faster than multiplications in hardware implementation. Because the number of multiplications in Winograd is greatly reduced, an acceleration method is also proposed here, which is to change the maximum frequency of the circuit, such as directly increasing the maximum frequency from 100 MHz to 200 MHz, to test whether the circuit can accelerate the calculation. Due to the reduction in the number of multiplications, it is obvious that the cycle required for the circuit calculation can be relatively reduced.  
The biggest sacrifice in the acceleration circuit is also the area. Because of the use of the conversion matrix, under the TSMC 0.18 \(\upmu\)m process, the area occupied by the Winograd circuit is very large, but with the help of EDA tools, it has reduced the original substantial growth.  
For the model part, because the chest X-rays and their information are not a public resource, although we tried to adjust the learning rate and expand the data, the amount of data also limited our training results. Although the types of data are very similar, after trying and comparing various models, a model with an accuracy rate of more than 85% was finally trained. If the X-ray information of the lungs becomes more transparent and public in the future, the increase in the amount of data is believed to be beneficial to us. The accuracy of the model is greatly improved, and it can also be trained with deeper and larger models.  
For the chip part of the future outlook, it is hoped that the neural network model can be used as a hardware circuit to realize a circuit that can fully connect the layer from the input of the model to the output and realize the function of the classification. Because the overall architecture is larger, more often, it needs to be implemented in a better process to achieve small-area, high-speed, high-performance chips. Because of the physical chip, we also hope to operate the results through the actual human-machine interface in the future, and it is also expected that the accuracy rate can be adjusted to more than 95% for the reference of real medical teams. The promotion model is also the final result we hope to achieve, and we hope that the results we have achieved can serve as a cornerstone and contribute to the development of this severe epidemic.
# R-Curve Behavior of Polyhedral Oligomeric Silsesquioxane (POSS)-Epoxy Nanocomposites
## 4 Conclusions  
Addition of the POSS molecule to the epoxy resin increases fracture toughness. POSS loading up to 1 wt.% of the POSS molecule exhibits brittle behavior. However, beyond 1 wt.% POSS addition, the nanocomposite exhibits ductile failure. Brittle initiation toughness, as found by using LEFM, is 50% higher than when using neat epoxy resin. Failure of brittle materials exhibits catastrophic behavior. From SEM images, reinforcement mechanisms in brittle nanocomposites observed are crack pinning and crack deflection. At a higher level of POSS inclusion in the epoxy resin, fracture behavior exhibited ductile phenomena. For ductile material, LEFM phenomena were no longer valid, but \(J\)-integral approach implemented successfully. Initiation toughness of the ductile nanocomposite increases with the POSS inclusion up to 5 wt.% and then decreases with further loading. The increase in the initiation toughness value is due to the blunting of the crack because of the formation of POSS-POSS compliant domain higher loading. Further inclusion of POSS leads to the deteriorating effect on the nanocomposite by creating the larger domain that acts as a critical site of a failure. The fracture surface of ductile nanocomposite exhibit formation of shear lips on the surface due to the shear yielding toughening mechanism.  
[PERSON] conceptualized, investigated, analyzed, and worked on original draft preparation. [PERSON] worked on conceptualization, review and editing of draft and funding acquisition. All authors have read and agreed to the published version of the manuscript.  
We gratefully acknowledge that this work is funded in part or wholly by a grant through the Oklahoma Nanotechnology Applications Project (ONAP) (Grant no. O9-20) and NASA Experimental Program to Stimulate Competitive Research (EPSCOR) (Grant no. NNX09 AP68A).  
Figure 8: Scanning electron microscopy image of 1 wt.% and 8 wt.% POSS loading shows the toughening mechanism due to POSS inclusion. (**a**) Fractured surface of 1 wt.% POSS inclusion. (**b**) Fractured surface of 8 wt.% POSS inclusion.  
Figure 7: Fracture behavior of POSS-epoxy nanocomposites at lower (**left**) and higher loading (**right**).
## 3 Results
### Changes in Terpenoids in P. thunbergii after Inoculation with PWNs
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & & & \multicolumn{6}{c}{**Relative Percentage Content (\%)**} \\ \cline{3-10}  & & & 0 h & & **24 h** & & **72 h** & & **168 h** \\ \cline{2-10}  & & **Resistant** & **Susceptible** & **Resistant** & **Susceptible** & **Resistant** & **Susceptible** & **Resistant** & **Susceptible** \\  & & _P._ & _P._ & _P._ & _P._ & _P._ & _P._ & _P._ & _P._ \\  & & _thunbergii_ & _thunbergii_ & _thunbergii_ & _thunbergii_ & _thunbergii_ & _thunbergii_ & _thunbergii_ \\ \hline
1 & Caryophyllene & 15.71 & 18.4 & 15.06 & 16.8 & 13.12 & 18.87 & 12.82 & 17.36 \\
2 & \(\gamma\)-Cadinene & 11.46 & 2.89 & 9.82 & - & 5.47 & 2.83 & 3.64 & 2.77 \\
3 & \(\alpha\)-Thujene & 10.82 & - & - & - & 14.14 & - & 9.1 & - \\
4 & Pseudolimonen & 10.37 & - & 9.27 & - & 7.63 & - & - & - \\
5 & \(\alpha\)-Pinene & 9.61 & 7.93 & 11.77 & - & 15.06 & 15.01 & 16.12 & 16.44 \\
6 & \(\beta\)-Pinene & 9.61 & 15.27 & 10.43 & - & 14.74 & 21.49 & 16.37 & 19.79 \\
7 & Tricylene & 7.85 & 0.11 & 7.15 & 18.78 & 0.12 & 0.3 & 0.1 & 0.18 \\
8 & Gemacrene D & 6.95 & 8.95 & 6.23 & 8.82 & 5.01 & 9.99 & 7.91 & 10.55 \\
9 & Terplinene & 6.94 & 4.13 & 5.95 & 4 & 6.313333 & 4.54 & 6.49 & 3.1 \\
10 & \(\beta\)-Myrence & 4.27 & 1.84 & 4.33 & 2.21 & 5.94 & 2.7 & 5.85 & 3.43 \\
11 & Sabinen & - & - & - & - & 15.97 & - & 18.89 & \\
12 & \(\beta\)-Cs-ocimene & - & - & - & - & 11.07 & 17.28 & 11.74 & 13.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Changes in the content of major terpenoids in _P. thunbergii_ with different levels of resistance following inoculation with PWNs. thunbergii_) was 10.37% before inoculation, decreased gradually after inoculation to 7.63% at 72 h, and was no longer detected 168 h after inoculation (Table 3). The relative percentage of \(\alpha\)-thujene (common to _P. thunbergii_ with different levels of resistance) was also large, 10.82% before inoculation, 14.14% after 72 h of inoculation and 9.1% after 168 h inoculation in resistant _P. thunbergii_, but only 0.22% before inoculation and 5.52% 24 h after inoculation in the susceptible one (Supplementary Table S2).
## 3 Results and Discussion
### Effect of Temperature on the UV-VIS Spectra  
In our study, the region between 270 nm and 290 nm was negatively correlated with the increase of temperature during the scanning of whisky samples. Therefore, to evaluate the effect of the scanning temperature on the UV-VIS spectra of the samples analysed, a linear regression between the absorbance at single wavelengths (e.g., 280 nm) and scanning temperature was performed. Figure 3 shows the linear regression between the average absorbance at 280 nm of all whisky samples and the scanning temperature. A negative and strong correlation (R\({}^{2}\): 0.9982) could be observed between the absorbance selected and the temperature, indicating that an increase in the scanning temperature decreased the absorbance values at specific wavelengths.
## 3 Survey of Techniques for Updating
### The Execution Environment
#### 3.3.13 ScatterWeb  
This is a distributed, heterogeneous platform for the _ad-hoc_ deployment of sensor networks [49]. Software is divided into a firmware core and modifiable tasks to provide a secure update environment. The software is fragmented into small packets to minimize packet loss. Lost packets are recovered in a two-step process: first from local nodes, and if this fails from the gateway. The update is first copied into EEPROM, and then written into flash. This allows the integrity of the received code to be checked, and allows synchronization of updates across the network (using a flash command, or a timestamp). A host tool supports over-the-air reprogramming via a USB/radio stick (ScatterFlasher) or via a www gateway (Embedded Web Server). Supported protocols, such as Directed Diffusion, can be used to distribute an update.  
As for other modular systems with dynamic linking, the updatable tasks provided by ScatterWeb provide flexibility which might prove to be a useful enabling technology for autonomous updates.