esa-sceva/satcom-chunk-collection huggingface dataset:
# Immersive Safe Oceans Technology: Developing Virtual Onboard Training Episodes for Maritime Safety
## 6 Areas of Further Research  
The research conducted on this topic indicated the critical roles of immersive futuristic technologies in sectors highly dependent on human expertise while operating in spaces where no help can be provided. A ship on the ocean or a plane on the sky stand alone with no or limited assistance to be provided when an accident occurs. The time to respond and the conditions to offer help can be undefined and unpredictable. The Japan Transport and Safety Board recorded 199 marine incidents from 1 January to 30 November 2019 (Figure 9) with 89% of them to be engineering and navigation related in the engine room and on the bridge [26].  
Furthermore, the Maritime Injury Guide notes that the lack of maintenance and lack of proper training need to be corrected to reduce human error in most maritime accidents [27]. This is related to the high turnover rate indicated in the seafarers (officers and unlicensed mariners), as it is challenging to recruit well trained human resources and retain existing ones [28].  
The test runs conducted during the development period of this research, especially with the MarSEVR technology where we had the opportunity to test it with experienced ship captains in a major shipping company, indicated not only the need and the market for such technologies but also further areas to extend this research related with the psychology of the captains and the engineers while they are on duty. We identified that it is not just the level of knowledge or expertise that contributes to the safe execution of a task but also the emotional and mental stage of the person at that specific  
Figure 8: Immersive Safe Oceans Technology’s training center.  
period or during the training session. These findings will lead our future research in this area towards emphasizing more on the behavioral elements of the technology in order to provide a holistic learning experience that will capture knowledge, skill, feelings, and emotions.  
This behavioral dimension that we already tapped into will be further extended to behavioral biometrics analysis, as people have unique ways of expressing themselves with their personal behavioral characteristics such as attention, body movement, and self-coordination [29]. With behavioral biometrics, we will be able to record, analyze, and understand the psychology of the trainee when executing a task. Today, the distance between VR and psychology has been shortened due to the commercial availability of the technology which can be acquired and adopted in many disciplines [30].  
Furthermore, we intend to extend our work on behavioral biometrics with the utilization of data analytics related to the trainee behavior during one or more training sessions. This will allow us to deliver reliable predictions on the performance of the crew, the safety of the vessel, and the crew on it and also the staffing process prior to the sailing of the vessel. Data analytics and data science are important factors often neglected in training.
# Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications
## 2 Meteorological Stations and Data
### Statistical Index for Accuracy Evaluation  
There are no well-defined error metrics standards, which makes the forecasting and estimation methods difficult to compare [25]. A benchmarking exercise was realized within the framework of the European Actions Weather Intelligence for Renewable Energies (WIRE) [26], with the objective to evaluate the state of the art concerning models' performances for short term renewable energy forecasting. They concluded that: \"More work using more test cases, data and models needs to be performed in order to achieve a global overview of all possible situations. Test cases located all over Europe, the US and other relevant countries should be considered, trying to represent most of the possible meteorological conditions\".  
In this paper, these five error metrics were used:  
* The mean absolute error (MAE) defined by: \[MAE=\frac{1}{N}\times\sum_{i=1}^{N}\left|\hat{X}_{t_{i}}-X_{t_{i}}\right|\] (1) \[\hat{X}_{t_{i}}\] being the forecasted outputs (or predicted values), \[X_{t_{i}}\] the observed data, and N the number of observations.
* The root mean square error (RMSE), more sensitive to important forecast errors, and hence suitable for applications where small errors are more tolerable than larger ones, as in utility applications. It is probably the reliability factor that is the most widely used: \[RMSE=\sqrt{\frac{1}{N}\times\sum_{i=1}^{N}\left(\hat{X}_{t_{i}}-X_{t_{i}} \right)^{2}}\] (2)
* The mean bias error (MBE), mainly used to estimate the bias of the model: \[MBE=\frac{1}{N}\times\sum_{i=1}^{N}\left(\hat{X}_{t_{i}}-X_{t_{i}}\right)\] (3)  
These errors were then normalized, and the mean value of irradiation is generally used as the reference:  
\[nRMSE=\frac{RMSE}{\overline{X}} \tag{4}\] \[nMAE=\frac{MAE}{\overline{X}} \tag{5}\]  
with \(\overline{X}\) the average value of X calculated on the N data.
## 3 Results and Discussion
### Effect of Extraction Solvents
In previous studies, many solvents were used to extract either fatty acids or hydrocarbons, such as petroleum ether with dichloromethane [33; 34], hexane with chloroform [35] and dichloromethane alone [15]. However, only acetonitrile demonstrated the ability to extract both fatty acids and hydrocarbon waves from insects. The results obtained from insect samples were compared with external standards of n-alkane, fatty acids, sterol, p-benzoquinone, methyl-p-benzoquinone and 1-pentadecene. The chemical compounds were identified using the NIST database after comparison with the mass spectra and retention indexes (RI) by running the external standards. The results of the LOD in Table 2 indicate that the new method could detect quinones (p-benzoquinone, 2-methyl-), fatty acids (arachidic acid), cholesterol and alkanes (heptacosane) at levels of 0.36, 0.65, 034 and 0.08 \(\upmu\)g/g respectively. Therefore, this method has been demonstrated to be a robust method to analyze a variety of lipids. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Solvent** & **Compound** & **The Rate of Total Expected** & **Number of Compounds** \\  & **Numbers** & **Compounds (\%)** & **Only Detected in Solvent** \\ \hline
1 & Acetonitrile & 41 & 77.4 & 5 \\
2 & Hexane & 23 & 43.4 & 1 \\
3 & Ethanol & 25 & 47.2 & 0 \\
4 & Methanol & 28 & 52.8 & 1 \\
5 & Acetonitrile and ethanol (1:1 \(\upmu\)v) & 34 & 64.2 & 0 \\
6 & Acetonitrile and water (1:1 \(\upmu\)v) & 22 & 41.5 & 0 \\
7 & Ethanol and water (1:1 \(\upmu\)v) & 27 & 50.9 & 0 \\
8 & Ethanol and acetonitrile and water (2:2 1 \(\upmu\)v/v) & 30 & 56.6 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of separated and identified compounds obtained in each solvent out of the total of 53 compounds from _T. castaneum_. Figure 2: Total signal chromatogram of lipids of cuticular extraction followed by homogenized body extraction of same insects of _Rhyzopertha dominica_, using acetonitrile as solvent.
## 7 Conclusions  
1. In the physical simulation experiment, through microseismic monitoring, the magnitude and location of microseismic event energy were analyzed. Based on the microseismic energy distribution cloud chart, the area of water-conducting fracture zone was divided. After fully mining, the overburden caving height was stable, and the height development range of the water-conducting fracture zone was 100-120 m, which is consistent with the height of the overburden caving envelope.
2. Using 3 DEC numerical calculation and similarity simulation theory and technology, we found that the concentrated distribution area of the fracture field formed a \"bridge arch\", with the fracture development model in the middle of the goat being higher than at both ends of working face, and roof strata deformation was obvious. The development height of the water-conducting fracture zone was 101.5-122.3 m, which is about 18.13-21.84 times that of mining height.
3. From the stress intensity factor, we deduced that when the energy of crack expansion and development reaches 108.708 J, the crack will form and continuously penetrate to form a water-conducting channel, that is, a water-conducting fissure zone. Compared with the focal line with concentrated energy, the development position and height of the water-conducting fissure zone were better verified.
4. We performed on-the-spot monitoring of the 8103 working face. A preliminary analysis was made by peeping through the hole with a drilling TV, and the hole position was comprehensively determined according to the topographic conditions, construction conditions, drilling protection requirements, and factors that did not affect the safety production of coal mine. The development heights of the two zones were explored. According to the drilling leakage and the return water of drilling fluid, the height of the water-conducting fracture zone was found to be between 98 and 123 m, which was consistent with the results of microseismic monitoring and numerical simulation.  
[PERSON] performed the experiments and wrote the manuscript; [PERSON] conceived, designed, and analyzed the test results. All authors have read and agreed to the published version of the manuscript.  
This study was sponsored by the National Natural Science Foundation of China (Grant No. 52104240) and Natural Science Basic Research Program of Shaanxi Province (Grant No. 2022)Q-320).  
The data used to support the findings of this study are available from the corresponding author upon request.  
The author sincerely thanks the Key Laboratory of Western Mines and Hazard Prevention of China Ministry of Education and Key Laboratory of Coal Resource Exploration and  
Figure 15: The height comparison of various water-conducting fractured zones.  
Comprehensive Utilization for the equipment and site support provided, as well as the funding provided for this work.  
The authors declare no conflict of interest.
## 2 Problem Description and System Modeling
Since the SJNR at the receiver at time \(t\) can be expressed as \(\mathrm{SJNR}(t)=P_{\mathrm{s}}(t)-P_{\mathrm{J}}(t)\), the system state is composed of the control input \(u(t)\) and the noise and jamming power at that time. Assuming that the received noise and jamming power can be accurately measured, it follows that:  
\[x_{i}(t+\Delta t)=u_{i}(t)-P_{\mathrm{J}}(t) \tag{3}\]  
According to Equation (3), the rate of change in the SJNR over time for the \(i\)-th modulation and coding scheme is:  
\[\frac{dx_{i}(t)}{dt}=A_{i}x_{i}(t)+B_{i}u_{p}(t)-P_{\mathrm{J}}(t)\quad X_{i} \leq x\leq X_{i+1},i=1,\ldots,M \tag{4}\]  
In the formula, \(u_{p}(t)\) represents the control parameter for the \(i\)-th modulation and coding scheme. According to Equation (2), the dynamic characteristic parameters are \(A_{i}=-1\) and the control parameters are \(B_{i}=-1\). Therefore, combining Equations (1) and (4), the state equation of the system can be modeled in the following switching system form:  
\[\left\{ \begin{array}{l}\frac{dx_{i}(t)}{dt}=-x_{i}(t)+u_{p}(t)-P_{\mathrm{J }}(t)\\ y_{\mathrm{BER}}(t)=C_{i}x_{i}(t)+D_{i}\end{array} \right.\quad X_{i}\leq x\leq X _{i+1},i=1,\ldots,M \tag{5}\]  
To simplify the calculations, define  
\[u_{1}(t)=u(t)-P_{\mathrm{J}}(t) \tag{6}\]Thus, Equation (5) is:  
\[\left\{ \begin{array}{l}\frac{dx_{i}(t)}{dt}=-x_{i}(t)+u_{1}(t)\\ y_{\text{BER}}(t)=C_{i}x_{i}(t)+D_{i}\end{array} \right.X_{i}\leq x\leq X_{i+1},i= 1,\ldots,M \tag{7}\]  
The control rule of the wireless communication switching system is to divide the switching intervals based on the SJNR, with each interval corresponding to a specific modulation and coding scheme. In the event of jamming, if the strength of the jamming is considerable, resulting in an instantaneous shift in the SJNR interval from one modulation and coding scheme to another, the system initiates a transition in the modulation and coding scheme and subsequently adjusts the transmission power in a manner that ensures the desired BER is maintained. In the event of weak jamming, which results in the instantaneous SJNR remaining within the same interval, the adjustment made is to the transmission power. This is illustrated in Figure 4.  
The stability control problem of the wireless communication switching system lies in how the system's BER can quickly converge to the target value when the system perceives the electromagnetic environment and adjusts its transmission power, modulation, and coding schemes accordingly. The following section designs the anti-jamming power controller introduced in each wireless communication switching subsystem.
## 1 Introduction
### Contribution of the Work  
The multistage, multicolumn layers improved spatial information along the attention features. In this approach, we introduced several modifications to improve the deep features from spatial information. The main contribution process of building the novel bilinear attention feature vector and fusion network process is explained as follows: The proposed model introduced three main parallel feature extraction methods to improve features at different levels. Mainly, bilinear CNN pooling networks extract different features at different depth levels. Therefore, bilinear feature extraction models extract richer features (deep features) than normal convolution networks. Our approach used a dual-channel feature extraction model (transfer learning) based on densenet121 and Efficientnetv2 for generating the bilinear pooling matrix in a given image. The generate bilinear feature matrix was used to calculate the attention score matrix of each point by multiplying it with the activation weight vector. Finally, we obtained the attention feature vector by aggregating all column elements together to generate a bilinear attention feature vector.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Input** & **3 \(\times\) 299 \(\times\) 299** & & \\ \hline
**Layer** & **Output** & **Filter Size** & **Neurons** \\ \hline Convolution & \(112\times 112\) & \(7\times 7\) & \(3\times 64\) \\ \hline Pooling & \(56\times 56\) & \(3\times 3\) & \(64\) \\ \hline Dense Block & \(56\times 56\) & \((1\times 1,3\times 3)\,6\) & \(64\times 256\) \\ \hline Transition & \((56\times 56,28\times 28)\) & \((1\times 1,2\times 2)\) & \(256\times 128\) \\ \hline Dense Block  
Second, the single-image multicolumn network was modified with a convolution layer and a fully connected layer to generate a feature vector instead of a feature map. The generated density feature vector and subsampled multistage convolution feature vector were parallelly concatenated with the bilinear attention feature vector. Finally, the two streams flattened the feature vectors, which were fed into a fully connected fusion network with a softmax classification.  
Experiments were conducted at different density levels using benchmarks, including PETS 2009, UMN and UCSD. The training and validation outcomes performed best, as mentioned in the results section, and were also compared with state-of-the-art algorithms.
## 5 Compressive Resistance of SDC Sandwich Wall  
### Code Method  
Methods to calculate the compressive resistance of normal-sectioned composite columns under axial load are included in Eurocode 4 [46], AISC360-10 [47], and other references.  
According to Eurocode 4, the compressive resistance of a composite cross-section should be calculated as the following;  
\[\mathrm{P_{u}}=\mathrm{f_{ya}A_{a}}+0.85\mathrm{f_{c}A_{c}}+\mathrm{f_{y}A_{s}} \tag{10}\]  
where \(\mathrm{A_{a}}\) and \(\mathrm{f_{ya}}\) denote cross-sectional area and yield strength of reinforcement; \(\mathrm{A_{s}}\) and \(\mathrm{f_{yd}}\) denote the cross-sectional area and design; \(\mathrm{A_{s}}\) and \(\mathrm{f_{y}}\) denote cross-sectional area and yield strength of steel face plate.  
In AISC 360-10, the compressive resistance of composite members is specified as the following,  
\[\mathrm{P_{u}}\ =\ \mathrm{P_{u0}}\big{(}0.658^{\mathrm{P_{u0}}/\mathrm{P_{e}}} \big{)},\ \mathrm{when}\ \mathrm{P_{n0}}/\mathrm{P_{e}}\leq 2.25 \tag{11}\]  
\[\mathrm{P_{u}}\ =\ 0.877\mathrm{P_{e}},\ \mathrm{when}\ \mathrm{P_{n0}}/\mathrm{P_{e}}>2.25 \tag{12}\]  
where \(\mathrm{P_{u}}\ =\ \mathrm{f_{y}A_{s}}+\mathrm{f_{ya}A_{a}}+0.85\mathrm{f_{c}A_{c}}\); \(\mathrm{P_{e}}\ =\ \mathrm{elastic\ critical\ buckling\ load}\), and equal to \(\mathrm{\pi^{2}(EI_{eff})}/\big{(}KL^{2}\big{)}\); \(\mathrm{K}\ =\mathrm{effective\ length}\) factor; \(\mathrm{L}\ =\mathrm{\ laterally\ unbraced\ length}\) of the member.  
Even though the confining effect of the out steel plate on the concrete is ignored in Equations (10) and (11), the two code methods still are found to over-predict the compressive resistance of the SCS sandwich wall [3] and SDC walls. The predictions are compared with the numerical results in Table 3. The average ratios of \(\mathrm{N_{FEM}}/\mathrm{N_{EC4}}\) and \(\mathrm{N_{FEM}}/\mathrm{N_{AISC}}\) are 0.953 and 0.975, respectively, with the same standard deviation of 0.045. This is because the criteria of codes were developed for composite columns and disregard the premature buckling of steel plates and incomplete material properties of concrete in composite walls. Furthermore, it can be observed from Figure 16 that the two codes obtain inferior predictions when the thickness of the rubber layer is higher than 5 mm, which means that the incomplete material properties of concrete are more influential, and it cannot be ignored in predictions of the SDC wall.  
Figure 20: Influence of the diameter (side length) of steel sheets on compressive resistance of SDC wall.  
improve the steel content and confinement effect on the concrete. Based on the above discussion, a new formula shall be developed to predict the compressive resistance of the SDC composite walls.
# Applications of Satellite Radar Imagery for Hazard Monitoring: Insights from Australia
## 1 Introduction
Sections 3-7 detail the applications identified to be of key significance for Australia (hydrological hazards; fire; natural resource extraction; seismic hazards; geomorphological changes) and summarise the past use of SAR based upon available case studies documented in the scientific literature and publicly available reports. For these applications, we also present new case studies (see Figure 2A for an overview map) that utilise Sentinel-1, plus commercial SAR data (see Table S3 in the Supplementary Material for details of the datasets and processing methods used). Our intentions are: to provide a baseline understanding of the potential role of SAR in national hazard monitoring and assessment in an Australian context; to provide a suite of new case studies that demonstrate SAR applications to a diverse portfolio of hazards that impact Australia and other regions globally; and to highlight opportunities for growth with the current and future influx of data from new SAR satellites (including NovaSAR-1) and other EO data types, plus advances in the provision of standardised SAR products via Analysis Ready Data and Open Data Cubes [10].
# Roles of Modeling and Artificial Intelligence in LPBF Metal Print Defect Detection: Critical Review
## 3 Advances in Numerical Modeling Techniques for LPBF Defect Detection
### Numerical Simulation Efforts of LPBF Defects
#### 3.2.2 Mechanical Simulations of Statistically Driven LPBF Defects Using FEM
Derived geometric data from XCT images were used to generate digital realizations of AM struts, including defects related to surface roughness and external geometrical discrepancies. Stochastic distributions of AM defects are accommodated into computationally effective beam models, allowing for large-scale lattice simulations at lower computational costs. An article presented an approach integrating defects in a density-based optimized topology of an LPBF build. As-built microstructure data collected via CT scans are utilized to determine the probability distribution of defects [106]. The investigated defects are a non-uniform strut wall thickness and the center shift of the ideal strut axis. Comparisons between the CT-generated scans and the idealized CAD models are compared to display the impact of unit cell anisotropy and mechanical properties. A micromechanical model was proposed that considers the quantitative pore characteristics from the XCT scan to predict the effective mechanical properties of LPBF-printed SS316L [107]. A study was conducted on the impacts of pore distances and the pore size distribution on the predicted elastic properties. The FEM predictions agreed with two analytical solutions and mechanical properties. An advanced numerical simulation model was developed to accurately predict mechanical properties and energy absorption according to the statistics of geometrical defects [108]. The probability distribution of geometrical defects was presented with a statistical function and implemented into the numerical model. The statistically driven LPBF-printed lattice strut model is compared to its idealized CAD model in Figure 8.  
Figure 8: Comparison between (**a**) idealized FE lattice strut model and (**b**) statistical model containing stochastic geometrical defects [108].
## 3 Optical Properties
### Edge Emitting Lasers
To the best of our knowledge edge-emitting, 1060 nm QW lasers demonstrate CW optical power as high as 18 W [73]. The pulsed output power of 37.5 W is achieved at room temperature (Figure 15b). The injection current is limited by the supply source. The decrease in differential efficiency at high injection currents (starting from ~40 A) is likely due to overheating and carrier pile-up in the active region as suggested by the redshift and broadening of the lasing spectrum, respectively. To the best of our knowledge apart of the QWDs there are only a few works on light-emitting devices based on nanostructures with mixed dimensionality. In [31], significant depolarization of the heavy-hole-related electroluminescence was observed and attributed to in-plane strain fields caused by modulations of In composition. In reference [27], a special InGaAs-based well-island composite quantum-confined structure was demonstrated, and both super-wide and very uniform gain and power distributions were obtained. The spectral flatness of the output power reached 0.1 dB, and the gain bandwidth was broadened to six-folds broader than FWHM (full width at half the maximum) of the standard gain spectrum from a classic InGaAs quantum well under the same carrier density. The formation of the well-island composite quantum-confined structure was associated with the Indium-rich island effect in the material growth. To conclude this part, QWD based edge-emitting lasers show a high optical gain, high differential efficiency, low threshold currents, low internal loss, and good temperature stability. They can find applications as efficient optical emitters for the spectral range around 1.1 \(\upmu\)m. We expect that using advanced waveguide design (broad waveguides, thin top-emitter and contact layer, etc. ), better heat dissipation, and up-to-date facet coatings will result in enhancement of output power levels. Figure 15: Dependencies of CW output power (left axis), voltage (right red axis), and power conversion efficiency (right blue axis) at room temperature on injected current for an edge-emitting laser based on 2 QWD layers (**a**); dependence of pulsed output power on injected current. Insets: spectra at two different currents (**b**).