esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Programme
### Sensitivity Analysis  
Sensitivity analysis is commonly used to evaluate how input parameters affect output variation derived by machine learning models [37]. As the best performing model, ResNet-based architecture 6 was exclusively selected for this analysis, which involved calculating FAGP concrete compressive strength by changing one input variable at a time while maintaining the other five as constants based on their mean values. For example, to assess the importance of the NaOH/Na\({}_{2}\)SiO\({}_{3}\) ratio, this value was varied from 0.4-2.5, while fly ash/aggregate, AL/Fly ash, NaOH concentration, curing time, and temperature values were kept constant at mean values of 0.23, 0.5, 14 (M), 8 h, and 85.6 \({}^{\circ}\)C, respectively. Data derived from this sensitivity analysis were returned to the training process to estimate compressive strength. For each parameter, a corresponding sensitivity analysis factor was given by the expression:  
\[I_{i}=f_{max}(x_{i})-f_{min}(x_{i}), \tag{5}\]  
\[SA_{i}=\frac{I_{i}}{\sum\limits_{i}I_{i}}\times 100, \tag{6}\]  
where \(f_{max}(x_{i})\) and \(f_{min}(x_{i})\) are the maximum and minimum estimated compressive strengths relating to the input variable \(x_{i}\), with all other input parameters kept constant at their mean values.  
Figure 10 shows the results of this sensitivity analysis, from which a pronounced influence (35.5%) of fly ash/aggregate ratio on estimated compressive strength can be seen. A similar effect was observed in the study by [PERSON] and [PERSON] [11], and can be explained by the fact that the internal void structure  
Figure 9: Error rate distribution of three proposed approaches: **(a)** validation ANN; **(b)** validation DNN; **(c)** validation ResNet.  
formed by fly ash and aggregates has direct effects on FAGP compressive strength. Additionally, shown in this figure are high sensitivity scores of 16.22%, 16.18%, and 14.93% for curing time, NaOH concentration, and curing temperature, respectively. This confirms the observations from previous studies by [22; 23] where curing conditions play significant roles in compressive strength of FAGP concrete. As such, various factors such as mix proportions, sodium hydroxide concentration, and curing regimes should be thoroughly considered in the prediction of FAGP mechanical properties using machine learning approaches. In particular, based on these findings, it is recommended that the fly ash/aggregate ratio is carefully determined and controlled in geopolymer manufacturing processes owing to its pronounced effect on FAGP strength.
## 3 Methodology
### Fuzzy Fault Tree Analysis
#### 3.3.1 Domain Expert Evaluations  
The failure probability of EMA's BEs needs to be evaluated by experts. Expert judgment methods have been widely adopted in different fields, including risk analysis, accident investigation, decision examination, and so on [60]. In practical applications, experts may have varying degrees of professional knowledge, background, and working experience. They may show different views on the same events and provide various assessments subjectively. Therefore, a weighting coefficient is introduced to represent the relative quality of various experts.  
According to the actual situation of the substation, modify the expert's weighting scores calculation table proposed by [PERSON] et al. [61], and Table 1 can be obtained. Refer to Table 1, using Equation (6) to calculate the expert's weighting coefficient.  
\[\left\{\begin{array}{l}WEI_{i}=\frac{SCO_{i}}{\sum_{i=1}^{i}SCO_{i}}\\ SCO_{i}=SPP_{i}+SEC_{i}+SPE_{i}\end{array}\right. \tag{6}\]  
where \(WEI_{i}\) is the \(i\)th expert's weighting coefficient, \(SCO_{i}\) is the \(i\)th expert's total score, \(SPP_{i}\) is the \(i\)th expert's professional-position score, \(SEC_{i}\) is the \(i\)th expert's education-level score or competency score, and \(SPE_{i}\) is the \(i\)th expert's professional-experience score.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Constitution** & **Classification** & **Score** \\ \hline \multirow{4}{*}{Professional position} & Foreman/Deputy foreman & 5 \\  & Shift foreman/Safety officer & 4 \\  & Deputy shift foreman/Technician & 3 \\  & Watcher/Deputy watcher & 2 \\  & Trainee & 1 \\ \hline \multirow{4}{*}{Education-level/Competency (The higher one is preferred)} & Doctor/Senior technician & 5 \\  & Master/Senior engineer & 4 \\ \cline{1-1}  & Undergraduate/Intermediate engineer & 3 \\ \cline{1-1}  & Junior college/Junior engineer & 2 \\ \cline{1-1}  & Below the junior college/Junior engineer & 1 \\ \hline \multirow{4}{*}{Professional experience time} & Greater than 20 years & 5 \\ \cline{1-1}  & 15 to 20 & 4 \\ \cline{1-1}  & 10 to 15 & 3 \\ \cline{1-1}  & 5 to 10 & 2 \\ \cline{1-1}  & Less than 5 years & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Expert’s weighting scores.
## 2 Research Scope, Methodology, Bibliometric, and Citation Analysis
### Research Methodology  
The primary goal of the systematic review is to give a comprehensive understanding of the current state of the art on a research topic or a section. The systematic review was carried out as described by (author). Two primary databases were considered for the systematic review: (1) SCOPUS (Elsevier Publishers) and (2) Web of Science (Institute for Scientific Information/Clarivate Analytics). As the search covers major scientific publications, we have confidence that all the major research approaches for creating a strategic, sustainable IoT-based composting facility in modern city ecosystems. The research criteria followed a TITLE-ABS-KEY schema using keyword terms' internet of things','smart waste management', 'intelligent waste sorting', 'composting automation','real-time monitoring', 'IoT-based composting', 'urban waste management', 'waste solutions', 'composting systems', 'organic waste recycling','machine learning', 'augmented reality', 'computer vision', 'artificial intelligence','sensor-based systems', 'data-driven decision-making', 'circular economy','sustainable development goals', 'pollution discharge monitoring', 'cost analysis', 'life cycle assessment','modern city'. The initial articles were filtered using pre-determined criteria for eligibility and relevance. The exclusion criteria for the articles include studies on non-composting waste management (e.g., electronic waste, hospitals, and industrial), outdated or non-specific research, general IoT applications non-related to composting, and non-peer-reviewed articles with non-urban focus unless applicable to urban settings. One hundred twenty-one were found to be directly related to the comprehensive IoT-based technology integration into sustainable modern city waste management. Next, the selected works were organized into categories. According to the scope of the study, 121 publications were analyzed across five key themes: (1) IoT-based urbanized composting automation; (2) intelligent composting process optimization; (3) urban composting sustainability assessment; (4) composting process pollution and environmental impact; (5) cost and life cycle analysis of modern city composting.
## 1 Introduction
In view of these technological disadvantages, there is a need for a new, 3D, robust, and reproducible measurement method that provides measured values directly and can integrate these measurements with various parameters, such as an applied force and the direction of the force [32]. These conditions can be met by the ARAMIS system (GOM GmbH, Braunschweig, Germany), which--based on triangulation and photogrammetry-- enables the dynamic measurement of surfaces in three dimensions, digitally and in real time. This system has already been introduced as a standard in connection with material testing and mechanical testing in the automotive and aerospace industries [33]. It is a two-camera system that provides noninvasive optical acquisition. This allows measurements to be made within specific limits for both in vitro and in vivo situations [34; 35]. As a limitation, it should be noted that only structures that are accessible to light optics can be investigated and that no phase boundaries, for example between air and water, may exist in the light path. Of scientific interest in materials science, the integration of various parameters, such as force magnitudes and time, are implemented in real time and fully digitally via the ARAMIS Professional software. The aim of this study is to show the comparability between the established method of strain gauges and the ARAMIS system and thus to introduce this optical, fully digital measurement method in the field of dental biomechanics as a new area of application.
# Ship Route Oil Spill Modeling: A Case Study of the Northeast Brazil Event, 2019
## 2 Materials and Methods
### Model Evaluation
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Number of liquid/solid particles & 2000 \\ X direction resolution (m) & 2980 \\ Y direction resolution (m) & 2900 \\ Z direction layers & 10 \\ Maximum concentration grid depth (m) & 1000 \\ Latitude & 22.424\({}^{\circ}\) S \\ Longitude & 39.957\({}^{\circ}\) W \\ Oil type & Blended Oil \\ \({}^{\circ}\)API oil & 22.8 \\ Viscosity (cP) & 28,000 \\ Start date & 2 March 2019 at 8 p.m. \\ Duration of spillage & 12 h \\ Volume & 122 m\({}^{3}\) \\ Simulation period & 12 days \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Model setting. Figure 2: Oil cell coverage superimposed on the daily surface currents (**top panels**) and hourly wind velocity (**bottom panels**) for 1 and 3 April (red lines) on left and right panels, respectively. The shaded gray area indicates the period of wind change direction from southeasterly to northeasterly, which probably drove the oil slick toward the coast. The red star marks the location of the wind data from ERA5, and the black cross indicates the location of platform P-53. The isobaths represent the depths of 200 and 100 m.
# The Extended SLM Combined Autoencoder of the PAPR Reduction Scheme in DCO-OFDM Systems
## 3 Results and Discussion
### PAPR Comparison  
First, we evaluated the PAPR performance of the presented system compared to AE, SLM, DCO-OFDM with different clipping ratio \(\gamma\) and the DCO-OFDM with no PAPR reduction scheme. CCDF curves are presented to illustrate the PAPR comparison results depicted in Figure 4. It can be observed from Figure 4 that the PAPR of the proposed ESLM-AE method outperforms other methods with a PAPR reduction gain of 10.8 dB compared to DCO-OFDM, while the SLM \(U=128\) gives the least PAPR reduction gain of 4.9 dB. To reach a CCDF of \(10^{-3}\), for example, the PAPR is 2.5 dB for the ESLM-AE, 3.1 dB for the AE, 8.2 dB for the SLM \(U=128\), 5.7 dB and 6.0 dB for the clipped  
DCO-OFDM with \(\gamma=1.2\) and \(\gamma=1.5\). Additionally, the AE method exhibits a higher PAPR than the proposed ESLM-AE, because the ESLM-AE signals are jointly processed with the extended SLM technique in the deep learning network. Therefore, the proposed ESLM-AE method enjoys a significant PAPR reduction in terms of CCDF, which can diminish the linearity requirement of the LED.
## 4 Discussion
Just the catalysis of silanol condensation is not enough to explain the bonding energy enhancement. Instead, asperity softening mechanisms seem to be much more significant. The asperity hydrolysis mechanism emerges as the most plausible explanation for the enhancement of direct bonding energy. This hydrolysis is driven by water itself, but elevated temperatures are necessary to facilitate water penetration into the roughness asperities. In this context, the addition of basic molecules catalyzes the hydrolysis, which explains the results observed during post-bonding annealing: the adherence increases more rapidly with chemical booster molecule. When plasma treatment is applied, the asperities are preemptively modified by ion bombardment, enabling water hydrolysis to occur effectively even at room temperature. This last phenomenon could be even amplified by adding chemical booster molecule. Naturally, if plasma treatment reduces the energy barrier for water to penetrate asperities at room temperature, the effect becomes even more pronounced during annealing. This explains the remarkable efficiency of plasma treatments, even at relatively low post-bonding annealing temperatures. It is not shown in this article, but we suspect to have an amplification of this effect adding chemical booster molecule after the plasma activation and post bonding annealing. The primary effect of plasma is to facilitate water penetration into the roughness asperities, enabling their hydrolysis. This is further supported by the observation that no enhancement in bonding energy occurs when plasma-treated surfaces are bonded within the plasma chamber without introducing water at the bonding interface, as demonstrated by [PERSON] et al. [39]. The system, therefore, requires water to improve silica adherence. A high concentration of water at the bonding interface and in the subsurface is beneficial for this purpose. However, excessive water concentration can weaken siloxane bonds between the wafers, shifting the equilibrium of Equation (7) to the left:  
\[\text{SiOH}+\text{ SiO}^{-}\rightarrow\text{ SiOSi}+\text{OH}^{-}\]0  
It is also noteworthy that, if all the bonding energy enhancement techniques (temperature, plasma, chemical molecule) is to enable the water penetration inside the roughness asperity to hydrolyze them, this will reduce water at the bonding interface, pushing the equilibrium of Equation (7) to the right and favoring covalent bonding between the wafers. While removing water from the bonding interface increases bonding energy, it is a side effect of the primary mechanism--the need to soften the roughness asperities through silica hydrolysis. Thus, it can be hypothesized that an optimal water amount exists that is sufficient to enable the mechanical softening of asperities but not excessive, and to minimize residual water at the bonding interface and maximize silanol condensation between the bonded surfaces.
# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 4 Results and Discussion
### Evaluation of PV Generation Forecasting Model Performance with Missing Data Imputation
#### 4.1.1 Evaluation of Forecasting Model Performance with Missing Data Imputation of Training Data  
We used the weather dataset of 2017 for the forecast and 12 training weather datasets with missing values (2016) with three missing data rates and applied the four missing data imputation methods. The simulation process was as follows: First, we generated a conventional training dataset using the missing data rates (i.e., 10%, 15%, and 20%). Second, we replaced the missing values with new values obtained by use of each missing data imputation method. Third, we initiated progressive learning with the training dataset by application of the missing data imputation and test data. Lastly, we predicted the solar power generation for 24 h by means of the learned model. This set of steps forms one cycle that was repeated five times. Finally, we calculated the RMSD and MRD for each of the five predicted values. Here, the error was calculated from the predicted value of the trained model by use of the reference model predicted value and the data obtained using the missing value replacement method.  
The reference model refers to the trained model with the original training data and test data with no missing values.  
Next, the values obtained with the iterations were averaged for each test period (i.e., 20-27 January, March, and June 2017) and missing data rates (10%, 15%, and 20%). The results corresponding to the prediction model error are listed in Table 1, while the deviation results are listed in Tables 2-4.  
Figure 8: _Cont._  
Figure 7: Forecasting results from 20–27 March for various missing data rates (10%, 15%, and 20%).  
.1.2 Evaluation of Forecasting Model Performance with Missing Data Imputation for the Test Data Case  
Here, we carry out the same process as that described in Section 4.1.1, except that the missing data are the test data and not the training data. The errors resulting from the estimation of the power generation with the application of the four missing data imputation methods according to the missing data rate with one training dataset are listed in Tables 5-7. As mentioned in Section 4.1.1, the mean value of the error was calculated over five trials, and the average of the predicted power generation was calculated by use of the four methods; the corresponding forecasting results are shown in Figures 9-11.  
Figure 10: _Cont._  
Figure 9: Forecasting results from 20–27 January for various missing data rates (10%, 15%, and 20%).  
Figure 11: _Cont._  
Figure 10: Forecasting results from 20–27 March for various missing data rates (10%, 15%, and 20%).
# A Review on the Lifecycle Strategies Enhancing Remanufacturing
## 5 Research Gaps and Future Work
### Future Work  
Remanufacturing, as earlier mentioned, has been gaining a lot of popularity due to the urgent need to attain sustainable development. There is still a lot of work that needs to be done to ensure that the ideas that scholars and research institutes come up with can be realized. Some of the future work includes the following:  
1. DfRem standards should be set for companies that manufacture the same products; this would improve the uniformity of the products during remanufacturing.
2. There needs to be a mechanism set in place that can ensure the safe sharing of data between stakeholders without the fear of information leakage.
3. An online monitoring system for lower value products could be established that would expand online monitoring beyond high value products.
4. More research needs to be done on how the RUL of a product can be calculated at any given time in its lifecycle.
5. More case studies should be used to demonstrate how emerging technologies like smart remanufacturing, I4.0 and SRDM can be applied in real circumstances.  
The bigger picture is to obtain a balance between the three aspects of the triple bottom line, namely social, economic and environmental needs. In the domain of remanufacturing, there needs to be more awareness and willingness from companies and nations to adopt remanufacturing as a main recovery option. More work needs to be done on the various actions that can be taken to ensure that a product can be recovered at its end-of-life stage. This worldwide awareness will definitely lead to sustainable development and a great decrease in the environmental pollution.
## 2 Study Area and Datasets
### Satellite Imagery
#### 2.3.1 Spatial Resolution  
There are five pushbroom satellites in the commercial RapidEye constellation, providing a daily off-nadir repeat cycle and a 5.5 day repeat cycle at nadir. The ground sampling distance of the multispectral sensors on board these satellites is 6.5 m but this is re-sampled to provide users with a 5 m spatial resolution for Level 3a orthorectified imagery. A 5 m spatial resolution will enable subtle changes in bathymetry to be identified and quantified (such as bedforms and channels), whereas larger pixel sizes such as a 30 m Landsat 8 pixel will cause a greater degree of generalisation/smoothing to be introduced and steep submerged gradients may not be resolved, leading to errors in bathymetric charts. The 5 m spatial resolution also corresponded with the 5 m MBES grid spacing and each pixel could then be matched with a corresponding MBES point.
## 2 Solar Radiation Components  
Modelling solar radiation is a very complex task because it is influenced by climatic zone, geographical area or seasons. Solar radiation provides the quantity of solar energy that reaches the Earth's surface during a particular time period [17]. It is important to know the whole phenomenon, starting from the definition of the sun, which is a star inside which a series of reactions take place that produce a loss of mass that is transformed into energy. Solar irradiance is the measured and recorded amount of energy that comes directly from the sun to the land surface [18]. There are three main types of solar radiation, which are the following: diffuse, direct and global solar radiation. Diffuse radiation is that which occurswhen radiation that comes directly from the sun is intercepted by the Earth's atmosphere, causing a scattering in the middle [19]. Diffuse solar radiation creates a problem in the generation of electricity by photovoltaic solar panels, reducing their generation capacity, that is, in solar radiation, clouds absorb all the incident energy and emit it again [20]. Direct solar radiation comes in a defined direction from the sun towards the earth, which, it is possible to concentrate it in a point for its use, however, it can also be reflected. This radiation is essential in the use and sizing of solar concentration systems [21]. With diffuse and direct solar radiation, it is possible to determine the global solar radiation in an area. Furthermore, it is possible to acquire information on global solar radiation by means of pyranometers that measure the solar irradiance from the sun to an area [22].
# Enhancing PAPR and Throughput for DFT-s-OFDM System Using FTN and IOTA Filtering
## 4 Simulation Results and Analysis  
In this section, we implement the proposed IOTA-based FTN-DFT-s-OFDM and evaluate the PAPR, BER and throughput performance under various MCS settings and acceleration rates. Link-level Monte-Carlo simulations are conducted to evaluate the performance gain of the proposed scheme over DFT-s-OFDM and non-orthogonal waveform (NOW) [32], which adopts RRC pulse as the prototype filter of FTN waveform. The main simulation parameters are presented in Table 2.  
Figure 3: The transceiver design of the proposed waveform.