esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Industry 4.0
### Challenges and Future Directions  
Established manufacturing environments operate automation processes with legacy equipment via robust communication means. During the 3 rd industrial revolution, when operations were automated via logic controllers and information technology, there was little to no requirement to send sensory information outside of the control of the local automation system. Industry 4.0 introduced technology that requires increased levels of connectivity [100]. This makes the practical implementation of Industry 4.0 difficult when attempting to integrate the enabling technologies whilst guaranteeing robust performance [110]. Barriers, such as legacy system integration with Industry 4.0 technologies, are challenging organisations to adopt new technology. Therefore, communication technology that assists with the integration of robust legacy platforms and IoT platforms whilst guaranteeing reliability and latency is fundamental for the development of Industry 4.0.  
Recent developments in sensory technology have the potential to enable large amounts of information to be collected from real-life systems. Strategic placing and access to these sensors enable the manufacturing system to make timely reactions to changes in customer demands. From this case study, it can be identified that for the sensing, analysis and control of a manufacturing system, such as the FMS described here, it is vital that there is a high level of connectivity. This presents some challenges for established manufacturing environments with investments in technology that are not designed with this level of connectivity in mind. A requirement for this case is also that the information distributed around the system is achieved in a timely manner.  
For successful implementation of machine learning technologies (also forecast to have high impacts to industry) time-sensitive datasets are required. Hence, this example would highly benefit from two of the three key drivers proposed for 6G: (1) connection of all things and (2) time-sensitive applications.
## 4 Results and Discussion
### Spatial and Temporal Characteristics
Of investigated research, 82%  
Figure 15: The effect of the number of Landsat imagery per year in time series analysis on the accuracy of change detection studies. collected reference data in the office from high-resolution images such as Google Earth or existing land cover maps. Recently, GEE has been one of the major infrastructures for geo-big-data processing. Amongst the studies using GEE, 92 are implemented at a large geographical scale. Parallel geospatial data processing is enabled by Google's computational infrastructure to reduce computational time. In addition to Landsat-1-Landsat-8, petabytes of other Earth observation data, such as Sentinel 1, 2, 3, 5-P, AVHRR, SRTM DEM, and MODIS, are available within the GEE data catalogue. GEE also provides APIs for JavaScript and Python and a cloud-based integrated development environment (IDE) for scriptwriting and visualizing the data. One of the key features of GEE is the existing ready-to-use packages to simplify coding for both scientific and commercial users [30].
# Detection of Crustal Uplift Deformation in Response to Glacier Wastage in Southern Patagonia
## 3 Geospatial Data Processing  
### Scientific Processing of the GPS Network  
Data daily solutions were obtained at GRCS, GBCS, LPAS, and PUMA stations that were referenced to the International GPS Service 2008 (IGS08) terrestrial reference frame by scientific processing of GPS data with BERNESE v5.0 software [39]. Accurate ephemerides and Earth rotation parameters consistent with the reference frame were used, as well as corrections to the phase center of the antennas. The FES2004 model was applied to perform the oceanic load corrections. For the tropospheric correction we used the Vienna Mapping Function with zenithal delay estimation every 2 h [40], and for the ionospheric corrections we followed [PERSON] et al. [41]. Finally, the ambiguity resolution strategy was QIF (quasi-ionosphere-free).  
The daily GPS time series allowed linear displacement trends to be estimated for each site after outlier removal. From these series, repeatability analysis of each component of the continuous station coordinates sites were performed to estimate the uncertainty in positioning and velocities. Uncertainty of 10 mm in the antenna position for the three components was added for the episodic stations since the antenna was mounted in each campaign. The total uncertainty obtained for each site was propagated according to the time interval between the first and last observation at each site. In particular, for the vertical component (Up) an uncertainty of 2 mm a\({}^{-1}\) was added in agreement with [PERSON] and [PERSON] [42].
# Predicting Road Traffic Accidents--Artificial Neural Network Approach
## 1 Introduction
#### Literature Review
Artificial Neural Network (ANN) approach was given in [PERSON] and [PERSON] [28] paper. Bayesian Network with Rough Sets for traffic accident analysis was the subject of research in [PERSON] et al. [29] paper. In their paper, [PERSON] and [PERSON] [30] analyzed traffic accidents by using Log-Linear Models. Bayesian Neural Network was the subject of research in [PERSON] et al. [31] paper. [PERSON] et al. [32] analyzed the impact of road and traffic characteristics on the occurrence of traffic accidents with fatalities on state roads in Serbia. A regression analysis was used for data analysis, which was the first attempt at this type of analysis in Serbia. [PERSON] et al. [33] made a calculation of traffic accident costs for major roads in the Republic of Serbia, with the costs per kilometer. Some of the influencing factors on the occurrence of traffic accidents were presented in the paper by [PERSON] et al. [34]. Overall, the influence of different factors on traffic safety and the occurrence of traffic accidents has been widely studied, with many researchers focusing on the impact of traffic volume, project geometry, traffic management characteristics, and other related factors. These studies have resulted in the development of various accident prediction models and methods for analyzing and predicting traffic accidents. These studies, each employing specific tools and methodologies, have contributed to our understanding of the factors influencing traffic accidents. However, there is a need to investigate the prediction of accidents in the Republic of Serbia and the Republic of Srpska (Bosnia and Herzegovina) using objective factors. By utilizing Artificial Neural Network models, this study aims to predict traffic accidents accurately and determine their severity based on factors such as road length, terrain type, road width, average daily traffic volume, and speed limit. The findings from this research will provide insights for improving road safety and supporting decision-making in transportation planning. As to our knowledge, this paper is the first to implement artificial neural networks to predict traffic accidents in the Republic of Serbia and the Republic of Srpska (Bosnia and Herzegovina) using objective factors: road length, terrain type, road width, average daily traffic volume, and speed limit.
## 5 Experimental Results
### Case Study of Vietnamese's Vicuna Benchmark  
The experimental results shown in Table 4 are the results related to the Vietnamese benchmark. In a comparative analysis we can see that the _Expert-B_ and Phoenix models displayed closer competition.  
For a total of 80 categories, the _Expert-B_ model showed an improvement of model's performance in 36 categories while the Phoenix model performed better in 44 categories for the Vietnamese benchmark. The Phoenix model dominated in more categories such as coding, fermi, and knowledge. In contrast, _Expert-B_ only managed to generate categories. In summary, the results for the Vietnamese benchmark are as follows:  
* The Phoenix model showed a better performance than _Expert-B_ in some categories, particularly in coding and fermi. However, _Expert-B_ demonstrated better performance in several other categories such as common-sense, counter-factual, and writing.
* The overall results for the two models for the Vietnamese benchmark were much closer than in the English benchmark, with the margin for the Phoenix model's performance improvements being relatively small.
## 4 Results
### Results for Deriving Local Preferences for the Alternatives  
Evaluation of local preferences for the alternatives (mining waste sites) with AHP produced the results presented in Table 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Position** & **Criterion Name** & **Weight [\%]** \\ \hline
1. & Use in road and railroad construction & 32.6 \\
2. & Use in construction & 25.0 \\
3. & Use in agriculture & 15.2 \\
4. & Use for reclamation/development of post-mining areas & 15.0 \\
5. & Use in the food industry & 9.2 \\
6. & Storage of mining waste & 2.9 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Weights of criteria (potential waste uses).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Alternatives** & **Storage** & \
\begin{tabular}{c} **Reclamation/** \\ **Development** \\ \end{tabular} & **Agriculture** &
\begin{tabular}{c} **Food** \\ **Industry** \\ \end{tabular} & **Road/R-Road** \\
**Construction** & **Construction** \\ \hline Site 1. Krzenów & 0.058 & 0.097 & 0.242 & 0.025 & 0.211 & 0.149 \\ Site 2. Lubieri & 0.197 & 0.136 & 0.093 & 0.260 & 0.067 & 0.080 \\ Site 3. Grabina Sl. & 0.143 & 0.262 & 0.258 & 0.111 & 0.308 & 0.309 \\ Site 4. Gniewkow & 0.136 & 0.206 & 0.276 & 0.182 & 0.287 & 0.318 \\ Site 5. Boguszyce & 0.163 & 0.171 & 0.070 & 0.192 & 0.063 & 0.073 \\ Site 6. Romanowo & 0.303 & 0.128 & 0.061 & 0.230 & 0.064 & 0.072 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Weights of local preferences with respect to potential waste uses (criteria).  
The Consistency Ratios (CR) for pairwise comparisons for alternatives with respect to criteria vary from 0.5% to 3.8% and are within the required limits (\(\leq\) 10%). The lowest value (0.5%) was obtained for the use in construction criterion and the highest (3.8%) for the use in agriculture criterion. The highest consensus (90%) of the seven experts was achieved for the pairwise comparison with respect to the use in road and railroad construction criterion, whereas the lowest one (73.3%) for the reclamation/development of post-mining area criterion.  
The overall priorities for the alternatives (waste sites) given the weight of each mining waste use criteria (synthesis of the AHP model) are presented in Table 4.
# Effects of Adding Batteries in Household Installations: Savings, Efficiency and Emissions
## 5 Data Analysis
### Electricity Costs  
The evaluation of the charging curve was established in December and June. Figure 4 shows the total costs of these months along the day.  
Due to the absence of battery (Figure 4a,b) the electrical network supplies the household demand along both months by means of the AC system.  
In both systems with battery in June, Figure 4c,e, the charge levels are similar to the demand produced during those months. As a consequence, when the battery is discharging, the costs are 0 \(\epsilon\) and an overcost is produced because of the charging process to recover the maximum. The costs in the combined AC/DC system begin to be due to the household demand at 2 a.m. On the contrary, the AC system starts at 3 a.m. This difference is due to the lower losses in the AC/DC system.  
In the AC system with battery in December, Figure 4d, as seen before, the costs are 0 \(\epsilon\) when the battery is discharged and an overcost arises when the maximum charging level must be recovered. The percentage of charge is lower than 20% at 8 p.m., so the battery must be charged. The costs at 10 p.m. are 0 \(\epsilon\), due to the charge during the previous hour and because it still happens during peak hours. Off-peak hours commence at 10 p.m., so the battery must charge; this produces an overcost. Finally, the costs begin to be due to the household demand at 2 a.m.  
Figure 3: Charging cycle for specific days of the year for the battery-powered systems: (**a**) AC system on 17 June. (**b**) AC system on 16 December. (**c**) Combined AC/DC system on 17 June. (**d**) Combined AC/DC system on 16 December.  
In the combined AC/DC system with battery in December, Figure 4f, as seen before, the costs are 0 \(\epsilon\) when the battery is discharged and an overcost is produced to recover the maximum charging level. The percentage of charge in the battery at 9 p.m. is lower than 20%, so the battery must be charged. The off-peak hours start at 10 p.m., so the battery continues charging. Finally, the costs begin to be due to the household demand at 2 a.m.
## 3 Materials and Methods
### Shrinkage
#### 3.2.3 Group-Lasso Regression  
In machine-learning problems, methods such as group lasso can be used to find groups of variables. Group lasso [33; 34] is another popular method for variable selection, which uses the \(\ell_{1}\)-norm for the selection and shrinkage of variables. This method is a generalization of lasso which performs the group-wise variable selection, applies the group constraints to variables, and estimates coefficients as a group due to the group structure. Consequently, the group of coefficients becomes either zero or non-zero. In contrast, lasso and elastic net perform shrinkage for each variable. The parameter vector \(\beta\) is divided into \(\mathcal{G}_{1},\ \mathcal{G}_{2},\dots,\ \mathcal{G}_{q}\) groups where \(\cup_{j=1}^{q}\mathcal{G}_{j}=\{1,2,\dots,p\}\). The vector \(\beta\) is  
\[\beta=\Big{(}\beta_{\mathcal{G}_{1}},\ \beta_{\mathcal{G}_{2}},\dots,\beta_{ \mathcal{G}_{q}}\Big{)}^{\prime},\beta_{\mathcal{G}_{j}}=\big{\{}\beta_{r}\ ;r\in\mathcal{G}_{j}\big{\}}\]  
Suppose \(\left(\mathbf{X},Y\right)\) is a dataset so that \(\mathbf{X}=\left(x_{1},\dots,\ x_{p}\right)^{\prime}\) is the prediction variable and \(Y\) is the response variable. The group lasso estimator is defined as follows:  
\[\hat{\beta}^{slasso}=\underset{\beta}{argmin}\left\{\frac{1}{n}Y-\mathbf{X}\beta^{ 2}+\lambda\sum_{j=1}^{q}m_{j}\Big{|}\beta_{j}\Big{|}\right\} \tag{7}\]  
where \(m_{j}\) is a coefficient to create balance in groups of different sizes. The \(m_{j}\) is selected as \(\sqrt{T_{j}}\) where \(T_{j}\) is the cardinality \(\big{|}\mathcal{G}_{j}\big{|}\) and \(\lambda\) is the tuning parameter that controls the amount of regularization [35].
## 2 Performance of IWP with Steel Anchor Beam
### FE Analysis and Results
#### 2.3.2 Model Validation  
Figure 16a compares the experimental and FEM Mises stress values on plate D under a 2.5P load (only the first row of measurement points on plate D is listed). Figures 16b and 16c show the comparison of experimental and finite element-calculated stress at measurement points p-10 and p-15 and relative slip at measurement point WY-3. The experimental stress and relative slip values show good agreement with the FE results. The slight difference in these curves can be attributed to the fact that bond strength was not considered in the FE model and that small gaps or imperfect contact likely existed at the steel-concrete interface in the physical test, causing the stress to increase gradually at the beginning. These results indicate that the FE model can accurately reflect the experimental results.
## 6 Discussion
As previously mentioned, the World Bank determines the LPI values by assigning equal weights to the indicators and calculating their weighted totals. We designated the Entropy-LPI scores as a fixed factor and obtained standard deviation values by comparing them with the LPI results derived from 105 different sets of weights. Consequently, the Entropy-LPI and Entropy-SAW analyses involve similar analytical processes and can be assessed under the same category. Figure 6 presents the six-year averages of the standard deviation values based on the rankings obtained from MCDM methods and the LPI. With an average standard deviation of 0.009, the MCRAT method was identified as the most sensitive method. It was followed by TOPSIS (\(\sigma\) = 0.008), SAW/LPI (\(\sigma\) = 0.003), and FUCA (\(\sigma\) = 0.002), respectively. Among all the rankings, the FUCA method was identified as the least sensitive method. In other words, it was determined that FUCA has the highest correlation with the fixed factor. As a result, we recommend the FUCA method to decision-makers for the calculation of LPI rankings because it is simple, practical, has low compensatory power, and has low sensitivity. \begin{table}
\begin{tabular}{c c c c c c} \hline
**Rank** & **MCRAT** & **SAW** & **TOPSIS** & **FUCA** & **LPI** \\ \hline
1. & Germany & Germany & Germany & Germany & Germany \\
2. & Singapore & Singapore & Netherlands- & Singapore & Singapore \\
3. & Netherlands & Netherlands & Sweden & Netherlands & Netherlands \\
4. & Sweden & Sweden & Belgium & Sweden & Sweden \\
5. & Belgium & Belgium & Japan & Belgium & Belgium \\  &. &. &. &. &. \\  &. &. &. &. &. \\
114. & Angola & Angola & Libya-Gabon & Cuba & Gabon \\
115. & Cuba & Cuba & Cuba & Libya & Haiti \\
116. & Haiti & Haiti & Haiti & Iraq & Cuba \\
117. & Iraq & Iraq & Iraq & Afghanistan & Iraq \\ \hline \end{tabular}
\end{table} 
Table 7: Countries with the highest and lowest logistics performance based on different rankings.
# A Novel Fused Multi-Class Deep Learning Approach for Chronic Wounds Classification
## 3 Methodology
### Swish-ELU EfficientNet-B4
#### 3.3.3 EfficientNet-B4 Concatenation and Global Average Pooling Layer  
After that, the two models (i.e., Swish-EfficientNet-B4 and ELU-EfficientNet-B4) are fused through the concatenation method. The features extracted from such a fused model are more informative and discriminative, which can help to further improve the overall classification accuracy of the model. The feature maps produced by the fusion of models are then passed to the global average pooling layer to reduce the spatial dimensions of the feature maps and to provide a compact representation that can capture more salient information. These feature representations are then propagated to the prediction module for classification.
## 3 Results
### Model Simulations of GPP, Re, and NEP under All Sky Conditions from 2003-2017
#### 3.1.2 Daily Sum Simulations of GPP, Re, and NEP
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**RMSE**} & \multicolumn{3}{c}{**MAD**} & & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{**(\%)**} & **(mgCO\({}_{2}\)** & \multirow{2}{*}{**(\%)**} & **(mgCO\({}_{2}\)** & \multirow{2}{*}{**(\sigma_{\rm obs}\)**} & **\(\sigma_{\rm cal}\)** & **NMSE** & **R\({}_{3}\)** & **R\({}_{2}\)** & **R\({}_{1}\)** & **obs** & **cal3** & **cal2** & **cal1** & **Model** \\  & & & & & & & & & & & & & & & \\ \hline
55.81 & 2.643 & 42.82 & 2.028 & 2.372 & 3.338 & 0.280 & 1.114 & 1.035 & 1.931 & 4.736 & 5.276 & 4.901 & 9.144 & EMGPP \\ \hline
151.01 & 2.533 & 126.42 & 2.121 & 0.801 & 3.192 & 2.159 & 1.056 & 0.592 & 3.611 & 1.678 & 1.771 & 0.993 & 6.058 & EMRe \\ \hline
39.06 & 2.547 & 34.99 & 2.282 & 1.411 & 0.608 & 0.235 & 0.650 & 0.599 & 0.473 & 6.522 & 4.240 & 3.906 & 3.084 & EMNEP \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Same as Table 1, but for daily GPP, Re, and NEP simulations. It is found that RMSE values were 2.60 gC m\({}^{-2}\) and 55.81% for the simulations of the mean daily sums of GPP from 2003-2017, which reasonably agreed with the simulations from 2013-2016, 3.55 gC m\({}^{-2}\) and 87.60% using the 3-factor EMGPP and solar radiation and meteorological inputs measured by another measurement system (Bai's group), and 3.43 gC m\({}^{-2}\) and 83.23% using the 2-factor EMGPP, respectively [40]. Under all sky conditions, the simulated Re values were higher than the observations from 2003-2017, similar to those from 2013-2016. However, the NEP simulations were underestimated from 2003-2017, which were different from that (overestimation) from 2013-2016, i.e., the calculated Re values overestimated the observations by \(\sim\)30% for the mean hourly and annual sums in 4 years [41].
## 2 Basic Theoretical Parameters of FE-SEA Method
### Internal Loss Factor  
#### 2.2.1 Internal Loss Factor of Structure  
The internal loss factor of the structure is used to represent the ratio of the energy loss per unit time of each subsystem to the average energy within the unit frequency interval. In general, the internal loss factor of the structure consists of three basic components, as follows [12; 13]:  
\[\eta_{i}=\eta_{is}+\eta_{ir}+\eta_{ib} \tag{17}\]  
where, \(\eta_{is}\) is the loss factor of the structure, which is generated by the internal friction of the structure, \(\eta_{ir}\) is the loss factor generated by the \"acoustic-vibration radiation\" of the structure, and \(\eta_{ib}\) is the loss factor generated by the damping at the boundary junction. \(\eta_{is}\) follows the change function of the material characteristics of the structure. \(\eta_{ir}\) is generated by the outward radiation of the vibration of the structural subsystem. The acoustic radiation loss factor in the lightweight plate contributes more to the internal loss factor of the plate. The acoustic radiation loss factor \(\eta_{ir}\) is a function of frequency \(f\), and the relationship is as follows:  
\[\eta_{ir}(f)=\frac{\rho_{0}c\sigma_{sa}}{2\pi f\rho_{s}} \tag{18}\]  
where \(\rho_{0}\) is the air density, \(c\) is sound velocity, \(\sigma_{sa}\) is the radiation ratio of the subsystem, \(\rho_{s}\) is the area mass density of the subsystem. The calculation formula is as follows:  
\[\frac{\partial^{2}u(x,t)}{\partial t^{2}}+R^{2}C_{l}^{2}\frac{\partial^{4}u(x,t)}{\partial t^{4}}=0 \tag{19}\]
# Analytical Approach in the Development of RF MEMS Switches
###### Abstract  
Currently, the technology of microelectromechanical systems is widely used in the development of high-frequency and ultrahigh-frequency devices. The most important requirements for modern and advanced devices of the ultra-high-frequency range are the reduction of weight and size characteristics, power consumption with an increase in their functionality, operating frequency and level of integration. Radio frequency microelectromechanical switches are developed using the technology of the manufacture of CMOS-integrated circuits. Integrated radio frequency control circuits require low control voltages, the high ratio of losses to the isolation in the open and closed condition, high performance and reliability. This review is devoted to the analytical approach based on the knowledge of materials, basic performance indices and mechanisms of failure, which can be used in the development of radio-frequency microelectromechanical switches.  
RF MEMS; switch; analytical approach; low control voltage; high switching speed; high reliability 2018
# Design and Performance Analysis of an In-Band Full-Duplex MAC Protocol for Ad Hoc Networks
## 5 Result and Performance Analysis
### Routing Time  
In this AdHoc-FDMAC, the AODV routing algorithm is used to select an efficient route. The AODV algorithm is used in the AdHoc-FDMAC using the IBFD communications. The comparison of the routing time of the AODV algorithm using the IBFD technique (described in Section 3.2.1) and routing time of the conventional AODV algorithm (without using IBFD technique) is shown in Figure 11. To calculate the routing time, we consider a node in the network from which three hops are required for a packet to reach the MS (final destination). Hence, if the packet requires more hops, the routing time becomes higher. The figure shows that routing time of AODV algorithm in AdHoc-FDMAC is 33.33% less than the conventional AODV routing.  
Figure 11: Routing time comparison.