esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Literature Review

Table 1: Methods exploited to mitigate nonlinearities. Along with the numerous techniques listed above, mitigation of the impairments of RoF system have also been studied previously [24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35]. A feedforward scheme has also been analyzed [26; 27] but is complex and to counter these problems, optical methods such as dual parallel modulation [28], mixed polarization [29; 30], etc. have been presented. In addition to the nonlinearities of optical fiber/modulators, RoF is liable to other issues such as amplified emission noise and phase noise [32; 33] that further limit performance of RoF increasing proportionally with RF carrier frequency increase. Moreover, Digital Signal Processing (DSP) methodologies have been exploited as well owing to their adaptive flexibility. A lot of such techniques have been studied to counter the phase noise issue [36; 37; 38], of optical modulator [39; 40]. Digital pre-distortion is presented for laser resonance, improving results in [41]. Apart from DSP approaches, Digitized RoF (DRoF) has also been utilized for nonlinearity mitigation [42; 43; 44; 45]. Another important concept proposed, Sigma Delta RoF (SD-RoF) is a method involving the plus points of the DRoF in addition to its own implementation [46; 47]. This is especially advantageous when utilizing optical transmitters having low linearity. On the other hand, considering Machine Learning as a domain of nonlinearity compensation, a lot of models and methodologies have been proposed such as the KNN/SVM [48]; SVM [49; 50]; k-means [51]; Convolution Neural Network (CNN) and Binary-CNN (BCNN) [52; 53] and Fully-CNN (FCNN) [54; 55; 56; 57; 58; 59; 60; 61].
# Heuristic Routing Algorithms for Time-Sensitive Networks in Smart Factories
## 4 Improved Genetic Algorithm to Solve TT Flow Routing Problem
### Description of GATTRP
#### 4.3.4 Offspring Breeding  
The optimization goal of the improved GA proposed in this section was to find the traversal route with the earliest \"latest arrival\" time of multiple traveling salesmen. Therefore, Equation (2) was determined as the fitness function for this section. We defined fitness \(fit_{i}\) as the evaluation criteria when breeding the \(i\)th chromosome, where \(fit_{i}=1/min(T)\). After generating the primary population according to the above chromosome encoding method, chromosomes with better fitness were selected to breed more offspring, so that they could keep their genetic advantages in the evolutionary iteration process. The specific number of reproducations of each chromosome was:  
\[O_{i}=\left\lfloor(O_{max}-O_{min})\frac{fit_{i}-fit_{min}}{fit_{max}-fit_{min}} \right\rfloor+O_{min}, \tag{7}\]  
where \(O_{i}\) represents the number of offspring of the \(i\)th chromosome, \(O_{max}\) and \(O_{min}\) are the maximum and minimum allowable numbers of breeding offspring, respectively, and \(fit_{max}\) and \(fit_{min}\) are the maximum and minimum fitness values in the formed population, respectively.  
In addition, we proposed an adaptive regeneration strategy to improve the searching ability and robustness of the GATTRP. We recorded the elite chromosomes with the top fitness rankings in each round of iterations. When the elite chromosomes were not evolved within 20% of \(I_{max}\) rounds in a row, the chromosomes in the bottom 10% of the genetic population were selected to perform a swap crossover with the elite chromosomes. By  
Figure 6: Example process of drift mutation.  
adopting an adaptive regeneration strategy to replace evolutionarily stagnant chromosomes, the diversity of the population was increased to improve the evolutionary ability of the algorithm.
## 3 Results and Discussion
### Physicochemical Properties of SEY
#### 3.1.5 FTIR Analysis  
The ATR-FTIR transmittance spectra, as depicted in Figure 1e, can be attributed to the functional groups present in lipids, proteins, and water within SEVs. There was little difference in the shape of the spectra for the microwave and baking groups, indicating that the chemical composition of their corresponding samples was more similar [28], whereas the differences with the steaming group were more pronounced. Seven bands were observed at 1085 cm\({}^{-1}\), 1172 cm\({}^{-1}\), 1236 cm\({}^{-1}\), 1464 cm\({}^{-1}\), 1743 cm\({}^{-1}\), 2850 cm\({}^{-1}\), and 2925 cm\({}^{-1}\), all of which were associated with functional groups of the yolk lipid constituents, in agreement with previous results [29]. The amide I region, spanning from wave number 1700 cm\({}^{-1}\) to 1600 cm\({}^{-1}\) (Figure 1f), encompasses the most informative spectral range in the FTIR spectra for characterizing protein secondary structure. In particular, the stretching vibrations of the protein carbonyl group (C=O) correspond to positions 1649 cm\({}^{-1}\), 1652 cm\({}^{-1}\), and 1654 cm\({}^{-1}\), where the vibrations of the microwave group were more intense. In contrast, the differences in fluctuations in the b-turn region of the three samples located at 1660 cm\({}^{-1}\) \(\sim\)1700 cm\({}^{-1}\) were not evident. Furthermore, the secondary structure of proteins in SEY was dominated by the \(\beta\)-sheet, followed by the \(\alpha\)-helix. Overall, it appeared that the peak shapes and peak positions in the secondary structure spectra of the proteins tended to be consistent, but the intensities varied, suggesting that the internal structural changes of the proteins were affected differently due to the different modes of heating.
# A High-Gain and Wideband MIMO Antenna for 5G mm-Wave-Based IoT Communication Networks
## 2 Proposed Antenna Design Procedure
### Antenna System Incorporated with a Dielectric Layer  
An MIMO antenna incorporated with a dielectric layer is shown in Figure 5. A dielectric layer with overall dimensions of 20 mm \(\times\) 26 mm \(\times\) 2 mm is placed below a proposed MIMO configuration at a distance of 4 mm. Moreover, the dielectric constant is maintained at 12 to ensure that the proposed dielectric layer has a significant beam concentration. The overall aim is to improve the radiation performance of the proposed antenna system. The reflection coefficient of the proposed MIMO antenna after incorporating a dielectric layer is analyzed in Figure 6a, and the isolation is shown in Figure 6b, which is more than \(-\)30 dB (maximally) within the operating band. Here, we suggest that after incorporating a dielectric layer, both antennas will still achieve similar results, which will be helpful to attain high data rates, which is among the main features of MIMO-based 5G transmission. Furthermore, a gain comparison is shown in Figure 7 for an antenna with and without a dielectric layer. The peak gain in the case of an antenna without an external layer is 5.48 dB, whereas when a dielectric layer is incorporated, the peak gain increases to 8.47 dB within the operating band due to the highly dielectric nature of the layer incorporated on the back  
Figure 4: Multiport antenna: (a) S-parameters, (b) isolation.  
of MIMO antenna. Owing to the highly capacitive nature of the dielectric layer, it serves as a suitable reflective surface, resulting in the focusing of the fringing fields and an overall improvement in the gain. In the future, a dielectric layer with nearly the same dielectric constant value will be utilized to validate the simulated results based on the dielectric layer.  
The following expression [31] can be utilized to estimate the distance between the proposed MIMO system and the dielectric layer to achieve a satisfactory output performance.  
\[\varphi-2\beta H=2n\pi;n=\ldots\ -1,\,0,\,1 \tag{2}\]  
The equation is based on a combination of components, i.e., free space propagation constant (\(\beta\)), reflection phase (\(\varphi\)), and distance (\(H\)) between the dielectric layer and the antenna configuration (\(\pi\) = 3.1415).
## 4 Algorithm Bias
### Estimators
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**ID** & **Year** & **Ref.** & **Domain** & **Data Category** & **Estimators** & **Most Suitable Estimator** \\ \hline CS1 & 2022 & [117] & Agriculture & Spectra Data & RF, SVM, Cubist & Ensemble Method \\ \hline CS2 & 2009 & [118] & Automobiles & Mixed & SWO, VBSS, BCO, TD & TS and SWO \\  & & & & (text–numeric) & & \\ \hline CS3 & 2016 & [119] & Biology & Numeric & SVM, KNN, DT & SVM \\ \hline CS4 & 2020 & [120] & Business & Text (reviews) & MNB, CLSTM & CLSTM \\ \hline CS5 & 2022 & [121] & Business & Numeric & RF, SVM, DNN & DNN \\ \hline CS6 & 2020 & [122] & Business & Time series & STL decomposition, ESM, ARIMA, & VAR or ARIMA (based on \\  & & & & LinearR, KNN, SVR, VAR, DT & missing values presence) \\ \hline CS7 & 2015 & [123] & Computer Vision & Remote Sensing & RF, ET, GBRT & ET \\ \hline CS8 & 2024 & [124] & Construction & Numeric & XGBoost, LightGBM, GEP & GEP \\ \hline CS9 & 2018 & [125] & Environment & Time series & CART, ANFIS, MLP NN & CART \\ \hline CS10 & 2021 & [126] & Environement & Seismic data & Dense NN, CNN, LSTM & CNN and LSTM \\ \hline CS11 & 2022 & [127] & Financial & Numeric & DDQN, SVM, KNN, RF, XGBoost & KNN \\ \hline CS12 & 2022 & [128] & Health & Numeric & LogR, NN, SGD, RF, NB, KNN, DT & LogR \\ \hline CS13 & 2021 & [129] & Health & Text & LinearSVC, LogR, MNB, RF & LogR with TF-IDF \\ \hline CS14 & 2020 & [130] & Health & Numeric & Cox regression, NN & NN \\ \hline CS15 & 2017 & [131] & Health & Numeric & SVM, NN, NB & SVM \\ \hline CS16 & 2021 & [132] & Health & Numeric & NN, C4.5 Classifier, NB, KNN, Logistic Classifier, SVM, DNN. & SVM \\ \hline SC17 & 2020 & [133] & Health & Numeric & LogR, DT, RF, NN & RF \\ \hline CS18 & 2022 & [134] & Health & Numeric & SVM, DT & SVM \\ \hline CS19 & 2021 & [135] & Health & Images & Simple NN, LSTM & LSTM \\ \hline CS20 & 2023 & [136] & Sentiment & Text & CNN-Bi-LSTM, LSTM, Bi-LSTM, CNN, GRU, RF, SVM & CNN-Bi-LSTM \\ \hline CS21 & 2012 & [137] & Smart Homes & Sensor Data & C-SVM, CRF, LDA & C-SVM \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Complete list of comparative studies of ML algorithms (estimators). multiple sensitive attributes but do not address the disparate impact's business necessity clause [144]. Furthermore, in the literature, there exists a type of neural network architecture named variational autoencoders which are able to achieve subgroup demographic parity with regard to multiple sensitive attributes, thus reducing bias [145]. Moreover, other approaches that are used in the literature for bias mitigation are contrastive learning [146] and neural style transfer [147]. In the first one, contrastive information estimators are utilized to control the parity of an estimator by limiting the mutual information between representations and protected attributes of a dataset. The second one is an approach for performing image-to-image translation which, in the context of dealing with bias and improving fairness, can be used for mapping from an input domain to a fair target domain.
###### Abstract  
While industrial plant projects are becoming bigger, and global attention to the plant as a construct is increasing, space arrangement in plant projects is inefficient because of the complex structure of required facilities (e.g., complex MEP (mechanical, electrical, and plumbing) installations, specialized tools, etc.,). Furthermore, problems during installation, operation, and maintenance stages caused by inconsistencies between floor plans and actual layout are on the rise. Although some of these conflicts can be addressed through clash detection using BIM (building information modeling), quality BIM models are scarce, especially for existing industrial plants. This study proposes a way to address the complexities caused by changes during plant construction and securing space for the installation of equipment during the construction and lifecycle of built facilities. 3D cloud point data of space and equipment were collected using 3D laser scanning to conduct space matching. In processing the space matching, data were simplified by applying the 3D grid and by comparing the data, easier identification of the space for target equipment was accomplished. This study also proposed a pre-processing method based on sub-sampling that optimizes the point cloud data and verifies the processing speed and accuracy. Lastly, it finds free space for various equipment layouts required in industrial plant projects by space analysis, proposed algorithms, and processes for obtaining the coordinates of valid space for equipment arrangement. The proposed method of this study is expected to help solve the problems derived from arrangement and installation of new equipment in a complex plant site.  
3D grid; 3D shape scanning; equipment and facilities relocation; space analysis; point cloud +
Footnote †: journal: _Sustainability_  
_Article_  
Automatic Space Analysis Using Laser Scanning and a 3D Grid: Applications to Industrial Plant Facilities
## Appendix A Extended Experiments
### Comparison with Evolutionary Algorithms  
We compared Adacomp with two evolutionary algorithms, the genetic algorithm (GA) and particle swarm optimization algorithm (PSO), on MNIST, Fashion-MNIST, and CIFAR-10 datasets. The code for GA was modified based on [[https://github.com/jishnup11/-Fast-CNN-Fast-Optimisation-of-CNN-Architecture-Using-Genetic-Algorithm](https://github.com/jishnup11/-Fast-CNN-Fast-Optimisation-of-CNN-Architecture-Using-Genetic-Algorithm)]([https://github.com/jishnup11/-Fast-CNN-Fast-Optimisation-of-CNN-Architecture-Using-Genetic-Algorithm](https://github.com/jishnup11/-Fast-CNN-Fast-Optimisation-of-CNN-Architecture-Using-Genetic-Algorithm)), (accessed on 24 October 2021 ) and the code for PSA was based on [[https://github.com/vinthony/pso-cnn](https://github.com/vinthony/pso-cnn)]([https://github.com/vinthony/pso-cnn](https://github.com/vinthony/pso-cnn)), (accessed on 24 October 2021). Specific parameter settings were as follows and detailed results are shown in Table A1, where results for Adacomp are the highest values corresponding to Table A2.  
* For the GA, we set 10 generations with 20 populations in each generation. Mutation, random selection, and retain probability were set as 0.2, 0.1, 0.4, respectively.
* For PSO, swarm size and number of iterations were both set as 100, inertia weight and acceleration coefficients were both set as 0.5.  
Two conclusions are obtained from Table A1. _First_, GA and PSO outperform Adacomp on MNIST and Fashion-MNIST. This is because GA and PSO explore too many parameter settings compared to Adacomp. However, this does not hold for CIFAR-10. Therefore, GA and PSO have a high probability of finding the better solutions by exploring a larger space.  
Second, Adacomp is more efficient than GA and PSO. For example, Adacomp performed similarly to GA and PSO; however, the consumed time was only about 1/10 compared to them.  
Based on Table 11, it may be possible to combine GA or PSO with Adacomp to simultaneously improve the efficiency and utility.
# Addressing the Non-Stationarity and Complexity of Time Series Data for Long-Term Forecasts
## 2 Methodology  
The proposed framework addresses both univariate and multivariate TSF problems, focusing on the non-stationarity of time series and simplifying time series data. This section outlines the concepts of \"spectral decomposition\", the blocks for addressing non-stationarity, (\"weak-stationarizing\" block and \"non-stationarity restoring\" block), and the ConvMixer architecture, and provides an overview of the architecture.
## 4 Discussion
This could indicate that the dispersion of the absolute bias values was greater with LATE than EARLY. Hence, in our case, EARLY showed better results despite being the IMERG product with the shortest latency. For the CMORPH family, the results were similar in the identification of both events; however, the quantification of the precipitation amounts revealed some differences locally (CMORPH-RT close to the JUR area during the April case; Section 3.2.2). Figure 20 shows that CMORPH tends to have more absolute biases close to 0 mm than CMORPH-RT, meaning that CMORPH estimations are closer to RADOME observations than CMORPH-RT. Figure 20 also shows that GSMaP-NRT products tend to have smaller absolute bias distributions and are more centred around 0 mm, supporting the results seen in Section 3.2.2. The GSMaP family products tend to underestimate the precipitation amounts compared to the RADOME observations, as well as for the rest of the MW-based precipitation products. Finally, the DATABOURG product shows relatively small bias distributions near 0 mm but a notable underestimation of the April case, while the June case was marked by an overestimation signal. In our case, both radars products showed good performances over France. For the satellite-based products, DATABOURG shows promising results while CMORPH and both IMERG products correctly identified both events. However, GSMaP-Now products and PDIR estimations tend to show poorer results. With the difficulty for PDIR to correctly identify and quantify the precipitation amounts on both events, this study confirms that products with finer spatial resolution do not always produce better representations of precipitation events, as found in the work of [PERSON] et al. [62]. This study also shows that all precipitation products tend to have difficulty in correctly estimating stratiform and light precipitation events over areas with high topography (e.g., [71]), such as in the case of the April case study. Topography has an important role in the contribution of precipitation on high mountainous areas, which are of particular interest for global climate projections [72].
## 2 Materials and Methods
### Estimation of fAPAR/fIPAR  
In this study estimation of fAPAR and fIPAR is based on the radiative budget (absorbed, scattered and intercepted radiation) produced by the PROSPECT-DART simulation. Values of fAPAR and fIPAR are defined by  
\[\text{fAPAR}=\frac{\text{absorbed PAR}}{\text{incident PAR}} \tag{1}\]  
\[\text{fIPAR}=\frac{\text{intercepted PAR}}{\text{incident PAR}} \tag{2}\]  
The fAPAR / FIPAR calculated here account not only for the single scattering radiation of the leaves but also for radiation scattered and reflected by the background, leaves, tree trunks, and other elements of the scene over multiple iterations. This is also consistent with the field-measured fAPAR/fIPAR from the above-mentioned optical instruments which measure the total absorbed/intercepted radiation and account for the contribution of multiple scattering.  
The radiative budget product was simulated along with the hyperspectral images for each scene (tree) using the amount of incident, intercepted and absorbed radiation with respect to the leaves in a three-dimensional grid. Five iterations were performed for each scene. The wavelength range of PAR is 0.4-0.7 \(\upmu\)m. Three band intervals (0.4-0.5 \(\upmu\)m, 0.5-0.6 \(\upmu\)m, and 0.6-0.7 \(\upmu\)m) were simulated separately to increase the simulation accuracy and were subsequently integrated into a single PAR band. The results of simulations performed using the DART model were averaged for each scene, except when the effect of the SZA on the proportion of sunlit and shaded leaves was investigated, in which case the pixel-wise average of the entire dataset comprised of all the scenes was considered. In the case of the diffuse ratio, the results were binned into discrete categories corresponding to each of six input SKYL values in the DART model. Figure 1 illustrates a typical HSI image converted to an RGB image for classification and the corresponding outputs of the ANN-based classification sunlit vs. shaded leaves and ground. The flowchart of the simulation and data processing in this study is illustrated in Figure 2.
# Research on the Tie Cable Replacement Method of Half-through Tied-Arch Bridge
## 4 Results
### Results of the Impact of Each Replacement Scheme on the Superstructure
#### 4.2.4 Cable Force of Adjacent Tie Cables  
The impact of the tie cable replacement on adjacent tie cables is shown in Figure 10.  
Figure 10: The cable force change in the adjacent tie cables.  
Figure 9: Displacement of the main arch crown.  
Figure 10 shows the maximum cable force variation of each tie cable in different replacement schemes. It can be seen that  
1. The influence of the non-substitution method's scheme 1 and scheme 3 on the cable force of adjacent tie cables is greater than that of temporary substitution method schemes, and the maximum value appears at 150 kN of scheme 3.
2. The change in the cable force of adjacent tie cables caused by scheme 5 is the smallest, followed by scheme 7.
3. When the tied cable on one side was replaced, the cable force of the tie cables on the other side will also be affected. For example, replacing #1 and #2 in scheme 6 will not only increase the cable force of 3#-6# but also increase the cable force of tie cables #7-#12 by 20 kN.
## 2 Results and Discussion  
### Gc-Ms-Spme  
Through the use of GC-MS-SPME this work could identify about thirty volatile compounds responsible for the identification and classification of our grated matrix, all of these compounds widely reported in literature [5]. These compounds are fundamental to obtain a solid results structure, able to unequivocally characterize the analyzed samples.  
After examining the chromatograms of individual samples and removing the insignificant compounds, we selected about 30 key compounds to be used to create the matrix. These compounds were then compared to draw some considerations (discussed below).  
The different mixtures of pulp/crust compounds identified through GC-MS-SPME were then grouped into different families to check whether there were any macroscopic differences in terms of type and number [3].  
The analysis identified specific markers for the crust: 2-heptanone, 2-octanone, and 2-nonanone (considering 18% crust is the limit to the maximum percentage by law, ketones are much more present in the crust than in the pulp). The specific markers identified in the pulp were: phenyethyl alcohol and 2-2'-Oxybis ethanol (alcohol) (Table 1) [1].  
It was also possible to identify other specific markers for the crust PR: 8-Nonen-2-one (ketone), 2-nonanol.  
SPME-GC-MS analysis suggests that, in general, alcohols typically characterize the pulp, while ketones, and free fatty acids can be more frequently found in the crust.  
Compound 5-Methyl-2-hexanol (Figure 1), identified through SPME-GC-MS is present in all analyzed samples. Its quantity decreases considerably between crust and pulp. In literature it is reported as typical VOCs present in the wood and used to ripen the cheese.  
This compound is in fact completely absent in the sample made of 100% pulp (which is not in contact with seasoning tables). This compound was identified as a specific marker.