esa-sceva/satcom-chunk-collection huggingface dataset:
# Advances on Water Quality Detection by UV-Vis Spectroscopy
## 6 Conclusions and Discussion  
This paper discusses the application of UV-Vis spectroscopy for water quality detection. As described in this paper, a variety of UV-Vis spectroscopy techniques have been widely used to detect pollutants in different water environments, including organic and inorganic substances. In this paper, the detection method of water quality parameters, data analysis method, and existing problems are introduced in detail. UV-Vis spectroscopy has become a rapid analysis tool for qualitative and quantitative detection of water quality.  
Although these studies show the ability of UV-Vis spectroscopy to detect pollutants in water environments, the practical application of UV-Vis spectroscopy in water quality detection is still difficult. One of the main problems is the detection limit. At present, the detection limit of many studies has been below the environmental safety value; however, it is worth noting that the experimental processes have been performed in the laboratory rather than in true field environments. The field environment is complex and contains many pollutants, which have significant impacts on the detection  
Figure 6: Schematic diagram of water quality parameter monitoring and an early warning system.  
Figure 7: Process of using multi-source data fusion to detect water quality parameters.  
results. Another problem is that it is difficult to detect the UV-Vis spectra of some pollutants in water, such as suspended solids, dissolved inorganic substances, and pathogenic microorganisms. In addition, there are significant differences in the methods of UV-Vis spectrometric determination of various pollutants, which may be affected by many factors, including sample preparation and instrument settings.  
Therefore, future research needs to include greater progress in the field of instrumentations. The miniaturization and integration of instruments for measuring UV-Vis spectra are the key factors for achieving accurate and rapid detection of water quality parameters in the 5G era. In addition, with the rapid development of other spectral detection technologies, the combination of Raman spectroscopy with UV-Vis or near-infrared spectroscopy with UV-Vis technology can also be used in rapid water quality quantitative detection.  
[PERSON] wrote the paper. [PERSON], [PERSON], and Q.D. reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.  
This research is supported by China's national key research and development program (Project No.: 2017 YFE0122100).  
We are deeply grateful to the reviewers and the editors for their valuable comments and suggestions, which improved the technical content and presentation of the paper.  
There are no conflicts to declare.
# Prediction of Intraday Electricity Supply Curves
###### Abstract
In the period of interest for this project (see Section 3.1), these markets were organized into six auction sessions, which are carried out sequentially, each  
Figure 1: Structure of the Spanish electricity sector. Source: [[https://www.energiaysociedad.es](https://www.energiaysociedad.es)]([https://www.energiaysociedad.es](https://www.energiaysociedad.es)) [5], accessed on 6 September 2024.  
with its own application horizon. Table 1 shows the schedules and market horizons for these sessions. In Table 1, we can see that the times at which the programs and results of a session are published allow that information to be used to predict the curve of the following session. For example, the results and trading programs of the first session are published at 16:20 at the latest on day D-1, and the second session occurs between 17:00 and 17:50 on the same day. Similarly, the results of the daily market are known before the first session of the intraday market, and, therefore, their information can be used to predict the supply curve of the first session. This is the predictive modeling proposal that we will use in this project, using data from the previous market session to predict the next session. We, therefore, pose the following research questions that will be addressed in this project:  
* Q1: Is it possible to predict supply curves more accurately than using the curves from the previous day? * Q2: Is it useful to incorporate information from the previous market to predict curves from intraday market sessions? The prediction of supply curves in the electricity market is not a new topic in the literature, but, as we will see in the next section, previous research has focused on the prediction of the day-ahead market or the reserve market, which are markets that occur once a day. Our research aims to close the gap in terms of a type of market structure in several daily sessions. The rest of the paper is organized into four sections. Section 2 presents the state of the art regarding the prediction of supply curves. Section 3 develops the proposed methodology and describes the considered models. The results are presented in Section 4. Finally, the conclusions and possible extensions are discussed in Section 5.
## 4 Empirical Evaluation
### Results Analysis
(5) (RQ5): Influence of the systematicity of review' abstracts on the number of citations of these reviews  
We computed the correlation between the _OConfidence_ values of the analyzed review abstracts and the number of Google citations (as of mid-July 2022). The corresponding value of the Spearman coefficient ([[https://www.soscistatistics.com/tests/spearman/default2.aspx](https://www.soscistatistics.com/tests/spearman/default2.aspx)]([https://www.soscistatistics.com/tests/spearman/default2.aspx](https://www.soscistatistics.com/tests/spearman/default2.aspx)) (accessed on 17 July 2022)) r\({}_{\text{s}}\) is 0.01235, with a \(p\)-value of 0.97685. By normal standards, the association between the two variables would not be considered statistically significant. We therefore cannot conclude whether there is a relationship between the systematicity of the review abstracts and the number of times these reviews get cited. Hence, there is no evidence that the systematicity of reviews has an impact on the influence, e.g., number of citations, of the review on the research community. Other factors may come into play and are worth investigating as future work. Such factors may include the reputation of the systematic reviewers in the research community, the importance, e.g., impact factor, conference ranking, conference acceptance rate, of the venue in which the review is published. (6) (RQ6): On the well-formedness of the review abstracts  
Thirty of the analyzed reviews had structured their abstracts by using specific keywords that mirrored the structure of the reviews they summarize. Based on the answers to  
Figure 3: Summary of the items answered by reviews. question 11 that we collected from these thirty reviews, we identified the various keywords that the systematic reviewers usually used to structure their abstracts. This allowed us to generate the word cloud shown in Figure 4, which was generated using the online tool called Free Cloud Generator ([[https://www.freewordcloudgenerator.com/](https://www.freewordcloudgenerator.com/)]([https://www.freewordcloudgenerator.com/](https://www.freewordcloudgenerator.com/)) (accessed on 19 July 2022)). Based on that figure, we can conclude that the most common structure of abstracts comprised the following sections: Context, Objectives, Method, Results, and Conclusion(s). Some of abstracts also include an additional Background section to improve the well-formedness of review abstracts. These various sections are expected to comprise sufficient information to allow readers to conclude that the review is transparent, reproducible, trustworthy, unbiased, and complete enough.
## 5 Conclusions  
A RFI mitigation algorithm has been presented. It is based on the estimation of the RFI signal using wavelet denoising techniques, and its subtraction from the original signal so as to obtain an estimate of the noise-only signal.  
It has been found that in general, the soft heuristic SURE thresholding method is the one that best performs for any type of signal, except for very weak RFI (\(INR\sim 10^{-3}\)... 10\({}^{-4}\)) in which the fixed thresholding performs slightly better.  
The minimum number of samples per period is \(\sim\)20, for which at least a \(\geq\)20 dB interference rejection is achieved using the Haar transform (depending on the signal type and the thresholding method). Higher rejections (\(\geq\)40 dB) are achieved at the expense of longer sequences (2\({}^{16}\) samples) and increased computational load (\(\sim\)3\(\cdot\)10\({}^{5}\) Flops for 20 samples/period to 7\(\cdot\)10\({}^{8}\) Flops for 2\({}^{16}\) samples/period, Figure 10a). Increasing the number of decomposition levels above 6-7 does not improve the quality of the RFI mitigation, except for PRN (pulsed) signals.  
Figure 10: \(Cont.\)  
The minimum inteference rejection (cancellation) is \(\sim\)40 dB for high _INR_s (_INR_\(\sim\)100) when using the Haar wavelet transform, but this value may increase up to \(\sim\)60-70 dB if the optimum wavelet type for each signal is selected: symlet 3 for the sinusoidal RFI, the reverse biorthogonal wavelets 1.5 for the Doppler signal, the discrete approximation of Meyer wavelet for the chirp signal, and the reverse biorthogonal wavelets 1.3 for the PRN signals. Therefore there is a trade-off between interference rejection and computational complexity which must be evaluated depending on the application.  
Even in the case of using the simple Haar transform, the proposed RFI mitigation algorithm allows one to make useful radiometric measurements in areas heavily corrupted by RFI. Since it is unlikely that with today's computers and FPGAs this technique can be applied for real time processing in microwave radiometers, the RFI cancellation could be applied _a posteriori_ to the recorded data. This can be of special interest in scenarios corrupted by human activity where the errors induced by RFI are equal or larger than the measurement uncertainty (\(\Delta\)T) and blanking is not an option due to the data loss. For example, if the antenna temperature is 100 K, the receivers noise temperature is 300 K, and the measurement uncertainty is \(\Delta\)T = 1 K, the minimum detectable power is 1 K/(100 K + 300 K) = 2.5\(\cdot\)10\({}^{-3}\) (assuming \(\sigma^{2}\) = 1), which determines the minimum useful _INR_ for which it makes sense to apply the RFI mitigation algorithm. In other demanding applications, such as sea surface salinity retrieval, where the required \(\Delta\)T is much smaller (\(\Delta\)T = 0.05 K), RFI mitigation can be useful down to _INR_ of \(\sim\)0.05 K/(100 K +300 K) \(\equiv\) 10\({}^{-4}\), which is at the limit of the performance of the RFI cancellation algorithm, regardless of the wavelet type used.
# Using Machine Learning for Web Page Classification in Search Engine Optimization
## 4 Machine Learning
### Classification Methods  
#### 4.1.1 Decision Trees  
Decision trees are one of the most popular classification methods that give interpretable results. Trees contain decision nodes from which they are branched further down until they reach the leaves, or nodes, at the lowest level, where a class is specified. These kinds of trees are used for classifying a new data sample (instance).  
Most popular decision trees algorithms are ID3, C4.5, CART, CHAID, and QUEST [39]. Even though they can be used for regression, their most common use is in classification tasks. The main advantages of decision trees are their interpretability (easy to explain), they do not have any underlying data assumptions, and they are not affected by outliers. Disadvantages are sensitivity to data variety, they can be complex and slow in training, and tend to overfit (if we do not prune it).
## 4 Results and Discussion
### LCNN Performance Comparison  
Table 4 shows the results of different LCNNs in Dataset A with transfer learning. When all the LCNNs were trained on Dataset A, all LCNNs had a lower true positive rate for crack images (TP) than for background images, indicating that the crack features were more challenging to learn than the background images (TN).  
All the LCNNs achieved an accuracy above 90% in the validation set. ShufflenetV2-0.5 had the lowest accuracy (91.73%) and complexity among all the LCNNs, and ShufflenetV2-1.5 had the highest accuracy (93.85%). However, ShuffleNetV2-1.0 only had 0.58% lower accuracy than ShuffleNetV2-1.5 despite having half the \"Parameters\", \"Complexity\" and \"Weights\" of ShuffleNetV2-1.5.  
At the same time, ShuffleNetV2-1.0-SE increased the accuracy of ShuffleNetV2-1.0 by about 15% but still had a much lower complexity than ShuffleNetV2-1.5. ShuffleNetV2-1.0-SE achieved the highest accuracy of 94.23% with transfer learning, which was 0.96% higher than that achieved with ShuffleNetV2-1.0, suggesting that utilizing the SE attention mechanism for information interaction between the channels will help improve the accuracy of crack detection significantly.
## 4 Experiments
### POD Flow Field Decomposition and Reconstruction Experiments  
The basic modes and corresponding eigenvalues can be obtained by the POD analysis of the simulation data, first arranging the eigenvalues according to the numerical value. The ratio of the sum of eigenvalues corresponding to the q-order fundamental mode to the sum of all eigenvalues is defined as the energy ratio of the q-order fundamental mode. It represents the amount of information contained in the q-order basic mode, which can be used to determine how many modes can contain most of the information in the original flow field. Figure 9 shows the changing trend of the energy contained in the n-order (n < 15) POD basic modes, the abscissa is the order of the basic modes, and the ordinate is the energy ratio.  
As shown in Figure 9, the energy of the ten basic modes accounts for more than 99.95%, indicating that the flow field characteristics are mainly concentrated in the first ten POD basic modes.  
In order to quantitatively analyze the reconstruction effect, 90% of the dataset is used as the training sample for the POD reduction, and the remaining 10% is used as the test sample for POD reconstruction. The reconstruction error of a single test sample is defined as:  
\[e=\frac{\sum_{i=1}^{N}\left|\frac{\left(\frac{a_{pre}^{i}-a_{real}^{i}}{a_{real }^{i}}\right)}{N}\right|}{N} \tag{15}\]  
where \(a_{pre}^{i}\) is the predicted data of the ith grid point, \(a_{real}^{i}\) is the real data of the ith grid point calculated by CFD with high accuracy, and \(N\) is the number of grid points.  
The overall error for all test samples is defined as follows, where \(T\) is the number of test samples:  
\[Error=\frac{\sum_{k=1}^{T}e^{k}}{T} \tag{16}\]  
Figure 10 shows the relationship between the number of basic modes and reconstruction error. Obviously, the flow field features are mainly concentrated in the first ten orders. Therefore, after the error analysis of POD reconstruction, the number of basic modes is selected as 10.  
Figure 9: Relationship between the number of basic modes and energy ratio.
## 3 Results and Analyses
### DesignBuilder Simulation Analysis
#### 3.4.1 Light Environment Simulation  
This experiment selected two periods (10:00-16:00 on 5 January 2002 in winter and 9:00-16:00 on 29 July 2002 in summer) to conduct indoor natural light simulation. As weather has significant impact on indoor light intensity and it is hard to predict the weather, this simulation used a daylighting coefficient (light transmittance) as the evaluation index. The simulation cloud at noon (12:00) is shown in Figure 8.  
According to Figure 8, there were obvious partitions for indoor light transmittance due to the shielding of photovoltaic modules. The light transmittances of the area covered by a film and the area covered by photovoltaic modules were significantly different. The changing trends of the light transmittance between the simulated data and the measured data were basically the same. When using photovoltaic modules, the daylighting coefficient in summer was lower than that in winter; but the daylighting coefficient of the photic area in summer was higher than that in winter when using films. In winter, as the solar altitude decreased, the transmittance boundary between the daylighting area and the power generation area gradually moved to the north, and the projected area of the photovoltaic modules was larger.  
To ascertain if weather has an effect on the light transmittance at a specific moment, we adopted software to simulate the light transmittance at each moment and calculated the average light transmittance of each simulation moment according to the simulation model. The light transmittance of each moment was averaged to obtain the average light transmittance of a day, which was then compared with the actually measured light transmittance. In winter, the simulated average light transmittance of the photovoltaic greenhouse was 33.09%, and the measured average light transmittance was 25.77%; in summer, the simulated average light transmittance was 37.54%, and the measured value was 32.35%. Regardless of winter or summer, the actually measured value was smaller than the simulated value. The main reason may be the influence of the sheltering of the greenhouse frame or roof grooves at certain moments.  
The simulated light transmittance was relatively reliable according to the analyses of the distribution trends and the average light transmittance. The influence coefficient of the greenhouse frame and affiliated components should be considered during the daylighting simulation of the photovoltaic greenhouse. The influence rate in winter was about 20.9%, compared with that of 15% in summer.  
Figure 8: Simulation cloud of the light transmittance of the photovoltaic greenhouse in winter (**a**) and summer (**b**).
# Real-Time Hybrid Multi-Sensor Fusion Framework for Perception in Autonomous Vehicles
## 2 Fusion Systems Literature Review
A multi-spectral pedestrian detection system is proposed using deep learning from a thermal camera and an RGB camera in [22]. In terms of architectures, [PERSON] [23] proposed a convolutional neural network to perform semantic segmentation called fully convolutional network (FCN). Reference [24] improved this architecture by proposing the deconvolutional network (DeconvNet) by using decoding layers with deconvolutional filters that upsample the down-sampled feature maps. In SegNet [25], DeconvNet was improved by using connections between encoder and decoder layers. In U-Net, another encoder-decoder-based architecture for semantic segmentation all the feature maps are transferred from the encoding layers to the decoding layers via residual connections. Mask-RCNN [26] was also proposed in which it combines Faster-RCNN [27] object detection network with an FCN in one architecture. Our goal is to provide a cost-effective, lightweight fusion system solution that can be implemented in an relatively cheap and efficient embedded edge computer.
## 2 Materials and Methods
### Analysis of DSC  
Calorimetric analyses were conducted using a differential scanning calorimetry (DSC) instrument in the Department of Biophysics at Bahcesehir University Faculty of Medicine. The Shimadzu DSC-60 Plus instrument was utilized for calorimetric measurements. Samples of were weighed at 3 mg and placed in sealed aluminum pans. The samples were then heated from 25 \({}^{\circ}\)C to a final temperature of 400 \({}^{\circ}\)C at a heating rate of 10 \({}^{\circ}\)C/min\({}^{-1}\) (\({}^{\circ}\)C/min). The measurements were performed under a nitrogen atmosphere with a flow rate of 50 mL/min\({}^{-1}\) (mL/min). Data obtained from DSC measurements include the peak temperature (T), time (t), heat flow (q) and heat capacity (\(\Delta\)Cp) between the sample pan and reference pan.  
The calculated area for the enthalpy change is initially in J/g, which is then converted to kcal/mol. TA60 software (version 8.0) calculates Tm value and Ep value; the energy obtained from the polymerization peak in DSC curves provides valuable information about the residual monomer content in polymers, which can be determined by integrating the area under the peak (see Supplementary Materials for calculation steps).  
Molar mass, known \(\Delta\)Hpol value (The C=C bond requires 60 kJ mol\({}^{-1}\) to cleave the \(\pi\) ligation, which is considered the standard enthalpy of polymerization) and the mass value (m\({}_{\mathrm{a}}\)) is the sample mass that is used in DSC measurement [19; 20].
## 5 Results and Analysis
### Quantitative Evaluation
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Metric** & **ECBSR** & **EDSR** & **SwinSR** & **RCAN** & **SRRepViT** & **HAT** \\ \hline params & 0.78 M & 1.52 M & 11.82 M & 15.59 M & 0.25 M & 40.26 M \\ FLOPs & 36.32 G & 130.27 G & 0.77 T & 1.05 T & 17.39 G & 2.53 T \\ Speed & 0.025 s & 0.083 s & 0.345 s & 0.223 s & 0.013 s & 1.443 s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: FLOPs, params, and inferencing speeds of different methods. that SRRepViT possesses the fewest parameters among all experimental networks. Consequently, the proposed SRRepViT network emerges as the most balanced solution, excelling in model inferencing speed, complexity, and super-resolution reconstruction performance simultaneously. This substantiates its efficiency in facilitating effective remote sensing image super-resolution reconstruction.
# A Self-Adaptive Approximated-Gradient-Simulation Method for Black-Box Adversarial Sample Generation
## 4 Experiment and Evaluation
### Experimental Setting
#### 4.2.3 Evaluation  
To sum up, we compared the SAGM with existing adversarial training algorithms and the results show that the algorithm that we proposed has an excellent performance in attack model evaluation metrics, such as attack success rate, transferability, robustness, sensitivity, and structural similarity (SSIM) measurement. The attack success rate is an important evaluation index of the adversarial training algorithm, which is equivalent to the accuracy of the recognition algorithm, Tables 1 and 2 show that the SAGM can reach a very high success rate in this index and it can carry out accurate and no-miss attacks. Figures 7 and 8 show the transferability of the SAGM. Table 3 shows the robustness of SAGM compared with other existing algorithms. Figures 9-18 show the sensitivity of SAGM, it has a better adversarial effect and higher clarity. Table 4 shows the result of SSIM that the SAGM compared with other existing algorithms.  
explore the sensitive gradient direction and generate the efficiency perturbation samples to attack DNNs. The extensive experiments showed that the SAGM achieves excellent performance comparable to other attack algorithms in success rate, perturbation distance, and transferability. In the future, the effect of this study will be a focus on improving the evaluation model to generate more efficient adversarial examples to achieve a balance between adversarial examples and the human visual system. In addition, we believe that some sensitive pixels can be explored to perturb partial pixels of clean images to improve the robustness of adversarial examples. Furthermore, another direction is the application of other intelligent optimization algorithms to adversarial attacks.  
Conceptualization, Y.Z. and S.-Y.S.; methodology, B.X.; writing--original draft preparation Y.Z. and X.T.; supervision, Y.Z., S.-Y.S. and X.T. All authors have read and agreed to the published version of the manuscript.  
This research is partially supported by Institute of Information and Telecommunication Technology of KNU.  
Not applicable.  
The authors declare no conflict of interest.