esa-sceva/satcom-chunk-collection huggingface dataset:
# Adaptive Cruise Control for Eco-Driving Based on Model Predictive Control Algorithm
## 2 Materials and Methods
### Emission and Fuel Consumption Model Development  
To quantitatively assess the performance index of eco-driving, fuel consumption is required to calculate through a specific fuel consumption model. In this research, the white-box fuel consumption model is applied to calculate fuel consumption, which relies on the working principle of the vehicle engine cycle [1]. Carbon balance method (CBM) and mean value phenomenological model (MVPM) are the main constituent of white-box models. The basic methodology of the carbon balance theory is the mass conservation, i.e., the aggregate mass of carbon after combustion in the exhaust emissions theoretically is equal to the fuel carbon content before combustion. Then, the prediction and  
Figure 3: The framework of the model predictive control (MPC)-based adaptive cruise control (ACC) system.  
measurement of vehicle emissions are required. According to [36; 37; 38], acceleration and velocity, as the direct representation of driving behavior, can be utilized to characterize fuel consumption and exhaust emissions, i.e., exhaust emissions are primarily decided by vehicle speed and its acceleration. Consequently, the following instantaneous exhaust emissions and fuel consumption model is applied to calculate the performance index of eco-driving. The structure of where to obtain the input data and how it works are shown in Figure 4.  
Emission data under a specific range of speed was collected from the Dynamometer Database of Argonne National Laboratory [39] to depict the emission maps, which shows each emission rate as a function of acceleration and speed.  
The input data to the instantaneous exhaust emissions and fuel consumption model is a driving speed-time series profile. In the data conversion process, the speed is manifest provided by the driving cycle speed profile data. Taking the first derivative of speed in terms of time, the acceleration is obtained directly:  
\[a_{t}=\frac{v_{t}-v_{t-1}}{\Delta t} \tag{27}\]  
where \(a_{t}\) refers to the acceleration at current time \(t\), \(v_{t}\) and \(v_{t-1}\) are the vehicle speed at current time \(t\) and previous time \(t-1\), respectively. Moreover, \(\Delta t\) is the speed profile time step.  
After obtaining the emission rates for each emission constituent (HC, CO, CO2), the carbon mass conservation concept was taken to identify the fuel consumption in liters per 100 km of the vehicle [1].  
\[FC=\frac{0.1154}{D}(0.886\mathrm{HC}+0.429\mathrm{CO}+0.273\mathrm{CO}_{2}) \tag{28}\]  
where, \(FC\) denotes the fuel consumption per hundred kilometers in liters, CO refers to carbon monoxide emission, HC refers to hydrocarbon emission, CO2 refers to carbon dioxide emission, and \(D\) represents the gasoline density at 288 K.  
Note that the vehicles discussed in this research are all conventional gasoline vehicles rather than hybrid or electric vehicles.
# FVR-Net: Finger Vein Recognition with Convolutional Neural Network Using Hybrid Pooling
## 4 Experiments and Results
### Training Stage  
The finger vein images generated after preprocessing were passed through the network to extract their corresponding features from each block of the network. The whole model was implemented with the Python programming language in a TensorFlow framework on a PC equipped with the Nvidia GeForce RTX 2080 Ti GPU. A cross entropy loss function was used for convergence of the model during training with the objective being to minimize global loss. To achieve faster convergence, the loss function was minimized using the RMS prop optimizer with a learning rate of 0.0001. A batch size of 32 was used, and a total of 100 epochs was used during training. It is necessary to stop training the model at the point where network hyperparameters are optimized and where saturation in training and validation losses is achieved. For this, we employed an early stopping technique where the training was halted when no further improvements to the loss of validation data were observed with respect to the training epochs. Figure 5 demonstrates the generalized learning curves of the proposed model on both training and validation datasets. We can clearly see that although the training epoch was set to 100, saturation in the validation loss was seen at epoch 25. At this point, training stopped, and the model became immune to overfitting from extensive training. Furthermore, we can also observe that the model provided very good convergence in the loss function for classification of the vein images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Databases** & **Augmentation Technique** & \begin{tabular}{c} **Total Images** \\ **Generated Per Class** \\ \end{tabular} & \
\begin{tabular}{c} **Total Images after** \\ **Augmentation** \\ \end{tabular} \\ \hline HKPU [3] &
\begin{tabular}{c} Width shift = 0.01 pixels, \\ Height shift = 0.01 pixels, \\ Shear range = 0.01 \\ \end{tabular} & 24 & 7488 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Augmentation Details about the Publicly Available Finger Vein Image Databases used in this Research.
## 4 Delivery and Tests Results  
The first module of the SIS100 cryogenic bypass line (Figure 15) was delivered by Wroclaw University of Science and Technology to GSI in the middle of 2016. This module is being used as part of the SIS100 String Test Facility (STF) to verify the operation of a short chain of SIS100 superconducting magnets. The first series module connects the STF feed box to the tested short chain of magnets.  
Before the magnets' short string verification test, the first SIS100 BPL module of the series was tested from the view of thermodynamics (heat losses) and electric performance. Figure 16 presents an example of heat-load measurement. The heat in-leak to the 4.5 K circuit, including the busbar system, was measured in two states:  
1. the static condition, when the busbar system was not powered and all heat losses were transferred through the thermal insulation and supports;
2. the transient condition, when superconducting busbars were powered with AC current.  
Figure 14: Thermal compensation loops on the busbar pairs on the support plates.  
Figure 15: Preparation of the first BPL module for testing at GSI, Darmstadt.  
Detailed test results will be the subject of a separate article.  
The first serial BPL linear module 1S42 YP was manufactured, tested at the manufacturer's premises, and delivered to the FAIR project in October 2020. A view of the module prepared for the site acceptance tests (SAT) is presented in Figure 17.  
In the next stage, one of the three feed boxes for liquid helium and electric power supply for the SIS100 ring will be designed and delivered. These components are to be tested in the Test String prior to the installation of a complete set of six bypass lines in the SIS100 tunnel.
# An Online Detection Method for Coal Dry Screening Based on Image Processing and Fractal Analysis
## 3 Methodologies
### Fractal Dimension for Coal Particle Image  
Fractals can be found in many self-similar natural objects. For non-self-similar objects, fractal dimensions can also be used to describe their geometric characteristics and properties. In our research, the images of coal do not strictly obey the law of self-similarity, yet the box dimension can be used to study the coal particle size distribution and screening efficiency of vibrating screens. For this purpose, a differential box-counting method is used to determine the coal's box size [17]. The basic equation of the differential box dimension is given by  
\[D=\frac{\log(N_{r})}{\log(1/r)} \tag{1}\]  
\(N_{r}\) represents the differential grayscale surface area under measure \(r\). Measure \(r\) signifies that the image of size \(M\times M\) pixels is scaled down to size s \(\times\) s under measure \(r\), and the relation is as follows:  
\[r=s/M \tag{2}\]  
For each different measure \(r\), the combination of the grid gray level and two-dimensional coordinates forms the three-dimensional surface of the whole image.  
Furthermore, the differential grayscale surface area \(N_{r}\) is given by  
\[N_{r}=\sum n_{r}(i,j) \tag{3}\]  
where \(n_{r}(i,j)\) is the maximum gray level difference of the scaled grid (\(i_{j}\)) and is represented as  
\[n_{r}=l-k+1 \tag{4}\]  
where \(l\) is the maximum grayscale in the grid(\(i,j\)) and \(k\) is the minimum grayscale.  
The fractal box dimension can be calculated in measure \(r\) by Equations (1)-(4). Subsequently, we can estimate \(D\), the fractal dimension, from the least square linear fit of \(\log(N_{r})\) against \(\log(1/r)\).  
Figure 3 is the least-squares fitting curve of one coal-edge image, and the curve is approximately linear. This proves that fractals do exist in coal-edge images and that the fractal dimension can be a characteristic of coal images and coal PSD.  
Figure 2: The flowchart of image-processing steps.
###### Abstract  
We present a novel optical metro node architecture that exploits the Wavelength Division Multiplexing (WDM) optical cross-connect nodes for interconnecting network elements, as well as computing and storage resources. The photonic WDM cross-connect node based on semiconductor optical amplifiers (SOA) allows switching data signals in wavelength, space, and time for fully exploiting statistical multiplexing. The advantages of using an SOA to realize the WDM cross-connect switch in terms of transparency, switching speed, photonic integrated amplification for loss-less operation, and gain equalization are verified experimentally. The experimental assessment of a \(4\times 4\) photonic integrated WDM cross-connect confirmed the capability of the cross-connect chip to switch the WDM signal in space and wavelength. Experimental results show lossless operation, low cross-talk \(<\)\(-\)30 dB, and dynamically switch within few nanoseconds. Moreover, the operation of the cross-connect switch with multiple WDM channels and diverse modulation formats is also investigated and reported. Error-free operation with less than a 2 dB power penalty for a single channel, as well as WDM input operation, has been measured for multiple 10/20/40 Gb/s NRZ-OOK, 20 Gb/s PAM4, and data-rate adaptive DMT traffic. Compensation of the losses indicates that the modular architecture could scale to a larger number of ports.  
semiconductor optical amplifiers; photonic integrated cross-connect switch; wavelength division multiplexing; optical metropolitan networks +
Footnote †: journal: Applied Physics Letters
## 3 Results and Discussion  
### Relative Abundance  
The rarefaction and rank abundance curves shown in Figure 2 are widely used for indicating the biodiversity of samples. A rarefaction curve is created by selecting randomly a certain amount of sequencing data from the samples, and then counting the number of the species they represent. The observed species richness varied between the five soil samples. The rarefaction curves in Figure 1 showed some of this variation in the microbial abundance of the soil samples, particularly between Cs2.S.Pro, Cs3.S.Pro, and Cs2.S.Pro. The Rarefaction curves can directly reflect the rationality of the sequencing data volume and indirectly reflect the richness of the microbial community in the samples. If the curve is steep, this means that a lot of the species remain to be discovered.  
Soil bacteria play an essential role in the cycling of organic matter in nature, soil formation, and soil fertility. Simultaneously, various secondary metabolites produced by soil bacteria during their life processes are also crucial to the soil ecological environment [26]. For a better visualization of the taxa with a higher relative abundance and their proportion in different classification levels for each soil sample, the most abundant 10 taxa at the phylum level were selected to form the distribution histogram of the relative abundance of taxa. Most major bacterial phyla such as Actinobacteria and Proteobacteria were present in Cs3.S.Pro, with nearly 20-60% of the total sequences identified as members of these phyla (Figure 3, in red and blue colour). Firmicutes are the most abundant in the rhizosphere soil  
Figure 1: Mobile experimental installation used by fluorescence spectroscopy.  
of the introduced amelina variety Cs1 and were decreased in the other soil samples. It is well known that intercropping can significantly affect soil properties and the composition of the microbial community. In our study, the dominant bacteria at the phylum level were Actinobacteria, Proteobacteria, Gemmatimonadota, Bacteroidetes, Firmicutes, and Acidobacteriota, accounting for approximately 92% of the total abundance of the bacterial community. Despite differences in relative abundance, Actinobacteria, Proteobacteria, Acidobacteria, and Chloroflexi were also the dominant bacteria found by [PERSON] et al. (2016) and [PERSON] et al. (2020)[27; 28]. Actinobacteria constitutes one of the largest bacterial phyla in many types of soil ecosystems [29]. They often live as plant commensals [30], nitrogen-fixing symbionts in soil [31; 32], and are also a significant source of extracellular enzymes and secondary metabolites [33]. From the data, it is clear that intercropping significantly decreased the Firmicutes abundance from 18.5% to 1.8% in the Sc2.S.Pro and Sc3.S.Pro and the intercropped plots Cs3.Vs.SPro and Cs3.Ps.SPro. In contrast, Actinobacteria and Acidobacteria abundance increased compared to the Cs1.S.Pro soil (Figure 3). Similar results have been obtained in intercropping experiments in which the bacterial biomass and activity in mono-cropping differed from those detected in intercropped systems [34].
## 6 Conclusions and Perspectives  
The interest in NDs and in particular fluorescent NDs in a large variety of science fields is pushing the research on this particular nanoparticle. This interest is due to the properties of fluorescent NDs, which in turn leads, as described in this review, to many different applications. Indeed, the significant and growing potential for commercial applications of nanodiamonds is testified to by the volume and timeline of published patents. A search using the \"nanodiamonds\" keyword on the Espacenet database yields 2077 results [146], steadily rising in a time window starting in 1995 and peaking in 2017. The largest group concerns fabrication and modification/functionalization methods (570), but patents regarding specific applications are the second largest group (240) and are concentrated in the last five years. To date, several industrial-grade producers are active, and commercial nanodiamonds for research purposes are available via both general chemical suppliers Sigma-Aldrich (St Louis, MO, USA) and specialized companies such as Ray Techniques Ltd. (Jerusalem, Israel), Adamas Nanotechnologies (Raleigh, NC, USA), PlasmaChem (Berlin, Germany), Tong Li Chemicals (Shenzhen, China), and TCI Chemicals (Tokyo, Japan), among others.  
However, many challenges remain, in particular concerning the synthesis process. To enable a wide use of nanodiamonds, large-volume cost-effective production is required, as well as a better  
Figure 21: The black line is the PL spectrum under a 532 nm pump of PLA NDs containing NV centers, while the blue and red curves are typical ERE emissions, pertinent to NGC2023 and NGC7023 nebulae Data reproduced from [64] (black curve), [133] (red curve) and [129] (blue curve).  
control of the surface chemistry, which is essential for certain applications. In addition, control of the size of NDs with limited dispersion and established content of NV centers are objectives to be pursued. Moreover, preventing aggregation during synthesis of ultrasmall fluorescent nanodiamonds (<4 nm) is a fundamental step towards improving sensing capabilities. In summary, synthesis techniques should be improved to obtain fluorescent NDs with a quality closer to bulk diamond. This research would lead to a better control of NDs' properties and thus to novel applications.  
Conceptualization, L.B., M.C., M.O. and A.M; writing--original draft preparation, L.B., M.C., M.O. and A.M.; writing--review and editing, L.B., M.C., M.O. and A.M.; supervision, A.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The authors declare no conflict of interest.
# PSF Estimation of Space-Variant Ultra-Wide Field of View Imaging Systems
## 6 Results
### Numerical Stability Verification

Table 2: Selected on-axis points results of verification of the algorithm, where normalized distance H is from 0 to 0.83 and \(\varphi\) is equal to zero. Image distance H is normalized according to the sensor, and it is related to an FOV of \(110^{\circ}\); all optical parameters are summarized in Table 1. Differences between the original PSF and the model are within the order of thousandths. Thus, the proposed algorithm can find the exact \(W_{klm}\) coefficients used for generating the test pattern illustrated in Figure 5.  
Figure 8: The dependence of SNR on MAXDIFF. When SNR is lower than 20 dB, the error of the model increases rapidly.
# Robust and Efficient Corner Detector Using Non-Corners Exclusion
## 4 Experimental Results and Analysis
### Analysis of Accuracy  
Accuracy requires that corners should be detected as close as possible to their correct positions [4]. The formula of ACU has been described in Section 2. We attempted the five test images, but due to lack of space only two test images are depicted in this paper. The ground truths of the block image and the lab image are illustrated in Figure 4, where the block image has 60 strong corners and the lab image has 242 strong corners. The corners for the two test images by the five algorithms are shown in Figures 5 and 6. Each detector includes adjustable parameters and their settings affect detection results [30]. To give a fair evaluation, several tests have been conducted with each algorithm to attain individual optimal parameter settings.  
Figure 4: Test images (**a**) block and (**b**) lab and their ground truths where corners are labeled.  
Figure 5: Corner detection on the block image. (**a**) Moravec. (**b**) Smallest unique segment assimilation nucleus (SUSAN). (**c**) FAST-9. (**d**) Harris. (**e**) Robust and efficient corner detector (RECD).  
We count the numbers of True Positives, False Negatives and False Positives for the five algorithms of the block and the lab. The results are listed in Tables 1 and 2, where the True Positives refers to positive examples that are classified as positive examples. The False Negatives refers to positive examples that are classified as negative examples. The False Positives refers to negative examples that are classified as positive examples. The ACU means accuracy of the correctness of corner detections. It can be seen from Table 1 that the number of true corners is 60. The RECD and the Harris corner detector give the same results. Both detect the highest number of true positives and have the least number of false negatives and false positives. The FAST-9 corner detector performs slightly worse than the Harris corner detector and the RECD, but it performs substantially better than the Moravec detector and the SUSAN detector. The detection performance of the SUSAN corner detector is the worst. Similar results are shown in Table 2. However, the FAST-9 corner detector is now slightly worse than the Moravec corner detector in detecting true positives and false negatives, while the RECD performs slightly better than the Harris corner detector. It is noted that the RECD always performs best on accuracy. Subjective observation of the two experimental figures shows that the RECD detects the largest number of the true positives while it detects the least false
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## Appendix A Details of the Used Backscatter Coefficients  
In this appendix section, we provide some details of the used backscatter data related to denoising and weather phenomenon, through some representative cases. Figure 11 shows an example of raw backscatter data and their denoised ones which were observed at a moment. We can see that noises are successfully removed. Figure 12 shows an extended time-height plot of raw backscatter data and their denoised ones which had been observed for one day (specifically on 18 March 2015 when daily precipitation was 61 mm). Figure 11 can be understood as a cross section at a point of Figure 12. For Figure 12, we chose a day during which both of precipitation and non-precipitation occurred while clouds are presented in both. In the right side of the figure, a gray box means a period that it rains continuously. We could find clear difference between each box boundary point and its adjacent one, but it does not seem easy to distinguish the two phenomena only by the values themselves.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **FAR** & **Recall (POD)** & **F1 Score** \\ \hline SMO & 0.8777 & 0.3649 & 0.6351 & 0.1606 & 0.2230 \\ ANN\({}_{1}\) & 0.8848 & 0.2122 & 0.7878 & 0.5147 & 0.3005 \\ ANN\({}_{2}\) & 0.8644 & 0.5340 & 0.4660 & 0.1849 & 0.2747 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Results of precipitation detection according to other ML techniques.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Accuracy** & **Precision** & **FAR** & **Recall (POD)** & **F1 Score** \\ \hline SMO & 0.8528 & 0.2404 & 0.7596 & 0.0203 & 0.0374 \\ ANN\({}_{1}\) & 0.8867 & 0.2820 & 0.7180 & 0.0311 & 0.0560 \\ ANN\({}_{2}\) & 0.8575 & 0.2334 & 0.7666 & 0.0204 & 0.0376 \\ \hline \hline \end{tabular}
\end{table}
Table 16: Results of low visibility detection according to other ML techniques.  
Figure 13 shows an example of the denoised backscatter data of CL51 and observation range data of PWD20 which had been observed for one day (specifically on 31 March 2015). For the figure, we chose a day during which both of low visibility and not-low visibility occurred while clouds are presented in both. In the left side of the figure, a gray box means a period that visibility is continuously low (i.e., less than 1000 m). Similar to the precipitation case of Figure 12, it is hard to distinguish the two phenomena only by the values themselves. Moreover, we can see that in the middle period it is not easy even to find some difference between the box boundary point and its adjacent one.
# Cost Analysis and Optimization of Modern Power System Operations
## Nomenclature  
* _Indices_ \(i\) **generating unit (thermal)** \(t\) **Time duration** \(k\) **blocks chosen for piece-wise linear cost function** \(Parameters\) \(L_{t}\) **Dynamic load** \(A_{i},B_{i},C_{i}\) **Coefficients of cost** \(U_{i}^{o}/S_{i}^{o}\) **At the operational horizon specifies the time for which \(i\)th generator is on/off** \(P_{i,ini/fin}^{i}\) **Initial and final value of power in kth block of linear cost of thermal unit \(i\) (MW).** \(C_{i,ini/fin}^{k}\) **Initial and final value of cost for block k of linear cost for thermal unit \(i\) (S/h).** \(P_{i}^{max/min}\) **Minimum/maximum power limitation of thermal unit** \(\Delta P_{i}^{k}\) **Length of block k of linear cost of thermal generators \(i\) (MW)** \(s_{i}^{k}\) **Slope for cost in segment k of linear cost function (S/MW)** \(P_{i,t}^{i}\) **& \(P_{i,t}^{i}\) **Minimum/Maximum time dependent power limit** \(RU_{i}/RD_{i}\) **with thermal unit's up/down ramp limitations** \(SD_{i}/SU_{i}\) **Ramp limit of start-up/shutdown thermal unit** \(DT_{i}/UT_{i}\) **Generator's minimum up/down time** \(SDc_{i,t}/STc_{i,t}\) **Time dependent shutdown/start-up cost of thermal unit \(i\) \(Cs_{i}/S_{i}\) **Shutdown/start-up cost of thermal unit \(i\) \(P_{t}^{up}\) **Wind energy at time \(t\)