esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
### Performance Issues  
The results clearly show that the proposed method has very promising performance, especially for the expected reference-disturbance pairs. This is expected since it is known in the AI domain that performance on the training set is always higher than performance on the test set. Since the system is trained with expected reference-disturbance pairs, it is understandable to have higher performance for the expected configuration. Note that this AI-based approach is unique in the literature to the author's best knowledge, so it is not possible to compare results from other works.  
As an automatic design method for automatic controller design, the proposed method automates the engineering process by largely performing critical tasks. Expected reference-disturbance pair selection is important in the performance of the proposed automatic control system. Even in the unexpected reference-disturbance pairs, the proposed system shows superior performance compared to the optimal PI controller, ensuring acceptable generalization performance in AI terms. This result may be due to the high flexibility of neural networks learning input to output mapping successfully.  
Note that the proposed system shows superior performance especially under difficult disturbances, which is important for realistic missions of robust systems. Under low-disturbance conditions, almost any rational controller will have acceptable performance, but the real quality of the controller is seen under harsh conditions. For example, for an air vehicle carrying humans, either fixed wing or mobile wing, it is important to fly safely under relatively high-power external wind disturbance in order to be utilized more effectively. Most of the works in the literature lack testing the system under high-amplitude disturbances, so the proposed system is compared with an optimal PI control system.  
The detailed results given in the Section 3 of this study will hopefully enable comparisons of performance results in future studies. It is important to give the details of the experiments conducted, which is the case in this study, and it is encouraged for automatic controller researchers to give the details of the disturbance signals' shapes and parameters in particular in order to make fair comparisons between controllers.  
Figure 12: The controller output is saturated between \(+12\) Volts and \(-12\) Volts.
# Fault Detection Algorithms in Medium-Voltage Direct-Current (MVDC) Grids
## 3 Study Case and Results
### Bus Faults  
In this section, all the considered algorithms are tested with identical bus fault scenarios in order to make a comparison between them. This way, pole-to-pole and positive-pole-to-ground faults have been analyzed at bus 2, employing various fault resistances. All the faults occur at t = 6.5 s. Graphs and tables depicting the detection times for each algorithm are presented.
## 1 Introduction
### Related Works  
We describe the techniques required for the indoor localization technique proposed in this thesis. In our previous paper, there is a possibility of errors in cluster formation during the process of clustering adjacent data points, and there is a problem in calculating the distance between clusters that include data points. This can be caused by measuring the distance between clusters and using an arbitrary threshold to connect clusters that are within a certain distance to establish a path. This problem can lead to the establishment of adjacencies between data points that are not actually adjacent to each other, forming incorrect clusters and generating movement paths that have structurally untraversable configurations. To solve the problem, this paper improves the adjacency of BB by representing data points as BBs and completely removing the threshold for adjacency judgment through IoU operations. We also applied grid cells to form clusters of BBs. Below we briefly describe the BB, IoU, and grid cell used in our proposed technique.
## 4 Results and Discussion
### Dispatching Scenario and Prediction Performance  
To simulate a real dispatch case scenario, the unit commitment and dispatched power plants for a day in December- 2019 were acquired coupled with the expected demands for that day as predicted by NEPCO. The detailed information of the dispatched power plants is given in Table 13. All units for that day operated with combined cycles with their real capacities shown. The available power from Egypt is assumed at 150 MW for the whole day with priority above IPP4 and Risha's power at maximum capacity with its cost not considered, being based on natural gas extracted from Jordan. The real hourly PV and wind energy from renewable power plants are assumed to be the predicted values and are subtracted from the demand, then the table is used to find the cheapest combination.  
Figure 17 shows the real and predicted values of electricity Sunday, 8 December 2019, as well as the PV and wind generation, where Sunday represents the start of the working week in Jordan. The proposed model's prediction achieved a MAPE of 3.59% and is compared to NEPCO's prediction, which performed worse. One reason is that NEPCO's prediction is usually provided before 4 PM in the previous day, while our model achieves the prediction of the whole day at the end of the previous day. Nevertheless, the proposed model achieved higher results throughout the period. It is to be noted that, hour 0:00 indicates the average demand from 0:00 to 1:00.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Unit** & **Name** & **Cost (JD/MW)** & **Min. Demand (MW)** & **Max. Demand (MW)** \\ \hline
**1** & Risha & 0 & 33 & 33 \\
**2** & AES CC & 59.85 & 210 & 410 \\
**3** & ACWA CC & 60.09 & 210 & 360 \\
**4** & SAMRA 4 CC & 61.06 & 127.5 & 220 \\
**5** & SAMRA 3 CC & 61.06 & 192.5 & 420 \\
**6** & SAMRA 1 CC & 61.06 & 210 & 310 \\
**7** & QPC CC & 64.89 & 210 & 424 \\
**8** & Wind & 72.79 & 0 & - \\
**9** & PV & 79.94 & 0 & - \\
**10** & Egypt & 52.79 & 0 & 150 \\
**11** & IPP4 & 121.17 & 0 & 240 \\
**12** & IPP3 & 231.04 & 0 & 570 \\ \hline \hline \end{tabular}
\end{table}
Table 13: Power plant dispatched for the case-study in December 2019.  
Figure 17: Selected case study day in December 2019.
_Applied_  
_sciences_  
_Article_  
**Complete Benzene Oxidation over Mono and**  
**Bimetallic Pd--Au Catalysts on Alumina-Supported**  
**Y-Doped Ceria**  
**[PERSON] 1,*(r)**, [PERSON] 1, [PERSON] 1, [PERSON] 2,**  
**[PERSON] 1, [PERSON] 2(r) and [PERSON] 3**  
1 Institute of Catalysis, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria; [EMAIL_ADDRESS] (L.I.); [EMAIL_ADDRESS] (P.P.); [EMAIL_ADDRESS] (Y.K.)  
2 Istituto per lo Studio dei Materiali Nanosrturat, CNR, 90146 Palermo, Italy; [EMAIL_ADDRESS] (A.M.V.); [EMAIL_ADDRESS] (L.F.L.)  
3 Institute of Physical Chemistry, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria; [EMAIL_ADDRESS]  
* Correspondence: [EMAIL_ADDRESS]  
Received: 30 December 2019; Accepted: 2 February 2020; Published: 6 February 2020  
**Featured Application: Design of well performing and cost-effective mono and bimetallic Pd--Au catalysts for complete benzene oxidation.**  
**Abstract:** The protection of environment and human health stimulates intensive research for abatement of volatile organic compounds (VOCs) in the atmosphere. Complete catalytic oxidation is an efficient, environmentally friendly and economically feasible method for elimination of VOCs. This study aims to design high performing and cost-effective catalytic formulations by exploration of appropriate and economically viable supports. Alumina-supported ceria (30 wt.%) and Y\({}_{2}\)O\({}_{3}\) (1 wt.%)-doped ceria were prepared by mechanical mixing and were used as support of mono Au (2 wt.%) and Pd (1 wt.%) and bimetallic Pd-Au catalysts. The characterization by textural measurements, X-ray powder diffraction (PXRD), X-ray photoelectron spectroscopy (XPS), EPR (electron paramagnetic resonance) and temperature-programmed reduction (TPR) was carried out in order to clarify the relationship between catalyst composition, textural, structural and surface properties, reducibility and catalytic performance for complete benzene oxidation. Among all studied catalysts, Pd-based catalysts exhibited the best combustion activity. In particular, monometallic Pd on alumina supported Y-doped ceria attained 100% of complete benzene conversion at 180 \({}^{\circ}\)C. These catalytic materials have potential to meet stringent emission regulations in an economical and effective way.  
**Keywords:** Au; Pd and Pd-Au catalysts; alumina-supported ceria; Y-doped ceria/alumina; VOCs oxidation; complete benzene oxidation
## 4 Experimental Results
### Capability of Estimating the Bias Field  
Synthetic inhomogeneous intensity brain MRI with different angles obtained from BrainWeb were used to show the capability of RC_LCI in estimating the bias field. Axial-, sagittal-, and coronal-sectioned images are shown in the first, second, and third rows of Figure 5, respectively. As shown in Figure 5, the estimated bias field in the second column satisfied the smoothly varying property due to the orthogonal basis functions. Hence, bias field correction images were more homogeneous than the original images, which can be seen from the third column. As shown in the last column, GM, WM, CSF, and background were distinctly segmented into four classes.  
The histograms of the original and corresponding bias field correction images obtained from Figure 5 are shown in the first and second rows of Figure 6, respectively. The histograms of the original and bias field correction images can be contrasted to further prove that the quality of the image was effectively improved. The first row shows that the intensity of GM, WM, and CSF could not be clearly distinguished, which meant that these images could not be segmented into four classes accurately. There was no obvious peak that represented each tissue in the histogram of the original inhomogeneous intensity image. On the contrary, the well-defined peak can be seen in the histogram of the bias field correction image. The above comparison demonstrates that RC_LCI can obtain accurate segmentation results from inhomogeneous intensity brain MRI.  
Figure 4: Jaccard similarity coefficients of MICO, BCFCM, FLICM, and RC_LCI: (**a**) Jaccard similarity (JS) for gray matter (GM); (**b**) JS for white matter (WM); and (**c**) JS for cerebrospinal fluid (CSF).  
Figure 5: Segmentation results of inhomogeneous intensity brain MRI: (**a**) original images; (**b**) estimated bias field; (**c**) bias field correction images; and (**d**) segmentation results.  
Figure 6: The histograms of the original and bias field correction images: (**a**) the histogram of axial-sectioned images; (**b**) the histogram of sagittal-sectioned images; and (**c**) the histogram of coronal-sectioned images.
# Applications of Pulsed Light Decontamination Technology in Food Processing: An Overview
###### Abstract  
Consumers of the 21 st century tend to be more aware and demand safe as well as nutritionally balanced food. Unfortunately, conventional thermal processing makes food safe at the cost of hampering nutritional value. The food industry is trying to develop non-thermal processes for food preservation. Pulsed light (PL) is one such emerging non-thermal food processing method that can decontaminate food products or food contact surfaces using white light. Exposure to intense light pulses (in infrared, visible, and ultraviolet (UV) regions) causes the death of microbial cells, rendering the food safe at room temperature. PL technology is an excellent and rapid method of disinfection of product surfaces and is increasingly being used for food surfaces and packaging decontamination, enabling the minimal processing of food. This paper aims to give an overview of the latest trends in pulsed light research, discuss principles of pulse generation, and review applications of various PL systems for the inactivation of microorganisms in vitro, in various food products, and on food contact surfaces. Effects of PL on food quality, challenges of the process, and its prospects are presented.  
p _Applied_  
_sciences_
## 4 The Proposed Routing Protocol: HPA-SR
### The Hybrid-Price Auction Model Process for The Proposed HPA-SR Protocol
There are two scenarios to compare the cost/bidding value with the cost/bidding threshold. If \(b_{i}\) is less than \(B_{th}\), the node will use FPA to determine the route to be pursued by the next node. Otherwise, if \(b_{i}\) is greater than \(B_{th}\), the node will use SPA to determine the route to be traversed by the next node. Accordingly, there are four transition events between the two states as follows:  
Event 1: The FPA-FPA event: \(S_{n}+S_{n+1}\), \(b_{in}<B_{th}\) and \(b_{in+1}<B_{th}\),  
Event 2: The FPA-SPA event: \(S_{n}+S_{n+1}\), \(b_{in}<B_{th}\) and \(b_{in+1}\geq B_{th}\),  
Event 3: The SPA-FPA event: \(S_{n}+S_{n+1}\), \(b_{in}\geq B_{th}\) and \(b_{in+1}<B_{th}\),  
Event 4: The SPA-SPA event: \(S_{n}+S_{n+1}\), \(b_{in}\geq B_{th}\) and \(b_{in+1}\geq B_{th}\),  
Where \(S_{n}\) represents the \(n\)-th session, \(S_{n+1}\) represents the next session, \(b_{in}\) represents the cost/bidding on node \(i\) in session \(n\), and \(b_{in+1}\) represents bidding on node \(i\) in the next session, respectively. From the transition events, it is noteworthy that: when \(b_{in}<B_{th}\), the node will choose the route with the first auction price and when \(b_{in}\geq B_{th}\), the node will choose the route with the second auction price, regardless of the conditions on each session. For steady-state probabilities, let \(p_{00}\), \(p_{01}\), \(p_{10}\), and \(p_{11}\) denote the transition probabilities of events one to four, respectively. Let \(\pi_{1}\) and \(\pi_{0}\) denote the steady-state probabilities of the FPA and SPA status, respectively. The relationship between \(\pi_{1}\) and \(\pi_{0}\) associated with the described Markov chain can be expressed as [41]  
\[\pi_{0} =p_{00}\pi_{0}+p_{10}\pi_{1},\] \[\pi_{1} =p_{01}\pi_{0}+p_{11}\pi_{1},\] \[1 =\pi_{0}+\pi_{1}. \tag{11}\]Relying on the fact that \(p_{00}=1-p_{01}\) and \(p_{11}=1-p_{10}\), and after some modifications, \(\pi_{0}\) and \(\pi_{1}\) can be written as  
\[\pi_{0}=\frac{p_{10}}{1-p_{00}+p_{10}}=\frac{p_{10}}{p_{01}+p_{10}}, \tag{12}\] \[\pi_{1}=\frac{p_{01}}{1-p_{11}+p_{01}}=\frac{p_{01}}{p_{01}+p_{10}}. \tag{13}\]
## 4 Discussions  
Having performed many rounds of experiments, the visually impaired user gave us some feedback as follows:  
1. This system is helpful to the user, and the user appreciates that we care about the needs of visually impaired people;  
2. The user likes to be informed by the system if there are objects around the user, but the prompts cannot play too often to bother the user;  
3. The walking guide strategy provided by the system gives the user a great sense of security and reduces the number and time of his orientation training;  
4. The user would prefer the system to be lighter and smaller just in case the user still needs the white cane;  
5. The user needs to take some time to become familiar with this system. The user hopes the system can be friendlier without long time training. We appreciate that the user gave us some positive feedback and suggestions to improve our development in future work.  
In Section 2.2.1, we have mentioned that the training data were collected on sunny days during daylight hours to train the Fast CNN. However, we also have done some experiments that used the trained Fast-SCNN on rainy days or at night. The experiment results are stated below. The system still works well in cloudy weather with sufficient brightness. However, it almost fails on rainy days when there is stagnant water affecting the segmentation result of the sidewalk. Furthermore, the system is unstable to recognize the sidewalk and crosswalk without stagnant water. Moreover, it did not work well at night since the environment is too dark, or streetlights of different brightness may confuse the segmentation result. Therefore, for the sake of safety, we suggest the visually impaired uses the proposed system on daytimes and not rainy days. In order to improve the system to be also feasible on rainy days and at night, it needs to collect a lot of extra training data on rainy days and at night. We need more time and more effort to study this issue in the future.  
We have to admit that we did not invite enough visually impaired persons to do the experiments since it is hard to find a large number of visually impaired people to test in a short time in Taiwan. However, we have to supplement that before we invited the natural visually impaired person to experiment one time, we would request several (about 5-6) students in my lab wear blindfolds to do many experiments in advance (see Figure 14). This paper just provides a prototype of the assistive system for visually impaired people and proposes a preliminary result for helping the visually impaired. We will continue finding more visually impaired users to experiment with verifying the effectiveness of the proposed system.  
Figure 14: Experiments by students with blindfolds.
## 3 Results and Discussion
### Critical Volatile Compounds  
It is worth noting that not all VOCs detected in the fermented grain samples fermented at different times contribute significantly to the flavor of the fermented grains. An ROAV analysis was conducted to assess the contribution of different volatiles to the fermented grains' aroma during the fermentation of the fermented grains. The ROAV value indicates the importance of key aroma compounds, with higher scores indicating a greater contribution to the aroma quality of the fermented grains [38]. Among the VOCs analyzed (Table 2), nine key VOCs with ROAV \(>\) 1 were identified in the fermented grains: 3-methyl-1-butanol (ROAV = 35.49), 1-octen-3-ol (ROAV = 3.48), 2-methyl-1-butanol (ROAV = 3.95), isoamyl acetate (ROAV = 100), ethyl caprylate (ROAV = 1.24), ethyl caprate (ROAV = 2.30), ethyl caproate (ROAV = 2.76), 2-methylbutyl acetate (ROAV = 8.36), and 1-nonanal (ROAV = 3.78). Isoamyl acetate had the highest ROAV and contributed the most to the flavor of the fermented grains. Its content decreased and then increased, giving the fermented grains a fruity aroma and a banana flavor. 3-methyl-1-butanol was the second most important odor-active substance, contributing to the floral and cocoa flavors of the fermented grains. 1-octen-3-ol, decanal, despite being present in low concentrations, had a high ROAV due to its low odor threshold, and it contributes a mushroom flavor to the fermented grains. 2-methylbutyl acetate provides a fruity and pear aroma to the fermented grains, ethyl caprylate provides a sweet and soft floral and brandy aroma, ethyl caproate provides a brandy and pineapple aroma to the fermented grains, and ethyl caprate provides a brandy and pear aroma to the fermented grains. All these volatile organic compounds enhance the floral and fruity aromas of the fermented grains. VOCs with 0.1 \(\leq\) ROAV \(<\) 1 included phenethyl alcohol, ethyl heptanoate, decanal, hexanal, 3-octanone, and 2-pentylfuran, which have a moderating effect on the aroma characteristics of the fermented grains. Phenethyl alcohol increased during fermentation, giving the fermented grains its honey and rose aromas.
# Small-Satellite Synthetic Aperture Radar for Continuous Global Biospheric Monitoring: A Review
## 1 Introduction  
Space-based radar observation has growing potentials for monitoring the global biospheric diversity subject to anthropogenic drivers at geological scales [1]. The performance of radar is less affected by weather and sunlight conditions than that of optical sensors. Satellites with onboard sensors can provide comprehensive coverage of remote areas or vast regions that may be too costly for unmanned aerial vehicles (UAVs) or ground-based platforms, provided that all platforms provide congruent results via calibrations [2; 3; 4]. Therefore, it was Seasat, the first satellite dedicated to remote sensing of the Earth's oceans, that carried the first synthetic aperture radar (SAR) and other radar instruments operable in space. Despite these advantages, miniaturization of radar-carrying satellites was rather slow compared to satellites carrying optical devices due to the lack of commercial-off-the-shelf (COTS) components as well as challenging design requirements for the satellite platform [5; 6]. Representative use cases of space-based radar include altimetry, sounding, scatterometry, and so forth in the studies of land, cryosphere, and oceans. Biospheric monitoring is another useful application because radar has high sensitivity in detecting surface changes in a target area and discriminating mobile targets against a background [7]. This paper will consider mainly SAR because of its three-dimensional mapping capability through interferometry. The heritage of Seasat has influenced many of later SARmissions for decades, as listed in Table 1[8; 9; 10; 11; 12; 13]; for instance, Shuttle Image Radar (SIR) missions used spare parts of the previous Seasat mission onboard Space Shuttles to test SAR image applications in land use, geology, hydrology, and forestry [14; 15]. European Remote-Sensing Satellite (ERS) could obtain radar data in polar regions thanks to its polar orbit. Two different operational modes were available for SAR of ERS, the imaging mode for high resolution (10m) and the wave mode with 30 m resolution for vector measurement of ocean waves. Figure 1 describes the acquisition of imagelets during the wave mode, which may be subdivided into the On-Ground Range Compression (OGRC, 5 km \(\times\) 5 km) mode and the Onboard Range Compression (OBRC, 5 km \(\times\) 9.6-12 km) mode [16]. The imaging mode, not shown in Figure 1, is identical to the wave mode except that the entire 100 km wide strip is scanned for high-resolution imagery without any movement profile information. This resolution-swath tradeoff is also shown in Advanced Land Observing Satellite (ALOS) whose ScanSAR mode has a coarse 100 m resolution but 250-350 km wide swath. This is 3 to 5 times wider than conventional SAR images and is especially useful for monitoring sea ice and rain forest extent with expansive areas of interest [13].