esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
Electromagnetic interference (EMI) is one of the crucial problems in semiconductor image analysis. Thus, in this research, four different types of denoising algorithms and twelve different edge detection algorithms are considered to investigate the influence of the EMI on the semiconductor SEM image analysis. From the experimental analysis, it is found that the Gaussian filter for denoising and both the Scharr \(\times\) filter and the Canny filter for edge detection are the best for characterizing distorted image objects. Additionally, from the statistical analysis, all the measures (i.e., image object area, image object contour, rectangular area of the image object, extend index, and solidity index) are very effective in describing the degree of distortion in semiconductor SEM images caused by EMI since the medians of all the extracted shape-related measures of high-EMI SEM images are higher than those of both medium- and weak-EMI SEM images and all the _p_-values of the test statistics are close to 0. As for future work, more accurate indices for calibrating the degree of distortion in semiconductor SEM images and the performance of other denoising and edge detection algorithms can be investigated. It is necessary to develop an automatic classification and analysis system for the EMI-generated semiconductor SEM images, including the EMI effect extraction function and yield analysis function.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Intercept** & **Value** & **Num. DF** & **Den. DF** & **F-Value** & _p-Value_ \\ \hline Wilks’ Lambda & 0.002 & 5 & 112 & 10,117.102 & 0.000 \\ [PERSON]’s trace & 0.998 & 5 & 112 & 10,117.102 & 0.000 \\ Hotelling–Lawely trace & 451.656 & 5 & 112 & 10,117.102 & 0.000 \\ [PERSON]’s largest root & 451.656 & 5 & 112 & 10,117.102 & 0.000 \\ \hline
**EMI Level** & **Value** & **Num. DF** & **Den. DF** & **F-Value** & _p-Value_ \\ \hline Wilks’ Lambda & 0.588 & 10 & 224 & 6.821 & 0.000 \\ [PERSON]’s trace & 0.429 & 10 & 226 & 6.169 & 0.000 \\ Hotelling–Lawely trace & 0.674 & 10 & 165.274 & 7.501 & 0.000 \\ [PERSON]’s largest root & 0.429 & 10 & 226 & 6.169 & 0.000 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of multivariate analysis of variance (MANOVA).  
**Author Contributions:** Y.-J.P.: conceptualization, investigation, data curation, experiment, writing--original draft, and writing--review and editing. R.P.: conceptualization, methodology, writing--original draft, and writing--review and editing. D.C.M.: conceptualization, methodology, supervision, and writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the Ministry of Science and Technology of Taiwan, grant number 110-2221-E-027-106-MY3.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study are available on request from the corresponding author. The data are not publicly available as it contains confidential information.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Antibacterial Activity of Crude Extract and Purified Acetogenins from _Amona muricata_ Seeds
## 2 Materials and Methods
### Obtaining Crude Extract and Purified Acetogenins  
The crude extract (CE) and purified ACGs (P-ACGs) were previously obtained as previously reported [21]. Briefly, CE was obtained from defatted endosperm of _A. muricata_ seeds using methanol and thermosonication (temperature (50 \({}^{\circ}\)C), time (50 min), amplitude (100%) and pulse cycles (0.5 s)) with an ultrasonic system UP400S (Hielscher Ultrasonic, Teltow, Germany). CE was dried with a rotary evaporator (Yamato RE300, Tokyo, Japan).  
To obtain P-ACGs, a portion of the CE was purified using open column chromatography; according to [PERSON] et al. [21] and [PERSON] et al. [22]. These authors concluded that P-ACGs are only ACGs and they are composed of pseudoannonacin, desacetyluvaricin, annoancin, squamostatin-D, isodesacetyluvaricin, squamocin, bullatacin, and other non-identified ACGs.
## 2 Geological Background  
The Moroccan Middle Atlas, shown in Figure 1, where our study area is located, is a chain structured during the Alpine orogen. It is subdivided into two units: to the west is the Tabular Middle Atlas (TMA) unit, and to the east is the Folded Middle Atlas (FMA) unit [26; 27]. The passage between these two units is underlined by a network of faults called the North Middle Atlas accident (NMAA). The Guigou plain is located at the south-eastern edge of the TMA and along the NE-SW NMAA [27; 28; 29]. It corresponds to a collapse ditch about 5 km wide and 40 km long, essentially filled with volcanic lava and traversed by Oued Guigou, which constitutes its alluvial plain [1; 7; 30]. The chronological succession of the geological formations of the Middle Atlas begins with the sandstone-pelitic series constituting a base attributed to the Paleozoic era, which is structured by the Hercynian orogen [26; 31; 32; 33; 34; 35]. In angular discordance, a thick series from the Triassic age composed of lower and upper argillites and framing a basaltic complex of doleritic-type rocks rests on this base [28; 35; 36; 37]. This series is underlain by the carbonate formations of the Lias, which are interspersed with marl and limestone of the Dogger age to the Mio-Pliocenee [32]. Locally at TMA, the Quaternary period is represented by volcanic lavas of a basaltic nature. These lavas come from craters located to the north of the study area, the most important of which are Jbel Habri, Chedifat and Bou-Ahsheine [28; 38]. At their exit, following the example of our study area in the Guigou plain, these lava flows used the slopes to occupy the depressions [28; 38]. In this plain, in addition to these basalt flows which predominantly outcrop, there are also some marl and limestone formations from the Mio-Pliocene period which outcrop mainly in the NE part in the form of small plateaus at the foot of the folded Middle Atlas [7; 34].  
Tectonically, the TMA is characterized by brittle rather than folded deformation [7, 28, 33, 39]. Indeed, two major fault networks can be distinguished, namely the NMAA fault network and the Tizi-n-Tretten (TNTA) fault network with a NE-SW direction. These two major accidents are part of the same fault system inherited from the Hercynian orogen, which was replayed several times during the Alpine orogen [26, 40, 41]. These faulted structures  
Figure 1: (**a**) General geological map of Morocco showing the location of the Middle Atlas Mountain; (**b**) Simplified geological map of the Middle Atlas extracted from the geological map of Morocco at 1/200,000, showing the location of the study area; (**c**) Sketch of the geological map of the Middle Atlas where the study area marked by the red polygon.  
that shape the TMA are easily mapped on the ground, except in areas covered by basaltic flows [28,38].
# Monitoring Forest Phenology in a Changing World
## 5 The Future for Forest Phenology Monitoring in a Changing World  
The impacts of climate and habitat change on phenology will likely vary across regional-to-continental scales and between biomes [2]. If we are to help mitigate these changes, a greater understanding of the phenological dynamics across biomes and across multiple scale is required. Detecting and quantifying the impacts of change on phenology across regions demands an increase in the spatial scale of phenological monitoring alongside an increase in spatiotemporal resolution. This will become possible through judicious use of the different quantitative methods currently available and currently under development. A key focus should be the increased incorporation of molecular methods and hyperspectral sensors in monitoring frameworks, with the ultimate goal of developing a mechanistic understanding of the phenological process. This ambition will be aided by the simultaneous deployment of multiple monitoring methods, allowing the strengths of one to compensate for the limitations of others, and the continued development of effective approaches to upscaling phenological monitoring from point observations on the ground to biome and global scale maps. This requires an expansion of monitoring approaches already in use [22; 159]; however, this is still heavily focused on temperate regions. Not all methods developed in these regions translate to the tropics, where high plant diversity and the asynchronous nature of phenology presents a more difficult challenge that is being addressed, but remains an obvious frontier for further methods development.  
[PERSON] led the writing of the manuscript. [PERSON] edited and provided comments on manuscript. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Environment Research Council Centre for Doctoral Training in Quantitative and Modelling Skills in Ecology and Evolution (QMEE), Grant Number: NE/P012345/1.  
No new data were created or analysed in this study. Data sharing is not applicable to this article.  
The authors would like to thank [PERSON] for her valuable comments on a prior draft of the manuscript.  
The authors declare no conflict of interest.
## 5 Simulations and Discussion  
### Simulation Parameters  
The simulation parameters of the two modified structures for the hybrid active sound quality control system were set up to study their performance. The mathematic models of the main path \(P(z)\) and the secondary path \(S(z)\) were in keeping with Reference [10] for convenience of comparison because the traditional structure was discussed therein.  
\[P(z)=0.0167+0.4833z^{-1}+0.4833z^{-2}+0.0167z^{-3}. \tag{22}\]  
\[S(z)=0.2037z^{-1}+0.5926z^{-2}+0.2037z^{-3}. \tag{23}\]  
At the same time, it is presumed that the estimated model of the secondary path \(\hat{S}(z)\) was equal to \(S(z)\).  
In the simulation, two components were contained in the noise signal; the first was the line-spectrum noise \(x(n)\), and the second was the Gaussian random noise \(v(n)\). The line-spectrum noise \(d(n)\) could be acquired by filtering the reference input signal \(x(n)\) via \(P(z)\). The frequency of the primary noise was 56 Hz, and the amplitude of the signal was 1 Pa. The Gaussian random noise \(v(n)\) was set to a bandlimited random noise such that 50 Hz was the center frequency and the bandwidth was 100 Hz. The lengths of the feedforward control filter \(W_{f}(z)\), the feedback control filter \(W_{b}(z)\), and the line-spectrum noise control filter \(W_{c}(z)\) were 512, and the step sizes \(\mu_{1}\), \(\mu_{2}\), and \(\mu_{3}\) were 0.0005, 0.005, and 0.005, respectively. Additionally, the sampling frequency was 10,240 Hz, and the length of the simulation was 4 s.
# Application of Digital Twin in Handling and Transportation of Hazardous Chemicals
## 4 Application of DT in LNG Road Transportation
### Road Transportation Phase  
(1) Analysis of tanker characteristics.  
Collecting driving behavior parameters such as reaction time, lane-change and avoidance behavior of drivers of hazardous chemical tankers, as well as integrating the driving characteristics of trolleys at high speed or on typical road sections, the driving behavior of tankers is simulated in the system.  
(2) Accident-prone roadway DT construction.  
Depict vehicle distribution characteristics on accident-prone road sections and monitor parameters that reflect traffic operation conditions such as vehicle ratio, travel speed, and headway spacing. Incorporate the prediction of lane changing behavior of small vehicles and unsafe driving category discrimination to synthesize a DT system with three-dimensional features.  
(3) Emergency hedging strategy development  
Develop emergency avoidance strategies for different types of rollovers, explosions, and other types of accidents during road transportation.  
(4) Rescue program selection.  
Simulate the specific implementation process of multiple accident rescue programs and corresponding traffic control measures in the system and compare the implementation effect of the programs.
## 2 NOAA Ocean Satellite Course
### Course Format  
The course is conducted over three days and has three main components: (1) lectures that provide participants with a basic knowledge of satellite remote sensing; (2) live demonstrations of tools developed to facilitate the use of satellite data; and (3) a hands-on workshop applying satellite data to a work-related project of the participants choosing. On the first day, lectures center around how satellite measurements are made and the types of measurements that are available. These lectures are not intended to provide detailed information about how satellite products are made (e.g., atmospheric corrections, radiative transfer models, etc.), but rather to give students sufficient background to select the best products for their applications. The first day also includes introductions to the tools used in the course to help them bring satellite data into their analysis software. To improve retention of introductory material and reduce presentation fatigue, lectures are interspersed with hands-on demonstrations of the course tools. During the first half of the second day, instruction on using the course tools begins in earnest. Students are walked through the steps of satellite data discovery, download, and visualization in a series of exercises that are based on common oceanographic applications, such as generating a timeseries of satellite data within a user-defined area. For the final day and a half of the course, students apply the tools and information from the course to a project of their choosing, with instructors providing guidance and troubleshooting problems.
# Advanced Optimization Algorithm Combining a Fuzzy Inference System for Vehicular Communications
## 2 System Models
### Sugeno Fuzzy Inference Systems
The intermediate functions L, M, and H are peaking at approximately 0.5, 0.6, and 0.7, respectively, with symmetric slopes on either side. Each adjacent pair of membership functions intersects at approximately 0.5 degrees of membership, ensuring smooth transitions between linguistic variables. The y-axis represents the degree of membership ranging from 0 to 1, while the x-axis shows the \(P_{rk}\) values, creating a comprehensive fuzzy logic control system representation. Figure 3: Fuzzy input membership function of distance.
# Collision Detection Method Using Self Interference Cancelation for Random Access Multiuser MIMO
## 3 Effectiveness of the Proposed Method by Computer Simulation
The median values are plotted for each SIR for all the trials by changing the channel response. As can be seen in Figure 7, the estimation error is less than 1 dB, when the SIR is less than 30 dB. Hence, the proposed method can successfully cancel the self-interference between AP antennas and estimate the interference with small power. Figure 5: Power versus subcarrier number (\(SIR=10\) [dB]). Figure 6: Power versus subcarrier number (\(SIR=45\) [dB]).
# Satellite-Based Wireless Sensor Development and Deployment Studies for Surface Wave Testing
## 2 Hardware Architecture
### Satellite Device  
As mentioned previously, wireless communication was achieved using a satellite device, RockBLOCK 9602, with dimensions 76 mm \(\times\) 52 mm \(\times\) 19 mm, weighing only 76 grams. The heart of the RockBLOCK 9602 is an Iridium 9602 modem, a commercial two-way satellite data transceiver, with a size of 41.0 mm \(\times\) 45.1 mm \(\times\) 13.1 mm. The Iridium 9602 transceiver does not require a SIM card. A GPS receiver is also embedded to provide relatively accurate positioning data. The device interface consists of a serial interface, power input, network available output, and power on/off control line. The Iridium 9602 is able to deliver up to 340 bytes mobile-originated messages and up to 270 bytes mobile-terminal messages via a simple AT command interface. Meanwhile, the two-way UART interface of the satellite device defines the possibilities for the integration of single-board computer, sensor, and server for delivering acquired data and remote controlling.  
The RockBLOCK 9602 hosts a small form factor Molex connector to link the ground, power, and signal lines to the USB port of Raspberry Pi 3 Model B+ as shown in Figure 1b. The baud rate of the RockBLOCK 9602 is 19,200. It exposes the modem's serial interface via a breakout connector over serial and, also, offers an SMA connector for external antenna usage. The antenna frequency range is 1616-1626.5 MHz combining the global coverage of the Iridium satellite constellation (66 satellites in orbit around the Earth) with the low latency of the short burst data (SBD) service to provide highly reliable satellite communications from any point on Earth including the polar regions, marine, and land areas. Its environmental operation temperature range and power consumption is \(-\)40-85 \({}^{\circ}\)C and 1 Watt, respectively.
# Adsorption of Ni2+ and Cd2+ from Water by Calcium Alginate/Spent Coffee Grounds Composite Beads
## 4 Conclusions  
Total removal of cadmium from solution was achieved at concentrations of 10 ppm and 20 ppm with SCGs and 98.07% (s = 0.95%) removal of nickel at 10 ppm. The CA-SCGs beads had a percent removal of 98.81% (s = 1.41%) for Cd\({}^{2+}\), and 80.15% (s = 1.94%) for Ni\({}^{2+}\) at 10 ppm. The SCGs is a very effective adsorbent of metals below its saturation levels. The CA beads have a larger adsorption capacity than SCGs, but this adsorption is less related to the level of metal concentration. A portion of the metal stays in solution instead of being adsorbed by the CA beads, no matter how low the metal concentration is. In this study, the percentages of adsorption by CA beads never reached average values over 90%. This poses a problem because the toxicity of metals, such as cadmium is possible even at trace levels. The encapsulation of SCGs in CA brings values closer to 100% of metal removal in the concentrations used in this study (10 ppm to 100 ppm). SCGs and CA-SCGs beads achieve percentages of removal closer to 100% due to the affinity of the adsorption sites of these adsorbents toward the metal. This characteristic can be related to the presence of phenolic groups in SCGs. Although the CA beads can have a larger number of adsorption sites, these adsorption sites have less affinity toward the metal, and so a portion of metal remains dissolved, despite the metal concentration. Also, the adsorption by CA beads involves mostly ion exchange between Ca\({}^{2+}\) and the metal of interest. This ion-exchange mechanism has a bigger role in the adsorption by CA beads than in the case of adsorption by SCGs because Ca\({}^{2+}\) does not have the same importance in the structure of SCGs. This study has showed the benefits of the encapsulation of a waste as SCGs in a renewable matrix, such as CA, to improve the adsorption of metals.  
Conceptualization, R.T.-C.; Investigation, R.T.-C.; Resources, C.A.V.-O. and N.M.-C.  
This research received no external funding.  
Special thanks to the USDA-ARS Tropical Agriculture Research Station for the use of the instrument of ICP-OES and to the Material Characterization Center of the University of Puerto Rico for the use of the SEM-EDX.  
The authors declare no conflict of interest.  
Figure 16: Adsorption/Desorption results for each cycle: (**a**) percent of removal of metal from the solution for each cycle and (**b**) percent of desorption of the metal from the bead for each cycle.
# A Novel Virtual Power Plant Uncertainty Modeling Framework Using Unscented Transform
## 6 Results and Analysis  
This section presents the results of the applicability of the UT method in a VPP. Comparisons were made with the persistence method, using all system users.  
The simulations were sufficient to obtain statistically significant results. The actual data from the third day were compared with the UT and the persistence to verify the error of each VPP user.  
Table 1 below shows the sum of the third-day forecast errors for each user logged into the VPP system, compared to actual observed data. The UT data and persistence of the entire connected VPP system are presented to the VPP aggregator from minute to minute.  
The values in Table 1 present a clear comparison of the errors. The predictability gain that the UT implies in a VPP system is noticeable.  
The software performed the prediction using the UT in 3 s for the 24 h horizon. Therefore, when the UT is executed within the VPP at a one-minute step ahead, the computational time is much smaller than the desired step, and there is a solution before the next event, which is considered real forecast time.  
A highly variable and unpredictable example that injects a high degree of error into the VPP is the entry of electric vehicles into the system. With the UT, the error was 25.2%, and with persistence, the error was 39.3%. Furthermore, for PV2, the UT resulted in a prediction error of 5.9%, whereas persistence resulted in a prediction error of 11.3%. These results show that predicting uncertainties using the UT can be beneficial to VPs with uncertainties in variable renewable generation and intermittent power demand.
# 3D Liver and Tumor Segmentation with CNNs Based on Region and Distance Metrics
###### Abstract  
Liver and liver tumor segmentation based on abdomen computed tomography (CT) images is an essential step in computer-assisted clinical interventions. However, liver and tumor segmentation remains the difficult issue in the medical image processing field, which is ascribed to the anatomical complexity of the liver and the poor demarcation between the liver and other nearby organs on the image. The existing 3D automatic liver and tumor segmentation algorithms based on full convolutional networks, such as V-net, have utilized the loss functions on the basis of integration (summing) over a segmented region (like Dice or cross-entropy). Unfortunately, the number of foreground and background voxels is usually highly imbalanced in liver and tumor segmentation tasks. This greatly varies the value of regional loss between various segmentation classes, and affects the training stability and effect. In the present study, an improved V-net algorithm was applied for 3D liver and tumor segmentation based on region and distance metrics. The distance metric-based loss function utilized a distance metric of the contour (or shape) space rather than the area. The model was jointly trained by the original regional loss and the three distance-based loss functions (including Boundary (BD) loss, Hausdorff (HD) loss, and Signed Distance Map (SDM) loss) to solve the problem of the highly unbalanced liver and tumor segmentation. Besides, the algorithm was tested in two databases LiTS 2017 (Technical University of Munich, Munich, Germany, 2017) and 3D-IRCADb (Research Institute against Digestive Cancer, Strasbourg Cedex, France, 2009), and the results proved the effectiveness of improvement.  
liver and tumor segmentation; full convolutional networks; distance metrics +
Footnote †: journal: Medical Image Computing