esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm for Producing Fuzzy Negations via Conical Sections
## 1 Introduction  
Fuzzy negations play a crucial role in the creation of De Morgan triples and also in the construction of fuzzy implications (c.f. [1; 2; 3]). As a general admission, the construction of fuzzy negations is the foundation stone of the triples De Morgan construction and fuzzy implications. [PERSON] in [4] presented a theoretical approach to fuzzy negations production and one important result ([4], Theorem 5.2.1). A constructive geometrical method of generating strong fuzzy negations, via conical sections has been presented in this paper, followed by the proof of the method and several final remarks, as well as some examples for the strong negations produced via conical sections. It should be particularly emphasized that the specific manner of geometrical construction of strong negations, covers the already known strong negations, such as the Sugeno negations, as well as the classical negation \(N(x)=1-x\) or \(N(x)=\sqrt{1-x^{2}}\).  
It should be noticed that Proposition 3 in paragraph 3 Main Results and particularly Equation (14), constitute new results that might be used in the production of implications, merely from negations (c.f. [2; 3]), and by extension in reaching conclusions. In general, the algorithmic approach of negations is virtually non-existent in the applications of fuzzy sets and implications, without any reasons explaining such a gap. This specific new process for producing negations is to address the present gap, since this production is simple and easily understandable. Changing just one parameter in Equation (14), as demonstrated in the examples in paragraph 4. Special Cases, is sufficient to produce a negation which is different from the negations already known, such as the Sugeno or Yager negations. The aforementioned fact led the writers to the extension of their scientific research interests and to the algorithmic approach of their subject matter, namely of the means of application of the present method to real data. The writers were looking forward to a future study on the specific issue.  
The key to this effortless production lies in the symmetry of a strong negation, such as the straight line \(y=x\), which is an essential attribute of the functions satisfying the condition \(N(N(x))=x\). All conical sections with their center at the beginning of the axis, satisfy this symmetry under the condition that they are directed towards \(\pi/4\) or \(3\pi/4\).  
The presentation of the authors line of reasoning, along with their methods of proof and their conclusions, follow the aforementioned pattern. The principle definitions and theorems needed to understand the basic theory and proof are set out in paragraph 2 Preliminaries. Then, in paragraph 3 Main Results, the basic propositions and the respective results leading to Proposition 3 are stated and in particular Equation (14) presents the new result. Finally, specific examples of negations through conical sections are presented in the next paragraph, while the study concludes with the remarks and conclusions, as well as the possible applications of the method presented above.
# IoTactileSim: A Virtual Testbed for Tactile Industrial Internet of Things Services
## 5 Conclusions  
In this paper, we proposed a virtual testbed termed as IoTactileSim to investigate and provision QoS and QoE strict requirements for tactile industrial IoT applications. The proposed IoTactileSim is equipped with a network emulator Mininet and an industrial simulator CoppeliaSim to mimic the real-world communication network and industrial IoT environment. It provides the users to evaluate the efficacy of their designed strategies under possible settings, including advanced core network technologies (SND, NVF), edge intelligence, and application-agnostic parameters (packet size, sampling rate, etc.) for improving QoS and QoE. The proposed IoTactileSim is investigated for two different industrial use case scenarios with haptic data traces and real-time remote interaction. The simulation results indicate that the IoTactileSim is able to handle real-time data traffic then offline scenario by providing communication latency ranges from 6 to 2.2\(\sim\)2.0 ms, and from 5.8 to 2.1 ms for 10 to 10,000 data packets, respectively. Moreover, the experimentation analysis indicates that the IoTactileSim allows the user to investigate network impairments (latency, jitter, reliability) and can support complex tactile industrial environments with a higher number of data packets. In the early future, we plan to extend the IoTactileSim with network coding and machine learning approaches like federated reinforcement learning at the tactile support engine to integrate it with the 6G network infrastructure.  
Methodology, R.A., A.H. and M.Z.I.; Software and coding, M.Z.I. and S.; Experimentation and formal analysis, M.Z.I, R.A. and A.H.; Writing--original draft preparation, M.Z.I. and A.H.; Writing--review and editing, M.Z.I, S., A.H. and R.A.; Supervision, H.K.; Funding acquisition, M.Z.I and H.K. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Modular representation of ITE in real-world scenario.  
Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (Nos. 2019R1A4A1023746, 2019R1F1A1060799) and the Strengthening R&D Capability Program of Sejong University. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The 3 DoF haptic datasets used in this work are available online with open access for academic research use. The static and dynamic interaction haptic dataset is available online at [[https://cloud.lmt.ei.tum.de/s/4](https://cloud.lmt.ei.tum.de/s/4) FmHUCsoUvwRle3]([https://cloud.lmt.ei.tum.de/s/4](https://cloud.lmt.ei.tum.de/s/4) FmHUCsoUvwRle3) (accessed on 1 November 2021), and the simulation experiment results files are availabe at [[https://github.com/zubair1811/loTactileSimV1.git](https://github.com/zubair1811/loTactileSimV1.git)]([https://github.com/zubair1811/loTactileSimV1.git](https://github.com/zubair1811/loTactileSimV1.git)), (accessed on 30 November 2021). Conflicts of Interest: The authors declare no conflict of interest. Abbreviations
# EmbeddedPigDet--Fast and Accurate Pig Detection for Embedded Board Implementations
## 3 Proposed Method
### Bottleneck Structuring (BS) module  
As previously explained, we apply the bottleneck structure [72] to reduce the computational workload of EmbeddedPigYOLO (with FC) further. In this study, we use the bottleneck structure (by a factor of four). For example, we apply the bottleneck structure by replacing each \(3\times 3\times i\) convolutional filters with \(1\times 1\times i\)/4, \(3\times 3\times i\)/4 and \(1\times 1\times i\) convolutional filters. Since the number of filters determined by the FC module can be even numbers, we derive the minimum number that can satisfy the bottleneck structure (by a factor of four). For example, for \(3\times 3\times 61\) convolutional filters in Conv3 shown in Table 2, the BS module generates the result as \(1\times 1\times 16\), \(3\times 3\times 16\) and \(1\times 1\times 64\) convolutional filters.  
Figure 4: Illustration of the filter clustering algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **TinyYOLOv2**[70] & **EmbeddedPigYOLO(v2)** \\ \cline{2-3}  & **Filter** & **Filter with FC** & **Filter with FC+BS** \\ \hline Conv1 & \(3\times 3\) (16) & \(3\times 3\) (16) & \(3\times 3\) (16) \\ \hline Conv2 & \(3\times 3\) (32) & \(3\times 3\) (32) & \(1\times 1\) (8) \\  & & & \(1\times 1\) (32) \\ \hline Conv3 & \(3\times 3\) (64) & \(3\times 3\) (61) & \(1\times 1\) (64) \\ \hline Conv4 & \(3\times 3\) (128) & \(3\times 3\) (114) & \(1\times 1\) (29) \\  & & & \(1\times 3\) (39) \\ \hline Conv5 & \(3\times 3\) (256) & \(3\times 3\) (197) & \(1\times 1\) (116) \\  & & & \(1\times 1\) (200) \\ \hline Conv6 & \(3\times 3\) (512) & \(3\times 3\) (256) & \(1\times 1\) (256) \\ \hline Conv7 & \(3\times 3\) (1024) & \(3\times 3\) (349) & \(1\times 1\) (88) \\  & & & \(1\times 1\) (352) \\ \hline Conv8 & \(3\times 3\) (1024) & \(3\times 3\) (353) & \(1\times 1\) (356) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison between TinyYOLOv2 [70] and EmbeddedPigYOLO(v2) with pig data set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **TinyYOLOv3**[70] & **EmbeddedPigYOLO(v3)** \\ \cline{2-3}  & **Filter** & **Filter with FC** & **Filter with FC+BS** \\ \hline Conv1 & \(3\times 3\) (16) & \(3\times 3\) (16) & \(3\times 3\) (16) \\ \hline Conv2 & \(3\times 3\) (32) & \(3\times 3\) (31) & \(3\times 3  
EmbeddedPigYOLO (with FC+BS) shown in Tables 2 and 3 represents the result of the BS module from EmbeddedPigYOLO (with FC). For Conv1 of TinyYOLO, the actual execution time of applying the bottleneck structure was longer than that of Conv1 and thus, we did not apply the bottleneck structure to Conv1. With this EmbeddedPigYOLO (i.e., light-weight version of TinyYOLO) and the three-channel composite image (through the IP module), the proposed EmbeddedPigDet can improve both execution speed and accuracy of TinyYOLO simultaneously.
# Autonomous Manipulator of a Mobile Robot Based on a Vision System
## 2 System Description
### Marker Detection Algorithm  
The algorithm and methods presented in [3; 14; 20] were used to estimate the position. This algorithm allows us to identify markers of various types that have a frame and an internal code of a fixed size, e.g., Aruco or ARTag.  
The image analysis for marker detection starts with converting the image to grayscale. Then, adaptive thresholding is performed in order to obtain the edges. Next, the algorithm searches for contours [21] and approximates them by means of a polygon [22]. The algorithm searches only for contours that meet the assumptions. The markers are square, so their image will be a convex quadrilateral. The remaining contours are discarded. The perspective view is removed from the image of the contours that remain after filtration so that the code can be read. A marker is considered correct if it has a black frame and a code compatible with the codes written in the program. To check this, thresholding [23] is carried out.  
For each detected marker, the position and orientation of the camera relative to the system associated with the marker are computed. The method of solving the Perspective-n-Point problem using the Levenberg-Marquardt optimization [24] was used to estimate the position. Upon obtaining a position in the global coordinate system in 3D space for at least four points and the projection coordinates of these points, it is possible to determine the orientation and position of the camera in relation to the global system. For exactly four points, the solution is unambiguous, and in the case of a greater number of points, the result is optimized with the chosen method.  
The algorithm was implemented in C++ using the OpenCV and QT libraries on the NVIDIA microcomputer.  
Figure 3 shows the image from the camera while positioning the manipulator.  
In order to identify the internal parameters of the camera, a calibration process was performed for a fixed sharpness value. The camera was calibrated using the Zhang method [25] in Matlab.  
Figure 2: Test panel with ARTag markers.
## 3 Results
### The Dominance of Water Stress to Vegetation Growth in Global Drylands
#### 3.4.1 Offset in the Contribution Among CO\({}_{2}\), SW, and VPD Based on Satellite Observations
Satellite observations found that CO2, increasing precipitation, and SRAD positively contributed to global dryland ecosystems, while enhanced atmospheric and SW constraints produced pervasive negative impacts on vegetation growth, as shown in Figure 11b. This paper also detected that P, T, SRAD, and human activities positively contributed to vegetation growth in dryland ecosystems (Figure S8 in supplementary Section S4.1). Eleven ecosystem models and satellite observations consistently found great positive contributions of CO2 concentration to vegetation dynamics in global drylands despite the observed differences existing in different ecosystem models. It is noteworthy that the positive contribution of CO2 concentration to the increasing LAI was multiple times greater than that of climate change. This indirectly supports the findings of earlier analyses that the beneficial effects of CO2 fertilization on vegetation growth in global drylands from 1982 to 2018 were not entirely counteracted by rising atmospheric and SW constraints. Furthermore, this study also employed 22 CMIP6 ESMs to explore the correlation relationships between the simulated LAI and its corresponding climatic factors under historical scenarios (1982-2014). The 22 CMIP6 ESMs revealed that the simulated LAI positively responded to water conditions but negatively to heat-related factors under historical scenarios (1982-2014), which was partially consistent with the satellite-observed results in the sensitivity analysis (Figure S9 in Supplementary Section S4.2).
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 8 Conclusions  
A non-deployable antenna called a Loop Hentenna, which uses the aluminum structure of a 1U CubeSat, was proposed. This antenna was mounted as a sub-mission on each of the three CubeSats of the BIRDS-4 project.  
The Loop Hentenna measured maximum gain of \(-5.7\) dBi before it was launched into orbit. The signals from the Loop Hentenna were successfully received after its launch. The signal was strong enough to decode its content. Analysis of the received signal showed that the antenna has a gain of \(-8.1\) dBi.  
As of January 2022, we expect that the satellite will stay in orbit for another year. We plan to do more on-orbit tests to collect data to characterize the antenna parameters.
# A Review on the Erosion Mechanism in Cavitating Jets and Their Industrial Applications
## 3 Erosion Mechanism in Cavitating Jets
### Effect of Standoff Distance
Both macroscopic (Figure 8) and microscopic (Figure 9) pictures showed that the damage pattern changes with the non-dimensional standoff distance (NSD). Under small NSD (Figure 8a,b), the central hole eroded by high-speed water jet is the dominant contributor for mass loss and the cavitation bubbles do not have enough time to grow before they reach the impinging plate. As NSD increases, cavitation erosion in the form of hemispheric pits is observed, and typical ring-shape distribution of cavitation pits is obtained (Figure 8c,d). The optimal NSD of 91.7 (Figure 8d) is characterized by a balance between bubble concentration and collapse intensity and cavitation pits much deeper than the central hole. As NSD further increases (Figure 8e-h), both the pits size and the erosion ring diameter decreases. Figure 8: Erosion pits; NSD: (**a**) 8.3, (**b**) 33.3, (**c**) 66.7, (**d**) 91.7, (**e**) 100, (**f**) 133.3, (**g**) 166.7, (**h**) 200 [56]. Figure 9: Erosion pits observed by laser microscope; NSD: (**a**) 33.3, (**b**) 66.7, (**c**) 100, (**d**) 200 [56]. From a microscopic point of view (Figure 9), the aggravation of cavitation erosion is the jointed results of the growth of single cavitation pits and the connection of adjacent pits. The ridges and protrusions are preferable sites for bubble collapse. It should also be noted that NSD decreases with the increase in jet nozzle diameter. This can explain the difference between the NSD values of the studies presented above.
# Spatio-Temporal Analysis of Surface Urban Heat Island and Canopy Layer Heat Island in Beijing
## 4 Discussion
### Analysis of Changes in Intensity and Center of SUHI and CLHI
During the daytime, the temperature difference between the green space and the surrounding areas decreases due to the heating effect of solar radiation, leading to a relative dispersion of SUHI and CLHI phenomena during the day. However, in the hot season, the higher intensity of solar radiation, even the surface with higher albedo to warm up faster due to prolonged exposure to solar radiation, leading to the movement of SUHI and CLHI towards the east and north. This is related to the four-seasonal NDVI changes in Beijing, and to the land use types. Figure 10 represents the NDVI changes in Beijing in summer, and it can be seen that from June to July, the NDVI gradually increases in the northwest and southwest directions of Beijing, which is opposite to the direction of the heat  
Figure 11: Scatter plot of surface temperature distribution versus (**a,b**) NDVI and (**c,d**) albedo for June and July 2020.  
island movement. Land use can reflect the types and distribution of land use within the city, such as residential areas, industrial areas, and green areas. Different types of land use have different impacts on the SUHI and CLHI. For example, industrial areas usually have higher surface temperatures and lower vegetation cover, which may lead to a concentration of the center of the SUHI and CLHI near industrial areas, whereas an increase in green spaces can help mitigate SUHI and CLHI effects. In order to better reflect the effect of NDVI and solar albedo on surface temperature, we analyzed the discussion of the effect of NDVI on latent heat flux (LHF) and albedo on net solar radiation (NSR) in the year 2020, as shown in Figures 12 and 13. From the figure it can be seen that NRS is significantly negatively correlated with albedo and LHF is significantly positively correlated with NDVI. The positive correlation between LHF and NDVI is around 0.5, and this positive correlation makes NDVI an important parameter for analyzing evaporative cooling caused by higher latent heat values [40]. In previous studies, we explored the effects of NDVI, albedo, and other factors on surface temperature, and these analyses have provided important clues to our understanding of the heat island effect. However, in order to reveal the intrinsic mechanism of heat island changes more comprehensively, we need to further analyze the correlation between surface temperature and latent heat and net radiation, as shown in Figure 14, where latent heat is negatively correlated with surface temperature and net radiation is positively correlated with surface temperature. Figure 12: Scatterplot of NDVI distribution versus LHF for (**a**) June and (**b**) July 2020. Figure 13: Scatterplot of albedo distribution versus NSR for (**a**) June and (**b**) July 2020.
## 5 Case Study
### DSM Model of Electric Bicycles Based on Core Parts
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ranking** & **Part Name** & **Score** & **Ranking** & **Part Name** & **Score** & **Ranking** & **Part Name** & **Score** \\ \hline
1 & Hub motor & 96.8 & 32 & Trumpet & 57.8 & 63 & Backrest strut & 33.5 \\
2 & Battery & 92.9 & 33 & Speed transmitter & 56.4 & 64 & Front brake reset spring & 33.3 \\
3 & Controller & 89.8 & 34 & Speed control handle & 55.9 & 65 & Back brake reset spring & 32.6 \\
4 & Headset & 89.3 & 35 & Back mudguard & 55.8 & 66 & Saddle & 32.6 \\
5 & Front hub & 88.5 & 36 & Front mudguard & 55.8 & 67 & Charging mouth & 32.4 \\
6 & Back axle & 87.5 & 37 & Battery holder & 55.7 & 68 & Back saddle & 31.6 \\
7 & Back brake drum & 87.4 & 38 & Right cranked arm & 55 & 69 & Seatpost collar & 30.9 \\
8 & Front brake drum & 86.6 & 39 & Lift cranked arm & 55 & 70 & Head lamp pedestal & 27.1 \\
9 & Fork & 84.9 & 40 & Right cranked & 54.9 & 71 & Kickstand & 26.7 \\
10 & Seat stay & 83 & 41 & Lift cranked & 54.9 & 72 & Back brake handle & 26.2 \\  & & & & & & bracket & \\
11 & Chain stay & 82.9 & 42 & Control cable & 54.9 & 73 & Front brake handle & 26.2 \\
12 & Front axel & 80.9 & 43 & Lower leg & 54.8 & 74 & Front brake lever & 25.3 \\
13 & Screen & 78.2 & 44 & Head lamp & 54.4 & 75 & Back brake lever & 25.3 \\
14 & Cassette & 77.5 & 45 & Power cable & 51.6 & 76 & Backrest scale board & 25.1 \\
15 & Backseat frame & 76 & 46 & Supporting leg & 50.4 & 77 & Screen cover & 24.8 \\
16 & Back tire & 74.8 & 47 & Seatpost & 50.2 & 78 & Basket studdle & 23.7 \\
17 & Front tire & 74.6 & 48 & Lift pedal & 50 & 79 & Lift damping spring & 22.8 \\
18 & Middle axle & 73.3 & 49 & Right pedal & 49.8 & 80 & Right damping spring & 22.8 \\
19 & Chain & 72.7 & 50 & Seat tube & 47.3 & 81 & Lampshade & 22.1 \\
20 & Back tube & 72.5 & 51 & Backrest & 45.6 & 82 & Right guard plate & 21.6 \\
21 & Chain ring & 68.8 & 52 & Battery holder cover & 44.8 & 83 & Lift guard plate & 21.6 \\
22 & Back inner tube & 66.1 & 53 & Crown & 44.3 & 84 & Back muquard & 20.3 \\  & & & & & & studdle & \\
23 & Front inner tube & 66.1 & 54 & Starting switch & 43.6 & 85 & Front mudguard & 20.3 \\  & & & & & & studdle & \\
24 & Middle tube & 65.1 & 55 & Bike lock & 43.4 & 86 & Grip & 19.9 \\
25 & Upright rod & 65 & 56 & Upright rod collar & 41.9 & 87 & License plate buckle & 17.4 \\
26 & Bike basket & 62.1 & 57 & Battery holder’s lock & 38.8 & 88 & Headset cap & 16 \\
27 & Head tube & 61.1 & 58 & Front brake cable & 38.8 & 89 & Reflector & 13.6 \\
28 & Basket cover & 60.4 & 59 & Back brake cable & 38.8 & 90 & Support leg return & 11.8 \\
29 & Rail & 59.6 & 60 & Back brake handle & 35.2 & 91 & Basket buckle & 11.7 \\
30 & Upper fork & 59.3 & 61 & Front brake handle & 35.2 & 92 & Back mudguard buckle & 7.5 \\
31 & Handlebar & 59 & 62 & Trumpet button & 34.7 & 93 & Front mudguard & 7.5 \\  & & & & & & buckle & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Importance value and ranking of electric bicycle parts.
## 3 Results and Discussion
### Winemaking Waste Analysis  
The PA fiber was used to analyze wine pomace ethanolic extracts obtained by macceration. The SPME-LC-F-UV analysis revealed Q (104.3 \(\pm\) 8.2 \(\mathrm{\SIUnitSymbolMicro g}\)/g dry weight) and R (38.5 \(\pm\) 4.1 \(\mathrm{\SIUnitSymbolMicro g}\)/g dry weight) in the extracts, with Q present at significantly higher levels. The optimized SPME procedure demonstrated high selectivity, yielding chromatograms similar to those of standard solutions despite matrix complexity. The accuracy of the extraction procedure was evaluated via recovery experiments in which varying amounts of mixed standard solutions were added at three concentration levels (one, two, and five times the estimated levels) to wine pomace extracts. Very high recoveries were obtained, i.e., 99.1 \(\pm\) 7.4% for quercetin and 98.5 \(\pm\) 9.8% for trans-resveratrol. The results obtained confirmed that ethanolic wine pomace extract contains high levels of quercetin [21], being a great source of these phenolic compounds. In addition, our results show that fluorescence detection is more effective for trans-resveratrol than for quercetin. Since Q and R are considered valuable fluorescent sensors for chemical and biological detection, further investigations are underway in our laboratory to combine the SPME-LC technique with fluorescence detection to elucidate aspects related to the dispersion of these molecules in water--factors that are responsible for their solubility and bioactivity.