esa-sceva/satcom-chunk-collection huggingface dataset:
# Self-Sovereign Identity-Based E-Portfolio Ecosystem
## 3 System Design
### Operation Model
#### 3.1.2 E-Portfolio Application Blockchain (EApp-Chain)
In the context of the e-portfolio ecosystem, the root-ca server issued identity certificates to reviewers, which are used to verify their identities when accessing the system. This helps to ensure that only authorized reviewers can access user data and provide feedback. * Local Education Unit (_LEU_): In the proposed e-portfolio ecosystem, _LEUs_ are responsible for auditing activity organizations. Once an _AO_ passes the audit, they are granted the right to create awards for students and receive a client credential to confirm userconsent on the _ACch_. Additionally, the _LEU_ has a root-ca server responsible for issuing identity certificates to the local _HS_ to create an intermediate CA. * High School (_HS_): _HS_s are responsible for managing and storing students' school grades and e-portfolio files in the proposed system. They also monitor events on the _CaAch_ to add award attributes for their students. To achieve this, _HS_s use an intermediate CA to create an identity certificate for each student, enabling them to participate in the e-portfolio ecosystem. * Activity Organization (_AO_): The _AO_ is an organization that manages students' extracurricular activity data, such as TOEFL, APCS, and online learning platforms. These institutes can apply for verification from local education units to improve the credibility of the data they manage. * Student (_STU_): In different ecosystems, a user may have multiple identities. In the e-portfolio ecosystem, a user may identify as a'student'. Each user also possesses a self-sovereign identity, which they can utilize to log in and sign up for _AO_s in the e-portfolio ecosystem. _STUs_ retain full control over their data authorization and can determine which data are used for reviews. * Reviewer (_RE_): In this scenario, the _RE_ represents the university. _REs_ are responsible for reviewing _HS_ student profiles and have direct access to trusted data that have been authorized by students from their _HS_ and _AO_.
## 6 Complexity Analysis
### Complexity of Clustering Algorithms  
The complexity of the clustering algorithms is dependent on how many clusters can be found. Since in most cases each cluster corresponds to a constellation point, the complexity will depend on the modulation format. However, in contrast to ANN, the complexity of the clustering algorithms is also dependent on the number of samples employed for clustering and their dispersion, which significantly impacts the convergence of the algorithm. Therefore, it is not possible to deterministically compute the number of required operations. A commonly adopted criterion is the worst-case scenario that represent a pessimistic upper bound to the complexity.
## 3 Results and Discussion
### Determination of CMC of Shell Breaking
#### 3.1.1 Determination of CMC of Shells  
To visualize the influence of MC on the degree of shell fragmentation, snapshots of the crushing state of the shells at different MCs are presented in Figure 8. It shows that when the MC decreases from 45.59 to 18.91%, the number of large fragments increases and the large fragment is dominant for the overall size of fragments (Figure 8A,B), which suggests that the degree of fragmentation weakens. Conversely, when the MC further decreases from 18.91 to 9.41%, the number of small fragments increases and the overall size of the fragments decreases significantly (Figure 8B,C), implying the degree of fragmentation strengthens significantly. In contrast, when the MC further decreases from 9.41% to 5.45%, the crushing state appears unchanged (Figure 8C,D). After statistical analysis, the numbers of shell fragments for 45.59%, 18.91%, 9.41% and 5.45% are 10,114, 8973, 10,326 and 10,259, respectively. From the comprehensive analysis above, we guess that compared to the high MC and medium MC, shells with low MC are easy to crush, as shown by the smaller and more numerous shell fragments.  
In order to quantitatively analyze the effect of MC on the fragmentation degree of the shell, Figure 9 shows the variation in \(D_{\text{b}}\) with MC. As shown, the \(D_{\text{b}}\) decreases rapidly when MC decreases from 45.59 to 18.91% (i.e., stage I). This is because, due to the decrease in the MC, the fiber comprising the shell is hardened mechanically, which allows the shell to resist fragmentation by dissipating energy and limiting crack expansion [34; 35]. In  
Figure 8: Crushing state of the shells at different MCs (45.59% (**A**), 18.91% (**B**), 9.41% (**C**) and 5.45% (**D**)).  
contrast, \(D_{\text{b}}\) increases rapidly when MC decreases from 18.91 to 9.41% (i.e., stage II). As MC continues to decrease, the properties of the fiber might transform from having a resilient or elastic character to being more and more brittle or fragile in nature [17]. As a result, the shell is more susceptible to fracture due to external loads, resulting in more crack generation and propagation due to multi-point compression, which in turn produces more fracture surfaces and smaller fragments. When MC falls below 9.41%, \(D_{\text{b}}\) almost remains constant (i.e., stage III). Obviously, this tendency is consistent with the results of the qualitative analysis mentioned above. In addition, the results show that when MC was 9.41%, \(D_{\text{b}}\) reached a maximum value of 1.73, corresponding to the larger fragmentation degree of the shell, which conforms to the objective of walnut shell breaking. Therefore, MC with 9.41% is treated as the CMC of shells in this work.
_Applied_  
_sciences_  
_Article_  
**Estimation of High-Resolution Daily Ground-Level**  
**PM\({}_{2.5}\) Concentration in Beijing 2013-2017 Using 1 km**  
**MAIAC AOT Data**  
**[PERSON] 1(c)**, **[PERSON] 1, [PERSON] 1, [PERSON] 2, [PERSON] 3 and [PERSON] 4,*c**  
1 School of Automation Engineering, University of Electronic Science and Technology of China,  
Chengdu 611731, China; [EMAIL_ADDRESS] (W.H.); [EMAIL_ADDRESS] (L.T.); [EMAIL_ADDRESS] (Y.C.)
2 College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China; [EMAIL_ADDRESS]
3 Lamont-Doherty Earth Observatory of Columbia University, Palisades, NY 10964, USA; [EMAIL_ADDRESS]
4 Center for International Earth Science Information Network, Earth Institute, Columbia University, Palisades, NY 10964, USA
* Correspondence: [EMAIL_ADDRESS]; Tel.: +1-845-365-8946  
Received: 22 November 2018; Accepted: 11 December 2018; Published: 14 December 2018  
**Featured Application: This work has several applications: (a) to reveal spatiotemporal variations of PM2.5 across urban landscapes; (b) to assess the long-term impacts of PM2.5 on human health; (c) to support government policy decision-making; and (d) to provide valuable information to the public.**  
**Abstract: High-spatiotemporal-resolution PM\({}_{2.5}\) data are critical to assessing the impacts of prolonged exposure to PM\({}_{2.5}\) on human health, especially for urban areas. Satellite-derived aerosol optical thickness (AOT) is highly correlated to ground-level PM\({}_{2.5}\), providing an effective way to reveal spatiotemporal variations of PM\({}_{2.5}\) across urban landscapes. In this paper, Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOT and ground-based PM\({}_{2.5}\) measurements were fused to estimate daily ground-level PM\({}_{2.5}\) concentrations in Beijing for 2013-2017 at 1 km resolution through a linear mixed effect model (DMEM). The results showed a good agreement between the estimated and measured PM\({}_{2.5}\) and effectively revealed the characteristics of its spatiotemporal variations across Beijing: (1) the PM\({}_{2.5}\) level is higher in the central and southern areas, while it is lower in the northern and northwestern areas; (2) the PM\({}_{2.5}\) level is higher in autumn and winter, while it is lower in spring and summer. Moreover, annual PM\({}_{2.5}\) concentrations decreased by 24.03% for the whole of Beijing and 31.46% for the downtown area from 2013 to 2017. The PM\({}_{2.5}\) data products we generated can be used to assess the long-term impacts of PM\({}_{2.5}\) on human health and support relevant government policy decision-making, and the methodology can be applied to other heavily polluted urban areas.  
**Keywords: urban pollution; remote sensing; PM\({}_{2.5}\); AOT**
## 3 Results and Discussion
### Effects of CO\({}_{2}\) to Carbon Ratio \(R_{\rm c}\)  
In this work, the co-gasification of MSW and BC in CO\({}_{2}\)/O\({}_{2}\) atmosphere is studied; the CO\({}_{2}\) flow rate into the gasifier is a key parameter for gasification performance. The range of \(R_{\rm c}\) is set to 0.1 to 1.0 by adjusting the CO\({}_{2}\) fed flow rate at a constant fuel feed rate of 100 kg/h. Besides, the gasification temperature and \(R_{\rm o}\) are kept constant. The effect of \(R_{\rm c}\) on raw syngas composition and system performance for three samples is presented in Figure 5. For the individual BC gasification, with the increasing of \(R_{\rm c}\) ratio, the mole fraction of CO first increased then slightly decreased when the demarcation point was at the position \(R_{\rm c}=0.4\), whereas the mole fraction of CO\({}_{2}\) remained stable when \(R_{\rm c}\) was less than 0.4, and subsequently increased. This indicates that the supplied CO\({}_{2}\) is consumed and the Boudouard reaction R4 is strengthened. Further, the mole fraction of CO\({}_{2}\) was affected by the supplied \(R_{\rm c}\) ratio, and the final composition fraction of the producer gas was also dominated by the CO\({}_{2}\) flow rate [47]. The mole fraction of H\({}_{2}\) decreased for gasification of all three samples. This decrease may be because the higher \(R_{\rm c}\) ratio supports the reverse water-gas shift reaction R9, resulting in converting CO\({}_{2}\) and H\({}_{2}\) into CO and H\({}_{2}\)O.  
According to the Figure 5a, the CGE of BC gasification system sharply increased when the \(R_{\rm c}\) was less than 0.5, and subsequently increased smoothly. Concurrently, the OEE of system showed a trend of increasing first and then decreasing, and obtained the maximum at approximately \(R_{\rm c}=0.5\). Therefore, the \(R_{\rm c}\) of 0.5 can be regarded as the recommended ratio for BC gasification. [PERSON] et al. [48] also came to the conclusion that the CO\({}_{2}\)/C ratio of 0.5 exhibited better performance by investigating the gasification of microalgae. However, the OEE of system always decreased as the increasing \(R_{\rm c}\) ratio for both MSW and the blends of \(R_{\rm M}=0.6\). This is because the mole fraction of H\({}_{2}\) significantly decreases and in the product syngas.  
Figure 4: Effect of \(R_{\rm o}\) on raw syngas composition and system performance (\(T=900\)\({}^{\circ}\)C and \(R_{\rm c}=0.5\)): (**a**) BC; (**b**) MSW; (**c**) \(R_{\rm M}=0.6\).
## 3 Electromagnetic and Temperature Characteristics of the Initial Scheme  
On the basis of the initial scheme, the motor model is established using Ansys Maxwell software. Zero current is set during no-load analysis, and a three-phase rated current is set during load simulation. After the boundary conditions are set, the electromagnetic properties under no-load state and rated load are calculated and analyzed by 2D-FEA.  
The magnetic flux density distributions of the motor under no-load state and rated load conditions are shown in Figure 2. In these two states, a small part of the stator teeth region is saturated in magnetic flux density, but the magnetic flux density of the other part behaves well.  
Figure 3 shows the variation of radial air-gap magnetic flux density with mechanical angle waveform and Fourier transform results under no-load state. As shown, the peak value of the radial air-gap magnetic flux density is 1.14 T. In addition, the 5 th and 7 th harmonic amplitudes of the radial air-gap flux density are small compared to the fundamental amplitude of the radial air-gap flux density. The waveform and Fourier transform result of line-to-line back electromotive force (back-EMF) under no-load state are shown in Figure 4. The amplitude of the no-load back-EMF line-to-line voltage is about 520 V, and its image is very smooth.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Rated power (kW) & 200 \\ Rated speed (rpm) & 56 \\ Rated voltage (V) & 380 \\ Stator outer diameter (mm) & 880 \\ Core length (mm) & 917 \\ Pole number & 30 \\ Stator slot number & 36 \\ Air-gap length (mm) & 3 \\ Current density (A/mm\({}^{2}\)) & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of initial scheme.  
Figure 1: Motor structure: (**a**) planar structure; (**b**) 3D structure.  
Figure 5 shows the cogging torque and the output torque under rated load. As shown in the figure, the output torque under rated load is 34,700 N\(\cdot\)m, and the ripple of the output torque is only 2%. The core loss and eddy current loss of PM under rated load are shown in  
Figure 4: Line-to-line back-EMF and Fourier transform results: (**a**) line-to-line back-EMF waveform; (**b**) Fourier transform.  
Figure 3: Radial air-gap magnetic flux density and Fourier transform results under no-load state: (**a**) radial air-gap magnetic flux density; (**b**) Fourier transform.  
Figure 2: Magnetic flux density distributions under no-load state and rated load: (**a**) no-load state; (**b**) rated load.  
Figure 6. After entering the steady state, the average values of core loss and eddy current loss of the PM are 3748 W and 667 W, respectively. In addition, the copper loss is 12,430 W.  
Figure 5. Cogging torque and output torque under rated load: (**a**) cogging torque; (**b**) output torque.  
Figure 6. (**a**) Core loss and (**b**) eddy current loss of the PM under rated load.  
Figure 7. Temperature distribution of motor under rated load: (**a**) whole temperature distribution; (**b**) partial temperature distribution.
## 3 Formulation of the Satellite Module Layout Problems
### Objective Function
Then, the inertias are calculated along the axes of the \(O^{\prime}x^{\prime}y^{\prime}z^{\prime}\) coordinate system using the Huygens theorem:  
\[I_{x^{\prime}x^{\prime}}(\mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^ {d},\mathbf{p}_{s}^{d})=I_{xx}(\mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^{d}, \mathbf{p}_{s}^{d})-(y_{c}^{2}+z_{c}^{2})m_{tot} \tag{6}\] \[I_{y^{\prime}y^{\prime}}(\mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^ {d},\mathbf{p}_{s}^{d})=I_{yy}(\mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^{d}, \mathbf{p}_{s}^{d})-(x_{c}^{2}+z_{c}^{2})m_{tot}\] (7) \[I_{z^{\prime}z^{\prime}}(\mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^ {d},\mathbf{p}_{s}^{d})=I_{zz}(\mathbf{p}_{I}^{c},\mathbf{p}_{\alpha}^{d}, \mathbf{p}_{s}^{d})-(x_{c}^{2}+y_{c}^{2})m_{tot} \tag{8}\]  
where \((x_{c},y_{c},z_{c})\) are the coordinates of the center of gravity of the module in the \(Oxyz\) system of coordinates and \(m_{tot}=(m_{sat}+\sum_{i=1}^{N}m_{i})\) is the mass of the whole module.
# Optimizing Retransmission Threshold in Wireless Sensor Networks
###### Abstract  
The retransmission threshold in wireless sensor networks is critical to the latency of data delivery in the networks. However, existing works on data transmission in sensor networks did not consider the optimization of the retransmission threshold, and they simply set the same retransmission threshold for all sensor nodes in advance. The method did not take link quality and delay requirement into account, which decreases the probability of a packet passing its delivery path within a given deadline. This paper investigates the problem of finding optimal retransmission thresholds for relay nodes along a delivery path in a sensor network. The object of optimizing retransmission thresholds is to maximize the summation of the probability of the packet being successfully delivered to the next relay node or destination node in time. A dynamic programming-based distributed algorithm for finding optimal retransmission thresholds for relay nodes along a delivery path in the sensor network is proposed. The time complexity is \(O\left(n\Delta\cdot\max_{1\leq i\leq n}\{u_{i}\}\right)\), where \(u_{i}\) is the given upper bound of the retransmission threshold of sensor node \(i\) in a given delivery path, \(n\) is the length of the delivery path and \(\Delta\) is the given upper bound of the transmission delay of the delivery path. If \(\Delta\) is greater than the polynomial, to reduce the time complexity, a linear programming-based \((1+p_{min})\)-approximation algorithm is proposed. Furthermore, when the ranges of the upper and lower bounds of retransmission thresholds are big enough, a Lagrange multiplier-based distributed \(O(1)\)-approximation algorithm with time complexity \(O(1)\) is proposed. Experimental results show that the proposed algorithms have better performance.  
packet delivery; optimal retransmission threshold; in time +
Footnote †: journal:
## 2 Study Area
### The Braina Landslide  
The Braina landslide (Figure 2) is located within the municipality of Gaggio Montano, in the province of Bologna (Italy), between 375 and 596 m a.s.l. It is 1250 m long and between 100 and 400 m wide, with an average slope of 12.6 degrees. The landslide reactivated as an earthflow [19] inside chaotic clay-shales belonging to the Ligurian domain, known as Palombini Shale Formation [40].  
Figure 1: Schematic map with elements of the geology of the study area; labeled landslides are those reactivated during the study period (Geological, Seismic and Soil Service of the Emilia Romagna Region).  
Since 1900, the landslide reactivated three times. The first event took place at the beginning of March 1934 after a prolonged rainfall [41]. Then, the landslide remained dormant for more than 60 years and reactivated a second time on 30 October 1999. According to the eyewitnesses, the landslide activated as a translational movement and then evolved into an earthflow. The reactivated area was 780 m long and between 60 and 17 m wide; the overall extent was about 47,000 m\({}^{2}\) and the approximate volume 470,000 m\({}^{3}\). The landslide disrupted the minor hydrographic network, damaged a building, and destroyed the municipal road. Local authorities carried out extensive works to consolidate the slope, such as draining trenches, hydrographic network arrangement, bridles, and wooden crib walls [41]. Despite these efforts, the landslide reactivated again in late 2019, between 15 and 21 November, due to the continuous precipitation in the second half of November. The reactivated area during this event was 1250 m long and between 100 and 450 m wide. The overall extent was about 23,000 m\({}^{2}\). This event partially damaged the municipal road and destroyed most of the ditches realized in 1999.
# Light-Controlled Microbots in Biomedical Application: A Review
## 4 Aspects of Design
### Manipulation of Microbots
#### 4.1.2 Optoelectronic Tweezers (OET)  
Optoelectronic tweezers (OET) can sometimes be more useful than optical tweezers. Although both mechanisms use light for the manipulation of small particles, the underlying technique is more distinctive from that of an optical tweezer [76]. An OET device consists of several layers of elements. The top layer consists of an ITO transparent electrode; then there is a photoconductive electrode layer made out of amorphous silicon (a-Si:H) [139]. In an OET, light is projected into the device using the help of a digital projector and a digital micromirror device (DMD), then it passes through an objective lens directed towards the a-Si:H layer, where it creates the image of the projected light. With an AC electric bias, the pattern turns into a temporary electrode that creates a nonuniform electric field, which exerts a force on the dielectric particle through the process known as dielectrophoresis (DEP). If the dielectric particle is less polarizable than the surrounding media, then where the light illuminates in the substrate, the particle gets repelled, and where there is no illumination, the particle does not feel any forces. What makes OET particularly distinctive from optical tweezers is that optical tweezers use highly focused light beams (intensity \(>10^{5}\) W/cm\({}^{2}\)) to trap the particle, which could damage the biocells during long exposures. Whereas, in OET, to create virtual electrodes, a lower intensive light source (around  
Figure 5: **(i)** Manipulation with Optical tweezers (**a**) working principle: momentum of light pushing a microparticle towards the center of the laser beam and (**b**) basic setup of a conventional optical tweezers system, (**ii**) all-fiber modular optical tweezers with same basic working principle but in a compact dimension, and (**iii**) 3D trapping capabilities of AFMOT (**a**) fiber schematic, (**b**) single, (**c**) multiple trapped microparticle (polystyrene beads, size: 15 \(\upmu\)m), and (**d**) trapped fibroblast cell [133]. Reproduced under the terms of the CC-BY license.  
\(1\) W/cm\({}^{2}\)) is enough. Even a commercial digital projector can be used as the light source in OET. OET does not require a coherent light source for trapping as optical tweezers because the light is only used to create the virtual electrodes, which create the DEP effect from the biased A.C. electric field. The photoconductive substrate a-Si:H is well conductive when illuminated and nonconductive when there is no illumination. Because of the use of a-Si:H (which is abundantly used in solar cells), the setup of OET is inexpensive compared to the optical tweezers system. However, this technique of creating a trap is not fully utilized in biomedical applications directly, but the separation of biological cells and, consequently, the medical diagnosis are proposed [139,140].
# Mathematical Modeling of Screw Press Configuration for Processing Safflower Oil
## 3 Results and Discussion  
### Construction Features of the Screw Press  
The equipment for oil separation works as follows: peeled safflower seeds from the feed hopper enter the pressing chamber. The pressure is generated by the gradual reduction of the inner diameter of the pressing screw on the raw material in the direction of the product flow, and the oil is released through the cone-shaped openings of the pressing cage. The pressure required for oil separation is regulated on the basis of a specially designed pressure control mechanism.  
During the experiment, it was observed that, under the initial conditions, the yield of safflower oil increased when the moisture content was reduced, the size of the oil outlet channel was reduced, and the rotational speed was decreased. However, this is not enough to obtain the optimal parameters of the process under consideration.  
For making the final decision on the selection of the optimal modes of the studied process, it is necessary to conduct a series of experiments on the change of humidity, pressure, and temperature. In this case, the screw rotation frequency is taken to be equal to 65 rpm; at this value, there is an increase in oil yield. It is noted that when humidity decreases below 8%, there is a decrease in the yield of safflower oil due to the increased temperature of the oil press, since oil \" burning\" occurs. Increasing humidity above 10% also reduces oil yield, as excess moisture makes it difficult to compress the oilcake effectively. Figure 5 shows the safflower oil and cake after the pressing process.  
Installing the pressure regulator mechanism in the press design intensifies the pressing process, increases oil yield, reduces energy consumption, and improves oil quality.