esa-sceva/satcom-chunk-collection huggingface dataset:
Effects of Epoxy Resin Value on Waterborne-Epoxy-Resin-Modified Emulsified Asphalt Mixture Performance  
[PERSON]  
1 Zhejiang Huadong Engineering Consulting Co., Ltd., Hangzhou 310030, China;  
[EMAIL_ADDRESS] (L.W)1 Zhejiang Huadong Engineering Consulting Co.  
as styrene butadiene styrene (SBS) and styrene-butadiene rubber (SBR) [12]. Nevertheless, investigations show that although the addition of SBS dramatically improves the mixture performance, SBS emulsified asphalt is hard to produce and store with high reliability. SBR emulsified asphalt, on the other hand, can improve the mixture performance at low temperatures but fails to meet the performance requirements in high-temperature zones [13].  
In recent years, researchers introduced the water epoxy resin system to modify emulsified asphalt [14]. The water epoxy resin system changes the dissolution state of epoxy resin through physical and chemical methods, resulting in a continuous phase of water-borne emulsion. When needed, curing agents are added to the waterborne emulsion, and the curing reactions in the system produce a solidification product with a linear or three-dimensional network structure, depending on the type and content of curing agents [15]. Waterborne epoxy resins have several advantages: (1) they are environmentally friendly, non-toxic, and have no risk of combustion or explosion; (2) they are low-cost because they use water as a solvent; (3) they have the ability to cope with a wide range of external environments to cure at room temperature, under high humidity, and even at low temperatures; and (4) the cured material has suitable bonding properties [16].  
Initially, the system was mainly applied in the coatings industry. Its advantages and performance in recent applications for modifying emulsified asphalt have attracted many researchers [17; 18]. However, waterborne epoxy resin emulsified asphalt and its mixture performance are complicated because the formation and breakage of emulsification, the emulsion and curing of water epoxy resin, the evaporation of water, the modification of asphalt, the bonding between asphalt and aggregates, the possible addition of cement, and the interactions between all these physical and chemical reactions must be considered [8]. Specifically, the curing agents and epoxy value are two significant factors that greatly impact the performance of WER-EA. Preliminary research has been published investigating the impact of three curing agents (TETA\(\backslash\)DETA\(\backslash\)593) with obvious differences in the molecular chain length and number of active groups [19]. However, more research should be conducted considering the potential benefits and complexity of WER-EA, especially the performance of asphalt mixtures.
# Research on Agricultural Product Price Prediction Based on Improved PSO-GA
## 4 Results  
### Parameterization  
For SSA, the window length L is selected based on the maximum period, and L decompositions are obtained. Decomposition contributions less than 0.1% are considered as noise, and the others are valid patterns. The final window length is set to 10, 10 decompositions are obtained, and the first three sequences are selected to reconstruct the denoising sequence. The scallion price sequence was decomposed into 10 sub-sequences using the one-dimensional EMD method, the mode with the highest frequency was treated as noise, and the other 9 modes were summed up as the denoised sequence. For VMD, the scallion price sequence was divided into 11 modes and a residual term, and all modes except the component with the highest frequency were summed to obtain the denoised sequence.  
All denoised price series on the validation and test sets are shown in Figure 4, below, for clearer comparison. All three denoising techniques are able to reduce the effect of noise and capture the overall trend and major fluctuations. SSA has the strongest smoothing. After smoothing, EMD is able to maintain amplitude during sharp changes. VMD retains more detail than SSA or EMD. In the absence of severe shocks to the scallion price series, EMD and VMD have similar smoothed series.
# Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis
## 2 Related Works
### Deep Learning on Point Clouds  
With the rapid development of deep learning, CNNs have been successfully used in 2D images and researchers have tried to explore its feature learning power for 3D point clouds. However, the implementation of typical convolutions relies on a regular data format that is not satisfied in point clouds. To solve this problem, various methods have been proposed.  
A straightforward extension is a voxel-based method that transforms the irregular point clouds into regular volumetric distribution [14; 15; 16]. Voxels are similar to the pixels in 3D space instead of 2D images. Then, the typical 3D convolution operation can be applied to extract its features. [PERSON] et al. [14] transformed the point clouds into binary occupancy of voxels, where 1 stands for the occupancy and 0 stands for empty. [PERSON] and [PERSON] [15] proposed three different representations of voxels and introduced a network named VoxNet for classification tasks. VoxNet can be trained by typical SGD algorithms. Though simple, this kind of method induces a loss of information, excessive consumption of memory, and a high computation cost.  
Another solution is multi-view-based methods [12; 13]. They used multiple 2D images to represent 3D objects so that it can benefit from these well-developed recognition methods in 2D images [23]. [PERSON] et al. [12] generated many projection images for 3D objects by placing several virtual cameras arounds it. Traditional networks on images such as VGGNet were utilized to extract features from each individual image. The extracted features were fused subsequently to obtain the overall representation of the object. [PERSON] et al. [13] introduced a relation network to learn the relation among different views; then, the features from all viewpoints were aggregated to produce a global feature. However, the determination of the number of 2D images is still a problem, and these methods would lead to information loss during the projection process.  
Pioneered by PointNet [6], point-based methods directly consume point clouds rather than transform them into an intermediate representation. An effective and simple architecture was constructed in PointNet to directly learn from point sets. First, point features were computed individually from shared multi-layer perceptrons (MLPs). Then, all of the features were aggregated as a global presentation of a point cloud. Furthermore, PointNet++ [7] was proposed to incorporate the local information since PointNet processes all points at one time. Although these methods significantly improve the speed of the 3D shape analysis, they still ignore structural information.
## 2 Background and Related Work
### Buffer-Based B-Trees  
Buffer-based B-trees employ the write buffer to reduce the number of write operations. Figure 3 shows a brief process of how buffer-based B-trees flush the inserted records from the buffer to the flash memory. In this example, the logical B-tree is built with four nodes A, B, C, and D by inserting records in the following key sequences: 1, 17, 9, 19, 13, 3, and 11. Assume that a page can store a maximum of four records in the flash memory. As shown in Figure 3a, Buffer-based FTL (BFTL) [27], the first buffer-based B-tree, temporarily stores the inserted records into the buffer. When the buffer overflows, BFTL flushes the data in a page unit to the flash memory. In this example, BFTL first flushes records with keys 1, 17, 9, and 19 to page #0 on the flash memory. Although BFTL reduces the number of write operations, its retrieval performance is very poor because it requires many read operations to find records that are scattered in the several pages.  
Figure 3: Buffer-based B-Trees for flash memory.  
Figure 2: B-Tree on the flash storage device.  
To store all the records that exist in the same logical node together, the recent buffer-based B-trees (e.g., IBSF [28], Lazy update B+-tree [29], and AS-tree [30]) reorder the inserted records in the buffer. Figure 2(b) shows an example of flushing nodes in IBSF. When the buffer overflows, IBSF finds all the victim records by simply referring to the key of the first inserted record in the buffer. Since leaf node B is the relevant victim node, all records associated with leaf node B (keys 1 and 3) are flushed from the buffer to page #0 on the flash memory. In contrast to IBSF, Lazy update B+-tree selects the victim node related to the least recently inserted record to improve the write throughput and the buffer hit ratio.  
AS-tree sorts all records in the buffer according to the logical node for batch writing. When the buffer overflows, all sorted records are sequentially flushed in a page unit to the flash memory. For avoiding overwriting data, it assigns more pages than the other index structures and requires garbage collection to remove invalid nodes. Generally, the overall performance increases as the buffer size increases. However, as the buffer size significantly increases, the overall performance could decrease because IBSF, Lazy update B+-tree, and AS-tree reorganize all the records in the buffer. In addition, there are other buffer-based B-trees such as MB-tree [31], FD-tree [32], and AD-tree [33]. They quickly create the index structure but they consume more time to find data due to the flexible node size.  
Most buffer-based B-trees guarantee fast performance without regard to FTL mapping algorithms. However, their performance is largely affected by the size of the high-cost memory buffer and the risk of data loss still remains in the case of a sudden power failure. Therefore, their solutions do not demonstrate the effective performance in the small embedded system that has limited memory resources and insecure power supply.
## 2 Materials and Methods
### Bioaccumulation Factor (BAF) and Bioaccumulation Index over Time (BAIt)  
The bioaccumulation factor (BAF) is used to evaluate the partitioning of natural or anthropogenic substances from the environment to the biota [44] and is calculated by the normalization of the concentration of the generic element or compound, x, with respect to a reference element, r. The reference element must be stable in the environment (soil, ambient air, and water) and possibly not associated with specific pollution sources. Examples of reference elements include Al, Fe, Mn, La, and Rb. In the present study, Al, a common terrigenous element, was chosen since it represents one of the predominant elements in natural atmospheric aerosols [35; 39].  
We evaluated the ion and metal accumulation levels by defining the bioaccumulation factor (BAF) as:  
\[\mathrm{BAF_{x}}=[(\mathrm{F_{x}}/\mathrm{F_{r}})_{\mathrm{POL}}/(\mathrm{F_{ x}}/\mathrm{F_{r}})_{\mathrm{CNT}}]\]  
where \(\mathrm{F_{x}}\) and \(\mathrm{F_{r}}\) are the deposition fluxes (\(\mathrm{\mu g\cdot m^{-2}\cdot day^{-1}}\)) of the x species and the r reference element on the pollen covered (POL) and control plates (CNT), respectively.  
Furthermore, to properly evaluate the accumulation of the elements naturally present in the pollen tissues and to interpret the temporal dependence of the exposure to pollutants, we calculated a bioaccumulation index over time (BAIt) [44] for the major species x originally present in the pollen grains, defined as follows:  
\[\text{BAIt}_{\text{x}}=(\text{P}_{\text{tx}}/\text{P}_{\text{tr}})/(\text{P}_{ 0x}/\text{P}_{0\text{tr}})-1\]  
where Ptx and Ptr are the amounts (ug) of the x and r species as a function of the exposure time t (t = 7 and 15 days). P0x and P0x (t = 0) have been obtained using the freshly harvested pollen, and Al is the reference, as above.
# Stress and Fatigue Analysis of Picking Device Gears for a 2.6 kW Automatic Pepper Transplanter
## 3 Results and Discussion
### Appropriate Gear Material and Face Width
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Cam Gear Set Maximum Stress, MPa**} \\ \cline{4-7}
**Load** & **Case** & \multicolumn{2}{c}{**Roll Angle, rad**} & \multicolumn{2}{c}{**SCM 420H Carbon Steel**} & \multicolumn{2}{c}{**AISI 1060 Steel**} \\ \cline{3-7}
**Type** & & & **3 mm Face** & **5 mm Face** & **3 mm Face** & **5 mm Face** \\  & & & **Width** & **Width** & **Width** & **Width** \\ \hline \multirow{3}{*}{Half load} & 1 & 0.01 & 151.05 & 147.17 & 155.23 & 151.00 \\  & 2 & 0.03 & 251.24 & 260.90 & 259.58 & 271.48 \\  & 3 & 0.04 & 575.63 & 504.17 & 630.88 & 580.26 \\  & 4 & 0.06 & 769.05 & 566.61 & 786.35 & 584.02 \\  & 5 & 0.07 & 702.44 & 567.04 & 745.25 & 576.09 \\  & 6 & 0.09 & 710.74 & 522.31 & 701.26 & 607.67 \\  & 7 & 0.10 & 675.44 & 507.65 & 672.49 & 524.94 \\  & 8 & 0.12 & 396.16 & 456.60 & 405.07 & 466.20 \\ Half load & 9 & 0.13 & 265.14 & 291.71 & 283.62 & 303.83 \\  & 10 & 0.15 & 173.74 & 199.77 & 184.79 & 211.27 \\ Full load & AGMA & & 620.63 & 480.74 & 626.54 & 485.31 \\  & (6.284 rad/s) & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Maximum stress obtained through contact stress analysis for cam gear set.
## 4 Experiments
### Test-rig for the PM-ASS  
Figure 5 shows the test-rig, which has a PM and two MacPherson struts installed in parallel between the vehicle body and the support frame, a wheel firmly installed on one side of the support frame and a road profile generator that generates road profiles for various road conditions. The road profile generator has rollers, an induction motor and a pneumatic cylinder, and is in close contact with the wheel. Figure 6 illustrates the system blocks for a quarter-car with a PM-ASS. The components in Figure 6 are detailed as follows:  
1. The sprung mass is the portion of the vehicle's total mass that is supported by the suspension, i.e., a chassis.
2. The unsprung mass represents the brake, the caliper and ancillaries. The un-sprung mass does not include the mass of the wheel.
3. Two MacPherson struts and one PM are used for the PM-ASS.
4. A proportional pressure regulator (PPR) regulates the flow of air (pressure) into the PM.
5. An inverter drives the induction motor to rotate the tire. The maximum speed of the tire is at 35 km/hr.
6. A pneumatic cylinder generates various road conditions. The proportional directional control valve regulates the flow of air into the pneumatic cylinder. The pneumatic cylinder can lift a load of more than 400 kg and generates road profiles that are more than 10 cm high with a frequency of 10 Hz.
7. On the plane of the support frame, one linear encoder and one accelerometer measures the displacement and the acceleration of the sprung mass.
8. Two linear scalars measure the vertical variation in the unsprung mass and the road profile.
9. The control \(u\) is bounded within \([-5V,\,5V]\).  
Figure 5: Test-rig of quarter-car using the PM-ASS.  
Figure 7 shows the mechatronics diagram for a quarter-car test-rig with a PM-ASS. The bottom of Figure 7 shows a control box, which has a NI cRIO-9074 embedded system, a NI-9263 D/A card, a NI-9215 A/D card and a NI-9411 encoder. The NI-9215 A/D card receives information (the green dashed line) about the displacement of the unsprung mass and the pneumatic cylinder and the acceleration of the sprung mass. The NI-9411 encoder receives a digital signal that shows the displacement of the sprung mass (the blue dashed line). The NI-9263 AO sends the control signals (the red dashed line) to the PDVC, the invertor and the PPR to drive the PM, the pneumatic cylinder and the induction motor. All of the measured data was sent to a personal computer through an Ethernet interface and this is displayed on the screen. The system parameters can be modified in the PC and were sent to the NI cRIO-9074 embedded system. The Butter Worth filter was adopted to attenuate the sensor noise for the linear encoders, which is expressed as:  
\[y_{out}(k)=0.97y_{out}(k-1)+0.015[y_{in}(k)+y_{out}(k-1)] \tag{45}\]  
where \(y_{out}(k)\) is the output of the filter at the \(k\)th sampling and \(y_{in}(k)\) is the displacement of the sprung mass measured by the linear encoder at the \(k\)th sampling.  
Figure 6: System blocks of quarter-car with the PM-ASS.
# Is Satellite Ahead of Terrestrial in Deploying NOMA for Massive Machine-Type Communications?
## 3 From S-MIM to F-SIM  
In the light of the good performance provided by S-MIM for mobile applications, it was a natural decision to extend it to other use cases, particularly for the case of fixed terminals using legacy GEO satellites in the C, Ku or Ka band. The objective was to offer cheaper VSAT-like services with a technology that would allow reducing both the terminal cost and service cost, more appropriate for applications such as interactivity, or IoT backhauling, where broadband speeds are not necessary. The new protocol, derived from S-MIM, was named the Fixed Interactive Multimedia Services (F-SIM) protocol. F-SIM was adapted from S-MIM specifically in order to efficiently support fixed terminal operations. As detailed in [5], the main differences are as follows:  
1. Support of higher frequency satellite bands: C, Ku and Ka--including adapted uplink power control algorithm in order to support propagation channel characteristics in these bands;
2. Use of digital video broadcasting DVB-S2/S2X protocol (instead of DVB-SH) in the forward link, including a network clock reference (NCR) counter, allowing the terminal to achieve an accurate frequency reference;
3. New physical layer configurations, in terms of bit rate and packet size;
4. Native support of Internet Protocol (IP) and flexible management of the different quality of service (QoS) classes at data link layer. F-SIM was designed in order to enable these two classes of services:
5. \"Satellite Over the Top\" services: additional interactive IP-based services on top of video satellite broadcasting, e.g., video to personal devices (multiscreen), digital rights management (DRM), voting, real-time audience measurement, targeted advertising, limited web browsing and datacast;
6. IoT/M2M connectivity: message-based or low-bit rate connectivity for objects or small networks. This includes IoT services, supervisory control and data acquisition (SCADA) and backhauling of terrestrial low-power wide-area networks (LPWAN).
# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 3 Proposed Architecture for Real-Time Flood Monitoring
### Phase 2: Postprocessing Filters
#### 3.2.1 Decimation Filter  
Since all the depth-generation processing was done on the chip on-board the camera, the rest of the postprocessing was configured on the platform used for the application. The decimation filter took in the raw depth frame of the scene and decimated it by a factor of n. Decimation in the context of image processing produces an approximation of the image as it compresses the desired frame. It typically involves lowpass filtering and subsampling of the data, which also aids in reducing the computational load as the computer does not need to process as many data [45; 46]. In this case, the depth data were subsampled, thus yielding an image with a reduced size and resolution as denoted by Equation (1). A kernel size of n by n was applied and the depth frame was scaled down by the given factor. The kernel size selected in this case was [2 \(\times\) 2], which was applied to the input resolution of the depth stream, i.e., 848 by 480 pixels. The resolution after the filter was applied was:  
\[[848,480]/2=[424,240] \tag{1}\]
## 3 Experimental Results and Discussion  
According to Equation (3), the induced voltage of the sensor output signal is related to the current, velocity, and magnetic field intensity, and the magnetic field intensity will change along the z-axis direction. To verify the relations between the induced voltage and the current, debris velocity and z-axis height, a series of experiments was conducted.
# Thermostable D-Allulose 3-Epimerase for Long-Term Food-Compatible Continuous Production Systems
## 3 Results and Discussion  
### Amino Acid Sequence Alignment of TcDAEase  
TcDAEase exhibited considerable sequence divergence from previously characterized DAEases. As shown in Figure 1, TcDAEase shared amino acid sequence identities from 26.12% to 48.08% with other known DAEases, with the highest similarity observed for the enzyme derived from _Arthrobacter globiformis_. Domain architecture analysis classified TcDAEase as a member of the xylose isomerase-like family (IPR036237). Multiple sequence alignment (Figure 2) revealed several conserved residues that are functionally important for the isomerization of D-fructose [30]. These include Glu149, Asp182, His208, and Glu243 [31], which participate in metal ion coordination, and residues Glu156, His185, and Arg214, which are involved in anchoring the O1, O2, and O3 positions of D-fructose [32]. These findings suggest that TcDAEase preserves the core structural features essential for efficient ketose epimerization.
Sensors_**2010**, _10_, 3389-3410; doi:10.3390/s100403389  
_Seriesors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Speedy Routing Recovery Protocol for Large Failure Tolerance in Wireless Sensor Networks**  
**[PERSON] and [PERSON] ***  
Department of Computer Science and Engineering, Kangwon National University, Chuncheon  
Gangwondo, 200-701, Korea; E-Mail: [EMAIL_ADDRESS]  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-33-241-9793; Fax: +82-33-252-6390.  
_Received: 3 March 2010; in revised form: 25 March 2010 / Accepted: 26 March 2010 / Published: 7 April 2010_  
**Abstract:** Wireless sensor networks are expected to play an increasingly important role in data collection in hazardous areas. However, the physical fragility of a sensor node makes reliable routing in hazardous areas a challenging problem. Because several sensor nodes in a hazardous area could be damaged simultaneously, the network should be able to recover routing after node failures over large areas. Many routing protocols take single-node failure recovery into account, but it is difficult for these protocols to recover the routing after large-scale failures. In this paper, we propose a routing protocol, referred to as ARF (**A**daptive routing protocol for fast **R**ecovery from large-scale **F**ailure), to recover a network quickly after failures over large areas. ARF detects failures by counting the packet losses from parent nodes, and upon failure detection, it decreases the routing interval to notify the neighbor nodes of the failure. Our experimental results indicate that ARF could provide recovery from large-area failures quickly with less packets and energy consumption than previous protocols.  
**Keywords:** failure tolerance; routing interval; failure detection; failure recovery