esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Surface Observation
#### 3.1.2 SEM Observation  
Figure 3A,B shows the fracture sections of post-2 nd-recycling and post-3 rd-recycling T/P/MA blends, respectively, after tensile tests. Figure 3A shows that T100 looks flatter and smoother than P100 because thermoplastic polyurethane is, comparatively, a more ductile material. Pure polypropylene reveals a homogeneous pattern, with a rough and flat surface that indicates a typical characteristic of tough fractures [33]. Figure 3B displays the surface roughness of P100 and T100 after the post-3 rd-recycling process and further explains that thermoplastic materials require appropriate post-treatment when reforming. T100 shows an ascending trend on roughness, which is ascribed to the poor thermal stability of thermoplastic polyurethane. The multiple melting-recycling cycles result in damage on T100 to a certain extent, so the surface is no longer being sleek. Polar thermoplastic polyurethane and nonpolar polypropylene are incompatible and immiscible when blended with each other, and further lead to phase separation derived from high interfacial tension produced at interphase [34]. The Figure 3A group (T90, T70, and T50) shows that with greater polypropylene content, the blends yield more pores, which means, equivalently, that both the differentiation effect and incompatibility between thermoplastic polyurethane and polypropylene are strengthened. Figure 3B (T90, T70, and T50) demonstrates that with the employment of the post-3 rd-recycling, thermoplastic polyurethane matrices are partially expanded, and polypropylene particles are wrapped in the matrices while the roughness is concurrently enhanced. Furthermore, the differentiation effect is clearly observed when comparing T70 and T50; the spherical particles in T50 blends appear prone to fall off from the pores due to enlarged interfacial tension and poor adhesion between thermoplastic polyurethane and polypropylene [33]. The Figure 3A group (T90/MA, T70/MA, and T50/MA) shows that the presence of MA stabilizes the morphology of all T/P/MA blends (i.e., MA-containing blends). Similarly, Figure 3B (T90/MA, T70/MA, and T50/MA) shows that MA improves the pores and defects triggered by the excessive interfacial tension between the thermoplastic polyurethane and polypropylene [33]. Moreover, the copolymer on the T/P interfaces prevents the occurring of coalescence. Comparing the MA-containing and MA-free groups, the former shows an attenuated differentiation effect. The results indicate that a proper introduction of compatibilizer improves the defects and reduces the differentiation effect caused by the diversity or blending ratio of the constituent materials.  
Figure 2: Surface observation of T/P and T/P/MA blends as related to post-2 nd and post-3 rd recycling. (**A**) Post-2 nd-recycling group; (**B**) post-3 rd-recycling group.  
Figure 3: SEM images of post-2 nd-recycling and post-3 rd-recycling T/P and T/P/MA blends. **(A)** Post-2 nd-recycling group; **(B)** post-3 rd-recycling group.
# Performance Testing and Evaluation of Drum-Type Stem-Separation Device for Pepper Harvester
## 2 Materials and Methods
### Field Test Method  
To evaluate the performance of the drum stem-separation device, field performance tests were conducted using two species of peppers. Stem-attached pepper samples were prepared for each variety, and five sets of stem-attached peppers were continuously added per each round of testing (Figure 5a). In the non-moving status, during the non-picking operation status of the pepper harvester, pepper samples were placed into the conveyor (Figure 5b). Figure 5c shows the non-moving status during the picking operation status of the pepper harvester, and the pepper samples were placed into the helix driven in the picking operation. Each performance test was repeated three times. In Figure 5d, the field test was conducted by driving at a running speed of 0.2 m/s in the moving status during the picking operation.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Variety** & \multicolumn{2}{c}{**AR Legend**} & \multicolumn{2}{c}{**Jeokyoung**} \\ \hline  & **Parameter** & **AVE** & **SD** & **AVE** & **SD** \\ \hline \multirow{4}{*}{Plant} & Length of pepper plant (mm) & 1052.5 & 136.5 & 866.6 & 90.7 \\  & Width of pepper plant (mm) & 814.6 & 111.2 & 776.7 & 125.2 \\  & Length of pepper stem (mm) & 198.3 & 20.3 & 163.3 & 15.3 \\  & Diameter of pepper stem (mm) & 20.5 & 3.8 & 24.6 & 4.1 \\ \hline \multirow{4}{*}{Fruit} & Length (mm) & 118.3 & 13.7 & 112.8 & 20.3 \\  & Maximum diameter (mm) & 23.1 & 2.1 & 22.6 & 2.7 \\  & Weight (g) & 22.4 & 2.3 & 18.7 & 3.2 \\  & Amount (ea/plant) & 88.0 & 6.5 & 96.4 & 5.8 \\  & Detachment force (kgf) & 2.3 & 0.6 & 1.4 & 0.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average (AVE) and standard deviations (SD) of the physical properties for peppers.  
Figure 5: Picture of the (**a**) pepper plants samples, (**b**) non-moving status during non-picking operation, (**c**) non-moving status during picking operation, and (**d**) moving status during picking operation.  
The rotation speed ratio of drums A, B, and C of the 3-shaft drum stem-separation device referred to the stem shredder of the vegetable harvester [42]. The A:B:C drum rotation speed ratio of the stem shredder was 1:2:3, and the drum teeth were rotated in opposite directions to shred the stems. As listed in Table 3, the weights of the stem-separated peppers, stem-attached peppers, and damaged peppers were measured at level 6, wherein the rotational speeds of drums B and C were increased based on that of drum A. The D drum speed of the 4-shaft drum device was set to the same rotation speed as the C drum in the 3-shaft drum.
## 4 Application of UWSN
### Monitoring
#### 4.1.3 Military  
UWSNs are extremely useful in military applications. The military uses UWSN to keep track of activities both over and underwater at the sea border. They use imaging, sonar, and other sensors for surveillance, mine detection, and locating submarines [82; 83].  
* Mines: Underwater mines pose a great danger to ships as they can drown the ship. Using UWSN, ships can detect mines and avoid the path. In [84], the authors designed a mine detection using AUV and UWSN sensors. The sensors are capable of capturing high-resolution underwater pictures, which helps in locating mines underwater.
* Submarines: UWSNs help in locating submarines. It is important for the military to track and locate submarines. In [85], the authors conducted a real-life experiment to detect submarines using UWSN. The results of the experiment show that it is safer to detect submarines with the help of AUV and UWSN.
3. Surveillance: UWSNs are useful in detecting unwanted ships and submarines [85]. They are also useful for logistical support for warfare. In [86], the authors proposed a new architectural layout for an underwater surveillance system. It consists of the deployment of sensors at both the surface and depth of the ocean to maximize the coverage of the sensors. Different types of sensors were used to gather different data and data mining was used to make use of the gathered data.
## 5 Experimental Results
### Experiments with Dataset
#### 5.1.1 Experimental Environment and Details
The experimental deep learning framework in this study is PyTorch, and the algorithm is implemented on the open-source detection tool library, mmdetection. The parameters in the model training process are as follows: global learning rate is set as 0.005, initial learning rate is set as 1.0/3, momentum factor is set as 0.9, and weight attenuation is set as 0.001. Furthermore, the stochastic gradient descent [38] optimization algorithm was adopted. Two images were used for each batch of training, and 20 epochs were trained. Moreover, the system's super-parameter learning rate adopts a warm-up strategy, that is, a relatively small learning rate is used at the beginning and the learning rate is gradually increased. This method can achieve a high accuracy rate and avoid the non-convergence of model training.
# A 100 Gbps OFDM-Based 28 GHz Millimeter-Wave Radio over Fiber Fronthaul System for 5G
###### Abstract  
Due to the unprecedented growth in mobile data traffic, emerging mobile access networks such as fifth-generation (5G) would require huge bandwidth and a mobile fronthaul architecture as an essential solution in providing a high capacity for support in the future. To increase capacity, utilizing millimeter waves (mm-waves) in an analog radio over fiber (RoF) fronthaul link is the major advancement and solution in achieving higher bandwidth and high data rate to cater for 5G mobile communication. In this paper, we demonstrate the feasibility of transmission and reception of a 100 Gbits/s data rate link at 28 GHz. The performance of three modulation formats (16-PSK, 16-QAM and 64-QAM) have been compared for an optical fiber length from 5 km up to 35 km for two detection systems; coherent and direct detection. Also, in this paper, the transmission impairments inherent to transmission systems are realized through the implementation of a digital signal processing (DSP) compensation scheme in the receiver system to enhance system performance. Quality factor (QF) and bit error rate (BER) are used as metrics to evaluate the system performance. The proposed system model is designed and simulated using Optisystem 16.  
5G; millimeter-wave (mm-wave); radio over Fiber (RoF); Orthogonal Frequency Division Multiplexing (OFDM); Digital Signal Processing (DSP); QF; BER  
opt202008
## 2 Computational Models
### Eulerian-Eulerian Two-Phase Flow Model
The turbulent stress \(\tau^{R}\), which utilizes the [PERSON]'s approximation, is also the product of the velocity gradient and the turbulent viscosity. In Eulerian-Eulerian model, \(K_{i}(u_{g,l}-u_{i,l})\) in Equation (2) is the \(i\)-direction interaction force from liquid to gas phase. As a result, the interaction force from gas to liquid in \(i\)-direction is \(K_{i}(u_{i,l}-u_{g,i})\), which switches the direction but has the same magnitude. \(K_{i}\) is the exchange coefficient of \(i\)-direction, and its value can be determined from the empirical relations as follows [6; 22; 32]:  
\[ \begin{array}{l}K_{i}=\frac{a_{i}a_{g}\rho_{g}f}{\tau_{r}}\\ f=\frac{C_{d}R_{e}}{24},\tau_{r}=\frac{\rho_{l}d_{l}^{2}}{18\rho_{g}},Re_{r}= \frac{\rho_{g}|u_{g,i}-u_{i,l}|d_{l}}{\mu_{g}},C_{d}=\text{max}(\frac{24(1+1.5 R_{e}^{0.687}}{K_{e}},0.4)\end{array}  \tag{4}\]  
In Equation (4), \(f\) is the drag function, \(C_{d}\) is the drag coefficient, \(\tau_{r}\) is the relaxation time, \(Re_{r}\) is the Reynolds number based on two-phase slip velocity and the liquid droplet diameters \(d_{l}\). The size of droplet diameter is chosen as 500 um in this study from preliminary results, which is also similar to the selection of ref. [11; 12]. After chemical reactions, the SO\({}_{2}\) within gas phase is reduced, and the slurry is produced in liquid phase, as indicated in Figure 1. This desulfurization process is controlled by Equation (5) with source term \(S\), which will be discussed in the next subsection. \[ \begin{array}{l}\frac{\partial\left(a_{g}\rho_{g}Y_{\text{SO}_{2}}\right)}{ \partial t}+\frac{\partial\left(a_{g}\rho_{g}Y_{\text{SO}_{2}}u_{g,i}\right)} {\partial x_{j}}=-S\\ \frac{\partial\left(a_{i}p_{l}Y_{slurry}\right)}{\partial t}+\frac{\partial \left(a_{i}p_{l}Y_{slurry}u_{i,l}\right)}{\partial x_{j}}=S\end{array}  \tag{5}\]  
An isothermal temperature around 58 \({}^{\circ}\)C is measured within the FGD tower by China Steel Cooperation. Therefore, the energy equation is not activated in this study. Similar situations can be seen in ref. [1; 5; 10; 20; 29]. Figure 1: This schematic shows the components in gas and liquid phases during the desulfurization process.
## 3 Result and Discussion
### Relationship between Protein Degradation or Oxidation and the Flavors of Bacon
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Gene Name** & **Accession** & **Protein** \\ \hline PRMT5 & A0A5G2 QR18 & Protein arginine N-methyltransferasease 5 \\ DPP3 & F1 RU52 & Dipeptidyl peptidase 3 \\ VCPKMT & A0A287B739 & Valosin-containing protein lysine methyltransferase \\ ALDH5A1 & F1 RUE3 & Succinate-semialdehyde dehydrogenase \\ LHPP & A0A480 STU2 & Phospholysine phosphohistidine inorganic pyrophosphate phosphatase phosphatase \\ PPP1 CC & Q2 EHH7 & Serine/threonine-protein phosphatase \\ AKR1B1 & P80276 & Aldo-keto reductase family 1 member B1 \\ CZIB & F1S765 & CXXC motif-containing zinc-binding protein \\ ANKRD2 & A0A5C2 QKX2 & Ankyrin repeat domain 2 \\ PREP & P23687 & Prolyl endopeptidase \\ MOBKL3 & F2Z5T8 & MOB-like protein phicein \\ HIBCH & A0A5G2 QLF4 & 3-hydroxyisobutyryl-CoA hydrolase \\ AIMP1 & A0A287 AKA2 & Aminoacyl tRNA synthetase complex interacting multifunctional protein 1 \\ STRAP & A0A4X1 VAU7 & Serine-threonine kinase receptor-associated protein \\ NAPI1A & A0A481C030 & Nucleosome assembly protein 1-like 4 \\ QDPR & A0A5C2 QZN1 & Quinoid dihydroprotein reductase \\ PA2G4 & A0A8D0X235 & Peptides M24 domain-containing protein \\ MAPK1 & A0A8W4 FG96 & Mitogen-activated protein kinase \\ PEPD & A0A5G2 QM19 & Peptides D \\ CMPK1 & Q29561 & UMP-CMP kinase \\ SUCLG1 & 019069 & Succinate—CoA ligase [ADP/GDP-forming] subunit alpha, mitochondrial \\ MPI & A0A287 BNL25 & Mannnose-6-phosphate isomerase \\ ELOC & A0A8D0F6A0 & Elengin-C \\ TXNDC17 & A0A286 ZZM7 & Thioredoxin domain-containing protein 17 \\ PIR & K7 GKW6 & Pirin \\ LOC100157017 & A0A287A5X0 & Glyoxylate reductase/hydroxypyruvate reductase \\ SGTA & A0A287A1V6 & Small glutamine-rich tetratricopeptide repeat co-chaperone alpha \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Information of flavor-related protein in Bacon during LTS. Figure 5: Correlation analysis between protein change and volatile flavor compounds. (**A**) Proteome positively associated with key volatile flavor compounds. (**B**) Proteome negatively associated with key volatile flavor compounds. (**C**) Correlation analysis of protein degradation and oxidation with key volatile flavor compounds. *: \(p\) < 0.05, **: \(p\) < 0.01.
## 3 Overview and Theoretical Foundations
### Big Spatial Multidimensional Data Analytics  
Analyzing big geospatial data has shifted through years from centralized deployments on beefed-up servers to massive deployments on distributed computing environments such as Cloud and in-premises counterparts. They are notably famous for their readily-offered capability of handling enormous amounts of data in an efficient seamless manner, that is transparent to the user. Geospatial frameworks built atop those baselines, such as geopandas, work by adapting the codebases (including the abstractions and data structures)and building up relevant spatial-aware extensions on top of them for efficiently analyzing big georeferenced data at scale with QoS guarantees.  
They offer new geospatial-aware primitive data types and functions for data representation, spatial indexing, spatial join processing, and the capability to run spatial queries that would be otherwise intractable with classic data types, such as proximity or intersection. However, they do not include intrinsic support for unifying heterogeneous multidimensional georeferenced data. For example, they are not able to provide over-the-counter support for joining meteorological data with mobility data in a unified view to allow sophisticated queries on the relationship between both domains in a specific geography.  
Python, for example, offers capabilities for spatial join and other geometric operations using Geopandas and other libraries. However, it does not offer over-the-counter support for cross-domain spatial join.
## 3 Results and Discussion
### Toxicity Tests in Laboratory  
#### 3.2.1 Amphipod Laboratory Bioassay  
Mean mortality results after the 10 day amphipod toxicity test are shown in Figure 2a. The highest mortality (60%) was associated with those organisms exposed to sediments at the SE station. An increasing toxicity gradient was shown downstream, while AR showed the lowest mortality values. Furthermore, stations with the highest toxicity values (SE and AL) showed significant differences (\(p<0.05\)) compared to the control station (RSP).
## 1 Introduction  
Mining-induced fractures are the principal pathways for groundwater seepage. During coal mining, changes in the structure of the overburden strata of the coal seam and the heights and spatial characteristics of the mining-induced fractures are the basic premise for water-preserving and damage-reducing coal mining techniques [1]. An extraordinary deal of practical and theoretical research has been conducted. [PERSON] et al. [2] established the widely accepted three horizontal zones and three vertical zones characteristics of rock formation movement-induced fractures and the mining-induced fracture distribution in the stope. [PERSON] [3] proposed a strategy for predicting the height of the WFFZ based on the location of the primary overburden strata. By studying the mining conditions of the shallow coal seams in Shendong and Northern Shaanxi, [PERSON] [4] established the step rock beam structure model of the overburden strata in shallow coal seams and determined the rules for upward fracture development and downward fracture development. [PERSON] [5]preliminary established an empirical equation for the height of the WFFZ during Jurassic coal seam mining in the western mining area, which provides guidance for the prevention and control of roof water hazards and water-preserving mining of the western shallow coal seams. [PERSON] et al. [6] and [PERSON] et al. [7] considered different influencing factors and used a back propagation (BP) neural network, support vector machine, and an improved model [8; 9; 10] to predict the height of the water-flowing fractured zone.  
The northwest-mining area is an important coal production base in China, where the structure of the overburden bedrock of most of the Jurassic middle-deep buried coal seams in the Inner Mongolia-Shaanxi border area is dominated by weakly cemented Cretaceous and Jurassic strata. The development started relatively late, and the exploration was less extensive. Affected by major factors, such as overburden strata, mining size, and mining rate, the predicted height of the water-flowing fractured zone according to the empirical formula diverges greatly from the actual height.  
Based on the geology and technical mining conditions of the middle-deep underground Jurassic coal seams in the Ordos Basin and height measurements of the WFFZ and comprehensively considering factors such as the burial depth, the structure of the weakly cemented overburden strata, and the high-intensity mining conditions, we used the sparrow search algorithm (SSA) to optimize the Elman neural network (ENN) and developed an SSA-Elman neural network coupling model to predict the heights of the WFFZ in the study area.
## 4 Performance Evaluation
### Simulation Results and Analysis
#### 4.2.1 Packet Delivery Ratio  
Figure 12a illustrates the comparison of packet delivery ratios as a function of the number of nodes. It is evident that all protocols exhibit a higher packet delivery ratio as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SIMULATION/SCENARIO** & \multicolumn{2}{c}{**MAC/ROUTING**} \\ \hline Simulator & NS-3.23 & MAC & IEEE 802.11p \\ Simulator Time & 500 s & Channel Capacity & 6 Mbps \\ Map Size & 3000 \(\times\) 2400 m\({}^{2}\) & Transmission range & 170 m \\ Mobility model & SUM 0.32 & Traffic Model & CBR \\ Number of vehicles & 100–400 & Packet size & 512 bytes \\ Vehicle speed & 20–60 km/h & Packet interval/s & 0.1–1 s \\ Weighting factors & \(\alpha=0.5\), \(\beta=0.5\) & Hello interval/s & 1 s \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation setup.  
Figure 11: Simulation of VANET topology.  
the number of nodes increases, with MJTAR demonstrating the highest packet delivery ratio among the protocols. This superiority can be attributed to MJTAR's multiple-junction-based traffic awareness mechanism, enabling it to avoid closed roads and select routes with higher connectivity. Conversely, GyTAR displays a lower packet delivery ratio than MJTAR, primarily because it assesses traffic one junction at a time, increasing the probability of encountering closed roads. Figure 12b presents the comparison of packet delivery ratios based on node movement speed. As the node travel speed increases, the packet delivery ratio decreases. This phenomenon occurs due to the rapid changes in vehicle positions. The greedy forwarding technique selects the node closest to the destination for packet forwarding. However, as node speed increases, these selected nodes may move out of range more quickly, leading to increased packet loss.  
Figure 12c depicts the packet delivery ratio for various packet sizes at a node traveling speed of 30 km/h. MJTAR maintains the highest packet delivery ratio compared to other protocols across different packet sizes. However, as the packet size increases, the ratio decreases. This decline can be attributed to the increased likelihood of packet collisions and fading as the packet size grows.
## 4 Experiments
### Parameter Analysis  
**The size of 3D voxels.** Point clouds typically consist of a large number of unordered sets of points. Due to the complexity of these data, it can be very time-consuming to store and process. To alleviate this issue, we embed the unordered points in 3D space into a regular grid structure through rasterization. This converts the point cloud into 3D voxels based on space occupancy, discretizing the continuous 3D space into a regular grid. This process provides a regular and well-understood structure, which reduces computational complexity and compresses the point cloud data, significantly reducing computational burden. It is essential to voxelize the point cloud appropriately because the size of the 3D voxel determines the strength of the point cloud compression and the granularity of the point cloud representation. To examine the impact of voxelization on the results, we evaluated the performance of SFCNet on the NTU RGB+D 60 dataset for different-sized voxels. The results are presented in Table 1, indicating that the model performs best for a 35 mm cube size. Setting the size too large or too small can lead to a decrease in accuracy.  
**The setting of threshold \(\theta\).** Human behavior usually involves only the movement of specific body parts, such as arm waving, leg walking, head rotation, etc. This locality means that behavioral analysis should be more focused on essential body parts rather than the whole body. With the help of the occupancy frequency variable \(l\) in the interval-frequency descriptor, we can describe the engagement of each voxel, which is positively related to the contribution of the body part in the action execution process. Since we sample the depth action sequence into groups of equal duration, the occupancy frequency \(l\) positively correlates with the number of occupancy \(u\) in Section 3.3. Then, a threshold \(\theta\) is employed to evaluate the attention given to the body part. To investigate the influence of threshold, we compare the performance of the SFCNet on the NTU RGB+D 60 dataset with various values. The findings are presented in Table 2. The optimal outcome can be obtained when \(\theta\) equals 30. Our research discovered that slight modifications in \(\theta\), by no more than 5, resulted in fluctuations in accuracy, underscoring the significance of investigating thresholds and breaking down significant body parts.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Voxel Size (mm)** & **Cross-Subject** & **Cross-View** \\ \hline \(25\times 25\times 25\) & 87.1\% & 94.9\% \\ \(35\times 35\times 35\) & **89.9\%** & **96.7\%** \\ \(45\times 45\times 45\) & 88.1\% & 95.5\% \\ \(55\times 55\times 55\) & 86.5\% & 93.6\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance on the NTU RGB+D 60 dataset with different-sized voxelization.