esa-sceva/satcom-chunk-collection huggingface dataset:
# Development of a Novel Shaft Dryer for Coal-Based Green Needle Coke Drying Process
## 5 Conclusions  
(1) A novel drying system and a shaft dryer are proposed for drying the wet coal-based green needle cokes by using medium-temperature gas as a heat source in an energy-efficient, direct-contact, and counter-current way.  
(2) The moisture content of green cokes reduces remarkably with the increase in gas temperature. Compared with lump and fine green cokes, mixed green coke has the lowest moisture content and is recommended in the real drying process. The average vertex angle of the pile of green cokes is 109.2\({}^{\circ}\), which is an important parameter for the structure of the shaft dryer distributor.  
(3) An approximately linear relationship is gotten between the pressure drop and the green coke bed height. The green coke with larger size has a less pressure drop. The specific pressure drops of the four test sets with median green coke size of 26.85, 29.00, 30.45, and 31.80 mm are 5714, 5554, 5354, and 5114 Pa/m, respectively.  
(4) Tooth spacing affects the mass of green coke leakage. The larger the spacing, the more the leakage. In real production, the optimal tooth spacing and rotary speed of the rollers should be determined by the required production yield.  
Conceptualization, G.X., X.Z. and J.C.; methodology, G.X.; validation, G.X., K.Z. and S.Z.; formal analysis, G.X. and W.S.; investigation, K.Z. and S.Z.; data curation, G.X.; writing--original draft preparation, W.S. and G.X.; writing--review and editing, W.S. and G.X.; visualization, W.S.; supervision, X.Z. and J.C.; project administration, G.X.; funding acquisition, G.X.  
This research was funded by the National Key R&D Program of China, grant number 2017 YFB0603502; the National Natural Science Foundation of China, grant number 51734004; and the Liaoning Revitalization Talents Program, grant number XLYC1805016.  
The authors declare no conflict of interest.  
Figure 10: Relationship between green coke leakage and tooth spacing.
# Big Data Perspective and Challenges in Next Generation Networks
## 2 Major Milestones of Next Generation Networks
### Massive MIMO
#### 2.3.1 Features of Massive MIMO  
The massive MIMO will provide: _improved spectral efficiency, smooth channel response_, and _simplified transceiver design_. Because of these promising benefits, massive MIMO has become an important part of 5G technology. Besides, in massive MIMO, front-end could be cheaper as multiple low power amplifiers are cheaper than a single high power amplifier one. Cabling in the RF chain also becomes cheaper. In addition, power efficiency is expected to be high in Massive MIMO, hence the cost of operation in the BS should be cheaper. However, there are several challenges for implementing massive MIMO. In the following text, we briefly outline some of the challenges for massive MIMO.
# Comparative Analysis of Kazakhstan and European Approaches for the Design of Shallow Foundations
## 4 Results and Discussion  
The calculated values of permanent and variable loads required for the comparative analysis of the design codes (particularly for the determination of bearing capacity and the elastic settlement of pad and strip foundations) are provided in Table 4. Figures 5 and 6 provide the calculated values of bearing capacity and elastic settlement of shallow foundations for each type of soil profile, respectively. The results show that the bearing capacity and the elastic settlement for pad and strip foundations are relatively comparable for the considered codes of practice. However, EC 7 overestimates the bearing capacity and the elastic settlement compared to SP RK. The obtained EC 7 results of bearing capacity are between 1.6 and 2.7 and are 1.4 to 2.4 times higher than SP RK values for pad and strip foundations, respectively. As both design codes apply to the ULS analysis for determining bearing capacity for shallow foundation design, the lower values of bearing capacity are provided due to the application of the lower values of partial factors adhering to the Kazakhstan approach.  
The divergence between the elastic settlement values is achieved by the application of different design methods and the determination of the vertical pressure acting on the foundation base. In particular, EC 7 estimates the settlement of shallow foundations using the adjusted elasticity theory, whereas Kazakhstan regulations use the method of layer summation. In addition, the obtained results of bearing capacity and elastic settlement for strip foundations indicate higher values than for pad foundations. The performed calculations for all types of soil profiles of Nur-Sultan city resulted in similar values for both the bearing capacity and elastic settlement of shallow foundations. However, the elastic settlement for strip foundations varied in the range of 66-69 mm when adhering to the Kazakhstan approach.  
Figure 7 represents the comparison of ODF values obtained for pad and strip foundations based on different design approaches. The European approach provides a higher ODF than the Kazakhstan approach, thus resulting in more conservative shallow foundation designs. As the ODF for the SP RK design was assumed to be equal to unity, the calculations of EC 7 ODF produced 1.32 to 1.85 and 1.12 to 1.57 times higher results for pad and strip foundations, respectively.  
Figure 5: Comparison of the values of bearing capacity for pad and strip foundations adhering to Kazakhstan and European approaches.  
Figure 6: Comparison of the values of elastic settlement for pad and strip foundations adhering to Kazakhstan and European approaches.
###### Abstract  
The study analyzed the effect of nitrogen (N) management and different sowing parameters of winter wheat on the flour quality, rheological properties of flour, and bread quality. Flour was obtained from winter wheat grain produced during a field experiment conducted in 2018-2021. The experiment involved three factors: (i) the sowing date (early (3-6 September), delayed by 14 days, and delayed by 28 days), (ii) sowing density (200, 300, and 400 live grains m\({}^{-2}\)), and (iii) split application of N fertilizer in spring (40 + 100, 70 + 70, and 100 + 40 kg ha\({}^{-1}\) in the full tillering stage and the first node stage, respectively). A 28-day delay in sowing increased the total protein content of the flour, water absorption capacity of the flour, dough development time and stability, and degree of softening. When sowing was delayed by 14 or 28 days, the crumb density decreased without affecting the louf volume. A sowing density of 400 grains m\({}^{-2}\) had a positive impact on the flour color, dough stability, and loaf volume. The flour color and dough stability were enhanced when N was applied at 100 + 40 kg ha\({}^{-1}\), respectively. In turn, the total protein content of flour peaked when it was applied at 40 + 100 kg N ha\({}^{-1}\). The quality of flour improved when winter wheat was sown at a density of 400 live grains m\({}^{-2}\) with a delay of 14 or 28 days and supplied with 100 kg N ha\({}^{-1}\) in the full tillering stage and 40 kg N ha\({}^{-1}\) in the first node stage.  
Trictium aestivum L; sowing date; sowing density; N fertilization; flour; protein and ash content; rheological properties; bread quality
# Dynamic Damage Mechanism and Seismic Fragility Analysis of an Aqueduct Structure
## 3 Seismic Fragility Analysis of the Aqueduct Structure
### Basic Principles of Incremental Dynamic Analysis (IDA)  
The numerical method is commonly used in the seismic fragility analysis of a structure. That is, if a house fails to withstand an earthquake and collapses totally, the fragility probability is 1; if the house remains intact, the likelihood is 0. The relationship between  
Figure 5: Damage development process.  
the structural engineering demand measure (DM) and the seismic intensity measure (IM) in fragility analysis is  
\[DM=\alpha(IM)^{\beta} \tag{11}\]  
Assuming the median \(\overline{D}\) of the seismic demand parameter and that the seismic intensity measure is subject to an exponential relationship, which is  
\[\overline{D}=\alpha(IM)^{\beta} \tag{12}\]  
Take logarithms of both sides of the above formula to get, where \(a\) and \(b\) are regression coefficients,  
\[Ln\overline{D}=a+bLn(IM)^{\beta} \tag{13}\]  
Let\(\lambda_{d}=Ln\overline{D}\), then  
\[\beta_{d}=\sqrt{\frac{1}{N-2}\sum_{i-1}^{N}\left(Ln(DM)-Ln(\overline{D}) \right)} \tag{14}\]  
In the formula, \(\lambda_{d}\) is the logarithmic mean of \(DM\); and \(\beta_{d}\) is the logarithmic standard deviation of \(DM\).  
Define the capacity parameter \(C\) of the structure with the logarithmic mean \(\lambda_{d}\) and logarithmic standard deviation \(\beta_{d}\); then:  
\[P_{f}=P(C/D<1) \tag{15}\]  
That is, \(P_{f}=P(C-D<0)\). If \(Z=C-D\), \(C\) and \(D\) are both independent random variables and subject to normal distribution so that \(Z\) is also subject to normal distribution, and its mean is \(\lambda_{z}=\lambda_{c}-\lambda_{d}\) and standard deviation \(\beta_{z}=\sqrt{\beta_{c}^{2}+\beta_{d}^{2}}\). The exceedance probability \(P_{f}\) for a certain performance level of a structure under a specific action of earthquakes can be expressed as:  
\[P_{f}=\Phi(-\frac{\lambda_{c}-\lambda_{d}}{\sqrt{\beta_{c}^{2}+\beta_{d}^{2}}} )=\Phi(-\frac{Ln(\overline{C}/\overline{D})}{\sqrt{\beta_{c}^{2}+\beta_{d}^{ 2}}})=\Phi(\frac{Ln(\overline{D}/\overline{C})}{\sqrt{\beta_{c}^{2}+\beta_{d}^ {2}}}) \tag{16}\]
## 7 Discussion of Results
### Aerodynamics of a Low-Rise Building with Photovoltaic Panels (Study 2)  
The second study investigated the impact of PV panels on wind-induced forces and the security of underlying components in gable-roofed buildings. ASCE 7-16 and 7-22 guidelines mandate the simultaneous application of wind loads for both the bare roof and the solar panels, excluding areas covered by the panels' plan projection [73; 74]. When the solar panel wind pressure is lower than Zone 1 pressures for the bare roof, reduced wind-induced forces are expected on the components and cladding beneath the panels.  
ASCE 7-16 underestimates the benefits of solar panels compared to this study's findings. However, ASCE 7-22 aligns with the current study. Solar panels provide enhanced protection for underlying components and act as a physical barrier against the potential lift or detachment of elements like shingles. Trims are recommended for roof-mounted solar arrays, with the study emphasizing the need for further research to refine standards and assess long-term performance effects. The solar panel study is poised to furnish prescriptive guidelines for superlative practices in robust roof-mounted PV systems, ensuring their steadfastness and durability amid extreme weather events. It aspires to contribute to advancing more efficacious design criteria and regulatory frameworks for these systems.  
In addition to wind loads, other factors impacting solar panel functionality should be considered. The response surface methodology (RSM) optimization of waste materials as reflectors for bifacial PV modules demonstrates a significant improvement in energy output [87]. [PERSON] et al. [88] proposed installing upside-down modules to address potential defects. [PERSON] et al. [89] eradicated the necessity of reconfiguration in PV arrays to improve the overall performance in the presence of partial shading and faults [90]. [PERSON] et al. [91] highlighted seismic considerations and assessment methodologies for isolated rooftop PV arrays, emphasizing their potential in mitigating building base shear while addressing concerns about potential movement induced by seismic and wind forces. Vibrations can adversely affect buildings [92]. Integrating solar panels with buildings can also serve as tuned mass dampers, providing additional seismic mitigation and vibration control [24].
## 3 Acoustic Emission Monitoring  
The AE monitoring is performed by analyzing the signals received from the transducers through a threshold detection device that counts the burst signals, which are greater than a certain voltage.  
The adopted USAM (Unit for Synchronous Multichannel Acquisition) acquisition system consists of six pre-amplified AE sensors, equipped with six units of data storage, a central unit for synchronization, and a trigger threshold. The output voltage signals of the piezoelectric transducers (PZT) were filtered with a pass-band from 50 kHz to 800 kHz and a detection threshold of 100 \(\upmu\)V. The obtained data from this device are the cumulative counting of each mechanical wave, the acquisition time, the measured amplitude in volts, the duration, and the number of oscillations across the threshold value for each AE signal [2,3]. Then, at the post-processing stage, we discarded signals with a duration shorter than 3 \(\upmu\)s and containing less than three oscillations across the detection threshold in order to filter out electrical noise spikes.
## 3 Experiment Setup
### Experiment Process
#### 3.2.2 Faults Definition and Test  
According to [PERSON] et al. [5], the following two conditions are defined as a \"non-fault state\". First, it should be ensured that the faulty parts of the drone are in good condition; second, the drone must be able to fly stably for several minutes. A quadcopter that passes the above two conditions is defined as being in a \"non-fault state\". In this study, follow-up research and testing of the UAV fault state were based on this definition of the \"non-fault state\".  
According to [PERSON] et al. [15], where there is no damage or fault in the power unit of the quadrotor UAV, the fault states of the power unit loading components can be roughly divided into three types: the damaged propeller, the loosening of the motor base, and the loosened propeller. In addition, [PERSON] et al. [5] believe that there is also a risk of loosening the screws of the arm base connecting the power distribution board and the upper cover, causing the arm to loosen, which is also a fault state.  
The three fault states tested in this study were considered to be critical but the drone was still flyable. The fault states were all occurred at the unique arm of the drone, namely, the two sides of the blades were broken shown in Figure 4a, the two diagonal screws of the motor base were loosened (red circle) shown in Figure 4b, and the two connecting screws between the arm base and the upper plate were loosened (red circle) shown in Figure 4c. In addition, we examined a mixture of the three fault states. Due to the combination of these three critical fault states, the quad is unable to execute normal flight, due to the poor mechanisms, but it can still hover stably. By collecting critical flight data, the ML model can classify the states that relate to the fault, while ignoring damage states that are too minor. The combination of the three fault states resulted in a table of eight fault states. The flight data was classified to determine if, in different kinds of damage situations, the types of damage can be effectively distinguished so that effective maintenance can be carried out and the damage immediately reduced.
## 3 Spectral Library and Dataset
### ROIs and Spectral Library  
In this section, the Image Labeler app provided by MATLAB ([[https://www.mathworks.com](https://www.mathworks.com)]([https://www.mathworks.com](https://www.mathworks.com))) is used to select ROIs for each objects [1]. Avoiding shadows and highlights, ROIs were chosen manually at perceived representative homogenous surfaces, and their locations at the false-color map are shown in Figure 4 with different colors indicating different materials. Among the ROIs, \"background\" refers to the lightless areas without objects. The labels and pixel numbers of the selected ROIs for different objects are listed in Table 1. They make up the spectral library and are used to provide training data for the classification.  
Figure 3: (**a**) The false-color map of the raw active HSI data cube, constructed with RGB channels using 640, 550 and 470 nm, separately. (**b**) The false-color map of the active HSI data cube after radiometric calibration, generated by RGB channels using 675, 606 and 520 nm, separately to show clearly.  
The mean spectra of different objects in the ROIs are shown in Figure 5. Note that the spectrum below ~500 nm is not credible, since the light of laser shorter than 500 nm is very weak. Although the spectra in spectral library show some variation in absolute reflectance, there is no obvious variation in feature position. For example, we can find that the calculated reflectance shows typical spectral signatures of green vegetation, like the small peak from 510 to 600 nm, sharp red edge from 670 to 750 nm and high infrared reflectance regions from 750 to 950 nm. Contrastively, the spectrum of green plastic leaves shows a gentle ascent in red edge and a different position of the troughs at around 650 nm. In conclusion, there exist giant spectral differences among different objects under the illuminating of SC laser, especially the troughs where the lowest reflectance appears. The differences show the feasibility of active HSI classification.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Object** & **Label** & **Pixel Number** & **Percentage to Whole Image (\%)** \\ \hline fresh leaves & 1 & 780 & 0.20 \\ plastic leaves & 2 & 578 & 0.15 \\ white plastic flowers & 3 & 578 & 0.15 \\ blue plastic flowers & 4 & 595 & 0.15 \\ red wood & 5 & 867 & 0.22 \\ green plastic bottle cap & 6 & 289 & 0.07 \\ reflectance plate & 7 & 480 & 0.12 \\ background & 8 & 1445 & 0.37 \\ Total & & 5612 & 1.42 \\ \hline \end{tabular}
\end{table}
Table 1: Pixel numbers of ROIs selected from each object.  
Figure 4: Regions of interest (ROIs) selected for spectral library.  
Figure 5: The mean spectra of different objects in the ROIs selected for analysis.
## 3 Results
### Effect of Degree of Polymerization of the Sacrificial Chains, \(N_{v-PDMS}\)  
In the next series of simulations, we focus on the effect of the degree of polymerization of \(v\)-PDMS, \(N_{v-PDMS}\), on the average characteristic size upon reaching an equilibrium, \(\bar{l}\). We vary \(N_{v-PDMS}\) within the following range: \(N_{v-PDMS}=[10,\,30,\,48,\,60,\,80,\,100,\,120]\). Representative snapshots upon reaching the equilibrium with selected values of \(N_{v-PDMS}\) are shown in Figure 3a. The shifted partial RDFs, \(g_{ss}(r)-1\), corresponding to the snapshots in Figure 3a are provided in Figure 3b for the same selected values of \(N_{v-PDMS}\). This plot shows that at the lowest degree of polymerization of the sacrificial component considered, \(N_{v-PDMS}=10\), \(g_{ss}(r,t)-1\) remains approximately zero beyond the initial small distances, indicating that the blend remains well intermixed on these length scales. An increase in \(N_{v-PDMS}\) leads to the distinct increase in \(\bar{l}\). To understand this effect, recall that at a constant fraction of the sacrificial components (\(f_{v-PDMS}=0.5\) in this series of simulations), higher values of \(N_{v-PDMS}\) correspond to a smaller number of chains and, respectively, to a lower number of reactive vinyl groups available, \(N_{l}(0)\) Equation (4). Hence, an increase in \(N_{v-PDMS}\) results in a decrease in the number of crosslinks formed in the system upon equilibration, in turn providing less constraints on the phase separation and resulting in formation of larger sacrificial domains. This tendency is clearly evident from the shift of zero crossing of \(g_{ss}(r)-1\) to the higher values of \(r\) for larger \(N_{v-\text{PDMS}}\) (Figure 3b).  
To characterize the average equilibrium length scale of the sacrificial domains, \(\overline{l}\), herein and in the subsequent series of simulations, we output the RDF data every \(5\times 10^{4}\) time steps and average the value of \(l\) calculated within the last five frames (upon reaching an equilibrium) for four independent simulation runs. An increase in \(l\) with an increase in \(N_{v-\text{PDMS}}\) is shown in Figure 3c, with error bars corresponding to standard deviation. Hence our mesoscale simulations demonstrate formation of larger sacrificial domains with an increase in \(N_{v-\text{PDMS}}\); similar trend showing an increase in the pore sizes with an increase in the molecular weight of \(v\)-PDMS was observed in a previous experimental study [21].
# High-Performance QC-LDPC Code Co-Processing Approach and VLSI Architecture for Wi-Fi 6
## 3 The Co-Processing Schemes and Architectures
### The Strategy and Architecture of Pre-Processing
As shown in Figure 8, the valid inputs at clock cycle \(i\) are x1, x2, x3, and x4, where x1, x2 fill the current block (Registers Group1) by shifting left and x3, x4 shift left to write another set of registers (Registers Group2). In processing the SBs, the symbol bits, and the numeric bits are filled with different values, and two submodules can complete the filling of the symbol bits and the numeric bits, respectively. Whenever a block is spliced, the block stitching module writes the block to the OB, the depths of which are set to 24; i.e., the total number of blocks of a codeword. When the idle indication of the decoding core module and the OB is whole, it is transmitted to the decoding core module in burst mode. In general, in addition to the first time the output cache is filled, the decoding iteration time is sufficient for the OB to fill up, so the delay in increasing the link is equivalent to the first fill time. In the case of high modulation, the pre-processing module only requires a few dozen clock cycles to meet the low latency requirements of the link.
# Resilience Evaluation of Multi-Path Routing against Network Attacks and Failures
## 3 Problem Statement
### Single Path or Multi-Path Routing  
Multihoming, SD-WAN and SCION appear to be multi-path approaches when viewed we simply look at them as a topology because it is feasible to make multiple connections to other ASes using them. However, if we consider them in terms of routing, each approach operates differently. For clarity, we define here the meanings of SP, FMP, and MMP, including their major differences from the routing mechanism of multihoming, SD-WAN, and SCION.