esa-sceva/satcom-chunk-collection huggingface dataset:
# Polynomial Cancellation Coded DFT-s-OFDM for Low-PAPR Uplink Signaling
###### Abstract  
The physical layer signaling of the 5G new radio still utilizes cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform-spread-OFDM (DFT-s-OFDM) to support 5G services for the sake of system-backward compatibility. However, the transmission requirements among these services differ, and this poses a challenge to the adaptability of the waveforms with regard to the peak-to-average power ratio (PAPR) issue. In particular, DFT-s-OFDM serving as a low-PAPR option for uplink signaling still has room for PAPR improvement in cases such as machine-type and device-to-device communications. We propose polynomial cancellation coded (PCC)-DFT-s-OFDM to flexibly reduce the PAPR of conventional DFT-s-OFDM. The principle of the proposed method, including its transform, is analyzed in the time domain. The results show that it can also be regarded as a novel spectral shaping scheme for PAPR reduction. Through a parameter designed for adjusting the cost of spectral efficiency, the proposed method can regulate the extent of improvement compared with the conventional DFT-s-OFDM, not only in the PAPR, but also for the spectral radiation and bit error rate when considering the nonlinearity of the power amplifier. The increase in computational complexity is neglectable owing to the simplicity of generalized PCC, making it apt to be deployed in existing systems.  
Keywords:discrete fourier transform spread orthogonal frequency division multiplexing (DFT-s-OFDM); peak-to-average power ratio (PAPR); polynomial cancellation coding (PCC); 5G waveform; spectrum shaping; moving filter +
Footnote †: journal: Accepted: 13 November 2019; Published: 14 November 2019
# The Application of Microplasma in the Terahertz Field: A Review
## 2 Application of Microplasma in THz Functional Devices
### Terahertz Filter
It is interesting to observe that the number of photonic crystal cycles is beneficial to the flatness of the passband. In terms of THz PPCs experiments, [PERSON] et al. reported a two-dimensional plasma photonic crystal based on hollow cathode discharge, as shown in Figure 7a. The electron density of the plasma is about 1 \(\times\) 10\({}^{13}\) cm\({}^{-3}\), the diameter of the plasma is 0.6 mm, and the lattice constant is 1.5 mm. A subterahertz bandgap around 0.1 THz is observed. The center frequency of the THz bandgap can be tuned by changing the lattice constant [56]. In 2017, [PERSON] et al. developed a 5 \(\times\) 10 microplasma jet array. The periodically aligned plasma jet and the surrounding air are the two dielectric types that form the two-dimensional microplasma photonic crystal. Each plasma jet has a diameter of 0.4 mm. The electron density is 3 \(\times\) 10\({}^{13}\) cm\({}^{-3}\). A narrow stopband at the center frequency of 157 GHz with a bandwidth of 1 GHz is observed for the first time. The maximum attenuation of THz radiation is only 5%, which is significantly different from the simulation results. One of the possible reasons is that the mixing of air and helium gas leads to the spatial nonuniformity of the collision frequency and plasma frequency [57]. [PERSON] et al. stated that the nonuniformity of plasma has a more significant impact on the propagation of terahertz waves in atmospheric microplasma [58]. The low electron density may also be responsible for the weak attenuation of THz radiation at the PPC stopband. Recently, [PERSON] et al. further developed a three-dimensional plasma/metal/ dielectric photonic crystal bandstop filter, as shown in Figure 8a. Two subterahertz stopbands at the center frequencies of 131 GHz and 138 GHz are obtained. When the electron density increases, the center frequency of the stopband increases [59]. Therefore, the simulation and experimental results above demonstrated that the microplasma photonic crystals served as THz filters are well suited to control the THz wave propagation. Figure 6: (**a**) Schematic diagram of one-dimensional collision microplasma photonic crystal; (**b**) relationship between the transmission coefficient of the first terahertz band gap and the frequency of the incident wave under different electron densities [53]. Figure 7: Microplasma column array and its filtering characteristic diagram. (**a**) Schematic diagram of the electrode structure with a lattice constant of 1.5 mm and the discharge diagram of the microplasma column array; (**b**) variation of millimeter-wave transmission coefficient with electromagnetic wave frequency in TE mode [56].
# The Evolution and Optimization Strategies of a PBFT Consensus Algorithm for Consortium Blockchains
## 3 An Introduction to Mainstream Consensus Algorithms
### The Mainstream Consensus Algorithms of Public Blockchains
#### 3.1.2 PoS
These optimizations reflect the fact that the core of the PoS development path lies in balancing security, efficiency, and participation. However, PoS significantly enhances energy efficiency through stake-based validation and dynamic incentive mechanisms, but its optimization strategies may exacerbate power imbalances. Large stakeholders inherently hold greater influence in the consensus process, increasing the risk of centralization. Additionally, the design of incentive and penalty mechanisms is highly complex, and any imbalance could lead to unfair competition among validators, ultimately compromising network security and stability. By dynamically adjusting economic incentives, strengthening security, and optimizing the network architecture, the blockchain consensus mechanism is evolving towards a higher robustness and lower energy consumption. Future research may further explore hybrid consensus schemes and cross-chain collaboration to meet the complex needs in heterogeneous environments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Year** & **Improvement Method** & **Core Mechanism** & **Limitations** & **Reward/Penalty Mechanism** \\ \hline Shashank & \multirow{2}{*}{2021} & Evolutionary Game & \multirow{2}{*}{ \begin{tabular}{c} Dynamically adjusts staking \\ rewards to incentivize \\ honest participation. \\ \end{tabular}  } & \multirow{2}{*}{\ \begin{tabular}{c} Requires continuous \\ monitoring and adjustment. \\ \end{tabular}  } & \multirow{2}{*}{ \begin{tabular}{c} Adjusts staking rewards \\ dynamically based on \\ participant behavior. \\ \end{tabular}  } \\ \cline{4-4} \cline{6-6} \cline{5-6
## 2 Materials and Methods  
### Object Detection  
Object detection models can be broadly classified into one-stage and two-stage detectors. One-stage detectors, which jointly learn to localize and classify objects using CNNs, are generally faster but less accurate than two-stage detectors. This makes them ideal for real-time object detection applications [17; 18]. Representative one-stage detectors include SSD [19] and YOLO [20]. YOLO, which is the most commonly used one-stage detector, excels at extracting and detecting object and background features, as it learns from individual objects as well as the surrounding information and entire image domain.  
The image input to YOLO is partitioned into a grid, \(S\times S\), to extract object features through a convolutional layer and generate predicted tensors through a fully connected layer. Subsequently, for each partitioned grid cell, localization and classification are performed simultaneously to produce \(B\) candidate bounding boxes, along with the corresponding confidence score (\(CS\)) for each bounding box. Each bounding box contains information of \((x,\ y,\ \omega,\ h,\ CS)\), where \((x,\ y)\) refers to the coordinates of the center point of the bounding box normalized to each grid cell, and \((\omega,\ h)\) refers to the width and height of the bounding box. \(CS\) reflects the probability that the bounding box contains an object, i.e., the accuracy of the predicted box, and is defined as follows:  
\[CS=P_{obj}\times IoU(\textit{pred},\ true) \tag{1}\]  
Here, \(P_{obj}\) refers to the probability that the bounding box contains the object, with a value of 1 if the grid cell correctly contains the object and 0 otherwise. \(IoU(\textit{pred},\ true)\) refers to the Intersection over Union (IoU) between the ground truth and predicted box,which is the width of the overlapping area and indicates how accurately the bounding box predicts the geometric information of the object. Moreover, the grid cell is expressed as a conditional probability of belonging to one of the \(C\) object classes within the bounding box, as presented below.  
\[P_{class}=\Pr(class_{i}|object) \tag{2}\]  
The class-specific confidence score (CCS), which represents the probability of an object being contained within each bounding box and the probability that the detected object matches the ground truth, is calculated as follows:  
\[CCS=CS\times P_{Class} \tag{3}\]  
The bounding box with the maximum \(CCS\) among the predicted \(B\) bounding boxes is chosen as the final bounding box for the target object.
## 4 Simulation
### Carrier Synchronization  
The carrier synchronization module used in our system is based on the Costas loop. In this module, the carrier loop phase detector constitutes an important component, as it is used to compare phases [22]. This paper uses CORDIC algorithm to implement an arc tangent phase detector [23,24]. The simulation result of the phase detector is shown in Figure 19, from which we can see that at the beginning of loop, a phase difference exists, but the output value of the phase detector will tend towards 0 as the carrier approaches synchronization.  
Figure 19: Simulation result of phase detector.  
Figure 18: Simulation result of low-pass filter.  
The simulation result of the carrier loop is shown in Figure 20. In this figure, we can see that high frequency components have been filtered and that the baseband signal will be obtained after the received signal is demodulated.
# Handover Decisions for Ultra-Dense Networks in Smart Cities: A Survey
## 2 Background  
Next-generation cellular networks have emerged due to the massive growth in connected devices and the high demands of a high mobile data rate. Sixth-generation cellular networks are expected to provide a revolutionary advancement over 5G cellular networks. Terahertz communication, artificial intelligence, and heterogeneous network architectures are examples of emerging technologies that are poised to revolutionize connectivity by facilitating smooth communication between satellite, aerial, and terrestrial infrastructures. Intelligent handover mechanisms that maximize performance and guarantee seamless connectivity must be integrated into 6G networks to support applications such as driverless cars, smart cities, and ultra-reliable low-latency communications. An outline of the core ideas of 6G networks, heterogeneous architectures, and handover decision-making is given in this section, along with an emphasis on significant developments and difficulties in these fields.
## 3 Results
### Characterization of Levan Polymer  
#### 3.3.1 Infrared Spectroscopy  
The IR spectrum of the levan from _Bacillus subtilis_ strain MZ292983.1 showed a characteristic broad stretching peak of OH around 3417 cm\({}^{-1}\), which is characteristic of polysaccharides. Aliphatic CH stretching was observed as two peaks at 2943 and 2885 cm\({}^{-1}\), and the peak at 1639 cm\({}^{-1}\) indicates the stretching vibration of C=O. Peaks between 1126 and 900 cm\({}^{-1}\) represent the fingerprint of polysaccharides. Finally, the FTIR spectrum featured an absorption at 2121 cm\({}^{-1}\), indicating the fingerprint of the \(\beta\)-glycosidic bond (Figure 2).  
Figure 1: Constructed phylogenetic tree of partial 16s rDNA gene sequence of _Bacillus_ isolate was constructed by neighbor-joining method with 1000 bootstraps using MEGAX. GenBank accession number of _Bacillus_ isolate sequences are indicated in top of the branch. _Pseudomonas gessardi_ AHB79N (GenBank accession number LN866625.1) was used as an outgroup. Bootstrap values (%) for 1000 repetitions are indicated by the numbers at nodes. The scale bar represents substitutions and nucleotide substitutions per site.  
Figure 2: FTIR spectrum of _Bacillus subtilis_ strain MZ292983.1.
###### Abstract  
The long-term immersion of coal rock may affect its mechanical properties and failure modes, potentially impacting the stability of coal pillars. This work aims to investigate the influence of the immersion duration on the mechanical properties and fracture evolution processes of coal, employing acoustic emission detection and the digital image correlation (DIC) method. The work focuses on the weakening law of the coal pillar dam in contact with water and obtains a model of the strength deterioration after different periods of water immersion. The stress-strain curves of coal specimens with varying immersion durations are obtained. The results show that the peak absorption rate of coal samples immersed in water transpires within 24 h, with fundamental saturation being achieved at between 25 and 30 days at saturation moisture content of 1.97%. The specimen's compressive stress after being immersed in water for 7 days is 3.34 MPa, with strain of 0.18%. The cracking stress is 15.60 MPa, with strain of 0.54%. The peak stress is recorded at 27.65 MPa, with strain of 0.92%. The complete rupture stress measures 23.37 MPa, with the maximum strain at 0.95%. During the yielding stage, the specimen has the highest strain increment of 0.38%. Short-term immersion brings about an increase in the coal sample's plasticity, exhibiting a relatively minor softening impact of water, resulting in comparatively intact fragmentation and modest breakage. A negative exponential function relationship is observed between the compressive strength of coal and the immersion duration. The mechanical reduction relationship is utilized to analyze the failure patterns of coal pillars in underground reservoirs. With prolonged water immersion, the damage area expands to include the coal pillars and the surrounding rock of the excavation area.  
Keywords:coal rock; water weakening; acoustic emission; strength reduction; fracture mode
## 3 Results and Discussion
### Mechanical Properties of Composites
Samples prepared with metakaolinite presented lower fiber volume fraction (25 vol%), but higher mechanical properties than the reference (cf. green dashed line in Figure 10). Hence, the presence of metakaolinite was beneficial for strength development, in consistency with calorimetry results on cumulative heat released (Figure 5). Another reason might be the small particle size of metakaolinite that allows for better contact and maybe even penetration in the textile. The substitution of copper slag by 5 wt.% OPC (95K50 PC) only had a negligeable effect on the flexural strength and a decrease in elastic modulus despite the positive effect of OPC on reaction heat. The flexural strength results are in some cases higher or comparable to the literature values of 40-45 MPa obtained with geopolymer laminate composites prepared with metakaolinite and basalt fabric [12]. These authors also observed that fiber reinforcement favored water evaporation from the composites at room temperature and improved the thermal stability in comparison to the pure matrix when thermally treated at 300-800 \({}^{\circ}\)C.  
In another study, a bending strength of 18 MPa was observed with blast furnace slag composite reinforced with 1 wt.% of 6 mm long carbon fiber, representing an increase of 19% compared to the pure matrix [37]. Meanwhile, an elastic modulus of 37 GPa and flexural strength of 133 MPa were achieved on potassium-based metakaolin geopolymers with 20-25% volume of unidirectional carbon fiber [38]. The analysis of these results shows some divergences in the bending strength depending on the matrix, fiber type, size and weaving, but in all the cases, the use of fiber was beneficial for the bending strength. In summary, for the mechanical properties, in comparison to composition K, the surfactant and OPC addition had no or little effect in the studied range of the prepared composites. However, metakaolinite addition had a beneficial effect on the mechanical properties, with about 100% increase in the composite made of matrix containing 20 wt.% MK (Figure 10).
## 4 Results and Discussion
### Crop Identification Results Based on Optimal Classifier  
Figure 8 compares the total area of maize and sunflower from official statistics and identified maps. From 2009 to 2015, the identified planting area of maize and sunflower are increasing progressively in general, which is consistent with statistical trends. This proves that this optimal classifier is suitable for multi-year maize and sunflower identification. However, there are also some years with a slightly large identification error. For 2012 and 2014, the areas of maize and sunflower are both underestimated. Especially for maize, the relative errors are \(-18.09\%\) and \(-21.17\%\), respectively. This may be attributed to the less qualified remote sensing images in these two years, which both have fewer available images during the growing season (from DOY 100 to 300) than other years. When images in the key growth period missed, the fitting quality of the logistic curve and the extraction of phenological features will be affected, which may lead to poorer identification results than other years.  
To further verify the accuracy of crop identification results, the correct identification percentage of different crops and Kappa coefficient (Table 6) were calculated using the verification points in 2014 and 2015 (Figure 1). The correct identification rates of different crops were all greater than 70% and the Kappa value of consistency test was 0.62, which indicated that the classifier performance was acceptable [15].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Identified Class**} & \multicolumn{6}{c}{**Actual Class**} \\ \cline{2-7}  & **Maize** & **Sunflower** & **Others** & **Total** & **Correct** & **Commission** \\ \hline Maize & 23.6 & 3.6 & 3.6 & 30.9 & 76 & 24 \\ Sunflower & 9.1 & 36.4 & 3.6 & 49.1 & 74 & 26 \\ Others & 2.7 & 1.8 & 15.5 & 20.0 & 77 & 23 \\ \hline Total & 35.5 & 41.8 & 22.7 & 100.0 & Kappa = 0.62 & N = 110 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The confusion matrix of crop identification accuracy.  
Figure 8: Comparisons of total area of maize (**a**) and sunflower (**b**) from official statistics and identified maps.  
We also selected a specific micro-scale area (19.36 ha) of Hetao Irrigation District to analyze the classification results; the detailed description of this area can be referred to [PERSON] et al. (2015). The crop planting structure of this area is similar to the whole Hetao Irrigation District, the planting area of maize and sunflower accounts for about half of the total area. According to the statistics data of planting area in 2012 and 2013, the mean relative errors of classifiers for maize and sunflower are 14.00% and \(-\)24.49%, respectively. This shows that the classifier is also applicable to micro-scale areas.
## 3 Theoretical Model and Computational Analysis of Aberrations in Double Prism Compensation Devices
### Aberration Calculation
\right) \tag{8}\]  
where \(W_{caj}\) is constant astigmatism of surface j, \(W_{aj}\) is anamorphism of surface j, \(W_{cgj}\) is constant coma of surface j, \(W_{laj}\) is linear astigmatism of surface j, \(W_{ftj}\) is field tilt of surface j, \(W_{qdlj}\) is quadratic distortion I (smile distortion) of surface j, \(W_{qdllj}\) is quadratic distortion II (keystone distortion) of surface j, \(W_{sqj}\) is spherical aberration of surface j, \(W_{lcgj}\) is linear coma of surface j, \(W_{qnj}\) is quadratic astigmatism of surface j, \(W_{fcj}\) is field curvature of surface j, and \(W_{cdj}\) is cubic distortion of surface j; the terms \"constant\", \"linear\", \"quadratic\", and \"cubic\" describe the algebraic power of the field dependence (constant astigmatism is represented by \"ca\", and similar notations apply for other terms.) For completeness, the magnitude of each surface aberration is calculated. This provides a detailed aberration analysis, which helps to identify the sources of aberrations in the entire system by quantitatively analyzing the aberrations in the combination of planar symmetric systems. The advantage of analyzing aberration coefficients is that it reveals how aberrations are influenced by system parameters such as curvature radius, OAR incidence angle, and paraxial ray data. This information is crucial for selecting an appropriate adjustment range to compensate for image plane deformation with minimal impact on imaging quality. In a doubly telecentric projection optical system with high imaging quality, a dual-prism system, as shown in Figure 2 is used to quantitatively verify the aberrations generated by the rotation of each surface by the prism. A 100 mm \(\times\) 100 mm the field of view is used to represent aberration characteristics. The structural parameters (wedge angle, refractive index, thickness) of the two prisms are consistent. Prism 2 rotates around a mechanical axis relative to Prism 1. The aberration orientation of the beam on each surface is represented by \(\overset{\rightarrow}{i}_{number}(number=1,2,3,4)\). The theoretical calculation values of aberration are shown in Figure 5.
## 2 Materials and Methods  
### Materials  
Commercial hop oils of the following American hop varieties (_Humulus lupulus_ L.) were examined: Amarillo, Azacca, Cascade, Centennial, Chinook, Saaz, and Ahhhroma. Hop oils were hydrodistilled in our laboratory from hop pellets of the same varieties (except for Ahhhroma, which was not available). Commercial hop oils and hop pellets were provided by Hopzoi\({}^{\text{TM}}\)( Glacier Hops Ranch, Whitefish, MT, USA). Analytical purity grade standards (\(\alpha\)-pinene, \(\beta\)-pinene, \(\beta\)-myrcene, methyl heptanoate, \(\beta\)-ocimene, d-limonene, \(\beta\)-caryophyllene, \(\alpha\)-humulene, \(\beta\)-farnesene, and geraniol) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Working samples of commercial and hydrodistilled hop oils and terpene standards were prepared for GC analysis as 5 mg mL\({}^{-1}\) solutions in \(n\)-hexane.  
Standard solutions of Trolox (TR) (Acros Organics, Geel, Belgium), gallic acid (GA), and ascorbic acid (AA) (P.P.H. POCh, Gliwice, Poland) were prepared for the needs of EPR spectroscopic analysis as a quantitative measure of the antioxidant activity of the commercial hop oils.  
Glass and aluminium-backed HPTLC silica gel 60 F\({}_{254}\) layers (all 20 cm \(\times\) 10 cm) and lipase (from porcine pancreas) were purchased from Merck (Darmstadt, Germany). Solvents of analytical grade were obtained from Molar Chemicals (Halasztelek, Hungary). Bovine serum albumin, \(p\)-anisaldehyde, 1,1-diphenyl-1-2-picrylhydrazyl (DPPH\(\bullet\)), and Fast Blue B Salt were obtained from Sigma-Aldrich (Budapest, Hungary). \(\alpha\)-Glucosidase (yeast maltase) was obtained from NOACK (Budapest, Hungary). 2-Naphthyl-\(\alpha\)-d-glucopyranoside, 4-methylumbelliferyl-\(\alpha\)-d-glucopyranoside, and 1-naphthyl acetate were purchased from Biosynth (Bratislava, Slovakia). _Aliivibrio fischeri_ (DSM 7151), a naturally luminescent marine bacterium strain, was supplied by Leibniz Institute DSMZ, German Collection of Microorganisms and Cell Cultures (Berlin, Germany).