esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 5 Simulation Results and Discussion
### Simulation of Time-Frequency Domain Feature Extraction
It is indicated that the proposed frequency domain feature extraction method has the ability to effectively extract jamming features. Then, the wideband blocking jamming scenario is considered: in our simulations, the available communication frequency band is set as 30 MHz\(\sim\)2.5 GHz, then the communication bandwidth is 5 MHz; the bandwidth of wideband blocking jamming is set to be 30 MHz\(\sim\)300 MHz (narrow), 300 MHz\(\sim\)1 GHz (medium), and 1 GHz\(\sim\)2.5 GHz (wide), respectively. Simulation results are shown in Figure 5.  
Figure 4: Frequency domain feature extraction recall.
# A Survey on Consensus Protocols and Attacks on Blockchain Technology
## 2 Overview of Blockchain  
Blocks in the Blockchain  
A block contains a block header and the block body as shown in Figure 2. In particular, the block header has:  
* Block version: decides which set of block validation guidelines should be used.
* Merkle tree root hash: the sum of the hashes for each transaction in the block.
* Time stamp: since 1 January 1970, the current time is expressed in seconds in universal time.
* nBits: optimum block hashing threshold.
* None: a four-byte field that typically starts at zero and gets bigger with each hash computation.
* Parent block hash: a 256-bit hash that directs the user to the prior block.
* Transaction counter: a transaction counter and transactions make up the block body. A block can contain a maximum number of transactions depending on the block size and the size of each transaction.  
Digitally signed transactions are sent across the network. A typical digital signature has two phases: a signing phase and a verification phase. For example, user [PERSON] wants to send a message to another user, [PERSON]. (1) At the signing stage, [PERSON] encrypts the data with her private key and sends the encrypted result and the original data to [PERSON]. (2) During the  
Figure 1: Architecture of Blockchain.  
verification phase, [PERSON] verifies the value with [PERSON]'s public key. This way [PERSON] could easily see if the data had been tampered with or not. A typical digital signature algorithm used in the blockchain is the elliptic curve digital signature algorithm (ECDSA).
# Non-Interactive Decision Trees and Applications with Multi-Bit TFHE
## 5 Applications of Private Integer Comparison: Decision Trees
### Extensions to Private Decision Tree Evaluation
#### 5.2.2 Random Forest Classification  
A random forest is a collection of decision trees, where the final decision is made by taking the majority vote of the decision trees. Even though the random forest algorithm can be used for regression, we focus on classification problems. Like multi-class classification, each decision tree in the random forest returns its output class as a one-hot encoded vector.  
Once the server obtains the individual decision tree results, the server computes the final result by adding element-wise again using the Parallel-Prefix-Sum algorithm. The final result vector is then sent to the client. The client decrypts the result, and the class with the highest vote is selected as the final classification.
# A New Computational Method for Arabic Calligraphy Style Representation and Classification
###### Abstract
However, for the other languages, several works have been made to this end such as the work [7] in which topological features have been proposed to recognize Hebrew handwriting styles. Nonetheless, the complexity of calligraphy styles makes it possible to use only two characters, the character [PERSON] and the character [PERSON]. Other attempts have been carried out for Chinese style recognition in [8], proposing a feature extraction method based on the regional guided filter (RGF) with reference images, which is generated by K-Nearest  
Figure 1: The different categories of text writing styles. Our work falls in the highlighted category of calligraphy text. Neighbor (KNN) matting and used as the input image for RGF, and in [9] using deep features with Support Vector Machine (SVM) and Neural Network (NN) classifiers. Despite the labor in other languages to find a common solution for style recognition, these features are not efficient and applicable for Arabic due to their nature. Arabic calligraphy texts are extremely smooth, interconnected, and have a unique characteristic in their form. Arabic has 28 letters; each one has isolated and connected forms (at the beginning, center, and end), as illustrated in Figure 2. Another difficulty in Arabic calligraphy is the possibility of letters overlapping one above the other, which is not the matter with other languages. Additionally, the shapes of letters are affected by both the writing and the writer's style, and also by the purpose of the writing itself (e.g., for decoration, documentation, or other purposes). In this paper, we propose a robust solution for AC style recognition from images. Our proposal consists of two main phases: (1) feature extraction from AC images and (2) classification through decision fusion. The extracted features, which represent the different morphologies of AC, have been used for the first time in this work and have been inspired by the characteristics experts (calligraphers) use for recognizing AC styles. Since each feature is dedicated to distinguishing one or more styles, multiple classifiers have been engaged in the process of recognition in which the decisions are fused to obtain a final decision. It should be mentioned that none of the former works on AC recognition has implicated such a wide variety of styles (nine styles) as we did in this work (Table 2). Our proposal achieved state-of-the-art and also provided stability against scale variation and learning from a few samples. The remainder of this paper is organized as follows. In Section 2, we clarify Arabic writing characteristics, styles, the writing complexity of each style, and the reasons we consider nine styles. Section 3 lists and discusses highly related works in the field of AC style Recognition. Thereafter, we introduce our proposal in Section 4 and experimentally evaluate it in Section 5. Finally, we infer a general conclusion and some perspectives.
## 3 Results and Discussion
### CFD Off-Design Pre-Analysis
At the inlet of impeller, the boundary layer thickens and even separates. This will cause great impact loss. Therefore, the turbine design in this paper adopts the negative impact angle design, which has less impact on energy loss than the positive impact angle. In addition, it can increase the power capacity of impeller [46; 47]. It can be seen from the figure that except for a small range of flow separation phenomenon from the leading edge to 25% chord length on the pressure side of the rotor blade, there is no secondary flow in other flow passage areas. S-CO\({}_{2}\) expands gradually from stator inlet to rotor outlet. The temperature and pressure decrease along the flow direction, and the value on the pressure side is larger than that on the suction side. There is no reverse pressure and temperature gradient. Therefore, the turbine design has good flow characteristics and aerodynamic performance. In order to explain the effect of prediction in detail, two off-design conditions, Case A (**x** = [950.13 K, 14.27 MPa, 45.58\({}^{\circ}\), 10.41 kg/s, 52,803.96 rpm]) and Case B (**x** = [752.05 K, 13.80 MPa, 39.31\({}^{\circ}\), 64,074.22 rpm]) are selected as examples in the design space. Figures 6 and 7 show the limiting streamline, pressure distribution, and temperature distribution of Cases A and B, respectively. The results show that the operating conditions of Cases A and B are both away from the design point. There is a small range of flow separation or local acceleration in the turbine. The power and efficiency deviate greatly from the design condition. The power of Case A is 882.48 kW and the efficiency is 87.69%. The power and efficiency of Case B are 1608.73 kW and 83.07%, respectively. Figure 5: Key parameter distributions of 50% blade height section under design conditions: (**a**) limiting streamline; (**b**) pressure distribution; (**c**) temperature distribution. Figure 6: Key parameter distributions of 50% blade height section of Case A: (**a**) limiting streamline; (**b**) pressure distribution; (**c**) temperature distribution. Figure 7: Key parameter distributions of 50% blade height section of Case B: (**a**) limiting streamline; (**b**) pressure distribution; (**c**) temperature distribution.
# A Compact Bat Algorithm for Unequal Clustering in Wireless Sensor Networks
## 5 Experiments for Clustering in WSNs Problem
### Objective Function  
The objective function is modelled with not only minimizing the optimized total communication distances from the cluster members to the CHs and the CHs to the BS but also by balancing energy consumption between the CHs and group members. Referring to Equation (3), the CHs is selected based on energy residual.  
\[f_{1}(x_{i})=\ \frac{\sum_{i=1}^{N}E_{i}}{\sum_{k=1}^{k}E_{CH_{k}}} \tag{19}\]  
In Equation (6), we use only the optimizable component that was independent of parameters related to the microcontroller and the supply voltage distances. Consequently, \(E_{frame}\) is the most affected factor of the energy loss depending on the distance of the clustering in WSN. Therefore, the consuming  
Figure 4: Comparison of four compact algorithms of rcGA, cDE, cPSO, and cBA for the selected testing functions: (**a**) is f1, (**b**) is f2, (**c**) is f5 and (**d**) is f7.  
energy of the network is based on the distances in the clusters. Sensor nodes are clustered by using entire distances, namely \(E_{distance}\), where \(k\) is the given clusters.  
\[E_{distance}=\sum_{j=1}^{k}\sum_{i=1}^{n_{j}}\left(d_{ij}^{2}+\frac{D_{j}^{2}}{ n_{j}}\right) \tag{20}\]  
Another factor that affected the fitness function is calculated as follows:  
\[f_{2}(x_{i})=min\left(\sum_{j=1}^{k}\sum_{i=1}^{n_{j}}\left(d_{ij}^{2}+\frac{D_ {j}^{2}}{n_{j}}\right)\right) \tag{21}\]  
where \(\sum_{j=1}^{k}n_{j}+k=N\) is the number of nodes in a network, \(d\) and \(D\) are the distances of member nodes to the CH and the CHs to the BS, respectively.  
Modelling for objective function suggests dense clusters with set of nodes which have sufficient energy to perform the CH tasks. The objective function for equal clustering in WSN is defined as follows:  
\[Minimize\ F=\theta\times f_{1}(x_{i})+(1-\theta)\times f_{2}(x_{i}) \tag{22}\]  
where \(\theta\) is weight of the objective function. The vector of bats \(x\) is represented by mapping from the distances of \(d_{ij}\) with the indexed \(D_{j}\). Both types of distances are considered the intra-cluster distance and the distance between the CHs and the BS. The intra-cluster distances are distances of member nodes and its CH.
## 3 Shake-Table Tests of Model with Innovative Infill Connection (Model IW-SB)
### Instrumentation Set-Up  
The model response was monitored by a high-speed data acquisition system consisting of 20 accelerometers (ACC), channels 1-20; 20 displacement transducers (LVDT), channels 25-44; 4 linear potentiometers (LP), channels 21-24 and 12 strain gages (SGs), channels 45-56, providing information about accelerations at different levels and points, relative displacements, deformations and strains at selected points. The complete instrumentation set-up is presented in Figures 15-17.  
Figure 15: Instrumentation of Model IW-SB—accelerometers (roof level).  
Figure 16: Instrumentation of Model IW-SB—LVDT and LPs (frame RA and RB).  
The characteristics of the transducers used for the measuring points are as follows:  
* KYOWA MCD-8A AND MCD-16A--Multi-conditioner system for strain gage and accelerometers (ACCs):  
Figure 17: Instrumentation of Model IW-SB—strain gages (frame RA and RB).  
KYOWA AS-5 GB  
Sensitivity 1 V/g full range \(\pm\) 5.0 g frequency response DC to 100 Hz  
KYOWA AS-20 GB  
Sensitivity 1 V/g full range \(\pm\) 20 g frequency response DC to 250 Hz  
KISTLER 8712A5M1  
Sensitivity 1 V/g full range \(\pm\) 5.0 g frequency response 0.5 Hz to 8000 Hz  
PCB 333B50  
ceramic shear ICP(r) accel, 1000 mV/g, 0.5 to 3 k Hz, full range \(\pm\) 5.0 g  
Linear Potentiometers (LPs):  
HSI Model 1850  
Sensitivity 0.13 mV/V/inch full range 15 inch  
LVDTs  
Macro Sensors Model DS/50
## 3 Results
### Experimental Results in Urban Canyon 2  
#### 3.3.1 Results of GNSS-RTK Availability Assessment  
To challenge the performance of the proposed method, the other dataset is further evaluated on a denser urban scenario, the urban-2. The ATE of the u-blox measurement and the correspondent mean elevation angle mask of the surrounding local map is depicted in Figure 8a. A similar trend can also be seen in that large mean elevation angle masks are accompanied by u-blox solutions with large ATE and vice versa. In detail, at the 1 place seen in Figure 8b1, the footbridge in the front of the vehicle and skyscrapers on both sides result in poor GNSS solutions with large ATE. More than half of the corresponding sky mask of the surrounding point cloud reaches up to 60\({}^{\circ}\) calculated as the Equation (3) shown in Figure 8c1. Then, the 2 place, displayed in Figure 8b2, is open near the bay which leads to accurate GNSS solutions with small ATE. Conforming to the scene, most elevation angles depicted in the mask in Figure 8c2 are smaller than 30\({}^{\circ}\).  
Figure 8: **(a)** The top panel shows the ATE of the u-blox RTK solutions produced by RTKLIB [7] collected in urban-2. The bottom panel shows the mean elevation angle mask of the local point cloud map around the correspondent u-blox epoch. The mean elevation angle mask is defined as that of a fixed-size local map with 50 m (Euclidean distance) both forward and backward the rover. Thus u-blox epochs within 50 m from the start and the end have no corresponding mean elevation angle masks. 1 and 2 denote representative cases. (**b1,b2**) are streetscapes of the three places denoted in (**a**) respectively. (**c1,c2**) are the sky masks of the two places denoted in (**a**) respectively.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multirow{2}{*}{\(el_{th}\) (\({}^{\circ}\))} & **ATE (m)** \\ \cline{3-3}  & & & **RMSE** \\ \hline \multirow{4}{*}{**Urban-1**} & Alone GNSS RTK & - & 42.02 \\ \cline{2-3}  & LIO & - & **3.99** \\ \cline{2-3}  & LIO-SAM & - & 19.89 \\ \cline{2-3}  & Adaptive Integrated GNSS-RTK/LIO & 90 & 25.330 \\ \cline{2-3}  & Adaptive Integrated GNSS-RTK/LIO & 35 & 18.881 \\ \cline{2-3}  & 15 & 4.123 \\ \hline \hline \end{tabular}
\end{table}
Table 3: ATE of the trajectory estimated by different pipelines on urban-1.
# Response of Corroded Steel Pipeline Without and with CFRP Reinforcement to Reverse Fault Movement
## 4 Results and Analyses  
### Validation of Numerical Model  
To validate the finite element model, its predictions are compared with the results of the full-scale physical model test conducted by [PERSON] et al. [36]. The developed model adopts the same material structure and size as in the physical test, and according to the test loading scheme, \(0.2\) m and \(0.6\) m fault displacements are applied to this model. Figure 4a shows the local buckling predicted by the finite element model in this study. Compared with [PERSON]'s test, it can be seen that the model successfully predicts the buckling position of the pipeline. Furthermore, Figure 4b compares the measured and calculated longitudinal strains after fault displacements of \(0.2\) m and \(0.6\) m. Figure 4b demonstrates that the finite element model successfully predicted the maximum strain and the position of the buckling pipeline section. The measured and calculated peak strains at the top of the pipeline at the hanging wall are \(0.02\) and \(0.018\), and the peak strains at the footwall are \(-0.095\) and \(-0.081\), respectively (i.e., discrepancy \(<20\%\)). This difference is attributed to the possible soil inhomogeneity and porosity in the test soil caused by fault displacement. In conclusion, the developed finite element model is suitable for conducting the response and failure mode analysis of buried pipeline crossing a reverse fault.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Failure Modes** & **Corresponding Indicators** & **Selected Limits** \\ \hline Tensile fracture & Allowable tensile strain [\(\varepsilon_{\text{T}}\)] & \(0.03\) \\ Local buckling & Allowable compressive strain [\(\varepsilon_{\text{C}}\)] & \(-0.016\) \\ Elliptic deformation & Allowable ovality [\(f\)] & \(0.15\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Identification of failure modes in this study.
## 4 Conclusions  
In conclusion, we have demonstrated the synthesis and characterization of CdSe quantum dot-embedded polypyrrole nanorods. The hybrid nanorods consisting of gold and PPy segments were electrochemically manufactured using a template synthesis method. Using this method we were able to successfully form nanostructures in which the interface area between the two components was maximized. The construction of the desired composite nanomaterials has been verified, and the hybrid structure exhibited optical and electronic properties distinguishable from each isolated component (polypyrrole and CdSe). Moreover, the fluorescence properties of CdSe-embedded PPy nanorods were confirmed, and their blue shift phenomenon was studied. Considering the wide variety of materials available for electrochemical synthesis and colloidal nanoparticles, scientists and engineers in the fields of photovoltaics, optoelectronics, catalysis, and biosensing may utilize this method for a variety of applications.  
Conceptualization, W.-S.K., J.-H.K., and J.-H.L.; methodology, T.O., H.-S.K., and G.-H.N.; analysis, W.-S.K., T.O., K.-S.K., and S.-H.P.; writing--original draft preparation, W.-S.K., T.O., and J.-H.L.; writing-review and editing, J.-H.K. and J.-H.L.; All authors have read and agreed to the published version of the manuscript.  
This study was supported by grants from the National Research Foundation of Korea (NRF), Creative Materials Discovery Program (2019M3 DIA1078938), and Priority Research Centers Program (2019R1A6A11051471). This work was also supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2019R1 FIA1059188). We would like to thank the BK21 Program of Molecular Science and Technology at Ajou University and R&D Center for Advanced Pharmaceuticals & Evaluation at Korea Institute of Toxicology.  
Figure 5: (**a**) Representative optical micrograph of a nanorod-bridged gapped Au electrode. (**b**) I-V characteristics of a CdSe-embedded PPy nanorod (black) and a control PPy nanorod without CdSe nanoparticles (red).
###### Abstract  
The effect of the addition of three types of tropical edible seaweeds, _Kappappiscus alvarezii_ (KA), _Sargassum polycystum_ (SP), and _Caulera lentilifira_ (CL), on sausages were studied. Nine sausage formulations with three levels of inclusion (2%, 4%, and 6%) of each seaweed were prepared, analysed, and compared with the control sample (without seaweed) in terms of their physicochemical properties, total phenolic content, and lipid oxidation. The modified sausages had low moisture and fat content (\(p<0.05\)) but high ash and dietary fiber content (\(p<0.05\)) compared to the control sausage. The addition of seaweed powder changed the texture of the sausages, mainly its hardness and chewiness (\(p<0.05\)), but no significant difference in cohesiveness and springiness was found (\(p<0.05\)). The modified sausages were shown to have high water holding capacities and cooking yields. The different types of seaweed modified the colour of the chicken sausages differently. In general, the \(L^{*}\) (brightness) and \(b^{*}\) (yellowness) values was low for all sausage samples containing seaweed powder (\(p<0.05\)), while the a* (redness) value increased with the addition of the KA and SP seaweed powder but decreased for the sausage sample with added CL seaweed powder (\(p<0.05\)). Moreover, the modified sausages have higher total phenolic contents and high antioxidant capacities, which contributed to slowing the oxidation of lipid in sausages during storage (\(p<0.05\)). Sensory evaluation showed that the panellists found up to 4% of KA and 2% of SP to be acceptable. Overall, the seaweeds, especially KA and SP, could potentially be developed as excellent additives for the manufacture of highly technological high-quality meat products.  
seaweed; meat emulsions; functional properties; lipid oxidation; sausages +
Footnote †: journal: Applied Sciences
## 2 Results
### Identification of Prenylated 4 HC Derivative Using LC-ESI(-)-QTOF-MS/MS  
The high-resolution LC-ESI-QTOF-MS/MS was used for the identification of the prenylated 4 HC derivative produced by clones carrying pCDFDuet-AtaPT-CstDXS. The in vivo formation for the peak corresponding to the 4 HC containing farnesyl moiety was detected on the extracted ion chromatogram (EIC) at the calculated _m/z_ 366.211670 [M - H]\({}^{-}\), in which the product was tentatively confirmed to be \"ferulenol (2)\" by comparing the MS/MS fragmentation profile against the authentic ferulenol (2) established by the previous work [57]. The results showed that there were two mass peaks (namely compound-1 and -2) which exhibited the identical _m/z_ 365.21 eluted at the RT 21.3 min and 22.3 min, respectively (Figure 3A). No product formation was observed in the culture medium clones harboring pCDFDuet-AtaPT-CstDXS grown without supplementing 4 HC (1) in the culture (Figure 3B). These products were therefore presumably farnesylated-4 HC, where the farnesyl moiety was incorporated on the C3 position of 4 HC (1) via C-C-bonded formation. Based on the direct comparison against previous mass spectra, only the compound-1 highly resembled the MS/MS profile to that of ferulenol (2) reported by [PERSON] et al. [57], which was characterized by the ion peak _m/z_ 365 [M - H]\({}^{-}\), 228 and 174 (Figure 3C). In the MS/MS spectrum of this product, the presence of product ions with _m/z_ 228.08 (base peak), 214.06, and 174.03 illustrated the partial losses of prenyl side chain attached to the C3 position of 4 HC (1), which was the core structure of this prenylated product (Figure 3D). This product (compound-1) is therefore believed to be ferulenol (2), where the obtained mass data (Figure S4) were further elucidated by many aspects of in silico tools, including MetFrag web service, SIRIUS, CSL:Finger ID web service, and CANOPUS to entirely support the structural elucidation of this prenylated product. [PERSON] et al. [47] revealed that 4 HC (1) is able to exist in tautomeric forms including 4-hydroxy-2-chromanone (a), 2,4-chromadione (b), and 2-hydroxy-4-chromenone (c) (Figure 3E). The compound-2 (_m/z_ 365.21; Rt = 22.3 min) was thus presumably the isomeric form of ferulenol (2) proceeded via the tautomerization, where the 2-hydroxy-4-chromenone (c) served as the aromatic core structure (Figure 3F). The presence of fragmented ions with _m/z_ 214.06 (base peak), 228.08, and 282.12 in the resulting MS/MS spectrum also indicated the partial losses of prenyl moiety that attached to the C3 position of this prenylated product (Figure 3G). Note that 4 HC (1) as the fed substrate was also detected in the culture medium of the clones carrying pCDFDuet-AtaPT-CstDXS (Figure 3F).