esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Structural Health Monitoring Techniques in Pipeline and Wind Turbine Industries
## 2 Health Monitoring Techniques in Pipeline Industry
### In Pipeline Industry
#### 2.1.3 Radiographic Inspection  
The energy is directed through a source to a detector with a number of modifications performed on the rays that then produce a measurable damage quantity. The basic elements of all radiographic measurements remain the same. The radiographic method is particularly used in industry when isotropic material is encountered. They are not very precise and may even be damaging to the structure in certain cases, but they still are widely popular. The use of traditional X-radiography in the checking of carbon fiber/epoxy composites is limited, since they are low absorbers of X-rays. Thus, techniques that involve the use of penetrants (i.e., zinc iodide solution) have been introduced for enhancing the sensitivity of the method for these materials.  
\[I=I_{o}e^{-\mu x} \tag{7}\]  
\[\mu=\frac{N\sigma\rho}{A} \tag{8}\]  
\[\sigma=\sigma_{pe}+\sigma_{\rm s}+\sigma_{pp}+\sigma_{pd} \tag{9}\]  
\[E=I_{f}T \tag{10}\]  
\[G_{D}=\frac{dD}{d(\log E)} \tag{11}\]  
\[D=\log\frac{I_{o}}{I_{t}} \tag{12}\]  
where \(I=\) intensity of a beam of radiation, \(I=\) radiation beam intensity entering the material, \(\mu=\) linear attenuation co-efficient, \(x=\) material thickness, \(\sigma=\) total atomic attenuation co-efficient, \(N=\) [PERSON]'s constant, \(\rho=\) material density, \(A=\) atomic mass, \(\sigma_{pe}=\) photoelectric affect, \(\sigma_{\rm s}=\) Compton scattering, \(\sigma_{pp}=\) pair production, \(\sigma_{pd}=\) photodisintegration,\(E\) = film exposure, \(I_{f}\) = radiation intensity on the Film, \(T\) = time of exposure, \(G_{D}\) = contrast measured at density \(D\), \(D\) = density, \(I_{t}\) = intensity observed on the film.  
These techniques are applied in various ways.  
Computed radiography involves digital scanning of the plates that are similar to the conventional films, and this allows reuse. Digital radiography nullifies the time lag between image production and ray penetration. This is tried in different orientations to produce different but predictable results.
# Wireless Traffic Prediction Based on a Gradient Similarity Federated Aggregation Algorithm
###### Abstract  
Wireless traffic prediction is critical to the intelligent operation of cellular networks, such as load balancing, congestion control, value-added service promotion, etc. However, the BTS data in each region has certain differences and privacy, and centralized prediction needs to transmit a large amount of traffic data, which will not only cause bandwidth consumption, but may also cause privacy leakage. Federated learning is a kind of distributed learning method with multi-client joint training and no sharing between clients. Based on existing related research, this paper proposes a gradient similarity-based federated aggregation algorithm for wireless traffic prediction (Gradient Similarity-based Federated Aggregation for Wireless Traffic Prediction) (FedGSA). First of all, this method uses a global sharing enhanced data strategy to overcome the data heterogeneity challenge of multi-client collaborative training in federated learning. Secondly, the sliding window scheme is used to construct the dual channel training data to improve the feature learning ability of the model; In addition, to improve the generalization ability of the final global model, a two-layer aggregation scheme based on gradient similarity is proposed. The personalized model is generated by comparing the gradient similarity of each client model, and the central server aggregates the personalized model to finally generate the global model. Finally, the FedGSA algorithm is applied to wireless network traffic prediction. Experiments are conducted on two real traffic datasets. Compared with the mainstream Federated Averaging (FedAvg) algorithm, FedGSA performs better on both datasets and obtains better prediction results on the premise of ensuring the privacy of client traffic data.  
wireless traffic prediction; federal learning; FedAvg; deep learning; gradient similarity
# Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
## 1 Introduction
For pictorial regions, since human vision is sensitive to texture information and luminance variation, texture features are used as structural features; meanwhile, the luminance information is used as the auxiliary feature. Finally, an activity weighting strategy is proposed to fuse the assessment values of textual and pictorial regions as the final assessment value of this degraded SCI. Experimental results show that the proposed BSRSF method achieves better prediction performance than other existing blind SCIQA methods on SIQAD and SCID, which are often employed as validation databases of SCIs. In contrast to the existing blind SCIQA methods, the main contributions of the proposed BSRSF metric are as follows:  
1. We propose improved histograms of the oriented gradients, which are extracted from the multi-order derivatives. In the proposed method, these histograms are adopted as structural features to predict the quality of textual regions of SCIs. 2. We extract texture features from both the spatial and shearlet domains as structural features of pictorial regions. The statistical histograms of the local derivative pattern are used as texture features in the spatial domain. We propose a new local pattern descriptor called the shearlet local binary pattern to represent texture features in the shearlet domain. To the best of our knowledge, this is the first attempt to extract texture features from the shearlet domain. 3. We propose an activity weighting strategy to combine the visual quality of textual and pictorial regions. This strategy is based on the activity degree of different regions in the SCI, in which the weights are extracted from gradient values of this SCI. The remaining content of this paper is organized as follows. The detailed content of the proposed BSRSF method is presented stage-by-stage in Section 2. Experimental results are given in Section 3. Finally, the conclusions of this paper are presented in Section 4.
# Effect of Slag Particle Size on Fracture Toughness of Concrete
## 1 Introduction
It is important to identify the fracture behavior and toughness for widely used high-performance concrete that normally incorporates slag or fly ash [16; 17]. Because of the non-homogeneous characteristics of concrete, its fracture behavior is quite complicated. It was proved that the methods developed in conventional fracture mechanics are unsuitable for the analysis of the influence of fracture toughness on the behavior of concrete structures [15]. Many theoretical models have been developed to make such an analysis possible. Among them, three well-known models are the fictitious crack model, the crack band model, and the two-parameter fracture model. By means of numerical techniques, for example, in the finite element method, it is possible using these models to make a theoretical analysis for the development of the damage zone and the complete behavior of the structure. As for the fracture mechanics of concrete, RILEM has put forward several methods to determine the fracture properties and parameters of concrete [18]. [PERSON] [15] determine the fracture energy according to the fictitious crack model. [PERSON] and [PERSON] [19] used the two-parameter fracture model to determine the critical stress intensity factor. A number of past studies on the concrete containing GGBS have been conducted, mainly dealing with the influence of the fineness of slag on the strength and durability of concrete. More rarely, investigations were conducted on the fracture behaviors of concrete incorporating finer GGBS. Consequently, there is a need for exploring the effects of the particle size of GGBS on the fracture properties of concrete. This study experimented using the three-point bend test to analyze the fracture energy and the critical stress intensity factor for evaluating the fracture mechanics or fracture toughness of concrete containing finer GGBS.
## 3 Results
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Measurement I**} & \multicolumn{4}{c}{**Measurement II**} & \multirow{2}{*}{**Stability**} \\ \cline{3-3} \cline{5-12}  & \multicolumn{2}{c}{**Translation**} & \multicolumn{2}{c}{**Translation**} & \multirow{2}{*}{**Stability**} & \multirow{2}{*}{**Translation**} & \multirow{2}{*}{**Stability**} & \multirow{2}{*}{**II-I**} & \multirow{2}{*}{**Stability**} \\  & **R** & **L** & & & & & & & & \\ \hline \multicolumn{2}{c}{Experimental group (\(N=68\))} & & & & & & & & & \\ \hline \multirow{2}{*}{FMS II} & 0.09 & 0.06 & 0.02 & 0.10 & 0.09 & \(-\)0.10 & \(-\)0.07 & \(-\)0.17 \\  & II & 0.24 \({}^{*}\) & 0.01 & 0.13 & 0.19 & \(-\)0.04 & 0.00 & \(-\)0.09 \\  & II-I & 0.14 & 0.32 \({}^{*}\) & \(-\)0.05 & 0.05 & 0.14 &the FMS test in the second measurement, as well as relative and absolute increments in the FMS scores. These relationships are positive and weak (0.24, 0.32 and 0.30). In the control group, only one significant relationship is observed, the strength of which is also at the level of 0.29. This relationship exists between translation R and the percentage (relative) increment of the FMS test. In the experimental group, there is also a significant relationship between the relative increase in stability and the relative increase in the FMS test (0.25; \(p<0.05\)). The significant fact is that this relationship is observed only in the experimental group, no such relationship is observed in the control group. The analysis of the relationship between the height, body weight, and BMI did not show any statistically significant relationships with the increases in stability and FMS (Table 4). The next stage of the analysis was a particular assessment of the influence of age, body weight, body height, gender and increase FMS (II %) on the increase in stability for experimental group. The analysis shows that the only variable that models the increase in stability is the variable describing the increase in points in the FMS test. The calculated regression model is characterized by a relatively low determination R\({}^{2}\) = 0.016. The calculated model has the form:  
\[\text{Stability II}-\text{I}\;(\%)=0.24+0.025*\text{FMS}\;\text{II}-\text{I}\;( \%);\text{R}^{2}=0.016 \tag{1}\]  
Equation (1) Calculated regression model.
## 3 Results and Discussion
### Antioxidant Activities of Fruit Extracts  
Antioxidant compounds can exert their effects through various mechanisms. The antioxidant activities of _C. azaroolus_ and _M. germanica_ extracts were assessed using the DPPH radical scavenging activity and FRAP (Table 3). The highest antioxidant capacity was observed in M3 extracts (14,744.50 \(\pm\) 226.27 mmol FeSO\({}_{4}\)/100 g fruit) for _C. azaroolus_ extracts, followed by M4 extract (13,499.62 \(\pm\) 1658.20 mmol FeSO\({}_{4}\)/100 g fruit), while for _M. germanica_, the highest antioxidant activity was observed for P8 (13,415.89 \(\pm\) 873.80 mmol FeSO\({}_{4}\)/100 g fruit), followed by P7 (12,972.12 \(\pm\) 554.99 mmol FeSO\({}_{4}\)/100 g fruit) and P4 (12,115.88 \(\pm\) 259.32 mmol FeSO\({}_{4}\)/100 g fruit). Free radical scavenging capacity was very high in all the _C. azaroolus_ extracts, with M4 (97.4% \(\pm\) 0.0) and M8 (94.1% \(\pm\) 1.0) extracts having the highest capacity, while in _M. germanica_ extracts, the extracts with the highest percentages were P3 (97.3% \(\pm\) 1.0), P4 (97.8% \(\pm\) 0.0), P7 (97.9% \(\pm\) 0.0) and P8 (97.8% \(\pm\) 0.0).  
The antioxidant capacity of _M. germanica_ and _C. azaroolus_ has previously been discussed. _C. azaroolus_' antioxidant activity was 0.79 \(\pm\) 0.10 mmol Fe\({}^{2+}\)/g of dry extract using the FRAP assay. Both fruit extracts exhibited notable antioxidant activities due to the presence of phenolic compounds [24]. The highest antioxidant activity was observed in the leaves of _C. azaroolus_, followed by the peel and pulp extracts, while syrup exhibited the lowest activity [20]. Similarly, the antioxidant activity of the methanolic extract of _M. germanica_ leaves using the DPPH assay reported a 69.43% radical inhibition in vitro [22].  
The high antioxidant activity of both fruit extracts is associated with the high content of polyphenols and flavonoids. It was also pointed out that this activity may also be influenced by the presence of some other compounds with high antioxidant activity, such as Vitamin C, pigments and tocopherools [25]. The results of this study showed that both fruit extracts can play an important role as a natural antioxidant source and could potentially provide health-promoting effects to consumers.
# Design and Performance Analysis of Hardware Realization of 3 GPP Physical Layer for 5G Cell Search
## 7 Conclusions and Future Works  
In this work, we studied and designed hardware IP cores for 5G cell search (CS) as per the third-generation partnership project (3 GPP) specifications. Our contributions include the design of CS transmitter PHY consisting of synchronization signal (SS) generation, resource mapping, scheduler, and orthogonal frequency division multiplexing (OFDM) modulation and receiver PHY consisting of blind primary SS search, OFDM demodulation, secondary SS (SSS) search, a demodulation reference signal (DMRS) search, and boundary detection for the frame, sub-frame, slot, and symbols. We have proposed a novel down-sampling approach for PSS search, which offers a substantial reduction in execution time and resource utilization. We have demonstrated the functional correctness and superiority of the proposed approach via the RFNoC framework. Future works include extending the proposed architecture for physical broadcast channel (PBCH) detection, physical random access channel (PRACH) generation, and detection.  
Conceptualization, S.J.D.; methodology, K.L. and J.C.; software, K.L., D.S. and J.C.; validation, K.L., D.S., J.C. and S.J.D.; writing--original draft preparation, S.J.D.; writing--S.J.D.; supervision, S.J.D.; project administration, S.J.D.; funding acquisition, S.J.D. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the funding received from core research grant (CRG) awarded to [PERSON] from DST-SERB, GoI.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results
###### Abstract  
A class of polyethyleneimine (PEI)-functionalized Fe\({}_{3}\)O\({}_{4}\) magnetic nanoparticles (MNPs) has been facilely produced through a solvothermal process. The synthetic MNPs have been characterized by multiple technologies and then used for Pb(II) ion sorption from the aqueous media in different conditions. It was found the Pb(II) adsorption behaviors could be well fitted by the pseudo second-order kinetic and Langmuir isotherm models. The maximum Pb(II) adsorption capacity at 25\({}^{\circ}\)C and pH5.0 was calculated to be 60.98 mg/g. Moreover, effects of temperature, pH, and electrolyte of aqueous phase on the Pb(II) adsorption capacity of MNPs have been carefully examined. The Pb(II) adsorbing capacity was enhanced with temperature or pH rising, but reduced with the addition of various electrolytes. Additionally, the recyclability of synthetic MNPs has been also assessed. The prepared PEI-functionalized MNPs could still maintain good adsorption performance after five cycles of Pb(II) removal. These results indicated that the PEI-functionalized Fe\({}_{3}\)O\({}_{4}\) MNPs could be readily synthesized and served as a desirable and economic adsorbent in Pb(II)-contaminated wastewater treatment.  
magnetic nanoparticles; polyethyleneimine; Pb(II) ion adsorption; recyclability +
Footnote †: journal: Applied Physics Letters
## 3 Four Methods of Surface Reconstruction from Point Clouds
### Poisson Reconstruction  
Poisson reconstruction [39] casts the surface reconstruction from oriented points as a spatial Poisson problem: The computed Laplace operator of the scalar function \(\chi\) is equal to the divergence of a vector field \(\vv{V}\), as follows:  
\[\Delta\chi=\
abla\cdot\
abla\chi=\
abla\cdot\vv{V} \tag{9}\]  
where the vector field \(\vv{V}\) is defined by the oriented points and \(\chi\) is a 3D indicator function. After finding \(\chi\) by solving Equation (9), the reconstructed surface is obtained by extracting an appropriate iso-surface from \(\chi\).  
The time and space complexities of Poisson reconstruction are proportional to the size of the reconstructed model, and it is highly resilient to data noise.  
One drawback of Poisson reconstruction is that it requires oriented normal vectors at the input points to define the vector field \(\vv{V}\).
# A Geospatial Approach to Improving Fish Species Detection in Maumee Bay, Lake Erie
## 3 Results
### Random vs. Hybrid Sampling  
In 2017, five species were detected at hot spot sites (combining all gear types) that were not detected at traditional sites despite the comparatively low effort at hot spot sites (three times more effort at traditional sites): Bowfin _Amia calva_, Golden Shiner _Notemigous crysoleucas_, Northern Pike _Esox lucius_, Shorhead Redhorse _Moxostoma macrolepidotum_, and Smallmouth Bass _Microperus dolomieu_ (Table 2). Traditional sampling sites captured seven species that were not detected at hot spot sites: Black Crappie _Pomoxis nigromaculatus_, Common Carp _Cyprius carpio_, Common Shiner _Luxilus cornutus_, Longnose Gar _Lepisosteus osseus_, Rock Bass _Amblopiltes rupstris_, Tadpole Madtom _Noturus syrinus_, and White Sucker _Catostomus commersonii_. Fourteen species were caught at the five electrofishing hot spot sites that were not caught at the 15 traditional electrofishing sites, while one species was caught at the traditional sites that was not caught at the hot spot sites (Table 2). Nine total species were caught at the 15 traditional electrofishing sites while 22 were caught at the five hot spot sites. Five species were caught at the five fyke net hot spot sites that were not caught at the 15 traditional sites, while five species were caught at the traditional sites that were not caught at the hot spot sites (Table 2).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Electrofishing** & **Paired Fyke Net** & **Bottom Trawl** \\ \hline Years & 2014, 2016 & 2013–2016 & 2013, 2015, 2016 \\ \# Sites & 24 & 60 & 45 \\ Range of Species & 0–17 & 4–25 & 3–11 \\ Mean of Species (SD) & 4.2 (4.2) & 10.2 (4.0) & 7.4 (1.7) \\ Moran’s I & \(p=0.08\) & \(p=0.02\) & \(p<0.01\) \\ First Peak (m) & 3490.55 & 1627.78 & 2547.81 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of Maumee Bay sampling results from 2013–2016.  
Twenty-two total species were caught at the 15 traditional fyke net sites and 22 were caught at the five hot spot sites. One species was caught at one of the five bottom trawling hot spot sites that was not caught at the 15 traditional sites, while six species were caught at the traditional sites that were not caught at the hot spot sites (Table 2). Eighteen total species were caught at the 15 traditional bottom trawling sites while 13 were caught at the five hot spot sites. A total of 12 unique or duplicate species (based on 2013-2016 data) were collected in 2017 (Table 2). Six of these species were collected at hot spot sites and seven were collected at traditional sites. No previously undocumented nonnative species were collected during the study period.
## 2 Methods
### Assembly Error Propagation Model
Step-3: Rotation. Rotor 2 is rotated by an angle of \(\beta_{2}\) around the Y axis so that the bottom mounting surface of Rotor 2 is parallel to the top mounting surface of Rotor 1. Then the rotation matrix of Rotor 2 relative to Rotor 1 about the Y axis can be expressed as follows:  
\[Ry_{2}=\left[ \begin{array}{ccc}\cos(\beta_{2})&0&\sin(\beta_{2})\\ 0&1&0\\ -\sin(\beta_{2})&0&\cos(\beta_{2})\end{array} \right] \tag{2}\]  
where \(\beta_{2}\) is an angle formed by the top and bottom mounting surfaces of Rotor 2 and can be calculated as follows:  
\[\beta_{2}=\arctan(\frac{P_{2}}{2D_{2}}) \tag{3}\]  
where \(D_{2}\) is the measured radius of the top mounting surface of Rotor 2. Step-4: Translation. Rotor 2 is translated so that the center of the bottom mounting surface of Rotor 2 coincides with the top mounting surface of Rotor 1, which is equivalent to directly accumulating the center coordinates of the top mounting surface of Rotor 1 on the basis of Step-2. Through the above steps, the assembly error propagation model of a 2-stage rotor can be expressed as follows:  
\[X_{2}=I_{2}Rz_{2}Ry_{2}+S_{1} \tag{4}\]  
where \(X_{2}\) refers to a coordinate vector of any point in Rotor 2 after assembly, and \(I_{2}\) to that in Rotor 2 before assembly. Rotor 1 does not move by default, and \(S_{1}\) is the coordinate vector of the center of the top mounting surface of Rotor 1. The assembly process of an \(n\)-stage rotor can be regarded as the assembly processes of \((n-1)\) two-stage rotor. Therefore, the assembly error propagation model of an \(n\)-stage rotor can be expressed as follows:\[X_{n}=I_{n}\left[\prod_{n^{*}\text{-}1:1}\left(Rz_{n}Ry_{n}\right)\right]+S_{1}(n \in N^{*},n\text{\textgreater}1) \tag{5}\]  
where \(X_{n}\) refers to a coordinate vector of any point in rotor \(n\) after assembly, \(I_{n}\) to that in rotor \(n\) before assembly, \(Rz_{n}\) to a rotation matrix of rotor \(n\) relative to rotor \((n-1)\) about the Z axis, and \(Ry_{n}\) to a rotation matrix of rotor \(n\) relative to rotor \((n-1)\) about the Y axis. For a rigid rotor, the coordinate vector of any point in rotors at different stages before and after assembly can be accurately predicted by Equation (5) on the premise that the geometric errors of rotors at different stages are known. The parameters needed to solve Equation (5) include concentricity (\(C_{n}\)), parallelism (\(P_{n}\)), measured radius (\(D_{n}\)), the angle between the calibrated screw hole and the highest point (\(\varphi_{n}\)), and the height of mounting surface (\(H_{n}\)), which can be obtained using a coordinate measuring instrument (CMM); only \(\theta z_{n}\) is unknown and is to be optimized.
## 2 System Design and Configuration
### Design and Control of the SEPIC
Beyond maximum power extraction, the SEPIC's control strategy also ensures a stable output despite variations in solar irradiance, as well as variations in the PV panel's temperature-dependent characteristics. The dynamic adjustment is achieved through a closed-loop feedback control system, involving a proportional-integral (PI) controller. The output voltage (\(V_{dc}\)) is continuously monitored and compared to a reference voltage, and the error is fed into a PI controller to generate a control signal. The PI controller adjusts the duty cycle \(D\) of the switch to bring \(V_{dc}\) closer to the reference voltage.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 4 High-Order QAM and APSK Modulation and Demodulation  
To improve communication speed and save spectrum resources, high-order modulation methods that combine amplitude and phase, such as Quadrature Amplitude Modulation (QAM) and Amplitude Phase Shift Keying (APSK), have advantages. By combining high-order QAM and APSK modulation with reliable polar code encoding through soft demodulation [25], the performance of PLC systems can be better improved.