esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Trust Concepts
### Trust Management Model  
As 6G networks are anticipated to underpin a diverse array of applications, including artificial intelligence, mobile, Internet of Things (IoT), and wireless sensor networks, the necessity for a robust trust management model becomes increasingly evident [17]. Such a model is designed to facilitate, reinforce, and perpetuate trust within the network ecosystem. The foundational step in establishing a secure relationship lies in fostering trust between a trustor and a trustee. Subsequently, trust monitoring constitutes the second stage, wherein pertinent and efficacious data on trust are gathered. These data may encompass evidence derived from trustee monitoring, the compilation of the trustor's relationship history and behaviors, solicitation of recommendations from neighboring nodes of the trustee (in the absence of direct communication), or feedback obtained from other monitoring systems such as Service Level Agreements (SLAs). The third stage involves the evaluation of trust, a process that may vary based on service type and employ diverse methodologies, culminating in decision-making outcomes. The final stage encompasses the control and maintenance of trust, highlighting the dynamic nature of trust relationships. The trust model is depicted in Figure 1.
## 3 Results and Discussion
### WSR of the Vulcanizates
According to the mechanism of lubrication effect stated above, Figure 6 vividly described the trick to breakdown thick water film by enlarging surface roughness provided by silica asperity, which is formed as long as silica migrates to the surface of the vulcanizate through polar interaction between silica and water [12]. As a result, the ease with forming asperity could be deduced from the interfacial polar interaction of water with silica that is close to the interface. The surface polarity of vulcanizates could be reflected by water contact angle, as that shown in Figure 7, which had a negative relationship with the content of MAH (Figure 8), making it clear that content of hydrophilic groups on the surface of the vulcanizates increased with increasing MAH content. Obviously, the differences in hydrophilicity among different samples are caused by the quantity of silica on which a large number of hydroxyl groups exist, as well as that of M-DCPD resin that grafted with anhydride. Moreover, the quantity is closely associated with the dispersion of silica. The polar interaction between M-DCPD resin and silica contributes to reduction of surface energy difference between rubber and filler, leading to both uniform dispersion of silica and high surface polarity of the vulcanizates. Therefore, we have ground to speculate that the enhanced polar interaction of water with vulcanizates facilitates the break of water film. In order to verify our theoretical inference about the effect of anhydride on filler dispersion, we therefore studied the BPST index. The BPST value directly reflects the degree of sliding friction between sample and testing surface by the following equation,  
\[BPST\ index=330\mu/(3+\mu) \tag{2}\]  
Figure 6: Interaction mechanism of vulcanizates and water effected by M-DCPD resin with anhydride and silica with hydroxy. Figure 7: Water contact angle images of vulcanizates. where \(\mu\) refers to the friction coefficient between the vulcanizate and the test surface [12]. As it is shown in Figure 8, the BPST value raises by 16% as the MAH content increases to 4 wt% in M-DCPD resin, while it changes slightly with increasing loading. The dynamic properties from Figure 4 show that R4 and R6 can effectively promote energy dissipation because of high hysteresis. At the same time, it is inferred from water contact angle (Figure 8) that the increasing amount of silica near the internal surface is more likely to expose as surface protrusions in these two samples. The friction coefficient is accordingly higher with a rougher surface. Figure 8 also shows that though R8 exhibits better interaction with water than R4 and R6, the three samples show comparable ability to overcome friction. The BPST test, as well as water contact angle, prove that whether resins could evenly disperse in the compounds is vital to the performances of vulcanizates.
## 3 Analytical Models of Lattice Structures Built with Polygon Hollow Shapes
### Analytical Models for Predicting the Behaviour of Selected Polygon Hollow Structures
#### 3.2.2 The Gibson-Ashby Model of an Equilateral Triangular Hollow Cell  
Figure 6 shows a unit planar triangular cell highlighting the directions for the planar in-plane loading of an equilateral triangular hollow cell. Equation (6) represents the analytical model describing the behaviour of a planar triangular polygon structure when subjected to loads in the in-plane directions [18; 20].  
For this triangular hollow structure, the Equation (6) represents stiffness in each of the two planar in-plane directions [18].  
\[E_{y}^{*}=E_{x}^{*}=1.15E_{s}\bigg{(}\frac{t}{L}\bigg{)} \tag{6}\]  
It is observed that the stiffness of the triangular hollow cell is directly related to its relative density, which from Equation (2) is known to be equal to the \(t/L\) ratio. This difference in the relationship of stiffness to the \(t/L\) ratio between a hexagonal hollow cell and a triangular hollow cell is because the triangular hollow cell deforms primarily through the axial deformation of the cell walls, as opposed to the hexagonal hollow cells, which deform predominantly through the bending of the cell walls [17; 18; 23]. Equations (5) and (6) show that the triangular hollow polygon cell is less stiff than the hexagonal hollow cell under planar in-plane loading by a factor of 2.
## 3 Statistical Analysis
### Hourly Values
#### 3.1.3 Monthly Variability of Irradiances of Solar Radiation Components  
The monthly variability of the irradiances of the shortwave radiation components is demonstrated with a graph of boxplots for each month of the year. The boxplot presents the median and the interquartile range (IQR) as well as the outliers (asterisks) of each variable (Figure 17). The smooth line represents the mean values of irradiances for each month of the year. As it can be seen no outliers are observed for \(B\) and \(G\) irradiances, while they are observed for the diffuse component in all months of the year. Generally, medians are closed to the mean values. The variability of the global irradiance in the summer months is greater than in other seasons as it is indicated from the length of the boxplots. Similar pattern is shown for the direct horizontal irradiance. On the other hand, the variability of direct normal and diffuse irradiances is higher during the winter and spring. Monthly descriptive statistics for the shortwave solar radiation components are presented in Table 5. Monthly mean values of global irradiances range between 300 and 635 W/m\({}^{2}\), while those for direct horizontal irradiances range between 130 and 435 W/m\({}^{2}\). The monthly mean values of diffuse irradiance are relatively constant throughout the year ranging mainly between 90 and 170 W/m\({}^{2}\).  
Figure 15: Solar global irradiances as a function of solar zenith angle (SZA) and clearness index (\(k_{t}\)).  
Figure 16: Isoline diagrams of monthly mean hourly: (**a**) direct normal; (**b**) direct horizontal; (**c**) diffuse; and (**d**) global irradiance values (W/m\({}^{2}\)).  
Figure 17: Boxplots of irradiances of the shortwave radiation components: (**a**) direct normal (\(B_{n}\)); (**b**) direct horizontal (\(B\)); (**c**) diffuse (\(D\)); and (**d**) global (\(G\)). The symbol of median values is represented as circle in the boxplot. The asterisks denote the outliers. The smooth curve represents the mean values of each month of the year.
## 6 IEC's Market Drivers  
The main drivers of advanced computing include existing 4G and 5G networks and Internet of Things (IoT). IEC market drivers are included in components of IEC's software and hardware, applications such as video surveillance, locations. Data services and data analytics. Infrastructures of networks will need to expand effectively to deliver more significant amounts of data. IEC depends on the flexibility and agility of end-user cloud-based to meet these requirements. IEC's networks are also evolving to encompass residential, commercial, mobile, and virtual converged networks.
## 3 Results and Discussion  
### Genetic Identification of Isolated Microalgae  
The isolated microalga was extracted for genomic DNA, and then PCR-amplified for ITS regions and chloroplast small subunit rRNA. The PCR products were purified, nucleotide-sequenced, and nucleotide-aligned for genetic identification. The alignment results from the BLASTn suite based on a megablast algorithm of the NCBI revealed that the ITS region of isolated microalga was closely related to _Chlorella variabilis_ Kb1(accession number LC420163.1) with a 99% query coverage and 99.10% identity, while the chloroplast small subunit rRNA of isolated microalga was closely related to _Chlorella variabilis_ YTU. ANTARCTIC.001 (accession number MN372092.1) had a 99% query coverage and 98.79% identity. The resulting E values obtained from the nucleotide alignment were zero. The phylogenetic trees generated by Seaview software with the BIONJ algorithm for 10,000 bootstraps are shown in Figures 2 and 3. Finally, the isolated microalga was designated as _C. variabilis_ RSM09. The resulting nucleotide sequences were deposited in the GenBank database of NCBI under the accession numbers ON167513 and ON161758.  
Figure 3: The circular phylogenetic tree of the isolated microalga (chloroplast small subunit rRNA) shown using the BIONJ algorithm for 10,000 bootstraps. The phylogenetic tree was analyzed by SeaView software version 4.7 and Figtree software version 1.4.4.  
Figure 2: The circular phylogenetic tree of the isolated microalga (ITS region) shown using the BIONJ algorithm for 10,000 bootstraps. The phylogenetic tree was analyzed by SeaView software version 4.7 and Figtree software version 1.4.4.
## 5 Applicability of NDT Methods to ABC Closure Joints
### Promising NDT Methods Comparison  
The selected methods were compared and rated, and the results are given in Table 2. The capabilities and attributes considered for rating the applicability of the methods used for ABC closure joints include: test speed, internal detection (ability to detect internal defects), accuracy, analyzing speed, cost, ease of use, safety for public and operator, required operator skill, and repeatability [1]. As a result of the evaluation shown in Table 2, based on the number of Gs, Fs, and Ps, the top five ranked NDT methods were identified as being IE; GPR; UT including PAU; IR; and IRT. Electro-chemical testing (e.g., the electrical resistivity test and half-cell potential method) for corrosion detection, though effective in certain conditions, has not been included among the NDT techniques considered in this study.  
\begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
**Capability** & **IE** & **GPR** & **UT** & **IR** & **IRT** & **RT** & **MFL** \\ \hline Test Speed & F & G & F & G & F & P & F \\ Internal Detection & G & G & G & P & F & G & F \\ Analyzing Speed & F & F & F & G & F & G & F \\ Cost & G & G & F & G & G & P & P \\ Accuracy & G & F & G & F & F & G & F \\ Ease of Use & G & G & G & G & G & P & P \\ Safety for Public and Operator & G & G & G & G & G & P & F \\ Required Operator Skill & G & G & G & G & G & P & P \\ Repeatability & G & F & G & F & F & G & F \\ \hline \hline \end{tabular}
\end{table}
Table 2: Qualitative comparison and preliminary rating of NDT methods for ABC closure joints:
# When 5G Meets Deep Learning: A Systematic Review
## 3 Results
### What are the Main Problems Deep Learning Is Being Used to Solve?
#### 3.1.2 Coding/Decoding Scheme Representation  
The generation of the information at the source and the reconstruction of such information at the receiver makes up the coding and decoding processes, respectively. However, due to the unstable nature of the channels, some disturbances and noise in the signal can cause data corruption [20]. Considering the 5G networks, where new technologies, such as MIMO, non-orthogonal multiple access (NOMA), mmWave will be deployed, the coding/decoding schemes must be adapted to work properly. These schemes need to characterize several phenomena that can impact the data transmission, such as signal diffraction, fading, path loss, and scattering.  
We identified a total of seven works that addressed the coding/decoding schemes using deep learning models.  
Three of these considered NOMA technology using deep learning models. In [21], the authors proposed a deep learning-based solution to parameterize the bit-to-symbol mapping and multi-user detection. Recall that as we are using non orthogonal modulation, multi-user detection becomes a cumbersome issue. In [22], the authors proposed a deep learning model to learn the coding/decoding process of MIMO-NOMA system in order to minimize the total mean square error of the users signals. In [23], the authors proposed a deep learning model to be used in sparse code multiple access (SCMA) system, which is a promising code-based NOMA technique, with the goal to minimize the bit error rate.  
The authors in [24] considered multiuser single-input multiple-output (MU-SIMO) systems. A simple deep learning model was considered for joint multi user waveform design at the transmitter side, and non coherent signal detection at the receiver side. The main goal was to reduce the difference between the transmitted and received signals.  
In [25], [PERSON] et al. proposed a novel peak-to-average power ratio (PAPR) reduction scheme using deep learning of OFDM systems. The presence of large PAPR values is harmful to battery life as high peaks tend to draw high levels of energy from sometimes energy limited devices. The model proposed map and demap symbols on each subcarrier adaptively and both bit error rate (BER) and the PAPR of the OFDM system could be jointly minimized.  
In [26], a deep learning based unified polar-low-density parity-check (LDPC) is proposed. The deep learning model was created to receive the observed symbols and an additional information introduced by the authors called \"indicator section\", and to output the signal decoded.  
In [27], a coding mechanism under low latency constraints based on deep learning was proposed. The idea was to create a robust and adaptable mechanism for generic codes for future communications.
## 1 Introduction
The region-based segmentation method of [PERSON] et al., used a deformable shape model to segment the prostate boundary using shape statistics and image texture information. The image texture is represented by Gabor features [6] and is used to distinguish the boundary between the prostate and non-prostate regions. The use of kernel support vector machine (KSVM) is for measuring the probability of belonging to a prostate voxel to distinguish the prostate from surrounding tissues. However, the segmentation accuracy of this method relies more on the statistical information of prostate voxel boundaries. Not only is the manual workload of the statistical information large, but when the statistical data is biased, it will directly lead to accurate segmentation results. Figure 1: Target area, artifact area, and non-prostate area in transrectal ultrasound (TRUS) image. In this paper, we propose a novel MFPN. The structure controls the scale and fuses multi-level feature maps to obtain feature fusion modules with rich semantic features. To avoid the artifact region from interfering with the prostate image segmentation, we base our algorithm on precise positioning of the target region before segmentation to solve this problem. To summarize,  
1. We propose that a MFPN network can strengthen a feature set by controlling the channel scale on a multi-scale feature map. 2. We solve the problem of inaccurate segmentation caused by weak edge features. 3. The Anchor size ratio of each anchor point in the Anchor mechanism was modified and a new bounding box regression algorithm was proposed, which not only improves the convergence speed of the target region. At the same time, the process effectively avoids the problem that the target region covers the inside of the prostate's contour and causes the segmentation accuracy to decrease.
# Experimental Study on Slender CFRP-Confined Circular RC Columns under Axial Compression
## 2 Experimental Program  
### Details of Test Columns  
An experiment was conducted on 6 CFRP-confined columns with a diameter of 200 mm and slenderness ratios (\(\lambda\)) of 12, 20, 32, 40, 48, and 56. The columns were constrained by one layer of CFRP cloth along the circumferential direction. The overlap length of CFRP was 100 mm. The thickness of concrete cover was 15 mm. The specimens were strengthened using \(\phi\)8 mm steel bars as the longitudinal reinforcement, and 100 mm spacing of \(\phi\)4 mm steel ties provided at the mid height region comprised the hoop reinforcement. To strengthen both ends of the column, the hoop reinforcement was changed by \(\phi\)6 mm ties, and the spacing was reduced to 50 mm. Figure 0(a) shows the characteristics of the specimens. Out of CFRP-confined columns, six reference RC columns were tested without any wrapping for each slenderness ratio. Each specimen was wrapped with CFRP cloth with 50 mm width at both ends of the specimen to ensure it would fail at the mid height region.  
Figure 1: Details of specimens and overview of test setup.  
Table 1 lists the characteristics of the test specimens. The test columns were identified by four characters. The first letter C represents a circular column. The next three numbers separated by hyphens stand for the number of CFRP layers, the slenderness ratio, and the serial number of the specimens, respectively.
## 4 Analysis of the Rock Breaking Process
### Stress Distribution  
For a better explanation of the rock failure mechanism subjected to different buttons, the force chain between particles at the penetration depth of 0.5 mm was analyzed, as shown in Figure 14. The thickness and direction of the line correspond to the magnitude and direction of normal force, respectively. Red represents tension, while blue represents pressure. The tension distribution is spherical, while the pressure is radially spread. This is in accordance with the famous Boussinesq elastic field. For conical, spherical, or parabolic buttons, a hemispherical contact force chain was established beneath these buttons under point contact load, forming uniform radial cracks. The force chain of the wedge button was distributed in an inverted pyramid shape, which was the reason why vertical cracks were more developed. In terms of stress distribution ranges, the wedge button had the largest, followed by saddle and spherical buttons, and conical and parabolic buttons had the smallest. It could explain the difference in indentation force required for each carbide button to penetrate into rocks at the same penetration depth.
## 2 OpenStreetMap Quality Assessment History  
Numerous scientists have investigated the quality of VGI and particularly OSM in recent years, and further research is currently still being conducted. In 2008 a discussion about the need for research with regard to the accuracy and correctness of the compiled information within the world of Web 2.0 was sparked [4, 7, 28, 29]. Preliminary direct comparison analyses with regard to OSM were conducted for Great Britain in 2008 comparing Ordnance Survey (OS) geodata with OSM [30, 31]; in Germany in 2009 [32] OSM data has been compared with the commercial Multinet dataset from TomTom (still known as TeleAtlas at the time). A few months later a similar comparison for Germany was conducted, but with the street dataset from Navteq, a different proprietary geodata provider [33]. Both analyses came to similar conclusions despite using slightly different methods: OpenStreetMap data shows a high degree of detail in urban areas; however, this detail richness declines significantly in rural areas. The main difference between the two studies was that one analysis included methodology to show the geographical discrepancies within Germany [32], while the other merely advised how complete OSM is in relative comparison with another dataset.  
In France, a similar approach was used to analyze OSM data and further studies were conducted at the same time [34]. The results showed the advantage and flexibility, but also the problem of the heterogeneity of the data specifically for this country. The latter is the result of the different data sources that have been used in OSM and also the differences in the work by the project participants in France.  
In 2011, the first studies that analyzed the quality of OSM outside of Europe were conducted [35]. In this particular case the OSM project data has been compared with proprietary data from TomTom (TeleAtlas) and Navteq for the entire state of Florida (USA) and four specific cities within the USA. In comparison to the results for Germany or England, the discrepancies between the rural and urban areas in the USA showed an opposite tendency. In Florida, the rural data was, in parts, even more complete than that of the proprietary datasets in the relative comparison conducted. This can probably be attributed to the TIGER (U.S. Census Bureau) street data import to the OSM database for the entire United States. Other analyses were conducted comparing the impact of OSM on shortest path generation for pedestrians in Germany and the US [36, 37]. Apart from England, no studies have been conducted to date over a period of several years and for an entire country [38].
## 1 Introduction
In the same section, two RC columns (the as-built columns), which represent the shorter pier of a bridge by proper scale factors [36], are built to be severely damaged in the plastic hinge region using cyclic tests in the laboratory according the test protocol presented in Section 5. The displacement-controlled method is used to apply an asymmetric displacement history to the top of the two columns. This history is representative of that recordable when the bridge is subjected to a strong, and then a very strong, seismic event. Then, the damaged columns are repaired using the proposed strategy, which are again tested applying the same cyclic displacement history used to damage the as-built columns (Section 5). Construction details and in situ operative difficulties are evaluated from an engineering point of view. The experimental results obtained by the cyclic tests on the as-built and repaired columns are presented in Section 6. In this section, the responses of the as-built and the repaired columns are presented in terms of the maximum base shear and ductility (Section 6.1), stiffness evolution (Section 6.2), dissipated energy (Section 6.3), hysteretic damping (Section 6.4), shear strength (Section 6.5) according to the guideline equations (Section 3), concrete jacket circumferential strain (Section 6.6), and the column damage and failure mode (Section 6.7). The comparisons among the as-built and the repaired columns permitted us to evaluate the effectiveness of the proposed repair strategy. In Section 7, a fiber model of the column section, built in the Open System for Earthquake Engineering Simulation (OpenSees, UC Berkeley, Berkeley, CA, USA) [46] was validated based on the experimental results. This model permitted us to numerically investigate, in detail, the maximum base shear measured experimentally. Finally, the conclusions in Section 8 show that the repair strategy is effective, rapid, and feasible on construction sites. The stiffness, maximum base shear, and displacement ductility were restored, and the energy dissipation capacity improved. The design code equations can correctly predict the column shear strength, and therefore can be a valid tool for designers.