esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Bending Test Results  
A bending test by its physical nature involves both tensile and compressive loading. This is a common case in dental medical practice, which justifies the research of materials and constructions based on this method. The outcomes of such tests on the aforementioned materials are summarized in Figure 14. Figure 14 shows that the load-displacement curves for 3D-printed and milled samples have different slopes up to about 0.5 mm of displacement. However, in the initial section, the angular dependence coefficient is analogous to that observed after about 0.5 mm sag of the specimen, which is taken as the region of linear development of the force-displacement curve. From the curves in Figure 14, it is also seen that the milled Ti specimens have a larger plastic strain region than the 3D printed ones. After the load was stopped (at a sag of \(\sim\)4.9 mm), the 3D-printed Ti specimens showed cracks in the region of maximum tensile stresses.  
Confirmation of the assumption about the different ability of the studied materials to develop plastic deformation can be seen in the pictures from the optical microscope in Figures 15 and 16. Those areas of the test specimens that have sustained the greatest tensile stress are shown there. An additional possibility was to check for the existence of a possible influence of the different strain rates. Upon careful comparison of the two pictures (a,b) in Figure 15, a violation of the specific texture of the surface of the object in Figure 15b can be noticed. This is due to the creep effect of the material, the analogue of which was observed in Figure 12. It is important to note that the strain rate did not lead to the appearance of visible cracks.  
The result of the same deformation impact on the 3D-printed Ti-6 Al-4V test specimens is significantly different. In order to better highlight the difference caused by the loading speed, the side view of the test specimens was chosen (Figure 16).  
In contrast to the mechanical processing of the surfaces, with the \"SLM\" technology, surfaces with a granular structure are obtained. In this sense, the natural lack of sufficient smoothness of the walls of the test specimens does not allow the sharp distinction of the edge from the adjacent walls of the specimen. This is the reason for the questionable accuracy when measuring the depth of the formed crack. A consequence of this is the impossibility of assessing the degree of influence of the loading rate. It is important to note that at the same degree of deformation, we observe consecutive plastic deformation  
Figure 14: Load–displacement curves, obtained by means of three-point bending test (1 mm/min).  
processes and, subsequently, the appearance of a crack. Because of the degree of deformation realized, no moment of final separation of the specimen into two parts was reached, regardless of the bending speed. The results of the three-point bending test are summarized in Table 4. The sample with second indices \"2\" was tested at an indenter displacement speed of 60 mm/min.
## 6 Transceiver Architectures' Classification and Basic Concepts
### 2D-Based Architectures
#### 6.2.2 PolMux \(Iq+\) Carrier (C)/Stokes Vector Receiver  
As the DP-IM/DD DP-IM/Stokes vector receiver, the PolMux \(IQ+C\)/Stokes vector receiver scheme exploits two DoFs to enhance the system performance, as illustrated in Figure 11b. For instance, it employs the signal \(I\) and \(Q\) parts along with the CW carrier (\(C\)) at the transmitter. Therefore, in the PolMux-\(IQ+C\) scheme, at the transmitter, two RF signals are encoded on two orthogonal phase quadratures (\(I\) and \(Q\)). Furthermore, a copy of the carrier is multiplexed with the \(IQ\) signal on the orthogonal polarization [23]. The polarization derotation can be realized at the receiver using a Stokes vector receiver and the associated MIMO DSP. Subsequently, the full-complex field information can be provided on the \(X\) polarization. Therefore, with this configuration, chromatic dispersion compensation at the Rx-DSP is possible after MIMO polarization derotation [14].
## 4 Field Data Processing
### Reconstruction of Virtual Shot Gathers by Cross-Correlation  
After preprocessing the raw field data, one geophone served as a virtual source for waves recorded by other receivers when using the cross-correlation calculation; thus, we could obtain all virtual shot gathers from every receiver without using an active source. In this paper, we showed the virtual shot gathered with the first receiver as the virtual source. In addition, in order to better characterize the intensity of the surface waves on the reconstructed virtual shot gathers, we used the dispersion curve image obtained by the phase-shift measurement method as the visualization tool.  
Figure 5a shows the virtual shot gather formed by the cross-correlation of raw passive seismic data acquired in Sichuan for a certain hour. The surface waves (indicated by red arrows) can be seen in the red box on the virtual shot gather but are concealed by the energy of a strong transverse axis. The corresponding dispersion curve image is shown in Figure 5b, and the dispersion curve (red part) is disordered and discontinuous.  
After preprocessing the raw field data, the virtual shot gather formed by the separated noise records is shown in Figure 6a. Compared with Figure 5a, the in-phase axis (indicated by the red arrow) of the internal surface waves in the red box for the processed virtual shot gather is clearer, but it is still concealed by the energy of the strong horizontal axis. The corresponding dispersion curve image is shown in Figure 6b, where the curve (red part) is clearer and more continuous than in Figure 5b.  
By comparing the virtual shot gathers and their corresponding dispersion curve images in Figures 5 and 6, we saw that the virtual shot gather formed by the separated noises had clearer surface wave in-phase axes and dispersion curves, which proved that the noise or signal removal algorithm based on SS-CWT could effectively enhance the reconstructed surface waves after preprocessing the raw field data.  
Figure 5: (**a**) Virtual shot gather formed by cross-correlation of raw one-hour field data and (**b**) the corresponding dispersion curve image.
# Image Classification for Automated Image Cross-Correlation Applications in the Geosciences
## 4 Results
### Shadow Impacts of ICC  
The MNS2_a dataset comprises three groups of ten images with different illumination states. As the acquired scene is fixed, it is valid to assume that any possible change in the results relates principally to the shadow effect. Figure 5 shows a boxplot of the mean (\(\mu\)) and standard deviation (\(\sigma\)) of the single CC maps of each group; these were computed for all possible image couples, and differentiated for the vertical (\(y\)) and horizontal (\(x\)) directions. It is evident that the dispersion of the two variables is at a minimum for the images acquired with diffuse illumination. Moreover, the \(\sigma\) obtained with images acquired at the same hour and in direct illumination are approximately two and three times greater, on average, than that obtained for images acquired in diffuse illumination. It is also evident that the \(\mu\) of the horizontal component has slightly greater variability, while there are no significant differences between the two components for the \(\sigma\).  
Figure 6 presents the \(\mu\) and \(\sigma\) of the ICC maps produced using all of the couples of the available images from the dataset MNS1, where the results are ordered according to the simulated position of the sun during the day. The results obtained with synthetic shaded relief show that both \(\mu\) and \(\sigma\) increase with an increase in the difference between the azimuth and elevation of the sun's position. In addition, both values are higher for the horizontal direction, which indicates that the change in the sun's azimuth has a greater effect on CC results than the change in elevation.  
Figure 5: Boxplots of (**a**) mean and (**b**) standard deviation of CC. Subscripts D, 12 and S are as follows: D represents images acquired respectively with diffuse illumination, 12 denotes images with direct illumination taken at a fixed hour of the day (i.e., 12:00), and S concerns images taken in direct illumination with different sun azimuths and elevation angles.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **SunLight** & **DiffLight** & **NoVis** & **SD** & **SN** & **DN** \\ \hline D1 & 24 & 78 & 11 & 16 & 0 & 10 \\ G1 & 838 & 386 & 171 & 105 & 15 & 34 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of images manually classified for datasets D1 and G1.
###### Abstract  
A new pre-supporting technology named the freeze-sealing pipe roof (FSPR) method was adopted in the construction of Gongbei tunnel (Zhuhai, China), a critical part of the Hong Kong-Zhuhai-Macau bridge (HZMB) project. The method combined pipe-roofing with artificial ground freezing (AGF). The pipe roof which included a number of large-diameter steel pipes was designed to play a primary role in load bearing, while the frozen wall between pipes was designed for water sealing. The refrigeration proceeded in two stages called the active freezing period and excavation period. This paper mainly focuses on the freezing characteristics of FSPR to explore how the frozen soil wall developed and changed over time during both periods based on field temperature data. The results show that the development of the frozen wall met the design requirements in fewer than 80 days of refrigeration considering the most unfavorable situation. The distribution of frozen soil along the entire tunnel was non-uniform. Frost heave and thaw weakening problems should be taken into account, since some of the bottom section of the frozen wall was more than 3 m. The frozen soil at the excavation side was visibly influenced by the replenishment of heat due to excavation, while the frozen soil outside the excavation face was much less influenced. The thermal effects of Hurricane Nicole on the frozen soil wall was also observed. The conclusions provide experience, reference, and guidance for the development of similar projects in the future.  
shallow buried tunnel; freeze-sealing pipe roof; ground freezing; freezing characteristics; field study; Hong Kong-Zhuhai-Macau bridge
# An Enhanced Non-Coherent Pre-Filter Design for Tracking Error Estimation in GNSS Receivers
## Appendix C \(C/n_{0}\) Estimation of GNSS Signals  
The power ratio method is used to estimate \(C/N_{0}\) in this paper, which involves the comparison between wideband power \(P_{\mathrm{w}}\) and narrowband power \(P_{\mathrm{n}}\). The estimation of \(C/N_{0}\) is shown as follows:  
\[P_{\mathrm{w}}=\sum_{n=1}^{M}\left(I_{P}(n)^{2}+Q_{P}(n)^{2}\right)\] (A19)  
\[P_{\mathrm{n}}=(\sum_{n=1}^{M}I_{P}(n))^{2}+(\sum_{n=1}^{M}Q_{P}(n))^{2}\] (A20)  
\[P_{\mathrm{n/w}}=\frac{1}{N}\sum_{r=1}^{N}\frac{P_{N,r}}{P_{w,r}}\] (A21)  
\[c/n_{0}=\frac{1}{T_{\mathrm{coh}}}(\frac{P_{\mathrm{n/w}}-1}{M-P_{\mathrm{n/w} }})\] (A22)  
\[C/N_{0}=10\mathrm{lg}(c/n_{0})\] (A23)  
where \(M\) and \(N\) are parameters of power ratio method, \(c/n_{0}\) is the carrier-to-noise density ratio in the unit of Hz, and \(C/N_{0}\) is the carrier-to-noise density ratio in the unit of dB-Hz.
# Design and Analysis of High-Accuracy Telecentric Surface Reconstruction System Based on Line Laser
## 4 Solution Concept
### Laser Stripe Extraction  
Laser stripe extraction is the fundamental step to locate the ROI. The laser line in the images is expected to be single-pixel width ideally. However, there is a line width for the laser employed in practice, which is much wider than the width expected. Thus, the first step of measurement is laser stripe extraction. As the laser stripe conforms to a Gaussian distribution in the vertical direction theoretically, a Gaussian filter is generally employed as the image preprocessing for laser stripe extraction. After the preprocessing, a weighted centroid localization algorithm is carried out to obtain sub-pixel location.  
As it is shown in Figure 8, the laser stripe in the image generally possesses the higher grey level. Therefore, it is easy to obtain the binary image of a laser stripe by a threshold method and some other image preprocessing. Furthermore, the minimum bounding box of laser stripe is achieved by a common connected components algorithm. Suppose the bounding box is a \(m\times n\) binary matrix \(L_{m\times n}\), in which the laser pixels are labeled as 1 while others are 0. Considering the grey level weighting, the weighted matrix of laser \(WL_{m\times n}\) is expressed as Equation (13).  
\[WL_{m\times n}=\text{times}(L_{m\times n},I_{m\times n}). \tag{13}\]  
\(I_{m\times n}\) is the corresponding region of the laser bounding box in the regular grey image. \(\text{Times}(a,b)\) represent the element-by-element multiplication of two matrixes \(a\) and \(b\), which are in the same size. Suppose that the direction of the laser line is along the columns of \(WL_{m\times n}\) as it is shown in Figure 8, the sub-pixel result of laser stripe extraction \(LoL_{m\times 1}\) is located by Equation (14).  
\[LoL_{m\times 1}=\text{times}\left(WL_{m\times n}\cdot C_{n\times 1},\,\frac{1}{ \text{sum}_{col}(WL_{m\times n})}\right), \tag{14}\]  
where \(C_{n\times 1}=\left[1,2,\cdots,n\right]^{\text{T}}\) represents a coordinate column vector. \(\text{sum}_{col}(*)\) is the function to calculate the sum of the matrix in columns. \(LoL_{m\times 1}\) is the sub-pixel location of the laser stripe with grey level weighting, which remains a single point in the direction of line width. The result is shown in Figure 8.
# Buckling-Restrained Bracing System with Ultra-High-Performance Fiber Concrete
## 7. BRB and RBRB Numerical Analysis Results Comparison
### BRB and RBRB Performance Comparison  
In this section, the effective damping (K\({}_{\text{effb}}\)), effective stiffness (\(\varphi\)) and dissipated energy (W) for both specimens were calculated. The energy dissipated in a single hysteresis loop (W\({}_{\text{i}}\)) is calculated by considering the area enclosed by the hysteresis loop using Equation (1) Figure 48. F\({}_{\text{i}}\)(u) and F\({}_{\text{ul}}\)(u) are forces at displacement 'u' throughout the loading and un-loading process. The summation of the total area enclosed by the hysteresis loop gives the cumulative energy dissipated in the specimen. The performance of BRB and RBRB subjected to cyclic loads is shown in Figure 47. As it can be seen in Table 16, both devices are capable of dissipating the energy, which was 8021 kN.mm for BRB and 11.806 kNmm for RBRB, which indicates an increase of 47.19%. The damping ratio was also calculated as 51 percent for BRB and 53 percent for RBRB as calculated based on BS EN 15,129:2009 in Equation (2). Additionally, the effective stiffness of both devices has been investigated, and was 12.76 kN/mm for BRB and 27.2 kN/mm for RBRB based on Equation (3), where V\({}_{\text{Ebd}}\) is the force corresponding to d\({}_{\text{bd}}\), obtained at the third load cycle.  
\[\Delta\text{W}_{\text{i}} =\int_{-\text{v}_{\text{min}}}^{\text{v}_{\text{max}}}(\text{F}_{ \text{i}}(\text{u})-\text{F}_{\text{ul}}(\text{u})) \tag{1}\] \[\varphi =\text{W}\left(\text{d}_{\text{bd}}\right)/(\pi\,\text{V}_{\text{ Ebd}}\,\text{d}_{\text{bd}})\] (2) \[\text{K}_{\text{effb}} =\text{V}_{\text{Ebd}}/\text{d}_{\text{bd}} \tag{3}\]  
The above analytical results approve the RBRB capability to overcome the drawbacks of conventional BRB systems and enhance a structure's resistance against seismic hazards.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Specimens** & **Effective Stiffness** & **Effective Damping** & **Dissipated Energy** \\ \hline BRB & 12.76 & 0.518 & 8021 \\ RBRB & 27.2 & 0.530 & 11.806 \\ Effectiveness & 46.9\% & 3\% & 47.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 16: Experimental comparison values for BRB and RBRB in effective stiffness, effective damping and dissipated energy.  
Figure 48: Effective stiffness equation parameters.
# Realistic Aspects of Simulation Models for Fake News Epidemics over Social Networks
## 4 Results
### Impact of Time Dynamics  
We now analyze the impact of time dynamics on the simulation results. In particular, we show that including time dynamics is key to obtaining realistic OSN interactions. We focus on Figure 6, where we show the all-time maximum fraction of believers and fact-checkers over time for different network configurations. We present results both with and without bots and eternal fact-checkers, as well as with and without time dynamics.  
In the absence of time dynamics, all types of agents access the network synchronously. Such synchronous access patterns make simulated OSN models inaccurate as, e.g., we expect that bots access social media much more often than common users, in order to increase and maintain the momentum of the fake news spreading. Conversely, enabling time dynamics allows us to model more realistic patterns. Specifically, we recall that we allow bots to access the network four times more often than a common user.  
In the absence of time dynamics, a larger fraction of the network remains unaffected by the fake news. For example, in the case that the network contains 10 bots, 30 influencers, and no eternal fact-checker, the maximum fraction of believers is 0.81 in the absence of time dynamics (light blue solid curve) and increases to 0.87 (\(+7\%\)) with time dynamics (brown solid curve), thanks to the higher access rate of the bots. Because time dynamics enable the fake news to spread faster, the number of fact-checkers increases more slowly. In Figure 6, we observe this by comparing the orange and green dashed lines.  
Figure 6 also shows the effect of eternal fact-checkers: their presence does not slow down the spreading process significantly, as negligibly fewer network users become believers. Instead, eternal fact-checkers accelerate the recovery of believers, as seen by comparing, e.g., the red and blue lines (for the case without time dynamics) and the green and brown lines (for the case with time dynamics). In any event, fact-checkers access the networks as often as common nodes; hence, their impact is limited. This is in line with the observation that time dynamic network access patterns slow down epidemics in OSNs [54].
## 2 Materials and Methods  
### Raw Material and Reagents  
_Adamsonia digitata_ L. fruits were collected from traditional local markets in three main municipalities of Luanda, Angola (Quicama: 9.7984\({}^{\circ}\) S, 13.7289\({}^{\circ}\) E; Icolo Bengo: 9.1871\({}^{\circ}\) S, 13.8216\({}^{\circ}\) E; and Belas: 9.0834\({}^{\circ}\) S, 13.1279\({}^{\circ}\) E). Luanda has a dry tropical climate, and its typical soil consists of luvisols, calcisols, and cambisols (calcareous and calcialitic soils), which give good claye soils for crops. Before analysis, the fruits were blended to create a representative sample from the Luanda region. A 2 mm sieve was used to separate the fruit pulp from the seeds. Following that, the fruit pulp was kept at room temperature (25-28 \({}^{\circ}\)C) in a hermetically sealed vial covered with aluminum foil until analysis.  
All chemicals and solvents used were of analytical grade. 2,2-Diphenyl-1-picrylhydrazyl (DPPH), 2,4,6-tris(2-pyridyl)-s-triazine (TPTZ), ferrous sulphate heptahydrate, sodium carbonate decahydrate, sodium nitrite, aluminium chloride, and ferric chloride hexahydrate were all purchased from Aldrich Chemical Co. (Steinheim, Germany). Catechin and gallic acid were obtained from Sigma Chemical Co. (St. Louis, MO, USA). Dihydrorhodamine 123 (DHR), sodium hypochlorite solution (NaClO) with 4% available chlorine, 3-(aminopropyl)-1-hydroxy-3-isopropyl-2-oxo-1-triazene (NOC-5), \(\beta\)-nicotinamide adenine dinucleotide (NADH), nitroblue tetrazolium chloride (NBT), phenazine methosulfplate (PMS), lucigenin, \(\alpha\),\(\alpha\)-azodiisobutyramidine dihydrochloride (AAPH), fluorescein sodium salt, 6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox), Iiron, ascorbic acid, and quercetin were all obtained from Sigma-Aldrich Co. (St. Louis, MO, USA). 4,5-Diaminobenz fluorescein (DAF-2), 30% hydrogen peroxide (H\({}_{2}\)O\({}_{2}\)), [PERSON]'s reagent, absolute ethanol, sodium acetate, and sodium hydroxide were obtained from Merck (Darmstadt, Germany). Ultrapure water was obtained using a Milli-Q water purification system (TGI Pure Water Systems, Leominster, MA, USA). The peroxynitrite anion (ONOO\({}^{-}\)) was obtained by synthesis, as described by [PERSON] et al. [26]
## 3 Concepts and Problems
### IploM  
IPLoM (Iterative Partitioning Log Mining) is a technique that clusters log entries in order to acquire event types [23]. It iteratively partitions the messages. The method consists of three steps that are based on different heuristics, as well as a fourth step that returns the different event types.  
Log entries that belong to the same message type are usually equivalent in their size (number of words). Based on this assumption, in the first step, the algorithm creates distinct groups of messages, each with a different size. At this stage, the messages of a group can be interpreted as n-tuples, where \(n\) is the size of the entry.  
Figure 1: Excerpt of log file generated by real-world networking device.  
The algorithm uses the location of tokens (words between two spaces) in the second step to further partition the messages. It uses the heuristic that the token position with the least number of unique words probably consists of tokens that are constant at that position. The messages are once again grouped by the recently discovered unique words. At the end of this step, each group will only involve messages that have the same unique value at the chosen location.  
The final clustering step is performed with the use of bijective relationships. The number of unique words at each position is used to determine the token count that appears most often. This value expresses the number of event types in that group. This indicates the existence of a bijective relationship between the words that are located in the position with the most unique words. This heuristic is used to further partition the data: the first two token positions that have an equal number of unique tokens as the most frequent token count are used to assign messages into different groups.  
At this point, it can be assumed that each group contains messages corresponding to an event type. As a final step, the distinction between constant words and parameters has to be performed. If, for each entry inside a group, there is only one token at a position, then it is considered to be a constant, otherwise, the token is a parameter that is then represented with a wildcard.