esa-sceva/satcom-chunk-collection huggingface dataset:
# Seismic Performance Assessments of RC Frame Structures Strengthened by External Precast Wall Panel
## 2 Experimental Investigation  
### Test Specimens  
In order to verify the seismic performance of RC frame structures strengthened with EPCWs, a total of four specimens were fabricated, using as variables the method of connection between the frame and the wall, and the size of the EPCW. In consideration of the laboratory conditions, the specimens were made of 1/3 scaled one-bay single-story frames. Specim FC shown in Figure 1 is a non-strengthened RC frame specimen, and the cross sections of the columns and beams are \(150\times 150\) mm and \(150\times 250\) mm, respectively. Specim FBC is a specimen in which the EPCW is connected to the beam and column, as shown in Figures 2 and 3, where Figure 2 shows the details of the anchoring. It should be noted that the PC walls were separately cast from the frame structures in the same way that they are cast for retrofitting in the field. The size of the EPCW is \(1200\times 1000\times 70\) mm. For the Specim FB, all the details are the same as Specim FBC; however, the EPCW is connected only to beams, not to columns. Specimens FBC and FB are used to investigate differences in behavior, according to the connection location of the PC wall and the existing RC frame.  
Figure 1: _Cont._  
Figure 1: Details of Specimen FC (unit: mm): (**a**) front view; (**b**) side view.  
Figure 2: Details of externally anchored precast wall panel.  
Figure 3: Details of Specimen FBC (unit: mm): (**a**) reinforcement details of frame; (**b**) reinforcement details of wall.  
Specimen FPB, shown in Figure 4, is a specimen of two strengthened EPCWs either side of a column, in the form of a wing wall. The dimensions of each EPCW are \(350\times 1000\times 70\) mm, which correspond to 1/4 of the EPCW applied to Specimens FBC and FB. The PC wall applied to Specim FPB is designed to facilitate lifting operations and transportation by reducing its size. Moreover, it has excellent constructability. The PC walls were connected to the foundation using D10 headed bars, as shown Figures 3b and 4b. The EPCW and RC frame are connected using high-strength anchor bolts. The high-strength anchor bolts have a diameter of 26.5 mm, their yield and tensile strengths are 900 and 1100 MPa, respectively, and their elastic modulus is 205,000 MPa. The high-strength anchor bolts are assembled using hexagonal nuts and bearing plates, as shown in Figure 2. In addition, a strain page is attached to the center of the anchor bolts to tighten the nuts until it they are about 300 \(u\varepsilon\), so that the same tensile force is introduced to the anchor bolts. In the frame and EPCW, concrete with a compressive strength of 21.8 MPa is used. The yield and tensile strengths of the D6 reinforcement are 381 and 426 MPa, and those of the D10 reinforcement are 502 and 619 MPa, respectively.
# GNSS RTK Positioning Augmented with Large LEO Constellation
## 5 Assessment of Combined GPS/LEO RTK for Different Length Baselines
### RTK for Short Baselines  
In this statistic analysis, 5 representative short baselines with lengths less than 10 km are chosen to evaluate the RTK performance with the augmentation of different numbers of LEO satellites, where the atmospheric residuals can be assumed absent through DD equations. Figure 7 shows the average convergence time and TTFF of different LEO constellations augmented GPS RTK solutions. It can be clearly found that the RTK solutions for short baseline schemes can easily converge or fix within a few seconds, whether or not LEO satellites are introduced. As the baseline range becomes much shorter, the augmented effectiveness gradually becomes unobvious for the LEO satellite numbers. After further statistics of average convergence time and TTFF of all short-baseline solutions, the 60- and 96-LEO schemes can achieve convergence and fixing in about 2 s, compared with that of about 4 s for the GPS-only scheme. And the slightly better augmentation performances can be realized within about 1 s for 192- and 288-LEO schemes.
# A Review on Head-Related Transfer Function Generation for Spatial Audio
## 4 Physical Modelling
### Boundary Element Method
The linear problem can be solved by matrix inversion to obtain pressure field values on the surface of \(\Sigma\):  
\[\mathbf{u}_{\text{c}}=\Big{(}H_{\text{c}}+\frac{1}{2}I\Big{)}^{-1}G_{\text{c }}\mathbf{q},\qquad\quad\mathbf{u}_{\text{c}}=\Big{(}H_{\text{g}}+\frac{1}{2} B_{\text{g}}\Big{)}^{-1}G_{\text{g}}\mathbf{q}. \tag{18}\]  
where \(I\) is the identity matrix. The solutions of either the first (for collocation schema) or second (for the Galerkin schema) of Equations (18) can be used to derive the pressure field value at a generic position in space by numerically evaluating the integrals (15) in Equation (14). Problem (17) is a computationally expensive and a memory-hungry task because the number of elements in the mesh ranges between tens of thousands to hundreds of thousands, and the matrices in (17) are fully populated and complex (except \(B_{\text{g}}\), which is sparse). Furthermore, in collocation schemata, the matrices are not symmetric. The direct solution to the problem has complexity \(O(N^{3})\). The use of iterative methods can reduce the computational complexity from \(O(N^{3})\) to \(O(N^{2})\), but the cost of repeated iterations remains high. Originally developed in 1980s for dealing with many body problems governed by long-range interactions [102], the fast multipole method (FMM) can reduce computational complexity, at the cost of approximation errors. This means that computing HRTFs in a reasonable amount of time without the need for super-computational resources is feasible. Roughly speaking, the FMM is based on a multipole expansion of Green's function, which allows neighboring sources to be grouped together and treated as a single source. When applied to BEM, the method groups together elements of the mesh and partially substitutes element-to-element interactions with cluster-to-element and cluster-to-cluster interactions. Methods where the FMM is applied to BEM are usually called fast multipole boundary methods (FMBEMs). A presentation of the method applied to the wave equation is given in [103], where a single-level application of the method is shown to reduce the computational complexity from \(O(N^{2})\) to \(O(N^{\frac{3}{2}})\). This method is applied in [104], where a multilevel clustering of elements is introduced. In this paper, the method is tested on HRTF generation in a frequency range between 1000 Hz and 18,000 Hz. The use of a multilevel approach can reduce the computational complexity from \(O(N^{2})\) to \(O(N\log(N))\) or \(O(N)\), making it possible for the simulation of large-scale systems with reasonable computational resources. [PERSON] et al. [105] contains a short exposition of the multilevel FMM applied to BEM (see references therein for further details).
## 4 Experiments
### Application of the Proposed System  
We want to show through the application that a hand gesture-based system is available on behalf of a keyboard or mouse-based system. Thus, we develop practical application with hand gesture recognition as shown in Figure 12. When the user stands in front of the RGB-D camera, video frames are captured and the hand is detected. When the user interacts with a computer, a deep learning technique is used for hand gesture recognition internally in the system. Therefore, users can easily use the hand gestures to interact and control applications. Figure 13 shows an example of how our hand gestures-based real-time HCI system controls the computer with Kinect V2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Models** & **Accuracy (\%)** \\ \hline
3 DCNN & 92.60 \\ Ensemble model with 5 different 3 DCNN networks & 96.42 \\ Ensemble model with 10 different 3 DCNN networks & 96.82 \\ Ensemble model with 15 different 3 DCNN networks & 97.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental result of the ensemble learning approach.  
Figure 12: Flowchart of virtual mouse system.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Target** & \multicolumn{5}{c}{**Selected**} & \multicolumn{5}{c}{**Acc**} \\ \hline  & **SL** & **SR** & **SU** & **SD** & **ZI** & **ZO** & **M** & \\ \hline SL & 149 & 0 & 0 & 0 & 0 & 0 & 1 & 0.99 \\ SR & 1 & 148 & 0 & 0 & 0 & 0 & 1 & 0.99 \\ SU & 1 & 0 & 145 & 0 & 2 & 0 & 2 & 0.97 \\ SD & 0 & 0 & 0 & 146 & 1 & 1 & 2 & 0.97 \\ ZI & 0 & 0 & 0 & 0 & 143 & 4 & 3 & 0.95 \\ ZO & 0 & 0 & 1 & 0 & 4 & 141 & 4 & 0.94 \\ M & 1 & 0 & 0 & 0 & 0 & 1 & 148 & 0.99 \\  & & & & & & & & 0.97 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Confusion matrix of the proposed method. SL, swipe left; SR, swipe right; SU, swipe up; SD, swipe down; ZI, zoom in; ZO, zoom out; M, meaningless.
## 3 Results and Discussion
### Characterization of Effluents Generated in Electro-Coagulation and Electro-Oxidation Experiments
#### 3.2.1 Monitoring of Chemical Oxygen Demand  
Reaction time is of significant importance in the EC and EO processes. Figures 4a and 5a show the evolution of COD values, shown as the standard COD in the EC and EO, respectively. The longer the treatment time, the lower the COD values. In the case of the EC process, this was attributable to the higher amount of hydroxide formed [78]. In the EO process, the longer the time, the greater the electron transfer, which promoted the direct oxidation of organic matter [79]. In the EC process, the greatest removal was achieved in experiment 5 (62 \(\pm\) 2) in 120 min. Other leachate studies have yielded different COD removal results using Fe anodes. [PERSON] et al. [31] obtained 53% in 60 min, [PERSON] et al. [23] removed 57% in 90 min, whereas [PERSON] et al. [80] achieved 68% in 60 min. The explanation for COD removal above 50% is that the Fe anode is very efficient through the generation of \"in situ\" ions that favor the removal of polluting organic compounds [81]. In the EO process, the highest COD removal was achieved in experiment 5 (52 \(\pm\) 2%). Other EO investigations using BDD anodes in leachates have generated different chemical oxygen demand (COD) removal results. [PERSON] et al. [82] achieved an 80% removal within 90 min. [PERSON] et al. [83] removed 51% of COD after 8 h of treatment. The efficacy of BDD anodes for the degradation of recalcitrant organic compounds during EO is known. The anode BDD favors the formation of \(\bullet\)OH, which can participate in the non-selective combination of all organic materials [84].  
In electrochemical processes, ELC is a crucial factor. The higher the ELC, the greater the efficiency of the process, the shorter the time required for treatment, and, consequently, the lower the energy consumption [78]. A comparison of the ELC values in the EC effluents showed a decrease in values in all experiments performed. Effluents with values of 2.2 to 2.9 mS cm\({}^{-1}\) were obtained. Under optimal conditions for greater COD removal, an effluent with 2.2 mS cm\({}^{-1}\) was obtained. This decrease could be explained by the decrease in dissolved organic matter, which indicated that, in addition to the EC process that decreased suspended organic matter (colloids), an indirect EO process occurred. During EC, EO could occur simultaneously [78]. The decrease in the ELC values of the effluents of the EC process could be a problem for the second stage of treatment, EO. A lower ELC value could decrease process efficiency. To remedy this situation, the addition of an additional electrolyte (NaCl) increased ELC. The electrolyte addition was an independent variable in the experimental design of the EO process.
# A Review of Seatbelt Technologies and Their Role in Vehicle Safety
## 3 Results
### Mechanical and Electronic Synergy of Seatbelt Components  
Modern occupant restraint systems rely on the coordinated operation of mechanical and electromechanical subsystems to optimize crash energy management and occupant kinematics. The seatbelt system, comprising the retractor, pretensioner, and load limiter, exemplifies this integration.  
The retractor, typically a spring-loaded spool with a locking mechanism, governs belt retraction and dynamic locking in response to vehicle deceleration or webbing acceleration. Pretensioners, which are often pyrotechnic or motor-driven, activate within milliseconds of crash detection, retracting the belt slack to secure the occupant firmly against the seat. Subsequently, load limiters allow controlled belt payout beyond a defined force threshold, thereby mitigating thoracic loading and reducing the likelihood of soft tissue or skeletal injury. These components are increasingly embedded within an intelligent network of sensors such as accelerometers, crash pulse detectors, occupant classification systems, and electronic control units, enabling real-time decision making and adaptive restraint strategies.  
In Table 3, the mechanical and electronic functions in a seatbelt retractor are presented.  
In Figure 11, the diagram of seatbelt mechanical components, one related to another, is presented.
## 4 Experimentation
### Results and Discussion
#### 4.2.3 Effect of Transfer Learning  
For this technique, we used two different approaches: (i) in the first approach, we freshly trained \(CNN_{1}\), \(CNN_{2}\), and \(CNN_{3}\) architectures with the corresponding image sizes, i.e., refreshed models were used; (ii) in the second approach, we used the trained \(CNN_{1}\) model as a retrained model for \(CNN_{2}\) such that only upper layers of the \(CNN_{2}\) model were trained with \(\mathcal{F}_{96\times 96\times 3}\) images. Similarly, the upper layers of the \(CNN_{3}\) architecture were trained by \(\mathcal{F}_{192\times 192\times 3}\) images, while for the remaining layers, the weights of the trained \(CNN_{2}\) model were used. Figure 14 presents the effect of transfer learning approaches on the performance of the proposed FERS. Here, only the performance of the \(CNN_{3}\) model trained with \(\mathcal{F}_{192\times 192\times 3}\) images is shown using both progressive image resizing and data augmentation techniques.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & \
\begin{tabular}{c} **Data** \\ **Augmentation** \\ \end{tabular} &
\begin{tabular}{c} **No Data** \\ **Augmentation** \\ \end{tabular} \\ \hline \multirow{3}{*}{KDEF} & 75.95 & 71.67 \\  & 78.18 & 74.92 \\  & **80.92** & 78.21 \\ \hline \multirow{3}{*}{GENKI} & 92.45 & 87.91 \\  & 94.13 & 89.77 \\  & **95.59** & 91.16 \\ \hline \multirow{3}{*}{CK+} & 95.89 & 88.15 \\  & 96.22 & 92.67 \\  & **96.71** & 95.20 \\ \hline \multirow{3}{*}{SFEW} & 34.05 & 32.56 \\  & 34.91 & 33.11 \\ \cline{1-1}  & **35.72** & 33.34 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Effect of the progressive image resizing on the performance of the proposed FERS where the first, second, and third row for each database shows the accuracy in percentage using \(CNN_{1}\) (\(\mathcal{F}\in\mathbb{R}^{48\times 48}\)), \(CNN_{2}\) (\(\mathcal{F}\in\mathbb{R}^{96\times 96}\)), and \(CNN_{3}\) (\(\mathcal{F}\in\mathbb{R}^{192\times 192}\)) models, respectively.
# Recent Advances and Applications of Semiconductor Photocatalytic Technology
## 3 Common Classification of Photocatalysts
### Non-Oxide Photocatalyst
#### 3.2.4 Nitride Series Photocatalyst  
In 1989, [PERSON] and [PERSON] [135] succeeded in replacing the Si atom with the sp\({}^{3}\) hybridization of C atoms according to the well-known \(\beta\)-Si\({}_{3}\)N\({}_{4}\) structure. Four sp\({}^{3}\) hybridized N atoms are bonded to the C atom, and three C atoms are bonded to each N atom to form a nearly planar structure of the carbon-nitrogen compound. In 1993, _Science_ published one paper on the successful synthesis of C\({}_{3}\)N\({}_{4}\) crystals with a hardness higher than that of diamonds using laser sputtering technology in the Harvard University laboratory [136; 137], which quickly attracted the attention of the global materials community. By 1996, [PERSON] and [PERSON] [138] believed that carbon nitride might have a structure of \(\alpha\) phase, \(\beta\) phase, cubic phase, quasi-cubic phase, and graphite-like phase.  
In 2009, [PERSON] et al. [139] reported on a polymer semiconductor material g-C\({}_{3}\)N\({}_{4}\) consisting entirely of non-metallic elements. g-C\({}_{3}\)N\({}_{4}\) is a graphite-like layered structure, and g-C\({}_{3}\)N\({}_{4}\) has an excellent electron/hole transporting ability, a high thermal stability and chemical stability, and a narrow band gap (2.7-2.8 eV). The forbidden bandwidth of g-C\({}_{3}\)N\({}_{4}\) allows it to have a strong light absorption in the visible light region (400-450 nm). Furthermore, the structure and properties of g-C\({}_{3}\)Nare easy to control, making it a hot research topic in the field of photocatalysts [140; 141]. g-C\({}_{3}\)N\({}_{4}\) shows excellent photocatalytic performance in the field of photo hydrolysis, hydrogen production, photodegradation of environmental pollutants, CO\({}_{2}\) reduction, sterilization, and synthesis of composite capacitor materials [141; 142; 143; 144; 145; 146; 147; 148; 149].  
The forbidden band width of semiconductors is an important factor limiting the wide application of photocatalysts.; as such, the forbidden band widths of some commonly used photocatalysts are summarized in Table 2.
## 2 Theoretical Background of Stemming
### Shock Wave Propagation Characteristics of Stemming Materials  
The impact force on the rock mass during a blast can be split into two components: the shock wave from the explosion and the pressure from the detonation gases. This occurs because the rock mass breaks apart due to both the force of the shock wave and the pressure of the detonation gases. Typically, near where an explosion occurs, the shock wave lasts between 10 to 100 microseconds [16].  
Various factors, like how the detonation gases expand cracks forming and connecting and their movements in directions, play a role in determining how long this pressure from detonation gases lasts. Studies suggest that in deep hole bench blasting with a decoupling charge setup, this duration is 10 ms based on both research and field tests [17].  
When rocks are blasted, shock waves move through where the explosive material meets the stemming material. Some of this shock wave goes into either stemming or rock (transmitted); some reflects back into the blast hole (reflected) upon hitting rock surfaces [7]. The behavior of these waves at boundaries is influenced by differences in impedance. Equation (2) is used to define a medium's impedance Z:  
\[\text{Z}=\rho\text{C}_{p}, \tag{2}\]  
In this context, \(\rho\) represents the density of the material (kg/m\({}^{3}\)), and \(C_{p}\) denotes the velocity of sound (m/s). When a shock wave travels from a material of lower impedance to one of higher impedance, there is an increase in shock pressure, and the reverse occurs when moving from high to low impedance. The impedance values for the shear-thickening fluids (STFs) used in this study, along with those for common stemming materials such as aggregate and sand, are detailed in Table 1.
## 4 Data Analysis and Results  
The combination of data from various and heterogeneous sources is a complex task that needs to be evaluated and prioritized to examine their compatibility grade. The field data analysis provides us with strong arguments for establishing plausible planes of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mission** & **Orbit** & **Track** & \begin{tabular}{c} **Incidence Angle** \\ **(Degrees)** \\ \end{tabular} & \
\begin{tabular}{c} **Observation** \\ **Period** \\ \end{tabular} &
\begin{tabular}{c} **Span after** \\ **Mainshock** \\ \end{tabular} \\ \hline Sentinel 1B-Sentinel 1A & Ascending & 102 & 43.8 & 23 September 2021–29 September 2021 & 2 days \\ Sentinel 1A-Sentinel 1B & Descending & 036 & 43.8 & 25 September 2021–1 October 2021 & 5 days \\ Sentinel 1B-Sentinel 1B & Ascending & 029 & 33.8 & 18 September 2021–30 September 2021 & 3 days \\ Sentinel 1B-Sentinel 1A & Descending & 109 & 33.8 & 24 September 2021–30 September 2021 & 3 days \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the interferometric pairs considered in DInSAR processing, including the LoS incidence angles of the measurements. The time span of each DInSAR pair following the mainshock is also indicated.  
Figure 4: Co-seismic LoS displacement maps, as derived using GEP DIAPASON service, for ascending track A102 (23 September 2021–29 September 2021) and descending D036 (25 September 2021–1 October 2021) (**c,d**) and the corresponding wrapped differential interferograms (**e,f**). The spatial extend of Sentinel-1 acquisition frames for both geometries are also indicated (**a,b**). The difference in the temporal span following the mainshock (27 September 2021) for the two opposite geometries is worth noting. While A102 spans approx. two days, D036 extends over five days after the earthquake, denoting larger contribution of post-seismic motion. Black lines correspond to fault zones (after [3]). The relocated epicenter of the 27 September 2021 mainshock is marked with a star.  
movement, which host the hypocenters of an earthquake. Therefore, it offers a tool for filtering them with higher relevance to the contemporary tectonic regime. In addition, the field data were used to validate the surface deformation detected through DInSAR data and attribute it to specific geological structures (e.g., active faulting).
## 4 Simulation Results and Discussions
### Utility Comparison under Different Algorithms with Feedback Error and Eavesdropping Error  
In one epoch, we average the user's utility every 20 time slots and obtain the comparison of the user's utility under the proposed HJOA algorithm and random selection algorithm, as shown in Figure 7. Moreover, we analyzed the influence of feedback error on utility. Under the HJOA  
Figure 5: Relay selection probability.  
Figure 6: Regret of jammer.  
algorithm, we can find the user's utility grows firstly and remains stable then, that is because the user has gradually obtained the optimal strategy with the iterations of algorithm. When the user can receive correct feedback, i.e., \(\varepsilon_{1}=0\), we can find the user's utility under the HJOA algorithm reaches the maximum value and is improved compared with random selection algorithm. The greater feedback error \(\varepsilon_{1}\) is, the larger deviation between the user's received feedback and actual value is, which causes the decrease of utility.  
In Figure 8, we average the jammer's utility every 20 epochs and obtain the comparison of the user's utility under the proposed algorithm and random selection algorithm. We analyze the influence of eavesdropping error on utility. Under the proposed the HJOA algorithm, we can find that the jammer's utility grows quickly firstly and grows slower then, that is because the jammer has gradually obtained the optimal strategy. When the jammer can eavesdrop the correct feedback, i.e., \(\varepsilon_{2}=0\), its utility reaches the maximum value and is higher than utility under random selection algorithm. The greater feedback error \(\varepsilon_{2}\) is, the lower the jammer's utility is because the existence of \(\varepsilon_{2}\) causes the jammer cannot eavesdrop the user's feedback information accurately and influences its decision-making.