esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Study on the Load-Bearing Performance of RC Beams Subjected to ASR Expansion
## 4 Load-Carrying Capacity of RC Beams with ASR Deterioration
### Results and Discussion
#### 4.5.3 Relationship between Aggregate Expansion Cracks Associated with ASR  
Deterioration and the Load-Carrying Capacity of RC Beams  
In this analysis, an RC beam model was created to reproduce ASR degradation, and the relationship between ASR degradation and load-carrying capacity was investigated. As a result, the failure behavior was obtained depending on the method of expansion, and the relationship between the method of ASR deterioration and the load-bearing capacity was also obtained.  
In the constant-expansion case, the maximum load capacity increased while the initial stiffness decreased in the expansion-degraded case. This may be due to the predominant effect of the chemical prestress caused by the expansion rather than the effect of cracking due to ASR degradation [42]. However, the microcracks propagated with bending loading and became macroscopic cracks, decreasing the maximum load. Leaving ASR-deteriorated cracks unattended is not advisable for structural performance reasons.  
The figure focusing on the water inflow condition shows that the top surface inflow increased the load-carrying capacity compared with the sound case. This can be attributed to the fact that the expansion of the top surface of the RC beams causes a convex upward warping deformation, which results in a resistance force against bending. The bottom inflow had little effect on the load-bearing capacity between 0 and 4 mm, and failure occurred at a displacement of about 7.5 mm. This is considered to be because fine cracks were generated at the bottom of the RC beams. Although these fine cracks had little effect on the load-bearing capacity, failure from the fine cracks progressed with loading, resulting in a decrease in the maximum load and failure.  
In the moisture-dependent case, the amount of cracking was small in the case of inflow from the z-direction, and cracking occurred only at the ends of the beams. This result suggests that the amount of cracking is not the only factor that affects the load-carrying capacity; factors other than cracking, such as chemical prestress and boundary conditions, have a strong influence.  
Figure 30: Load-displacement curve.  
Figure 29: Bending fracture situation.  
In addition, the model assumed perfect adhesion between the rebar and mortar, which did not accurately reflect the decrease in rebar adhesion due to ASR deterioration. Thus, the load-bearing capacity did not decrease.
## Materials and Methods
### Handover Prediction Models
#### 2.2.3 Trigger Sensor  
Unlike the two aforementioned models, the trigger sensor method is a reactive handover prediction model. Due to the fact that the positions of the LEDs fixed to the back of the operator's hand and the UR3 robotic gripper were detected by the PhaseSpace during the assembly task and then read using Vizard, the position information from the repetitive assembly task could be used as the basis for specifying when the robot initiated a handover. When the operator completed the assembly of three orange blocks and reached beyond a specific plane toward the handover point, the UR3 robot was triggered to initiate the handover action. A total of 0.6 s was required for one complete handover task (0.45 s for the UR3 robot to move from the starting point to the handover point, and 0.15 s for network transfer latency). Therefore, the trigger plane was set to the location corresponding to 0.6 s before the operator reaches the handover point. Given the average speed of the operator, the trigger plane was set to 12 cm in front of the assembly point. When the PhaseSpace LEDs on the back of the operator's hand were detected to pass this plane, the program immediately sent a command to trigger the robot. Theoretically, in this reactive handover prediction model, the robot and operator should arrive at the handover point simultaneously, minimizing the waiting time and cycle time of the robot and operator.  
The trigger sensor model was used to benchmark the other two models but is unsuitable for predicting robot handover times in actual production systems. First, the trigger sensor must be relatively complicated if it is to instantly detect an operator's hand movements and position. Taking this study as an example, the PhaseSpace system must detect the LED position, which is difficult to implement on production lines due to complexity and cost considerations. Compared with a trigger sensor, the MTM uses the standard cycle time determined beforehand. Moreover, when the Kalman filter is executed on an actual production line, the only system input required is the interval between the removal time of previous and current objects. For a robotic gripper with force feedback, these data are relatively easy to obtain. Therefore, the MTM and Kalman filter are easier to implement on actual production lines than a trigger sensor. Second, in the trigger sensor method, once the network transfer latency time is deducted, the robot has only 0.45 s to respond once the operator's hand has passed the trigger plane. The present study focused on the effect of time variability on handover fluency; no other tasks were given to the robot except for this task. Therefore, this robot had to be kept in a handover-ready state during the experiment to achieve the robot reaction time of 0.45 s. However, in actual collaborative assembly tasks, robots generally have their own tasks in addition to handovers and thus cannot be kept ready for a handover task. The trigger sensor method is thus a suitable point of comparison with the MTM and Kalman filter.
# Detection and Optimization of Photovoltaic Arrays' Tilt Angles Using Remote Sensing Data
###### Abstract  
Maximizing the energy output of photovoltaic (PV) systems is becoming increasingly important. Consequently, numerous approaches have been developed over the past few years that utilize remote sensing data to predict or map solar potential. However, they primarily address hypothetical scenarios, and few focus on improving existing installations. This paper presents a novel method for optimizing the tilt angles of existing PV arrays by integrating Very High Resolution (VHR) satellite imagery and airborne Light Detection and Ranging (LiDAR) data. At first, semantic segmentation of VHR imagery using a deep learning model is performed in order to detect PV modules. The segmentation is refined using a Fine Optimization Module (FOM). LiDAR data are used to construct a 2.5D grid to estimate the modules' tilt (inclination) and aspect (orientation) angles. The modules are grouped into arrays, and tilt angles are optimized using a Simulated Annealing (SA) algorithm, which maximizes simulated solar irradiance while accounting for shadowing, direct, and anisotropic diffuse irradiances. The method was validated using PV systems in Maribor, Slovenia, achieving a 0.952 F1-score for module detection (using FT-UnetFormer with SwinTransformer backbone) and an estimated electricity production error of below 6.7%. Optimization results showed potential energy gains of up to 4.9%.  
solar energy; photovoltaics; semantic segmentation; optimization; LiDAR; VHR imagery Article
# Effectiveness of Laser-Assisted Teeth Bleaching: A Systematic Review
## 3 Results
### Main Study Outcomes  
A diode laser was used in 29 publications, of which 10 concluded that it increases the whitening effect [5; 34; 38; 40; 42; 43; 48; 52; 53; 55]. Of the four publications in which the Nd:YAG laser was used, only one showed its positive effect on whitening [59]. In two out of three tests conducted using the Er,Cr:YSGG laser, a greater color change was found compared to the conventional method [8; 10; 55]. None of the studies involving argon or AsGaAl lasers showed that they increased whitening efficiency [1; 62; 63]. At the same time, the authors of all articles describing whitening assisted with KTP or Er:YAG lasers concluded that they increased the whitening effect [48; 52; 53; 55; 64]. In 31 papers, a spectrophotometer was used to measure color, of which only 1 decided not to calculate the \(\Delta\)E value [1; 2; 3; 4; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 44; 46; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60]. As many as seven researchers decided to use the VITA shade guide as the only assessment of whitening effects [5; 42; 43; 47; 57; 58; 60]. The bleaching of ceramics typically results in the restoration of their original factory color, with no significant improvement beyond that [36]. Moreover, bleaching composite materials has generally yielded limited or no noticeable color improvement [4; 8]. For whitening endodontically treated teeth, laser-assisted techniques have demonstrated superior efficacy compared to conventional methods, as shown in two studies [43; 64]. Furthermore, lasers have been effective in whitening non-vital primary teeth, presenting a promising alternative to traditional approaches [51]. Among the whitening agents, 35% hydrogen peroxide (HP) was the most commonly used, featured in 17 of the 40 studies reviewed [1; 2; 7; 10; 37; 40; 45; 51; 52; 54; 55; 56; 57; 58; 59; 60; 61; 63; 65; 66]. (See Table 2.)  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
## 3 Results and Discussion
### XPS Analysis before and after Adsorption  
For further characterization of the resins before and after adsorption, The XPS spectra of SD1 and SD5 before and after adsorption are exhibited in Figure 7. As seen, all S2p spectra of the two resins before adsorption were not detected to any peaks. Nonetheless, the S2p spectra of SD1 and SD5 were revealed at the increased peak of 168.18 and 168.00 eV, respectively, after adsorption, which illustrated that sulfur (sulfonic acid groups in PTSA) was adsorbed on the surface of the adsorbents [16; 40].  
As seen in Figure 7c, before adsorption, three N1s spectra of SD1 resin peaks were detected, which were assigned as either free tertiary amine groups (399.55 eV), protonated tertiary amine groups (402.43 eV), or quaternary ammonium groups (403.35 eV) [41]. As seen from Table 1, all resins possessed a strong base exchange capacity according to the basic parameters of different functional group density resins, which demonstrates the presence of quaternary ammonium nitrogen N3 in the resin. Furthermore, it can be found that the structure of SD1 resin no longer contains the free tertiary amine group N1 after adsorption on the basis of the area ratio of different forms of nitrogen atoms after adsorption of PTSA. In addition, the area ratio of N1 in SD5 resin decreased from 66.63% to 10.14% after adsorption, as seen in Figure 7d. We speculate that this is due to the free tertiary amine group having been protonated and having participated in the electrostatic interaction between the resin and PTSA. In addition, the area ratio of protonated tertiary amine groups in the two resins after adsorption was 93.01% and 85.86%, respectively, implying that the degree of protonation of the amination group may be related to the density of the functional group of the resin. Hence, the higher the functional group density, the weaker the conversion ability of free tertiary amine groups to protonated tertiary amine groups.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Adsorpents**} & \multicolumn{3}{c}{**Langmuir**} & \multicolumn{3}{c}{**Freundlich**} \\ \cline{2-7}  & \(Q_{m}\) **(mmol/g)** & \(K_{L}\) **(L/mmol)** & \(R^{2}\) & \(K_{F}\) & \(n\) & \(R^{2}\) \\ \hline SD1 & 2.7589 & 9.5222 & 0.9906 & 2.3169 & 8.4754 & 0.9972 \\ SD2 & 2.9988 & 11.1786 & 0.9920 & 2.5355 & 8.3759 & 0.9982 \\ SD3 & 3.2578 & 9.6817 & 0.9925 & 2.7110 & 7.5409 & 0.9928 \\ SD4 & 3.4916 & 13.3726 & 0.9935 & 2.9623 & 7.9107 & 0.9930 \\ SD5 & 4.2401 & 13.1659 & 0.9939 & 3.5859 & 6.9177 & 0.9804 \\ \hline \end{tabular}
\end{table}
Table 4: Characteristic properties of adsorption isotherms.  
Figure 6: Adsorption isotherms of PTSA onto resins at 303 K.
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 3 Dynamic Buckling Criteria
### Comparison of Two Models  
Figure 7 shows the initial state of X-stress, which is caused by the static pressure only without the time effect. Figure 7a shows the X-stress of the cylinder model, while Figure 7b shows that of the spherical shell model. It can be found that the maximum stress for each model is \(-648\) MPa and \(-660\) MPa (the negative sign implies that the member is in a compression state), respectively, which only has a 2.0% discrepancy. In addition, the X-stress at the apex will be \(qR/2t=650\) MPa. The result agrees very well.  
Figure 8 illustrates the displacement in the velocity direction versus time in the cases of both the cylinder and spherical shell, respectively. This figure depicts that the displacements are identical before contact (\(t<0.02\) s), and almost the same during the contact time (\(0.02\) s \(<t<0.06\) s); lastly, it has very small discrepancy after contact (\(t>0.06\) s).  
Figure 6: The geometry and mesh model for the cylinder and spherical models. (**a**) The mesh of the cylinder model; (**b**) The mesh of the spherical model.  
Figure 7: The initial state of X-stress (Pa) with \(R=2.0\) m, \(t=0.03\) m, \(q=20\) MPa, \(v=4\) m/s. (**a**) Cylinder shell model; (**b**) Spherical model.  
Figure 9 presents the time history of contact forces during collision or impact. The max forces are 17.3 MN and 17.1 MN, and the mean forces are and 9.93 MN and 9.89 MN, respectively. Therefore, it can be observed that the contact forces can be considered identical values.  
Figure 10a depicts the time history of the kinetic energy. It shows that the kinetic energy is almost the same for both cases up until the half of the contact time (\(t<0.045\) s); then, there is some time delay (phase difference) between the cases of the cylinder model and the spherical model. After the impulse contact, the values of kinetic energy remain mostly the same (\(t>0.08\)). Figure 10b depicts the internal energy and total energy time history of both models. It could be noticed that the internal energy of the cylinder is two times of that of the spherical shell model before collision (\(t<0.02\)). Note that the cylinder is set to be the rigid material, so that it cannot absorb energy [59]. Nevertheless, the changing trends of the internal energy and total energy are the same.  
Figure 8: The plot of displacement vs. time with \(R=2.0\) m, \(t=0.03\) m, \(q=20\) MPa, and \(v=4\) m/s.  
Figure 10: \(Cont\).  
Figure 9: The plot of contact force vs. time with \(R=2.0\) m, \(t=0.03\) m, and \(q=20\) MPa.  
According to the discussion above, it can be seen that all of the physical quantities of force, stress, displacement, and energy of both models could be the same. Thus, it is necessary to consider the spherical model instead of a simplified cylinder model in order to determine the dynamic buckling of the structure subjected to the collision impacts.
# Parallel-Coupled-Line Bandpass Filter with Notch for Ultra-Wideband (UWB) Applications
## 4 Fabrication and Measurements  
The presented bandpass filter with a notch was designed utilizing a microstrip substrate with a 10.8 relative dielectric constant. The thickness for the microstrip substrate was chosen to be 1.27 mm. The filter design was successfully manufactured using printed-circuit-board technology. Figure 15 depicts a photograph of the fabricated filter. In addition, pairs of SMA 212-500 SF connectors were attached at the input/output feedlines to allow the measurement of the scattering parameters (S11 and S21). The overall size of the manufactured microstrip filter with a notch was very small, with a physical size of 3.5 mm \(\times\) 4.2 mm. A comparison between the measured and EM-simulated insertion and return losses is demonstrated in Figure 16 for the developed UWB microstrip filter with a notch. As can be seen in Figure 16, an outstanding agreement was attained among the expected and experimented insertion and return losses. In addition, the filter showed a very wide passband with a FBW of around 110% at a midband frequency of nearly 6.9 GHz. The fabricated filter showed an insertion loss with a very high selectivity and with a couple of transmission zeros near the passband edges. The measured insertion loss is recorded to be very low with a maximum value of 0.9 dB over the whole passband. Moreover, the measured return loss was recorded with a minimum value of 12 dB within the passband. The measured filter displayed an excellent out-of-band performance with rejection levels of more than 25 dB at the lower band and more than 18 dB at the upper band. In addition, the fabricated filter exhibited a notch with a narrow band, which has a fractional bandwidth of around 4.3% at a mid-band frequency of about 6.3 GHz, inside the passband. The rejection level of the insertion loss was better than 20 dB at the center frequency of the notched band. The filter exhibited a flat group delay of less than 0.4 ns at the midband frequency of each passband, as demonstrated in Figure 17. Table 1 provides a comparison with other reported filters [2; 3; 4; 5; 6; 7; 8]. The proposed UWB bandpass filter showed a very small circuit size compared to those filters which are presented in [2; 3; 4; 5; 6; 7; 8]. It also showed an excellent performance with a very low insertion loss compared to those filters in [2; 3; 4; 5; 6; 7]. In addition, the return loss of the proposed filter was better than 12 dB inside the desired passband, which is better than the return loss of the filters in [3; 5; 6; 7; 8].  
Figure 16: _Cont._  
Figure 15: A photograph of the fabricated filter.
## 3 Results and Discussions
### Pile Group vs. Piled Raft under Vertical Loading
#### 3.1.3 9-Pile Group vs. 9-Piled Raft  
With the increase in the number of piles, the stiffness of the system is going to increase, and hence, it can be seen in Figure 16 below which 9-pile group vs. 9-piled raft response under the vertical load of 5250 N is shown which is more than the previous cases. Corresponding to this maximum load, the settlement was reported as 12.97 mm in the pile group, and in comparison, the pile raft experienced a settlement of 5.14 mm at the same load, leading to it being 2.52 times less than the pile group.
## Materials and Methods
### Data Acquisition  
The Free Electron Laser FERMI currently operates at a repetition rate of 50 Hz (10 Hz at the time of the experiment). This is determined by the operation rate of the LINAC that accelerates the electron bunches used to generate the FEL pulses. Single-shot ion TOF traces (Figure 3) are acquired, tagged with a progressive integer (\"bunchnumber\") and stored along with a wide selection of experimental parameters and machine parameters, for post-processing. The relevant single-shot quantities for us are the FEL energy per pulse (\(I0\)), the UV energy per pulse (\(I_{\text{UV}}\)), and the integrated area \(a\) of the TOF peak of interest (specifically the \({}^{20}\)Ne\({}_{2}^{+}\) peak).  
A preset number of single-shot values \(a_{j}\) at nominally identical conditions are acquired and aggregated into a datapoint \(A_{i}\); one in three shots is a blank shot (no gas sample), which is subtracted from the average to eliminate spurious signals (Equation (9)). Although in reality the bunchnumber is never reset, i.e., each bunchnumber is unique since the inception of FERMI, for the purpose of this work we will think of it as spanning a range \(j=\{1..m\}\) for each datapoint. A set of datapoints at a sequence of delay values constitutes a delay scan. Delay scans are averaged into a global delay scan, where each scan enters with the weight determined in Equation (8).  
Figure 3: Single-shot ion-TOF trace (gray) and average over \(12,200\) shots (red). One in three shots is a blank shot (no gas sample), which is subtracted from the average to eliminate spurious signals, such as that at \(m/z=28\) in the single shot spectrum, due to residual nitrogen gas. Note that for \(m/z>25\) both spectra are magnified by a factor 5.
## 3 Methodology
### Data Pre-Processing  
In the data pre-processing phase, we performed data clean and data normalization steps. In the data clean step, we dealt with missing value examples and processed category data, after determining the 18 input and output variables (learning performance). In this step, we removed all examples that contain missing values, and encoding category data.  
In data normalization step, the data was normalized according to Equation (1).  
\[\mathrm{X_{mon}}=\frac{\mathrm{X-X_{min}}}{\mathrm{X_{max}-X_{min}}} \tag{1}\]  
where \(\mathrm{X_{max}}\) is the maximum value, \(\mathrm{X_{min}}\) is the minimum value, and \(\mathrm{X_{mon}}\) is the normalized value.
## 2 Experimental  
### Test Rig Setup  
Figures 1 and 2 illustrate an absorber test rig at Technische Universitat Darmstadt. The absorber test rig consists of four main parts: a gas mixing unit, an absorber column, a regeneration unit, and a gas analysis unit. The mixing unit consists of two lines, one is connected with a compressed-air source, and the other is combined with a CO\({}_{2}\) gas cylinder. The mixing unit is connected to a manifold upstream of the absorber. The absorber is made of a glass column that has a height of 1500 mm, and its internal diameter is 152 mm. The column has 12 glass nozzles to which metal flanges can be attached, ten nozzles used to measure pressures and temperatures in the absorber, and two nozzles for introducing the inlet gas and liquid to the absorber. At the bottom and the top, it is closed by suited metal flanges as well. The top flange contains the exit of the gas, and the bottom flange contains the exit of liquid. Five sieve trays are fixed by threaded rods and inserted inside the absorber. The diameter of the tray is 150 mm, the diameter of the hole is 2 mm, the fraction of the sieve hole area to active area is 0.071, and the weir height is 15 mm. The tray spacing is 240 mm, and the space between the tray and glass wall is closed with rubber seals. A gas analysis unit is connected at the gas outlet line to measure the volumetric fraction of CO\({}_{2}\) at the outlet of the absorber.  
Figure 1: Side view of the absorber test rig: 1, control panel; 2, absorber column; 3, recycling pump; 4, liquid level control valve; 5, make-up pump; 6, pressure difference transmitter; 7, Coriolis device; 8, packed column; 9, reboiler; 10, gas analysis unit; 11, pressure control valve; 12, gas outlet.  
The objective of the absorbent regeneration unit is to regenerate the absorbent and recycle it to the absorber as a lean absorbent. It consists of two heat exchangers, a reboiler, a packed column, a make-up pump, and a recycle pump. The packed column is a glass column with a diameter of 152 mm and a high of 1300 mm, which is filled to a height of 1 m with a metal packing material from type Pall-Ring 15 mm, with a specific surface of 360 m\({}^{2}\)/m\({}^{3}\), and free volume 95 %. The rich absorbent enters the packed column through a liquid distributor on the top of the packed column. The purpose of the liquid distributor is to spray the absorbent uniformly on the top of the packings; the manufactured liquid distributor is from a spray type that contains 10 holes distributed uniformly on the liquid distributor. The packed column is placed on the reboiler, which has a thermal power of 4.5 kW. The recycle pump is connected directly to the reboiler, which pumps the water from the reboiler to the absorber. The lean hot absorbent is precooled in the first heat exchanger by exchanging heating with the absorbent, which comes out from the absorber. After that, the precooled absorbent cools down through the second heat exchanger, which is installed before the absorber, by exchanging heating with cool water.