esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
#### 4.2.3 The Reason for the Permeability Difference under the Sole Influence of Temperature
The reason for the above results is that the porosity and average pore throat radius of subcategory I\({}_{2}\) sandstone were larger than those of subcategory II\({}_{2}\) sandstone, and the water pressure thus had a more obvious expansion effect on the pore walls, while the fine pore throats of subcategory II\({}_{2}\) sandstone were more sensitive to the microscale volume change caused by the temperature change. Analysis of Similarities and Differences between the Results of This Study and the Findings of Previous Researchers Mentioned in the Literature Review  
Comparing the results of this study with the works of the previous researchers mentioned in the literature review, it is evident that the works of some researchers are similar to the results of this study. For example, [PERSON] (1968) found that the pore permeability of granite decreased with increasing effective confining pressure [21]. [PERSON] et al. (2014) studied the influence of water pressure on the time-dependent deformation of surrounding rock in the process of deep rock mass excavation under high stress, and found that water pressure can enhance the performance of rock creep deformation [24]. [PERSON], [PERSON] and [PERSON], [PERSON] (2009) carried out research on the variation in the permeability of carbonate rocks and found that the permeability began to decrease when the temperature increased from 25 \({}^{\circ}\)C to 50 \({}^{\circ}\)C [26]. However, the works of some researchers are inconsistent with the results of this study. For example, [PERSON] (1965) conducted heating tests on a large number of sandstone cores at 400-800 \({}^{\circ}\)C and found that the permeability of broken cores increased by 50% [20]. [PERSON] (1982) heated adamellite to 300 \({}^{\circ}\)C under different confining pressures, which made the rock fracture and its permeability increase several times [22]. In summary, the conclusions about the influence of confining pressure and water pressure on the permeability evolution of sandstone are similar, but the conclusions about the influence of temperature on the permeability evolution of sandstone have both similarities and differences. Further analysis shows that when the temperature range was similar to that of this study, the conclusion was consistent with that of this study; but when the temperature variation range was very different from that of this study, the conclusion was different. The reason for this phenomenon is that the temperature variation range in this study was small, and only a small elastic deformation occurred in sandstone with increasing temperature. When the temperature was too high, such as 800 \({}^{\circ}\)C, the sandstone was broken and produced a large number of fractures, which led to a sharp increase in sandstone permeability. At the current mining depth of deep coal mines, the underground temperature was generally less than 60 \({}^{\circ}\)C. Therefore, the conclusions of these researchersare of little significance for the prevention of water seepage from sandstone pores in deep coal mines at present.
# Optical Flow-Based Fast Motion Parameters Estimation for Affine Motion Compensation
## 3 Proposed Method  
### Optical Flow Estimation  
For each picture using inter-picture prediction, optical flow is computed using the current picture and the first picture in the reference picture list. While computing it for every picture in the reference picture list leads to better approximations, the required time is much higher, and the proposed method aims to provide good encoding efficiency with a faster encoding than similar methods. For the reference picture, the picture before encoding is used. This offers two advantages: first, this allows optical flow to be computed before the picture is encoded, and second, the motion estimation is more accurate and follows the real movement better, especially when the quantization parameter is large and the reconstructed picture is of lower quality.  
After obtaining an approximate displacement for each pixel in the current picture, the estimation is performed for each CTU. As in Reference [3], using smaller blocks improves only slightly the encoding efficiency, but it would require a lot more time. The estimation is based on resolving the linear equation for the 4-parameter model transform with two points in the block.  
As the translation parameter can be more accurately estimated with the standard motion estimation technique, only the parameters \(a\) and \(b\) are considered. Using \(x\) and \(y\) as the distance between the input points and \(x^{\prime}\) and \(y^{\prime}\) as the distance between the output points, we can estimate \(a\) and \(b\) with the following equation:  
\[\begin{split} a=1+s&=\frac{xx^{\prime}+yy^{\prime}} {x^{2}+y^{2}}\\ b=-r&=\frac{x^{\prime}y-xy^{\prime}}{x^{2}+y^{2}} \end{split} \tag{8}\]To get good results, the points should be far enough apart, so points around the edge of the current block are used. If the points are too close together, cancellation is likely to occur, as the subpixel motion estimation through optical flow is imprecise. To remove the risk of bad estimations from outliers, the values of \(a\) and \(b\) are estimated for multiple couples of points, and the median value is retained. When the block is on the edges of the picture and contains pixels outside the reconstructed picture, we cannot compute optical flow on these samples. This happens when the input size is not a multiple of the largest coding block size. In this case, we use samples that are within the reconstructed picture for the computations.
## 7 Simulation Parameters
### Analysis of BIN-TANVEER and PL-ROBINA with Number of Dead Nodes and Packet Transmitted  
The BIN-TANVEER and PL-ROBINA relation is presented the same scenario of finding the number of dead nodes in Figure 16. The path-loss mechanism is supported by _[PERSON]'s model_ and the _Colored Gaussian's Surface (CGS)_ for optimizing the routing path and acute signal between two communicated nodes, so the path loss is dubbed according to Equation (1). Typically, with the use of the practical value of '\(K\)' with _'Throp's formula'_ for derivative by integration, it has been observed that the existing monitoring IoT system with some routing mechanism ignores the factor of path loss (here, we considered the path loss between only two communicated IoT devices (whatever the way underwater) and the main concern was its recovery as well as detection to find out the health of the network when the path was not found, for the time being, between two devices) to ensure if it recovers from two connected devices and if recovery is so for all devices (connected) in the routing path by the help of _'Tylor and Maclaren Series'_ that already used in sensor data cryptography [40; 41; 42]. Therefore, the path loss is best described in the PL-ROBINA scheme over BIN-TANVEER. Although the PL-ROBINA outperformed even the largest number of nodes (say 750 from Figure 16), the BIN-TANVEER has only handled the nodes and their dead quantity with suitable energy tax when the network was in the category of the small to medium scale, as contrary to PL-ROBINA for medium to large-scale networks. Ultimately, the packet transmission is also attached as for the energy tax from Figures 17 and 18. From Figure 17, the performance of PL-ROBINA is satisfactory as evidenced by its large number of packets transmitted due to the non-loss of the path between two connected devices; BIN-TANVEER cannot handle a large amount of data, as the Binary Inter Nodes (BIN's) are not functional enough and supported with this kind of situation. It is only built on purpose in TANVEER, and as mentioned in [22], the packet success ratio achieved 94% of ROBINA and AVH-AHH-VBF. In addition, the WDFAD-DBR has the least ratio, which is only 62%. Meanwhile, the same situation will be observed for energy tax with BIN-TANVEER and PL-ROBINA respectively. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Schemes** & **Features** & **Achieved Parameters** & **Trade-Offs** \\ \hline \multirow{6}{*}{ROBINA} & First time introduce the path & Avoidance of path loss and & The maximum probability of \\  & rotation idea with path loss and & increased the reliability of \\  & adjustment variants in the & energy-efficient routing that & path rotation and internodes \\  & acoustic environment. & supports IoT-based & adjustment is achieved with \\  & & dynamic devices. & affordable AE2 ED \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Overall performance trade-offs.
## 3 Photon Beamlines
### Photon Diagnostics
#### 3.3.2 Temporal Diagnostics  
The measurement of the arrival time of the X-ray pulse relative to a laser pulse is crucial for pump-probe experiments that are an important category of research performed at FELs. The monitoring of temporal lengths of X-ray pulses is similarly important for both experiments and for the optimization of the performance of the machine. PSI has installed several diagnostics to monitor and record both of these properties over a wide range of X-ray intensities and photon energies.  
The first device is based on techniques developed at LCLS [102; 103] and SACLA [104] that measure the arrival time of the FEL pulse relative to a pump laser using spectral encoding. The method works by using a chirped laser pulse that passes through a dielectric substance and with the change in transmission being measured. The wavelength corresponding to the change in transmission is directly linked to the arrival time of the FEL on the dielectric material due to the spectrally chirped nature of the laser beam. As the chirped laser pulse is derived from the same laser beam that is used for the experiment, the time-arrival information can be used to directly sort the experimental data. The photon spectral encoder (PSEN) is located a few meters before the experimental chamber and is projected to have an accuracy of measurement of the arrival time of 20 fs or better. It has a large number of thin membranes of different materials and thicknesses to allow for online measurement of the arrival time over a range of 1-1.5 ps without a significant loss of X-rays for the experiment. However, the device will require a minimum of 5-10% transmission losses to be able to measure the arrival times accurately.  
The second device SwissFEL will have for temporal diagnostics is the photon arrival and length monitor (PALM), which uses THz streaking [7; 105] to measure both the arrival time and pulse length of the incoming XFEL pulses relative to a THz beam generated from the same source as the experimental pump laser [106], as shown in Figure 9. The method has proven to work well with soft X-rays at FLASH [7] and with hard X-rays at SACLA [107], where the PALM demonstrated measured arrival time accuracies to a precision of about 5 fs [108]. The device measured pulse lengths with an HHG source down to 25 fs rms with accuracies typically between 4 and 10 fs [109]. The device is gas based, and is virtually non-destructive, with X-ray absorption typically being less than 0.1% of the incoming flux. However, unlike the PSEN with its large time window, the PALM has an acceptance window for arrival time measurement that is typically between 400 and 600 fs. If the X-ray/laser timing jitter is larger than this value the PALM requires additional timing information, for example from the PSEN, to be used. Future upgrades are planned to both increase the range of the PALM and to improve the time resolution of both the arrival time and pulse length measurements.  
Figure 9: Schematic drawing of the photon arrival and length monitor (PALM) setup.
## 4 Algorithm Verification
### Data Analysis  
#### 4.2.1 Shadow Occlusion Verification  
The shadow shielding relationship between the extended flange and the web of the box girder is shown in Figure 14. The following formula [38] can be used to calculate the theoretical value of the shadow shielding height of the box girder web:  
\[L_{s}=L_{c}\frac{\tan\alpha_{s}}{\tan\alpha_{s}\mathrm{cos}\,\beta+\sin\beta \mathrm{cos}(\gamma-\gamma_{s})} \tag{31}\]  
where \(L_{s}\) is the height of sunshine shadow occlusion; \(L_{c}\) is the width of the box girder flange; \(\alpha_{s}\) is the solar elevation angle; \(\beta\) is the inclination angle of the outer surface of the box girder web relative to the horizontal plane; \(\gamma\) is the angle between the normal direction of the outer surface of the web and the south direction; and \(\gamma_{s}\) is the solar azimuth angle.  
The sunshine shadow distribution of the sunny side web of the verification example at 7:00 a.m. on 14 August 2013 was displayed according to the shadow display method described in Section 3.4, and the shading height of the sunny side web shadow recognized by the algorithm in this paper was compared with the theoretical value calculated by Formula (15) to verify the accuracy of the sunshine shadow recognition algorithm proposed in this paper, as shown in Figure 15. From the diagram, it can be seen that the simulated value of sunshine shadow length was in good agreement with the theoretical value; the maximum error between the theoretical calculation value and the simulated value of sunshine shadow was only 19.1 mm. Therefore, the sunshine shadow recognition algorithm proposed in this paper can accurately identify the sunshine shadow of the structural surface.  
Figure 14: The shadow occlusion relationship diagram of the box-girder web.  
Figure 15: Comparison of Shadow Occlusion Results.
# RMFNet: Redetection Multimodal Fusion Network for RGBT Tracking
## 4 Implementation Details  
### Offline Training  
This section outlines the offline training process of RMFNet. Firstly, the weights of the first three convolutional layers of the VGG-M [6] model trained on the ImageNet dataset are loaded and used to initialize the convolutional layers of our two specific modal branches and fusion branch. Then they trained the entire RMFNet using the Adam algorithm, with the learning rate of the convolutional layers set to 0.0001 and the learning rate of the fully connected classification layer formed to 0.001, which is 10 times higher than that of the convolutional layers. For each training iteration, 8 frames of images and their corresponding ground-truth targets are randomly extracted for each video sequence. Samples are then Gaussian-sampled around the ground truth targets on the image. In total, 256 positive samples and 768 negative samples are collected from the 8 frames of images, forming a small batch of samples for network training. The only criterion for dividing positive and negative samples is the IOU between the sample box and the ground truth box, with two thresholds set at 0.7 and 0.5. Boxes with IOU greater than 0.7 are positive samples, and those with IOU less than 0.5 are negative samples. RMFNet is trained for 120 and 220 rounds on the GTOT and RGBT234 datasets. These two datasets serve as each other's training and testing sets, with the RGBT234 dataset as the testing set when GTOT is used as the training set, and vice versa. It is worth noting that to save training time, we randomly select 78 video sequences for each iteration when training the network on the RGBT234 dataset.
## 2 Proposed Energy Cost Model for Offshore Wind Farms
### Annual Energy Production  
Normally, the \(AEP_{i}\) of the offshore turbines is estimated based on the Weibull probability distributions of the wind statistics, a standardized power curve, a physical description of the turbine and physical constants. Although this method can effectively estimate the AEP of single wind turbines, it was simplified in the present context to ignore the wake effects and wind turbine layout when applied to estimate the \(AEP_{total}\) of the whole offshore wind farm. So, in this paper, the existing AEP model is modified by taking wind turbine's wake effect and layout into consideration.  
The \(AEP_{i}\) of a turbine can be given by the average power production, \(P_{avg,i}\), of the wind turbine \(i\) during one hour and the total hours of one year:  
\[AEP_{i}=8760(1-\varsigma)P_{avg,i}, \tag{7}\]  
where \(\varsigma\) is the total power generation loss, including power converter loss, electrical grid loss, availability loss and so on. In this study, \(\varsigma\) is assumed to be a constant of 0.16 [26].  
Meanwhile, the mean power production, \(P_{avg,i}\), of a single wind turbine can be calculated as:  
\[P_{avg,i}=\int_{0}^{\infty}P(v)f_{i}(v)dv, \tag{8}\]  
where \(v\) is the wind speed, \(P(v)\) denotes the power curve model as a function of the wind speed, and \(f_{i}(v)\) is the Weibull distribution corresponding to the installation site, which is also as a function of the wind speed.
## 1 Introduction
This approach ensures that this study accounts for potential variations in electron behavior due to changes in the temporal characteristics of the laser pulse, thereby providing more robust and reliable results. Based on the experimental results, it can be observed that the beam waist radius influences the oscillation direction of the electron trajectory and the deflection exit angle, and the delay time plays a crucial role in the deflection direction of the electron. Simultaneously, with the beam waist radius increasing, the spatial radiation distribution of the electrons exhibits a comet-shaped pattern, and the power peak approaches the z-axis. Regarding the time distribution, it presents the symmetrical three-peak structure. The spectrum reveals the property of transitioning from a supercontinuum to a multi-peak state spectrum as the beam waist radius enlarges, and the phenomenon of the interchange between the main peak and the secondary peak is also witnessed. These accomplishments offer novel insights for the study of the cross-collision between high-energy electrons and tightly focused linearly polarized laser pulses, and exert a certain facilitating effect on the advancement of medical treatment and the field of optical physics.
## 3 Results
### Simulation Results
Figure 6: PSF of a numerical point scatterer at 70 mm depth for parameters chosen to obtain PSF images with minimum-side lobe energy (**a,b**) (array configuration: \(\theta=149^{\circ}\), gap = 3 mm) and with minimum main lobe width (**c,d**) (array configuration \(\theta=124^{\circ}\), gap = 35 mm), and using rectangular (**a,c**) or Tukey (**b,d**) apodization on transmit. Corresponding lateral (**e**) and axial (**f**) profiles of PSF with minimum-side lobe energy (dashed line) and with minimum main lobe width (solid line) and using rectangular (black and gray) or Tukey (blue and red) apodization on transmit. Results obtained by coherently compounding 7 PWs per array with \(\alpha_{max}\) equal to \(13^{\circ}\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Configuration** & \multicolumn{2}{c}{\(\theta=149^{\circ}\), gap = 3 mm} & \multicolumn{2}{c}{\(\theta=124^{\circ}\), gap = 35 mm} \\  & \multicolumn{2}{c}{**(Minimum-Side Lobe Energy)**} & \multicolumn{2}{c}{**(Minimum Main Lobe Width)**} \\ \hline
**Tx. Apodization** & **Rectangular** & **Tukey** & **Rectangular** & **Tukey** \\ \hline
**FWHM [mm]** & 0.49 & 0.49 & 0.29 & 0.29 \\
**PSMR [dB]** & \(-23.7\) & \(-20.7\) & \(-8.3\) & \(-7.2\) \\
**SMER [dB]** & 0.0 & 0.9 & 9.4 & 10.3 \\
**CR [dB]** & \(-22.7\) & \(-22.6\) & \(-23.4\) & \(-23.6\) \\
**CNR [-]** & 1.65 & 1.64 & 1.72 & 1.72 \\
**gCNR [-]** & 0.97 & 0.97 & 0.97 & 0.97 \\
**Speckle resolution [mm]** & 0.53 & 0.54 & 0.38 & 0.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Imaging metrics at 70 mm depth for parameters chosen to obtain images with minimum-side lobe energy (array configuration: \(\theta=149^{\circ}\), gap = 3 mm) and with minimum main lobe width (array configuration \(\theta=124^{\circ}\), gap = 35 mm) and using rectangular or Tukey apodization on transmit (Tx). FWHM, PSMR and SMER are calculated from the PSFs in Figure 6. CR, CNR, gCNR and speckle size are calculated from the numerical B-mode images in Figure 7.  
Figure 7: B-mode images of the numerical phantom obtained with Tukey apodization on transmit and the configurations of (**a**) minimum-side lobe energy (\(\theta=149^{\circ}\), gap = 3 mm) and; (**b**) minimum main lobe width (\(\theta=124^{\circ}\), gap = 35 mm). Regions used for contrast (circles) and speckle size (square) calculations are highlighted. (**c**) Corresponding lateral sections of (**a**) (blue) and (**b**) (red). Results obtained by coherently compounding 7 PWs per array with \(\alpha_{max}\) equal to \(13^{\circ}\).
###### Abstract  
New sources of supplementary cementitious materials (SCMs) are needed to meet the future demand. A potential new source of SCM is spent pot lining, a residue from aluminum production. The present work showed that the refined aluminosilicate part of spent pot lining (SPL) has a moderate chemical reactivity in a cementitious system measured in the R3 calorimetry test, comparable to commercially used coal fly ash. The reaction of SPL led to the consumption of Ca(OH)\({}_{2}\) in a cement paste beyond 7 days after mixing. At 28 and 90 days a significant contribution to strength development was therefore observed, reaching a relative strength, which is similar to composite cements with coal fly ash. At early age a retardation of the cement hydration is caused by the SPL, which should most likely be associated with the presence of trace amounts of NH\({}_{3}\). The spent pot lining is also investigated as silica source for autoclaved aerated concrete blocks. The replacement of quartz by spent pot lining did not show an adverse effect on the strength-density relation of the lightweight blocks up to 50 wt% quartz substitution. Overall, spent pot lining can be used in small replacement volumes (30 wt%) as SCM or as replacement of quartz (50 wt%) in autoclaved aerated concrete blocks.  
supplementary cementitious materials; cement replacement; spent pot lining; autoclaved aerated concrete; cement hydration; lightweight blocks
## 3 Multi-Strategy Improved Chimpanzee Optimization Algorithm
### Hybrid Good Point Set and Chaos Initialization Strategy
#### 3.1.1 Good Point Set Initialization  
The good point set is an effective method for generating uniformly distributed points, particularly in high-dimensional search spaces, where it ensures better distribution than random initialization.  
Originally proposed by [PERSON] et al. [25], its basic definition and construction are as follows:  
The deviation \(P_{n}(k)=\left\{(\left\{r_{1}^{(n)}\cdot k\right\},\left\{r_{2}^{(n)}\cdot k \right\},\cdots,\left\{r_{s}^{(n)}\cdot k\right\})\middle|1\leq k\leq n\right\}\) satisfies \(\varphi(n)=C(r,\varepsilon)N^{-1+\varepsilon}\), where \(C(r,\varepsilon)N^{-1+\varepsilon}\) is a constant only related to \(r\) and \(\varepsilon\). \(n\) denotes the number of points. The set \(P_{n}(k)\) is called a good point set, with \(r\) as the good point. \(\left\{r_{s}^{(n)}\cdot k\right\}\) represents the fractional part, with \(r=\left\{2\cos(\frac{2\pi k}{p})|1\leq k\leq s\right\}\), where \(p\) is the smallest prime number satisfying \((\frac{p-3}{2}\geq s)\).  
Good point set initialization generates low-discrepancy points within a given dimension, ensuring uniform spatial distribution. This improves the quality of the initial population and mitigates early convergence to local optima. However, the strong regularity of good point sets limits randomness and dynamism, potentially reducing the algorithm's exploration capability in complex or high-dimensional problems.