esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Video Processing
Our prompting algorithm [9] selects the prompt to segment the seed frame. The selection process for determining prompt points begins with identifying the contours within the mask. Initially, a bounding box encompassing all mask contours for the seed frame is established, followed by identifying the centroid of each mask contour. However, due to the irregular shape of the lungs, the centroid may occasionally fall outside the mask boundary. To address this, the centroid is adjusted by using it as a seed to find intersections on the X and Y axes with the mask. The centroid is then repositioned to the intersection's midpoint, which contains the most points, ensuring it is placed within the mask. This adjustment is applied even when the centroid is inside the mask to ensure it is more accurately centered within the corresponding mask. A negative prompt is also included, representing the center of the frame. Once segmented, SAM 2 can propagate the segmentation to the rest of the frames of the two halves of the video. Figure 4: Proposed segmentation process. (**a**) Proposed segmentation process using SAM 2. (**b**) Previously proposed segmentation process using SAM [9]. In the case of the Montgomery dataset samples, there is no temporal relation between the slices since they are independent chest radiographs belonging to different patients. However, there is a spatial relation since the nature of these samples is similar. In this case, we can also take advantage of SAM 2 memory to improve the segmentation of the radiographs. Assuming a folder with the slices in JPEG format exists, Algorithm 1 shows the algorithm for dividing the slices into two parts, selecting the seed, adding the prompts needed for the seed frame, propagating them to the rest of the slices, and combining the results.
## 3 Results and Discussion
### Coating Formation Stages
#### 3.5.3 Crystallization and Lateral Growth  
The third stage is the crystallization of calcite and lateral growth through the attachment of calcium carbonate nanoparticles until the formation of a compact crystalline film coating the Mg disk. The process of transforming ACC into calcite takes place at the same time as ACC nuclei continue to be deposited from the solution, incorporating into growing calcite crystals until full protective coverage is formed and the calcite bi-dimensional growth comes to an end due to the coalescence of the growing crystals. We can name this stage crystallization and lateral growth.  
When the coverage of the disk by calcite crystals is complete, the deposition of additional carbonates from the saturated solution results in non-homogeneous, irregular deposits that cannot be considered a constituent part of the calcite coating layer, forming loosely attached deposits on top of the calcite layer, particularly aragonite colonies. We can name this last stage excessive growth (in height, so three-dimensional excessive growth). The four stages are illustrated in Figure 14.  
Figure 14: Flux diagram illustrating the identified stages of carbonate coating.  
Figure 13: pH evolution of the carbonated water solution in the presence of Mg disks and without them (free solution).
# A Novel SIW Leaky-Wave Antenna for Continuous Beam Scanning from Backward to Forward
## 3 Experimental Verifications  
In this section, experimental verification of the proposed LWA array is described by comparing the simulation results with those obtained by measurement. For this purpose, a prototype of the LWA array with 19 elements was fabricated based on the dimensions given in Table 1. The fabricated antenna is depicted in Figures 19 and 20 showing the S-parameters of the proposed antenna. The results showed that the proposed design effectively suppressed OSB at broadside frequency. In addition, one can easily see an adequate level of agreement between simulation and measurement.  
The normalized radiation pattern of the proposed LWA array, obtained by simulation and measurement, is shown in Figures 21-27 for different frequencies. Results confirmed the capability of the proposed antenna to provide continuous beam scanning from backward to forward through the broadside. The measurement and simulation results agreed very well with each other.  
Figure 19: Prototype of fabricated SIW LWA.  
Figure 20: Simulated and measured S-parameters for LWA array.  
Figure 23: Simulated and measured radiation patterns at 17 GHz.  
Figure 21: Simulated and measured radiation patterns at 14.5 GHz.  
Figure 22: Simulated and measured radiation patterns at 16 GHz.  
The result shows the main beam direction of the proposed antenna continuously varied from \(-60^{\circ}\) to \(+57.5^{\circ}\), with a maximum gain of 16 dBi. The simulated and measured maximum gain values of the antenna are shown in Figure 28. For a maximum gain of 16 dBi, the measurement results agreed well with the simulated result.  
The performance of the proposed antenna was compared with different types of reported SIW LWAs, which are listed in Table 2. For approximately the same length, the maximum gain of the proposed antenna is greater than that of the other reported  
Figure 28: Measured and simulated maximum gain values of antenna.  
Figure 26: Simulated and measured radiation patterns at 21 GHz.  
Figure 27: Simulated and measured radiation patterns at 22.5 GHz.  
Figure 25: Simulated and measured radiation patterns at 19 GHz.  
SIW LWA. Furthermore, the proposed antenna can provide beam-scanning capability in a narrower frequency bandwidth, which is appropriate for applications requiring fast frequency beam scanning.
## Materials and Methods
### Determination of Volatile Compounds  
Volatile compounds in Xuanwei hams were determined using headspace-solid phase microextraction (HS-SPME) combined with gas chromatography-mass spectrometry (GCMS-QP 2020 NX, Shimadzu Co., Kyoto, Japan). The minced ham (3.00 g) was put into a sealed headspace vial (VFAP-606050F-18M-100, CNW Technologies, Shanghai, China) with added 1 \(\mu\)L 1,2-dichlorobenzene as the internal standard. Subsequently, the 50/30-\(\mu\)m-thick divinylbenzene/carboxen/polydimethylsiloxane (DVB/CAR/PDMS) fibre (57328-U, Supelco Inc., Bellefonte, PA, USA) was exposed to the headspace of a headspace vial for extraction at 45 \({}^{\circ}\)C for 40 min. Finally, the volatile compounds absorbed were desorbed in the GC injector at 240 \({}^{\circ}\)C for 5 min, identified, and quantified using the GC-MS system equipped with a DB-WAX (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m, Agilent Technologies, Santa Clara, CA, USA). The GC oven temperature programme was as follows: first increase, 40 \({}^{\circ}\)C for 3 min, 3 \({}^{\circ}\)C/min to 90 \({}^{\circ}\)C for 5 min; second increase, 3 \({}^{\circ}\)C/min to 200 \({}^{\circ}\)C; and third increase, 15 \({}^{\circ}\)C/min to 230 \({}^{\circ}\)C for 5 min; split ratio of 5:1. The flow rate of the high-purified helium as the carrier gas was 2.0 mL/min. The mass spectrum parameters were electron ionisation mode at 70 eV and a scanning range of \(m/z\) 35-500. The MS ion source and quadrupole temperatures were 200 \({}^{\circ}\)C and 230 \({}^{\circ}\)C, respectively.  
Volatile compounds were identified by comparing the mass spectra with reference mass spectra from the NIST21 library data with a similarity of 90%. For further confirmation, the retention index (RI) was calculated using a mixture of n-alkanes (C\({}_{5}\)-C\({}_{25}\)). Finally, the odour activity value (OAV) of the volatile compounds was calculated according to [PERSON] et al.'s method [38].
## 3 Discussion
### Evolution of the Scalping Kinetics of VSC  
In both matrices (model and Shiraz wines), flavour scaling of VSCs on micro-agglomerated closures were identified and evaluated, up to 25% of initial DMS levels, specifically by considering whole-surface closures and a scalping duration of 7 days. A previous study had shown similar tendencies [19], with approximately 50% of DMS scalped for natural cork closures after 7 days of soaking. This gap could have been attributed to the difference in the initial concentration of DMS between the two studies, with 267 ug/L in the present experiment and 20 ug/L in [19].  
While scalping seemed to increase between 1 h and 6 h, remaining stable afterwards, in the model wine conditions, the analysis of the Shiraz wine showed a decrease after 7 days, probably due to the chemical mechanisms that were not in equilibrium. Indeed, VSCs were likely to interact with other components of the wine, or closures could also interact with other components such as other flavours or polyphenols.  
For each VSC, the same behaviour was observed in the model and red wines. Sulphur compounds with very low molecular weights, such as EtSH, DMS, and DES, had decreasing amounts scalped on closures, compared to heavier molecules such as DMDS, SMTA, and ETA, suggesting that molecule volatility could play a role on the ability of flavours to be sorbed. Additionally, previous studies demonstrated that hydrophobicity would be the major effect on flavour scalping, with a higher adsorption observed with more hydrophobic molecules [8]. As the logP for all seven VSCs ranged between 0.7 (SMTA) and 1.8 (DMDS), these molecules could indeed be scalped on micro-agglomerated closures, but these values seemed to be too close to each other to conclude on the role of hydrophobicity on the evolution of each scalped VSC during time.  
This experiment was conducted on the whole surface of the closure. However, in bottling conditions, only the mirror (i.e., the part of the cork in contact with the wine during storage) could undergo flavour scaling. Then, levels adsorbed on closures were divided by the factor dividing the surface of the mirror by the surface of the entire closure (factor equal to 9.3) to determine the actual scalping effect during wine evolution (Table 1).  
For both the model and red wines, the scalping corresponded to 1% to 5% of the initial VSC concentrations. By extrapolation, the losses accounted for approximately 40% to 80% for the model wine and for 8% to 70% for the Shiraz wine. Therefore, flavour scalping of VSCs could be considered as a minor effect towards the evolution during bottle ageing compared to other phenomena such as VSC chemical reactions or volatility. Consequently, the adsorption of VSCs on micro-agglomerated closures could not significantly impact the formation or disappearance of reduced off-flavour during wine ageing under our conditions.
# Numerical Study of Dig Sequence Effects during Large-Scale Excavation
## 3 Modeling and Scheme Design
### Modeling  
The depth of the foundation pit was 44 m, which was planned to be excavated in five layers, and the specific excavation depth of each layer is shown in Figure 4. The first excavation layer was 5.6 m, the second excavation layer was 5.4 m, the third excavation layer was 7.0 m, the fourth excavation layer was 8.2 m, and the fifth excavation layer was 17.8 m. The DW, which was 64.5 m long and 1.5 m thick, was made of HRB400 reinforcing bars cast into C30 concrete. The common parameters of the concrete and steel reinforcing materials show that the C30 concrete typically has a density of about 2400 kg/m\({}^{3}\), Poisson's ratio of about 0.2, and elasticity modulus of between 28 and 35 GPa (there may be minor variations in the literature). HRB400 steel reinforcement has an elasticity modulus of about 200 GPa, a Poisson's ratio of about 0.3, and a density of about 7850 kg/m\({}^{3}\). Therefore, the modulus of elasticity of a wall made of C30 concrete with HRB400 reinforcing bars, when properly designed and constructed, is in the range of 32 to 40 GPa, Poisson's ratio in the range of 0.2 to 0.3, and density in the range of 2.6 to 2.8 t/m\({}^{3}\). It should be noted that these values are also affected by the actual site environment, construction form, quality standards, and other factors, and then change. The WD parameters in this study are shown in Table 1.  
Figure 3: Schematic of soil pressure evolution before and after foundation pit excavation.  
Using Rhino (7.0) and Griddle (2.0), a foundation pit excavation mode that was 500 m \(\times\) 200 m \(\times\) 100 m (long \(\times\) wide \(\times\) high) was created, which had a total of 238,431 zones and 164,512 gridpoints (Figure 5). According to [PERSON]'s principle [34], the dimension of the model does not affect the results of the excavation of the foundation pit in this case. The soil was modeled as a zone structural element with the Mohr-Coulomb [35; 36] model commonly used in geotechnical simulation, and the DW was a shell structural element. The boundary conditions of the model were set: the top was a free interface without constraints, the sides were constrained by velocity in the horizontal direction, and the bottom was a fixed boundary, which constrained the velocity in the horizontal and vertical directions at the same time. Considering the self-gravity stress, the gravity acceleration was taken as 10 m/s\({}^{2}\). In addition, this study focused on the influence of the excavation sequence on the foundation pit and WD, so some parts were simplified, such as the construction process of the WD; the foundation pit precipitation and the beam support were neglected. Likewise, the lateral pressure coefficients of the different soil layers ranged from 0.2 to 0.6, and 0.25 was taken here for simplification and evenly distributed on the side of the model. Details are shown in Figure 6.
## 2 Concepts and Theoretical Analysis of SDC Wall  
### The SDC Wall  
Figure 2 shows the configuration of an SDC wall which consists of SCS wall components (steel plates, the concrete core, and binding bars), embedded damping (the damping layer and damping rolls), and reinforcement enhancements (steel sheets and sleeves). The bilateral steel plates and the concrete core act as the main force member of the composite wall. Whilst, binding bars are used to bond these layers of materials as integrity, similar to the SCS wall. The damping layer is sandwiched between the concrete core and external steel plates and expected to dissipate part of the input energy based on its deformation. Damping rolls are stuck to the surface of binding bars to coordinate the deformation of other components.  
Figure 2: Configuration of the steel-damping-concrete (SDC) wall.  
However, setting damping layers between the concrete core and steel plates, separates the concrete and steel plates and results in the decrease of wall compressive and shear capacity due to the loss of confinement on the concrete core as discussed in Section 4. For compensating the decreased confinement, the steel sheets and sleeves are utilized. The bilateral steel sheets are thread connected with bonding bars to enhance the confinement on the concrete core. In order to place steel sheets, circle holes in the damping layer are drilled and the thickness of the steel sheet is half of that of the damping layer which means another half of the hole in the damping layer is filled with concrete. This design produces the extra contact surfaces between the damping layer and the concrete core, which increases the interaction and integrity of the two components. Meanwhile, sleeves are stuck to the outer surface of damping rolls and designed to provide internal confinement. Compared with the SCS walls, there are three beneficial effects of the SDC walls listed as follows:  
1. Embedding a damping layer in composite walls can improve its energy consumption capacity. This is because (i) when the composite wall is under compressive or seismic loading, compression and shearing deformation occurs to the damping layer sandwiched between steel plates and concrete due to their relative displacement, which can achieve energy dissipation. (ii) the damping layer can provide a space for lateral dilation for the concrete core and allow the concrete core in SDC walls to have more deformation than in SCS walls.
2. Embedding a rubber layer in the composite wall can reduce the structural response under impact load by acting as a cushion to absorb the impact energy and mitigate the internal damage.
## 4 Discussion  
To guide the discussion, Table 4 provides the results of the experiments.  
Despite the current numerical weather forecast method being excellent compared to other regional forecast methods, a post-processing method could increase the accuracy significantly. For the purposes of post-processing, the GRIDPP has been shown to be a wonderful tool to improve and downscale the forecast result, greatly reducing the forecast error. Thanks to the GRIDPP result, one can train neural networks in a better way.  
First, looking at Table 4, it is possible to see that both proposed neural network architectures performed well on the test set, achieving a smaller RMSE than the benchmark and significantly fewer errors greater than 3 K. It is also interesting to notice in Figures 7 and 11 how the quantity of errors greater than 3 K increases with the growth of the forecast range. This is expected on real data, and we can see in each range value that both NN models achieved a better result.  
Although the MLP and CNN models have some errors greater than 3 K, both models obtained excellent results, reaching a smaller error in, respectively, 74.8% and 73.8% of the samples between the test samples where the GRIDPP error was greater than 3 K. The RMSE in these two cases also decreased by approximately 0.5 K, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**RMSE**} & \multirow{2}{*}{**Percentage of Errors Greater**} & \multicolumn{2}{c}{**Percentage of Smaller Errors**} & \multirow{2}{*}{**RMSE Where GRIDPP Error Is**} \\  & & & **than 3** & **Error Is Greater than 3** & \\ \hline MEPS & 5.258 & 54.05\% & - & 5.995 \\ GRIDPP & 1.886 & 9.38\% & - & 4.484 \\ MLP & 1.786 & 8.20\% & 74.8\% & 3.912 \\ CNN & 1.790 & 8.23\% & 73.8\% & 3.902 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Test set results.  
Figure 13: Comparing the RMSE by hours ahead.
# Application of a Geotextile in the Treatment of Post-Subsidence in Karst Areas
## 4 Results and Discussion
### Effects of Anchorage Length of Geotextile
#### 4.1.3 Soil Displacement
For example, the \(\alpha\) values are 1.05%, 6.47%, 20.72%, 32.21%, and 34.18% for \(L/B\) values of 0.25, 0.35, 0.5, 0.75, and 1.0, respectively, representing that the surface settlement values under anchorage ratios \(L/B\) of 0.25, 0.35, 0.5, 0.75, and 1.0 were reduced by 1.05%, 6.47%, 20.72%, 32.21%, and 34.18%, respectively, compared to \(L/B=0\) (i.e., without using a geotextile). According to BS 8006 [35] and [PERSON] et al. [4], the maximum differential surface deformation (i.e., the radio of depression at surface \(d_{s}\) to the subsidence width at surface \(D_{s}\)) should be limited to 1% if the subgrade is subjected to subsidence, which is one of the important indicators to evaluate the treatment effect of subsidence. The differential surface deformations \(d_{s}/D_{s}\) are 0.21%, 0.22%, 0.25%, 0.30%, and 0.33% for anchorage lengths of \(L=1.0B\), \(L=0.75B\), \(L=0.5B\), \(L=0.35B\), and \(L=0.25B\), which are smaller than the specified value of \(d_{s}/D_{s}\) of 1% in BS 8006. It is indicated that the application of a geotextile has a great effect on the treatment of subsidence. The application of a sufficient anchorage length, i.e., \(L=1.0B\) and \(L=0.75B\), results in an approximately suitable soil pressure, displacement of soil, tensile force and deformation of the geotextile at the final state of subsidence and effectively treats the subsidence. As such, an appropriate anchorage length for a geotextile should be chosen as 0.75 times length of subsidence width \(B\), which is approximately equal to the appropriate anchorage length of \(L=1.0B\) obtained from model test conducted by [PERSON] et al. [31]. Considering better safety of the treatment in terms of subgrade subsidence, it is suggested a greater anchorage length \(L\) of \(L=1.0B\) is used.
## 4 Experimental Set Up and Result Analysis
### Data Set and Simulation Tool  
In this paper, we applied two real data sets for our experiments. In the first case study, the NSL-KDD, [[https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)]([https://www.unb.ca/cic/datasets/nsl.html](https://www.unb.ca/cic/datasets/nsl.html)) (accessed on 8 May 2023), data set [10,21] is used, consisting of 42 features with a total of 148,517 instances. NSL-KDD is a data set to solve some of the inherent difficulties of the KDD'99 data set. This data set classifies existing instances into two main categories as \"Anomaly\" and \"Normal\" labels [22]. For the second case study, UNSW_2018_IoT_Botnet, [[https://www.unb.ca/cic/](https://www.unb.ca/cic/)]([https://www.unb.ca/cic/](https://www.unb.ca/cic/))  
Figure 2: General view of the cyber-attack detection in the IoMT healthcare system [18].  
datasets/nsl.html (accessed on 8 May 2023), Refs. [23; 24; 25; 26] data set is applied to evaluate prediction factors for the proposed GA-RF algorithm in IoMT environment [27; 28]. This data set has 19 features for a total of 3,668,522 instances. This data set categorizes all instances into five main classes including DoS, DDoS, Reconnaissance, Theft, and Normal labels. We completed the simulation process using a laptop with the Windows 10 Pro 64-bit, Processor type AMD Ryzen 9 PROP 5945 12-Core 3.00 GHz and 32 GB RAM for experimentation. Further, the WEKA tool was used for the implementation of prediction algorithms. Table 2 shows a brief illustration of NSL-KDD and UNSW_2018_IoT_Botnet data sets with the number of instances, type of attacks, and number of existing attributes for the prediction phase. For implementing the proposed GA-RF algorithm in WEKA tool, Table 3 shows a brief illustration of specific parameters for this algorithm that was used during the experiments and the prediction process.
## 4 Experimental Stations
### 2D X-ray Detectors
#### 4.3.3 Readout Chip Design  
The JUNGFRAU readout chip (ROC) is designed in United Microelectronics Corporation (UMC) 110 nm CMOS technology with aluminum-only interconnects. JUNGFRAU is a charge-integrating detector rather than a single photon counter. Most hybrid pixel detectors for synchrotron radiation are based on the photon counting principle, which cannot meet the requirements of XFEL applications. In photon counting detectors, an event in the sensor causes a response in one or more ASIC pixels. Whenever such a response pulse reaches a user-set threshold level in a photon counting ASIC, the event is \"counted\". Photon counting is inaccurate when multiple photons arrive at the detector the same time, since two or more photons are counted as one. Charge integrating ASICs record a signal proportional to the number of photons interacting with the sensor, at least in the case of monochromatic light, independently of the time structure of the incoming beam.
## 8 Mechanics  
The fundamental mechanical structure of the laser beacon is centered around a titanium container (see Figure 1 for more details). Conceptually, it can be characterized as a cylinder measuring 500 mm in length and a diameter of 142 mm. This container serves as the housing for the laser, electronic attenuator, batteries, and associated electronics, all securely ensocored within an aluminum chassis. The material of choice for the container is grade 5 titanium (Ti6 Al4V), a selection justified by its exceptional resistance to marine-based corrosion. A comprehensive overview of the mechanical attributes inherent in grade 5 titanium is provided in Table 2. Comprising three distinct components, the container assembly incorporates a cylindrical tube alongside two caps. The two caps are shown in Figure 7. The uppermost cap accommodates a glass rod, a strategic element that facilitates the egress of light from within the container. Conversely, the lower cap is equipped with a penetrator, affording the essential link to the external environment. Because of the glass rod's susceptibility during transit, a dedicated protective cover has been thoughtfully devised. This cover, painted in red, serves the dual purpose of enhancing visibility and mandating its removal prior to deployment.