esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
As saturation increases, the ice in the main pores continues to grow due to sufficient unfrozen water and drives some of the water into the secondary pores. If the pore is closed, a water pressure \(\rm P_{\rm w}\) is created in the pore. When the \(\rm P_{\rm w}\) exceeds the tensile strength of the rock skeleton, triggering the spreading of the secondary pore is the primary reason for the change in the second peak of the \(T_{2}\) spectrum. When the pore is connected, the hydrostatic pressure \(\rm P_{\rm i}\) generated by the pore ice drives the unfrozen water seepage to form damage and exerts a dynamic water pressure on the rock skeleton, weakening the deformation resistance of the rock matrix [38]. The pressure \(\rm Pi\) also provides a supporting force for thepore wall. At the same time, the mechanical properties of ice are similar to those of rock, which reduces the wave impedance under the compressive stress wave and effectively reduces the dissipated energy. Therefore, cracks are less likely to develop in samples under impact loading due to the effect of pore ice, indicated macroscopically by an increase in the elastic phase and a decrease in the plastic deformation phase (Figure 8). In addition, the ice in the main pore gradually shifts to the secondary pores during the freezing process, with the capillary mechanism playing an important role in this process [25]. Due to the relatively low chemical potential of the ice in the main pore, the supercooled water in the small pores migrates along the thin water film to the larger pores [6], resulting in a continuous growth of ice in the main pores and a gradual increase in pressure P\({}_{i}\). When the difference between the ice pressure P\({}_{i}\) and the water pressure P\({}_{w}\) is \((27_{\text{sl}}/R)\cdot\cos\theta\) (\(\gamma_{\text{sl}}\) is the interfacial tension between ice and water; \(R\) is the equivalent capillary radius; and \(\theta\) is the contact angle [27]), the pore ice gradually develops toward the capillary pores and generates frozen expansion, which promotes the pore development. In general, the dynamic mechanical properties of frozen rock are not always enhanced with increasing saturation, with the strengthening effect suppressed when the saturation exceeds a critical value. The strengthening effect is less than the weakening effect when the saturation of the red sandstone exceeds 80%. Therefore, from the test results, the dynamic strength, modulus, and dissipated energy of the frozen sandstone gradually decrease when the saturation exceeds 80%, while the ultimate deformation capacity and broken degree progressively increase. In summary, as saturation increases, pore ice gradually transitions from filling, supporting, and bonding effects to frost damage.
## 3 Results and Discussions
### FTIR Analysis  
According to the fundamentals of IR spectroscopy, there are 3 absorption domains (Figure 4): 4000-2500 cm\({}^{-1}\) (I), 2000-1500 cm\({}^{-1}\) (II) and the fingerprint region (1500-400 cm\({}^{-1}\)) [67]. In this study, we performed the first spectroscopic characterization of buckwheat achenes sprouted after three and seven days. The spectral domains most often used in the qualitative analysis of buckwheat achenes are those in region I, characteristic of O-H bonds, in the composition of phenols [68]. Thus, in the unsprouted buckwheat achenes (Figure 4) the wide band from 3273.8 cm\({}^{-1}\) was observed. In the sprouted achenes after 3 days, there was an increase in the transmittance to 3278 cm\({}^{-1}\) due to the accumulation of -OH groups in the phenol structures, showing accentuated growth after 7 days.  
Adjacent peaks at 2924 and 2853.1 cm\({}^{-1}\) corresponded to the tensile vibrations of the -C-H bond within the -CH\({}_{3}\) or -CH\({}_{2}\) groups [69]. Region II showed a considerable increase due to the tensile vibrations in the carbonyl groups, as a result of the accumulation of carboxylic acids and amino acids.  
In summary, our experimental studies have shown that the analyzed Moldavian buckwheat extracts contain important lignans, flavonoids and isoflavonoids (Figure 5), which have known beneficial effects such as antioxidant, anti-inflammatory, antiallergic, antistrogenic, antiviral and anticarcinogenic properties, and can be recommended for exploration in functional food due to their protective effects.  
In the long run, understanding the effects of food ingredients on human health will lead to a change in the way we eat and thus a decrease in the risk of morbidity and mortality due, in particular, to obesity and cardiovascular disease. Both expanding the range and the insight into the role of functional foods would lead to easier choices for consumers.  
Figure 4: FTIR footprint of bioactive compounds in buckwheat achenes.
# Performance Optimization of Object Tracking Algorithms in OpenCV on GPUs
## 3 Analysis of OpenCV Object Tracking Algorithms  
Improving the application's performance typically requires source code analysis and execution profiling. In this paper, we optimized two types of algorithms for object tracking in the OpenCV library. This section demonstrates an analysis of the two algorithms with a GPU profiler. The first is the object detection algorithm; it detects target objects in images. The second is the optical flow algorithm; it estimates the motion of objects using a series of images.
## 4 Experimental Validation
### Improved BIT* Algorithm with Adaptive Parameter Optimization for Optimal Path Planning
#### 4.1.2 Improved BIT* Algorithm
However, in practical applications, it is necessary to carefully weigh the choice of thread count, hardware resources, and thread management costs to ensure that the algorithm achieves optimal performance in real-world environments. Increasing the thread count can enhance computational throughput, but excessive multi-threading may increase communication costs and reduce efficiency. Therefore, it is necessary to strike a balance between efficiency and thread count. Figure 9: Impact of different numbers of threads on planning efficiency. In this research, after careful consideration and experimentation, we selected four threads as the optimal number for optimizing the path algorithm. This selection is based on the balance between algorithm performance and resource utilization efficiency. To investigate the algorithm's effectiveness, we compared single-threaded and multi-threaded processing for different map sizes and recorded their processing times. Figure 10 clearly illustrates that multi-threading methods are more efficient than single-threading methods when processing large-scale map data. As map size increases, the processing time for both single-threaded and multi-threaded methods increases, but the time required for multi-threading methods remains significantly lower than that for single-threading methods. The faster processing speed achieved by multi-threading is primarily attributed to several factors: 1. Parallel Processing Capability: Multi-threading enables the simultaneous processing of multiple grid cells, and this parallel processing mechanism substantially reduces the overall computation time. 2. Reduced I/O Operation Time: Parallelization reduces the number of disk I/O operations because multiple threads can share data, thereby minimizing the need for disk read and write operations. 3. Full Utilization of Multi-core CPUs: Multi-threading fully leverages the computational power of modern multi-core CPUs by distributing tasks across different processor cores, thus achieving faster processing speeds.
# Evaluation of Land Subsidence during Groundwater Recovery
## 3 Methodology  
A changing groundwater level is an important cause of land subsidence. This study focused on the evaluation of soil surface displacement during groundwater level recovery in the Bangkok area. Soil profiles and soil parameters were collected from the Department of Public Works and Town & Country Planning, then they were evaluated and verified with the data from the Department of Groundwater Resources. All verified data were used to analyze soil surface displacement through theoretical calculations.
# Advanced Scanning Technology for Volume Change Measurement of Residual Soil
## 1 Introduction
In addition, there are only limited studies with respect to the advanced measurement of soil volume change for residual soil. Therefore, the advanced methodologies for measuring soil volume change utilizing modern 3D scanning technology are proposed in this work. The change in soil volume caused by SWCC measurement is modeled by combining a shrinkage curve with the measured SWCC in terms of gravimetric water content. Under unsaturated conditions, the coefficient of permeability, which is a function of void ratio and water content, is estimated from the SWCC. A commonly used technique is a statistical method proposed by [PERSON] and [PERSON] [38], which uses parameters that account for soil texture and pore structure along with the SWCC to provide an alternative to direct measurement of permeability. However, the shape of the permeability function is affected by the shape of the SWCC. Hence, the unsaturated permeability estimated from SWCC with soil volume change will be different from that without soil volume change. The research entails the creation of methods for accurate assessment of soil volume utilizing the proposed 3D scan technology, laboratory experiments involving the SWCC test, shrinkage test, 3D scan, and determination of the air-entry value of a residual soil from Singapore.
## 2 Materials and Methods  
### General  
The methodology framework of our study relies on two main components: VAEs and Monte Carlo simulations of BE studies. These components were used to demonstrate the applicability and advantages of VAEs in the context of BE studies of highly variable drugs. Specifically, BE trials were simulated under various conditions of within-subject variability, sample size, and differences between the T and R products [3]. Using MonteCarlo simulations, two populations (termed the \"original\" dataset) of subjects were created for both the T and R groups across two periods, which were then randomly subsampled with different proportions (\"subsampled\"). Subsequently, the subsampled dataset was utilized to train a VAE model, and that trained VAE model was then used to generate new datasets (\"generated\").  
The conditions for creating the original data varied in terms of sample size, within-subject variability, and the mean ratio of the T and R endpoints (T/R). The VAE model generated datasets of varying sizes relative to the original sample size (1 to 3 times the original sample size). Additionally, the (unscaled) VAE-generated datasets were compared against the original and the subsampled datasets with the scaled BE approach in order to achieve a stricter evaluation of the performance of the VAE method (Figure 1).  
BE assessment was applied to all three datasets (original, subsampled, and generated), adjusting the acceptance limits appropriately based on within-subject variability as per the guidelines of the EMA and the FDA [1; 2]. This procedure was repeated thousands of times (5000) to ensure robust estimates. The total of 5000 Monte Carlo trials is based on findings from prior research [3]. These studies have demonstrated that conducting this quantity of repetitions ensures the acquisition of reliable and consistent estimates [3]. Each step of the analysis is described below.
## 3 Results and Discussion  
### Residual Stress Distribution Characteristics on the Nugget Surface of the Spot Weld Joint  
Figure 8 presents the experimentally measured residual stress distribution characteristics of the spot weld joint. The results obtained from the XRD and HDR methods indicate the distribution of residual stress with respect to the distance from the nugget center. The largest tensile residual stress was observed at the nugget center, while the tensile residual stress decreased progressively with increasing distance from the nugget center. The maximum compressive residual stress occurred at a distance of 6 mm from the nugget center. In the specimen length direction (y-direction), substantial tensile residual stress was observed up to a distance of 3 mm in the region in contact with the electrode, beyond which the stress level diminished. Similarly, in the specimen width direction (x-direction), tensile residual stress extended up to a distance of 3 mm in the region in contact with the electrode, after which significant compressive residual stress was detected. The residual stress values obtained using the XRD and HDR methods were found to be in good agreement for both directions. Due to the small nugget size (approximately 4 mm), HDR measurements were conducted only at two positions: the nugget center and a point 6 mm from the center. Although this limited the resolution of HDR measurements, the results were consistent with those obtained at 2 mm intervals using XRD. This consistency suggests that the residual stress of the spot weld joint was accurately measured by both methods.  
Figure 7: Temperature dependence of various physical properties: (**a**) thermal properties; (**b**) physical properties; (**c**) electromagnetic properties.  
Figure 9 compares the residual stress distribution obtained through FEM analysis with the experimental results. The agreement between the experimental and numerical results was strong for the residual stress in the x-direction. However, in the y-direction, the numerical values were slightly higher than the experimental results. Despite this discrepancy, the residual stress distribution patterns were comparable. Within the nugget, the y-direction residual stress values derived from FEM analysis were higher than those obtained experimentally. Outside the nugget, the experimental and numerical values aligned closely. The observed differences in the nugget region, where the temperature was relatively high, are likely attributable to variations in material properties at elevated temperatures. In the FEM analysis, the same spot-welding conditions as in the experiment were used, and the temperature-dependent mechanical properties were predicted based on the material's chemical composition. Overall, the FEM analysis model demonstrated strong validity and was applied in this study to conduct various examinations of the spot weld joint.
## 5 Performance Evaluation
### Results and Discussion  
Assessment results attained by implementing the aforementioned simulation framework are described in this subsection. Here, the results and discussion are presented according to the impact of varying the number of requests, as well as the QoS requirements of requests.  
The percentage of successfully executed service requests in terms of maintaining QoS level is enumerated here for varying number of arrived requests, and is delineated in Figure 4a. However, the graphs of Figure 4a illustrate that the percentage of successfully executed requests satisfying QoS level sharply declines in all of the studied resource allocation strategies with increasing arrived requests. The growing number of requests augments the waiting time for the requests, which imposes many of the service requests to overstep the maximum allowable delay.  
Figure 4: _Cont._However, the rate of decrease is significantly less in the FFS algorithm, as this is especially designed for maximization of user Quality-of-Experience (QoE). In FFS, one BBU which can execute a request with the fastest processing time is allocated for a request. On the other hand, FFP targets on maximization of profit while maintaining the minimum QoS level. As in QEPRA, a multi-objective optimization problem is formulated, the proposed FFS algorithm outperforms QEPRA and FFP. Yet, the QEPRA provides a better result compared to the NvG and QoSM protocols because the proposed QEPRA technique prioritizes the scheduling of requests following their QoS constraints. The rationality for better performance of QEPRA compared to the other two reviewed schemes from the literature can be described by a scenario as, suppose that in the system three requests are being executed using the total system resources. Then, two other requests come one after another. The first request contains flexible QoS requirements, while the second is stringent in terms of QoS. As there is no priority based on the QoS requirements in the NvG approach, generally at first the system will adjust the total system resources for the first application request. After adjusting the request, it may find that the later request cannot be sent for execution since the required adjustment to allocate resources for that request cannot be made in the present system condition. In this circumstance, the QoS of the later service request will be violated. As the number of requests increases in the system, the rate of this type of QoS violation will increase. In this case, our proposed execution scheduler works significantly well, since it prioritizes the newly arrived requests based on their QoS requirements. Moreover, as the number of requests increases in the system, the genetic solution in QoSM consumes much more time than the proposed QEPRA and affects the QoS requirement of the service request. In addition, QEPRA makes a tradeoff between profit and QoE and FFP maintains minimum QoS level. On the other hand, FFS is particularly designed for QoE maximization and thus FFS provides the best result here.
# Unmanned Aerial Vehicle Landing on Rugged Terrain by On-Board LIDAR-Camera Positioning System
###### Abstract  
Safety landing unmanned aerial vehicles (UAVs) in unknown environments that are denied by GPS is challenging but crucial. In most cases, traditional landing methods are not suitable, especially under complex terrain conditions with insufficient map information. This report proposes an innovative multi-stage UAV landing framework involving (i) point cloud and image fusion positioning, (ii) terrain analysis, and (iii) neural network semantic recognition to optimize landing site selection. In the first step, 3D point cloud and image data are fused to attain a comprehensive perception of the environment. In the second step, an energy cost function considering texture and flatness is employed to identify potential landing sites based on energy scores. To navigate the complexities of classification for precise landings, the results are stratified by the difficulty of various UAV landing scenarios. In the third step, a network model is applied to analyze UAV landing site options by integrating the ResNet50 network with a convolutional block attention module. Experimental results indicate a reduction in computational load and improved landing site identification accuracy. The developed framework fuses multi-modal data to enhance the safety and feasibility of UAV landings in complex environments.  
onvolutional neural network; LIDAR; landing scene recognition; landing site selection; point cloud; unmanned aerial vehicle
## Oil Shale Chemical Characteristic
### Oil Shale Pyrolysis
#### 4.2.2 Pyrolysis Products
Figure 2a-d present a clear correlation between the ascending temperature regime and the increased rate of transformation during the pyrolysis of oil shale, verifying the influence of the rate of heat application. The rate of change for FSOL1 and FSOL2 is quite similar, but a noticeable extension in the transformation duration for FSOL1 underscores the role of silicates in curtailing the time required for organic matter transformation. The transformation rates heighten further for FSOL3 (Figure 2c) and reach a peak for FSOL4 (Figure 2d), where the enhanced organic matter proportion significantly contributes to the boost in transformation rates [56]. Also, the physical dimensions of the oil shale granules impact the concentration ratios of different constituents within the produced oil and gas. An upsurge in the size of oil shale granules leads to a reduction in 1-olefins and an augmentation in aromatic hydrocarbons. The proportion of n-paraffins and heteroatom-incorporated compounds also exhibit variations with granule size changes. Nevertheless, the aggregate yield of shale oil and non-condensable gases does not exhibit a significant correlation with granule size. The retort temperature and the size of oil shale granules influence the extent of the secondary reactions happening during the pyrolysis process. These reactions, taking place within the confines of the oil shale granule and the packed bed, substantially modify the chemical composition of the end-product gases and shale oil. In addition, the spatial heterogeneity of kerogen and minerals in the oil shale matrix can also intervene in the pyrolysis reaction, thereby modifying the composition of the resulting products. Therefore, the inherent properties of oil shale, the conditions inside the retort, and the specific characteristics of the pyrolysis process collectively govern the yield and the constituent makeup of the products obtained from oil shale pyrolysis. Empirical models have been extensively used in the modeling of kerogen and heavy oil pyrolysis, such as the Braun and Burnham model and the Wellington model. A series of decomposition reactions are involved in these models, which are important for determining product distributions. However, it is important to note that these kinds of models have  
Figure 2: Oil shale of different samples conversion at different temperature. Reproduced with permission from ref [56]. Copyright 2022 Royal Society of Chemistry. limitations and do not capture all the intricacies of the pyrolysis process. The models' predictive accuracy can vary depending on the specific oil shale composition and pyrolysis conditions. The frequency factor and reaction enthalpy may be adjusted based on more realistic scenarios.