esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Considering the ALS Workflow and Its Impact on the Efficacy of Data Products
### Planning and Data Collection
#### 3.1.1 Flight Planning and System Configuration  
The planning phase takes into account the requirements of the investigator, local flying conditions and aviation regulations, as well as aircraft performance specifications to create a flight plan and system configuration that will yield a target laser shot density (laser pulses per unit area), which in turn will produce a given return density (as a single pulse can produce multiple returns) and more importantly a ground point density that will adequately support the desired raster resolution. It is also important to point out that even though LASER shot density is the metric most often used to plan and evaluate LiDAR surveys, for archaeology and other applications it is useful to keep in mind the fraction of the surface area that is being illuminated by the laser footprints. From an aircraft perspective the three factors that can be controlled are: flying altitude above the ground (AGL), airspeed (which in conjunction with the wind speed and direction determine the ground speed), and the flight line orientation. For the ALS system the main parameters that can be controlled are the pulse repetition frequency (PRF), scan angle and scan frequency, and in some systems the divergence of the LASER beam. All of these variables affect the shot density and the fraction of surface that is illuminated. In general higher flight altitudes will produce larger swath widths (for a given scan angle) which will spread the number of laser shots fired in a given interval of time over a larger area and thus reduce the shot density (Figure 2). The same goes for air/ground speed; the faster an aircraft moves over the ground (ground speed) the more the laser shots will spread out, thus reducing the shot density. The flight line orientation has no practical effect on the shot density, however the orientation of the lines is adjusted primarily to minimize the flying time while maximizing the surveying time. In some cases the orientation of the flight lines may be constrained by the local terrain, safety factors, or local regulations. Finally it should be noted that higher flying altitudes bring two additional drawbacks besides a reduced shot density; they also reduce the horizontal accuracy of the returns, and reduce the probability of detecting weak returns such as the ground returns that suffer degradation as they make their two-way travel through the atmosphere and vegetation. For system parameters, increasing the scan angle increases the swath width and thus reduces the shot density. Increasing the scan frequency reduces the across-track spacing of LASER footprints, but decreases the scan line spacing, with no net effect on the overall shot density. However, the scan frequency is normally chosen to provide similar along-track and across-track spacing for uniform
# A Method for Generation of a Sizing System and Representative Models for a Facial Mask Design
## 4 Discussion
The RFMs found as the closest to the centroid of sizing categories generated by two key dimensions might not be representative for other body dimensions [6; 7; 28]. This study used all the 18 facial dimensions in the selection of RFMs closest to the average face of each sizing category. Not all the RFMs in this study locate close to the centroid of each sizing category drawn based on only the two key dimensions, but it might show better representativeness within the population than the RFMs close to the centroids. The representativeness was evaluated in this study by comparison between different RFM sets found based on different conditions. Types of measurement value (measured value or normalized value) and weight (equal weight or unequal weight: H = 9, M = 3, L = 1) of each facial dimension were considered as the analysis conditions for RFMs selection in this study. The normalized values in WSED calculation were used to consider the differences in measurement range by facial dimensions (e.g., nose protrusion = 12.9-23.9 mm; birtagion-menton arc = 269.0-361.1 mm) that can affect the results of RFM selection. The RFM selection conditions were evaluated using NED. RFMs showing better representativeness were found under the conditions using measured values with unequal weights in the case of the oxygen mask for KAF pilots. The RFM selection and representativeness analysis methods proposed in this study can be usefully applied to finding representative models for a multiple-size product design. As the follow-up study, the RFMs having facial sizes that are exactly the same as the average (or median for the key dimensions) face would be created by applying the skin deformation technology. The present study found the RFMs among the existing faces of the KAF pilot data. Due to the limited amount of sample size (\(n\) = 336), an identified RFM could be located at a significant distance from the average/median face in the sizing category. This issue can be resolved by artificially creating 3D RFMs that have the average/median facial size in each sizing category by applying the skin deformation technology [29; 30; 31; 32]. The 3D form of the selected RFM can be slightly deformed to have exact sizes of the average/median face by adjusting meshes around facial landmarks. The sizing system and RFMs derived in this study were applied in the design of a pilot oxygen mask for KAF pilots. Four-sized oxygen masks were designed based on the proposed sizing system [6; 26; 28] and the RFMs. Prototypes of the oxygen masks for KAF pilots were manufactured with similar materials to the existing MBU-20/P mask. Then, the prototypes were compared to the MBU-20/P with 88 KAF pilots in terms of subjective and facial contact pressure analyses [27]. As a result, the discomfort level and facial contact pressure of the newly designed mask showed 33-56% and 11-33% lower on average than those of the MBU-20/P, respectively.
## 5 Conclusions  
The outcome of the study is the positive effects of seed priming on eggplant seed germination and early seedlings growth. It is thoughtfully concluded that AGE at a concentration of 200 \(\upmu\)g mL\({}^{-1}\) for 12 h was the best priming treatment. Additionally, MeJA and SA also improved seed germination and early seedling growth, which confirm previously suggested functions of these plant growth regulators to induce priming effects on the seed germination of various plants. Our findings indicated that seed priming stimulated germination and enhanced numerous physiological responses such as antioxidant enzymes, ROS modulation, and soluble protein abundance in the randomly selected eggplant seeds. These physiological responses may, therefore, be suggested to have influenced the morphological improvements such as root/shoot length and fresh and dry weights. AGE could also successfully improve the germination properties of the eggplants by reducing the time taken to germinate and increase the number of seedlings germinated in a given time. Furthermore, stimulated antioxidant enzymes system may be important for the protection and maintenance during long-term survival of the resulting plants. Current findings also provide a platform for future research work to understand the biological functions of AGE, MeJA, and SA during seed germination involving ROS regulation and the scavenging through antioxidant enzymes or, the accumulation of soluble proteins interacting with the germinating seeds. Preparation of AGE is, nonetheless, handy and economical, and bears less or no potential hazards to the users and thus is proposed as a promising seed priming agent for enhanced seed germination and improved growth during early stages of plant development. Although the results are very convincing, additional studies involving molecular approaches and evaluating the anti-pathogenic effects of AGE treated plants should be performed.  
Conceptualization, M.A., S.H. and Z.C.; data curation, H.A., M.I.G., B.A. and M.J.A.; formal analysis, M.A. and H.A.; Funding acquisition, Z.C.; investigation, M.A., H.A. and M.I.G.; methodology, M.A. and S.H.; project administration, Z.C.; Resources, Z.C.; software, M.A.; supervision, Z.C.; Validation, M.A., S.H. and Z.C.; writing--original draft, M.A.; Writing--review and editing, M.A. and S.H.  
This research received financial support from the Shaanxi Provincial Sci-Tech Innovation project (Grant Number 2016 KTCL02-01) and National Natural Science Foundation of China (Grant Number 31772293).  
**Acknowledgments:** The authors would like to extend their sincere appreciation to the China Scholarship Council and Shaanxi Provincial Science and Technology Innovation project of China for their financial support.  
**Conflicts of Interest:** The authors have no conflict of interests to declare.
## 2 Materials and Methods
### Methods
#### 2.2.4 Algorithm of XDAWN + RGC  
The overall steps of our algorithm are as follows (Figure 5). Training stage: We use signal Xn and label Yn as the input of the filter XDAWN, calculate the SPD covariance matrix of the filtered signal Xn *, perform affine transformation on the SPD covariance matrix, and then use the transformed SPD covariance matrix to train the RGC classifier. Testing stage: When a new test set Xp comes in, the previously-trained XDAWN is used to perform spatial filtering to obtain Xp*; then, the SPD covariance matrix is obtained and affine transformed according to Xp* and its own RGM, respectively; and, finally, it is classified using RMDM.
# An Accurate Direction Finding Scheme Using Virtual Antenna Array via Smartphones
## 2 Related Work  
### Direction Finding via Smartphones  
As smartphones have become common personal belongings in daily life, more and more indoor direction finding systems are being deployed on smartphones. Swadloon [3] leverages the Doppler effects of the acoustic waves for direction finding and indoor localization. Users need to shake their smartphones and walk a few steps before using Swadloon. It only requires off-the-shelf speakers and supports an arbitrary number of users. However, this scheme does not take the multipath effect into consideration. In addition, it has a non-line of sight problem, which means if the user turns his back on the acoustic source, the results become unstable. Besides, there are other schemes solving the localization problems by Doppler effects, e.g., [4]. [PERSON] et al. [5] propose a way to locate outdoor access points (APs) using smartphones. Their insight is that, by rotating a wireless receiver (smartphone) around a signal-blocking obstacle (the user's body), they can effectively emulate a directional antenna. By the periodic received signal strength (RSS) measurements of WiFi signals, the users can detect the outdoor APs' locations. However, RSS measurements are easily affected by the multipath effect in indoor environments. It will cause a large angular error on the results of indoor direction finding. There are some similar methods using body as an antenna array for localization, e.g., [6]. In addition, there is an indoor localization technique based on an ambient sound fingerprint called the Acoustic Background Spectrum (ABS) [7]. As with other fingerprint-based localization techniques [8; 9; 10], location is determined by measuring the current fingerprint and then the ABS chooses the \"closest\" fingerprint from a database. This scheme is accurate and reliable, but it also demands a lot of preparation before localization, and it is easily affected by the environment change.
# Recent Advances in Geographic Information System for Earth Sciences
## 3 GIS for Mining  
Systematic and strategic mine planning, operation, and environmental management are necessary to improve mineral productivity, operational efficiency, and stability in the mining environment. To accomplish these objectives, GIS has been effectively used to design and optimize mine development. [PERSON] et al. [7] review GIS-based methods and applications utilized in mine development, especially for mine planning, operation, and environmental management. They observe that GIS-based methods, including database management, spatial analysis, mapping, and visualization, are effectively used for all stages of mine development at global, regional, and local scales. In the mine planning phase, GIS-based methods are adopted for ore reserve estimation, open-pit boundary optimization, mine infrastructure design, and potential conflict analysis. Various mine operation systems based on GIS have been implemented in mining sites for ore haulage operations, wireless communication, ore management, safety monitoring, underground ventilation, and drainage systems. Moreover, various GIS applications have been developed to support decision-making in mine reclamation planning and re-utilization designs.  
As an example of a GIS application for mining, [PERSON] et al. [8] present a spatiotemporal model tightly coupled with GIS for simulating methane emissions in underground coal mines. Such a tight coupling approach is achieved by developing a lattice Boltzmann method (LBM)-based turbulent model with an underlying shared FluentEntity model within the GIS. A case study demonstrates that the proposed GIS-based model is capable and effective in providing functionalities for lattice domain decomposition, simulation, visualization, and analyses, as well as improving computational efficiency compared with traditional computational fluid dynamics (CFD) methods. The tight coupling approach for integrating GIS and simulation models is applicable to underground coal mine disasters.
## 2 Materials and Methods
### SVM Classifier and Post-Processing  
After features were extracted from each image, an SVM classifier was used to determine each texture class. The SVM became very popular within the machine learning community due to its great classification potential [41; 42]. The SVM maps input vectors in a non-linear transformation to a high-dimensional space where a linear decision hyperplane is constructed for class separation.  
Figure 8: Feature vector extracted from one image of the Vistex Database (Food0007). (**a**) The original image is divided into five partitions (**b**): four local partitions and one global (the same image, bottom). (**c**) The feature vectors obtained from each partition. The color feature vectors and texture feature vectors are concatenated. (**d**–**g**) The global sub-image as a real example using one iteration (**e**), two iterations (**f**), or three iterations (**g**) of the BQMP method.  
A Gaussian SVM kernel was used, and a coarse exhaustive search over the remaining SVM parameters was performed to find the optimal configuration on the training set.  
A grid search with cross-validation was used to find the best parameters for the multiclass SVM cascade. We used half of the training set to determine the SVM parameters, and the other half in validation. For testing, we used a different set as explained in Section 3.1. In the case of bagging, we took repeated samples from the original training set for balancing the class distributions to generate new balanced datasets. Two parameters were tuned: the number of decision trees voting in the ensemble, and the complexity parameter related to the size of the decision tree. The method was trained for texture classification using the training sets as they are specified for each database.  
In order to have a fair comparison between our obtained classification rates and those previously published, we employed the same partitions used for training and testing as in [PERSON] et al., 2011 [1], [PERSON] et al., 2015 [7], [PERSON] et al., 2004 [9], [PERSON] et al., 2004 [27], [PERSON] et al., 2011 [28], [PERSON] et al., 2013 [4], and [PERSON] et al., 2017 [50]. The training and test sets came from separate sub-images, and the methods never used the same sub-image for both training and testing.  
In general, combining multiple classification models increases predictive performance [51]. In the post-processing stage, a bagging predictive model composed of a weighted combination of weak classifiers was performed with the results of the SVM model [52]. Bagging is a technique which uses bootstrap sampling to reduce the variance and improve the accuracy of a predictor [51]. It may be used in classification and regression. We created a bagging ensemble for classification using deep trees as weak learners. The bagging predictor was trained with new images taken from the training set of each database. This result was assigned as the final classification for each image.  
We compared our results with those published previously on the same databases.
# Investigations into Picture Defogging Techniques Based on Dark Channel Prior and Retinex Theory
## 1 Introduction
Section 3 introduces a novel defogging algorithm that integrates the dark channel prior with Retinex theory to address the limitations of existing defogging methods. Section 4 presents both subjective assessments and quantitative comparisons with current state-of-the-art defogging algorithms, followed by conclusions and discussions in Section 5.
## 4 Results and Discussion  
We validated a dynamic model through simulations that verified the behavior of coupled dynamics and no lineality. Calculations were performed in a Mac OS Big Sur (Intel core i9, RAM 24 GB, and 1 TB hard disk), using MATLAB 2015a (MathWorks) for each algorithm (PSO, GWO, HGS, LSHADE, SPACMA, MPA, SMA, WOA). The solution space for each parameter to optimize (total 12, Equation (16)) was constrained to [0,10]. We performed simulations for 20, 30, and 40 individuals using 20 iterations during 30 independent runs for PD controllers. We ran simulations with 80 and 100 individuals for PID controllers using 40 iterations and 30 independent runs. The evaluation performance of the algorithms is reported through statistics that considers four variables to check the dispersion or variability of the solutions, which are the best, the mean, the standard deviation and the worst. In the following tables, the best results are presented in bold, and the purpose is to quickly identify the best performing controller as well as its gains.
# Blade Shape Optimization and Analysis of a Propeller for VTOL Based on an Inverse Method
## 5 Analysis
### Analysis of the Level Flight Energy Ratio  
The level flight mode and vertical flight mode have different, even contradictory, requirements for the optimization of a VTOL propeller. To investigate the weights of these two flight modes on the optimization result, we changed the ratio of energy of level flight to the profile energy of the whole mission \(\xi\) by changing the flight time of the different stages and reconducting the optimization. The relationship between the energy-saving advantage of the VPP over the FPP \(\delta\) and \(\xi\) is explored and shown in Figure 30.  
Figure 28: Optimization results with different hover disk loading for a VPP. (**a**) Pareto fronts; (**b**) extreme values of two objectives.  
Figure 27: Optimization results with different hover disk loading for an FPP. (**a**) Pareto fronts; (**b**) extreme values of two objectives.  
Given several fixed maximum thrust-to-weight ratios, the variations of \(\delta\) along with \(\xi\) are shown in Figure 30b. For the researched tilt-wing aircraft, when the maximum thrust-to-weight ratio at hover \(\kappa\) was higher than 1.30, the energy-saving advantage of the VPP \(\delta\) increased with an increase in the level flight energy ratio \(\xi\). However, when \(\kappa\) was lower than 1.30, \(\delta\) had a contradictory trend with \(\xi\). Regardless, the energy-saving advantage of the VPP over the FPP was over 10.5%. This section quantitatively validated this advantage. Compared with the FPP, the application of the VPP meant a more complicated mechanism and a larger weight cost, which means less reliability and more operation power required. Combining these factors with the rules shown in Figure 30b, a practical reference to choose a VPP or an FPP for VTOL aircraft could be given.
# The Use of Earth Observation Data for Railway Infrastructure Monitoring--A Review
Additionally, the integration of EO data with in situ measurements and AI algorithms remains underdeveloped, restricting the ability to leverage full-scale predictive analytics [18]. Addressing these gaps will have profound implications for railway infrastructure management. Enhanced data integration and resolution will enable more precise monitoring of track conditions, mitigating risks such as deformation and subsidence. Incorporating multispectral and thermal data can improve vegetation management and flood risk assessments, resulting in better safety outcomes and fewer service disruptions [19; 20]. The adoption of near-real-time EO monitoring will facilitate proactive maintenance strategies, reducing downtime and maintenance costs [21]. Moreover, advanced AI-driven analytics can transform the management of railway assets, enabling predictive maintenance and improving resource allocation [22]. In summary, while EO data applications in railway infrastructure monitoring are still nascent, addressing these knowledge gaps can unlock transformative improvements in safety, efficiency, and sustainability. This paper aims to provide a comprehensive review of EO data use cases with a focus on the diverse range of established and emerging EO-based applications highlighting opportunities for innovation and practical implementation in the railway sector.
# A Single-Rate Multicast Congestion Control (SRMCC) Mechanism in Information-Centric Networking
## 4 Rate Adaptation
### Rate Calculation  
In this section, the rate adjustment method in each stage is introduced in detail. In a round of rate adjustment, the maximum queue occupancy ratio \(O_{max}\) and the minimum queue occupancy ratio \(O_{min}\) are used, respectively, as the input of the rate calculation module, and the minimum rate \(R(t+1)_{min}\) and the maximum rate \(R(t+1)_{max}\) are output, respectively. Finally, the final sending rate is obtained according to the rate selection strategy.
## 6 Limitations  
While wearable technologies have seen rapid adoption and widespread use in sports performance monitoring, several limitations remain that hinder their full potential [89].  
These limitations include the following:  
_Validity:_ The accuracy of wearable devices in measuring specific metrics can vary significantly. For instance, heart rate monitors might provide accurate readings under controlled conditions, but may fail to capture accurate exertion levels during high-intensity activities [26].  
A potential solution is to develop advanced algorithms that can better interpret data under varying conditions and integrate multi-sensor data to enhance accuracy.  
* Reliability: The reliability of data from wearable devices can be influenced by environmental factors and device placement. GPS accuracy can be compromised in urban areas with tall buildings, and biometric readings can be affected by improper device usage or physiological conditions like dehydration. Improving sensor technology and incorporating redundant systems can help mitigate these issues [17; 89].
* Interpretability: Wearable devices generate vast amounts of data, which can be challenging to interpret effectively. Athletes and coaches may struggle to make actionable decisions based on complex datasets. Developing user-friendly interfaces and employing artificial intelligence to provide clear, actionable insights can enhance the usability of these devices [25].
* Cost: High-quality wearable devices can be expensive, limiting their accessibility to amateur athletes or teams with limited budgets. Potential solutions include reducing production costs through technological advancements and economies of scale, and developing lower-cost alternatives that still provide valuable insights.
* Ethical and Privacy Concerns: Continuous monitoring of physiological data raises concerns about data privacy and the ethical use of the collected information. Establishing robust data protection regulations and ensuring transparency in data usage policies are essential steps in addressing these concerns.