esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussions
### Study of a Wasserstein-Based Evolutionary Algorithm
#### 4.1.1 Alternating vs. Successive Boundary and Full Domain Mutations  
In this experiment, we compare the performances of the three mutations defined in Algorithms 2-4. They are all combined with the crossover defined in (2), and correspond to the algorithms named WBGEA_1, WBGEA_2 and WBGEA_3, respectively. Figures 10-12 show (on their lefts) statistics of the best so far objective function values throughout the iterations for the wind farm, the inertia and the point minimum distance cases, respectively. In all of the plots, the implementation WBGEA_1 (which has the alternating mutation) clearly outperforms WBGEA_2 and WBGEA_3 (which have successive mutations). In a more subtle way, WBGEA_2 yields better results than WBGEA_3 in all tests with a compromised speed on \(F_{minDist}\). When considering the associated diversity measures in Figures 10-12 (on their rights), it is observed that populations in WBGEA_1 always converge faster than those in WBGEA_2 and WBGEA_3. The diversity drops faster with WBGEA_3 than with WBGEA_2 except on \(F_{intert}\).  
The fact that the boundary and the full domain mutations occur one at a time is the determining advantage of WBGEA_1 over WBGEA_2 and WBGEA_3. If one of the two mutations is detrimental, it will be avoided in fifty percent (prob = 0.5) of the sample cases. The difference between WBGEA_2 and WBGEA_3 follows the same independence idea in a less perceptible way: there will be situations where \(\epsilon_{1}\) is large and \(\epsilon_{2}\) small, or vice versa, creating some independence between the two mutations. On the contrary, in WBGEA_3, the boundary and full domain mutations are always applied together with the same intensity. These tests suggest a _mutation independence principle_: for composite mutations made of different types of perturbations, like the boundary and the full domain mutations, the perturbations should be applied independently. The population's diversity drops faster in WBGEA_1 than in WBGEA_2 and WBGEA_3, suggesting, in conjunction with its better objective values, that WBGEA_1 is more efficiently converging towards the global optima. Conversely, WBGEA_3 is more prone to premature convergence than WBGEA_2, therefore losing diversity faster. With the inertia function, \(F_{intert}\), neither WBGEA_2 nor WBGEA_3 manage to converge: the population diversity plateaus at a relatively high level (about 8).  
Figure 10: Alternating vs. Successive Boundary and Full Domain Mutations. The test function is the wind-farm layout problem averaged over 4 directions, \(F_{4d}\). (**Left**): mean \(\pm\) std. deviation of the best so far solution. (**Right**): mean \(\pm\) std. deviation of the population diversity. Statistics calculated from 20 independent runs.
## 2 Materials and Methods
### WindSim  
In the case of a mesoscale numerical model, modeling is carried out with a resolution of 1 to 10 km, but with this resolution, it is possible to obtain a numerical value close to reality in a system requiring high-resolution, such as a decrease in power generation due to a fluid flow in a complex terrain, or a wake effect at the rear of a turbine. It is difficult to determine, and accordingly, Computational Fluid Dynamics (CFD) models that can be modeled at a high resolution of tens to hundreds of meters are mainly used in wind farm design. WindSim, one of the computational fluid dynamics programs, is a program mainly used for site selection for wind farm construction, and was developed by WindSim A/S of Norway. The purpose of WindSim is to find an accurate solution of the flow characteristics generated as the wind passes through the turbine, taking into account all the natural properties of the fluid, such as compression, viscosity, friction, and turbulence. Since WindSim is a non-linear flow program, it can simulate special vortex phenomena, such as flow separation in complex terrain, much more accurately than a linear model. WindSim calculates pressure, wind speed, Turbulent Kinetic Energy (TKE), and turbulent dissipation rate using the Reynolds Averaged Navier-Stokes (RANS)equation for each grid through the mesh system to calculate the flow field. The turbulence dissipation rate refers to the amount of energy transferred in the energy cascade process, in which large eddies are converted into several small eddies. In WindSim, Terrain creates a domain based on terrain elevation and roughness length, Wind Fields calculates a flow field using a turbulence model based on meteorological data, Objects can designate observation points and turbine locations, and flow value characteristics are identified. Six modules are sequentially performed: results that can confirm the results; wind resources that create an overall wind resource map; and energy that calculates annual power generation with wake effect applied. In other words, after analyzing the terrain data including the terrain height and roughness length in the Terrain module, the wind conditions in the corresponding domain are analyzed using the RANS equation through the Wind Fields module. Energy is calculated based on the wind resource map created after setting the location of the meteorological data and the coordinates to install the turbine with the Objects module. The Shuttle Radar Topography Mission (SRTM) 1\({}^{\circ}\) (30 m) data provided by the National Aeronautics and Space Administration (NASA) was used for the topographical data of the wind resource map analysis area, and the ground roughness (Roughness length) was Vegetation Continuous Fields (VCF) data, the horizontal grid resolution of the domain is set to 120 m, and the total number of grids is 3,120,500. WindSim used in this study is ver. 11.0, and Table 4 shows the model settings.
# Video Quality Modelling--Comparison of the Classical and Machine Learning Techniques
## 4 Results
### Subjective Quality Assessment  
The results obtained from the video quality assessment are presented in Figure 4 and Table 2. As seen in Figure 4, the video quality is directly proportional to the encoding bitrate used. For both codecs and both image resolutions, the greatest quality changes are observed at low bitrates, i.e., in the range of up to approximately two megabits per second. Above this threshold, the video quality in all cases analysed was above four on the MOS scale, indicating that most users rated this quality as good or very good.  
It can also be noted that the general rule is that for a given encoding rate, the video quality for the H.265 codec is higher than for the H.264 codec. At the same time, it can be noted that higher image resolution results in higher quality perceived by users. It can also be seen that changes in encoding rates in the range of values below two megabits greatly affect the change in video quality, while for higher values of encoding rates, the changes in quality are much smaller. The detailed results of the subjective tests conducted by the authors on the examined video material are presented in Table 2, which shows the mean assessment of video quality (MOS), standard deviation (S), and confidence interval coefficient (8).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RMSE 1** & **MSE 2** & **MAE 3** & **MaxAE 4** \\ \hline Linear Regression & 0.27 & 0.07 & 0.19 & 0.83 \\ \hline Ridge Regression & 0.52 & 0.27 & 0.39 & 1.37 \\ \hline Lasso Regression & 0.55 & 0.30 & 0.42 & 1.53 \\ \hline Elastic Net & 0.56 & 0.32 & 0.41 & 1.55 \\ \hline Support Vector Regression & 0.34 & 0.12 & 0.23 & 0.96 \\ \hline Decision Tree Regressor & 0.36 & 0.13 & 0.22 & 1.23 \\ \hline Random Forest Regressor & 0.35 & 0.13 & 0.24 & 1.13 \\ \hline Gradient Boost Machines & 0.36 & 0.13 & 0.23 & 1.26 \\ \hline k-Nearest Neighbours & 0.40 & 0.16 & 0.25 & 1.39 \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: Performance comparison results of the tested machine learning models.
# Performance Evaluation of a Mesh-Topology LoRa Network
## 3 LoRaWAN Overview
### LoRa and LoRaWAN MAC
The maximum timing uncertainty measured for the unmodified LoRaWAN stack was 15 ms.
# A Finite State Machine-Based Improved Cryptographic Technique
## 4 Results and Analysis of Proposed Work
### Bit Error Rate of Cryptographic Turbo Code  
Bits that are sent across a digital communication system incur the danger of getting corrupted if the channel through which they travel is impaired in some way, such as by noise, distortion, interference, fading, or other similar phenomena. It is necessary for a communication system to be able to recognize and rectify problems such as these for it to be reliable. The bit error rate, sometimes referred to as the BER, is a statistic that is used to quantify how well an electronic communication system corrects errors. To calculate it, take the total number of bits that were entered and deduct from that number (Table 8).
## 2 Material and Methods
### Data Clustering  
Regularized partitions in the training dataset must be clustered to gather similar glucose responses prior to local modelling, as required by seasonal techniques. Fuzzy C-Means (FCM) clustering [29] requires data to be complete and, therefore, it is not directly applicable to the available partitions with missing data resulting from the seasonality enforcing step. The partial distance strategy FCM (PDSFCM) algorithm is used instead, combined with the partial distance as defined in [30].  
Most clustering algorithms, such as FCM and its modification PDSFCM, require knowing the expected number of clusters in the data before running the process. Fortunately, there exists a large variety of indices to help determine the optimal number of clusters. In this work, minimization of the Fukuyama-Sugeno (FS) index has been used [31] for this purpose, due to the good results obtained with it in previous works [18].  
Additionally, provided that a local model will be identified later for each cluster, a minimum number of time series is required in a cluster for the identification process to be performed. A threshold of 10 time series has been found adequate in this work, then ensuring subsequent local models a minimum set for identification and validation.  
Figure 1 shows an example where FS decreases as the number of clusters increases, with some local minimum in the evolution. If only FS is considered, five or seven clusters are the bests candidates (local minimum with less clusters). However, when five or more clusters are considered, the number of time series drops below 10 in (at least) one cluster (marked with a cross in the figure), thus leading to possible problems in the subsequent identification step (due to the small number of data for training and validation). Therefore, four clusters are chosen in this case.  
Figure 1: Example of number of clusters selection using the FS index in combination with a minimum number of time series in a cluster. The X indicates the existence of one or more clusters with less than 10 time series.
# Effects of Core Stabilization Exercise Programs on Changes in
## 3 Results
### Analysis of Isokinetic Muscle Function of Trunk  
At an angular velocity of 60\({}^{\circ}\)/s, there was a significant post-exercise change in the maximum (\(z=-6.605;\)\(p<0.001\)) and relative muscle strength per bodyweight of the extensor (\(z=-6.681;p<0.001\)), but there was not a significant post-exercise change for the flexor (\(z=-0.686;p=0.493,z=-0.887;p=0.375\)) (Table 4).  
### Analysis of Isokinetic Muscle Function of Trunk  
At an angular velocity of 60\({}^{\circ}\)/s, there was a significant post-exercise change in the maximum (\(z=-6.605;\)\(p<0.001\)) and relative muscle strength per bodyweight of the extensor (\(z=-6.681;p<0.001\)), but there was not a significant post-exercise change for the flexor (\(z=-0.686;p=0.493,z=-0.887;p=0.375\)) (Table 4).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Variables**} & \multicolumn{2}{c}{**Pre**} & \multicolumn{1}{c}{**Post**} & \(z\) & \(p\) \\ \hline \multirow{4}{*}{\(60^{\circ}\)/s} & \multirow{2}{*}{Flexor} & PT (Nm) & \(132.19\pm 35.39\) & \(135.61\pm 29.74\) & \(-0.686\) & \(0.493\) \\ \cline{3-6}  & & PT (\%BW) & \(227.52\pm 49.23\) & \(235.01\pm 37.99\) & \(-0.887\) & \(0.375\) \\ \cline{2-6}  & \multirow{2}{*}{Extensor} & PT (Nm) & \(101.54\pm 37.79\) & \(118.92\pm 34.66\) & \(-6.605\) & \(<\)0.001 *** \\ \cline{3-6}  & & PT (\%BW) & \(174.21\pm 57.58\) & \(206.11\pm 55.59\) & \(-6.681\) & \(<\)0.001 *** \\ \cline{2-6}  & & Ratio & \(139.43\pm 38.16\) & \(120.72\pm 31.86\) & \(-5.424\) & \(<\)0.001 *** \\ \hline \multirow{4}{*}{\(90^{\circ}\)/s} & \multirow{2}{*}{Flexor} & PT (Nm) & \(130.25\pm 34.65\) & \(133.55\pm 31.24\) & \(-1.461\) & \(0.144\) \\ \cline{3-6}  & & PT (\%BW) & \(224.20\pm 46.92\) & \(232.03\pm 41.68\) & \(-1.950\) & \(0.051\) \\ \cline{2-6}  & \multirow{2}{*}{Extensor} & PT (Nm) & \(88.55\pm 31.71\) & \(106.83\pm 30.75\) & \(-7.218\) & \(<\)0.001 *** \\ \cline{2-6}  & & PT (\%BW) & \(152.30\pm 48.66\) & \(183.65\pm 46.55\) & \(-7.232\) & \(<\)0.001 *** \\ \cline{2-6}  & & Ratio & \(159.21\pm 52.48\) & \(132.07\pm 31.52\) & \(-6.285\) & \(<\)0.001 *** \\ \hline \hline \end{tabular} Note. Data are mean \(\pm\) standard deviation, ***\(p<0.001\). Abbreviations: PT, Peak torque; BW, Body weight.  
\end{table}
Table 3: Results of tensiomyography of erector spinae of the participants.  
At an angular velocity of \(90^{\circ}/\)s, there was no significant post-exercise change in the maximum and relative muscle strength (\(z=-1.461\); \(p=0.144\), \(z=-1.950\); \(p=0.051\)) of the flexor muscle (Table 4). However, there was a significant post-exercise change in the maximum and relative muscle strength per bodyweight of the extensor muscle (\(z=-7.218\); \(p<0.001\), \(z=-7.232\); \(p<0.001\)).  
Regarding the muscle strength ratio, measurements at an angular velocity of \(60^{\circ}/\)s (\(z=-5.424\); \(p<0.001\)) and \(90^{\circ}/\)s (\(z=-6.285\); \(p<0.001\)) showed significant differences (Table 4).
# Semi-Direct Point-Line Visual Inertial Odometry for MAVs
## 3 Proposed System Implementation
### Front-End Tracking Based on Semi-Direct Methods
#### 3.2.3 Semi-Direct Method for Tracking  
(a) For keyframes, we detected point features using the Accelerated Segment Test (FAST) [31] algorithm and tracked adjacent frames using an optical flow based on Kanade-Lucas-Tomasi (KLT) [32]. Then, we eliminated outliers by Random Sample Consensus (RANSAC) combined with the essential matrix model. Meanwhile we detected line segments using the Line Segment Detector (LSD) [33] algorithm, and matched them using Line Band Descriptors (LBD) [34] between the current frame and the reference frame. In addition, we also removed outliers for line-matching using geometric constraints.  
(b) For non-keyframes, as shown in Figure 2, we first extracted the key points \(\mathbf{p}\) from the last keyframe \(\mathbf{I}_{n}\) in the sliding window, and used transformation to construct the matching 3D points \(\mathbf{P}_{w}\) in the world coordinate. Then, we projected the 3D points onto the current frame \(\mathbf{I}_{n+1}\), and obtained the pixel intensity residual \(\delta\mathbf{I}(\mathbf{T},\mathbf{p})\) as:  
\[\delta\mathbf{I}(\mathbf{T},\mathbf{p})=\mathbf{I}_{n+1}(\pi_{c}(\mathbf{T} \cdot\mathbf{P}_{w}))-\mathbf{I}_{n}(\mathbf{p}),\forall\mathbf{p}\in\mathcal{R} \tag{7}\]  
where \(\mathcal{R}\) is the image area formed by the key points \(\mathbf{p}\). By minimizing the photometric error, the relative pose \(\mathbf{T}_{n}^{n+1}\) can be calculated [15]:  
\[\mathbf{T}_{n}^{n+1}=\arg\min_{\mathbf{T}}\iint_{\mathcal{R}}\rho[\delta \mathbf{I}(\mathbf{T},\mathbf{p})]d\mathbf{p} \tag{8}\]  
where \(\rho[\cdot]=\frac{1}{2}\|\cdot\|^{2}\).  
However, the current frame pose, which was only obtained by Equation (8), is insufficiently accurate. Therefore, we need to find more co-visibility feature points between the current frame and the co-visibility keyframes in the sliding window. As shown in Figure 3, based on the pose solved by Equation (8), we projected the co-visible 3D points \(\mathbf{P}_{w}\) in the world coordinate onto the current frame. By minimizing the photometric error  
Figure 2: Direct image alignment.  
of co-visibility points, the corresponding 2D feature points \(\mathbf{p}_{i}{}^{\prime}\) in the current frame can be obtained:  
\[\mathbf{p}_{i}{}^{\prime}=\arg\min_{\mathbf{p}_{i}{}^{\prime}}\frac{1}{2}{\sum \left\|\mathbf{I}_{n+1}\big{(}\mathbf{p}_{i}{}^{\prime}\big{)}-\mathbf{I}_{n+1} (\pi_{c}(\mathbf{T}_{w}^{c}\cdot\mathbf{P}_{wi}))\right\|^{2}},\forall i \tag{9}\]  
After feature-points-matching was completed, we again optimized the camera pose \(\mathbf{T}_{w}^{c}\) to minimize the re-projection errors:  
\[\mathbf{T}_{w}^{c}=\arg\min_{\mathbf{T}_{w}}\frac{1}{2}{\sum\limits_{i}{{{{ \left\|\mathbf{p}_{i}-\pi_{c}(\mathbf{T}_{w}^{c}\cdot\mathbf{P}_{wi})\right\|} ^{2}}}}} \tag{10}\]
# Using Imaging Techniques to Analyze the Microstructure of
## 3 Scanning Electron Microscope (SEM)
### AC Microstructure Analysis Using SEM
SEM image analysis showed that the size of micro-cracks in the mixtures was reduced under healing conditions for a period of 0 to 6 days. After 6 days, the healing efficiency was 70-83% at room temperature and 50-70% at high temperatures. [PERSON] et al. [45] used SEM to evaluate the impact of freeze-thaw (F-T) cycles on AC cross-sections. After F-T cycling, the SEM images indicated that the mixture's surface hadchanged from a smooth to a rougher surface. SEM images also showed that the microcracks within samples increased in size with the increase of F-T cycles, which led to damage to the sample structure. [PERSON] and [PERSON] [46] used the environmental scanning electron microscope (ESEM) to evaluate the impact of F-T cycling on aggregates coated with binder, and they found that F-T cycling caused an increase in cracking and stripping between the asphalt binder and the aggregate particles. The presence of cracks is one of the factors that affect the performance of AC mixtures. [PERSON] et al. [47] used SEM to analyze the microstructure of AC before and after microbially induced calcite precipitation (MICP) treatment. The SEM images revealed that the MICP treatment resulted in calcium carbonate deposition that filled the cracks and improved the bonding of the AC mixture at both interfaces, which contributed to the mechanical behaviour and durability of the treated AC mixture. Moreover, SEM images showed that the MICP treatment reduced the width of cracks by filling them with precipitated calcium carbonate [47]. SEM reveals the self-healing properties of asphalt binder. [PERSON] et al. [48] investigated the use of ethylene methacrylic acid (EMAA), an ionic copolymer, as a self-healing agent to improve the performance of AC mixtures. Various parameters and testing methods, including SEM analysis, were employed to examine the impact of EMA addition. Results showed that EMA significantly enhances stiffness, self-healing capacity, and cracking resistance in both virgin and modified binders and mixtures. However, caution is advised when using EMAA as a modifier for virgin binders and mixtures due to poor cracking resistance [48].
# Innovative Approach Integrating Machine Learning Models for Coiled Tubing Fatigue Modeling
## 7 Correlation Analysis
### Influence of Dominant Features on Fatigue Behavior
#### 7.3.4 Internal Pressure: Limited Direct Effect on Fatigue Life  
While internal pressure contributes to hoop stress, its impact on fatigue life was lower than anticipated. This is because the fatigue damage in coiled tubing is primarily driven by cyclic bending stress, with hoop stress playing a secondary role, unless the tubing operates near the plastic deformation limits. The models assigned less importance to pressure, confirming that mechanical strain dominates fatigue behavior over internal loading effects.
# 6D Pose Estimation of Objects: Recent Technologies and Challenges
## 5 Challenges  
### Textureless and Reflective Objects  
As a special case in pose estimation, the pose estimation of textureless objects is challenging. Due to the lack of reliable texture information on the surface of such objects, it is difficult to extract feature points on them. Therefore, many methods based on the surface texture information of objects cannot effectively measure the 6D pose of such objects.  
However, textureless objects are common in industry. Therefore, the pose estimation of textureless objects is very important.  
[PERSON] et al. [82] transformed sliding windows to scale-invariant RGB-D patches and applied a hash voting-based hypothesis generation scheme to compute a rough 6D pose hypothesis and then employed particle swarm optimization to improve the result, which achieved high precision and good performance on three datasets. [PERSON] et al. [83] coarsely segmented the object in the point cloud and precisely measured the pose results in the gray image using a view-based matching method.  
The methods mentioned above used a depth camera or 3D scanning device to obtain the depth image or the point cloud of objects. However, for objects with reflective surfaces, such as metal parts, depth information is hard to acquire. Therefore, some other kinds of information are used in pose estimation. Geometrical information is a reliable kind of information for textureless objects. Based on the above perspective, [PERSON] et al. [84] proposed a new method (Figure 7) making full use of the geometrical information of objects. It used geometric features, such as straight lines, to generate descriptors of the objects, and proposed the GIIBOLD algorithm for matching the input image and the template image. Furthermore, accurate 2D-3D point pairs were acquired on the basis of the matched geometric features. Finally, the 6D pose was measured using the PnP-RANSAC algorithm. This method leveraged simple geometrical information, achieving fast matching and accurate measurement without the requirement for plenty of storage and time. [PERSON] et al. [85] first detected the object in the RGB image using a 2D bounding box and then measured the pose result in the edge image. [PERSON] et al. [86] used multiple appearance features including color, size and aspect ratio to distinguish objects from environmental clutter and measured the 6D pose. [PERSON] et al. [51] proposed a novel method for measuring textureless objects on the basis of RGB images. It followed a coarse-to-fine procedure, using only the shape and contour information of the input image. Several template images with poses similar to the input image were selected to match with the input. Then the contour and shape information, specifically the ORB features, were used to establish 2D-3D correspondence and finally to calculate the 6D pose. On the basis of the studies above, without reliable texture information, there are many kinds of information can be leveraged, such as contour, color, shape, and so on. Accurate results can be obtained through the proper use of this information.