esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Data Preprocessing
#### 2.2.4 DeepLabCut Data Labeling and Model Training  
The process for DeepLabCut data labeling, model training, and 3D kinematic analysis is illustrated in Figure 3. Video data from 12 of the 16 subjects were used for DLC model training. Two trials from each of the 12 subjects were selected randomly from the 72 trials without replacement, resulting in 24 total trials used for labeling. As the 24 trials were recorded from 4 different camera angles, there were a total of 96 unique view/posture combinations used for labeling.  
A k-means clustering function from DLC was used to extract 20 frames from each of the 96 labeling videos resulting in 480 frames per camera field of view for a total of 1920 labeled frames. The k-means clustering function downsamples each video, treats each frame as a vector, and clusters them together based on appearance [44]. Labeling manually identified the reflective marker associated with each anatomical landmark (Table 1) from an image using the DLC GUI (Figure 2). Labeling was performed by a single researcher. The labeled frames were then used to train a DLC model using a resnet-152 neural network for 250,000 iterations with the learning loss plateauing at 0.0013 error.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Arm Position** & \multicolumn{2}{c}{**Wrist Posture and Range of Motion Tasks**} \\ \hline
90\({}^{\circ}\) from torso, pointing to front camera & 3 cycles of & 3 cycles of & 3 cycles of \\
90\({}^{\circ}\) from torso, pointing to oblique camera & a radial/ & a flexion/ & a radial/ & 3 cycles of \\
90\({}^{\circ}\) from torso, pointing to right-side camera & uhar & extension & ulnar & a flexion/ \\
90\({}^{\circ}\) from torso, pointing to right-side camera & deviation & range of & deviation & extension \\
45\({}^{\circ}\) from torso, pointing to front camera & range of & motion in & range of & range of \\
45\({}^{\circ}\) from torso, pointing to oblique camera & promoted & promotion & motion in & motion in \\
45\({}^{\circ}\) from torso, pointing to right-side camera & posture & posture & handshake & posture \\ \hline \hline \end{tabular}
\end{table}
Table 2: Arm position relative to the torso and wrist posture during the required range-of-motion tasks.
## 3 Results
### Analysis of Group of Experiments: Experiment A  
The contact efficiency of wood ash after 24 h under laboratory conditions resulted in 90 \(\pm\) 10% (N = 10) mortality of the slugs treated with oak wood ash (see Figure 1A). High slug mortality was also achieved after 24 h of treatment with spruce wood ash (50 \(\pm\) 17%, N = 10) (see Figure 1A). Except for oak and spruce, wood ash from other plant species did not cause slug mortality after 24 h. Our results showed that 100 \(\pm\) 0% (N = 10) of the slugs treated with oak and spruce wood ash died after 48 h. Besides oak and spruce, 48 h after the slugs' exposure to fir wood ash also caused the slugs mortality (20 \(\pm\) 10%, N = 10). However, wood ash from other tested plant species did not impact slug mortality (see Figure 1A).  
The effect of wood ash on slug feeding after 24 and 48 h is presented in Figure 1B. The results obtained reveal that only 10 \(\pm\) 10% (N = 10) of slugs treated with oak wood ash fed on plant seedlings after 24 h. The inhibition of feeding was observed also in the treatment with spruce wood ash. Similar to results obtained for contact efficacy after 24 h, wood ash from plant species other than oak and spruce did not impact slugs feeding (see Figure 1B). Slugs did not damage the lettuce seedlings 48 h after treatments with oak and spruce, and total feeding inhibition (100 \(\pm\) 0%; N = 10) was achieved. Fir wood ash reduced slugs' food consumption by 50% (N = 10). Wood ash from plant species other than oak, spruce, and fir did not impact slugs feeding behavior after 48 h (see Figure 1B).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{Source} & \multicolumn{3}{c}{Slug Mortality} & \multicolumn{3}{c}{Feeding Ability} \\ \cline{3-8}  & & F & df & \(p\) & F & df & \(p\) \\ \hline Exp. & Treatment (T) & 87.59 & 7 & \textless{}0.0001 & 75.43 & 7 & \textless{}0.0001 \\ A & Exposure time (DAT) & 66.61 & 2 & \textless{}0.0001 & 69.33 & 2 & \textless{}0.0001 \\ \hline \multirow{4}{*}{Exp.} & \multirow{2}{*}{Source} & \multicolumn{3}{c}{Barrier crossing} & & \\ \cline{3-8}  & & F & df & \(p\) & & & \\ \hline Exp. & Treatment (T) & 16.27 & 7 & \textless{}0.0001 & & & \\ B & Exposure time (DAT) & 87.04 & 2 & \textless{}0.0001 & & & \\ \hline \multirow{4}{*}{Exp.} & \multirow{2}{*}{Source} & \multicolumn{3}{c}{\% damaged plants} & \multicolumn{3}{c}{\% damaged leaves} \\ \cline{3-8}  & & F & df & \(p\) & F & df & \(p\) \\ \hline Exp. & Treatment (T) & 33.84 & 4 & \textless{}0.0001 & 62.25 & 4 & \textless{}0.0001 \\ C & Exposure time (DAT) & 29.82 & 2 & \textless{}0.0001 & 24.21 & 2 & \textless{}0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of ANOVA showing data for experiments A (df for the error term: 239), B (df for the error term: 239), and C (df for the error term: 74).
# A Novel Global Key-Value Storage System Based on Kinetic Drives +
## 7 Performance Evaluation
### Simulation Setup  
We simulated a global key-value store system with 16 and 32 regions respectively. In each region, there is a cluster of Kinetic Drives accessed by users. The locations of those clusters and users are randomly generated and distributed in a normalized 1000 * 1000 map.  
For each Kinetic Drive in each region, we assume it is accessed by all the users following a Poisson distribution with a mean of 500 times.  
Network latency is an important performance metric of a large-scale networked system. Propagation delay is a typical component of the network latency. It is the amount of time taken by the first bit to travel from the sender to the receiver over the physical medium of the network link. Propagation delay depends on the distance between sender and receiver and the propagation speed, as shown in the following equation.  
\[Propagation\ Delay=Distance/Propagation\ Speed\]  
We can see that it is proportional to the distance between the source and destination in the network, and a shorter distance can reduce the propagation delay. Since clusters of Kinetic Drives and users are dispersed among different locations, the propagation delay directly reflects the distance between a user and the target Kinetic Drive, to show the cost of a data access. Moreover, with a shorter distance, the number of intermediate networking devices (e.g., routers) along the path may be decreased as well, possibly reducing processing and queuing delays, so that the entire network latency can be further shortened.  
This paper discusses the high-level architecture design of a global key-value store system, especially focusing on the backup rules and coordination among metadata servers, Kinetic Drives, and users. Hence, in order to evaluate this design, we use the average propagation delay per data access request for all drives in all regions by all the users as the metric. As mentioned above, this propagation delay reflects the distance between users and the target Kinetic Drives, which also has influence on the entire network latency.  
We compare our result with the random approach, which just randomly chooses two regions to find two available Kinetic Drives for the _original data_'s backups, without considering the access frequencies and distances. To fairly show the advantage of our approach, however, in the random approach, when a user tries to access a Kinetic Drive, we still use the same way as our approach to choose a Kinetic Drive in the region with the shortest distance to the user as the target drive to calculate the result. In other words, for the random approach, the randomness is only applied for the selection of backup drives.
# Machine Learning Applications and Uncertainty Quantification Analysis for Refload Tests
## 3 Results and Discussions
### Machine Learning Predictions and Comparisons  
In this section, the dataset presented in Section 2.4 was employed for training the machine learning models. Within this context, the predictions of machine learning models, including LR, RF, and DNN, were deployed and compared. In the LR, the model assumes a linear relationship between the input features and the output; therefore, for this nonlinear dependency of the system state in the reflooding phenomena prediction, LR may not capture the underlying patterns effectively, resulting in high uncertainty in the prediction compared with the actual values of the test dataset (see Figure 7). These results illustrated that the system states and features are firmly nonlinear dependent. Furthermore, due to these inaccuracies, the ranked parameters most influential to the system state predictions were also not well consistent with the STD evaluation and DNN identification.  
In addition, RF is a learning method based on decision trees. It builds multiple decision trees during training and merges them for a more accurate and stable prediction. However, it may not capture highly complex patterns as effectively as DNNs when massive training datasets are employed. It can be illustrated that the predictions of the DNN with selected hyperparameters have the highest accuracy compared to LR and RF (see Figures 7-9), delivering an acceptable consistent identification of the most influential physical model. These results trends were also indicated in the previous studies [27, 29] where the DNNs tended to capture better predictions compared to RF.  
However, DNNs often require large datasets to obtain good predictions. If the dataset is small, a simpler model like LR and RF might better capture the actual system states and identify the most influential physical model, as DNNs may be more unsuitable for overcoming such scenarios. Therefore, the choice between LR, RF, and DNN depends on  
Figure 6: The cladding temperature propagations.  
the nature of the data, the linearity of the system, the correlations between input parameters, the size of the dataset, and the computational cost.  
Additionally, we conducted a comparison of computation times. In this analysis, we assumed that each method is tasked with predicting 4000 samples. The findings indicate that the total computation time required by the SPACE is approximately 16,000 min, utilizing an AMD Ryzen Threadripper 3970X computer with 64 processors. In contrast, the LR, RF, and DNN models were executed on a desktop computer with a Core i7-6700 processor, taking a maximum of approximately 3 min, representing a substantial reduction in computation time. The noteworthy reductions presented in Table 4 underscore the potential application of these machine learning models, particularly the DNN.  
Figure 8: Random forest predictions.  
Figure 7: Linear regression predictions.
# Path Planning of Underground Robots via Improved A* and Dynamic Window Approach
## 2 Improved A* Algorithm
### Improvement of Heuristic Function
#### 2.3.4 Diagonal Distance
The smaller \(\beta\) will strengthen the dominant position of Euclidean distance in heuristic evaluation, which is equivalent to relaxing the constraint condition of roadway width on path selection, so that the algorithm pays more attention to the relative distance of the target position in the search process, and gives less consideration to the spaciousness of the surrounding environment. The latter part is the penalty factor. If \(d\) is very small, the penalty term becomes larger, which means that the path is very poor. The algorithm will increase the heuristic function value of this region, thereby reducing the possibility of selecting a narrow region. If \(d\) is large, the penalty term is reduced, indicating that the path is more spacious and the traffic is good, the algorithm will give priority to these areas.
# A Study of Colormaps in Network Visualization
## 4 Results  
### Task Completion Time  
On average, each task took 7.33 s (\(SD=6.34\) s) to complete. A repeated measures ANOVA showed that the colormaps had a significant effect on the task completion time (\(F_{3,102}=9.4\), \(p<1.38\times 10^{-5}\)). The average task completion time (see Figure 3) was 7.92 s for _jet_ (\(SD=6.61\) s), 7.55 s for _viridis_ (\(SD=6.83\) s), 7.25 s for _RdYIBu_ (\(SD=6.16\) s), and 6.60 s for _blue_ (\(SD=5.54\) s). In terms of task completion time, _blue_ outperformed the other colormaps.  
The post-hoc test using the Bonferroni correction showed that the task completion time for _blue_ was faster than _viridis_ (\(p<0.02\)) and _jet_ (\(p<8.7\times 10^{-7}\)), respectively. _RdYIBu_ was significantly faster than _jet_ (\(p<0.02\)). Furthermore, _viridis_ was significantly different and faster than _jet_ (\(p<0.04\)). However, the difference between _viridis_ and diverging _RdYIBu_ was not significant.  
The dataset used had a significant effect on task completion time (\(F_{2,68}=53.6\), \(p<1.04\times 10^{-14}\)). However, as expected, the largest network took more time than the smaller graphs. The average task completion time (see Figure 3) was 6.29 s for _lesmis_ (\(SD=5.70\) s), 7.01 s for _football_ (\(SD=5.65\) s), and 8.69 s for _jazz_ (\(SD=7.25\) s).  
The span values of _15_ and _40_ had no significant effect on the task completion time. Similarly, the node structural (_PageRank_) and non-structural (_Random_) attributes had no significant effect on the task completion time.
## 2 Materials and Methods
### Data Processing and Analysis  
This section describes the processing of the data and the methodology applied. The following 4 steps were applied for the processing and quality control of the data.  
Step 1. The downloaded hourly data in UTC from the PV-GIS website were converted into Greece's LST (i.e., UTC + 2 h). It must be mentioned here that the PV-GIS solar radiation values were provided at different UTC times for the various sites considered, e.g., at hh: 48 or hh: 09, where hh stands for any hour in the range 00:00-23:00.  
Figure 1: Distribution of the 43 sites across Greece. The numbers refer to those in column 1, Table 1 in [23]. This map is reproduction of Figure 1 in [23] with some additions.  
Step 2. All meteorological and solar radiation values were assigned to the nearest LST hour (i.e., values at hh: 48 LST or hh: 09 LST were assigned to just hh: 00 LST). That was performed in order to have all values in the data base at integer hours.  
Step 3. Only those H\({}_{\rm g}\) hourly values were retained for analysis that were greater than 0 W\(\cdot\)m\({}^{-2}\). In addition, the criterion of H\({}_{\rm d}\leq\) H\({}_{\rm g}\) should be met at an hourly level [24; 32; 33; 34].  
Step 4. Hourly values of k\({}_{\rm d}\) were also calculated in the same data base of the location from the hourly values of the ratio H\({}_{\rm d}/\)H\({}_{\rm g}\).  
The methodology applied is described in the fifth step.  
Step 5. As mentioned in the Introduction, [PERSON] et al. [23] have derived a mathematical methodology for determining the upper and lower k\({}_{\rm d}\) limits that classify the sky into clear, intermediate, or overcast. The main conclusion of that study was that global values of k\({}_{\rm d}\) could be used in the following ranges: 0.78 \(<\) k\({}_{\rm d}\)\(\leq\) 1 for overcast skies, 0.26 \(<\) k\({}_{\rm d}\)\(\leq\) 0.78 for intermediate, and 0 \(\leq\) k\({}_{\rm d}\)\(\leq\) 0.26 for clear skies. By adopting these k\({}_{\rm d}\) limits in the data bases of the present study, the hours belonging to the 3 posed k\({}_{\rm d}\) intervals were counted (summed up). In this way, at each site the number of hours corresponding to clear, intermediate, and overcast skies was recorded. Annual, seasonal, and monthly values of these hours were further estimated. One should note that the clear-sky hours are equivalent to SSD (in h), according to the WMO directive [35]. This statement has been used in the present article with emphasis on the sunshine, as mentioned in the title.  
In a further analysis of the above-derived sky conditions hours, the major conclusion from [23] was taken into account; this refers to the interpretation of k\({}_{\rm d}\) as an atmospheric scattering index (ASI) in the Earth's atmosphere due to the scattering effect of clouds and aerosols on solar radiation. Therefore, k\({}_{\rm d}\) was considered and used in the present work as ASI, too, thus justifying the second emphasis in the title of the article.
## 2 Numerical Simulation  
### Physics Model  
The physical model of a circular tube inserted with BTs is shown in Figure 1. The total length and inner diameter of the copper tube are \(L\) = 1300 mm and \(D\) = 26 mm, respectively.  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs.** & **Method** & **Research Focus** & **Conclusions** \\ \hline
[5] & Experiment & New twisted tape; decrease the pressure drop; full-length twisted tap & Regularly-spaced twisted tape performed better than the full-length twisted tape; the improved structure could reduce the pressure by 40\% \\ \hline
[6] & Experiment & Multiple short-length twisted tapes; decrease the pressure drop & Yielded at least a 50\% pressure drop compared with that of the full-length twisted tapes \\ \hline
[7; 8; 9; 10; 11] & Experiment & Other modified patterns; improve the overall thermo-hydrodynamic performance & Improving the overall thermo-hydrodynamic performance of the tubes fitted with twisted tapes \\ \hline
[12; 13; 14] & Experiment & Heat transfer and pressure drop; coil-wire inserts & The use of coiled wire inserts led to significant increases in both heat transfer and pressure drop \\ \hline
[15; 16; 17; 18] & Experiment & Combine different techniques; enhanced heat transfer & Coupled methods are more able to enhance the heat transfer process as well as increase the friction factor than the use of a single method \\ \hline
[19; 20; 21; 22] & Numerical methods & A new type of insert; ball turbulator (BT) & Heat augmentation was achieved at the cost of a higher pressure drop \\ \hline This study & Numerical simulation & A new kind of BT; optimize the heat transfer performance & The Nusselt number increases and friction factor decreases as the Reynolds number increases; smaller ball diameter ratio is more able to effectively decrease the friction factor; larger ball diameter ratio leads to a higher heat transfer rate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Contents summary of the current literature involving heat transfer and friction characteristics.  
Figure 1: Physical model of a circular tube inserted with ball turbulators (BTs): (**a**) 3D view; (**b**) several elements of insert; (**c**) basic dimensions.
# Dynamic Stability Finite Difference Time Domain Analysis of Landfill Based on Hypergravity Test
## 1 Introduction
Grounded on prior experimental studies on the analysis of seismic stability of landfills with different water levels, the primary objective of this study is to clearly present the dynamic response of landfills under various water levels and seismic intensities and to provide a more effective approach to analyze the seismic stability of landfills using the finite difference nonlinear analysis method. The secondary objective is to investigate the seismic stability of landfills under different breaking strains based on the provided approach since the strength parameters of MSW are greatly influenced by the breaking strain value. In this study, the finite difference nonlinear analyses are achieved using FLAC3D 6.0 software. Hysteretic damping was applied to the analysis based on the modulus reduction curve. This study provides a basis for the seismic stability assessment and seismic design of landfill slopes.
## 5 Discussion
### Geomorphic Changes
#### 5.2.3 Adjustment of Hydraulic Geometry  
Figure 9 presents statistics illustrating the changes in water depth, river width, cross-section area, and river regime coefficient after the TGD operation for each sub-reach in the MYR under a given bank-full discharge at Station 1. The results indicate that there is an increase in cross-sectional area (by \(\sim\)19%; from 20,678 m\({}^{2}\) to 24,608 m\({}^{2}\)), water depth (by \(\sim\)11%; from 13.5 m to 15.0 m), and river width (by \(\sim\)7%; from 1516 m to 1629 m). Additionally, the channel becomes deepened and narrower (with a \(\sim\)7% reduction in the river regime coefficient; from 2.91 m\({}^{-1/2}\) to 2.70 m\({}^{-1/2}\)).  
Viewed along the MYR, the reduction in the river regime coefficient is greater in the near-TGD reach (\(\sim\)15% in sub-reaches 2-6) than in the far dam reach (\(\sim\)5-8% in sub-reaches 8-9). Concurrently, the increase in water depth in sub-reaches 2-6 is relatively obvious (\(\sim\)20%), while the increase in the downstream reach near Hukou station is more modest (\(\sim\)10%), with the increase in sub-reach 1 being only 8%. The primary reason for the minor depth increase in sub-reach 1 may be attributed to its riverbed being composed of gravel and cobble, which is more resistant to scouring compared to the downstream sandy reach. Consequently, the riverbed can easily form a protective layer due to roughening, resulting in a smaller erosion magnitude.  
Figure 8: Annual bar and channel deposition volume in different sub-reaches after the operation of the Three Gorges Dam.  
In general, although the MYR exhibits channel undercutting and bank widening throughout its course, there are subtle variations along the way. These include relatively minor changes in channel morphology in sub-reach 1, an increase in water depth in sub-reaches 2-6, and an increase in river width in sub-reaches 7-9.
# Audio Fingerprint Extraction Based on Locally Linear Embedding for Audio Retrieval System
###### Abstract  
With the appearance of a large amount of audio data, people have a higher demand for audio retrieval, which can quickly and accurately find the required information. Audio fingerprint retrieval is a popular choice because of its excellent performance. However, there is a problem about the large amount of audio fingerprint data in the existing audio fingerprint retrieval method which takes up more storage space and affects the retrieval speed. Aiming at the problem, this paper presents a novel audio fingerprinting method based on locally linear embedding (LLE) that has smaller fingerprints and the retrieval is more efficient. The proposed audio fingerprint extraction divides the bands around each peak in the frequency domain into four groups of sub-regions and the energy of every sub-region is computed. Then the LLE is performed for each group, respectively, and the audio fingerprint is encoded by comparing adjacent energies. To solve the distortion of linear speed changes, a matching strategy based on dynamic time warping (DTW) is adopted in the retrieval part which can compare two audio segments with different lengths. To evaluate the retrieval performance of the proposed method, the experiments are carried out under different conditions of single and multiple groups' dimensionality reduction. Both of them can achieve a high recall and precision rate and has a better retrieval efficiency with less data compared with some state-of-the-art methods.  
audio fingerprint; sub-regions; dimensionality reduction; audio retrieval Article