esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Result  
In this section, the computational overhead incurred for a user to calculate hash values is analyzed. It is assumed that the data have \(K=2^{k}\) segments.  
In the original MHT operation, a user must compute \(k+1\) hash values to verify each segment. Hence, the user computes \(2^{k}(k+1)\) hash values to verify the data. Using the proposal 1 of Algorithm 1, the user computes each leaf node value once and each internal node value twice. Hence, the user computes \(2^{k}\) hash values for leaf nodes and \(2(\sum_{i=0}^{k-1}2^{i})=2\left(2^{k}-1\right)\) hash values for internal nodes. That is, the user computes \(3\times 2^{k}-2\) hash values to verify the data. When using the proposal 2 of Algorithm 2, it reduces the hash value computations by \(2^{k-1}\). That is, the user computes \(5\times 2^{k-1}-2\) hash values to verify the data. In this case, the user must need \(k\) additional memory spaces.  
Figure 5 shows the comparison results of the computational overhead for four cases: [proposal1] is the hash operation time of the proposal 1 of Algorithm 1, [proposal2] is the hash operation time of the proposal 1 of Algorithm 2, [mht] is the hash operation time of the original MHT, and [retriee] is the data transmission time of NDN. The same configuration as Figure 3 was utilized. As shown in Figure 5, the performance of the proposed schemes was improved by more than three times. Practically, the greater the number of segments, the more obvious the improvement.  
Figure 6 shows the evaluation comparison results of computational overheads for the four cases when transmitting \(2^{n}\) segments, \(10~{}\leq n\leq 19\). If \(n=10\), it means that a user retrieves \(2^{n}\) (=1024) segments for [retriee] seconds and verify the segments for [mht] (or [proposal 1] or [proposal 2]) seconds. Especially, as shown in Figure 6, if \(n\geq 14\), verifying segments using MHT takes longer than verifying the segments using proposal 1 and 2 as well as receiving the segments. Practically, to transmit 1 GB size content through NDN, the content publisher generates 262,144 segments and transmits the segments to a user. In this case, the size of MHT is \(n=14\). Compared to [mht], an increase in the number of segments has little effect on [proposals 1 and 2].  
Figure 5: The performance comparisons of the proposed schemes with the computing time of the original MHT and with the downloading content time of NDN.  
Figure 6: The performance evaluation of the proposed schemes for authenticating \(2^{n}\) segments.
## 4 Methodology
### Adaptive Position Estimation  
The adaptive position estimation for motion includes three primary components: step segmentation detection, adaptive step length estimation, and position estimation. First, based on the inertial data features collected from the chest of rescuers during movement, a step segmentation is defined. An adaptive step detection method is then proposed to accurately delineate step segmentations, adjusting to various motion states that may occur during the rescuer's movement. Next, based on the results of the feature discriminability analysis and the selected twelve key features in Section 3, an adaptive step length estimation dataset is constructed. The parameters of the adaptive step length estimation model are then trained using this dataset. The improved step length estimation model is capable of adapting to multiple motion states such as walking, running, crouch-walking, and side-stepping. Finally, the estimated step lengths are integrated into a position estimation model to reconstruct the trajectory and estimate the real-time position of the individual.
## 4 The Proposed AAC-IoT System Model
### Registration and Authentication  
The entities in the network system begin with the process of registration. The DO and the DU use different types of credentials for registration and authentication since the DO submits only the data, whereas the DU uploads a request query and then receives data. The registration procedure is illustrated in sequential steps.
## 3 Test Results and Analysis  
### Sandstone Mechanical Experimental Results  
The analysis of the experimental results revealed that the failure process of the samples could be categorized into four distinct stages: compaction, elastic, plastic and final failure. Figure 4 illustrates the stress-strain curve for the specimen at each stress stage throughout the compression process. Upon processing the sample test data, we derived the mechanical parameters of the samples corresponding to different durations of corrosion, with the specific results presented in Table 3. The findings depicted in Figure 3 indicate that during the corrosion process, the compressive strength of the samples exhibits an increasing trend in the later stages of immersion. Furthermore, the presence of brine-CO\({}_{2}\) significantly influenced the rock strength during immersion.  
Figure 4: Stress–strain curves for different soaking times.
## 3 The Proposed Routing Protocol--CRD
### Underlying Technology--Necessary Algorithms  
In this subsection, we propose the underlying technology comprising three algorithms which not only optimizes cluster selection but also achieves energy balancing in clustering phase.
## 2 Materials and Methods
### Imputation Models
#### 2.3.7 Performance Measurements  
In this study, the performance of the imputation models was evaluated using the root-mean-squared error (RMSE) [58], establishing as the best model the one with the lowest RMSE value.  
The k-fold cross-validation method was used to use part of the data for missing value imputations and another part to check the predicted values against the available values, being \(k=5\) parts. More information on this method can be found in [59].
## 3 Results and Discussion
### Binding Interaction Analysis with PLIP
#### 3.5.2 Interactions with PPAR-\(\alpha\)
Overall, binding studies conducted showed that highest binding free energies according to FireDock were observed for CSAMut3, CGAMut4, and RMAMut3 as well as the neat Mut3 peptide. According to Autodock Vina, however, the results showed that the highest binding affinities were for Mut1, Mut2, RMAPep, GLAMut2, and RMAMut1. Several residues were significantly implicated in binding with peptides and peptide-polyphenol conjugates as predicted according to PLIP, including Thr279A, Thr285A, Tyr334A, Leu254A, Leu321A, Ile228A, Ala333A, Val255A, and Val281A, attesting to the relative hydrophobicity of the binding site. Though the binding sites seen for peptides and conjugates varied, molecules bound in or near the ligand binding domain found in other studies, as indicated by the overlap with residues such as Thr279A, Leu254A, Leu321A, among other interacting residues [64].
## 4 Case Study of Punjab OSM Data
### Data Preparation and Tools
#### 4.1.1 History Data Preparation  
The OSM full history dump contains the entire history of the OSM data ([[http://planet.openstreetmap.org/planet/full-history/](http://planet.openstreetmap.org/planet/full-history/)]([http://planet.openstreetmap.org/planet/full-history/](http://planet.openstreetmap.org/planet/full-history/)), accessed on 28 July 2016) sized 56 GB in pbf format. The tool OSM-history-splitter ([[https://github.com/joto/osm-history-splitter](https://github.com/joto/osm-history-splitter)]([https://github.com/joto/osm-history-splitter](https://github.com/joto/osm-history-splitter)), accessed on 3 October 2016) was used to extract Punjab (India) data. This tool took two inputs: (1) history file; (2) polygon for the region. The polygon data (.poly format) for the region was created using the QGIS clip feature. The polygon was prepared from the administrative boundaries (.shp) of Indian districts collected during the census of 2011 ([[https://github.com/datameet/maps/tree/master/Districts/Census_2011](https://github.com/datameet/maps/tree/master/Districts/Census_2011)]([https://github.com/datameet/maps/tree/master/Districts/Census_2011](https://github.com/datameet/maps/tree/master/Districts/Census_2011)), accessed on 26 June 2016). The steps followed for splitting are available at ([[https://github.com/joto/osm-history-splitter](https://github.com/joto/osm-history-splitter)]([https://github.com/joto/osm-history-splitter](https://github.com/joto/osm-history-splitter))), and the obtained.osm.pbf format data were converted to.osh using the tool osmconvert ([[https://gitlab.com/osm-c-tools/osmctools](https://gitlab.com/osm-c-tools/osmctools)]([https://gitlab.com/osm-c-tools/osmctools](https://gitlab.com/osm-c-tools/osmctools)), accessed on 3 October 2016). The converted data was imported into PostgreSQL ([[https://www.postgresql.org](https://www.postgresql.org)]([https://www.postgresql.org](https://www.postgresql.org)), accessed on 13 March 2016) database for the readiness of the data using the history importer module of OSM-history-renderer ([[https://github.com/MaZderMind/osm-history-renderer](https://github.com/MaZderMind/osm-history-renderer)]([https://github.com/MaZderMind/osm-history-renderer](https://github.com/MaZderMind/osm-history-renderer)), accessed on 3 October 2016).
## 3 The Design of the EKF Algorithm  
Assuming that the state equation of a discrete nonlinear system is given as follows:  
\[\left\{\begin{array}{l}\textbf{X}_{k+1}=f(\textbf{X}_{k},\textbf{W}_{k},k)\\ \textbf{Z}_{k}=c(\textbf{X}_{k},\textbf{V}_{k},k)\end{array}\right. \tag{11}\]  
where **X** is the state vector; \(f(\bullet)\) is the n-dimensional nonlinear state vector transfer function; **Z** is the output vector; \(c(\bullet)\) is the m-dimensional nonlinear vector-valued function; **W** and **V** are the system process noise and measurement noise, respectively, both being Gaussian white noises with zero mean.  
The recursive process for the parameter estimation of the system based on the EKF algorithm is shown in the following Figure 1:  
When parameter identification is performed based on the EKF algorithm, the parameters to be identified need to be included in the state vector, and the identification parameters are obtained through the overall estimation of the state variables. The EKF algorithm is mainly divided into prediction and update phases, with the specific calculation formulas for each stage shown as follows:  
(1) The prediction phase includes the calculation of the state matrix \(\hat{\textbf{X}}_{k+1/k}\) and the error covariance matrix \(\textbf{P}_{k+1/k}\):  
\[\left\{\begin{array}{l}\hat{\textbf{X}}_{k+1}=f(\textbf{X}_{k},k)\\ \textbf{P}_{k+1/k}=\boldsymbol{\Phi}_{k+1/k}\textbf{P}_{k}\boldsymbol{\Phi}_{k +1/k}^{T}+\textbf{Q}_{k+1}\end{array}\right. \tag{12}\]  
Figure 1: Schematic diagram of the EKF algorithm.  
(1) Operational Condition 1: \(10^{\circ}/-10^{\circ}\) Steering Experiment: The USV was operated at its rated speed with a steering angle of \(10^{\circ}\) for 50 s, followed by a steering angle of \(-10^{\circ}\) for the same duration. To minimize the impact of random occurrences, the experiment was repeated 4 times. The experimental data obtained were primarily used for the identification modeling of the USV's maneuverability parameters and to verify the accuracy of the resulting model.  
(2) Operational Condition 2: \(10^{\circ}/-10^{\circ}\) Turning Experiment: The USV was operated at its rated speed with a steering angle of \(10^{\circ}\) or \(-10^{\circ}\) for a duration of 50 s.  
In this study, the motion data required for identification include the yaw rate, yaw angle, and steering angle. The yaw angle is measured by an inertial navigation sensor, while the steering angle is obtained from the control-end sensor. To more effectively handle nonlinear data and the dynamic performance of varying systems, we applied filtering to the experimental data collected from the sensors. The yaw rate is derived by differentiating the filtered yaw angle.
# Numerical Study of Coolant Flow Phenomena and Heat Transfer at the Cutting-Edge of Twist Drill
## 5 Conclusions  
In this study, an extensive investigation into the coolant flow dynamics and heat dissipation in drilling operations was conducted through computational fluid dynamics (CFD) modelling. The study encompassed four different drill models, namely, the standard model, standard model with a notch, profile model, and profile model with a notch. The main focus was on understanding the impact of coolant channel design on fluid flow at the cutting-edge area and subsequent heat dissipation.  
The results of the current research draw attention to the fact that the cutting-edge zones are not sufficiently supplied with cooling lubricant when using modified drill models such as standard model drill with a notch and profile model drills with and without a notch. The investigations reveal that an improved coolant supply at the cutting-edge can be achieved using the standard model drill, thus providing comparatively better heat transfer. Thus, it can be said that among the four different drill models, the standard model drill provides better results in terms of coolant flow velocity at the cutting-edge and heat transfer. On the other hand, despite providing evenly distributed flow dynamics, the profile-shape coolant channel drill models are not efficient enough in effective heat transfer.  
Furthermore, it was also observed that the standard twist drill with a notch exhibited superior performance in reducing thermal load. The presence of a notch at the exit of the coolant channel facilitated an efficient escape passage for the cooling agent to the flute, contributing to improved heat dissipation at the cutting-edge. Despite the challenges posed by dead zones in the cutting-edge area, especially in profile models, the standard-with-notch model demonstrated the most promising results.  
Further analysis under constant pressure conditions at 40 and 60 bar revealed a notable enhancement in fluid flow rates, particularly at the cutting-edge, leading to improved heat dissipation. The temperature distribution along the cutting-edge and outer corner exhibited a decrease as the pressure increased.  
In conclusion, this study underscores the critical role of coolant channel design and inlet pressure in optimizing coolant flow dynamics and heat transfer in drilling operations. The findings provide valuable insights for the design and improvement in coolant systems in machining processes, emphasizing the significance of not only coolant channel geometry but also the inlet pressure for effective heat dissipation and enhanced tool performance.
## 2 Related Work  
### CNN-Based Network  
Deep learning is being broadly employed in CD tasks due to its potent ability to process computer vision tasks. Among them, the powerful feature representation capability of CNN enables it to play a great role in early CD. [PERSON] et al. [6] framed a two-branch structure with shared weights, and the difference image of the input images could be obtained by calculating the feature maps output from the two-branch network with Euclidean distance. Weighted contrastive loss was used to distinguish the changed pixels from the unchanged pixels more effectively, so as to reduce the influence of data imbalance.  
However, the network was not well altered to effectively extract image features, and the small sample of datasets used was not convincing enough to perform comparison experiments. [PERSON] et al. [7] brought fully convolutional into the CD domain by proposing three CD algorithms, namely the FC-EF, FC-Siam-conc, and FC-Siam-diff. The FC-EF is an early fusion-based model that concatenates bitemporal images along the channel dimension and later passes them into a fully convolutional network, while the other two, FC- Siam-conc and FC-Siam-diff, are siamese architectures. Moreover, the models achieved better performance than the previously proposed methods, while being at least 500 times faster than related systems. Similarly, these networks are not a good solution to the problem of pseudo-change and small change targets that are difficult to detect. Aiming to solve the circumstance of pseudo-change and the difficult detection of change edges in remote sensing image CD, much recent research has been directed to the strategy of feature fusion. STANet [10] designed a siamese neural network through obtaining illumination invariance and misalignment robustness features, but the proposed BAM and PAM only considered the spatial attention weights between bitemporal images. [PERSON] et al. [11], using dual branches, presented a depth-supervised strategy to optimize the change boundary by means of merging multilayer deep features and differential image features. In addition to this, various works have been conducted by researchers to expand the receptive field, such as the use of atrous convolution [25] and the use of various attention mechanisms. SNUNet [14] presented a dense connection network based on UNet++ that incorporated multiscale features, and finally, the ECAM module enhanced the feature representation by an attention mechanism [26]; however, ECAM only employed a channel attention mechanism and ignored spatial relations. [PERSON] et al. [12] described a dual-attention module to obtain both the spatial and channel attention at the same time.  
Although a growing number of CNN-based works consider CD from the perspective of multiscale feature fusion, these works still lack the modeling of global contextual features; therefore, we remedy this shortcoming in our work.
## 5 Experimental Results and Discussion  
### Experimental Results of the UCI Datasets  
The proposed ISDTSVM algorithm was used for the classification of the multi-class UCI data sets. The features of the multi-class UCI data sets are presented in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline
**UCI Data Sets** & **Samples** & **Number of Attributes** & **Data Categories** \\ \hline Vowel & 528 & 10 & 11 \\ Zoo & 104 & 17 & 7 \\ Wine & 178 & 13 & 3 \\ \hline \end{tabular} The optimized results of the SVM parameters \(C\) and \(\gamma\) (replaced by the letter: g) based on the UCI data sets using an inertial weight PSO algorithm are presented in Figure 6.  
\end{table}
Table 1: The features of the University of California Irvine (UCI) data sets.  
Figure 5: The SVM parameter optimization process using an inertia weight Particle Swarm Optimization (PSO) algorithm.  
Figure 6: \(Cont\).  
Accuracy of the k-fold cross-validation was taken in to account as a criterion to evaluate the effectiveness of a classification algorithm. The five-fold cross-validation were carried out. The classification testing results of the UCI data sets are presented in Table 2 by using the ISDTSVM algorithm and the conventional SDTSVM algorithm with optimized parameters (Figure 6) and two kinds of kernel functions. As can be observed, for the Vowel, Zoo and Wine data sets, the ISDTSVM algorithm improves the average classification accuracy by 3%, 3% and 1% respectively. The classification accuracy of the radial basis function kernel was higher than the polynomial kernel. The classification results of the Wine data sets are presented in Table 3 using the SDTSVM algorithm with all of the possible classification orders and radial basis function kernel. As can be observed, the determined classification order by the ISDTSVM algorithm has the highest classification accuracy.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**UCI Data Sets** & **Kernel Functions** & \begin{tabular}{c} **Average Accuracy of** \\ **Conventional SDTSVM** \\ \end{tabular} & \begin{tabular}{c} **Average Accuracy** \\ **of ISDTSVM** \\ \end{tabular} & \begin{tabular}{c} **Classification Order** \\ **of ISDTSVM** \\ \end{tabular} \\ \hline Vowel & \
\begin{tabular}{c} Radial basis function \\ (C = 4.81, \(\gamma\) = 0.86) \\ \end{tabular} & 96\% & 99\% & \(1{-}8{-}10{-}2{-}3{-}3{-}9{-}77\) \\ \hline Vowel &
\begin{tabular}{c} Polynomial (d = 3) \\ \end{tabular} & 95\% & 97\% & \(\rightarrow\)5\({-}4{-}4{-}6{-}11\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: The classification results of the UCI data sets using the Improved Skewness Decision Tree Support Vector Machine (ISDTSVM) and the conventional Skewness Decision Tree Support Vector Machine (SDTSVM) algorithm with different kernel functions and a five-fold cross-validation.  
Figure 6: SVM parameters optimized results of the UCI data sets.
## 5 Conclusions  
Under seismic excitation, the horizontal response accelerations in the two slopes show typical effects of elevation amplifications, surface amplification, and lithology. The arrangement of stabilizing piles suppressed the downward trend in the motion of the overlying deposit. This results in a decrease in the difference between internal accelerations and external accelerations of the bedrock at the same elevation within the slope. A more obvious wave reflection superposition effect in the passive zone caused by the piles under ground shaking leads to the slope surface PGA amplification factor increasing in front of the piles and decreasing below the piles. In addition, through the transfer function between the slope bottom and slope crest accelerations, it can be found that the installation of stabilizing piles significantly amplified the 15 to 17 Hz frequency component.  
The permanent settlements and angular rotation of the stabilizing pile-reinforced slope crest are reduced by about 30% compared with the unreinforced slope after five-stage seismic excitation is applied in the centrifuge tests. The installed piles will increase the Newmark yield acceleration of the underlying bedrock deposit slope. The horizontal displacement at the top of the stabilizing pile is closely related to the slope crest settlement and angular deformation.  
The bending moments at each point on the pile do not reach their maximum simultaneously. There is a significant phase difference in the bending moment time-history curve pertaining to each measurement point, and the bending moment at the pile top lags behind that at the pile bottom. The short-term response amplitude of the dynamic bending moment is significantly larger than the corresponding post-earthquake permanent residual bending moment. The lateral pressure between pile and deposit gradually increases as the input acceleration increases. The point of action of the distributed load (when expressed as a single resultant force) is 7.46 m above the sliding surface, indicating that the earthquake mainly causes shallow sliding of the deposit to produce a residual sliding force. Under strong seismic excitation, the main bearing stratum of the pile-socketed bedrock section moves downwards, indicating that the stress state in that section of the pile embedded in the bedrock changes. In seismic design terms, each stabilizing pile should have sufficient rock-socketed depth to ensure stability.  
Conceptualization, Z.S. and L.K.; methodology, Z.S.; validation, W.B. and Y.W.; data curation, Z.S.; writing--original draft preparation, Z.S.; writing--review and editing, Z.S.; visualization, Y.W.; supervision, L.K.; project administration, L.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, grant number 2018 YFC1505304; the National Natural Science Foundation of China, grant numbers 41702348 and 51979269; and the Hubei Provincial Natural Science Foundation of China, grant number 2017 CFB373.  
Not applicable.  
The authors declare no conflict of interest.