esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### Analysis of the Overall Consumer Liking
#### 3.4.3 Open Comments from Consumers  
The open comments are encompassed in Figure 3(a). When describing their liking for the vinegar samples, no significant variation or specific frequency was detected in the verbiage used by the consumers, and included terms, such as \"rich vinegar aroma\", \"medium vinegar aroma\", \"fruity vinegar aroma\", and \"sweet aroma.\" Consumers also mentioned terms like \"too pungent vinegar aroma\", \"not rich vinegar aroma\", \"alcohol aroma\", and \"smeally aroma.\" Furthermore, the heatmap showed the differences between the six samples as per the participants.  
Regarding liking, B1, B3, and B4 were more often described as presenting a \"medium vinegar aroma\" than the remaining samples, while more participants ascribed a \"rich vinegar aroma\" to B2. More consumers described B6 as \"having a sweet aroma and fruity vinegar aroma\" than the other samples.  
Many consumers (32 out of 76) described B5 as exhibiting a \"too pungent vinegar aroma\", while significantly fewer participants ascribed this characteristic to B3 and B6,  
Figure 4: The heatmaps of open comments from all consumers (**a**) and each cluster (**b**). The code of vinegar corresponds with Table 1. The left 18 columns were the terms that consumers liked, and the right 18 columns were the terms that consumers disliked.  
Figure 3: The heatmap of respondents’ liking scores for six kinds of vinegar in different regions. The code of vinegar corresponds with Table 1. These regions are explained in the supplementary data (Figure S2).  
when asked what they disliked about the product. Comments indicated B6 as \"having an alcohol aroma\" for 15 consumers out of 76, while fewer than five participants ascribe this attribute to the other samples. B3 was attributed a \"Chinese medicine aroma\" by 15 out 76 consumers, who disliked the characteristics of this sample, representing the highest percentage of the six products.
# Remote Sensing Image Target Detection Method Based on Refined Feature Extraction
## 4 Conclusions  
Object detection algorithms based on deep learning have made great progress in natural scene images. However, remote sensing images have difficulties, such as complex backgrounds, many small objects, and arbitrary arrangement directions. Therefore, this paper proposes a remote sensing image object detection algorithm, DCN-YOLO, for remote sensing image detection tasks. First, the model introduces DCNv2 to reconstruct the backbone network and enhance its feature extraction capability. Then, the model improves the multi-scale feature fusion structure by incorporating the SimAM attention mechanism to suppress background interference and enhance the model's ability to extract target features, thus improving localization accuracy. Moreover, a detection head is added to the head network, and anchor boxes are generated using the K-means++ algorithm to increase the model's priors and significantly improve its detection performance for small targets.  
Figure 7: Illustrates a comparative visualization of feature maps. In panels (**a–f**), we present visualizations of feature maps from 6 sets using two distinct methods.  
Finally, the model employs the normalized Gaussian Wasserstein distance loss function for regression loss calculation, effectively enhancing the model's multi-scale detection capability. Experimental results show that the proposed method achieves good detection results on large-scale public remote sensing image object detection datasets, DOTAv1.0 and DIOR. However, the method in this paper has the disadvantages of slow detection speed and large consumption of GPU resources, so further research will be carried out on the lightweight version of the network in subsequent work.  
Conceptualization, H.C. and B.T.; methodology, B.T.; software, B.T.; validation, B.T.; formal analysis, B.T.; investigation, B.T.; resources, B.T.; data curation, B.T.; writing--original draft preparation, B.T.; writing--review and editing, H.C. and B.T.; visualization, B.T.; supervision, H.C.; project administration, H.C.; funding acquisition, H.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62163023, 61873116, 61763029), the Industrial Support Project of the Education Department of Gansu Province (2021 CYZC-02), and the Gansu Provincial Science and Technology Planning (20 JR10 RA184).  
Not applicable.  
The data used for training and test set DOTAv1.0 are available at: [[https://captain-whu.github.io/DOTA](https://captain-whu.github.io/DOTA)]([https://captain-whu.github.io/DOTA](https://captain-whu.github.io/DOTA)), accessed on 8 May 2023. The data used for training and test set DIOR are available at: [[http://www.escience.cn/people/gongcheng/DIOR](http://www.escience.cn/people/gongcheng/DIOR)]([http://www.escience.cn/people/gongcheng/DIOR](http://www.escience.cn/people/gongcheng/DIOR)), accessed on 8 May 2023.  
The authors declare no conflict of interest.
# Experimental and Numerical Studies on the Impact Energy Absorption of Cutting Shear Rings
## 3 Drop Hammer Test
### The Test Results  
The test data collected by the sensor was the curve of platform force and time. To capture the energy absorbed by the device during the collision, the curve needs to be processed as follows:  
\[I=\int F(T)dT \tag{1}\]  
\[I=mv_{1}-mv_{2} \tag{2}\]  
where \(I\) is the impulse of the platform force; \(F(T)\) is the platform force-time curve of the acquisition; \(m\) is the weight of the drop hammer; \(v_{1}\) is the speed at which the hammer is in contact with the energy absorption rods; \(v_{2}\) is the speed at which the hammer drop after the energy absorption process is completed.  
To obtain the energy absorption during the collision, the following calculations are also required:  
\[mgh=\frac{1}{2}mv_{1}^{2} \tag{3}\]  
\[Q=\frac{1}{2}mv_{1}^{2}-\frac{1}{2}mv_{2}^{2} \tag{4}\]  
Figure 3: Test apparatus diagram. (**a**) drop hammer impact test setup; (**b**) pedestal and bolts.  
The energy absorption rods after the impact of the drop hammer are shown in Figure 5. For case 1, the maximum peak impact force is 88.01 kN, and the last shear ring is left on the outside of the energy absorption rods. The energy absorption is 2362.58 J, with an average of 118.12 J per shear ring. For case 2, the maximum peak impact force is 385.71 kN, and four shear rings are retained on the outside of each absorption rod, and the suction energy reaches 8155.56 J, with an average of 159.91 J per shear ring. For case 3, the maximum peak impact force is 104.50 kN, all shear rings on the outside of each rod are sheared, and the energy absorption is 1402.42 J, with an average of 175.30 J per shear ring.
## 3 Experiments
### Dataset Acquisition
#### 3.2.4 VGG16_BN Network Data Set Production  
The creation of the VGG16_BN network dataset involves several steps. Firstly, UAV aerial images are utilized for manhole cover acquisition by performing manhole cover positioning. The acquired manhole cover images are then processed to extract the text present on them. Subsequently, the extracted text is inputted into the super-resolution reconstruction network for the purpose of reconstruction. In this study, a total of 3249 manhole cover text images were collected, consisting of 1530 images from rainwater manhole covers and 1719 images from sewage manhole covers. The dataset was partitioned using the K-fold cross-validation method, with a value of K set to 5 for the division.
# An Overview of Distributed Firewalls and Controllers Intended for Mobile Cloud Computing
## 3 SDN, NFV, and CASB Technologies
### Network Function Virtualisation (NFV)  
NFV revolutionises network infrastructure by virtualising network functions and deploying them as software instances on commodity hardware [45]. NFV eliminates the dependency on proprietary hardware appliances by decoupling network functions, like firewalls and intrusion detection systems, from physical devices and enabling their implementation in software [46]. This approach offers greater flexibility and scalability, allowing network functions to be deployed wherever and whenever needed without being tied to specific hardware platforms [36].
# Calculation Method for Electricity Price and Rebate Level in Demand Response Programs
## 3 Calculation Methodology of Electricity Prices and Rebate Levels
### Definition of the Optimal Rebate Level and Its Calculation  
If there is no incentive payment, the utility of the consumers decreases, depending on contribution to the DR. This is because the consumers must accept less satisfaction than that in the standard electricity consumption. In the incentive-based DR, values of the utility of the power suppliers and the consumers and their changes are separately calculated as:  
\[U_{1,t}\big{(}d_{t}^{\prime}\big{)} = p_{t}^{*}d_{t}^{\prime}-E_{t}\big{(}d_{t}^{\prime}\big{)}, \tag{21}\] \[U_{2,t}\big{(}d_{t}^{\prime}\big{)} = F_{t}\big{(}d_{t}^{\prime}\big{)}-p_{t}^{*}d_{t}^{\prime},\] (22) \[\Delta U_{1,t} = p_{t}^{*}\Delta d_{t}-\Delta E_{t},\] (23) \[\Delta U_{2,t} = \Delta F_{t}-p_{t}^{*}\Delta d_{t}. \tag{24}\]  
These equations are similar to Equations (13)-(16); however, the electricity price is fixed to \(p_{t}^{*}\) in the incentive-based DR. Although the consumers accept less satisfaction, their utility recovers to its original level by compensating the decrement of Equation (24). Therefore, we can set the acceptable condition for the unit price of money rebate \(r_{t}\) as:  
\[\frac{p_{t}^{*}\Delta d_{t}-\Delta F_{t}}{|\Delta d_{t}|}\leq r_{t}\leq\frac{p _{t}^{*}\Delta d_{t}-\Delta E_{t}}{|\Delta d_{t}|}. \tag{25}\]  
Under this condition, the DR does not bring negative impacts to both the power suppliers and the consumers. The authors defined the optimal rebate level to induce the active cooperation of the consumers as:  
\[r_{t}^{\prime}=\frac{p_{t}^{*}\Delta d_{t}-\Delta E_{t}}{|\Delta d_{t}|}. \tag{26}\]
# The Influence of Interlock Loss between Rebar and Concrete on Bond Performance of RC Member
## 5 Analytical Case Study Assuming Lug Loss Due to Rebar Corrosion
### Investigation on the Effect of Non-Uniform Lug Loss Region on Cross Section of Rebar  
Figure 15 shows the load-deflection curves and strain distribution obtained by the FE analysis. In the following figures showing load-deflection curves, the result of sound case (ST-100) is included for comparison. In the case of Side-0%, where two of the three main rebars were perfectly round, stiffness decreased after the onset of flexural cracks, and cracks were localized, as well as strain being concentrated at the beam ends, indicating bond loss. On the other hand, in the cases of Bottom-0% and Outer-0%, the load-deflection curves and the strain distributions were almost identical to those of the sound case, so the bond of the entire beam can be almost the same condition as the sound one. In the Bottom-0% case, lug loss region was the same as the ST-50, and the difference between the two cases was whether cover concrete was present or not. However, the two cases clearly differed in behavior. In the ST-50 case of analysis, the main rebar detachment from the upper concrete caused interlock loss, while in the Bottom-0%, the concrete under the rebar pushed up the rebar to concrete so that contact between the lugs and the concrete was maintained, resulting in a sound bond. Although the sound interlock area was \(1/2\) for the Bottom-0% and \(1/3\) for the Outer-0%, behaviors of these beams were almost same as  
Figure 14: Pull-out test results.  
Figure 15: Analysis results (non-uniform lug loss region).  
the sound interlock case. As long as some interlocking region remains on every main rebar, tensile stress can be transferred to the rebars, and they can work as tensile members, as is expected in design.
# Design of a Chaotic Trajectory Generator Algorithm for Mobile Robots
## 6 Multiple Robots
### Presence of Chaos in Generated Trajectories for Multiple Mobile Robots
#### 6.4.3 Box-Counting Test  
After applying the box-counting test (see Section 5.3) to \(1\leq n\leq 10\) mobile robots, the results shown in Table 3 were obtained.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Robots** & **Coverage (\%)** \\ \hline
1 & 18.1250 \\
2 & 33.7500 \\
3 & 46.4583 \\
4 & 55.8333 \\
5 & 63.3333 \\
6 & 70.2083 \\
7 & 75.8333 \\
8 & 79.3750 \\
9 & 82.0833 \\
10 & 85.8300 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average coverage percentage for \(n\) robots simultaneously in the same work area.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Robots** & **Result for \(x\)** & **Result for \(y\)** \\ \hline
1 & 0.9837 & 0.9254 \\
2 & 0.9461 & 0.9107 \\
3 & 0.9540 & 0.9307 \\
4 & 0.9863 & 0.9633 \\
5 & 0.9554 & 0.9417 \\
6 & 0.9899 & 0.9601 \\
7 & 0.9919 & 0.9645 \\
8 & 0.9621 & 0.9774 \\
9 & 0.9619 & 0.9445 \\
10 & 0.9769 & 0.9552 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Gottwald–Melbourne results for 10 robots simultaneously in the same work area.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Number of Robots** & **Coverage (\%)** \\ \hline
1 & 18.1250 \\
2 & 33.7500 \\
3 & 46.4583 \\
4 & 55.8333 \\
5 & 63.3333 \\
6 & 70.2083 \\
7 & 75.8333 \\
8 & 79.3750 \\
9 & 82.0833 \\
10 & 85.8300 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Box-counting results for \(n\) robots simultaneously in the same work area.  
We can observe that a fractal dimension is present in the generated trajectories even with multiple mobile robots, which is a chaotic property. Based on the obtained result of the tests applied, we can conclude that every generated trajectory by means of the proposed algorithm presented in this work is chaotic.
## 2 Multipath Theory and Signal-to-Noise Ratio
The carrier-phase multipath contribution \(\delta\phi(\theta,\alpha,H_{o},\lambda)\) is a function of the satellite elevation angle, \(\theta\), the damping factor, the antenna height, \(H_{o}\), and the wavelength (\(\lambda\)) of the signal  
\[\delta\phi(\theta,\alpha,H_{o},\lambda)=tan^{-1}\frac{\alpha\ sin(\Delta\phi_{r} (t))}{1+\alpha\ cos(\Delta\phi_{r}(t))}=tan^{-1}\frac{\alpha\ sin\left[4\pi \frac{H_{o}}{\lambda}\sin\theta\right]}{1+\alpha\ cos\left[4\pi\frac{H_{o}}{ \lambda}\sin\theta\right]} \tag{3}\]  
In general, the multipath footprint amplitudes depend on the damping factor, while the period is intimately related to the GNSS antenna height, satellite elevation and rate of change of the elevation angle of the satellite. The multipath period is longer for a short height antenna and for slowly descending or ascending satellites; a longer multipath period also occurs for close-by reflectors. The multipath carrier phase error from Equation (3) can be converted to units of distance multiplying by \(\lambda/2\pi\). The phase error can be expressed in units of distance for a specific GNSS,  
\[\delta\phi(\theta,\alpha,H_{o},\lambda)_{L}=\frac{\lambda}{2\pi}\delta\phi( \theta;\alpha,H_{o},\lambda) \tag{4}\]  
The contributions of the multipath error to the GNSS coordinates following Equation (3) attain its maximum values when the multipath phase angle is \(90^{\circ}\) or \(270^{\circ}\). Alternatively, the maximum phase can be deduced from the amplitudes of the direct and reflected multpath signals:  
\[\delta\phi(\theta,\alpha,H_{o},\lambda)=sin^{-1}\left(\frac{A_{r}}{A_{d}}\right) \tag{5}\]  
In Equation (5), the amplitude of the direct signal can be modelled from the SNR time series for a particular satellite by fitting a polynomial. The remaining residual SNR time series largely contains the power of the reflected/scattered signal. The amplitude of the reflected signal is not easy to estimate but can be modelled using wavelet transform applied to the residual SNR (\(\delta SNR\)) time series ([34; 35], and see also Equation (12).
## 5 Births in Last Five Years for Women in Bangladesh
\begin{table}
\begin{tabular}{l r r r r r r r r} \hline \hline  & \multicolumn{6}{c}{**GP(I)**} & \multicolumn{6}{c}{**CMP**} \\ \hline
**Parameter** & **MLE** & **Std** & \(\mathbf{z}\)**-Value** & \(\mathbf{p}\)**-Value** & **MLE** & **Std** & \(\mathbf{z}\)**-Value** & \(\mathbf{p}\)**-Value** \\ \hline Intercept & 4.455 & 0.3685 & 12.09 & \(<\)0.0001 & 4.843 & 0.4700 & 1.31 & \(<\)0.0001 \\ \(\alpha\) & 0.913 & 0.0052 & \(-\)16.71 & \(<\)0.0001 & – & – & – & – \\ \(\
u\) & – & – & – & – & 1.828 & 0.0666 & 27.44 & \(<\)0.0001 \\ Age & \(-\)0.147 & 0.0097 & \(-\)15.05 & \(<\)0.0001 & \(-\)0.157 & 0.0121 & \(-\)12.98 & \(<\)0.0001 \\ Education & & & & & & & & \\ Primary & \(-\)0.083 & 0.0401 & \(-\)2.06 & 0.0391 & \(-\)0.032 & 0.0508 & \(-\)0.63 & 0.5271 \\ Secondary & \(-\)0.085 & 0.0405 & \(-\)2.10 & 0.0361 & 0.002 & 0.0512 & 0.04 & 0.9685 \\ Higher & 0.223 & 0.0534 & 4.18 & \(<\)0.0001 & 0.403 & 0.0671 & 6.01 & \(<\)0.0001 \\ No education & 0.000 & & & & & & & \\ Religion & & & & & & & & \\ Islam & \(-\)0.342 & 0.1797 & \(-\)1.90 & 0.0573 & \(-\)0.247 & 0.2387 & \(-\)1.04 & 0.3002 \\ Hinduism & \(-\)0.638 & 0.1868 & \(-\)3.42 & 0.0006 & \(-\)0.684 & 0.2478 & \(-\)2.76 & 0.0057 \\ Christianity & 0.000 & & & & & & & \\ Division & & & & & & & & \\ Chittagong & 0.064 & 0.0551 & 1.16 & 0.2461 & 0.103 & 0.0685 & 1.50 & 0.1331 \\ Dhaka & \(-\)0.060 & 0.0572 & \(-\)1.05 & 0.2946 & \(-\)0.067 & 0.0711 & \(-\)0.94 & 0.3492 \\ Khulina & \(-\)0.320 & 0.0640 & \(-\)5.00 & \(<\)0.0001 & \(-\)0.351 & 0.0794 & \(-\)4.42 & \(<\)0.0001 \\ Mymensingh & 0.052 & 0.0584 & 0.89 & 0.3759 & 0.072 & 0.0727 & 0.99 & 0.3210 \\ Rajshahi & \(-\)0.319 & 0.0630 & \(-\)5.06 & \(<\)0.0001 & \(-\)0.359 & 0.0781 & \(-\)4.59 & \(<\)0.0001 \\ Rangpur & \(-\)0.093 & 0.0597 & \(-\)1.56 & 0.1198 & \(-\)0.116 & 0.0745 & \(-\)1.55 & 0.1208 \\ Sylhet & 0.433 & 0.0540 & 8.03 & \(<\)0.0001 & 0.576 & 0.0682 & 8.45 & \(<\)0.0001 \\ Barisal & 0.000 & & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Comparisons of goodness-of-fit among the GP(I) regression model, CMP regression model, log-lambda based GP regression model with constraint \(\lambda\geqslant 0\) and the log-lambda based GP regression model without constraint on \(\lambda\). \begin{table}
\begin{tabular}{l r r r r r} \hline \hline
**Model** & **Log-Likelihood** & **AIC** & **BIC** & \(\mathbf{\chi^{2}_{n-p-1}}\) & **Sys. Time** \\ \hline GP(I) & \(-\)7624.63 & 15,279.26 & 15,385.94 & 8974.17 & 11.0384 s \\ CMP & \(-\)7623.66 & 15,277.32 & 15,384.01 & 9017.33 & 52.4071 s \\ genpoisson0 & \(-\)7645.95 & 15,321.90 & 15,428.58 & 7675.93 & 3.0545 s \\ genpoisson & \(-\)7706.93 & 15,443.87 & 15,550.55 & 7526.34 & 3.7786 s \\ \hline genpoisson0 means using the function ‘genpoisson0’ in ‘vglm’; genpoisson means using the function ‘genpoisson’ in ‘vglm’; Sys. Time represents the time cost of the system when the algorithm converged.
## 3 Momentum-Based Motion Detection
### Dynamic MRSS  
In the MRSS method, the smooth factor \((\alpha)\) is usually fixed and never changed. However, when the smooth factor is set according to an MN's state of motion, the sensitivity of MRSS can be improved. Sensitive detection can determine the correct instant to execute or terminate a network discovery process, and prevent unnecessary power consumption. We proposed a Dynamic MRSS (DMRSS) scheme to improve the sensitivity of MRSS based on the following observations:  
* Before an MN starts moving, EWMA only has a small fluctuation, hence using S-EWMA to detect the change of an MN's motion state is easier than L-EWMA.
* When an MN is in the leaving/approaching state, EWMA has large fluctuations, thus using L-EWMA to detect the change of an MN's state is more accurate than S-EWMA.  
DMRSS uses two different pairs of smooth factors \((\alpha)\) for various motion states of an MN. In DMRSS, when an MN is in the moving state (approach or leaving), EWMA uses a smaller smooth factor to make the EWMA curve smooth so that we will not ignore the MN's state change. In the stationary state, EWMA uses a larger smooth factor to make the EWMA curve sensitive to RSS changes. Thus, when a MN starts moving, MRSS will quickly increase and make motion detection easy.
## 3 Experimental Results and Discussion
### Practical Sample 3: Forecasting the Residential Electricity Consumption in Chong Qing
The _RMSE_ and _MAPE_ values of the MDGM(2,1),FDGM(2,1) and FMDGM(2,1) models are obviously smaller than that of original DGM(2,1) model. Therefore, the accuracy of the modified models is also increased compared with the original DGM(2,1) model. Referring the forecasting level of [23], the performances of the FDGM(2,1) and FMDGM(2,1) are classified placed at good and excellent forecasting levels, in which the precision rate falls from 92.47% to 98.46%. Although the modified FDGM(2,1) and FMDGM(2,1) have a higher accuracy than the original DGM(2,1), some accuracy measurements showed that the performance of the FDGM(2,1) and FMDGM(2,1) seem to be equal. It is difficult to confirm what is the best model. Despite this, the role of the Fourier correction approach is to increase the predictive capability of the considered input data set, and does not change the characteristic of grey prediction model [12]. For this reason, we can conclude that the FMDGM(2,1) is the most accurate model among all of the proposed models in this study.
## 3 System Design and Implementation
### Training Methods
#### 3.4.1 ML Training Model  
The ML models summary is listed as follows.  
Figure 3 shows the binomial logistic regression model.  
LogisticRegression(c=1.0, class_weight=None, dual=False, fit_intercept=True, intercept_scaling=1, max_iter=100, multi_class=\"warn\", n_lobs=None, penalty=\"12\", random_state=None, solver='warn', tol=0.001, verbose=0, u_iter=false)  
Naive Bayes Classifier  
MultinomialNB (alpha=1.0, class_prior=None, fit_prior=True)  
Alpha set = 1 means Additive (Laplace/Lidstone) is smoothing. Class_prior = None means prior probabilities of the classes re adjusted according to the data. Fit_prior = True means whether to learn class prior probabilities or not.  
Support Vector Machines  
C is the regularization parameter, the default is 1.0, cache size is the size of the kernel chache, the default is 200.  
class weight is none means that all classes supposed to have weight one.  
Coef0 is 0.00, this is the the default parameter for independent term in kernel function. Because we used \"linear\" for the kernel type, then no need to set the coef0, it is only significant in \"poly\" and \"sigmoid\".  
Decision function shape \"ovr\" means return a one-vs-rest (ovr) decision function of shape. Degree 3, the degree setting is ignored by all other kernels, except \"poly\".  
Gamma setting is auto deprecated means that we used 1/n_features.  
Kernel is \"linear\" means that we specify the linear kernel type to be used in the algorithm.  
max_iter-1 means no limit iterations.  
Probability is False means that we did not enable the probability estimation.  
random_state 1 is to shuffling the data to generate the pseudo random number.  
Shrinking default is True means that we use the shrinking heuristic.  
tol 0.001 means the tolerance for stopping criterion, the default is \(1\times 10^{-3}\), we set it at 0.001.  
Verbose false means we disable verbose output.  
Figure 4 shows the model of support vector machines.  
Random Forest  
_'max_depth'_ : 60,'_n_estimators_': 200  
Max_depth = 60 means that the maximum depth of the tree is 60. N_estimator = 200 means that the number of trees in the forest is 200.