esa-sceva/satcom-chunk-collection huggingface dataset:
# MEC Server Sleep Strategy for Energy Efficient Operation of an MEC System
## 5 Performance Evaluation
### Performance of Sleep Control Vector Decision Method  
We use GA to determine \(\Omega(t)\). To focus on the influence of GA on the energy consumption and the queue length, we use true task arrival rates (i.e., \(\{\hat{\lambda}_{i}(t)\}\)) instead of the predicted task arrival rates (i.e., \(\{\hat{\lambda}_{i}(t)\}\)) when determining \(\Omega(t)\) at the beginning of each time slot. Given \(\hat{\lambda}_{i}(t)\)s, we compare the performance of GA and that of the exhaustive search (ES) method. Since ES explores all possible instances in the solution space, \(\Omega(t)\) found by ES is globally optimal for the given input state.  
We measure the difference between \(E(t)\) when ES is applied and that when GA is used and draw the distribution of the difference in \(E(t)\) in Figure 5. The circles in the box plot (Figure 5a) represent the outliers. We observe that the distribution of \(E(t)\) when GA is applied is very similar to that of \(E(t)\) when ES is applied. Specifically, the Q-values in the box plot are \(Q_{1}=-12.46\), \(Q_{2}=-0.43\), and \(Q_{3}=0.50\). In addition, we observe in Figure 5b that the difference in \(E(t)\) is densely concentrated around zero. We also examine the Q-values of \(E(t)\) when ES and GA were applied, respectively. When ES is used, it is \(Q_{1}=8.53\), \(Q_{2}=10.64\), and \(Q_{3}=12.54\), but while GA is employed, it is \(Q_{1}=8.61\), \(Q_{2}=10.69\), and \(Q_{3}=12.70\). Thus, the difference between \(Q\)-values obtained by GA and those when ES is applied is marginal.  
In Figure 6, we show the distribution of the differences between the average queue length (i.e., \(\bar{Q}(t)=\frac{1}{|\mathcal{M}|}\sum_{i\in\mathcal{M}}Q_{i}(t)\)) when ES is applied and \(\bar{Q}(t)\) when GA is utilized. When compared to the ideal case where the error is zero, the median value (\(Q_{2}\)) of \(\bar{Q}(t)\) is 0.54. When we inspect the histogram, the average difference in \(\bar{Q}(t)\) is 0.87. To further inspect the variance of \(\bar{Q}(t)\), we examine the interquartile range (IQR), which is the difference between the 75 th percentile and the 25 th percentile. In the case of ES, the IQR for \(\bar{Q}(t)\) is 4.40, whereas with GA, the IQR is 3.91, resulting in a difference of 0.49 between them. Considering that \(Q_{th}=37.5\), such a difference can be regarded as very small.  
We also compare ES and GA in terms of their run time. ES takes an average of 80 ms to find \(\Omega(t)\) while GA takes an average of 29 ms. By using GA, we reduce the time needed to determine \(\Omega(t)\) by 63.75%.
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 3 Sound Source Localization Methods for Two-Dimensional (2D) Structures
### Artificial Neural Networks
Then, the PSO algorithm was adopted to localize the AE sources, which were excited by a pencil lead fracture on a concrete slab (1000 mm \(\times\) 1000 mm) and the two algorithms are compared. The computational time of PSO was 1.23 s, whereas that of the IPS algorithm was 0.9 s. The IPS algorithm was superior to PSO in terms of algorithmic consistency. A rectangular sensor array produced better localization results. The maximum localization error of the IPS algorithm was 125 mm. [PERSON] et al. [58] studied the acoustic source localization method in large reinforced concrete slabs and proposed an algorithm using a combination of time-of-flight and MAE source localization. Most of the algorithms for acoustic source localization assume that the algorithm is independent of the nature of the propagation medium, assuming that the propagation path is a straight line between the transducer and the source. [PERSON] et al. [59] proposed a method for acoustic source localization called Fastway, which accounted for discontinuities in material, including cracks and voids, and considered the fastest wave propagation path between source and the transducer, instead of the shortest (straight) path. The experiments demonstrated that the Fastway method determined the location of the sound source more accurately.
## 5 Discussion  
As shown Figures 16 and 17, no improvement was observed in the case of a lap length of 12.5 mm, because the peel stress was not significant. The improvements shown for lap lengths of 25, 40, and 100 mm were significant; the values were much higher than the converted mean shear stress of the peel stoppers because the peel stress is significant due to the long lap length. These results indicate that the effect is caused by the stiffness of the peel stoppers, not the shear strength.  
As shown in Figure 14, a single sample with a lap length of 100 mm with steel pins significantly improved compared with the samples with steel pins and lap lengths of 25 and 40 mm. The reason is that the steel pins stuck in spite of the release agent, generating the peel stopper effect.  
As shown in Figure 15 (lap length of 100 mm), for brass bolts, the failure load reached the tension strength of the adherends. On the other hand, lap lengths of 25 and 40 mm with brass bolts experienced adhesive failure, as shown in Figures 9, 11 and 13. These results show that failure load increases with increasing lap length for brass bolts, despite lap shear stress decreases with increasing lap length.  
A simulation is required for predicting the strength of adhesive joints for design purposes. The author, however, cannot find a reliable analytical or numerical method for predicting the strengths of lap shear joints without peel-stop fasteners. Thus, at present it remains hard to establish a strength prediction method for lap shear joints with peel-stop fasteners, and more analytical and experimental studies are required.
# Calibration of a Rotating or Revolving Platform with a LiDAR Sensor
## 4 Evaluation
### Experiments
Second, to investigate how the method behaves if the initial pose guess is far from the true pose, the actually measured pose was tested in comparison to one with an additional error of +0.03, added to all five dimensions. Figure 6: Point cloud of the scene, recorded to compare calibration qualities with different configurations. The scene contains different oriented planar regions, about 3–5 m away. They cover the test of all orientation dimensions of the LiDAR. Moreover, a cube with about 0.4 m side length and a cylinder with ca. 0.4 m diameter and height are placed in the scene. They should be congruent after calibration. The calibration runs result in different transformations that were used to record the surroundings in a second phase. While running the platform in operation mode, it was configured to rotate continuously with 0.0175 Hz (i.e., one full rotation in about a minute) to sample the environment. The records of the continuous runs were cropped to an excerpt of the room to avoid negative influence of clutter or salient structures of the building. The plane deviation measure was applied to each of these records to rate the quality of the respective point cloud. We set the parameters of the measure as followed. The RANSAC of the plane deviation has a parameter to set the allowed distance between the plane model and the data points to distinct plane inlier from other points. This was set to \(2\sigma=0.06\), i.e., the doubled sensor noise, to prevent the potential over-segmentation of calibration results with low quality, i.e., high noise and thus also high point deviations. The same applies for the allowed point distance during the region growing. The qualities of the recorded point clouds are plotted against the different target distances in Figure 7.  
Additionally, to study the calibration runtime with different configurations, several calibration runs with static range limiting the search space and from a single initial pose guess were done. The runtime depends on the grid resolution, runtime of the local optimiser and the number of detected promising starting points for the gradient descent. All calibration runs to log the optimisation durations were performed on a _Lenovo Thinkpad T460p_ laptop with an _Intel(R) Core(TM) i7-6700 HQ_ CPU. The CPU has four physical cores and another four logical ones. Each core is working at 2.60 GHz. Furthermore, the system has 16 GB of RAM and a 128 GB SSD storage. The logged runtimes are displayed in Table 1.
# Random-Specific Activity in Drinking Water and Radiological Health Risk Assessment: A Case Study
## 3 Results and Discussion  
In Table 2, the annual mean value (\(\pm\)standard deviation) of the _Rn_-222-specific activity in the investigated drinking water samples is reported for each collection site.  
It can be noticed that that the radon activity concentration varies from a minimum of (7.4 \(\pm\) 1.4) Bq L\({}^{-1}\) for the site ID2 to a maximum of (164 \(\pm\) 46) Bq L\({}^{-1}\) for the site ID1, thus suggesting that the provenance of these drinking water samples is different and that they originate from different depths and pass through distinct geological strata. This uneven distribution of the specific activity may be dependent on the amount of _Rn_-222 in the aquifer rocks of different locations and on the residence time of water/rocks-soils in contact [36]. Specifically, the highest value was recorded for a site situated within the geological context of the \"Calabrian-Peloritan arc\" [37]. This particular geological setting is known for its abundance of uranium-rich rocks, resulting in elevated levels of radon gas.  
In addition, the relative uncertainty for the site ID1 is higher than in other cases; this is due, according to Equation (2), to the higher _Rn_-222 activity concentration measured in the four investigated samples picked up from this sampling location.  
Moreover, in all cases, with the only exception of site ID1, the activity concentration values were found to be always lower than 100 Bq L\({}^{-1}\), i.e., the parameter value according to the Italian legislation [24]. Then, in this case, the assessment of the total annual effective dose due to the ingestion and inhalation of _Rn_-222 by infants, children and adults was carried out by using Equations (3) and (4), respectively. Specifically, the annual effective dose due to ingestion of _Rn_-222 was 0.56 mSv y\({}^{-1}\), 0. 4 mSv y\({}^{-1}\) and 0.42 mSv y\({}^{-1}\) for infants, children and adults, respectively. These values fall within the acceptable range of 0.2-1.8 mSv y\({}^{-1}\) reported in the literature [38]. Furthermore, the annual effective dose due to inhalation of _Rn_-222 in the analyzed water sample was 0.41 mSv y\({}^{-1}\). Therefore, the total annual effective dose was found to be 0.97 mSv y\({}^{-1}\), 0.75 mSv y\({}^{-1}\) and 0.83 mSv y\({}^{-1}\) for infants, children and adults, respectively. All these results, obtained in a fully precautionary scenario, are below the 1 mSv y\({}^{-1}\) limit value recommended by the World Health Organization (WHO), thus allowing us to reasonably exclude any possible radiological health risk related to the radon exposure for the population living in the investigated area [39].
# A Symbol Recognition System for Single-Line Diagrams Developed Using a Deep-Learning Approach
## 5 Discussion
### Comparison with Different Data Augmentation Techniques  
Table 3 shows the occurrences of 16 different classes present in both datasets. After generating fake SLD images using the DCGAN and LSGAN, it was observed that the new samples were nearly balanced, although the class imbalance problem can be further improved by adding unlike and distinct images to the original dataset.  
The detection results of two additional dataset versions, the original SLD images without synthetic images, were compared with the detection results of the augmentation method shown in Table 4. Along with different preprocessing strategies, the subsequent detection networks, including the YOLO V5-based symbol detection model, were consistent.  
It is important to acknowledge that enhancing datasets for complex images such as single-line diagrams (SLDs) can present certain challenges. These challenges may include: (i) acquiring authentic SLD images: obtaining SLD images from reliable and trustworthy sources can be problematic; (ii) the manual labor required for labeling the SLD images: labeling SLD images often requires manual effort, as it entails annotating specific elements or regions of interest within the images; and (iii) obtaining a sufficient number of random samples: constructing a comprehensive dataset for SLD images typically requires an adequate number of diverse and random samples. In light of these challenges, the proposed method has demonstrated improvements and offers an alternative approach to address them. By employing the proposed method, it is possible to overcome the difficulties associated with acquiring authentic SLD images by generating a sufficient number of synthetic data, thus reducing the manual labeling effort, and generating a satisfactory number of random samples for a balanced dataset.
## 2 Review of the MCC and Zero-Attraction (ZA) Technique  
### Conventional MCC  
We consider a sparse system identification within the MCC framework shown in Figure 1. From Figure 1, we can see that the MCC-based adaptive filtering is to mimic the entropy error \(e(n)\) that is a difference between output \(y(n)\) and desired signal \(d(n)\) under non-Gaussian noise \(v(n)\). As we know, the entropy is to quantify the uncertainty with designated random variables, and, hence, the minimized errors are concentrated. Herein, we discuss the MCC algorithm based on a linear system given in Figure 1. From the system identification theory, \(d(n)\) is depicted by:  
\[d(n)=\mathbf{x}^{T}(n)\mathbf{w}_{o}(n)+v(n), \tag{1}\]  
where \(\mathbf{x}(n)=[x(n),x(n-1),\cdots,x(n-N-1)]^{T}\) represents an input vector, \(\mathbf{w}_{\mathbf{o}}(n)=[w_{0},w_{1},\cdots,w_{N-1}]^{T}\) denotes an unknown system which is an FIR channel, \({}^{T}\) is the transpose operation, \(v(n)\) denotes noise or interference signal which is non-Gaussian. Here, the memory size of the channel is \(N\). In the AF framework, the estimated output is \(y(n)=\mathbf{x}^{T}(n)\mathbf{\tilde{w}}(n)\), and, hence, \(e(n)\) is given by:  
\[e(n)=d(n)-y(n). \tag{2}\]  
\(\mathbf{\tilde{w}}(n)\) represents the estimated system. The MCC algorithm is to calculate the equation [23; 27]:  
\[\begin{split}&\min\frac{1}{2}\left\|\mathbf{\tilde{w}}(n+1)- \mathbf{\tilde{w}}(n)\right\|^{2},\\ &\text{subject to }\hat{e}(n)=\left[1-\xi\exp\left(-\frac{e^{2}(n)}{2 \sigma^{2}}\right)\right]e(n).\end{split} \tag{3}\]  
Herein, \(\hat{e}(n)=d(n)-\mathbf{x}^{T}(n)\mathbf{\tilde{w}}(n+1)\), \(\left\|\cdot\right\|\) represents \(l_{2}\) norm, and we use \(\xi=\chi_{\text{MCC}}\left\|\mathbf{x}(n)\right\|^{2}\).  
To get the solution of Equation (3), the Lagrange multiplier (LM) method is used in this paper. Therefore, the MCC's cost function is [27]:  
\[J_{\text{MCC}}(n)=\frac{1}{2}\left\|\mathbf{\tilde{w}}(n+1)-\mathbf{\tilde{w} }(n)\right\|^{2}+\lambda\left(\hat{e}(n)-\left[1-\xi\exp\left(-\frac{e^{2}(n) }{2\sigma^{2}}\right)\right]e(n)\right), \tag{4}\]  
where \(\lambda\) denotes Lagrange multiplier. Then, we have:  
\[\frac{\partial J_{\text{MCC}}(n)}{\partial\mathbf{\tilde{w}}(n+1)}=\mathbf{0},\]  
and  
\[\frac{\partial J_{\text{MCC}}(n)}{\partial\lambda}=\mathbf{0}.\]  
Thus, we can get the following formula:  
\[\mathbf{\tilde{w}}(n+1)=\mathbf{\tilde{w}}(n)+\lambda\mathbf{x}(n). \tag{5}\]  
Here, \(\lambda\) is obtained:  
\[\lambda=\xi\frac{\exp\left(-\frac{e^{2}(n)}{2\sigma^{2}}\right)e(n)}{\left\| \mathbf{x}(n)\right\|^{2}}. \tag{6}\]We substitute Equation (6) into Equation (5), the updating of the MCC is [23; 27]:  
\[\widehat{\mathbf{w}}(n+1)=\widehat{\mathbf{w}}(n)+\chi_{\text{MCC}}\exp\left(- \frac{e^{2}(n)}{2\sigma^{2}}\right)e(n)\mathbf{x}(n). \tag{7}\]  
Compared to the basic LMS, an exponential weighting is considered and used in MCC, which is for eliminating the large errors. The MCC algorithm is robust for dealing with impulsive noises for system identifications.
## 5 Materials and Methods
### Pathogenicity Test  
Peanut and maize seeds were used for pathogenicity assay according to the previously illustrated procedure [56; 57]. In brief, the maize seed and peanuts cotyledons were inoculated with _A. flavus_\(10^{5}\) conidia/mL and mock inoculation was used as a control. The seeds were placed in a petri dish (4 replicates for each sample) with a piece of moisture filter paper and incubated for 6 days at 29 \({}^{\circ}\)Cin a dark condition. Peanuts and maize seeds were collected and placed into 50 mL centrifuge tubes containing 0.05% tween 80 and 15 mL of sterilized water and vortexed for 1 min. For conidia counting, 500 uL aliquot of conidia was diluted and counted haemocytometrically. Aflatoxin was extracted as previously described method by [PERSON] et al. (2018). The extracted samples were spotted on TLC plate and viewed under ultra violet (UV) light at 365 nm. Gene Tools image analysis system software version 4.03.05.0 was used for the quantification of AFB\({}_{1}\).  
Reverse Transcriptase Polymerase Chain Reaction (RT-PCR) and Quantitative Real Time Polymerase Chain Reaction (qRT-PCR)  
qRT-PCR were carried out following the descriptions and guidelines that were previously described ([PERSON] et al., 2018; [PERSON] et al., 2018) with minor modifications. Consistent with the aforementioned, _A. flavus_ wild-type and mutant strains were inoculated into YES agar media and grown in the dark for 2 days at 37 \({}^{\circ}\)C. Their mycelia were then collected and strongly grounded in liquid nitrogen to break down the cell wall material and get access to RNA molecules isolation. The total RNA was extracted from 100 mg smashed mycelia for every sample using Total RNA Extraction kit (Promega, Madison, WI, USA). The first strand cDNA for each sample was synthesized from the extracted total RNA using revertAid first strand cDNA synthesis kit (ThermoFisher scientific, Waltham, MA, USA). To analyze qRT-PCR 2 xSYBR Green mix kit (Takara Dalian China), a pair of primers (Table 3) for target genes were used and amplified with ThermoFisher scientific Real-time PCR system (PikoReal 96 Real-Time PCR system/from Vantaa, Finland). The valuation of relative transcript level of every target gene was done by the 2\({}^{-\Delta\Delta\text{CT}}\) method ([PERSON] et al., 2018) and _A. flavus actin_ gene was used as a control. The qPCR test was carried out with technological quadruplet for wild-type, \(\Delta\)_A/saka_ and \(\Delta\)_A/saka_::_A/saka_ and the experiments was repeated at least in triplicate.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Primers** & **Sequence (\(\mathbf{5^{\prime}}\)-\(\mathbf{3^{\prime}}\))** & **Application** \\ \hline \hline \(\mathcal{A}\)/
## 2 Modelling of Base-Isolated Buildings with DFP Bearings
### Considering the Vertical Excitation of Earthquakes  
Equation (2) considers the acceleration of ground motion in the two horizontal directions \(X\) and \(Y\). In order to include the effect of the vertical component, an additional mass caused by vertical action should be added. The corresponding weight \(V(t)\) varying over time is determined as  
\[V(t)=W\Bigg{(}1+\frac{\ddot{u}_{g2}}{g}\Bigg{)}, \tag{6}\]  
where \(\ddot{u}_{g2}\) is the vertical acceleration component of the ground motion; \(g\) is the acceleration of gravity, and \(W\) is the total weight above the bearing.  
The stiffness of springs from the above series model is recalculated as  
\[\left\{\begin{array}{l}k_{bz1}=\frac{V(t)}{K_{eff}}\\ k_{bz2}=\frac{V(t)}{K_{eff}}\end{array}\right.. \tag{7}\]  
Similarly, Equation (3) is rewritten considering the effect of the vertical component of the ground motion,  
\[\left\{\begin{array}{l}F_{f21}=\mu_{1}V(t)Z_{1}\\ F_{f22}=\mu_{2}V(t)Z_{2}\end{array}\right.. \tag{8}\]  
As a result, a system of differential equations of motion of the structural system under the impact of the ground acceleration of earthquakes in 3 directions \(X\), \(Y\), and \(Z\) is obtained as  
\[\left\{\begin{array}{l}m_{b1}\big{(}\tilde{u}_{b1}+\tilde{u}_{g}\big{)}+k_{ b21}u_{b1}+F_{f21}+F_{r1}+k_{b22}(u_{b1}-u_{b2})-F_{f22}-F_{r2}=0\\ m_{b2}\big{(}\tilde{u}_{b2}+\tilde{u}_{g}\big{)}+k_{b22}(u_{b2}-u_{b1})+F_{f22}+ F_{r2}+k_{1}(u_{b2}-u_{1})+c_{1}\big{(}\dot{u}_{b2}-\dot{u}_{1}\big{)}=0\\ m_{1}\big{(}\dot{u}_{1}+\tilde{u}_{g}\big{)}+k_{1}(u_{1}-u_{b2})+c_{1}\big{(} \dot{u}_{1}-\dot{u}_{b2}\big{)}+k_{2}(u_{1}-u_{2})+c_{2}\big{(}\dot{u}_{1}-\dot {u}_{2}\big{)}=0\\...\\ m_{n}\big{(}\ddot{u}_{n}+\ddot{u}_{g}\big{)}+k_{n}(u_{n}-u_{n-1})+c_{n}\big{(} \dot{u}_{n}-\dot{u}_{n-1}\big{)}=0\end{array}\right. \tag{9}\]  
The motion equations can be solved by the fourth-order Runge-Kutta numerical method using the ode15s solver in the MATLAB software [17].
## 4 Results and Discussion
### Experimental Results  
The SL-CNN model continuously compared and optimized the initial network model structure and four operational parameters, resulting in further improvements in the performance and accuracy of the network model. For the hyperspectral images, after applying the band selection and model optimization techniques, the SL-CNN model achieved an increase of 0.46% in its overall accuracy (OA) compared to that of the initial model with the Windowsizes parameter set to 7, and there was a decrease of 61 s in the training time compared to that of the initial model with the Windowsizes parameter set to 11. Therefore, the band selection method and parameter optimization used in this study were confirmed to be beneficial for improving the classification performance of desert grassland hyperspectral images and accelerating the model construction.  
To verify the validity of the SL-CNN model, four widely used hyperspectral model classification algorithms were selected for a comparative study that is, ResNet34, GoogLeNet, DenseNet121, and MLP. In addition, to verify the advantages of the improved depth-separable convolution, the conventional convolution method was used for reconvolution under the SL-CNN model to generate the 2D-CNN model. All the classification algorithms were executed in the same programming environment using the same data preprocessing method to ensure experimental reliability. The single-feature recognition classification accuracy results are shown in the confusion matrix (Figure 14). Table 5 presents the results.  
As shown in Figure 14, the SL-CNN model constructed in this study had the most accurate overall performance, with recognition accuracies of 99.56%, 99.31%, 98.40%, and 96.49% for Features 1, 2, 3, and 4, respectively. This indicated that the SL-CNN model had a high capability for grassland feature extraction. As shown in Table 5, regarding the overall classification performance, the SL-CNN model achieved kappa coefficient, OA, and AA values of 98.735, 99.216%, and 98.442%, respectively. Its training time and generated model required the lowest memory compared to other models at 367 s and 16.3 MB, respectively, and the total number of parameters run during the model construction comprised 4.73 MB of the memory. The results showed that the SL-CNN had a high generalization ability and could be applied to desert grassland feature classification tasks.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**RBC-F** & **CBAM-F** & **Kappa \(\times\) 100** & **OA (\%)** & **AA (\%)** & **Test loss (\%)** & **F1** \\ \hline ✓ & & 98.386 & 99.000 & 98.382 & 0.028 & 0.983 \\  & ✓ & 98.154 & 98.857 & 98.387 & 0.031 & 0.980 \\ ✓ & ✓ & 98.735 & 99.216 & 98.442 & 0.022 & 0.985 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of ablation experiments.