esa-sceva/satcom-chunk-collection huggingface dataset:
# Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing
## 6 Conclusions  
This paper aimed to address the low utilization of storage space and high data replacement rates in traditional edge computing cache placement strategies. The objective was to maximize the cache service rate and minimize the system overhead and data response time. The cache value, acquisition cost, placement cost, and replacement cost for data blocks is considered as a joint optimization problem. A cache placement optimization strategy based on improved tabu search was proposed for solving this problem. The replacement overhead of data blocks was quantified by taking into account the available storage space and the number of data blocks cached on the edge server. The experimental results demonstrated that the CPOSCU algorithm can boost the cache space utilization, reduce the system overhead, and effectively improve the system performance, thereby enhancing the user experience of mobile internet applications.  
In future work, two aspects will be investigated. First, various content caching and placement operations will be performed using deep reinforcement learning methods based on different content types. Second, as the scale of the edge cloud environment built in this experimental environment was limited, the experiments will be scaled up further by expanding the number of edge servers and improving the deployment.  
Conceptualization, Y.L.; methodology and writing, Y.B.; visualization, N.C.; data curation, S.Z. All authors have read and agreed to the published version of the manuscript.  
The work was supported by the National Natural Science Foundation (NSF) under grant no. 61802353, the Natural Science Foundation of Henan Province (No. 202300410505), and the Project of Science and Technology of Henan Province (No. 192102210270).  
Not applicable.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data Availability Statement: Data sharing not applicable. No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
# Energy Efficiency Analysis of MIMO Wideband RF Front-End Receivers
## 3 Transmission Schemes
### Effect of the SAW-Filters at the LNA Receiver  
Another important element that affects the transmitted power is the SAW-filter at the LNA receiver. For the LNA architecture we consider the insertion loss of the off-chip SAW-filters and the necessary arrangements for the transmission of the simultaneous carriers. For the LNTA architecture this was not necessary because integrated reconfigurable filters are implemented. As a result, there is a 2.1 dB loss, per active receive antenna, in the received SNR of the LNA, compared to the LNTA. The value of 2.1 dB is extracted from the datasheet of a commercial low insertion loss RF SAW filter [26], compliant with the carrier frequency denoted in Table 2.  
Then, in order to provide the same SNR \(\xi_{0}\) required to meet the target outage probability \(\mathcal{O}^{\star}\) for both architectures, we compensate such insertion loss of the LNA by increasing the transmitted power with respect to that required with the LNTA architecture, i.e.,  
\[P_{\text{out,LNA}}^{\text{(sch)}}\left[\text{dBm}\right]=P_{\text{out}}^{\text{ (sch)}}\left[\text{dBm}\right]+n_{\text{r}}\times 2.1\ [\text{dB}]. \tag{15}\]
# The State of Ethereum Smart Contracts Security: Vulnerabilities, Countermeasures, and Tool Support
## 4 Smart-Contracts Vulnerabilities and Countermeasures
### Vulnerabilities and Countermeasures
#### 4.2.2 Arithmetic Issues  
In Solidity, integer type has a specific range. During the arithmetic operations on variables, their values may go beyond the upper or lower bound. If that happens, the value will warp to the other side of the bound. For example, if the real value is larger than the upper bound, the actual value will be the real value less the upper bound. In earlier versions of Solidity, this abnormality does not trigger any alarm. Attackers could increase or decrease specific integer values to trick the smart contract into unwanted behaviour. Solidity does not support floating points yet. The representation of floating numbers in smart contracts must be dealt with integer types. For example, in the design of ERC20 (a token standard), decimal is used to represent the number of digits after the decimal point. The division in Solidity always rounds down, so the precision is only to its nearest lower integer. The loss of information after the decimal points could cause severe problems when higher precision is needed.  
_Countermeasures_. Since arithmetic vulnerability lies in Solidity, using a well-designed, audited library such as SafeMath [75] provided by Openzepplin [76] instead of built-in arithmetic operations will mitigate the risk.
# Acoustic Emission Analysis of the Cracking Behavior in ECC-LWSCC Composites
###### Abstract  
Acoustic emission (AE) analysis was utilized to assess the cracking behavior of six lightweight self-consolidating concrete (LWSCC)-engineering cementitious composite (ECC) beams under flexural loading. Two control beams were fully cast with ECC containing either polyvinyl alcohol (PVA) fibers or steel fibers (SF). The remaining four beams were ECC-LWSCC composite beams, with the ECC layer containing PVA fibers or SF placed on either the tension or compression side. The results showed that the control beams had the highest ultimate load capacity, followed by beams repaired in tension, and then beams repaired in compression. PVA fibers exhibited higher performance compared to steel fibers at the first crack load, while steel fibers enhanced the beam's performance at the ultimate load stage. During the flexural testing, AE parameters such as the number of hits, signal amplitude, and cumulative signal strength (CSS) were collected until failure. The analysis of these AE parameters was effective in detecting the first crack and evaluating cracking propagation in all beams. Changing the type of fibers (PVA and SF) in the ECC layer showed a significant effect on AE parameters. Moreover, adding a new ECC layer to an existing LWSCC layer resulted in variations in the signal amplitude. Finally, the flexural failure mode was confirmed with the aid of the rise time/maximum amplitude vs. average frequency analysis.  
acoustic emission analysis; ECC-LWSCC composite beams; first crack detection; damage quantification; failure modes Article
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 2 Methodology
### Attitude Model  
There will be six main external surfaces in a CubeSat without deployable solar panels, whose orientations are described by their normal vectors (\(\vec{n}_{w}\)). Similarly to the previous section, their inclination may be defined by other two angles, now around the ORF. Figure 2 illustrates the axes of the rotational matrix, the position, and orientation of the CubeSat at the first step in orbit.  
Two rotational matrices will be used to simulate the attitude models of this work, as follows:  
\[\vec{n}_{w}^{*}(t) =\left[M_{a}\right]_{\text{ERF}}\vec{n}_{w,0} \tag{13}\] \[\vec{n}_{w}(t) =\left[M_{b}\right]_{\text{ORF}}\vec{n}_{w}^{*} \tag{14}\]  
In the first step, the six CubeSat's sides rotate around the axis \(\vec{u}_{a}\) at the angle \(\Delta\theta_{a}\). After that, the satellite suffers one more rotation, now around the axis \(\vec{u}_{b}\), at the angle \(\Delta\theta_{b}\). The inclinations after this last rotation (\(\vec{n}_{w}\)) are \(\vec{n}_{w,0}\) at the next iteration \(it+1\). For a spin in two axes, the first rotation will be around a fixed axis at the ERF, and the second will occur around the transient axis \(\vec{n}_{w}^{*}(t)\) at the ORF.
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
## 3 Experiment and Analysis
### Evaluation Methodology  
This study employs the area under the receiver operating characteristic (ROC) curve (AUC) and partial AUC (pAUC) as evaluation metrics. pAUC is a metric that calculates the area under a certain range of interest on the ROC curve, specifically calculated as the AUC over a low false-positive rate ranging from 0 to p. The definitions of AUC and pAUC are given by the following formulas:  
\[AUC=\frac{1}{N_{-}N_{+}}\sum_{i=1}^{N_{-}}\sum_{j=1}^{N_{+}}\mathcal{H}( \mathcal{A}_{\theta}(x_{j}^{+})-\mathcal{A}_{\theta}(x_{i}^{-})) \tag{17}\]  
\[pAUC=\frac{1}{\lfloor pN_{-}\rfloor N_{+}}\sum_{i=1}^{\lfloor pN_{-}\rfloor} \sum_{j=1}^{N_{+}}\mathcal{H}(\mathcal{A}_{\theta}(x_{j}^{+})-\mathcal{A}_{ \theta}(x_{i}^{-})) \tag{18}\]  
Here, the symbol \(\lfloor\cdot\rfloor\) denotes floor function. Function \(\mathcal{H}(x)\) returns 1 if \(x\) is greater than 0; otherwise, it returns 0. \(\{x_{i}^{-}\}_{i=1}^{N_{-}}\) and \(\{x_{j}^{+}\}_{j=1}^{N_{+}}\) represent the sorted normal and abnormal test samples, respectively, and their anomaly scores are arranged in descending order. Here, \(N_{-}\) represents the number of normal test samples and \(N_{+}\) represents the number of anomalous test samples.  
The pAUC is used to evaluate model performance, particularly in improving the true positive rate (TPR) at low false positive rates (FPR). This is essential because if the ASD system consistently generates inaccurate alerts, it becomes unreliable, as in the fable of \"the boy who cried wolf\", who lost his credibility. The value of \(p\) in this experiment is set to 0.1.  
In this study, we evaluate model complexity using two metrics: Floating-Point Operations (FLOPs) and the number of parameters. FLOPs represent the number of floating-point operations required for a single inference, providing insight into the model's computational complexity and inference efficiency. The number of parameters refers to the total count of trainable parameters in the model, reflecting its storage capacity.
## 2 Materials and Methods  
### Wood Plastic Composite Sample and Initial X-ray Micro-CT  
An uncapped sample of wood plastic composite (WPC) docking measuring 24 mm (thick) and 140 mm squared provided by Rio Tinto was air freighted to the University of British Columbia. The WPC was composed of a polyethylene polymer, wood particles, zinc borate, and other unspecified additives. However, the relative proportions of the different components in the WPC sample, including the wood fraction, were not known. A cylindrical core (5.8 mm (diameter) \(\times\) 9.68 mm (length)) was removed from the WPC sample using a plug cutter and vertical drill press. The sample was wrapped in pliable plastic, air-bubble packaging and sealed in a padded envelope, which was air freighted by courier to the National Laboratory for X-ray Micro-Computed Tomography at The Australian National University in Canberra. The sample was sealed in a polyether ether ketone (PEEK) tube and imaged using a helical cone-beam X-ray micro-CT device designed and built in the Department of Applied Mathematics at the Australian National University [24]. The sample was fixed to the stage of the X-ray micro-CT device. X-rays from a cone-beam source passed through the sample to a detector, where they created a two-dimensional radiograph of the sample's interaction with X-rays. A series of 2880 such projections were collected over a period of 16 h by rotating and moving the sample relative to the X-ray source. The resolution of the CT scan was 2600 \(\times\) 2600 voxels with a voxel size of 3.1 \(\upmu\)m. Projection data were used to create 3D volumes (tomograms) of the X-ray attenuation property of the sample. Projection data were segmented to identify discrete phases in the WPC (wood, plastic, void space, and zinc borate).
## 4 Proposed Non-Linear Diffusion Method
### Architecture  
Figure 2 illustrates the proposed framework for SAR image despeckling, involving a multi-stage process to enhance image quality while preserving essential details. The framework begins with log transformation applied to the input speckled SAR image, followed by recursive non-linear diffusion to address noise. Discrete Wavelet Transform is then employed to decompose the image into various components. The approximation component is processed with a Weighted Graph Image Filtering (WGIF) technique, while method noise thresholding is applied to refine the despeckling process. The final step includes applying inverse DWT and exponential transformation to reconstruct the despeckled image, achieving significant noise reduction and detail preservation. The methodology is validated against various performance metrics, demonstrating superior capabilities in maintaining structural integrity and reducing noise in SAR images.
## 3 Results and Discussion
### Vitamin C, Total Polyphenols, and Antioxidant Activity Analysis of Fruit Juice Formulations  
The AA, DHAA, TAA, and TPC content and the antioxidant activity of the mixed fruit juice formulations are shown in Table 6. There are two biological active forms of vitamin C, L-ascorbic acid (AA) and L-dehydroascorbic acid (DHAA). L-ascorbic acid (AA) is highly sensitive to technological processes, as well as temperature and the availability of oxygen, which causes its decomposition [46]. AA degradation involves the conversion of AA to the form of DHAA, and then DHAA undergoes further reactions such as hydrolysis to 2,3-diketo-L-gulonate or oxidation to a series of products such as cyclic oxalyl threonine, oxalyl threonine, L-threonine acid, and oxalic acid [47]. As a water-soluble vitamin, AA is easily extracted into mash juices, but it degrades quickly after pressing and therefore its concentration in juices is lower compared to intact fruit or parts thereof [48]. The initial values of TAA were 253.60, 54.56, and 1.14 mg/100 mL in fresh rose, strawberry, and apple juices, respectively. According to the fitting results, percentages of AA in TAA were 78.2%, 86.5%, and 46.5% in fresh rose, strawberry, and apple juices, respectively. The average percentages of DHAA in TAA were 21.8%, 13.5%, and 53.5% in fresh rose, strawberry, and apple juices, respectively. These DHAA percentages suggest that irreversible degradation of DHAA may occur in fresh fruit juices, and that the oxidation of AA to DHAA and further degradation of DHAA might have different equilibriums in these three types of fruit juices. The obtained values for ascorbic acid were similar to those obtained by other authors. [PERSON] et al. [49] obtained a vitamin C content of 268.3 \(\pm\) 15.7 mg/100 mL in rosehip juices. Similar values were obtained by [PERSON] et al. [35], who in their research on _Rose rugosa_ juice determined the content of L-ascorbic acid at the level of 259 \(\pm\) 1.9 mg/100 mL. In strawberry juices determined by other researchers, the AA content ranges from 20 to 40 mg/100 mL. The same researchers showed that the reaction rate constants of AA degradation decrease in juices after adding sugar and also in juices stored at refrigerated temperatures [14]. [PERSON] et al. [48] reported 3.1-10 mg/L of AA in freshly
## 3 Results
### Artificial Neural Network (ANN) Performance
Figure 16: Performance of the best ANN model to predict the damping ratio for (**a**) training and (**b**) testing datasets. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & **Observations** & **Minimum** & **Maximum** & **Mean** & **SD** \\ \hline Damping Ratio, \(D\) (\%) & 29 & 6.900 & 25.500 & 13.355 & 5.370 \\ Number of Cycles (\(-\)) & 29 & 0.000 & 120.000 & 56.897 & 51.555 \\ Sphericity, \(S\) (\(-\)) & 29 & 0.702 & 0.807 & 0.753 & 0.028 \\ Roundness, \(R\) (\(-\)) & 29 & 0.502 & 0.592 & 0.552 & 0.025 \\ Regularity, \(\rho\) (\(-\)) & 29 & 0.612 & 0.695 & 0.653 & 0.025 \\ Vertical Stress (kPa) & 29 & 50.000 & 250.000 & 143.103 & 84.223 \\ CSR (\%) & 29 & 0.200 & 0.500 & 0.348 & 0.112 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics for the training dataset. to the five independent variables employed, namely the three particle shape parameters (i.e., \(S\), \(R\) and \(\rho\)), vertical stress, the number of loading cycles and the CSR. The number of neurons in the hidden layer was initially set to 50, but through an iterative process of trial and error, the optimum number of neurons was determined to be 28. This process is essential for optimizing the model's performance and ensuring that it is not overfitting the training data. Table 7 presents various performance metrics (i.e., MAE, MSE, RMSE, MSLE, RMSLE and \(R^{2}\)) for the proposed ANN model, trained using a Levenberg-Marquardt (LM) algorithm, to predict the damping ratio as a function of the sand particle shape, vertical stress, number of loading cycles and CSR. The results indicate that the ANN model performs well in predicting the damping ratio, as evidenced by the high \(R^{2}\) value of 0.962 for both the training and testing datasets, indicating that 96.2% of the variations in the actual output variable (i.e., the damping ratio) is captured and explained by the proposed ANN model. The MAE, MSE and RMSE values for the training dataset were found to be higher than those obtained for testing, indicating that the model performs better on the testing dataset. This could be due to overfitting, which occurs when the model fits the training data too closely, leading to relatively poorer performance on new (unseen) datasets. The MSLE and RMSLE values were low for both the training and testing datasets, implying that the predictions were associated with low forecast errors. In conclusion, the results suggest that the ANN model trained using the LM algorithm is a promising approach for predicting the damping ratio of sand based on the sand particle shape, vertical stress, number of loading cycles and CSR. However, further research is needed to validate the model's performance on larger (and/or more diverse) datasets.
# Numerical Investigation of Tonal Trailing-Edge Noise Radiated by Low Reynolds Number Airfoils
## 4 Results of the Parametric AFL Sensitivity Study
### Variation in Angle of Attack for Re\({}_{\rm c}\) = 180,000: No Tone-Producing Regimes
Instead, the turbulent BL shows many small and random vortices to occur along the suction surface. This, along with the lack of strong vortex shedding, results in the suppression of the AFL and transforms the surface spectra to just a \"broadband hump\" near the natural shedding frequency. It should be noted, the noise generated by this regime still occurs at the TE as indicated by the dilatation field. This is mainly due to the interaction of the turbulent structures with the TE. In addition, the dilatation field shows the LE to radiate sound through the unsteady pressure fluctuations caused by the LE vortex. However, the high-frequency contributions are quickly suppressed and do not show up in the far-field. To ensure the LE contributions are not dissipated by an inadequate grid resolution, several checks were conducted at \(0^{\circ}\), \(90^{\circ}\), \(180^{\circ}\), and \(270^{\circ}\), and \(360^{\circ}\) around the LE, to confirm the wavelength of the pressure perturbations is accurately captured. The analysis revealed the frequency of the LE contributions to be approximately 2000 Hz, while the maximum resolvable frequency at those locations, based on the computational mesh, is approximately 3300 Hz. It is worth mentioning that the dilatation fields obtained from these three cases all show high amplitude contours in the wake region (between 5c and 10c), indicating the presence of hydrodynamic effects. However, the wake is steady in time and does not radiate any additional noise. Results from the full 3D ILES reveal that the original hypothesis of the tonal noise suppression based on 2D simulations (suggesting the lack of co  
Figure 17: NACA-0012 at an \(\alpha=4^{\circ}\) AoA and \(R_{c}=180,000\). (**Left**) Instantaneous Z-vorticity contours; (**Right**) the dilatation field. Red circle indicates the origin of the acoustic wave to be at the TE. Figure 18: NACA-0012 at an \(\alpha=8^{\circ}\) AoA and \(R_{c}=180,000\). (**Left**) Instantaneous Z-vorticity contours; (**Right**) the dilatation field. Red circle indicates the origin of the acoustic wave to be at the TE. interacting with the TE and scattering as acoustics) is, in fact, misleading. Instead, the actual 3D mechanism responsible for the AFL suppression at a high AoA is the transition to a fully turbulent flow regime with strong spanwise mixing.
## 2 Three-dimensional Object Detection Techniques
### Projection of Point Cloud in Bird's Eye View (BEV) or 2D Plane
#### 2.1.2 Camera/Vision  
Vision-based systems have been successfully applied for both object detection and tracking [45; 46; 47; 48]. [PERSON] et al. performed vision-based moving object detection using semantic convolutional features and achieved a greater detection rate than YOLO V3 and faster than RCNN [46]. Besides single objects, researchers even estimated dynamic crowds from UAV imagery using a vision-based system [49]. While this research focuses on 2D object detection, vision-based moving object detection is also possible in three dimensions [50; 51]. Three-dimensional detection using cameras can be performed using two methods, i.e., i. monocular and ii. stereo images.  
Monocular (RGB)  
Monocular camera is a single camera that is used to generate 2D images. From single 2D images, 2D object detection task i.e., object classification and localization can be done. For 3D object detection, additional depth information is inferred from geometric cues, contrast or prior shape information of the known object.  
Stereo  
Stereo cameras utilize two cameras, one capturing the left and another the right image. Its working principle follows that of human vision. By comparing the disparity between the same pixels of two camera images, it is possible to perceive the depth information of the object. Stereo images are proved to be usable for 3D object detection, with a slight compromise in precision compared to LiDAR [56; 57; 58]. Their research proves that stereo images can be used for 3D object detection precisely and economically.  
[PERSON] et al. applied the CNN-based network TrackleNet for detecting and tracking pedestrians [50]. Stereo R-CNN [59], Pseudo-LiDAR from Visual Depth [56] and Triangulation Learning Network [60] are 3D deep learning networks developed and successfully applied for autonomous vehicle navigation. [PERSON] et al. utilized stereo images guided by an economical few point laser for improving precision [57].