esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussions
### Impact on Energy Consumption  
In a wireless communication network, energy consumption is high due to the movement of devices from one position to another. Here, the energy is consumed during caching \(E_{c}\) and transmitting the content \(E_{t}\). To minimize the overall energy consumption, the Transport energy \(E_{t}\) needs to be minimized, as most of the energy is consumed during the transmission of content. Efficient caching will reduce the average latency and minimize the transportation energy.  
The Figure 9 represents the relationship of energy saving ratio with cache size. It is computed for CEE, Random Caching \(p\) = 0.9, Random Caching \(p\) = 0.5, and the Proposed caching scheme. At 10 MB cache size, CEE saves only 20% energy, which is worst, while the Random Caching \(p\) = 0.9 performed quite better due to its probability strategy. The proposed scheme \(SOCC\) save high energy of 31.53%. At 210 MB of cache size, CEE, \(p\) = 0.9, \(p\) = 0.5 and \(SOCC\) achieve 19.2%, 38.4%, 46.78% and 52.32% energy saving, respectively. At 500 MB, CEE, \(p\) = 0.9 and \(p\) = 0.5 achieved energy saving of 26.11%, 15.97% and 5.91%, respectively while \(SOCC\), achieved the highest energy saving of 55.45%. All of these experimental results validate that the \(SOCC\) scheme performs efficiently with the maximum storage capacity of the node.
## 3 Methodology
### Data Set and Data Information  
The data set used in this paper is the Melbourne public dataset [44], since it is hard to obtain open parking data from official websites elsewhere. The data set is from 1 January 2017 to 31 December 2017. The minimum and maximum values of regional longitude are 144.9275761 and 144.9833373 respectively, while the minimum and maximum values of the regional latitude are \(-37.83126814\) and \(-37.78907112\). [PERSON] et al. [27] gave some geographical locations of placed sensors. The detailed information of placed sensors refer to reference [44]. From the data set, we selected the eight districts (streets) as parking spaces, and the details are shown in Table 1. The map of Melbourne in this region is shown in Figure 4. In Table 1, PL1-PL8 are labels of commercial parking lots. These 8 parking lots are located near the CBD of Melbourne, including Swanston Street, Collins Street, Flanders Lane, Elizabeth Street, Bourke Street, Little Bourke Street, William Street, and Queensberry Street. This area is home to retail, finance, tourism, entertainment, and educational establishments. These lots are busy every day with frequent traffic, and the parking lots are mainly of a commercial type. The detailed format of parking sensor data is shown in Table 2. The weather data set is used from Bureau of Meteorology, Commonwealth of Australia, including temperature, wind speed, pressure, and relative humidity [45]. The detailed format of weather data is shown in Table 3.  
The parking occupancy data was obtained through sensors. Due to the long working time of the sensor, some irresistible or human factors including data reading and writing errors may occur during the recording process. Therefore, it is necessary to preprocess the data, which will cause the loss of the collected data and reduce prediction accuracy. The mean filling method was used to deal with these missing and error values in this paper. The formula for calculating the average value of normal time in these days is shown in Equation (1).  
\[\overline{m}(t)=\frac{1}{d}\sum_{i=1}^{d}m_{i}(t) \tag{1}\]  
where \(\overline{m}\) (\(t\)) represents the average value in the time period of \(t\), \(m_{i}(t)\) represents the actual value in the time period of the previous days, and d represents the number of days in the time period.  
At the same time, some parking lots have conducted time-series noise processing, such as turning on the sensors at about 7:30 a.m. and turning off the sensors at about 6.30 p.m. This means that the parking data of the parking lot changes greatly, which greatly interferes with the prediction of parking occupancy. Therefore, the first and last data of the day will be truncated.
# Research on Visual Positioning of a Roadheader and Construction of an Environment Map
## 4 Map Construction and Test
### Construction of 3D Map and Test  
#### 4.3.1 Principle of Vision Mapping  
According to Table 1, the map constructed using the ORB-SLAM1/2 algorithm is a sparse point cloud map, which cannot be used for navigation and obstacle avoidance of the roadheader. In addition, the algorithm can easily miss a target when the airborne camera rotates and it is sensitive to noise. Considering the complex environment of underground roadways and the requirements of tunneling work, the performance of running and being in real-time, the accuracy of algorithm should be given more priority. The RTAB-MAP framework provides a map construction scheme that is independent of scale and time. As shown in Figure 12[19], its internal memory management is implemented through three memory nodes, i.e., WM (working memory), LTM (long-term memory), and STM (short-term memory). The weight of the registration point is updated in STM, and the closed-loop detection of the registration point is completed in WM. When the number of registration points exceeds a certain threshold, the registration point with the longest storage time is placed in WM from STM. To ensure real-time Bayesian estimation of the number of registration points, two functional nodes, namely fetch and transfer, are defined between WM and LTM during closed-loop detection in WM. When the closed loop is detected in the WM, the adjacent location points in the closed loop will be retrieved from the LTM and placed in the WM. When the processing time for closed-loop detection exceeds a certain threshold, the anchor point with the lowest weight is transferred to the LTM [19]. Based on the above memory management mechanism, the RTAB-MAP framework avoids the problem of having map data that are too large, which can meet the engineering needs of long-term, real-time, and online mapping in a large-scale environment.  
The RTAB-MAP algorithm can build a dense point cloud map. Considering the disadvantage of a large amount of data in a dense point cloud, a portable stored octree map can be formed after octree compression [44; 45], which can be used for stereoscopic navigation of the roadheader. The data structure of the octree is shown in Figure 13; each parent node contains eight child nodes, among which the leaf layer node corresponds to the map with the highest resolution. In addition, each node points to a cube voxel, and  
Figure 11: Cost map of simulated roadway based on the Karto algorithm.  
Figure 12: Memory management mechanism of RTAB-MAP.  
the probability of the \(n\)-th node corresponding to the voxel being occupied is calculated according to Bayesian theory, as shown in Equation (26) [45].  
\[p(n|z_{1:t}^{c})=\left[1+\frac{1-p(n|z_{1:t}^{c})}{p(n|z_{1:t}^{c})}\cdot\frac{1- p(n|z_{1:t-1}^{c})}{p(n|z_{1:t-1}^{c})}\cdot\frac{1-p(n)}{p(n)}\right]^{-1} \tag{26}\]  
where \(z^{c}\) represents the observation data of the airborne camera at the corresponding subscript time.
# AI-Based Resource Allocation in E2E Network Slicing with Both Public and Non-Public Slices
###### Abstract  
Network slicing is a key technology for 5G networks, which divides the traditional physical network into multiple independent logical networks to meet the diverse requirements of end-users. This paper focuses on the resource allocation problem in the scenario where public and non-public network slices coexist. There are two kinds of resources to be allocated: one is the resource blocks (RBs) allocated to the users in the radio access network, and the other is the server resources in the core network. We first formulate the above resource allocation problem as a nonlinear integer programming problem by maximizing the operator profit as the objective function. Then, a combination of deep reinforcement learning (DRL) and machine learning (ML) algorithms are used to solve this problem. DRL, more specifically, independent proximal policy optimization (IPPO), is employed to provide the RB allocation scheme that makes the objective function as large as possible. ML, more specifically, random forest (RF), assists DRL agents in receiving fast reward feedback by determining whether the allocation scheme is feasible. The simulation results show that the IPPO-RF algorithm has good performance, i.e., not only are all the constraints satisfied, but the requirements of the non-public network slices are ensured.  
network slicing; resource allocation; non-public network; deep reinforcement learning; machine learning +
Footnote †: journal: Applied sciences  
0
Footnote †: journal: Applied sciences  
0
Footnote 0: Copyright** © 2023 by the authors. Liconse MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/)]([https://creativecommons.org/licenses/py/4.0/](https://creativecommons.org/licenses/py/4.0/))).
## 2 Extraction Methods
### Non-Conventional Extraction
#### 2.2.4 Pulsed Electric Field  
Pulsed electric field (PEF) treatment is a non-thermal technology that exposes a sample to repetitive short voltage pulses at relatively low energy and moderate intensity. The application of PEF in plant tissues improves the permeabilization of the cell membranes which thus enhances the release of liquid and targeted compounds from the cells [47].  
To the best of our knowledge, there has been only one study on the effect of PEF treatment on BCA extraction. It reported that under optimum treating conditions an increment of 6% for total monomeric anthocyanins extracted from blackcurrants was found [38]. Given the limited study on PEF treatment, the effect of PEF on BCA extraction needs further investigation.  
The adoption of non-conventional extraction methods did not show a remarkable increase in the extraction yield of anthocyanins from blackcurrants. However, the reduced extraction time and solvent consumption were significant. For example, MASE greatly reduced the extraction time from 300 min to 10 min and reduced the solvent to sample ratio from 40:1 to 20:1 [32]. UAE also reduced the extraction time from 60 min to 10 min [33]. In addition, the studies on enzyme-assisted extraction showed they should be carried out with great caution given the possible negative effect of cell wall degrading enzymes on BCA extraction.
# Potential of Photovoltaic Generation in the Putumayo Department of Colombia
## 2 Materials and Methods
### Statistical Analysis for Comparison  
The results of the irradiation calculation with the Angstrom-Prescott method were compared with NASA's POWER database and NREL's NSRDB (The National Solar Radiation Database), for which the following statistical parameters were evaluated. For the Mean Bias Error (MSE), which refers to the systematic error, positive values indicate overestimation and negative values indicate underestimation. For the Mean Percentage Error (MPE), which is an overall measure of the precision of the estimate, deviations of less than 10% are acceptable. The Root Mean Square Error (RMSE) shows the variation of the estimates with respect to the recorded data and is always positive. The t-statistic establishes the significance and allows the objective comparison of the models [21]. The results of these parameters or statistics should be close to zero. The mentioned statistics are calculated as indicated in Equations (4)-(7).  
\[MBE=\frac{\sum_{1}^{n_{o}}RGe-RGm}{n_{o}} \tag{4}\]  
\[MPE=\frac{\sum_{1}^{n_{o}}\frac{RGe-RGm}{RGm}\cdot 100}{n_{o}} \tag{5}\]  
\[RMSE=\sqrt{\frac{\sum_{1}^{n_{o}}(RGe-RGm)^{2}}{n_{o}}} \tag{6}\]  
\[t=\bigg{[}\frac{(n_{o}-1)MBE^{2}}{RMSE^{2}-MBE^{2}}\bigg{]}^{0.5} \tag{7}\]  
where: \(RGm\) is the radiation measured or calculated with the data from the stations, \(RGe\) is the estimated radiation, and \(n_{o}\) is the number of observations.
## 3 Results
### COSMIC Satellite RO-Electron Density Data
One field line connects the Beirut port explosion site (marked by the magenta dot) to the southern COSMIC trajectory, whereas the other lines link the northern COSMIC trajectory to its conjugate point in the southern hemisphere. This mapping highlights the geomagnetic connection between the ionospheric disturbances observed in both hemispheres by the COSMIC satellites. Figure 8: Trajectories of the COSMIC RO path numbers: (**a**) 2635, (**b**) 2634, and (**c**) 362. Dot in magenta colour indicates the position of Beirut port. The middle column (**d–f**) shows the corresponding electron density data (blue) with the cubic spline fit (red). The far-right column (**g–i**) presents variations in the COSMIC electron density data, where the red lines represent the residuals, and the blue lines indicate the first-time derivative.
# Efficient Implementation of Polymer Microwave Fiber Links Employing Non-Galvanic Coupling Mechanism
## 2 PMF Overview
### PMF Propagation Characteristics
#### 2.2.1 Attenuation  
The attenuation of a PMF is affected by multiple factors. For a given frequency band, the attenuation constant depends on the geometry and the material of the PMF. Consequently, a PMF is designed to operate in a specific frequency band. Moreover, for a given geometry, the attenuation of the PMF is frequency-dependent. As the frequency increases, the attenuation increases as well. Moreover, the attenuation is affected by the dielectric loss tangent of the core and cladding materials, although the core material has a higher impact on the attenuation, as most of the field is propagating inside the core.  
To minimize the attenuation of the PMF, we must consider two key aspects of the waveguide's design. First, we must select a material with a low dielectric loss tangent. Second, we need to find the ideal conditions of operation, where the attenuation is at its lowest value, while, simultaneously, good field confinement is obtained. This can be achieved by carefully adjusting the cross-sectional area of the PMF. In Equation (1), the attenuation of a PMF is shown [25; 26]:  
\[a=\pi R\frac{f}{c}\text{tan}\delta\left[\frac{\text{Nepper}}{\text{m}}\right] \tag{1}\]  
where R is the geometric loss factor and shows how the field confinement affects the attenuation. As the field is concentrated inside the core (the geometric loss factor becomes higher), the attenuation increases. However, the waveguide is less prone to bending and other mechanical strains, which is very important for practical applications. An example with two rectangular PMFs of different cross-sectional areas at 140 GHz is shown in Figure 3a,b, respectively. The PMF with the larger cross-sectional area (Figure 3a) exhibits stronger field confinement, as described earlier.
# Bioinspired Kirigami Structure for Efficient Anchoring of Soft Robots via Optimization Analysis
## 2 Design, Composer, and Experimental Setup
### Theoretical and Analytical Design
#### 2.3.1 Pop-Up Height Analysis  
The pop-up height of the kirigami skin emerges from localized buckling triggered by axial compression applied via a PM linear actuator with a 30 mm stroke. Drawing upon classical buckling theory for thin elastic sheets [13; 21; 28], the pop-up height is evaluated by the relation:  
\[h=kt\sqrt{\frac{\epsilon}{\epsilon_{c}}} \tag{1}\]  
where \(t\) is the sheet thickness (100 \(\upmu\)m); \(\epsilon=\Delta L/L_{0}=0.214\) is the applied compressive strain; \(\epsilon_{c}\) is the critical buckling strain; and \(k\) is a geometric amplification factor determined via experimental calibration. Assuming \(l=10\) mm with \(L_{\text{cut}}=9\) mm, \(t=0.1\) mm, and \(\
u=0.4\) yields. The critical strain for uniaxial buckling is given by:  
\[\epsilon_{c}=\frac{\pi^{2}}{12(1-\
u^{2})}\bigg{(}\frac{t}{L_{\text{cut}}} \bigg{)}^{2} \tag{2}\]  
Substituting values with an initial geometric factor \(k=1.0\) yields: \(\epsilon_{c}\approx 1.207\times 10^{-4}\) and \(h\approx 4.21\) mm. Experimental validation across six measured locations (Figure 3b) evaluated via ImageJ (version 1.54 g) for samples with \(l=10\) mm, \(L_{\text{cut}}=9\) mm, yielded average pop-up heights of 8.99 mm for triangular cuts and 7.85 mm for trapezoidal cuts.  
To adjust the theoretical predictions with experimental data, the factor \(k\) was calibrated to 2.14 for triangular patterns and 1.87 for trapezoidal ones. This yielded refined predictions of \(h\approx 9.00\) mm for triangular and \(h\approx 7.87\) mm for trapezoidal geometries. These elevated \(k\) values reflect the additional geometric amplification introduced by kirigami patterns, which lead to more complex tent-like deformations beyond simple Euler buckling, with sharper triangular cuts exhibiting greater vertical displacement. For further comparison, when \(L_{\mathrm{cut}}=6\) mm, the critical buckling strain increases to \(\epsilon_{c}\approx 2.72\times 10^{-4}\), and using the previously calibrated \(k=2.14\), the predicted pop-up height becomes approximately 5.61 mm for triangular and 4.58 mm for trapezoidal cuts. This prediction corresponds closely with experimental results for triangular and trapezoidal patterns as observed in Figure 3a, which further confirms the robustness of the model in capturing the influence of cut length and geometry on vertical deformation.
In this paper, we conduct a systematic literature review with the aim of contributing to the field of blockchain-based reputation systems by providing an overview of the existing proposals for offering trust to e-commerce users. It should be noted that this work is directed at user reputation, because we consider product reputation to be a distinct problem that is independent of user reputation, despite complementing the trust given to a buyer.  
Our work focuses on finding the existing blockchain-based models that define the reputation calculation process and data storage so that they can provide fairness and transparency to reputation scores. Thus, we formulate the following question: _\"What blockchain-based reputation systems exist to determine user reputation in e-commerce?\"_  
In order to answer this question, as mentioned above, we conduct a systematic literature review based on three databases, following the PRISMA methodology [3], which we discuss in this paper, and we conclude that reputation systems have been effectively studied over a long period of time.  
The proposed systems found in the literature are based on models that involve several data sources, such as personal data, social interactions, commercial transactions, and feedback given by other users. As this involves sensitive data, risk mitigation techniques are required, such as cryptography and protection of the users' private data. Moreover, the model on which the calculation of users' reputations is based should be immune to possible attacks and frauds that misrepresent the results of the calculations.  
Despite the reliability of the results provided by the proposed models, to determine the reputation score of a user, in general, they are based on a trusted third party (TTP), which does not give process the necessary transparency. In the recent literature, the authors have proposed blockchain-based systems to overcome the lack of transparency of the reputation score given to a user or product by these TTP-based systems. The blockchain approach, since it is an approach based on a distributed ledger, grants the immutability of the data and transparency about the reputation scores. However, the blockchain is not a silver bullet solution, since it can introduce new types of attack, such as a 51% attack, as well as performance issues and user privacy.  
Next, we present some very basic concepts that introduce a non-expert reader into the e-commerce field, providing support to fully understand the remaining sections. In Section 3, we discuss the methodology applied to our systematic review, as well as how it was applied in our work, enabling us to select the most relevant literature to be discussed in Section 4. We conclude the paper by presenting our conclusions and pointing out the future direction of our work.
## 2 Literature Review
### State Representations  
As the key components of state, the vehicle-related features were encoded in a point-based mode, a grid-based mode, or a block-based mode (see Table 1). The point-based mode utilized conventional point detectors, such as inductive loop, to collect features like gap time or occupancy time. The grid-based mode divided roadway space into many grids. Each grid was several meters in length to contain at most one vehicle. The features collected in a grid included vehicle's presence, speed, and so forth. Quite often, the grid-based mode was used in combination with convolutional neural networks. The block-based mode divided roadway space into a few blocks. Each block was tens of meters in length to contain multiple vehicles. The features collected in a block included vehicles' count, lengths, speeds, wait times, and so forth. Note that literature [15; 25; 38] combined the grid-based and block-based modes to construct hybrid state features.  
Basically, the point-based mode failed to capture rich spatial information about vehicles. The grid-based mode saved a lot of effort for feature engineering, whereas the block-based mode required useful features to be handcrafted. Compared with the block-based mode, the grid-based mode led to more informative state representation but made training more difficult.
# A Novel Cross-Correlation Algorithm Based on the Differential for Target Detection of Passive Radar
## 3 Result
### Measured Data, Measured Results, and Analysis  
For the measurement of data, audio signals from China's digital TV signal and FM broadcast signal were adopted. The echo signal and reference signal in the measured data were received by the echo antenna and the reference antenna, respectively, and the reference antenna points to the direction of the radiation source.
# Recent Developments in the Field of the Metal-Insulator Transition in Two Dimensions
## 1 Introduction  
The zero-magnetic-field metal-insulator transition (MIT) was discovered in a strongly-interacting two-dimensional (2D) electron system in silicon metal-oxide-semiconductor field-effect transistors (MOSFETs) [1; 2; 3] and later reported in a wide variety of 2D electron and hole systems: \(p\)-type SiGe heterostructures, \(p\)- and \(n\)-type GaAs/AlGaAs heterostructures, AlAs heterostructures, ZnO-related heterostructures, etc. (for reviews, see [4; 5; 6; 7]). The strongest drop of the resistance on the metallic side of the transition (up to a factor of 12) at sub-Kelvin temperatures was reported in 2D systems in SiGe/Si/SiGe quantum wells [8]; in spite of lower disorder, the drop of the resistance in GaAs-based structures never exceeded a factor of about three. This discrepancy has been attributed particularly to the fact that electrons in silicon-based structures have two almost degenerate valleys, which further enhances the correlation effects [9; 10].  
Here, we briefly review the experimental data obtained in different strongly-correlated 2D electron and hole systems in zero and non-zero magnetic fields on both sides of the metal-insulator transition; the term strongly correlated means that the interaction parameter given by the ratio of the Coulomb and Fermi energies, \(r_{\rm s}=g_{\rm v}/(\pi n_{\rm s})^{1/2}a_{\rm B}\), exceeds \(r_{\rm s}\sim 10\) (here, \(g_{\rm v}=2\) is the valley degeneracy, \(n_{\rm s}\) is the areal density of carriers, and \(a_{\rm B}\) is the effective Bohr radius in the semiconductor). Then, we will discuss the behavior of the effective electron mass and the Lande \(g\)-factor on the metallic side of the transition. Finally, transport evidence for the formation of a quantum electron solid on the insulating side of the transition will be reviewed.