esa-sceva/satcom-chunk-collection huggingface dataset:
# Hard Fusion Based Spectrum Sensing over Mobile Fading Channels in Cognitive Vehicular Networks
## 6 Conclusions  
In this paper, we have researched the application of cognitive radio technique to vehicular environments for the purpose of improving the reliability for vehicular communications. For this, we evaluated the detection performance of spectrum sensing in mobile vehicular environments. Simulation results demonstrate that temporal correlation cannot be neglected and be one of the considerable factors that may impact the sensing performance. In particular, high mobility of the vehicles provides the opportunity to exploit temporal diversity at each vehicle. In spite of the fact that cooperative sensing has normally been considered as a viable means of producing better detection performance by making the most of spatial diversity among vehicles, we do believe that due to high mobility, temporal diversity may be used in preference over vehicles' cooperation in the future.  
This work was supported by the National Natural Science Foundation of China (NSFC) project (No. 61271245 and No. 61731017). The authors would like to thank the anonymous reviewers for their insightful comments and suggestions, which helped significantly improve the quality of this paper. [PERSON] designed the system model, performed performance evaluations by theoretical analysis and simulation, and prepared the manuscript. [PERSON] conceived the system model, carried out theoretical analysis and supervised the activities. [PERSON] and [PERSON] discussed the main ideas and analyzed the results together. All authors approved the publication. The authors declare no conflict of interest.
## 4 Influences of Slag on the Growth of Microalgae
### Influences of Ca in slag on the Growth and Accumulation of Metabolites of Microalgae  
There was a lot of Ca in slag and it existed in the form of CaO. After dissolving in seawater, the final content of Ca in slag was from 400 mg/L to 1400 mg/L. Ca was one of the necessary nutrients for microalgae's growth, which played an important role in the process of microalgae's metabolism, such as being involved in signal transduction and ion channels, as well as many cell metabolic activities. Ca was the main component of cell membranes. It could improve the contents of chlorophyll and protein. Ca had an important impact on the formation and transformation of carbohydrates [70].  
[PERSON] [75] and [PERSON] et al. [76] found that Ca had a negative impact on the accumulation of _Chlorella_ sp.'s biomass. [PERSON] et al. [49] studied the influences of electric-arc-furnace slag with two different technologies on _Chlorella_ sp. Metallic element was leached from slag by hydrochloride and was filtered by 0.45 \(\upmu\)m pore filters to remove slag particles. Thus, a slag leaching solution was attained. After using every type of leaching solution to deal with _Chlorella_ sp. for a week, a quantitative detection was conducted through a blood cell counter technique to test _Chlorella_ sp.'s growth. When more than 30% of slag leaching solution was added into the culture, _Chlorella_ sp.'s growth rate was 150%. There was no slag leaching solution in the control group and _Chlorella_ sp.'s growth rate was 100%. Onthe contrary, the addition of slag extract into the culture did not directly promote _Chlorella_ sp.'s proliferation. Ca in slag leaching solution increased the concentration of CO\({}_{2}\), which promoted _Chlorella_ sp.'s photosynthesis rate and proliferation rate. [PERSON] et al. [60] studied effects of Ca on the growth and lipid accumulation of _Desmodesmus_ sp. WC08. The biomass concentration, lipid content and lipid production rate of _Desmodesmus_ sp. WC08 were tested when the Ca concentration was 0 mmol/L, 0.05 mmol/L, 0.20 mmol/L, 0.60 mmol/L and 1.00 mmol/L. Research findings showed that when Ca concentration was 1.00 mmol/L, the biomass concentration of _Desmodesmus_ sp. WC08 reached the highest value (2.96 \(\pm\) 0.16 g/L), which was 1.28 times as much as that of the blank group. When the Ca concentration was 0.20 mmol/L, the lipid content of _Desmodesmus_ sp. WC08 reached the highest value (30.22%), after which the lipid content was 0.05 mmol/L, slightly lower than 30%. When the Ca concentration was 1.00 mmol/L, the lipid production rate of _Desmodesmus_ sp. WC08 reached the highest value (0.87 g/L). Taking costs and the influence of medium precipitation caused by the high concentration of Ca into account, the 0.05 mmol/L Ca concentration was helpful to the accumulation of biomass and lipids of _Desmodesmus_ sp. WC08, under the condition in which the biomass concentration was 2.85 \(\pm\) 0.25 g/L and the lipid production rate was 0.85 g/L. The results were inconsistent with the research on _Chlorella_ sp. of Ge [75], which might be caused by algae species.
# Calibration of a Rotating or Revolving Platform with a LiDAR Sensor
## 4 Evaluation  
The optimisation quality probably depends on the arrangement of the calibration targets. There are two quantities that could affect the calibration result: (1) is the distance between the targets and the LiDAR and (2) the angle between the direct lines corresponding to the pairs of spheres.  
The distances \(||L_{02}||\), \(||L_{13}||\), \(||L^{\prime}{}_{02}||\) and \(||L^{\prime}{}_{13}||\) in Figure 5 have a direct influence on the _sensitivity_ of the objective function for the orientation optimisation. The sensitivity of a function is the magnitude of the change of its response when its arguments are changed, that is, how steep the gradient is, relative to the change in a certain argument. Thus, the sensitivity has an influence on the optimisation behavior and should be high. For the distance, this means that the sensitivity with respect to \(\phi\) is of interest:  
\[E_{ij}(R) =||\vec{d}_{i}\times(-\vec{d}_{j})||=||\vec{d}_{i}||*||-\vec{d}_{ j}||*\sin(\phi),\ \ \ \phi=\sphericalangle(\vec{d}_{i},(-\vec{d}_{j})),\] \[\frac{dE_{ij}(R)}{d\phi} =||\vec{d}_{i}||*||-\vec{d}_{j}||*cos(\phi). \tag{12}\]  
The derivative of \(E_{ij}(R)\), with respect to the angle \(\phi\), in Equation (12) is scaled by the squared distance between the spheres of a pair. Therefore, we would expect that a larger distance between the platform and the targets is beneficial for the optimisation results.  
The influence of the angles \(\omega\) and \(\omega^{\prime}\), however, does not directly affect the objective function. However, it affects the sensitivity of the coupling between two target pairs. If a change of the extrinsic parameters of the LiDAR results in a rotation of angle \(\alpha\) around the direct line \(L_{ij}\) between a certain pair of spheres, this does not change the residual contributed by this particular pair. Moreover, this rotation would increase the residual contributed by the other pairs. How strong this contribution gets is defined by the orthogonal portion \(r_{kl}=||L_{kl}||\sin(\omega)\), from one direct line \(L_{ij}\) to another \(L_{kl}\). Rotating around \(L_{ij}\) would displace the spheres centers defining \(L_{kl}\). The magnitude of the displacement can be described as an chord of length \(l\):  
\[l =2r_{kl}\sin(\alpha)=2(||L_{kl}||\sin(\omega))\sin(\alpha),\] \[\frac{dl}{d\alpha} =2(||L_{kl}||\sin(\omega))\cos(\alpha). \tag{13}\]  
The derivative, with respect to \(\alpha\) in Equation (13), suggests that the coupling of two target pairs is more sensitive if \(\omega\approx\pi\). Therefore, better optimisation results are expected if the direct lines between the pairs of spheres are orthogonal.  
Further investigations about these expectations will be done in the following sections. To evaluate the calibration quality, a measure to rate recorded point clouds will be introduced in Section 4.1. The experiments in Section 4.2 will assess the calibration qualities of different target arrangements and calibration configurations followed by a discussion of the results in Section 4.3.
# A New Approach to Preform Design in Metal Forging Processes Based on the Convolution Neural Network
## 2 Preform Design Methodology
### Training Database Definition  
A training database consisting of the forging shapes and the corresponding preform shapes should be constructed to avoid user bias. According to the preform shape, in the training database, satisfying the objective design criterion, the weight values of the filters can be set to meet the considered criterion. In this study, both the forging and preform shapes for the training database were parametrically designed to avoid any bias. The design criterion was defined as the reduction in the forging load and the avoidance of metal flow overlapping and die unfilling.  
Regarding the forging shape, a combination of four levels (0.24, 0.36, 0.48, and 0.6 D) at four heights (H1, H2, H3, and H4) was utilized to design all 96 geometries with the same outer diameter D, as shown in Figure 5a. All cases had similar volumes in the \(-2\%\leq V\leq 2\%\) range. An R = 5 mm filet radius was used to connect the four regions for all cases. An example shape is shown in Figure 5b.  
To define the preform shape geometries, three levels (0.3, 0.4, and 0.5 D) were considered for five heights from H1 to H5. A B-spline function was utilized to generate 240 preform geometries, as shown in Figure 6a. H6 was changed to match the volume of the forging shapes. Two examples of preform shapes are shown in Figure 6b. The parametric design procedure was implemented in SolidWorks 2016 (Dassault Systemes SolidWorks Corporation, Waltham, MA, USA) CAD software. To reduce the computation  
Figure 4: The preform design step during the implementation of the CNN algorithm.  
time, the forging shapes were considered as axisymmetric with a flat surface, and the preform shapes were presented using B-spline in axisymmetric shapes.  
Among the 240 preform shapes, one shape satisfying the defined design rules was selected as the preform shape of the corresponding forging shape based on the FEM simulation results. The FEM simulations were implemented in the DEFORM 2D Version 11.0 (Scientific Forming Technologies Corporation, Columbus, OH, USA) software, as summarized in Appendix B. The preform billet is defined as a deformable plastic considering AA6061 aluminum alloy using the Ludwick-type flow stress in Equation (6), calibrated by tensile test experiments on ASTM-E8 specimens at room temperature.  
\[\overline{\sigma}=225.43\overline{e}^{0.257}+144.79\ [\text{MPa}] \tag{6}\]  
Although the AA6061 aluminum alloy was utilized in the implemented FEM model, the proposed procedure is independent of the material, as will be shown in the results section. Because the objective of the FEM investigation is to compare the forging load reduction and the plastic flow of the preform, and not a detailed investigation of the stress distribution, all simulations were implemented under cold and isothermal forging conditions.
# Use of Wearable Sensor Technology in Gait, Balance, and Range of Motion Analysis
## 4 Design Issues in Gait, Balance and RoM Wearable Systems
### Noise  
Noise is irregular fluctuations within the signal monitored. If noise is not filtered out, the results attained may be inaccurate. Wearable sensor noise is generated by the electrical and mechanical components. Common considerations to reduce noise in a system using accelerometers are cables and shield [47]. Most wearable sensors are wireless, eliminating cable noise. Modern wearable devices shield the sensors embedded in the device to protect them from noises produced by external signals. Sometimes these techniques are not sufficient and the measured signal contains measurement errors. In this case, noise is known to be the high-frequency portion of the measurement errors and thresholds and filtering techniques are used to clean the signals extracted from the sensors [34; 44; 45; 54; 46].
# Supporting Privacy of Computations in Mobile Big Data Systems +
## Complexity
### Communication Cost to Compute One Garbled Table Entry  
In general, a 1-out-of-2 OT [15] exchange between two parties involves the exchange of: (i) a random element \(C\) from the prime order subgroup, \(G\), of \(Z_{p}^{*}\); (ii) a public key, \(PK_{0}\); and (iii) the encryptions, \(E_{0},\ E_{1}\), of the plaintext messages \(M_{0},\ M_{1}\). Let \(k\) denote the security parameter, which equals the size of the plaintext messages, \(M_{0},\ M_{1}\). Let \(s_{1:2}\) denote the total number of bits that are exchanged during a 1-out-of-2 OT. Then, \(s_{1:2}=|C|+|PK_{0}|+|E_{0}|+|E_{1}|=|p|+|p|+(|p|+k)+(|p|+k)=4|p|+2k\).  
A 1-out-of-4 OT [16] exchange between two parties includes: (i) two 1-out-of-2 OTs, and (ii) four encryptions, \(E_{00}\), \(E_{01}\), \(E_{10}\), \(E_{11}\). Let \(s_{1:4}\) denote the total number of bits exchanged during a 1-out-of-4 OT. Then, \(s_{1:4}=2(s_{1:2})+4k=8(|p|+k)\); here \(|p|\) and \(k\) are public and symmetric key security parameters, respectively. For example, \(|p|=3072\) achieves the equivalent of \(k=128\)-bit security [17]; in this case, the sum of the sizes of all messages exchanged during a 1-out-of-4 OT is \(s_{1:4}=3200\) bytes.  
Figure 3: Number of gates in the circuit that computes one garbled table entry as a function of \(n\).  
For each \(AND\) gate in the circuit \(B^{\prime}\), each pair of servers, \((p_{i},p_{j}),1\leq i<j\leq n\), engage in a 1-out-of-4 OT, and there are a total of \(n(n-1)/2\) combinations of \((p_{i},p_{j})\). The total number of 1-out-of-4 OTs is at most \(t_{1:4}=(nk+1)\times n(n-1)/2\) since the number of \(AND\) gates in the circuit \(B^{\prime}\) is at most \((nk+1)\).  
To create the desired garbled table entry, \(A_{ab}\), at the completion of the secure multiparty computation protocol of Goldreich, each server, \(p_{i}\)\((1\leq i\leq n)\), sends its share \((A_{ab})_{i}\) to another server, \(p_{c}\), which receives a total of \(s^{\star}=n(nk+1)\) bits from the other \(n\) servers since \(|(A_{ab})_{i}|=nk+1\).  
To summarize, in order to create one garbled table entry, \(A_{ab}\), the total amount of network traffic, \(T=(t_{1:4}\times s_{1:4})+s^{\star}=(nk+1)[(4(|p|+k)\times n(n-1))+n]\). The network traffic is a cubic function of \(n\) when the security parameters, \(k\) and \(|p|\), are fixed.  
Figure 4 shows the network traffic to create one garbled table entry as a function of \(n\). For example, when \(n=5\), the cloud servers exchange 19.56 MB of data in order to create a single entry in the garbled table.  
In the process of creating the garbled circuit, \(GC\), the total amount of network traffic equals \(4N_{g}\times T\), where \(N_{g}\) denotes the total number of gates in the circuit \(B\) that corresponds to the desired computation.
## 7 Results and Discussions
### Verification of the Solution  
With respect to the same parameters, the present solution for a pile head's complex stiffness is reduced to compare with the existing solution of [PERSON] et al. [29] by setting \(\zeta^{\mathrm{s}}\to 1\). Figure 3 shows that the present solution for longitudinal impedance with different values of pile length \(H\) is in very good agreement with that derived by [PERSON] et al. [29]. Moreover, the material damping of the present solution is viscous-type, which differs from the hysteretic-type damping used for the solution of [PERSON] et al. [22]. For convenience, the effect of material damping is not considered in the following comparison. The present solution is reduced to compare with the existing solution of [PERSON] et al. [22] by setting \(c_{j}^{\mathrm{s}}\to 0\) (\(j\)=1, 2,..., \(m\)) and \(v^{\mathrm{P}}\to 0\). It is illustrated in Figure 4 that the obtained solution with a different pile length agrees well with the existing solution achieved by [PERSON] et al. [22]. Hence, the accuracy of the present solution can be validated with these independent comparisons.  
Figure 3: Comparison between the present solution (\(\zeta^{\mathrm{s}}\to 1\)) and the solution of [PERSON] et al. [29]: (**a**) true stiffness; (**b**) equivalent damping.
## 3 Approach for Reliable Data Collection in IoT-Based Smart Farming
### Failure Recovery Mechanism  
FRM infers data to replace outliers without losing significance in data, aiming to achieve reliability and, consequently, accurate models in IoT-based applications. Figure 2 depicts inputs and outputs in FRM. From a high-abstraction level, FRM operates as follows. It receives the dataset with the outliers tagged. Then, it removes and replaces them with data inferred by interpolation techniques. Finally, it delivers the corrected data to applications in the Things-Fog-Cloud continuum for further processing. These applications will be reliable regarding data due to the FDM and FRM operation.  
FRM infers data to replace outliers by considering three algorithms: Cubic Spline, Linear, and Nearest Neighbor. Cubic Spline is an interpolation method that returns the straight line connecting data points with a polynomial function to obtain a continuous and smooth curve [47]. Linear interpolation is a curve fitting method using linear polynomials to construct new data points within the range of a discrete set of known data points [48]. Nearest Neighbor interpolation is a proximal interpolation method of multivariate interpolation in one or more dimensions used in image processing [49]. The Nearest Neighbor algorithm takes a rounded value of the expected position and finds the closest data value at the integer position [50].
### Motivation and Contribution  
In the era of Big Data, where a vast amount of data is produced on a daily basis, there is the necessity for the implementation of new tools or platforms that are able to facilitate the selective collection, filtering, and processing of this information so useful information can be extracted and used in different fields of the market. One of these fields is the media industry. Journalism is closely related to the retrieval, evaluation, and filtering of raw information, as well as, to the combination of knowledge retrieved from multiple sources and the publishing of news stories that are based on these data. Thus, Web 3.0 applications that are able to facilitate the above journalistic practices are of great significance and comply with the transition towards Journalism 3.0.  
In this work, we present EarthPress, an interactive web-platform whose scope is to facilitate journalists and editors in synthesizing news articles about disasters. The platform combines various methods from different scientific fields, such as information retrieval and artificial intelligence and, more specifically, natural language processing and image processing. EarthPress services allow its users to have access to multimedia data (e.g., documents, videos, images, etc.) from multiple sources regarding disasters such as floods and fires. Moreover, the EarthPress platform detects automatically, and in real-time, breaking news related to disasters. The latter is achieved by monitoring publications in social media. The detected breaking news is presented to journalists through the platform's interactive user interface (UI). Additionally, the platform consists of models that are able to retrieve, process, and extract useful information and statistics from EO data, which can be used by editors and journalists in order to enrich the content of their publications. Finally,the platform provides services that can combine all the above and generate automatic ready-to-print news articles, considering also the writing style of the journalist.  
In summary, the platform can facilitate the following journalistic practices:  
* Data collection: raw data need to be available (search for data on the web).
* Data filtering: the process of filtering relevant information from the news story.
* Data visualization: the process of transforming data and creating visualizations to help readers understand the meaning behind the data.
* Story generation (publishing): process of creating the story and attaching data and visuals to the story.
# Research on Key Parameters and Engineering Experiments of Coal Gangue Slurry Filling Technology
## 4 Industrial-Grade Circular Pipe Conveying Test of Gangue Slurry
### Experimental Analysis of Long-Distance Pipeline Transportation
#### 4.3.1 The Relationship Between the Pumping Flow Rate and Conveying Resistance
By substituting the data in Table 2 into Equation (2) and after calculating and taking the average value, it can be seen that when the pumping flow rate was 80 m\({}^{3}\), \(l_{e0}\) was 48.01 m and \(l_{e1}\) was 29.76 m. When the pumping flow rate was 100 m\({}^{3}\), \(l_{e0}\) was 54.64 m and \(l_{e1}\) was 33.74 m. When the pumping flow rate was 120 m\({}^{3}\), \(l_{e0}\) was 62.15 m and \(l_{e1}\) was 38.11 m.  
Combining the above calculation with Table 2, the relationship between the pumping flow rate and conveying resistance was obtained under two conditions: considering local resistance and not considering local resistance, as shown in Figure 7. As shown in the figure, as the pumping flow rate increased, the conveying resistance also increased, and the influence of the bent pipe sections on the conveying resistance also increased; when the pumping flow rate was the same, the measured and calculated conveying resistances were higher than the actual designating resistance, and as the pumping flow rate increased, the difference rate between the estimated and actual communicating resistance increased. Compared with the case of not considering local frictional losses, the fitting degree of the pumping flow and conveying resistance obtained by considering local frictional losses was higher.
## 2 The Microphone Model and Proposed Nested Microphone Array
### The Proposed Uniform Circular Nested Microphone Array
The multirate sampling by the use of up-samplers and down-samplers is implemented to provide the frequency bands. As shown in Figure 3a, the analysis filter bank \(H_{i}(z)\) and down-sampler \(D_{i}\) are realized as a multi-level tree structure. Each stage of the tree requires a high-pass filter (HPF) \(HP_{i}(z)\), a low-pass filter (LPF) \(LP_{i}(z)\), and a down-sampler \(D_{i}\) (for the analysis filter bank) \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Band** & **Bandwidth** & **Analysis Filter Bank** & \(f_{\epsilon}\) & \(d_{\rm{lim}}\) \\ \hline \multirow{4}{*}{1} & \multirow{4}{*}{B1 = [3900–7800] Hz} & B1,1 = [6825–7800] Hz & & \\  & & B1,2 = [5850–6825] Hz & & \\  & & B1,3 = [4875–5850] Hz & & \\  & & B1,4 = [3900–4875] Hz & & \\
2 & B2 = [1950–3900] Hz & B2,1 = [2925–3900] Hz & & \\  & & B2,2 = [1950–2925] Hz & & \\
3 & B3 = [975–1950] Hz & B3,1 = [1425–1950] Hz & & \\  & & B3,2 = [975–1425] Hz & & \\
4 & B4 = [50–975] Hz & B4,1 = [512–975] Hz & & \\  & & B4,2 = [50–512] Hz & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The information to design the proposed uniform CNMA. Figure 2: The proposed uniform CNMA and allocated microphones for each subarray. or up-sampler \(D_{i}\) (for the synthesis filter bank). The relation between the analysis filter bank \(H_{i}(z)\), the LPEs, and HPFs in the tree structure is expressed as:  
\[ \begin{array}{l}H_{1}(z)=HP_{1}(z)\\ H_{2}(z)=LP_{1}(z)HP_{2}(z^{2})\\ H_{3}(z)=LP_{1}(z)LP_{2}(z^{2})HP_{3}(z^{4})\\ H_{4}(z)=LP_{1}(z)LP_{2}(z^{2})LP_{3}(z^{4}).\end{array}  \tag{3}\]  
The synthesis filters \(G_{i}(z)\) are the mirror image of analysis filters \(H_{i}(z)\), which are implemented by the tree structure as seen in Figure 3b. In each level of the tree, a 52-tap finite impulse response (FIR) LPF and HPF are implemented by the Remez method. The parallel filters have a stop-band attenuation of 50 dB and a transition band 0.0575. Figure 4 shows the frequency response for the analysis filter banks. Figure 4: The frequency response for the analysis filter banks. Figure 3: The tree structure for (**a**) analysis and (**b**) synthesis filters in CNMA.