esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey on 5G Coverage Improvement Techniques: Issues and Future Challenges
## 3 5G Coverage Enhancement Techniques
### Non Orthogonal Multiple Access  
To enhance the coverage and capacity, the cellular networks use many access techniques, such as frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and orthogonal frequency division multiple access (OFDMA). The concept of orthogonality is used in these accessing techniques to reduce the interference between users, but it also reduces the number of users multiplexed to access the spectrum.  
Therefore, the concept of NOMA has been proposed in 3 GPP long-term evaluation release 15 as the latest multiple access technology, and it is also implemented in 5G [112]. Initially, NOMA was used with a single cell to improve spectral efficiency. The spectral efficiency is increased by increasing the number of multiplexed users accessing the single channel with different channel gains [113]. NOMA is basically divided into two types: power domain NOMA (PDNOMA) and code domain NOMA (CDNOMA). In PDNOMA, multiplexing is based on the transmitted power, and in CDNOMA, multiplexing is based on code [114; 115; 116]. Along with CDNOMA and PDNOMA, there are some other NOMA techniques such as signature NOMA (S-NOMA) and compressing-based NOMA (CS-NOMA) [117]. NOMA works with MIMO, cognitive radio (CR), HetNets, millimetre waves, mobile edge computing, visible light communication, vehicles for any communication, etc. This will provide high spectral efficiency, high data rates, and massive connectivity while decreasing inter-cell and intra-cell interference [118]. In NOMA, fairness in power allocation is obtained by assigning less power to strong channel gain users and more power to weak users. This power efficiency will increase the overall throughput and efficiency. NOMA can be implemented with a single cell or with multiple cells. The authors in Ref. [119] implemented the NOMA on the multi-cell and identified some issues that occurred due to the multi-cell NOMA.  
By utilising the idea of high- and low-gain users, NOMA increases the throughput and spectrum efficiency by eliminating interference. It is called successive interference cancellation (SIC). A SIC receiver is one of the most important blocks in NOMA transmission, which is shown in Figure 12[114]. The quality detection of the strongest user who has a strong signal is identified by subtracting the other user's signal. Similarly, the weak user detects its own signal by subtracting the others' signal, assuming noise or interference, which will improve the system's performance by minimising the propagation effects. However, while implementing the SIC in NOMA, security issues will arise. These issues are classified as resource allocation issues, signalling issues, and security issues, which are discussed in the following subsections.
## 3 Results
### Identification of Dominant LAB in Pecorino di Farindola Cheese  
Predominant LAB involved in the maturation of Pecorino di Farindola cheese were isolated from each growth medium at different maturation times, obtaining 206 isolates. Although the exact load of each isolate in the sample of origin could not be determined, its order of magnitude can be estimated from the count on the respective isolation medium. Isolate identification based on 16S rRNA gene sequences is jointly reported in Table 1 with  
Figure 1: Evolution of LAB in nine lots of Pecorino di Farindola cheese from three manufacturers (Dairy 1, 2 and 3). source and time of isolation. Isolates of the same species are listed separately according to the detected genes. In milk, curd and cheese in early ripening, the dominant microorganisms present in all three dairies, although not detected in all lots, were _Lactococcus lactis_ subsp. _lactis_ and _Leuconstoc mesenteroides_. In particular, _L. lactic_ subsp. _lactis_ with the nisin structural gene _nis_A was predominant for five lots from two dairies. The heteroformatterative species _Lveliactobacillus brevis_ was the only microorganism isolated from all cheese lots and generally retrieved until advanced maturation, while other LAB species predominated according to the dairy. The _L. brevis_ isolates did not possess amino acid decarboxylase encoding genes except for an isolate from Dairy 1 in which an ornithine decarboxylase gene, _odc_, was identified. _Lactiplantiobacillus plantarum_ was also isolated from all three dairies but not from all cheese lots. _L. plantarum_ isolates from Dairy 2 were obtained from late ageing cheese, thus indicating their capacity to persist during maturation. The presence of _Lacticaseibacillus paracasei_ was demonstrated only in two dairies. Other species found to predominate in particular maturation phases, i.e., _Loigolactobacillus corporiformis_, _L. pseudomesenteroides_, _L.
# A Multi-Frame PCA-Based Stereo Audio Coding Method
## 4 PCA-Based Parametric Stereo  
The stereo coding method proposed in this paper, as the extension of mono coding method mentioned before, is shown in Figures 12 and 13. The encoder and decoder for stereo audio use the same module of PCA and quantization as mono audio. The differences between mono coding and stereo coding are elaborated in the following sections. In encoder, the two channels' signal carries out MDCT and the two channels' coefficients gather to generate an original matrix to do PCA; then, an improved parametric stereo module is used to downmix and calculate parameters of the high-band. Finally, a module based on PVQ is used for quantizing coefficients of matrix, and so on. In decoder, coefficients of mid downlink matrix and rotation matrix are used to generate mid channel; then, spatial parameters and other information are introduced to restore stereo signals. After inverse MDCT (iMDCT) and filtering, the signal can be regarded as the output signal.
## 4 Analysis of SMA-Compensated Torque Performance
### Friction Torque of the Drives  
The composite transmission device pushes the piston to squeeze the hydraulic fluid via the spring restoring force, so that the friction ring of the input disc squeezes the output housing to produce additional friction torque. A schematic diagram of the hydraulic transmission friction is shown in Figure 16.  
\begin{table}
\begin{tabular}{c c c c} \hline \(A_{\rm S}\)/\({}^{\circ}\)C & \(A_{\rm F}\)/\({}^{\circ}\)C & \(A_{\rm F}\)/\({}^{\circ}\)C & \(G_{\rm A}\)/\({}^{\circ}\)Ga \\ \hline
39.85 & 60.85 & 80.85 & 200 \\ \hline \(M_{\rm S}\)/\({}^{\circ}\)C & \(M_{\rm F}\)/\({}^{\circ}\)C & \(M_{\rm F}\)/\({}^{\circ}\)C & \(G_{\rm M}\)/\({}^{\circ}\)Ga \\ \hline
31.85 & 26.85 & 22.85 & 70 \\ \hline \end{tabular}
\end{table}
Table 4: Material parameters of the shape memory alloy spring (Ni51 Ti49 (at. %)) [21].  
Figure 15: Relationship between SMA spring restoring force and temperature.  
According to [PERSON]'s principle, the fluid pressure thrust can be expressed as  
\[F_{2}+F_{3}=n_{2}\eta_{m}F_{5}\left(\frac{D_{1}^{2}-D_{2}^{2}}{D_{3}^{2}}\right) \tag{15}\]  
where \(D_{1}\) and \(D_{2}\) are the inner diameter and the outer diameter of the input disc ring piston, respectively, \(D_{3}\) is the diameter of the driving piston, \(n_{2}\) is the number of SMA springs, and \(F_{s}\) is the thrust force on the piston, that is, \(F_{s}=F_{r}\). The hydraulic thrust forces \(F_{2}\) and \(F_{3}\) are equal, and \(\eta_{\rm m}\) is the hydraulic transmission efficiency.  
The input disc is extruded and rubbed by the output housing under the action of fluid pressure, and the stress on the unilateral friction ring is  
\[\sigma=\frac{F_{2}}{\pi\left(r_{2}^{2}-r_{1}^{2}\right)} \tag{16}\]  
where \(r_{1}\) and \(r_{2}\) are the inner and outer diameters of the input disc friction ring, respectively.  
The bilateral friction torque \(M_{\rm f}\) generated by the hydraulic thrust is  
\[M_{\rm f}=2\int_{r_{1}}^{r_{2}}\mu r\sigma{\rm d}S=\frac{4\mu F_{2}\left(r_{2 }^{2}+r_{1}r_{2}+r_{1}^{2}\right)}{3(r_{1}+r_{2})} \tag{17}\]  
where \(\mu\) is the friction coefficient.  
We set the friction coefficient \(\mu=0.29\), the number of springs \(n_{2}=6\), and the hydraulic transmission efficiency \(\eta_{\rm m}=0.85\). The piston diameters \(D_{1}\), \(D_{2}\), \(D_{3}\) were 70 mm, 50 mm, and 20 mm, respectively, and the inner diameter \(r_{1}\) and outer diameter \(r_{2}\) of the friction ring were 66 mm and 75 mm, respectively. The SMA spring compensation torque was derived from Equation (17).
# Model Test and Numerical Simulation of Single Pile Response under Combined Loading in Slope
## 3 Test Results Analysis
### Displacement and Horizontal Load  
The curve of load-displacement is shown in Figure 3. Because of the soil pressure load on the slope, the horizontal displacement of 0.19 mm was produced before the pile was subjected to horizontal load. As the lateral load increased to 450 kN, the displacement reached 6.71 mm. The pile's horizontal displacement was small at the beginning of loading. With the growth of the load, the soil around the pile had plastic damage. The horizontal displacement increased significantly.
# Analog Circuit Fault Diagnosis Using a Novel Variant of a Convolutional Neural Network
## 3 The Proposed Method
### Multi-Scale Order Mean Difference Layer  
The mean difference is also called the difference quotient, for discrete functions with equal steps \(y=f(x)\), the \(n(n\geq 1)\) order mean difference at the node \(f(x)x_{i}\), \(x_{i+1},\ldots,x_{i+n}\) is defined as Equation (4).  
\[f[x_{i},x_{i+1},\ldots,x_{i+n}]=\frac{f[x_{i+1},x_{i+2},\ldots,x_{i+n}]-f[x_{i },x_{i+1},\ldots,x_{i+n-1}]}{x_{i+n}-x_{i}}, \tag{4}\]  
The mean difference data of different order can effectively characterize the average rate of change of the circuit response signal at different scales in the time domain. The calculation operation of the mean difference is simple, and the model complexity and calculation cost are low. Not only that, for the actual measured circuit time domain response signal, the mean difference data can filter out high-frequency disturbance and random noise to a certain extent. Therefore, this paper designs a multi-scale mean difference layer as the first layer of the convolutional neural network, and calculates the 0 th order, 1 st order and 2 nd order mean difference sequences to extract the average rate of change feature to facilitate subsequent deep-level feature extraction. The 0 th order, 1 st order and 2 nd order mean difference sequences are shown in Table 1.  
As shown in Table 1, the 0 th mean difference is the original time domain response signal of the circuit. In addition, it is easy to see from the table that the lengths of the 0 th order, 1 st order, and 2 nd order mean difference are different. The length is N, and the lengths of the 1 st order and 2 nd order mean difference sequences are N-1 and N-2, respectively. In order to facilitate the design of CNN network, the length of the 1 st order, and 2 nd order mean difference is the same as that of the 0 th order mean difference by means of zeroing.
# Intelligent Reflecting Surface Aided Wireless Systems with Imperfect Hardware
## 3 Performance Analysis
### Outage Probability for RIS-DH Scheme  
The outage probability of the system is written as follows:  
\[P_{DH,out}=\Pr(\gamma_{D}<\gamma_{th}), \tag{8}\]  
where \(\gamma_{th}\) denotes the target rate. With the help of (5), we can rewrite (8) as follows.  
\[\begin{split}& P_{DH,out}=\Pr\biggl{(}\eta<\frac{\gamma_{th} \varpi_{3}}{\varpi_{1}-\gamma_{th}\varpi_{2}}\biggr{)}\\ &=F_{\eta}\biggl{(}\frac{\gamma_{th}\varpi_{3}}{\varpi_{1}- \gamma_{th}\varpi_{2}}\biggr{)}.\end{split} \tag{9}\]
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 4 Simulation Results
### Communication Quality Performance Evaluation
#### 4.3.2 Analysis of the Impact of Different Velocities on the Average Network Throughput  
Figure 11 shows that different mobile velocities have different effects on the average network throughput. The change in movement speed affects the dwell time and communication blind zone residence time under the VLC network, and as the movement speed increases, the network dwell time and communication blind zone residence time become shorter accordingly. Since the I-VHO and D-VHO solutions are vertical handover types, the average network throughput shows a decreasing trend, while the CBDHVHO solution focuses on the horizontal handover type. The shorter the communication blind zone residence time, the more horizontal handover there is, so the average network throughput shows an increasing trend. When the mobile speed increases from 0.3 m/s to 0.7 m/s, the average network throughput of the I-VHO scheme decreases from 162.4467 Mbps to 131.5774 Mbps; the D-VHO\({}_{\text{t=0.5}}\) scheme decreases from 162.8908 Mbps to 137.7859 Mbps; the D-VHO\({}_{\text{t=1}}\) scheme decreases from 167.0896 Mbps to 127.5167 Mbps; and the average network throughput of the CBDHVHO scheme increases from 172.4695 Mbps to 231.5928 Mbps. The above data show that the scheme has good applicability for mobile velocity.
# Application of Internet of Things in a Kitchen Fire Prevention System
## 1 Introduction
### Problems to be Solved  
To provide a complete kitchen accident prevention mechanism, problems that must be solved include the following:  
1. Users may forget they have something cooking on the stove when they are busy. Therefore, when a pot or pan boils dry, a sensing element is required to detect the high temperature and issue light or sound alarms to warn residents and neighbors.  
When a pot or pan catches fire because of an overly high temperature or a high alcohol content in food, the fire incident may become more serious if it is not handled appropriately. Therefore, an automatic gas shutoff device is necessary in such a situation.
3. When a household kitchen catches on fire, the owner must report the accident to the management center or emergency units immediately via a communication mechanism. However, if the main entrance door to the house is locked, the optimal time for rescue may be missed. Therefore, the kitchen fire prevention system automatically unlocks the door to enable others to enter the house for rescue operations.
4. When users are unsure whether they have turned off the stove, they can view the live video of the stove by using the remote monitoring system. If the stove is still turned on, a remote control system is required for the users to shut off the gas supply to the stove.
# GPS Time Series Analysis from Aboa the Finnish Antarctic Research Station
## 2 Materials and Methods
### Data Processing
#### 2.3.3 Validation Solution Processing Settings (APPS, GIPSY-OASIS)  
All available observation data at Aboa were also processed using the APPS web-service. The main intent was for validation purposes. APPS solution is based on the GIPSY-OASIS 6.4 software and uses Jet Propulsion Laboratory (JPL) final orbit and clock precise products. The web-interface allowed us to set two parameters: the elevation cut off angle was set to \(10^{\circ}\), whereas the observation weighting scheme was chosen \(srtq(sin)\). The station positions were estimated at 5 min intervals. All other processing parameters and correction models were defined automatically by the service and inaccessible to the user. According to the information on the service webpage, several signal and ground models were also applied by GIPSY, such as GPS yaw attitude, phase windup, GMT troposphere mapping function, a-priori hydrostatic delay as function of the station height above the ellipsoid, a-priori constant value for the wet delay of 10 cm, tropospheric gradients, relativity, pole and solid tides, ocean tides, and second order ionospheric delay. APPS produces several GIPSY and generic output files, including a summary of the PPP processing. APPS provides coordinates in ITRF2014 starting 5 June 2018.
# Metasurface Source Antenna Gain Improvement Using Simple Side Metal Structure
## 2 Design and Analysis of Side Metal Structure for Metasurfaces
### Patch Source with Sidewall Configuration  
The next side metal structure employed in this study is the sidewall, which is made of copper with a thickness of 0.6 mm. As mentioned earlier, the sidewall acts as a reflector, taking advantage of the properties of metal to reflect electromagnetic waves radiated from the antenna. The design of the sidewall requires specifying the focal length, as the height of the sidewall can affect the reflection, beam pattern, and impedance of the electromagnetic waves. Therefore, it is crucial to establish appropriate specifications for optimal performance.  
To determine the focal length, [19] standardized the maximum aperture efficiency based on the ratio (F/D), where F represents the distance from the source antenna to the array, and D is the optimal size of the array. For instance, when a patch antenna with a gain of 10 dB is used as the source antenna for a transmission antenna, the maximum aperture efficiency is approximately 62%, and F/D can be designed to be around 0.2. In this study, the maximum gain of the source antenna using the side plate is 10.83 dB, corresponding to an F/D ratio of 0.25. Consequently, the optimal size (D) of the TA, which will be detailed in Section 3, is determined to be 160 mm, with a focal length of 40 mm. Considering the thickness of the patch antenna's PCB board, the sidewall was designed to have a height of 41 mm.  
The effect of the sidewall on the source antenna's gain is shown in Figure 5, where gains were observed at 9 GHz and 9.61 GHz. Comparing (a) and (b), there was no significant change in gain at these frequencies when only a single SP was applied. However, when both SP 1 and SP 2 were applied, the gain improvement was notably larger with the addition of the sidewall. As a result, the overall gain increased from 6.2 dB to 10.83 dB upon applying the side metal structure to the single patch.  
The gain variation in the patch antenna due to the addition of the SP structure was observed for both cases, with and without the application of the SW. As shown in Figure 6, when the SW was not applied, the gain of the patch antenna improved by 4.25 dB, increasing from 6.2 dB to 10.45 dB with the addition of the SP. In the case where the SW was applied, the gain increased by 2.63 dB, from 8.2 dB to 10.83 dB, with the addition of the SP. This improvement highlights the effectiveness of using side metal structures, including both SPs and the SW, in enhancing the gain of the patch antenna.  
Figure 5: Effect on gain at 9 GHz and 9.61 GHz. (**a**) Single patch without SP. (**b**) Single patch with one SP. (**c**) Single patch with two SPs.
## 2 Materials and Methods
### Selection Equation of Deep Coalbed Directional Hydraulic Splitting Angle
#### 2.1.2 An Angle Selection Equation for the Directional Hydraulic Splitting of Deep Coalbeds
\tag{6}\]  
Substituting Equation (6) into the yield criterion (3), the yield region boundary equation is obtained as:  
\[R=\frac{1}{2\pi}\bigg{(}\frac{K_{\rm I}}{\sigma_{5}}\bigg{)}^{2}\cos^{2}\frac {\theta}{2}\bigg{[}(1-2\mu)^{2}+3\sin^{2}\frac{\theta}{2}\bigg{]} \tag{7}\]  
As the width of the damaged area is perpendicular to the crack when \(\theta\) = 90\({}^{\circ}\), the expression of the width of the damaged area \(R\) is:  
\[R=\frac{1}{4\pi}\bigg{(}\frac{K_{\rm I}}{\sigma_{s}}\bigg{)}^{2}\bigg{[}(1-2\mu )^{2}+\frac{3}{2}\bigg{]} \tag{8}\]  
The evolution of coal damage [25] is the further crack expansion of macroscopic cracks. In the stable expansion stage of cracks, the relationship between crack expansion resistance and crack expansion increases. The relationship between crack expansion resistance and crack expansion is established as follows:  
\[\frac{r^{\prime}}{r}=\left(\frac{H}{a}\right)^{n} \tag{9}\]  
In the relationship between the propagation stress G of the plane crack tip of coal and rock, the stress intensity factors \(K_{\text{I}}\) and \(K_{\text{II}}\) are:  
\[G=\frac{1-v_{0}^{2}}{E_{0}}\left(K_{\text{I}}^{2}+K_{\text{II}}^{2}\right) \tag{10}\]  
Among them, \(K_{\text{I}}=\sigma\sqrt{\pi a}\) and \(K_{\text{II}}=\tau\sqrt{\pi a}\) are substituted, and the expansion stress at the tip of the crack is:  
\[G=\frac{1-v_{0}^{2}}{E_{0}}\pi a\Big{(}\sigma^{2}+\tau^{2}\Big{)} \tag{11}\]  
According to the crack propagation criterion [26], combined using Equations (9) and (11), the length of the crack after expansion can be obtained as:  
\[H=a^{\frac{n}{n-1}}\Bigg{[}\frac{\pi(1-v_{0}^{2})}{E_{0}r}\Big{(}\sigma^{2}+ \tau^{2}\Big{)}\Bigg{]}^{\frac{1}{n-1}} \tag{12}\]  
Combining the above expressions of the width of the damaged area and the length of the crack, we obtain:  
\[\tan\alpha=\frac{R}{H}=\frac{\frac{1}{4\pi}\left(\frac{K_{\text{I}}}{\sigma_{ \text{s}}}\right)^{2}\left[\left(1-2\mu\right)^{2}+\frac{3}{2}\right]}{\text{a }^{\frac{n}{n-1}}\left[\frac{\pi(1-p^{2})}{E_{0}r}(\sigma^{2}+\tau^{2})\right] ^{\frac{1}{n}}} \tag{13}\]  
In the formula: \(K_{\text{I}}\)--stress intensity factor, \(K_{\text{I}}=\sigma\sqrt{\pi a}\), \(\sigma_{\text{s}}\)--principal stress around the borehole, \(\mu\)--[PERSON]'s ratio, and \(E_{0}\)--disturbed modulus. The n-crack expansion resistance growth index generally takes 1/2-3/2, with \(r\)--crack expansion resistance. To obtain the appropriate splitting angle, the coal sample data measured in the laboratory were brought into the formula. Due to the anisotropy of the coal, the parameters measured by each numbered coal sample were sorted and brought into the formula to obtain \(\alpha\). The range is 70.5-112.26\({}^{\circ}\).
# Robust DOA Estimation Using Multi-Scale Fusion Network with Attention Mask
## 5 Experiments and Discussion
### Experiments
#### 5.4.5 Inference Time of Methods  
In Figure 6, the inference time required for DOA estimation of the three methods for a single sample was presented. The inference time is the average time required to perform 100 independent inferences. The software platform includes Python 3.7, Tensorflow 2.4.1, and CUDA 11.0.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Noises** & **Acc5\({}^{\circ}\) (\%)** & **Acc10\({}^{\circ}\) (\%)** & **Acc15\({}^{\circ}\) (\%)** & **MAE (\({}^{\circ}\))** \\ \hline \multirow{3}{*}{CV-CNN} & Gaussian white noise & 94.07 & 99.54 & 99.95 & 2.029 \\  & Diffuse noise & 88.08 & 98.92 & 99.85 & 2.986 \\  & Pink noise & 92.56 & 97.42 & 99.38 & 2.676 \\ \hline \multirow{3}{*}{CVREAN} & Gaussian white noise & 89.21 & 98.06 & 99.68 & 2.690 \\  & Diffuse noise & 80.97 & 97.67 & 99.21 & 3.435 \\  & Pink noise & 85.05 & 97.5 & 99.44 & 3.139 \\ \hline \multirow{3}{*}{MF-AMnet} & Gaussian white noise & 94.07 & 99.54 & 99.95 & 2.029 \\  & Diffuse noise & 88.08 & 98.92 & 99.85 & 2.986 \\ \cline{1-1}  & Pink noise & 92.56 & 97.42 & 99.38 & 2.676 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Performance of MF-AMnet in different types of noise.  
Compared with CV-CNN, CVREAN and MF-AMnet have higher inference time with residual structure. CVREAN has the longest inference time due to its many layers. The proposed MF-AMnet was allowed to capture multi-scale features, but the use of multi-branch and attention mechanism also increases the computation load. Compared with CV-CNN algorithms, the MF-AMnet requires longer inference time to achieve DOA estimation due to the higher complexity of the algorithm itself. Overall, MF-AMnet performs well in the balance of performance and complexity.