esa-sceva/satcom-chunk-collection huggingface dataset:
# Fast Converging Gauss-Seidel Iterative Algorithm for Massive MIMO Systems
## 4 Simulation Results and Analysis
### BER Performance
Figure 6 concerns the fully correlated channel, i.e., \(\zeta_{r}=0.3\), \(\zeta_{t}=0.3\). The BER performances of the GS iterative algorithm, CPGS iterative method, JA-GS detection algorithm, PGS detection method, and CJGS iterative algorithm are compared and analyzed for different antenna configurations with 16\(-\)QAM modulation. As shown in Figure 6, the detection capability of all the algorithms becomes better with more repetitions. From Figure 6a, the designed algorithm is shown to perform as well as the performance of MMSE detection at one iteration when the MIMO antenna configuration is \(256\times 64\), while all other iterations require more than five iterations to achieve the same results. If the S/N ratio is 16 dB, the BER achievable via the MMSE detection algorithm is \(7.813\times 10^{-6}\), while the BER of the CJGS iterative algorithm is \(5.208\times 10^{-6}\) after five  
Figure 4: BER performance for related channel in base station side. Figure 5: BER performance for related channels on the user side. iterations. Thus, the CJGS iterative method can achieve a lower BER. From Figure 6b, it can be observed that to achieve the performance of MMSE detection when the MIMO antenna configuration is \(128\times 64\), the CJGS iterative algorithm needs to perform three iterations, while the other four iterative algorithms need more than seven iteration cycles. Figure 6c shows evidence that all algorithms achieve lower BER after five iterations when the MIMO antenna configuration is \(128\times 32\), but the suggested method has superior performance for equal iterations. According to the previous description, near MMSE detection performance is achieved with the proposed method with only a few iterations. The novel scheme outperforms the other four iterative algorithms. With a reduction in the quantity of the received side and an increase in the sending side, the ratio between them decreases. The proposed algorithm still shows a reliable performance, which can be well adapted to both channel-uncorrelated and channel-correlated scenarios. As shown in Figure 7, baseline1, baseline2, and baseline3 are the initial values of schemes 1 and 2 and the zero vector mentioned in Section 3.2, part B, respectively. When the Massive MIMO antenna configuration is \(128\times 32\), the number of iterations is two, and the baseband modulation method is 16-QAM, the CJGS iterative method has superior BER performance and faster convergence when the initial values proposed in scheme 1 are used.
# Phase Balancing and Reactive Power Support Services for Microgrids
## 5 Conclusions  
The proposed MGCC allows concurrent provision of reactive power and phase balancing service by grid tied inverters. The solution requires advanced functionalities by the inverters that are not currently available in commercial inverter. Therefore, an advanced inverter controller is proposed to enable the on purpose asymmetric current injection. Further, a new PQ controller has been proposed and developed to estimate the available capacity for reactive power and negative sequence current according to its real time operating condition in order to allow the proper provision of ancillary services by the new inverter. The proposed inverter with new ancillary services capabilities has been validated using experimental tests in a laboratory scale inverter. Thereafter, a real-time simulation was carried out to investigate the effectiveness of the MGCC during an entire day. The results indicate that the proposed solution achieves significant benefits for the distribution system operator by enhancing the power quality and efficiency and utilization of existing capacity of the distribution grid.  
Conceptualization, E.K., L.H. and A.C.; methodology, E.K., L.H. and A.C.; software, A.C., L.H. and L.Z.; validation, A.C., investigation, A.C., L.H., L.Z., A.D.B. and A.C.T.; writing--original draft preparation, A.C., L.H. and L.Z.; writing--review and editing, E.K., D.T., A.D.B. and A.C.T.; visualization, L.H. and A.C.; supervision, E.K.; project administration, L.H. and A.C.T.; funding acquisition, E.K. and D.T.  
This work is supported by the Cyprus Research Promotion Foundation (RPF, Cyprus, Project KOINA/ERANETMED/1114) through the ERANETMED initiative of Member States, Associated Countries and Mediterranean Pantner Countries (3 DMgrid Project ID enatemed_energy-11-286). The work is also supported in part by the European Union's Horizon 2020 research and innovation programme under grant agreement No 739551 (KIOS CoE) and from the Government of the Republic of Cyprus through the Directorate General for European Programmes, Coordination and Development.  
Figure 11: Reactive power and negative sequence current at the PCC with and without the proposed MGCC.  
**Acknowledgments:** This paper is dedicated to the memory of [PERSON] who has unfortunately pass away on 28 October 2019. He had a great contribution in this paper including the conceptualization of the idea, the formulation of the methodology and the review, editing and supervise the writing of this paper.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Simulation Results  
This section contains the simulation analysis of the proposed schemes. In order to investigate the effect of adding feedback loop in the FIS, a 2 kW heater is considered for a small room, whereas impact of adding a humidity parameter is observed using the 10 kW HVAC in a residential building. The objective of enhancing user comfort was achieved by initializing the set-points according to the temperature and relative humidity. The effect of adding a feedback loop in the FIS for energy consumption minimization is also discussed in this section.
# Arbitrary Sampling Fourier Transform and Its Applications in Magnetic Field Forward Modeling
## 2 Methods
### 3d As-Ft  
A three-dimensional Fourier forward transform and inverse transform are performed on function \(f(x,y,z)\), respectively:  
\[\left\{\begin{array}{l}F(k_{x},k_{y},k_{z})=\int_{-\infty}^{\infty}\int_{- \infty}^{\infty}\int_{-\infty}^{\infty}f(x,y,z)e^{-i(k_{x}x+k_{y}y+k_{z}z)}dxdydz \\ f(x,y,z)=\frac{1}{8\pi^{3}}\int_{-\infty}^{\infty}\int_{-\infty}^{\infty}\int _{-\infty}^{\infty}F(k_{x},k_{y},k_{z})e^{i(k_{x}x+k_{y}y+k_{z}z)}dk_{x}dk_{y }dk_{z}\end{array}\right.. \tag{11}\]  
where \(k_{x}\), \(k_{y}\), and \(k_{z}\) represent the wavenumbers in the directions \(x\), \(y\), and \(z\). \(f(x,y,z)\) is a spatial domain function and \(F(k_{x},k_{y},k_{z})\) is a wavenumber domain function. \(f(x,y,z)\) and \(F(k_{x},k_{y},k_{z})\) are each other's Fourier transform functions.  
For the Fourier transform integral of Equation (11), similarly, since the kernel function is continuous in the integral interval, the triple integral can be converted into three one-dimensional integrals. First, the \(x\)-directional one-dimensional integral is performed, then the \(y\)-directional one-dimensional integral on this basis, and finally the \(z\)-directional one-dimensional integral, to realize the 3D Fourier transform.  
Taking the forward Fourier transform in Equation (11) as an example, first a 1D Fourier transform is performed for the \(x\) direction:  
\[F_{x}(k_{x},y,z)=\int_{-\infty}^{\infty}f(x,y,z)e^{-ik_{x}x}dx. \tag{12}\]  
Then a 1D Fourier transform of the \(y\) direction is performed:  
\[F_{xy}(k_{x},k_{y},z)=\int_{-\infty}^{\infty}F_{x}(k_{x},y,z)e^{-ik_{y}y}dy. \tag{13}\]  
Finally, one-dimensional integration in the \(z\) direction is performed on (13) to complete the Fourier transform in the \(z\) direction:  
\[F(k_{x},k_{y},k_{z})=\int_{-\infty}^{\infty}F_{xy}(k_{x},k_{y},z)e^{-ik_{z}z}dz. \tag{14}\]  
The integrals of Equations (12)-(14) above are solved by a 1D Fourier transform. The process of the three-dimensional inverse transform is the same as the forward transform, and will not be repeated.  
The AS-FT algorithm calculates the Fourier transform coefficients in advance. It can flexibly set the sampling interval according to the distributions of field and spectrum, sparsely, and encrypt the sampling points according to demand. At the same time, the semi-analytical solution of the Fourier oscillation operator can be obtained in the integral interval, which improves the accuracy and efficiency of the Fourier transform.
## 3 System Setup
### Tx/Rx Dsp
#### 3.1.2 Tdma Tx/Rx Dsp  
The offline DSP blocks for the TDMA-based channel aggregation/de-aggregation are shown in Figure 3. At the transmitter DSP side, the complex NR channels are first aggregated by the time interleaving each time sample. The aggregated waveforms in the time domain are upsampled in frequency to the DAC sampling rate and filtered for image rejection. Frequency up conversion is used to obtain a real output signal from the complex baseband signal. The output signal is clipped to the desired clipping level. At the receiver side, the received digital signal from the ADC output shown in Figure 1 is down converted in frequency in order to recover the baseband complex signal, which is then filtered to prevent aliasing. The complex baseband signal is then down sampled in frequency to the original aggregated channels sampling frequency, which is then de-aggregated in time. The equalization and EVM estimation block is the same as the one used for the FDMA scheme shown in Figure 2[15].
# A Feasibility Study of the Use of PZT Actuators for Active Control to Enrich Engine Sound
## 4 Experiment Results and Discussions
### Spectrogram Analysis after ASE  
A real-time control experiment of the proposed ASE system was carried out while the engine was swept from 1000 to 3900 RPM for about 9 s at the neutral mode. The amplitude values in terms of SPL of the three orders were set to have greater SPLs than its original SPLs, but gradually higher as the engine RPM increased.  
Figure 12a,b show the comparison of the spectrograms of the engine sound measured at the microphone before and after ASE. In Figure 12a, it can be observed that there were a number of engine order sounds of the four-cylinder car, and the C2 order was the most dominant. The C4, C6, and C8 orders, which are integer multiples of the C2, were also noticeable, but they were less dominant than the C2 order. Among them, it was found that especially the C6 order had the poorest SPL. In addition, the odd integer orders such as C3 and C5, which are important in terms of sound quality, were not observed clearly.  
In Figure 12b, it can be seen that the ASE system changed the spectrogram dramatically with the three orders becoming more distinct as their SPLs were significantly enriched. The C6 order became the most dominant order of the enriched engine sound, and the C3 and  
Figure 11: Comparison of the magnitude responses of \(S(z)\), \(A(z)\), and \(S(z)A(z)\) indicated by dashed, dotted, and solid lines, respectively.  
Figure 12: Engine RPM-frequency spectrograms of the measured engine sounds at the driver’s ear location. (**a**) Before ASE. (**b**) After ASE.  
C5 orders became noticeably observable. Moreover, it was found that the SPLs of the three orders gradually increased with the engine RPM, which followed the target sound (Please see Figure 13).
## 4 Conclusions  
Although both pDMYO-gel, derived from cardiac tissues, and pDSIS-gel, derived from small intestinal submuc, have a beneficial effect on the repair of myocardial injuries, they exert differential effects on the behavior of cardiomyocytes (NRCMs) and human umbilical vein endothelial cells (HUVECs), mainly due to their different protein components. pDSIS-gel is more conducive to the proliferation of HUVECs, while pDMYO-gel is more conducive to the adhesion, growth, spreading, and maintenance of the normal beating of NRCMs in vitro and pDMYO-gel has a better effect on improving cardiac function, inhibiting myocardial fibrosis, and maintaining ventricular wall thickness in acute myocardial infarction models in vivo. The unique proteins found in pDMYO, including annexin-6, agrin, cathepsin D, and galectin-1, may be the reason why pDMYO-gel has good performance in vitro and in vivo. The differential effects exerted by these two gels on cell has been correlated in this study. To design or prepare a close mimic of myocardial ECM for the repair of myocardial injuries, the molecular ingredients shall be considered to match with that of myocardial ECM, especially for some unique proteins. Further studies on the stoichiometric ratio of pDMYO-gel will help us understand how to design better materials for myocardial infarction repair and myocardial tissue engineering.  
The following are available online at [[https://www.mdpi.com/article/10.3390/app1117768/s1](https://www.mdpi.com/article/10.3390/app1117768/s1)]([https://www.mdpi.com/article/10.3390/app1117768/s1](https://www.mdpi.com/article/10.3390/app1117768/s1)). Figure S1: Gene symbol and relative content of matrisome subgroup proteins of pDMYO matrix; Figure S2: Gene symbol and relative content of matrisome subgroup proteins of pDSIS matrix; Figure S3: the relative abundance of proteins in pDMYO and pDSIS; Video S1: the beating cardiomyocytes after seven days cultured on pDSIS-gel; Video S3: the beating cardiomyocytes after seven days cultured on pDSIS-gel; Video S3: the beating cardiomyocytes after seven days cultured on pDMYO-gel.  
Conceptualization and methodology, X.Y.; writing-original draft preparation, X.Y.; validation, S.C.; formal analysis, J.C.; resources, Y.L.; writing-review and editing, Y.B. and D.Q.; supervision, Y.B. and D.Q.; funding acquisition, S.Y. and D.Q. All authors have read and agreed to the published version of the manuscript.  
This research was supported by Science and Technology Planning Project of Guangdong Province, China (2015B010125001).  
All of the procedures involving animals in this study were approved by the Institutional Animal Care and Use Committee (IACUC), Sun Yat-Sen University (approval No. SYSU-IACUC-2020-000509). Animal welfare complied with the Laboratory Animal Guidelines for Ethical Review of Animal Welfare, General Administration of Quality Supervision, Inspection and Quarantine of the People's Republic of China/Standardization Administration of China (GB/T 35892-2018).  
Not applicable.  
The data presented in this study are available in the article.  
The authors declare no potential conflicts of interest.
# Time Segmentation-Based Hybrid Caching in 5G-ICN Bearer Network
## 3 Path Segmentation-Based Hybrid Caching
### Caching Idea Based on Time Segmentation  
The main objective of mobile wireless networks is to ensure that users can still receive high-quality services in a high-speed mobile state. We are aware of the existence of tidal phenomena in mobile networks. For instance, roadside base stations experience an increase in network demands during morning and evening rush hours. This change in access frequency is periodic. Cycle T = 24 h. Mobile users are numerous and mobile speeds are high during peak hours. At other times, the situation is reversed. This gives us the concept of dividing a cycle into high liquidity periods and low liquidity periods.  
With the combination of GIS technology, the base station can make intelligent traffic statistics in custom areas by user-requested messages [40], and the access frequency is reflected by time series. The base stations in a certain region have similar time series trends, thus, can be clustered together. We start by clustering base stations that have a cycle of consistent time series. The time series data set for the clustered area is then fitted as a single series. The result of the partition is communicated to the cache nodes of the BN in this region. Finally, we identify partition points of the fitted time series.  
The TSBC scheme is based on path partitioning, and we use network packets to deliver messages to the ICN routers in the BN. In addition to normal request packets and data packets, we need to add several fields to the header of the packet to carry some special information like partition result. Next, we will introduce the four fields that need to be added in TSBC. The first field carries the division result message, and the other three are important fields used in the caching strategy of different time periods.  
* The period identification field: After finding special points of final time series, we record it in the ICN-GW between the base station and the 5G-ICN BN. Each request packet transmitted by the base station into the BN passes through the ICN-GW, where the ICN-GW fills in the identification of the corresponding period in the field of the packet according to the time. Select different caching strategies by copying the same period identity in the packet that carries the content when the content hits.
* The min_heat and node_addr field: min_heat is used to store the minimum popularity value of the content of the return path. Popularity value is the number of content visits in nodes. The node_addr field is used to store the node address of the minimum popularity value. The min_heat is initialized to the lowest content popularity value in the next node of the node hit on the path.
* The hop_count field: record information about \(Hop\) in low liquidity period.
* The popularity and time_stamp field: record information about \(Popularity\) and \(Freshness\) in low liquidity period.
# SMT Assembly Inspection Using Dual-Stream Convolutional Networks and Two Solder Regions
## 7 Experimental Result
### Comparison of CNN Structures  
First, we compared the classification performance according to the CNN structure. To evaluate the performance according to the merge point of the network, we implemented both single and dual-stream CNN. The structures of the CNNs are shown in Figures 9 and 11. Each early- and late-merge dual-stream CNN has the same weight.  
Table 3 shows the results of the classification performance according to the network structure. For the average accuracy and recall, the difference is small at 0.1%. However, the late-merge dual-stream CNN displayed a 10.1% higher performance due to differences in precision and had a 5.3% higher F1-score. The single-stream CNN had a 4.8% higher F1-score than early-merge dual-stream CNN, but a 5.3% lower F1-scores than late-merge dual-stream CNN. Please refer to appendix A below for more information.  
Table 4 shows the inference times and network weight for the single-stream and late-merge dual-stream CNN. In the table, the late-merge dual-stream CNN takes 2.3 ms longer than the single-stream CNN during image transformation. However, the inference time of the late-merge dual-stream CNN is 3 ms faster than the single stream CNN. When comparing the total time required, the proposed method is 0.3 ms faster than the conventional method. As for weight, the proposed method is about 4000 KB lower than that of the single-stream CNN. This difference is due to the fact that single-stream CNN have a larger network structure, as they insert the entire chip component image into the network. In contrast, dual-stream CNN can reduce the size of the network because they only use the solder region image, which contains only the information necessary for classification.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**DefectDataset** & **Train** & **Test** \\ \hline Over-Solder & 108 & 36 \\ Manhattan & 106 & 35 \\ Missing & 922 & 580 \\ Normal & 5018 & 3263 \\ Under-Solder & 387 & 223 \\ Tombstone & 297 & 172 \\ \hline Total & 6838 & 4309 \\ \hline \hline \end{tabular}
\end{table}
Table 2: SMT defect dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**MeasureMethod** & **Single-Stream** & \multicolumn{3}{c}{**Dual-Stream**} \\ \cline{3-4}  & & **Early-Merge** & **Late-Merge (Proposed)** \\ \hline Average & 95.9 & 90.1 & 96.0 \\ Accuracy (\%) & 87.8 & 83.5 & 97.9 \\ Precision (\%) & 95.9 & 90.3 & 96.0 \\ score (\%) & 91.6 & 86.8 & 96.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of classification for CNN structure.
# Correlation Analysis between UBD and LST in Hefei, China, Using Luojia1-01 Night-Time Light Imagery
## 2 Study Area and Data
### Experimental Data
#### 2.2.2 Landsat8  
Landsat8 thermal infrared sensor (TIRS) data were utilized to analyze the LST. They were issued by the United States Geological Survey (USGS) Global Visualization Viewer (GloVis, [[https://glovis.usgs.gov](https://glovis.usgs.gov)]([https://glovis.usgs.gov](https://glovis.usgs.gov))). To be consistent with the time of the Luojia1-01 data, we analyzed all images (with less than 10% cloud cover) available for a three-months time period from 1 June to 30 August 2018. After this analysis, we chose the cloud-free image from 30 July, with path 121 and row 038. As we were looking to reduce the influence of terrain, illumination, and atmosphere on the spectral information, we performed radiation calibration and atmospheric correction on the image. The Radiation Correctiong module sub-tool, Radiometric Calibration in ENVI software, is used to perform  
Figure 2: The study area.  
the radiometric calibration on the TM image in the study area, and the FLAASH module is used for atmospheric correction.