esa-sceva/satcom-chunk-collection huggingface dataset:
# Analytical Performance Evaluation of Massive MIMO Techniques for SC-FDE Modulations
## 6 Performance Results
### MIMO Scenario  
In this scenario, a set of 3 iterations with \(P=4\) and \(R=8\) antennas is considered.  
In Figure 3, the theoretical/simulated BER performance for both receivers (IB-DFE and MRC) is depicted in Figure 3, as well as the correlation factor, shown in Figure 3.  
According to Figure 3, it is possible to verify that the IB-DFE receiver has a BER performance closer to the MFB. In its third iteration, for a BER of \(10^{-4}\), this receiver presents a difference of less than 1 dB to the MFB. For the same BER, MRC receivers present a worse performance. When comparing the simulated and theoretical values of BER performance, it should be pointed out that the theoretical curves achieve better performance, with the exception of the first IB-DFE iteration where the two curves are identical. In Figure 3, both receivers, for the first iteration, present values for the correlation factor that never reach the optimum value 1, which implies a worse BER performance, as depicted in Figure 3. From the second iteration onward, the correlation factor values are close to 1, allowing the achievement of BER performance values close to the MFB.  
The performance results of a receiver that combines IB-DFE in the first iteration with MRC in the remaining iterations are depicted in Figure 4. According to Figure 4, the BER performance of the receiver, in its first iteration is about 3 dB away from MFB and in the remaining iterations, the result approaches the MFB. The theoretical and simulated BER performance values are also very similar. The poor values of BER performance are accompanied by the correlation factor values. As represented in Figure 4 in its first iteration, these values are quite far from the optimal value 1. In the remaining iterations, the value of the correlation factor converges faster to 1.  
Figure 3: Theoretical/simulated BER performance and correlation factor for IB-DFE and MRC receivers in a Multiple Input Multiple Output (MIMO) system with 3 iterations. (**a**) BER performance. (**b**) Correlation factor (\(\rho\)).  
In fact, in the third iteration, this receiver has a difference of 0.5 dB when compared with the MFB, for a BER of \(10^{-2}\) while for a BER of \(10^{-4}\) this difference is reduced to approximately 0.1 dB.
# NPP-VIIRS DNB Daily Data in Natural Disaster Assessment: Evidence from Selected Case Studies
## 3 Results
### Floods  
For the Louisiana flood in the U.S., because the cloud cover was heavy every day of the 10 days following the flood, the available images during that period could not cover the whole study area. Therefore, we chose the first three images covered the whole study area as the post-flood images (Table 9). Figure 8a-c show pre- and post-flood averaging composite DNB images and the PNL image. Figure 9d shows the Flood Proxy Map produced by ARIA on 18 August. The patterns of pre- and post-flood images were almost the same through visual analysis. The mean radiances of pre- and post-flood image were 7.87 nW\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\) and 7.79 nW\(\cdot\)cm\({}^{-2}\cdot\)sr\({}^{-1}\), respectively. The brighter pixels and darker pixels were intertwined, with an area size of 2004.2 km\({}^{2}\) and 1891.7 km\({}^{2}\), respectively. The \(p\)-value of the Mann-Whitney U-test was 0.514, which indicated that the difference between the pre-and post-flood DNB images was not statistically significant at a 95% confidence interval. This is probably because the images used were captured 10 days after the flood and, in most areas, the power supply had been restored by then [78]. Furthermore, by comparing the FPM with the NTL map, we observed that the flood water was mainly located in areas with dim light. The light changes caused by the flood damage may not have been noticeable enough to be captured by NTL. Therefore, the NTL data were not useful in this case.  
Figure 8: (**a**) The pre- and (**b**) post-flood averaging composite DNB images, (**c**) the PNL image, and (**d**) the Flood Proxy Map (FPM) of the Louisiana flood.  
For the Yulin flood in the Guide district in China, the dates and cloud coverages of pre- and post-flood DNB images are shown in Table 10. Pre- and post-flood averaging composite DNB images and the PNL image are shown in Figure 9a-c, respectively. The difference between the pre-and post-flood DNB images was statistically significant (Mann-Whitney U-test, \(p<0.001\)).  
Comparing the PNL image with the land cover map (Figure 1f), most of the urban areas became darker, which was consistent with the news report that the main county had undergone a large area of power outage [79]. It was reported that 194 houses collapsed during the flood [58]. As the number of housing collapses was relatively small, the main explanation for the light decrease could be due to power outages. Apart from the darker areas, there were still some pixels that became brighter, most of which were located along highways (Figure 9c of the highways). As all of the highways were seriously damaged by the flood [57], the brighter areas are probably attributable to the presence of rescue vehicles at night and the road repair work.
# Two-Stage Single-Channel Speech Enhancement with Multi-Frame Filtering
## 2 Related Research
### Speech Enhancement Based on DNNs  
Existing speech enhancement methods can be divided into time-frequency (T-F) domain and time domain methods. For T-F domain methods, the input features are usually the magnitude or the real and imaginary (RI) components of the short-time Fourier transform (STFT) spectrum but the training targets can be different. Based on the training targets, T-F domain methods can be further divided into mask-based methods and mapping-based methods. Mask-based methods estimate a mask, which multiplies with the noisy T-F spectrum to get the enhanced spectrum. Commonly used masks include ideal ratio mask (IRM) [14], phase-sensitive mask (PSM) [15], and complex ratio mask (CRM) [16]. Mapping-based methods estimate the enhanced spectrum directly. Common neural network architectures used in T-F domain include long short-term memory (LSTM) [17], convolutional recurrent network (CRN) [18], and U-Net [19].  
Time domain methods directly estimate clean speech waveforms from noisy speech waveforms. This end-to-end manner can work around the difficult phase estimation problem and help the model learn proper representations that are suitable for enhancing speech. Conv-TasNet [20] and dual-path recurrent neural network (DPRNN) [21] are two typical works in time domain speech enhancement.
# Enhanced Routing Algorithm Based on Reinforcement Machine Learning--A Case of VoIP Service
## 5 Results and Discussions
In relation to the RLRP, the gain approaches 8%. Therefore, RL in routing protocols improves the user's QoE in a speech communication service. The e-RLRP not only reduces overhead but also provides a positive impact in the quality of VoIP communication, mainly because the \(Ppl\) is decreased.
## 3 Methodology and Overall Framework
### The Digital Twin Model  
We need twin models of the machinery system to be able to simulate and obtain nominal conditions of the system during an operation, as well as abnormal conditions for training and testing purposes. A commonly used approach in modeling the operation of servo motors under changing load is to model the relationship between speed, torque and input power readings. Therefore, our goal is to obtain a mathematical approximation of the real system that is able to generate a power reading given a speed and a torque (load) value. For this purpose, we start from an existing servo motor simulation configured with the datasheet specifications of our servo motor and the conveyor belt mechanism to generate data under ideal conditions. Since the load on the real system is dynamic and nonlinear, we also collect many data from the conveyor setup under various nominal operating conditions, that is, various expected loads under various operation requests. Both the ideal case data and the real data collected are used to obtain an approximation model. The model is generated using polynomial regression over the obtained final readings of speed, torque and power, using a curve fitting approach. The polynomial approximation that serves as the final twin model generating nominal values is given in (1).  
\[P(v,\tau)=a+b\cdot v+c\cdot\tau+d\cdot v^{2}+e\cdot v\cdot\tau+f\cdot\tau^{2}, \tag{1}\]  
where \(v\) is the rotation speed of the conveyor belt, \(\tau\) is the torque (load) on the motor and \(a,b,c,d,e,f\) stand for the constants derived after curve fitting. During runtime, to calculate a nominal power (\(P_{nom}\)) approximation given the current operation, we use the function above with the requested speed value (\(v_{req}\)) and the current load (\(\tau_{cur}\)) carried on the conveyor system, that is, \(P_{nom}=f(v_{req},\tau_{cur})\). However, the actual torque on the motor driving the belt is assumed unknown during the operation due to several abnormal conditions like glitches on the gears of the motor or extra friction on the belt. Therefore, we generate speed-power curves for different allowed loads on the conveyor using the data generated from the digital twin model and the real system under normal conditions (as depicted in Figure 2). The combination of all nominal data is named as the _fingerprint_ of our conveyor system, which is then used in the training of the predictive maintenance algorithm, as explained in the next section.
## 5 Furrowing Depth Stability Control--Automatic Control System
### System Feedback Regulation  
In order to reduce the profiling error brought by the profiling advance or lag of the system, the relevant experts and scholars began to use the system feedback regulation to reduce the error of the profiling control system. [PERSON] et al. designed a furrowing depth control system based on displacement sensors and hydraulic cylinders, utilizing PLC closed-loop control and compensating for the delay time to realize the synchronous profiling of the furrowing depth. The experimental results show that the stability coefficient of the furrowing can reach more than 90% at the advancing speed of 5 km/h and the depth of the furrowing below 70 mm [79]. [PERSON] et al. designed a constant pressure profiling control system based on PLC closed-loop control. The system is simulated and analyzed using MATLAB. The results show that the control system operates stably with an overshoot of 5.02%, a response time of 0.25 s, and a steady-state error of 0.79%. The bench test shows that the average steady-state error of the system is 1.4-1.8 N, the maximum steady-state error is 2.7 N, and the standard deviation is 0.78-6.94%, which can meet the test requirements. It can ensure that the furrow opener operates under constant pressure and provides a reference for the design of the furrow depth profiling control system [80]. In order to improve the electrohydraulic control of the seeding depth system, [PERSON] et al. combined feed-forward compensation PID and IPRC for dynamic regulation of downforce. The integrated control of sowing depth and soil downforce was realized, and the sowing quality was improved. The test results showed that the seeding depth qualification rate of self-weight adjustment, spring adjustment, and electrohydraulic adjustment was 89.2%, 96.7%, and 98.6%, respectively, and the corresponding maximum coefficients of variation of seeding depth were 16.7%, 12.9%, and 6.4%, respectively [81]. [PERSON] et al. developed a furrowing depth control system. An angle sensor was installed in the corner of the furrow opener to provide angle feedback to the system, and the system provided a constant furrowing depth through an electrohydraulic actuator. Experimental results showed that the furrowing depth of the system was independent of the resistance of the seedbed and maintained high stability of the furrowing depth when the resistance of the seedbed changed [82].
## 4 Experimental Results and Discussion
### Effect of the Longitudinal Tilt Angle of the Secondary System on Total Useful Energy Gain  
The total useful energy in the configurations studied is the sum of the electrical energy generated and the useful thermal energy. As shown in Table 5, the incident solar irradiation on the \(PV\) system increases with increasing longitudinal tilt angle. This is an important factor in this study. Figure 19 shows the total useful energy gain for the two configurations as a function of two wind speeds. The longitudinal tilt configuration shows the best total useful energy gain in the absence of wind. The lowest latitude location (Medellin) has the smallest increase. On the other hand, for higher latitudes, the total useful energy gain reflects values up to 53%. The longitudinal tilt configuration still performs the best when the wind speed is 10.03 (m/s), except at the lowest latitude location. The total useful energy gain at this wind speed decreases to 25% for the highest latitude location (Almeria). Therefore, it can be concluded that secondary system designs with longitudinal tilt angles perform well at locations with latitudes above 10 (\({}^{\circ}\)). These results are quite good if the latitude is higher than 24 (\({}^{\circ}\)) and lower than 37 (\({}^{\circ}\)).  
Figure 16: Useful heat for power not converted into electricity by the PV cells of 300 (W).  
Figure 17: Thermal efficiency for power not converted into electricity by the PV cells of 400 (W).  
Figure 18: Thermal efficiency for power not converted into electricity by the PV cells of 300 (W).
## 5 Measurement of GAF MIMO Antenna Array  
### Measurement of GAF Antenna Element  
The GAF antenna, FSS and isolation strip are fabricated by laser engraving method with the accuracy of 25 \(\upmu\)m. The illustration of Figure 9a shows the digital photo of GAF antenna element. GAF is as the radiator. The performances of GAF antenna element are measured. Figure 9a depicts the reflection coefficient and gain of the GAF antenna element measured by vector network analyzer (Keysight NS247A PNA) in microwave anechoic chamber. In the 3.41-3.58 GHz frequency band, \(|\)S\({}_{11}\)\(|\) is lower than \(-10\) dB and the gain is greater than 2 dBi, which is consistent with the simulation results. The radiation patterns at 3.5 GHz are displayed in Figure 8b. The GAF antenna element is used as the receiving antenna, and the standard gain horn antenna is used as the transmitting antenna. Both the transmitting antenna and the receiving antenna are placed in a microwave anechoic chamber and connected with the vector network analyzer. As shown in Figure 9b, the maximum radiation directions of the E-plane and H-plane of the GAF antenna element are both in the normal phase.
## 1 Introduction
When the SOC and/or frequency operating point deviates from the desired range, all grid-forming sources change their output frequencies. Grid-feeding DGs restore the frequency by changing their output power. To this end, we propose for grid-feeding DGs to use an integral control; however, if there are two or more units with integral control for frequency restoration, they conflict since the frequency measurements and/or the set points may differ from each other in the real world [25]. To prevent this problem, which is known as a hunting effect, an On/Off scheme for frequency integral controllers in grid-feeding DGs is also included in the proposed method, which is implemented to use local controllers and local information only without any communication devices to improve system reliability and reduce the installation cost. However, since this paper is focused on proposing the simple concept of the frequency recovery control without communication for the first time, the detailed analysis, such as stability analysis, has not been implemented. Hence, the scope of this paper is limited to the basic concept of the controller. The rest of this paper is structured as follows. Section 2 briefly reviews conventional _P-f_ droop control and introduces a droop coefficient determination method to synchronize the output powers of grid-forming units (ESSs in this paper), and hence permit other grid-forming units to estimate the SOC of the ESSs. Section 3 describes a method to integrate the SOC information into the frequency. Section 4 shows a controller for frequency restoration, Section 5 illustrates case studies verifying the effectiveness of the proposed methods, and Section 6 outlines the conclusions.
# Optimization of Image Capture Distance for Facial Thermograms in Dentistry
###### Abstract  
Thermography has not yet been validated for the screening of oral disorders and no clear guidelines or methodology for this purpose have been defined in the literature. The current pilot study was aimed as an initial step to evaluate the influence of shooting distances on the detection of the temperature asymmetries on the face for the purpose of dental thermography. Facial thermograms were taken in three views at three shooting distances for each participant. The mean temperature of the overall image and at the region of interest (ROI) were measured from the analysis software. Thirty adult volunteers participated in this study [\(n\) = 30, mean age: 25.8 \(\pm\) 6.0]. The differences between the mean temperature values at the ROI for all different shooting distances within each profile view were statistically significant [Front: H(2) = 80.176, \(p\) < 0.001; Left: H(2) = 21.399, \(p\) < 0.001; Right: H(2) = 49.451, \(p\) < 0.001]. The mean ROI temperature was influenced by personal effects (\(p\) < 0.001), medications (\(p\) < 0.001), undergoing dental treatments (\(p\) < 0.001) as well as the consumption of food (\(p\) < 0.001). This study concludes that the detection of temperature asymmetries on the face in adult volunteers are affected by the capture distance as well as factors such as medication, oral-care, and alimentation. Therefore, it is advised that personal as well as medical histories are obtained in detail prior to making the facial thermograms.  
g pacs: **Keywords:** geriatric dentistry; dentistry; thermography
# Augmented MIMO: Body-Mounted Antennas for Tiny Wearable Devices
###### Abstract  
Multiple-input-multiple-output (MIMO), which uses multiple antennas at the transmitter and receiver, is now an essential technique for increasing communication capacity without widening the occupied radio bandwidth. However, antenna arrays within a deep subwavelength dimension degrade MIMO performance due to mutual coupling between the antenna elements. In particular, very small devices such as smartwatches encounter this problem. To address this, we propose Augmented MIMO, mounting a larger antenna array on the human body, for small wearable devices. The experimental results demonstrate throughput improvement with the proposed scheme, even if the overall antenna gain decreases with external body-mounted antennas. This work contributes to the future development of yet another scheme to improve the communication performance of small wearable devices--using the human body as a spacious antenna fixture.  
multiple-input-multiple-output (MIMO); wearable systems; wireless communications +
Footnote †: journal: Applied Sciences  
1
# Estimation of Antenna Pose in the Earth Frame Using Camera and IMU Data from Mobile Phones
## 6 Experimental Results
### Setup and Datasets  
We captured multiple datasets and organized them into two groups for various evaluations. In the first group, there are three datasets, named BoardF, BoardL and Motion, which are for evaluations of camera-IMU calibration methods; in the second group, there are two datasets, named AntennaL and AntennaS, which are for evaluations of visual pose estimation methods and the overall pipeline.  
All of these datasets are captured using a Samsung(c) Galaxy S4 Zoom smart phone with an Android application that we developed ourselves, and a tripod is used to ensure stable sensor data when necessary. Examples are presented in Figure 11, and details are given below. The datasets are publicly available at [[http://zju-capg.org/antenna/data](http://zju-capg.org/antenna/data)]([http://zju-capg.org/antenna/data](http://zju-capg.org/antenna/data)).  
Figure 11: Data capture tools and examples from the datasets. The top part of (**a**) shows the tools used to measure the downtilt and azimuth angles of the checkerboard used for calibration; the bottom part of (**a**) shows an example image from BoardF in which the board is aligned with the Earth frame; (**b**) shows examples of checkerboards in various orientations from BoardL; (**c**) shows two snapshots from data prepared for camera-to-IMU calibration and synchronization toolbox (CRISP) in Motion; (**d**) shows examples from multi-view images of antennas from AntennAL (left) and AntennaS (right).
## 4 Experimental Results and Discussion
### Security Test
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Greyscale** & \multicolumn{2}{c}{**With GA**} & \multicolumn{2}{c}{**With ABC**} & \multicolumn{2}{c}{**With FO**} \\
**Imagee** & \multicolumn{2}{c}{**NC**} & **BER** & **Alpha** & **NC** & **BER** & **Alpha** & **NC** & **BER** & **Alpha** \\ \hline Lena & 1 & 0 & 9.18692 & 1 & 0 & 4.2647 & 1 & 0 & 4.47517 \\ Baboon & 1 & 0 & 8.84809 & 1 & 0.0004 & 4.2157 & 1 & 0 & 4.04169 \\ Lighthouse & 1 & 0 & 9.34731 & 1 & 0.0002 & 4.1358 & 1 & 0 & 3.63865 \\ Desert & 1 & 0 & 6.06826 & 1 & 0.0002 & 4.2014 & 1 & 0 & 3.82451 \\ Grain & 1 & 0 & 6.18985 & 1 & 0 & 4.0224 & 1 & 0 & 5.04537 \\ MRI & 1 & 0 & 5.69925 & 1 & 0 & 3.8877 & 1 & 0 & 2.70833 \\ \hline Average & 1 & 0 & & 1 & 0.0001 & & 1 & 0 & \\
**Color Images** & **NC** & **BER** & **Alpha** & **NC** & **BER** & **Alpha** & **NC** & **BER** & **Alpha** \\ \hline Koala & 0.9999 & 0 & 9.62690 & 0.9996 & 0.0004 & 4.7327 & 0.9996 & 0 & 4.1267 \\ Penguins & 1 & 0.0004 & 9.88692 & 0.9997 & 0.0004 & 4.7606 & 0.9996 & 0.0004 & 3.9985 \\ Tulips & 1 & 0.0002 & 6.67544 & 1 & 0 & 5.1535 & 1 & 0 & 4.9423 \\ Tiffany & 0.9997 & 0.0002 & 9.80044 & 0.9990 & 0.0004 & 4.7247 & 0.9993 & 0 & 4.2167 \\ Splash & 0.9995 & 0 & 5.32084 & 0.9997 & 0 & 6.5745 & 0.9990 & 0 & 5.9291 \\ Skin & 0.9999 & 0 & 9.26637 & 0.9996 & 0 & 4.8773 & 0.9996 & 0 & 4.5178 \\ \hline Average & 0.9998 & 0.0001 & & 0.9996 & 0.0002 & & 0.9991 & 0 & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: NC, BER under zero attack using optimized ISF with GA, ABC and FO.
## 5 Simulation Results and Discussion
### Performance for Single Sensor FDI with External Disturbance  
The mechanical power fed to generator varies slowly to simulate real wind power in all simulation. Type a and type c fault occur at \(t=0.4\) s and \(t=2.0\) s as shown in Figure 3 and Figure 4. Fault detection variable \(f_{d}\) changes instantly and corresponding isolation flag \(f_{a}\) and \(f_{c}\) change subsequently at \(t=0.42\) s and \(t=2.01\) s. In addition, the mechanical power variation starts at \(t=0.5\) s, which causes sudden variation of the rotor speed. During this period, \(f_{d}\) and \(f_{a}\) remain higher than the threshold while \(f_{b}\) and \(f_{c}\) are lower than detection threshold, which indicates that the isolation algorithm is robust to the disturbances.  
The current waveforms of the healthy phase will be distorted by the faulty sensor because of feedback control. As shown in Figure 4, **Type c** fault of phase c current sensor distorts phase a and phase b currents. When phase c fault is triggered at \(t=2\) s, fault detection flag \(f_{d}\) and isolation flag \(f_{c}\) change from '0' to '1' while \(f_{a}\) and \(f_{b}\) remain '0'. This reports that the proposed isolation variables are only sensitive to the corresponding fault phase and makes it possible to isolate all phase current sensor faults. The next section will show the multiple sensor fault diagnosis performance.  
Figure 3: Simulation results on the single phase fault isolation, under varying mechanical disturbance (from top to bottom are phase a current \(i_{abc}\), rotor speed \(\omega_{r}\), isolation variables \(f_{a}\),\(f_{b}\),\(f_{c}\) and FDI flags).