esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessing Ambisonics Sound Source Localization by Means of Virtual Reality and Gamification Tools
## 2 Related Work  
### Sound Source Localization in Ambisonics Systems  
Virtual sound source localization tasks have been widely used for measuring the performance of spatial audio systems and auditory research [16; 17]. Ambisonics is often preferred by researchers for experiments involving both users' natural auditory conditions with loudspeakers and binaural reproduction using HRTF with headphones. [PERSON] et al. conducted an evaluation of sound source localization accuracy across various elevation and azimuth positions [18], comparing different Ambisonics orders using a loudspeaker array. This study focused on the performance of Ambisonics systems in accurately localizing virtual sound sources in the vertical plane. [PERSON] assessed the spatial resolution of a 3 rd-order Ambisonics system by conducting a listening test in which participants identified the direction of sound sources [19]. This approach allowed for an evaluation of the system's perceived spatial accuracy, providing insights into the effectiveness of Ambisonics-encoded sound sources. [PERSON] et al. investigated the influence of Ambisonics order and encoding bitrate on the localization of virtual sound sources reproduced over both loudspeaker arrays and binaural systems [20]. Similarly, [PERSON] et al. assessed the performance of multiple-order Ambisonics systems through a sound source localization test comparing real and virtual sound sources [21]. In these works, it was highlighted that the Ambisonics order had a direct effect on sources' localization, with HOA showing a clear improvement over the 1 st-order setup. Another key finding was that this trend is more prominent when conducting loudspeaker-based tests.
# Applications of Capacitive Imaging in Human Skin Texture and Hair Analysis
## 2 Materials and Methods  
### Measurement Apparatus  
To capture hydration information in pharmaceutical and cosmetic industries non-invasive capacitance sensing systems are employed. In the field of skin research, the most widely known single pixel capacitive system is the Corneometer\({}^{\textregistered}\) CM825 (Courage+Khazaka, Cologne, Germany) [25]. This instrument has a 7 \(\times\) 7 mm sensor in interdigital electrode geometry with 250 \(\upmu\)m spatial wavelength and 50 \(\upmu\)m electrode width (Figure 2). Its sensor is covered with a 20 \(\upmu\)m thick layer of glass for galvanic contact protection [26]. The capacitive imaging sensors are based on the same technology with Corneometer and they are widely known as fingerprint sensors. Their sensing surface consists of a two-dimensional array of miniaturized capacitive pixels that capture the hydration map of the sample. In this work the Epsilon E100 (Bior Systems Ltd., London, UK) [27] is used. It has a sensing surface of 12.8 \(\times\) 15 mm filled with 300 \(\times\) 256 pixels and a 2 \(\upmu\)m coat of silicon dioxide for galvanic contact protection. The accompanying software exports hydration information in a format of color-coordinated images. In those images, brighter pixels denote higher while darker pixels denote lower hydration levels.  
Figure 2: **Left, 3D representation of scalar potential for Corneometer’s electrodes as produced using Agros2D [28]. Right, representation of the fingerprint sensor pixels embedded in Epsilon E100 [27].**
# DNN Beamforming for LEO Satellite Communication at Sub-THz Bands
## Appendix A  
For the given link design parameters in Table 1, we provide the signal-to-noise ratio as the elevation angle of LEO satellite changes in Figure 11. The SNR at the receiver array output at the ground station would be expressed as [50]  
\[SNR=\frac{\eta_{R}\eta_{T}\mathrm{P}_{T}\mathrm{G}_{T}\mathrm{G}_{R}}{kTBL_{FS }L_{A}} \tag{11}\]  
where \(k\) is the Boltzmann constant, \(B\) is the bandwidth, \(T\) is the noise temperature, \(L_{FS}\) and \(L_{A}\) denote the free-space path loss and atmospheric absorption loss respectively. The value of noise temperature \(T_{A}\) is taken to be 500 K and the plot of SNR as a function of elevation angle is shown in Figure 11.  
We observe that the SNR is decreasing as the elevation angle is decreasing. This is expected as with lowering elevation angle, as the path length through the channel increases leading to higher absorption and free-space path loss. It should be noted that for a SNR of 4 dB, the bit-error rate (BER) of a Quadrature PSK (QPSK) would be \(1.53\times 10^{-7}\) for the convolution coding with soft decision and code rate of 2/3. The simulation results have been shown upto the elevation angle of \(10^{\circ}\) as for the next-generation of LEO satellite networks, there will thousands of satellite in the orbit and the handover will be done at higher elevation angles to maintain high link quality [51].
# A Software-Defined Radio Platform for Teaching Beamforming Principles
## 5 Conclusions  
In this paper, we present the development, calibration, validation, and testing of a hybrid beamforming system aimed at providing telecommunication engineering students with an interactive, user-friendly platform for observing and testing beamforming principles. By integrating a commercial multichannel SDR device with traditional RF equipment and open-source software, the proposed platform contributes to the development of students' practical abilities and a deeper understanding of signal-processing techniques in real-world applications.  
Along with its main objective, this paper also documents the challenges associated with using an SDR platform for beamforming, emphasizing the need for accurate phase and amplitude calibration and parametrization to ensure optimal system performance in beamforming applications.  
The system is tested on a customized 1 \(\times\) 4 patch antenna, designed to operate in the 3.53 GHz frequency range. The system validation was achieved through experimental measurements, with results demonstrating the main differences in the behavior of a beamforming system in indoor and outdoor environments. The reported results also validate the effectiveness of the beamforming platform in achieving the desired steering angles, with good agreement between experimental and simulated/analytical results.  
The use of the proposed platform will engage students in the fundamental principles of telecommunication and will trigger the development of essential professional competencies. By exploiting the versatility of SDR devices, they could enable, observe, and test various beamforming technologies, preparing them for the future challenges in telecommunication engineering.  
We have thus demonstrated that utilizing open-source hardware for system experimentation in telecommunication education is feasible and beneficial for promoting cost-effective educational initiatives. The proposed SDR platform, paired with open-source software and affordable hardware components, can effectively support hands-on learning experiences. This setup not only allows students to experiment with real-time beamforming but also provides a flexible, scalable system adaptable to a range of experimental configurations and frequencies.  
Conceptualization, A.S. and C.I.; data curation, R.P. and C.I.; formal analysis, A.D.; funding acquisition, C.I.; investigation, A.S. and R.P.; methodology, A.S.; project administration, C.I.; resources, A.D. and C.I.; software, R.P.; supervision, A.S. and A.D.; writing--original draft, A.S.; writing--review and editing, A.S. and A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.
# Performance Analysis of Ocean Surface Topography
## 2 Altimetry Simulation of OST
### Altimetric Precision Simulation
Compared to the above mentioned factors, other decorrelation factors can be neglected during the simulation [22]. The coherence of the interferometric data can be expressed by the multiplication of the decorrelation factors, as given in (11):  
\[\gamma=\gamma_{N}.\gamma_{v} \tag{11}\]  
The coherence of InIRA acquired data is then simulated, as shown in Figure 5c, and the decrease in the far range is mainly caused by the decrease of SNR. By averaging the neighboring pixels, the phase error can be suppressed and follows the Cramer-Rao bound [21], which is given by:  
\[\Delta\phi=\sqrt{\frac{1-\gamma^{2}}{2\gamma^{2}N}} \tag{12}\]  
where \(N\) is the multi-look number, \(\gamma\) is the coherence. Though the SNR of InIRA acquired data is very high, a large number of pixels still need to be averaged to achieve centimeter-level precision. We have simulated the altimetric precision on a 1-km grid, as shown in Figure 5d. In the near range, the precision drops due to the volumetric decorrelation. In the far range, when the incidence is above \(\sim\)7.4\({}^{\circ}\), the precision appears to have a steep drop, this is caused by the increase of the incidence apart from the SNR decline. As mentioned before, the altimetric precision can be increased by multi-looking, thus altimetric precision on a 1-km, 5-km and 10-km grid are simulated and compared, the SWH is set to be 2 m. As shown in Figure 5e, the precision on a 5-km grid is better than 7 cm and improves to 3 cm on a 10-km grid due to larger averaged pixel number. Figure 5: _Cont._
###### Abstract  
This study focuses on improving the defect recognition accuracy under weak partial discharges (PDs) in epoxy resin through phase-resolved partial discharge (PRPD) analysis. The method is to refine the data rather than enhance the algorithm. Two measurement conditions are compared until PRPD pattern saturation is achieved: one-minute and one-hour durations. The PD data specifically target three void types located at different positions within the epoxy material. The aim is to evaluate how the presence of weak PDs at the PD extinction voltage (PDEV) influences defect recognition accuracy. This research sheds light on the potential implications of neglecting the significance of weak PD signals in defect detection. A convolutional neural network (CNN) model is trained using PRPD data recorded at the repetitive PD inception voltage (RPDIV) and tested using the new PRPD data from both conditions recorded from a lower PDIV to a PDEV. The trained CNN model achieves a defect recognition accuracy of 100% for a one-hour duration, highlighting the importance of not neglecting weak PD signals. This emphasizes the significance of extended measurement duration and pattern saturation in capturing and analyzing weak PD signals for an improved defect recognition. This study contributes to the advancement of practical applications by understanding the behavior of the epoxy material and enhancing defect detection techniques.  
Keywords:convolutional neural networks; defect recognition; partial discharge measurement durations; epoxy resin; PRPD +
Footnote †: journal: Applied sciences
## 4 Discussion  
### Optical Effects  
At first glance, the three prominent absorption signatures of Figures 4 and 5 might appear to be caused by standing wave interference in the substrate etalon, since their peak-to-peak separation is comparable to the plane-wave etalon prediction: \(\Delta\)f \(\approx\) c/(2 nd) \(\approx\) 77 GHz for the fused quartz chip. However, on further inspection, we see that the center frequencies are not equally spaced, as one would expect from etalon behavior. In Figure 4 (si-RNA), the separation between signatures (1) and (2) is 46 GHz, and the separation between (2) and (3) is 72 GHz. In Figure 5a (50-bp DNA), the separation between signatures (1) and (2) is 68 GHz, and the separation between (2) and (3) is 55 GHz. In Figure 5b (1-kbp DNA), the separation between signatures (1) and (2) is 75 GHz, and the separation between (2) and (3) is 85 GHz. Furthermore, the precipitous behavior of the signatures in Figures 4 and 5a does not extrapolate to lower or higher frequencies, as one would expect for a strong etalon effect.  
This reasoning does not apply to \(\lambda\)-DNA with its quasi-sinusoidal behavior, shown in Figure 6a,b, and its lack of prominent signatures. We reiterate that the background spectrum in Equation (1) (used in the calculation of transmittance) is taken with the nanochannels containing buffer solution, and the sample spectrum is taken with the nucleic acid solution added. Therefore, the transmittance calculation should cancel out the etalon effect, and the undulations of Figure 6 likely arise from weak nucleic acid absorption in the nanochannels comprising the top facet of the etalon. In Figure 6, the uniformity in the peak-to-peak separation and the weakness in amplitude together suggest that the \(\lambda\)-DNA absorption is broadband and lacking in measurable frequency-specific signatures, as displayed especially by the si-RNA and 50-bp DNA samples.  
We do not expect high-Q etalon effects in our fused quartz substrates anyway. Because of the moderate refractive index (n \(\approx\) 1.95 at \(\sim\)1.0 THz), the reflectance at each silica-air interface is only \(\approx\)10%. Furthermore, fused silica is somewhat lossy at THz frequencies (\(\alpha\) = 2.5-3.0 cm\({}^{-1}\) at 1.0 THz; [20]), which dampens the etalon cavity Q, especially compared to the high-resistivity Si substrate in the nanofluidic chip that we studied previously [15]. Hence, we believe it is coincidental that the frequency separations of our three strongest signatures, especially those in Figures 4b and 5a, are comparable to the value expected for the 1 mm-thick fused silica etalon, \(\Delta\)f \(\approx\) 77 GHz.
# Key Issues on Integrating 5G into Industrial Systems
## 5 Integration of 5G with Industrial Systems
### Key Technologies for Replacing Field Buses with 5G  
At present, diverse communication protocols are adopted at various layers of real-time IoT networks in the field of intelligent manufacturing, undoubtedly increasing the complexity of maintenance and operation. To achieve a \"one network to the bottom\" communication architecture, 5G end-to-end solutions are used, where all the four layers'  
Figure 8: The Architecture of Puao ProudThink cloud platform.  
Figure 9: The case of the ProudThink cloud platform.  
network protocols are replaced with 5G technology. To replace the field bus with 5G, it is necessary to implement relevant technologies, including real-time IoT technology as well as technologies closely related to 5G.
## 4 Compensation with Unbalanced Voltages
### Calculation of SVC compensator Reactances with Unbalanced Voltages  
Consider the system shown in Figure 2 at unbalanced voltages. It is known that the reactive power consumed by the load, \(Q_{zL}\), is determined by Equation (35), and that consumed by the SVC compensator, \(Q_{zP}\), is determined by Equation (36).  
\[Q_{zL}=Q_{zL+}+Q_{zL-}\ z=\{a,b,\ c\} \tag{35}\]  
\[Q_{zP}=Q_{zP+}+Q_{zP-}\ z=\{a,b,\ c\} \tag{36}\]  
On the other hand, the reactive power \(Q_{zF}\), consumed by the NSCC compensator, is determined by Equation (37).  
\[Q_{zF}=Q_{zF+}+Q_{zF-}\ z=\{a,b,\ c\} \tag{37}\]  
If we consider that the balance of powers is met:  
\[Q_{zL+}+Q_{zP+}+Q_{zF+}=0, \tag{38}\]  
\[Q_{zL-}+Q_{zP-}+Q_{zF-}=0. \tag{39}\]  
Substituting condition (34), defined for the NSCC compensator, into Equations (38) and (39), Equation (40) is obtained.  
\[Q_{zL+}+Q_{zP+}=Q_{zL-}+Q_{zP-} \tag{40}\]  
Considering the condition Equation (33) defined for the SVC compensator in Equation (40), Equation (41) is obtained.  
\[Q_{zL+}+Q_{zP+}=Q_{zL-}+\delta_{-}^{2}Q_{zP+} \tag{41}\]  
Equation (41) indicates that the reactive power consumed by the SVC compensator in each phase, \(Q_{zP+}\), at unbalanced voltages is equal, and its value is determined by Equation (42). Considering the values of \(Q_{zP+}\) in (14) for each phase, Equation (43) is obtained, which allows us to determine the values of the SVC compensator reactances (\(X_{aP}\), \(X_{bP}\) and \(X_{cP}\)). \(X_{zP}\) with a positive value represents a coil, and that with a negative value represents a capacitor. Obviously, \(X_{aP}=X_{bP}=X_{cP}=X_{P}\).  
\[Q_{zP+}=\frac{Q_{zL-}-Q_{zL+}}{1-\delta_{2}^{2}} \tag{42}\]  
\[X_{zP}=\frac{V_{z+}{}^{2}}{Q_{zP+}}=3\frac{V_{+}{}^{2}}{Q_{P+}} \tag{43}\]  
With balanced voltages, Equations (14) and (43) are identical, as \(\delta_{-}=0\) and \(Q_{zL-}=0\).
## 3 The Proposed Fuzzy Adaptive Filter Strategy
### The Fuzzy Logic Adaptive System (FLAS)  
Fuzzy logic was first developed by [PERSON] in the mid-1960s for representing uncertain and imprecise knowledge. It provides an approximate but effective means of describing the behavior of systems that are too complex, ill-defined, or not easily analyzed mathematically. A typical fuzzy system consists of three components, that is, fuzzification, fuzzy reasoning (fuzzy inference), and fuzzy defuzzification, as shown in Figure 2. The fuzzification process converts a crisp input value to a fuzzy value, the fuzzy inference is responsible for drawing calculations from the knowledge base, and the fuzzy defuzzification process converts the fuzzy actions into a crisp action.  
Figure 1: The block diagram of the FUZZY-IMMUKF algorithm. (one cycle with two models).  
The fuzzification modules: (1) transforms the error signal into a normalized fuzzy subset consisting of a subset for the range of the input values and a normalized membership function describing the degree of confidence of the input belonging to this range; (2) selects reasonable and good, ideally optimal, membership functions under certain convenient criteria meaningful to the application. The characteristics of the fuzzy adaptive system depend on the fuzzy rules and the effectiveness of the rules directly influences its performance. To obtain the best deterministic output from a fuzzy output subset, a procedure for its interpretation, known as defuzzification should be considered. The defuzzification is used to provide the deterministic values of a membership function for the output. Using fuzzy logic to infer the consequent of a set of fuzzy production rules invariably leads to fuzzy output subsets.  
The fuzzy modeling is the method of describing the characteristics of a system using fuzzy inference rules. In this paper, a Takagi-Sugeno (T-S) fuzzy system is used to detect the divergence of EKF and adapt the filter. [PERSON] and [PERSON] proposed a fuzzy modeling approach to model nonlinear systems.  
The output is the weighted average of the \(y_{k}\):  
\[y=\sum\limits_{k=1}^{M}w_{k}.y_{k} \tag{28}\]  
where the weights \(w_{k}\) are computed as:  
\[w_{k}=\frac{\prod\limits_{i=1}^{n}\mu_{F_{i}^{k}}\left(x_{i}\right)}{\sum \limits_{j=1}^{M}\left[\prod\limits_{i=1}^{n}\mu_{F_{i}^{j}}\left(x_{i}\right) \right]} \tag{29}\]  
with \(\sum\limits_{i=1}^{M}w_{i}=1\), and the \(\mu\)'s represent the membership function.
###### Abstract  
This paper devotes to the robust stability problem for an uncertain linear time invariant (LTI) feedback system with actuator saturation nonlinearity. Based on a three degree of freedom (DOF) non-interfering control structure, the robust stability is enforced with the describing function (DF) approach for an uncertain LTI system to avoid the limit cycle. A new type of anti-windup (AW) compensator is designed using the quantitative feedback theory (QFT) graphical method, which results in a simple design procedure and low-order AW control system. One of the most significant benefits of the proposed method is free of the non-convexity (intractable) drawback of the linear matrix inequality (LMI)-based approach. The analysis conducted on the benchmark problem clearly reveals that the proposed QFT-based anti-windup design is able to handle both saturation and uncertainty in a very effective manner.  
Keywords:quantitative feedback theory (QFT), input saturation; robust anti-windup; limit cycle; describing function; uncertain linear systems +
Footnote †: journal: Applied
# Evaluation of the Landsat-8 Albedo Product across the Circumpolar Domain
## 3 Results  
### Overview of Landsat Circumpolar Albedo  
The Landsat circumpolar albedo for 2018 is presented in Figure 2. The product provides comprehensive coverage across much of the domain with some data gaps resulting from solar angle limitations and the presence of clouds across the landscape. The albedo results are generally high-quality returns with the greatest data loss occurring in November through February due to polar night and insufficient illumination conditions. The data loss during this period is not typically critical for forcing applications, as solar insolation is low and the impact of albedo on the energy balance is minimal.
## 3 Test Results and Analysis
### The Results of the NMR Experiment
The proportion of micropores declines from 58.69% to 41.12%, while the proportion of mesopores increases from 40.41% to 52.66%, and the proportion of macropores rises from 0.9% to 6.22%. This indicates that supercritical CO\({}_{2}\) solution molecules predominantly enter the mesopores. In the mineralization stage, the compressive strength of coarse sandstone not only stabilizes, but also exhibits an upward trend. At this point, the proportion of micropores decreases from 41.12% to 40.06%, the proportion of mesopores increases from 52.66% to 54.49%, and the proportion of macropores declines from 6.22% to 5.45%. This change suggests that regenerated calcium carbonate molecules primarily fill the micropores and macropores. During the secondary dissolution stage, the compressive strength begins to decrease again, with the proportion of micropores increasing from 40.06% to 45.03%, while the proportion of mesopores decreases to 49.81%, and the proportion of macropores slightly reduces to 5.16%. These results indicate that, at this stage, supercritical CO\({}_{2}\) solution molecules predominantly re-enter the micropores. Figure 5: Pore size distribution curves of the samples after soaking in different months. Figure 6: The change trend for sample porosity after soaking in different months.
## 3 Experimental Studies
### Flowthrough Measurements  
The flowthrough experiments mainly measure three-phase relative permeability. In these experiments, changes in recovery rate are determined through different displacement methods. After the displacement experiment, cumulative injected pore volumes (PV), experimental data of the core flooding are obtained. These data are then input into the simulator with proper relative permeability hysteresis model to perform the history matching and obtain the relative permeability curves, thereby analyzing the hysteresis phenomenon.  
Unlike direct permeability experiments, the results of displacement-type experiments require numerical simulation. The accuracy of the numerical model directly affects the final experimental results, and the fitting results depend on the two-phase relative permeability curves. Therefore, different WAG hysteresis model and parameters are required for different relative permeabilities studies.  
Gas trapping ultimately affects other characteristics of hysteresis modeling. In mixed-wet systems during gas injection, the cyclic hysteresis of water relative permeability can be neglected, while in water-wet systems, water relative permeability decreases as alternating imbibition and drainage processes occur. In oil-gas two-phase systems, gas relative permeability is much higher than in three-phase systems, mainly due to the significant impact of water presence on three-phase gas relative permeability. From the simulation results, it is observed that under the same gas saturation, gas-phase relative permeability exhibits cyclic decrease. The main reason is that during the drainage process after imbibition, some gas is trapped in the core, causing each new drainage cycle after imbibition to start with a higher content of trapped gas. Therefore, with the progress of WAG, the gas injection capacity significantly decreases and hysteresis occurs. Additionally, due to the cyclic water and gas injection, the trapped gas saturation continuously increases, and the residual oil saturation decreases accordingly.