esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Channel Estimation for Candidate Waveforms of Next Generation Networks
## 3 Fundamentals of Channel Estimation
### Channel Estimation for Filter Bank GFDM
The performances of OFDM CE is used as a benchmark for analysing the impact of the proposed frequency domain IFPI method on signal properties such as peak-to-average power ratio (PAPR) and power spectral densities (PSD)/OOB emission in comparison to GFDM based transceivers. The results obtained in terms of OOB emission demonstrate that the proposed IFPI GFDM system has slightly OOB emission in comparison to the conventional GFDM signals while windowed GFDM offers equivalent OOB radiation in comparison to the proposed IFPI GFDM. The MSE of this IFPI proposed technique is also compared with conventional OFDM systems and basic GFDM CE. It is observed that the MSE performance of the IFPI technique is identical to that of conventional OFDM as a result of the orthogonality of the pilot symbols to the data symbols. The basic GFDM CE demonstrates a large error floor at high SNR due to the significant effects of interference. The PAPR performance of the basic GFDM is also investigated in comparison to the proposed IFPI GFDM method where it is observed that the PAPR of IFPI GFDM increases due to orthogonal pilot insertion in comparison to basic GFDM. Pilot-assisted CE is also studied in [52] for MIMO-GFDM systems in the presence of ISI, ICI and inter-antenna interference (IAI) which constitute substantial challenges to the design of the MIMO-GFDM based system. The performance of two known CE schemes (LS and LMMSE) is analysed for pilot-assisted MIMO-GFDM CE in comparison with OFDM systems. The results obtained show that the MSE CE performance of GFDM system contains an error floor resulting from the generated interference precipitating from the data symbols while the MSE is observed to decrease linearly with SNR for OFDM systems. When the CE performance is analysed in terms of SER under OFDM and MIMO-GFDM genie-aided receiver, it is observed (from the obtained results) that the SER of the GFDM system consists of an error floor at higher SNRs as a result of the CE error floor while the SER of OFDM CE proportionally decreases with increase in SNR. The LMMSE estimation which exploits a priori knowledge of channel statistics offered better CE quality in comparison to LS estimation but at high computational complexity. This means that CE for basic GFDM and MIMO-GFDM systems is still an open research area. The performances of semi-blind and blind CE for GFDM system is an open research area that can be evaluated for improving the performances of existing non-orthogonal waveforms for the emerging next generation (5G) networks. In adaptive CE schemes, the parameters of the estimator are updated continuously/adaptively in such a way that the channel and noise statistics are not necessary. The performance analysis of pilot, semi-blind and blind-based adaptive CE schemes for the mentioned potential non-orthogonal waveforms for 5G is another open research problem especially as the signals propagate across highly frequency selective multipath fading communication channels.
# Reducing the Cost of Implementing Filters in LoRa Devices
## 4 Conclusions  
Two methods were presented to reduce the complexity of implementing multiplier-less SRRC pulse shaping filters in LoRa transmitters. These methods focus on reducing the required number of samples in the ROM used to generate basic chirp signals, as well as those required for the multiplier-less filter LUT. Chirp segmentation can be used to generate the entire basic LoRa chirp waveform from only a quarter of its samples without adding any additional error to the signal. Quantization can also  
Figure 5: Bit-error-rate (BER) results for quantized and non-quantized signals with \(BW=125\) kHz and \(SF\) ranging from 6 to 12.  
be used to exponentially decrease the number of unique samples at the input to the multiplier-less pulse shaping filter at the cost of introducing small errors to the transmitted signal.  
Using both methods allows for a reduction in the number of stored samples so as to not only match, but also improve upon the results obtained from a standard LoRa device that does not contain a multiplier-less filter. For example, a system using 10-bit fractional precision and a length-17 multiplier-less pulse shaping filter requires fewer samples to be stored in memory compared to a standard LoRa system when supporting spreading factors 6 to 12. Even a device with a length-81 filter requires fewer stored samples than a standard device by quantizing the LoRa chirp samples to 8 fractional bits.  
Furthermore, it was shown that moderate levels of quantization do not hinder the decoding performance of LoRa devices, even under harsh channel conditions. Therefore, the quantization factor can be chosen based on the complexity requirements of the system. For example, devices intended for long-range communication require larger spreading factors and, as a result, a higher quantization factor to compensate for the added complexity. In conclusion, using the proposed sample reduction methods can aid in further alleviating the complexity concerns associated with implementing SRRC filters in LoRa devices.  
[PERSON] and [PERSON] contributed to the conception and design of the study; analysis and interpretation of results; and writing of the manuscript. [PERSON] and [PERSON] contributed to interpretation of results; and writing of the manuscript.  
This work was supported an Industrial Research Chair (IRC) Program, jointly sponsored by Natural Sciences and Engineering Research Council (NSERC) of Canada, Cisco Systems Canada, and the University of Saskatchewan.  
The authors declare no conflict of interest. The funding sponsors contribute to define and approve the NSERC/Cisco IRC Program, which includes this research project.
# Calculation Method of New Assembled Corrugated Steel Initial Support Structure of Highway Tunnel
## 3 Rotational Stiffness of Corrugated Steel Flange Joint
### Results and Analysis  
The numerical simulation of the CSF employed the same loading conditions as those used in the full-scale test. Specifically, a vertical load (F) was applied in incremental steps, while horizontal loads (N3) and (N4) were set at 5 kN each. Solving the three-dimensional fine FE model of the CSFJ yielded the relationship between the vertical load and the deformation of the CSFJ. The deflection near the wave crest of the CSFJ's flange is selected as the deformation indicator, and its corresponding curve is depicted in Figure 10.  
As shown in Figure 10, several observations can be made: (1) The deflection of the CSFJ gradually increases with the vertical load, following a roughly S-shaped curve. (2) Under negative bending moment conditions, an apparent inflection point in the load-deflection curve occurs at 110 kN vertical load. Conversely, this inflection point appears at 80 kN under positive bending moment conditions. Examination of the CSP and flange yield range with respect to the vertical load reveals localized yielding occurring at 110 kN and 80 kN. Thus, the vertical load capacity for the CSFJ (model dimensions: 380 mm \(\times\) 140 mm \(\times\) 5 mm) is determined to be 110 kN (under negative bending moment) and 80 kN (under positive bending moment).  
Figure 10: Vertical load–deformation curve of CSFJ (negative bending moment working condition).  
Figure 9: Plastic strain contour of flange joint under positive bending moment condition: (**a**) front view; (**b**) back view; (**c**) experimental results [10].
# Design of External Rotor Ferrite-Assisted Synchronous
## 4 Finite Element Simulations
### Cogging Torque and Back-EMF under No-Load  
The developed cogging torques in the proposed ferrite PM-based external rotor motor and rare-earth PM-based motor are shown in Figure 12. The result shows that under no load and at 1800 rpm rotor speed, the peak-to-peak cogging torque developed in the proposed Fe-PMaSynRM design is around 0.2 Nm, which is nearly the same in the RE-PMaSynRM. Therefore, both motors will develop comparable torque pulsation at no load, which is nearly 1% (peak-to-peak) of the average torque.  
The back-emfs of the proposed rare-earth free and rare-earth-based external rotor PMaSynRM are shown in Figure 13. The result shows the developed induced voltage in phase-A winding at 1800 rpm rotor speed. The RMS values of the phase back-EMFs in  
Figure 11: (a) 2D FEA model of the benchmark five-phase internal rotor RE-PMaSynRM, (b) proposed five-phase external rotor Fe-PMaSynRM, and (c) exploded view of the Fe-PMaSynRM.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Name**} & **Benchmark** & **Proposed** \\  & **RE-PMaSynRM** & **Fe-PMaSynRM** \\ \hline Rated power (kW) & 3.8 & 3.7 \\ Rated speed (rpm) & 1800 & 1800 \\ Slot/Pole & 25/12 & 25/12 \\ Rated Current (A) & 9 & 10.5 \\ Stator outer diameter (mm) & 190 & 190 \\ Magnet Material & NdFeB, Nd35 & Ferrite, Y30H-2 \\ Total Volume of Magnet (L) & 0.065 & 0.19 \\ Stack length (mm) & 65 & 65 \\ Air-gap length (mm) & 0.7 & 0.7 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Specifications of motors.  
ferrite and neodymium-based external rotor PMaSynRMs are 63.7 and 47 V, respectively. The differences between the back-EMFs are significant differences in the remnant flux density and flux barrier shapes of Fe and Nd-based motors.
# Using Transfer Learning to Realize Low Resource Dungan Language Speech Synthesis
## 2 Models and Methods
### Text Analyzer of Dungan Language
#### 2.1.3 Word Segmentation  
Word boundaries play a significant role in predicting prosodic boundaries. Thus, it is essential to identify the word boundaries of a sentence post-normalization. Dungan sentences exhibit clear distinctions between words and syllables, making segmentation relatively straightforward. We employed a maximum matching-based word segmentation algorithm to extract Dungan words from the input sentence. We compiled a Dungan word dictionary comprising 49,293 words to facilitate this process. The longest word spans eight characters in this dictionary, while the shortest is a single character. The dictionary primarily encompasses core Dungan terms, as referenced in sources like \"Common Dictionary of Dungan Language\" [39], \"A Survey on Tungan Language in Central Asia\" [40], \"A Survey of Dungan Language\" [41], and additional searchable Dungan terms available online.
# Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
## 5 Studies toward Clinical Applications of TD-NIRS
### Time-Domain Fluorescence Diffuse Optical Spectroscopy (TD-FS) and Tomography (TD-FT)
#### TD-FT Using Full TOF-Distributions and Effects of Featured Data Types  
For TD-FT, [PERSON] et al. reported FT images of the fluorescence yield (\(\gamma\varepsilon N\)) and lifetime (\(\tau\)) using full TOF distributions in numerical simulations [202]. Reconstructed FT images using featured TD data can be evaluated by comparing with those using the full TOF distributions. To improve the quality of the images reconstructed from full TOF distributions, [PERSON] et al. proposed an overlap time-gating approach which simultaneously achieves high time-resolution and high signal-to-noise ratio for input data extracted from the measured TOF distributions [203]. [PERSON] et al. discussed the effect of featured data types on reconstructions of the depth and lifetime of the fluorophore, and concluded that local data types such as the peak values, peak times, FWHMs (full widths at half maximum), and slopes of decaying period of measured TOF distributions have advantages over global data types such as the CW intensities, mean TOFs, variances, and skews [204].
## 9 Performance Validation and Analysis
### Open Sky Scenario
#### 9.1.1. Emulated Scenario 1  
The statistics of the first emulated scenario are presented in the following Table 4. This scenario is designed to evaluate the positioning accuracy in the presence of fixed outliers. Measurement errors stay fixed for a satellite over a defined period of time. The measurement error is kept small because a large error is easy to detect by a simple algorithm however it is more difficult to detect if the error is not very large and show significant effect. Results, in Figure 5, showed that the performance of our proposed estimation scheme is better than LSE and WLSE in both RCEP and PMD.
## 2 Numerical Models
### Comparison of the Numerical and Analytical Model for the Circular Tunnel Case Study
#### 2.3.3 Effect of the Lining Thickness  
The tunnel lining thickness was assumed to vary between 0.2 to 0.8 m, corresponding to a lining thickness vs. tunnel dimension ratio of 3 to 8.5% [28], while the other parameters are based on the reference case assumed in Table 1. Similar to what happens when  
Figure 6: Effect of \(E\) on the incremental internal forces of the circular tunnel lining. (**a**) Extreme incremental bending moments; (**b**) extreme incremental normal forces.  
considering the soil Young's modulus \(E\) and horizontal seismic acceleration \(a_{H}\) effects, the results presented in Figure 7 show good agreement between analytical and numerical models for both no-slip and full-slip conditions. The discrepancy is under 1% for both the incremental bending moments and normal forces.  
In general, the absolute extreme incremental bending moment and normal force values gradually increase when the lining thickness \(t\) increases from 0.2 to 0.8 m. This concerns both the full-slip and no-slip conditions. The incremental bending moments for the no-slip condition are always smaller than the full-slip ones (Figure 7a). The biggest difference of 14% was obtained for a lining thickness of 0.8 m. It should be noted that the incremental normal forces variations caused by the lining thickness increase are less significant when compared to the incremental bending moment ones (Figure 7a,b).  
Based on the above comparison between the analytical solution and numerical model when considering Young's modulus \(E\), horizontal seismic acceleration \(a_{H}\), and tunnel lining thickness \(t\), which show good agreement between the analytical solution and numerical model, it is reasonable to conclude that the circular tunnel numerical model developed can be used to investigate the behavior of circular tunnels subjected to seismic loadings.
# Crossband Filtering for Weighted Prediction Error-Based Speech Dereverberation
## 3 Proposed WPE with Crossband Filtering  
### Conventional WPE  
The conventional WPE for multichannel input predicts the components of the late reflections based on the LP of an inverse filter [19]. To leverage the spatial information to improve the estimation of the late reflections, the inverse filter predicts the late reflectionsbased on observations from all channels. More specifically, let \(y_{f,t}^{(m)}\) be an observed signal in the STFT domain captured by the \(m\)-th microphone of an \(M\) length microphone array, and let  
\(\mathbf{g}_{f}^{(m)}\in\mathbb{C}^{LM}\) be an \(LM\)-order prediction filter. The enhanced signal \(z_{f,t}^{(m)}\) is achieved as  
\[z_{f,t}^{(m)}=y_{f,t}^{(m)}-\mathbf{g}_{f}^{(m)H}\mathbf{y}_{f,t;L}\in\mathbb{C}, \tag{8}\]  
\[\mathbf{y}_{f,t;L}=\left[\mathbf{y}_{f,t;L}^{(1)},\dots,\mathbf{y}_{f,t;L}^{(M )}\right]^{T}\in\mathbb{C}^{LM}, \tag{9}\]  
\[\mathbf{y}_{f,t;L}^{(m)}=\left[y_{f,t-D}^{(m)},\dots,y_{f,t-D-L+1}^{(m)}\right] \in\mathbb{C}^{L}, \tag{10}\]  
where \((\cdot)^{T}\) and \((\cdot)^{H}\) represent the transpose and Hermitian transpose, respectively, and \(D>0\) is the predefined prediction delay. Note that given a frequency bin \(f\), based on the definition of \(\mathbf{y}_{f,t;L}\) in (9), the enhanced signal is obtained using only information from samples of the observed signal in the band-to-band frequency bin, ignoring samples from the crossband frequency bins. For simplicity, we select an arbitrary value for \(m\) and omit the microphone designation from this point onward.
# Analysis and Reduction of Nonlinear Effects in Optical Fiber Frequency Transfer
## 1 Introduction
We specifically focus on two primary nonlinear effects: Brillouin scattering and Raman scattering. Generally, the threshold power for Brillouin scattering is two orders of magnitude lower than that for Raman scattering. Our calculations reveal that the threshold powers for both Brillouin and Raman scattering decrease with increasing fiber length. However, these threshold powers remain relatively constant when the fiber length exceeds 10 km. When the fiber length \(L\) is kept constant, the threshold power for both Brillouin and Raman scattering decreases as the gain coefficients increase. Conversely, this trend reverses for the attenuation coefficient. Furthermore, when the fiber length is predetermined, and the linewidth of the laser source ranges from 1 Hz to 1 MHz, the linewidth of the laser source does not impact the threshold powers required to generate Brillouin scattering. To the best of our knowledge, this study represents the first comprehensive exploration of nonlinear effects with multiple parameters in optical fiber frequency transfer. Our work aims to offer valuable design guidance for mitigating nonlinear effects in optical fiber frequency transfer and, in particular, long-distance and high-precision field fiber optical frequency transmission. The calculation results hold significant potential for guiding various applications utilizing Brillouin and Raman scattering characteristics, such as laser technology and optical fiber sensing. This paper is structured as follows. We present the fundamental principle, including the high-precision optical frequency signal transmission, the Brillouin scattering, and the Raman scattering, in Section 2. In Section 3, the calculation results of the threshold powers for the Brillouin scattering and the Raman scattering, including the forward and backward scattering, are presented. The conclusions are summarized in Section 4.
## 2 Materials and Methods
### Formulation of Exergy Analysis
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Equations** & **Formula** & **Description** \\ \hline Exergy balance & \(\dot{\text{Ex}}_{\text{loss}}=\dot{\text{Ex}}_{\text{mass}}+\dot{\text{Ex}}_{ \text{heat}}+\dot{\text{Ex}}_{\text{work}}\) & (1) & \(\dot{\text{Ex}}_{\text{loss}}\) is exergy loss, \(\text{Ex}_{\text{mass}}\) mass exergy, \(\dot{\text{Ex}}_{\text{heat}}\) heat exergy, \(\dot{\text{Ex}}_{\text{work}}\) work exergy \\ Heat exergy & \(\dot{\text{Ex}}_{\text{heat}}=(1-\text{T}_{\text{o}}/\text{T})\dot{\text{Q}}\) & (2) & \(\text{T}_{\text{o}}\) is reference temperature, T stream temperature, \(\dot{\text{Q}}\) heat flow \\ Exergy of work & \(\dot{\text{Ex}}_{\text{work}}=\dot{\text{W}}\) & (3) & \(\dot{\text{W}}\) system work \\ Mass exergy & \(\dot{\text{Ex}}_{\text{mass}}=\dot{\text{Ex}}_{\text{phy}}+\dot{\text{Ex}}_{ \text{che}}\) & (4) & \(\dot{\text{Ex}}_{\text{phy}}\) physical exergy, \(\dot{\text{Ex}}_{\text{che}}\) chemical exergy \\ Chemical exergy & \(\dot{\text{Ex}}_{\text{ch}-i}=\Delta\text{G}_{\text{t}-i}^{0}+\sum\text{v}_{j} \text{Ex}_{\text{ch}-j}^{0}\) & (5) & \(\dot{\text{Ex}}_{\text{ch}-j}\) chemical exergy of element \(j\), \(\dot{\text{v}}_{j}\) number of atoms, \(\Delta\text{G}_{\text{t}-i}^{0}\) \\ Physical exergy & \(\dot{\text{Ex}}_{\text{phy}}=(\text{H}-\text{H}_{0})-\text{T}_{0}(\text{S}- \text{S}_{0})\) & (6) & \(\dot{\text{Ex}}_{\text{phy}}\) physical exergy H enthalpy, H\({}_{0}\) enthalpy at reference conditions \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Governing equations of exergy analysis. analysis determines the thermodynamic inefficiencies or irreversibility of a system. It also includes the underuse work and energy efficiency [48]. \[\dot{\mathrm{Ex}}_{\mathrm{loss}}=\sum\dot{\mathrm{Ex}}_{\mathrm{in}}-\sum\dot{ \mathrm{Ex}}_{\mathrm{product-out}} \tag{10}\]  
\(\dot{\mathrm{Ex}}_{\mathrm{loss}}\) represents process irreversibilities. The exergy efficiency (n\({}_{\mathrm{energy}}\)) provides the thermodynamic yield of the process. This analysis also considers calculating the influence of each process on the irreversibilities of the process (% \(\dot{\mathrm{Ex}}_{\mathrm{product-out}}\)). \[\mathrm{n}_{\mathrm{energy}}=1-\left(\dot{\mathrm{Ex}}_{\mathrm{loss}}/\sum \dot{\mathrm{Ex}}_{\mathrm{in}}\right) \tag{11}\]  
\[\%\ \dot{\mathrm{Ex}}_{\mathrm{loss-}k}=\left(\dot{\mathrm{Ex}}_{\mathrm{loss -}k}/\sum\dot{\mathrm{Ex}}_{\mathrm{in}}\right) \tag{12}\]  
According to the described mathematical structure, the formulation is normalized in a functional unit (1 kg of product). This setting lets a direct comparison be made between different projects with different sizes and settings.
## 3 Results and Discussions  
### Model Calibration  
The simulation analysis was conducted with the RETScreen Expert 8 tool, and it was calibrated with readings from the bill of the thermal and electricity energy consumption. The numerical model was implemented taking into account the characteristics of the envelope and the plants used for heating, cooling, lighting, and all the electrical equipment used inside the building. Furthermore, information regarding the occupancy profile was considered in order to setup the power of the plants and to consider the thermal internal gains.  
The calibration consists of a comparison of the annual heating, cooling, and electrical demand simulated with the numerical model with the data taken from the bills. In order to minimize the discrepancies between the observed and simulated data, we varied the gainsin the numerical model. For the calibration of the electrical demand, considering that, in most of the cases, it is not possible to accurately quantify the amount of electrical equipment, we introduced in the numerical model more or less electrical equipment. About the heating and cooling demand, the calibration was focused on the variation of thermal gains. It is worthwhile to notice that the introduction of thermal gains can vary the cooling demand that brings a variation in the electricity demand due to the use chillers that work with this kind of energy. For these reasons, the calibration method consists of an iterative process in order to minimize the differences in the numerical, thermal, and electrical demand and the readings from the bills.  
In the case study, a calibration with a discrepancy in the annual thermal and electricity consumption was achieved; they are, respectively, \(-2.2\%\) and \(+3.0\%\). In particular, in Figure 4, the comparison of the bill and the ante-operand scenario that corresponds to the actual situation of the building is shown.
# Multi-Wavelength Quantum Key Distribution Emulation with Physical Unclonable Function
## 6 Future Work  
### Studying the Emulation System Error Rates  
With the emulation system built, the next steps include further testing the ternary protocol and implementing the quaternary protocol in order to study the error rates. The next steps also include implementing an eavesdropper. Running the ternary and quaternary protocols allows us to measure throughput, statistical changes from eavesdropper presence, and overall efficiency. We can also study the error rates. Two potential sources of error include: (1) error from the emulation hardware and physical components (e.g., alignment, lighting issues/read noise); (2) error from the PUF (e.g., inconsistencies between the image of the PUF and PUF). To isolate the error introduced by the emulation physical components, we can hardcode identical PUF ternary and quaternary messages for the server and client, which eliminates error from the PUF. To isolate the PUF error, we can study the differences between the server and client independent of the emulation QKD protocol. Future work also includes modifying the ternary protocol to switch the unused states' different probabilities, as mentioned earlier in Section 2. We also aim to write the software for quaternary (and higher) protocols and repeat the error studies. Adding a third wavelength is also a possible direction for future work.