esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Harmonic Detection, Suppression, Aggregation, and Estimation Techniques
## 4 Harmonic Aggregation
This technique is applied to three low-voltage networks illustrated in Figure 11. The results indicate that it can effectively represent their stochastic behavior while greatly reducing the computational load compared to modeling the downstream network. Another finding from [58] is that resonances occur in a narrower frequency band as the number of customers increases. The previous standard IEC/TR 63000-3-6 states the general summation law but with a limited number of harmonic sources [59]. Therefore, ref. [59] provides a methodology that uses more sophisticated equipment to allow facility operators to plan accordingly after an accurate harmonic contribution and summation estimate. This new standard aims for a 95% probability of the estimated value not exceeding the actual value. Another popular method for harmonic aggregation is the harmonic peak picking method [60], which is a non-parametric method that estimates the fundamental frequency  
Figure 11: Flowchart of the proposed method for stochastic aggregation model. Figure 10: Third current harmonic simulations of aggregated electric vehicle loads for different time aggregation intervals [57]. of a signal by selecting the peak harmonic frequency of the signal. This method is also simple to implement and computationally efficient, but it can also be sensitive to noise and other distortions. A more recent method for harmonic aggregation is the harmonic cepstral method [61], which is a parametric method that estimates the fundamental frequency of a signal by fitting a harmonic model to the cepstral coefficients of the signal. This method is more computationally intensive than peak picking methods. However, it can provide more accurate estimates of the fundamental frequency of a signal in the presence of noise and other distortions. In addition, recent research has also used machine learning-based approaches, like neural networks, to improve the robustness and accuracy of harmonic aggregation [62; 63]. A comparative analysis of harmonic aggregation models is presented in Table 3.  
In conclusion, traditional methods face challenges due to variations in non-linear sources and unpredictable system parameters, which impact precision. Techniques mentioned in IEC 61400-21 show limitations with renewable energy generators, where aggregation errors arise due to phase angle dispersion and harmonic cancellation. More recent approaches include advanced modeling, such as stochastic models for load variation, harmonic peak picking, and cepstral methods for improved accuracy. Machine learning models further enhance robustness and accuracy, addressing complex system dynamics and noise.
# A Novel Channel Coding Scheme for RFID Generation-2 Systems
## 1 Introduction
Apart from improving the access control mechanism, it is also possible to tackle the collisions at the signal coding level by increasing the robustness of the signal transmitted on the noisy radio channel, so that the receiver is able to correctly reconstruct the original signal, even in case of collisions. Moreover, coding scheme and collision-avoidance protocol are not mutually exclusive; on the contrary, they are complementary approaches that can be combined to increase the overall tag detection probability. In particular, the current Gen-2 standard supports two types of baseband data encoding schemes prior to the amplitude shift keying (ASK) or phase shift keying (PSK) modulator: a scheme of bi-phase space (FM0) and a Miller baseband data encoding scheme. A new scheme for the structure of Gen-2 tags [10; 16] has been proposed in [8], which includes a channel encoder block based on a convolutional coding scheme and a corresponding decoder block in the receiver system. However, this type of complex decoder impacts the energy efficiency and the cost of passive tags. In [17], the same idea has been further investigated and expanded by considering a turbo coded scheme where an outer convolutional code is serially concatenated to the FM0 and Miller baseband codes, regarded as inner codes. Since RFID applications require low-cost and low-complexity solutions, we propose a novel Gen-2 modulation scheme inspired by the superposition codes concept and featuring low complexity hardware implementation. Actually, the coding process acts on very short codewords, with a length equal to four bits, and the decoding algorithm may be implemented by an analog comparator which chooses the maximum signal among four. We apply the proposed encoding method to the Gen-2 FM0 modulated signal, with the same code rate of Miller subcarrier modulator. The proposed scheme has been verified and evaluated through MATLAB simulation of Gen-2 MAC level [18]. Results show that our approach outperforms the more complex Viterbi hard decoding-Miller 4 modulation scheme under different noise levels and interrogation interval conditions, despite the low complexity of the proposed decoder. We have also considered the performance of the Viterbi soft decoding using the same modulation: surprisingly, the performance of our novel and simple decoding algorithm is not far from the best attainable performance for the Miller 4 case. The rest of the article is organized as follows. Section 2 describes the system model and details the proposed coding and decoding schemes. The simulation environment used for performance evaluation is provided in Section 3, and Section 4 compares the results of the proposed algorithm with the hard and soft Viterbi schemes. Finally, some concluding remarks wrap up and close the article in Section 5.
# Frost Resistance of Coal Gangue Aggregate Concrete Modified by Steel Fiber and Slag Powder
## 3 Results and Discussion
### Air-Void Characteristics before F-T Test
#### 3.3.1 Effect of Steel Fiber Type on Air-Void Structure  
The air-void distribution and cross-sectional morphology of CGAC with and without steel fibers are shown in Figure 15. Similar to ordinary concrete, coarse and visible pores of low frequency constitute a high proportion of pore volume, while it was the opposite for fine pores, which is well illustrated in Figure 4b. For ordinary CGAC, most air-voids had a chord length of 0.02-0.1 mm. Air-voids with chord lengths larger than 0.5 mm covered a large volume fraction. Especially for the chord length of 0.5-1.0 mm, the air-void content was up to 0.61%. The air-voids became smaller after incorporating steel fibers and more isolated closed pores were found in the cross-sectional morphology. For CGAC with 1 vol.% HESF, the frequencies of the air-voids with chord lengths of 0.01-0.02 mm and 0.02-0.03 mm were 15.2% and 0.98%, respectively, which were much more than that of the ordinary CGAC. Compared with the air-void size distribution of CGAC with HESF, the CPSF-reinforced CGAC had a smaller proportion of small air-voids. This may be related to the smooth surface and flat shape of CPSF, which makes it better for CPSF to be mixed with the concrete mixture, thus resulting in less air being brought into concrete.  
It can be seen from the cross-sectional morphology that as a kind of lightweight aggregate, porous coal gangue aggregate can internally cure the concrete, making the interfacial transition zone denser, but it can also weaken the mechanical properties of concrete. On the other hand, the ordinary CGAC has a large number of coarse pores, and the water in the coarse pores generates pressure on the pore wall of concrete when it freezes, which is harmful to the frost resistance of the concrete. The incorporation of steel fibers can reduce the development of microcracks and the formation of coarse pores, and increase the number of isolated closed pores, which will alleviate the expansion pressure caused by water freezing and improve the frost resistance of concrete.  
The characteristic parameters of air-voids in CGAC with different steel fiber types are shown in Table 7. It is easy to see that compared with that of the ordinary CGAC, the spacing factor and the mean chord length of air-voids of CGAC with 1 vol.% HESF declined 19.6% and 17.7%, respectively, and the quantity of voids and specific surface area increased by 27.8% and 9.9%, respectively. Fine closed pores are beneficial to the frost resistance of concrete [45], which also helps explain why the frost resistance of HESF-reinforced CGAC is better than that of CPSF-reinforced CGAC.  
Figure 14: Effect of slag powder content on RDME of CGAC.  
Figure 15: Air-void distribution and cross-sectional morphology of CGAC (**a**) without steel fiber; (**b**) with 1% HESF; (**c**) with 1% CPSF (1: Air-void distribution; 2: Cross-sectional morphology).
# Prediction of Phytochemical Constituents in Cayenne Pepper Using MIR and NIR Spectroscopy
## 2 Materials and Methods
### Collection of MIR Spectra  
Mid-infrared spectra were collected from the dried, ground chili powder using a Bruker Alpha Fourier transform infrared (FTIR) spectrophotometer (Ettlingen, Germany). A single-reflection diamond crystal attenuated total reflectance (ATR) module was used to ensure firm contact between the sample and the infrared beam. The spectra were recorded between 4000-400 cm\({}^{-1}\) in triplicate for each sample (n = 300 spectra), with each spectra being the average of 24 scans at 4 cm\({}^{-1}\) resolution. Spectra were exported in Opus (*.0) format.
## 2 Materials and Methods
### Data Preprocessing  
Atmospheric correction of remote sensing images can correct the influence of aerosols in the atmosphere on electromagnetic wave propagation and improve the accuracy of the signals received. It is a necessary step for quantitative remote sensing inversion and analysis. In this study, Korea Ocean Satellite Center standard atmospheric correction technology was used for GOCI data to generate water-leaving radiance [36]. Atmospheric aerosol properties were estimated using two near-infrared channels, which makes the results more suitable for GCOI images [37; 38]. For the Landsat 8 OLI data, the 6S atmospheric correction model was used. The 6S model is a basic radiative transfer code that can accurately simulate satellite and plane observations, simulate the real mixed atmosphere, and calculate gaseous absorption. It is one of the most widely used, rigorously verified, and extensively recorded radiative transfer codes known for satellite remote sensing [39]. The 6S atmospheric correction process automatically generates the corrected result image by setting the satellite altitude, acquisition time, aerosol inversion mode, and other parameters [40].  
Figure 2: Overall workflow in this study.  
The GOCI Chl-a data used in this study were generated by GOCI's official data processing software, the GOCI Data Processing System (GDPS), and the retrieval algorithm was OC3g.  
\[\log_{10}(\text{Chl}-\text{a})=a_{0}+\sum_{i=1}^{4}a_{i}(R)^{i} \tag{1}\]  
\[R=\log_{10}\left(\frac{R_{rs}(443)>R_{rs}(490)}{R_{rs}(555)}\right) \tag{2}\]  
where \(a_{0}\), \(\ldots\), \(a_{4}\) are the Chl-a calculation parameters, and \(R\) is the band combination data.  
Landsat Chl-a concentration was retrieved with the OC3 algorithm provided by the Ocean Color website. The form of the OC3 algorithm is consistent with OC3g, but the value of \(R\) is different.  
\[R=\log_{10}\left(\frac{R_{rs}(\lambda_{blue})}{R_{rs}(\lambda_{green})}\right) \tag{3}\]  
The values of the coefficients are shown in Table 2.
# Experimental and Numerical Research on End-Wall Flow Mechanism of High-Loading Tandem Stators
## 2 Research Object and Method
### Research Methodology  
The experimental measurement and numerical simulation were used in the study. The stage characteristics of tandem blades, the spanwise parameter distribution of tandem stators, and the outlet flow field were measured experimentally, and the end-wall flow structure of the stator was shown by the oil flow method. The numerical simulation was used to supplement the flow details in the stator channel.
# Investigation of Biochars in Terms of Vitamin E Adsorption Capacity
## 3 Results and Discussions
### Adsorption Capacity of \(\upalpha\)-Tocpheryl Acetate
#### 3.4.3 Correlation of Adsorption Capacity and Biomass and Pyrolysis Processes  
To test the hypothesis that the biomass and pyrolysis processes affect adsorption, the data were sorted. For the biomass, the main biomass was used, and biochar E, without specified biomass, was removed. However, as seven of the biochars contained mainly beech or had been as the first biomass listed, and biochar I consisted of spruce and biochar B of poplar, no correlation could be tested. For correlation, biochars C and D were removed, due to unspecified pyrolysis processes. The remaining biochars were either produced with 'PYREG' (\(n=6\)) or with a kiln (\(n=2\)). Since no quantitative descriptors of the processes exist, the grouped adsorption capacities were compared using a t-test. As the normality failed (\(p=0.008\)), a Mann-Whitney Rank Sum t-test was conducted. This test revealed no significant differences (\(p=0.286\)). Thus, the variation between the biochars does not correlate with the pyrolysis process. Referring to the previously reported results and findings from the literature [46; 58; 63], it is assumed that the adsorption capacity depends on multiple internal and external factors.
# Evaluation of IEEE802.15.4g for Environmental Observations
## 5 Discussion
### On the Longer Range of FSK-FEC and O-QPSK  
Throughout the experiments we can observe that the longest radio links were obtained when using the FSK-FEC and O-QPSK PHYs. This is expected as both set of PHYs have the highest sensitivity of all PHYs, delivering up to 141 dB of link budget (see Table 6, OQPSK-6.25).  
OFDM radio signals are more robust against interferences and multi-path effects. OFDM1-100 uses 104 sub-carriers (96 data and 8 pilot tones) with 4x frequency repetition (there are 4 sub-carriers with the same information), providing a high level of robustness. OFDM2-50 has the same characteristics but half of the sub-carriers and thus, half of the data rate.  
Each OFDM option 1 channel occupies 1.2 MHz and the 4 equal sub-carriers are separated by the same distance (in frequency). Even if 3 sub-carriers get compromised during one symbol transmission on the path towards the receiver, this can still recover the information. Even though that robustness is provided, in Table 7 the PDR for this PHY is just 31% and 5% for short and long packets at 700 m. For OFDM2-100, the PDR increased to 92% and 47%. This PHY is 2 dB more sensitive than OFDM1-100. We see in Table 6 that the maximum power for OFDM is +11 dBm whereas for the rest is +14 dBm.  
Our believe is that the longer range of the O-QPSK and FSK-FEC PHYs can be attributed to their higher sensitivity in comparison to the rest of the PHYs tested. FSK-FEC holds 5 dB, 5 dB and 8 dB higher sensitivity than its non-FEC counterpart (2 FSK-50, 2 FSK-100, 4 FSK-200). When compared 2 FSK-50 with OQPSK-50, the difference is 8 dB.  
The reason why OFDM PHYs do not reach as far as these PHYs is that they present a lower sensitivity and a lower maximum TX power.  
These values of sensitivity and maximum TX power are directly dependent of the hardware and can vary depending on the manufacturer.
## 2 Proposed Approach
### Linear Fitting Correction Model Based on Guided Filtering  
Nonuniformity has been effectively corrected by the guided filter model. However, the image's high-frequency information may be unnecessarily blurred by the process of guided filtering. Since the infrared focal plane linear array scan detector imaging speed is fast, there would be tens of thousands of columns of images per second, and the guided filter needs to calculate the linear coefficients \((a_{k},b_{k})\) of each window, as shown in Equations (3) and (4). The algorithm has a high time complexity and large computational overhead, making it difficult to ensure real-time performance. Traditionally, the nonuniformity correction methods of an infrared array detector involve collecting the temperature of a blackbody and employing a linear model with two or more points to adjust. This is consistent with the assumption that the guided image and the output image are linearly related in the window. Therefore, the small window of guided filtering is discarded. The guided filtering window is a one-dimensional horizontal window, and its length is equal to the width of the guided image. The linearity coefficients are calculated using the image \(I_{q}\) after the guided filtering as the input image and the raw infrared image \(I_{p}\) as the guided image [29]. At this moment, the coefficients of the guided filter only need to be calculated once, and the computation of the linear coefficients is turned into least squares linear regression, which significantly decreases the algorithm's temporal complexity, as follows:  
\[a(i)=\frac{\sum\limits_{j=1}^{W}I_{q}(i,j)(I_{p}(i,j)-\sum\limits_{k=1}^{W}I_{p }(i,k)/W)}{\sum\limits_{j=1}^{W}(I_{p}(i,j)-\sum\limits_{k=1}^{W}I_{p}(i,k)/W) ^{2}} \tag{10}\]  
\[b(i)=\frac{\sum\limits_{j=1}^{W}I_{q}(i,j)-a(i)\sum\limits_{j=1}^{W}I_{p}(i,j)} {W} \tag{11}\]  
where \(W\) is the width of the image after a guided filtering process, \(I_{p}(i,j)\) is the image that was partially extracted from the full-frame raw infrared image with a horizontal strip, and \(I_{q}(i,j)\) is \(I_{p}(i,j)\) after guided filtering, as shown in Figure 3. The obtained linear correction coefficients are used to correct the whole frame of the raw infrared image:  
\[\hat{I}(i,j)=a(i)\times\hat{I}_{p}(i,j)+b(i) \tag{12}\]  
where \(a(i),b(i)\) are the correction coefficients of the \(i\) th row of the image, \(\hat{I}_{p}(i,j)\) is the whole frame raw infrared image, and \(\hat{I}(i,j)\) is the whole frame image after nonuniformity correction.
## 3 Cochleogram and Mel Spectrogram Generation  
Both Cochleogram and Mel Spectrogram represents speech in two-dimensional time-frequency using the Equal Rectangular Band (ERB) scale and Mel Scale, respectively. At the beginning steps of Cochleogram and Mel Spectrogram generation, the steps such as: Pre-emphasis, framing, windowing, Fast Fourier Transform (FFT) and power spectrum generation are applied in common.  
Mel Filter banks, which simulates non-linear human auditory perception is computed from FFT, then it is stacked together to form the Mel Spectrogram of the speech. The sample Mel Spectrogram is shown in the Figure 1.  
Gammatone filter bank (GFTB) provides a closer approximation to the bandwidths of filters in the human auditory system. It is specified on the Equivalent Rectangular Bandwidth (ERB) scale, which is a psychoacoustic measure of the width of the auditory filters. Representing low frequency speech in higher resolution [24], non-linear rectification [7] and measuring of psychoacoustics feature from the speech make Gammatone filter banks more preferable speaker characterization in noisy condition. GFTB is computed from the FFT, then stacked together to form Cochleogram. Sample Cochleogram is shown in Figure 2 below.
# Acoustic Emission for Structural Monitoring of Historical Masonry: An In-Field Application
## 2 Objectives and Methodology  
This study aims to assess the potential of Acoustic Emission (AE) monitoring as a non-destructive technique for the structural evaluation of historic masonry. The experimental campaign was carried out on a restored arch of the Santa Maria delle Grazie complex in Barano d'Ischia (Italy) during a cyclic load test.  
The main objectives are  
* To verify the responsiveness of AE sensors in a real-site load test on masonry;
* To detect the onset and progression of damage under increasing load levels;
* To correlate AE activity with mechanical displacements measured by comparators.  
The methodology includes
* A three-cycle semi-static load test, with incremental loading and unloading steps;
* AE monitoring using four piezoelectric sensors, integrated with displacement measurements;
* A data acquisition strategy dynamically adjusted to background noise and signal thresholds.  
The identification of key AE parameters includes
* The Kaiser effect, to detect whether emission occurs only beyond previous load levels;
* The Felicity ratio, to assess the structural memory and degree of internal damage;
* Source localisation, through the Arrival Time Method, to spatially map AE events along the structure.  
This combined methodological approach aims to deepen the understanding of AE behaviour in complex, historically significant masonry structures and explore its compatibility with established modelling tools.
## 4 Ensemble Learning IDS for IoV: Research Trends and Gaps
### Distributed Deployment
#### 4.1.5 Based on Application Specificity  
Network-Based IDSs  
These systems track network traffic looking for hostile activity. Ensemble learning improves the precision and scalability of these systems. Within a network-based intrusion detection system, [PERSON] and [PERSON] [69] used ensemble learning to evaluate traffic from several IoV nodes. To address the difficulty of spotting scattered attacks over large IoV networks, their method used classifiers such as Random Forest and Gradient Boosting. The studies show improved detection accuracy and the capacity to identify complex network-level intrusions. [PERSON] and [PERSON] [74] looked at network-based ensemble learning models housed under centralized systems. Their work focused on combining traffic data for ensemble classifier processing, thereby enhancing the identification of volumetric attacks, particularly to protocols. Two highlighted shortcomings in real-time scenarios were scalability and latency.  
Host-Based IDSs  
These systems seek to monitor certain devices, such as cars, for signs of illegal access. This method lowers transmission overhead and supports more localized data analysis. Using host-based IDS and ensemble learning approaches, [PERSON] et al. [75] examined statistics from individual automobile systems to find host-level abnormalities with light-weight classifiers like AdaBoost and DTs. This method effectively solves resource constraints in IoV devices and maintains excellent detection accuracy. To improve security and privacy, [PERSON] et al. [77] used federated learning in concert with host-based IDS. Analyzing data on host devices and distributing model changes allowed their ensemble approach to meet privacy protection and real-time adaptation.  
Hybrid IDSs  
In a hybrid IDS, ensemble learning helps to detect threats completely. In 2020, [PERSON] et al. [57] created a hybrid IDS architecture combining network traffic analysis with host-level log monitoring. To address the difficulty of identifying intricate multi-vector attacks, the study applied ensemble classifiers incorporating neural networks and SVM. While improving detection coverage, the hybrid approach lowered false positives. In 2022, [PERSON] et al. [61] presented a hybrid ensemble learning system using networked topologies. Their method guaranteed dependable detection by combining host-level and network data, hence reducing transmission costs. This approach was particularly effective in spotting coordinated attacks across different IoV zones.