esa-sceva/satcom-chunk-collection huggingface dataset:
# Low-Complexity Synchronization Scheme with Low-Resolution ADCs
## 1 Introduction
However, the key bottleneck to doing this is the ADC; the cost and power consumption of high-resolution ADCs become prohibitive at multi-GHz sampling rates [17]. Thus, it is natural to ask whether DSP-centric architectures with samples quantized at significantly low-resolution (e.g., 1-4 bits) can be effective. A phase-quantized carrier-asynchronous system model was studied in Reference [18], and it employed block non-coherent demodulation, approximating the phase as constant over a block of symbols. This approach incurred a loss of about 2 dB with respect to unquantized block non-coherent demodulation. A receiver architecture was implemented for a gigabit/s 60 GHz system in Reference [19], including blocks for both carrier synchronization and equalization. While the emphasis in Reference [19] was on establishing the feasibility of integrated circuit implementation rather than algorithm design and performance evaluation, it makes a compelling case for low-power mixed signal designs at high data rates. In Reference [20], the authors investigated a canonical problem of blind carrier phase and frequency synchronization with coarse phase quantization. Then, they developed a Bayesian approach to blind phase estimation, jointly modeling the unknown data, unknown phase, and the quantization nonlinearity. Performance evaluations for a quadrature phase shift keying (QPSK) system shown that excellent BER performance, close to that of an unquantized system, was achieved using eight phase bins. In order to improve the power consumption and complexity of the systems, synchronization performance with low-resolution ADCs was studied in this paper. Considering that the autocorrelation of the pseudo-noise (PN) sequence is not altered by low-resolution ADCs, we propose a frequency offset estimation scheme based on one-bit timing synchronization. We firstly performed the timing synchronization under one-bit quantization, and then implemented the carrier synchronization under multi-bit quantization. In this way, the cyclic summation process at full resolution could be simplified effectively. By equipping low-resolution ADCs, the power consumption and complexity of the systems are greatly ameliorated. This paper is split into five sections. In the second section, the effects of orthogonality and frequency offset on OFDM systems are described in detail. In the third section, a novel synchronization scheme with low-resolution ADCs is proposed. The fourth section analyzes the performance of the proposed synchronization scheme in a comprehensive way through simulation results. In the last section, the conclusions are drawn and future research directions are envisioned.
## 4 Non-Cooperative Game of Customers
### Formulation of Non-Cooperative Game
\right.\] (19) which means that the utility in this game is an increasing function before getting the pole point and then it is a decreasing function. In summary, the utility in this game is a concave function, while being monotone increasing or monotone decreasing, or increasing at first and then decreasing. Therefore, this function is quasi-concave in the strategy space. According to the lemma about existing Nash equilibriums, they do exist in this game. It should be noted that if \(K(t)\leq 1\) after each customer chooses an ideal strategy, which means the actual rebate ratio equals the ideal rebate ratio, i.e., \(k_{i}^{*}(t)=k_{i}(t)\), the whole system reaches the equilibriums. However, if \(K(t)>1\), which means the actual rebate ratio does not equal the ideal rebate ratio, i.e., \(k_{i}^{*}(t)\
eq k_{i}(t)\), customers will recalculate an ideal ratio based on the current \(K(t)\). In this situation, customers need to interact with each other and finally reach a balance. The iterative step is as follows:  
(1) For customer \(i\), initialize \(K^{(m=0)}(t)=1\), and calculate an initial ideal rebate ratio \(k_{i}^{opt\,(m=0)}(t)\) to maximize his/her own welfare, and \(m\) represents the iteration number. Because the monotonicity of the welfare function has been analyzed according to (17)-(19), the optimal \(k_{i}^{opt}(t)\) can be determined according to (20):  
\[k_{i}^{opt}(t)=\arg\max_{k_{i}(t)}w_{i}^{DR}(t)=\left\{ \begin{array}{ll} \overline{k}_{i}(t)&\hat{k}>\overline{k}_{i}(t)\\ \hat{k}&\underline{k}_{i}(t)\leq\hat{k}\leq\overline{k}_{i}(t)\\ \underline{k}_{i}(t)&\hat{k}<\underline{k}_{i}(t)\end{array} \right. \tag{20}\]  
(2) Update the value of \(K^{(m)}(t)\) and recalculate an ideal rebate ratio \(k_{i}^{opt(m)}(t)\) according to (20)until it reaches the converge condition:  
\[\left|k_{i}^{opt\,(m+1)}(t)-k_{i}^{opt\,(m)}(t)\right|<\xi \tag{21}\]  
where \(\xi\) is a small positive constant. This iterative process can be characterized by Algorithm 1. Algorithm 2 is a part of Algorithm 1, which is the process for each customer to evaluate the value of the total rebate ratio of all the DR customers and will be introduced in the next part. ```
1:for each \(i\in\mathcal{I}\)do
2: Obtain global configure information from LSE day-ahead;
3:\(m\) = 0;
4: Keep initial estimation value of total rebate ratio \(\hat{K}_{i}^{(m=0)}=1\)
5:repeat
6: Calculate optimal ideal rebate ratio \(k_{i}^{opt(m)}\) according to (20);
7:\(m=m+1\);
8: Execute Algorithm 2 to update the estimation value of total rebate ratio \(\hat{K}_{i}^{(m)}\);
9:until (21) is satisfied;
10: Report the optimal rebate ratio \(k_{i}^{opt(m)}\) to LSE;
11:endfor
```  
**Algorithm 1**Non-cooperative process executed by DR customer \(i\in\mathcal{I}\)
## 5 Experiments
### Diagnosis by VMD-Based Envelope Analysis  
To show the superiority of the proposed method, the envelope demodulation method in the order domain, based on variational mode decomposition (VMD) is selected for comparison. The following steps can be used:  
1. Order tracking. The same as (1) in Section 5.3.
2. Signal decomposition and reconstruction with VMD are used to remove noises [16], in order to obtain the signal in the order domain after de-noising.
3. The envelope demodulation method based on the Hilbert transform is applied to achieve the envelope order spectrum.  
Then, the envelope order spectrum is obtained, as shown in Figures 20 and 21.  
Figure 20 shows the envelope order spectrum of the fault signal of the intershaft bearing's outer ring. In this spectrum, the highest amplitude order is 1.2. An order of 15.9, corresponding to a fault in the outer ring, can be seen but it is not the dominant component.  
Figure 19: Envelope order spectrum of inner ring fault (envelope demodulation based on wavelet de-noising).  
Figure 18: Envelope order spectrum of outer ring fault (envelope demodulation based on wavelet de-noising).  
Figure 21 shows the envelope order spectrum of the fault signal of the intershaft bearing's inner ring. In this spectrum, the dominant order is 2. An order of 18.1, corresponding to the fault of the inner ring, is completely buried by noise.  
The analysis results for the above two sets of data show that it is very difficult or even impossible for the algorithm to extract the weak fault features of an intershaft bearing.
## 2 Materials and Methods
### Preparation of Microcapsules  
Nanoemulsions were mixed for at least 30 min with 30% (\(w/w\)) maltodextrin solution in buffer solution (imidazole/acetate, 5 mmol/L) at two different pHs, 3 and 7, at ratio 1:1 (\(w/w\)). Each emulsion was mixed with a maltodextrin solution having the same pH. Next, the emulsion-maltodextrin mixture solution was divided into two parts for drying with two different techniques. The moiety of this mixture solution was dried in a laboratory-scale spray-dryer equipped with a 0.5 mm nozzle atomizer (Mini spray-dryer B-290, BUCHI, Buchegg, Switzerland). The mixture solution was pumped to the spray-dryer at a feed rate of 1 L/h at room temperature and dried at an inlet temperature of 180 \({}^{\circ}\)C and an outlet temperature of 80 \({}^{\circ}\)C with an air pressure of 3.2 bar. The other moiety of the mixture solution was dried with a laboratory-scale freeze dryer (Lyovapor(tm) L-200 Pro, BUCHI, Buchegg, Switzerland) operating at a vacuum pressure of 0.08 mbar and condensing temperature of -54 \({}^{\circ}\)C. Before freeze-drying, the mixture solution was frozen at -20 \({}^{\circ}\)C for at least 12 h. The freeze-drying process took 4 days to completely dry the samples. All dried powders were collected and stored in airtight containers at 4 \({}^{\circ}\)C for further analysis. Figure 1 shows a schematic representation of the preparation procedure and Table 1 shows the abbreviations of the SD and FD samples.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample Abbreviation** & **Buffer pH** & **Emulsifier** & **Drying Method** \\ \hline \multirow{4}{*}{Emulsions} & **L3** & 3 & Lecithin & - \\ \cline{2-4}  & **L7** & 7 & Lecithin & - \\ \cline{2-4}  & **P3** & 3 & Pea protein & - \\ \cline{2-4}  & **P7** & 7 & Pea protein & - \\ \hline \multirow{4}{*}{Powders} & **L3S** & 3 & Lecithin & Spray-dryer \\ \cline{2-4}  & **L7S** & 7 & Lecithin & Spray-dryer \\ \cline{2-4}  & **P3S** & 3 & Pea protein & Spray-dryer \\ \cline{2-4}  & **P73** & 7 & Pea protein & Spray-dryer \\ \cline{2-4}  & **L3L** & 3 & Lecithin & Freeze-dryer \\ \cline{2-4}  & **L7L** & 7 & Lecithin & Freeze-dryer \\ \cline{2-4}  & **P3L** & 3 & Pea protein & Freeze-dryer \\ \cline{2-4}  & **P7L** & 7 & Pea protein & Freeze-dryer \\ \hline \hline \end{tabular}
\end{table}
Table 1: Abbreviations for samples of emulsions and dried powders.
# Modelling Pollutant Dispersion in Urban Canyons to Enhance Air Quality and Urban Planning
## 3 Results
### Airflow Pattern
In addition, AR morphologies in a single-vortex regime now have two recirculations, such as AR = 2.5. Secondly, lower vortices become larger compared to previous results, displacing their centre towards the roof level. This implies that a little uncertainty in wind speed magnitude may trigger crucial changes in airflow patterns because of the formation of unexpected vortices. Figure 5: Air pattern for different \(AR\)s of street canyons with \(Re\) = 2,030,387. Figure 6: Air pattern for different \(AR\)s of street canyons with \(Re\) = 507,597. Finally, the information regarding airflow patterns can be summarised in Figure 7 where the number of vortices is displayed depending on the aspect ratio and Reynolds number. With this, the characterisation of the airflow pattern for a wide range of typical street configurations is provided. This is very useful for the guidance of the initial steps of outdoor conditioning because the number of vortices directly determines the ventilation capacity of the pedestrian level. This finding is fundamental because, according to other authors [25; 33], the airflow pattern does depend on the Reynolds number for deep street canyons. This means that the previous work from the literature that was carried out with reduced-scale models might be insufficient to explain the real behaviour of flow across streets, because the range of Reynolds numbers typically used does not resemble the ones found in reality, so the findings referring to the airflow pattern are not feasible for its use in real projects, due to the uncertainty of the vortices formed. For example, [PERSON] et al. [32] intended to characterise the pollutant dispersion by using a model with \(\mathbf{Re}\)-12,000, which can lead to inaccurate results. Furthermore, the studies that do use a common Reynolds number regime usually only focus on a specific urban layout with few cases, for instance, [PERSON] et al. [30] studied different external conditions but only for a fixed street aspect ratio, as well as [PERSON] et al. [33]. These studies are incomplete, as they do not provide enough information for a wide range of scenarios. Therefore, this work addresses the existing gap in information about the number of recirculations formed in a narrow street when the exterior wind speed changes for a range of common Reynolds numbers for an average building.
## 3 Results and Discussion
### Cluster Analysis  
Hierarchical cluster analysis (HCA) of the forty-two peach cultivars was performed using the nine volatile compounds characterized as peach aroma volatiles from the previous literature, as listed in Table 4. The Euclidean method was used for the distance calculation for the cluster analysis. The forty-two peach cultivars can be grouped into two different clusters (Figure 8). Cluster 1 was made up of 37 peach cultivars. Cluster 2 was made up of 5 peach cultivars. From the dendrogram, the genotypes in Cluster 2 are further apart than the genotypes found in Cluster 1. The \"Fairtime\" and \"Southern Pearl\" cultivars both were found to have the highest levels for multiple volatiles detected in this study (Table S1). The results of this cladogram may represent the complexity of their flavor profile, with Cluster 1 representing cultivars with unique flavor and aroma characteristics.  
Figure 8: Hierarchical agglomerative cluster analysis based on a similarity matrix using the Euclidean method of 42 peach cultivars based on 9 major volatile compounds that characterize peach aroma.  
The peak characteristic aroma volatiles such as benzaldehyde, linalool, \(\gamma\)-decalactone, and \(\delta\)-decalactone have been reported in the literature to increase their levels during maturation [3; 8]. Since all the cultivars in this study were processed fully ripened, these compounds were compared across the three harvest seasons (early, mid, and late) to determine which harvest season had more of these volatiles (Figure 9). It could be seen that overall, mid- and late-season peaches had significantly higher benzaldehyde than the early-season cultivars. For linalool and \(\delta\)-decalactone, there was no significant difference between the three harvest seasons. Mid-season cultivars overall were significantly higher than both early- and late-season cultivars for \(\gamma\)-decalactone. Since there are not many differences among the three seasons with only a few exceptions, it could be suggested that peach aroma/flavor may be attributed by the interaction of multiple factors such as fruit growth characteristics, environmental factors, genetic components, and others.
# CFP-AL: Combining Model Features and Prediction for Active Learning in Sentence Classification
## 6 Conclusions and Future Work  
We have attempted various interpretations in active learning for sentence classification, an area where research has been limited due to the dominance of uncertainty-based sampling as the most effective method. Through prior studies that explain the characteristics of fine-tuning well, we understood that the model tends to cluster data, leading to the insight that there are not many data points near the decision boundary, which we confirmed through visualized plots. Additionally, simple experiments using only the model's features showed which data are beneficial or harmful to the model. Therefore, we found that the features of the data passing through the model can play an important role in improving performance. And we proposed CFP-AL, which combines the features of the model and the output probabilities to score and showed the novelty of improving performance by combining features. This will enable future research on feature-based methods, an area that has been less explored due to the heavy reliance on the uncertainty of model outputs. Our study, supported by various experiments and visualizations, aligns with many previous studies, making it easily comprehensible. We believe that our research and analysis will provide insights not only for active learning tasks but also for various other fields in the future.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & & \multicolumn{8}{c}{**Acquired Dataset Size (\%)**} \\ \cline{3-10}
**Dataset** & \(\mathbf{\alpha}\) & **1** & **3** & **5** & **7** & **9** & **11** & **13** & **15** \\ \hline \multirow{3}{*}{IMDB} & 0.5 & 61.46 & 86.97 & 87.22 & 88.66 & 89.14 & 90.02 & 89.80 & 90.02 \\  & 0.1\(c\) & 66.06 & 86.27 & 87.79 & 88.81 & 89.13 & 89.47 & 89.76 & 90.54 \\  & 1-0.1\(c\) & 63.92 & 86.63 & 87.32 & 88.41 & 89.48 & 87.35 & 89.32 & 89.80 \\ \hline \multirow{3}{*}{SST-2} & 0.5 & 84.96 & 88.06 & 88.63 & 89.32 & 89.44 & 90.01 & 89.90 & 90.76 \\  & 0.1\(c\) & 84.50 & 88.98 & 89.44 & 88.75 & 88.40 & 89.32 & 90.08 & 91.23 \\  & 1-0.1\(c\) & 85.76 & 86.45 & 88.06 & 88.98 & 88.98 & 90.70 & 90.47 & 91.04 \\ \hline \multirow{3}{*}{QNLI} & 0.5 & 79.37 & 83.54 & 81.29 & 85.58 & 83.87 & 86.51 & 87.10 & 88.45 \\  & 0.1\(c\) & 80.87 & 81.46 & 80.63 & 86.03 & 86.55 & 86.95 & 87.75 & 88.69 \\  & 1-0.1\(c\) & 79.28 & 82.87 & 84.95 & 84.44 & 86.38 & 82.74 & 87.28 & 88.30 \\ \hline \multirow{3}{*}{QQP} & 0.5 & 79.63 & 82.13 & 83.33 & 82.02 & 83.76 & 84.36 & 84.98 & 86.47 \\  & 0.1\(c\) & 78.56 & 81.90 & 83.30 & 85.05 & 85.61 & 86.38 & 86.68 & 87.11 \\  & 1-0.1\(c\) & 78.71 & 82.49 & 82.01 & 84.34 & 82.30 & 83.12 & 84.79 & 85.43 \\ \hline \multirow{3}{*}{AGnews} & 0.5 & 89.70 & 91.54 & 92.59 & 92.72 & 93.29 & 93.33 & 93.42 & 93.88 \\  & 0.1\(c\) & 89.42 & 91.18 & 92.41 & 93.08 & 93.22 & 93.41 & 93.88 & 94.11 \\ \cline{1-1}  & 1-0.1\(c\) & 89.68 & 91.09 & 92.38 & 92.47 & 93.39
# P-Wave Detection Using a Fully Convolutional Neural Network in Electrocardiogram Images
## 2 Materials and Methods  
### Used Databases  
The presence of standard ECG databases is essential for the evaluation of ECG signal detection algorithms. Most of these databases contain annotation files for QRS, but lack P-wave annotation. In this study, two databases were used to evaluate the performance of the proposed algorithm. A self-annotated database (MIT-BIH AF database) and a standard annotated database (QT database). In Table 1, we provide a summary of the aforementioned databases.
# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 3 Methodology
### Automatic Point of Interest Recognition and Tracking
#### 3.2.1 GPS Range Checking  
The automatic approach to recognize POIs begins with a check of the helicopter's GPS position. This simple and efficient step can significantly reduce computation time and resource usage by pausing the pipeline when no POIs are in close proximity.  
The haversine formula is employed to calculate the distance between the helicopter's location and the POIs in the reference database. When no POI is detected within a 500 m range, the POI recognition pipeline is stopped and reattempted after a 1 s wait. The 500 m threshold is an adjustable parameter that can be set based on specific requirements. A shorter range will reduce computing resource utilization but increase the likelihood of missing distant POIs, for example when the helicopter's camera is zoomed in. Conversely, a larger radius will minimize the chances of missing far-away POIs but will lead to increased resource consumption due to a more frequent execution of the detection pipeline.
## 5 Conclusions  
In this paper, we analyze the impact of correlation in the large-scale fading on the DASs in composite fading channels characterized by Rayleigh and log-normal distributions. While the existing literature assumes independent shadowing for composite fading channels, the simulation results indicate that the correlated shadowing causes significant outage performance degradation, which requires more power up to 7 dB to achieve the same outage rates. Assuming a realistic shadowing correlation model that reflects measured data, we show that there exists an optimal inter-separation between the two transmitters in a dual diversity MISO link. In addition, through analysis and simulation, we present how the optimal inter-transmitter distance changes under variations in path loss exponent and standard deviations of shadowing. From the network-level system perspective, the observations in this paper should sensitize system designers to use more realistic physical-layer performance model. Possible future extensions include generalization of DAS configurations (i.e., MIMO channel with more than two transmit antennas) and investigation with more accurate but complex shadowing correlation models.  
The contributions of [PERSON] were to perform simulations and summarize the results, while [PERSON] advised the key ideas about the system model and crystallized mathematical framework and theoretical analysis.  
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (Grant number: NRF-2016R1D1A1B03930060). The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DAS & Distributed antenna system \\ MIMO & Multiple-input-multiple-output \\ IoT & Internet-of-Things \\ SISO & Single-input-single-output \\ MISO & multiple-input-single-output \\ RAU & Remote antenna unit \\ CT & Cooperative transmission \\ SNR & Signal-to-noise-ratio \\
5G & Fifth-generation \\ Tx & Transmitter \\ Rx & Receiver \\ AWGN & Additive white Gaussian noise \\ PDF & Probability density function \\ CDF & Cumulative distribution function \\ MRC & Maximal ratio combining \\ SC & Selective combining \\ \end{tabular}
## 5 Experiment and Results
### Experiments and Discussion
#### 5.4.1 Meter Reading Recognition Results  
At the outset of our experiment, we assessed the effectiveness of various models using the meter reading recognition dataset to determine their capacity for identifying meter readings. When referring to Table 2 and Figure 17, it is evident that the models presented in this paper exhibit superior performance compared to other models with comparable parameters. Additionally, once we integrated meter data augmentation, the mAP@50:95 of our model surpassed that of YOLOv8s while significantly reducing the number of parameters required.
# Review of Energy Management Systems in Microgrids
## 2 Energy Management Systems in Microgrids
### Meta-Heuristic Approach-Based Energy Management Systems  
To solve difficult nondifferentiable optimization problems, heuristics and meta-heuristics are applied in a variety of engineering domains, including communications, power systems, microgrid energy management systems, transportation, and power systems. Genetic algorithms and particle swarm optimization (PSO) techniques are two common metaheuristic approaches used in the development of microgrid energy management systems due to their parallel computing capabilities. Apart from the widely recognized PSO and genetic algorithm approaches for energy management systems, there exist novel approaches like Gray Wolf Optimization (GWO). Energy management in microgrids makes use of techniques like Ant Colony Optimization (ACO) [21], Bacterial Foraging Optimization (BFO) [22], Artificial Immune System (AIS) [23], Particle Swarm Optimization (PSO) [24], Genetic Algorithm (GA) [25], Artificial Bee Colony (ABC) [26], and Gray Wolf Optimization [27].  
Various algorithms, such as GA and PSO, must be evaluated for computational complexity, scalability, and accuracy in real-world microgrid scenarios. As a result of this evaluation, problems are modeled with various dimensions in real-world optimization scenarios. The purpose of this is to estimate the scalability and adaptability of the proposed algorithm to unexpected changes.