esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Background
### Transfer Function Method for Active Control Termination  
In the past, researchers eliminated the transmission-reflection wave through placing absorption materials at the end of an impedance tube. However, the transmission-reflection wave is difficult to cancel completely, especially in a low-frequency range. Thus, researchers began trying to solve this problem with the active control method. For instance, [PERSON] and  
Figure 1: Four hydrophone measurement model.  
[PERSON] designed a measurement system with active absorption termination downstream of an impedance tube, and the simplified system model is shown in Figure 2[40].  
They regarded the relationship between \(P_{3}\) and \(P_{4}\) as an error signal and determined the transfer function between the error signal and input of active control termination. The transmission-reflection wave D will be cancelled by the corresponding sound wave excited by active control termination.  
The aim of this system is eliminating the transmission-reflection wave \(D\) with active control termination by minimizing the error signal. Initially, they determined the transfer function between the error signal and the sound pressure amplitude of \(D\). The sound pressure of hydrophones at locations 3 and 4 can be written as follows:  
\[p_{3}=Ce^{-jkx_{3}}+De^{+jkx_{3}}, \tag{9}\]  
\[p_{4}=Ce^{-jkx_{4}}+De^{+jkx_{4}}. \tag{10}\]  
From Equations (9) and (10),  
\[D=\frac{\left[p_{4}-p_{3}e^{-jk(x_{4}-x_{3})}\right]e^{-jkx_{3}}}{2j\sin k(x_{4 }-x_{3})}. \tag{11}\]  
The error signal extracted from Equation (11) is as follows:  
\[E=p_{4}-p_{3}e^{-jk(x_{4}-x_{3})}, \tag{12}\]  
Assuming that \(G_{1}(w)\) represents the transfer function between primary source and error signal and \(G_{2}(w)\) is the transfer function between control termination and signal error, the error signal in the form of frequency domain is expressed as follows:  
\[E(w)=[G_{1}(w)+G_{2}(w)H(w)]X(w), \tag{13}\]  
where \(H(w)\) is the transfer function between error signal and active control termination that should be measured, and \(X(w)\) is the form of frequency domain of the test signal. Then, the quadratic error \(|E(w)|^{2}\) is minimized to zero to obtain the transfer function \(H(w)\), as shown as follows:  
\[H(w)=\frac{G_{1}(w)G_{2}^{*}(w)}{\left|G_{2}(w)\right|^{2}+r^{2}}, \tag{14}\]  
where (*) indicates the complex conjugate, and \(r^{2}\) is the regularization level [41]. In this way, the reflected sound wave D can be absorbed by active termination in the downstream section of the tube. In this condition, the reflection coefficient R and transmission coefficient T can be obtained using Equations (7) and (8).  
Figure 2: Transfer function method measurement model.
# Digital Pre-Distortion of Carrier Frequency Offset for Reliable Wi-Fi Enabled IoTs
###### Abstract  
The Internet of Things (IoTs) will change the requirements for wireless connectivity significantly, mainly with regard to service coverage, data rate, and energy efficiency. Therefore, to improve robustness and reliability, WiFi-enabled IoT devices have been developed to use narrowband communication. However, narrowband transmission in WiFi such as IEEE 802.11 ah causes relatively higher frequency error due to the reduced subcarrier space, which is larger than legacy wireless local area networks (WLANs) in 2.4/5 GHz frequencies. In a direct conversion receiver, this error degrades the signal quality due to the presence of direct current (DC) offset cancellation circuits. In this paper, a digital carrier frequency offset (CFO) predistortion scheme is proposed for a reliable communication link in dense networks. Evaluation results demonstrate that the proposed scheme can improve received signal quality in terms of packet error rate and error vector magnitude.  
digital predistortion; carrier frequency offset; DC canceller; WLAN; narrowband Internet of Things +
Footnote †: journal: Future Internet
## 3 RS Applications in Ocean
### Sea Level (SL)
#### 3.4.1 Gnss-R  
GNSS-R systems use direct or reflected GNSS signals to measure the SL. To this end, different techniques, including floating carpet boats [234], unmanned surface vehicles [234], and spaceborne/airborne GNSS-R [235], can be employed. The first two approaches carry the GPS antenna to directly compute the elevation value of the corresponding instrument, which is then converted to SL. These approaches are only suitable for relatively small regions. On the other hand, GNSS-R employs reflections of signals transmitted from navigation satellites to infer several geophysical parameters [236; 237]. GNSS-R can provide SL data at a global coverage with high spatiotemporal resolutions [212; 229]. The concept of GNSS-R relies on measuring the delay between direct and reflected signals reaching the receiver (e.g., aircraft, satellite) above the ocean. In particular, the computed delay waveforms (from delay Doppler maps) enable the estimation of the SL through geometric models [238]. It was reported that the nominal precision of SL measurement from an individual 1-s GNSS-R was 5 m [239]. Further enhancements, such as employing more observations from multiple GNSS-R data over more time ranges, could improve the precision to about tens of centimeters [240]. For example, [PERSON] and [PERSON] [212] employed the data acquired by the eight satellites of the CYGNSS mission to estimate the GMSL. The calculated GMSL was validated by satellite altimetry and the DTU-10 sea surface model. The results showed a significant correlation of 0.97 with both datasets, demonstrating the high potential of the CYGNSS for global SL measurements. Likewise, [PERSON] et al. [241] implemented different ML algorithms to retrieve SL from GNSS-R data. They reported that the ensemble of ML algorithms, along with three input features (i.e., the 70% peak correlation power, peak first derivative, and leading-edge slope) from airborne delay waveform dataset, led to the best results, with an RMSE of 0.23 m concerning the DTU15 model.
# Hybrid Forecasting Models Based on the Neural Networks for the Volatility of Bitcoin
## 1 Introduction and Review of Models
### Introduction
However, GARCH models have limitations that are hard to capture complex fluctuation and nonlinear correlation of time series data. In order to overcome these limitations, many researchers have proposed the non-parametric forecasting methods based on machine learning approaches such as ANN for better forecasting of Bitcoin volatility [21, 22, 23]. Over the past few years, there have been various hybrid models based on ANN to improve the forecasting ability of the time series data. In particular, the hybrid models based on ANN and GARCH models have been proposed to improve forecast accuracy for the time-series data such as market indices, exchange rate, stock volatility, gold price, oil price and metal, etc. [24, 25, 26, 27, 28, 29, 30]. These results have shown that the hybrid models have an advantage compared to ANN models. The so-called ANN-GARCH models are the hybrid models that incorporate the GARCH forecasts as the explanatory variables to the ANN models and have been developed consistently by many researchers. For instance, [PERSON] et al. [31] proposed two ANN-GARCH models to improve the forecasting performance of the S&P 500 index volatility. They used various input variables including financial indicators and the simulated volatility by GARCH models, and the proposed hybrid model with EGARCH model show better accuracy than the traditional GARCH models and ANN models. [PERSON] et al. [32] provided the methodology and the application for the volatility forecast of three Latin American stock indexes using a hybrid ANN-GARCH model. [PERSON] and [PERSON] [33] presented an ensemble system based on a hybrid EGARCH-ANN model which is trained with a different distributional assumption. In addition, [PERSON] et al. [34] constructed the hybrid ANN-GARCH model with Google domestic trend and various activation functions for better forecasting accuracy of S&P 500 index volatility. In this paper, we also employ the ANN-GARCH models for accurate forecasting of the realized volatility of Bitcoin. Specifically, we develop ANN-GARCH models with HONN and Google trends (GT) data and compare the proposed models to find the best fitting model for Bitcoin volatility. The contribution of this work is to find the optimal hybrid model for forecasting Bitcoin's volatility. To present our result, this paper is structured as follows. In the next subsection, we review the models used in this paper. In Section 2, we describe the data used for the proposed hybrid models. In Section 3,we construct efficient hybrid models and provide the results of the experiments by the proposed models. In Section 4, we present the concluding remarks.
# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 4 Experiment
### Results
#### 4.3.1 Subjective Evaluation
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Score**} \\ \cline{2-3}  & **Mean \(\uparrow\)** & **Std \(\downarrow\)** \\ \hline CycleGAN & 2.347 & 1.021 \\ MUNIT & 3.731 & 1.392 \\ WarpGAN & 2.718 & 1.113 \\ Image2 StyleGAN & 4.231 & 0.985 \\ S-Embedding & 3.076 & 1.207 \\ Hierarchical Optimization & 2.725 & 1.113 \\ CariGAN & 3.985 & 1.028 \\ XGAN & 3.154 & 1.114 \\ SpleCariGAN & 2.986 & 1.217 \\ CariMe & 3.012 & 1.034 \\ Proposed & **4.017** & **0.962** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Subjective score of experimental results. Figure 7: Comparison between the experimental results of ours and those of others. The red and green boxes represent detail problems in the highlights of the face and artifacts at the edges of the portrait, respectively. The research data indicate that the Improved Detail-Enhancement CycleGAN method proposed in this paper consistently achieves higher scores in both mean and standard deviation, signifying its superiority in imaging quality over other methods in terms of subjective vision. It effectively addresses issues such as incomplete style transfer, unnatural color transitions, and image artifacts prevalent in other methods. These experimental findings strongly validate the effectiveness of the proposed method outlined in this paper.
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 5 Comparative Analysis of Different Algorithm Experiments
### Experimental Evaluation Indicators  
The evaluation of abnormal crowd behavior recognition integrates multiple indicators to ensure that the recognition is not only accurate but also precisely located. During the evaluation, frame-level and pixel-level standards are usually considered. Frame-level detection focuses on determining whether there is any abnormality within the video frame. Even if the specific location of the abnormality is not precise, as long as there is abnormal behavior in the frame, the entire frame is marked as abnormal; the pixel-level standard further requires the precise positioning of the spatial location of the abnormal behavior, usually requiring that the predicted abnormal pixels cover at least 40% of the real abnormal area to evaluate the accuracy of abnormal positioning.  
The performance of abnormal crowd behavior recognition is usually evaluated by indicators such as the confusion matrix, ROC curve and AUC, and Equal Error Rate (EER).  
By comprehensively considering the four basic indicators of True Positive (TP), True Negative (TN), False Positive (FP), and False Negative (FN), the confusion matrix is used for basic classification performance evaluation.  
The ROC curve depicts the classification performance of the algorithm through the changes in True Positive Rate (TPR) and False Positive Rate (FPR). AUC, that is, the area under the ROC curve, reflects the average performance of the classifier under all thresholds. The closer the AUC value is to 1, the better the performance of the classifier.  
It is defined as the error classification rate when the True Positive Rate is equal to the False Positive Rate. The smaller the EER value, the better the performance of the abnormal detection method because it balances missed alarms and false alarms and is an important indicator to measure the performance of a binary classification system.
# Multi-Source Information Fusion Diagnosis Method for Aero Engine
## 3 Case Study  
To validate the proposed method, a case study is conducted on a two-shaft turbofan aero engine, focusing on performance degradation. Figure 3 shows the sectional structure diagram of the turbofan engine. The main components include the air inlet, fan, low-pressure compressor (LPC), high-pressure compressor (HPC), combustion chamber, high-pressure turbine (HPT), low-pressure turbine (LPT), bypass, mixing chamber, and exhaust nozzle. To obtain vibration signals, two sensors are placed on the bearing housing and two sensors are placed on the intermediate case. The temperature and pressure measurement points are the same as in the introduction in Table 1. In addition, four vibration points are set. The meanings of the vibration measurement points in Figure 3 are indicated in Table 2. As shown in Figure 4, the trends of the rotational speed of the low-pressure system (N1) and high-pressure system (N2) after 1000 s of operation are presented.  
The values in the graph are relative to the rated speed. N1 is 0.36 times the rated speed at 1000 s. It reaches its peak (0.92 times the rated speed) at 1759.8 s, then rapidly drops to zero as the engine shuts down. N2 is 0.80 times the rated speed at 1000 s. It reaches its peak (1.02 times the rated speed) at 1759.8 s. The rapid drop to zero after 1759.8 s coincides with the engine failure, suggesting a sudden and complete loss of power.  
In the case study, the vibration and gas path parameters are analyzed, respectively, and fault features are extracted. Then, D-S evidence theory and a Bayesian network are used to carry out information fusion diagnosis, so as to compare the effect of different information fusion algorithms.  
\begin{table}
\begin{tabular}{l c} \hline \hline
**Symbolic Meaning** & **Symbolic Representation** \\ \hline Vibration measurement point 1 of the bearing housing & V11 \\ Vibration measurement point 2 of the bearing housing & V12 \\ Vibration measurement point 1 of the intermediate case & Vic1 \\ Vibration measurement point 2 of the intermediate case & Vic2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information of vibration measurement points.  
Figure 3: The sectional structure diagram of the turbofan engine.
## 5 Results  
Extracted textural features, including GLCM and Ranklet features, were tested to see if they could distinguish stroke patterns from normal patterns. For GLCM features, three (cluster prominence, cluster shading, and entropy) of 14 features were significant (\(p<\) 0.05). Among 42 Ranklet features,23 of them were determined to be significant, and 8 features were selected to establish the prediction model in the classifier.  
Performances of prediction models using GLCM and Ranklet features are compared in Table 1. GLCM features achieved an accuracy of 71% (115/160), a sensitivity of 57% (32/56), a specificity of 79% (83/104), and an Az of 0.73; while Ranklet had an accuracy of 81% (130/160), a sensitivity of 64% (36/56), a specificity of 90% (94/104), and an Az of 0.81. Among the performance comparisons, accuracy, specificity, and Az were statistically significant at \(p=0.0478\), \(=0.0322\), and \(=\)0.028, respectively. The corresponding ROC curves of both models are illustrated in Figure 4. Figure 5 shows two acute stroke cases correctly classified using Ranklet features (80% and 87%, respectively) but misclassified using GLCM features (19% and 37%, respectively).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **GLCM** & **Ranklet** & **GLCM vs. Ranklet (_p_ Value)** \\ \hline Accuracy & 71\% (115/160) & 81\% (130/160) & 0.0478 * \\ Sensitivity & 57\% (32/56) & 64\% (36/56) & 0.4390 \\ Specificity & 79\% (83/104) & 90\% (94/104) & 0.0322 * \\ Az & 0.73 & 0.81 & 0.028 * \\ \hline \hline \end{tabular}  
* \(p<0.05\) indicates a statistically significant difference. Az, the area under the receiver operating characteristic curve.  
\end{table}
Table 1: Performance comparisons between gray level co-occurrence matrix (GLCM) and Ranklet features in classifying strokes  
Figure 4: Receiver operating characteristic (ROC) curves of the gray level co-occurrence matrix (GLCM) and Ranklet features regarding trade-offs between sensitivity and specificity.
## 2 Issues in Topology Design
### Data Related Considerations  
WSNs are very much data oriented. Usually WSNs are deployed to collect environmental/monitoring data. Thus, data related considerations during the designing of the topology are crucial. The two most important issues of data related considerations are data collection and data aggregation, as considered below.  
_Data collection._ According to the system model, based on which the multiple chain oriented logical topology is proposed, sensed data are continuously/periodically collected at all of the sensor nodes, and forwarded through wireless communications to a central base station (BS) for further processing. Sensor data collection requires that all sensing data are correctly and accurately collected and forwarded to the BS, as sometimes the complexity of the data requires global knowledge in order to be processed. This feature thus prevents the use of data aggregation/fusion techniques that are typically employed to enhance the network performance. As a result, the major traffic in sensor data collection is the reported data from each sensor to the BS. Such a \"many-to-one\" traffic pattern, if not carefully handled, causes high unbalanced and inefficient energy consumption in the entire network. For example, [29] reports on the energy hole problem, where sensor nodes close to the BS are depleted quickly due to traffic relays and create a hole shape area that leaves the remaining network disconnected from the BS.  
Figure 1 depicts an example of such a scenario. One possible solution to alleviate the issue of uneven energy dissipation is to avoid the construction of complex chains, where two or more nodes send their data to a single node. Another solution is to exclude the set of sensor nodes from performing the same task repeatedly.  
_Data aggregation._ Besides considering the data collecting technique, another important issue to consider is data aggregation. The information gathered in a sensor network is highly correlated, due to a spatial and temporal correlation between successive measurements. Exploiting the data-centricity and the spatial-temporal correlation characteristics allows the application of effective in-network  
Figure 1: Uneven energy dissipation by sensor nodes.  
data aggregation techniques, which further improve the energy-efficiency of the communication in WSNs [30]. Aggregation can eliminate the inherent redundancy of the raw data collected as well as diminish the traffic in the network, thereby reducing congestion and induced collisions [31]. Thus, data aggregation policies are adopted in WSNs to increase the lifetime of the network. However, designing aggregation points for data aggregation requires careful attention. Data aggregating points consume more energy in processing the aggregation method, and unplanned, non-distributed aggregation points can drastically affect the lifetime of the network [32].
# In-Depth Analysis of GAF-Net: Comparative Fusion Approaches in Video-Based Person Re-Identification
## 4 Experimental Settings
### Models and Implementation  
Our Gait-Appearance Fusion Network (GAF-Net) integrates both gait and appearance features for video-based person Re-ID. This is crucial for efficient person Re-ID in multi-camera scenarios. GAF-Net consists of the following three modules: gait feature extraction and appearance feature extraction, followed by a fusion module that combines the extracted features.
# Damage Detection in Wind Turbine Blades Based on an Improved Broad Learning System Model
## 4 Conclusions  
A novel CBNLM-BLS model for damage detection in wind turbine blades was proposed in this work. It could overcome the weakness of large time consumption and high complexity of structure in deep learning neural networks such as ResNet, AlexNet and VGG-19. Compared with the traditional BLS model, the CBNLM-BLS had less time complexity due to the use of chunked parallelism. Comparison experiment for damage detection of wind turbine blades was performed and it showed that the proposed CBNLM-BLS had the best performance and achieved a classification accuracy of 99.716% and took 28.662 s.  
Future work would be concentrated on further validation of the CBNLM-BLS model and its application for real time and online damage detection in wind turbine blades.  
Conceptualization, Y.W. and J.B.; methodology, Y.W.; software, J.B.; validation, L.Z. and Y.S.; formal analysis, L.Z.; investigation, Y.W.; resources, J.B.; data curation, Y.W. and J.B.; writing--original draft preparation, L.Z. and Y.W.; writing--review and editing, L.Z.; supervision, L.Z.; project administration, L.Z.; funding acquisition, L.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science Foundation of China under Grant [52005071] and the Liaoning Provincial Educational Department Project under Grant [JDL2020004].  
Not applicable.  
The datasets analyzed during the current study are available from the corresponding author on reasonable request. The data that support the findings of this study are openly available in \"Wind-turbine-blade\" at [[https://github.com/KaKoYu007/Wind-turbine-blade](https://github.com/KaKoYu007/Wind-turbine-blade)]([https://github.com/KaKoYu007/Wind-turbine-blade](https://github.com/KaKoYu007/Wind-turbine-blade)), (accessed on 18 January 2022).  
We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted.