esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Design Overview
### Gateway Design and Reordering Process
When buffer resources are insufficient, the buffer enters a restrictive state and performs replacement operations, resulting in some out-of-order packets being directly forwarded to the IP terminal. This triggers the built-in out-of-order correction mechanism in TCP/IP network (typically a sliding-window-based buffer). If these out-of-order packets are successfully delivered, the IP terminal sends an acknowledgment message to the gateway, ensuring that the corresponding sequence number is removed from the mapping table. However, if reordering fails and packet loss occurs, the IP terminal sends a retransmission request to the data source through the gateway.
# Clipping Noise Compensation with Neural Networks in OFDM Systems
## 3 Results
### MSE and BER Performances  
In this section, two performance measures are presented: the MSE between the reconstructed and the original unclipped signal, and the BER of the un-coded OFDM system. For the MSE, two signal parts are evaluated separately, e.g., the clipped and unclipped part. The reason for doing this is to evaluate the capability of the NN to recover the clipped part while assess the potential damage that may occur to the unclipped part. The performance of the CS-based method [18] is compared.  
Figure 5a,b show the MSE of the clipped parts with (a) clipping ratios = 1.0 and (b) 1.3. The blue dashed line indicates the MSE without clipping noise compensation. The other lines represent the MSE of various algorithms' MSEs. In general, the NN (yellow lines) outperforms the CS-based algorithm (orange lines), especially when the clipping is severe (cr = 1.0). The reason for this is because the necessary assumption of the CS-based algorithm that clipping occurs sparsely is violated when clipping is severe. The best performance happens when NN works with DAR-inspired post processing.  
Figure 5: Mean square error (MSE) versus Signal-to-Noise Ratio (SNR): (a) clipped part, cr = 1.0 dB (b) clipped part, cr = 1.3 dB (c) unclipped part cr = 1.0 dB (d) unclipped part, cr = 1.3 dB.  
Figure 5c,d show the MSE of unclipped parts. The DL-based algorithm suffers from signal distortion caused by the NN and has the worst MSE when the protection of the unclipped part is not activated (Figure 5d). DAR-inspired post processing, as described in Section 2, can be applied to improve the performance of the DL-based algorithm. As a result, the MSE performance is greatly improved. Notice that the protection can be applied to the CS-based algorithm as well. Overall, taking into account the MSE of both the clipped and unclipped parts, the DL-based scheme achieved a better performance.  
Next the BER performance is presented under various clipping ratios and channel conditions. Only two NN models are needed to cover the wide range of clipping ratios. They are trained with the clipping ratio 1.08 and 1.27, and neighboring clipping ratios can be covered by them. However, note that the design of the NN is not scalable yet. The NN model can only work for a fixed number of subcarriers.  
In Figure 6a,b, with AWGN and Rayleigh fading channels, respectively, the dashed lines and solid lines represent the BERs with and without compensation by the DL-based method, and the improvement is significant. Figure 6c,d, with AWGN and Rayleigh fading channels, respectively, the BERs of the CS- and the DL-based method are compared; again, the DL-based method outperforms the CS-based algorithm by a large margin.  
Figure 6: BER performance in AWGN and Rayleigh fading channels with different clipping ratios. In (**a**,**b**), dashed line: no clipping noise compensation; solid line: Neural Network (NN). In (**c**,**d**), dashed lines: CS; solid lines: NN.
# A Novel Method of Seismic Signal Detection Using Waveform Features
## 3 Signal Model  
### Waveform Features  
Based on the statistics and observations about a series of seismic events, we analyze waveform features in the time domain and energy features in the frequency domain. We can identify some useful features to distinguish seismic signals and noise. These features include the amplitude \(\alpha\) and the ratio \(\rho\) of the STA/LTA trace of the seismic waveform, signal energy mean \(\gamma\) in the same length time window near the arrival of the signal and the power \(\lambda\) of the intrinsic mode function \(1\) (IMF1) after the waveform decomposed by EMD.  
In Figure 1, we can see the seismic signal amplitude is higher than noise. Statistically, the mean value of the Gaussian distribution of seismic signals is higher than the mean of noise signals, as shown in Table 1. So the probability of noise is usually smaller compared to the seismic signal obtained by the predictive distribution from the Gaussian process. Additionally, as shown in Figure 1, it can be seen that the noise has many visible fluctuations. The ratio value is the largest peak divided by the average of other spikes. The ratio of the signal is higher than the noise. In Figure 2, we can notice the difference between the spectrogram of the seismic signal and noise. The waveform energy average of the seismic signal is higher than the noise for 1-2 Hz, so the band energy average can be utilized as another feature to distinguish signals and noise. In addition, EMD can decompose the non-stationary non-linear signal into a series of various frequency sub-components, namely the IMF, reflecting many inherent information of the signal. In Figure 3, we can note that the signal is decomposed into a group of IMF components, arranged in descending order of frequency. After statistics, it was found that the main component containing most seismic signal energy was the IMF1 part. Then, we used the energy of IMF1 to distinguish seismic signals and noise.
# Research on Laser Dual-Mode Fusion Detection Method of Ship Wake Bubbles
## 1 Introduction
A multi-timescale hierarchical fusion signal processing approach is proposed, enabling the graded fusion processing of ns-level and ms-level laser echo signals. Additionally, a time-differential accumulation image processing method is introduced, effectively distinguishing between wake bubble targets and the water background. By combining the high sensitivity and large dynamic range of laser detection with the high resolution and large field of view of image detection, the dual-mode laser fusion detection method can achieve high-sensitivity, large-dynamic-range, high-resolution, and large-field-of-view detection of wake microbubbles against a strong reverberation background. This paper primarily adopts a research approach that combines theory, simulation, system design, and experimental verification. The first section focuses on analyzing the laser detection characteristics of ships' wakes. In the second section, based on Monte Carlo simulations, the laser dual-mode fusion detection characteristics of different bubbles are analyzed. In the third section, based on theoretical and simulation studies, system design and signal processing algorithms are investigated. Finally, in the fourth section, experimental verification tests are conducted using the system.
# Development of Wearable Textile MIMO Antenna for Sub-6 GHz Band New Radio 5G Applications
## 4 Results  
Figure 5 displays a prototype of a proposed textile two-port irregular octagon MIMO antenna design. It was constructed on a substrate composed of jeans textile and copper foil with thicknesses of 1 mm and 0.07 mm, respectively. Modeling for performance assessment using the finite element method (FEM) in HFSS software v21 was then conducted while measurements using an Anritsu Model: S820E MS2038C Vector Network Analyzer were performed within an anechoic chamber as demonstrated in Figure 6. Section 3 provides an analysis of the bending characteristics that the proposed antennas possess and conducts an experiment to verify them with slightly bending wearable antenna; this test validates their bending properties and further establishes their effectiveness within their operating ranges.  
Figure 4: Comparison of the S-parameters of bending analysis.  
Figure 5: Proposed textile irregular octagon antenna. (**a**) Top view. (**b**) Bottom View.
## 2 Related Works
### Spread Factor Allocation in LoRa Networks  
The performance of LoRa networks has been aided by appropriate resource allocation strategies, one of which is SF, which defines the number of bits that can be encoded by a symbol. SF assignment schemes include, but are not limited to, random, area-proportional, and distance-based distribution schemes from the GW [10; 21]. Furthermore, SF assignments in the LoRa network are more attributable to the work in [22], which indicated that SF allocation was primarily based on the power level of the signal that the GW receives from the end devices and the GW's sensitivity without taking end device location into account. Similarly, in [23], an efficient interference-aware SF allocation strategy was introduced. In reality, SF can be assigned based on the signal-to-noise ratio (SNR) of the received packets, and as a result, devices in a certain range can use the same SF [24]. However, the challenge to optimize the SF assignment over each channel realization and high-density building obstacles was ignored. In [25], the authors considered a fair SF clustering of end devices in LoRa networks using k-means clustering. Their algorithm determined the minimum cost function, which was defined as the distance between each point in the data set and its nearest centroid. Furthermore, the k-means clustering method achieved robust clustering with ease. However, this method of SF allocation did not take into account the SF overlap of edge nodes at different annulus boundaries as well as the distance of the end devices from the GW. Studies in [26] proposed an SF allocation algorithm based on matching theory to maximize the minimum achievable average rate in LoRa networks to improve network throughput and fairness. This theory classifies resource allocation as a many-to-one matching problem with conventional externalities and peer effects. In this case, a device prefers to be paired with the SF that provides the most utility. That algorithm, however, performed better with only heterogeneous LoRa devices. [PERSON] et al. [27] proposed a heuristic SF allocation scheme in which users with similar path losses were simply assigned to the same channel, then to each SF based on their distance from the GW. The work in [28] proposed a suboptimal SF allocation strategy to maximize packet success probability. Inter-SF interferences, on the other hand, were ignored in that work. In particular, the proposed SFPCR scheme allocates SF based on a combination of the device's distance and the SNR of the received packets. The SF allocation technique utilized in the SFPCR scheme is modified by assuming that all LoRa devices inside the same contour lines have the same SF.
## 3 Results and Discussion
### Comparison between Vasari Corridor and La Specola  
Figures 26 and 27 compare the average monthly values of indoor temperature and relative humidity, respectively, monitored in Vasari Corridor and La Specola. The results clearly show that La Specola room X, characterized by low thermal inertia and with two windows exposed to solar radiation, behaves similarly to the zones of Vasari Corridor, in terms of both temperature and relative humidity. On the contrary, room XXII, characterized by higher thermal inertia and located inside the building without external walls and, thus, more protected from the outdoors, presents a flatter trend of indoor temperatures along the year, up to about 8 \({}^{\circ}\)C higher in colder months and 3 \({}^{\circ}\)C lower in hotter months, and significantly lower values of relative humidity throughout the year, up to about 26% difference. Table 6 compares the values of the statistical parameters characterizing the historical climate of temperature and relative humidity in each analyzed zone compared with the reference value coming from D.M. 10.05.2001 on the annual basis. In all four zones, the range of preservation coming from the analysis of the historical climate, to which the objects are now acclimatized, is much wider and rather different than that suggested by the D.M. standards. Figure 25: Trend of historic climate hourly values of RH and statistical limits of 93 rd and seventh percentiles in room XXII vs. the range recommended by the Italian D.M. 10.05.2001 (16) (gray band). Figure 27: Trend of average indoor monthly relative humidity (RH) measured in the analyzed zones of Vasari Corridor and La Specola museum. Figure 26: Trend of average indoor monthly temperature (T) measured in the analyzed zones of Vasari Corridor and La Specola museum. Table 7 summarizes the maximum daily gradients of T (\(\Delta\)T\({}_{24\max}\)) and RH (\(\Delta\)RH\({}_{24\max}\)) together with the PI of the hygothermal parameters (T, RH, \(\Delta\)T\({}_{24}\), and \(\Delta\)RH\({}_{24}\)) for the four analyzed zones in the two buildings, i.e., Lungarno (point A) and Ponte Vecchio (point B) in Vasari Corridor and room X and room XXII in La Specola. The PI yearly values calculated accordingly to the reference values recommended by the Italian standard D.M. 10.05.2001 (third column) were compared with those calculated on the basis of the historical climate (fourth column). The comparison shows that Vasari Corridor zones present much higher peak daily gradient, in terms of both temperature and relative humidity, than La Specola rooms. Accordingly, on average, the percentage of time when the daily gradients of temperature and relative humidity are within the range of acceptability is higher in La Specola rooms than in Vasari Corridor. In particular, the zone with higher PI\({}_{\Delta\)T24 (about 33%) and better PI\({}_{\Delta\)RH24 (about 74%) is room XXII, as expected from previous results. Considering the reference values recommended by the Italian standard D.M. 10.05.2001, for both the museums, the analysis of indoor microclimate parameters demonstrates that
# Free Space Optical Communication Networking Technology Based on a Laser Relay Station
## 2 Theory and System Design
The resulting signal-to-noise ratio is:  
\[SNR=\frac{P}{\sigma_{n}^{2}} \tag{2}\]  
The probability of misidentifying datum 1 as datum 0 is set to \(P_{1-0}\), and the probability of misidentifying datum 0 as datum 1 is set to \(P_{0-1}\); then, we can obtain that [22; 27]:  
\[P_{1-0}=\frac{1}{2}[1+erf(\frac{k-\sqrt{P}}{\sqrt{2\sigma_{n}^{2}}})]=\frac{1} {2}[1+erf(\frac{k}{\sqrt{2\sigma_{n}^{2}}}-\sqrt{\frac{SNR}{2}})] \tag{3}\]  
\[P_{0-1}=\frac{1}{2}[1-erf(\frac{k}{\sqrt{2\sigma_{n}^{2}}})] \tag{4}\]  
where the error function \(erf(x)=\frac{2}{\sqrt{\pi}}\frac{x}{\sqrt{\pi}}e^{-t^{2}}dt\), \(k\) is the decision threshold, and then the bit error rate of the PWM method is [44; 47]:  
\[P_{se}=\frac{1}{Z}[P_{1-0}+(Z-1)P_{0-1}] \tag{5}\]  
where \(Z=2^{M}+1\), therefore obtaining:  
\[BER_{PWM}=\frac{1}{2^{M+1}}[1+erf(\frac{k}{\sqrt{2\sigma_{n}^{2}}}-\sqrt{\frac {SNR}{2}})+(2^{M+1}-1)erf(\frac{k}{\sqrt{2\sigma_{n}^{2}}})] \tag{6}\]  
According to \(\text{Q}=\sqrt{2}\text{erfcinv}(2 BER)\), it further is obtained that:  
\[\text{Q}=\sqrt{2}\text{erfcinv}(\frac{1}{2^{M}}[1+erf(\frac{k}{\sqrt{2\sigma_ {n}^{2}}}-\sqrt{\frac{SNR}{2}})+(2^{M+1}-1)erf(\frac{k}{\sqrt{2\sigma_{n}^{2} }})]) \tag{7}\]
# A Ku-Band GaAs Multifunction Transmitter and Receiver Chipset
## 1 Introduction  
Demand for phased-array systems has continued to increase for satellite/military communications and radars [1]. In the past, multiple individual modules were assembled to implement each channel element in the phased array, which increases the size, weight, complexity and cost of the system [2]. However, a multifunction chip integrates most of the functionalities required for the array element in a single chip, including the phase and amplitude control, signal amplification and digital control. This significantly reduces the system complexity and cost [3]. In particular, as the array size increases to a large scale, the benefit of the multifunction chip becomes more prominent. For this purpose, the chip should retain high integration level and compact size.  
In recent years, several multifunction chips have been reported in GaAs or silicon technologies [1; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13]. Silicon-based multifunction chips are favorable for their easy integration with digital circuits and low cost in the case of mass chip production [4]. However, GaAs technologies offer superior noise figures and output power over silicon technologies. In addition, digital circuits can be integrated even in GaAs technologies using a combination of enhancement-mode (E-mode) and depletion-mode (D-mode) transistors [14]. Therefore, it is more attractive to use GaAs technology for broadband multifunction chips with high gain, high output power and low noise figures. There are several articles which have reported on GaAs multifunction chips [1; 8; 9; 10; 11; 12; 13]. Nevertheless, many chipsintegrate only some, and not all, of the essential circuit blocks required for phased arrays, including a phase shifter, attenuator, amplifier and digital control circuit [1, 8, 9]. Other reports show narrowband operation [8, 10] or relatively limited output power and high noise figures [11, 13].  
In this paper, a GaAs multifunction transmitter and receiver chipset operating over the full Ku-band (12-18 GHz) frequency is presented. A 4-bit digital phase shifter, 5-bit digital attenuator and 9-bit serial-to-parallel converter (SPC) are all integrated with a medium power amplifier (MPA) or a low-noise amplifier (LNA). This high integration level enables high output power and low noise figure and thus makes this chipset suitable for a large-scale phased-array system. This paper is organized as follows. In Section 2, the architecture of the multifunction transmitter and receiver are described. The design and measurement of each circuit building block are presented in Section 3. In Section 4, the measurement of the multifunction transmitter and receiver chipset is presented. The conclusion is presented in Section 5.
# A Novel Heart Rate Robust Method for Short-Term Electrocardiogram Biometric Identification
## 2 Methods
### Classifier
#### 2.4.5 Support Vector Machines (SVM)  
Support Vector Machine (SVM) is one of the state-of-the-art classifiers which can split a dataset into two or more categories. By using a function called kernel, support vector machine can transform the input samples into a higher dimensional space and classify them linearly. In this paper, since features extracted by PCANet are high-dimensional and sparse, we adopt a linear kernel support vector machine (linear-SVM) according to [25]. Linear-SVM can be realized by freely available Liblinear toolkit [39]. During the use of Liblinear, parameter C known as error penalty factor, which expresses the tolerance to error, was set to 1 for good performance of classifier.
# Signal Modulation Recognition Algorithm Based on Improved Spatiotemporal Multi-Channel Network
## 4 Experiment and Analysis
### Comparative Experiments of Different Networks
To more intuitively see the recognition accuracy of each type of signal in each SNR, the recognition accuracy diagrams on three datasets are drawn here, represented by the IQGMCL network, as shown in Figure 11.  
Figures 12-14 show several representative confusion matrices of IQGMCL and MCLDNN networks under three datasets. In a confusion matrix, the accuracy of the recognition result is expressed by the color depth of each square. The abscissa of the confusion matrix represents the results of classifying various modulated signals with networks. The ordinate represents the true modulation methods. If the diagonal of the matrix is darker, then the model's prediction accuracy is better. If the color blocks are scattered throughout the matrix and are not concentrated at the diagonal, then the recognition effect is not ideal. Figure 12 shows the confusion matrices in the RML2016.10a dataset when using IQGMCL and MCLDNN networks for signal modulation recognition, and SNRs of 0 dB, 4 dB, and 12 dB are selected. Where (a)(b)(c) are the results of the IQGMCL network, (d)(e)(f) are the results of the MCLDNN network. It can be concluded that the overall recognition effect of the IQGMCL network is better than that of MCLDNN. In particular, IQGMCL performs better in distinguishing between QAM16 and QAM64 signals. The improved network also improves the recognition accuracy of WBFM signals. Figure 13 shows the confusion matrices in the RML2016.10b dataset when using IQGMCL and MCLDNN  
Figure 10: Training and validation process curves. (**a**) Train-accuracy contrast; (**b**) Train-loss contrast; (**c**) Val-accuracy contrast; (**d**) Val-loss contrast. networks for signal modulation recognition, and SNRs of 0 dB, 4 dB, and 12 dB are selected. It can be found that when the SNRs are 0 dB and 4 dB, the IQGMCL recognition effects are better. When the SNR is 12 dB, the two effects are comparable. Figure 14 shows the confusion matrices when the SNRs are 6 dB, 8 dB, and 14 dB on the RML2016.04c dataset. Similarly, (a)(b)(c) are the results of the IQGMCL network, (d)(e)(f) are the results of the MCLDNN network, and it can be derived that the recognition effect of the IQGMCL network is better than that of the MCLDNN network. Figure 11: Accuracy for each category at each SNR on three datasets. (**a**) RML2016.10b; (**b**) RML2016.04c; (**c**) RML2016.10a. Figure 12: Partial confusion matrices on the RML2016.10a dataset about two networks. (**a**) IQGMCL-0 dB; (**b**) IQGMCL-4 dB; (**c**) IQGMCL-12 dB; (**d**) MCLDNN-0 dB; (**e**) MCLDNN-4 dB; (**f**) MCLDNN-12 dB. Figure 13: Partial confusion matrices on the RML2016.10b dataset about two networks. (**a**) IQGMCL-0 dB; (**b**) IQGMCL-4 dB; (**c**) IQGMCL-12 dB; (**d**) MCLDNN-0 dB; (**e**) MCLDNN-4 dB; (**f**) MCLDNN-12 dB.