esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Digital Pulse Compression
### Classical Digital Pulse Compression Methods
#### 3.1.1 Pulse Compression in Time Domain  
For narrow bandwidth pulse compression, a digital convolution in the time domain is adopted. The procedure can be described as follows:  
\[y(n)=s(n)*h(n)=\sum_{k=0}^{N-1}s(k)h(N-k) \tag{14}\]  
A digital FIR filter structure is usually used to implement digital convolution in the time domain. When N is large, the implementation of the FIR filter is difficult, which limits its application to the pulse compression of large bandwidth chirp signals.
# QoS-Guaranteed Radio Resource Management in LTE-A Co-Channel Networks with Dual Connectivity
## 3 Proposed Schemes
### Q-Learning Dynamic Almost Blank Subframe  
In order to explore the dual connectivity mechanism combined with CRE and ABS, the ABS configuration learning procedure is introduced in this section. We propose a novel Q-learning dynamic  
Figure 5: Signal coverage in dual connectivity mode.  
ABS method based on reinforcement learning. Furthermore, it is effective to adjust the ABS ratio and reduce the interference in the co-channel networks by coordinating the adjacent Macro BSs. Thus, we also propose a centralized decision to adjust the ABS ratio which requires all Macro BSs to synchronize their ABS ratio.
# A Review on Neural Network Based Models for Short Term Solar Irradiance Forecasting
## 3 The Architecture of Deep Learning Algorithms
### Bi-LSTM Network  
The LSTM was modified into the Bi-LSTM, which was presented by [109], to enhance forecasting accuracy by combining forward and backward information from the input sequence. The forward hidden sequence was calculated first, and the reverse hidden sequence was then merged to find the result. The architecture of Bi-LSTM was enhanced by integrating the best features of BRNN and LSTM to improve the accuracy of the state. Furthermore, double processing was beneficial for learning complex temporal correlations.
## 3 Hierarchical Modulation and Physical Layer Network Code Scheme
Let us assume that \(\phi_{1}=\xi_{1}\), \(\varphi_{1}=\xi_{2}\), \(\phi_{2}=\xi_{3}\), \(\varphi_{2}=\xi_{4}\) can get \(J_{1}=\sqrt{\alpha}\xi_{1}+\sqrt{1-\alpha}\xi_{2}\) and \(J_{2}=\sqrt{\alpha}\xi_{3}+\sqrt{1-\alpha}\xi_{4}\). Figure 2: Constellation of 4/16-QAM hierarchical modulation. After HM-PNC, the relay node transmits the encoded information to the four user nodes. The received information for each user node can be expressed as:  
\[ \begin{array}{ll}y_{RN}(i)&=h_{RN}j_{i}+n_{N}\\ &=h_{RN}\big{(}\sqrt{\beta}\phi_{i}+\sqrt{1-\beta}\varphi_{i}\big{)}+n_{N},&i=1,2\end{array}  \tag{11}\]  
where \(h_{RN}\) is the channel fading coefficient from the relay node \(R\) to the user node \(S_{N}\), \(N\in\{1,2,3,4\}\). \(n_{N}\) is the Gaussian white noise corresponding to the channel. Finally, each user node can be decoded using serial interference cancellation (SIC), in the following steps:  
The high-priority information required by the user node is decoded as:  
\[\widehat{\phi_{i}}=\text{argmin}\big{\|}y_{RN}-h_{RN}\sqrt{\alpha}\phi_{i} \big{\|}^{2} \tag{12}\]  
Each user node uses the SIC method to eliminate high-priority information from \(y_{RN}(i)\), and the low-priority information required by the user is:  
\[\widehat{\phi_{i}}=\text{argmin}\big{\|}y_{RN}-h_{RN}\Big{(}\sqrt{\alpha} \widehat{\phi_{i}}+\sqrt{1-\alpha}\varphi_{i}\big{)}\big{\|}^{2} \tag{13}\]  
Each user node can decode data from other nodes using high-priority data obtained by a direct link with neighboring nodes. For example, the user node \(S_{1}\) has four messages, \(S_{1h}\), \(S_{1I}\), \(S_{2h}\) and \(S_{4h}\), from neighboring nodes. By decoding \(S_{2l}\oplus S_{4h}\oplus S_{4l}\oplus S_{2h}\oplus S_{1l}\oplus S_{3h}\), and \(S_{1h}\oplus S_{3l}\), you can get \(S_{2l}\oplus S_{4l}\oplus S_{3l}\), and \(S_{3h}\). The details are as follows:  
\[S_{2l} =S_{4h}\oplus(S_{2l}\oplus S_{4h}) \tag{14a}\] \[S_{4l} =S_{2h}\oplus(S_{4l}\oplus S_{2h})\] (14b) \[S_{3l} =S_{1l}\oplus(S_{1l}\oplus S_{3h})\] (14c) \[S_{3h} =S_{1h}\oplus(S_{1h}\oplus S_{3l}) \tag{14d}\]  
Other user nodes require the same information as the user node \(S_{1}\).
# Research on the Cleaning Method of Unmanned Sweeper Based on Target Distribution Situation Analysis
## 4 Experimental Results
### A Sweeping Path Accuracy Method Based on Situation Analysis and BF-CPP + SA  
#### 4.3.1 Multi-Region Situational Analysis and Route Planning  
We set up three sets of experimental scenes. For Scene 1, we designed multiple small garbage to verify the effectiveness of the SA algorithm. For Scene 2, we set up a non-rectangular unit to verify the effectiveness of the BF-CPP algorithm. For Scene 3, we set up small garbage with multiple non-rectangular units to verify the synergistic effect of BF-CPP and SA. Finally, we combine the three experimental scenarios together to test the coherence and smoothness of the algorithm between different camera frames.  
As shown in Figure 14, the first line is the initial camera image, the second line is the effect image after IPM, the third line is the effect image after YOLOv5+GMM, the fourth line is the path calculated under the three scenarios, respectively, and the last one is the resulting image after the three experimental scenarios are combined. From the figure, it can be seen that both IPM and GMM can complete the analysis of the distribution situation  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Time/s** & **Precision/\%** & **Recall/\%** & **Coverage/\%** \\ \hline Faster RCNN & 5.1 & 65.8 & 47.7 & 49.2 \\ YOLOv3 & 6.7 & 69.4 & 46.4 & 53.2 \\ YOLOv4 & 7.2 & 72.7 & 50.8 & 55.9 \\ YOLOv5 & 6.5 & 80.8 & 57 & 64.7 \\ \hline \end{tabular}
\end{table}
Table 1: Performance comparison of YOLOv5 with different network models.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Algorithm** & **Time/s** & **AP/\%** & **mAP/\%** & **MIoU/\%** \\ \hline UNet++ & 7.4 & 87.5 & 47.6 & 39.5 \\ DUNet++ & 6.8 & 92.1 & 69.3 & 51.8 \\ YOLOv5+self-cluster & 8.1 & 90.3 & 74.1 & 55.2 \\ YOLOv5+PE & 15.3 & 98.6 & 80.1 & 75.7 \\ \hline \end{tabular}
\end{table}
Table 2: Performance comparison of YOLOv5+PE algorithm with different network models.  
of the garbage target better, and the final planned path can also complete the cleaning task better.
## 4 Evaluation of One-Hop Neighbor Wireless Link Status Based on Fuzzy Logic Model  
Fuzzy logic-based systems have attracted considerable interest and are widely accepted in many industrial systems and research communities to develop communication applications [6; 25; 41], due to their ability to simulate human brains reasoning in interpretation of uncertain information. The fuzzy logic model handles imprecise and uncertain information well when the boundaries are not clear. Uncertainty in a classical set theory arises when an attempt is made to define a measurement value as either member or non-member; instead, values in the fuzzy logic set theory can have different degrees of membership [6; 25]. Accordingly, the fuzzy logic model can handle imprecise and uncertain information by approximating data rather than precise information analysis. Therefore, fuzzy logic can perform well in decision making and analyzing different metrics.  
In the opportunistic network, wireless communication links are vulnerable due to nodes mobility and wireless signal impairment. Accordingly, an evaluation of neighbors' wireless links status is required to improve network connectivity and routing performance. The neighbor's wireless links status depends on multiple factors, such as transmission range capability, available throughput, link stability, neighbor mobility, etc. Therefore, the attempt to derive any mathematical model facilitating the selection of the forwarding candidate set by taking into consideration multiple network metrics is a complex and challenging task, and the solution would be inflexible.  
For routing in MANETs, a fuzzy logic model can cope with unreliable and uncertain information about candidate nodes due to rapid nodes mobility and wireless channel conditions. Whenever a forwarder node has a packet to forward, a fuzzy logic model is used to evaluate each candidate's wireless link status and select the most suitable next-hop for forwarding the packet. The proposed fuzzy logic model is developed to combine all considered network metrics discussed in Section 3.3 to contribute to improve the efficiency and reliability of selecting a next-hop forwarder from the potential next-hop candidate. Figure 2 illustrates the fuzzy logic structure that evaluates the neighbors' wireless link status. It consists of three phases: fuzzification, fuzzy inference, and defuzzification. In the first phase, numerical values accepted as inputs are converted into nonnumerical linguistic variables to express the facts. Then, the defined fuzzy rules are used to drive a linguistic output. The final phase interprets the linguistic output into a numerical value that represents the fuzzy weight of each neighbor. The phases that illustrate the calculation operation of the fuzzy logic weight for candidate nodes \(C_{i}\) are elaborated in the following subsections.
# PnV: An Efficient Parallel Consensus Protocol Integrating Proof and Voting
###### Abstract
Unfortunately, while fusion consensus protocols have demonstrated greater scalability compared with other consensus types [24], the incorporation of parallelization approaches into them has not been thoroughly investigated. Consequently, this paper aims to present PnV, an efficient parallel fusion protocol integrating proof-based and voting-based consensus features. It enhances the data structure, consensus process, transaction allocation, and timeout handling mechanisms to enable concurrent block generation by multiple nodes within a consensus round. Here, the letter P carries a dual meaning, i.e., parallelization and proof, whereas V denotes voting. In our previously published conference version [25], PnV was referred to as PPoV, a parallelization improvement of the PoV protocol. To enhance its practical functionality, we additionally incorporate transaction allocation and timeout handling mechanisms in this paper. Furthermore, we implement the proposed PnV protocol by using Golang [26] and conduct both theoretical and experimental analyses for comprehensive evaluation. The main contributions of PnV encompass a novel data structure called block group, an innovative consensus process featuring parallel bookkeeping. Other crucial mechanisms, including transaction allocation and timeout handling, further optimize the protocol. These advancements empower the blockchain to facilitate simultaneous block generation by multiple nodes within a consensus round, thereby significantly enhancing throughput with an acceptable level of delay. Experimental results demonstrate that PnV achieves peak throughput ranging from approximately five times higher than PoV and BFT-SMART in the best-case scenario. In comparison with FISCO BCOS, a prominent enterprise-level consortium blockchain platform, the PnV-based blockchain system exhibits an impressive four-fold performance enhancement. The remainder of the paper is organized as follows: Section 2 provides an overview of related work. In Section 3, we establish a preliminary definition for both the node and data model. The PnV consensus protocol is presented in detail in Section 4, followedby theoretical and experimental analyses in Sections 5 and 6, respectively. Finally, our comprehensive conclusion is drawn in Section 7.
# Superpixel Segmentation of Hyperspectral Images Based on Entropy and Mutual Information
## 1 Introduction
proposed to pre-cluster the hyperspectral image for the non-linear graphs into Simple Linear Iterative Clustering (SLIC) superpixels, and then use the superpixels as the input of the dimensionality reduction algorithm [16]. In this method, only the first principal component map was used for segmentation, which may cause the loss of important information. Based on the region growth method in [17], [PERSON] et al. proposed a super-pixel segmentation method [18]. In this method, on the basis of fast region growth, the low-dimensional representations of hyperspectral images were achieved without sacrificing subsequent classification performance. [PERSON] and [PERSON] used principal component analysis as a preprocessing technique to classify hyperspectral images [19]. Based on super pixels, [PERSON] et al. proposed a hyperspectral image classification method to effectively identify the type of land cover [20]. [PERSON] et al. proposed a band selection method to effectively visualize the spectral images [21]. The results showed that the superpixel segmentation scale had sufficient potential in hyperspectral image segmentation. Therefore, although superpixel segmentation is still under study, it has been proved to have promising prospects in HSIs segmentation. In this paper, a new hyperspectral image superpixel segmentation algorithm (IM-CHD) was proposed, in which the new techniques and methods, such as band selection method based on information measures, CMF, and color histogram driving function, were used to achieve more accurate and effective HSIs segmentation. In our previous work [22], we have proposed an HSIs classification method based on information measure, and proved the effectiveness of obtaining the main spectra of HSIs through information measure. In this paper, we synthesize the false color image based on the spectral components selected by information measure, and realize the segmentation of hyperspectral image based on this. The results of this study have significant reference value in two major aspects. 1. The information measures theory, including entropy, mutual information, and normalized mutual information, was applied to superpixel segmentation for HSIs. When information measures were used in the superpixel segmentation, CMF was applied to HSIs at the same time. The combination of both information measures and CMF achieved dimensionality reduction of hyperspectral images. Thus, the superpixel segmentation based on information measures is different from the existing spectral band selection methods based on information measures. As one of the most innovative and effective methods, superpixel segmentation based on color histogram driving and hill climbing optimization was used to segment the false color image obtained in previous steps.
# Applications of Thermal Plasmas for the Environment
## 2 Thermal Plasma Waste Treatment
The amount of added oxygen (oxygen, air, water, steam or CO\({}_{2}\)) determines the type of plasma-assisted thermochemical process in the reactor. * _Pyrolysis_ if no oxygen is added (plasma supplies all energy needed for destruction and volatilization of material and for chemical reactions between the produced gases and the plasma)
* _Gasification_ if oxygen is added to balance the number of carbon and oxygen atoms to suppress the production of solid carbon and provide maximum production of CO and maximum heating value of the produced gas. The combination of different oxidizing agents can control the ratio of hydrogen to carbon monoxide, characterizing the syngas. * _Combustion_ if oxygen is added for the production of CO\({}_{2}\) and H\({}_{2}\)O, and energy is produced through oxidation (a small part of the energy is supplied by plasma). The treatment of raw materials is optimized with regard to the quality of the syngas and the recovery process according to criteria determined by the reaction products end use, namely the energy content of the syngas for combined electricity/heat production (_thermal conversion_), or sustainable recovery of valuable products from the syngas such as hydrogen, methanol, synthetic fuel feedstock, ammonia, olefins or other liquid hydrocarbons via Fischer-Tropsch synthesis (_chemical conversion_). The high temperatures in the reactor volume volatilize the organic material components and dissociate molecules of the produced gas into atoms and/or simpler molecules. Gases produced by material volatilization react with plasma components, and the resulting reaction product's composition depends on the temperature in the reactor volume. The temperature is determined by the energy balance between energy carried by the plasma, energy used for material heating, energy used or produced by chemical reactions, and energy spent for melting of inorganic components. In the Treatment of Waste, Plasma Plays the Following Roles:  
* Transport of energy into the reactor volume, necessary for the material decomposition, melting and volatilization;
* Energy transfer to the waste to be treated;
* Temperature control in the reactor volume;
* Reaction products composition control by supplying the necessary substances.
## 3 Influence on Noise Suppression and Pulse Shape by Filtering System
We use SPD to measure the converted light field after the filtering system, and accumulate the measurement results by FPGA to acquire the photon statistics distributions of the converted pulse, as shown by the gray histogram in Figure 4. The sampling accuracy of FPGA is 10 ns. By fitting the photon distribution with Gaussian function, the pulse shape of the converted field after the filtering system is obtained, as shown by the red solid lines in Figure 4. For \(\Delta t_{in}=53\) ns, 112 ns and 182 ns, the FWHM of the fitted Gauss distributions are \(\Delta t_{out}=102\) ns, \(\Delta t_{out}=133\) ns and \(\Delta t_{out}=190\) ns, respectively. We can see that the input pulse will be significantly broadened when its FWHM is less than 100 ns. So the tolerable narrowest bandwidth of the filtering system depends on the duration of the pulse to be filtered. When the frequency conversion  
Figure 3: Noise counting rate as a function of the pumping power under different filtering conditions. When the etalons and F-P cavity are bypassed, only the first-stage filter is remained, the noise level is shown by the blue triangles. The red circles show the noise level when the second-stage filter is added into the filtering system. The black squares represent the noise level when the third-stage filter is further added. The solid lines are corresponding fitting curves. is used as a quantum interface to connect quantum storage and fiber-based communication, one needs to choose proper bandwidth of the filtering system according to the pulse width of light field emitted from the storage medium, so as to ensure the converted pulse passes through without distortion and the noise is filtered to the greatest extent.
# Improvement of Power Converters Performance by an Efficient Use of Dead Time Compensation Technique
## 3 Proposed Dead Time Compensation Technique
### Total Harmonics and Individual Harmonics Distortions Calculation by FFT Analysis  
The third harmonic distortion is the main problem due to the non-ideal characteristics of power converters. By nature, 'each regularly distorted waveform may be defined as a number of pure sine waves in which the frequency of each sinusoid is an integer multiple of the fundamental frequency of the distorted wave. The sum of the sinusoids is referred to as the \"Fourier series.\" In recent years, they have also concentrated on the harmonic distortion of the power field.  
Dead-time is unavoidable in inverter circuitry as it prevents short-circuiting. However, it comes with the side effect of total harmonic distortion, thus necessitating DTC. The proposed DTC can alleviate the side effects. As can be seen from Figure 10, the third harmonic distortion is 16.26% without DTC. However, after the application of the novel DTC, the distortion is mitigated by 3.77% to 12.49%, as presented in Table 4. This improvement of almost 4% will be instrumental for the health of the motors operating in industrial load.  
Individual harmonic distortion (IHD) represents the relation between the root mean square (RMS) value of the fundamental (RMS) value of the individual harmonics in Equation (29) [28]:  
\[IHD_{n}=I_{n}/I_{1} \tag{29}\]  
For third harmonic, \(n\) is represented by 3. From Figure 10, the RMS of the fundamental current is equal to 100. Also, the RMS of the third harmonic current without DTC is 11.615 A and with DTC is 7.475 A. Therefore, \(IHD_{3}=11.615\) / \(100*100=11.615\) without DTC and \(IHD_{3}=7.475\) with DTC.
## 5 Results
### Results Obtained by Using Data Augmentation  
The effect of augmenting the training data (of adult speech) with the method described in Section 2 was studied by testing the ASR system with noise-corrupted children's speech. To generate noisy test data, the children's speech signals of the PF-STAR database were corrupted with additive noise using four noise types (babble, white, factory, volvo) and four SNR categories (between 0 dB and 15 dB). For each individual noise condition, we built a baseline ASR system, which was trained using solely the noise-corrupted adult  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Database** & **WSJCAM0** & **PF-STAR** \\ \hline Language & British English & British English \\ \hline Use & training & testing \\ \hline Speaker type & adult & child \\ \hline No. of speakers & 92 & 60 \\ (males / females) & (53/39) & (32/28) \\ \hline Age & \textgreater{}18 years & 4–13 years \\ \hline No. of words & 132,778 & 5067 \\ \hline Duration (h) & 15.5 & 1.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Information on the two speech databases used in the current study.  
speech of the corresponding condition (i.e., no data augmentation was used in the training of the baseline systems). The results of the ASR experiments are reported in Table 3.  
From Table 3, the following observations can be made. (1) The data augmentation based on the \"same\" condition improved the ASR performance compared to the baseline and the condition \"different\" worsened the performance in all noise types and SNR categories. (2) The data augmentation based on the \"all\" condition improved the performance compared to the baseline for babble and factory noise but decreased the performance for white and volvo noise. (3) Despite the fact that the data augmentation improved the performance compared to the baseline systems in many of the scenarios studied, overall the WER values were still unacceptably high.