esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiple Extended Target Tracking Algorithm Based on Spatio-Temporal Correlation
## 1 Introduction
Therefore, finding a combination of multiple clustering algorithms can bring more accurate division results and provide a guarantee for subsequent high-precision filtering. A comparison of the characteristics of existing extended object tracking algorithms based on different partitioning methods is presented in Table 1. In addition, in recent years, Ref. [17] proposed an algorithm to create data particles in two-dimensional feature space through its geometric partition (GD). This method solves the classification problem of datasets with similar centers of gravity and overlapping objects described by different labels. Therefore, the data particle geometric partition method has broad prospects for the measurement partition problem of multiple close distance targets. However, the drawback of this method is that it can only be applied to 2D datasets. The number of measurements in the measurement set is the main factor that affects the speed and number of partitions. How to effectively reduce the number of measurements participating in clustering without affecting the accuracy of partitioning is the focus of this paper. To solve this problem, this paper proposes an extended multi-target tracking algorithm based on temporal correlation, which is based on ET-GM-PHD and GM-PHD filtering algorithms. The measurement set is hierarchically processed at the same time, which improves the filtering accuracy and reduces the computational complexity. Experimental results show that the proposed method improves the overall filtering performance compared with the DP partition algorithm. To summarize, the main contributions of this work are listed as follows. * A hierarchical measurement partitioning method based on spatio-temporal correlation is proposed;
* The ET-GM-PHD filter based on spatio-temporal correlation is proposed, and the corresponding implementation scheme is given;
* The performance of the proposed method is assessed via simulations. The rest of this paper is organized as follows. Section 2 introduces relevant mathematical modeling. In Section 3, the ET-GM-PHD filter based on the spatio-temporal correlation method is proposed. The simulation results of the proposed method are analyzed in Section 4. Conclusions are presented in Section 5.
## 3 Avocado's Composition in Nutrients and Bioactives
### Avocado Bioactive Compounds  
Bioactive compounds are traced in minuscule amounts in various plants and foods, including avocado, and are considered extra-nutritional constituents, some of which are tannins, carotenoids, fatty acids, polar lipids, and polyphenols, with an important biological role. These compounds may differ chemically and structurally, but their positive pharmacological effect categorizes them as functional compounds [38; 39]. The importance of bioactive compounds not only \"lies\" in their ability to act as a protective agent but in their ability to do so without any additional side-effects, while reducing economic or environmental costs, especially when retrieved from the bio-wastes of avocado fruit, as a large quantity of these compounds is treated as waste by food industries, especially regarding the peel and seed part of the avocado, which account for the 15-16% of the whole avocado fruit [40; 41]. Thus, the anti-inflammatory, antioxidant, anti-thrombotic, neuroprotective, and cardioprotective properties of these compounds from avocado and its by-products against inflammation-related chronic diseases are explored in this section (Tables 2 and 3).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Phenolic and Flavonoids** & **Avocado Fruit** & **Avocado By-Products** \\
**Compounds (GAE) *1** & & \\ \hline TPC & 0.825–5.6 & 66.1–4.250 \\ Chlorogenic acid & 0–10.77 & 0–34.96 \\ \(p\)-hydrobenzoic acid & 18.25–25.34 & 0–11.19 \\ Caffeic acid & 4.63–10.19 & 0–4.53 \\ Benzoic acid & - & 128.51–147.73 \\ Catechin & 58.67–121.61 & 2.58–2.62 \\ Epigallocatechin & - & 0.82 \\ Rutinoside–3–O–quercetin & - & 0.35–0.45 \\ Glycoside–3–O–kaempfer & - & 0.87–0.93 \\ Quercetin & 5.06–8.84 & 2.72–2.9 \\ Gallic acid & 0.007–32.75 & 0.004–9.22 \\ References & [26; 42; 43; 44; 45] & [26; 31; 42; 45] \\ \hline \hline \end{tabular}
\end{table}
Table 2: Proximate composition of phenolic and flavonoids compounds and the antioxidant capacity of avocado’s fruit, by-products, and oil *.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 1 Introduction
### Contributions and Scope  
This survey presents an overview of the latest advancements in real-time SR techniques. The goal is to offer foundational knowledge and set the stage for future developments in real-time SR tasks. It aims to identify key variations and parameters that could be considered for future innovative approaches. While there are many valuable surveys covering various aspects of SR, there is a notable lack of comprehensive sources specifically focused on real-time SR and emerging methodologies in this area.This study aims to address this gap.
## 3 The PML-V2 Model
### Model Calibration and Validation  
To evaluate the PML-V2 model, we calibrated the model parameters for each growing stage (P\({}_{3}\), P\({}_{4}\),..., P\({}_{9}\), in Table 1) separately, in 2017, and tested the model performances in 2018, where parameters in each growing stage (P\({}_{11}\), P\({}_{12}\),..., P\({}_{17}\), in Table 1) in 2018 were taken as those in the periods of P\({}_{3}\), P\({}_{4}\),..., P\({}_{9}\), respectively, in 2017. The definitions and ranges of all parameters of the PML-V2 model are summarized in Table 2. For each period, we first calibrated the parameters of V\({}_{\text{m25}}\), \(\beta\), \(\eta\), D\({}_{\text{min}}\), D\({}_{\text{max}}\), and K\({}_{\text{Q}}\) by minimizing the RMSE in GPP predictions and then calibrated the rest of the parameters by minimizing the RMSE in ET predictions. In addition, to explore the seasonal variations in the model parameters that are related to the light responses, canopy conductances, and water-carbon coupling characteristics of the rice paddy ecosystem, we also calibrated the model in each period of early rice and late rice in both 2017 and 2018 (P\({}_{3}\), P\({}_{4}\),..., P\({}_{9}\) and P\({}_{11}\), P\({}_{12}\),..., P\({}_{17}\)), separately.
# Options for Performing DNN-Based Causal Speech Denoising Using the U-Net Architecture +
## 3 Speech Denoising
### Input Arrangements  
Input arrangement is crucial to the learning speed and ultimate performance of the U-Net. As indicated earlier in the introduction, both temporal and spectral representations of noisy speech can be adopted as input for denoising tasks. When performing speech denoising in the temporal domain, the input consists solely of sequences of noisy speech waveforms. Figure 7 illustrates the input arrangement in the temporal domain. When considering a transformed domain, the noisy speech signals must be converted into the designated domain. For instance, the STDCT sequence is obtained by applying Equation (7) to a frame of the noisy speech signal. Since an STDCT sequence contains only real values, it can directly serve as input for the U-Net. Figure 8 shows the input arrangement of STDCT coefficients.  
Figure 5: The composition of GLFB.  
Figure 6: Encoder and decoder submodules used in the advanced U-Net.  
Figure 7: U-Net’s input adopted in the temporal domain.  
The situation is somewhat different when using STFT coefficients as the U-Net's input, as STFT coefficients consist of complex values with conjugate symmetry in their first and second halves. Let us assume the number of points used in the DFT equals the frame length. Due to the conjugate symmetry of the STFT coefficients, only half of the coefficients are needed to conserve all available information. For a DFT sequence with an even length, the coefficients corresponding to the direct current (DC) and Nyquist frequency (NF) only have real values and require special attention in data arrangement. Researchers typically adopt the first half of the DFT coefficients plus one extra (i.e., NF) as input. However, this arrangement appears redundant, as the imaginary parts of both ends are essentially null. To address this issue, we take the first half of the DFT coefficients as input, arranging each coefficient's real and imaginary parts in alternating order to form a real-value sequence [30].  
Additionally, we insert the real NF value into the imaginary position of the DC term. The final arrangement is shown in Figure 9. This arrangement maintains a consistent input dimension regardless of the domain selected for denoising, allowing the proposed U-Net to be applied across various domains without the need for dimensional rescaling.
## 2 Materials and Methods
### Ex-Vivo Experiment
#### 2.1.2 Experimental Equipment  
The microwave ablation system includes MTC-3 microwave ablation instrument, microwave operating frequency is 2450 MHz (When using a single insulated microwave antenna of 2450 MHz, the diameter of the ablation area and power deposition is greater than 915 MHz [21]), MTC-3 CA-II7 water-cooled circulating microwave antenna (17 g in diameter and 180 mm in length) (produced by Nanjing Viking Kyushu Medical Instrument Co., Ltd., Nanjing, China), and is equipped with low loss coaxial cable transmission line with peristaltic pump to provide water-cooled circulation of microwave antenna. Table 1 describes the main performance parameters of the microwave ablation instrument.  
The air compressor used in the inflation system is 380-4L silent oil-free air compressor produced by Daertuo company (Shanghai, China), which is used with GM510 high-precision handheld digital pressure gauge produced by Benetech company (Palo Alto, CA, USA), so as to control the inlet pressure of isolated pig lung.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Performance Parameters** & **Value** \\ \hline Ambient temperature/\({}^{\circ}\)C & 5–40 \\ \hline ambient humidity/\% & \(\leq\)80 \\ \hline supply voltage/V & 220 \(\pm\) 22 \\ \hline Microwave operating frequency/MHz & 2450 \(\pm\) 50 \\ \hline Microwave ablation power/W & 0–120 \\ \hline Microwave output working time/min & 0–30 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance parameters of microwave ablation system.  
The data acquisition instrument is the 34972A multipoint data acquisition instrument produced by Agilent. It is used with thermocouples with 1 mm diameter. The Agilent Benchlink Data Logger software (version 1.0, Agilent Technologies Co., Ltd., Beijing, China) is used to set the test parameters, collect and archive the measurement data, and display and analyze the input measurement data in real time.  
During the experiment, the thermocouples was directly punctured into the ex-vivo inflatable pig lung, and the tip of the thermocouples and the microwave antenna were in the same horizontal plane. The distribution of temperature field during microwave ablation can be obtained by monitoring the data of thermocouples. The insertion direction of the microwave antenna is the Y-axis, and the thermocouples are evenly distributed on the right side of the microwave antenna at 5 mm intervals. In addition, a thermocouple is arranged at the emission point of the microwave antenna slot to obtain the highest temperature of the ablation area. The ablation length along the Y-axis is the long diameter (DI) of ablation area, and the ablation length along the X-axis is the short diameter (Ds) of ablation area. The thermocouple distribution is shown in the Figures 1 and 2.
# SPACNet: A Simulation Platform of an Acoustic Cognitive Network
## 2 Design of SPACNet
### State Machine-Based Process Model  
The state machine-based process model is an important part of cognitive network simulation platforms. The network protocols conform to the six-tuples of finite state machines, which can be abstracted into state set, initial state, input, action, output, and state transition [34]. The protocol stack based on the state machine can intuitively represent the logical structure of the protocol, and the process model uses the finite state machine to describe the logical behavior of nodes. The state transition process has no aftereffect, which can be modeled by a nonhomogeneous Markov chain, and the finite state space is defined based on the adopted protocol. The one-step transition matrix can be expressed as:  
\[\mathbf{P}(m)=\left[\begin{array}{cccc}p_{11}(m)&p_{12}(m)&\cdots&p_{1N}(m) \\ p_{21}(m)&p_{22}(m)&\cdots&p_{2N}(m)\\ \vdots&\vdots&\ddots&\vdots\\ p_{N1}(m)&p_{N1}(m)&\cdots&p_{NN}(m)\end{array}\right], \tag{6}\]  
and the one-step transition matrix satisfies Equation (7):  
\[\left\{\begin{array}{l}0\leq p_{ij}(m)\leq 1\\ \sum\limits_{j=1}^{N_{s}}p_{ij}(m)=1\end{array}\right., \tag{7}\]  
where \(p_{ij}(m)\) is the one-step transition probability of the node from state \(i\) to state \(j\) at time \(m\), and \(N_{s}\) is the total number of states in finite state space. For example, Figure 4 shows the state transition diagram of link quality cognitive routing based on a multiparameter comprehensive evaluation protocol, and the one-step transition matrix can be written as:  
\[\mathbf{P}(m)=\left[\begin{array}{cccc}p_{11}(m)&p_{12}(m)&p_{13}(m)\\ p_{21}(m)&p_{22}(m)&0\\ p_{31}(m)&p_{32}(m)&p_{33}(m)\end{array}\right], \tag{8}\]  
where the state transition probability is related to the actual network running status and network protocols. Note that, because \(p_{23}(m)=0\) as expressed in (8), in Figure 4, there is no state transition from state 2 to state 3.
## 3 Results and Discussion
### Detection of IGF-1  
In traditional ELISA, after TMB substrate is added, a blue color develops in proportion to the amount of IGF-1, and the color turns different degrees of yellow with the addition of termination solution. IGF-1 was detected by a colorimetric assay with the OD value monitored at 450 nm, and the standard curve is shown in Figure 4a. The OD value gradually increased with the increase in IGF-1 concentration, revealing an excellent linear relationship between the OD values and IGF-1 concentrations in the range of 1.25-100 ng/mL. The regression equation was y = 0.00463x + 0.05547, the regression coefficient was 0.9998, and the LOD was calculated to be 1.018 ng/mL.  
In the fluorescent sandwich ELISA, the addition of the optimized fluorescent working solution initiated a gradual color transition from colorless to faint pink, and the color became deep pink with increasing concentrations of IGF-1. After adding the termination solution, the reaction mixtures maintained their developed color without further visible changes. The fluorescence intensities measured at the emission wavelength of 585 nm were analyzed by a four-parameter logistic regression model, which is usually performed in ELISA methods [32]. The standard curve was constructed and is shown in Figure 4b, in which all IGF-1 concentrations are closely located on the four-parameter fitting curve (R\({}^{2}\) = 0.9968), with a detection range in the lower concentration region of 0.1-30 ng/mL and a much lower LOD of 77.29 pg/mL.  
Figure 3: (**a**) Excitation and emission spectra of resorufin generated by ADHP in HRP enzymatic reaction. Optimization of (**b**) concentration of ADHP, (**c**) total volume, (**d**) temperature, and (**e**) pH of fluorescent sandwich ELISA working solution.  
Compared with traditional ELISA, the fluorescent sandwich ELISA method has a faster experimental process within 1 h, while commercial ELISA kits usually need at least 3 h, so the fluorescent sandwich ELISA method saves time and obtains test results quickly. The LOD of this fluorescent sandwich ELISA method is reduced by about 10 times, which is suitable for measuring ultra-low concentrations of IGF-1. In addition, the signals of traditional ELISA are ineffective within 15 min after the addition of the termination solution, whereas the ADHP reaction obtains a stable fluorescent signal for up to 1 h after termination, which helps in saving results for longer time. Moreover, a comparison between the present method and other reported methods for IGF-1 determination with regard to the detection range and LOD is summed up in Table 1. It demonstrates that liquid chromatograph-mass spectrometer (LC-MS) methods usually have higher LOD values and are suitable for the detection of IGF-1 with high concentrations. ELISA methods tend to be specific to lower concentrations of IGF-1, especially the fluorescent ELISA method proposed in this work, which is ultrasensitive in IGF-1 detection, reaching a magnitude of pg/mL.
# The Role of Macrophage Dynamics in Atherosclerosis Analyzed Using a Petri Net-Based Model
## 3 Results and Discussion
### Presentation of the Model and the Results of Its Formal Analysis  
This section introduces and analyzes a Petri net-based model that illustrates the interplay between cholesterol homeostasis disruption, macrophage equilibrium, and atherosclerosis progression. Illustrated in Figure 3, the model comprises 41 transitions and 45 places. These components are described in Tables 1 and 2, respectively. The network is covered by 12 t-invariants, each detailed in Table 1, along with their respective biological significance. Based on these t-invariants, a series of MCT sets have been computed, resulting in six distinct, non-trivial ones (i.e., the ones containing more than one transition). Table 3 comprehensively describes all MCT sets. Each of them represents interconnected subnetworks, and they partition the structure of the network into separate regions. Figure 2: Macrophage dynamics analyzed in this study. 1. Activation of IL-23/IL-17-G-CSF pathway; 2. HSCs’ mobilization in the bone marrow; 3. monocyte polarization (Ly6 Chigh and Ly6 Clow); 4. macrophages M1 and M2 in subendothelial space; 5. M1 phagocytes oxLDL forming foam cells; 6. foam cells secrete pro-inflammatory cytokines and enhance local inflammation, increasing atherosclerotic plaque. Parts of the figure were created using images from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License ([[https://creativecommons.org/licenses/by/3.0/](https://creativecommons.org/licenses/by/3.0/)]([https://creativecommons.org/licenses/by/3.0/](https://creativecommons.org/licenses/by/3.0/)), accessed on 21 March 2024). Figure 3: A Petri net-based model depicting the role of macrophages and disturbances in cholesterol homeostasis in the development and progression of atherosclerosis. This model is segmented into six subprocesses corresponding to MCT sets and is distinguished by various transition colors. Detailed descriptions of these subprocesses can be found in Table 3. In this model, transitions are represented as squares, whereas circles represent places. The locations depicted as two concentric circles refer to identical locations in the model and are referred to as logical places. To improve clarity and comprehension, we have selectively integrated crucial species’ names from Tables 1 and 2 directly into the figure. \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Place** & **Biological Meaning** & **References** & **Place** & **Biological Meaning** & **References** \\ \hline \(p_{0}\) & M1 macrophages & [3,34] & \(p_{23}\) & splenic macrophages and dendritic cells mobilized & [38,53] \\ \hline \(p_{1}\) & monocytes in intima & [34,54] & \(p_{24}\) & M-CSF & [9] \\ \hline \(p_{2}\) & strongly adherent monocytes to EC & [34,54] & \(p_{25}\) & IFN-\(\gamma\) & [9,55] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The list of places and their biological meaning.
## 6 Forecasting System Hard-Ware Implementation for Autonomous Photovoltaic Power Plants  
The discussed above forecasting algorithms were implemented using a distributed computing system that included a low-cost x86 embedded computer and an FPGA (Figure 12).  
In general, the architecture of the implemented computing system repeats the one proposed in [44]. FPGA in this architecture is used as a custom accelerator, which is built on a basis of one of more specialized computational cores (SCCs) and Ethernet POWERLINK communication core [45]. Each SCC, which includes a combination of a matrix coprocessor [46] and a general-purpose processor and performs computations requested by managing device, built on the basis of an x86 embedded  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Confidence Interval**} & \multicolumn{2}{c}{**The Share of the Forecasts within the Interval**} \\  & **STF, \(K\)-Means Method** & **Operational Forecast, AR(2)** \\ \hline \(\pm\)1 MW (6.7\% from PVPP \(P_{\text{inst}}\)) & 78.9\% & 88.5\% \\ \(\pm\)2 MW (13.3\% from PVPP \(P_{\text{inst}}\)) & 92.2\% & 97.5\% \\ \hline \hline \end{tabular}
\end{table}
Table 10: The reliability parameters comparison of PVPP generation operational and short-term forecast.  
Figure 12: Hardware architecture used for forecasting algorithms implementation.  
computer running Linux operating system. This architecture is inspired by the ones used in space robotics [47], which makes it suitable and attractive for adaptation to other application areas where the robust and maintenance-free operation of equipment is required in long terms; for example, for autonomous PVPPs.  
The developed x86 software algorithms and FPGA firmware were first verified using the Vmodel toolbox [48] simulation tool, and then uploaded into Intel NUC8 IN computer and a Digillent Nexys 4 DDR kit FPGA (Figure 12). The estimation evaluated during model verification was the same as the ones provided by the real hardware. The worst-case difference between evaluated forecasts and reference ones presented in Table 7 was 0.04883%. This error is caused by the use of fixed-point calculations on FPGA accelerators. Meanwhile, as it can be seen from Table 7, it is significantly less than the error of the forecasting methods themselves.  
This experiment demonstrates that the discussed forecasting methods can be implemented using embedded equipment and integrated into autonomous photovoltaic power plants. Moreover, the proposed implementation approach is scalable and, as it was shown in [44], even a significantly larger amount of data can be processed without the use of high-performance servers just by increasing the number of distributed FPGA-based accelerators.
# A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors
## 5 Conclusions  
Although the distributed machine learning model training with SSP can improve the utilization of computing nodes and the computing efficiency by reducing communication and synchronization overhead, the cumulative errors can significantly influence the performance of the algorithm and lead to low convergence speed. Frequent communication can reduce the stale threshold and parallel error, thereby improve the performance of the algorithm, but this is subject to the network transmission rate limit.  
In this paper, we optimize SSP, extend the fault tolerance of the machine learning iteration-convergence algorithm, propose the DEFT, and implement DSP which is a parameter communication optimization strategy based on the DEFT. DSP dynamically adjusts the stale threshold according to the performance of each worker node to ensure the balance between the computational  
Figure 14: This figure compares the difference of training time between the distributed machine learning model using SSP with different stale thresholds and DSP.  
efficiency and the convergence rate. The weak threshold is added to solve the problem that SSP is inefficient to train the distributed machine learning model in clusters which are composed of nodes with similar performance. Finally, the experimental results show that the efficiency and the convergence rate of DSP are better than that of BSP and SSP under the premise that the accuracy is guaranteed. At present, the convergence rate and accuracy of the distributed machine learning system based on DSP are not very good for large-scale instances. In our future work, we will improve the scalability of this strategy, especially in large-scale sensors systems.