esa-sceva/satcom-chunk-collection huggingface dataset:
# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 3 Proposed Method
### Template Filtering  
Currently, most mainstream trackers utilize similarity matching for object tracking, which involves measuring feature similarities between the template and the search images. However, the template image may contain substantial background information, as shown in the first row of Figure 4. In this case, due to the significant scale gap between the target and the background, using self-attention to highlight the target features also amplifies the background noise. Moreover, performing self-attention calculations between the template and all search region tokens leads to excessive redundant computations.  
To alleviate the above issues, accompanied by providing more refined target information to the transformer backbone, we propose a template-filtering (TF) approach. As depicted in the second row of Figure 4, we crop a smaller yet more accurate target region \(t^{*}\in\mathbb{R}^{3\times H_{t}^{*}\times W_{t}^{*}}\) from the template image according to the size of the target bounding box and serialize it into image patches \(t_{p}^{*}\in\mathbb{R}^{\mathbb{N}_{t}^{*}\times 3\cdot P^{2}}\), where \(N_{t}^{*}=H_{t}^{*}W_{t}^{*}/P^{2}\). Next, the image patches are fed into a linear projection layer, and the obtained mapped features are then summed with the position embedding to obtain the sequences that are more focused on the target. Consequently, the proposed TF approach mitigates the background noise so that the template image can be more focused on the target. This prompts FETrack to increase the tracking accuracy while reducing a large number of redundant computations.
# Statistical Method Based Waveform Optimization in Collocated MIMO Radar Systems
## 4 Algorithms for Waveform Optimization  
The availability of closed-form expressions derived in the previous section enables us to perform an unsupervised constrained optimization and thus decrease the probability of outage given in (21) for the collocated MIMO radar. In what follows, we break down this section into first outlining a sub-optimal, albeit closed-form, solution of the optimization variable. Next, we use the closed-form expression as an initialized vector and perform the outage probability minimization using heuristic approaches.
## 3 Proposed SNNtrainer3D
### Programming Languages and Technologies Involved  
Figure 1 summarizes the programming languages and technologies involved in developing the SNNtrainer3D.  
Specifically, the Python programming language is used for the application's backend and deep learning parts, and JavaScript for the front end, visualization, and GUI parts. Python and JavaScript were chosen due to their distinct advantages in developing web applications and machine learning systems. Python's extensive libraries and ease of use make it ideal for backend development, data processing, and machine learning tasks. With its ubiquity in web development, JavaScript enables sophisticated frontend interfaces, ensuring seamless user interaction. Together, they offer a powerful combination for full-stack development, facilitating the complex computational requirements of the server and interactive, user-friendly client sides.  
For deep learning, the PyTorch framework [45] and the snnTorch library [27] are used. PyTorch is a popular open-source machine learning library based on the Torch library, widely used for applications such as natural language processing. It was used in this context because of its ease of use, flexibility, and efficient tensor computation. PyTorch supports dynamic computational graphs that allow for more intuitive development of complex architectures, making it a preferred choice for research and development projects involving neural networks. snnTorch is a Python library designed specifically for building and training SNNs, leveraging the capabilities of PyTorch. It provides tools and functionalities that simplify the creation, simulation, and optimization of SNNs, which are neural networks that mimic how real neurons in the brain communicate through discrete spikes. It was used here to harness these biological neural network properties, offering advantages in efficiency and performance for specific tasks, particularly those involving time-series data or requiring low-power computation.  
Flask [46] is used for the backend part. Flask is a micro web framework written in Python known for its simplicity and flexibility, allowing developers to build web applications quickly and scale up to complex applications. It's considered modern due to its continued updates and compatibility with current web standards. While it may not be the \"best\" for every scenario, given the diversity of project requirements and preferences for frameworks, Flask is highly regarded for cases where a lightweight, extensible framework is desirable. It was used in this paper because of its lightweight nature, enabling rapid development and prototyping.  
For the front end, visualization and GUI, JavaScript, and Three.js [18] are used. Three.js is a cross-platform JavaScript library and API used to create and display animated 3D graphics in a web browser, utilizing WebGL underneath. It was used in this context to enable the development of rich, interactive visualizations for the SNNtrainer3D software, allowing users to intuitively understand and interact with the neural network models and their training progress directly through the web interface.
# Control of Corner Separation with Plasma Actuation in a High-Speed Compressor Cascade
## 3 Results
### Performances of Plasma Actuations in Suppressing the High-Speed Compressor Cascade Flow Separation
According to Figure 11b,c, both the suction surface and the endwall plasma actuations can improve the total pressure of the high-speed compressor cascade flow, but the regions where the flow total pressure is improved are obviously different for these two plasma actuation layouts. From Figure 11a,b, it can be found that the suction surface plasma actuation improves the total pressure of the high-speed  
Figure 10: Influences of plasma actuations on the high-speed compressor cascade total pressure loss and blockage coefficients, with plasma actuators inducing different body forces. (**a**) Suction surface plasma actuation; (**b**) Endwall plasma actuation. compressor cascade flow mainly through suppressing the airfoil separation and the WV, as well as its interaction with the FV. However, from Figure 11a,c, it can be found that the endwall plasma actuation improves the total pressure of the high-speed compressor cascade flow mainly through suppressing the PV, as well as its interaction with the WV. Since the mechanisms behind the control effects of the suction surface and the endwall plasma actuation in suppressing the high-speed compressor cascade corner separation are different, their behaviors in Figures 9 and 10 are resultantly different too. Figure 12 shows the influences of plasma actuations on the pitch-averaged blockage coefficients of the high-speed compressor cascade. According to Figure 12a, strong flow blockage can be observed within the core region of the high-speed compressor cascade corner separation, and the flow blockage at the boundary of the corner separation is slightly reduced by the suction surface plasma actuation, which is associated with the suppression of the WV. On the contrary, as shown in Figure 12b, flow blockage within the core region of the high-speed compressor cascade corner separation is obviously reduced by the endwall plasma actuation, which is associated with the suppression of the PV. Figure 11: Influences of plasma actuations on the total pressure at plane p6 and the corresponding main flow structures. (**a**) Main flow structures at plane p6 of baseline condition; (**b**) Variance of total pressure with the suction surface plasma actuation; (**c**) Variance of total pressure with the endwall plasma actuation. Figure 12: Influences of plasma actuations on the pitch-averaged blockage coefficients of the high-speed compressor cascade. (**a**) Suction surface plasma actuation; (**b**) Endwall plasma actuation.
# Mine Intelligent Receiver: MIMO-OFDM Intelligent Receiver for Mine Information Recovery
## 2 Theoretical Basis
### Mine Wireless Channel Model  
The mine communication environment is complex, and there are many branches and bends in the narrow space, as well as obstacles, etc. Therefore, the mine wireless channel is susceptible to damage from external factors, such as noise and interference, and there is serious fading. Generally, underground wireless channels can be divided into two types: large scale fading and small scale fading.  
The large-scale fading of the mine channel mainly consists of free-space path loss and specific electromagnetic wave transmission loss in the mine environment [22]. For an arbitrary distance, the path loss \(\zeta(t,d)\) at a specific time and location follows a normal distribution in dB.  
\[\zeta(t,d)=\zeta(t,d)+10n\lg\frac{d}{d_{0}}+X_{\sigma}(t) \tag{7}\]  
where \(t\) is time, \(d\) is signal transmission distance, \(d_{0}\) is the reference distance, \(n\) is the path loss index, indicating the path loss growth rate, and \(X_{\sigma}(t)\) is the shadow fading, obeying a normal distribution with mean 0 and variance \(\sigma^{2}\).  
The electromagnetic wave propagation loss in the mine tunnel is related to the roughness of the tunnel wall, the inclination degree, and the polarization mode of the antenna, etc., where the vertical polarization loss is  
\[L_{ver}=4.343\lambda^{2}z\left(\frac{w^{2}}{a^{3}\sqrt{\varphi_{1}-1}}+\frac{ \rho^{2}\varphi_{2}}{b^{3}\sqrt{\varphi_{2}-1}}\right) \tag{8}\]  
where \(\lambda\) is the wavelength of the electromagnetic wave signal, \(z\) is the distance between the transmitting and receiving antennas, \(\omega\) is the half-wave number in the horizontal propagation direction, \(a\) and \(b\) are the height and width of the alleyway, \(\varphi_{1}\) and \(\varphi_{2}\) are the relative dielectric constants of both sides and the top and bottom plates, and \(\rho\) is the half-wave number in the vertical propagation direction. The total loss of horizontal polarization wave is  
\[L_{total}=L_{hor}+L_{rough}+L_{tile} \tag{9}\]  
where \(L_{hor}\) is the horizontal polarization loss, \(L_{rough}\)is the roughness loss, and \(L_{tile}\) is the tilt loss.  
\[L_{hor}=4.343\lambda^{2}z\left(\frac{w^{2}\varphi_{1}}{a^{3}\sqrt{\varphi_{1}- 1}}+\frac{\rho^{2}}{b^{3}\sqrt{\varphi_{2}-1}}\right) \tag{10}\]  
\[L_{rough}=8.636\pi^{3}\Delta h^{2}z\lambda\left(\frac{1}{a^{4}}+\frac{1}{b^{4 }}\right) \tag{11}\]  
\[L_{tile}=4.343\pi^{2}\vartheta^{2}  
and \(\Omega\) is the average power. The Nakagami-m distribution is used to accurately characterize the fading of multipath signals in complex scenes. The actual channel scenarios, such as long straight tunnels and turnouts in mine tunnels, are characterized by varying the value of \(m\).
## 2 Methods
### GPS PPP Observation Models  
This study uses an undifferenced and uncombined GPS PPP model as compared with the traditionally used IF-PPP model. These models include a standard SF GPS PPP model, troposphere-constrained SF GPS PPP models, ionosphere-constrained SF GPS PPP models, and SF GPS PPP models with troposphere- and ionosphere-constrained.
# Entropy-Regularized Federated Optimization for Non-IID Data
## 2 Related Work
### Entropy and Mirror Descent
#### 2.2.2 Entropy in Federated Learning  
Integrating entropy-based objectives into federated optimization is still uncommon. [PERSON] et al. [27] embed an entropic proximal term into mirror descent, but the formulation introduces dual variables that must be stored and transmitted for every client, increasing communication volume as the federation scales. [PERSON] et al. [28] treat per-client entropy as a proxy for model uncertainty; their server therefore requests and aggregates additional entropy scores each round, doubling the message size and incurring latency on low-bandwidth links.  
ERFO takes a lighter approach: it maximizes the entropy of the _update distribution_ through a single-scalar regularizer that is evaluated locally during back-propagation. Consequently, each client sends only its standard weight delta--no dual variables, uncertainty vectors, or side metadata--so the protocol remains bandwidth-neutral while still providing a principled, information-theoretic control on non-IID drift.
## 2 Queue Model
### Implementation of Real-Time Simulation with EV Charging  
To study the impact of EV charging on the grid load demand, a real-time simulation is essentially required. Hence, in this work, QSTS simulation is used to compute a 24-h simulation period. In contrary to classical snapshot power flow simulation, QSTS is a sequential time series power flow and can be performed based on a varying load demand during a period. The classical loads such as commercial, industrial, residential are varying based on the daily load curves. However, to simulate the random behavior of EV load demand during 24 h without control, a statistical model can be used based on the lognormal distribution of the traveling millage to calculate the SOC and normal distribution of the plugin time \(t_{p}\). Then, at each instant time \(T\), the load of the EV can be estimated based on the number of the plug-in EVs and their SOC at this instant plus the classical load.  
To manage and control the EVs load demand, a DSM is proposed in this work based on the queue as presented in Figure 6.  
Figure 4: Real-time ETR with the owner reference price.  
Figure 6 shows the implementation of the proposed DSM for a set of EVs connected to bus k at instant time \(T\). The figure demonstrates the interface of a single EV with the grid. The EVs status is updated at the instant time \(T\) with four control variables \(SOC_{N_{EV}}\), \(t_{p}\), \(OPR_{N_{EV}}\), and \(EPTD_{N_{EV}}\). When the \(OPR_{N_{EV}}\) is greater than the ETR, the queuing algorithm generates the waiting time \(Tq\), and the time spent in the system \(Ts\) for each EV. The controller then decides when the battery should charge or be idle. After the demand is determined, the total EV load is added to the classical demand, then the QSTS simulation is applied to compute a 24-h simulation period.  
Figure 5: Flowchart of real-time EV queue charging based on owner’s preferences.
# Towards Bio-Hybrid Energy Harvesting in the Real-World:
## 3 Research Progress in Energy Harvesting from Microorganisms  
MFCs extract electrical energy from different microorganisms by utilizing their catabolic metabolism over various organic components [17; 18]. Bacteria are the most used microbes for this purpose, whose activity can be boosted with the aid of other microorganisms like algae [43; 44].  
The following paragraphs depict research progress in MFCs, their structural development, a pragmatic classification of the MFCs, adaptations performed to optimize their operations in different scenarios and their diverse conjugated applications along with electricity generation.
# A Node Density Control Learning Method for the Internet of Things
## 6 Experimental Results
In addition, the power consumption fluctuation of this method is small. When the number of samples increased from 100 to 400, the control power of the proposed method increased by 5 Joules, and the power of control of an integrated sensing control method for joint sensing wireless sensors increased by 7 Joules. The control power of the group control method increased by 14 Joules based on evolutionary game theory. The control power of the multi-source fault-tolerant topology node control method in the wireless sensor actuator network increased by 13 Joules, and the control power of the 3D WSN topology \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number of Samples/One**} & \multicolumn{5}{c}{**Power Consumption (J)**} \\ \cline{2-6}  & **Method 1** & **Method 2** & **Method 3** & **Method 4** & **Method 5** \\ \hline
100 & 20 & 63 & 61 & 65 & 63 \\
200 & 21 & 65 & 62 & 71 & 69 \\
300 & 23 & 69 & 69 & 75 & 72 \\
400 & 25 & 70 & 75 & 78 & 86 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different methods of network node control power consumption. Figure 2: Comparison of node density control completion time. control method based on the node degree estimation increased by 23 Joules. It can be seen that the power consumption of the proposed method is the smallest and the stability of the control process is good. This paper analyzes the probability of signal interconnection between mobile nodes in the process of analyzing and determining the minimum node density. Therefore, the accuracy of signal interconnection probability calculation between mobile nodes has an important influence on the accuracy of the research results in this paper. The formula for calculating the accuracy of signal interconnection probability between mobile nodes is as follows:  
\[G=\frac{C}{J}\times 100\% \tag{17}\]  
In Equation (6), \(C\) represents the node interconnection probability calculated by the method herein, and \(J\) represents the actual probability of node interconnection. To further verify the effectiveness of this method, the accuracy of signal interconnection probability calculation between mobile nodes is analyzed and compared with References [5; 6; 7; 8] method, as shown in Table 2.  
It can be seen from Table 2 that when using this method to calculate the probability of signal interconnection between mobile nodes, the accuracy of the calculation results is about 90%, and the accuracy of Method 2 is not more than 76.3%. The accuracy of Method 3 is relatively high, but it is still lower than that of this method. The accuracy of Method 4 is lower than that of Method 3 and not more than 80.2%. It can be seen that the accuracy of the probability of signal interconnection between mobile nodes is high, which shows that the accuracy of node density control in this method is high. The main reason is that in the process of calculation, any pair of removable nodes are calculated, which has a certain comprehensiveness and improves the accuracy of the results.
# Research on the Robustness of Active Headrest with Virtual Microphones to Human Head Rotation
## 2 Theory
### Secondary Sound Field  
Secondary sources can be approximated as monopoles in low frequency range. Due to the close distances between secondary sources and the error microphones of the active headrest, only the direct sound from the secondary sources is considered. With the diffracting rigid sphere shown in Figure 1, the sound pressure at the receiving point \(\mathbf{r}=(r,\theta,\phi)\) generated by a secondary source at \(\mathbf{r}_{\mathrm{s}}=(r_{s}\theta_{s},\phi_{s})\) can be expressed as [20]  
\[p_{\mathrm{s}}(\mathbf{r})=Z_{\mathrm{s}}(\mathbf{r})d_{\mathrm{s}}, \tag{3}\]  
\[\begin{array}{rl}Z_{\mathrm{s}}(\mathbf{r})=&\frac{k\omega\rho}{4\pi}\sum \limits_{l=0}^{\infty}\ (2l+1)[j_{l}(kr_{<})-\frac{j_{l}^{\prime}(k\alpha)}{h_{l}^{ \prime}(k\alpha)}h_{l}(kr_{<})]h_{l}(kr_{>})\\ &\times\bigg{\{}P_{l}(\cos\theta_{s})P_{l}(\cos\theta)+\sum\limits_{m=1}^{2}2 \frac{(l-m)!}{(l+m)!}p_{l}^{m}(\cos\theta_{s})P_{l}^{m}(\cos\theta)\cos[m( \phi-\phi_{s})]\bigg{\}},\end{array} \tag{4}\]  
where \(\omega\) is the angular frequency, \(\rho\) is the air density, \(q_{\mathrm{s}}\) is the source strength of the point source, \(Z_{\mathrm{s}}\) is the acoustic transfer impedance from the secondary source to the receiving point, \(r_{>}=\max(r_{s},r)\) and \(r_{<}=\min(r_{s},r)\). Equation (4) can be used in the calculation of plant responses, including the physical plant response between the secondary source and the physical microphone, as well as the virtual plant response between the secondary source and the virtual microphone.
# MIMO Radar Imaging Method with Non-Orthogonal Waveforms Based on Deep Learning
###### Abstract  
Transmitting orthogonal waveforms are the basis for giving full play to the advantages of MIMO radar imaging technology, but the commonly used waveforms with the same frequency cannot meet the orthogonality requirement, resulting in serious coupling noise in traditional imaging methods and affecting the imaging effect. In order to effectively suppress the mutual coupling interference caused by non-orthogonal waveforms, a new non-orthogonal waveform MIMO radar imaging method based on deep learning is proposed in this paper: with the powerful nonlinear fitting ability of deep learning, the mapping relationship between the non-orthogonal waveform MIMO radar echo and ideal target image is automatically learned by constructing a deep imaging network and training on a large number of simulated training data. The learned imaging network can effectively suppress the coupling interference between non-ideal orthogonal waveforms and improve the imaging quality of MIMO radar. Finally, the effectiveness of the proposed method is verified by experiments with point scattering model data and electromagnetic scattering calculation data.  
multiple-input multiple-output (MIMO) radar; non-orthogonal waveform; radar imaging; deep learning +
Footnote †: journal:  
doi:10.3390/a15090306
# Low-Cost Software Evaluation of Communication Delays in Networked Industrial Systems
## 4 Delay Measurement
### Delay Measurement Technique
 \tag{6}\]  
According to (6), \(\delta\) can be calculated only if \(c_{R}\), \(c_{M}\), and \(\delta_{\textit{TAP}}\) are known. However, let us consider the total delay incurred by a packet \(p\) in the two distinct configurations of the system as follows:  
1. Delay \(\delta\), corresponding to configuration \(\mathcal{T}_{\textit{base}}\) that excludes DUT (upper half of Figure 2). 2. Delay \(\delta^{\prime}\), corresponding to configuration \(\mathcal{T}_{\textit{DUT}}\) that includes \(\textit{DUT}_{1}\) (lower half of Figure 2). Assuming that the delay \(\delta_{SW}\), introduced by \(SW\), is the same in the two configurations \[\delta_{\textit{DUT}}=\delta^{\prime}-\delta.\] (7)  
Denoting by \(t_{R}\) and \(t_{M}\) the transit times of \(p\) through ports \(n_{R}\) and \(n_{M}\) in configuration \(\mathcal{T}_{\textit{base}}\), and by \(t_{R}^{\prime}\) and \(t_{M}^{\prime}\) the analogous transit times in configuration \(\mathcal{T}_{\textit{DUT}}\), from (6) and (7) we obtain the following:  
\[ \begin{split}\delta_{\textit{DUT}}&=(u_{R}(t_{R} ^{\prime})-u_{M}(t_{M}^{\prime})-(c_{R}-c_{M})+\delta_{\textit{TAP}})\\ &-(u_{R}(t_{R})-u_{M}(t_{M})-(c_{R}-c_{M})+\delta_{\textit{TAP}} )\\ &=(u_{R}(t_{R}^{\prime})-u_{M}(t_{M}^{\prime}))-(u_{R}(t_{R})-u_{ M}(t_{M})).\end{split}  \tag{8}\]  
In summary, (8) shows that the following properties hold:  
* \(\delta_{\textit{DUT}}\) can be measured by means of timestamps taken at ports \(n_{R}\) and \(n_{M}\). * The exact values of \(c_{R}\), \(c_{M}\), and \(\delta_{\textit{TAP}}\) are irrelevant for the measurement, as long as they remain unvaried. The reasoning can be further generalized to prove that, as \(\delta_{\textit{TAP}}\) cancels in (8), so does any other delay that is fixed and common to both configurations being considered. In turn,this makes _TM_ very robust with respect to any fixed source of delay (not due to the DUT) along the measurement path, which may be hard to characterize at times.
## 2 Theoretical Framework and Methods
### Analysis Process
#### 2.3.4 Confidence Level Determination Category  
Determine categories based on confidence criteria. \(\lambda\) predetermined confidence threshold \(\lambda\) is set to judge whether sample \(x_{i}\) belongs to category \(k_{0}\), with a confidence level of \(\lambda\). The range of \(\lambda\) is \(0.5\leq\lambda<1.0\); the larger the \(\lambda\) value, the more conservative the result ([PERSON] et al., 2021) [29].  
\[k_{0}=\min_{k}\left(\sum\limits_{k=1}^{p}\mu_{ik}\geq\lambda,1\leq k\leq p\right) \tag{24}\]