esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Recurrent Neural Network (RNN)  
Recurrent Neural Network (RNN) [1] is a feed-forward neural network with the addition of feedback connections. The output of a unit in an RNN model at the current timestep is supplied back to the unit of the RNN model as an additional input for the subsequent step. This architecture innately enables the RNN model to learn temporal dependence in the data. For this reason, we consider RNN as one of our approaches to perform long-term prediction in this research.  
In this section, we briefly discuss the Jordan Network [2], one of the widely recognized classical RNN architectures. Let each step \(t\), with input \(x_{t}\), hidden state \(h_{t}\), and the output \(y_{t}\), then the architecture and operation of the Jordan Network can be expressed as follows:  
\[h_{t}=\tanh(W_{h}x_{t}+U_{y}y_{t-1}+b_{h}) \tag{1}\]  
\[y_{t}=\tanh(W_{y}x_{t}+U_{y}h_{t-1}+b_{h}) \tag{2}\]  
where \(W_{h}\) and \(U_{h}\) are hidden state parameter matrices, \(W_{y}\) and \(U_{y}\) are the output parameter matrices, \(b_{h}\) is parameter vectors, and \(tanh\) is the hyperbolic tangent activation function for non-linearity. The gradients of the above units (Equations (1) and (2)) are computed using back-propagation through time (BPTT) algorithm [3]. BPTT unfolds RNN's hidden units backward over time, and back-propagates the gradients of the hidden units at the corresponding time step. The parameters of the hidden units are updated according to the gradients in the respective time steps.  
Several studies have reported that RNN has a problem with a long-term dependency issue [4; 5]. One of the main reasons is vanishing and exploding gradient problems [5], which can happen when a gradient-based machine learning algorithm tries to fit its model using data with long-term dependencies. Long short-term memory (LSTM) addressed this issue by introducing gates to control the flow of information [6]. In this work, we consider LSTM and Sequence-to-Sequence (Seq2 Seq) LSTM architecture as our long-term estimator candidate.
## 3 Implementation
### Integral Average Lowpass Filter Error Analysis  
It can be seen from the previous analysis of Equation (10) that the frequency deviation \(\Delta f\) affects the amplitude measurement accuracy. When there is a multiple frequencies low SNR signal (18), the frequency components \(f_{k}\pm\hat{f}_{m1}\) as shown in Equations (20) and (21) should be regarded as frequency deviations for IAF.  
The continuous form (4) of the IAF can be rewritten to the discrete form:  
\[y[n]=\frac{1}{N}\sum_{k=0}^{N}x(n-k) \tag{23}\]  
where the \(N\) is the integral length of the integral average filter. The transfer function of the integral average filter is:  
\[H(z)=\frac{1}{N}\sum_{k=0}^{N}z^{-k}=\frac{1}{N}\frac{1-z^{-N}}{1-z^{-1}} \tag{24}\]  
The amplitude-frequency response characteristic [18] is:  
\[H(f)=\frac{\sin(\frac{\pi Nf}{f_{s}})}{N\sin(\frac{\pi f}{f_{s}})}e^{\frac{-| \pi(N-1)f}{f_{s}}} \tag{25}\]  
When at low frequencies, the frequency response Equation (25) of IA can be approximately simplified:  
\[H(f)=\text{sinc}(\frac{Nf}{f_{s}})e^{\frac{-|\pi(N-1)f}{f_{s}}} \tag{26}\]  
For large filter length \(N\), the \(-3\) dB frequency is about \(0.443~{}f_{s}/N\) which satisfies \(|H(F)|=0.707\). Figure 5 represents the amplitude-frequency responses of different integral length \(N\). It can be seen that the longer the filter length \(N\), the faster roll-off in the low-frequency passband. Moreover, the narrow bandwidth can reduce the residual noise \(n^{\prime}(t)\) effectively in Equation (5) if the IAF has a large length \(N\). In the high-frequency region, the IAF with a longer length \(N\) has greater attenuation capability. Because the frequency deviations \(f_{k}\pm\hat{f}_{m1}^{\prime}\) caused by other frequency components are usually large, an integral average filter with a long length \(N\) can effectively filter out their interference. However, the frequency deviation \(\Delta f\) between frequency component \(f_{m1}\) and its corresponding reference signal \(\hat{f}_{m1}\) is very small and in the passband of the integral average filter. Thus, the long filter length \(N\) has no significant improvement for amplitude estimation. That also indicates the frequency estimation method is necessary.
## 1 Introduction
[18,19] used the magnetic gradient tensor method to extract the fine signal of the nine-dimensional component of the magnetic signal of a magnetic field. The influence of magnetic memory detection direction on the detection signal was eliminated by scaling, so the multidimensional magnetic memory signal was showed to have an important role in accurately positioning defects [20]. Since the magnetic memory signal is a weak magnetic field signal, the measured magnetic signal in the actual inspection is affected by the roughness of the test piece's surface, the ambient background magnetic field, and the system noise, which results in the magnetic signal containing a large amount of interference noise [21; 22]. Since wavelet transform has the characteristics of a \"mathematical microscope\" and has multi-resolution, research [23; 24] has found that it has more applications in magnetic memory signal processing, and the interference of random noise in magnetic memory signals can be eliminated by using wavelet noise. In this paper, with the help of magnetic charge theory and electromagnetism theory, theoretical support for the difference in magnetic permeability in the two regions is unearthehed by using the phenomenon wherein the number of magnetic charges gathered in pores and cracks far exceeds that in the region where the residual stress is located. Additionally, considering that information on the defect site cannot be accurately extracted from a single directional signal, three-dimensional magnetic modulus gradient extremes [25; 26] are proposed to characterize air-type defects among the welds. Finally, the feasibility and validity of the method are demonstrated with the help of simulations and experimental validation based on the elimination of the effect of random interference noise on the 3D magnetic modulus gradient polarity. This study is the first to complete a coupled thermal force-magnetic analysis, and provides a reference for finite element simulation studiesfocused on magnetic memory. The proposed determination characteristics accurately locate welding defects and provide a good judgment for defect detection in practical engineering.
## 4 Results
### Numerical Results
#### 4.1.1 Peak Signal-to-Noise Ratio  
As depicted in Figure 8a, the PSNR method exhibited four distinct patterns:  
* Period 1 \(\rightarrow\) ball shapes: The initial 259 phantoms representing a ball shape showed a wide range of similarity values.
* Period 2 \(\rightarrow\) H shapes: Phantoms 260-521, representing an H shape, demonstrated consistent similarity within each quality level, with clear differences between qualities.
* Period 3 \(\rightarrow\) L shapes: Phantoms 522-783, representing an L shape, exhibited a tight clustering of Low-Quality values and a wider, evenly distributed spread for High-Quality values.
* Period 4 \(\rightarrow\) rod shapes: Phantoms 784-end, representing a rod shape, showed a similar lack of consistency as Period 1, but with a narrower spread.  
These patterns were observed in all the evaluated methods. The differences between the periods likely stemmed from variations in the phantom shapes within the dataset.  
The PSNR method produced 43 errors, indicating that in 43 cases it incorrectly assigned a higher quality score to the Low-Quality image compared to the High-Quality image. The average execution time for the PSNR was 0.0034 s.
# Multi-Scale Deep Feature Fusion with Machine Learning Classifier for Birdsong Classification
## 3 Experiments and Analysis of Results
### Performance Metrics  
The experimental setup consisted of a desktop computer equipped with 128 GB of memory, a 16-core, 32-thread CPU running at a main frequency of 3.40 GHz, and an NVIDIA GeForce RTX 4090 24G GPU. The operating system was a 64-bit Ubuntu OS. The software tools used included Anaconda3, PyCharm 2023.3.2 and Python 3.10 as the deep learning platforms.  
For the log-power spectrogram, the short-time Fourier transform (STFT) parameters were as follows: 512 frequency points, a window length of 512 samples and a 75% frame overlap. The log-Mel spectrogram was computed using 40 Mel filter banks, with the same STFT parameters applied. The log-wavelet spectrogram was computed with a frame length of 257 samples, 75% overlap between frames and 258 wavelet bases, enabling detailed time-frequency analysis for feature extraction.  
The performance of the proposed method was evaluated using four standard metrics: accuracy, precision, recall and \(F1\) score. These metrics assess the classification effectiveness across all classes, providing a robust evaluation framework. The calculation of each indicator is as follows:  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+ \text{FN}} \tag{5}\]  
\[Precision=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{6}\]  
Figure 7: Dataset B statistics of 400-millisecond audio segments for each bird species.  
\[Recall=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
\[F1=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{8}\]  
TP, TN, FP and FN, respectively, represent true positives, true negatives, false positives and false negatives. The \(F1\)-score is a metric used to evaluate the accuracy of a model. Precision indicates the proportion of true positive samples among all samples classified as positive. Recall denotes the proportion of true positive samples among all actual positive samples.
# High Throughput Data Relay in UAV Wireless Networks
## 4 Proposed Solution
### UAV Trajectory Optimization  
For any given victim bandwidth scheduling \(\mathcal{A}\), problem (5) is simplified as  
\[\max_{\mathcal{C}}R, \tag{8}\]  
subject to  
\[R_{n}\geq R,\ \forall n \tag{8a}\] \[||\mathbf{c}[k+1]-\mathbf{c}[k]||\leq V_{\max}\delta_{t},\ \forall k\in\{1,\ldots,K\}\] (8b) \[\mathbf{c}[1]=\mathbf{c}_{I},\ \mathbf{c}[K+1]=\mathbf{c}_{F}, \tag{8c}\]  
The constraint (8a) is equivalent to the following expression  
\[\frac{1}{K}\sum_{k=1}^{K}a_{n}[k]\log_{2}(1+\frac{P\gamma_{0}}{||\mathbf{c}[k] -\mathbf{c}_{n}||^{2}+H^{2}})\geq R,\ \forall n,\]  
where \(\gamma_{0}\triangleq\frac{\beta_{0}}{\sigma^{2}}\). Note that (8a) is a non-convex constraint regarding the UAV trajectory variable \(\mathbf{c}[k]\). To deal with it, the expression in (8a) is replaced by its lower bound at a given local point. We denote the input UAV trajectory for sub-problem (7) as \(\{\mathbf{c}^{\prime}[k],k\in\{1,\ldots,K,K+1\}\}\). Recalling that the logarithmic function is lower bounded by its first order Taylor expansion, we can obtain the following lower bound with the given local point \(\mathbf{c}^{\prime}[k]\) when treating \(||\mathbf{c}[k]-\mathbf{c}_{n}||^{2}\) as the variable  
\[\begin{split} R_{n}[k]&=a_{n}[k]\log_{2}(1+\frac{P \gamma_{0}}{||\mathbf{c}[k]-\mathbf{c}_{n}||^{2}+H^{2}})\\ &\geq a_{n}[k]\left[A_{n}[k](||\mathbf{c}[k]-\mathbf{c}_{n}||^{2}- ||\mathbf{c}^{\prime}[k]-\mathbf{c}_{n}||^{2})+B_{n}[k]\right]\\ &\triangleq R_{n}^{lb}[k],\end{split} \tag{9}\]  
where  
\[A_{n}[k] =\frac{-P\gamma_{0}\log_{2}e}{(||\mathbf{c}^{\prime}[k]-\mathbf{ c}_{n}||^{2}+H^{2})(||\mathbf{c}^{\prime}[k]-\mathbf{c}_{n}||^{2}+H^{2}+P \gamma_{0})} \tag{9a}\] \[B_{n}[k] =\log_{2}(1+\frac{P\gamma_{0}}{||\mathbf{c}^{\prime}[k]-\mathbf{ c}_{n}||^{2}+H^{2}}),\ \forall n,k. \tag{9b}\]  
With the lower bound (9), sub-problem (8) is approximated as the following sub-problem  
\[\max_{\mathcal{C}}R^{lb}, \tag{10}\]  
subject to  
\[R_{n}^{lb}=\frac{1}{K}\sum_{k=1}^{K}R_{n}^{lb}[k]\geq R^{lb},\ \forall n \tag{10a}\] \[||\mathbf{c}[k+1]-\mathbf{c}[k]||\leq V_{\max}\delta_{t},\ \forall k\in\{1,\ldots,K\}\] (10b) \[\mathbf{c}[1]=\mathbf{c}_{I},\ \mathbf{c}[K+1]=\mathbf{c}_{F}, \tag{10c}\]  
For (10a) the victim bandwidth scheduling variable \(a_{n}[k]\) is determined by solving the sub-problem (7). Hence both (10a) and (10b) are convex quadratic constraints and (10c) is a linear constraint. Therefore, sub-problem (10) is a convex quadratically constrained quadratic program which can also be solved efficiently by the MATLAB CVX toolbox.
# Effect of Blood on Synovial Joint Tissues: Potential Role of Ferroptosis
## 5 In Vivo Models of Hemarthrosis
### Rodent  
Ferrostatin studies have been performed using rodents; the current studies represent a translational advance with preclinical and clinical relevance [11; 59; 76; 114]. These small animal model findings provide proof-of-concept data to warrant studies using large preclinical animal models, such as the canine model, where established models of hemophilic arthrosis and PTOA can be adopted [10; 67; 73; 96; 107; 108; 109; 123]. In most rodent studies, ferrotposis was triggered by blood and ROS as a result of OA biomechanical instability. Although Fer-1 mitigated OA changes in rodents, we are unsure if it would be successful in treating blood-induced injury. Nevertheless, these studies provide an in vivo experimental drug protocol that can be used to investigate the effects of blood exposure.  
While rodents do not accurately recapitulate the human anatomy, they are useful in determining mechanisms of pathology, as mentioned above [96]. In a 2019 study, mice models were used to discover that ferrotposis is a mechanism of cardiomyopathy induced by doxorubicin and ischemia/reperfusion [59]. Using mice served as a useful disease model to study the efficacy of cardiomyopathy-inducing drugs and to test the therapeutic effect of inhibitors on a diseased subject. With this information, the group discovered that ferrotposis in cardiac injury is a result of the upregulation of heme oxygenase Hmox1 that causes the heme degradation and release of free iron that leads to ferrotposis [59]. Importantly, using small animal models like rodents makes it easier to test different factors and environments that could promote therapeutic effects. In addition to identifying mechanisms of cardiomyopathy, this group tested the role of low-iron diets and certain inhibitors in reducing the risk of death, paving the way for future studies to utilize these findings [59].  
In another study, rats were used to further investigate the mechanisms of ferrotposis in an in vivo model. In vitro studies utilized IL-1\(\upbeta\) to simulate inflammatory conditions, and iron overload was replicated using ferric ammonium citrate (FAC) in order to validate these reagents as inducers of ferrotposis [76]. Their studies demonstrated that these two reagents did cause changes in the protein expression of markers indicative of ferrotposis (GPX4, ACSL4, P53, SLC7A11) [76]. After in vitro validation testing, the inhibitory effects of Fer-1 were tested in a mouse OA model. The OA model was established in the mice by performing destabilized medial meniscus (DMM). With unaffected and DMM mice, the group tested the effects with and without Fer-1 injections. Safranin-O staining revealed that the injection of 1 mg/kg Fer-1 lessened cartilage degeneration. Additionally, GPX4 (a key regulator of ferrotposis) staining showed that the injection of Fer-1 increased the amount of GPX4-positive cells in the DMM groups [76]. Overall, this study proved that Fer-1 exhibits anti-OA effects in vivo.
## 1 Introduction
To identify appropriate mechanical parameters for tunnel surrounding rocks, intelligent optimization techniques such as Genetic Algorithms [18; 19] and Particle Swarm Optimization [20; 21; 22] have been widely applied. However, the complexity of tunnel projects often results in back-analysis methods becoming trapped in local minima, limiting practical applications. The Grey Wolf Optimizer (GWO), an efficient meta-heuristic approach, effectively addresses this issue with its straightforward concept, minimal adjustable parameters, rapid convergence, and robust global optimization capacity, rendering it well suited for nonlinear and complex problems [23]. The effectiveness of the algorithm hinges on various elements, including the initial placement of grey wolves, convergence factors, and the position update equations. In pursuit of optimizing the Grey Wolf Optimizer (GWO) algorithm's precision and search capabilities, numerous researchers [24; 25; 26; 27; 28; 29] have innovatively refined it from multiple angles. The GWO algorithm features impressive search efficiency and rapid convergence. By integrating it with other intelligent algorithms, its capacity to tackle complex problems can be significantly improved [30; 31; 32]. Despite its strengths, the GWO algorithm has limitations in balancing global and local search abilities, with the alpha wolf's position not always yielding the optimal solution, leading to premature convergence to local optima and slow overall convergence. In response, this study integrates an Improved Grey Wolf Optimization (GWO) algorithm with a Backpropagation (BP) neural network, employing cubic chaotic mapping, nonlinear convergence factors, and updated position update formulas to enhance both global and local search capabilities. This optimized IGWO-BP neural network model adjusts the weights and thresholds, establishing a correlation between the mechanical parameters of surrounding rock and displacement. Validation of the method's effectiveness in inverting the mechanical parameters of surrounding rock masses was conducted through practical examples from rock tunnel projects. However, it is important to note that the experimental scenario employed in this study is relatively simplified, primarily focusing on the behavior of elastic materials. This limitation restricts the broader applicability of the findings, particularly in more complex, non-elastic conditions.
## 2 Materials and Methods
### Selected Algorithms
The DSM open-source code is publicly available in [63], which was implemented for comparisons on Ubuntu 18.04 with Eigen (v3.1.0), OpenCV (v2.4.3), and Ceres solver, which were provided in the official repository. * 8. **DynaSLAM. ** The Dyna-SLAM algorithm [32] is a lighter version of ORB-SLAM2 exceeded by adding the detection, segmentation, and inpainting of dynamic information on scenes' machine learning capabilities. In addition, the Mask R-CNN of [64] was integrated with the classic sparse-indirect method to detect and segment regions of each image that potentially belonged to movable objects. The authors also incorporated a multiview geometry approach calculating backprojections to define the key point parallax angles to detect additional information the CNN cannot recognize. The authors reported that this combination contributed to overcoming the ORB-SLAM2 initialization issues; so, it works in dynamic environments. The DynaSLAM is publicly available in [17], and it was implemented in Ubuntu 16.04 with ROS Kinetic, Cuda 9, Tensorflow 1.4.0, and Keras 2.0.8. * 9. **CNN-DSO. ** In the literature, DSO neuronal methods like D3 VO [65], MonoRec [14], and DDSO [66] can be found. Nevertheless, they are not publicly available, or in the case of MonoRec, its monocular VO pipeline is not available for testing; so, the CNN-DSO was selected for this comparison, which is publicly available in [15]. This method includes a CNN depth prediction module enabling the DSO system to execute its estimation modules using additional depth prior information obtained by the network. The CNN used for this study was the MonoDepth system of [67], a single image depth estimation network that outputs a depth value for each pixel position by chains of feature maps processing. The network was built over the ResNet backbone using a variant of its encoder-decoder architecture. The CNN-DSO requires building TensorFlow (v1.6.0) from source and MonoDepth from its official repository [68], and it was implemented in Ubuntu 18.04, with Eigen (v3.1.0) and OpenCV (v2.4.3). * **CNN-SVO**. In the study of [11], an extension of the hybrid method SVO was proposed by fusing the same Single Image Depth Estimation (SIDE) CNN MonoDepth module used in the CNN-DSO with the original geometric-based hybrid method. In this case, MonoDepth was included to add preliminary depth information to the SVO pipeline, minimizing the uncertainty in the feature correspondence identification steps; then, the system is initialized, obtaining high uncertainty maps. Then, the SIDE CNN creates filters to approximate the current values' mean and variance, considerably reducing the amount of information separating inliers/outliers in the depth map. The CNN-SVO is publicly available in [46] and was implemented in Ubuntu 16.04 to allow the SVO modules to work with ROS Kinetic. For more information on the taxonomy, definitions, SLAM, VO, and SFM basics, and further details of the methods described in this review, we encourage the reader to see the prior works listed in [9].
# Theoretical Derivation and Optimization Verification of BER for Indoor SWIPT Environments
## 5 Numerical Results
### Simulation Result  
Figure 5 shows the compared results of the objective values versus the generations for the SADDE and APSO algorithms in our simulation. The horizontal axis is the number of generations and the vertical axis is the fitness value that is equal to the inverse of the harvested power efficiency. It is found that the objective value almost converges near 400 generations with SADDE, and near 100 generations with APSO. According to this result, APSO has a faster convergence speed than SADDE. However, the final harvesting power ratio compared to the harvesting power ratio at 100 generation had only 1% improvement with APSO.  
Figure 4: Environment model.  
Figures 6a-d and 7a-d show the radiation pattern for Tx to Rx in the x-y plane by SADDE and APSO at 100, 150, 250, and 600 generations. It is seen that the patterns of both algorithms point to all receivers at early generations, and they have similar radiation patterns as a result. In our case, we know that Rx1 uses half of the power received to decode information. To give the best power efficiency, Tx should send just enough power to Rx1 and try to maximize the power transfer to other receivers. Both SADDE and APSO adjusted the lowest amplitude gain for Rx1. For other receivers with SADDE, it was found that the amplitude gain point to Rx2, Rx3, and Rx4 reached near 15 to 13 at 250 generation. However, the distances of Rx2 and Rx3 were closer to the transmitter than Rx4. The amplitude gain point to Rx4 adjusted from near 15 to 12 at 600 generations. With APSO, the amplitude gain point to Rx2 and Rx3 reached 15 at 150 generations, and that to Rx4 adjusted from near 12 to 13 at 150 generations. The improvements of the total power harvesting ratio were about 2.45 by two algorithms.  
Figure 5: Objective value generations for the self-adaptive dynamic differential evolution (SADDE) and asynchronous particle swarm optimization (APSO) algorithms.  
Figure 6: _Cont._  
Both SADDE and APSO adjust a strong side-lobe at 247\({}^{\circ}\). Figure 8 shows this side-lobe will reflect to Rx4 by the concrete wall at the point of 4.4736, 0.0, and 0.5, with reflection angle of 23\({}^{\circ}\), making a constructive interference for power transfer.  
Figure 6: Radiation pattern for Tx to Rx by SADDE in different generations: (**a**) 100, (**b**) 150, (**c**) 250, and (**d**) 600.  
Figure 7: Radiation pattern for Tx to Rx by APSO in different generations: (**a**) 100, (**b**) 150, (**c**) 250, and (**d**) 600.