esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### BiLSTM Network
#### 2.4.1 Improved Particle Swarm Optimization Algorithm  
The traditional PSO algorithm tends to fall into the local optimal solution and cannot converge to the global optimal solution every time, despite a global search being performed [18; 19; 20; 21]. Therefore, in this paper, an Improved Particle Swarm Algorithm (IPSO) is employed. Compared to the traditional particle swarm optimization algorithm, the \(\lambda\mathrm{P(x)}\) term is added to the velocity update formula, where \(\lambda\) is the force parameter, which is more particle nature-dependent.  
\[\lambda=\left\{\begin{array}{l}1\ \mathrm{good\ particle}\\ -1\ \mathrm{bad\ particle}\end{array}\right.\]  
\(\mathrm{P(x)}\) is the interparticle force function defined as:  
\[\mathrm{P(x)}=\frac{\mathrm{gbest-x_{min}}}{\mathrm{x_{max}-x_{min}}} \tag{6}\]  
where \(\mathrm{x_{min}}\) and \(\mathrm{x_{max}}\) denote the minimum and maximum values of the particle positions, respectively. Through this function, the magnitude of inter-particle forces is related to the relative relationship between the particle positions and the global optimal position. Good particles (fitness value greater than the average fitness) are attractive to other particles, and bad particles (fitness value less than the average fitness) are repulsive to other particles, so that the particle swarm can better explore the solution space and avoid falling into the local optimal solution during the search process. The improved particle velocity update function \(\mathrm{v_{i}(k+1)}\) is as follows:  
\[\mathrm{v_{iF}(k+1)}=\mathrm{v_{i}(k+1)}+\lambda\mathrm{P(x)} \tag{7}\]  
The algorithm of IPSO is as follows:  
Figure 2: Schematic diagram of BiLSTM neural network operation. Cyan circles indicate the original input data, grey circles indicate the data after model calculation.  
Step 1: Initialize the particle swarm, randomly generate a group of particles, including position and velocity. And calculate the current individual optimal solution, pbest, and global optimal solution, gbest.  
Step 2: Calculate the per-particle fitness, which is used as a measure of how well the particle represents the solution.  
Step 3: Compare the fitness of the current particle with the average fitness (higher than the average is a good particle and the opposite is a bad particle) and determine the particle's \(\lambda\).  
Step 4: Update the velocity and position of the particles.  
Step 5: Update pbest and gbest if there is a better individual or global optimal solution than the current one.  
Step 6 If the number of iterations is reached or if the value of the objective fitness function is satisfied, the algorithm is stopped and the optimal solution is output. Otherwise, go to Step 2.
## 3 Problem Description
Another important parameter to be set in the tape recorder is the equalization: a post-equalization curve is applied during the reading, in order to compensate the pre-equalization curve applied during the recording, essentially acting as the integrator to make the overall transfer function nearly flat [29]. Several standards exist, and they are commonly called using the name of the association that proposed the standard. For 30 ips, the most diffused is the standard AES derived by Audio Engineering Society, whereas for 15 ips and 7.5 ips the most used standards are CCIR and NAB, from International Radio Consultative Committee (in French) and National Association of Broadcasters, respectively [30; 31; 32]. Applying the wrong post-equalization curve implicates the wrong frequency response and, thus, a non-flat overall transfer function. Furthermore, the recording may be encoded with a noise reduction system. The most common are Dolby A and Dolby SR (professional), Dolby B and Dolby C (domestic) and dbxTypes I (professional) and II (domestic) [28]. When reading the tape, the same noise reduction system must be used in order to compensate the one adopted in the recording phase. Again, the lack of compensation or the wrong system choice deeply changes the signal. In this paper, only the former problem is tackled, together with the opposite case: the use of a noise reduction system in the decoding phase when the original system was not encoded. Considering the combination of all these configurations, it is evident how many different digitized versions of the same tape could be obtained. The approach proposed in this work handles this aspect considering all the versions of the same tape as a single node in the phylogenetic tree. The problem could be further extended considering multiple copies recorded and digitized with different tape recorders and the possibility of finding some audio documents obtained by pieces of tape recorded by different machines (professional or not). In this case, the analogue filters and characteristics of tape recorders could lead to differences in digitized copies. The same could happen with machines that were not correctly calibrated. A further variable that could be considered in the analysis is the possibility of having different copies of the same tape and the presence of damage and/or syndrome (such as SSS [7]) that impact the digitization results, creating dissimilarities and artifacts despite the original tape being the same. This extension has been provided to better explain the overall complexity of the problem, but it goes far beyond the scope of this article, which seeks to prove the effectiveness of this new approach with respect to the problem with a simplified model that nonetheless includes the main alterations.
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Researcher** & **Method** & **Merit** & **Defect** & **Contribution** \\ \hline \multirow{8}{*}{[PERSON] [9]} & \multirow{8}{*}{A digital notch filter is used to suppress periodic interference.} & \multirow{8}{*}{Certain filtering effect.} & \multirow{8}{*}{The ability to suppress interference is limited, resulting in poor performance.} & \multirow{8}{*}{Through the organic combination of experimental modeling and spectrum analysis, [PERSON] solved the problem that it is difficult to accurately identify the fault type in PD detection and promoted the leap from qualitative judgment to quantitative analysis of insulation diagnosis of high-voltage equipment.} \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{8}{*}{[PERSON] [10]} & The spectrum of partial discharge signal is obtained by the fast Fourier transform. & More effective extraction of partial discharge spectrum from interference. & There is still some interference in the partial discharge spectrum. & The research of [PERSON] solved the problem of feature degradation and misclassification of acoustic partial discharge detection in a strong noise environment through noise robustness enhancement, feature engineering innovation, and a hybrid intelligent classification framework design. \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Advantages and disadvantages of current research on partial discharge characteristics. The empirical mode decomposition (EMD) can obtain the intrinsic mode function, and the useless information is filtered out. The filtering effect is higher than the other filtering methods, and only the IMF containing the characteristic signal is left. The IMF signal as the input of the classifier will greatly improve the recognition accuracy [15]. Deep convolutional neural networks (DCNNs) are widely used in various fields [16, 17, 18, 19]. It can effectively alleviate the phenomenon of gradient disappearance and overfitting. It has the advantages of short training time and stable convergence, which can effectively improve recognition accuracy [20,21]. Therefore, this paper proposes a method based on empirical mode decomposition and deep convolutional neural network (DCNN) for partial discharge type identification of a 10 kV T-type terminal. In this method, the partial discharge signal obtained by the experiment is decomposed by EMD, and the IMF signal is obtained and used as the input of the deep convolutional neural network. Compared with the EMD-SVD-RF method, the effectiveness of the proposed method is verified.
# FuGeF: A Resource Bound Secure Forwarding Protocol for Wireless Sensor Networks
## 3 The DWSIGF Protocol  
This section describes the DWSIGF protocol. The protocol utilizes both MAC and network layer for routing. The MAC (IEEE 802.11 DCF) uses a four-way handshake semantics for the node to node coordination within a 60-degree sextant, centered on a direct line with the destination, as shown in Figure 1.  
The handshake is initiated when the Sender S's NAV timer is zero and an idle channel is sensed for a DIFS time period, before broadcasting an Open Request to Send (ORTS) signals (containing its location and destination location) to nodes within its broadcast range. The nodes within the broadcast range consist of the forwarding candidates (within the 60-degree sextant area) and non-forwarding candidates shown in Figure 1. On receiving the broadcast, the non-forwarding candidates suppress their responses by setting their NAV timer in accordance with 802.11 semantics to avoid interference. On the other hand, the forwarding candidates respond by setting up a CTS response time. When the candidate's CTS response time expires, a CTS signal containing candidate's location is sent to the ORTS sender S as the response [2; 3; 30; 2].  
The sender S collects multiple responses within the dynamic collection window period. The window period creates a possible time shift in the protocols semantics [3; 4]. The responses gathered within the collection window period are immediately sampled and a node is selected based on either DWSIGF-P or DWSIGF-R forwarding strategy. The strategies use Increasing Distance Towards the Destination (IDTD), which is a parameter that defines a node's progress towards the destination and maps out its position on the network. DWSIGF-P utilizes the greedy forwarding strategy, which means the node with the highest IDTD is selected as the forwarding node. On the other hand, DWSIGF-R employs a loop-free random forwarding strategy, it randomly selects any node as long as it has a positive IDTD value. On selecting the forwarding node, sender S proceeds in accordance with the 802.11 DCF semantics (ORTS =>CTS=>DATA=> ACK) using an isolated channel with the receiver [3; 4].  
Figure 1: Forwarding area.  
DWSIGF mitigates selection of attacker to minimize packet losses. However, the utilization of IDTD parameter (only) for forwarding node selection in both strategies(DWSIGF-P and DWSIGF-R) has resulted in the simplification of spatio-temporal predictions. This has allowed malicious nodes to succeed in causing substantial packet loss in the network.
## 2 Background
### Hyperband  
Hyperband is a bandit-based approach to hyperparameter optimisation introduced by [PERSON] et al. [15]. The algorithm formulates hyperparameter optimisation as an infinite-armed bandit problem where a predefined resource, such as iterations, data samples, or features, is allocated to randomly sampled configurations. Unlike Bayesian optimisation methods that focus on intelligent configuration selection, Hyperband emphasises speeding up random search through adaptive resource allocation and early-stopping strategies.  
The algorithm builds upon the successive halving algorithm (SHA), which allocates a budget uniformly to a set of hyperparameter configurations. After the budget is depleted, half of the configurations are eliminated based on resources. This process continues until only one configuration remains.  
A key limitation of successive halving is the requirement to choose the number of configurations as input. This presents a trade-off; a small number of configurations means longer average training times per configuration, while a large number means shorter training times but broader exploration. Hyperband addresses this by running multiple successive halving instances with different configuration counts, called brackets. Each bracket represents a different balance between exploration and exploitation.  
Hyperband begins with the most aggressive bracket to maximise exploration, with each subsequent bracket reducing exploration until the final bracket allocates maximum resources to each configuration, equivalent to random search. Each bracket is designed to consume approximately the same total computational budget.
# Enrichment of Oesophageal Speech: Voice Conversion with Duration-Matched Synthetic Speech as Target
###### Abstract  
Pathological speech such as Oesophageal Speech (OS) is difficult to understand due to the presence of undesired artefacts and lack of normal healthy speech characteristics. Modern speech technologies and machine learning enable us to transform pathological speech to improve intelligibility and quality. We have used a neural network based voice conversion method with the aim of improving the intelligibility and reducing the listening effort (LE) of four OS speakers of varying speaking proficiency. The novelty of this method is the use of synthetic speech matched in duration with the source OS as the target, instead of parallel aligned healthy speech. We evaluated the converted samples from this system using a collection of Automatic Speech Recognition systems (ASR), an objective intelligibility metric (STOI) and a subjective test. ASR evaluation shows that the proposed system had significantly better word recognition accuracy compared to unprocessed OS, and baseline systems which used aligned healthy speech as the target. There was an improvement of at least 15% on STOI scores indicating a higher intelligibility for the proposed system compared to unprocessed OS, and a higher target similarity in the proposed system compared to baseline systems. The subjective test reveals a significant preference for the proposed system compared to unprocessed OS for all OS speakers, except one who was the least proficient OS speaker in the data set.  
pathological speech; voice conversion; intelligibility; speech recognition Article
## 3 Methodology
### Assessment of the UAV and VIIRS/DNB Images  
As shown in Figure 1, we found that the UAV moonlight remote sensing mosaic eliminated the need for a linear stretching process to clearly distinguish the land surface features. Therefore, the whole image classification process mainly consisted of three steps, geometric correction, multi-resolution segmentation, and land surface classification using the RF algorithm. The coverage area of UAV moonlight remote sensing mosaic was relatively small, while each land surface type in the mosaic was too big, so that the multi-resolution segmentation in which the optimal segmentation scale was 200. We randomly selected 226 samples (113 samples for training, 113 samples for testing). Five different land surface classes (farmland, bare land, pond, vegetable greenhouses, and others) were used to map in this image.  
Because the brightness ranges of VIIRS/DNB were small, numerical stretching (\(\times 10^{10}\)) operations were performed on the entire image. Then, the image was also selected to obtain the optimal thresholding value (170.00 in this case) for separating the low-light suburban areas from the bright urban areas (Figure 5).  
For the low-light suburban area, four types of land surface were selected, namely snowy mountains (Snow), farmlands (Farmland), rivers (River), as well as other types and city light areas (Other type). The optimal segmentation scale in the multi-resolution segmentation process was set as 20. We randomly selected 189 (93 samples for training, 96 samples for testing). Some parameters are shown in Table 3.
## 3 Results and Discussion
### Comparison of Different Methods  
The results shown in Figure 3 indicate the yields of flavonoids that were obtained by UEAATPE, EAATPE, UAAATPE, ATPE, and SE. Among the above five methods, the highest yield of total flavonoids was extracted by UEAATPE. At the same time, the remaining four methods in order of extraction yield were UAE > EAE > ATPE > SE (53.48, 49.59, 47.46, and 24.79 mg/g, respectively). The extraction yield of total flavonoids with SE was lower than that with the other four methods. Therefore, UEAATPE is a prospective method for the extraction of flavonoids.
# Adaptive-Size Dictionary Learning Using Information Theoretic Criteria
## 1 Introduction
Section 4 is dedicated to experimental results on synthetic data that show that our algorithm is able to recover the size of the true dictionary, in various noise and sparsity level conditions and with various initializations. Our algorithm also gives, almost always, recovery errors on test data that are better than those given by the underlying DL algorithm in possession of the true dictionary size. Comparison with the method in [9] is also favorable.
# Pruning Deep Neural Network Models via Minimax Concave Penalty Regression
## 3 Comparison Experiment  
Ablation experiments were conducted on three networks, VGG19_Simple, VGG19, and ResNet34, using two datasets, CIFAR10 and CIFAR100. The VGG19_Simple model was employed, where the three fully connected layers in the original VGG19 model were reduced to two layers. The parameters of the original 4096 hidden layer neurons were adjusted to 512, significantly reducing the fully connected layer parameters. The VGG19 model, initially designed for the ImageNet dataset with 1000 categories, was adapted in our experiments to classify 100 categories. The training epoch for all models was set to 300. As ResNet34 was initially designed for the ImageNet dataset, its original input dimensions were 224 \(\times\) 224. In the experiment using CIFAR100 with input dimensions of 32 \(\times\) 32, adjustments were made to the first convolutional layer's kernel size from 7 \(\times\) 7 to 3 \(\times\) 3. The learning rate for retraining was uniformly set at 0.001, with 40 epochs for the VGG model and 20 epochs for the ResNet34 model. The Top1 accuracy after MCP-L1 and L1-Norm pruning represents the average value of three repetitions.  
He [28] demonstrated that the latter layers of the VGG model contain comparatively less information. Considering that the dimensions of the model's input data are already reduced significantly by preceding pooling layers, and the computational requirements for these layers are notably lower than those of earlier convolutional layers, pruning the last few layers has a limited impact on overall computation reduction. Consequently, in the ablation experiments involving the VGG19_Simple and VGG19 models, the number of channels in the last four convolutional layers remains unchanged; in other words, no pruning is applied to these final four layers. Unlike employing distinct pruning ratios for varying filter quantities, our approach uses global ratios across all parameter-clipped layers, ensuring consistent ratios for all layers subjected to pruning.
## 3 Results
### RNA-Seq Analysis in EPS Myotubes  
To clarify global transcriptional regulation with 24-h EPS, EPS-applied C2C12 myotubes were analyzed by RNA-seq (Figure 4a). A total of 575 genes were identified as differentially expressed genes (DEGs) with statistical significance (exactTest raw \(p\) < 0.05) and a fold change more than 2-fold. In addition to _Myth7_ and _Myth2_, RNA-seq revealed that sarcomeric genes, such as _Acta1_ and _Myom1_, were also induced by EPS. _Slc2a1_, which codes GLUT1 (glucose transporter 1), was also increased, which might contribute to insulin sensitivity increased by exercise. We validated _Gsta1_, _Gsta4_, _Slc2a1_, _Acta1_, and _Myom2_ with qPCR experiments, in which expression levels were greatly increased in the EPS-applied myotubes (Figure 4b). _Gsta1_, _Gsta4_, and _Slc2a1_ were identified as new targets of EPS. These results suggested that EPS increased antioxidant pathway and glucose metabolism. The enriched pathways are analyzed by KEGG [29] (Figure 4c) and DIRECT BP (Figure 4d). Both analyses identified cytokine-cytokine receptor interaction and glutathione metabolism (Figure 4c).  
Figure 2: RNA-sequencing analysis revealed clenbutterol-dependent transcription. (**a**) Volcano plot for RNA-seq data obtained from C2C12 myotubes treated with 30 ng/mL. clenbutterol, low glucose media for 1 h; (**b**) mRNA expression levels of _Nrta1_, _Nrta2_, _Vegfa_, _Cxcl1_, _Cxcl5_, and _Per1_ were determined in C2C12 myotubes supplemented with 30 ng/mL. clenbuterol for 1 h in low glucose media (_n_ = 4/each condition). **_p_ < 0.01, ***_p_ < 0.001 and ***_p_ < 0.0001 compared with untreated C2C12 myotubes; (**c**) KEGG pathway analysis for clenbuterol-dependent upregulated genes (red) and downregulated genes (blue). Number of genes in enriched terms are indicated in brackets. (**d**) Direct BP analysis for clenbuterol-dependent upregulated genes (red) and downregulated genes (blue). Number of genes in enriched terms are indicated in brackets.  
Figure 4: RNA-sequencing analysis revealed EPS-dependent transcription. (**a**) Volcano plot for RNA-seq data obtained from C2C12 myotubes with 10 pulses at 1 Hz for 24 h; (**b**) mRNA expression levels of _Slc2a1_, _Acta1_, _Myom2_, _Gsta1_ and _Gsta4_ were determined in C2C12 myotubes with10 pulses at 1 Hz for 24 h (\(n=4\)/each condition). * \(p<0.05\), ** \(p<0.01\); (**c**) KEGG pathway analysis for EPS-dependent upregulated genes (red) and downregulated genes (blue). Number of genes in enriched terms are indicated in brackets. (**d**) Direct BP analysis for EPS-dependent upregulated genes (red) and downregulated genes (blue). Number of genes in enriched terms are indicated in brackets.  
Figure 3: EPS-induced chronicle exercise-like transcriptional changes. mRNA expression levels of _Myth?_, _Myh2_, _Myth1_, and _Myth4_ were determined in C2C12 myotubes with 1 pulse or 10 pulses at 1 Hz for 24 h (\(n=4\)). * \(p<0.05\), ** \(p<0.01\), *** \(p<0.001\) and *** \(p<0.0001\).
###### Abstract  
Brain-computer interfaces (BCIs) enable people to communicate with others or devices, and improving BCI performance is essential for developing real-life applications. In this study, a steady-state visual evoked potential-based BCI (SSVEP-based BCI) with multi-domain features and multi-task learning is developed. To accurately represent the characteristics of an SSVEP signal, SSVEP signals in the time and frequency domains are selected as multi-domain features. Convolutional neural networks are separately used for time and frequency domain signals to extract the embedding features effectively. An element-wise addition operation and batch normalization are applied to fuse the time- and frequency-domain features. A sequence of convolutional neural networks is then adopted to find discriminative embedding features for classification. Finally, multi-task learning-based neural networks are used to detect the corresponding stimuli correctly. The experimental results showed that the proposed approach outperforms EEGNet, multi-task learning-based neural networks, canonical correlation analysis (CCA), and filter bank CCA (FBCCA). Additionally, the proposed approach is more suitable for developing real-time BCIs than a system where an input's duration is 4 s. In the future, utilizing multi-task learning to learn the properties of the embedding features extracted from FBCCA can further improve the BCI system performance.  
Keywords:brain-computer interface; machine learning; multi-domain feature; multi-task learning; steady-state visual evoked potentials +
Footnote †: journal: Applied sciences