esa-sceva/satcom-chunk-collection huggingface dataset:
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 3 Proposed Autoencoder-Based Image Transmission System  
The mathematical model for the proposed architecture is presented in the section below:  
\[\mathbf{h}=f(\mathbf{W}_{\text{enc}}\cdot\mathbf{x}+\mathbf{b}_{\text{enc}}). \tag{1}\]  
Equation (1) represents the encoder function, where \(\mathbf{x}\in\mathbb{R}^{mm\times c}\) is the input data. Here, \(m\) and \(n\) denote the height and width of the input image, respectively, and \(c\) denotes the number of color channels. \(\mathbf{W}_{\text{enc}}\in\mathbb{R}^{p\times mm\times c}\) is the encoder weight matrix, \(\mathbf{b}_{\text{enc}}\in\mathbb{R}^{p\times 1}\) is the encoder bias vector, \(\mathbf{h}\in\mathbb{R}^{p\times 1}\) represents the latent vector where \(p\) is much smaller than \(mm\times c\), and \(f(.)\) denotes the activation function.  
\[\mathbf{\hat{x}}=f(\mathbf{W}_{\text{dec}}\cdot\mathbf{h}+\mathbf{b}_{\text{ dec}}). \tag{2}\]  
Equation (2) represents the decoder function, where \(\mathbf{\hat{x}}\in\mathbb{R}^{mm\times c}\) is the reconstructed output, \(\mathbf{h}\in\mathbb{R}^{p\times 1}\) is the latent vector, \(\mathbf{W}_{\text{dec}}\in\mathbb{R}^{mm\times c\times p}\) is the decoder weight matrix, \(\mathbf{b}_{\text{dec}}\in\mathbb{R}^{mm\times c}\) is the decoder bias vector, and \(f(.)\) denotes the activation function.  
\[\mathcal{L}(x,\hat{x})=-(x\log(\hat{x})+(1-x)\log(1-\hat{x})). \tag{3}\]  
In Equation (3), \(\mathcal{L}(.)\) denotes the binary cross-entropy loss, which represents the true binary label (0 or 1), and \(\mathbf{\hat{x}}\in\mathbb{R}^{mm\times c}\) indicates the predicted probability of the positive class (between 0 and 1).  
\[\mathbf{\hat{y}}=f(\mathbf{W}_{\text{dec}}\cdot\mathbf{\hat{h}}+\mathbf{b}_{ \text{dec}}). \tag{4}\]  
The equation to train the decoder with a noisy latent vector is defined in (4), where \(\mathbf{\hat{y}}\in\mathbb{R}^{mm\times c}\) represents the reconstructed output, \(\mathbf{W}_{\text{dec}}\in\mathbb{R}^{mm\times c\times p}\) is the weight matrix of the decoder, \(\mathbf{b}_{\text{dec}}\in\mathbb{R}^{mm\times c}\) is the bias vector of the decoder, and \(\mathbf{\hat{h}}\in\mathbb{R}^{p\times 1}\) is the noise added latent vector. The noise is typically applied to the latent vector before passing it to the decoder, aiding in learning a robust representation.  
\[\mathbf{\hat{h}}=\mathbf{h}+\mathcal{N}(0,\sigma^{2}). \tag{5}\]  
Equation (5) illustrates how \(\mathbf{h}\in\mathbb{R}^{p\times 1}\) and \(\mathbf{\hat{h}}\in\mathbb{R}^{p\times 1}\) are related through the AWGN distribution with 0 mean and \(\sigma^{2}\) variance.  
The following subsections illustrate the main features of the proposed autoencoder-based image transmission system.
# Development and Evaluation of Light-Weight Active Noise Cancellation Earphones
## 2 ANC Algorithms for Earphones  
This section introduces the single-channel adaptive feedback ANC algorithm, which requires only one error sensor for each side of the earphone.
## 5 Experimental Evaluation
### General Performance  
Both models, MiniatureVQNet-Noise and MiniatureVQNet-Noise-Network, outperformed the ITU-T P.563's accuracy. The mean squared error (MSE) of the models compared to the PESQ score for ITU-T P.563, MiniatureVQNet-Noise, and MiniatureVQNet-Noise-Network was 2.19, 0.34, and 0.21, respectively. A further examination of the difference in the MSE of P.563 and MiniatureVQNet variation shows that the difference is attributed to the failure of the P.563 model to accurately predict the quality when speech distortions are low, or the SNR is high, as shown in the Figure 2 below.  
While both the MiniatureVQNet-Noise and MiniatureVQNet-Noise-Network model show a higher performance than that of P.563, the model trained on the noise-network speech dataset MiniatureVQNet-Noise-Network has a higher accuracy than the model trained on noise-only MiniatureVQNet-Noise. Therefore, single-ended speech quality monitoring in the VoIP applications model trained on noisy datasets is outperformed by models trained on noise-network speech datasets.
# Blind Image Quality Assessment Based on Multi-Window Method and HSV Color Space
## 4 Conclusions  
In this paper, we extract statistical features from the spatial and the color domains to form a powerful quality-aware feature vector for image quality pooling. By using the multi-window method, the proposed BIQA-SC algorithm can better characterize the degradations in the distorted images from receptive fields of different sizes. Quantifying the correlation between adjacent coefficients in the multiple NFMs, and measuring the difference between different NFMs can also improve the prediction accuracy of the image quality. The color entropy and the low-level gradient statistics extracted from the HSV color space make BIQA-SC capable of evaluating the quality more accurately for a variety of distorted images. Experimental results on LIVE, TID2013, CSIQ, MD1, MD2, and WIQCD databases show that BIQA-SC can considerably improve the prediction accuracy on a broad range of synthetically distorted images as well as authentically distorted images. BIQA-SC performs much better than state-of-the-art algorithms with relatively low computational cost, which makes it more suitable for practical applications.  
Conceptualization, Y.T., S.X., and S.J.; Methodology, Y.T. and S.X.; Software, Y.T. and T.L.; Validation, Y.T., T.L., and C.L.; Formal analysis, Y.T. and S.X.; Writing--original draft preparation, Y.T. and S.X.; and Writing--review and editing, Y.T., S.X., and S.J.  
This work was supported by the National Natural Science Foundation of China under Grants 61662044, 6163023, and 51765042, and in part by the Jiangxi Provincial Natural Science Foundation under Grant 20171 BAB202017.  
The authors declare no conflict of interest.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development
### Stringent Response-Related Molecules
#### 3.6.2 Poly(P)  
Inorganic polyphosphate (poly(P)) is another modulator of the stringent response [49]. Mtb expresses two polyphosphate kinases--PPK1 and PPK2--and two exopolynophatases--PPX1 (Rv0496) and PPX2 (Rv1026) [50]. These enzymes work collaboratively to maintain the homeostasis of poly(P). PPK1 promotes poly(P) synthesis, while PPK2, despite its name, predominantly exhibits the poly(P) hydrolysis function of PPX [50]. The balance of intracellular poly(P) is essential for not only Mtb cell survival during host infection but also biofilm formation in vitro [33; 50]. Biofilm formation is absent or reduced in _ppx1_ and _ppk2_ mutants [33; 49; 50]. Therefore, it is conspicuous that both PPKs and PPXs interfere with biofilm formation, likely because they destroy the homeostasis of poly(P).  
In addition, (p)ppGpp and poly(P) have strong interplay, as shown in Figure 1. Firstly, poly(P) has a positive impact on (p)ppGpp generation. Poly(P) can be used to phosphorylate MprA via PPK1 when facing ATP depletion; phosphorylated MprA then enhances _mprAB-sigE-rel_ signaling and, consequently, promotes the production of (p)ppGpp [51]. Secondly, (p)ppGpp displays an inhibiting effect on Mtb PPX1 and, consequently, promotes poly(P) accumulation [52]. Therefore, (p)ppGpp and poly(P) are closely linked, together maintaining their level balance.
## 3. Results and Discussion
### Volume-Free Near-Equal Optimal Bale Dimensions Design
#### 3.1.1 Algorithm 1  
Assume the inequality expressed by Equation (24) is satisfied and film parameters \(b_{f}\), \(v_{f}\), \(\epsilon_{lf}\), upper layers overlap ratio \(k_{f}\), Equation (20), number of global film layers \(p_{l}\), and the order of near-equilibrium \(\epsilon_{0}\) are given. Take minimal \(\delta_{min}\), \(k_{fb,min}\) and maximal \(\delta_{max}\), \(k_{fb,max}\) values of the overlaps \(\delta\), \(k_{fb}\) based on the knowledge and experience of baling to guarantee an appropriate tightness of the wrappings.  
Determine the set \(\mathcal{P}_{1}\) of all integer \(p_{b}\) defined by the inequalities \[\max\Bigg{\{}p_{\frac{1-k_{B,max}}{1+2k_{0}}-k_{B,max}},p_{\frac{1}{1+2k_{0}}b_{ jr,max}},p_{\frac{1}{1+2k_{0}}b_{jr,-2k_{min}}}\Bigg{\}}\leq p_{b}\leq\min \Bigg{\{}p_{\frac{1-k_{B,min}}{1+2k_{0}}-k_{B,max}},p_{\frac{1}{1+2k_{0}}b_{jr,-2k_{min}}},\] (28) for which there exists \(2\leq q\leq 5\) satisfying applicability condition, Equation (21).
2. If the set \(\mathcal{P}_{1}\) is empty, then the solution to Problem 1 does not exist--go to step 3. Otherwise, go to step 4.
3. Change the lower \(k_{fb,min}\), \(\delta_{min}\) or upper \(k_{fb,max}\), \(\delta_{max}\) bounds of wrapping parameters, or the order \(\varepsilon_{0}\), or the film width \(b_{f}\) and repeat the computations starting from step 1.
4. For any \(p_{b}\in\mathcal{P}_{1}\) compute the overlap ratio \(k_{fb}\) according to Equation (27) and, next, the overlap \(\delta=\frac{1}{2}b_{f}k_{fb}\).
5. If a bale volume \(V_{b0}\) is assumed, then for any \(p_{b}\in\mathcal{P}_{1}\) compute film usage \(FC\) obtained for optimal \(D_{b}^{*}\), \(H_{b}^{*}\), Equations (5) and (6), using Equation (A2) and choose that \(p_{b}\) which yields the minimal film consumption.  
Application of Proposition 4 and Algorithm 1 is illustrated by an example.
# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 5 Results
### Overview of Algorithms Used and Results from Each Article  
Different ML algorithms were evaluated across the studies, including regression models, neural network, decision trees, and gradient boosting. Table 3 presents the key performance metrics reported for each algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Method** & **Algorithm** & **Metric** & **Values** \\ \hline
[28] & Regression & Logistic Regression & MAE & 198,379,877,732,011.9 \\  & & Ridge Regression & MAE & 0.82131 \\  & & Lasso Regression & MAE & 0.96865 \\  & & ElasticNet & MAE & 0.95121 \\ \hline
[29] & Neural Network & Default Parameters & Accuracy & 94.66\% \\  & & Grid Search & Accuracy & 94.74\% \\  & & Optimization & Accuracy & 94.74\% \\  & & Random Search & Accuracy & 94.74\% \\  & & Optimization & Accuracy & 94.74\% \\ \hline
[30] & Regression & Random Forest & R\({}^{2}\) Score & 0.7780 \\  & & XGBoost & R\({}^{2}\) Score & 0.7608 \\  & & Gradient Boosting & R\({}^{2}\) Score & 0.7651 \\  & & Algorithm & Logistic Regression & R\({}^{2}\) Score & 0.6466 \\  & & K-Nearest & R\({}^{2}\) Score & 0.7306 \\  & & Neighbors & R\({}^{2}\) Score & 0.7306 \\ \hline
[32] & Survival & Various & Concordance & 0.7 \\  & Analysis & Various & Index & 0.7 \\ \hline
[33] & Decision Trees & Decision Trees & Accuracy & Above 80\% \\ \hline
[34] & Regression & Random Forest & Accuracy & 59.78\% \\  & Neural Network & Neural Network & Accuracy & 47.52\% \\ \hline
[35] & Classification & Logistic Regression & Accuracy & 80.54\% \\  & & Modified Random & Accuracy & 81.09\% \\  & & Forest & Gradient Boosting & Accuracy & 82.41\% \\ \hline
[36] & Classification & Logistic Regression & F1 Score & 0.59705 \\  & & Decision Trees & F1 Score & 0.59273 \\  & & Neural Network & F1 Score & 0.67248 \\  & & Random Forest & F1 Score & 0.66797 \\  & & Gradient Boosting & F1 Score & 0.64848 \\ \hline
[37] & Regression & Multiple Linear & & \\  & Regression & R\({}^{2}\) (Pre-DTAP) & 0.63 \\  & & & Std. Error (Pre) & 3.12 \\  & & & R\({}^{2}\) (Post-DTAP) & 0.50 \\  & & & Std. Error (Post) & 5.08 \\ \hline
[38] & Regression & XGBoost & RMSE & 2.03 \\  & & & R\({}^{2}\) & 0.89 \\ \hline
[39] & Classification & Naive Bayes & Accuracy & Not Specified \\  & & Random Forest & Accuracy & Not Specified \\  & & Support Vector & Accuracy & Not Specified \\  & & Machine & & \\ \hline
[40] & Neural Network & GPT-3 & MAE & 2.37 days \\  & & & MAE & 1.88 days \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparation of algorithms and metrics.
###### Abstract
investigated the mechanism of FA influence in heavyweight concrete, concluding that 15% FA replacement yields early compressive strength comparable to the control group. For higher levels of FA and slag replacements, prolonged curing time is required to achieve the desired strength levels [25]. [PERSON] et al. used FA, red mud, and calcium carbide slag as raw materials to prepare full solid waste cementitious materials. The results showed that when sufficient calcium carbide slag is present, adding FA can significantly enhance the long-term strength of specimens [26]. Compared to prior studies, the innovations of this study are as follows. First, while some researchers have focused on using multiple SCMs to reduce cement usage and carbon emissions, this study analyzes the synergistic effects and carbon emission changes of FA and QZ in cementitious systems. Second, it uses regression analysis to quantitatively evaluate the strength changes in cement-based materials with QZ and FA. Third, unlike most previous studies that only focus on macro-properties, this study also assesses the evolution of micro-structures and refines the correlation between macro-properties and micro-structures. Considering a mixture of FA and QZ, this study investigated the mortar specimens and paste samples, including compressive strength (selecting the dosages of FA and QZ as independent variables, 28-day compressive strength as dependent variable to establish a regression analysis model), ultrasonic pulse velocity (UPV), heat of hydration, X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), thermogravimetric (TGA), and scanning electron microscopy (SEM). Based on the analysis of these experimental results, the macroscopic properties and microstructure of the samples, as well as the correlation between the experimental data, were discussed.
## 4 Implementation  
### Device Design  
The DFF design consists of two poles connected to actuators (Figure 6). To achieve the research objective, we needed actuators that met the following conditions: (1) accurate speed control of the motor must be possible; (2) tracking of the motor's actual drive should be done in real time; (3) sufficient torque is required to move the weights (1 newton-meter (N*m) is required when stopping, 5 N*m or more when moving); (4) high motor position encoder resolution is required. As illustrated in Table 1, the Robotis Dynamixel MX product family is a module that includes motor, controller, driver, sensor, reducer, and network functions. It can decompose 360 degrees into 4096 steps (0.088 degrees per step) using a magnetic encoder and has up to 4.5 Mbps high speed communication. Therefore, we used the Dynamixel MX-64R, which has a stall torque of 6 N, can track the rotation angle with high resolution (12 bit), and can rotate in 0.08-degree increments. The performance of the MX-64 model was sufficiently satisfactory to meet the objective of the research. Each pole has joints to adjust the placements of the attachable weights. The current version of DFF allows three different weights (100, 200, and 500 g) to be used. Various weights can be attached to DFF with customized weight clamp designs.
## 5 Results
### Ablation Study
After incorporating data augmentation techniques, accuracy increased to 88.62% and EER further decreased to 9.81%. Compared to the model without data augmentation, accuracy improved by 1.25% and EER decreased by 0.16%. In summary, the ablation experiment results indicate that optimizing the ECAPA-TDNN model with the Bi-LSTM module, multi-head attention mechanism parallel branch architecture, Sub-Center Loss function, and data augmentation techniques can effectively improve the model's classification performance. These optimization strategies complement each other, enhancing the model's classification capabilities and further optimizing its application effectiveness.
## 4 Common/Unifying Theoretical-Conceptual Framework
### qEEG Phenotype and Neurophysiological, Cognitive, and Behavioral Traits
#### 4.2.1 Tonic Vigilance  
Vigilance (or arousal) here refers to the individual's predominant level of supply energy available to the brain's regulatory systems. It is responsible for generating the activation of neural systems appropriate for various contexts and providing appropriate homeostatic regulation of systems such as energy balance and sleep.  
There are more or less stable individual differences in the baseline vigilance: some individuals are constantly in a highly activated state (_hypervigilance_), while others have a chronically low level of activity (_hypovigilance_) [519], thus reflecting an individual's _predominant_ vigilance state--_tonic vigilance_. The underlying cause is believed to be different levels of activity in the loop connecting the brainstem reticular formation with the cortex. As such, tonic vigilance reflects the predominant baseline cortical and mental arousal that sets the overall level of activity of the entire brain and, as a consequence, the body. In this way, tonic vigilance, as the brain's trait energetic capacity, sets the stage for cognitive and behavioral performance.  
It seems that tonic vigilance is one of the most important traits of an individual since it determines the optimal range of activating factors (a sympathovagal balance) on the cerebral cortex that is necessary for the formation of an active, energetic state [520]. Outside this optimal range, work capacity drops and the subjective sense of well-being decreases, while psychopathology vulnerability increases [521]. Indeed, studies have demonstrated that hypervigilance is a common feature of various anxiety and affective disorders (depression), including PTSD [522; 523; 524; 525], hyperaroused fatigue (overloading) with reduced sleep propensity, inhibition of drive, and eventual exhaustion [526; 527; 528]. Conversely, hypovigilance is associated with hypoaroused fatigue with increased sleepiness, a lack of drive, sickness behavior [526; 529; 530; 531], burnout [532], impulsivity [533], ADHD [241; 534; 535], and AD [135]. This is supported by the Arousal Regulation Model of Affective Disorders [269], where hypostable and hyperstable levels of arousal contribute to manic or ADHD and depressive-like behavior, respectively [270; 536]. Importantly, tonic vigilance has been suggested to causally contribute to mental illness [269]. Additionally, the relationship between tonic vigilance, personality traits [501; 537; 531], and psychopathology have been proposed [538; 539] (see also Section 4.1).  
Besides (a) being expressed along a health-pathology continuum and (b) being transdiagnostic, tonic vigilance is reflected in the characteristics of the qEEG phenotype [540; 541; 542; 543; 544; 545; 546; 547]. Additionally, vigilance-relevant qEEG characteristics are \(\sim\)80% heritable [548].  
To summarize, tonic vigilance affects cognition, emotions, and behavior and is implicated in the etiology of psychiatric disorders and reflected in the characteristics of qEEG phenotypes.
# A Flexible FPGA-Based Channel Emulator for Non-Stationary MIMO Fading Channels
## 3 Flexible Hardware Architecture and Implementation
### FPGA-Based Implementation
#### 3.3.2 Fading Generation Module  
For an arbitrary \(U\times S\) MIMO channel, the number of channel fading generation module should be \(U\times S\times N\) and they could consume huge of hardware resources. As the maximum Doppler frequency is usually much smaller than the system sampling rate, in this paper we use a low initial sampling rate \({f_{s}}^{\prime}\) to generate the channel fading, which can greatly reduce the hardware consumption. The implementation scheme of channel fading generation is showed in Figure 5. First, the parameter module updates the Doppler frequency and phase in real time. Then, it passes them to the subtractor (SUB), accumulator (ACC), multiplier, and adder (ADD) to complete the corresponding integral operations and generate a look-up table (LUT) address. The values of the cosine function stored in the cosine table can be found by the LUT address, and they are superimposed by the accumulator to obtain the channel fading coefficient. Finally, the cascaded integrator comb (CIC) filter is used to interpolate and match the data rate.  
Figure 4: Hardware simulation of delay module.  
Figure 3: The implementation scheme of delay module.  
According to the central limit theorem, the larger the number of sub-path, the closer the output channel fading is to the theoretical distribution. Considering a trade-off between the resource consumption and complexity, the number of sub-path is set as \(N=64\). The data width of LUT is set to 16 bits and the data depth is set to 12 bits. We use the idea of serial and time-division multiplexing to find the phase address in the LUT efficiently. Considering the symmetry of cosine function, only a quarter of cosine period needs to be stored, and thus the data width and depth are 15 bits and 10 bits, respectively. Note that this can significantly save the RAM resource when the sub-path number becomes large. Figure 6 shows the simulation result of hardware implementation. In this figure, only the first three sub-paths, i.e., three FM signals, and the superposition of 64 branches are given. As we can see that the output fading envelope is random fluctuation and it should approximate to the Rayleigh distribution according to the central limit theorem. For the latency of hardware, with the help of integrated logical analyzer (ILA) debugging tool, we find that it takes three clock cycles to reach the steady state and 16 clock cycles totally to output the first valid channel data. As the system clock is 100 MHz, the latency of proposed hardware emulator is about \(16\times 10^{9}/(100\times 10^{6})=160\) ns.
## 4 Experiments and Analysis
### Datasets  
The UIEB dataset contains 890 high-resolution raw underwater images, corresponding high-quality reference images, and 60 challenge images (C60) for which no corresponding reference images were obtained. UIEB covers a variety of underwater scenes, and the image content covers a wide range of areas, such as marine life, divers, submarine corals, and coral reefs. The quality of underwater images is significantly lower. The creators of the dataset devised a method for generating high-quality reference images. Firstly, they applied twelve popular image-enhancement methods to generate enhanced results. Then, fifty volunteers were recruited to evaluate the quality of the enhanced results. The reference image for each original underwater image was determined by majority voting based on pairwise comparisons.  
The U45 dataset consists of 45 degraded small images without reference images. Considering that the underwater enhancement task does not have publicly available datasets for a test like the single-image super-resolution task, [PERSON] et al. carefully selected 45 authentic underwater images, named U45. It is divided into three subsets of green, blue, and haze, where the subsets correspond to the color cast of underwater degradation, low contrast, and blur effects.  
The EUVP dataset contains separate sets of paired and unpaired image samples of poor and good perceptual quality. The creators employed seven distinct camera models, including duPros, uEye cameras integrated into the Aqua AUV, low-light USB cameras, and high-definition cameras mounted on the Trident ROV, to capture the image data. The data acquisition occurred at diverse locations and under various conditions during marine exploration and robot navigation. In our experiments, two paired sets were used: Underwater Dark (5550 pairs) and Underwater ImageNet (3700 pairs).