esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results
The learning curves showing the changes in the training and validation accuracies of the model across epochs are presented in Figure 6. The model learned rapidly on the training data for training accuracy and reached an accuracy rate of approximately 90%. However, this situation shows that the model is vulnerable to overfitting. For validation accuracy, although the accuracy on the validation dataset was initially low (30%), it increased over time and reached approximately 60%. However, the significant difference between the training and validation accuracy reveals that the model is poor at generalizing the validation data. Figure 5: Confusion matrix for the proposed method. Figure 6: Learning curves for ANN. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**First author** & **Dataset** & **Features** & **Classifiers** & **Differences** & **Similarity** \\ \hline [PERSON] [6] & NICU recorded (draw attention, cry, diper) & LPC, LPC, MCC, BCC & KNN, ANN & Different dataset and classifiers & Baby cry classification \\  & change-needed, cry, and bumpypy) & LPC, LPC & LPC, MCC, BCC & KNN, ANN & Different dataset and classifiers & Same feature extraction method \\ \hline [PERSON] [10] & Self-recorded (bungry, need to bump, skppy, pain) & Fish short-time energy MCC & KNN & Different dataset and classifiers & Baby cry classification \\  & disconfort) & Statistical properties of MCC & KNN & Different dataset and classifiers & Same feature extraction method \\ \hline [PERSON]  
The ROC curve presented in Figure 7 was used to evaluate the model's inter-class discrimination power. The AUC values for all classes are quite high (0.99 for Belly pain, 1.00 for Burping, 0.99 for Discomfort, 0.95 for Hungry, and 0.98 for Tired), which shows that the model has good discrimination power in general. The One-vs-Rest (OvR) approach was used in calculating the ROC curves. The slight decreases in AUC values, especially for hungry and tired (0.95 and 0.98, respectively), indicate a result consistent with the confusion between these classes. Feature engineering and/or dataset balancing are recommended to separate these classes better. To analyze the robustness of the proposed method in this article, the 10-fold cross-validation method was also applied. The dataset was divided into 10 different subsets, and the model was trained and tested in each of them. After the model training was completed in each layer, predictions were made on the validation set, and the predicted class labels were stored. Finally, the model's overall performance was calculated by combining the results of all layers. The results show the model's performance against different classes and measure the classification quality. The model accuracy is presented with the average accuracy calculated as a result of cross-validation. As a result of 10-fold cross-validation, an accuracy rate of 87% was obtained.
## 4 Results  
### Experimental Settings  
All analyses were conducted using Python 3.8.0 on a workstation running Linux OS with 48 GB of RAM, an Intel Quad-Core Xeon CPU (2.3 GHz), and a Tesla K80 GPU (4 GB VRAM). Data were processed with NumPy 1.19.5 [69] and pandas 1.1.5 [70]. Statistical methods and correlation tests were performed using SciPy 1.4.1 [71], and other data-wrangling tasks were carried out with scikit-learn 1.0.0 [72]. Visualizations were prepared using Matplotlib 3.2.2 [73] and UMAP-learn 0.5.3 [74]. Additionally, for the deep learning models and Grad-CAM pipeline, Keras 2.7.0 [75] and TensorFlow 2.8.0 [76] were used. HRV and PRV indices were calculated using NeuroKit 0.2.0 [77].  
The dataset instances were divided into a standard 60-20-20 training-validation-hold-out testing split at the patient level (so that there was no overlap in the data samples in each split), using a five-fold cross-validation approach. To mitigate sampling bias, 5-fold cross-validation was repeated three times and averaged.  
Before the signals and HRV indices were fed into the model, \(Z\)-score normalization was performed, wherein all ECG and PPG datasets' signals were fixed with zero mean (\(\mu\)) and unit standard deviation \(\sigma=1\). This sped up convergence and reduced the amplitude scaling and drastic variability caused by outlier values.
# High Speed Decoding for High-Rate and Short-Length Reed-Muller Code Using Auto-Decoder
###### Abstract  
In this paper, we show that applying a machine learning technique called auto-decoder (AD) to high-rate and short length Reed-Muller (RM) decoding enables it to achieve maximum likelihood decoding (MLD) performance and faster decoding speed than when fast Hadamard transform (FHT) is applied in additive white Gaussian noise (AWGN) channels. The decoding speed is approximately 1.8 times and 125 times faster than the FHT decoding for \(\Re(1,4)\) and \(\Re(2,4)\), respectively. The number of nodes in the hidden layer of AD is larger than that of the input layer, unlike the conventional auto-encoder (AE). Two ADs are combined in parallel and merged together, and then cascaded to one fully connected layer to improve the bit error rate (BER) performance of the code.  
Reed-Muller (RM) code; machine learning; auto-decoder; auto-encoder +
Footnote †: journal: Applied Sciences
# Advances in Wearable Smart Chemical Sensors for Health Monitoring
## 1 Introduction  
Wearable technologies and devices are evolving from faddish consumer electronics to become deeply integrated into human lives [1; 2], with smart wearable sensors becoming increasingly popular due to their flexible, simple [3] and portable nature. From smart watches and eyewear to health monitoring devices [4], the scope of wearable devices is widening [5; 6], bringing convenience and comfort to our lives. Health and fitness are not the only areas where wearable technology and devices are used [7; 8; 9]. As technology continues to evolve, wearable devices have begun to infiltrate healthcare, entertainment, industry, education and security [10; 11; 12; 13; 14]. Wearable chemical sensors, a technology on the rise, have garnered significant interest in recent years. A wearable chemical sensor is a portable, wearable electronic device that detects chemicals in the human body and converts the results into electrical or digital signals for monitoring human health and providing early warnings [15; 16; 17]. Such sensors offer advantages such as small size, lightweight, easy operation and reusability and usually consist of sensing elements, signal-processing circuits, display devices, etc. [18; 19; 20]. Current research is focusing on wearable chemical sensors, which have a variety of potential uses in medical care, monitoring health [21], sports [22; 23] and so on.  
Various uses of chemical sensors in wearable technology have been documented, including those utilizing triboelectric nanogenerators (TENGs), piezoelectric nanogenerators (PENGs), solar cells, thermal energy and energy storage devices [24; 25], as illustrated in Figure 1. The integration of chemical sensors into wearable devices has a variety of advantages, which are mainly reflected in their small size, lightweight [26], easy operation, strong adaptability [27; 28] and ability to meet a variety of application scenarios [29; 30]. While these sensors play an important role in wearable devices, some challenges need to be further addressed in terms of improving the sensitivity and selectivity of the sensors, as well as reducing power consumption and improving stability [31; 32; 33].  
However, there has been considerable progress in the practical applications of wearable smart chemical sensors, including the real-time monitoring and analysis of human motion [34], improving the efficiency and accuracy of healthcare systems, assisting smart wearable devices [35] and recycling energy [36; 37]. This article examines the latest advancements in and benefits of wearable intelligent chemical detectors for monitoring health, exploring wearable chemical detectors utilizing TENGs, PENGs, solar cell-based sensors, thermal energy-based sensors and energy storage device-based sensors in terms of their energy consumption [38; 39]. In the conclusion, the potential and challenges of wearable chemical sensors are discussed, offering potential insights for the advancement of self-powered chemical sensors based on TENGs technology for future use in wearable devices.
## 3 Results and Discussion
### Electrochemical Test Analysis  
The potentiodynamic polarization curves of the coatings with different Mo contents and 45 steel substrate are shown in Figure 10. The potential for all of the coatings is higher than that of the 45 steel substrate, which indicates that the corrosion resistance of the 45 steel substrate is significantly improved by the coatings. Laser cladding is a rapid heating and cooling process. From the XRD results, it can be seen that complex chemical reactions take place and new phases generate during the solidification process. The addition of Mo will refine the microstructure of the coatings, which can improve the corrosion resistance of the coating. With the increase of Mo content in the coatings,  
Figure 9: Worm surfaces of laser cladding coatings with different Mo contents: (**a**) Mo-free; (**b**) 2.0 wt. % Mo; (**c**) 4.0 wt. % Mo; (**d**) 6.0 wt. % Mo.  
the potential first increases and then decreases. The coating with 2.0 wt. % Mo has the best corrosion resistance. The reasons are as follows. When the corrosion test is carried out in NaCl solution, the Cl\({}^{-}\) will be adsorbed on the surface of the coating, which can react with the metal ion and form soluble chloride. This process can break the surface of the coating, and result in the formation of pitting (i.e., corrosion) on the surface of the coating. The addition of Mo can lead to a passivation phenomenon and greatly improve the pitting resistance of the coating [48]. When a certain amount of Mo is added in the coating, it can form a passivation film on the surface of the coating, which hinders the Cl\({}^{-}\) entering the coating. So the pitting of the coating reduces and the corrosion resistance improves. When the Mo content is too high, the corrosion resistance decreases. Through the mapping scanning analysis, it can be seen that the Mo element is mainly enriched at the grain boundaries. Such an uneven distribution reduces the corrosion resistance of the coating.
## 3 Elicitors and Their Classifications
### Methyl Isamonate (MeI)  
Methyl Jasmonate (MeI) is an elicitor capable of triggering the synthesis of secondary metabolites by inducing plant defense mechanisms [136]. Methyl Jasmonate (MeI), as a derivative of jasmonic acid (JA), is widely used in viticulture due to its significant impact on grape metabolites such as volatile compounds [35; 61; 120; 137]. Most recent MeI studies are focused on the phenolic and volatile contents of grapes and wines since these compounds influence their sensorial quality to a greater extent. [PERSON] et al. [61] and [PERSON] et al. [120], in their studies of MeI application to Sangiovese and Grenache grapes, reported an increase in the concentration of volatile compounds in the treated samples compared to the respective control samples. However, after treating Tempranillo grapevines with MeI, grape volatiles concentrations in the treated samples decreased relative to the control samples [35]. An indication that grape variety is a factor influencing the effectiveness of MeI. Similarly, a study on the impact of MeI on the concentrations of grape amino acids was influenced by variety and vintage [121].  
In the context of phenolic compounds, the grape variety is not a limiting factor, as most studies have reported increased levels of phenolic compounds after MeI application, irrespective of the variety [124; 138; 139; 125]. However, seasonal variations impact the effectiveness of MeI on grape quality components, especially in consecutively studied seasons. Methyl Jasmonate (MeI) often improves the content of metabolites in one season and decreases the levels in the subsequent season, as reported in the following studies [121; 137; 136; 137; 139]. Regarding the time of treatment, MeI, like other elicitors, is usually applied on grapes during the veraison stage. However, few studies sprayed MeI on grapevines during veraison and mid-ripening and reported that phenolic compounds were enhanced better in samples treated during mid-ripening than samples treated during veraison [140; 141]. Hence, they postulated that the optimum time for MeI application could be a few weeks after veraison.
# Double-Scale Adaptive Transmission in Time-Varying Channel for Underwater Acoustic Sensor Networks
## 2 Related Works
### Channel State Prediction
From the perspective of divide and conquer, the prediction based on decomposition can enhance the prediction ability of the model [33], as the original non-linear and non-stationary sequence is decomposed into a finite number of subsequences, which have simpler frequency components. Thus, the difficult prediction task is divided into several relatively easy subtasks [34].
# InMemQK: A Product Quantization Based MatMul Module for Compute-in-Memory Attention Macro
## 2 Transformer and CIM Accelerator
### Voltage-Domain MAC and Time-Domain MAC  
NVM-based MAC macros have gained widespread adoption in compute-in-memory (CIM) systems owing to their capability to substantially decrease weight data transfer. NVM-MAC macros are primarily categorized into two types: time-domain MAC and voltage-domain MAC. Time-domain NVM-MAC has garnered growing interest in recent years, attributed to its advantages in energy efficiency and scalability.  
Specifically, voltage-domain MAC designs, such as PRIME [18] and ISAAC [19], use ReRAM to accelerate the inference of Convolutional Neural Networks (CNNs). The computation process is shown in Figure 2a, taking ReRAM [20] as an example, where digital input signals are first converted to voltage signals, multiplication is performed by activating word lines, products are calculated based on [PERSON]'s law, and results are accumulated along each column according to [PERSON]'s law. Nevertheless, these designs necessitate high power consumption and physically large analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) to interface the intermediate results from the NVM array with digital circuits; notably, in the ISAAC design, the power consumption of ADCs accounts for 61% of the total chip power consumption. With the technology scaling down, the decreased supply voltage constrains the signal-to-noise ratio, imposing more stringent requirements for mitigating mismatch and thermal noise, often necessitating larger passive or active devices.  
In contrast, the computational process of time-domain MAC is depicted in Figure 2b, where the distinction lies in utilizing pulse width as the input signal, representing neural network weights with programmable current sources, executing multiplication through current integration, and computing accumulated results based on the principle of charge conservation. Time-domain MAC not only substantially reduces energy consumption per conversion (as illustrated in Figure 2c) but also exhibits higher frequency in designs involving multi-input parallel computation and offers superior scalability [21; 22].  
In light of these advantages offered by time-domain MAC, this paper employs a design based on the time-domain NVM-MAC macro and presents the design and simulation verification of an InMemQK prototype module for time-domain MatMul.
###### Abstract  
In complex and dynamic environments, traditional motion detection techniques that rely on visual feature extraction face significant challenges when detecting and tracking small-sized moving objects. These difficulties primarily stem from the limited feature information inherent in small objects and the substantial interference caused by irrelevant information in complex backgrounds. Inspired by the intricate mechanisms for detecting small moving objects in insect brains, some bio-inspired systems have been designed to identify small moving objects in dynamic natural backgrounds. While these insect-inspired systems can effectively utilize motion information for object detection, they still suffer from limitations in suppressing complex background interference and accurately segmenting small objects, leading to a high rate of false positives from the complex background in their detection results. To overcome this limitation, inspired by insect visual neural structures, we propose a novel dual-channel visual network. The network first utilizes a motion detection channel to extract the target's motion position information and track its trajectory. Simultaneously, a contrast detection channel extracts the target's local contrast information. Then, based on the target's motion trajectory, we determine the temporal variation trajectory of the target's contrast. Finally, by comparing the temporal fluctuation characteristics of the contrast between the target and background false positives, the network can effectively distinguish between the target and background, thereby suppressing false positives. The experimental results show that the visual network performs excellently in terms of detection rate and precision, with an average detection rate of 0.81 and an average precision as high as 0.0968, which are significantly better than those of other comparative methods. This indicates that it has a significant advantage in suppressing false alarms and identifying small targets in complex dynamic environments.  
small object motion detection; local contrast; motion trajectories; insect vision +
Footnote †: journal: Applied Sciences
# Research on Prediction of TBM Performance of Deep-Buried Tunnel Based on Machine Learning
## 3 Prediction Model of TBM Penetration Rate Based on Machine Learning
### Prediction Model of TBM Penetration Rate Based on Support Vector Regression
#### 3.3.1 Principle of Support Vector Regression Algorithm
Combined with Formulas (22)-(25), this can be achieved by minimizing the objective function:  
\[\min_{w,b}\left[\frac{1}{2}\|w\|^{2}+C\sum_{i=1}^{m}\left(\zeta_{i}^{+}+\zeta _{1}^{-}\right)\right] \tag{22}\]Constraints are (23)-(25):  
\[f(x_{\rm i})-y_{\rm i}\leq\varepsilon+\zeta_{\rm i}^{+} \tag{23}\]  
\[y_{\rm i}-f(x_{\rm i})\leq\varepsilon+\zeta_{\rm i}^{-} \tag{24}\]  
\[\zeta_{\rm i}^{+},\zeta_{\rm i}^{-}\geq 0,\ i=1,2,\ldots,m \tag{25}\]  
The constant \(c>0\) is a penalty parameter used to adjust the regression error and flatness weight. The relaxation variables \(\zeta_{\rm i}^{+}\) and \(\zeta_{\rm i}^{-}\) are the training errors calculated from the \(\varepsilon\)-insensitive loss function. Here, \(\frac{1}{2}\|w\|^{2}\) represents the structural risk, which is used to control the smoothness or complexity of the function (regularization term); and \(C\sum\limits_{i=1}^{m}\left(\zeta_{\rm i}^{+}+\zeta_{\rm i}^{-}\right)\) represents the empirical risk. Therefore, the support vector regression can be understood as the minimization of the structural risk and empirical risk. Because the hyperplane of support vector regression is linear, the prediction accuracy of the linear equation may not be high for most nonlinear problems. This problem can be solved by mapping samples to high-dimensional space through the kernel function, and then the objective function can be transformed as shown in Formula (26) and constraint (27). \[\underset{\alpha,\alpha^{*}}{max}\sum\limits_{i=1}^{m}[y_{\rm i}(\alpha_{\rm i }^{*}-\alpha_{\rm i})-\varepsilon(\alpha_{\rm i}^{*}+\alpha_{\rm i})]-\frac{ 1}{2}\sum\limits_{i=1}^{m}\sum\limits_{j=1}^{m}\left(\alpha_{\rm i}^{*}-\alpha _{\rm i}\right)(\alpha_{\rm j}^{*}-\alpha_{\rm j})K(x_{\rm i},x_{\rm j}) \tag{26}\]  
\[\sum\limits_{i=1}^{m}\left(\alpha_{\rm i}^{*}-\alpha_{\rm i}\right)=0\ \ \ \alpha_{\rm i}^{*},\alpha_{\rm i}\in[0,C] \tag{27}\]  
Among them, \(\alpha_{\rm i}\) and \(\alpha_{\rm i}^{*}\) are Lagrange multipliers, and \(K(x_{\rm i},x_{\rm i}^{*})\) is the kernel function of input vector \(x\). Its main function is to convert data points from low-dimensional space to high-dimensional space, so that data points can be divided by linear functions.
## 3 Results and Discussion
### Wetting Ability  
In tertiary oil recovery, an important mechanism of surfactant displacement is to change the wettability of formation rocks. In the process of displacement, surfactants with amphiphilic groups can be arranged at the interface between the rock and the liquid, so that the rock surface changes from the oleophilic surface to the hydrophilic surface. A change in wettability reduces the adhesion of oil droplets on the rock surface, and it is easier to be displaced by oil displacement agents [24,33].  
With pure water as a blank control group, it was found that the droplet volume and contact angle did not change significantly over time, so the effect of water evaporation could be ignored. Figure 7 shows that the contact angle decreased sharply with the increase in surfactant concentration [19]. This is due to the concentration difference between the solution body and the solid-liquid interface continuing to increase, and the trend and probability of surfactant molecules diffusing to the interface increase, and the adsorption process becoming easier [34]. Meanwhile, ignoring the effect of water droplets evaporation at room temperature, the contact angle gradually tended toward stability, indicating that the adsorption of surfactant molecules on the interface is a dynamic process, and when the adsorption and desorption reached a dynamic equilibrium, the contact angle reached a stable value.  
Figure 6: Effects of divalent ion on IFT between the optimum concentrations of surfactants and Xinjiang crude oil at 40 \({}^{\circ}\)C for DCB (**a**) and DSB (**b**) of CaCl\({}_{2}\), DCB (**c**) and DSB (**d**) of MgCl\({}_{2}\).  
Figure 8 shows the dynamic contact angle changes of DCB and DSB using paraffin film as substrate. DCB and DSB showed the lowest contact angles of 28.36\({}^{\circ}\) and 35.26\({}^{\circ}\), respectively, which can transform the oil-philic interface into a strong hydrophilic interface. DCB has a stronger wetting reversal ability and faster change rate of interface wettability, which can reduce the contact angle to less than 80\({}^{\circ}\) in a few seconds. This is because the carboxyl group volume is smaller than the hydroxypropyl sulfonic group, and the molecules are more tightly arranged and have stronger wettability when adsorbed on the interface [35].