esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work  
### Generating Adversarial Network  
GAN is designed from game theory, mainly the idea of two-person zero-sum game in game theory, and this idea is introduced into the training of generator (G) and discriminator (D), and the process of G and D training is the process of gaming these two networks [25].  
The generator produces generated data that are close to the real data, and then these data are scored by the discriminator. The higher the score, the closer the discriminator thinks the image is to the real image. So, the generator will improve its ability to generate images that are as close to the real data as possible, so that the discriminator can give it a high score. The discriminator, moreover, will improve its ability to discriminate the image and maximize the distinction between the real data and the generated data. In the process of distinction, it will give the generated data a low score, as close to 0 as possible. In contrast, it will give the real data a high score, as close to 1 as possible.  
The network structure of GAN is shown in Figure 1.  
The input noise z passes through the generator G, which generates the data. At this point, the generator G wants to confuse the discriminator and get a higher score, that is, the generator's purpose is to output \(D(G(z))\) as close as possible to 1. The role of discriminator D is to discriminate between true and false, and D wants to give the generated data a low score and the real data a high score, so the discriminator's purpose is to output \(D(G(z))\) as close as possible to 0 and \(D(x)\) as close as possible to 1, so as to achieve the purpose of correctly distinguishing the true and false data.  
The ultimate goal of the network is to get a generator with good performance. This will ensure that, eventually, the data generated by the generator is close to the real data, when the discriminator has discriminated it.  
The objective function of GAN is as follows [25]:  
\[\min_{G}\max_{D}V(D,G)=\mathbb{E}_{x\sim p_{data}(x)}[\log D(x)]+\mathbb{E}_{z \sim p_{z}(x)}[\log(1-D(G(z)))] \tag{1}\]  
where Z is noise, and X is real data.  
In Equation (1), the discriminator model expects to distinguish the real data from the generated data to the greatest extent possible. The image passes through the discriminator, the discriminator will score the image, the closer the score is to 1, the more the discriminator tends to think that the input is the real data; the closer the score is to 0, the more the discriminator tends to think that the input is the generated data, so to maximize \(\log D(x)\) and \(\log(1-D(G(z)))\). The generator aims to produce a generated image that is close to the real image, to let the discriminator give itself a high score. The expected score is close to 1, to minimize \(\log(1-D(G(z)))\). That is, for the generator G to make \(\mathrm{V(G,D)}\) as small as possible, the discriminator D has to make \(\mathrm{V(G,D)}\) as large as possible.  
With the development of GAN, many variants of GAN have been developed, such as DCGAN, CGAN, and CycleGAN. In this paper, we chose CGAN to improve it.
## 1 Introduction
The analyses on the experiments validate the promising performance of the presented method on both synthetic and real data. We show that our method can not only eliminate corruption, but also remove the clutter from the test data. Figure 1 depicts sample results of CMD-Net. To summarize, our main contributions are as follows:  
* To the best of our knowledge, this is the first time we adopt representation learning through intrinsic structure points for 3D shape denoising. * We alleviate the requirement of pre-aligned data by canonicalizing input clouds through a transformer and present a self-supervised method for the denoising problem. * Our method does not only deal with 3D synthetic shapes, but also handles cluttered real objects. **Organization of the Paper. ** Section 2 presents the relevant studies available in the literature. In Section 3, we detail CMD-Net along with our training strategy. Experimental results are provided in Section 4. We conclude the paper in Section 5 with several remarks and future works. **Denotation. ** Throughout the paper, we denote scalar variables by lowercase letters (e.g., \(x\)) and vector variables by bold lowercase letters (e.g., \(x\)). Capital letters (e.g., \(P\)) are adopted for specific functions or variables. We use bold capital letters (e.g., \(\mathbf{I}\)) to denote a matrix or a set of vectors and use calligraphic letters (e.g., \(\mathcal{B}\)) to express batches or datasets. The symbols not commonly employed will be defined in the context. The objects in all figures in this paper are color-coded: blueish gray is for noise-free ground truth 3D shapes in the canonical space; gray depicts noise-corrupted (noisy) input; orange represents canonicalized instances; magenta shows denoised objects.
## 3 Problem Statement  
In UWSN, each sensor has limited resources and requires effective use of these resources. Efficient energy consumption has a major contribution to stabilizing the network for long-term communication. In UWSNs, the packet is sent from the source node to the sink node using different relay nodes. If a node cannot find a forwarder node in its transmission range, it causes hindrance in the network during communication.  
To avoid the void holes in UWSNs, a routing protocol namely GEDAR presented in [22]. GEDAR addresses the issue by adjusting the depth of nodes; however, the process of depth adjustment consumes lots of energy. In [23], LMPC routing technique addresses the efficient data transmission by making the binary tree from the root node. However, binary tree generation consumes high energy and lead towards the transmission overhead. To overcome the aforementioned problems, two routing protocol namely GEDPAR and E2 EVHR are proposed for avoiding the void holes and eliminating the extra energy consumption.
## 4 Machine Learning Models
### Artificial Neural Network Model  
ANNs are ML models that draw inspiration from the human brain's structure and functions [35]. They comprise interconnected neurons that use weighted connections to process and transmit information. ANNs can learn data patterns and relationships by modifying the connection strength based on the input and output. ANN models typically contain three layers, including input, hidden, and output layers. Figure 5 presents an example of an ANN model that includes an input layer with 6 neurons, 2 hidden layers with 16 and 8 neurons, and an output layer. Deep learning is commonly used to describe neural networks with many hidden layers.  
In the ANN models, weights (the connection strength between neurons) and activations (output of a neuron in the network) are fundamental elements that enable the network to learn patterns and relationships in the data. The weights in an ANN are adjusted during the learning process to optimize the model's performance. At the same time, activation applies a mathematical operation to the input and transmits an output to the other neurons in the network.  
Choosing the proper activation function is essential when dealing with an ANN model. There are several types of activation functions, namely, Sigmoid function (commonly used for binary classifications), ReLU (rectified linear unit) function, Tanh (hyperbolic tangent) function, and Softmax function (commonly used in the output layer).  
Our study considers an ANN model with 2 hidden layers containing 128 and 32 neurons and an output layer. We implemented our models using the Keras package [55], which provides an easy-to-use interface for building and training neural networks. A multi-layer perceptron (MLP) model provided the soil classification with the ReLU activation function in both hidden layers, and the Softmax activation function in the output layer. The Keras library in R aided the model development, which was compiled using the categorical cross-entropy loss function, the Adam optimizer, and accuracy as the evaluation metric. The model was trained on the training data for 200 epochs, using a batch size of 32 and a validation split of 0.2. The model would learn from the data and adjust its weights and  
Figure 5: Example of visualized neural network plot with 2 hidden layers, 16 and 6 neurons, and 6 features.  
biases to minimize the loss, which measures the difference between the predicted and actual values.  
The categorical cross-entropy loss function is used to measure the difference between the predicted and actual values in a classification task. To minimize this loss, the Adam optimizer adjusts the weights and biases of the model during training. Accuracy, on the other hand, is a metric that evaluates how well the model generalizes to new, unseen data by measuring the percentage of correct predictions. The performance of the model was improved through the Bayesian optimization fine-tune of its hyperparameters including dense units 1, dense units 2, dropout 1, dropout 2, and batch size.
# Applicability Study on Modified Argillaceous Slate as Subgrade Filling for High-Speed Railway
## 3 Experimental Evaluation of Cement/Lime-Modified Soil  
### Results  
#### 3.1.1 Analysis of Main Mineral Components for Natural and Modified Soil  
Mineral composition of the modified soil after 7 days of curing was carried out by performing EDS. These analyses help in understanding and appreciating the increase in geotechnical properties of lime/cement-modified soil at a microscopic level. The mineral composition of the soil modified by 3% and 5% cement and 6% quicklime are presented in Table 8.  
The muscovite content is recorded to decrease from 36.42% to 26.85%; simultaneously, illite decreases lightly. It weakens the hydrophilicity of the soil, enhances water stability and reduces the plasticity of the soil.  
The quartz content is noted to increase from 47.54% to 55.90%; simultaneously the contents of kaolinite, feldspar and calcite increase slightly, showing that the total content of clastic minerals--particularly the amorphous phase that is mostly formed by SiO\({}_{2}\)--increases sharply. Similar observations were reported while studying the effect of lime on expansive clays [38; 39; 40]. This may be attributed as the result of crystallization, a part of amorphous phase generated by ion exchange, condensation and pozzolanic reaction after mixing additive; the other part is from the additive (quicklime, cement) itself which is generated by calcination. It not only increases the size of soil particles, but also makes the soil form a new slab structure, thus enhancing the strength and stiffness of the soil.
# Second Harmonic Revisited: An Analytic Quantum Approach
## 1 Introduction  
Following the first observation of second-harmonic generation (SHG) by [PERSON] et al. [1], the process has been the subject of a huge amount of theoretical and experimental investigations. The exact solution of the equation of motion for the classical field amplitudes, provided by [PERSON] et al. [2], paved the way toward a formal analytic treatment of the problem. On the other hand, the quantum dynamics does not yield exact solutions, so that the quantum description of the process has been approached in a number of different ways, such as the analogy with the Dicke model [3, 4] or the assumption of a factorization hypothesis on the moments of the photon number [5, 6]. However, the most common strategy is the perturbative approach [7], often followed by a numeric analysis [8, 9, 10].  
Here, we review the simplest quantum description starting from a perturbative approach to the basic interaction Hamiltonian of the process, and we find a series of analytic results on the evolution of the pump field, the SHG probability, and the transformation of the statistics. In Section 2.1, we briefly report the well-known classical description of SHG focusing on the transformation of the statistics in the cases of coherent and chaotic light. In particular, we resume the results of a previous work by us, where, by applying a semiclassical approach, we have demonstrated [11] that the second-harmonic of a fundamental beam featuring multi-mode thermal statistics exhibits a more-than-thermal, namely superthermal, statistics. In Section 2.2, we approach the quantum model. Firstly, we retrieve some general results on the generation of second-harmonic (SH) light by inspecting the perturbative evolution of both the photon-number operator and the SH state. Then, we find that, in some simple cases, the SH conversion probability converges to periodic analytic functions. Finally,supported by the comparison with the classical results and by the quantum description, we move to the analysis of the output statistics by inspecting the Glauber autocorrelation function [12] of the SH field. In particular, we analytically retrieve it for the cases of coherent, chaotic, and squeezed light.
## 2 Damage Detection Using a Stationary Vehicle
### Decomposition of Contact-Point Response  
The vibration signal of the bridge carries information about the bridge properties that contain damage indications. To separate the contact-point response into several meaningful mono-component signals, several techniques could be used. Empirical mode decomposition (EMD) has been used widely for structural damage detection to decompose the vibration signal of the structure [30]. EMD is an innovative technique that has been proposed as part of the Hilbert Huang transform (HHT), which is able to preserve the nonlinear response of the bridge at the damage location [31; 40]. EMD decomposes the signal into several mono-component signals called intrinsic mode functions (IMFs). The first IMF contains a higher frequency signal, which contains damage information, which is usually used for damage detection [41; 42; 43]. The nonlinear effects of structure damage have the tendency to be of higher frequencies. Therefore, higher frequency bands are more likely to contain the damage information. [PERSON], [PERSON] [43] proposed a damage detection technique employing EMD to detect multiple damage in a simple-beam bridge. However, the road roughness of the bridge is not considered in their study. [PERSON], [PERSON] [35] proposed an indirect damage detection technique based on EMD while considering the road roughness effects. However, the proposed method relies on subtracting the difference of the IMFs from damage and healthy signals (baseline-based technique). Therefore, it is necessary to have prior information of the intact bridge to implement the method. In general, most damage detection EMD-based studies employ baseline-based methods that require a prior response of the undamaged bridge [31]. In addition, EMD is an empirical method; being empirical, it fails sometimes to decompose the non-stationary signal into perfectly narrow-banded mono-component signals.  
Recently, [PERSON] and [PERSON] [44] introduced a new method, called variational mode decomposition (VMD), to deal with the limitations of the EMD method. The VMD is fundamentally different from the recursive EMD as it preforms adaptive decomposition of the signal into its components [44]. VMD has shown a superior performance in decomposing non-stationary signals over the EMD method [31; 44]. The method is a generalization of the classic Wiener filter. The main algorithm and MATLAB code of the VMD method can be found in [PERSON] and [PERSON]'s original paper [44]. The number of IMFs in the VMD method can be controlled, unlike in the EMD method. The ideal number of modes depends on the knowledge of physics of the non-stationary signal. Therefore, in this paper, VMD will be used for signal decomposition of the contact-point response for identifying damage locations.
# CFD Simulation of Dry Pressure Drop in a Cross-Flow Rotating Packed Bed
## 4 Results and Discussion
### Cavity Zone B  
#### 4.4.1 Effect of Rotating Speed  
The effect of rotating speed on the pressure drop in the cavity zone B is shown in Figure 11. Figure 11a shows that the pressure drop at a gas flow rate of 800 m\({}^{3}\).h\({}^{-1}\) increases markedly from 1.5 Pa to 27 Pa with an increase of rotating speed from 200 rpm to 1000 rpm. The gas turbulence in the cavity zone B is enhanced by the rotating packing and more gas will collide with each other, leading to the loss of more energy and, consequently, the increase of the pressure drop. An interesting finding is that the pressure drop at a gas flow rate of 200 m\({}^{3}\).h\({}^{-1}\) is larger than that at 800 m\({}^{3}\).h\({}^{-1}\) when the rotating speed is kept at 200 rpm. At a low gas flow rate, the contact time between packing and gas is prolonged, and collision and pressure drop occur simultaneously. However, when the rotating speed is higher than 200 rpm, the wall friction loss will increase, resulting in a larger pressure drop at a high gas flow rate. Figure 11b shows that the static pressure drop decreases from \(-2\) Pa to \(-15\) Pa as the rotating speed is increased from 200 rpm to 800 rpm at a gas flow rate of 800 m\({}^{3}\).h\({}^{-1}\). It is also seen that the difference between a gas flow rate of 200 m\({}^{3}\).h\({}^{-1}\) and 800 m\({}^{3}\).h\({}^{-1}\) becomes less noticeable with increasing rotating speed, which is attributed to the increase of collision and conversion of kinetic energy to static pressure and internal  
Figure 8: The static pressure drop in the packing zone. (**a**) Plane a\({}_{3}\) to a\({}_{4}\); (**b**) plane a\({}_{4}\) to a\({}_{5}\); (**c**) gas flow rate 200 m\({}^{3}\) h\({}^{-1}\), rotating speed 1000 rpm; (**d**) gas flow rate 800 m\({}^{3}\) h\({}^{-1}\), rotating speed 1000 rpm.  
Figure 9: The dynamic pressure drop in the packing zone. (**a**) Plane a\({}_{3}\) to a\({}_{4}\); (**b**) plane a\({}_{4}\) to a\({}_{5}\).  
energy at high rotating speed. Figure 11c shows that dynamic pressure drop increases markedly from 1 Pa to 43 Pa with the increase of rotating speed from 200 rpm to 800 rpm. The same trend is observed at a low gas flow rate.
## 2 Experimental Setup  
### Device under Test  
This study focused on JM001, a low-power asynchronous timing monolithic SRAM with a storage capacity of 8 K x 8 bits. This device consisted of an address input buffer circuit, a row decoder circuit, a column decoder circuit, a data input/output buffer circuit, a small signal readout amplifier circuit, a data output driver circuit, a logic control module, and a 256 x 256-bit memory cell array. It was powered by a single 5 V supply, with a read-write cycle of 30 ns. A schematic block diagram of the device is shown in Figure 1.  
Figure 1: Schematic block diagram of JM001.  
When the SRAM device is working normally, the serial port sends data periodically, but if an error occurs, it sends error data continuously. A description of the serial port data of the host software is shown in Figure 2.  
* (3) 0000 indicates the read error count;
* (8) 00000 indicates the address of error data when an error occurs;
* (10) 00 indicates the error data when an error occurs;
* (12) 00 indicates the expected correct data.
# Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
###### Abstract
It is urgent to develop a new method that can adaptively integrate the features extracted by convolution and Transformer and effectively capture the information obtained from multiple stages to overcome the limitations of existing methods. Studies have noted that the deep learning methods normally suffer from the data-hungry problem [39]. This problem is particularly acute in HSI classification due to a lack of high-quality benchmark dataset. Most of the previous studies have used small-scale datasets, such as Indian Pians, Salinas, and Pavia University [17,20,23,27,30,40], which comprise only hundreds of rows by hundreds of columns of pixels. It is often the case that deep learning methods yield nearly perfect classification results on these datasets, possibly because the overlapping pixels between training data and test data will lead to information leakage [40,41]. The new data partition method solves the problem of information leakage to some extent [40,41,42]. However, the partitioned training data usually contains only thousands of pixels, which is difficult to support the training of deep learning models (usually including millions of parameters). At the same time, a small number of test samples are insufficient to comprehensively evaluate the performance of the model. Recently, a series of large-scale and challenging datasets have been developed [43,44]. It is of interest to further test existing deep learning models using these benchmark datasets to better understand their comprehensive performances. The main objectives of this study are to: (1) develop a convolution-Transformer adaptive fusion network (CTAFNet) using an adaptive hybrid strategy to fuse high-frequency and low-frequency signals so as to extract more robust and discriminating local-global fusion representation and improve the performance of HSI classification; (2) to evaluate the performance of several widely used deep learning models (i.e., FSSANet [31], SS3 FCN [42], UNet [45], etc.) using large-scale and challenging benchmark datasets to provide a fair and comprehensive comparison between the models.
## 2 Methods
### Data Annotation and Medical Diagnosis  
The medical characterization of the PSG recordings was performed by the clinical team of the Sleep Study Unit of Sismanoglio General Hospital of Athens. The respiratory events and sleep stages labeling followed the standard protocol for Sleep Apnea Diagnosis [2]. The respiratory episodes related to this research study included apnea and hypopnea events: Apneas are defined as events of at least 10 s duration (3 respiratory cycles) with a severe reduction in the airflow (more than 90%), while hypopneaas are defined as events of the same duration that are characterized by a moderate (at least 30%) reduction in the airflow, provided the event is associated with a relative oxygen desaturation drop of at least 3% or with arousal [2]. Further categorization of apneic events includes their characterization as \"central\"--in which no respiratory effort is present--, \"obstructive\"--in which respiratory effort is present during the entire event--, or \"mixed\" apnea, involving characteristics of both previous categories. According to the latest update of the Sleep Apnea Syndrome annotation protocol, hypopnea should not be accompanied by further subcategorization and are considered obstructive hypopneaas.  
The identification and classification of respiratory events lead to the extraction of the total count of apnea/hypopnea events per sleeping hour. This value defined as the AHI is the most frequently used for the extraction of a final diagnosis of sleep apnea syndrome, by characterizing the patient in one of the following classes: non-apnea (AHI < 5 episodes/h), mild apnea (5 episodes/h \(\leq\) AHI < 15 episodes/hour), moderate apnea (15 episodes/h \(\leq\) AHI < 30 episodes/hour), severe apnea (AHI \(\geq\) 30 episodes/h). In this study, we kept and examined only the patients that suffered from severe or moderate SAS.  
The acquired data and the annotations of sleep stages and apnea events are publicly available [32]. The online dataset is part of an ongoing research project, thus, further expansion is expected in the following months/years.