esa-sceva/satcom-chunk-collection huggingface dataset:
# A Multi-Resolution Approach to GAN-Based Speech Enhancement
## 3 Multi-Resolution Approach for Speech Enhancement
### Multi-Scale Discriminator  
When generating high-resolution image and speech data in the early stage of training, it is hard for the generator to produce a realistic sample due to the insufficient model capacity. Therefore, the discriminator can easily differentiate the generated samples from the real samples, which means that the real and fake data distributions do not have substantial overlap. This problem often causes training instability and even mode collapses [30]. For the stabilization of the training, we propose a multi-scale discriminator that consists of multiple sub-discriminators treating speech samples at different sampling rates.  
As presented in Figure 3, the intermediate enhanced speech \(G_{n}(z,c)\) at each layer restores the down-sampled clean speech \(x_{n}\). Based on this, we can utilize the intermediate enhanced speech and down-sampled clean speech as the input to each sub-discriminator \(D_{n}\). Since each sub-discriminator can only access limited frequency information depending on the sampling rate, we can make each sub-discriminator solve different levels of discrimination tasks. For example, discriminating the real from the generated speech is more difficult at the lower sampling rate than at the higher rate. The sub-discriminator at a lower sampling rate plays an important role in stabilizing the early stage of the training. As the training progresses, the role shifts upwards to the sub-discriminators at higher sampling rates. Finally, the proposed multi-scale loss for discriminator with gradient penalty is given by  
\[\begin{array}{ll}L_{D}(q)=&\sum\limits_{\begin{subarray}{c}n\geq q\\ n\in N_{D}\end{subarray}}L_{D_{n}}+\lambda_{GP}L_{GP}(D_{n}),N_{D}\in\{1k,2k,4 k,8k,16k\},\\ =&\sum\limits_{\begin{subarray}{c}n\geq q\\ n\in N_{D}\end{subarray}}-\mathbb{E}_{(x_{n},x_{f_{n}})\sim(\mathbb{P}_{n}, \mathbb{P}_{f_{n}})}[log(\sigma(C_{n}(x_{r_{n}})-C(x_{f_{n}})))]+\lambda_{GP} \mathbb{E}_{\widetilde{x_{n}},c_{n}\sim\widetilde{\mathbb{P}}_{n}}[(||\ \bigtriangledown_{ \widetilde{x_{n}},c_{n}}C(\widetilde{x_{n}},c_{n}))||_{2}-1)^{2}]\end{array} \tag{12}\]  
where \(\widetilde{\mathbb{P}_{n}}\) is the joint distribution of the down-sampled noisy speech \(c_{n}\) and \(\widetilde{x_{n}}=\epsilon x_{n}+(1-\epsilon)\hat{x_{n}}\), \(\epsilon\) is sampled from a uniform distribution in \([0,1]\), \(x_{n}\) is the down-sampled clean speech, and \(\hat{x_{n}}\) is the sample from \(G_{n}(z,c)\). \(N_{D}\) is the possible set of \(n\) for the proposed discriminator, and \(q\) is the minimum sampling rate at which the intermediate enhanced output was utilized as the input to a sub-discriminator for the first time. The adversarial losses \(L_{D_{n}}\) are equally weighted.
# Onsite Testing for Nonlinear Analysis of an Earthquake Damaged Historical Church in Italy
## 5 Nonlinear Analysis
### Nonlinear Static Analysis  
The non-linear static analysis or pushover analysis simulates the response during an earthquake through the application of incremental horizontal forces until collapse. In this work, two distributions of lateral forces are applied to the model: uniform load pattern, in which the lateral forces are proportional to the mass, and modal pattern, in which the lateral forces are proportional to the main mode shapes (from the numerical modal analysis: the first mode with 45% of modal effective mass in the y-direction and the second mode with 22% of modal effective mass in the x-direction). Thus, a total of eight pushover analyses were performed according to the previously mentioned load distributions and the positive and negative principal directions. The convergence criterion is based on the energy control with a tolerance of 0.01 from the \(i\)th to the \(i\)th + 1 iteration step. The response of the structure is represented by the capacity curve, which reports the value of the seismic coefficient (6) versus the displacement of a control point.  
\[\alpha_{i}=\frac{\sum_{i}Horizontal\ Force}{Self\ Weight} \tag{6}\]  
where \(i\) is either the horizontal direction x or y.  
The MDOF system of the church is transformed to a SDOF by dividing the seismic coefficient and the nodal displacement by the modal participation factor \(\Gamma\).  
Figure 18: Stress–strain and damage-strain relationships for the materials with reference to the experimental data.  
In Figure 20a, the capacity curves are compared with the seismic demand represented by the acceleration-displacement response spectrum (ADRS), the analysis in the longitudinal X direction return capacity curves for both load patterns with higher capacity of the structure in the positive direction (east side) are constrained by Palazzo della Provincia, while the negative X direction highlights the high risk of the facade to overturn out of plane.  
In Figure 20b, the analysis in the longitudinal Y direction returns capacity curves of both the load patterns with higher capacity of the structure in the positive Y direction (north side, uphill). Although, the geometry of the church is essentially symmetric, the higher ground level of the north side together with the mechanical characteristic of the north exterior masonry (lower stiffness but larger resistance than the south side) contributes to the larger seismic coefficient and admissible deformation of the church in the positive Y direction.  
Figure 19: Stress-strain and damage-strain relationships for untested materials.  
Figure 20: Capacity curves from nonlinear static analysis, (**a**) in the X direction, (**b**) in the Y direction.
# The Stock Index Prediction Based on SVR Model with Bat Optimization Algorithm
## 2 Literature Review
[10] mixed support vector regression model with quantum computer mechanism, chaotic mapping function and bat algorithm. They proposed a novel prediction method that optimized the parameters and showed superiority in improving the prediction accuracy. [PERSON] et al. [11] proposed a method that combined wavelet neural network (WNN) and adaptive mutation bat algorithm (AMBA). AMBA was used to optimize the network parameters of WNN. This method improves the accuracy of prediction and speeds up the training. [PERSON] [12] employs a support vector regression model with hybrid kernel functions. A chaotic efficient bat algorithm based on chaos, niche search and evolutionary mechanism is proposed for optimizing its parameters with satisfactory prediction accuracy. Moreover, [PERSON] [13] developed a least squares support vector machine model. The relevant parameters are optimized by the bat algorithm to improve the prediction performance. Further, in stock index prediction, the bat algorithm will play a pivotal role in combination with other population intelligence algorithms or machine learning algorithms as well. In 2014, [PERSON] et al. [14] used the investment satisfaction ability index to screen potential candidates and used the evolutionary bat algorithm to construct stock portfolios. [PERSON] [15] designed a multi-agent framework for predicting stock prices with a bat neural network multi-agent system (BNNMAS), which tested a model for predicting stock prices in a global facing financial crisis for predicting DAX stock prices over a period of time. It is considered as a suitable tool for predicting stock prices, especially in the long-term period. While [PERSON] [16] emphasized artificial neural networks (ANNs) with time series data and nonlinear parameters to predict the stock price the next day. The proposed ANNs were trained with the bat algorithm, which was first applied to stock price prediction in 2015. The approach was to first preprocess the data to predict the closing price of the stock using three types of ANNs. Subsequently, the performance of the three methods is evaluated by mean absolute percentage error (MADE). This literature provides an innovative idea and lays a theoretical foundation for the research in this paper. [PERSON] and [PERSON] [17] examine linear, nonlinear, artificial intelligence, frequency domain, and hybrid models to predict stock returns in developed, emerging, and frontier markets. [PERSON] [18] uses artificial neural networks to predict stock price indices and uses social spider optimization (SSO) and Bat Algorithm (BA) to train it. Mean absolute error (MAE) is used as an error evaluation criterion. Some time series models of forecasting such as ARMA and ARIMA are used to predict stock prices.
# Mechanical Structure Design and Experimental Study of Gamma-ray Monitor for Small Satellite Payload
## 5 Discussion  
According to the dynamic simulation and experimental results, it can be concluded that the response of sinusoidal vibration is much smaller than that of random vibration. The reason is that the basic frequency of the structure calculated in Section 4.2.1 is 420.25 Hz, and the excitation condition of sinusoidal vibration is 5-100 Hz, which will not cause obvious resonance response. From this perspective, it seems that the sinusoidal vibration test is unnecessary. In fact, sinusoidal vibration test can be equivalent to a quasi-static inertial force process, simulating low frequency transient events.Sinusoidal vibration is the actual working state of satellite from ignition to orbit loading, especially in the atmosphere. The structure must undergo the test of this working condition, otherwise it is impossible to predict whether the structure or internal components will be damaged in this process.  
Because the integrity of sodium iodide crystal and structural adhesive in the novel GRM cannot be judged intuitively in the process of finite element analysis and mechanical vibration test. Therefore, before the mechanical test, the photomultiplier tube (PMT) is used to test the performance of the crystal package, as shown in Figure 14a. The energy spectrum before the test is shown in Figure 14b. Through the analysis of the data in the energy spectrum diagram, it is found that the half-peak width size meets the requirements of the DRO satellite file KSW-DRO-Q09-2021, indicating that the sodium iodide crystal has a good energy resolution for gamma rays. Then it is considered that there are no cracks or defects in the bonding site at this time. After the mechanical vibration test, PMT was used again to test the performance of the crystal package. The energy spectrum after the test is shown in Figure 14b. Comparing the mechanical test before and after the vibration of the crystal packaging performance test found that the two full-energy peaks almost coincide, obtained before and after the mechanical vibration test structural adhesive and sodium iodide crystal does not exist cracks or defects.
# AI-Enabled Intelligent Visible Light Communications: Challenges, Progress, and Future
## 3 Machine Learning in Physical Layer of IVLC
### Channel Equalization
#### 3.2.8 Postequalization MPANN  
Although the ML-based equalizers for mitigating both the linear and nonlinear distortions in VLC systems have been booming recently, the computational complexity is still a problem that needs to be further solved. Therefore, an ML-based equalizer with relatively optimal equalization performance while still maintaining a low complexity is needed in the field of VLC. One promising way is to greatly relieve the equalizer's complexity by moderately sacrificing partial performance.  
Researchers have developed a simplified ML-based equalizer, namely the memory-polynomial artificial neural network (MPANN) [63], to prune the network structure and still maintain similar equalization performance as MLP or other NNs. Likewise, the input data to be fed into MPANN could be obtained by windowing the received time-serial signal. The length of the window is usually called the memory length, which also represents the dimensions of the features. The major characteristic of MPANN is that its input layer, namely the memory-polynomial layer (MP layer), would expand the input features by one certain function, which is memory polynomial expansion. In addition, the Gaussian, Fourier basis, and other trigonometric polynomials (e.g., Legendre, Chebyshev, etc.) could be the function in the input layer. It is believed that the demanded nodes of the modified NN structure could be significantly decreased if one could provide a prior knowledge of the nonlinear model. Therefore, the memory polynomial expansion is adopted to map the input features to higher dimensional data space. Then the output pattern of the MP layer is multiplied by the corresponding weights and fed into the following hidden layer of the NN. A regular activating (ReLU) and weighting process are conducted in the hidden layer and back propagation (BP) algorithm is utilized to update the parameters. Then, finally, the output layer is utilized to output the equalized symbol. The experimental results confirmed that the MPANN could achieve the same equalization performance as the regular MLPs and only requires less than a quarter of the complexity [63].
# Outlier Detection for Multivariate Time Series Using Dynamic Bayesian Networks
## 4 Experimental Results
### Pen-Digits  
A distinct application is the recognition of drawn digits. Measurements are taken along time from each drawing phase. Data is available at [46] and studied in [47]. Handwriting samples are captured using a sensitive tablet which outputs the \(x\) and \(y\) coordinates of the pen at fixed time intervals. The goal is to model the system to a certain character being simultaneously unwanted digits amid the data. A set comprising 1143 MTS (\(N=1143\)) along 8 time frames (\(T=8\)) representing digit 1 is assembled from 44 different writers. The original MTS are discretized with an alphabet size of 8 (\(r_{i}=8\) for all \(i\)). The dataset is injected with 130 subjects (\(N=1143+130\)) belonging to a different digit. The aim is to detect the aforementioned and subsequently understand similarities between digits.  
Results are present in Table 4, where \(D_{i}\) represents the anomaly digit \(i\) introduced. A first-order \((m=1)\) non-stationary tDBN is modeled, since a pair of coordinates is more easily explained by its immediate precedent. Every attribute can possess at most one parent from its preceding slice (\(p=1\)). Thresholds are selected manually. The objective is not only to capture the performance of the outlier detection system but further understand which digits are more commonly resembled with digit 1. Results show that distinguishing digit 7 from 1 is difficult due to their similarity, proved by the low F\({}_{1}\) score obtained. Such reflects the blending of both class distributions. Digits 8 and 9 proved to be more easily discerned from 1.
# Directional Support Vector Machines
## 1 Introduction  
Several phenomena and concepts in real-life applications are represented by angular data or, as they are referred to in the literature, directional data. Examples of data that may be regarded as directional include temporal periods (e.g., time of day, week, month, year, etc.), compass directions, dihedral angles in molecules, orientations, rotations, and so on. The application fields include the study of wind direction as analyzed by meteorologists and magnetic fields in rocks studied by geologists.  
The fact that zero degrees and 360 degrees are identical angles, so that for example 180 degrees is not a sensible mean of two degrees and 358 degrees, provides one illustration that special methods are required for the analysis of directional data.  
Directional data have been traditionally modeled with a wrapped probability density function, like a wrapped normal distribution, wrapped Cauchy distribution, or von Mises circular distribution. Measures of location and spread, like mean and variance, have been conveniently adapted to circular data.  
The design of pattern recognition systems fed with directional data has either relied completely on these probabilistic models or just ignored the circular nature of the data.  
In this work, we formulate for the first time a non-probabilistic model for directional data classification. We adopt the max-margin principle and the hinge loss, yielding a variant of the support vector machine model.  
The theoretical properties of the model analyzed in the paper, together with the robust behavior shown experimentally, reveal the potential of the proposed method.
## 3 Methodology
### Robust Counterpart of Sub-Problem  
For any demand locations, we assume the uncertainty demand between \(t-1\) and \(t\) with the following non-negative bounded box uncertainty set:  
\[U_{t}=\Big{\{}d_{t}|\tilde{d}_{t}-\hat{d}_{t}\leq d_{t}\leq\tilde{d}_{t}+ \hat{d}_{t},t=1,2,\dots,H\Big{\}} \tag{22}\]  
Let \(z_{t}\) denote the inventory cost between \(t-1\) and \(t\), the robust counterpart of the inventory model can be formulated as the following three situations:When a demand location is not replenished in the planning cycle, the inventory cost is \[\min : \sum_{t\in H}z_{t}\] (23) s.t. \[: z_{t}\geq h(I_{0}-\sum_{\delta=1}^{t}\left(\bar{d}_{t}-\hat{d}_{t} \right))\quad\forall t\in\{1,\ldots,H\}\] \[z_{t}\geq-s(I_{0}-\sum_{\delta=1}^{t}\left(\bar{d}_{t}+\hat{d}_{t }\right))\quad\forall t\in\{1,\ldots,H\}\]
2. When a demand location is replenished by only one mode in the planning cycle, the inventory cost is \[\min : \sum_{t\in H}z_{t}\] (24) s.t. \[: z_{t}\geq h(I_{0}-\sum_{\delta=1}^{t}\left(\bar{d}_{t}-\hat{d}_{t }\right))\quad\forall t\in\{1,\ldots,l\}\] \[z_{t}\geq-s(I_{0}-\sum_{\delta=1}^{t}\left(\bar{d}_{t}+\hat{d}_{ t}\right))\quad\forall t\in\{1,\ldots,H\}\] \[q\geq 0\] where \(l\) is the lead time, and \(q\) is the replenishment quantity.
3. When a demand location is replenished by both two modes in the planning cycle, the inventory cost is \[\min : \sum_{t\in H}z_{t}\] (25) s.t. \[: z_{t}\geq h(I_{0}-\sum_{\delta=1}^{t}\left(\bar{d}_{t}-\hat{d}_{ t}\right))\quad\forall t\in\left\{1,\ldots,l^{f}\right\}\] \[z_{t}\geq h(I_{0}+q^{f}-\sum_{\delta=1}^{t}\left(\bar{d}_{t}-\hat{d} _{t}\right))\quad\forall t\in\left\{l^{f}+1,\ldots,l^{s}\right\}\] \[z_{t}\geq-s(I_{0}+q^{f}-\sum_{\delta=1}^{t}\left(\bar{d}_{t}+\hat {d}_{t}\right))\quad\forall t\in\left\{l^{f}+1,\ldots,l^{s}\right\}\] \[z_{t}\geq h(I_{0}+q^{f}-\sum_{\delta=1}^{t}\left(\bar{d}_{t}-\hat{d} _{t}\right))\quad\forall t\in\left\{l^{s}+1,\ldots,H\right\}\] \[z_{t}\geq-s(I_{0}+q^{f}+q^{s}-\sum_{\delta=1}^{t}\left(\bar{d}_{t }+\hat{d}_{t}\right))\quad\forall t\in\{l^{s}+1,\ldots,H\}\] \[q^{f}\geq 0\] \[q^{s}\geq 0\] where \(l^{f}\) and \(l^{s}\) are the faster and the slower replenishment lead time, respectively. Similarly, \(q^{f}\) and \(q^{s}\) are the faster and the slower replenishment quantity.
## 1 Introduction
Due to the fact that positional methods are an expensive and recent technology, few studies have analyzed distances covered and specific velocity thresholds using indoor positioning systems [8; 23]. However, these studies evaluate it during 20-min simulated games [8] and female matches [8]. In turn, these studies differ to each other at the time to specify velocity thresholds, as some consider \(>\)24 km\(\cdot\)h\({}^{-1}\) as a sprinting action [8] and others \(>\)14 Km h\({}^{-1}\)[8]. These differences can be a result of gender differences or not considering the dimensions of a basketball court, which could prevent athletes from reaching a sprint speed \(>\)24 Km h\({}^{-1}\), and if the athletes do reach that speed, it would be during an insignificant range of time [8]. However, there is some confusion and inconsistency when determining velocity thresholds [24], extending this debate to the duration of the actions. Previous research from field sports stipulates that in order to consider an action as a sprint, this has to occur in a period over one second [25; 26; 27], while other studies do not state the duration for a sprint to occur [28; 29]. In addition, there exists a lack of absolute thresholds derived from the local positioning system (LPS) due to the fact that this technology is usually allocated in different installations where the games are played or the devices are not allowed by federations and institutions during official games. In addition, knowing the specific velocity thresholds would permit the development of more precise conditioning programs for basketball players. Consequently, further research is needed to clarify the prior features mentioned. For these reasons, the aim of this study was (I) to establish absolute specific velocity thresholds during basketball games using LPS, for the same team during one competitive season, and (II) to compare the activity and speed profiles between various levels of competitions within the same team throughout the same season.
## 4 Discussion
Taking the Jincheng mining zone as an example, according to the Jincheng mining zone's environmental impact report, mining land has decreased by 6.28% over a 10-20 year period, and the average vegetation coverage has increased from 57% to 61%. This indicates that, with the introduction of various environmental protection policies and the construction of ecological conservation projects, the ecological conditions in mining areas are gradually improving. Numerous studies have indicated that meteorological factors play a significant role in influencing NDVI [47; 48; 49]. When analyzing the impact of these two factors in the Shanxi section of the Yellow River Basin, it becomes evident that precipitation has a greater influence compared to temperature. In over 90% of the study area, vegetation cover exhibits a positive correlation with precipitation. This correlation is especially pronounced in mountainous and hilly regions, where the impact of precipitation on vegetation change is more significant. On the other hand, vegetation cover shows a lower response to temperature variations. In the Shanxi section of the Yellow River Basin, the relationship between vegetation cover and temperature change is not significant, with only 1% of the area exhibiting an extremely significant positive/negative correlation. The Shanxi section of the Yellow River Basin is located in the middle-latitude region and is geographically shielded by the Taihang Mountains to the east [50]. The climate is characterized as a temperate continental monsoon climate, with annual average temperature variations within 1 \({}^{\circ}\)C from 2001 to 2020. This may explain the relatively weak correlation between the NDVI and temperature in the study area. This study has analyzed the overall trends in vegetation cover changes in the Shanxi section of the Yellow River Basin, including planned mining areas. It holds significant implications for ecological monitoring and management in the Yellow River Basin and the mining areas. However, there are some limitations in this research: (1) The study lacks a detailed analysis of the impacts of human activities, such as changes in population, ecological conservation policies, and coal mining activities within the mining areas, on vegetation cover. (2) The research has focused primarily on climate factors as drivers of vegetation cover changes in mining areas, and it has not extensively examined other factors such as land use changes and alterations in groundwater patterns resulting from mining activities. Future research should address these gaps, with a particular focus on delvinginto the patterns of surface disruption and mechanisms driving vegetation damage in mining areas. This will provide essential theoretical support for sustainable coal mining and ecological preservation in the Shanxi section of the Yellow River Basin.