esa-sceva/satcom-chunk-collection huggingface dataset:
# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
###### Abstract  
Convolutional neural networks (CNNs) with log-mel audio representation and CNN-based end-to-end learning have both been used for environmental event sound recognition (ESC). However, log-mel features can be complemented by features learned from the raw audio waveform with an effective fusion method. In this paper, we first propose a novel stacked CNN model with multiple convolutional layers of decreasing filter sizes to improve the performance of CNN models with either log-mel feature input or raw waveform input. These two models are then combined using the Dempster-Shafer (DS) evidence theory to build the ensemble DS-CNN model for ESC. Our experiments over three public datasets showed that our method could achieve much higher performance in environmental sound recognition than other CNN models with the same types of input features. This is achieved by exploiting the complementarity of the model based on log-mel feature input and the model based on learning features directly from raw waveforms.  
Keywords: environmental sound classification; convolutional neural network; DS evidence theory; audio processing; fusion model +
Footnote †: journal: Applied Sciences
# A Lightweight Residual Model for Corrosion Segmentation with Local Contextual Information
## 4 Experiment
### Ablation Study  
We evaluated the various design decisions for our method as well as its components.  
**Ablation Study for the Mixture of Small and Large Kernel.** Since both the small and large kernels are meant to capture different types of corrosion features, we ran an ablation  
Figure 5: Visualization comparison results between our model and other SOTA methods, where Background, Fair, Poor and Severe represented the four degrees of corrosion. Best viewed in color.  
by removing each of these kernels. The results are presented in Table 3. We found that both large and small kernels had improved accuracy, which confirmed that precise segmentation results need not only a larger range of receptive fields but also local detailed information. It should be noted that the large kernel brings a larger computational overhead but improves accuracy over the small kernel. The best performance was achieved when a mixture of small and large kernels was used.  
**Ablation Study for Residual Connection.** Inspired by ResNet [19], we used residual learning in the local contextual blocks to improve feature representation ability (Table 4). We found that the residual connection improved the mIoU from 74.67 to 75.61% without adding extra parameters. One possible reason is that the residual connection had a stronger ability to facilitate the flow of information in the network.  
**Ablation Study for Local Contextual Block.** To verify the effect of local contextual information, We replace the local contextual block with an inverted residual block, and the ablation study result is shown in Table 5. For convenience, we referred to the inverted residual and local contextual blocks as IRB and LCB, respectively. The IRB was a classical lightweight block from MobileNetV2 [4]. It first used 1 \(\times\) 1 convolution to expand the dimension of the input feature map, then performed the convolution operation with 3 \(\times\) 3 depthwise convolution. Finally, it used 1 \(\times\) 1 convolution to reduce its dimension. We set the expansion rate to 1 to ensure our model had the same settings. We found that the parameters of the model with the IRB was 2.04 MB, which was slightly lighter than our model caused by the large kernel, but the other evaluation metrics were inferior to ours. The results indicated that the local contextual feature was efficient for corrosion segmentation.
# Research and Development of Automatic Monitoring System for Livestock Farms
## 3 Research Process
### Data Retrieving and Analysis  
After the data were collected to the PLC's designated register, the MX sheet package was used to set up the data retrieving time and lengths to retrieve the required data and export the retrieved data into an Excel file database for the researchers' computation and analysis purposes. Meanwhile, the human-machine interface also retrieved the data from the register, and the data from different sensors were displayed in the human-machine interface for observation by staffs in the livestock farm, as shown in Figure 9.  
In this study, the data exported to the Excel file were used to calculate the temperature-humidity index (THI) based on the formula of (1), where T was Celsius temperature and RH was humidity. The temperature-humidity index could be divided into 4 levels [27]: THI \(<72\) for no heat stress, \(72\leq\) THI \(<79\) for mild heat stress, \(80\leq\) THI \(<89\) for moderate heat stress, \(90\leq\) THI \(<98\) for severe heat stress, the detailed description is as shown in Table 3. We acquired and analyzed the data of lactation yield every 10 minutes during time interval 9:00 am to 4:00 pm for March, August and November and the lactation yield for dairy cows under different seasons and climates were also discussed.  
\[\text{THI}=\frac{9}{5}\times T+32-0.55\times(1-RH)\times\left(\frac{9}{5}\times T -26\right), \tag{1}\]  
Figure 9: The data from different sensors were displayed in the human-machine interface (HMI).  
In addition, the apparent temperature index (AT) of the dairy cows was established based on the formula (2) [28], where \(T\) was Celsius temperature (\({}^{\circ}\)C), \(RH\) is humidity, I was illuminance, e is vapor pressure and \(V\) was wind speed (m/s). We compared the apparent temperature index (AT) and the temperature-humidity index (THI), and analyzed the difference between the temperature-humidity index (THI) and lactation yields for dairy cows.  
\[\text{AT}=1.07T+0.2e-0.65V-2.7, \tag{2}\]  
\[\text{e}=\frac{RH}{100}\times 6.105\times\exp\biggl{(}\frac{17.27T}{237.7+T} \biggr{)}.\]
# Recurrent Neural Networks and ARIMA Models for Euro/Dollar Exchange Rate Forecasting
## 2 Materials and Methods
### Data and Sampling  
ARIMA, Elman, and LSTM were used to forecast the time series to analyze the accuracy of the model. For this proposal, the time series represents the EUR/USD exchange rate's daily value. The data were obtained from the records on Investing.com from 2 January 1998, to 31 December 2019, with a total of 5737 observations. Each observation represents the daily price of the EUR/USD exchange rate from Monday to Friday. To apply the prediction techniques, the time series were divided into training and validation sets. For training, the dataset (\(train\)) used the expression \(train=n-valid\), where \(n\) represents the total observations, and \(valid\) stands for the validation dataset, which includes windows with different forecasting horizons of 5, 11, 22, 35, 44, and 55 observations.  
The window that provided the best forecasting in the short term was selected to evaluate the performance of the three forecasting methods. [PERSON]'s U coefficient and the Diebold-Mariano test were used to evaluate the forecasting method's accuracy.  
Finally, we obtained the mean absolute percentage error (MAPE) of the selected prediction window to identify the observations where the method provided the greatest accuracy.
## 3 The Proposed IAVMD Method
### Parameter Optimization of VMD Using GWO
(2) Calculate the fitness value \(mean(WSK_{i})\) of each wolf \(X_{i}(i=1,2,\cdots,m)\), look for the minimum fitness value (i.e., \(\min\{mean(WSK_{i})\}\)) between the four agents (i.e., the fitness value of the individual grey wolf \(\alpha\), \(\beta\), \(\delta\), and \(\omega\)) and record the current best wolves \(X_{best}\). (3) Update the position of wolves in terms of the gray wolf movement pattern shown in Equation (13):  
\[X_{i}(t+1)=\frac{X_{i,\alpha}(t)+X_{i,\beta}(t)+X_{i,\beta}(t)}{3} \tag{13}\]  
\[\left\{ \begin{array}{l}X_{i,\alpha}(t+1)=X_{\alpha}(t)-A\cdot\left|\mathbb{ C}\cdot X_{\alpha}(t)-X_{i}(t)\right|\\ X_{i,\beta}(t+1)=X_{\beta}(t)-A\cdot\left|\mathbb{C}\cdot X_{\beta}(t)-X_{i}(t) \right|\\ X_{i,\delta}(t+1)=X_{\delta}(t)-A\cdot\left|\mathbb{C}\cdot X_{\delta}(t)-X_{i} (t)\right|\end{array} \right. \tag{14}\]  
where \(A\) is the convergence factor and meets \(A=2d\cdot r_{1}-d\), \(C\) is the swing factor and meets \(C=2\cdot r_{2}\), \(d\) is the range control parameter which linearly decays from 2 to 0 over the whole iteration, and \(r_{1}\) and \(r_{2}\) are the random numbers between 0 and 1. (4) Calculate the fitness value \(mean(WSK_{i}^{\text{new}})\) of new wolves, and record the best wolves \(X_{best}^{new}\) through the comparison of the fitness value. That is, if the fitness value \(mean(WSK_{i})\) of the updated wolves overmatches that of the previous wolves, the updated wolves \(X_{i}^{(t+1)}\) will take the place of the previous wolves \(X_{i}^{(t)}\). Otherwise, retain the previous wolves \(X_{i}^{(t)}\). (5) Determine whether the stopping condition is satisfied. Concretely, decide whether the current iterations are lower than the maximum iterations (i.e., \(t\leq T\)) or whether the opposite of the fitness value \(mean(WSK_{i})\) is small enough. If that is the case, stop the iteration and output the best wolves \(X_{best}\) (i.e., the best parameters \(x_{alpha}^{best}\) and \(x_{K}^{best}\) of VMD). Otherwise, let \(t=t+1\), back to step (2) to keep working. In short, this section has two contributions. First, the WSK indicator is proposed and is more reliable and robust to the outlier of the signal compared with the previous WK and three single indicators (i.e., the sparseness, kurtosis, and correlation coefficient). Second, two key parameters (\(\hat{\alpha}\), \(K\)) of VMD can be determined automatically by using the GWO method, where the mean of the proposed WSK for all modes is regarded as the objective function of the optimization procedure. Figure 5: A population hierarchy of the GWO algorithm.
# The Emerging Roles of RNA Modifications in Glioblastoma
## 3 RNA Modifications in GBM
### Other RNA Modifications in GBM  
Although most of the other RNA modifications are not well described, there are also some studies regarding the effect of these RNA modifications on GBM progression. For instance, ADAR2 and ADAR3 binds to the pre-mRNA of glutamate receptor subunit B, glutamate receptor ionotropic AMPA (GRIA2), contributing to a RNA editing at the Q/R site by modifying a codon replacing the glutamine (Q) with arginine (R). ADAR2/3 deficiency in GBM leads to increased unedited GRIA2 subunits, thereby leads to a calcium-permeable glutamate receptor, which can promote cell migration and tumor invasion [146]. Cleavage factor Im 25 (CFlm25) restricts mRNA proximal poly(A) site usage via alternative polyadenylation (APA). CFlm25 knockdown leads to at least 1,450 genes with shortened 3'UTRs and marked increases in the expression of several oncogenes in GBM, including cyclin D1 (CCND1), glutaminase (GLS) and methyl-CpG-binding protein 2 (MECP2), thereby enhancing their tumorigenesis [147]. Mechanically, shortened 3'UTR truncation of oncogenic transcripts relieves intrinsic microRNA- and AU-rich-element-mediated repression. These new findings unravel a wide horizon for us to explore the implements of RNA modifications in cancer treatment. With the developing technology, much more modifications in RNA and their RMPs, as well as their connections with human diseases, especially cancers, will be disclosed.
# Timed-SAS: Modeling and Analyzing the Time Behaviors of Self-Adaptive Software under Uncertainty
## 6 Related Work and Discussion
To make up for this deficiency, we proposed the metrics of Self-adaptive Steady Time and Self-adaptive Adjusting Time, which can be used to quantitatively evaluate the performance and response time of the self-adaptation logic. Furthermore, on the basis of the Statistical Model Checking technique, we created a set of quantitative analysis templates to analyze the self-adaptation properties and the time properties under uncertainty. To summarize, our work concentrated on definition, formal modeling and quantitative analysis of the self-adaptation processes and the time behaviors for the SAS systems. To the best of our knowledge, this is the first attempt to systematically tackle the time behaviors within the SAS systems.
# A Flexible FPGA-Based Channel Emulator for Non-Stationary MIMO Fading Channels
## 3 Flexible Hardware Architecture and Implementation
### FPGA-Based Implementation
#### 3.3.3 Interpolator Module  
The interpolator module performs a linear interpolation by \(I\) times to match the data rate between the channel fading and the input signal. The channel sampling rate \(f_{s}{}^{\prime}\) is much smaller than the system sampling rate \(f_{s}\), so the channel fading rate should be interpolated to \(f_{s}=I\times f_{s}{}^{\prime}\). Let us denote two adjacent channel fading samples as \(h[mI]\) and \(h[(m+1)I]\), then the linear interpolation can be realized as  
\[h[(mI+k)]=\frac{(h[(m+1)I]-h[mI])k}{I}+h[mI] \tag{10}\]  
where \(k=0,1,\cdots I-1\). The scheme of interpolator module in this paper includes one SUB, one multiplier, and one ADD shown in Figure 7. In this figure, two input ports of subtractor represent the adjacent channel fading samples, and the difference value is multiplied by the weight coefficient \(k/I\). Finally, the output of multiplier and the first channel fading sample are summed up by an adder to obtain the interpolated channel fading sample.  
Figure 5: The implementation scheme of channel fading module.  
Figure 6: Hardware simulation of channel fading module.
## 2 Background and Related Work
### English-Chinese Machine Translation
#### 2.2.2 Leveraging Chinese Features  
Chinese phonologic features play an important role in the sentence pronunciation. To improve the machine translation performance, [PERSON] et al. propose a novel phonology-aware neural machine translation (PA-NMT) model where Chinese phonologic features are leveraged for translation tasks with Chinese as the target [36]. A separate recurrent neural network (RNN) is constructed in the NMT framework to exploit Chinese phonologic features to facilitate the generation of more native Chinese expressions. Experimental results on the English-to-Chinese task show that the proposed method significantly outperforms state-of-the-art baselines.  
Neural machine translation (NMT) faces the challenge of out-of-vocabulary (OOV) word translation. [PERSON] et al. address this OOV issue and improve the NMT adequacy with a harder language, such as Chinese, whose characters are even more sophisticated in composition [37]. They integrate the Chinese radicals into the NMT model with different settings to address the unseen word challenge in Chinese-to-English translation. The experiments on standard Chinese-to-English NIST translation shared task data from 2006 and 2008 show that their designed models outperform the baseline model in a wide range of state-of-the-art evaluation metrics.
# Fuzzy PID Control of the Three-Degree-of-Freedom Parallel Mechanism Based on Genetic Algorithm
## 6 Analysis of Simulation Results  
According to the determined structure and control method of the servo control system, a simulation experiment was carried out on the position control of the servo motor combined with the engineering tuning method. The superiority of the control strategy designed in the work was verified by comparing the control effect of the fuzzy PID (FPID) control strategy and genetic algorithm optimized fuzzy PID (GAFPID) control strategy on the position signal tracking of the servo control system.
# Exploring the Benefits of Photovoltaic Non-Optimal Orientations in Buildings
## 2 Materials and Methods
### Case Study #1: Residential PVSCS  
The residential PVSCS is a single-home building located in the Madrid metropolitan area (40\({}^{\circ}\)27\({}^{\prime}\) N, 3\({}^{\circ}\)48\({}^{\prime}\) W). It is a two-story detached building equipped with heating and ventilation air conditioned (HVAC), as shown in Figure 2[38]. The climate in Madrid is continental Mediterranean, with cold winters and hot summers. Hence, the yearly consumption is high, 14,189 kWh for the full-year period under study. The installation has a peak power of 3.85 kW and uses 10 Canadian Solar Ku Max CS3U-385 MS monocrystalline PERC PV modules with power optimizers connected in a single string to a 4-kW SolarEdge inverter. The modules are installed on the roof, with a southeast orientation aligned with the building and with an inclination of 30\({}^{\circ}\).  
The methodology used for the analysis is as follows. Using the reference building, several suitable alternative PVSCS with non-optimal energy-producing orientation were simulated with PVSYST. The energy balances with the grid were calculated using the original installation energy consumption data. Then, the economic balance was performed using the electricity prices for the energy imported and exported from the grid under Spanish regulation. This economic balance served as the cashflow for the financial study.  
The biggest issue when comparing the performance of these different orientations was that the annual production varies for each case. Different annual PV production is the result of different instantaneous PV power; this means that, as the electricity consumption is the same, the balance of self-consumption and surplus are different, affecting the economic results. In fact, for a less-producing layout, there would be a higher share of self-consumption and a lower proportion of surplus energy. As long as self-consumed electricity is valuated at retail price and the surplus near at wholesale price, the less-producing layouts economic results would be overestimated.  
The method that was used for a proper comparison. For all the different layouts, the former PVSYST simulations results were rescaled for yearly productions between 5000 kWh and 15,000 kWh, and the energy and economic balances were performed. In this way, there would be a better comparison, and the effects of the different hourly PV production profiles of each layout would be distinguished.  
Figure 2: Residential building under study before PV installation. (**a**) Satellite image (Google Earth) (**b**) Floorplan with basic dimensions and orientation.
## 1 Introduction
The results were encouraging: Flexible Start saved up to 26.6% in emissions for short-duration workloads, while Pause & Resume achieved up to 11.4% carbon savings for the longest workloads, such as a 6.1B parameter language model. Unfortunately, this study is the only one providing quantitative evaluations of these strategies to date. To confirm their validity across various AI tasks and regions and to encourage their adoption in the industry, further research is needed to assess the consistency of these results under different conditions. The research questions we address in this work are the following:  
* RQ1: How effective are the Flexible Start and Pause & Resume carbon-aware AI training strategies in reducing carbon emissions when applied to different Anomaly Detection algorithms? * RQ2: What impact do geographical variations, specifically the carbon intensity of different regions, have on the carbon savings achieved by the Flexible Start and Pause & Resume strategies? * RQ3: How do the carbon savings from Flexible Start and Pause & Resume strategies differ when applied to Machine Learning (ML) and Deep Learning (DL) Anomaly Detection algorithms, and what factors influence these variations? To answer the provided research question, in this work we benchmark Flexible Start and Pause & Resume for four Anomaly Detection (AD) algorithms [10], utilising a large dataset of real financial transactions provided by an Italian bank. These algorithms include a variety of approaches, encompassing both Machine Learning (ML) and Deep Learning (DL). We apply the strategies in the North Carolina region as a reference, following the methodology of other works in the field. Additionally, we average their outcomes across seven European data centres within the AWS network. Energy consumption for the four training algorithms was measured on a DGX machine by NVidia (Santa Clara, CA, USA) equipped with 8 A100 GPUs per node, provided by CINECA (Consorzio Interuniversitario dell'Italia Nord Est per il Calcolo Automatico), a university consortium supporting research activities through the use of supercomputers. Our results confirm the effectiveness of the proposed carbon-aware training strategies, revealing an interesting correlation between the amount of carbon savings, the region in which these strategies are applied, and the time extension assigned to the strategies. The rest of the paper is structured as follows. In Section 2, we report an analysis of the state-of-the-art. Section 3 summarises the formulation of the two benchmarked strategies. Section 4 presents the research methodology adopted in this paper, also highlighting some technical details on the conducted experiment. Section 5 presents the results of our benchmark. In Section 6, we discuss the main results, comparing them with those already present in the state-of-the-art. Finally, Section 7 outlines the main outcomes of this research and sketches future research challenges.
# Effective Receiver Design for MIMO Visible Light Communication with Quadrichromatic LEDs
## 3 Proposed Receiver Design
### Problem Formulation
On the other hand, since the ML detector is utilized, the minimum ED between any two received symbols should be maximized in order to minimize the error probability [3]. Therefore, the orientation  
Figure 2: Transmitter and receiver geometry. of receivers can be optimized to improve the system performance by solving the following ED-based decision metric optimization  
\[\{\mathbf{u}_{1},...,\mathbf{u}_{N_{r}}\}=\arg\max_{\mathbf{u}_{i}}\min_{ \mathbf{x}_{m}\
eq\mathbf{x}_{n}\in\mathcal{S}}\lVert\mathbf{H}\left(\mathbf{x }_{m}-\mathbf{x}_{n}\right)\rVert^{2}, \tag{10}\]  
where \(\mathbf{u}_{i}\) denotes the coordinate of the normal vector of the \(i\)-th receiver. The normal vector \(\mathbf{u}_{i}\) consequently determines the orientation of the multi-color PDs belonging to the \(i\)-th receiver and the receiver design problem becomes the problem of finding the optimal set of \(\{\mathbf{u}_{1},\mathbf{u}_{2},...,\mathbf{u}_{N_{r}}\}\). We assumed that the PD cluster belongs to each multi-color receiver as a point; thus, the orientation of the four PDs of the \(i\)-th receiver can be adjusted according to the normal vector \(\mathbf{u}_{i}\). Therefore, in theory, to improve the performance of multiple transmitter elements in a VLC system, we can carefully alter the receiver orientation according to (10). The later sections will explain the novel methods used to design the normal vectors such as orientation parameters can be utilized to aid the information transmission for a practical VLC system.