esa-sceva/satcom-chunk-collection huggingface dataset:
# Effective Monoaural Speech Separation through Convolutional Top-Down Multi-View Network
## 5 Experimental Setup
### Training Objectives  
The objective function used for training the speech separation networks is the scale-invariant source-to-distortion ratio (SI-SDR). This function calculates the difference between the estimated speech signal \(\hat{s}\) and the clean source signal \(s\). SI-SDR is developed to address the misuse of SDR and improve the accuracy of evaluation results.  
The SI-SDR is calculated by:  
\[\rho(\hat{s},s)=10\log_{10}\left(\frac{\left\|\frac{\langle s\rangle}{\langle s \rangle}s\right\|^{2}}{\left\|\hat{s}-\frac{\langle\hat{s},s\rangle}{\langle s \rangle}s\right\|^{2}}\right), \tag{10}\]  
where \(\langle\cdot,\cdot\rangle\) denotes the inner product operation. The term \(\frac{\langle\hat{s}\rangle}{\langle s\rangle}s\) denotes the orthogonal projection of the estimated speech signal \(\hat{s}\) on the line spanned by the clean source signal \(s\), while the term \(\hat{s}-\frac{\langle s\rangle}{\langle s,s\rangle}s\) denotes the (error) residual in \(\hat{s}\) that is orthogonal to the clean source signal \(s\). As the result, the SI-SDR is not affected by the absolute power (loudness) of \(\hat{s}\) and \(s\) serves as a robust measure for speech separation.  
In speech separation, the multiple clean source signals may be swapped, leading to a permutation problem. To overcome this issue, permutation invariant training (PIT) [17] is used to find the maximum SI-SDR value.
# Fault Detection in Wireless Sensor Networks through the Random Forest Classifier
## 3 Faults in WSNs
### Offset Fault  
Offset fault is when collected data are out of line [1]. This happens when a constant is added to expected data due to the bad calibration of the sensing unit. Offset fault can be modeled as follows:  
\[x^{\prime}=\alpha+x+\eta \tag{3}\]  
where \(\alpha\) represents the constant value added in the normal reading.
## 3 Methodology
### Data Augmentation
#### 3.3.2 PSD Fault Data Augmentation Based on TimeGAN  
The platform screen door fault data enhancement process based on TimeGAN is shown in Figure 4. The optimization of the generator loss, discriminator loss, and embedding function loss is accomplished through the application of gradient descent methods.  
* Generator loss \(L_{G}\)  
The generator loss comprises three components: the unsupervised loss, denoted by \(L_{unsup}\); the supervised loss, represented by \(L_{sup}\); and the recovery loss, indicated as \(L_{R}\). \(L_{unsup}\) denotes the cumulative cross-entropy between \(c_{fake,t}\) and the value of 1, in addition to the cross-entropy between \(c_{fake,t}\) and the value of 1. This relationship suggests that the data produced by the generator are discernible by the discriminator. \(L_{sup}\) denotes the root-mean-square error between the variables \(h_{t}\) and \(\hat{h}_{sup,t}\). The underlying principle involves inputting the preprocessed fault data from the platform screen door system into the generator and subsequently calculating the error between the generated data and the initially provided input data. The recovery loss (\(L_{R}\) ) quantifies the aggregate difference between the variance and the mean of the estimated values \(\hat{p}_{t}\) and the actual values \(p_{t}\). This indicates that the recovery function is capable of effectively reconstructing the data. The calculation of the generator loss (\(L_{G}\) ) is conducted as follows [43]:  
\[L_{G}=0.1\times L_{unsup}+100\sqrt{L_{sup}}+100\times L_{R} \tag{12}\]  
* Discriminator loss \(L_{D}\)  
The discriminator loss \(L_{D}\) represents the sum of the cross-entropy of \(c_{real,t}\) with 1, the cross-entropy of \(c_{fake,t}\) with 0, and the cross-entropy of \(c_{fake,t}\) with 0, which indicates  
Figure 4: Fault data augmentation process for a PSD system based on TimeGAN.  
that the discriminator can accurately distinguish the generated data from the historical input data.  
* Embedding function loss \(L_{E}\)  
The embedding function loss \(L_{E}\) is composed of the supervised loss \(L_{sup}\) and the root-mean-square error of \(\hat{p}_{t}\) and \(p_{t}\). The calculation method is as follows [43]:  
\[L_{E}=10\times\sqrt{MSE(p_{t},\hat{p}_{t})}+0.1\times L_{sup} \tag{13}\]
# Enhanced Multistream Fast TCP: Rapid Bandwidth Utilization after Fast-Recovery Phase
## 3 Enhanced Multistream Fast TCP (EMFast TCP)  
In this section, we extend the Fast TCP congestion control algorithm to resolve the issue of rapid convergence after the fast recovery in long-distance and high-bandwidth network. Further, it narrates the CCAFR algorithm.
# Wi-Senser: Contactless Head Movement Detection during Sleep Utilizing WiFi Signals
## 5 Discussion  
Wi-Senser utilizes the multipath propagation effect of WiFi signals to achieve contactless, low-cost, easy-to-deploy human head movement detection during sleep. However, at present, there are also some limitations in practical applications, as follows:  
* The relative position of sensing devices: In order to overcome this limitation, Wi-Senser can try to deploy multiple pairs of transceivers in future work to expand the effective sensing range of the system, and fuse the CSI measurement values collected by multiple pairs of transceivers to achieve more comprehensive and three-dimensional detection of human movement.
* The relative distance of sensing devices: In order to overcome this limitation, Wi-Senser can try to use high-gain directional antennas to increase the transmission power of WiFi signals in future work, enabling receivers to collect effective CSI measurement values at greater distances.
* Multiple-user sensing: At present, Wi-Senser can only sense the head movements of a single person. In future work, Wi-Senser can try to use multi-signal classification algorithms such as MUSIC to achieve sensing of multi-person sleep activities.
## 3 Experimental Verification
### Verification for the Case of Continuous EPCB  
The specimen PCCB-4 conducted by [PERSON] et al. [13] is employed to verify the effectiveness of the proposed model for continuous EPCBs. The structural details and layout of PCCB-4 are shown in Figure 15. The properties of the concrete slab are as follows: \(f_{\text{c}}=30\text{ MPa}\), \(E_{\text{c}}=30\text{ GPa}\) and \(f_{\text{t}}=2.5\text{ MPa}\). For the steel beam, the yield strength is 249.3 MPa for the top flange, 272.3 MPa for the bottom flange and 287.7 MPa for the web. The elastic modulus of the steel beam is 200 GPa. The composite beam is prestressed by two unbonded steel tendons in the steel box beam. The cross-section area of the tendon is 139 mm\({}^{2}\) and the initial effective prestress is 885.8 MPa. The yield strength of the external tendons is 1860 MPa and the elastic modulus is 200 GPa. The loading scheme is shown in Figure 15.  
Figure 13: Structural details of specimens B3 and B4.  
Two models, including the proposed model and the traditional model presented above, are built to simulate the flexural behaviors of PCCB-4 up to failure. Figure 16 shows the load-deflection curve comparisons, in which a better agreement of the proposed model with the tests is observed. For the prediction of external tendon stress increments, the three segments of the tendon in the traditional model have different behaviors. The stress increment in the seg-3 is small and the one in the middle segment is larger than that found in the tests, causing the overestimation of the flexural capacity. For PCCB-4, the tendon ratio is low and the effects of the overestimated tendon stress on the behaviors are not obvious in consequences. However, for EPCBs with a high external tendon ratio, the traditional model overestimates the flexural capacity significantly. In the proposed model, the external tendon is modeled by the strain-compatible slipping tendon element which was close to the actual behavior of the external tendon. The proposed model can be used to model the behaviors of EPCB with multiple segments of external tendons.  
Figure 14: Analysis results of specimens B3 and B4.  
Figure 15: Structural details of specimen PCCB-4.
## 4 Extraction Methods for Coriander Bioactive Compounds
### Non-Conventional Extraction Methods  
One of the main challenges to technological innovation in the direction of \"Green chemistry\" is the creation of new extraction techniques [112]. An appreciable number of innovative extraction techniques have been developed to extract bioactive compounds or secondary coriander metabolites, but the most exploited are microwave-assisted extraction, ultrasound-assisted extraction, supercritical fluid extraction, and subcritical water extraction. MAE and UAE are both aligned with the principles of green chemistry, reducing the use of solvents and energy consumption. Together, these techniques represent major advances in the search for efficient, environmentally friendly extraction methods [112].  
Microwaves are generally superior to ultrasound in cases where higher yields, shorter extraction times, and uniform heating are required, particularly for more complex or dense matrices [113; 114; 115].
# Modelica-Based Energy Management of PEMFC Hybrid Power System of Vehicle
## 2 Mathematic Model of Hybrid Power System
### PEMFC Model
#### 2.1.2 Stack Temperature
After absorbing heat, a phase change occurs, considering that the evaporation of water will absorb a portion of the energy from the battery stack, and its heat flow rate can be calculated by Equation (13):  
\[Q_{H_{2}O}^{evap}=m_{v,gen}H_{evap} \tag{13}\]  
where \(m_{v,gen}\) is the amount of water produced by the reaction by Equation (14), and \(H_{evap}\) is the enthalpy of evaporation of liquid water given by Equation (15):  
\[m_{v,gen}=\frac{I_{FC}N_{cell}M_{H_{2}O}}{2F} \tag{14}\]  
\[H_{evap}=\frac{4.507\times 10^{4}-\xi_{5}T_{ca}+\xi_{6}{T_{ca}}^{2}+\xi_{7}{T_{ ca}}^{3}-\xi_{8}{T_{ca}}^{4}}{M_{H_{2}O}} \tag{15}\]  
where \(T_{ca}\) stands for the average temperature of cathode, \(M_{H_{2}O}\) is the molar mass of water, and \(\xi_{5}\), \(\xi_{6}\), \(\xi_{7}\), and \(\xi_{8}\) are the empirical coefficient. The heat rate is taken by the excessive air liquid as the following Equation (16):  
\[Q_{air}^{excess}=(m_{air,inlet}-m_{air,react})C_{p,air}(T_{ca,out}-T_{ca,in}) \tag{16}\]  
where \(m_{air,inlet}\) and \(m_{air,react}\) are the mass flow of air inlet and reaction consumption in PEMFC, \(C_{p,air}\) is the heat capacity of air, and \(T_{ca,out}\) and \(T_{ca,in}\) are the temperature of outlet and inlet. \[m_{air,react}=\frac{I_{FC}N_{cell}M_{O_{2}}}{4F} \tag{17}\]  
where \(M_{O_{2}}\) is the molar mass of oxygen. The heat exchange of cooling water is described as:  
\[Q_{cooling}=h_{cooling}S_{cooling}(T_{FC}-T_{cooling,in}) \tag{18}\]  
where \(h_{cooling}\) is the convective heat transfer coefficient between cooling water and stack, \(S_{cooling}\) is the heat exchange area, and \(T_{cooling,in}\) is the inlet temperature of cooling water. The heat exchange between the stack and the ambient air can be calculated by the ambient temperature and PEMFC' working temperature. \[Q_{amb}=h_{amb}S_{FC,amb}(T_{FC}-T_{amb}) \tag{19}\]  
where \(h_{amb}\) is the convective heat transfer coefficient between ambient air and stack, \(S_{FC,amb}\) is the corresponding heat exchange area, and \(T_{amb}\) is the temperature of ambient air.
## 5 Experiments and Data Analysis
### 5G Experimental Base Station Testing
#### 5.1.1 Unilateral Target  
The radius of the unilateral rotation target is 0.3 m, and the data with a speed of 0.5 rps and clockwise rotation are analyzed as an example. To obtain better display results, the initial 1 s of data are discarded. Figure 14a shows the time-frequency analysis results of the channel response without interference suppression, Figure 14b shows the time-frequency analysis after random phase noise suppression, and Figure 14c shows the time-frequency analysis after suppressing the zero frequency component. Figure 14d shows the smoothed frequency offset curve extracted by peak detection, in which the blue solid line represents the actual measurement results, and the red dashed line represents the theoretical values derived from the rotating target model. Figure 15a shows the error between the measured Doppler frequency and the theoretical value. The initial large error is due to the poor filtering effect of the filter when it first acts on the data. It can be seen that the overall error is small, and the experimental results are consistent with the theoretical model. The errors are mainly caused by model measurement errors and the FFT spectral resolution. From the spectrum graph Figure 15b, it can be observed that the measured speed is 0.500488 rps,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Parameter Value** & **Parameter** & **Parameter Value** \\ \hline Num RB & 273 RB & Subcarrier Location & 0 \\ Symbol Locations & 4 & Period & 40 slots \\ Density & 3 & Slot Offset & 24 slots \\ \hline \hline \end{tabular}
\end{table}
Table 4: CSI-RS parameter table for 5G base station in 5G laboratory.  
Figure 13: (**a**) Measurement scenario in 5G laboratory; (**b**) measurement model in 5G laboratory.  
with an error of 0.1%. For the frequency offset curves corresponding to different rotation directions at the same position, as shown in Figure 15c, it can be seen that the relationship between the maximum and minimum values of the frequency offset curves corresponding to different rotation directions is different.
# Partial Fake Speech Attacks in the Real World Using Deepfake Audio
## 3 Background
### Speech Synthesis Generation  
Speech synthesis is a process that involves the artificial production of human speech using computer algorithms and synthetic voices. Speech synthesis encompasses two main categories: text-to-speech (TTS) and voice conversion (VC). Both TTS and VC systems have been significantly improved by deep neural networks (DNNs), which enable more natural-sounding voice synthesis. TTS systems convert any given text into spoken words, mimicking the voice of a specified target speaker [7; 11; 37; 38; 39; 40]. Traditional TTS systems used concatenative synthesis, which involved stitching together short pre-recorded speech segments to form longer sentences [41]. By contrast, VC is a category of speech synthesisfocused on modifying the characteristics of a source speaker's voice to make it sound like a target speaker's voice [12; 13; 14; 42].  
**Zero-shot TTS and VC.** Recently, researchers have dedicated significant attention to zero-shot speech synthesis generation [3; 43; 5; 6; 10; 44; 45]. This technique allows for the adoption of a new voice with just one utterance or a few seconds of target voice speech without requiring additional training. The main advantage of this technique is that it enables the adaptation of a new voice without the need for retraining [46].  
**Several Methods May Be Used to Carry Out Synthetic Speech Generation.** There are two main methods for creating synthetic speech using DNN: commercial cloud services and open-source tools. In order to use a commercial cloud service to train a model for a specific voice, the person's consent must be obtained. If consent is not obtained, the user can utilise the voices provided by the cloud service to generate TTS audio. Commercial services offer full voice training [47], a zero-shot TTS service [16], and a zero-shot TTS and VC service [15]. On the other hand, numerous DNN speech synthesis open-source tools are publicly accessible for both TTS [48; 9; 49] and VC [50; 51; 52] including support for zero-shot tools.
# Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network
## 2 Theoretical Analysis and Proposed CNN Denoiser
### Proposed CNN Denoiser
#### 2.2.2 Loss Function  
To better deal with the case of wrapped-phase boundary blurring, this work uses smooth\({}_{L1}\) to replace the \(L_{2}\) loss function. The \(L_{2}\) loss function typically results in a significant loss of texture details when dealing with speckle maps. On the contrary, the denoising model based on the \(L_{1}\) loss function can obtain an image with relatively clearer edges. The \(L_{1}\) loss function calculates the average distance between the denoised image \(f(x)\) and its corresponding noise-free image \(y\). The definition and gradient calculation equations are as follows  
\[L_{1}=\sum\
olimits_{i=1}^{N}\lvert f(x_{i})-y_{i}\rvert/N, \tag{10}\]  
\[\frac{\partial L_{1}}{\partial d}=\left\{\begin{matrix}1,&d>0\\ -1,&d<0^{\prime}\end{matrix}\right. \tag{11}\]  
where \(x_{i}\) is the block of the input, \(x\), \(y_{i}\) is the block of the corresponding position of the real value y, and \(d=f(x_{i})-y_{i}\). From Equations (10) and (11), for any input other than \(d=0\), the absolute value of the gradient is the same. The increased stability of the gradient suggests its resilience to outliers and reduced likelihood of gradient explosions.. However, when the \(d\) value is very small, the absolute value of the gradient is still one, and the model convergence is difficult. In addition, the non-differentiability of \(L_{1}\) at \(d=0\) is also a shortcoming that cannot be ignored. Therefore, this paper uses the smooth\({}_{L1}\)[22] loss function. The definition of the loss function and its gradient calculation equations are as follows  
\[\text{smooth}_{L_{1}}(d)=\left\{\begin{matrix}0.5e^{2},&if|e|<1\\ |e-0.5|&\text{otherwise}\end{matrix}\right. \tag{12}\]  
\[\frac{\partial\text{smooth}_{L_{1}}(d)}{\partial d}=\left\{\begin{matrix}d,&if |d|<1\\ \pm 1,&\text{otherwise}\end{matrix}\right. \tag{13}\]  
In Equations (12) and (13), the smooth\({}_{L1}\) loss function retains the advantage that \(L_{1}\) is not easy to gradient explosions and can be derived at any point. Meanwhile, when the \(d\) value is small, the corresponding gradient value is also small enough. Therefore, without increasing the complexity of the model and affecting the real-time performance, a low-cost solution is proposed; smooth\({}_{L1}\) is used to replace the traditional \(L_{1}\) and \(L_{2}\) as the loss function. Based on this, the trained denoising model can better preserve the image details while denoising and reduce the loss of edge information.
# Predictive Optimization of the Heat Demand in Buildings at the City Level
## 3 Methods
### Predictive Optimization of the Heat Demand
#### 3.3.2 Peak Load Cutting  
In the earlier work, the cost function for peak load cutting minimized the heat demand [28]. However, in the case were no measured indoor temperatures are available and with the assumption that the simulated indoor temperature is the set point temperature, minimizing the heat demand would mean that the indoor temperature would just be decreased to the minimum level. Therefore, in this work the cost function for peak load cutting minimized the difference between the forecasted and the optimized heat demand keeping the change in the total heat demand at minimum:  
\[J=|\sum_{t=1}^{N}\big{(}P_{forecast,k}-P_{optimal,k}\big{)}|+\sum_{t=1}^{N} \big{(}P_{ChangePenalty,k}(t)+T_{Penalty,k}(t)\big{)}, \tag{7}\]  
where \(P_{changePenalty,k}\) is the penalty for too large hourly changes in the heating power. The optimization was subject to the constraints in Equations (5) and (6), and the following:  
\[\big{|}P_{optimal,k}(t)-P_{optimal,k}(t-1)\big{|}\leq P_{change,k}, \tag{8}\]  
\[0.3P_{forecasted,k}(t)\leq P_{optimized,k}(t)\leq P_{forecast,k}(t) \tag{9}\]  
where \(P_{change,k}\) is the building specific maximum hourly change in heating power. The heating power was constrained to be 30-100% of the forecasted heat demand for the hours when the peak load cutting was realized depending on the heat storage capability of the building. This means that a maximum of 70% peak load cut was assumed to be possible. This is consistent with the results in [28]. The _pattern search_ algorithm from the Global Optimization Toolbox of MATLAB(r) was utilized in the optimization.
## 2 Materials and Methods
### Study Area and Crop Yield Data  
The study area included two major agricultural states, Iowa and Illinois, that are key contributors to United States maize and soybean production [43]. These states lie within the Corn Belt, a region known for its fertile soils with high organic matter and nitrogen content [44]. The climate across the study area is classified as a humid continental type (\"Dfa\" per Koppen climate classification), characterized by warm summers and significant seasonal precipitation variability [45]. The high-resolution maize and soybean yield data was obtained from the Quantile Loss Domain Adversarial Neural Network (QDANN) database, which included 30 m resolution yield maps for maize and soybean by integrating county-level yield statistics with remote sensing inputs [46]. This approach mitigated the scarcity of ground truth yield data while maintaining subfield spatial accuracy. However, while the QDANN dataset was developed using comprehensive ground truth crop yield datasets from yield mapping systems from combine harvesters, it results from statistical modeling and thus contains a bias [46]. The time frame of the research included a  
Figure 1: Workflow of the crop yield potential prediction using machine learning based on phenological modeling using vegetation indices from Sentinel-2 images.  
subsequent four-year period from 2019 to 2022, which represents the most recent available data in the QDANN dataset. For model training and testing, a total of 1000 randomly distributed sample points per crop annually was generated from the QDANN database to capture spatial heterogeneity in soil, climate, and management conditions (Figure 2). The sampling grids were adjusted each year to reflect crop rotation patterns, thereby preventing repetitive sampling of the same fields and increasing interannual variability in environmental conditions and management practices. A spatial autocorrelation test of the input crop yield values per dataset was performed using Moran's I.