esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Modern Audio Deepfake Detection Methods: Challenges and Future Directions
## 4 Fake Audio Detection Datasets  
The previous section discussed various methods used to distinguish fake and real voices. The detection methods discussed use models that must first be trained on a sample of data. Different datasets were published in the literature along with the detection method, while other studies focused on explaining their dataset and its characteristics. This section describes eight recent datasets created for use in fake detection methods, which are summarized in Table 2.  
More datasets have been reported for AD detection and were published separately. A German audio dataset named M-AILABS Speech was published for use with speech recognition and synthetic audio. It is freely accessible and contains 9265 real audio samples along with 806 fake samples. Each sample differs in length from 1 to 20 s, and the set has a total length of 18.7 h. The company Baidu published another public dataset called the Baidu Silicon Valley AI Lab cloned audio dataset, which was generated by a neural voice cloning tool. This dataset contains 6 h of high-quality and multi-speaker audio clips, each 2 s long. In 2019, the Fake or Real (FoR) dataset was released, which included eight synthetically generated English-accented voices by the Deep Voice 3 and Google-WavNet generation models. It is available for public access, and its most crucial feature is that it includes samples in two types of formats, MP3 and WAV. The total dataset includes 198,000 files divided into 111,000 real samples and 87,000 faked samples, with each sample being 2 s long. There was also a faked audio dataset of Arabic speakers called the Ar-DAD Arabic Diversified Audio gathered from the Holy Quran audio portal. It contains the original and imitated voices of Quran reciters, while the audio speech is for 30 male Arabic reciters and 12 imitators. Specifically, the reciters are Arabic people from Saudi Arabia, Kuwait, Egypt, Yemen, Sudan, and the UAE. The data consist of 379 fake and 15,810 real samples, each being 10 s long. The dataset language is named Classical Arabic (CA) since it is in the Quranic language. Furthermore, the H-Voice dataset was generated recently based on imitation and synthetic voices speaking in different languages, namely, Spanish, English, Portuguese, French, and Tagalog. It contains samples saved as a histogram, which is in the PNG format. This dataset contains 6672 samples and has many folders, as illustrated in Figure 4, which also depicts the number of imitated and synthetically created samples in each folder. However, Table 2 combines the number of samples as 3332 real and 3264 fake imitated samples, as well as 4 real and 72 fake synthetic samples. It is public access, and the model generation for the synthetic-based files is Deep Voice 3. Figure 4: The Structure of the H-Voice Dataset.
# Resource Allocation for Reconfigurable Intelligent Surface Assisted Dual Connectivity
## 2 System Model
### Link Rate and Scheduling  
We consider flat fading channels where all subchannels and channels over elements have similar channel gains. We also consider an adaptive modulation and coding scheme (MCS) as in [21] with the SINR as in (3). Let \(\Gamma(.)\) denote the spectral efficiency obtained from MCS. Given SINR as in (3) and spectral efficiency \(\Gamma(.)\) (bits/symbol), the direct link rate of user \(u\) from BS \(j\) is  
\[l_{u,j}^{0}=\frac{\Gamma(\omega_{u,j}^{0})\ SC_{OFDM}SY_{OFDM}\text{C}}{T_{sc}}, \ j\in\mathbf{j},\ c\in\mathbf{C}, \tag{4}\]  
where, \(SC_{OFDM}\), \(SY_{OFDM}\), and \(T_{sc}\) represent the number of subcarriers per subchannel, number of symbols per subcarrier, and time duration of a subframe, respectively. Given the direct link rate \(l_{u,j}^{0}\) as in (4), the net data rate of user \(u\) from BS \(j\) for a conventional direct link is given as follows.  
\[\lambda_{u}=\sum\limits_{j\in J}x_{u,j}^{0}\delta_{u,j}^{0}l_{u,j}^{0},\forall u \in\mathbf{U}, \tag{5}\]  
where the user association \(x_{u,j}^{0}\) is as in (1), link rate \(l_{u,j}^{0}\) as in (4), and \(\delta_{u,j}^{0}\) is the user scheduling time fraction for user \(u\) by BS \(j\). The \(\delta_{u,j}^{0}\) for the direct channel between BS \(j\) and user \(u\) is given as  
\[\delta_{u,j}^{0}=\frac{(l_{u,j}^{0})^{\frac{1-\alpha}{\alpha}}}{\sum\limits_{u \in\mathbf{U}}x_{u,j}^{0}(l_{u,j}^{0})^{\frac{1-\alpha}{\alpha}}},\ \forall j\in\mathbf{J}, \tag{6}\]  
where \(\alpha\) is the fairness parameter. The channel modeling parameters explained in (1)-(6) are related to direct channel parameters between BS \(j\) and the user \(u\). These does not involve the RIS as an intermediate node. The channel modeling and user scheduling parameters of RIS based links are explained in the next subsection.
## 3 Datasets and Methods
### Methodology
#### 3.2.1 Feature Extraction  
In this subsection, two types of features are extracted from the sign images-hand coordinates which are a non-convolutional feature and four convolutional-related features that are obtained from the CNN architecture described in Table 4.  
The hand coordinates extracted in Table 5 are robust because even if the depth or orientation of the hand varies, the relative distance between the joints will be equal for the same sign. However, this procedure is constrained by the quality of the sign images and in certain scenarios may not detect the hand due to lighting conditions as well as image noise. Overall, it gives a feature vector of size 63. Coming to the convolutional features, the convolutional neural network (CNN) described in Table 5 is able to extract 2D feature maps using the convolutional filters, and the pooling layers scoop these features in a summarised form from specific windows/regions of the image along with reducing dimensionality. In the context of sign images, these features can indicate the position of fingers, hand orientation, etc. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Operation** & **Kernel** & **Stride** & **Filters** & **Pool** & **Activation** \\ \hline Conv2D & \(3\times 3\) & \(1\times 1\) & 64 & & \\ Conv2D & \(3\times 3\) & \(1\times 1\) & 64 & & \\ MaxPooling2D & \(1\times 1\) & & & \(2\times 2\) & \\ Conv2D & \(3\times 3\) & \(1\times 1\) & 128 & & ReLU \\ Conv2D & \(3\times 3\) & \(1\times 1\) & 128 & & ReLU \\ MaxPooling2D & & \(1\times 1\) & & \(19\times 19\) & \\ Flatten & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Architecture for convolutional-related features. \begin{table}
\begin{tabular}{l l} \hline \hline
**Input:** & **Sign Images Annotated with Labels** \\ \hline
**1:** & Extract numerical features from sign images: \\ _Hand coordinates_–3D coordinates of hand joints; \\ _Convolutional features_–features obtained from a CNN after convolution and pooling operations; \\ _Convolutional features_ + _finger angles_–convolutional features from a CNN along with cosine of angles between the fingers; \\ _CNN features on hand edges_–convolutional features on sign images whose background is removed and only hand edges are retained; \\ _CNN features on BRISK_–convolutional features on hand edges retained sign images that are infused with BRISK keypoints; \\
**2:** & Perform dimensionality reduction on convolutional-related features: \\ _PCA_–dimensionality reduction through orthogonal transformation; \\
**3:** & Apply random forest and XGBoost on hand coordinates and all PCA-applied \\
**4:** & Apply neural networks on the features: \\ _Hybrid ANN_–applied on convolutional features and CNN features on hand edges; \\ _CNN_–applied directly on NUS I and II datasets; \\ _ANN_–applied on hand coordinates extracted from ASL finger-spelling dataset for testing \\ leave-on-out-accuracy. \\ \hline
**Output:** & Predicted label of sign images. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Methodology for sign image classification.
###### Abstract
The MA method can also be used to obtain supersaturated solid solutions [45, 46, 47] and amorphous materials [48, 49, 50]. The use of MA in the Al-TiO\({}_{2}\) system contributes to the appearance of an energy stimulus to initiate a reduction reaction, which results in the formation of other hardening phases: Al\({}_{2}\)O\({}_{3}\) and Al\({}_{3}\)Ti [51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64]. In general, a large number of works deal with the Al-TiO\({}_{2}\) system. However, in the works that use the MA method, the microstructure and reduction reactions are mainly studied without attention to the mechanical properties of the prepared composites. Table 1 summarizes the data on the composition and mechanical properties of Al-TiO\({}_{2}\) system composites obtained by different methods. As can be seen from Table 1, the use of MA to obtain an Al-TiO\({}_{2}\) composite gives good results in strengthening the aluminum matrix. However, no studies have dealt with the effect of various additives on the microstructure and mechanical properties of composites or their stability as a result of thermal exposure. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Initial Composition** & **Preparation Method** & \begin{tabular}{c} **Final Phase** \\ **Composition** \\ \end{tabular} & **Hardness** & \
\begin{tabular}{c} **Strength** \\ **(Compressive/** \\ **Tensile/Bending**) \\ \end{tabular} & **Ref.** \\ \hline AA7075–(5–30) vol.\% TiO\({}_{2}\) & MA + sintering &
\begin{tabular}{c} Al, TiO\({}_{2}\), \\ MgTiO\({}_{3}\), ZnO \\ \end{tabular} & 100–168 HV & — & [18] \\ \hline Al–(3–12) wt.\% TiO\({}_{2}\) & ball milling + sintering & Al, TiO\({}_{2}\) & 86 HV (max.) & \(\sigma_{\text{UCS}}\) = 158 MPa (max.) & [19] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Composition, preparation methods, and mechanical properties of Al-TiO\({}_{2}\) composites obtained in other works. In our previous work [65], we studied the effect of copper and stearic acid additives on the features of phase and structural transformations in the Al-5 wt.% TiO\({}_{2}\) material as a result of heating during aluminum matrix melting. The aim of this work is to study for the first time the effect of these additives on the thermal stability of the microstructure and the mechanical properties of the mechanically alloyed Al-5 wt.% TiO\({}_{2}\) material in the solid state.
# Harnessing Digital Twins for Sustainable Agricultural Water Management: A Systematic Review
## 4 Results
### AI Integration  
Agricultural growth and productivity assessments, traditionally conducted through direct observation, can now be minimized by integrating AI. ML and DL serve as simulation models. Real data are input into the prediction model, which then produces a decision. In agriculture, once the model is properly trained, validated, and verified digitally, it can offer insights on factors like soil moisture, irrigation, fertilizer recommendations, and plant growth for real-world applications. In the context of decision-making regarding irrigation requirements, the integration of advanced machine learning techniques such as the Fuzzy Inference System offers a promising approach [104]. In our review, [PERSON] et al. [96] use Adaptive Neuro Fuzzy Inference System (ANFIS) with a cloud computing environment. With a comparative testing accuracy of 95.85%, the ANFIS-GA model demonstrates superior precision in predicting soil conditions. This is particularly striking when compared to the standalone ANFIS model, which achieves 91.77% accuracy, alongside the Fuzzy Inference System (FIS) (89.68%) and ANN (90.86%). Its application could significantly reduce resource consumption. In another study, [PERSON] et al. [93] use a fuzzy inference system for irrigation recommendation. The findings of their study show that the proposed system can effectively gather the necessary data to generate daily irrigation recommendations. [PERSON] et al. [95] introduce an automated system for plant irrigation within the greenhouse, using moisture data and their relationship with temperature to control irrigation. Among the various ML algorithms applied, including RF, SVM, and Adaptive Boosting, ANN demonstrated the highest accuracy, achieving 98%. In aquonics, AI can be used to predict the system variables like water quality parameters, plant growth, and fish growth. [PERSON] et al. [100] develop models using LR, SVR, and CART Decision trees, in addition the ensemble method of XGB boost with decision trees. The best model for predicting plant growth was a simple linear regression.
# Speech Enhancement for Hearing Aids with Deep Learning on Environmental Noises
## 3 Results
### Speech Enhancement
As shown by the average of ten noises outlined in Table 1, all objective measurements of the proposed enhancement algorithm's application (After) demonstrated improved speech quality over the noisy speech which did not apply the algorithm (Before). In particular, the improvement of speech quality using QQCM, which was the closest subjective measure, increased 45% in the 0 dB SNR, 45% in the 5 dB SNR, 41% in the 10 dB SNR, and 35% in the 15 dB SNR tests, respectively.
# Comparative Analysis of Current Control Techniques to Support Virtual Inertia Applications +
## 4 Simulation Results and Comparative Analysis
### Hysteresis Controller Simulation Results  
The output current of the grid-connected inverter when a hysteresis controller was used in the current control loop is shown in Figure 10a. Similar to the PR controller, in this case, the transition of current during the step change in current reference was smooth without any distortions. Figure 10b shows the change in the active power of the grid-connected inverter corresponding to the change in the output current. The response speed of the output power was quicker than both the PI and PR controllers. Moreover, the overshoot in the active power response was negligible. One disadvantage, however, is that there was increased ripple in both the active and reactive power output, which can be attributed as an effect of the variable switching frequency of this type of controller. Moreover, the ripple seemed to increase for higher values of reference currents.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 1 Introduction
In contrast, optical and SAR data can provide gridded AGB estimates over large areas, but are affected by well-known saturation problems that greatly affect the accuracy of estimated biomass. No single sensor on any satellite mission, whether optical, radar or LiDAR, can provide consistently infallible estimates of biomass, so a combination of these measurements is often used to overcome individual limitations [17]. Therefore, many scholars have integrated field measurements, LiDAR, optical and/or SAR data using advanced methods to generate maps of the spatial distribution of forest AGB on a regional scale [18; 19]. Currently, many regional and global AGB maps are available, and some of them have been even used for further analysis in the fields of climate change and ecology [20; 21; 22; 23]. However, published papers mainly review biomass estimation methodologies, focusing on the datasets used, modelling algorithms, uncertainties and other related issues [9; 24; 25; 26], and a systematic review of forest AGB maps is still lacking. While in this paper, we collected currently available regional and global forest biomass maps, and described their retrieval and limitations, to address recent progress and practical challenges of large-area biomass mapping. Forest biomass estimation or mapping is a complex procedure involving many factors, so we focused only on the major aspects of estimating forest AGB. The paper is organized as follows: Section 2 introduces the principles for using remote-sensing-derived parameters for AGB estimation; Section 3 describes the currently available regional and global forest biomass maps; Section 4 compares the available forest biomass maps over several regions; Section 5 includes the limitations of current forest AGB maps and possible directions to improve their accuracy; and Section 6 is a brief summary.
# Coupled Vibration Analysis of Multi-Span Continuous Cable Structure Considering Frictional Slip
## 4 Engineering Examples
### Analysis of Results
#### 4.3.4 Design Parameter Analysis  
In order to analyze the influence of the traveling speed and load of the crane on the vibration, this section takes \(\alpha=V_{\rm n}/V_{0}\) and \(\beta=G_{\rm n}/G_{0}\), where \(V_{\rm n}\) is the different speeds and \(V_{0}\) is the design speed. When \(\alpha=0\), there is a static effect. \(G_{\rm n}\) is the different loads, and \(G_{0}\) is the design load. When \(\beta=0\), only the crane is acting on the main cables. The top view of the relationship between the design parameters is shown in Figure 17, in which \(y(L/2t)\) indicates the vertical displacement in the span at different moments. \(y_{0}\) indicates the maximum vertical displacement in the span in the design state. \(F(L/2t)\) indicates the cable force in the span at different moments, and \(F_{0}\) indicates the maximum cable force in the span in the design state, as shown in Figure 18.  
From Figures 17 and 18, it can be seen that (1) the traveling speed of the crane is proportional to the vertical displacement in the span, but when the speed exceeds 2 times  
Figure 16: Vertical displacement–time–span 3D relationship of the main cable at design velocity: (**a**) plot of time–varying displacements of mid–span cables distributed along the span, (**b**) top view of time–varying displacements of mid–span cables, (**c**) plot of time–varying displacements of the left–span cords distributed along the span, (**d**) top view of the time–varying displacement of the left–span cable, (**e**) plot of time-varying displacements of the right–span cords distributed along the span, (**f**) top view of time–varying displacements of the right–span cable.  
Figure 17: Relative values of mid–span displacement and cable force versus time and velocity parameters: (**a**) top view of relative values of mid–span displacements, (**b**) top view of relative values of mid–span cable forces.  
the design speed, there is an obvious decreasing trend. The cable force in the span will have an obvious sharp increase when it exceeds the design speed, but there will also be an obvious decreasing trend when it exceeds 2 times the design speed. (2) The load capacity presents an obvious proportionality with the span displacement and cable force, and the vertical displacement will increase sharply when it exceeds 0.7 times the design load capacity. The cable force in the span will increase obviously and reach its peak moment much faster when it exceeds 1.5 times the design load capacity. When the load exceeds 0.7 times the design load, the vertical displacement will increase rapidly.
# Towards Feasible Capsule Network for Vision Tasks
## 4 Experiments
### Results
#### 4.3.2 Performance on Segmentation Task  
In this study, we aim to test the efficacy of the capsule head in solving segmentation tasks, specifically in the third setting. Our design is optimized to preserve the spatial size required for making predictions at each pixel, making it suitable for segmentation.  
Table 2 provides a comprehensive comparison between CapsHead (setting (3) with dynamic routing and no projection) and traditional CNN baselines (with one hidden layer) on the segmentation task for two distinct datasets: CT-Lung-Scan and VOC-2012. For the CT-Lung-Scan dataset, CapsHead demonstrates remarkable performance, achieving a Dice score of 97.59, surpassing the results of the CNN baselines. Specifically, the FCN model achieves a Dice score of 96.06, while Deeplab achieves 97.69. Similarly, on the VOC-2012 dataset, CapsHead showcases its proficiency by achieving a Dice score of 86.58. In comparison, the FCN baseline only manages a Dice score of 80.24, and Deeplab obtains 80.27. These findings further validate the efficacy of CapsHead in segmentation tasks and illustrate their ability to outperform traditional CNN models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Classifier** & **Settings** & **Accuracy** \\ \hline \multirow{4}{*}{CIFAR 10} & SVM & & 89.2 \\  & FC & 2 hidden layers & 89.1 \\  & FC & 1 layer & 87.97 \\  & CapsHead & Max-Min, setting (3) & **89.18** \\ \hline \multirow{4}{*}{CIFAR 100} & SVM & & 68.02 \\  & FC & 2 hidden layers & 66.82 \\ \cline{1-1}  & FC & 1 layer & 66.43 \\ \cline{1-1}  & CapsHead & Dynamic, setting (3) & **69.47** \\ \hline \hline \end{tabular}
\end{table}
Table 1: Linear evaluation performance of CapsHeads (dynamic routing with setting (3) and no projection) compared to baseline models on the classification task.
## 6 Conclusions  
This study investigates the effects of vibration frequency and vibration duration on the mechanical properties of Zhanjiang Formation structural clay, as well as the mechanisms of action, by conducting vibration disturbance tests on the frequency-duration relationship. The main conclusions are as follows:  
1. Both the disturbance factor \(RD_{q}\), defined by strength, and the disturbance factor \(RD_{s}\), defined by deformation, exhibit a linear increasing trend with the increase of vibration duration and vibration frequency. As vibration duration and vibration frequency increase, so does the degree of soil disturbance. These two parameters are important factors in the vibrational disturbance of soil.  
Figure 5: Changes in microstructure before and after disturbance: (**a**) Undisturbed sample; (**b**) Disturbed sample (\(RD_{q}\) = 0.42 and \(RD_{s}\) = 33.3%).  
Vibration duration and vibration frequency both disturb the structural integrity of clay. Through a two-factor, three-level orthogonal experiment assessing disturbance degrees, whether defined by strength as disturbance degree \(RD_{q}\) or by deformation as \(RD_{s}\), range analysis reveals \(R_{t}>R_{f}\), indicating a greater impact of vibration duration on disturbance compared to vibration frequency. Grey relational analysis indicates that the relational degrees of the vibration duration and vibration frequency factors are quite close, both significantly impacting disturbance, with the duration having a greater effect than frequency. The grey relational analysis results align with the patterns observed in the orthogonal experiment.
* 3. Zhanjiang Formation structural clay, characterized by its strong cementation, is not easily destroyed. However, external vibration can gradually weaken and even destroy the cementation bonds between soil particles. Moreover, vibration disturbance functions as a \"fatigue damage effect\", wherein prolonged exposure weakens the cemented bonds between soil particles due to \"cumulative\" energy, eventually causing fracture and destruction. Both frequent small-scale and large-scale vibrations can destroy the cemented bonds between soil particles, contingent upon sufficient duration.  
Writing-original draft, Writing--review and editing, methodology, Y.X.; writing--review and editing, supervision, B.T.; software, Y.S. and S.L.; data curation, J.H.; writing--original draft, B.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Guangxi Natural Science Foundation, China [Grant No. 2024 GXNSFAA010450].  
Not applicable.  
Data a contained within the article.  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.