esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Domain Communication Systems and Networks: A Tensor-Based Approach
## 5 Conclusions  
This paper presents a tensor-based perspective on multi-domain communication systems. Multi-domain communication systems can be considered as an evolution of MIMO where the presented tensor framework leads to the exploitation of not only the space domain through multiple antennas, but also joint use of various other domains. A generic system model where the input and output are represented using tensors, which are linked through a tensor multi-linear channel using the Einstein product, was presented. The proposed system model can be used to model many practical communication systems. As a demonstration of the use of our framework for multi-domain signal processing in communication systems, we considered the notion of TPRS which allows the introduction of controlled inter-domain interference for shaping the spectrum and cross-spectrum of multi-domain signals. Further tensor-based multi-linear MMSE detection, which utilizes the interference terms for joint data estimation across all the domains, was also presented. It was shown that for a fixed transmit tensor, better MSE performance is achieved with an increase in the number of receive domains. In addition, the tensor framework is best suited to unravel the latent trade-off that exists among domains. This was illustrated through an information theoretic analysis of the tensor channel. We presented numerical examples demonstrating the domain trade-off benefits in the capacity and mean square error performance. It was shown that the dimensions of the individual domains can be mutually adjusted while maintaining the same performance, depending on the tensor channel eigenvalues. It was also shown that a multi-domain transmit precoding can be easily devised using the tensor framework which can be seen as a beamforming operation across all the domains under both sum-power and per antenna power constraints. While in this work we focus on the physical layer, this framework can be extended to include also higher layers, providing a convenient basis for communication system cross-layer design and signal processing. In particular, the use of such tensor framework for multi-domain resource allocation can be of considerable interest.  
[PERSON] and [PERSON] contributed part of their graduate thesis research to this paper. [PERSON] initiated the NSERC supported project on tensors. Furthermore, he served as a mentor and supervisor for the graduate students involved in this project. All authors have read and agreed to the published version of the manuscript. This work was supported by grant RGPIN-2016-03647 entitled \"Tensor modulation for space-time-frequency communication systems\", awarded by Natural Sciences and Engineering Research Council of Canada (NSERC). The authors declare no conflict of interest.
# Meta-Learning for Mandarin-Tibetan Cross-Lingual Speech Synthesis
## 5 Experiments
### Experimental Setup  
In the experiment, we selected training corpus from the DataBaker and Tibetan_Lasa_1. First, two sets of 150 utterances were randomly selected from the DataBaker to serve as the Mandarin test set and Mandarin development set, respectively. Similarly, two sets of 150 utterances were randomly selected from the Tibetan_Lasa_1 to serve as the Tibetan test set and Tibetan development set, respectively. Then the remaining Mandarin utterances were marked as [M], while the remaining Tibetan utterances were marked as [T].  
We build the following four TTS models to verify the effectiveness of the proposed meta-learning-based Mandarin-Tibetan cross-lingual TTS.  
* seq2 seq-M or seq2 seq-T: Based on the original Tacotron2, we trained Mandarin and Tibetan monolingual TTS, called seq2 seq-T and seq2 seq-M, respectively.
* shared-MT: We call the shared encoder Mandarin-Tibetan (MT) cross-lingual TTS as shared-MT, which is trained on training subsets [M] and [T], and its architecture is shown in Figure 2.
* separate-MT: We call the separate encoder Mandarin-Tibetan cross-lingual TTS as separate-MT, which is trained on training subsets [M] and [T], and its architecture is shown in Figure 3.
* meta-MT: We call the meta-learning-based Mandarin-Tibetan cross-lingual TTS as meta-MT, which is trained on training subsets [M] and [T], and its architecture is shown in Figure 4.  
The Adam optimizer was used in the experiment to train the above TTS models. The learning rate stepped from \(10^{-3}\) and halved every 10k step. All models were trained until the loss of the development set started increasing. Two NVIDIA Tesla P4 GPUs were used in the experiment, and batch_size was set to 64. The dynamic network consists of fully connected layers. The input of the dynamic network is the 20-dimensional language embedding, and the size of each convolution kernel determines its output dimension. The bidirectional LSTM of the prosody generator consists of a two layers LSTM with 128 units. The other parameters of all models are similar to the original Tacotron2.
## 4 Results and Discussion
### Comparison Experiments with Advanced Baseline Models  
Table 2 shows the experimental results of the proposed model and all baselines on three datasets. Table 3 shows the training time of the proposed model and several baselines. Based on the experimental results, it can be seen that DHGAR can achieve the best experimental results on all the datasets. In addition, to directly demonstrate the forecasting performance of the proposed model, this section visualizes the forecasting results of the proposed model and several baselines. Figures 5-7 show the visualizations on all datasets. Based on Tables 2 and 3 and Figures 5-7, the following conclusions can be drawn:  
1. Compared to other deep learning-based models, classical models have the largest forecasting errors. The main reason is that classical models rely on stationarity assumptions, which limits their effectiveness in modeling non-stationary wind power sequences. In addition, it is difficult for these models to effectively model the implicit correlations between different variables. 2. Compared with other baselines, the spatiotemporal graph neural network can obtain more accurate forecasting results. Specifically, the spatiotemporal graph neural network uses adaptive graph learning to effectively mine the correlations between different variables. In this way, the model can effectively use other variables to help improve the accuracy of wind power prediction. 3. DHGAR achieves the most satisfactory forecasting accuracy on all datasets. The experimental results fully prove the application value of the proposed model in the field of wind power prediction. First, different from the traditional single-variable wind power time series prediction model, we introduce other variables to improve the prediction effect of the model. Then, we introduce the dynamic graph structure to construct the correlation between other variables and wind power, aiming to dynamically select other features to assist wind power prediction. Next, we propose heterogeneous graph attention to further explore the implicit correlations between heterogeneous variables. Finally, we replace all fully connected layers in the GRU with the proposed component to realize the mining of the wind power time series context. Through the above method, DHGAR can dynamically and deeply explore the spatiotemporal correlation between other variables and wind power, which ensures the DHGAR's effectiveness. Figure 5: Experimental results of different models for dataset #1.
# Kirchhoff Migration for Identifying Unknown Targets Surrounded by Random Scatterers
## 6 Conclusions  
In this paper, we have considered Kirchhoff migration techniques for locating small anomalies when they are surrounded by small random scatterers. In order to investigate the mathematical structure of imaging function and examine the imaging performance, we carefully established a relationship with Bessel functions of the first kind of order zero and one based on the asymptotic  
Figure 8: (Example 7) Imaging results via the subspace migration, MUSIC, direct sampling method, and factorization method for \(\lambda=0.3\) with \(N=128\).  
expansion formula in the presence of anomalies and random scatterers. Based on the established relationship, we showed that Kirchhoff migration can be regarded as an effective non-iterative technique for identifying the locations of small anomalies when the sizes and permittivities of the random scatterers are smaller than those of the anomalies and the applied frequency is high enough. However, when one of these conditions is not satisfied, the imaging results are somehow poor but Kirchhoff migration yields better results than various non-iterative techniques such as subspace migration, MUSIC, direct sampling method, etc. We have also presented simulation results for several different cases to validate these relationships and observed properties. Although we have discovered various properties of Kirchhoff migration, some phenomenon cannot yet be explained. Further development of mathematical theory for explaining such phenomenon will be a remarkable research topic. Here, we considered the two-dimensional problem, the analysis could be extended to the three-dimensional problem.  
**Author Contributions:** Formal analysis, C.Y.A., T.H. and W.-K.P.; Investigation, C.Y.A., T.H. and W.-K.P.; Writing--original draft, C.Y.A., T.H. and W.-K.P.; Writing--review & editing, C.Y.A., T.H. and W.-K.P.  
**Funding:** [PERSON] and [PERSON] was supported by National Institute for Mathematical Sciences (NIMS) grant funded by the Korean government (No. NIMS-B19610000) and [PERSON] was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2017R1D1A1A09000547).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 V2X System Architecture When Integrating Blockchain Technology and MEC
### System Model
#### 3.4.2 Computation Model  
In this subsection, the computation model is presented, where our system environment is composed of a set of vehicles \(\mathbb{A}\) that is connected t a set of RSUs \(\mathbb{B}\) via a wireless channel. Additionally, each vehicle \(i\) has an intensive task to be processed locally at the vehicle or preserved as a set of transactions and then offloaded to one of the available servers (i.e., edge or cloud) through the blockchain architecture. Consequently, the computation overhead in terms of time and energy for processing the vehicles' tasks, wherever locally or remotely, is discussed in the next subsections.  
The local execution approach takes into account that different vehicles may have different capabilities of computation. Moreover, each vehicle is to execute its task locally.  
Therefore, for each vehicle \(i\), the execution time and energy consumption for performing the computation tasks locally can be calculated accordingly, as follows:  
\[T_{i}^{l} =\frac{c_{i}}{f_{i}^{l}} \tag{3}\] \[E_{i}^{l} =\vartheta_{i}c_{i} \tag{4}\]  
where the computational capability of vehicle \(i\) in CPU cycles per second is indicated by \(f_{i}^{l}\) and the energy consumed per CPU cycle is represented by \(\vartheta_{i}\).  
Remote Execution Approach  
As part of the remote execution approach, the computation task for vehicle \(i\) is offloaded and executed on one of the connected servers \(j\).  
Therefore, for each vehicle \(i\), the execution time for performing the computation tasks remotely at the edge or cloud servers can be computed accordingly, as follows:  
\[T_{i}^{e}=T_{i}^{tr}+T_{i}^{e,ex} \tag{5}\]  
\[T_{i}^{c}=T_{i}^{tr}+\Delta+T_{i}^{e,ex} \tag{6}\]  
where the propagation delay for edge and cloud communication is indicated by \(\Delta\). Moreover, the transmission, edge and cloud execution are indicated by \(T_{i}^{tr},T_{i}^{e,ex}\) and \(T_{i}^{e,ex}\), respectively, which are expressed as  
\[T_{i}^{tr}=\frac{a_{i}}{r_{i,j}} \tag{7}\]  
\[T_{i}^{e,ex}=\frac{c_{i}}{f_{i}^{e}} \tag{8}\]  
\[T_{i}^{e,ex}=\frac{c_{i}}{f_{i}^{e}} \tag{9}\]  
where the computational capability allocated for each vehicle \(i\) at the RSUs and cloud are indicated by \(f_{i}^{e}\) and \(f_{i}^{c}\), respectively.  
Subsequently, for each vehicle \(i\), the energy consumption for transmitting the computation tasks remotely at the edge or cloud servers can be computed accordingly, as follows:  
\[E_{i}^{tr}=p_{i}T_{i}^{tr} \tag{10}\]  
In this study, the computational resources of each edge server are equal and assumed to be equally shared between all the connected vehicles.  
Finally, based on Equations (3), (4) and (8)-(10), the total time and energy for performing all the computation tasks of vehicle \(i\) can be respectively computed as  
\[T_{i}=\alpha_{i,0}T_{i}^{t}+\alpha_{i,N+1}T_{i}^{c}+\sum_{j=1}^{N}\alpha_{i,j }T_{i}^{e} \tag{11}\]  
\[E_{i}=\alpha_{i,0}E_{i}^{l}+\sum_{j=1}^{N+1}\alpha_{i,j}E_{i}^{tr} \tag{12}\]
# SGM-EMA: Speech Enhancement Method Score-Based Diffusion Model and EMA Mechanism
###### Abstract
[40] proposed a unified speech enhancement and editing model using conditional diffusion models to handle various tasks in a generative manner. Based on these developments, this paper adopts a score-based diffusion model, which consists of a forward process that gradually corrupts clean speech with noise and a reverse process that starts from the noisy input to iteratively estimate the original signal. However, diffusion-based methods may encounter challenges in generalizing to unfamiliar conditions. To address this problem, we adopt a score-based diffusion model integrated with the EMA mechanism. This approach effectively captures multi-level information and utilizes a U-Net structure for speech restoration. The main contributions of this study are as follows:  
* We pay special attention to the score-based diffusion model and the multi-resolution U-Net model and make corresponding modifications to the U-Net structure to better handle speech enhancement tasks. * We introduce an EMA mechanism that captures both multi-scale contextual information and local details, thereby enhancing the performance of speech enhancement. * To verify the effectiveness of our proposed method, we conducted experiments on the VB-DMD and TIMIT-TUT datasets. The results show that our SGM-EMA method performed well on the speech enhancement task. * We also conducted ablation studies to evaluate the contribution of the method in the predictor of the PC sampler and the EMA mechanism to the overall model performance.
###### Abstract  
Gravity survey is one of the passive geophysical techniques commonly used to delineate geological formations, especially in determining basement rock and the overlying deposit. Geologically, the study area is made up of thick quaternary alluvium deposited on top of the older basement rock. The Muda River basin constitutes, approximately, of more than 300 m of thick quaternary alluvium overlying the unknown basement rock type. Previous studies, including drilling and geo-electrical resistivity surveys, were conducted in the area but none of them managed to conclusively determine the basement rock type and depth precisely. Hence, a regional gravity survey was conducted to determine the thickness of the quaternary sediments prior to assessing the sustainability of the Muda River basin. Gravity readings were made at 347 gravity stations spaced at 3-5 km intervals using Scintrex CG-3 covering an area and a perimeter of 9000 km\({}^{2}\) and 730 km, respectively. The gravity data were then conventionally reduced for drift, free air, latitude, Bouguer, and terrain corrections. These data were then consequently analyzed to generate Bouguer, regional and total horizontal derivative (THD) anomaly maps for qualitative and quantitative interpretations. The Bouguer gravity anomaly map shows low gravity values in the north-eastern part of the study area interpreted as representing the Main Range granitic body, while relatively higher gravity values observed in the south-western part are interpreted as representing sedimentary rocks of Semanggol and Mahang formations. Patterns observed in the THD anomaly and Euler deconvolution maps closely resembled the presence of structural features such as fault lineaments dominantly trending along NW-SE and NE-SW like the trends of topographic lineaments in the study area. Based on power spectral analysis of the gravity data, the average depth of shallow body, representing alluvium, and deep body, representing underlying rock formations, are 0.5 km and 1.2 km, respectively. The thickness of Quaternary sediment and the depth of sedimentary formation can be more precisely estimated by other geophysical techniques such as the seismic reflection survey.  
gravity methods; power spectrum; Euler deconvolution; basement detection; Muda River basin +
Footnote †: journal: Physics Letters B  
0021
## 3 Results and Discussion
### The Influence of Sulfur and Development Time
#### 3.4.3 LAS Test  
During extended development, the SBS-modified asphalt faces the risk of aging and weakened fatigue resistance. Therefore, the LAS test is conducted to evaluate the samples' fatigue resistance. The results are shown in Figure 11. Without sulfur, the evolution trend of fatigue life against development time is inconsistent. When sulfur is added, the fatigue life of SBS-modified asphalt first increases and then decreases. The asphalt's fatigue life reaches its maximum after 8 h of development. It is also noted that when the development  
Figure 10: Comparison of MSCR test results of SBS-modified asphalt with or without sulfur.  
time exceeds 16 h, the asphalt's fatigue life is lower than its initial state (e.g., 4423 versus 4723 for 5% strain level).  
The stress-strain curve obtained from the LAS test is shown in Figure 11c. It shows that at 25 \({}^{\circ}\)C, SBS-modified asphalt with sulfur has lower peak stress and stiffness, which suggests it is less likely to fracture during fatigue loading. It shows that the chemical reac  
Figure 11: The effect of sulfur on SBS-modified asphalt LAS test. (**a**) 5% strain, (**b**) 15% strain, (**c**) amplitude scan.  
tion between sulfur and the SBS-modified asphalt can improve the strength and toughness of SBS-modified asphalt, thus prolonging the fatigue life [41].  
Based on previous performance testing, it can be concluded that without sulfur addition, the networks structure strength, high-temperature performance, and fatigue performance of SBS-modified asphalt are significantly weakened. Sulfur crosslinks the asphalt and SBS to form C-C and C-S bonds, effectively improving the asphalt's performance [42].  
However, during the development process of the sulfur-added SBS-modified asphalt, the SBS polymer swells during the high-temperature development process on the one hand, which improves the performance of the SBS-modified asphalt. On the other hand, the chemical bonds formed between sulfur and the SBS-modified asphalt are gradually destroyed due to the high temperature, resulting in impaired performance [43]. The performance of sulfur-added SBS-modified asphalt is affected by the coupling effect of these two aspects. It appears that SBS swelling dominates the first 6 h of high-temperature development. Afterwards, the degradation of the sulfurized polymer network prevails. As for the SBS-modified asphalt without sulfur, it faces less degradation during development, and thus its performance always shows an increasing trend.
## 2 Wireless Multimedia Sensor Networks
### Battery-Powered
#### 2.1.2 Energy-Efficient Communication  
Like traditional WSNs, data communication also accounts for the majority of energy consumption in WMSNs. In fact giving that there is significantly larger quantity of data to transmit, it is of great importance to have communication protocols designed to be highly energy-efficient. This applies to every communication layer in the protocol stack. For example, transport protocols can adjust the event reporting frequency according to the level of reliability achieved [38], routing protocols can use energy prediction technics and load balancing mechanisms to even energy distribution across the network [108; 109], and MAC protocols can accommodate dynamic duty cycle to conditionally put some nodes to sleep so as to avoid idle listening [40; 41]. Furthermore, a tradeoff between computation and communication power usage can also be derived to minimize overall energy consumption [110].
# Signal Detection Based on Separable CNN for OTFS Communication Systems
## 3 Problem Formulation
### Delay-Doppler Domain Channel Model  
The signal received after propagation through a time-varying channel is given by:  
\[r(t)=\iint h(\tau,\
u)\cdot s(t-\tau)\cdot e^{2\pi\
u(t-\tau)}\,d\tau\,dv+w(t), \tag{3}\]  
where \(h(\tau,\
u)\) is the DD-domain channel response and \(w(t)\) is zero-mean complex white Gaussian noise with power \(N_{0}\).  
In practice, the channel is modeled as a sparse sum of propagation paths:  
\[h(\tau,\
u)=\sum_{p=1}^{p}h_{p}\delta(\tau-\tau_{p})\delta(\
u-\
u_{p}), \tag{4}\]  
where \(h_{p}\), \(\tau_{p}\), and \(\
u_{p}\) are the gain, delay, and Doppler shift of the \(p\)-th path, respectively.
# Joint Deployment Optimization of Parallelized SFCs and BVNFs in Multi-Access Edge Computing
## 3 System Model and Problem Statement
### Problem Statement  
Now, we describe all constraints related to the deployment of P-SFCs and BVNFS. Constraint 1: Ensures that all sub-flows k of traffic flow \(r_{i}\) are processed by the ordered SFC. \[\sum_{k}\in r_{i}\sum_{VNF^{l}\in SFC_{i}}r_{i,j}^{l,k}=1,\forall VNF^{l}\in SFC _{i},\forall r_{i}\in R \tag{7}\]  
Constraint 2: Ensures that the number of sub-flows cannot exceed the maximum number of sub-flows for user request \(r_{i}\). That is, the number of all instances for \(VNF^{l}\) cannot exceed the maximum number. \[0\leq\sum_{V_{i}\in V}X_{i,j}^{l,k}\leq n_{i},\ \forall VNF^{l}\in SFC_{i}, \forall r_{i}\in R \tag{8}\]  
Figure 2: The operation to calculate reliability. The reliability of any single VNF instance is 0.8. All feasible sets are sequentially sorted by the ID of edge servers in each column while ensuring that the elements in each row are unchanged. The overall reliability \(r_{VNF^{l}}\) of \(VNF^{l}\) on \(SFC_{i}\) is the sum of the reliability of all feasible sets. Constraint 3: For each user request, if it can be successfully accepted, we should make sure the end-to-end delay will not exceed its delay requirement. \[max\big{\{}d(P(s_{i},d_{i},k))+d_{pro}(f_{i,k})\big{\}}\ \leq\ D_{i},\forall r_{i}\in R \tag{9}\]  
Constraint 4: For each user request, if it can be successfully accepted, we should make sure the reliability is no less than its reliability requirement. \[R_{SFC_{i}}\geq R_{i},\forall r_{i}\in R \tag{10}\]  
Constraint 5: To guarantee that the total resource preemption of each edge server \(V_{j}\) does not exceed the resource capacity \(C_{j}\), we have:  
\[\sum_{r_{i}\in R}\sum_{k\ \in r_{i}}\sum_{VNF^{l}\in SFC_{i}}X_{i,j}^{l,k}x_{i,j}^ {l,k}+Z_{i,j}^{l}z_{i,j}^{l}\leq C_{j},\forall V_{j}\in\ V \tag{11}\]  
We use \(x_{i}=1\) to represent the fact that the user request \(r_{i}\) is successfully accepted.