esa-sceva/satcom-chunk-collection huggingface dataset:
# An Investigation into Audio-Visual Speech Recognition under a Realistic Home-TV Scenario
## 2 End-to-End AVSR Framework  
In this paper, we employ an advanced encoder-decoder based end-to-end AVSR framework as the baseline system, which is presented in Figure 1. In the encoder part, we utilize two extractors for audio and video modalities, respectively, and then splice the extracted features as the input of the conformer [38] to model the context dependencies within the inputs. In the decoder part, we employ the transformer [39] to generate the posterior probabilities of the character sequence. 'Fusion-cat' means directly splicing the features of audio and video.  
Moreover, we utilize the multi-task learning framework to improve the robustness of the system, as in [40]. The overall multi-task learning (MTL) loss function is defined by the following equations:  
\[\mathcal{L}_{\text{CTC}}=-\ln p_{e}(z^{*}\mid\mathbf{X}) \tag{1}\]  
\[\mathcal{L}_{\text{Attention}}\ =-\ln p_{d}(z^{*}\mid\mathbf{X})=-\sum_{u}\ln p_{d} \big{(}z^{*}_{u}\mid\mathbf{X},z^{*}_{1:u-1}\big{)} \tag{2}\]  
\[\mathcal{L}_{\text{MTL}}\ =\alpha\times\mathcal{L}_{\text{Attention}}\ +\beta\times\mathcal{L}_{\text{CTC}} \tag{3}\]  
where \(z^{*}\) denotes the ground truth character sequence. \(\mathbf{X}\) means the input features. \(p_{e}\big{(}z^{*}\mid\mathbf{X}\big{)}\) and \(p_{d}\big{(}z^{*}_{u}\mid\mathbf{X},z^{*}_{1:u-1}\big{)}\) are the probability distributions estimated from the shared encoder and attention decoder, respectively. \(u\) is the index of ground truth characters. \(z^{*}_{1:u-1}\) is the label of the previous \(u-1\) characters.  
A compact E2E audio-visual framework can efficiently learn multi-modal information. However, direct training procedures do not guarantee good results, especially when the amount of training data is small. Various problems will occur, such as overfitting, non-convergence, and even training failure. Hence, pre-training becomes the most popular method to alleviate the training difficulty.
# Experimental Study on a Prediction Model of the Shrinkage and Creep of Recycled Aggregate Concrete
## 2 Shrinkage and Creep Test of Recycled Aggregate Concrete
### Mix Proportion Design  
According to JGj55-2011 [26] \"Ordinary concrete mix design rules\", the mixing ratio of natural aggregate concrete (NAC) was calculated. The mixing ratio of RAC was replaced by equal mass. The natural coarse aggregate in ordinary concrete was replaced by RCA. Considering that the water absorption of the RCA is higher than that of the natural coarse aggregate, a certain amount of additional water was added, which was 3.83% of the mass of the RCA. The concrete slump of each component during mixing is about 90 mm. The proportions of each group and the compressive strength of the concrete cubes are shown in Table 2.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**Aggregate Type** & **Apparent Density/kg-m\({}^{-3}\)** & **Crushing Index/\%** & **Moisture Content/\%** & **Water Absorption Rate/\%** & **Initial Store/\%** & **Secondage/\%** & **Mortage/\%** & **Imparity/\%** \\ \hline Recycled coarse aggregate & 2458 & 17.0 & 1.33 & 3.83 & 29.5 & 51.2 & 16.0 & 3.3 \\ \hline Natural coarse aggregate & 2658 & 10.6 & 4.21 & 0.69 & 98.7 & — & 1.3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Physical properties of recycled coarse aggregate (RCA) and natural coarse aggregate (NCA).
# Self-Sovereign Identity-Based E-Portfolio Ecosystem
## 2 Background and Related Work
### Hyperledger Fabric (HLF)  
Hyperledger Fabric [16] is a blockchain framework tailored for enterprise applications. It operates as a permissioned blockchain, restricting access to authorized participants--a departure from public blockchains like Bitcoin [2], in which anyone can participate.  
Hyperledger Fabric boasts a modular architecture as one of its key features. This allows enterprises to customize the framework to fit their specific needs, making it more flexible to use. For example, Hyperledger Fabric provides functional components like Fabric CA (Certificate Authority), which is used for identity management, and channel and offline sign, which provides privacy and security features.
# EEG Emotion Classification Based on Graph Convolutional Network
## 3 Method
### Regional Feature Learning Block  
Many studies have argued that individual emotions can be related to activity in multiple brain regions and a single region can be associated with the formulation of multiple emotions [12; 36; 37; 38]. It is therefore vital to investigate the function that each region plays to determine how the activity of each region helps to recognize different emotions. In this study, we intend to explore the emotional functionality of brain regions by gathering channels within the same region and then feeding them into the corresponding region encoder.  
Based on the spatial location of channels, we divide EEG channels into \(n\) different regions \(R_{1},R_{2},\cdots,R_{n}\), where \(R_{i}\) is defined as a set of channels grouped together, i.e., \(R_{i}=\{k_{1},k_{2},\cdots,k_{m}\}\), and \(k_{j}\) refers to the channel index. The regional input \(X_{R_{i}}\in\mathbb{R}^{m\times T_{i}}\) consists of the signals of channels in the same region:  
\[X_{R_{i}}=\{x_{k_{j}}|x_{k_{j}}\in\mathbb{R}^{1\times T_{i}},k_{j}\in R_{i}\}, \tag{3}\]  
where \(x_{k_{i}}\) is the signal of channel \(k_{i}\). Firstly, we apply a multi-layer perceptron (MLP) to process the raw input signal \(X_{R_{i}}\in\mathbb{R}^{m\times T_{i}}\):  
\[X^{\prime}_{R_{i}}=MLP(X_{R_{i}}). \tag{4}\]  
The processed data \(X^{\prime}_{R_{i}}\in\mathbb{R}^{m\times F_{in}}\) will be taken as the input to be fed into the region encoder \(f_{i}\):  
\[F_{R_{i}}=f_{i}(X^{\prime}_{R_{i}}),\forall i\in\{1,2,\cdots,n\}. \tag{5}\]  
The structure of region encoder \(f_{i}\) is displayed in the third section in Figure 2. We employ the graph-based network by mapping channels into nodes. The benefit of this approach is its ability to model the underlying patterns of each region since it provides a natural way to explore the relationships between channels, rather than focusing on the integration of individual channel features. In the graph, all nodes are fully connected to each other at the beginning due to the observation that channels in the same region tend to have similar activities. We adopt a graph convolutional network (GCN) to propagate the information among nodes. Finally, the derived node features are flattened and passed into fully connected layers to produce the output \(F_{R_{i}}\). The detail of the GCN is described in Section 3.3.  
Figure 2: Different types of region encoders.
## 4 Results and Discussion
### SEM Analysis and Effects of Glass Substitution on Mechanical Properties  
It is shown that the replacement of glass sand for normal riverbed sands generally provided acceptable mechanical properties for up to 30% substitution. This is generally attributed to the physical factors associated with the geometrical effects arising from the smaller particle size and the faceted morphology of the glass sands. The interaction of glass sands with the matrix was followed by scanning electron microscopy. A typical microstructure observed for glass particles in mortar is shown in Figure 17. The acicular and sharp edges found in glass particles are potential sources of stress concentration that could cause a premature failure and weakness. For small volume proportion, the contributions from such stress concentrators was not enough to weaken the concrete; hence, acceptable strength was observed for substitutions less than 30%. However, as the volume proportion of glass sands exceed 30%, the lower strength observed could be attributed to increased stress concentrating effects of sharp cornered CWG sand substitutes where the advantage from the increased specific surface is nullified.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mixture** & **28 Days** \\ \hline
0\% & 20 \\
10\% & 23 \\
15\% & 22 \\
30\% & 20 \\
50\% & 23.3 \\
100\% & 24 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of MoE in GPa.  
Figure 16: Representative stress-strain data from video extensometer for 15% glass substation, indicating modulus of elasticity (MoE) to be 22.3 GPa.  
The formation of ASR gel has been generally implicated with the use of soda lime glass in cement mix. Evidence exists in the present that shows the formation of ASR around the sharp edges of glass particles. Normally, it has been proposed that ASR by siliceous particles in cement binder is particle size-dependent and is prone to forming around sharp corners and cracks that have high specific surface. The EDS around glass particles showed compositions with a CaO/SiO\({}_{2}\) ratio consistent with ASR gel compositions [48]. However, the fine glass particles with an average diameter less than 100 \(\upmu\)m are known to exhibit pozzolanic activity that would normally preclude the formation of ASR gel.  
Evidence exists along some embedded glass particles where cracks were found at the boundary of the glass mortar matrix, as shown in Figure 18. Chemical probe by EDS indicated an Na-Ca-Si ratio suggestive of ASR gel. It is known that the Na/Si ratio determines the hydrophilic nature of ASR gels and their potential to cause cracking. It is no surprise that the Na/Si ratio would be higher in the vicinity of glass particles, and the potential for expansive cracks is high for high volume substitution.  
Figure 17: Glass-mortar interfacial zone with alkaline silica reaction (ASR) cracking. Accompanying EDS point probe indicate an Na-Si-Ca ratio consistent with compositions of ASR in lime glass.
## 4 Simulation Analysis of PER and EE for Three-Stage Incremental Cooperative Communication
### Per  
PER is plotted against distance to see the effect of various distances between the source and destination, \(r_{sd}\). For cooperative communication, the distances between the source and relay, \(r_{sr}\), and the relay and destination, \(r_{rd}\), are kept half of the distance between the source and destination. Figure 2a,b shows the PER for on-body LOS and NLOS, the direct and cooperative communication schemes. It is observed from Figure 2 that PER for direct communication is higher than the PER for cooperative communication. When the direct link is not reliable enough for efficient transmission, cooperative communication proves to be a better solution by providing redundant links for packet transmission. It is seen from the figures that path loss increases with the increase in distance. Therefore, for a larger distance, direct communication has more PER. Thus, for the increased hop length between the source and destination, cooperative communication is useful. It is obvious from the plots that two-relay communication is better than single-relay communication; the same is the case for three-relay communication, which is better than single- and double-relay communication. When the first and second stage of relaying fails, relay \(R_{3}\) provides an extra redundant link to the destination and enhances network reliability. It is also shown that the PER of LOS communication is less than NLOS communication due to more path loss in NLOS communication. Therefore, LOS communication offers less PER for longer hop lengths between the source and destination.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameters** & **NLOS** & **LOS** \\ \hline \(d_{o}\) (cm) & 10 & 10 \\ PL(\(d_{o}\))(dB) & 48.4 & 35.2 \\ n & 5.9 & 3.11 \\ \(X_{\sigma}\) & 5 & 6.1 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Channel model parameters. NLOS, non-LOS.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Packet size & 500 bits \\ Overhead & 80 bits \\ ACK/NACK & 64 bits \\ Transmission power & \(-12\) dBm \\ Data rate & 2 Mbps \\ \(E_{TX_{slice}}\) & 50 nJ/bit \\ \(E_{RX_{slice}}\) & 50 nJ/bit \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation parameters. NACK, non-ACK.
# Seismic Reliability Assessment of Water Supply Systems Considering Critical Paths
## 3 Application and Results
### Application Results
#### 3.4.2. Pipe Replacement Cost  
Although seismic reliability can be greatly increased, the impact of increasing overall reliability decreases when the economic costs of doing so are high. Therefore, in this study, the cost of seismically reinforcing pipelines in accordance with the specifications of each critical path was calculated, and the results were evaluated in combination with the effect of increasing reliability. Table 4 shows the cost of making each critical path earthquake resistant. The cost of seismic reinforcement pipes was calculated using the regression equation in Figure 3. The cost calculation showed that the RES2 Pipeline, which consists of large-diameter, long pipes, costs the most, at an estimated USD 210,000. This is about 37.9% of the cost of making the entire pipeline system earthquake resistant, which means that a higher cost versus the supply rate (35.9%) must be invested to render the RES2 Pipeline earthquake resistant.
## 2 Datasets, Feature Extraction, and Classification Methods  
Radar sensing uses an electromagnetic signal that hits an object. This signal might be directly reflected or scattered/absorbed by it, therefore giving an overview of the properties of the material the object is made of or of the distance or position the object is at. The type of radar whose data form the repository we used is a Frequency-Modulated Continuous-Wave (FMCW) radar, and this type of radar system has shown its potential to be used for detection/recognition purposes.  
Soli [18] is a mono-static, multi-channel (8 channels = 2 transmitters \(\times\) 4 receivers) radar device, operating in the 57-64 GHz range (center frequency of 60 GHz), using an FMCW principle, where the radar transmits and receives information on a continuous basis. When an object is placed on the top or nearby the Soli sensor, the energy transmitted from the sensor is absorbed and scattered by the object, which varies depending on its distance, thickness, shape, density, internal composition, and surface properties, commonly described as the Radar Cross Section (RCS). As a result, the signals that are reflected back represent rich information about the contributions from a range of surface and internal properties.  
Nevertheless, the complex mixing nature of the signals obtained by the radar sensor (due to these above-mentioned multiple factors that are contributing to the final, detected, and signal) make them have non-smooth and complex shapes, which contribute with an additional complexity degree to a potential signal classification strategy.  
The aim of Section 2 is to present a coherent explanation of the different feature extraction techniques and classification strategies applied on a series of datasets obtained by [PERSON] et al. [18], using the Soli radar sensor. It is composed of the following subsections: Section 2.1 gives a detailed overview about the different types of datasets used in our classification performance analysis framework. Section 2.2 explains the feature extraction methods that are applied. Feature selection methods are considered taking into account their number in relation to the number of instances of each dataset (the so-called curse of dimensionality). Section 2.3 describes the different classification strategies applied.
# An Efficient Routing Scheme Based on Node Attributes for Opportunistic Networks in Oceans
## 3 The Proposed Scheme: RSNA
### The Description of RSNA
#### 3.2.3 Utility-Based Forwarding  
Combining node attributes, the utility-based forwarding is described by  
\[\begin{split} U(l,m)&=P_{FC}(l)\cdot(FW_{l}\cdot m \cdot R_{l}-T_{l}^{m})^{-1}\\ U(r,m)&=P_{FC}(r)\cdot(FW_{r}\cdot m\cdot R_{r}-T_{r }^{m})^{-1^{\prime}}\end{split} \tag{8}\]  
where \(R_{l}\) and \(R_{r}\) are the resource values of forwarding messages for source/carrier node \(l\) and relay node \(r\). \(T_{l}^{m}\) and \(T_{r}^{m}\) denote the costs of transmitting message \(m\) of node \(l\) and node \(r\), respectively. Simultaneously, the utility metric needs to satisfy  
\[U(r,m)\geqslant U(l,m)+\delta, \tag{9}\]  
where \(\delta\) is threshold, the value of which can differ depending on network requirement and design goal. The utility metric is made by joining the delivery competency and forwarding willingness evaluation together to make decisions on the relay node selection. Here, we not only predict the intermediate nodes of the current node \(l\), but also whether the relay node \(r\) has the next hop. If link availability \(p_{la}(l,r)\) exists, it implies that the node \(l\) has intermediate nodes. Furthermore, the prediction of \(FW(r)\) is used to evaluate the device capacity of the relay node \(r\) and the existence of its further relay nodes. The predictions of further relay nodes effectively diagnose the unsuitability of the seemingly qualified nodes, thus avoiding the unnecessary route requests and waste of network resources.
# A Survey on Energy-Aware Security Mechanisms for the Internet of Things
## 4 Energy-Aware Security Mechanisms in IoT
### Energy-Security Trade-off in the IoT  
There is an intrinsic conflict between security and energy efficiency in resource-constrained IoT systems. Traditional security approaches usually require intensive computation and communication, including complex cryptographic algorithms, key management, and authentication processes, which conflict with the energy efficiency of IoT devices. High energy-consuming operations desire frequent battery replacements, which is a non-negligible factor for jeopardizing the battery life of IoT devices. As IoT devices are widely deployed, this issue will exacerbate the high energy consumption of IoT devices, harming network infrastructure. Therefore, taking into account factors of energy consumption and device lifetime becomes indispensable when designing sustainable yet secure IoT systems.
## Materials and Methods
### Data Analysis  
We first calculated the daily values of EC-based GPP, ET and ecosystem WUE, and then averaged to the eight-day values for analyzing the seasonal dynamics of each year. After revealing the environmental controls from biotic and abiotic factors at the two flux sites, this study aimed to establish a reliable method for tracking the grassland and cropland WUE patterns in near real-time. Meanwhile, performance of the indirect MODIS WUE estimates from GPP and ET products was also evaluated using the EC observations at eight-day time scale, and the main error sources were examined by comparison of both GPP and ET components. In total three years of data during 2013-2015 for each site (Arou and Daman) are obtained for analyses in this study, with 2/3 of these site-level data as the training set (2013 and 2014) and the remaining 1/3 as a validation set. All statistical analyses are performed using SPSS 19.0 (IBM, Chicago, IL, USA). Prediction performance is assessed using two widely-used indicators: the root-mean-square error (RMSE) and the coefficient of determination (\(R^{2}\)). The equations of \(R^{2}\) and RMSE are as follows:  
\[R^{2}=1-\sum{(\hat{y}-y)^{2}}/\sum{y^{2}} \tag{1}\]  
\[\text{RMSE}=\sqrt{\sum{(\hat{y}-y)^{2}}/n} \tag{2}\]  
where \(y\) and \(\hat{y}\) represent the observed and predicted values of GPP, ET, and ecosystem WUE, respectively; \(n\) is the total number of observations (eight-day periods).
# Interferometric DEM-Assisted High Precision Imaging Method for ArcSAR
## 3 Analyzing the Error Caused by ArcSAR Imaging on the Reference Plane
### The Error Model of ArcSAR Imaging on the Reference Plane
The maximum phase error acceptable for SAR image is \(\pi\)/4 [21]. If the phase error exceeds \(\pi\)/4, the SAR image will severely defocus (when the phase error is less than \(\pi\)/4, this error is too small for SAR imaging to be ignored). Therefore, the condition for severe defocusing of the ArcSAR imaging result on the reference plane is:  
\[ \begin{array}{c}\Delta p_{\max}\geq\pi/4\\ \frac{4\pi f_{\xi}}{c}\cdot\Delta R_{\max}\geq\pi/4\\ \Delta R_{\max}\geq\frac{\lambda}{16}\end{array}  \tag{16}\]  
where \(\lambda\) represents the wavelength.
## 4 Approach
### Extractors
#### 4.2.2 Extractor Model  
Our extractor model is created with a base model and three fine-tune layers. We used a YNN layer, start indices layer, and end indices layer.  
For our base model, we compared BERT (tiny, base, large) [18] along with RoBERTa [31], AlBERT [32], and distillBERT [33]. We implemented the same strategy as the original papers to fine-tune these models. We also used the same hyperparameters as the original papers: L is the number of transformer blocks (layers), H is the hidden size, and A is the number of self-attention heads.  
The YNN fine-tune layer takes the pooled output from the base BERT model and feeds it into a three-node dense layer with a Softmax activation to obtain the YNN answer. Furthermore, our model takes the sequence output from the base BERT model and feeds it into two sets of dense layers with sigmoid as activation. The first layer aims to find the start indices of the answer sequences, and the second layer aims to detect the end indices of the answer sequences. Figure 2 illustrates the extractor model architecture.  
Figure 2: Extractor model architecture.  
We define a training set data point as a four-tuple \((d,s,e,yn)\), where \(d\) is a document containing the answers, \(s,e\) are indices to the start and end of the text answer, and \(yn\) is the yes-no-none answer.  
The loss of our model is  
\[L=\log p(s,e,yn|d)\]  
\[L=\frac{1}{3}(\log ps(s|d)+\log pe(e|d)+\log pyn(yn|d))\]  
where each probability \(p\) is defined as  
\[ps(s|d)=\frac{1}{1+\exp(-f_{start}(s,d;\theta))}\]  
\[pe(e|d)=\frac{1}{1+\exp(-f_{end}(e,d;\theta))}\]  
\[pyn(yn|d)=\frac{\exp(-f_{yn}(yn,d;\theta))}{\sum yn^{\prime}\exp(-f_{yn}(yn^{ \prime},d;\theta))^{\prime}}\]  
where \(\theta\) is the base model parameters and \(f_{start}\), \(f_{end}\), \(f_{yn}\) represent three outputs from the last layer of the model.  
At inference, we pass through all text from each document and return all start and end indices with scores higher than a threshold (implementation details are presented in Section 5.2). We used F1 and exact match (EM) metrics to evaluate our extractor models.