esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Bearing Fault Diagnosis Method using Acoustic Emission Signals
### Creating and Processing Bearing Faults Spectrograms  
Optimizing STFT depends on (1) finding compatible window size; (2) overlap between segments (hop size), which affects the density in time; (3) zero-padding for FFT calculation; and (4) choosing a suitable signal segment size. Those STFT parameters can be determined by the trade-off relation between frequency resolution and time resolution. For applying the STFT to bearing fault signals efficiently, the STFT matrix is determined by a new routine with the MATLAB tool to achieve high accuracy and computational efficiency [15]. The segment duration of the signal is also considered based on the fault frequency range, which depends on the rotational speed range. Besides, the parameters are chosen to ensure that signal statistic features are as steady as possible. The window length (_when_) is set to 1024, and the hop size is set to _when_/4, as referred in various experiments [15]. Figure 2 shows our AE signal spectrograms at various rotational speeds (250, 300, 350, 400, 450, and 500 RPM) for three kinds of single faults (inner race faults, outer race faults, and roller faults). Similar results are obtained for compound faults (inner and outer raceway cracks (BCIO), outer and roller cracks (BCOR), inner and roller cracks (BCIR), inner-outer-roller cracks (BCIOR)) and normal condition (BNC).  
Figure 1: Proposed bearing fault diagnosis method using acoustic emission (AE) signals.  
Spectrogram image normalization:  
Normalization [16] is applied to change the range of pixel intensity values of created spectrogram images to make those images more suitable to analyze. Figure 3 shows the difference between spectrogram images before and after the normalization. Through the normalization process, a tensor image is calculated by the mean and standard deviation; the output of each image's channel is illustrated in Equation (1):  
\[Output[channel]=\frac{Input[channel]-mean[channel]}{std[channel]} \tag{1}\]  
where \(mean[channel]\) is the sequence of the mean for channels and \(std[channel]\) is the sequence of standard deviations for channels. After the normalization, spectrogram images are resized to \(224\times 224\) to fit the input layer size of the used CNN model. The image normalization is used as image pre-processing to yield better input data. Therefore, training time and the number of samples used for training the CNN model can be decreased through image normalization.  
Figure 3: Bearing fault spectrogram images: (**a**) before normalization; (**b**) after normalization.  
Figure 2: Spectrograms of bearing AE signals at six different shaft speeds: (**a**) inner race bearing faults; (**b**) outer race bearing faults; and (**c**) roller faults.
## 3 Results and Discussion
### Compressive Strength  
All compressive strength testing results for the hardened paste samples are present in Figure 2.  
In the case of no gypsum, the sample with RO reject water (i.e., sample RO) developed the greatest compressive strengths among mixtures throughout all ages up to 28 days. In particular, the use of RO reject water clearly enhanced the strength by 6.2 MPa at 1 day compared to the sample with purified water (i.e., sample C); however, after one day, the degrees of strength increase were similar between RO and C, without any further significant enhancement in RO up to 28 days. On the other hand, the use of seawater for CSA cement (i.e., sample SW) showed insignificant enhancements in strength until 7 days and even yielded a lower strength at 28 days than sample C.  
When gypsum was used, all samples (i.e., CG, SWG, and ROG) clearly showed higher compressive strengths than the samples without gypsum (i.e., C, SW, and RO) at all days. However, the distinct differences in strength, which were observed in the samples without gypsum when different mixing waters were used, nearly disappeared, and thus all mixtures showed similar strength developments; however, the use of RO reject water still showed slightly enhanced strengths, which entailed a 4.5-7.8% improvement over those of purified water at corresponding days of curing. Unlike the case of no gypsum, the use of seawater also produced a higher strength than that of purified water in the presence of gypsum at 28 days.
# Wireless Home Energy Management System with Smart Rule-Based Controller
## 2 Proposed HEM System  
The HEM system developed in this paper consists of three major modules, namely the appliance monitoring and controlling circuitries, room condition monitoring circuitry, and main scheduling terminal. These three modules are connected by a ZigBee wireless communication protocol through XBee microcontrollers that are installed in the modules. The XBee microcontrollers are configured to the API 2 communication mode, which allows for the transmission of data as packets. One of the major advantages of using such packets is that address information it can be integrated into them, enabling the main controller to identify the module sending and receiving any data. However, the data should be coded as string bytes at the transmitting end and decoded as float values at the receiving end to fit into a transmission packet. Figure 1 shows an illustration of the proposed HEM system. Further details on each module are provided in the following subsections.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 4 Role of Permissioned DLTs
### Election Permissioned DLT in the Front-End System  
The front-end system makes use of DL technology. This DL is integrated into the Selene protocol as a key element to obtain the necessary assurances ahead of the election, e.g., the first half of commitments to tracking numbers. It is also responsible for storing the election's verifiability evidence as it is generated, and it handles the mixing and decryption of the votes and tracker numbers at the end of the ballot. This is carried out in this manner to ensure that the commitments that were placed in the ledger before the election started do not change after the election. Using the ledger guarantees that the same verifiability information is seen by all observers, and that the verifiability information cannot be changed at a later point in time.  
In the front-end system, a permissioned DL is used. It is considered to be the appropriate design choice. This is because the DL is purpose-built for the election. In their current forms, EAs are necessary to manage the election roll. The need for the EA motivates the requirement for having trusted authorities. The EA's role is to start the election and to take responsibility for the election outcome. A benefit of the proposed system is that only the individuals responsible for conducting the election, who are already trusted, need to be granted write access to the ledger. Consequently, malicious nodes do not have the ability to take part in the DL's network. As a result, the security of the proposed system is increased. An additional advantage of permissioned DLs is that they permit faster and lighter consensus than the permissionless DLs. One side effect of the permissionless public-ledgers approach is that they give less control over consistency. This is because they support _eventual consensus_, which means that forks are possible and are eventually resolved. A good example of this is the branch rule of Bitcoin [31; 32].
## 4 Results and Discussion
### Gas Content after Extraction  
To determine the gas extraction effectiveness and whether the coal seam meets the standards for mining in China, some cross-measure boreholes were drilled to test the gas pressure and content of the coal seam after 9 months of extraction (including the drilling period). The government of China stipulates that a coal seam cannot be mined if the gas pressure exceeds 0.74 MPa or the gas content exceeds 8.0 m\({}^{3}\)/t. The average gas pressure after extraction in the W2708N working face was 0.31 MPa, which was 5.42 MPa lower than the original gas pressure. The average gas content was 6.44 m\({}^{3}\)/t, 12.97 m\({}^{3}\)/t lower than the original gas content. The gas pre-extraction rate was 66.8%, and the gas contents in the nontectonic region and the tectonic region of the W2708S working face were 6.61 m\({}^{3}\)/t and 10.52 m\({}^{3}\)/t, respectively, which did not meet the requirements of the standard. After 4 months of additional extraction, the gas content in the tectonic region was 7.13 m\({}^{3}\)/t (12.28 m\({}^{3}\)/t lower than the original gas content), and the gas pre-extraction rate was ultimately 63.3%. The average gas pressure after extraction was 0.51 MPa, which was 5.22 MPa lower than the original gas pressure.
# Building Information Modeling Impact on Cost Overrun Risk Factors and Interrelationships
## 3 Methodology
### Analyze Survey Data Using PLS-SEM
#### 3.3.3 Evaluate Outer Model  
For each model, the assessment of the outer model focuses on identifying and removing insignificant risk factor variation. Construct validity and discriminant validity were performed. Construct validity investigates indicators (risk factors) that positively contribute to their group. Magnitudes of Cronbach's alpha (\(\alpha\)), composite reliability (CR), and average variance extracted (AVE) were calculated for the three groups. Outer loading of risk factors influences the reliability coefficients of constructs. Values of \(\alpha\), CR, and AVE are associated with the outer loading of risk factors. The \(\alpha\) and CR can be estimated using Equations (2) and (3), respectively.  
\[\alpha=\bigg{(}\frac{M}{M-1}\bigg{)}\bigg{(}1-\frac{\sum_{i=1}^{M}s_{i}^{2}}{s _{t}^{2}}\bigg{)} \tag{2}\]  
\[CR=\frac{\Big{(}\sum_{i=1}^{M}l_{i}\Big{)}^{2}}{\Big{(}\sum_{i=1}^{M}l_{i} \Big{)}^{2}+\sum_{i=1}^{M}var(e_{i})} \tag{3}\]  
The variance of \(a\) risk factor is denoted by \(s_{i}^{2}\), while \(s_{t}^{2}\) signifies variance linked to observed total factors within a specific group. The standardized outer loading value of the \(i\)th risk factor in a group is represented by \(l_{i}\) M, which represents a number of factors within a group, and \(var(e\_i)\) indicates variance attributed to measurement errors.  
Additionally, the purpose of \(AVE\), which can be calculated utilizing Equation (4), is to evaluate the degree to which risk factors of a latent group share common variance and reflect underlying constructs. This provides information about convergent validity, which is the degree to which construct risk factors are related to each other and converge onto latent groups. It will be higher than the accepted level value (0.50) by omitting the risk factor with the lowest outer loading (\(l\)) value [34].  
\[AVE=\frac{\sum_{i=1}^{M}l^{2}}{M} \tag{4}\]  
[PERSON] et al. [35] summarized the construct validity steps followed in this paper;  
1. Choose a specific group (Financial, Experience, or Time).
2. Display outer loading values for factors within that group.
3. Remove any factors with an outer loading value below 0.4.
4. Evaluate the removal of factors with outer loading values between 0.4 and 0.7.
5. A factor is removed if elimination leads to enhancements in the group's \(\alpha\), CR, and AVE; otherwise, the factor is retained.
6. Repeat these steps for the remaining factors until Cronbach's \(\alpha\) and CR exceed 0.7 and AVE surpasses 0.5.  
Crucial metrics for assessing discriminant validity are cross-loadings and the Cornell-Larcker criterion. The Cornell-Larcker criterion can be determined if a group contributes higher variance with itself than other groups. To meet this requirement, \(AVE^{2}\) for each group must be greater than the covariance between that group and the other group [36]. According to the cross-loading method, a risk factor should load more with the corresponding group than any other group [36].
# Emotionally Controllable Talking Face Generation from an Arbitrary Emotional Portrait
## 3 Method
### Emotional Landmark Animation Generation Network
In addition, the decoder \(\psi_{\rm e}\) consisting of three-layer MLP accepts the fused feature \(F_{t}\) and reconstructs the emotional landmark displacement \(\Delta Q_{t}\) for each frame of the identity source. Lastly, \(\Delta Q_{t}\) is used to revise the lip-sync landmark sequence \(P_{t}\) to obtain the target portrait facial landmark sequence with emotional source expressions, \(Q_{t}\). To summarize, the module uses the following modifications to describe the output landmarks in sequential order:  
\[F_{a}=\phi_{c1}(L_{a};W_{c1}) \tag{5}\]  
\[F_{b}=\phi_{c2}(L_{b};W_{c2}) \tag{6}\]  
\[F_{t}=concat(F_{a},F_{b},S_{t}) \tag{7}\]  
\[\Delta Q_{t}=\psi_{\rm e}\ (F_{t};W_{e}) \tag{8}\]  
\[Q_{t}=P_{t}+\Delta Q_{t} \tag{9}\]  
where \(\{W_{c1},W_{c2},W_{e}\}\) are the learnable parameters of the network; the encoder \(\{\phi_{c1},\phi_{c2}\}\) internal hidden state vectors are all 256 in size; the decoder \(\psi_{\rm e}\) for the three layers is 512, 256, and 204 (68 \(\times\) 3) in size; \(\{L_{a},L_{b},F_{a},F_{b},S_{t},F_{t},\Delta Q_{t},P_{t},Q_{t}\}\) are consistent with those described above; and _concat_ denotes the horizontal connectivity. In the training phase, the overall loss is given by the sum of the three loss terms, \(\mathcal{L}_{L}\), \(\mathcal{L}_{D_{L}}\) and \(\mathcal{L}_{D_{T}}\):  
\[\mathcal{L}_{s}=\lambda_{1}\mathcal{L}_{L}+\lambda_{2}\mathcal{L}_{D_{L}}+ \lambda_{2}\mathcal{L}_{D_{T}} \tag{10}\]  
where \(\lambda_{1}=1\) and \(\lambda_{2}=0.001\) are set through hold-out validation. **Landmark Loss. **\(\mathcal{L}_{L}\) is defined as the distance between the minimized reference landmark \(\hat{Q}_{t}\) and the predicted landmark \(Q_{t}\), with the aim of promoting the correct placement of the emotional landmarks while preserving the target identity. \[\mathcal{L}_{L}=\sum_{t=1}^{T}\sum_{i=1}^{N}\left\|Q_{i,t}-\hat{Q}_{i,t} \right\|_{2}^{2} \tag{11}\]  
**Adversarial Loss. ** Generates facial landmarks with stable frame rates and rich facial expressions during the training process. To this end, we created two discriminator networks following GAN design ideas and inspired by [37]. The goal of discriminator \(D_{L}\) is to determine the authenticity of the predicted landmarks. To ensure the fluidity of the landmark sequence and to prevent the interval frame landmarks from shifting too much,we used the discriminator \(D_{T}\) to determine the similarity of the interval frame identities. \(\mathcal{L}_{D_{L}}\) and \(\mathcal{L}_{D_{T}}\) are then their loss functions:  
\[\mathcal{L}_{D_{L}}=\log D_{L}(\hat{Q}_{t})+\log(1-D_{L}(Q_{t})) \tag{12}\]  
\[S_{t}=Attn\Big{(}LSTM_{c^{\prime}}\big{(}Ec_{t\to t+\lambda};W^{\prime}_{lstm} \big{)},MLP_{s}\Big{(}Es;W_{mlp,s}\Big{)};W_{attn}\Big{)} \tag{4}\]0  
where \(\hat{Q}_{t-1}\) denotes the reference landmark of the previous frame
## 2 QSAR--A Brief Overview  
The roots of QSAR can be traced back about 100 years, when [PERSON] and [PERSON] made an important observation that the narcotic properties of anesthetizing gases and organic solvents correlated with their solubility in olive oil [27; 28; 29; 30]. A significant advancement in the development of QSAR was the introduction of the so-called Hammett constants, which represent the effects induced by substituents in organic molecules on the rate of chemical reactions [31]. The reaction equilibrium shifts due to different substituents. [PERSON] proposed a simple equation (Equation (1)) involving a substituent constant, \(\sigma\), specific to each substituent in the molecule, and a reaction constant, \(\rho\), dependent on the reaction type.  
\[\text{logK}=\text{logK}^{0}+\rho\times\sigma \tag{1}\]  
where logK\({}^{0}\) and logK are the dissociation constants of the unsubstituted and substituted benzoic acid, \(\sigma\) is the Hammet constant, and \(\rho\) is the reaction constant.  
QSAR formally began in the early 1960s with the works of [PERSON] and [PERSON] [32; 33] and [PERSON] and [PERSON] [34]. [PERSON] and [PERSON] extended [PERSON]'s equation by incorporating the electronic properties of substituents, as follows:  
\[\text{log1/C}=\text{b}_{0}+\text{b}_{1}\sigma+\text{b}_{2}\text{logP} \tag{2}\]  
where log1/C stands for the biological activity defined as the logarithm of the reciprocal of effective dose (C = ED) or inhibitory concentration (C = IC) that is needed to produce a certain biological effect, and P is the octane/water partition coefficient.  
The Free-Wilson method quantifies the observation that changing a substituent at one position of a molecule often has an effect independent of substituent changes at other positions. This effect is considered additive in nature [34].  
Briefly, QSAR modeling begins with a library of chemical compounds that are assayed for their biological activity using a suitable assay system. Chemical descriptors are then calculated for these compounds, and the resulting numerical data are correlated with biological activities using appropriate data analysis or machine learning algorithms. This process leads to the development of a QSAR model, as schematically presented in Figure 1.  
The generated model must be validated; if found valid, it can be used to interpret chemical effects involved in biological activity and/or to predict the biological activities of new compounds.  
QSAR modeling focuses on the chemical properties of a series of compounds. The chemical variation within this series defines a theoretical space known as chemical space. A compound's position in this space determines its biological activity. As a result, QSAR models typically consider a small subset of the entire chemical space, where model predictions for a specific set of compounds are considered reliable [35].
# Graph Dilated Network with Rejection Mechanism
## 5 Graph Dilated Network with Rejection Mechanism
### Training
#### 5.3.2 Overall Algorithm  
The overall algorithm of GraphDRej is summarized in Algorithm 1. First, in each iteration, we sample a batch of nodes from the training nodes in Line 4. At each layer \(k\), multiple graph dilated convolution kernels are applied to aggregate information from neighbors (Lines 8-9), and a rejection mechanism based combination is adopted to combine the information from the neighbors and the center node (Lines 10-11). Then, the node representation vector is updated by averaging the representation produced by different graph dilated convolution kernels (Line 13). We calculate the label prediction and the cross-entropy loss in Lines 16-17. After the forward propagation (Lines 4-17), backward propagation is carried out to update the parameters in Line 18. Finally, after the convergence, we take the last layer output as the embeddings of nodes in Line 20.  
```
Input: Graph \(G^{s}=(V^{s},E^{s})\);  Node features \(\{X_{v},\forall v\in V\}\);  The number of layers \(K\);  The dimension of the representation vector;  The number of the dilated convolution kernels \(T\);  The value of each \(\gamma\); Output: the node embedding \(h_{v},\forall v\in V\)
1 Set \(h_{v}^{(0)}=X_{v},\forall v\in V\);
2 Initialize the parameters of GraphDRej;
3 whilenot convergingdo
4 Sample source node batch \(B\) from the training nodes;
5 fork=1,...,Kdo
6 for\(v\in B\)do
7 for\(t=1,...,T\)do
8 //AGGREGATE;
9 Aggregate neighbors information by the graph dilated convolution kernel in the current \(\gamma\), i.e., calculating \(m_{v}^{(k,t)}\) according to Equation (10);
10 //COMBINE;
11 Combine neighbor information and the information of the center node, i.e., calculating \(h_{v}^{(k,t)}\) according to Equation (11);
12
13 end for
14 Update the representation of vector of node \(v\) in the \(k\)-th layer by averaging all the representation vectors produced by \(T\) graph dilated convolution kernels, i.e., calculating \(h_{v}^{(k)}\) according to Equation (12);
15
16 end for
17
18 Calculate the prediction label of each node in \(B\) according to Equation (13);
19 Calculate the loss function according to Equation (14);
20 Backward propagate and update parameters in GraphDRej;
21
22 end for Return \(h_{v}\gets h_{v}^{(K)},\forall v\in V\).
```  
**Algorithm 1**The Algorithm of GraphDRej.
## 2 Algorithm
### Localization Using Spring-Relaxation Technique
Recall the notations defined previously, \(\overrightarrow{V_{i}}\) is the location of \(Sensor_{i}\), where \(i\in\mathbb{S}\); \(\overrightarrow{V_{j}}\) is the location of \(Beacon_{j}\), where \(j\in\mathbb{B}\); and \(d_{i,j}\) is the estimated distance between \(Sensor_{i}\) and \(Beacon_{j}\) according to the measured signal strength. Define \(\overrightarrow{F_{i,j}}\) to be the force that the spring between \(Sensor_{i}\) and \(Beacon_{j}\) exerts on \(Sensor_{i}\). We show that  
\[\overrightarrow{F_{i,j}}=(d_{i,j}-\parallel\overrightarrow{V_{i}}- \overrightarrow{V_{j}}\parallel)\times u(\overrightarrow{V_{i}}- \overrightarrow{V_{j}})\]  
The scalar quantity \((d_{i,j}-\parallel\overrightarrow{V_{i}}-\overrightarrow{V_{j}}\parallel)\) is the displacement of the spring from natural length, which gives the magnitude of the force exerted by the spring between \(Sensor_{i}\) and \(Beacon_{j}\). The unit vector \(u(\overrightarrow{V_{i}}-\overrightarrow{V_{j}})\) gives the direction of the force on \(Sensor_{i}\). The spring constant is ignored. The net force on \(Sensor_{i}\), defined as \(\overrightarrow{F_{i}}\) is the vector sum of all forces  
\[\overrightarrow{F_{i}}=\sum_{j\in\mathbb{B}}\overrightarrow{F_{i,j}}\]  
To mimic the evolution of the spring network, our algorithm updates the locations of sensors in iterations. In each iteration, the algorithm moves \(Sensor_{i}\) a small distance in the direction of \(\overrightarrow{F_{i}}\) and then recomputes all the applied forces. Let \(\delta\) be the step size of location adjustment. Considering a linear relationship between the net force and the displacement, the location of \(Sensor_{i}\) is then updated as  
\[\overrightarrow{V_{i}}\leftarrow\overrightarrow{V_{i}}+(\delta\cdot \overrightarrow{F_{i}})\]