esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data and Methods
### MODIS Land Products Subsets
#### 2.1.1 MOD13 NDVI and EVI  
MOD13 is the 250 m, 16-day composite NDVI and EVI product, derived from MOD09 reflectances. Even though vegetation indices are a unitless metric of vegetation greenness, they are widely used as monitoring tools because of the simplicity of their formulas. NDVI is defined as:  
\[NDVI=\frac{\rho_{\textit{NIR}}-\rho_{\textit{red}}}{\rho_{\textit{NIR}}+\rho_ {\textit{red}}} \tag{1}\]  
where \(\rho_{\textit{NIR}}\) is near infrared reflectance (841-876 nm) and \(\rho_{\textit{red}}\) is red reflectance (620-670 nm).  
Among all vegetation indices, NDVI is the most widely used for monitoring and modeling vegetation dynamics [33, 34]. However, NDVI-based monitoring of vegetation dynamics suffers from several drawbacks: it saturates over dense vegetation, is frequently contaminated by clouds and aerosols, and is sensitive to soil background reflectance. EVI [20] was introduced as a partial solution to these limitations of NDVI, and is calculated as:  
\[EVI=G\frac{\rho_{\textit{NIR}}-\rho_{\textit{red}}}{\rho_{\textit{NIR}}+C_{ \textit{i}}\rho_{\textit{red}}-C_{\textit{2}}\rho_{\textit{NIR}}+L} \tag{2}\]  
\begin{table}
\begin{tabular}{l l c c c} \hline  & **Products** & **Spatial Resolution** & **Compositing Period** & **Citation** \\ \hline MOD13Q1 & NDVI and EVI & 250 m & 16 day & [PERSON] _et al._[20] \\ MOD15A2 & LAI and FPAR & 1 km & 8 day & [PERSON] _et al._[30] \\ MOD17A2 & GPP/NPP & 1 km & 8 day & [PERSON] _et al._[31] \\ \hline \end{tabular}
\end{table}
Table 1: Description of Moderate Resolution Imaging Spectroradiometer (MODIS) product subsets.  
EVI empirically corrects for aerosols with the reflectance in the blue band (\(\rho_{\rm blue}\), 459 to 479 nm) and adds L (1) as a background brightness correction factor. G (2.5) is the gain factor and C\({}_{1}\) (6) and C\({}_{2}\) (7.5) are band-specific atmospheric resistance correction coefficients.  
We used the MOD13 quality assurance fields to identify the presence of snow, in which case we set the values of both NDVI and EVI to zero.
# An Efficient Location Verification Scheme for Static Wireless Sensor Networks
## 4 The Proposed Scheme: Mutually-Shared Region-Based Location Verification
### Handling the Verification Failure of the Honest Sensors  
In this section, the purpose of D-filtering is described. If MSRLV does not perform D-filtering, the fake location report of any malicious sensor would cause an unintentional location verification failure of the honest sensor. When the malicious sensor who actually is located outside MSR between the honest verifier and claimant fakes its location at a position in the MSR, the verification for the honest claimant may not succeed. Figure 6 shows this case. In the example, assume that all of the sensors but \(z\) (\(z^{\prime}\)) are honest. When the verifier \(v\) tries to verify the location of the claimant \(c\), \(v\) and \(c\) require the information from the shaded sensors in their \(MSR_{vc}\), including black sensor \(z^{\prime}\) for the successful verification. However, the information from \(z^{\prime}\) cannot be acquired by \(c\) since the actual position of \(z^{\prime}\) (i.e., \(z\)) is beyond the communication range of \(c\). Thus, the information is not symmetric for \(v\) and \(c\), and verification will fail. This situation may happen frequently and eventually degrade the successful verification.  
Equipped with D-filtering, MSRLV can handle the case of the example in Figure 6. A sensor \(v\) will filter out the information from the sensor \(z^{\prime}\) since the distance it measures through the filtering is different from its estimated distance through the reported location of \(z^{\prime}\). Finally, the sensor \(v\) and sensor \(c\) will have the same _msrToken_ value with the help of D-filtering.  
From the perspective of the whole network, it is obvious that an attacker can influence the verification of the honest sensor if it passes D-filtering by conforming to the rule of the filtering. Those cases will be analyzed in Section 5.1.3.  
Figure 5: An example of detecting a malicious sensor with MSRLV.
## 4 Measurement Formation Process  
According to the description in Section 2, CDG schemes require a certain number of measurements of network sensed data, and these measurement results are generated and aggregated on measurement-formation paths (_i.e._, trees). As each measurement is a linear combination of multiple contributions, each of which is generated by multiplying the reading of an interested node and its corresponding measurement coefficient, an underlying question surfaces that how the measurement coefficients are generated and distributed. For the example proposed in Figure 2D, before responding to the requirement of the formation of the measurement \(y_{1}\), interested nodes #1, #3 and #9 should receive their coefficients first. One may think that the sink node can pack and broadcast each measurement coefficient vector \(\Phi_{i}\) to the network during each data gathering epoch, and every node in the network can directly acquire its measurement coefficient vector by monitoring these packets. However, for a WSN which has dozens of sensor nodes deployed in a large harsh field, such a process will incur a very heavy overhead of the time slice. What's worst, such broadcasting requires the synchronization among nodes in the network, thus it is impractical, especially for those WSNs applying sleeping mechanisms.  
Considering the tree-type and sink-rooted measurement-formation path which covers all interested nodes, we propose to generate the measurement coefficients during constructing these paths. We achieve this object by associating a pseudo-random generator, which is an algorithm publicly known by both the sink node and sensor nodes in the network. In each measurement-coefficient-generating procedure, the sink node first produces a global seed corresponding to this measurement (in fact,  
Figure 4: Main procedure of the proposed scheme.  
the sink node's running time would be a greater candidate for this global seed). Once the interested nodes in the network receive this global seed, it can generate its own measurement coefficient by adding the node ID. The sink node can generate a sparse coefficient vector \(\Phi_{i}\), where each element \(\Phi_{ij}\) is randomly generated by using the group seed: (global seed \(S_{i}\), node ID \(j\)). Thus, coefficients in \(\Phi_{i}\) obey an independent and identical distribution (i.i.d.) and the sink node can programme the measurement-formation path according to \(\Phi_{i}\). For the measurement \(y_{1}\) proposed in Figure 2D, such measurement-formation process is depicted in Figure 5, and two contribution packets can be merged on node #3. Now, there's still an issue remaining, _i.e._, how to energy efficiently build an optimal measurement-formation path which covers all interested nodes. In other words, how to ensure that all interested nodes in the network can receive the global seeds efficiently, and meanwhile how to minimize the total transmission cost. In the following sections, we would like to present our measurement-formation method.
## 4 Study Design
### Procedures  
Detailed ways in which MOCCA hosted participants of each group are as follows. During each question, the MOCCA robot presented four choices displayed around her head on the screen as shown in Figure 1. Each choice was intentionally placed at each corner side so that the participant would be able to notice the eye movements of MOCCA while reading through the four choices. In order to provide a cue revealing the answer to participants, the MOCCA robot made a quick, head-eye combined movement toward the right choice as shown in Figure 1.  
Participants in the OC group were asked to read through the four choices independently and select an answer at their leisure. We intended MOCCA to provide participants with a hint after they had read through all the choices, which we estimated would take two seconds after all choices first appeared on the screen. For the LC group, the MOCCA robot loosely controlled the participants' pace and attention by reading out the four choices.  
Once MOCCA completed this action, she instructed participants to answer on the count of three, and then provided a hint on the count of two. We expected the participants' visual attention to be directed to the MOCCA robot on the countdown by audibly counting to three.  
After the sessions ended, a researcher asked each participant to answer a brief post-survey on the likeability of MOCCA, the trust level of the social engagement, and the participant's awareness of each of the robotic cues. The survey questions are as follows:  
Q1: Do you like this robot? (Please answer using the 5-level Likert scale: 1: I hate it, 2: Dislike a little bit, 3: Neither dislike or like, 4: I like it a bit, or 5: I love it).  
Q2: Do you trust MOCCA? (1: I do not trust it at all, 2: I have a bit of distrust, 3: I neither distrust or trust it, 4: I trust it a bit, 5: I have total trust in it).  
Q3: Were you aware that MOCCA gave you hints during the quiz sessions? (1: I was not at all aware, 2: I was slightly aware, 3: I was somewhat aware, 4: I was moderately aware, 5: I was clearly aware).
## 3 Case Study
### Comparative Analysis of Factor Sets  
This paper mainly follows the method from reference [41], using cosine similarity to initially quantify the lag duration. Subsequently, to facilitate the establishment of a factor set for the MIMO model, the lag time is approximately set to 120 days. The traditional HST factor set and the recently proposed HT\({}_{\text{A}}\)T factor set (a lag of 180 days is chosen for comparison in this study) are selected for comparison with the proposed HT\({}_{\text{H}}\)T factor set in this paper, and the constructed factor sets are shown in Table 2. A total of 13 measurement points are selected to predict displacement under the single-output PSO-LSSVM model with different factor sets. The average value of the prediction performance of all the measurement points is taken as the evaluation index to compare the prediction accuracy under different factor sets. The results of the analyses are shown in Figure 9. In each area, a single representative measurement point is selected to plot the displacement prediction  
Figure 8: Correlation analysis of the selected dam section displacement.  
fitting curve. The prediction results are displayed in Figure 10. The analysis indicates that the proposed HT\({}_{\text{H}}\)T factor set demonstrates the highest prediction accuracy. Compared with the HST factor set, the average enhancement rate in the HT\({}_{\text{H}}\)T factor set of R\({}^{2}\), MAE, and RMSE has reached 0.60%, 25.59%, and 22.10%, respectively. In subsequent studies, the HT\({}_{\text{H}}\)T factor set, which considers the hysteresis effect of temperature on displacement, will be employed in the analysis.
## 4 The Modified MSRS Method
### Method 1: Making the Structural Displacement Vector  
This method is used to make the vector, \(\mathbf{X}_{a}\), including all the translational displacements of the structure, that is,  
\[\mathbf{X}_{a}=\biggl{\{}\begin{array}{c}\mathbf{X}\\ \mathbf{X}_{g}\end{array}\biggr{\}}, \tag{20}\]  
where \(\mathbf{X}_{a}\) is called the translational displacement vector. The transform vector, \(\mathbf{q}_{a}\), is also divided into two parts corresponding to \(\mathbf{X}_{a}\)  
\[\mathbf{q}_{a}=\biggl{\{}\begin{array}{c}\mathbf{q}\\ \mathbf{q}_{g}\end{array}\biggr{\}}, \tag{21}\]where \(\mathbf{q}\) and \(\mathbf{q}_{g}\) correspond to \(\mathbf{X}\) and \(\mathbf{X}_{g}\). Hence, when \(\mathbf{q}_{a}\) and \(\mathbf{X}_{a}\) replace \(\mathbf{q}\) and \(\mathbf{X}\) in Equation (11), the new expression of the structural response, \(z(t)\), can be written as  
\[z(t)=\mathbf{q}_{a}^{T}\mathbf{X}_{a}=\left\{\begin{array}{c}\mathbf{q}\\ \mathbf{q}_{g}\end{array}\right\}^{T}\left\{\begin{array}{c}\mathbf{X}^{s}+ \mathbf{X}^{d}\\ \mathbf{X}_{g}\end{array}\right\}=\mathbf{q}^{T}\Big{(}\mathbf{X}^{s}+ \mathbf{X}^{d}\Big{)}+\mathbf{q}_{g}^{T}\mathbf{X}_{g}. \tag{22}\]  
Then, Equation (23) can be yielded by substituting \(\mathbf{X}^{s}=\mathbf{R}\mathbf{X}_{g}\) and \(\mathbf{X}^{d}=\sum\limits_{j=1}^{n}\sum\limits_{k=1}^{m}\mathbf{\varphi}_{j} \beta_{k}\delta_{jk}(t)\) into Equation (22)  
\[z(t)=\sum\limits_{k=1}^{m}\Big{(}q_{gk}+a_{k}\Big{)}x_{gk}+\sum\limits_{k=1}^{ m}\sum\limits_{j=1}^{n}b_{jk}\delta_{jk}. \tag{23}\]  
If \(\Big{(}q_{gk}+a_{k}\Big{)}\) in Equation (23) is written as  
\[c_{k}=q_{gk}+a_{k}=q_{gk}+\mathbf{q}^{T}\mathbf{r}_{k}, \tag{24}\]  
Equation (22) can be simplified as Equation (25)  
\[z(t)=\sum\limits_{k=1}^{m}c_{k}x_{gk}+\sum\limits_{k=1}^{m}\sum\limits_{j=1}^{ n}b_{jk}\delta_{jk}, \tag{25}\]  
and Equation (12) can be simplified as Equation (26)  
\[\begin{array}{rl}\mu_{\text{i=max}}=&\left[\sum\limits_{k=1}^{m}\sum \limits_{l=1}^{m}c_{k}c_{l}\rho_{x_{gk}x_{gl}^{\prime}}\mu_{x_{gk,\text{max}}} \mu_{x_{gl,\text{max}}}+2\sum\limits_{k=1}^{m}\sum\limits_{l=1}^{m}\sum \limits_{j=1}^{n}c_{k}b_{lj}\rho_{x_{gk}x_{gl}^{\prime}}D_{l}\Big{(}\omega_{j},\xi_{j}\Big{)}\right.\\ &\left.\hskip 14.226378 pt+\sum\limits_{k=1}^{m}\sum\limits_{l=1}^{m}\sum \limits_{i=1}^{n}\sum\limits_{j=1}^{n}b_{lk}b_{lj}\rho_{x_{gk}x_{gj}^{\prime}} \mu_{x_{gk,\text{max}}}D_{k}(\omega_{i},\xi_{i})D_{l}\Big{(}\omega_{j},\xi_{j }\Big{)}\right]^{1/2}\end{array} \tag{26}\]  
It should be noted that Equation (26) includes the effect caused by the displacements at the structural support degrees of freedom, so that this method could be considered as the best one.
## 4 Results
### Self-Trained Analysis  
Starting from the findings reported in the previous section, we decided to carry out new experiments in which we carefully selected the training and test sets to evaluate different interoperable scenarios. We chose the same training sets of LivDet 2021 to compare the results directly and added the LivDet 2019 DigitalPersona dataset, keeping the same sensing technology of GreenBit and Dermalog scanners. We then merged these three datasets in different proportions while maintaining a regular training set size. Accordingly, we used half or one-third of the datasets when we combined two or three of them, respectively. This approach helps evaluate the impact of heterogeneous data on classification. Thus, we designed three different PADs: two hand-crafted, based on a linear SVM classifier trained with two of the most adopted textural algorithms at the state of the art, namely BSIF and LBP [7; 8]; and one based on the SpoofBuster algorithm [11]. We did not carry out any parameter optimization since our purpose was not to design the optimal PAD, but to enhance the improvement achievable when introducing samples coming from multiple scanners. As a first analysis, we investigated the PADs' accuracies at a decision threshold of 0.5 score (range [0,1]) (Tables 8 and 9). Figure 3 illustrates the accuracy analysis visually to make it easy to read. From these results, it can be seen that, except for some tests related to LBP, the use of training on different types of data benefits the performance of the PADs. Again, this benefit is nil in the case of the Orcanthus 2019 test, in which all our self-trained PADs fail to classify the images. This is because this dataset is the only cross-sensing one. In fact, its PAs were acquired with thermal swipe technology, while all the training sets are related to optical technology, albeit with different sensors.
## 4 Results and Analysis
### Time Execution Analysis  
The time spent running an algorithm is a crucial metric for gauging a cryptographic technique's effectiveness. The proposed procedure is put through its paces on the following hardware: The PC has a 1.60 GHz Intel(R) Core(TM) i5-8265U processor, 8 GB of RAM, and Windows 10. Computer simulations in MATLAB R2016a are used to carry out the encrypting and decrypting processes. It took 1.320452 s to encrypt the test image.
# An Optimal Resonant Frequency Band Feature Extraction Method Based on Empirical Wavelet Transform
###### Abstract  
The Empirical Wavelet Transform (EWT), which has a reliable mathematical derivation process and can adaptively decompose signals, has been widely used in mechanical applications, EEG, seismic detection and other fields. However, the EWT still faces the problem of how to optimally divide the Fourier spectrum during the application process. When there is noise interference in the analyzed signal, the parameterless scale-space histogram method will divide the spectrum into a variety of narrow bands, which will weaken or even fail to extract the fault modulation information. To accurately determine the optimal resonant demodulation frequency band, this paper proposes a method for applying Adaptive Average Spectral Negentropy (AASN) to EWT analysis (AEWT): Firstly, the spectrum is segmented by the parameterless clustering scale-space histogram method to obtain the corresponding empirical mode. Then, by comprehensively considering the Average Spectral Negentropy (ASN) index and correlation coefficient index on each mode, the correlation coefficient is used to adjust the ASN value of each mode, and the IMF with the highest value is used as the center frequency band of the fault information. Finally, a new resonant frequency band is reconstructed for the envelope demodulation analysis. The experimental results of different background noise intensities show that the proposed method can effectively detect the repetitive transients in the signal.  
empirical wavelet transform; scale-space histogram; spectral negentropy; correlation coefficient; rotating machinery +
Footnote †: journal:  
Article
# Lateral Pressure of Poured Concrete: Arguments from Ultra-Deep Diaphragm Wall (120 m) Construction
## 2 Materials and Methods
### Underground Diaphragm Wall Filling Test  
The project is located in Shanghai, and the underground diaphragm wall reaches a depth of 120 m, crossing 9 layers of soil from top to bottom. The foundation soil of the site comprises Quaternary loose sediments within the scope of the survey depth, mainly composed of saturated clay soil, silty soil and sandy soil. The groundwater depth is 0.5 m. The foundation was first excavated with a trench-milling machine; then, the wall was protected with mud, and after completion, a reinforcement cage was placed, and finally, concrete was casted instead of mud. The site conditions are shown in Figure 1.  
For the test, we used a fiber optic grating earth pressure meter (model MW12-BGK-4810) with a range of 5 MPa and a resolving power of 0.04% FS. The reinforcement cage was divided into three sections, each with a height of 40 m. In the actual installation process, premixed, quick-setting cement mortar or epoxy mortar was smoothed on the surface of the underground diaphragm wall; then, a manometer was pressed into the cement mortar so that the excess cement mortar was squeezed out. The manometer was held still until the cement mortar set, then mounted on the reinforcing cage by adding bolts and tightening or using steel nail side manometers with the sensing side of the side manometer facing inward. The sensor wires on each section of the cage were brought together at the end of the cage and extended along the outer side wall to the collection system as the cage was lifted into the tank.  
The lateral pressure was monitored in situ by constructing an ultra-deep underground diaphragm wall at a depth of 120 m and a trench with dimensions of 2.8 \(\times\) 1.2 m. Four fiber Bragg grating lateral pressure gauges were installed at \(-\)40 m, \(-\)80 m, \(-\)100 m and \(-\)120 m along the height direction. The diameter of its carrier plate was 230 mm, and its range was 5 MPa. The specific layout details are shown in Figure 2, where the black box represents the reinforcement cage, and the blue Y icon represents the side pressure gauge.  
The submerged concrete used in the present study has a compressive strength of 40 MPa after 28 days. The percentage of longitudinal reinforcement in this underground diaphragm wall is 0.7%. The casting of this underground diaphragm wall lasted 16 h, with 375 m\({}^{3}\) of concrete casted from the bottom up in 21 concrete mixer trucks, each containing 18 m\({}^{3}\) of concrete, with an average casting speed of 16.846 m/h. The peak casting speed of one truck reached 32.148 m/h if the casting was easy.
# Gaze Estimation via Strip Pooling and Multi-Criss-Cross Attention Networks
## 3 Proposed SPMCCA-Net Models
### Robust Multi-Loss Combination Function  
The multi-loss combination function incorporates cross-entropy loss and mean-squared error using each gaze direction. It can provide more supervised information than a single cross-entropy loss or mean-squared error, and these two quantities are coordinated with one another, improving training efficiency and performance. The multi-loss combination function is typically expressed as:  
\[CLS(y,p)=H(y,p)+\beta\cdot MSE(y,p), \tag{7}\]  
where \(H(\cdot,\cdot)\) denotes cross-entropy loss, \(MSE(\cdot,\cdot)\) denotes mean-squared error, \(p\) denotes predicted values, \(y\) denotes ground-truth values, and \(\beta\) is the regression coefficient. We found better gaze estimation by changing the value of \(\beta\).  
We also found that the mean-squared error in the multi-loss combination function results in unstable and slow convergence during training, which makes the model unable to produce optimal outcomes. This occurs primarily because the inputs of the predicted and labeled values in the regression loss are respectively represented by the predicted angle values (obtained from calculating expectation values from the softmax probability distribution) and the actual angle value data, not the normalized data. For this reason, there is a large difference between predicted and labeled values, which leads to instability and slow convergence when adopting mean-squared error as the regression loss. We therefore adopted a smooth L1 loss, enabling the network to change the size of gradient values adaptively during the backpropagation process, which in turn enhances stability and convergence speed during training.  
The smooth L1 loss is defined as:  
\[SLL(y,p)=\frac{1}{n}{\sum_{i=1}^{n}}\begin{cases}0.5\times(y_{i}-p_{i})^{2},&if |y_{i}-p_{i}|<1\\ |y_{i}-p_{i}|-0.5,&otherwise\end{cases}. \tag{8}\]  
Smooth L1 loss can limit the gradient in two ways: when the difference between the predicted value and the ground-truth value is too great, the gradient value will not be too large, and when the predicted value is less different from the ground-truth value, the gradient value can be sufficiently small. As a result, smooth L1 loss is more resistant to outliers, and the gradient value can be adjusted to reduce the likelihood that training will fail, somewhat increasing the precision of gaze estimation. The more robust multi-loss combination function is expressed as:  
\[CSLS(y,p)=H(y,p)+\beta\cdot SLL(y,p). \tag{9}\]  
Figure 7 illustrates the framework underlying multi-loss combination.