esa-sceva/satcom-chunk-collection huggingface dataset:
# Using Remote Sensing Techniques to Improve Hydrological Predictions in a Rapidly Changing World
## 2 Contributed Papers  
### Estimating Evapotranspiration  
In this category, two papers are published. [PERSON] and [PERSON] et al. [5] estimated the Evapotranspiration (ET) of Beijing city at a 10-m resolution, using vegetation information from Sentinel-2 satellite data. For the first time, monthly ET at a 10 \(\times\) 10-m resolution for the Beijing Sponge City was estimated, using a water-carbon coupling model (PML-V2) driven by the 10-m resolution of the Sentinel-2-based Leaf Area Index (LAI), Sentinel-2-based land cover classification data, and surface meteorological data. Model validations show that the Sentinel-2-based LAI has a good correlation (R\({}^{2}\) = 0.74) with the observed values. The PML-V2-estimated ET compares well (R\({}^{2}\) = 0.64-0.90) with the flux measurements at three fields within the Beijing region. Model simulation results show that LAI, ET, and the Gross Primary Productivity (GPP) are 0.83 m\({}^{2}\) m\({}^{-2}\), 1.6 mm d\({}^{-1}\), and 2.8 gC m\({}^{-2}\) d\({}^{-1}\), respectively, during June 2018. Water bodies, including lakes and rivers, show the highest ET of \(>\)8 mm d\({}^{-1}\), followed by mixed forest and croplands that have ET varying in 4-6 mm d\({}^{-1}\) and grasslands with ET varying in 2-4 mm d\({}^{-1}\). Compared to these land cover types, the impervious surface occupying \(-\)60% of the sponge city areas shows the smallest ET of \(<\)2.0 mm d\({}^{-1}\). This study demonstrates that it is feasible to use the high-resolution satellite data to have detailed simulations of hydrological processes in urban ecosystems. [PERSON] et al. [6] analyzed the suitability of remotely sensed ET for calibrating a hydrological model in the upper Kings River watershed (3999 km\({}^{2}\)) of California's Sierra Nevada, a snow-influenced watershed in a Mediterranean climate. They compared a spatiotemporal pattern of ET from a remote-sensing product, MODIS MOD16A2, to that from a hydrological model (SWAT) calibrated against an observed streamflow. The ET estimates from both MOD16A2 and SWAT modelling were evaluated against observations from three flux towers at elevations of 1160-2700 m. It was found that the SWAT-modelled ET performs better than MOD16A2 ET, indicated by the Nash-Sutcliffe efficiency (+0.36 versus \(-\)0.43) and error in the elevational trend (+7.7% versus +81%). For this particular modelling experiment, the authors concluded that it is challenging for the remotely sensed ET product used for watershed-model parameter estimation. By analyzing ET-weather relationships, the authors found that the relatively large errors in MODIS ET may be related to weather-based corrections to water limitation not representative of the hydrology of this snow-influenced, Mediterranean-climate area. Therefore, attention should be paid when using global ET products (such as MOD16A2) at a watershed scale, particularly when the watershed involves snowmelt processes. Moreover, it is necessary to have bias corrections of the ET data before use for model parameterization [7].
# A Review of Acoustic Emission Source Localization Techniques in Different Dimensions
## 4 Acoustic Emission Source Localization Techniques for Three-Dimensional (3D) Structures
### Point Location Based on Arrival Time Approach
#### 4.5.2. Inglada's Method  
This method is most suitable for simple source location problems, where the minimum number of sensors used can be assumed to be a constant velocity model [99].  
The algorithm is as follows:  
\[(x_{i}-x)^{2}+(y_{i}-y)^{2}+(z_{i}-z)^{2}=v^{2}(t_{i}-t)^{2} \tag{4}\]  
for i = 1, 2, 3 and 4, and then the system is linearized by subtracting the first equation from the three remaining ones, which yields:  
\[a_{i}x+b_{i}y+c_{i}z=e_{i}+f_{i}t \tag{5}\]  
where  
\[\begin{array}{c}a_{i}=x_{i}-x_{1}\\ b_{i}=y_{i}-y_{1}\\ c_{i}=z_{i}-z_{1}\\ e_{i}=0.5({R_{i}}^{2}-{R_{1}}^{2}-v^{2}({t_{i}}^{2}-{t_{1}}^{2}))\\ f_{i}=v^{2}(t_{i}-t_{1})\end{array}\]  
And  
\[{R_{i}}^{2}=x_{i}^{2}+{y_{i}}^{2}+{z_{i}}^{2}\]  
\[{R_{1}}^{2}={x_{1}}^{2}+{y_{1}}^{2}+{z_{1}}^{2}\]  
for \(i\) = 2, 3 and 4.  
Three linear equations defined in (5) can be solved for \(x,\;y\) and \(z\) in terms of t by using Cramer's rule. The solution is as follows:  
\[x=\frac{M_{1}+N_{1}t}{D},\;y=\frac{M_{2}+N_{2}t}{D},\;z=\frac{M_{3}+N_{3}t}{D} \tag{6}\]  
where D is the determinant given by  
\[D=\begin{bmatrix}a_{2}&b_{2}&c_{2}\\ a_{3}&b_{3}&c_{3}\\ a_{4}&b_{4}&c_{4}\end{bmatrix}\]  
\(M_{j}\) and \(N_{j}\), for j = 1, 2, 3, are also the determinants. They are similar to D, but with the jth column of D replaced by \((e_{2},\;e_{3},\;e_{4})\) and \((f_{2},\;f_{3},\;f_{4})\), respectively.  
Substitute three equations defined in (6) back to one of the equations defined in (4). Since x, y and z in (6) are all expressed in terms of t, this back substitution yields a second order equation of t with the form  
\[At^{2}+Bt+C=0 \tag{7}\]  
where A, B and C are constants.  
Equation (7) is a quadratic function of t and there are two solutions, say, \(t_{01}\) and \(t_{02}\). Substituting \(t_{01}\) and \(t_{02}\) into Equation (6), the corresponding solutions are \((x_{01},\;y_{01},\;z_{01},\;t_{01})\) and \((x_{02},\;y_{02},\;z_{02},\;t_{02})\), respectively.
###### Abstract  
Water samples were collected and analyzed for conductivity, pH, temperature and trihalomethanes (THMs) during the fall of 2014 at two monitored municipal drinking water source ponds. Both spot (or grab) and time weighted average (TWA) sampling methods were assessed over the same two day sampling time period. For spot sampling, replicate samples were taken at each site and analyzed within 12 h of sampling by both Headspace (HS)- and direct (DI)- solid phase microextraction (SPME) sampling/extraction methods followed by Gas Chromatography/Mass Spectrometry (GC/MS). For TWA, a two day passive on-site TWA sampling was carried out at the same sampling points in the ponds. All SPME sampling methods undertaken used a 65-\(\upmu\)m PDMS/DVB SPME fiber, which was found optimal for THM sampling. Sampling conditions were optimized in the laboratory using calibration standards of chloroform, bromoform, bromodichloromethane, dibromochloromethane, 1,2-dibromoethane and 1,2-dichloroethane, prepared in aqueous solutions from analytical grade samples. Calibration curves for all methods with \(R^{2}\) values ranging from 0.985-0.998 (\(N=5\)) over the quantitation linear range of 3-800 ppb were achieved. The different sampling methods were compared for quantification of the water samples, and results showed that DI- and TWA- sampling methods gave better data and analytical metrics. Addition of 10% wt./vol. of (NH4)2 SO4 salt to the sampling vial was found to aid extraction of THMs by increasing GC peaks areas by about 10%, which resulted in lower detection limits for all techniques studied. However, for on-site TWA analysis of THMs in natural waters, the calibration standard(s) ionic strength conditions, must be carefully matched to natural water conditions to properly quantitate THM concentrations. The data obtained from the TWA method may better reflect actual natural water conditions.  
T +
Footnote †: journal: _Appl. of the Journal of the American Society_
# Interface Growth and Void Formation in Sn/Cu and Sn0.7 Cu/Cu Systems
## 4 Conclusions  
In this work, the interface growth and void formation in the Sn/Cu as-reflowed joints and aged joints were comparatively studied in the combination of pure Sn solder and HP Cu substrate, pure Sn solder and EP Cu substrate as well as Cu addition (0.7%) of Sn solder and EP Cu substrate. The EP Cu and Cu addition (0.7%) seemed to slightly affect the total IMCs thickness. However, they showed different effects on the growth behaviors of different IMCs, i.e., Cu\({}_{6}\)Sn\({}_{5}\) and Cu\({}_{3}\)Sn. The EP Cu substrate significantly suppressed the growth of the Cu\({}_{6}\)Sn\({}_{5}\) layer; while it promoted the growth of the Cu\({}_{3}\)Sn layer. In the case of Cu addition (0.7%), it had the function of suppressing growth of the Cu\({}_{3}\)Sn layer, but retarded the growth of the Cu\({}_{3}\)Sn layer. During the thermal aging, a number of voids appeared at the Cu\({}_{3}\)Sn/Cu interface in Sn/EP Cu joints, but not in Sn/HP Cu joints. These voids could be mainly caused by the impurities and fine grain incorporated during electroplating. The addition of Cu element (0.7 wt.%) retarded the growth of the Cu\({}_{3}\)Sn layer and suppressed the formation of voids.  
[PERSON] conceived and performed the experiments, analyzed the data, and wrote the paper. [PERSON] and [PERSON] provided the experimental materials and laboratory equipment, and directed the research. [PERSON], [PERSON], [PERSON] and [PERSON] contributed to the discussion and interpretation of the results.  
This project is supported by National Natural Science Foundation of China (Grant Nos.51805316, 51805315 and 51605276), Shanghai Science and Technology Development Funds (18 FY1424900) and Shanghai Science and Technology Committee Innovation Grant (17 JC1400600, 17 JC1400601).  
The authors declare no conflicts of interest.
## 2 Related Work
### Graph Neural Networks for Node Classification  
GNNs [8] have become a widely used class of models in recent years. They transform complex graph-structured data into meaningful representations for downstream mining tasks through information propagation and aggregation, based on the dependencies in the network. Among all GNN models, graph convolutional networks (GCNs) are considered a major solution and can be categorized into two types: spectral methods and spatial methods. In the spectral domain, [PERSON] et al. [8] proposed using Fourier basis vectors for graph convolution in the spectral domain. ChebNet [9] introduced smooth filters in spectral convolution, which can be approximated well by K-order Chebyshev polynomials. [PERSON] et al. [10] further simplified and constrained the parameters of ChebNet by introducing a convolutional architecture based on the local first-order approximation of spectral graph convolution. Alternatively, spatial methods directly operate on the graph by defining operations on the target node and its topological neighbors, enabling aggregation operations on the graph structure. For example, [PERSON] et al. [11] proposed the GraphSAGE model, which samples and aggregates neighbor nodes through graph sampling to ensure each node has a limited number of sampled neighbors. By combining graph sampling and feature aggregation, GraphSAGE can perform node embedding and representation learning by utilizing local node information while considering global information from the entire network. This approach overcomes the challenge of loading features for all nodes in large-scale networks while still considering global information. Another improvement made to the GCN resulted in the Graph Attention Network (GAT) model [12], which incorporates node feature expressions into the definition of aggregation functions using attention mechanisms. The weights of the nodes are computed and aggregated to the central node in a weighted sum manner. However, these methods do not address the bias introduced by majority class nodes during the training process, making them unsuitable for directly classifying imbalanced nodes.
## 2 Dyadic Green's Function for the Electric Field
### Source and Scattered Green's Functions: Scattering at a Planar Interface
Therefore, the total field for \(z>0\) can be written as  
\[\mathbf{E}\left(\mathbf{p}_{\parallel},z>0,z_{0},\omega\right)=\mu_{1}\mu_{0} \omega^{2}\left[\overleftrightarrow{G_{0}}\left(\mathbf{p}_{\parallel},z-z_{ 0},\omega\right)+\overleftrightarrow{G_{r}}\left(\mathbf{p}_{\parallel},z,z_{ 0},\omega\right)\right]\cdot\mathbf{d}_{0}, \tag{33}\]  
where we have introduced the reflected Green's function  
\[\overleftrightarrow{G_{r}}\left(\mathbf{p}_{\parallel},z,z_{0},\omega\right)= r_{s}\frac{i}{2\beta_{1}}\hat{e}_{s}\hat{e}_{s}e^{i\hat{p}_{1}(z+z_{0})}+r_{p} \frac{i}{2\beta_{1}}\hat{e}_{p,1}^{+}e^{-}e^{i\hat{p}_{1}(z+z_{0})}. \tag{34}\]  
Similarly, the transmitted field for \(z<0\) can be written as  
\[\mathbf{E}_{0}\left(\mathbf{p}_{\parallel},z,z_{0},\omega\right)=\mu_{1}\mu_{ 0}\omega^{2}\overleftrightarrow{G_{0}}\left(\mathbf{p}_{\parallel},z,z_{0}, \omega\right)\cdot\mathbf{d}_{0}. \tag{27}\]0  
with the transmitted Green's function being written as  
\[\mathbf{E}_{0}\left(\mathbf{p}_{\parallel},z,z_{0},\omega\right)=\mu_{1}\mu_{ 0}\omega^{2}\overleftrightarrow{G_{0}}\left(\mathbf{p}_{\parallel},z,z_{0}, \omega\right)\cdot\mathbf{d}_{0}. \tag{27}\]1At this point, we have now in our possession all the relevant tools to study the renormalization of the polarizability of a nanoparticle in the vicinity of a planar interface. Renormalization of the Polarizability of a Quantum Emitter Near a Graphene Sheet and a Graphene-Based Grating  
The dyadic Green's function method is a powerful tool for describing the modification of the properties of a quantum emitter near interfaces, as it takes into account the change in the density of electromagnetic modes induced by the presence of the interface. Problems such as the calculation of the Purcell factor and Forster energy transfer are two examples [42; 43] well-suited for the Green's function approach. Here we consider another problem that also depends on the density of electromagnetic modes, the calculation of the effective polarizability of a quantum emitter.
# Backdoor Attack Against Dataset Distillation in Natural Language Processing
###### Abstract  
Dataset distillation has become an important technique for enhancing the efficiency of data when training machine learning models. It finds extensive applications across various fields, including computer vision (CV) and natural language processing (NLP). However, it essentially consists of a deep neural network (DNN) model, which remain susceptible to security and privacy vulnerabilities (e.g., backdoor attacks). Existing studies have primarily focused on optimizing the balance between computational efficiency and model performance, overlooking the accompanying security and privacy risks. This study presents the first backdoor attack targeting NLP models trained on distilled datasets. We introduce malicious triggers into synthetic data during the distillation phase to execute a backdoor attack on downstream models trained with these data. We employ several widely used datasets to assess how different architectures and dataset distillation techniques withstand our attack. The experimental findings reveal that the attack achieves strong performance with a high (above 0.9 and up to 1.0) attack success rate (ASR) in most cases. For backdoor attacks, high attack performance often comes at the cost of reduced model utility. Our attack maintains high ASR while maximizing the preservation of downstream model utility, as evidenced by results showing that the clean test accuracy (CTA) of the backdoored model is very close to that of the clean model. Additionally, we performed comprehensive ablation studies to identify key factors affecting attack performance. We tested our attack method against five defense strategies, including NAD, Neural Cleanse, ONION, SCPD, and RAP. The experimental results show that these defense methods are unable to reduce the attack success rate without compromising the model's performance on normal tasks. Therefore, these methods cannot effectively defend against our attack.  
machine learning; deep neural network; natural language processing; dataset distillation; backdoor attack +
Footnote †: journal: Applied  
Article
## 4 Result and Discussion
### Fault Signature Characterization  
Initially, the fault occurred in transmission line system is simulated based on signature obtained in raw neutral current and voltage signal. Single line to ground fault have been chosen for this study due to the fact that it is the most prominent fault types among the line to line fault, the double line to ground fault, and the three phase fault. Moreover, four types of faults namely lightning, insulator degrading, tree encroachment, and crane encroachment are chosen according to their prominent tripping occurred in Malaysia within the year 2016 to 2020. The signatures of faults based on neutral current and voltage waveform, which have different patterns, can be observed in Figures 14 and 15a-d.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Matrix** & **Definition** \\ \hline ACC & Accuracy & \(\frac{TP+TN}{TP+TNEP+FN}\) \\ SNS & Sensitivity & \(\frac{TP}{TP+TN}\) \\ SPC & Specificity & \(\frac{TP+FP}{TP+FN}\) \\ PRC & Precision & \(\frac{TP+FP}{PRC+SNS}\) \\ F\({}_{1}\) & F\({}_{1}\) Score & \(2\frac{PRC+SNS}{PRC+SNS}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: List of rates definition computed from the confusion matrix.  
Figure 14: Fault signature of neutral current sinusoidal waveform.  
Figure 13: Validation result for generated current waveform due to lightning (a), insulator degrading (b), tree encroachment (c), and crane encroachment (d).  
Based on the Figure 14, tree encroachment shows a gradual current increase that lasted for six cycles. Whilst lightning fault shows current increase which lasting for two cycles and with high magnitude of about 2 kA Figure 14. In some cases, the lightning fault can last for three to three and half cycle. As for the crane encroachment, fault current indicates a gradual increase lasting for three cycles which contains harmonic content at the pre-fault as shown in Figure 14. Finally, for insulator degrading (Figure 14), the fault current increases within three cycles, which is the same pattern as per lightning fault but with a lower magnitude. However, fault current for lightning is usually higher than insulator degrading due to its high current which can be up to thousands of kiloamps, which is dependent on the load carried in the lines. The higher the loads, the greater the fault current magnitude.  
In addition, the criteria of faults were observed on the voltage signature. Based on the Figure 15, sinusoidal voltage pattern for tree encroachment and crane encroachment has no difference from the normal condition until the circuit breaker is closed. Meanwhile, Figure 15 shows a voltage dip of more than 20% was produced in the system for lightning with a slight dip in the case of insulator degrading as in Figure 15. Based on the observation, raw voltage and current fault did give several signatures but insufficient to be analyzed due to similar characteristics obtained in several cases. Therefore, further analyses such as converting the raw current and voltage into RMS and DWT have been considered and proposed in this paper.
## 2 Materials and Methods
### Sample Analysis  
#### 2.2.1 Soil Analysis  
The soils were subjected to analysis of physical and chemical parameters prior to the experiment. These included soil texture, pH, and organic-matter concentration. The pH was measured potentiometrically in a 1:2.5 (m V\({}^{-1}\)) soil suspension in H\({}_{2}\)O (PN-ISO10390, 1997). The particle size distribution was analyzed via the aerometric method (PN-R-04032, 1998), while soil organic matter content was determined after sulfochromic oxidation followed by titration of the excess of K\({}_{2}\)Cr\({}_{2}\)O\({}_{7}\) with FeSO\({}_{4}\)(NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\)\(\cdot\)6H\({}_{2}\)O (PN-ISO 14235, 2003).  
Trace-element concentrations (Zn, Pb, Cd, Cu, Ni, Cr) were determined by the aqua regia digestion (0.5 g air-dried, sieved through 2-millimeter mesh and ground on mortar-ground soil), with involvement of middle-pressure (32 bars) microwave digestion system (Mars Xpress from CEM Corp., Matthews, NC, United States). The extracts were analyzed by the inductively coupled plasma mass-spectrometry technique (Agilent quadrupole 7500 CE ICP-MS equipped with a torch, micro mist nebulizer, nickel sampler and skimmer cones, and double-pass spray chamber). Argon was used as a carrier gas, and hydrogen and helium as reaction gases for the elimination of interferences. To minimize the matrix effect and ensure long-term stability, all determinations were made in the presence of internal standard consisting of 1 mg L\({}^{-1}\) of 45 Sc, 89Y and 159 Tb. A blank sample and the certified reference material (CRM028-050) were included in analyses for quality control. The recovery for analyzed trace elements was from 90 to 97%, while precision of the method defined as a relative standard deviation (RSD) was \(<\)3%. The LOD values were determined at the level 0.085 mg kg\({}^{-1}\), 0.081 mg kg\({}^{-1}\), 0.007 mg kg\({}^{-1}\), 0.050 mg kg\({}^{-1}\), 0.032 mg kg\({}^{-1}\), 0.099 mg kg\({}^{-1}\), respectively, for Zn, Pb, Cd, Cu, Ni, Cr.  
The soluble forms of metals (Zn, Pb, Cd, Cu, Ni, Cr) were analyzed using 0.01M calcium nitrate (Ca(NO\({}_{3}\))\({}_{2}\)) extraction (liquid-to-soil ratio = 1:2.5) in an end-over-end shaker for 2 h. The metal concentrations in the extracts were determined by the inductively coupled plasma mass-spectrometry technique, as described above.
# A Novel Approach to EEG Speech Activity Detection with Visual Stimuli and Mobile BCI
## 4 Methods
### Minimum-Phase Signal Calculation  
The first step before calculating the features that would define the signal was to calculate the minimum-phase signal from the obtained EEG recordings. Creating this equivalent signal is one of the features we want to use to improve speech detection. From a theoretical point of view, we can describe the minimum-phase signal as follows.  
System with a minimal phase has the poles and zeros of its rational transfer function in the domain of the transformation \(\mathbb{Z}\) lie inside a unit circle in a complex plane. The minimum-phase signal is defined as a signal in which the energy is concentrated near the front of the signal [17].  
The method for obtaining the minimum-phase phase equivalent uses the conversion of a signal to its real cepstrum using fast Fourier transforms and a recursive process from its real cepstrum backwards. The real cepstrum is the inverse Fourier transform of the real logarithm of the magnitude of the Fourier transform of a sequence [18]. First of all, we calculated the real cepstrum from the signal \(x(n)\). The real cepstrum analysis can be expressed as [19]:  
\[c_{r}(n)=\Re(\mathcal{F}^{-1}[\log\lvert\mathcal{F}\{x(n)\}\rvert]). \tag{1}\]  
By a recursive procedure can be found the minimum-phase equivalent of a signal \(x(n)\) from its real cepstrum \(c_{r}(n)\) :  
\[x_{min}(n)=\Re(\mathcal{F}^{-1}\exp[\mathcal{F}\{c_{r}(n)\}]). \tag{2}\]  
From the obtained minimum-phase signal \(x_{min}(n)\), the features were calculated according to the following chapter.