esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Terrestrial Datasets  
Data related to both canopy structure and plant physiological activities were included in this study to characterize surface-level drought impacts. We used the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectrometer (MODIS) MOD13C2 product. The resolution was 250 m, and we used data from 2007-2012 at 16-day, 0.05-degree resolution to characterize the change in potential or accumulated productivity [29]. We used solar induced chlorophyll fluorescence (SIF) as an indicator of actual photosynthetic activity from the Global Ozone Monitoring Experiment-2 (GOME-2)) [11]. GOME-2 measures this fluorescence signal at 9:30 am local time and provides global coverage every 1.5 days. The nadir footprint size is 40 km \(\times\) 80 km; here we used both monthly and weekly GOME-2 products from NASA at a spatial resolution of 0.5\({}^{\circ}\).  
Fluorescence occurs when a solar photon is absorbed and is elevated to an excited state in the light reaction of photosynthesis. Typically, between 2 and 5 percent of photons absorbed by chlorophyll are re-emitted at longer wavelengths as fluorescence [30]. Canopy-level SIF measurements demonstrate that fluorescence capture productivity decreases even when NDVI remains constant [31]. SIF retrievals are also sensitive to seasonal dynamics of vegetation, independent from the structure of the canopy, and have been employed for stress detection in ground [32], aircraft [33], drone [34], and satellite-based instruments at wavelengths surrounding the oxygen A and B bands or Frauhoefer lines [35].  
To assess the impact of the 2010-2011 drought on GPP, we scaled SIF to GPP using a linear relationship between monthly SIF and a GPP product, Fluxnet Multi-Tree Ensemble (MTE) GPP [36] during 2007-2011 (Figure 3). The Fluxnet GPP data were calculated using a machine-learning approach with eddy covariance datasets from flux towers, climatic variables, and remote sensing products. The dataset spanned from 1983 to 2011. The GOME-2 monthly SIF overlapped with Fluxnet-MTE over the 2007-2011 period. We then calculated the difference between 2010-2011 SIF and SIF climatology (2007-2012). We limited our analysis between 2007-2012 because GOME2 SIF showed a decreasing trend after 2013, likely caused by a sensor drift [37]. We calculated the GPP reduction during the drought period by using this relationship between SIF and GPP.  
Figure 3: The relationship between solar-induced chlorophyll fluorescence (SIF) and gross primary production (GPP): (**top**) spatiotemporally-averaged SIF and GPP for the black box region in Figure 1; (**bottom**) the relationship between SIF and GPP.
# Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
## 1 Introduction
### Adversarial Machine Learning  
[PERSON] et al., 2013 [1], and [PERSON] et al., 2014 [2], pioneered the research of DNN vulnerability, and they indicated that adversarial samples could easily fool DNNs. An adversary formulates adversarial examples by applying almost imperceptible human perturbations to examples from the dataset. These perturbations lead the model to make wrong predictions with high confidence. Adversarial machine learning is crafting perturbations (_adversarial samples_) to fool machine learning models. Adversarial sample generation can be performed at all phases of machine learning models, as shown in Figure 2. An attacker can craft perturbations by modifying input data during training or prediction phases.  
Figure 2: Adversarial machine learning. Reproduced with permission from [13], CEUR ([[http://ceur-ws.org/Vol-2057/](http://ceur-ws.org/Vol-2057/)]([http://ceur-ws.org/Vol-2057/](http://ceur-ws.org/Vol-2057/))), 2017.  
Adversarial attacks can be categorized based on the adversary's goal or based on the adversary's knowledge. Classification of adversarial attacks based on adversarial goals can be further classified into poisoning attacks and evasion attacks [14], where poisoning attacks refer to a process where an adversary modifies the training dataset by inputting malicious samples. During an evasion attack, adversaries do not have the power or access to change the model or its parameters, but they can create malicious samples foreign to the model; hence during testing, the classifier will not recognize these samples resulting in wrong classifications. In addition, an attack can be targeted or non-targeted under the adversary's goal classification. A targeted attack is where the adversary's objective is to trick the model into producing the specified output. While a non-targeted attack, the adversary's goal is to make the model perform poorly by misclassifying some samples with high accuracy.  
Classification based on the adversary's knowledge can also be further categorized into white-box attacks, black-box attacks, and grey-box attacks based on the amount of information the adversary has on the model. A white-box attack refers to a situation where the adversary has full access to the target model. They have information about the model at their disposal. Under the black-box attack scenario, the adversary does not have access to the model's inner configuration. They can only input data and query the output of the model, and then the adversary can be able to create their dataset, which can be used to develop surrogate models. Finally, they can use transfer learning techniques to develop adversarial samples for a target model. In the grey-box scenario, the adversary has partial knowledge about the model architecture but does not have access to the weights in the model [15]. This paper used evasion, non-targeted and white-box attacks for the proposed framework evaluation. The attacks are FGSM, PGD, and DeepFool.
# Bluetooth Low Energy Mesh Networks: A Survey
## 6 Discussion and Open Issues
### Open Issues
#### 6.2.1 Security  
Security is of the utmost importance in IoT networks, given the impact that compromising such networks may have on physical world activities. However, security is currently a challenge in BLE mesh networks.  
Since SMP services are not available over advertising packets, only packets transmitted through data channels are encrypted. Moreover, authentication is only performed within a connection. Thus, routing and data packets that are transmitted through advertising channels are not secured unless the application layer provides a security solution.  
On the other hand, since BLE was originally designed for star topology networks, data channels are protected by per-hop security. End-to-end encryption and authentication are not currently supported in BLE mesh networks.  
Future IPv6-based BLE mesh solutions will be able to leverage existing end-to-end security functionality for IP-based protocols. A first option is Transport Layer Security (TLS), which is commonly used to secure HTTP communications [53]. Furthermore, specific work has been recently done on Datagram TLS (DTLS) for constrained environments [54], as in fact DTLS is the default security protocol for the Constrained Application Protocol (CoAP), a lightweight application-layer protocol that has been designed for constrained devices and is therefore a good candidate for BLE devices [37]. Object security is another recent approach being developed for securing application-layer payloads (i.e., end-to-end) [55], which may be suitable for BLE mesh networks as well. Non-IP-based BLE mesh networks will need to develop end-to-end security functionality that might be equivalent to the approaches described.
## 4 Numerical Study
### Simulation Results  
#### 4.3.1 Simulation Results According to Map Sizes  
The results for map scales of \(20~{}\times~{}20,30~{}\times~{}30\), and \(50~{}\times~{}50\) without and with \(5\%\), \(10\%\), and \(20\%\) obstacles are displayed in Figure 7, Figure 8, and Figure 9, respectively. The five metrics of \(\bar{t}_{\text{search}}\), \(\text{std}_{\text{search}}\), \(\overline{n}_{\text{search}}\), \(\text{std}_{\text{node}}\), and \(r_{c}\) are separated into (a), (b), (c), (d), and (e) in each figure. The results from the proposed LH algorithms are shown by the orange curve, and the results from the three benchmark algorithms of A-star (\(l_{1}\)), BFS (\(b_{w}^{*}=0.7\)), and RRT-star (\(\eta=3\)) are shown by yellow, green, and maroon curves.  
Illustrated in Figures 7-9, the proposed LH algorithm almost always returns the best computational cost with the lowest average searching time and the smallest number of traversed nodes in all three map scales with or without randomly settled obstacles. The proposed LH algorithm also returns the best performance stability with the highest completion rate and the smallest standard deviation of the searching time, as well as the number of traversed nodes, if a solution/path can be returned/planned.  
Figure 8: Numerical study results for map size of 30 \(\times\) 30. **(a)** The average values of the searching time, (**b**) the standard deviation of the searching time, (**c**) the average of traversed nodes, (**d**) the standard deviation of traversed nodes, (**e**) the completion rate.  
Figure 9: Numerical study results for map size of 50 \(\times\) 50. **(a)** The average values of the searching time, (**b**) the standard deviation of the searching time, (**c**) the average of traversed nodes, (**d**) the standard deviation of traversed nodes, (**e**) the completion rate.
## 4 Case Study
### Noise Measurement and Analysis on a Real Power System  
For further verification of the proposed algorithm's effectiveness, simulation tests are also carried out on a real power grid system. One generator outgoing circuit has a short-circuit fault, and the generator parameters are based on the actual data. The initial value of the state variable is the steady value, and the initial error covariance matrix is the unit matrix. Assuming that the actual value of the standard deviation of power angle measurement noise is 2.5 degrees, the value is set to 2 degrees in the filtering program.  
Comparing EnKF with the proposed method, Figures 7 and 8 show the simulation results of DSE of generator in the presence of measurement noise deviation. The simulation results of G2 illustrate that under the AEnSRF, the RMSE of power angle estimation is 0.0435, the RMSE of electric angular velocity estimation is 0.0010. Under the EnKF, the RMSE of power angle estimation is 0.0438 and the RMSE of electric angular velocity estimation is 0.0013.  
Figure 6: Tracking curve of G2 measurement noise by AEnSRF in the case of measurement noise deviation: (**a**) Power angle measurement noise tracking curve of G2; (**b**) angular velocity measurement noise tracking curve of G2.  
Figure 8: _Cont._  
Figure 7: Contrast chart of EnKF and AEnSRF for generator state filtering with deviation of measurement noise: (**a**) Power angle dynamic estimation curve of generator; (**b**) dynamic angular velocity estimation curve of generator.  
Comparing with EnKF, AEnSRF has higher filtering-precision. In AEnSRF, the noise estimator tracks the power angle measurement noise at 0.3 s and stabilizes at about 2.5 degrees. This shows that AEnSRF has a good tracking effect on measurement noise.
## 2 Materials and Methods
### Cell Culture and Differentiation  
The thawed cells were resuspended in standard cell culture medium in culture flasks to expand the cells in a monolayer culture. Once the cells reached confluency, they were collected, split, and partly redistributed to four six-well plates (Greiner Bio-One GmbH, Frickenhausen, Germany) for osteogenic and adipogenic, as well as eight 15 mL tubes for chondrogenic differentiation as pellet cultures. For adipogenic and osteogenic differentiation, we used standard adherent monolayer cultures and six-well plates. Differentiation was promoted by exposing cells to osteogenic and adipogenic differentiation media, as described in our earlier studies [22]. Osteogenic differentiation medium was supplemented with 100 nM dexamethasone, 50 \(\upmu\)g/mL ascorbate, and 10 mM \(\beta\)-glycerophosphate (all Sigma-Aldrich, Schnelldorf, Germany), while the control cultures lacked osteogenic supplements. An adipogenic differentiation medium was supplemented with 1 \(\upmu\)M dexamethasone, 1 \(\upmu\)g/mL insulin, 0.5 mM 3-isobutyl-1-methylxanthine, and 100 \(\upmu\)M indomethacin (all Sigma-Aldrich), while the control cultures lacked adipogenic supplements.  
For chondrogenic differentiation, we used a pellet cell culture, according to our previous studies [23]. Briefly, to form the pellets, cells were suspended in serum-free DMEM, containing 1 mM pyruvate, 1% ITS + Premix (insulin, transferrin, and selenous acid containing culture supplement), 37.5 mg/mL ascorbate -2-phosphate, and 100 nM dexamethasone (all Sigma-Aldrich). Following this step, 200 \(\upmu\)L aliquots (2 \(\times\) 10\({}^{5}\) cells) were placed in v-bottomed 96-well plates (Greiner Bio-One GmbH, Frickenhausen, Germany) to initiate aggregate formation. To induce chondrogenesis, pellet cultures were supplemented with TGF-\(\beta\)1 recombinant protein (Sigma-Aldrich) [23].  
Half of the samples were exposed to the specific differentiation media, while the other half was used as a negative control group and, hence, only exposed to standard cell culture medium, as mentioned above. During the differentiation period, the cells were stored in an incubator to assure a permanent outer temperature of 37 \({}^{\circ}\)C and 5% CO\({}_{2}\) level for optimal growth. The adipogenic and osteogenic media were changed every second day, while the chondrogenic media was changed every third day. We stopped the adipogenic and osteogenic differentiation after a differentiation period of 27 days (d). Pellets were harvested after 21 d of chondrogenic differentiation.
# Sediment Erosion Characteristics and Mechanism on Guide Vane End-Clearance of Hydro Turbine
## 1 Introduction
The sediment impacts are simulated by means of comprehensive physical models on the microscale and turbulent sediment transport and erosion accumulation are calculated on the macroscale. [PERSON] [15] studied the mechanisms of sediment erosion at guide vanes, where erosion was categorized as turbulence erosion, secondary flow erosion, leakage erosion, and acceleration erosion. In addition, the influence of end-clearance on turbine efficiency was studied, where the loss in efficiency is greater than the increase of leakage flow. [PERSON] [16] showed that high quartz content, inefficiency of the flushing system, higher concentration of sediment load, high impingement velocity, and operation at low guide vane angles were the major contributors to sediment erosion in guide vanes. The end-surface clearance flow of the turbine guide vane was simplified as circular-cylinder flow and backward-facing step flow by [PERSON] [17; 18; 19]. The erosion of the flow-passage walls with inlet velocity, particle diameter, and particle density has been studied by the discrete phase model (DPM) and mixture model, and the sediment-wear morphology prediction method based on the differential-quadrature method has been proposed. Through a comparison and analysis of the above references, few studies have been conducted on the turbulence characteristics and erosional mechanism at the end-clearance flow of guide vanes with a large eccentric shaft. This paper used the end-clearance flow of the turbine guide vanes with a large eccentric shaft at the Dongshuixia hydropower station as the research object, and simulated the flow in the clearance and around the guide vane with a large eddy simulation (LES) and discrete phase model (DPM). Using the actual wear pattern of the unit, the turbulence characteristics and the wear mechanism of the guide vane were analyzed and studied.
###### Abstract  
The construction of complex networks from electroencephalography (EEG) proves to be an effective method for representing emotion patterns in affection computing as it offers rich spatiotemporal EEG features associated with brain emotions. In this paper, we propose a novel method for constructing complex networks from EEG signals for emotion recognition, which begins with phase space reconstruction to obtain ordinal patterns and subsequently forms a graph network representation from the sequence of ordinal patterns based on the visibility graph method, named ComNet-PSR-VG. For the proposed ComNet-PSR-VG, the initial step involves mapping EEG signals into a series of ordinal partitions using phase space reconstruction, generating a sequence of ordinal patterns. These ordinal patterns are then quantified to form a symbolized new sequence. Subsequently, the resulting symbolized sequence of ordinal patterns is transformed into a graph network using the visibility graph method. Two types of network node measures, average node degree (AND) and node degree entropy (NDE), are extracted from the graph networks as the inputs of machine learning for EEG emotion recognition. To evaluate the effectiveness of the proposed construction method of complex networks based on the visibility graph of ordinal patterns, comparative experiments are conducted using two types of simulated signals (random and Lorenz signals). Subsequently, EEG emotion recognition is performed on the SEED EEG emotion dataset. The experimental results show that, with AND as the feature, our proposed method is 4.88% higher than the existing visibility graph method and 12.23% higher than the phase space reconstruction method. These findings indicate that our proposed novel method for constructing complex networks from EEG signals not only achieves effective emotional EEG pattern recognition but also exhibits the potential for extension to other EEG pattern learning tasks, suggesting broad adaptability and application potential for our method.  
emotion recognition; complex network; ordinal patterns +
Footnote †: journal:
# Image Human Thorax Using Ultrasound Traveltime Tomography with Supervised Descent Method
## 3 Traveltime Tomography with SDM
### Online Predicting Process  
The online predicting process is to minimize the following function after offline training:  
\[S^{\mathbf{P}r}(\Delta\mathbf{m})=\left\|\Delta\mathbf{m}-\mathbf{\tilde{K}}\Delta\mathbf{d}_{ \mathbf{obs}}\right\|^{2} \tag{20}\]  
With the model update direction \(\mathbf{\tilde{K}}\) obtained from offline training process, we can directly minimize Equation (20) as follows:  
\[\mathbf{m}_{k+1}=\mathbf{m}_{k}+\mathbf{\tilde{K}}(\mathbf{d}_{\mathbf{obs}}-F(\mathbf{m}_{k})) \tag{21}\]  
However, traveltime tomography is a ill-posed problem. The result obtained from Equation (21) might have artifacts. Here, Tikhonov regularization is applied to mitigate the ill-posedness [53]. New objective function can be written as  
\[S_{R}^{\mathbf{P}r}(\mathbf{m}_{k+1})=P(\mathbf{m}_{k+1})+\alpha_{v}R_{v}(\mathbf{m}_{k+1})+ \alpha_{h}R_{h}(\mathbf{m}_{k+1}) \tag{22}\]  
where  
\[P(\mathbf{m}_{k+1})=\left\|\mathbf{m}_{k+1}-\mathbf{m}_{k}-\mathbf{\tilde{K}}_{k}(\mathbf{d}_{\mathbf{ obs}}-F(\mathbf{m}_{k}))\right\|^{2} \tag{23}\]\[R_{v}(\mathbf{m}_{k+1}) =\frac{\|\mathbf{d_{obs}}-F(\mathbf{m}_{k})\|}{\|\mathbf{d_{obs}}\|}\int_{\Omega} \Bigl{(}\bigl{|}\
abla_{v}\mathbf{m}_{k+1}\bigr{|}^{2}+\delta_{k}^{2}\Bigr{)}d\mathbf{r} \tag{24}\] \[R_{h}(\mathbf{m}_{k+1}) =\frac{\|\mathbf{d_{obs}}-F(\mathbf{m}_{k})\|}{\|\mathbf{d_{obs}}\|}\int_{ \Omega}\Bigl{(}\bigl{|}\
abla_{h}\mathbf{m}_{k+1}\bigr{|}^{2}+\delta_{k}^{2}\Bigr{)} d\mathbf{r}\] (25) \[\delta_{k}^{2}=\frac{\|\mathbf{d_{obs}}-F(\mathbf{m}_{k})\|^{2}}{\|\mathbf{d_ {obs}}\|^{2}\hat{\Delta}} \tag{26}\]  
\(R_{v}\) and \(R_{h}\) are regularization terms along vertical and horizontal direction and they are proportional to the data misfit. \(\hat{\Delta}\) is a positive real parameter related to the mesh size [54]. At the begining of the inversion, the data misfit is large and the regularization terms are strong, which prevents the appearing of extreme values and helps to converge to a better model. As the iteration goes on, the data misfit decreases and the model is closed to the local optimal model, regularization terms becomes weak and the model converges faster.  
Equation (22) can be minimized directly as follow:  
\[\frac{\partial S_{R}^{Pr}(\mathbf{m}_{k+1})}{\partial\mathbf{m}_{k+1}}=0 \tag{27}\]  
We have  
\[\mathbf{m}_{k+1}=\mathbf{A}_{k+1}^{-1}\mathbf{b}_{k+1} \tag{28}\]  
where  
\[\mathbf{A}_{k+1}=\mathbf{I}+\frac{\|\mathbf{d_{obs}}-F(\mathbf{m}_{k})\|}{\|\mathbf{d_{obs}}\|} \Bigl{(}\alpha_{v}\
abla_{v}^{T}\
abla_{v}+\alpha_{h}\
abla_{h}^{T}\
abla_{h} \Bigr{)} \tag{29}\]  
\[\mathbf{b}_{k+1}=\mathbf{m}_{k}+\mathbf{\tilde{K}}_{k}(\mathbf{d_{obs}}-F(\mathbf{m}_{k})) \tag{30}\]  
Equation (28) is used to update the model every iteration during the inversion.
## 4 Discussion
### Effect of Soil Reflectance on Estimating \(f_{\text{esc}}\)
[PERSON] et al. [28] proposed the NIRv/FAPAR model, which replaced the near-infrared reflectance with NIRv; however, the NDVI used as the pure vegetation signal in this model still depended on soil reflectance. The accuracy of \(\mathrm{f_{esc}}\) estimated by the NIRv/FAPAR model is reduced when a real soil background, rather than a non-reflecting background, is present, especially for sparse scenes [51]. This is because soil background pollutes the NIRv used to calculate \(\mathrm{f_{esc}}\). The variability of the value of \(i_{0}\) is also high for sparse vegetation, which indicates that soil background can affect \(i_{0}\) and thus \(\mathrm{f_{esc}}\)[15]. The simulations using the SCOPE model in this study also show that soil reflectance significantly affects the estimation of \(\mathrm{f_{esc}}\), particularly for sparse vegetation (Figure 11). When NIRv < 0.439, soil reflectance is the dominant factor in \(\mathrm{f_{esc}}\), influencing the scattering process between the canopy and soil background. The canopy structure is the dominant factor of \(\mathrm{f_{esc}}\) when NIRv > 0.439. Thus, ignoring the reflection characteristic of the soil background and treating it as \"black\" will introduce significant uncertainty in the calculation of \(\mathrm{f_{esc}}\), especially for sparse canopies where soil reflectance has a greater influence.