esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Multi-Gateway Model  
[PERSON] [PERSON] [43], and [PERSON] [49] proposed a multi-gateway communication model, including users, gateway nodes (GWNs), and sensor nodes. Here, we divide gateway nodes into two categories: home gateway nodes (HGWN) and foreign gateway nodes (FGWN) according to the distance to other nodes--relatively close gateway nodes are called HGWNs, and the rest of them are called FGWNs. Sensors and gateway nodes are stationary after they are placed. The computing power of the gateway nodes is powerful, while sensors have low memory, low bandwidth, low battery power and limited computing power. Sensor nodes monitor and collect data, then send the sensed data to the nearest gateway node, i.e., HGWN. The HGWN forwards the received data to other FGWNs, users or sensors. For example, when a user wants to communicate with a sensor node, they need to authenticate each other.  
As shown in Figure 4, if the user and the sensor belong to a home network managed by the same HGWN, the authentication process is as follows: Case 1; Firstly, the user sends a login message to HGWN. Second, HGWN authenticates the user and sends a message to the sensor node. Then, the sensor authenticates HGWN and returns messages to HGWN. After HGWN completes the authentication, it returns messages to the user. Finally, the user completes the authentication of HGWN and computes a session key with the sensor and HGWN. When a user wants to communicate with a sensor node in different networks, the detailed steps are shown in Figure 3. We describe the process as follows: Case 2; The user Ui sends a login message to its HGWN. The HGWN then broadcasts request messages to the sensor node that the user wants to request for a communication. After FGWN receives the broadcast messages, it checks whether the sensor node is in its database. If so, FGWN sends a message to HGWN. The HGWN returns reply messages to the user. Finally, the user and FGWN perform mutual authentication and negotiate the session key as shown in Figure 4.  
Figure 3: Single-gateway communication model.
## 2 Materials and Methods
### Data Analysis  
#### 2.3.1 Parameters of Red Edge  
The canopy reflectance spectra in the domain 680-760 nm are called \"red edge\". The red edge, which contains considerable information and has a strong correlation with the chlorophyll abundance, nitrogen concentration, and leaf area index, can well characterize the growth status of rice. In general, there are two ways to derive red-edge parameters from red-edge reflectance spectra.  
In the first method, a first-derivative curve of the spectra at the red edge was retrieved by Equation (1). The maximum value at this curve is defined as the red-edge amplitude (\(D\lambda\)), and the corresponding wavelength is the red-edge position (\(\lambda_{0}\)). The integral area of the first-derivative curve at the red edge is defined as the red-edge area (\(\alpha_{0}\)). \(D\lambda\), \(\lambda_{0}\), and \(\alpha_{0}\) are red-edge parameters.  
\[R^{\prime}(\lambda_{i})=\frac{dR(\lambda_{i})}{d\lambda}=\frac{R(\lambda_{i+1 })-R(\lambda_{i-1})}{\lambda_{i+1}-\lambda_{i-1}} \tag{1}\]  
where \(\lambda_{i}\) is the \(i\)-th wavelength, \(R(\lambda_{i})\) is the reflectance corresponding to the i-th wavelength, and \(R^{\prime}(\lambda_{i})\) is the first-derivative reflectance corresponding to the \(i\)-th wavelength.  
In the second method, the inverse Gaussian model [45] was applied to derive \(\lambda_{0}\) since the model can fit the shape of the reflectance curve at the red edge well.  
\[R(\lambda_{i})=R_{\rm s}-(R_{\rm s}-R_{0})\exp\left(\frac{-(\lambda_{0}- \lambda_{i})^{2}}{2\sigma^{2}}\right) \tag{2}\]  
In Equation (2), \(\lambda_{i}\) is the wavelength, and \(R(\lambda_{i})\) is the reflectance. R\({}_{\rm s}\) is the maximum reflectance in the near-infrared range. R\({}_{0}\) is the minimum reflectance in the red-light range. In this paper, the average reflectance in 780-795 nm and 670-675 nm was set as R\({}_{\rm s}\) and R\({}_{0}\), respectively. In the above equation, \(\lambda_{0}\) and \(\sigma\) represent the red-edge position and the red-edge absorption band width, respectively. Both values were derived from an optimized Equation (2), with \(\lambda_{i}\) and \(R(\lambda_{i})\) data used as input.  
In this paper, the two methods were used separately to calculate \(\lambda_{0}\). The results are presented in the box plots shown in Figure 2. From the figure, it can be seen that the average value of \(\lambda_{0}\) (728 nm) estimated by the first method was higher than the value of \(\lambda_{0}\) (687 nm) by the second method, but many outliers resulted from the first method. Against the possible high uncertainty in the further analysis, we chose the estimate by the second method as the default value of \(\lambda_{0}\) in this paper.
## 4 Discussion
### Regulation of SIF by Environmental Factors
In addition, the decline rate of MTCI in September-October was higher than that in NDVI (Figure 1), indicating that the decrease of Ta in autumn led to the decomposition of leaf chlorophyll [91], which could be another reason why Ta affected \(\mathsf{f_{esc}}\). The environment not only affected SIF directly by changing the allocation of the light energy, but also indirectly by regulating stomatal behavior. Plant stomata are accesses that regulate and control photosynthesis and transpiration. Some physiological processes also require stomata for gas exchange. Stomata are closed at night, even when the temperature and water are optimal for plants; light is the necessary condition for the stomata to open. Circadian rhythm can affect the relationship between photosynthesis and fluorescence efficiency by controlling stomatal conductance [92]. The VPD is an indicator of meteorological drought, and leaf curl/stretch can change canopy structures (\(\mathsf{f_{esc}}\)) to affect SIF. Moreover, the VPD can affect stomatal opening and closing; however, this effect was mainly reflected in the half-hour scale. Previous research has shown that the influence of the VPD on SIF had a critical value; thus, a high VPD will have a negative effect on SIF [93]. Our results showed that the VPD had a positive effect on SIF(Figure 5). However, after removing the covariant effect of the VPD and PAR, VPD had a negative effect on SIF (Figure 6). PAR mutated the effect of the VPD on SIF, and the asynchronous response of SIF and the GPP to the VPD may be one of the reasons for decoupling in drought events. Ws affected SIF in physical and physiological directions. First, wind affected the canopy structure by moving or rolling the blades, causing changes in APAR (half-hour). Second, the wind affected stomatal conductance by changing transpiration pull, causing changes in light utilization.
# Online SoC Estimation of Lithium-Ion Batteries Using a New Sigma Points Kalman Filter
## 4 Experimental Results
### The Experimental Validation
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Condition** & **Algorithm** & **RMS** & **Error-MAE** & **SI** & **MAPE** & **VAF** \\ \hline \multirow{3}{*}{The initial SoC guess} & UKF & 0.0081 & 0.0072 & 0.0201 & 1.3542 & 1.0028 \\  & CDKF & 0.0201 & 0.0187 & 0.0503 & 3.9068 & 1.0404 \\ \cline{1-1}  & EKF & 0.0141 & 0.0113 & 0.0353 & 1.9676 & 1.0500 \\ \cline{1-1}  & Proposed & 0.0065 & 0.0058 & 0.0163 & 1.1100 & 1.0011 \\ \hline \multirow{3}{*}{The initial SoC guess} & UKF & 0.0091 & 0.0074 & 0.0227 & 1.4363 & 0.9906 \\ \cline{1-1}  & CDKF & 0.0122 & 0.0080 & 0.0305 & 2.1100 & 0.9523 \\ \cline{1-1}  & EKF & 0.0141 & 0.0115 & 0.0353 & 2.0290 & 1.0350 \\ \cline{1-1}  & Proposed & 0.0061 & 0.0045 & 0.0153 & 0.8467 & 0.9884 \\ \hline \multirow{3}{*}{The initial SoC guess} & UKF & 0.0058 & 0.0043 & 0.0149 & 0.7612 & 1.0071 \\ \cline{1-1}  & CDKF & 0.0192 & 0.0171 & 0.0495 & 3.6600 & 1.0482 \\ \cline{1-1}  & EKF & 0.0138 & 0.0104 & 0.0355 & 1.5805 & 1.0346 \\ \cline{1-1}  & Proposed & 0.0046 & 0.0030 & 0.0119 & 0.6148 & 1.0052 \\ \hline \multirow{3}{*}{The initial SoC guess} & UKF & 0.0067 & 0.0044 & 0.0173 & 0.8196 & 0.9956 \\ \cline{1-1}  & CDKF & 0.0129 & 0.0099 & 0.0332 & 2.4148 & 0.9601 \\ \cline{1-1}  & EKF & 0.0135 & 0.0100 & 0.0349 & 1.5113 & 1.0198 \\ \cline{1-1}  & Proposed & 0.0047 & 0.0026 & 0.0120 & 0.5110 & 0.9964 \\ \hline \hline \end{tabular} Note: The true initial SoC value was 0.8.  
\end{table} 
Table 6: Comparison of SoC estimation errors of four algorithms. \begin{table}
\begin{tabular}{c c c} \hline
**Condition** & **Algorithm** & **Time(s)** \\ \hline \multirow{3}{*}{The initial SoC guess was 0.7} & UKF & 55 \\  & CDKF & 1595 \\  & EKF & 67 \\  & Proposed & 53 \\ \hline \multirow{3}{*}{The initial SoC guess was 0.9} & UKF & 74 \\  & CDKF & 852 \\  & EKF & 68 \\  & Proposed & 44 \\ \hline \multirow{3}{*}{The initial SoC guess was 0.7} & UKF & 60 \\  & CDKF & 1377 \\  & EKF & 70 \\  & Proposed & 51 \\ \hline \multirow{3}{*}{The initial SoC guess was 0.9} & UKF & 66 \\  & CDKF & 831 \\ \cline{1-1}  & EKF & 69 \\ \cline{1-1}  & Proposed & 32 \\ \hline \end{tabular}
\end{table} 
Table 7: The time of initial error convergence.
## 3 Urban Water System Issues and Potential Smart Solutions
### Energy Consumption  
The most energy intensive portions of water delivery are usually source pumping and wastewater treatment [6,37]. The U.S. Environmental Protection Agency (EPA) estimates that it takes an average of 1.5 kilowatt-hours of energy to convey, treat, and distribute one thousand gallons of drinking water in the U.S. [38]. In the southern Los Angeles basin the estimate is 9.9 kilowatt-hours per thousand gallons [6]. This larger energy consumption is mainly due to the long distances and altitude changes for conveying water from source to drinking water treatment plant.  
Undetected leaks and biofilms can also increase energy consumption. Leaks in the distribution system result in a loss of water pressure. Energy is required to rebalance this pressure loss. In turn, increasing the pressure actually increases the severity of the leaks, which means more water and energy lost [39]. Biofilms increase the frictional resistance in pipes, slowing the water down, resulting in increased pumping to compensate [40].  
If pumping distances in the Southwest U.S. cannot be reduced, the use of smart pumps has the potential to at least reduce the energy consumption of pumping water, because they adjust their power levels based on environmental conditions--ramping up when water is flowing slowly and ramping down when water is flowing quickly [13]. Smart pumps can also be used in the distribution system as part of a smart pressure management program. Used in the same way as smart step testing described previously, smart pressure management with smart pressure meters, smart valves, and smart pumps can reduce pipe deterioration. This saves energy in the long-run, because leaky pipes lose pressure and require more energy to balance. Lastly, biosensors can locate problematic biofilms that slow the water flow.
## 5 Discussion and Analysis
### Why ConvProtoNet Works: Some Comparisons
#### 5.2.1 With Hybrid Attention-Based Prototypical Network  
Hybrid Attention-Based Prototypical Network [15] (HABPN) is one of the few-shot baselines in experiments and accuracy of HABPN is mostly 10% lower than that of ConvProtoNet. In HABPN's architecture, feature attention receives the embedded support set \(S\) and generates a score vector \(z\) to weight the dimensions in L2 distances between class prototypes and embedded query samples. However, the last ReLU in feature attention module makes all negative values in score vector zero which attempts to produce a sparse score vector. When backpropagating through queried loss \(\mathcal{L}\), the gradient of parameter \(\phi^{e}\) in embedding module will be also sparse for having a factor of a sparse weight vector. In specific, let \(f_{\phi^{e}}\) be the embedding function, \(\hat{x}\) be the class prototype of a class, \(e_{i}^{Q}\) be the embedding of a query input \(x_{i}^{Q}\), \(d\) be the distance between the class and the queried input, it goes:  
\[d=z^{T}(e_{i}^{Q}-\hat{x})^{2}, \tag{9}\]  
\[e_{i}^{Q}=f_{\phi^{e}}(x_{i}^{Q}). \tag{10}\]  
Figure 7: Gradient visualization of the malware image. Left column indicates the original malware images, and the right column indicates the corresponding gradient.  
The gradient of parameter \(\phi\) can be calculated using (9), (10) as following:  
\[\frac{\partial\mathcal{L}}{\partial\phi^{e}}=\frac{\partial\mathcal{L}}{\partial d }\cdot\frac{\partial d}{\partial\phi^{e}}=2\cdot z^{T}\cdot(e_{i}^{Q}-\hat{x})( \frac{\partial f_{\phi^{e}}}{\partial\phi^{e}}-\frac{\partial\hat{x}}{\partial \phi^{e}})\cdot\frac{\partial\mathcal{L}}{\partial d}. \tag{11}\]  
From (11), it is obvious that the sparsity of \(z\) can simultaneously lead to the sparsity of gradient of \(\phi^{e}\) and parameters will remain unchanged even after an episode due to vanishing gradient. This negative effect will continue to grow like a snowball making learning process stop eventually. ConvProtoNet regards the output of convolution over support set as the class prototypes instead of feature attention to prevent from gradient vanishing.  
Meanwhile, generation of class prototype in HABPN uses instance attention from instance attention module to suppress noise in samples. Let \(W^{I}\) denote the instance attention, \(e^{S}\) denote the embedded support set \(S\), the class prototype can be obtained as:  
\[\hat{x}=W^{I}\cdot e^{S}. \tag{12}\]  
Although it can reduce the effect of noise by decreasing the attention of noisy instance, in essence it performs linear combination over support set, and this can generate shallow prototype sometimes. Instead, ConvProtoNet uses stacked convolution layers to generate prototype which is capable to output deep class prototype through non-linear transformation.
# Optimal Steady-State Range Prediction Filter for Tracking with LFM Waveforms
## Appendix A Derivation of Equation (15)  
Because \(r_{\text{t},k}^{\text{cv}}\) is the true range of a target moving with constant velocity, this is expressed as  
\[r_{\text{t},k}^{\text{cv}}=r_{\text{t},k-1}^{\text{cv}}+v^{\text{cv}}, \tag{16}\]  
where \(v^{\text{cv}}\) is the true range rate. With Equations (1) and (16), the variance of the range prediction error is found to be  
\[E[(r_{\text{t},k}^{\text{cv}}-r_{\text{p},k})^{2}]=E[(x_{\text{s},k-1}-x_{ \text{ts},k-1})^{2}]+2 TE[(x_{\text{s},k-1}-x_{\text{ts},k-1})(v_{\text{s},k-1} -v^{\text{cv}})]+T^{2}E[(v_{\text{s},k-1}-v^{\text{cv}})^{2}]. \tag{17}\]  
Here, \(E[(x_{\text{s},k-1}-x_{\text{ts},k-1})^{2}]\), \(E[(x_{\text{s},k-1}-x_{\text{ts},k-1})(v_{\text{s},k-1}-v^{\text{cv}})]\), and \(E[(v_{\text{s},k-1}-v^{\text{cv}})^{2}]\) are \(s_{11}\), \(s_{21}\), and \(s_{22}\) of [10], which are the components of a sensor-noise only covariance matrix corresponding to the smoothed target range and range rate. Hence, Equation (17) is rewritten as  
\[E[(r_{\text{t},k}^{\text{cv}}-r_{\text{p},k})^{2}]=s_{11}+2 Ts_{21}+T^{2}s_{22}, \tag{18}\]  
where \(s_{11}\) is expressed as given in Equation (11), and \(s_{21}\) and \(s_{22}\) are expressed as [10]:  
\[s_{21} =\frac{\beta(2\alpha-\beta)}{\alpha(4-\beta-2\alpha)-c_{\text{RD} }(4\beta(\alpha-1)+\beta^{2}(2c_{\text{RD}}+1))}\frac{\sigma_{\text{w}}^{2}}{T}, \tag{19}\] \[s_{22} =\frac{2\beta^{2}}{\alpha(4-\beta-2\alpha)-c_{\text{RD}}(4\beta( \alpha-1)+\beta^{2}(2c_{\text{RD}}+1))}\frac{\sigma_{\text{w}}^{2}}{T^{2}}. \tag{20}\]  
Substituting Equations (11), (19), and (20) into Equation (18), we obtain Equation (15).
# Volumetric Analysis of Aortic Changes after TEVAR Using Three-Dimensional Virtual Modeling
## 2 Materials and Methods
### Volumetric Analysis
#### 2.3.1 Image Segmentation  
Segmentation of the descending thoracic aorta was achieved using D2 PTM software, v. 1.0.2.2055 ('DICOM to PRINT'; 3D Systems Inc., Rock Hill, SC, USA). D2 PTM is a cleared medical device software intended for pre-operative surgical planning and the production of 3D-printed anatomic models for diagnostic purposes. D2P focuses on creating a workflow consisting of as few steps as possible to provide a segmentation tool that is useable for clinicians with limited experience in image processing [15].  
A combination of semi-automatic tools (multi-slice interpolation and threshold segmentation) in D2 PTM software was used to segment each anatomical structure of interest (aorta, thrombus, and stent graft) in the descending thoracic aorta, from the left subclavian artery to the celiac trunk.  
In detail, the contrast containing aorta was automatically segmented using the automatic vascular segmentation option, and the Smart Brush tool was used to manually extend the mask to unmasked areas with the use of a threshold range (HU 100-500). The aneurysm and the intraluminal thrombus were segmented using the Multi-Slice Interpolation tool plus Brush tool and Eraser tool. For the stent graft, the Threshold tool (HU 600-2500) was used.  
The segmentation results and the anatomical accuracy of the reconstructed 3D virtual models were reviewed by two experienced radiologists. To validate the reconstructed 3D virtual models, the two radiologists carefully examined the 3D anatomical reconstructions in relation to the CTA scan projections (axial, coronal, and sagittal) that D2 PTM software provides contextually for the segmentation masks.
# Internet of Things and Blockchain Integration: Security, Privacy, Technical, and Design Challenges
## 3 RQ 1 and RQ 2--Current Challenges and Recommendations to Enhance BIoT Integration
### BC-Based Security Issues
#### 3.1.4 Authentication  
Authentication challenges in BIoT range from the increase in storage size and computing power overhead to openness against certain types of attacks [68]. In the IoT, public key infrastructures (PKIs) make authentication and identity management easier. At the moment, the most widely used PKIs are certificate authorities and a web of trust grounded on somewhat excellent privacy [119]. However, because the growing number of devices would necessitate significant processing and storage requirements to allow for continuous message exchange and confirmation of identity, these traditional solutions cannot fulfill identity management needs [75].  
To resolve these challenges, combining smart contracts with lightweight encryption methods to handle identities automatically is an interesting research proposal. We may develop specific smart contracts that contain [75] (1) a device recording containing device type, expiry date, public key, and so on, (2) information updating when a new IoT device enters the network that may include firmware upgrade and expiration date, and (3) obsolescence of devices that may include device registration, identity verification, and information update. Furthermore, by synchronizing the data stored in the BC, the full node may synchronize the state of identity-related smart contracts to offer identity authentication. Similarly, the lightweight node may successfully authenticate additional IoT devices after querying the complete node [75]. The decentralized identity management strategy, which makes use of BC immutability and automated smart contract execution, not only avoids identity fraud but also lowers the cost of developing trust in IoT systems when compared to CA-based techniques [75].  
The user's identification in several services (e.g., smart city) is currently given via central authority-managed digital identity management systems. Self-sovereign identity (SSI) and decentralized identity (DID) allow people to fully manage their digital identities without the need for a third-party intermediary. This gives consumers control over how their identifiable data are shared. BC-enabled IoT services, SSI, and DID can be used for decentralized authentication and authorization. DID and SSI, on the other hand, present several difficulties, including human dependence such as losing the private user's key [57]. Identity identification and message authentication are two types of network authentication. To ensure dependability, identity authentication requires a key. One of the most often used authentication techniques in IoT is message identification and authentication, which is a protective mechanism for ensuring the integrity and security of data when transferring data [76].
## 4 Conclusions  
In summary, our findings revealed the significant role of droplet impingement in determining the weld pool profile and grain morphology in the welding of aluminum alloys. The main conclusions were as follows:  
1. The grains along the wall of the FLP region were more refined than those in the shallow region because of the more violent flow driven by droplet impingement running in a confined space created by the FLP. In summary, grain morphology is determined by the magnitude of contradiction between the room required by fluid flow and the space supplied by the weld pool.
2. When the torch is inclined, the SIP disappears and the curved columnar grains emerge along the NIAS wall because of the gradually weakened impingement at that location. Then, when the penetration becomes shallow, the trajectory of droplet impingement is split into two branches under the combined effect of pushing aside and inducing flow which is driven by EMF.
3. Grains along the wall of the upward branch undergo three stages due to the change in momentum and heat carried by droplets with increasing distance. However, grains before the IP survive to grow into a columnar morphology with a markedly larger size than in the neighboring region because the columnar grain region may not be impacted by droplets, due to the sensitivity of the impingement trajectory to the effect of pushing aside.
4. Droplet impingement can refine grains directly through impact, as evidenced by the sharply shortened columnar grain at the impact point in case 3.  
**Author Contributions:** [PERSON] and [PERSON] conceived and designed the experiments, and wrote the paper; [PERSON] and [PERSON] performed the experiments; and all the authors analyzed and discussed the data.  
**Funding:** This work was supported by the High-level Leading Talent Introduction Program of GDAS (No. 2016 GDASRC-0106), the National Natural Science Foundation of Guangdong Province (No. 2016A0303117), and Natural Science Foundation of Fujian (No. 2018J01503).  
**Conflicts of Interest:** The authors declare no conflict of interest.