esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 2 Materials and Methods
### Materials
#### 2.2.6 Topography Datasets  
The Shuttle Radar Topography Mission (SRTM) is an international project spearheaded by the National Geospatial-Intelligence Agency (NGA) and (NASA), and it was launched in February 2000. The SRTM project provided high-resolution DEM data with a spatial coverage ranging from 56\({}^{\circ}\)S to 60\({}^{\circ}\)N and a spatial resolution of one arc second (\(\sim\)30 m). In this study, the dataset with three arcs second (approximately 90 m) were adopted as the basic topographical data ([[http://srtm.csi.cgiar.org](http://srtm.csi.cgiar.org)]([http://srtm.csi.cgiar.org](http://srtm.csi.cgiar.org))). Other topographical parameters, including slope [35], aspect [36], curvature [37], radiation [38], slope length and steepness (LS) [39], the topographic wetness index (TWI) [40], the multiresolution valley bottom flatness index (MrVBF) [41] and the terrain ruggedness index [42], were derived from DEM data. Curvature, radiation, and ruggedness were abbreviated as Curv, Radi, and Rugg, respectively.
## 2 The Permanent Facility for Altimeter Calibration
### Infrastructure and Instrumentation
#### 2.1.5 The GNSS Monitoring Network
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Site** & **vN** & **vE** & **vUP** & **\(\sigma\)vN** & **\(\sigma\)vE** & **\(\sigma\)vUP** & **Time Span** \\  & **(m/Year)** & **(m/Year)** & **(m/Year)** & **(m)** & **(m)** & **(m)** & **(Years)** \\ \hline CDN0 & \(-\)0.0129 & 0.0092 & 0.0009 & 0.0003 & 0.0005 & 0.0015 & 2014.49–2018.44 \\ CDN2 & \(-\)0.0129 & 0.0063 & 0.0002 & 0.0007 & 0.0006 & 0.0036 & 2016.40–2018.44 \\ CRS1 & \(-\)0.0124 & 0.0068 & \(-\)0.0011 & 0.0002 & 0.0001 & 0.0003 & 2008.18–2018.44 \\ GVD0 & \(-\)0.0128 & 0.0081 & 0.0000 & 0.0002 & 0.0001 & 0.0004 & 2003.04–2017.35 \\ GVD7 & \(-\)0.0138 & 0.0086 & \(-\)0.0004 & 0.0001 & 0.0002 & 0.0005 & 2009.37–2018.44 \\ GVD8 & \(-\)0.0143 & 0.0082 & \(-\)0.0007 & 0.0002 & 0.0002 & 0.0007 & 2010.50–2018.44 \\ IMS1 & \(-\)0.0131 & 0.0078 & \(-\)0.0004 & 0.0002 & 0.0002 & 0.0006 & 2010.19–2015.19 \\ MEN2 & \(-\)0.0146 & 0.0058 & \(-\)0.0001 & 0.0005 & 0.0005 & 0.0007 & 2013.26–2018.25 \\ RDK1 & \(-\)0.0128 & 0.0081 & 0.0010 & 0.0004 & 0.0002 & 0.0009 & 2009.18–2017.44 \\ SELI & \(-\)0.0112 & 0.0077 & \(-\)0.0010 & 0.0004 & 0.0005 & 0.0009 & 2009.52–2013.80 \\ TUC2 & \(-\)0.0124 & 0.0075 & \(-\)0.0006 & 0.0001 & 0.0001 & 0.0002 & 2004.47–2018.44 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Velocities and uncertainties (weighted root mean square error) for 14 GNSS stations in western Crete from the established monitoring network in support of the Permanent Altimeter Calibration Facility. (ITRF 2008). In the past, a Geodetic Mobile Solar Spectrometer (GEMOSS-I), developed by ETH Zurich, Switzerland, was used at the PFAC to retrieve the precipitable water vapor content in the atmosphere [39] during a satellite altimeter pass. From 11 January to 11 January 2003, a GEMOSS-I radiometer campaign was conducted in Rethymnon, Crete, to measure the zenith wet path delay during the overflight of Jason-1 on 11 January 2003 (Figure 17). Figure 16: The DORIS beacon (**Left** and **central image**) and system (**right image**) installed in Gavdos calibration facility. It was collocated with a GNSS receiver (**Left image**). This GAVB Doris station was decommissioned on 27 March 2014. A new installation is underway elsewhere in western Crete. Figure 17: Time variation of the zenith wet path delay as measured by GEOMOSS-I on 11 January 2003, at Rethymnon, Crete. (**Upper left**) image shows the results of wet troposphere delays as a function of time. (**Upper right**) images show a close view of this prototype GEOMOSS-I instrument and the (**lower**) image depicts the instrument set up on the roof of the building at the Institute of Mediterranean Studies in Rethymnon.
# Applications of Probabilistic Forecasting in Demand Response
## 2 Materials and Methods
### Quantile Regression Forest  
The Quantile Regression Forest (\(QRF\)) is an adaptation of the \(RF\) algorithm, considered an effective method to estimate the mean of the response variable given the values of the predictors. It was firstly introduced in [36], which shows that the \(RF\) not only provides relevant information about the conditional mean of the response variable, but also about the conditional distribution.  
It is well known that standard regression aims to estimate the conditional mean of the response variable. For that, the cost function corresponding to the expectation of the squared error is minimized:  
\[E(Y|X=x)=\arg min_{z}E\Big{\{}(Y-z)^{2}\Big{|}X=x\Big{\}} \tag{13}\]  
However, estimating the conditional distribution of the response variable is more informative than just focusing on the mean, even in the form of the conditional quantiles, given respectively by the following:  
\[F(y|X=x)=P(Y\leq y|X=x) \tag{14}\]  
\[Q_{\alpha}(x)=\inf\{y\::F(y|X=x)\geq\alpha\} \tag{15}\]  
Note that we can obtain prediction intervals for a given coverage level \(\alpha\in(0,\:1)\) from the estimation of the conditional quantiles, so this will be our goal.  
It is important to highlight that the standard regression minimizes the mean squared error whereas the quantile regression minimizes the mean of the quantile loss function. Therefore, the function to minimize is as follows:  
\[Q_{\alpha}(x)=\arg min_{q}E\{L_{\alpha}(y,q)|X=x\} \tag{16}\]  
where the quantile loss function is:  
\[L_{\alpha}(y,q)|=\left\{\begin{aligned} \alpha|y-q|& y>q\\ (1-\alpha)|y-q|& y\leq q\end{aligned}\right. \tag{17}\]  
Nevertheless, the \(QRF\) does not minimize the quantile loss function. Instead, the \(RF\) is employed to estimate the conditional distribution of the response. It results in a non-parametric method to estimate the quantiles of the response for given values of the predictors, which is efficient in the case of high-dimension datasets.  
Recall that in RFs, for each input (\(X=x\)), the estimation of the response conditional mean is given by a weighted average of the sample observations, as follows:  
\[RF:\:\hat{E}(Y|X=x)=\hat{\mu}(x)=\sum_{i=1}^{n}w_{i}(x)y_{i} \tag{18}\]  
In the case of QRFs, for each input (\(X=x\)), the estimation of the response conditional distribution is given by the weighted distribution of the sample observations, using exactly the same weights provided by the \(RF\) algorithm:\[QRF:\ \hat{F}(y|X=x)=\sum_{i=1}^{n}w_{i}(x)\mathbf{1}_{(y_{i}\leq y)} \tag{19}\]  
Therefore, the hyperparameters to be tunned are the same as in RFs--that is, the number of trees (ntree), the number of predictors to be considered in each split (mtry), and the minimum number of elements in a node (min.node.size).
# Multibeam Wideband Transmit Beamforming Using 2D Sparse FIR Trapezoidal Filters
## 3 Proposed Low-Complexity Multibeam Wideband Transmit Beamformer  
In this section, we present the proposed multibeam wideband beamformer in detail. To this end, we first present an overview of the architecture of the proposed multibeam wideband beamformer.
# Investigations on Millimeter-Wave Indoor Channel Simulations for 5G Networks
## 2 Previous Related Work
The RMS-DS was examined at 60 GHz within LOS and NLOS scenarios in indoor corridors and office environments [28]; for LOS scenarios, RMS-DS were in the range of 12.3-21.1 ns, while, for NLOS scenarios, the RMS-DS were in the range 18.5-31.7 ns.
## The Living Building Blocks of MESs  
### The Bacteria-Electrode Interface  
Thus far, we have discussed the role that alternative architectures and materials play in influencing MES function. In this section, we will focus on the biological aspect of these systems, describing the complex array of microbial electron transfer mechanisms within electrode biofilms as a means to understand how to optimize these mechanisms for optimal MES productivity. Over the past five decades, many patents have been filed, describing inventions in which bacterial metabolic activity is harnessed to enhance MES function [27; 28; 29; 30]. In the anode compartment, electroactive microorganisms (EAMs) called electricgions utilize the anode as an extracellular electron acceptor, thereby generating small amounts of current [31; 32]. In the cathode compartments of these systems, EAMs called electrotrophs catalyse the production of useful biofuels such as hydrogen [33; 34] by utilizing the electrode as an extracellular electron donor.  
There are a range of currently known EAMs which are capable of interacting with electrodes. Among the most well characterized electricgions are those belonging to _Geobacter_ and _Shewanella_ genera, including _Geobacter sulfurreducens_ and _Shewanella oneidensis_[6; 35; 36]. Other known examples of EAMs include those from the genera _Pseudomonas_[37], _Geothrix_[38], _Clostridium_[39], _Desulfuromonas_[31], _Desulfoviibrio_[40], _Desulfobulbus_[41] and _Enterobacter_[42]. Not as many electrotrophs have been identified to date, and these include _Geobacter_[33; 44; 45], _Clostridium_, _Moorella_[4], _Sporomusa_[4; 46; 47]. Given this broad range of EAMs, it is not surprising that they often differ in the types of mechanisms they utilise for extracellular electron transfer (EET).
## 3 Method and Model Preparation
### Surface Meteorological Analysis of Taishan Camp in Princess Elizabeth, Antarctica
At the same time, the wind speed and direction data are shown in Figure 5. The wind speed and wind direction data of 2m and 4m height were obtained, respectively. It can be seen that the highest wind speed of 19.7 m/s at 2 m appeared on July 30. At this time, the annual maximum wind speed at 4 m above the ground was 22.4 m/s. The minimum wind speed at the two altitudes appeared on January 9 at 0 m/s. The annual average wind speed at 4m height is 10.84 m/s, and the average yearly wind speed at 2 m is 10.17 m/s. It can be seen from Figure 5 that the annual wind direction is mainly distributed between 40\({}^{\circ}\) and 100\({}^{\circ}\)and combined with the data, the average yearly wind direction at 2 m is 76.94\({}^{\circ}\) and 69.61\({}^{\circ}\) at 4 m. The average wind speed and direction obtained here provide environmental conditions for the subsequent establishment of the physical model. The physical model defaults the annual wind speed and direction as the average value for calculation. The wind direction data gives the characteristics of the on-site wind field, based on which the most suitable placement method for the ground observation module is found. Comprehensive weather data predicts that the local factors that affect the regular operation of the equipment may include the following:  
1. The low-temperature environment during the polar night makes the voltage of the lead-acid wound battery continue to decrease. The transmitting current is not enough to make the Iridium module transmit. This is the most critical one. We use the acquired temperature information to heat the equipment to avoid this phenomenon. 2. There are strong winds in the Princess Elizabeth area in the east of Antarctica, which has a significant impact on wind speed and direction and ground optical observation equipment. Figure 4: (1) Battery case temperature, (2) Power supply voltage, (3) Solar radiation, and (4) Sonar distance data observed by the weather station installed at Mount Tai in TYUT in 2016. Continuous high radiation intensity may cause ageing of the equipment's non-metallic enclosure. 4. In the absence of supplementary energy in the photovoltaic system, the wind turbine has a high probability of failure, resulting in no supplement of renewable energy. In the case of the continuous low battery voltage, the battery is eventually damaged and the system cannot be restarted.
# MP-GCN: A Phishing Nodes Detection Approach via Graph Convolution Network for Ethereum
## 4 Experiments
### Performance and Comparisons  
In our experiments, we randomly segregated the dataset into a training set and a test set at a certain ratio (rate = 80%). Each labeled node and its first-order neighbor nodes are used as samples. For the classification model, the state of the labelled node should be distinguished; therefore, we set the output dimension to two. The input dimension \(d\) = [4, 8, 16, 32, 64] and the nodes embedding in dimension d are randomly initialized by using module nn.Embedding from Pytorch, which is for saving word embedding [44]. In the forward propagation function, we used one of four different aggregation functions {sum, max, min, mean} to aggregate the nodes' embedding, and we used one of two features {timestamp and amount} as the weights of the edges. We used relu, which is an activation function to fit the nonlinear relationship, and set the dropout rate \(p\) = 0.5. The \(p\) is a parameter to prevent overfitting and improve model generalization. For the model training,we used Adam [45] as the loss optimizer and the binary cross-entropy loss as the loss function with a learning rate of 0.01.  
In our experiments, to demonstrate the prediction precision of our MP-GCN, we compare our method with the following methods:  
**Deepwalk**[38]: The sequence of the node's neighboring nodes is obtained using the random walk strategy, which consequently yields structural information.  
**Node2 vec**[39]: Based on Deepwalk, wandering is specified such that a tradeoff can be achieved between depth-first and breadth-first.  
**Trans2 vec**[25]: Based on Node2 vec, the best attribute value is obtained by balancing the weights of two attributes (amount and timestamp) based on the two attributes of the edge.  
**Timestamp-GCN**: One of our methods, represents a GCN with the timestamp as the weight.  
**MP-GCN**: Our method represents a GCN with the amount as the weight.  
Our experiments set embedding dimension d = 32, epoch = 500, and the sum as the aggregation function to obtain the best experimental results. The comparison with other embedding methods is shown in Table 1. Deepwalk and Node2 vec consider only the structural information of the graph thus their classification precision rate is less an 87.0%. The method Trans2 vec, which considers the transaction amount and timestamp, yielded classification precision approximately 92.7%. Deepwalk and Trans2 vec only use the characteristics of the neighboring nodes of the nodes considered; they do not consider the overall network topology information and cannot fully utilize the node characteristics in the transaction network. The classification precision of the MP-GCN, which considers the transaction amount and structural information, can reach 93.5%, which is the best performance achieved hitherto. The Timestamp-GCN, which uses timestamp as the transaction weight, its classification performance is not as good as above, is only 73.2%.
# Crasworthiness Analysis and Multi-Objective Optimization for Concave I-Shaped Honeycomb Structure
## 4 Multi-Objective Optimization of Concave I-Shaped Honeycomb Structure
### Introduction to the Optimization Objectives  
The concave I-shaped honeycomb structure requires particular energy absorption and peak cellular force as the optimization targets to have greater crashworthiness. The idea of making lightweight materials is becoming more and more common nowadays. Mass is another objective of optimization, as people desire materials that are as light as feasible while still having a particular amount of impact resistance. Therefore, specific energy absorption, peak crushing force, and mass are the optimization objectives of this study. The cellular length \(S\), the vertical short cellular height \(h\), the horizontal short cellular length \(b\), and the cellular thickness \(t\) all influence the Poisson's ratio of the structure according to the theoretical calculation. Thus, \(S,h,b\), and \(t\) are selected as the variables. An appropriate range of parameters is chosen following Section 3 to guarantee the adaptability of the concave I-shaped honeycomb structure and the reasonableness of its macroscopic dimensions while taking into consideration the density and level of concavity of the honeycomb structure. The concave I-shaped honeycomb structure crashworthiness multi-objective problem is as follows:  
\[\begin{array}{c}Min[-SEA,PCF,m]\\ S_{1}\leq S\leq S_{2}\\ h_{1}\leq h\leq h_{2},\\ b_{1}\leq b\leq b_{2}\\ t_{1}\leq t\leq t_{2}\end{array} \tag{28}\]  
In Equation (28), \(S_{1}\) and \(S_{2}\) are the lower and upper limits of the cellular length and are taken as 6 mm and 14 mm, respectively; \(h_{1}\) and \(h_{2}\) are the lower and upper limits of the vertical short cellular height \(h\) and are taken as 2 mm and 3 mm, respectively, \(b_{1}\) and \(b_{2}\) are the lower and upper limits of the horizontal short cellular length \(b\) and are taken as 1.75 mm and 2.75 mm, respectively; and \(t_{1}\) and \(t_{2}\) are the lower and upper limits of the cellular thickness \(t\) and taken as 0.5 mm and 1.1 mm, respectively.
# Analysis of Security Issues and Countermeasures for the Industrial Internet of Things
## 3 Security Issues in IIoT Systems
### Security Issue 10--Non-Trusted Network Connection  
In an IIoT system, the majority of the devices often perform networking functions in an open (and maybe public) wireless networking environment [110]. The edge devices could be attacked by the de-synchronization attack in which an attacker can take control over communication between two devices and disconnect an established dialogue [111]. In this attack, an attacker de-synchronizes a tag's key (e.g., RFID tag) that is stored in the back-end server and the key that is located in the tag's memory. Therefore, in a future session establishment, it is impossible to get synchronized with the correct pair of keys required for authentication. In addition, in a non-trusted network connection environment, the IIoT systems can face DoS attacks where an attacked node would send more and more data packets to a trusted node to disintegrate the whole network [68, 94].
## 2 Establishment of the Atmospheric Wind Field Model
### Shaping Filter  
During the response of the aircraft to atmospheric turbulence, the spectral function matrix of the random input signal \(r(t)\) is known. According to the transfer relationship between the input vector and the output vector in the linear system, \(r(t)\) is taken as a constant value and white noise as a unit value, i.e., \(\Phi_{rr}(\omega)=1\). Next, the output spectrum is shown in (4):  
\[\Phi_{xx}(\omega)=|G(i\omega)|_{2}\Phi_{rr}(\omega)=G^{*}(i\omega)G(i\omega). \tag{4}\]  
Equation (3) is decomposed in the form of Equation (4) to obtain the transfer function \(G(s)\) of the shaping filter that generates the output spectrum \(\Phi_{xx}(\omega)\) on the turbulent flow component.  
* U-direction shaping filter \[G_{U}(s)=K_{U}/T_{U}s+1,\ K_{U}=\sigma_{U}\sqrt{L_{U}/\pi d},\ T_{U}=L_{U}/V,\] (5)
* V-direction shaping filter \[G_{V}(s)=K_{V}/T_{V}s+1,\ K_{V}=\sigma_{V}\sqrt{L_{V}/\pi d},\ T_{V}=2L_{V}/ \sqrt{3}d,\] (6)
* W-direction shaping filter \[G_{W}(s)=K_{W}/T_{W}s+1,\ K_{W}=\sigma_{W}\sqrt{L_{W}/\pi d},\ T_{W}=2L_{W}/ \sqrt{3}d.\] (7)  
Considering that the shaping filters in the \(U\) and \(W\) directions are actually second-order forms, \(G_{V}(s)\) and \(G_{W}(s)\) are appropriately simplified to first-order forms for easy implementation. According to the flight quality specification approved by the authoritative organization [31]; under the condition of low-altitude flight, the turbulence intensity \((\sigma_{U},\sigma_{V},\sigma_{W})\) on the component is related to the flight altitude, and the turbulence scale \((L_{U},L_{V},L_{W})\) is related to the wind speed. The relationship between them is as follows:  
\[\left\{\begin{array}{l}2L_{W}=h\\ L_{U}=2L_{V}=\frac{h}{(0.177+0.000823h)^{12}}\\ \sigma_{W}=0.1u_{20}\\ \frac{\sigma_{U}}{\sigma_{W}}=\frac{\sigma_{V}}{\sigma_{U}}=\frac{1}{(0.177+ 0.000823h)^{04}}\end{array}\right., \tag{8}\]  
In the research of this paper, the flying height of the UAV on the sea level is taken as 20 m. \(u_{20}\) is the wind speed value at an altitude of 20 feet, taken as 10 \((\,\mathrm{m/s})\). Substitute \(h\) and \(u_{20}\) into (8) and calculate the specific value of the sum in the direction as shown in Table 1, and then deduce the transfer function of the shaping filter.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \(L_{U}\) (\(\mathrm{m}\)) & \(L_{V}\) (\(\mathrm{m}\)) & \(L_{W}\) (\(\mathrm{m}\)) & \(\sigma_{U}\) (\(\mathrm{m/s}\)) & \(\sigma_{V}\) (\(\mathrm{m/s}\)) & \(\sigma_{W}\) (\(\mathrm{m/s}\)) \\ \hline
143.59 & 71.79 & 10 & 1.93 & 3.72 & 1 \\
## 3 Results  
### Plantarum KG Lysates Significantly Increased hBD3 Production in HaCaT Cells  
KG lysates significantly increased hBD3 mRNA expression from HaCaT cells after 48 h treatment. It further increased by 460-fold at 72 h. On the other hand, K8 lysates increased hBD3 mRNA by 50-fold. Data shows that KG lysates were 9-fold more inducible in hBD3 mRNA expression than K8 lysates (Figure 1A). Protein level of hBD3 was increased by 24 h stimulation of KG lysates and more hBD3 protein was noted at 72 h (Figure 1B). Next, the expression pattern according to concentration was observed. Both KG lysates and K8 lysates significantly increased hBD3 with 10\({}^{9}\) CFU/mL bacterial lysates (Figure 1C). The protein level of hBD3 was also dramatically increased by 10\({}^{9}\) CFU/mL KG lysates (Figure 1D). Viability assay was performed to determine whether KG lysates affect HaCaT viability. When cells were treated with 10\({}^{9}\) CFU/mL KG lysates for 72 h, viability was slightly reduced compared to the control, but there were no significant differences (Figure 1E).  
2 L. plantarum KG Lysates Inhibited \(S\). aureus Internalization into HaCaT Cells via hBD3 Induction  
The effect of _S. aureus_ on hBD3 expression was examined in HaCaT cells. Although it was less than in KG lysates, _S. aureus_ induced hBD3 mRNA expression in HaCaT cells. Furthermore, _S. aureus_ increased hBD3 mRNA expression more in cells pretreated with K8 lysates or KG lysates, suggesting that K8 lysates and KG lysates have a synergistic effect on the expression of hBD3 against pathogen infection (Figure 2A). Although K8 lysates also increased hBD3 mRNA expression, it did not effectively inhibit intracellular infection of _S. aureus_. On the other hand, KG lysates significantly inhibited intracellular infection of _S. aureus_ (Figure 2B). When cells were treated with the indicated dose of KG lysates, intracellular _S. aureus_ was decreased in a dose-dependent manner (Figure 2C). To examine whether the inhibition of _S. aureus_ infection in KG lysate-treated cells was due to hBD3 induction, conditioned media (CM) excluding hBD3 was prepared. The treatment of normal CM containing hBD3 inhibited intracellular infection of _S. aureus_, but inhibition did not occur in cells treated with CM excluding hBD3, suggesting that hBD3 affected _S. aureus_ infection (Figure 2D).  
Figure 1: _L. plantarum_ KG lysates increased hBD3 expression in HaCaT cells. HaCaT cells were treated with the indicated doses of K8 lysates and KG lysates for the indicated times. (**A,C**) hBD3 mRNA level was examined by real-time PCR. (**B,D**) hBD3 protein level was examined by Western blot. (**E**) Cell viability assay was performed with HaCaT cells treated with KG lysates. * _p_\(<\) 0.05, **_p_\(<\) 0.01, and *** _p_\(<\) 0.001.