esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Results and Analysis
### Results from Scenario I
function were compared with the forecasting results of the hybrid activation function (\(w_{1}=0.6\) and \(w_{2}=0.4\)) proposed in this study at the high water surface elevation above the specified elevation for flood management (EL. 3.9 m) which caused the flood damage at the riverside. The results are summarized in Table 9.  
When using the hybrid activation function, the RMSE decreased by 0.22 m compared with the hyperbolic tangent function and decreased by 0.25 m compared with the rectified linear function and the NSE increased by 79.3% compared with the hyperbolic tangent function and increased by 97.0% compared with the rectified linear function. Finally, the PE was 0.11 m and 0.21 m less than the hyperbolic tangent function and rectified linear unit function, respectively. From these results, the use of the hybrid activation function instead of the existing activation function had the effect of improving the forecasting accuracy at a high water surface elevation. However, it can be confirmed that the forecasting accuracy did not improve in all the data. Therefore, the hybrid activation function was more effective only for forecasting a high water surface elevation than the existing activation function because the forecasting water surface elevation in this study was highly affected by seasonal characteristics in Korea. Moreover, the time series data of water surface elevation, tidal level, precipitation and discharge have characteristics such as trend and seasonality. Even though the LSTM was generally used to forecast the time series data [22], the high water surface elevation was under-forecasted because of the lack of samples at a high water surface elevation. In particular, the Hangang River has a large coefficient of river regime in terms of river discharge. In addition, the original activation functions have the problems of a vanishing gradient and a dying neuron that occurred in the backpropagation training. Thus, the original activation functions have the limitation of needing to resolve the under-forecasted problem. Therefore, the hybrid activation function proposed in this study to partially solve the problems of the vanishing gradient and the dying neuron show the more accurate forecasting than the original activation functions, as shown in Table 9. Therefore, we conclude that the hybrid activation function was suitable for forecasting a high water surface elevation in Hangang River.
## 2 Data Set and DSM Preparation
### Terrestrial Forest Inventory  
Two terrestrial forest inventories were conducted at the study site by staff of the Chair of Forest Growth and Yield Science at TUM in summer 2008 and 2013, respectively, based on the standard design of the Bavaria State Forest Enterprise [40; 41].  
For this inventory, 221 field plots were placed in the forest area using a regular grid of \(100\text{ m}\times 100\text{ m}\), with each one assumed to represent a forest area of 1 ha. The plot centers are permanently marked and each field plot consists of three concentric circles: an inner one with radius \(r_{1}=3.15\text{ m}\) (\(31.15\text{ m}^{2}\)), a middle one with radius \(r_{2}=6.31\text{ m}\) (\(125\text{ m}^{2}\)) and an outer one with radius \(r_{3}=12.62\text{ m}\) (\(500\text{ m}^{2}\)). In this study, the last (\(500\text{ m}^{2}\)) were used as field reference. The trees measured within the concentric circles depended on their diameter at breast height (DBH): all trees with a DBH < 10 cm, 10 cm \(\leq\) DBH < 30 cm and DBH > 30 cm were callipersed inside the smallest, middle, and largest circle, respectively. During field work, the species and age class were determined for each measured tree, which was then assigned to one of the following vertical layers: regeneration, understory, overstory or emergent trees (i.e., trees with crowns that emerge above the rest of the canopy). Then, at least one tree height was measured for every existing combination of tree species, age class and vertical layer affiliation occurring at a plot. If available, the height of at least two of the highest trees was measured for each of the following species: _Picea abies_, _Pinus sylvestris_, _Abies alba_, _Larix decidua_, _Pseudotsuga menziesii_, _Fagus sylvatica_, and _Quercus_ spp.  
In order to take only the inventory plots of the unchanged regions (after damage), a forest mask is manually extracted from the multispectral channels of WorldView-2 data. Thus, 184 plots are selected for the evaluation procedure. The distribution of the inventory plots is shown in Figure 3. Using these data, the top height H100 (which is defined as the average height of the 100 stems with the largest diameter at breast heights (DBHs) per hectare) was modeled for each plot using the algorithm described in [37].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Data**} & \multicolumn{3}{c}{**3D Shifts**} \\ \cline{2-4}  & \(\mathbf{X}\) **(m)** & \(\mathbf{Y}\) **(m)** & \(\mathbf{Z}\) **(m)** \\ \hline LiDAR2001 & 2.26 & \(-5.38\) & \(-0.49\) \\ HRSC2003 & \(-1.38\) & \(-2.98\) & \(-0.61\) \\ Alos2008 & 0.41 & \(-0.38\) & \(1.90\) \\ Cartosat2008 & 0.60 & \(-0.79\) & \(2.47\) \\ RapidEye2009 & \(-3.26\) & \(0.98\) & \(-5.24\) \\ WorldView2012 & 8.20 & \(-4.82\) & \(-0.79\) \\ Aerial2012 & \(-2.36\) & \(1.55\) & \(1.25\) \\ TanDEM-X2013 & \(-4.11\) & \(5.46\) & \(-3.21\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculated shift values for three directions (reference: LiDAR2010).
# A Modified Residual-Based RAIM Algorithm for Multiple Outliers Based on a Robust MM Estimation
###### Abstract  
The residual-based (RB) receiver autonomous integrity monitoring (RAIM) detector is a widely used receiver integrity enhancement technology that has the ability to rapidly respond to outliers. However, the sensitivity and vulnerability of the residuals to the outliers are the weaknesses of the method especially in the case of multi-outlier modes. It is an effective method for enhancing the validity of residuals by robust estimation instead of least squares (LS) estimation. In this paper, a modified RB RAIM detector based on a robust MM estimation with a higher detection performance under multi-outlier modes is presented. A fast subset selection method based on the characteristic slope that could reduce the number of subsets to be calculated is also presented. The experimental results show that the proposed algorithm maintains a more robust performance than the RB RAIM detector based on the LS estimator and M estimator with an IGG III function especially with the increase in the number of outliers. The proposed fast subset selection method can reduce the calculation time by at least 80%, demonstrating the practical application value of the algorithm.  
RAIM; robust statistics; MM estimator; characteristic slope +
Footnote †: journal: Sensors
# DAB Signal Preprocessing for Passive Coherent Location
## 3 Results
### Replacing of Null, PR and FIC Symbols by Noise  
The replacement of the Null, PR, and FIC symbols by the noise leads to a raised noise floor in the detection results (as shown in Figure 16) since the introduced noise cannot be suppressed with the clutter-removal filter. The bistatic \(v/R\) plane looks similar to the previous case from Figure 15 with the bypassed clutter-removal filter, but the periodical artifacts are even more intensified. These artifacts are approx. 20 dB more intensive than in the reference (Figure 1). The moving object at \(R\) = 1.2 km is obscured by the noise and is therefore undetectable.  
Figure 14: PCL detection results after clearing of PR and FIC symbols in the DAB frame: (**a**) bistatic velocity/range plane; (**b**) its cross-section at selected range.  
Figure 15: PCL detection results after the clearing of the PR and FIC symbols (bypassed clutter-removal filter).  
After reducing the power of the inserted noise by 6 dB, the noise floor is lower and the moving object at \(R=1.2\) km emerges from the noise, as marked in Figure 17 with a red rectangle. Further reduction of the inserted noise power results in an even lower noise floor. When the inserted noise power is at least 9 dB lower than the DAB signal power, the situation converges toward the case of clearing the samples of initial frame symbols, shown in Figure 14. However, it should be emphasized that with this method, the periodical artifacts are constantly visible in the bistatic \(v/R\) plane.
## 3 System Model
### Local Computing  
Each MD has limited computing power, denoted as \(f_{i}(n)\). The quantity of CPU cycles needed to complete one computation task in one bit is \(\rho\). Therefore, the computation bits performed at user \(i\) at time slot n can be obtained, as shown in the Formula (4).  
\[R_{i}^{l}(n)=\frac{f_{i}(n)\tau}{\rho} \tag{4}\]  
According to [21], \(p_{i}^{l}(n)=\kappa f_{i}^{3}(n)\) is a model for how much power user \(i\) uses for local computation. The value of \(\kappa\), the CPU's effective switched capacitance, depends on the design of the chip. Therefore, the energy consumption of user \(i\) in the period \(\tau\) can be expressed as Formula (5).  
\[E_{i}^{l}(n)=\kappa f_{i}^{3}(n)\tau \tag{5}\]  
Figure 1: The overview of system model.
# DRLLA: Deep Reinforcement Learning for Link Adaptation
## 4 Method
### RL Algorithms
#### 4.1.1 Natural Actor-Critic (NAC)  
Based on the proposed algorithm in [13], the neural network (NN) only consists of three layers: an input layer, a hidden layer, and an output layer. Both the actor and the critic network share a common input layer of size 52, which is the size of our observation space since the IEEE 802.11p standard uses 52 active subcarriers. A hidden layer of size 128 follows. The output layer for the actor consists of eight neurons representing the eight possible actions defined in the action space. A softmax activation function is then applied to the output neurons to model the probability density function of the action space. The agent then samples an action from this distribution to determine the proposed action to apply to the following communication. The critic, on the other hand, has a single output neuron that represents the expected return. All layers are fully connected and use ReLU as an activation function.
# Quadratic Model-Based Dynamically Updated PID Control of CSTR System with Varying Parameters +
###### Abstract
This control approach uses a local quadratic model [18; 19], of the plant control error to improve the robustness of the conventional PID against the change of the plant parameters. Based on this model the PID control value is adapted directly without retuning its coefficients, to steer the plant output in close proximity of the set point. The DUPID controller is mainly meant to work well for plants found in the process industry. The plants encountered there are often monotone (inert), stable and primarily affected by slow and gradual degradation in their physical components over time. The DUPID control scheme is made of two parts: a PID controller and a supervisory mechanism (SM). The SM plays the role of the adaptation mechanism; it detects the change in plant parameters by locally modelling the recent history of the control errors and uses that model to produce an incremental value that is added to the PID control value. The control performance of the DUPID control scheme is dependent on the quality of the predictions the error model can deliver. To establish a locally precise error model, we must ensure that the data used for modelling meet certain criteria [18]. In this regard, this paper discusses two versions of the DUPID algorithm, the 1D DUPID and the 2D DUPID. The effectiveness of both versions of the DUPID controller was compared against the conventional PID controller, when each controller was applied to control the CSTR system with varying parameters [20]. We assumed that the varying parameters in question are: the heat transfer coefficient \(h\) and the feed fluid temperature \(T_{f}\). Based on this assumption we defined three different scenarios in which these parameters vary linearly over time. The observed control performance of each controller was quantified and compared against the other controllers based on the IAE metric values. The rest of the paper is structured as follows: Firstly, the mathematical background of the PID and DUPID are presented, and the DUPID and its two versions are discussed in more detail. Secondly, the CSTR system with varying parameters is analyzed. Thirdly, the PID controller and both versions of the DUPID controller are tested when applied to the CSTR system. Finally, conclusions and an outlook for future work are given.
## 3 SWIPT-Based Multi-Hop Uneven Clustering Routing Protocol Design for RF EH-CRSNs
### Theoretical Derivation of Cluster Radius  
This paper derives the relationship between the cluster radii of adjacent rings, aiming to equalize the net energy expenditure of CHs in adjacent rings. With the cluster radius of the outermost ring as the initial condition, the cluster radii of other rings are ascertained.
# Generalized Frequency Division Multiplexing-Based Low-Power Underwater Acoustic Image Transceiver
## 3 Simulation Results
Figure 6a,b shows the received underwater image 1 signals using 16-QAM, with the transmission BERs of \(10^{-3}\) and \(10^{-4}\), respectively, in the GFDM-based underwater image transceiver with a CEE of 10%. The PSNR is 60.45 dB and 71.63 dB, respectively. Figures 6b and 7a show the received underwater image 2 signals using 16-QAM, with the transmission BERs of \(10^{-3}\) and \(10^{-4}\), respectively, in the GFDM-based underwater image transceiver with a CEE of 10%. The PSNR is 38.18 dB and 48.79 dB, respectively. Figure 8a,b shows the received underwater image 3 signals using 16-QAM, with the transmission BERs of \(10^{-3}\) and \(10^{-4}\), respectively, in the GFDM-based underwater image transceiver with a CEE of 10%. The PSNR is 38.21 dB and 49.87 dB, respectively. The simulation results show that the received underwater image 1, 2, and 3 signals with a  
Figure 2: BER performance of the GFDM-based UWA image transceiver. (line: 4-QAM, dotted line: 16-QAM, \(\alpha\): perfect, +: CEE of 5%, \(\square\): CEE of 10%, and *: CEE of 20%). The power-saving ratio (PSR) of the GFDM-based low-power UWA image transceiver for the \(k\)-th user is expressed as  
\[PSR_{k}=(1-\mu_{k})\times 100\quad\% \tag{22}\]  
Figure 8: Received underwater image 3 signals using 16-QAM with the CEE of 10% and transmission BERs of \(10^{-3}\) and \(10^{-4}\), respectively. (**(a)** PSNR = 38.21 dB; **(b)** PSNR = 49.87 dB). Figure 6: Received underwater image 1 signals using 16-QAM with the CEE of 10% and transmission BERs of \(10^{-3}\) and \(10^{-4}\), respectively. (**(a)** SNR = 10.46 dB, PSNR = 60.45 dB; **(b)** SNR = 12.44 dB, PSNR = 71.63 dB). Figure 7: Received underwater image 2 signals using 16-QAM with the CEE of 10% and transmission BERs of \(10^{-3}\) and \(10^{-4}\), respectively. (**(a)** PSNR = 38.18 dB; **(b)** PSNR = 48.79 dB). Figure 9 shows the PSR performance of the proposed transceiver for the \(k\)-th user, with the PCE and CEEs of 5%, 10%, and 20% with 4-QAM and 16-QAM strategies. The AWGNk is \(\omega_{k}\). The UWA image transmission BER is \(10^{-4}\). When PSR values are 90%, 80%, 70%, 60%, 50%, and 40%, the AWGNk values are 0.00630, 0.012, 0.0179, 0.0235, 0.0285, and 0.0353, and 0.00089, 0.00176, 0.00263, 0.00343, 0.0042, and 0.0054 when using 4-QAM and 16-QAM with CEE of 10%, respectively. With the 4-QAM strategy, the PSR of 90% outperformed the PSR of 40% as the AWGNk decreased by 0.0290. The 4-QAM outperformed 16-QAM, under a PSR of 60%, with the AWGNk decreasing by 0.0201. At PSR of 60%, the PCE outperformed the CEEs of 20% under 4-QAM and 16-QAM with the AWGNk decreasing by 0.0111 and 0.0051, respectively. As the CEEs increased, the AWGNk values decreased. The simulation results show that the proposed GFDM-based low-power UWA image transceiver is a suitable image signal transmission method for future advanced underwater image sensor network technology.
## 2 Materials and Methods
### Methods
#### 2.2.1 XDAWN Spatial Filter  
XDAWN is a spatial filter used to find a transformation that can improve the signal-to-noise ratio and reduce the dimension of the data. The specific process is as follows: The EEG signal containing the P300 component is expressed as \(X\in R^{n\times d}\), where n represents the feature dimension and d represents the number of channels of the EEG signal. We need to find the projections \(W\in R^{n\times f}\), where f represents the number of filters for projection. The data filtered by this filter are \(\widehat{X}=XW\). We suppose a real P300 signal \(A\in R^{\varepsilon\times d}\), where e represents the length of the P300 components, and we also have a noise signal \(N\in R^{n\times d}\). The noise signal contains noise that conforms to a normal distribution. The position of the P300 component in the P300 signal can be given by \(D\in R^{\varepsilon\times n}\) through the Toeplitz matrix. Therefore, the signal can be given by \(X=D^{T}A+N\), and the enhanced P300 signal we try to find can be expressed as \(XW=DAW+NW\). We can estimate A by a least squares estimate using the pseudoinverse:  
\[\widehat{A}=\underset{A}{\text{argmin}}=\|\mathbf{X}-\mathbf{DA}\|_{2}^{2}=\left(\bm {D}^{T}\mathbf{D}\right)^{-1}\mathbf{D}^{T}\mathbf{X} \tag{1}\]  
the optimal filters W can be found by maximizing the signal-noise-ratio (SNR) as given by the generalized Rayleigh quotient [23].  
\[\widehat{\mathbf{W}}=\underset{\mathbf{w}}{\text{argmax}}\frac{\text{Tr}\left(\mathbf{W} ^{T}\widehat{\mathbf{A}}^{T}\mathbf{D}^{T}\mathbf{D}\widehat{A}\mathbf{W}\right)}{\text{Tr} \left(\mathbf{W}^{T}\mathbf{X}^{T}\mathbf{X}\mathbf{W}\right)} \tag{2}\]  
In the traditional XDAWN algorithm, we can combine QR matrix decomposition (QRD) and singular value decomposition (SVD) to solve this optimization problem. XDAWN has proven to be very effective in increasing ERPs signals [23].
## 3 Methodology
### TLS-Aided Survey at Porto Katsiki  
A series of reasons led us to use a TLS instead of a UAV for the point cloud generation at the Porto Katsiki white limestone steep slope, which bounds the beach zone and increases the rockfall risk, especially during the tourist season. The crowd presence during the data acquisition and the unstable NRTK signal due to the bad reception of the cellular network prevented the surveying team from using a UAV at this site. Thankfully, the morphology of the surrounding area provided us with a rather ideal place for establishing the LiDAR equipment and using it as a base for the tripod on which the TLS was placed (Figure 6).  
Figure 6: (**a**) Spatial location of the TLS point clouds acquired from two base stations (in red and blue colors). The black dots delineate the common scanned area of the steep slope with the minimal shadowing effect. (**b**) Panoramic view of Porto Katsiki beach during the fieldwork. Note the scanning equipment established at the bottom right (TLS base 2).  
The state-of-the-art, long-range Terrestrial Laser Scanner, Leica P50, was used, with the ability to acquire high-precision point data at distances up to 1 km. According to its specifications, the point accuracy may reach the order of 3 mm over the range that it was used at Porto Katsiki, since the TLS base was established at a distance between 120 and 430 m opposite the steep slope. This resulted in a rather dense point cloud as the laser beam adds a point almost every 0.9 cm at a distance of 120 m or every 3.6 cm at the furthest slope areas, producing a rather reliable and valuable dataset for further processing. For the latter, in addition to the xyz coordinates, RGB values (using the internal 4 MP camera) are included along with intensity laser signal values as well. We acquired two point clouds from two different TLS bases, with known coordinates, 25 m away from each other (Figure 6) in order to change the acquisition angle towards the slope as much as possible and therefore reduce the shadowing during the scanning. Both point clouds were processed together and merged, after co-registration and geolocation.  
The final dataset consisted of 90 million points, and after the cleaning procedure, a point cloud of 65.5 million was generated for further interpretation (Figure 7). The latter includes several types of point classification and meshing, leading to detailed morphotectonic analysis of the rock slope and calculation of structural discontinuities' orientation (Figure 7).  
Figure 7: **(a)** The point cloud of the cliff uphill from the Egremnoi beach, cleaned from the noise caused by the vegetation, which might alter its steep topography. Classification of the extracted facets according to their calculated dip in degrees (**b**) and each one’s dip direction that is color-coded regarding the statistical analysis (**c**). The cliff was segmented into three segments (North, Middle and South) and below each one, the wedge-sliding kinematic analysis is presented.
# Exploring Synergy of Denoising and Distillation: Novel Method for Efficient Adversarial Defense
## 4 Experiment and Evaluation
### Experimental Result and Evaluation  
We verify and analyze an adversarial defense model designed to respond effectively to adversarial attacks. Examination of the experimental results concentrated on defensive performance and efficiency. The aim was to execute individual evaluations centered on these focal points. First, we performed an analysis focusing on the model accuracy and defense success rate for the performance assessment of the model. Subsequently, we conducted an analysis focusing on the defense success rate in relation to the model parameters and the model learning time to assess the efficiency of the model.