esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experimental Studies
### Vision-Based Tracking  
In the second study, the vision-based sensing presented in Section 5 is applied. The control loop is closed by state measurements from the camera system instead of the incremental encoders. This yields challenges, for example, concerning sampling times or accuracy.  
Due to the necessarily increased sampling interval of \(\Delta T=200\) ms due to image processing, major drawbacks in the overall control loop performance are natural. Therefore, a much simpler trajectory from \(\mathbf{r_{\rm ee}}(t_{0}=0\ \mathrm{s})=[0.6\ \mathrm{m},0.2\ \mathrm{m},0.0\ \mathrm{m}]^{\rm T}\) to \(\mathbf{r_{\rm ee}}(t_{t}=10\ \mathrm{s})=[0.0\ \mathrm{m},1.0636\ \mathrm{m},0.0\ \mathrm{m}]^{\rm T}\) is considered,  
Figure 12: Control error of the end-effector in the encoder-based experimental study.  
which the control system with such a long sampling interval is still able to handle. The resulting path of the end-effector is shown in Figure 13, whereas the complete motion of the manipulator can be seen in the deposited video ([[https://www.itm.uni-stuttgart.de/en/research/vision-based-control-of-a-powercube-robot/](https://www.itm.uni-stuttgart.de/en/research/vision-based-control-of-a-powercube-robot/)]([https://www.itm.uni-stuttgart.de/en/research/vision-based-control-of-a-powercube-robot/](https://www.itm.uni-stuttgart.de/en/research/vision-based-control-of-a-powercube-robot/))).  
Figure 14 shows the experimental results of the considered point-to-point motion with the visually determined joint angles being fed back to the controller.  
The joint angles and their corresponding angular velocities as well as the motor currents are depicted. The angular velocities \(\hat{\mathbf{q}}_{\text{vis}}(k)\) at time step \(k\) with sampling interval \(\Delta T\) are calculated from the visually measured angles \(\mathbf{q}_{\text{vis}}(k)\) and \(\mathbf{q}_{\text{vis}}(k-1)\) based on finite differences according to  
\[\dot{\alpha}_{\text{vis}}(k)=\frac{\alpha_{\text{vis}}(k)-\alpha_{\text{vis}} (k-1)}{\Delta T}\,\qquad\hat{\beta}_{\text{vis}}(k)=\frac{\beta_{\text{vis}}(k)-\beta_{\text{ vis}}(k-1)}{\Delta T}. \tag{34}\]  
As a reference, results for the same scenario with a sampling interval of 200 ms but with feedback from the encoders are plotted in the figure as well. Furthermore, the desired trajectories for the generalized coordinates and velocities as well as the previously computed feed-forward signals are part of the plot. The results are explained and interpreted in the following with respect to the quality of the visual feedback and control accuracy.
## 2 Problem Statement  
### American Options as Sequential Decision-Making: A Framing Perspective  
The optimal exercise time of an American Option can be framed as a Sequential Decision-Making (SDM) problem. SDM is a type of decision problem ubiquitous in many fields, including finance, engineering, and healthcare [22]. SDM involves a sequence of decisions over time that can be represented as a sequence of the following:  
_decision, information bit, uncertainty, decision, information bit, uncertainty,..._  
After making one decision, additional information is received, which is used to update our uncertainty and support the next decision. We incur costs or receive a contribution (reward) each time we decide. The goal is to make a sequence of decisions (known as policy, guiding a decision maker (DM) on how to act according to information) that maximizes the cumulative reward over time. Reward is a quantity defined by the decision maker to measure what he/she desires. The SDM problem in the context of optimal exercise time for an American Option can be represented in a compact form, as follows:  
\[s_{0},\;a_{0},\;W_{1},\;s_{1},\;a_{1},\;\ldots,\;W_{T},\;s_{T},\;a_{T}\]  
where  
* \(s_{t:}\) is the State variable that captures what we know at time \(t\). In the context of the American Option (AO), \(s_{t}\) is a two-dimensional vector that includes the current time step and the underlying asset's price \(x_{t}\), i.e., \(s_{t}=(t,x_{t})\).
* \(a_{t:}\) is the Decision variable that captures the decision made at time \(t\). The AO problem has two alternatives Exercise the option or Continue holding the option, at each time step before the option is exercised.
* \(W_{t}\): is the Information variable that emerges after decision \(a_{t-1}\). In an AO context, it is the underlying asset's price at time \(t-1\).
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results
### Uncertainties Analysis
#### 3.3.6 Minimum Time Interval for Time Series Analysis with Sentinel-2  
Finally, we have tried to establish the required minimum repetition cycle for Sentinel-2 that would allow to detect a 10% change in glacier surface velocity, which generally corresponds to the expected variation in glacier velocity with the seasons. We assume that this requirement would be met when the 2-\(\sigma\) precision of the velocity maps (Figure 4C) is smaller than one-tenth of the magnitude of the ice flow, which would therefore provide a sufficient signal-to-noise ratio to capture the variation. Illustrated in Figure 9, this analysis shows a consistent pattern between the length of the cycle to be used and the magnitude of the glacier's surface velocity: long cycle lengths are required in slow areas of the glaciers while short cycles can be used in the fastest. For example, a minimum ten-day repetition cycle would be required for glacier surface speed time series at speeds greater than 400/year (e.g., Figure 9C, Fox Glacier), a repetition cycle greater than 30 days would be required to capture glacier flow at a rate of 200-400 m/year (typically the Bossons and Mer de Glace glaciers in Figure 9B) and lower glaciers at speeds of 100-200 m/year should be monitored with 60-day or longer cycles. In summary, this highlights the current limitations of Sentinel-2 (and therefore Landsat 8) of capturing seasonal variations for glaciers flowing at a rate slower than 100 m/year, i.e., 10 m/year speed change, which is the case for the large proportion of the glaciers of the study regions: the European Alps, the Cordillera Blanca in the Peruvian Andes and the southern Alps of New Zealand).  
Figure 8: Comparison of glacier surface velocity fields obtained by [38] (**left**), mosaicking of GoLIVE products (**middle**); and our study (**right**).
## 3 Materials and Methods
### MagEMNet Architecture  
#### Convolutional Layers  
The initial convolutional layers serve as the primary feature extractors, designed to learn spatial representations from the dual-channel input data (representing EM and magnetic field responses). The first convolutional layer operates with a kernel size of \(3\times 3\), a stride of 1, and padding to preserve spatial dimensions. This layer maps the input data  
Figure 5: Part of dataset for joint inversion. (**a**) electromagnetic data1 (**b**) magnetic data1 (**c**) electromagnetic data2 (**d**) magnetic data2.  
Figure 6: MagEMNet diagram.  
to a 32-channel feature space, allowing the model to disentangle the spatial dependencies within the geophysical data. A second convolutional layer further extends the feature space to 64 channels, enhancing the model's capacity to capture increasingly abstract features.  
The convolutional blocks are designed to detect spatial patterns at different scales, which is particularly advantageous in geophysical inversion, where both large-scale and fine-grained information about subsurface properties is essential. By employing a hierarchical feature extraction approach, these layers enable the network to capture both localized details and broader structural information.
# Meta-Learning with Graph Community Detection for Cold-Start User Clustering
## 3 Related Works
### Meta-Learning Recommendation Algorithm Applying Clustering
#### 3.3.1 Graph Community Detection  
Graph community detection algorithms are generally used when the samples can be represented as a network or a graph to find out the more closely connected samples among them. A graph group is usually defined as a subset of vertices, where the vertices in each subset are more tightly connected relative to the other vertices in the network. The criterion used to measure the quality of graph group partitioning is called modularity, whose larger value means better quality of graph group partitioning and it is calculated as in Equation (3):  
\[M=\frac{1}{2L}\sum_{i,j=1}^{N}{(A_{ij}-\frac{k_{i}k_{j}}{2L})\delta(c_{i},c_{j} )}, \tag{3}\]  
where \(L\) denotes the number of edges contained in the graph, \(N\) denotes the number of vertices, the value of \(A_{ij}\) denotes the number of edges between vertex \(i\) and vertex \(j\), \(\frac{k_{i}k_{j}}{2L}\) denotes the expected value of the number of edges between node \(i\) and node \(j\) if the edges are placed randomly, where \(k_{i}\) and \(k_{j}\) denote the degree of each vertex, that is, how many vertices are connected to it, and lastly, Kronecker's delta function, where two parameters return 1 if they are equal or 0 if they are not. In Equation (3), if the vertices belong to the same cluster, then it returns 1, and 0 otherwise.  
The overall execution process of the graph community detection algorithm is as follows:  
1. Each vertex is first formed into a cluster independently, and the modularity \(M\) of the whole network is computed.
2. Select two clusters for fusion and calculate the modularity \(M_{1}\) of the whole network to derive the change \(\Delta M\).
3. Take the two cluster fusions that show the largest increase in \(\Delta M\) and compute the new modularity \(M\).
4. Repeat steps 2 and 3 until all vertices are grouped into a clustered position and find the \(M\) value that returns the highest among them.
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 8 Discussion  
In this study, we used pcap files provided in a previous study to analyze attacks, extract features, and detect attacks using the Seq2 Seq model. Future studies will involve setting up IoT systems and verifying our ability to detect attacks in real time.  
In this study, we used sequence data, including \(k\) consecutive packets per client; the arrival time of each packet was not considered. However, if there is a significant time gap between two consecutive packets, it may be difficult to detect attacks. Therefore, we plan to investigate how the time gap between two packets affects attack detection.  
Recently, several studies have investigated the ability of generative adversarial networks to avoid detection [27]. In the future, we intend to investigate the possibility of evading malicious MQTT traffic detection using a generative adversarial network and propose a method to prevent these adversarial attacks.
# Assessment of the Length and Depth of Delamination-Type Defects Using Ultrasonic Guided Waves
## 4 Verification of Proposed Delamination Depth and Length Estimation Technique
### Verification with 2D Model
#### 4.1.2 Evaluation of Defect Length  
As the depth of the defect for both samples has been determined, the length is estimated by measuring the delay between the \"first A\({}_{0}\) arrival\" \(u_{\text{IA0}}(t)\) and \"direct A\({}_{0}\) transmission\" \(u_{\text{IIA0}}(t)\) of A\({}_{0}\) mode. The delays are measured according to the maximum envelope values of each wave packet by using Equation (6). Then, the length of the defect is estimated according to Equation (7).  
In this particular case, the delay values are estimated at a single excitation frequency \(f_{9}\) = 130 kHz. The vertical component of the particle velocity at nodal point \(r_{2}\) for sample No. 1 and sample No. 2 is presented in Figure 14a,b. The picture shows the wave packets of \"first A\({}_{0}\) arrival\" \(u_{\text{IA0}}(t)\) and \"direct A\({}_{0}\) transmission\" \(u_{\text{IIA0}}(t)\), along with the estimated delay values according to the Hilbert envelope.  
According to Equation (7), the delay time values correspond to the defect length of 67.6 mm for sample No. 1, meanwhile, for sample No. 2, the delay indicates the length of 83.4 mm. The results demonstrate that the average error in the length estimation is approximately 6% relative to the actual size. Such error is valid for frequency range between 50 and 200 kHz for defect sizes of 70 mm and 90 mm. The amount of error depends both on the accuracy of damage depth estimation and the delay time measurement. For different defect sizes and excitation frequencies, the errors may change. However, the accuracy of defect size and depth estimation can still be improved by adding scattering coefficients to the reference model and measuring delay time according to the mass center of the signal instead of using Hilbert envelope.
# The HD-Domain Metalloprotein Superfamily:
### HD-Domain Hydrolases: The Phosphatase Subfamily
#### 1.2.1 Monophosphatases  
YfbR and YGK1  
YfbR is a dimeric 5'-deoxyribonucleotidase that is specific to 2'-deoxyribonucleotide-5'-monophosphates (dNMPs) and hydrolyzes dAMP with a k\({}_{\text{cat}}\)/K\({}_{\text{M}}\) of \(25.6\times 10^{3}\) M\({}^{-1}\)s\({}^{-1}\)[3]. YfbR is most active with a mononuclear Co\({}^{2+}\) cofactor, while Mn\({}^{2+}\) and Cu\({}^{2+}\) can also support activity to a lesser extent [3]. The Co ion is bound in an octahedral geometry by the HD-domain residues H33, H68, D69, and D137, the phosphate oxygen of the nucleotide, and a water molecule that is activated by Co\({}^{2+}\) for nucleophilic attack of the  
Figure 3: Active sites of HD-domain phosphohydrolases. Mononuclear HD-domain phosphohydrolases utilize a conserved motif “H \(\ldots\) HD \(\ldots\) D” to bind a variety of metals including cobalt (pink), zinc (purple), magnesium (light green), nickel (dark green), or iron (orange). Small red spheres represent water molecules. The dinuclear phosphatase YqeK harbors two extra histidines between the HD and D residues to stabilize the second metal ion. Phosphatases are classified into mono-, di-, or triphosphohydrolases, labeled in light green, dark green, and blue, respectively. All phosphohydrolases have a conserved arginine (shown in teal), which is located typically three residues prior to the first histidine of the HD motif and in the vicinity of the oxygens of the substrate phosphate group. Other important residues are shown in pale blue and are described in the text.  
phosphate group. YfbR shows no preference for the chemical nature of the substrate base because the latter makes no significant interactions with the protein [3]. However, YfbR exhibits specificity for deoxyribomonophosphates, afforded by steric hindrance from W19 and additional hydrogen bond contacts between the deoxyribose moiety with residues R18 and D77. Substitutions of R18, D77, W19, and the metal binding ligands completely inactivate the enzyme [3].  
The HD-domain monophosphate YGK1 has high structural similarity to YfbR and acts on dNMPs with catalytic efficiencies between \(0.78\times 10^{3}\) and \(6.7\times 10^{3}\) M\({}^{-1}\)s\({}^{-1}\)[5]. Similar to YfbR, the cofactor is mononuclear and held to the protein polypeptide by H61, H89, D90, and D159 (Figure 3). The metal ions that support catalysis follow the trend: Mn\({}^{2+}\)\(>\) Co\({}^{2+}\)\(>\) Mg\({}^{2+}\)[5].
## 4 Experimental Results and Analysis
### Analysis of Different Tampering Test Results
#### 4.2.5 Content-Only Attack  
A content-only attack is an operation that tampers with the image content without changing the embedded watermark information. After tampering, the watermark information in the image is well preserved. However, the new watermark generated by the tampered image will be different from the original embedded watermark. The results of each method are displayed in Figure 12 and Table 6 gives their PSNR and SSIM values.  
Figure 11: Partially enlarged details of restored images: (**a**) details in Reference [8]; (**b**) details in Reference [9]; (**c**) details in Reference [7]; (**d**) details in the proposed method.  
Figure 10: Tamper detection and recovery results of an image deletion attack on the Venice image: (**a**) original image; (**b**) watermarked image; (**c**) tamper reference; (**d**) tampered image; (**e**) tamper detection result of [8]; (**f**) tamper detection result of [9]; (**g**) tamper detection result of [7]; (**h**) tamper detection result of the proposed method; (**i**) recovery result of [8]; (**j**) recovery result of [9]; (**k**) recovery result of [7]; (**l**) recovery result of the proposed method.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Indexes** & **[PERSON] et al. [8]** & **[PERSON] et al. [9]** & **[PERSON] et al. [7]** & **Proposed** \\ \hline PSNR (dB) & 35.19 & 35.21 & 37.27 & 37.05 \\ SSIM & 0.7194 & 0.7911 & 0.9925 & 0.9930 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Objective comparison of the recovery performance for the image deletion attack on the Venice image.  
In Figure 12c, the airplane stems from the original image Airplane and the 5 MSBs of its information are inserted into the watermarked image Clock. If the authentication watermark is completely irrelevant to the image content, then this type of attack cannot be detected and located, as shown by the detection results in Figure 12e. Therefore, the three methods shown in Figure 12f-h all generate watermarks from the image itself. In Figure 12k-l, Figure 12k eliminates several of the black noises in Figure 12i through its neighborhood adjustment, while Figure 12l has a gap in the dark vertical line on the left due to the filtering operation. The background line in the tampered area interferes with the median filtering operation, making it unable to apply its full advantages. Combining the results of the deletion attack and the content-only attack, we can conclude that while the proposed method improves the subjective quality of the restored image, it is unsuitable for tampered regions containing details within a single pixel or small area.
# PD-Free Design of Insulation Systems: An Application to Laminated Busbars
## 1 Introduction
Simulations showed that the electric field distribution in the gap was far from being almost uniform, and a good agreement with experimental measurements could be obtained by considering the non-uniformity. Another interesting contribution regarding deviations from Paschen law is given in [8]. In this work, it is shown that at triple points, which are typically present in LBBs along the connector contour, the expected PDIV deviates from the PDIV estimated by Paschen law. On the other hand, if the inception occurs sufficiently far from the triple point, and the electrons responsible for the charge follow a trajectory parallel to an electric field line, Paschen law might still provide reasonable estimates. In general, all the cited works show that non-uniform electric fields challenge the typical assumptions relevant to uniform electric field distribution, as the creepage concept for the design of surface insulation systems and Paschen law. Recent work, refs. [9; 10; 11] has been dealing with divergent fields at triple points with the purpose being either achieve a PD-free design or at least to mitigate the risk of PD inception. Focus was on reducing the maximum electric field at the critical points of the system to avoid the inception of PD. In this paper, we discuss and apply a new partial discharge inception field model that works not only for uniform fields but also for highly divergent fields (including internal and surface discharges), considering the complete field profile and not just the peak value. Also, we refer to both insulation bulk and surface design, applying an intrinsic life model for intrinsic aging (determining insulation thickness) and partial discharge inception model for surface insulation design. The PD-free concept can find useful applications also in the design and acceptance testing (besides diagnostics). LBBs are used in power and industrial electronics as well as in industrial distribution and electrified transportation (e.g., ships). An LBB is made by multiple conductors generally embedded in epoxy resin insulation and typically supplied by mixed/hybrid mode voltage (AC, DC, or AC-modulated waves). The advantage, compared to conventional polymeric cables, is to increase power density while limiting the bus inductance [12]. It has been speculated that internal and particularly surface discharges can occur in LBBs during service, causing sudden and dramatic failures affecting the functionality of the whole electrical asset [13; 14]. Therefore, developing PD-free design and testing specifications for LBBs seems to be an important and stringent topic, seeing the fast and massive trend toward power electronics and increase in specific power. This paper addresses the challenge of PD-free design by an innovative procedure, the three-leg approach [5], which is explained in Section 2 and is applied to the AC and DC insulation subsystems of an LBB in Sections 3 and 4.
## 4 Case Studies
### Wind Power, Photovoltaic Power and Load Power Forecasting
#### 4.1.5 The Results of Forecasting
The forecasting errors of wind power, photovoltaic power and load power follow the Gaussian distribution as follows:  
\[\varepsilon_{wind} \sim N(0,0.779) \tag{49}\] \[\varepsilon_{pv} \sim N(0,0.207)\] (50) \[\varepsilon_{load} \sim N(0,0.284) \tag{51}\] \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**MAE (MW)**} & \multicolumn{3}{c}{**RMSE (MW)**} & \multicolumn{3}{c}{**MAPE (\%)**} \\ \cline{2-10}  & **Wind** & **Photovoltaic** & **Load** & **Wind** & **Photovoltaic** & **Load** & **Wind** & **Photovoltaic** & **Load** \\ \hline LGBM [26] & 0.621 & 0.109 & 0.183 & 0.798 & 0.226 & 0.297 & 29.123 & 10.640 & 3.079 \\ XGBoost [25] & 0.624 & 0.122 & 0.185 & 0.813 & 0.238 & 0.289 & 27.712 & 11.348 & 3.120 \\ RR [27] & 0.857 & 0.123 & 0.254 * & 1.055 & 0.228 & 0.347 * & 44.474 & 12.986 & 4.385 * \\ SVR [28] & 0.846 & 0.155 * & 0.216 * & 1.060 & 0.216 * & 0.346 * & 40.634 & 11.275 * & 3.542 * \\ KNN [29] & 0.756 & 0.127 & 0.255 & 0.977 & 0.260 & 0.369 & 35.309 & 13.491 & 4.317 \\ LR [30] & 0.835 & 0.123 & 0.278 & 1.040 & 0.228 & 0.369 & 41.702 & 10.372 & 4.805 \\ LSTM [45] & 0.611 * & 0.115 * & 0.189 * & 0.828 * & 0.213 * & 0.288 * & 28.187 * & 11.161 * & 3.248 * \\
**Stacking** & **0.597** & **0.108** & **0.178** & **0.779** & **0.207** & **0.284** & **27.462** & **9.977** & **3.005** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Forecasting results comparison of stacking and different algorithms. Figure 6: The point forecasting results: (**a**) Wind power; (**b**) Photovoltaic power; (**c**) Load power. where \(\epsilon_{wind}\), \(\epsilon_{pv}\) and \(\epsilon_{load}\) represent the forecasting errors of wind power, photovoltaic power and load power, respectively. The forecasting probability distribution can be obtained by superimposing the distribution of forecasting errors on the point forecasting value. For example, if the point forecasting value of wind power at the next moment is 5, its forecasting probability distribution is \(N(5,0.779)\), correspondingly.
## 4 Examined Distributed Consensus Gossip-Based Algorithms
### Push-Pull Protocol  
The last analyzed algorithm is \(PP\), where each agent periodically (i.e., at each time instance \(k\)) sends a message containing its current inner state (referred to as a Push message) to one of its neighbors chosen uniformly at random [3; 34]. The message receiver answers with its current inner state (a so-called Pull message) so that these two agents can perform the pairwise averaging operation [3; 34]. Similar to the previous approach, \(PP\) is very sensitive to losing messages [34]. Again, the agents are assumed to be synchronized, so no collisions may occur while the messages are sent.