esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The paper presents a novel data-embedding method based on the Periodic Haar Piecewise-Linear (PHL) transform. The theoretical background behind the PHL transform concept is introduced. The proposed watermarking method assumes embedding hidden information in the PHL transform domain using the luminance channel of the original image. The watermark is embedded by modifying the coefficients with relatively low values. The proposed method was verified based on the measurement of the visual quality of an image with a watermark with respect to the length of the embedded information. In addition, the bit error rate (BER) is also considered for different sizes of a watermark. Furthermore, a method for the detection of image manipulation is presented. The elaborated technique seems to be suitable for applications in digital signal and image processing where high imperceptibility and low BER are required, and information security is of high importance. In particular, this method can be applied in systems where the sensitive data is transmitted or stored and needs to be protected appropriately (e.g., in medical image processing).  
w +
Footnote †: This paper is an extended version of “A Novel Watermark Method for Image Protection Based on Periodic Haar Piecewise-Linear Transform” published in the Proceedings of the 10 th International Conference, MCSS 2020, Krakow, Poland, 8-9 October 2020.
## 4 Robustness Testing
### Signal Coverage Test  
Two iBeacons were deployed on a column with tape, with a long line of sight, to test the maximum signal coverage and the stability of signals; the two mobile devices were fixed on camera tripods at 1.5 m height as shown in Figure 2a. The RSSI was measured using the two mobile devices from 5 m to 133 m distance ranges, at 5 m intervals from 5 m to 120 m, and at 1 m interval from 121 m to 133 m. At each of these distances, 1 min of data was collected.  
The outdoor maximum coverage test demonstrated that the iBeacons could provide outdoor coverage for up to 133 m with the data from two iBeacons. The signal packet loss rate, RSSI and the standard deviation of RSSI from the two devices at 5 m intervals from 5 m to 120 m, and at 1 m intervals from 121 m to 133 m, were recorded and plotted in Figure 3. From Figure 3a, the packet loss rate from device 1 became unstable after 120 m. For device 2, it started to fluctuate at 80 m. The graph shows that signals were attainable and stable when the distance was short, but different results obtained  
Figure 2: Experimental setup of signal coverage test and environmental conditions test where the red circles indicate the location of iBeacons installed. (**a**) Signal coverage test. (**b**) Obstruction of tree. (**c**) High vehicle traffic. (**d**) Pedestrian traffic. (**e**) Control dataset.  
by the two devices after 80 m suggest that differing wireless capabilities of the devices could lead to different sensitivities to wireless signals. However, implications for long-range outdoor use-cases are probably unusual, as any deployment of iBeacons should entail more than one iBeacon for a distance of over 100 m.  
In terms of the RSSI, a logarithmic nature was found in both Figures 3b,c. The RSSI collected by the two devices (Figure 3b) and their standard deviations (Figure 3c) were very similar, with an average RSSI of \(-\)88.02 dBm (for device 1) and \(-\)87.94 dBm (for device 2), respectively, and a standard deviation of 3.56 dBm (for device 1) and 3.50 dBm (for device 2) from the range of 5 m to 120 m. The result suggests the RSSI was stable when collected by different devices at different distances, and the estimation of distances from RSSI was practical.
# Distinction of Scrambled Linear Block Codes Based on Extraction of Correlation Features
## 5 Results
### Network Model Parameters
#### 5.2.2 Effect of CNN Layers and Parameters on Recognition Rate  
We analyzed the effects of the number of CNN layers and cells on the recognition rate of the algorithm, as shown in Figures 9 and 10, respectively.  
As can be seen in Figures 9 and 10, the highest recognition rate is achieved when the number of CNN layers is 2; the recognition effect is better when the number of convolutional layer cells in the first layer is 32, and the number of convolutional layer cells in the second layer is 68.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Network Parameters** & **Numerical Value** \\ \hline batch size & 20 \\ learning rate initial value & \(1\times 10^{-4}\) \\ final number of epochs & 100 \\ validation part of training set & 25\% of the training set \\ initial value of the weight constraint coefficient & 0.01 \\ number of LSTM units with shared weights & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Network model parameters.  
Figure 8: The effect of the number of cells of LSTM on the performance of the algorithm.  
Figure 7: Effect of the number of LSTM layers on the performance of the algorithm.
## 4 Conclusions  
The first results from the application of the high-resolution modeling system WRFCAMx in the GAA have revealed its overall satisfactory performance being improved with the use of the VBS OA scheme with respect to the SOAP one. PM overestimation by CAMx during the cold period of the year is related mostly to OA values and provides an indication for adjustments in the estimated bottom-up biomass burning emissions. Future work will involve the validation of the modeling system on a winter/summer seasonal basis.  
Conceptualization, A.P. ([PERSON]); methodology, A.P. ([PERSON]), S.K. and N.L.; software, S.K., N.L. and D.T.; validation, A.P. ([PERSON]), S.K., N.L., D.T., I.K. and S.S.; data curation, E.L., E.A., G.G., E.D., V.V., S.P. and A.P. ([PERSON]); writing, A.P. ([PERSON]).; review, C.Z., D.M., P.K., E.G., K.E. and A.P. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
The authors would like to acknowledge the support of the EU project, titled \"Copernicus Atmosphere Monitoring Service CAMS2_82: Evaluation and quality control (EQA) of Global products\".  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**PM\({}_{\mathbf{10}}\)*****} & \multicolumn{4}{c}{**PM\({}_{\mathbf{2.5}}\)*****} & \multicolumn{4}{c}{**PM\({}_{\mathbf{2.5}}\)*******} \\ \hline  & \multicolumn{2}{c}{**Jan 2019**} & \multicolumn{2}{c}{**Jul 2019**} & \multicolumn{2}{c}{**Jan 2019**} & \multicolumn{2}{c}{**Jul 2019**} & \multicolumn{2}{c}{**Jul 2019**} \\ \hline
**Evaluation Metrics******* & \multirow{2}{*}{**SOAP**} & \multirow{2}{*}{**VBS**} & \multirow{2}{*}{**SOAP**} & \multirow{2}{*}{**VBS**} & \multirow{2}{*}{**SOAP**} & \multirow{2}{*}{**VBS**} & \multirow{2}{*}{**SOAP**} & \multirow{2}{*}{**VBS**} & \multirow{2}{*}{**SOAP**} & \multirow{2}{*}{**VBS**} \\ \cline{1-1} **(Uniteless)** & & & & & & & & & & \\ \hline
**NMB** & \(\mathbf{+0.39}\) & \(\mathbf{+0.25}\) & \(\mathbf{-0.35}\) & \(\mathbf{-0.33}\) & \(\mathbf{+0.41}\) & \(\mathbf{+0.21}\) & \(\mathbf{-0.41}\) & \(\mathbf{-0.38}\) & \(\mathbf{-0.19}\) & \(\mathbf{-0.14}\) \\ \hline
**NMSE** & \(1.42\) & \(1.31\) & \(0.57\) & \(\mathbf{0.50}\) & \(1.71\) & \(1.50\) & \(0.71\) & \(0.62\) & \(\mathbf{0.35}\) & \(\mathbf{0.30}\) \\ \hline
**IOA** & \(\mathbf{0.58}\) & \(\mathbf{0.58}\) & \(0.48\) & \(\mathbf{0.50}\) & \(\mathbf{0.56}\) & \(\mathbf{0.59}\) & \(0.43\) & \(0.44\) & \(0.44\) & \(0.46\) \\ \hline
**Fac2** & \(\mathbf{0.59}\) & \(\mathbf{0.62}\) & \(\mathbf{0.65}\) & \(\mathbf{0.69}\) & \(\mathbf{0.58}\) & \(\mathbf{0.61}\) & \(\mathbf{0.60}\) & \(\mathbf{0.65}\) & \(\mathbf{0.78}\) & \(\mathbf{0.81}\) \\ \hline
**Fb** & \(\mathbf{+0.25}\) & \(\mathbf{+0.15}\) & \(\mathbf{-0.44}\) & \(\mathbf{-0.41}\) & \(\mathbf{+0.26}\) & \(\mathbf{+0.13}\) & \(\mathbf{-0.54}\) & \(\mathbf{-0.48}\) & \(\mathbf{-0.22}\) & \(\mathbf{-0.16}\) \\ \hline
**Fs** & \(\mathbf{+0.20}\) & \(\mathbf{+0.07}\) & \(\mathbf{-0.
# Elastic Modulus Prediction Model of Foamed Concrete Based on the Walsh Formula
## 2 Micromechanical Analysis of Foamed Concrete Elements
### Spherical Shell Elastic Modulus of Foamed Concrete  
Owing to the existence of a large number of closed air bubbles in foamed concrete, mechanical analysis of foamed concrete microelements can be conducted on the basis of the assumption of spherical pores. First, a foamed concrete unit containing spherical pores is selected, which can be regarded as a spherical shell with inner and outer diameters a and b, respectively. When the internal pore connectivity is poor, it is assumed that no pore water pressure exists. The inner diameter variation for the spherical shell has the following relationship with volume variation, as shown by Equations (4)-(6).  
\[\Delta V_{c}=4\pi a^{2}\Delta a=-4\pi a^{2}u_{a} \tag{4}\]  
\[V=\frac{4}{3}\pi b^{3} \tag{5}\]  
\[\frac{\Delta V_{c}}{V}=\frac{9n(1-v)\Delta\sigma}{E_{1}(1-n)} \tag{6}\]  
When Equation (6) is substituted into Equation (2), the calculation formula of the elastic modulus of foamed concrete element with respect to porosity can be obtained, as shown by Equations (7) and (8).  
\[E_{2}=\frac{1-n}{1+\beta n}E_{1} \tag{7}\]  
\[\beta=\frac{1+v}{2(1-2v)} \tag{8}\]  
The initial porosity of foamed concrete can be measured experimentally. Nevertheless, in the actual compression process, the porosity changes with the pore deformation of foamed concrete. Therefore, this study analyzes it from the perspective of stress. The derivation process is shown in Equations (9) and (10).  
\[n_{0}=\frac{V_{c}}{V} \tag{9}\]  
\[n=\frac{V_{c}-\Delta V_{c}}{V-\Delta V_{c}}=\frac{V_{c}/V-\Delta V_{c}/V}{1- \Delta V_{c}  
in the porous material theory is proposed. The basic principle of the model is that foamed concrete is regarded as a two-phase composite material, and the overall elastic modulus is obtained through the homogenization process. From a microscopic analysis, the holes in foamed concrete can be regarded as spherical shells, so its mesoscopic structure can be regarded as the form of a matrix unit wrapping a spherical shell unit. The schematic of the calculation model is shown in Figure 4. From the figure, foamed concrete conforms to the two-layer embedded wrapping model [36; 37; 38] on the mesostructure.
## 4 Framework
The values of attributes with continuous domains are clustered into three groups, usually labelled'small','medium', and 'large', using the 33% and 66% percentiles as thresholds, e.g., a value is mapped to the categorical value'medium' if it falls in a percentile comprised between the 33% and 66%. Next, to train and test ML-based predictive models, an event log is pre-processed using prefix-length bucketing and index-based encoding [27]. That is, we create a different model for each prefix length, trained/tested using prefixes pre-processed to obtain a fixed-length features vector. All attributes of an event log are pre-processed using one-hot encoding. Figure 1 shows an example of pre-processing an event log where the continuous attributes are first discretised and then attributes are one-hot encoded to obtain an item matrix. The figure shows that the categorical attributes, such as activity labels, are one-hot encoded index-based, i.e., for each position in a trace, an item is generated for all possible activities in the event log, and only the item corresponding to the activity that was executed is set to 1. The encoding of the numerical attributes (attributes C2 and timestamps in the example) occurs through the discretisation described above. Finally, all the items obtained are integrated into the item matrix that is used for the classification task. For ARM-based models, the objective of pre-processing is to obtain an extended item matrix from which rules can be extracted. Similarly to the case of ML-based models, we consider prefix-length bucketing and index-based encoding, that is, we create different item matrices for each prefix length.
# Vision Transformer in Industrial Visual Inspection
## 4 Experiments and Results
### Results
#### 4.2.1 Full Images  
In the sheet metal (SM) use case, the DDETR model achieves the highest \(mAP_{50,test}\) of 69.9%, when looking at \(F1\)-\(Score_{initial}\) it is surpassed by Retina Swin by 6.5%p. Probably, this is due to DDETRs matching loss, which optimizes the model to preferably output only one prediction per ground truth bounding box. This, in turn, leads to lower recall compared to the Retina architecture models and, therefore, to a lower F1-Score. After adjusting the IoU threshold, Retina Net takes the lead with 81.3% \(F1\)-\(Score_{adjusted}\), while Retina Swin comes in second at 79.1%. The reason for this could be the fact that the weights of Retina Nets backbone and head used for transfer learning have been optimized jointly on COCO before, while the Swin weights and Retina head of Retina Swin are loaded separately before training on the SM use case. Yolo also slightly outperforms DDETR by 0.9%p. The DETR model significantly suffers from the low batch and general dataset size, as expected, resulting in the worst performance of all evaluated models in all metrics.  
For the wooden load space (WLS) use case, the best performing models are Yolo V3, considering 52.2% \(mAP_{50,test}\), respectively, 53.1% \(F1\)-\(Score_{initial}\) and Retina Net with the best \(F1\)-\(Score_{adjusted}\), amounting to 64.4%. This can be explained by the greater number of generated predictions before NMS of Retina Net compared to Yolo V3 (100k vs. 10k), which results in a higher recall and, consequently, a higher \(F1\)-\(Score\). DDETR ranks third in \(mAP_{50,test}\), second in \(F1\)-\(Score_{initial}\) and third again in \(F1\)-\(Score_{adjusted}\), with values of 41.4%, 49.1% and 52.6%, respectively.  
Generally, the models performed worse compared to the SM use case, due to the higher difficulty caused by more damage classes, as well as higher intra-class variability. The complexity of the character recognition (CR) use case cannot be handled by any of the evaluated models, none of them were able learn to predict overlapping bounding boxes on the full images at the utilized resolution. Higher resolution training was not explored as for the DETR family models, it would have exceeded the memory capacity of our hardware.
## 2 Antenna Design
### Single Antenna Design Evolution Steps
\begin{table}
\begin{tabular}{c c c} \hline
**Radius A1** & **Radius A2** & **Radius A3** \\ \hline N1 = Number of circles & N2 = Number of circles & N3 = Number of circles \\ = 5’1 & = 5’2 & = 5’3 \\ \hline L1 = Scaling factor & L2 = Scaling factor & L3 = Scaling factor \\ L1 = (1/3)’1 = 0.333 & L2 = (1/3)’2 = 0.111 & L3 = (1/3)’3 = 0.037 \\ \hline A1 = 0.333 \(\times\) 9.31 & A2 = 0.111 \(\times\) 9.31 & A3 = 0.037 \(\times\) 9.31 \\ = 3.10 mm & = 1.03 mm & = 0.344 mm \\ \hline \end{tabular}
\end{table} 
Table 1: Calculation of radius A1, A2 and A3. \begin{table}
\begin{tabular}{c c c} \hline
**Parameters** & **Values** & **Parameters** & **Values** \\ \hline L\({}_{\text{S}}\) & 28.22 mm & F\({}_{\text{L}}\) & 11.007 mm \\ \hline W\({}_{\text{S}}\) & 28.22 mm & F\({}_{\text{W}}\) & 2.99 mm \\ \hline A & 9.31 mm & \\ \hline \end{tabular}
\end{table} 
Table 2: Dimensions of single-element antenna. Figure 3 illustrates the gain of a single antenna. It is evident from the figure that the suggested antenna attains a maximum gain of 8.46 dB. Figure 4 illustrates the simulated E and H plane radiation patterns of the single element patch antenna operating at frequencies of 9.2 GHz, 21.5 GHz, and 26.7 GHz. The antenna exhibits partly omnidirectional radiation characteristics with multiple lobes at higher operating bands resonating at 21.5 GHz and 26.7 GHz. Figure 4: _Cont._  
Figure 3: Gain of single antenna. \begin{table}
\begin{tabular}{c c c c} \hline
**Parameters** & **Bandwidth (MHz)** & **f\({}_{\textbf{r}}\) (GHz)** & **S11 (dB)** \\ \hline
0 th iteration & 400, 5400, 3800 & 9, 18.8, 26.4 & \(-\)19.6, \(-\)24.2, \(-\)23.5 \\ \hline
1 st iteration & 1200, 5200, 3800 & 9.3, 21.7, 26.2 & \(-\)23.8, \(-\)26.2, \(-\)23.4 \\ \hline
2 nd iteration & 1000, 700, 5500, 3700 & 9.2, 10.6, 18.8, 26.6 & \(-\)24.7, \(-\)12.1, \(-\)24.5, \(-\)23.7 \\ \hline
3 rd iteration & 1000, 700, 200, 5500, 3800 & 9.2, 10.6, 16.3, 21.5, 26.7 & \(-\)26.8, \(-\)12.4, \(-\)10.8, \(-\)28.6, \(-\)25.5 \\ \hline \end{tabular}
\end{table} 
Table 3: Iteration effect on single element antenna.
# Delamination Localization in Multilayered CFRP Panel Based on Reconstruction of Guided Wave Modes
## 1 Introduction
The phase and group velocities of guided waves are important features to inspect the structure. These features are dependent on the ply direction, elastic properties, and the product of frequency-thickness [46]. A phase velocity technique was proposed by [PERSON] to detect, locate, and size the through-thickness hole in composite material by measuring phase velocity variation within the defect region [47]. [PERSON] presented a technique to evaluate matrix cracking and fiber damage caused by cycle loads by monitoring a significant drop in the speed of phase velocity [48]. [PERSON] proposed a technique to monitor the delamination growth between the laminates by monitoring group velocity variation in the multilayered composite [49]. Identification of guided wave modes in the presence of low amplitude, converted, and overlapped modes has been a long-standing problem. Zero-crossing and Hilbert transform methods were used to calculate the ToF of well-isolated modes to determine the location of delamination. However, the challenges arise when ToF needs to be determined using zero-crossing and Hilbert transform techniques due to phase shift, signal-to-noise ratio, distorted signal, and significant dispersion caused by anisotropic material properties [50; 51; 52]. In this study, the semi-analytical finite element method was used to obtain dispersion curves to calculate the guided wave mode velocity at various frequencies. The GW tuning curves were obtained to evaluate the dominant mode and the optimal excitation frequency for inspection. A 2D FFT-based spatial filtering was used to filter the GW modes from the B-scan data. To separate the specific mode, a frequency-dependent bandpass filter with a cosine-tapered window was used to eliminate the S\({}_{0}\) mode in the frequency wavenumber domain. A phase spectrum approach was used to obtain the phase velocity of A\({}_{0}\) mode. Then the analytical GW modes were reconstructed following different propagation scenarios such as reflected, converted, and overlapped modes. By selecting the A\({}_{0}\) mode, analytical modeling, numerical simulation, and experiments were performed to detect and estimate the location of the delamination. The objective of this research is to present a novel analytical model to locate the delamination in the presence of reflected, converted, overlapped, and low-amplitude GW modes. A phase velocity reconstruction approach was proposed to reconstruct the velocity of unknown materials from the baseline reference data. An efficient SHM technique was developed to detect and localize the delamination in the multi-layered CFRP material with a minimum number of sensors. Theoretical analysis of GW interaction with delamination analytical, numerical, and experimental analysis of GWs to increase the probability of detection in CFRP material was used. The quantitative and qualitative analysis for the estimation of delamination position is assessed.
# Comparative Study of Physics Engines for Robot Simulation with Mechanical Interaction
## 4 Simulation and Results
### Multipoint Contact Scenario  
In this scenario, we used the direction of the friction force acting on the cylinder as a performance index since most physics engines include approximation to the direction of the friction force (e.g., polyhedral approximation of friction cone). In particular, when multipoint contact occurred, the direction of the friction force could be significantly changed because the contact point was approximately reduced to a small number of contact points to reduce the computational load as explained in Section 2. Therefore, we applied force only in the \(x\) direction while pressing down to maintain the contact between the cylinder and box, and measured how much the \(y\) direction component of the friction force occurred compared to the \(x\) direction component by calculating an angle. Here, a large angle value means that a large error in friction force occurred, since the \(y\)-direction friction force should not be generated. In addition, we measured the penetration depth as in the simple scenario.  
As shown in Figure 6, similar to the result of [17] referenced when including the Vortex as the physics engine to be compared, the Vortex showed the best performance in terms of contact force when multipoint contact occurred on a simple mesh surface. Here, in the case of ODE and Bullet, a failure occurred in which the cylinder fell as shown in Figure 7 at a specific time step, and the time step without data in Figure 6 was a failure case. Like in the simple scenario, Bullet had little penetration, and MuJoCo had large penetration. In addition, because of ignoring the nonpenetration constraint (i.e., soft contact) in MuJoCo, the cylinder continuously moved up and down.  
Figure 5: Performance of each physics engine for a simple scenario: (**left**) average percentage error of the friction force acting on all upper boxes with an analytic solution; (**right**) maximal penetration depth of all upper boxes.
# Performance Analysis of Existing ITS Technologies: Evaluation and Coexistence
## 4 Coexistence of ITS Technologies  
This section presents the spectrum band dedicated to vehicular networks and channel coexistence problems between ITS technologies. Subsequently, we define a coexistence solution based on the evaluation model and the results obtained in the previous section.
# A Component-Based Approach to Early Warning Systems: A Theoretical Model
## 1 Introduction
### Aims and Contributions of This Article  
The aim of this paper is to define and categorise EWSs and their components, with a focus on the timeline of natural disaster development and the need for information distribution based on warnings or alerts during different phases of their progression. This approach is unique, as there is currently no widely adopted similar classification, despite the fact that information requirements vary significantly at different stages of a crisis event. The proposed classification of components highlights the importance of real-time data and the targeted distribution of this information, which is crucial for the effective implementation of EWSs.  
This paper identifies the primary and secondary causes of crisis events and their manifestations, which play a key role in preventing and mitigating natural hazards. Understanding these factors is essential for comprehending the dynamics of such events and is critical for the development of EWSs that can help mitigate their negative impacts. The identification of these factors also underscores the importance of implementing sensors to monitor different aspects of natural disasters, enabling the early detection of risks and the provision of relevant data for decision-making.  
For the identified manifestations of natural hazards, an analysis of sensors used for measuring the respective parameters was conducted. The sensor list was compiled based on an analysis of scientific publications, with the objective of identifying 5-10 types of sensors for each natural hazard manifestation.  
The main contribution of this paper lies in consolidating and aligning information on EWSs and their functionality. Rather than focusing on a specific type of natural hazard, this approach provides a general perspective on the requirements these systems should meet across all types of natural hazards. By doing so, the article reflects the universal criteria necessary for the effective operation of EWSs. The proposed classification of EWSs into three components based on the disaster timeline offers a new perspective on this issue. These components not only describe the data sources used but also address the methods of information dissemination to the public.