esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Waveform Matching  
Each real GEDI waveform was compared against hundreds of simulated waveforms to determine the state of the forest. This process is referred to as waveform matching [19]. Lidar matching was used to determine the current state of the forest under consideration of each GEDI waveform. Based on the forests simulated in FORMIND, virtual lidar waveforms were simulated and stored every five years. Each real GEDI waveform was compared to the dataset of all simulated lidar waveforms in the respective environmental region, and a relative overlap of the waveforms was determined starting at a height of 5 m [19; 31]. The lowest 5 m waveforms were discarded in the waveform matching because the shapes of the real GEDI waveforms in these low heights are often influenced by uneven terrain and understory vegetation, leading to blurred ground return peaks. These influences were absent in simulated waveforms, which possessed sharp peaks at ground level.  
The virtual waveforms with the best overlaps were used to determine the current forest condition, as these waveforms best matched the GEDI data and thus reflected reality (Figure S4).  
Fifty simulated waveforms with the best overlaps were selected, with at least 70% overlap [19]. By linking the simulated data with the real data, it was possible to derive, for example, the simulation time steps of the best-matching waveforms and, from that, various forest parameters. For each of the 50 waveforms, the respective forest parameters were derived and then averaged over all 50 values (Figure 1). In this work, the focus was on the productivity of the forest at a given time point, which was determined by the gross primary production. About 155 million GEDI waveforms in the considered area were available for analysis after the quality flag check. After rejecting some waveforms due to mismatches (no waveform matching possible because all overlaps <70%), and discarding waveforms whose RH95 values were <5 m, a total of about 110 million GEDI waveforms were included in our study. From these, it was possible to use waveform matching to determine, for example, the structure and productivity of the forest at the time the GEDI waveforms were recorded. Although most of our analyses were at the footprint level, for some results and figures, forest variables were aggregated to a 1 km\({}^{2}\) scale by averaging; 1 km\({}^{2}\) cell grids where no waveforms were located were not included in this analysis.
# Applications of Nanomaterials in RFID Wireless Sensor Components
## 4 Nanomaterials for RFID-Based Sensing System
### Polymer
Despite having lower conductivity than copper, organic polymer-based antennas demonstrated readable distances of up to 75 cm, indicating sufficient performance as antennas. Additionally, these antennas were printed as films on transparent glass substrates, providing optical transparency useful for next-generation displays. Conducting polymers can selectively respond to specific analytes and are widely used as detection materials due to the ease of adjusting selectivity by controlling various parameters. Nanostructures of conductive polymers, in particular, enhance effective detection of target substances by increasing the interaction sites with the analyte due to a high surface-area-to-volume ratio. For instance, [PERSON] et al. utilized carboxyl-functionalized polypyrrole (C-PPy) nanoparticles as the active material for a sensing tag for detecting ammonia gas (Figure 5b) [64]. While conducting polymers can respond to external environmental stimuli, their resistance changes are relatively small compared to other materials. To overcome this limitation, conducting polymers can form heterojunctions with their opposite semiconductor mate \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Substrate**} & **Dielectric Constant** & \multirow{2}{*}{**RH Low [\%]**} & \multirow{2}{*}{**RH High [\%]**} & **Frequency Shift [MHz/\%]** & \(\Delta\)1S\({}_{21}\)**1 min** \\  & & & & & **[dB/\%]** \\ \hline DiClad & 2.33 & 15 & 70 & 3.909 & 0.012 \\ RO4003C & 3.88 & 15 & 75 & 2.683 & 0.018 \\ FR4 & 4.6 & 10 & 65 & 3.454 & 0.017 \\ RO3010 & 11.2 & 10 & 90 & 1.562 & 0.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Frequency shifts and signal amplitude variation of humidity sensor based on dielectric substrates [97]. rials to enhance sensitivity. [PERSON] et al. improved the sensitivity of a RFID sensor by introducing n-type phosphonium-functionalized reduced graphene oxide (P-rGO) into p-type semiconductor PANI, forming localized heterojunctions (Figure 5c) [114]. The p-n heterojunction induced a rapid resistance change in the material by altering the depth of the depletion layer, depending on sensing conditions, enhancing the sensor's performance.
# Unmanned Aerial Vehicles Enabled IoT Platform for Disaster Management
## 2 Literature Review and Applications in Disaster Management
### Literature Review
In [36], an NDN based emergency message delivery mechanism is proposed by using multi-casting. An NDN-based solution is proposed in [37] that uses the satisfied interest table (SIT) along with push-based messages for communication among nodes. The SIT can help nodes to quickly find relative information in a fragmented network that can be useful in scenarios such as disaster management. In [38], the authors proposed an NDN based UAV's communication system in which authors emphasize the importance of light weight ICN (NDN) based communication mechanism in UAVs. Moreover, lightweight interest and data packet payload and the inbuilt security feature of NDN architecture can help the smart gadgets to communicate with each other with increased reliability and less power consumption. These NDN advantages further secure and enhance the IoT-UAV based systems. In this paper, we propose NDN-based ground IoT mechanism for wildfire management. The trigger based NDN communication in ground IoT network increases the efficiency of the system in terms of reliability and response time of the nodes besides reducing the overall power consumption.
## 2 Collision Avoidance System  
### General Layout  
The assistance system is based on four main modules:  
* Obstacle detection module: The mission of this module is to detect and track obstacles and determine their speed and direction while distinguishing between obstacles that are within the area of interest and may present a risk, and those that are outside. Positioning of the vehicle and the obstacles in accurate and detailed digital maps provide extra information.
* Decision module. This module decides the best possible action to take to avoid an accident or reduce its consequences based on the information from the surroundings. Its premise is not to generate any additional risks for other road users. This decision module should take into account the road characteristics, the own vehicle movement, the obstacles and should generate manoeuvres that are feasible in practice according to vehicle dynamics and should not be surprising for the drivers.
* Autonomous manoeuvring module. The mission of this module is to carry out the manoeuvre selected by the previous module by acting on the vehicle's controls, without eliminating the capability of the driver of acting over those controls if the wants.
* Communications module. This module sends messages to nearby vehicles in the event of risk situations being detected that lead to avoidance measures so that those vehicles can be made aware of these anomalous moving traffic circumstances. Communications are not used for obstacles detection but to warn of sudden manoeuvres that could be performed by the autonomous system.
# Analysis of Permanent Magnet Motors in High Frequency--A Review
## 3 Analysis Tools
### Finite Element Methods (FEM)
#### 3.1.2 Analysis of Coil Conductors
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Case**} & **Frequency** & \multirow{2}{*}{**Geometry**} & \multirow{2}{*}{**Mesh**} & **Rotor** & **End** & **Skin** & **Proximity** & **Iron** \\  & & & & **Model** & **Winding** & **Effect** & **Effect** & **Core** \\ \hline
[73] & 30–5 M & 3-D.1 slot. & & x & x [85] & & (Bulk turn) & \(R_{core}\) \& \(L_{core}\)\({}^{1}\)[11] \\
[54]\({}^{2}\) & 100–100 M & 2-D.1/8 Model. & & x & & x & x & - \\
[62] & 20–4 M & 2-D.1/4 Model. & & x & & (Bulk turn) & Equivalent \(\sigma\) \\
[52,86] & 10 k & 2-D.1/2 Model & \(<\)\(\delta\) & x & & x & x & Nonlinear BH \\
[58] & 100–1 M & 2-D slot & & x & & x & Complex \(\mu\). Linear BH \\
[61] & 0–100 k & 2/3-D & & x & x & x & x & nonlinear BH and losses \\
[39] & 10–10 M & 2-D.1 slot & & x & & x & x & x \\
[51] & 1 k–10 M & 2-D.1 slot & & & x & x & Constant \(\mu\), No lamination \\
[55] & 10 k–20 M & 3-D.3 slot & \(<\)\(\delta\) & & x & x & Constant \(\mu\). One sheet \\
[67] & 0–10 M & 2-D.1 slot & \(<\)\(\delta\) & & & x & x & Coreless \\
[68] & 0–200 k & 2-D.1 slot & \(<\)\(\delta\) & & & x & x & x \\ \hline
[71] & 0–800 & 2-D Full & \(<\)\(\delta\) & x & & x & x & \\
[72] & 500–3 k & 3-D.3 coils & Some \(<\)\(\delta\) & x & & & Coreless \\
[84] & 0–333 & 2-D. Half & Fine & & x & x & x & nonlinear BH and losses \\
[78] & 0–14 k & 3-D.1 Phase & Fine & & x & x & x & nonlinear BH and losses \\
[66] & 0–600 & 2-D.1 pole & & x & & x & x & \\
[70] & 10 k–1 M & 2-D.1 slot & \(<\)\(\delta\) & & & x & Complex \(\mu\). One sheet \\ \hline \hline \end{tabular} \({}^{1}\) Analytical equations are used. \({}^{2}\) Rotor position is taken into account for R and L calculations.  
\end{table} 
Table 1: FEM Electromagnetic simulation comparison.
## 3 Procedure, Data and Methodology
### Construction of Lexical Situation Type System
#### 3.1.4 Automatic Generation of Lexical Situation Type System  
Based on the annotation results, verbs exhibit distinct distribution patterns in terms of aspectual marker features. Although [PERSON] [12] classified the situation type system of verbs according to objective grammatical criteria, his approach remains insufficiently thorough. This is because, when determining the presence and intensity of the three elements--inception, finish and duration--he merely aggregated these features without employing a more scientific method to examine the impact of differences in element intensity. Therefore, to address the limitations of previous research and further measure the similarity and structural relationships among verbs, we transformed the categorical variables in the original matrix into dummy variables. Based on these dummy variables, we performed hierarchical clustering on the verbs and visualized the clustering results using a dendrogram. Finally, we evaluated the clustering outcome using the Cophenetic Correlation Coefficient (CPCC) metric.  
Prior to conducting hierarchical clustering, it is necessary to calculate the distance between each pair of samples and assemble these distances into a distance matrix to determine the optimized clustering structure during the clustering process. Since there are multiple methods for calculating the distance matrix and linking methods, different parameters can yield varying clustering results and construct different dendrograms. To achieve a more desirable hierarchical clustering outcome, we first employed the grid search method to identify the optimized parameters for hierarchical clustering.  
Grid search can exhaustively explore all possible combinations of parameters, selecting the optimized set for hierarchical clustering. Using this set of parameters in hierarchical clustering yields the best CPCC, and the dendrogram with the highest CPCC value is chosen as the optimized dendrogram.  
After determining the parameters, we employ agglomerative hierarchical clustering to process the matrix, automatically generating the hierarchical structure of situation types from the bottom up. In hierarchical clustering, each sample initially forms its own cluster. Based on the distance information in the distance matrix, the similarity between clusters is calculated, and at each step, the two closest clusters are merged. This process continues until all samples are merged into a single cluster. The system of situation types is then automatically generated based on the semantic distances between verbs and is presented in the form of a dendrogram.  
Finally, based on the results of hierarchical clustering, the verb situation types are mapped to the senses of the verbs. We adhere to the principle of a one-to-one correspondence between verb senses and situation types, meaning that each sense of a verb is assigned to a specific situation type in the hierarchical clustering, thereby obtaining the corresponding situation type labels.
## 2 Results and Discussion
### Farfield Radiation Pattern
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multicolumn{1}{c}{**Design Name**} & \multicolumn{1}{c}{\begin{tabular}{c} **Resonant Frequency** \\ **(f\({}_{\text{c}}\)) (THz)** \\ \end{tabular} } & \multicolumn{1}{c}{\begin{tabular}{c} **Return Loss** \\ **(dB)** \\ \end{tabular} } & \multicolumn{1}{c}{\
\begin{tabular}{c} **Gain (dBi)** \\ \end{tabular} } & \multicolumn{1}{c}{
\begin{tabular}{c} **Directivity (dBi)** \\ \end{tabular} } \\ \hline One segment & Ordinary state & 0.54 & \(-\)6.61 & 1.73 & 7.35 \\  & Extra ordinary state & 0.504 & \(-\)14.5 & 4.29 & 8.43 \\ Two segment (without arm cut) & 0.558 & \(-\)8.07 & 0.981 & 5.9 \\ Two segment (large electrical length) & 0.555 & \(-\)10.7 & 2.09 & 7.22 \\ Two segment (small electrical length) & 0.579 & \(-\)9.4 & 2.42 & 7.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parametric comparison of different geometries of the fractal antenna at resonant frequencies. arm (no change in electrical length), as shown in Figure 9a,b. However, after varying the electrical length value, a significant amount of gain is observed for the two segment fractal antenna. The gain of 2.09 dBi at 0.555 THz is observed when the fractal geometry was cut into small physical lengths (large electrical length). On the other hand, a gain of 2.42 dBi at 0.579 THz is achieved when the fractal geometry was cut into large physical lengths (small electrical length). The farfield gain of all the fractal antennas at resonant frequencies are summarized in Table 2.  
Figure 6: **The 3D and polar plot of the farfield radiation pattern of the one segment Hilbert fractal reconfigurable antenna from (a,b) ordinary (at 0.54 THz) to (c,d) extraordinary (at 0.504 THz) polarization in the PDLC. **  
Figure 7: **The 3D and polar plot of the farfield radiation pattern of different electrical length two segment Hilbert fractal reconfigurable antenna in ordinary polarization in the PDLC. (a,b) Two segments with no cut (at 0.558 THz), (c,d) two segment with cut (large electrical length) left shift (at 0.555 THz), and (e,f) with cut (small electrical length) right shift (at 0.579 THz) of the fractal geometry. **  
Figure 8: The 3D and polar plot of the farfield gain of the one segment Hilbert fractal reconfigurable antenna from (**a**,**b**) ordinary (at 0.54 THz) to (**c**,**d**) extraordinary (at 0.504 THz) polarization in the PDLC. Figure 9: Cont.
## 4 Discussion  
The results from Table 1 regarding the multi-depth encryption performance reveal that the phase encryption algorithm proposed in this study by encrypting and decrypting images through random keys could effectively enhance image qualities and reduce the interference of out-of-focus images. In the same optical axis, the multi-depth phase encryption result, the RDE results reveal that the image reconstruction quality is greatly enhanced, to at least 80% of the quality level, due to the reduction of out-of-focus image interference. When examining the distortion of diffraction reconstruction images based on the RMSE value, the reduced RMSE value indicates lower image distortion. Moreover, if the SNR values are compared, the noise of different reconstruction depths can be found to decrease, i.e., reducing image interference. To verify the results in Figure 8, the method proposed in this study, compared to the previous MGSA algorithm, could effectively reduce out-of-focus image crosstalk.  
In terms of the performance of angle/depth encryption, Table 2 shows that the phase encryption algorithm could enhance the quality of multi-view images. From the RDE results, the reduction of out-of-focus image interference tremendously enhances the image reconstruction quality to more than 80% of the quality level. Judging from the distortion of the diffraction reconstruction image with RMSE values, the results reveal that the reduced RMSE value indicates lower image distortion. Finally, the comparison of SNR values demonstrates that the noise of each angle was obviously reduced, i.e., reducing image interference. Figure 9 reveals that when calculating multi-angle images with the MGSA algorithm, images with different views interfere in focusing images. On the contrary, the method proposed in this study could effectively reduce out-of-focus image crosstalk.  
Based on the analysis of the more detailed intensity distribution, out-of-focus images have great effects on the results. As shown in Figure 13a, the impacts of the out-of-focus image appear to be serious. Figure 13b-d display the reconstruction results with phase-locked encryption, which is shown to effectively remove the effects of out-of-focus images. Figure 14a shows the result of the side view, where the effect of the out-of-focus imaging actually exists. Figure 14b-d displays the result of side view reconstruction with phase-locked encryption, where the effects of the out-of-focus image are significantly decreased.  
Figure 11: Experimental results of coaxial reconstruction with distinct depth (**a**) with phase-locked algorithm and (**b**) without phase-locked algorithm.  
Figure 12: Experimental results of reconstruction with same depth but different view (**a**) with phase-locked algorithm and (**b**) without phase-locked algorithm.  
Figure 14: _Cont._  
Figure 13: Front view analysis results (**a**) without phase-locked algorithm and (**b–d**) with phase-locked algorithm.
# Automatic Symbol Resolution on Embedded Platforms by the Example of Smart TV Device
## 5 Materials and Methods
### Distinctive Functions  
The same method could not be used with Samsung TVs as the vendor does not provide any symbol file with the firmware, and as a consequence, a different approach is needed. In the case of Samsung firmwares, most of the reverse engineering was done as a part of SamyGO project [26], which started around 2009 when B series TVs were out in the market. Developers were not only able to reverse engineer a lot of security mechanisms, but even have provided custom firmwares as well as extracted security keys. Therefore, it should not be a surprise that removing symbols was considered as a good idea to stop or at least slow down reverse engineering of C series firmware [27]. The main flaw of this idea was that during the preparation of new firmwares, developers used the old code. Therefore, the new firmware with removed symbols was based on the previous series, and the previous versions of the firmware could be used to retrieve removed symbols.  
This can be illustrated in the example of TVs movie player seek patch. There was already a developed version for the B series ([[https://forum.samygo.tv/viewtopic.php?t=1270](https://forum.samygo.tv/viewtopic.php?t=1270)]([https://forum.samygo.tv/viewtopic.php?t=1270](https://forum.samygo.tv/viewtopic.php?t=1270)), accessed on 13 April 2021), which was based on patching seek values in function CWProVideoPlayer::ProcessDirectionKey. Unfortunately, the same approach cannot be directly applied to C series as there is no information where this method is located because of the removed symbols. However, through the function content analysis, it is possible to find where the same function is located on the C series firmware.  
In the analyzed case, the most interesting part of the function are two specific places with default seek values for _down_ and _up_ keys, see Listings 2 and 3, respectively.  
```
LDAR2,[R6,#0 xC];onDown LDAR4,=0 xCE800000;float-1073741824.00 MOVR0,R2
```
Listing 3: Part of the function CWProVideoPlayer::ProcessDirectionKey which sets the value 0x4D4 CCCCCD when the _up_ key is pressed.
```
LDRR2,[R6,#0 XC];onUpLDRR4,=0x4D4 CCCCD;float214748368.00 WOVRO,R2
```
Those are simply huge positive and negative float values, which as a result cause the TV to jump to the beginning or end of the movie on key down and key up. At this point, symbols do not matter anymore as it can be safely assumed that the same or at least similar code was used on the C series. A quick binary search for 0x4D4 CCCCD gets us to the patch location (Listing 4).
```
LDRR3,[R5,#8];onUpLDRR4,=0x4D4 CCCCD;float214748368.00 WOVRO,R3
```
As different firmware versions are often completed with the same or at least similar compiler, it is also possible to locate missing symbols by searching for specific assembler instruction sets. The approach described above has been implemented in find_func_by_binary_data. The function with a removed symbol is localized based on characteristic, binary values, or a set of assembler instructions unique for specific functions (signatures).
# Numerical Investigation of the Hydrodynamic Characteristics of 3-Fin Surfboard Configurations
## 2 Materials and Methods  
### Fin Design  
To ensure all simulations are comparable, a baseline fin template was produced. This neutral template will mimic a popular commercial fin, which is described as having all-rounded and balanced performance [8]. Normally produced using injection moulding, these fins can be produced using other means, such as additive manufacturing [9] or hand shaping. This baseline fin has a rake of 33.7\({}^{\circ}\), a depth of 115 mm, a base length of 111 mm, and a cart of 5\({}^{\circ}\) (Figure 1). Autodesk Inventor 2020 was used to produce the CAD models.  
Each of the parameters in this study (rake, depth, base length) had a large and small template simulated. In total, seven fin templates were simulated (one baseline + three parameters \(\times\) two variations). The only dimensional change between each template and the baseline design is the named parameter. The change for these maximum and minimum templates was: 37\({}^{\circ}\) and 28\({}^{\circ}\) for the rake, 121 mm and 105 mm for the depth, 119 mm and 100 mm for the base length, listed in Table 1. Each of these dimensions is the maximum and minimum values listed in the thruster fin product range of a commercial fin manufacturer [8].  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \begin{tabular}{c} **Template** \\ **Name** \\ \end{tabular} & **Rake (\({}^{\circ}\))** & \begin{tabular}{c} **Base Length** \\ **(mm)** \\ \end{tabular} & **Depth (mm)** & **Foil Centre** & **Foil Side** & \begin{tabular}{c} **3-Fin Reference** \\ **Area (mm\({}^{2}\))** \\ \end{tabular} \\ \hline Baseline & 33.7 & 111 & 115 & NACA 0006 & \begin{tabular}{c} Half NACA \\ 0012 \\ \end{tabular} & 21,994.9 \\ \hline Large-Rake & 37 & 111 & 115 & NACA 0006 & \begin{tabular}{c} Half NACA \\ 0012 \\ \end{tabular} & 22,804.8 \\ \hline Small-Rake & 28 & 111 & 115 & NACA 0006 & \begin{tabular}{c} Half NACA \\ 0012 \\ \end{tabular} & 21,268.3 \\ \hline Large-Base & 33.7 & 119 & 115 & NACA 0005 \({}^{\star}\) & \begin{tabular}{c} Half NACA \\ 0005 \({}^{\star}\) \\ \end{tabular} & 23,453.7 \\ \hline Small-Base & 33.7 & 100 & 115 & NACA 0014 \({}^{\star}\) & \begin{tabular}{c} Half NACA \\ 0014 \({}^{\star}\) \\ \end{tabular} & 20,039.9 \\ \hline Large-Depth & 33.7 & 111 & 121 & NACA 0006 & \begin{tabular}{c} Half NACA \\ 0012 \\ \end{tabular} & 23,617.5 \\ \hline Small-Depth & 33.7 & 111 & 105 & NACA 0006 & \begin{tabular}{c} Half NACA \\ 0012 \\ \end{tabular} & 19,788.5 \\ \hline Optimised & \begin{tabular}{c} (Side: 39) \\ (Centre: 30) \\ \end{tabular} & 111 & \
\begin{tabular}{c} (Side: 115) \\ (Centre: 100) \\ \end{tabular} & NACA 0006 &
\begin{tabular}{c} Half NACA \\ 0012 \\ \end{tabular} & 20,388.2 \\ \hline \hline \end{tabular}  
* The chord length has been changed. Therefore, the foil has been changed, so the chord/thickness ratio remains constant.  
Figure 1: Baseline fin dimensions (dimensions are mm unless otherwise stated).