esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application Examples
### Partially Oxidized Horizontal Rod  
As a first example of application, consider a horizontal rod with length \(L=40\) m and initial radius \(r_{C}=5\) mm, buried \(0.5\) m from the surface of a uniform soil with resistivity \(\rho=100\)\(\Omega\)m and half covered with a layer of conductive material with resistivity \(\rho_{ox}=500\)\(\Omega\)m and thickness \(e=1.5\) mm. The resistivity of the oxide layer and the thickness are fictitious in order to evaluate the effect on the electrical parameters of these added properties. The rod material has an internal resistivity \(\rho_{e}=7\times 10^{-6}\)\(\Omega\)m. The current injection point is at one of its ends, and the fault current is set at \(1\) A. The rod is segmented with \(30\) pieces per meter.  
Figure 2 shows the Ground Potential Rise (GPR) produced by the resistive half-covered rod. A different profile is observed in the two halves. In order to compare the behavior of the ideal electrode with the real one, Figure 3 is included.  
Figure 2: GPR contour plot from the partially oxidized horizontal rod the example \(1\).  
In the upper left panel of Figure 3, a surface plot of the GPR created by the ideal horizontal rod is shown. In the right panel, the potential at the nodes that join the 1200 segments into which the rod is divided is shown. The calculations have been made with copper as the internal resistivity, so it is really a PEC. It is observed that the potential can be considered constant along the length of the rod. In the lower panels, the results for the same rod, but considering both the finite internal resistance \(\rho_{e}\) and the influence of the covering layer of half its length, are shown. In this case, a strong potential gradient is observed along the conductor in addition to a potential profile on the surface that shows a clear difference between the two halves of the rod. In this simple example, given that the representative potential of the electrode is that associated with the injection node, it can be seen that there is a difference in grounding resistance between the two assumptions of more than 20%.
## 4 Conclusions and Outlook
As long as these transmissive materials are chiefly aselective in the visible regime [136, 137, 138] and, thus, only diminish the entering daylights' absolute levels of illuminance without changing their spectral composition, a similar accuracy as reported for the outdoor measurements can be expected to be observed when applying the proposed method to calculate CS for daylight conditions that are measured indoors. However, in the case that the spectral transmittance of the window material is strongly wavelength dependent, [PERSON] et al. 's approximation method needs to be applied, which, as shown in this work, considerably reduces CS prediction accuracy (at least in its current form; see the previous paragraph). For both cases, though, dedicated experiments are required to confirm these expectations and eventually quantify the respective accuracies. Regarding the issue of proper system integration and testing, ongoing research by our lab further addresses the lighting control problem in realistic application scenarios. For this purpose, a multi-channel IoT floor lamp prototype has recently been developed with contributions from some of the present authors [139] that allows for the implementation of additional color sensor devices to monitor the ambient lighting conditions as sketched in the Introduction. Thus, in order to account for potential changes in the ambient lighting conditions, color sensor feedback is intended to serve as suitable input for adaptively updating the lighting control parameters to achieve constant or pre-defined indoor lighting conditions that may for example keep the circadian effectiveness always at an optimal level. Corresponding field tests are currently under preparation, for which the current work provides the theoretical and methodological background. At the same time, it will guide others to develop similar sensor-based strategies for an advanced lighting control,which will help to drive the momentum towards the integration of human-centered lighting solutions into our daily lives. Conceptualization, V.Q.T., S.B. and T.Q.K. ; data curation, V.Q.T. and P.M.; formal analysis, V.Q.T., S.B. and P.M.; methodology, V.Q.T., S.B. and T.Q.K. ; software, V.Q.T. and P.M.; supervision, T.Q.K. ; validation, S.B. and P.M.; visualization, V.Q.T. and P.M.; writing--original draft, V.Q.T. and S.B. ; writing--review and editing, V.Q.T., S.B., P.M. and T.Q.K. ; project administration, V.Q.T. All authors have read and agreed to the published version of the manuscript  
This work received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. The publication of the manuscript is supported by the Open Access Publishing Fund of the Technical University of Darmstadt. Not applicable. All data generated or analyzed to support the findings of the present study are included this article. The raw data can be obtained from the authors, upon reasonable request. The authors declare no conflict of interest.
## 3 Effective Thermal Conductivity Analysis
### Solid Conduction + Surface Radiation + Solid Conduction  
[PERSON] and [PERSON] [11] proposed a cell model in 1970, which can be used to describe this type effective thermal conductivity. A unit cell consists of two half spheres with point contact and void between spheres. The pebble bed in the cell model is formed by orderly arrangement of this kind unit cells and heat transfers in the pebble bed by radiation of sphere surfaces and conduction in spheres. [PERSON] and [PERSON] [6] noted the cell model did not consider the radiation from gaps outside the cell and improved the formula. In this paper, we adopt this developed formula, as shown below:  
\[\lambda_{e}^{r}=\left\{[1-(1-\varepsilon)^{1/2}]\varepsilon+\frac{(1- \varepsilon)^{1/2}}{2/\varepsilon_{r}-1}\cdot\frac{B+1}{B}\cdot\frac{1}{1+ \frac{1}{(2/\varepsilon_{r}-1)\Lambda}}\right\}\cdot 4\sigma T^{3}d \tag{1}\]  
where \(\lambda_{e}^{r}\) is the first type effective thermal conductivity due to the solid conduction-surface radiation-solid conduction heat transfer process; \(F_{E}=[1-{(1-\varepsilon)}^{1/2}]\varepsilon+\frac{(1-\varepsilon)^{1/2}}{2/ \varepsilon_{r}-1}\cdot\frac{B+1}{B}\cdot\frac{1}{1+\frac{1}{(2/\varepsilon_{ r}-1)\Lambda}}\) is defined as the radiation exchange factor; \(\varepsilon\) is the porosity of the pebble bed; \(\varepsilon_{r}\) is the pebble emissivity; \(B\) is the deformation parameter related to the porosity, which was given by [PERSON] and [PERSON] as \(B=1.25(\frac{1-\varepsilon}{\varepsilon})^{10/9}\). However, [PERSON] et al. found that \(B=1.364(\frac{1-\varepsilon}{\varepsilon^{1005}})\) led to a more accurate prediction, which is adopted in the following analysis. \(\Lambda=\frac{\lambda_{s}}{4\sigma T^{3}d}\) is the dimensionless solid conductivity. \(\sigma\) is the Stephan-Boltzmann constant. \(\lambda_{s}\) is the heat conductivity of the pebble, which is temperature dependent. \(d\) is the diameter of the pebble.  
The first type effective thermal conductivity calculated by Equation (1) is shown in Figure 3, using parameters of the SANA experiment under helium condition, which can be found in [12]. The pebbles used in SANA experiment were also graphite spheres with the same diameter in our experiment. As we can see, this type effective thermal conductivity grows quickly with temperature rising due  
Figure 2: Heat transfer mechanisms in packed pebble bed.  
to the radiation effect. Although the heat conduction ability of the pebble itself declines with rising temperature, the whole heat transfer ability of the pebble bed increases quickly due to the sharp enhancing of the surface radiation of the pebbles.
# Material Characterization for Reliable Resin Transfer Molding Process Simulation
## 2 Materials and Methods  
### Test Material  
The naval component was made of a polyester resin reinforced by glass fibers. In particular, the following materials were used:  
* a stitched glass fiber textile, i.e., SAERTEX SAERcore Max BX600/PP18/450, made of a layer of 600 g/m\({}^{2}\) E-glass biaxial fibers [+/-45\({}^{\circ}\) orientation] and a 450 g/m\({}^{2}\) chopped strand mat stitched by means of a 18 g/m\({}^{2}\) PP (polypropylene flow media layer). The nominal thickness of the dry textile was 3.2 mm.
* orthophthalic resin Lavesan LERPOL 666/S RAL 9010. The resin was diluted with 2% of styrene and added with 1% of catalyst (CUROX M-303 methyl ethyl ketone peroxide) prior to injection into the mold.
* an ultralight foam made of polyurethane, polyethylene, and polyisocyanurate, i.e., SAERTEX Saerfoam. Good resin flow through the core thickness was achieved by 1 mm diameter holes within the foam, spaced 20 mm from one another. The holes were filled with glass fibers to increase shear and compression resistance. The nominal thickness of the foam was 10 mm.  
The nominal properties of the resin are reported in Table 1. Accurate characterization of the flow properties of the resin is vital for manufacturing control, as well as for numerical modelling of the process. To this end, experimental tests were conducted to assess the resin viscosity and the fiber volume fraction and permeability of the textile.  
Moreover, in order to provide references respect to other fibres and styles, carbon fiber reinforcement textiles were tested in permeability tests. These materials are a unidirectional fiber 12K T700 300 g/m\({}^{2}\), a triaxial fiber (30/90/\(-\)30) 24K T700 300 g/m\({}^{2}\), and a Twill 2\(\times\)2 fabric 12K T700 630 g/m\({}^{2}\).
## 3 Results and Discussion  
### Profile in VOCs  
#### 3.1.1 Chemical Groups of Volatiles  
The volatile organic compounds were directly extracted from fresh materials using the headspace-solid phase microextraction technique (HS-SPME). The extraction of VOCs without the addition of solvents has been successfully used in plant materials including leaves such as salad rocket, water celery and parsley [20; 22; 26]. In addition, the HS-SPME technique was previously applied to several plant materials such as leaves and fruits [21; 25; 26; 27]. Overall, the methodology used (i.e., the extraction of VOCs from the materials and tentative identification according to mass spectra match) was adequate to analyze the main compounds in wall rocket chromatograms. The populations showed significant differences in their total levels of volatiles identified (Table 2). Thus, there was a considerable diversity in this trait, with several accessions showing total levels below \(500\times 10^{6}\) (GC-peak area units), such as DER014, DER045 and DER068, while others like DER039, DER056, DER057, DER087, DER089 and DER103 showed levels higher than \(1000\times 10^{6}\). Further analyses combining olfactory tests to study the aroma of these materials (e.g., sniffing port) could clarify whether these differences might correlate with differences in the aroma intensity, which would be probably due to a combination of intensity and relative abundances of some individual VOCs [28]. Overall, accessions were rich in isothiances (ITCs, on average \(703.7\times 10^{6}\) GC-peak area units) and esters (\(116.3\times 10^{6}\)) (Table 2). Interestingly, differences in the total level of volatiles as reported above were mainly due to significant differences in the levels of ITCs among accessions
# Enhancing Recognition of Human-Object Interaction from Visual Data Using Egocentric Wearable Camera
## 3 Proposed System
### Classification  
In the final step of the proposed framework, we performed classification for HOIR based on two different approaches, i.e., ODIR and OIIR. In the first approach, we employed the ODIR method, which consisted of two steps. In the first step, ODIR classified the object based on hand joint features. Then, in the next step, using the classified object label and hand joint features, ODIR subsequently recognized the interaction being performed with the object. For example, for ODIR, we aimed to detect an object first, e.g., \"Bottle\", and then recognize its relevant interaction, such as \"pick and place\". Thus, in this case, we had 16 different object classes in the first step, with varying numbers of classes in the second step (depending upon the number of interactions with each object).  
The second approach, i.e., OIIR, omitted the object classification step and directly focused on the recognition of HOI as a whole, e.g., \"Pick and Place Bottle\". Thus, we had 54 classes in this case based on the combination of all objects and their related interactions.  
We chose the random forest (RF) classifier for the tasks involved in this step. For ODIR, we trained 17 RF classifiers (one for object classification and 16 for object-wise interaction recognition) separately, whereas in the case of OIIR, we trained one RF classifiers for recognition of the HOI as a whole, with 54 classes in total.
# Thermodynamic Analysis of In-Cylinder Steam Assist Technology within an Internal Combustion Engine
## 3 Results and Discussion
### Effects of Intake Pressure on Cycle Performance  
In the mixtures used in conventional SI engines, the pressure difference between each side of the throttle determines the intake mass, and the injected fuel mass is based on the AFR. The output power is applied to measure the engine load. Intake pressure and intake mass increase with increases in engine load, which enhances the indicated work carried out per unit volume of a cylinder, improves the indicated thermal efficiency and speeds up the evaporation in the combustion chamber. Therefore, the intake pressure and AFR usually indicate and control the engine load. In this study, the AFR of the proposed cycle is set to the stoichiometric ratio. The quantity of stoichiometric mixture in the combustion chamber is only decided by the intake pressure, which is the only parameter reflecting the engine load.  
Figure 7a shows the relationship between in-cylinder/exhaust temperatures and steam injection mass at various intake pressures for a steam injection strategy of 450 K at 20 MPa. The range of intake pressures is 0.1-0.3 MPa, considering that naturally aspirated, turbocharged and supercharged engines will be investigated experimentally in the future. As shown in Figure 7a, the trends in the in-cylinder and exhaust temperatures of these three cases are similar. With increases in intake pressure, both temperatures rise slightly. For example, the in-cylinder temperature rises from 2193 K to 2238 K while the exhaust temperature rises from 1278 K to 1305 K at steam injection mass = 0.2 g/cycle as the intake pressure rises from 0.1 MPa to 0.3 MPa. This suggests that the intake pressure only has a slight influence on the in-cylinder temperature. It enhances the temperatures by increasing the density of the in-cylinder charge from 12.29 kg/m\({}^{3}\) to 36.88 kg/m\({}^{3}\) at TDC. Thus, as presented in Figure 7b, thermal efficiency increases linearly with steam injection mass because of the additional work generated by the high-temperature and -pressure water steam. The increase in intake pressure does not have much benefit for thermal efficiency. Thermal efficiency increases from 51.1% at 0.1 MPa intake pressure to 52.3% at 0.3 MPa intake pressure when steam injection mass = 0.2 g/cycle.  
Figure 6: Relationships between in-cylinder/exhaust temperatures and steam injection mass at various injection pressures (T\({}_{\text{steam}}\) = 450 K; P\({}_{\text{nistate}}\) = 0.1 MPa; CR = 10).
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 5 Real-Time UWB Indoor Positioning Systems (RTIPSs)
### Indoor Positioning Systems for Robotics Applications
The system testing showed that the extended Kalman filter with a low-pass filter (EKF + LPF) had the lowest root-mean-square error (RMSE) of 40.22 mm on the x-axis, 78.71 mm on the y-axis, and reduced mean absolute error (MAE) percentages of 3.39% for square paths, 4.21% for circular paths, and 6.16% for free paths. [PERSON] et al. [125] proposed an enhanced IPS using human-robot collaboration. This method aims to reduce the number of fixed anchors in classical IPSs by using the mobile robot as a reference anchor of positioning systems, while also enhancing the positioning accuracy using inertial navigation data from pedestrian smartphones. The system was tested in three scenarios, the first with only three fixed anchors and pedestrians holding smartphones. In the second scenario, a mobile anchor (mobile robot with UWB module) was added, which added the LiDAR positioning information to enhance the system's performance. In the third scenario, only two mobile anchors and two fixed anchors were used. The test results indicated that the best system performance was achieved in the second scenario, with an RMSE of 0.355 m. The authors did not provide technical details about the used hardware components. [PERSON] et al. [126] developed a UWB-based IPS for grid-confined robotic platform position control. The system hardware used the commercial Qorvo MDEK1001 kit, which mainly uses the Qorvo DW1000 UWB transceiver. Eight modules were used as fixed anchors, one was used as a movable tag attached to a Mecanum four-wheel drive robot, and one was used as a listener for providing the streaming coordinates of the UWB system to the monitoring laptop via a USB COM port. The mobile robot was controlled by the Atmel ATmega2560 (Atmel Corporation, San Jose, CA, USA) microcontroller, and the robot communicated with the control station wirelessly using the HC-12 Bluetooth module (Guangzhou Huicheng Information Technology Co., Ltd., Guangzhou, China). The open-source Tera Term terminal software, Arduino IDE (Arduino LLC., Boston, MA, USA), and MATLAB (MathWorks Inc., Natick, MA, USA) were used for data processing and monitoring. The practical tests of the system achieved an X-Y location accuracy of 7 cm. Other robotics-based IPSs are presented in [127; 128; 129; 130; 131; 132; 133; 134; 135].
## 3 TDoA Method Experimental Validation
### Anchor Switched Beam Antenna Design
In the latter, the phase ambiguity is manifested for several ranges of \(\vartheta\), for example the most evident in the plots are those around \(\vartheta=90\) deg. Figure 9 shows the expected phase differences considering an equivalent signal at frequency \(f_{\text{FIT}}\). The overall smaller values of phase differences related to signal fit could lead to consider about phase measurement sensitivity problems. Nevertheless, actual measurements are not carried out in phase, but, in time, where the time sampling is given according to phase measurement resolution requirements as described in Section 2.3.
# A Novel DFA on AES: Based on Two-Byte Fault Model with Discontiguous Rows
## 4 Simulation Result and Discussion  
### Experimental Result  
The DFA on AES based on the Dcor fault model was discussed in this work, and it was implemented using Dev-C++ on a PC with a 3.20 GHz Intel processor and 16 GB of RAM. The experimental results showed that the Dcor fault model could improve the cracking efficiency of AES.  
As shown in Table 4, we summarized DFA on AES and contrasted the multi-byte fault models in terms of the number of PCFCs, the depth of fault injection, and the application of the models. Although alternative fault models can be used to break AES [22], they are not fixed, with M1 having a limit of eight-byte faults and M2 having a limit of twelve-byte faults. In [22], M1 is used to break AES, while the Dcor fault model requires fewer PCFCs and thorough searches for AES-192 and 256. While the multi-byte fault models used in [13; 21; 26] have not been expanded to include more AES variations, the Dcor fault model proposed can be utilized to recover three different AES versions.  
Additionally, the depth of the fault injection is another factor in cracking AES. In Table 4, such as [19], two variants were cracked of AES by injecting faults between R-2 and R-3. Another paper [13] induced faults at K\({}^{9}\) for AES-128 and at K\({}^{\text{R}-1}\) and K\({}^{\text{R}-2}\) for AES-192 and AES-256. The location of the fault induced at \(S^{\text{R}-2}\) with the Dcor model proposed in this paper does not require changing the fault induction conditions, such as voltage and frequency. Considering the required PCFCs and model scalability, we propose that the Dcor model is more efficient and applicable to all forms of AES.
## 4 Experiments
### Results & Analysis
#### 4.2.2 Recognition Performance of Diff-Swin-Transformer  
In this part, we evaluate the recognition performance of our proposed Diff-SwinT algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Jamming Type** & **Parameter** & **Range** \\ \hline \multirow{3}{*}{NAMJ} & Amplitude & 5–10 \\  & Frequency & 100–150 MHz \\  & bandwidth & 10–30 MHz \\ \hline \multirow{3}{*}{NFMJ} & Amplitude & 5–10 \\  & Frequency & 100–150 MHz \\  & bandwidth & 10–30 MHz \\  & effective modulation index & 0.5-0.75 \\ \hline \multirow{3}{*}{NCJ} & Amplitude & 5–10 \\  & Frequency & 100–150 MHz \\  & bandwidth & 10–30 MHz \\ \hline \multirow{3}{*}{NPJ} & Amplitude & 5–10 \\  & Frequency & 100–150 MHz \\  & bandwidth & 10–30 MHz \\ \hline \multirow{3}{*}{CSJ} & Amplitude & 5–10 \\  & Frequency & 100–150 MHz \\  & bandwidth & 10–30 MHz \\  & Comb teeth number & 4–8 \\ \hline \multirow{3}{*}{SMSPJ} & Amplitude & 5–10 \\  & Frequency & 100–150 MHz \\  & bandwidth & 10–30 MHz \\  & Sampling Multiple & 2–4 \\ \hline \multirow{3}{*}{CJ} & Amplitude & 5–10 \\  & Frequency & 100–150 MHz \\  & bandwidth & 2–10 MHz \\  & Sub-pulse Number & 2–10 \\  & Retransmission Number & 4–8 \\ \hline \multirow{3}{*}{ISRJ} & Amplitude & 5–10 \\  & Frequency & 100–150 MHz \\ \cline{1-1}  & bandwidth & 2–10 MHz \\ \cline{1-1}  & sampling number & 3–5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameterization of Radar Jamming signals.  
The recognition accuracy under different jamming types and JNR levels is shown in Figure 5. It can be observed that compared to the first experiment without using the diffusion-based denoising module, the proposed algorithm demonstrates stronger robustness for all the tested jamming. At low JNR of \(-16\) dB, the identification accuracy is improved by 22%, 9%, 35%, 15% for NPJ, CSJ, NCJ, NAMJ respectively. As JNR increases to \(-10\) dB, the improvement becomes 26%, 7%, 35%, 27% for these four jamming types. Overall, the recognition rate for all jamming types exceeds 70% at JNR = \(-10\) dB, exceeds 80% at JNR = \(-8\) dB, exceeds 90% at JNR = \(-4\) dB, and gradually increases to around 95% as JNR rises.  
Additionally, we show the confusion matrix obtained using the proposed algorithm at JNR = \(-12\) dB in Figure 6, where the vertical axis is the true label and the horizontal axis is the predicted label. It can be observed that under low SNR, NAMJ and NCJ, two radar jamming types with very different generation mechanisms, are highly confused. This is because their time-frequency representations look similar under comparable modulation bandwidth and chirp rate, as can be seen from Figure 2. The same situation occurs between CSJ and NCJ as well.  
Figure 4: The Recognition Accuracy of Swin-T for 8 Radar Jamming Types.  
Figure 5: The Recognition Accuracy of Diff-SwinT for 8 Radar Jamming Types.