esa-sceva/satcom-chunk-collection huggingface dataset:
# Clutter-Masked Waveform Design for LPI/LPD Radarcom Signal Encoding
## 3 Clutter Distribution Deconvolution from Noise  
Let us consider a scenario in which an ultra-wideband (UWB) radar illuminates a segment of a terrain with an underlying clutter model. The received signal can then be represented as:  
\[S_{R}=C+N, \tag{4}\]  
where \(C\) is contribution from clutter and \(N\) is noise contribution, and distributions of each are not known, although it is known that clutter and noise are statistically independent. We then identify two cases:  
Case 1: Radar receiver is on, but the transmitter is off.  
Case 2: Both the transmitter and receiver are on.  
In Case 1, the only received component with a non-zero value in (4) is \(N.\) In Case 2, the return is the sum of independent random variables \(C\) and \(N.\) In the actual system implementation, the received signal is recorded in terms of its magnitude samples [5]. Owing to the independence of \(C\) and \(N,\) we then formulate the expression for their joint PDF, expressed via their individual pdf's:  
\[p_{C+N}(x)=p_{C}(x)\otimes p_{N}(x), \tag{5}\]  
where the \(\otimes\) sign denotes convolution.  
Let us introduce characteristic function of the joint clutter and noise disturbance, which, by definition [19], is the Fourier transform of the corresponding pdf:  
\[\Im(p_{C+N}(x))=\varphi_{C+N}(\omega)=\int\limits_{-\infty}^{\infty}p_{C+N}(x )\cdot e^{i\omega x}dx. \tag{6}\]  
Following radar return collection in Case 1, we can obtain the characteristic function of noise-only returns, \(\varphi_{N}(\omega);\) similarly, Case 2 will yield \(\varphi_{C+N}(\omega)\). Then, the problem of finding clutter-only pdf from the collected data reduces to solving for \(p_{C}(x)\) via deconvolution:  
\[p_{C}(x)=\Im^{-1}\bigg{(}\frac{\varphi_{C+N}(\omega)}{\varphi_{N}(\omega)} \bigg{)}, \tag{7}\]  
where \(\Im^{-1}\) denotes inverse Fourier transform (IFT).  
Radar returns for Cases 1 and 2 were collected at an indoor laboratory location using UWB software-defined radar system (SDRS), described in detail in [20]. The indoor setting was an empty 10-meter-long hallway with concrete floor, walls, and ceiling. The system has useful baseband bandwidth of 600 MHz, which is determined by the sampling rate of the digital-to-analogue converter (DAC). Transmissions are performed at the center frequency of 7.5 GHz, which was selected due to cost, availability, and efficiency considerations pertaining to the local oscillator (LO) and the antennas. A block diagram of the SDRS is shown in Figure 1a and an image of SDRS with collocated transmitter and receiver antennas is shown in Figure 1b. We used our SDRS in monostatic configuration, where the transmit and receive antennas were collocated.  
To comply with the U. S. Federal Communications Commission (FCC) requirements for maximum permitted emissions in UWB frequency range, the output power was limited to that provided by the LO input to the upconverting mixer, as shown in Figure 1a. This restriction limits the useful range of our experiments to approximately 5 m.
# Traits and Transports: The Effects of Personality on the Choice of Urban Transport Modes
## 6 Discussion
### Limitations of the Study  
The results of this study must be viewed considering its limitations. The cross-sectional design only permits exploration of relationships and not determining causal effects. From a theoretical point of view, it might be argued that personality traits cause the behavior and not the other way around [12,13].  
Web-based surveys allow the involvement of many participants and may easily be used in Sweden where almost everyone has access to the Internet in their home or via smartphones [39]. However, one potential problem with our setting is that the sample is neither representative for Sweden nor the city of Gothenburg. Among our respondents, there was a higher proportion of men, older people, people with higher income and people without children in their households [34,37,38,39].  
Another limitation is the self-reporting of choices of transport modes rather than measuring actual transport behavior. To estimate actual transports from introspective self-reports and questionnaire ratings cause problems familiar in the field of social and personality psychology [41]. We recommend future research to combine personality inventories with actual choices of transport modes, for instance through direct observations, automatic behavioral registrations, or geotagging through smartphones.  
The Big Five personality traits were measured by a short scale to promote a higher response rate. This scale entails substantial losses and clear psychometric disadvantages in comparison to a full-length scale [35]. Further, the Environmental personality scale can be questioned in terms of construct validity. However, the internal consistencies for the five items of the Environmental personality is acceptable (Cronbach alpha = 0.7). It is also worth noting that the relations between the Big Five personality traits and the Environmental personality are consistent with previous research (Table 2, 19-21).  
Although the Big Five personality traits show universal characteristics, modes of transport, and the relationship between personality traits and choice of modes of transport, might differ across regions [1,24]. Therefore, we cannot comment on the extent to which present and previous results might have been influenced by regional culture characteristics, related to Sweden in general and the urban area of Gothenburg in particular. In the light of our findings, future studies can replicate the study in different regions and cultures that may behave differently. The consistency with previous research regarding control variables strengthen the reliability of the present study. Frequencies in car driving is positively related to male, age, presence of child in the household, while frequencies in usage of public transportation is negatively related to male, age, and presence of child in the household [18,22,23,32].
## 2 Main Blocks for Wirelessly-Powered Cage System  
Figure 2 shows a simplified block diagram of the wirelessly-powered cage system and the IMD attached to or implanted in the freely-moving animal subject. The wirelessly-powered cage system typically includes a main controller, a power amplifier (PA), a pair of data Tx/Rx, a Tx coil array, and a position sensor to localize the Rx coil in the IMD. The wireless link for the power and data transmission is established between the Tx coil array and the Rx coil through the skin/air while the PA driving the Tx coil array tuned at the power carrier frequency, \(f_{p}\). The amount of wireless power driven from the PA is typically controlled by the main controller, namely PC or microcontroller unit (MCU), depending on the amount of received power by the Rx. Unlike the WPT system including Tx and Rx coils with fixed distance, the coupling between the Tx and Rx coils in the wirelessly-powered cage is varying due to the movements of the animal subject, resulted in the received power variations. The CLPC, composed of the received data from the IMD, the main controller, and the PA, increases the Tx power in the PA until enough power is delivered to the Rx while it reduces the Tx power when the Rx receives enough power.  
The Tx coil array is one of the important design considerations in the wirelessly-powered cage system to provide the high and homogeneous PTE and power delivered to the load (PDL) within the cage. The Tx coil array can be optimized for a designated arena while it also can be extended for a larger area with the modular type design. Although the achievable PTE and PDL by the inductive link are important for the Tx coil array, the homogeneity of the PTE across the cage should also be considered due to the animal subject's freely movements. In most of the wirelessly-powered cages with the Tx coil array, the position sensor selects the nearest Tx coil among the Tx coil array to the Rx coil. Since only the selected Tx coil will be activated, this position sensing mechanism helps to reduce power loss significantly. Furthermore, each single Tx coil in the modular design of Tx coil array is optimized with the Rx coil to improve the PTE within the entire wirelessly-powered arena. The near-field data telemetry between the Tx and Rx coils can be used to control the IMD, monitor the received power, and/or acquire the biomedical data from the IMD. Even though far-field communication has the advantage of longer data transmission distance, near-field communication is regarded as a more suitable method in terms of power saving in IMDs. Given that the IMD is always located within the wirelessly-powered cage or arena, the coupling between Tx and Rx coils is always enough to deliver both power and data [7]. Here are some practical considerations for different types of wirelessly-powered cage used in the freely-moving animal experiments.  
Figure 2: Simplified block diagram of the wirelessly-powered cage system as a stationary unit and the IMD as a mobile unit attached to or implanted in the freely-moving animal subject.
## 3 Results and Discussion
### PLSR by Preprocessing Using Savitzky-Golay Smoothing
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Soil Properties** & **Spectral Band 1** & **No.F 2** & **R2\({}^{2}\)c 3** & **RMSEC 4** & **R2\({}_{\text{P}}\)5** & **RMSEP 6** & **RPD** \\ \hline \multirow{6}{*}{Dried Soil} & \multirow{2}{*}{pH} & VIS & 7 & 0.81 & 0.16 & 0.72 & 0.20 & 1.87 \\  & & NIR & 7 & 0.69 & 0.20 & 0.51 & 0.26 & 1.43 \\ \cline{2-8}  & EC & VIS & 4 & 0.64 & 0.47 & 0.62 & 0.49 & 1.60 \\  & NIR & 5 & 0.54 & 0.53 & 0.41 & 0.60 & 1.30 \\ \cline{2-8}  & SOM & VIS & 6 & 0.56 & 3.53 & 0.47 & 3.88 & 1.38 \\  & [g/kg] & NIR & 4 & 0.37 & 4.22 & 0.26 & 4.59 & 1.17 \\ \cline{2-8}  & TN & VIS & 6 & 0.64 & 0.01 & 0.51 & 0.02 & 1.43 \\  & [g/kg] & NIR & 5 & 0.49 & 0.02 & 0.37 & 0.02 & 1.26 \\ \cline{2-8}  & TOC & VIS & 6 & 0.56 & 0.20 & 0.49 & 0.22 & 1.39 \\  & NIR & 3 & 0.32 & 0.25 & 0.28 & 0.27 & 1.16 \\ \cline{2-8}  & Clay & VIS & 7 & 0.77 & 0.01 & 0.64 & 0.02 & 1.67 \\  & NIR & 7 & 0.79 & 0.01 & 0.72 & 0.01 & 1.90 \\ \hline \multirow{6}{*}{Wet Soil} & \multirow{2}{*}{pH} & VIS & 7 (5) 7 & 0.39 (0.31) & 0.29 (0.30) & 0.19 (0.25) & 0.34 (0.32) & 1.09 (1.13) \\  & NIR & 7 (4) & 0.58 (0.43) & 0.28 (0.28) & 0.27 (0.38) & 0.32 (0.29) & 1.16 (1.26) \\ \cline{2-8}  & EC & VIS & 4 (5) & 0.25 (0.28) & 0.68 (0.66) & 0.19 (0.20) & 0.71 (0.71) & 1.09 (1.10) \\  & NIR & 5 (3) & 0.35 (0.24) & 0.63 (0.68) & 0.19 (0.20) & 0.71 (0.70) & 1.11 (1.11) \\ \cline{2-8}  & SOM & VIS & 6 & 0.40 & 4.17 & 0.12 & 5.07 & 1.06 \\  & [g/kg] & NIR & 4 (2) & 0.32 (0.17) & 4.40 (4.85) & 0.09 (0.12) & 5.10 (5.02) & 1.05 (1.07) \\ \cline{2-8}  & TN & VIS & 6 (3) & 0.27 (0.19) & 0.02 (0.02) & 0.13 (0.15) & 0.02 (0.02) & 1.09 (1.07) \\  & [g/kg] & NIR & 5 (5) & 0.42 & 0.02 & 0.29 & 0.02 & 1.18 \\ \cline{2-8}  & TOC & VIS & 6 (3) & 0.17 (0.10) & 0.28 (0.29) & 0.02(0.05) & 0.31 (0.30) & 1.01 (1.03) \\  & [\%] & NIR & 3 (2) & 0.24 (0.17) & 0.27 (0.28) & 0.07(0.13) & 0.30 (0.29) & 1.03 (1.07) \\ \cline{2-8}  & Clay & VIS & 7 (6) & 0.46 (0.43) & 0.02 (0.02) & 0.24 (0.25) & 0.02 (0.02) & 1.15 (1.15) \\  & NIR & 7 (4) & 0.56 (0.43) & 0.02(0.36) & 0.02(0.23) & 0.02 (0.02) & 1.12 (1.23) \\ \hline \hline \end{tabular}  
* The spectral band was selected using the best estimation model between VIS at 400–700 nm and NIR at 700–2500 nm. 2 No.F means the number of PLS factors. 3 R\({}^{2}_{\text{C}}\) represents the R\({}^{2}\) of the calibration model. 4 RMSEC represents the RMSE of the calibration model. 5 R\({}^{2}\) represents the R\({}^{2}\) of the prediction model. 6 RMSEP represents the RMSE of the prediction model. 7 Parentheses indicate the number of PLS factors most suitable for the regression model.  
\end{table} 
Table 2: PLSR analysis results using Savitzky–Golay smoothing. values decreased. Dry soils had a minimum of about 7% and a maximum of about 72% increase in the RPD values, which were compared to those of the wet soils.
## 4 Experiment Setup
### Testbed Setup
During training, each compute resource \(c\) (CPU and GPU) operates at an average dynamic power \(P_{c}\); multiplying by its active time \(t_{c}\) gives the energy devoted purely to model computation. * Communication energy--\(P_{tx}\dfrac{B_{i}}{R_{i}}\). Exchanging model parameters and gradients forces the network interface to transmit \(B_{i}\) bytes at a data rate of \(R_{i}\). The term \(B_{i}/R_{i}\) is the transmission time; when multiplied by the NIC's transmit power \(P_{tx}\), it captures the energy spent on networking. Because all power coefficients \((P_{idle},P_{c},P_{tx})\) are pre-profiled using the powertop command for the power meter and remain nearly constant during the experiments, Equation (7) offers a fast, sensor-free way to predict per-round energy from easily observable variables (execution times and payload size). The scheduler can therefore optimize client selection simultaneously for latency and energy.
# A Consensus-Based Likert-LMPP Model for Evaluating the Earthquake Resistance of Existing Buildings
## 3 Methodology
### Building Seismic Performance Scores (Outputs of the Proposed Model)  
A damage score indicates the level of damage likely to be sustained by a structure. Building damageability is computed based on the building vulnerability parameters. Some studies categorize building damage into five damage levels: very low, low, moderate, high, and very high ([PERSON] et al., 2017; [PERSON] et al., 2017). There are, however, three damage levels according to some studies: low risk, medium risk, and high risk ([PERSON] et al., 2017), or four damage grades: none, light, moderate, and severe/collapse ([PERSON] et al., 2017). Damage grades are generally defined as follows. No damage: the structure is not significantly damaged; slight damage: capillary bending or shear cracks in columns, beams, and slabs, as well as capillary shear cracks in walls; moderate damage: local or longitudinal cracks in most columns and beams; severe damage: poured concrete covers, buckled longitudinal reinforcement bars, and wide cracks; and collapse: excessive deformation causing a structure to collapse. Damage grades were classified into four categories in this study: none, slight, moderate, and severe or collapse, as in the study by [PERSON] et al. (2017). As explained in detail in the introduction, determining building seismic performance varies depending on the proposed model. The use of linear functions is suggested by some studies, while the use of fuzzy functions, neural networks, and multi-criteria decision-making methods is suggested by others. A consensus-based Likert-ANN model is recommended in this study.
# Digital Twin Technology-Based Networking Solution in Low Earth Orbit Satellite Constellations
## 5 Networking Protocol Based on Digital Twins  
To meet the requirements of constructing a digital twin for low Earth orbit (LEO) constellation systems, it is necessary to first research and address the challenges of simulating LEO constellation network scenarios with multiple nodes, complex constellation configurations, and high-dynamic inter-satellite topology constraints. The research involves building a foundational platform for digital twinning, which is essential to support the development of LEO constellation network protocol digital twins. Therefore, establishing a highly realistic, easy-to-deploy, and cost-effective LEO constellation network simulation platform on the ground is a crucial research topic. It forms the basis for the construction of digital twin systems of network protocols and holds significant engineering practicality. Figure 2: Routing protocol in satellite broadband internet system. Currently, there are two mature methods used in the industry: software simulation based on simulation software such as NS-2, NS-3, and OPNET and physical emulation based on actual hardware devices. Software simulation primarily involves building a simulated network using probabilistic statistical models of network devices, communication links, and network protocols. It abstracts and models the transmission of network traffic at different granularities, such as flows or packets. Software simulation methods typically rely on discrete event-driven models and employ Monte Carlo methods, performing a large number of repeated experiments within the same simulation scenario to obtain a series of key performance data and metrics required for network design or optimization. The advantages of software simulation include low costs, as software models can replace the hardware and network behavior of the actual target network. Typically, complex system simulations can be performed using only a regular computer. However, software simulation also has the following two main drawbacks: (1) it is inherently an approximation of the actual target system and may have certain differences from the real system; (2) the portability of the implemented device software in software simulation is poor and not interchangeable with the software of actual deployed devices. Physical testing refers to replicating all or part of the hardware structure of the target network and conducting tests and performance evaluations on the actual physical links [24], network protocols, and various upper-layer application software. All experiments must be conducted in real time, so the test environment in physical testing has strong consistency with the real target system scenario. Compared to software simulations, the test results obtained on the physical testing platform can typically sufficiently and accurately demonstrate the performance of the target network's various protocol layers and upper-layer application software under real-world conditions.
# Boiling Flow Pattern Identification Using a Self-Organizing Map
## 2 Materials and Methods  
### Experimental Setup  
The schematic diagram of the boiling system is shown in Figure 1. A detailed description of the experimental setup is described in [22]. Working fluid (distilled water) was pushed out from the supply tank (1--Figure 1) floated through the surge tank (3--Figure 1) to the heated section. The constant overpressure was maintained in the supply tank, which was 50 kPa. The ball valve (2--Figure 1) was used to regulate the liquid flow rate. The heated section (4--Figure 1) was made of a single round brass pipe with an inner diameter of 1 mm and an outer diameter of 2 mm. The heat was supplied to the pipe at a length of 100 mm. The outlet of the brass pipe was connected to the glass tube to allow the two-phase flow observation and recording. The pressure difference (5--Figure 1)between the inlet of the brass pipe and the outlet of the glass pipe is measured. The outlet pressure was atmospheric pressure.  
The Phantom v1610 high-speed camera (6--Figure 1) was used to record the flow patterns inside the minichannel at a frame rate equal to 1000 fps. During the experiment, high-amplitude density wave oscillations (_DWO_) superimposed on the long-period pressure drop oscillations (_PDO_) were observed over a short period. Pressure drop and videos were recorded for a whole _PDO_ cycle occurring for constant mass flow rate \(G\) and input heat flux \(P\). The pressure drop oscillations during a single _PDO_ cycle shown in Figure 2 (acquired at 1000 Hz) were used for analysis. In a single _PDO_ cycle, the following flow patterns were observed: liquid flow, bubble flow, confident bubble flow, slug flow, elongated slug flow, churn flow, and wavy annular flow.
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**Scaling Factor**} & \multirow{3}{*}{**Dataset**} & \multirow{3}{*}{**Bit Resolution**} & \multicolumn{4}{c}{**RMSE of Respiratory Rate Estimation**} \\  & & & & \multicolumn{2}{c}{**RR Estimator**} \\  & & & & \multicolumn{2}{c}{**Aggregation Operation**} & \\ \cline{3-6}  & & & **eRR\_sp** & **eRR\_sp** & **eRR\_as** & **eRR\_as** \\  & & & **Average** & **Skewness** & **Average** & **Skewness** \\ \hline \multirow{3}{*}{**0**} & \multirow{3}{*}{**Lepton**} & 8 bits & 4.97 & 6.28 & 15.61 & 12.80 \\ \cline{3-6}  & & 16 bits & 5.15 & 6.35 & 5.68 & 7.39 \\ \cline{3-6}  & & 8 bits & 5.58 & 7.04 & 9.40 & 11.94 \\ \cline{3-6}  & \multirow{3}{*}{**Lepton * EVM**} & 16 bits & 5.58 & 6.81 & 7.98 & 11.28 \\ \hline \multirow{3}{*}{**1/2 and 2**} & \multirow{3}{*}{**Lepton Bicubic**} & 8 bits & 5.66 & 7.21 & 9.14 & 7.96 \\ \cline{3-6}  & & 16 bits & 4.93 & 7.20 & 8.08 & 7.77 \\ \cline{2-6}  & \multirow{3}{*}{**Lepton SR**} & 8 bits & **4.89** & **5.64** & **4.95** & **6.21** \\ \cline{3-6}  & & 16 bits & 4.93 & 6.72 & 6.29 & 7.41 \\ \hline \multirow{3}{*}{**1/4 and 4**} & \multirow{3}{*}{**Lepton Bicubic**} & 8 bits & 5.61 & 7.64 & 8.57 & 7.90 \\ \cline{3-6}  & & 16 bits & 6.40 & 7.32 & 8.37 & 7.34 \\ \cline{3-6}  & & 8 bits & 4.96 & 5.93 & 7.41 & 12.25 \\ \cline{3-6}  & & 16 bits & **4.89** & 6.10 & 10.31 & 8.00 \\ \hline \multirow{3}{*}{**0**} & \multirow{3}{*}{**SC3000**} & 8 bits & 3.48 & 3.59 & 17.19 & 11.06 \\ \cline{3-6}  & & 16 bits & 3.61 & 5.61 & 12.11 & 14.72 \\ \cline{3-6}  & & 8 bits & 5.00 & 6.15 & 5.98 & 7.82 \\ \cline{3-6}  & & 16 bits & 4.56 & 6.09 & 5.84 & 7.65 \\ \hline \multirow{3}{*}{**1/2 and 2**} & \multirow{3}{*}{**SC3000 Bicubic**} & 8 bits & 6.35 & 6.04 & 17.05 & 11.26 \\ \cline{3-6}  & & 16 bits & 5.91 & 8.46 & 34.43 & 14.52 \\ \cline{3-6}  & & 8 bits & **2.94** & **2.46** & **5.56** & **4.27** \\ \cline{3-6}  & & 16 bits & 4.09 & 3.59 & 8.39 & 8.90 \\ \hline \multirow{3}{*}{**1/4 and 4**} & \multirow{3}{*}{**SC3000 Bicubic**} & 8 bits & 5.73 & 8.23 & 17.05 & 11.65 \\ \cline{3-6}  & & 16 bits & 5.73 & 6.32 & 14.31 & 11.35 \\ \cline{3-6}  & & 8 bits & 3.48 & 5.11 & 13.36 & 12.12 \\ \cline{3-6}  & & 16 bits & 3.48 & 5.38 & 14.48 & 14.61 \\ \hline \hline \end{tabular}  
* Lepton dataset had to be upscaled due to requirements of the EVM tool [50], which does not accept input with spatial resolution smaller than 100 \(\times\) 100 pixels.  
\end{table} 
Table 2: Root Mean Square Error calculated using a difference between reference RR values and RR values estimated from thermal sequences using two respiration rate estimators: _eRR_sp_ and _eRR_as_. Raw pixel values inside manually selected regions of interests were aggregated using average or skewness operators. Best result for each dataset was marked in **bold** (separately for 8 and 16-bit data).
## 4 Experiments and Evaluation
### Hypothetical Scenario
#### 4.2.3 Analysis of Location of Underperforming Area Procedure  
The evaluation has been performed for the first floor of the case building. Table 3 provides an overview of the percentage of area available to be eliminated. In the situations where the result of a meter or submeter is irrelevant, such as a situation evaluating only one-half floor, NA has been used in the table.  
According to Table 3, the lowest benefit is in the scenario of the north half floor. The highest benefit is obtained in the event that the meter for overall floor PT fails, while one of the half floors fail. Given the large amount of area that was eliminated due to the presence of three submeters, in this case, we conclude that the benefits are proportional to the number of submeters.