esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Signal Processing Method for Time-of-Flight and Center Frequency Estimation
## 3 Experimental Signal Processing by the Proposed Method  
For the qualitative evaluation of the proposed signal processing method, processing of various experimental signals was carried out. In order to demonstrate the advantages of this method, instead of conventional ultrasound signals, Lamb wave (LW) signals were chosen for signal processing. The advantage of these waves is that they propagate in plates over long distances with low amplitude attenuation. However, they are also characterized by a special property dispersion, i.e., depending on the frequency of the signals, their phase and group velocities vary. Therefore, after the signal travels a certain distance, its shape changes without changing the frequency response. In order to evaluate the versatility of the proposed method, several different cases of experimental data processing were selected.
## 4 Methodology
### Temporal Batch  
Larger time intervals may cover specific behavioral changes of an entity affecting its detection. Moreover, the amount of data in a larger time interval increases the complexity of the problem, often leading to the creation of highly non-transparent \"black-box\" models. Our approach involves dissecting data temporally, so dividing everything into smaller batches that can then be studied by themselves (thus using \"smaller black-boxes\", which may help to understand better the internal dynamics within the large black-box of the full data).  
The idea behind our approach is that a model trained on a batch with a certain time-interval can be used for unveiling behavioral entity patterns when testing it on another temporal batch. Thereby, higher classification performance would relate to higher similarity between behavioral entity patterns within the training and test batch. Hence, in this analysis, it is important to divide the whole Bitcoin dataset into temporal batches, where each batch represents a dataset composed by the transactions belonging to a fixed number of consecutive blocks.  
Let \(min\_blk\) be the minimum block height and \(max\_blk\) the maximum block height in the (considered) Bitcoin blockchain data, and let \(batch\_size\) be the size (in blocks) of a single batch, then the number of generated batches can be computed through Equation (2)  
\[\#batches=\frac{max\_blk-min\_blk}{batch\_size}. \tag{2}\]  
In our analysis, we considered \(min\_blk\) with a value of 390,000 blocks and \(max\_blk\) with a value of 570,000, and four different values for \(batch\_size\). We started creating batches of 60,000 consecutive blocks (representing approx. 12 months) in the first experiment. In the second one, we chose a value of 30,000 (representing approx. six months), in the third one, a value of 20,000 (representing approx. four months) was used and, finally, in the last experiment, the \(batch\_size\) was fixed to 10,000 consecutive blocks (representing approx. two months).  
Figure 2: Example of 2_motif graph topology.  
Table 2 shows the number of batches considered in each experiment and the related distinct known entities divided per class. This table shows that the majority of the detected entities belonged to batches with \"old or past\" data, since the number of distinct known entities for almost all the classes decreases over time.
## 5 Simulations and Results
### Oil Leakage Monitoring  
In this section, mobile sensors were considered in the simulation. An \"event-driven\" simulation was implemented. More specifically, it was assumed that a leakage occurs on one of the pipelines, which reduces the oil flow at the end of the pipeline. In order to find the leakage, several sensors were temporarily installed along the pipelines. After the problems were solved, the sensors were removed gradually.  
For long-term monitoring applications, the network's lifetime is especially important. The system's lifetimes corresponding to CWCA and LEACH are shown in Figure 10. Figure 11 shows normalized network lifetimes for networks with different sizes and cluster-based routing protocols. And the red dash line illustrates the beginning of the leakage event. Once the leakage occurs, the amount of sensor nodes increases from 100 to 120. When using LEACH, the first dead node is discovered soon after the leakage event occurs. It is believed that the instantaneous growth of data traffic is responsible for this. To verify this, we have changed the beginning time at which the leakage event occurs, and a similar phenomenon has been observed.  
Figure 11: Normalized lifetimes for different network sizes in oil leakage monitoring.  
Figure 10: Number of sensors still alive for different time steps in oil leakage monitoring.  
On the other hand, by using CWCA, it seems that the round in which the first node dies has nothing to do with the beginning time of the leakage. It should also be noted that after the leakage was handled, the amount of sensor nodes decreased to 100 gradually from the 60 th rounds. The actual round in which the first node dies occurs at about the 150 th round. There is no doubt that the CWCA would behave better than the LEACH when using mobile sensors.  
The effect and power test was conducted through the above experiments. The above results show that this model can run well, and the working life of the nodes has been extended efficiently. It is able to conduct real-time pipeline monitoring, accurately reflecting the pipeline's working state and alarming in a timely way.
## 4 Scanning Algorithm Using Omnidirectional Antenna at BS
Applying Equations (5)-(11), the misdetection probability of the cell search procedure at each of the 46 distributed UEs with an omnidirectional antenna at BS is calculated as shown in Figure 4.  
The solid red line in Figure 4 shows that with the use of an omnidirectional antenna at BS, the detection of PSS signals at UEs does not perform well at low SNR conditions, and the performance depends on the search time, that is, the duration that an UE receives the signal. With the use of only one time slot within 5 ms of search time, only the UEs within the \(-13\) dB S  
Figure 4: Misdetection probability of the Generalized Likelihood Ratio Test (GLRT) detector as a function of PSS SNR when omnidirectional antenna is used at the base station (BS) for a search time of one time slot and 25 time slots. Figure 3: Signal-to-noise ratio (SNR) levels, (**a**) for line-of-sight (LOS) channels as a function of distance; (**b**) color mapping of SNR levels over the simulated microcell. PSS signal with a high probability. In the SNR range of \(-25\) to \(-13\) dB, the UEs can still detect the PSS signals, but the misdetection rate sharply increases as a function of SNR levels. For UEs located where the SNR is lower than \(-25\) dB, it becomes almost impossible to detect the PSS signal. Thus, if a UE wants to detect the PSS signal with a low misdetection probability within one timeslot, the SNR at the UEs should be greater than \(-13\) dB. In the case where 25 time slots are used within 125 ms of search time, the misdetection rate improves as observed by the dashed blue line in Figure 4. The detection is achieved with high probability for UEs beyond the SNR level of \(-25\) dB. As SNR levels decrease from \(-25\) to \(-32\) dB, the misdetection rate increases to almost 1. For UEs at locations with SNR levels lower than \(-32\) dB, the PSS detection becomes impossible.
# OmniSR-M: A Rock Sheet with a Multi-Branch Structure Image Super-Resolution Lightweight Method
## 3 Experimental Results and Their Analysis
### Comparative Analysis of Different OSAG Structural Designs  
In this paper, a test dataset consisting of 130 rock thin section data was used to quantitatively evaluate different OSAG structures, and the results are shown in Table 1, which mainly includes comparing the results of different OSAG layer count algorithms and the comparison of the results between different OSAG structures. As can be observed from Table 1, the performance metrics of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) experience an increasing and then decreasing trend as the number of OSAG layers increases in the OmniSR model. As the number of layers continues to increase to five, the performance decreases dramatically, indicating that too deep an OSAG  
Figure 4: Fully aggregated network structure with multi-branch structure.  
structure is not conducive to generalization. This means that increasing the number of layers can improve the feature representation of the model, but too many layers may lead to overfitting, which in turn affects the performance. Among them, OmniSR obtained a PSNR of 35.715 and an SSIM of 0.8741 at with a four-layer OSAG, a result that suggests that the four-layer OSAG seems to be a good choice for this task and dataset, enabling the best reconstruction.  
In this paper, we propose that the multi-branch structural design of OmniSR-M achieves a performance close to that of a four-layer OmniSR when using only two layers of OSAG, which highlights the effectiveness of structural optimization. In addition, the new model achieves a significant improvement in reducing the computational and parametric counts by 27.66% and 26.56%, respectively, relative to the four-layer OmniSR model, an observation that highlights the importance of the trade-off between resource efficiency and performance, providing strong support for the lightweight and efficient nature of the model.
## 2 Research Work
### Siamese Network Framework
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Definition** & **Layer** & **Kernel Size** & **Stride/Channel** & **Template** & **Search** \\ \hline \hline Input & & & /3 & \(127\times 127\) & \(255\times 255\) \\ \hline \multirow{4}{*}{conv1} & CONV1-BN & \(3\times 3\) & \(1/64\) & \(125\times 125\) & \(253\times 253\) \\ \cline{2-6}  & CONV2-BN & \(3\times 3\) & \(1/128\) & \(123\times 123\) & \(251\times 251\) \\ \cline{2-6}  & CONV3-BN-ReLu & \(1\times 1\) & \(1/64\) & \(123\times 123\) & \(251\times 251\) \\ \cline{2-6}  & MaxPool & \(2\times 2\) & \(2/64\) & \(61\times 61\) & \(125\times 125\) \\ \hline \multirow{4}{*}{conv2} & CONV4-BN & \(3\times 3\) & \(1/128\) & \(59\times 59\) & \(123\times 123\) \\ \cline{2-6}  & CONV5-BN & \(1\times 1\) & \(1/64\) & \(59\times 59\) & \(123\times 123\) \\ \cline{2-6}  & CONV6-BN-ReLu & \(3\times 3\) & \(1/128\) & \(57\times 57\) & \(121\times 121\) \\ \cline{2-6}  & MaxPool & \(2\times 2\) & \(2/128\) & \(28\times 28\) & \(60\times 60\) \\ \hline \multirow{4}{*}{conv3} & CONV7-BN & \(3\times 3\) & \(1/256\) & \(26\times 26\) & \(58\times 58\) \\ \cline{2-6}  & CONV8-BN & \(1\times 1\) & \(1/128\) & \(26\times 26\) & \(58\times 58\) \\ \cline{2-6}  & CONV9-BN-ReLu & \(3\times 3\) & \(1/256\) & \(24\times 24\) & \(56\times 56\) \\ \cline{2-6}  & MaxPool & \(2\times 2\) & \(2/256\) & \(12\times 12\) & \(28\times 28\) \\ \hline \multirow{4}{*}{conv4} & CONV10-BN & \(3\times 3\) & \(1/512\) & \(10\times 10\) & \(26\times 26\) \\ \cline{2-6}  & CONV11-BN & \(1\times 1\) & \(1/256\) & \(10\times 10\) & \(26\times 26\) \\ \cline{1-1} \cline{2-6}  & CONV12-BN & \(3\times 3\) & \(1/512\) & \(8\times 8\) & \(24\times 24\) \\ \cline{1-1} \cline{2-6}  & CONV13-BN-ReLu & \(1\times 1\) & \(1/256\) & \(8\times 8\) & \(24\times 24\) \\ \hline conv5 & CONV14 & \(3\times 3\) & \(1/256\) & \(6\times 6\) & \(22\times 22\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Backbone features extract network specific information. where \(z\) denotes the input object template, \(\alpha\) denotes downsampling, \(f\) denotes the fused object features, and \(f\in R^{W\times H\times C}\), \(\lambda\) denotes the scale factor of the mid-level features after downsampling.
# Model Reference Adaptive Control of Vehicle Slip Ratio Based on Speed Tracking
## 4 Simulations Results and Analysis
### Simulation Comparison with SMC Method  
To better evaluate the proposed MRAC method, simulation comparisons with the SMC method are discussed. From the simulation results on the dry asphalt road in Figures 10 and 11, both methods have achieved good vehicle speed tracking and desired slip ratio control under the same road conditions and the same initial braking velocity (80 km/h). Table 3 shows the detailed braking performance of the two methods.  
From the simulation results, the MRAC has shorter braking distance, shorter braking time, and smaller tracking error compared to SMC. The braking distance and braking time of MRAC are correspondingly shortened by approximately 20% regardless of any road circumstances. The slip ratio output of SMC indicates the typical character of chattering around the desired value. Therefore, the brake torque is fully utilized through the control of the slip ratio and MRAC has better control performance and road condition adaptability.  
Figure 11: Slip ratio of MRAC and SMC.  
Figure 10: Vehicle speed and wheel speed of MRAC and SMC.  
From the results, although the load wheel and rolling radius of the vehicle fluctuate and change in a certain range of friction coefficient, the controller can still achieve good control performance by the adaptive mechanism and the slip ratio is well controlled. The MRAC has good performance and strong robustness for the parameter uncertainties and modeling errors based on the road observer.
# Reconfigurable Antennas: Switching Techniques--A Survey
## 7 Conclusions  
A detailed study and exhaustive review about different implementation methodologies and techniques of reconfigurable antennas is presented in this paper. The investigated reconfiguration techniques are classified into electrical, optical, mechanical and smart-material-loaded reconfigurable structures. Various examples of reconfigurable antennas with different implementation applications are discussed and their characteristics are described.  
The implementation of different functionalities requires topological reconfigurability to achieve radiation pattern, polarization and frequency agility. The mechanisms for reconfiguration add levels of complexity that can have effects that are difficult to predict in advance. The best reconfiguration technique is the one that is more satisfying to the constraints of the application for which the antenna is designed.  
The future of reconfigurable antennas is filled with self-adaptation and is to achieve a well-defined and energy-efficient communication link with highly dynamic and ever-changing properties. The future reconfigurable antennas should be multifunctional and have software-controlled/machine-learning capabilities to detect and react to various changes in their RF environment. Applications of the future reconfigurable antenna should be implemented based on a new generation of wireless systems and communication protocols.  
Writing--original draft preparation, N.O.P., H.J.B., Y.I.A.A.-Y., A.M.A. and R.A.A.-A.; writing--review and editing, N.O.P. and R.A.A.-A.; investigation, N.O.P., H.J.B., Y.I.A.A.-Y. and A.M.A.; resources, N.O.P. and R.A.A.-A.; for other cases, all authors have participated. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.  
The authors wish to express their thanks to the support provided by the innovation programme under grant agreement H2020-MSCA-ITN-2016 SECRET-722424.  
The authors declare no conflict of interest.
# Risk-Sensitive Rear-Wheel Steering Control Method Based on the Risk Potential Field
## 2 Design of Rear-Wheel Steering Control Based on the Risk Potential Field
### Design of the Risk Potential Field  
As this study aims to design the collision avoidance system, \"risk\" in this paper refers to the collision risk for the obstacle and the lane boundary. When the relative distance between the vehicle and those objects are closer, the risk becomes higher. This is based on the concept of collision avoidance explained by [PERSON] and [PERSON] [15] and the intuitive feeling of the driver's physiological status studied by [PERSON], which explained that the heart rates of drivers get higher when they are getting closer to the object or obstacle [16; 17]. The risk potential (\(U_{risk}\)) is expressed as the sum of the road center potential and road boundary potential. It can be calculated using the following equation:  
\[U_{risk}(X,Y)\ =\ U_{r}(X,Y)+U_{l}(X,Y), \tag{1}\]  
where \(U_{r}\) and \(U_{l}\) indicate the values of the road center and boundary potentials, respectively. The road center potential is expressed as  
\[U_{r}(X,Y)\ =\ w_{r}\Bigg{\{}1-\exp\Bigg{(}-\frac{\left(Y_{center}-Y\right)^{2}}{2 \sigma_{r}{}^{2}}\Bigg{)}\Bigg{\}}, \tag{2}\]  
where \(w_{r}\) denotes the weighting coefficient, \(Y_{center}\) indicates the road center, and \(\sigma_{r}\) represents the variance. Conversely, the road boundary potential is expressed as follows:  
\[U_{l}(X,Y)\ =\ w_{l}\Bigg{\{}-\exp\Bigg{(}-\frac{\left(Y_{l}-Y\right)^{2}}{ \sigma_{l}{}^{2}}\Bigg{)}\Bigg{\}}, \tag{3}\]  
where \(w_{l}\) indicates the weighting coefficient, \(Y_{l}\) denotes the road boundary, and \(\sigma_{l}\) represents the variance. In this paper, the weighting coefficients and the variances are set as follows: \(w_{r}\) = 7.4 \(\times\) 10\({}^{4}\), \(w_{l}\) = 1.0 \(\times\) 10\({}^{5}\), \(\sigma_{r}\) = 2.0, and \(\sigma_{l}\) = 0.6.
# Progress in Remote Sensing of Photosynthetic Activity over the Amazon Basin
## 2 Remote Sensing of Non-Photochemical Quenching from PRI  
Numerous studies [6; 7; 9; 10; 11; 12] have related \(\varepsilon\) to the PRI where reflectance measurements were acquired close to the target. The dependency of this index on extraneous effects and atmospheric conditions, however, has hampered its use beyond the leaf and canopy scale [8]. Recent work has shown that the status of the xanthophyll cycle may be inferred across vegetation types from multi-angle observations of sunlit and shaded leaves: In cases where GPP is limited by factors other than light (\(\varepsilon<\varepsilon_{\text{max}}\)), PRI is closely related to canopy shadow fractions (\(\alpha_{\text{s}}\)) [13; 14], because sunlit leaves are more likely to be exposed to excess radiation levels than shaded leaves [15]. However, this relationship disappears under conditions where light is limiting GPP. In this case, photosynthesis will not be downregulated in either sunlit or shaded leaves (\(\varepsilon_{\text{sunlit}}=\varepsilon_{\text{shaded}}\)). As a result, the slope of the relationship between PRI and as is proportional to the light-use efficiency [16; 17; 14]. This multi-angle retrieval is largely insensitive to changes in vegetation type because slope is derived from comparing the same area of the canopy [13]. The sampling approach is theoretically sound [13; 14] and has been validated from both tower-mounted [16; 18] and satellite-borne sensors [16; 17; 19]. In the latter, it proved critical to implement a sophisticated atmospheric correction algorithm (MAIAC) to preserve the anisotropy of surface reflectance at 531 nm [17].  
One of the main sampling limitations for broad-scale application of the approach is the shortage of multi-angle observations acquired along a satellite's track at 531 nm. Such sampling is available with ESA's Compact High Resolution Imaging Spectrometer (CHRIS) [16], but only for pre-determined sites. Alternatively, NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) acquires spectral reflectance data at 531 nm (Band 11), but samples across rather than along the satellite track. As a result, repeated overpasses are required to obtain multi-angle assessments of individual pixels. The relatively large pixels sampled by MODIS (1 km) also make it difficult to assess PRI over heterogeneous landscapes. Nonetheless, over comparatively homogeneous types of vegetation, data acquired over a few days with MODIS instruments on Terra (morning) and Aqua (afternoon) satellites provide backscatter and forward-scattered reflectance data from multiple angles that permit the calculation of PRI (Figure 2).  
Figure 2: On the first overpass, when the sensor faces the Sun, forward-scattered reflectance data are retrieved; on the second overpass, with the sensor facing the opposite direction, back-scatter reflectance from shaded portions of the canopy is obtained.
## 4 Results
### Assessment with Spatial Averaged Magnetic Field Outside the Vehicle  
The relationship between the external magnetic and internal electric fields was evaluated outside the vehicle [18]. The averaged magnetic field strength (\(H_{\text{ave}}\)) was calculated based on the measurement points and averaging scenario defined in Table 2. Table 3 presents the magnetic field strength outside the vehicle at the measurement points and \(H_{\text{ave}}\) for vehicle body with different materials. As shown in Table 3, the magnetic field strengths for different materials are consistent. Figure 9 shows the conformity assessment in each definition against the ICNIRP guidelines and IEEE standard in terms of \(a_{c}\) outside the vehicle. As shown in the figure, the stringer assessment methods were in the order of Definitions 3, 2, and 1 because a high magnetic field was distributed near the ground. All definitions exhibited a conservativeness with more than a margin of a factor of four with respect to the ICNIRP guidelines, whereas only Definition 1 was an unconservative assessment for the IEEE standard. The magnetic field limits for the ICNIRP guidelines and IEEE standard are 21 and 163 A/m for public exposure.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{6}{c}{**(a)**} \\ \hline \multirow{2}{*}{**Setting**} & \multicolumn{4}{c}{**Magnetic Field Strength [A/m]**} \\ \cline{2-5}  & \multicolumn{4}{c}{**Measurement Points**} & \multicolumn{1}{c}{\(\boldsymbol{H_{\text{ave}}}\)} \\ \hline \multirow{2}{*}{Definition 1} & 0.5 m & 1.0 m & 1.5 m & \multirow{2}{*}{-} & 0.40 \\  & 0.78 & 0.28 & 0.13 & & \\ \hline \multirow{2}{*}{Definition 2} & 0.06 m & 0.5 m & 1.0 m & 1.5 m & \\  & 1.54 & 0.78 & 0.28 & 0.13 & \\ \hline \multirow{2}{*}{Definition 3} & 0.06 m & 0.31 m & 0.56 m & \multirow{2}{*}{-} & 1.15 \\  & 1.54 & 1.23 & 0.67 & & \\ \hline \multicolumn{6}{c}{**(b)**} \\ \hline \multirow{2}{*}{**Setting**} & \multicolumn{4}{c}{**Magnetic Field Strength [A/m]**} \\ \cline{2-5}  & \multicolumn{4}{c}{**MeasurementPoints**} & \multicolumn{1}{c}{\(\boldsymbol{H_{\text{ave}}}\)} \\ \hline \multirow{2}{*}{Definition 1} & 0.5 m & 1.0 m & 1.5 m & \multirow{2}{*}{-} & 0.41 \\  & 0.82 & 0.27 & 0.13 & & \\ \hline \multirow{2}{*}{Definition 2} & 0.06 m & 0.5 m & 1.0 m & 1.5 m & \\  & 1.55 & 0.82 & 0.27 & 0.13 & \\ \hline \multirow{2}{*}{Definition 3} & 0.06 m & 0.31 m & 0.56 m & \multirow{2}{*}{-} & 1.18 \\  & 1.55 & 1.27 & 0.72 & & \\ \hline \end{tabular}
\end{table}
Table 3: Heights of magnetic field strength measurement points and average magnetic field strength averaged for each definition with outside the vehicle body made of (**a**) iron and (**b**) CFRP.  
Figure 8: Internal electric field distributions in human body model inside the vehicle sitting on the driver’s seat for different materials: (**a**) iron and (**b**) CFRP.