esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Outlook  
The effect of FCW installation varies depending on driver-specific characteristics and, thus, in some cases FCW can actually have negative effects. Taking into account the ADAS functions suitable for each driver's driving ability and usual driving habits, considerations should be made on driver-specific safety support functions, ADAS training for improvement in driving habits, and additional safety devices.  
In the _PBC_ analysis of this study, environmental variables (weather conditions, road pavement conditions) that may affect the behavior of drivers were not reflected. There was a technical limitation in reflecting the conditions of rain and snow, which are highly likely to cause a traffic accident while driving, in the simulation without any difference from reality. In the future, it is necessary to conduct research that can optimize and apply weather conditions and road pavement conditions in simulations.  
In the _ATB_ analysis, due to the Personal Information Protection Act, only detailed IoT logs for one year could be used in this study, and only the average data for each period was available for the data for the next two years. In the future, if the scope of data utilization is expanded in research for public interest, detailed analysis will be possible.  
Also, road information (road geometry, weather, pavement quality, road operation status) could not be reflected, owing to limitations in the data collection methods in this study. In South Korea, an IoT pavement quality management system (PQMS) platform is under development, as shown in Figure 10. This new PQMS platform will enable the collection of road information that could not be incorporated in this study. With the incorporation of road information collected through the developed platform in further studies, a high-quality effect evaluation will be achieved.  
Figure 10: Data connection and analysis by using the IoT platform in fields ranging from road construction to operation.  
The comprehensive evaluation method proposed in this study can change the evaluation method for newly developed technologies, thereby ensuring the effectiveness and safety of the research and development results of these techniques.  
Conceptualization, H.K. and D.J.; methodology, H.K.; software, B.K.; validation, H.K. and D.J.; formal analysis, B.K. and D.J.; investigation, H.K. and D.J.; resources, H.K.; data curation, B.K.; writing--original draft preparation, H.K.; writing--review and editing, H.K. and B.K.; visualization, H.K.; supervision, D.J.; project administration, H.K.; funding acquisition, B.K. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the Korea Agency for Infrastructure Technology Advancement (KAIA) grant funded by the Ministry of Land, Infrastructure and Transport (Grant 21 POQW-B152690-03).  
Not applicable.  
The data included in this study are available upon request by contact with the corresponding author.  
The authors declare no conflict of interest.
## 6 Experiments
### Qualitative Registration Results over Real Datasets  
In addition to the quantitative point cloud registration results rendered above, we provide qualitative results of the registration problems over multiple real point clouds, including the \"bunny\" and \"Armadillo\" from [52], the \"Mario\" and \"Squirrel\" from the SHOT dataset ([[http://vision.deis.unibo.it/list-all-categories/78-cvlab/80-shot](http://vision.deis.unibo.it/list-all-categories/78-cvlab/80-shot)]([http://vision.deis.unibo.it/list-all-categories/78-cvlab/80-shot](http://vision.deis.unibo.it/list-all-categories/78-cvlab/80-shot)), accessed on 30 July 2021), and the \"city\" and \"castle\" from the RGB-D Scans dataset [26], for concrete evaluation.  
The experiments were set up as follows: (i) we transformed a certain point cloud with a random transformation (with both a known scale and unknown scale); (ii) we adopted the FPFH 3D feature descriptor [54] to match and build putative correspondences between the transformed point cloud (colored in magenta) and the initial one (colored in blue), where outliers are bound to exist; (iii) we applied ICOS to solve the transformation and then used it to reproject the transformed point cloud back to its initial place. The qualitative results are shown in Figure 8. After reprojection, the better the magenta point cloud overlaps with the blue one, the better the registration can be.  
From Figure 8, we see that ICOS yielded rather satisfactory robust registration results.  
Figure 6: Inlier recall ratio of ICOS in the rotation search and both known-scale and unknown-scale point cloud registration w.r.t. an increasing outlier ratio (0–99%).  
Figure 7: Benchmarking of point cloud registration on scale estimation. Left: scale errors w.r.t. an increasing outlier ratio (0–99%). Right: runtime w.r.t. an increasing outlier ratio (0–99%).
# Wireless Energy Harvesting for Internet-of-Things Devices Using Directional Antennas
## 3 System Model
### Sector Model  
We express the harvesting environment topology in the Cartesian coordinate system. The directional antenna has a fixed transmission angle. We divide the coordinate into \(S\) transmission regions, as shown in Equation (4). The charging station transmits RF energy to each region iteratively. The devices in each sector are identified by response messages from devices receiving RF signals [7]. When a device is fully charged, it will also transmit a message to inform the AP.  
We assume that the charging station can change the transmission direction without switching time. The charging sequence is performed in a counterclockwise direction. \(\phi\) is  
Figure 2: Simplified antenna model.  
the angular coordinate representing the start end of the transmission direction based on polar coordinates. \(\Delta\) is an offset angle. \(\phi\) will be changed by \(\Delta\) counterclockwise until it completes one cycle. We show an example in Figure 3. Figure 3 indicates that the initial transmission direction is represented by \(\phi=0\) and \(\phi_{t}=90\). Figure 3b shows the next transmission direction, \(\phi=30\), where the angle offset \(\Delta\) is 30.  
\[S=\frac{360-\theta_{t}}{\Delta}+1 \tag{4}\]
# Energy Consumption Framework and Analysis of Post-Quantum Key-Generation on Embedded Devices
## Appendix A Practical Setup and Operation
### Test Equipment Details
#### a.1.3 Windows Data Collection PC  
Connect as per Figure 2, with the USB connecting to the MicroUSB on the meter, and an Ethernet connection to the Pi using a static IP address. In the experimental setup for this paper, the desktop PC used had a high specification; this is absolutely not required. The responsibilities of this machine are logging data from a USB serial connection, so are relatively lightweight. Its pertinent hardware and software configuration is as follows:Windows PC--the experiment's PC was running Windows 11 Professional 24H2, Build: 26100.3775. Fully Patched through 7 May 2025.
* A USB connection able to attach to the TC66C meter, using a generic Microsoft Serial USB Adaptor device driver.
* Python 3.x--the test PC used Python v3.13.3.
* Pip packages for 'pycryptodome' and 'pyserial' to communicate to the TC66C meter.
* Running the'responder.py' code from the GitHub project repository.
* Trivial PC hardware requirements, but for transparency, the test Collection PC was an AMD Ryzen 9 7900X 12C 24T Processor with 96 GB DDR5 RAM and 2 TB SSD.
* Size the Collection PC according to the requirements:
* The PC is running a master control thread, which spawns two additional threads for networking and polling data from the TC66C meter.
* Listening and responding to Network messages for START and STOP commands. Reading a small segment of data from the USB/Serial connection periodically at less than or equal to 10 Hz. Printing a short update to the screen to keep the user updated on status. Writing out single lines of data to files at a maximum frequency of 10 Hz.  
It would be relatively trivial to convert the Collection software to permit a Linux-based collection device (or Apple) if required. The current test to ensure the device is a Windows PC could be adjusted to adapt to serial port identifications and file permissions on other platforms, without requiring platform-specific versions.  
During experimentation, attach to the collection PC via SSH or via standard keyboard, mouse, and video connections to run the required scripts and see the energy collection outputs from the experiments. An example of an interaction using this framework is provided in the following section for reference.
## 3 Results
### Experiments on Synthetic Data  
In order to quantify the performance of DP-BCD simulations with synthetic data are performed. We use standard normally distributed data and normally distributed \(\beta\) parameters (\(\mu=2\), \(\sigma=1.5\)). In the baseline scenario, there are nine predictors, with a correlation of \(0.3\), \(N=1000\), \(R^{2}=0.3\), \(\varepsilon=1\), and \(\gamma=1.2\) with two parties. Because preliminary analyses have indicated that five iterations is a favourable cut-off in the trade-off between privacy and noise-accumulation, this is the number of iterations used.  
For comparison with this baseline scenario, each of the following factors are varied separately: the sample size \(N\in\{100,250,1000,5000,10,000\}\), the correlation between predictors \(\{0.1,0.3,0.5\}\), \(R^{2}\in\{0.1,0.3,0.8\}\), \(\varepsilon\in\{0.1,0.3,0.5,0.8,1.0,1.5,2.5,10\}\), and \(\gamma\in\{1.15,1.25,1.5,1.8,2.2,5,3\}\). The \(\gamma\) values are chosen big enough to avoid an abortion of the protocol run with high probability. For low values of \(\gamma\), the algorithm may terminate (see Algorithm 1, because \(\gamma\) is too low. This could lead to an unbalanced comparison between scenarios where the \(\gamma\) is sufficiently high and those where the algorithm could not carry out all iterations for each repetition. Each of the variations is repeated 500 times with the exception of the sample size experiment, which is repeated 100 times per variation. At every iteration, a different data set (\(\mathbf{X}\) and \(y\)) is generated. In experiments where the privacy parameters \(\varepsilon\) and \(\gamma\) are varied, different \(\beta\) parameters are generated for every iteration.  
To evaluate the utility, two results are considered. These are the \(R^{2}\) and the \(\beta\) estimates. These outcomes are also generated in the centralized setting and using BCD without differential privacy. Because the results for these two algorithms are practically identical, we only compare it to the centralized results. For several scenarios, we compute the average absolute proportional distance (AAPD) for these \(\beta\) estimates. For \(r\) repetitions of a scenario with \(m\) predictors, the corresponding AAPD is defined as
## 5 Experimental Results and Analysis
### Experimental Study on Speed Prediction of Section Vehicles Based on WT-LightGBM  
#### 5.2.1 Evaluation Indicators and Parameter Settings  
The experiment uses preprocessed data for in transit vehicle speed prediction. Firstly, WT is used to decompose and reconstruct the speed data of each section of the vehicle, and noise reduction is performed. Then, put it into the LightGBM model for training and learning. In order to evaluate the predictive performance of the model, the experiment uses root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R\({}^{2}\)) as evaluation indicators. The metrics are defined as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{N}{\sum_{i=1}^{N}{(y_{i}-\hat{y})}^{2}}} \tag{14}\]  
\[\text{MAE}=\frac{1}{N}{\sum_{i=1}^{N}}{\left|{{\text{y}_{i}}-\hat{y}}\right|} \tag{15}\]  
\[\text{R}^{2}=1-\frac{{\sum_{i=1}{(y_{i}-\hat{y})}^{2}}}{{\sum_{i=1}{(y_{i}- \overline{y})}^{2}}} \tag{16}\]  
where y\({}_{i}\) is the actual vehicle speed, \(\hat{y}\) is the predicted vehicle speed, and \(N\) is the sample size. RMSE and MAE reflect the degree of deviation between the real value and the predicted value. The smaller the value, the better the quality of the model and the more accurate the prediction. R\({}^{2}\) is used to observe whether the forecast error is greater than or less than the mean reference error, and the larger the value, the better the forecast effect.  
In this experiment, when using WT for data denoising processing, sym5 wavelet was used, with three decomposition layers. At the same time, the LightGBM model used the hyperopt module to perform Bayesian optimization on the parameters. The final determined model parameters are shown in Table 8.  
Figure 9: Comparison of traffic flow before and after conversion.
# Encapsulated Probiotics: Potential Techniques and Coating Materials for Non-Dairy Food Applications
## 4 Biomaterials Utilized for Probiotics Encapsulation
### Lipids  
Lipid matrices, such as fatty acids, diglycerides, monoglycerides, vegetable-based oils, waxes, and resins, are commonly used to encapsulate hydrophilic probiotics [81, 82]. Lipid-based biomaterials are naturally low in polarity, exhibit excellent water barrier properties, and are thermally stable [76, 92]. However, lipid-based biomaterials have weak mechanical properties and are chemically unstable. Therefore, lipids are often combined with other biomaterials, such as polysaccharides or proteins, to increase their performances in probiotic encapsulation [76]. In addition, when used with other biomaterials, capsules with low gas migration can be produced [41, 76]. Compared to free _L. casei_ and _B. pseudolongum_, lipid encapsulated probiotics were observed to have higher viability under simulated intestinal conditions [41]. However, this improvement was not observed during storage. In addition, lipid-based biomaterials were reported to have adverse effects on the overall sensory characteristics of the food product carrying the probiotics owing to lipid oxidation [41, 76].
## 2 Materials and Methods
### Data Representation and Analysis  
The Spearman coefficient was used to analyze the correlation between the measurements from the capacitive soil moisture sensors and data from the RADAR sensor. This method evaluates the monotonic relationship between two series. A monotonic relationship is a relationship that does one of the following:  
* When one series increases, the other increases as well;
* When one series increases, the other decreases.  
Since the ground-truth signal is far from having a linear correlation with the SWC [42], the non-parametric Spearman coefficient is well suited because the rates of increasing/decreasing of the series do not need to be constant. In contrast, in a linear relationship, the rate of increase/decrease is constant.  
Since the signals from the RADAR and from the control signal (SEN0193 sensor) can be out of sync, a Cross-Correlation Function (CCF) allows to better assess the correlation between the two sensors, taking into consideration potential delays [46]. The delays are most likely related to water spread time in the soil. This CCF measures the similarity between possibily related signals that might include an unknown delay. This way, the same correlation algorithm gives the highest value of correlation as well as it allows to estimate the delay that maximizes the correlation [46].  
\begin{table}
\begin{tabular}{c c} Range Resolution & 4.4 cm \\ Frequency & 77 GHz \\ Field of View & 15\({}^{\circ}\) \\ Frame Duration & 50 ms \\ Doppler Detection Threshold & 15 dB \\ No. of transmitters & 3 \\ No. of receivers & 4 \\ \end{tabular}
\end{table}
Table 1: Main configuration of Texas instruments AWR1843-BOOST RADAR.
# The Identification and Assessment of Rail Corrugation Based on Computer Vision
## 3 Rail Corrugation Identification Based on Improved SPM Model
### Rail Corrugation Identification Based on IMFLLC  
In this paper, to achieve better identification accuracy, an improved SPM model for rail corrugation identification is proposed, which integrates multi-features and locality-constrained linear coding, named IMFLLC. The diagram of IMFLLC is shown as in Figure 7.  
More specifically, the Uniform LBP and dense_SIFT of all training rail images are first extracted to construct two corresponding feature sets. Secondly, the K-means clustering algorithm is used to cluster the two feature sets of the training image, respectively. As a result, two visual codebooks are obtained which can represent different aspects of the image information. After that, based on the two obtained visual codebooks, LLC is used to encode each image, followed by SPM pooling to receive two different image representations of each image. Then, different image representations are integrated to form the final image representation of each image. Finally, the final image representations with artificial labels are used to train a linear SVM classifier [8].  
For new or test rail images, the Uniform LBP and dense_SIFT feature sets are first extracted. Subsequently, based on the corresponding two different visual codebooks, each image feature is encoded by using LLC and the final feature representation of each image is obtained after SPM pooling  
Figure 6: Comparison of two coding methods. (**a**) Vector quantization (VQ); (**b**) Locality constrained linear coding (LLC).  
Figure 7: The general structure diagram of integrated multi-features and locality constrained linear coding (IMFLLC).  
and feature integration. Finally, the linear SVM classifier obtained from the training procedure is used to test the image and get the final class label to judge whether it is a corrugation rail image or not.
## Appendix A Method Used for the Interpolation of Data Observed in Vineyards  
Measurements in the three experimental sites CF (Castiglione Falletto), CO (Coconato), and FB (Fubine) were mainly carried out during the three vegetative seasons from 2008 to 2010, even if most instruments remained active throughout that time. To run simulations with the UTOPIA, to eliminate the spin-up problem, we decided to start all simulations one year earlier, disregarding the first year, thus the complete data period needed was of four years, e.g., 2007-2010. In order to have in this 4-year period the complete dataset of variables needed by UTOPIA (temperature, humidity, pressure, precipitation, horizontal wind speed components, and solar radiation), some interpolation procedures were performed, using the data of the available neighbor meteorological stations coming from the regional meteorological and agrometeorological networks. For some data, such as pressure and precipitation (and, for CF, also humidity), measurements within vineyards were unavailable, thus the reconstruction was performed on the basis of the data of neighboring stations, using as weight the inverse square distance among the station and the vineyard. For humidity, data were converted in dew-point temperatures before interpolation, and then reconverted into relative humidity after. For precipitation, the data were converted in rates. For all other variables, the regression between daily mean data recorded at the experimental sites and those measured in all neighbor stations was firstly performed. Subsequently, an interpolation was performed on half hourly data, using as weights the fourth power of the correlation coefficients evaluated for daily mean values. For air temperature, it was investigated if, using separate regressions for minimum and maximum temperatures (and then weighting the instantaneous value with the difference between minimum and maximum temperatures), the quality of interpolated data was improving. Since the result was not positive, the method was not used. The comparison between the data reconstructed with this method and those measured in the experimental sites [30; 31; 32] (Table A1) revealed a good agreement for all variables. The errors were larger considering daily extreme values (minima and maxima), their average remaining under the uncertainty of the instrument.  
Despite the fact that some variables (such as pressure and precipitation) were entirely reconstructed using neighbor stations, we think that the dataset created with this method can be considered as the closest possible group of complete data, close to the observations, to drive UTOPIA. In the paper, these data will be hereafter referred to as \"observations\".
# Modeling DGNSS Pseudo-Range Correction Messages by Utilizing Satellite Repeat Time
## 4 PRC Modeling
### Accuracy Analysis
However, in Figure 6e,f, which show the ratio of the residuals to the true values, a relatively large ratios are observed in the region of higher elevation angles. That is to say, the absolute magnitude of the error decreases as the elevation angle increases, but the relative error is larger at medium and high elevation angles. The reason for the high absolute value of the PRC error at low elevation angle is that as the elevation angle of the satellite decreases, the atmospheric path length of the signal becomes longer and the time rate of change of the range gets larger. According to [PERSON] and [PERSON] and [PERSON] (2017), the signal delays vary from about 55 m to 300 m at the elevation angle of 15 depending on the signal frequency and the ionospheric activity. Figure 5: Mean value (bullet point) and standard deviation (error bar) of differences between real and predicted PRC values from (**a**) GPS satellites and (**b**) BeiDou satellites.
## 6 Performance Evaluation
### Simulation Model, Parameters and Network Topology  
In the simulations we set a network area of 100 m \(\times\) 100 m, the transmission range of 15 meters, and neighbors distance of around 10 meters. We use the _two-ray ground model_ as a radio propagation model. The network consists of 46 wireless nodes that are evenly distributed in the simulation area. The network topology is shown in Figure 18. This regular topology helps to evaluate the behavior of D-MSR and WirelessHART more accurately. For instance, in Section 6.5.2, we can evaluate the effect of increasing interference regions on the data delivery ratio rather precisely, by controlling the number of edges that were affected by interference in each step.  
The length of management superframes, which is defined to allow for the exchange of management messages, is set to be 2 seconds. All the obtained results are based on the 2 seconds management superframes. In addition, in D-MSR, the size of link table entries, which are included in the advertisement payload, may reach 400 bytes. In the simulations, we assume that these amounts of data can be compressed in the advertisement payload with a size of 100 bytes. The detailed parameters are presented in Table 3.
# A Source Anonymity-Based Lightweight Secure AODV Protocol for Fog-Based MANET
## 4 Source Anonymity-Based Lightweight Secure AODV Protocol  
In this section, we propose a source anonymity-based lightweight secure AODV Protocol, which involves three parts: a source anonymity algorithm, lightweight tamper-proof scheme, and random delayed transmitting scheme. Then, we simulate and analyze SAL-SOADV by using NS-2.
## 4 Case Study
### Analysis of Active Bearing Effect  
The vault settlements with different lateral stiffnesses were calculated when the spring was preloaded by 5 cm. The curve is shown in Figure 14. The effects of the active and passive bearing schemes were compared and analyzed. The change in the two schemes was the same, but the vault settlement was significantly reduced after preloading. With the increase in stiffness, the effect of the controlling settlement was more apparent. When the preload was 5 cm, the spring stiffness corresponding to the vault settlement of 16.5 cm was approximately 5570 kN/m, so the required stiffness of the FLSP was 4180 kN/m. Installing one \(\phi\)108 \(\times\) 4-millimeter FLSP can meet the settlement control requirements. The passive bearing scheme needs to install two \(\phi\)108 \(\times\) 3-millimeter FLSPs. According to Table 1, the steel consumption of the passive bearing scheme is 54.4 kg, and the steel consumption of the active bearing scheme is only 35.9 kg, which reduces the steel consumption by 34%. As a result, the active bearing can significantly reduce the amount of steel and reduce the project cost.