esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Discussion
### Aerosol Classification
Furthermore, for the purpose of training a supervised machine learning model, particularly a random forest (RF) model, lidar data were compartmentalized into eight groups: low clouds, high clouds, rain, three aerosol types distinguished by their origins and interaction with atmospheric water vapor, other non-noise elements, and noise. This categorization was based on distinct physical parameters, such as CNR values and DBSCAN clustering. For example, high clouds were demarcated by their distance from the boundary layer and isolated using the DBSCAN method, while different types of aerosols were classified based on backscatter and depolarization ratio variances influenced by atmospheric moisture levels. Rain was among the most challenging to label, due to the Doppler lidar's insensitivity to it. Rain classification was based on its low depolarization ratio and descending motion, complicated by instances of high background depolarization ratios. There were cases where it became difficult to distinguish between low clouds and precipitation. Any errors in labels, such as those marked as rain without the associated descending movement, were manually corrected. The dataset, comprising more than 7.8 million data points, was labeled according to these categories. To train the RF model, four features were selected: height, CNR, backscatter coefficient, and depolarization ratio. After a system breakdown and re-calibration in July 2019, there was a slight variance in the absolute measured values between the datasets from June and July. Therefore, lidar data were normalized daily to maintain consistency. Due to the massive size of the dataset, a 10% random selection was used for model training. In conclusion, with the trained noise discrimination and classification models in place, the lidar data were processed in two steps. First, CNR was used as input for the noise discrimination model, to segregate noise from non-noise data. Second, height, normalized CNR, backscatter coefficient, and depolarization ratio were used as inputs for the classification model, categorizing the non-noise data points from the first step. Enhancing the accuracy of lidar data labeling would contribute to further improvements in the classification model. Additionally, the performance of the model could be improved by employing a larger and more diverse dataset that encompassed different weather conditions. Additionally, the calibration and correction of the lidar data played a role in the result accuracy; that is, in certain cases, an artifact layer at approximately 600 m was discovered due to the absence of focal correction. On the basis of their findings, they concluded that the continuous incorporation of new training data would further enhance the results. Consequently, the algorithms presented in this study hold promise for real-time interpretation of lidar observations, providing valuable information to end-users such as aviation service providers and air quality observers.
# Depletion-of-Battery Attack: Specificity, Modelling and Analysis
## 4 DoB Analysis
### DoB Based on Excessive Packets
Let \(p_{N},p_{A},p_{S}\) denote the corresponding equilibrium probabilities, i.e., the vector \(\overrightarrow{p}=(p_{N},p_{A},p_{S})\) satisfies the balance equations:  
\[\overrightarrow{p}G=0,\]  
and  
\[\sum_{j\in\Omega}p_{j}=1.\]  
From here, the equilibrium probabilities can easily be derived in closed form. We get:  
\[ \begin{array}{l}p_{A}=\rho(G),\\ p_{N}=\frac{\alpha_{2}}{\alpha_{1}}\rho(G),\\ p_{S}=\frac{\alpha_{2}}{\alpha_{1}}\frac{\alpha_{1}+\beta_{1}}{\beta_{2}}\rho (G),\end{array} \]  
Figure 3: State diagram of sensor modes. where  
\[\rho(G)=\left(1+\frac{\alpha_{2}}{\alpha_{1}}+\frac{\alpha_{2}}{\beta_{2}}+\frac{ \alpha_{2}\beta_{1}}{\alpha_{1}\beta_{2}}\right)^{-1}.\]  
Therefore, in the considered scenario, we get:  
\[\text{MTTF}=\frac{N_{0}}{\lambda_{A}\rho(G)+\lambda},\]  
and  
\[\text{SSF}=\mathcal{H}(h,\lambda_{A}\rho(G)+\lambda,N_{0}).\]  
From the obtained results, it can be concluded that for the same duty cycle rate, it is advisable to increase the sleep mode duration in order to reduce the influence of DoB.
# Review of Soft Actuators Controlled with Electrical Stimuli: IPMC, DEAP, and MRE
## 2 Recent Progress in Development and Applications of Soft Actuators
### Dielectric Electroactive Polymers (DEAPs)
#### 2.2.2 Actual Trends in the Application of DEAP in Soft Robotics
The interesting result is also magnetic, where two magnets attract to each other, causing the movement of the DEAP membrane [67; 80; 83]. In comparison to the mechanical spring, the connection by two magnets is done by a magnetic field. Lately, the cone actuator is analyzed in various configurations. The most popular combination is the single membrane  
Figure 7: Proposed flexible vibrotactile actuator based on dielectric elastomer: overall structure of the proposed actuator and assembled vibrotactile actuator (**a**); the bottom surface of the upper layer (**b**); proposed actuator attached to a foldable smartphone (**c**) (source: [73]). and a bias. However, in recent times the advantage of the double cone has shown higher power efficiency [84, 85]. The typical application of a DEAP is an intelligent actuator with a single input as voltage and single output as displacement or force. As described above, most DEAP actuators work with applied bias. This kind of structure can be applied to various devices like actuators, pumps, or loudspeakers [80, 86, 87]. As an example, we can see the pump designed for soft robotics application, which is studied in [80]. Another example is shown in [88], where the actuator applied in the rehabilitation system is presented. It is also possible to create a linear DEAP actuator as presented in [89].
## 2 Materials and Methods
### Method  
Steps in the processing chain for urban flood delineation are shown in Figure 4. These include preprocessing operations carried out prior to SAR image acquisition and Flood Foresight model output, and near real-time operations carried out after the georegistered SAR image and model output have been obtained. As stated in [PERSON] et al. (2018) [42], \"for the SAR data, the approach involves first detecting the flood extent in rural areas, and then detecting it in adjacent urban areas using a secondary algorithm guided by the rural flood extent. A rural area is considered to be one not significantly affected by shadow and layover. Note that this means that the method will not work in a situation where a flood is totally contained within an urban area. But even in a city, rural areas (e.g., parks) can often be found not far away from urban ones.\"  
Figure 4: Steps in the processing chain for urban flood delineation (numbers in brackets refer to relevant section numbers; case numbers in italics; brackets refer to cases considered in the Results Section).  
\begin{table}
\begin{tabular}{|p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}|} \hline
**Date and Time** & **River** & **Location** & **SAR** & **Resolution (m)** & **Pass** & **Angle of Inclination (\({}^{\circ}\))** & **Angle of Incidence (\({}^{\circ}\))** \\ \hline
12 February 2014 & \multirow{2}{*}{Thames} & \multirow{2}{*}{Wraysbury SkyMed} & COSMO-SkyMed & \multirow{2}{*}{2.5} & \multirow{2}{*}{Descending} & \multirow{2}{*}{97.9} & \multirow{2}{*}{43.4} \\
19:05 & & & & & & \\ \hline
13 February 2014 & \multirow{2}{*}{Thames} & \multirow{2}{*}{Wraysbury Stines} & COSMO-SkyMed & \multirow{2}{*}{2.5} & \multirow{2}{*}{Descending} & \multirow{2}{*}{97.9} & \multirow{2}{*}{31.6} \\
18:11 & & & & & & \\ \hline
14 February 2014 & \multirow{2}{*}{Thames} & \multirow{2}{*}{Wraysbury Stines} & COSMO-SkyMed & \multirow{2}{*}{2.5} & \multirow{2}{*}{Descending} & \multirow{2}{*}{97.9} & \multirow{2}{*}{35.9} \\
18:05 & & & & & & \\ \hline
25 July 2007 & \multirow{2}{*}{Severn/ Avon} & \multirow{2}{*}{Tewkesbury TeraSAR-X} & \multirow{2}{*}{3.0} & \multirow{2}{*}{Descending} & \multirow{2}{*}{97.4} & \multirow{2}{*}{24} \\
06:34 & & & & & & \\ \hline \end{tabular}
\end{table}
Table 1: Parameters of SAR images.
## 3 Circuit Design
### Variable-Gain Amplifiers  
Optical receivers with enough gain assure the signal for other subsequent reliable implements. The two-stage VGA provides the optical receiver with high gain. A schematic of each stage is given in Figure 9. A complete schematic of the one-stage VGA consists of the capacitive degeneration structure, two adjusting load resistors, and two identical variable current sources.  
The adjusting resistor consists of a fixed resistor and a PMOS. The PMOS transistors controlled by V\({}_{\text{ctrl}}\) in Figure 9, created by the RSSI module in Figure 1, act as variable resistors. Then, the PMOS resistors vary with the LDO output current, which denotes the input signal strength. Since it is unaffordable to extend the bandwidth by using inductors for area minimization, capacitive degeneration as an active inductor was applied for bandwidth extension. The variable current source controlled by the power mode equips the VGA stage with variable gain. Each VGA stage is followed by an emitter follower stage to drive the next module. Finally, the simulated two-stage VGA frequency response with different LDO output currents in low-power mode and high-power mode is shown in Figure 10.  
Figure 8: The simulated optical receiver discrepancy. (**a**) phase discrepancy; (**b**) gain discrepancy.  
Figure 9: A schematic of the variable-gain amplifier.  
The simulated gain of the VGA varies from 16.5 dB to 9.1 dB in high-power mode, and the gain of the VGA varies from 9.1 dB to 1.8 dB with the current reduced in low-power mode.
## 3 Simulation Setup
### Evacuation Simulation Settings  
The simulation utilized PATHFINDER 2018, which was developed by Thunderhead Engineering in the US. The evacuation simulation aimed to calculate the time taken by residents in multi-unit buildings to open fire doors during evacuation, and then to apply this time to the fire simulation to assess the smoke control effect of the air curtains while the fire doors are open.  
Accordingly, the start time for the evacuation simulation was set by adding the smoke detector detection time derived from the fire simulation and a delay time of 120 s. Simulations were executed to assess the operation times of smoke detectors in each case. All buildings in every scenario were equipped with smoke detectors. The respective operational times for each case were as follows: 25 s for Case 1, 28 s for Case 2, and 28 s for Case 3. The occupancy standard was based on the residential density in apartment buildings of 18.6 persons/m\({}^{2}\), as recommended by NFPA 101. The walking speeds employed were obtained from experimental data from the Korea Institute of Construction Technology, and the age distribution of the population was based on the demographic statistics of South Korea. The occupancy for each case was as follows: 456 individuals for Case 1, 342 individuals for Case 2, and 363 individuals for Case 3, as input by the user in the simulation(Table 2).  
Figure 3: Comparison of simulation results according to grid resolution. (**a**) Heat release rate, (**b**) visibility, (**c**) temperature.  
door opens after evacuation begins. Based on the evacuation simulation results, the average values of CO, CO\({}_{2}\) and O\({}_{2}\) concentrations; temperature; and visibility distance due to the fire were derived from the time the door opened to when it closed in the fire simulation. The assessment of toxic gases, temperature, visibility, and other hazards was conducted in accordance with South Korea's fire safety standards. Accordingly, the evaluation criteria were set as follows: CO (carbon monoxide) levels exceeding 1400 ppm, CO\({}_{2}\) (carbon dioxide) levels below 5%, O\({}_{2}\) (oxygen) levels below 15%, temperature below 60 \({}^{\circ}\)C, and visibility exceeding 5 m.
## 3 DIC Algorithm Configuration for Nanofiber Mat and Application Test
### FFT-Based Image Correlation Measurement Algorithm by using Displacement Feedback Loop  
Displacement change in material continuously influences the adjacent area for the deformation by applied force. This change increases along the axis of applied force. Thus, the DIC method needs to expect the maximum displacement of initial guess area and then applies to every tracing point. The initial guess area is widened due to the large deformation of material, which is time consuming as the iterations of image analysis increase.  
Figure 3 shows the displacement variation of the solid material by a uniaxial load. Material deformation is not dependent on each position but on adjacent area, because material is a continuum. This changes along the same direction of applied load, as mentioned above. Therefore, the same amount of deformation (\(\beta\)) is assumed for the same length (\(\alpha\)) by a uniaxial load: then, Equation (8), as follows, is obtained.  
\[\begin{gathered} X_{1}^{\prime\prime}=X_{0}+\alpha+\beta=X_{1}^{ \prime}+\beta\\ X_{2}^{\prime\prime}=X_{1}^{\prime\prime}+\alpha+\beta=X_{1}^{ \prime}+\alpha+2\beta=X_{0}+2(\alpha+\beta)\\ \ldots\\ X_{n}^{\prime\prime}=X_{n-1}^{\prime\prime}+\alpha+\beta=X_{n-1}^{ \prime}+\alpha+2\beta=X_{0}+n(\alpha+\beta)\end{gathered} \tag{8}\]  
The conventional DIC method specifies the initial guess area based on displacement tracing position from reference image subset. The value of initial guess area should be set over \(2\beta\), which represents material deformation because the value of initial guess position is set on \(X_{n}\) position. Finally, the initial guess area becomes wider at a further position from \(X_{0}\), which is set as the origin.  
Figure 2: The correlation comparison procedure with FFT images.  
When the subset image to be analyzed is separated from the reference image, the FFT region area can be much reduced by updating the measured strain value. The following algorithm was established for FFT DIC. The algorithm, shown in Figure 4, applies a double feedback loop to FFT DIC--the image analysis result around the adjacent region is applied to the next result of deformed images in the feedback. The first displacement measurement is done to the next initial guess area in the same way. Because the amount of displacement is small for each image, the upper left-side initial guess area can be set small. Thus, the small initial guess area size is conserved, regardless of local deformation, and finally, a faster strain measurement is made because the area is small.  
Figure 4: The optimization of the initial guess area size to reduce the calculation of the image correlation between the reference image and the deformed image for the auto captured images by using FFT, based on the digital image correlation measurement devices. (Red-dot box: added displacement feedback process).  
Figure 3: The displacement variation of the solid material by a uniaxial load.
## 2 Related Works  
### Traffic Information Center  
The traffic information center is an institution that exists on a national or local basis and serves as a hub for linking, integrating, analyzing, and providing traffic information collected from various traffic sites [6; 27]. Its primary functions include real-time monitoring of traffic conditions, signal control, traffic management, provision of traffic information, operation of bus management systems, and operation of unmanned enforcement systems. Additionally, many countries operate traffic monitoring/control centers worldwide to enhance road utilization efficiency, improve road traffic safety, enhance convenience for public transportation users, and promote orderly traffic flow [28; 29].  
In New York, U.S., the New York City Traffic Management Center [25] actively monitors and manages traffic conditions across New York City by utilizing incident information provided by police agencies, road safety equipment (RSE), Closed-circuit Television (CCTV), and VDS. Additionally, the center optimizes signals to minimize traffic congestion. Moreover, it provides real-time traffic information, traffic incident/regulation updates, CCTV location, and video information, as well as public transportation route/route information through 511 NY, a traffic information platform.  
The Berlin Traffic Information Center [26] in Berlin, Germany, collects traffic information through RSE and CCTV on 1360 km of major roads and uses it to monitor real-time traffic conditions or provide traffic information to drivers and users.  
The Japan Road Traffic Information Center (JARTIC) [30] in Japan provides real-time traffic conditions by collecting and integrating road traffic information across Japan. Traffic information provided by the JARTIC includes real-time traffic flow, real-time accident and construction section, disaster, and traffic statistics information.  
South Korea has the National Transport Information Center (NTIC) [31] operated by the MOLIT and Urban Traffic Information Center (UTIC) [32] operated by the National Police Agency. The NTIC integrates and provides nationwide traffic information in connection with the Korea Expressway Corporation and Private Expressway Traffic Information Center. Traffic information provided by the NTIC includes traffic flow, traffic incidents, CCTV, vehicle detection, dangerous section, and standard node-link information. In addition, the UTIC has established local transportation centers in major cities across South Korea to integrate traffic information collected through on-board equipment installed in taxis and patrol cars, RSE installed on roads, and highway/national road traffic flow/incident information received from NTIC. Traffic information provided by the UTIC includes traffic flow, traffic incidents, CCTV video, traffic safety, and protected area information.
# A Needs-Based Augmented Reality System
## 6 Discussion  
The present research explores the technologies and classification of augmented reality. To provide personalized AR experiences, it connects the concept of AR with that of human needs. In this case, the human need is considered to be the trigger of the AR experience.  
The needs prediction results from the CAP-AR dataset show low accuracy for the Eat/Drink subsistence need, this is due to the varying context information associate with one certain need. Basic human needs may occur at different times and contexts, correct prediction of needs requires an ample number of records in the dataset  
Despite the low accuracy of the prediction results, the guidelines demonstrate a coherent means to develop a needs-based AR application that should be further tested.  
While a need is a primary controller of the experience trigger, it might stir an argument: if a user has deficiencies in basic human needs, what makes the user capable of possessing the technology to operate the experience? This question might be answered in two folds:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & \begin{tabular}{c} **Accuracy of all** \\ **Needs Prediction** \\ \end{tabular} & \
\begin{tabular}{c} **Eat/Drink Need** \\ **Class Precision** \\ \end{tabular} &
\begin{tabular}{c} **Eat/Drink Need** \\ **Class Recall** \\ \end{tabular} \\ \hline Decision Tree & 49.32\% & 44.44\% & 42.11\% \\ Random Forest & 32.88\% & 40.00\% & 21.05\% \\ Naive Bayes & 38.36\% & 42.11\% & 42.11\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Need prediction results.  
Figure 10: Records of the dataset portraying required fields.  
* Technology is now reaching many people in various living conditions.
* People undergo changing levels of needs daily regardless of capabilities.
* AR experiences are custom-made for specific reasons; however, these reasons are usually generic and not user-related, even in the case of personalized AR, the experiences are product, location, or service related more than user related. This research questions that from a plethora of AR experiences that shall be available in the future, how do we make AR more user-specific and find the best experience for a user at a certain time? This study answers this question by incorporating basic human needs into the equation. Therefore, the novelty is in connecting the concepts of augmented reality, needs detection, and satisfier recommendation.  
The goal of this research is to create the means to trigger the experience based on needs. However, the actual design and the AR experience's purpose are left to the creators and designers of the experience.
# Comparing Winds near Tropical Oceanic Precipitation Systems with and without Lightning
## 2 Materials and Methods
### Ascat  
ASCAT is a C-band scatterometer that measures ocean vector winds. ASCAT consists of a dual-fan-beam radar, which measures along two parallel swaths. Currently, there are three ASCAT instruments in orbit onboard the Meteorological Operational (Metop) A, B, and C satellites. The time period of May 2017 through December 2019 was analyzed, so this study only focused on ASCAT-A and -B. For each of these instruments, the 12.5-km coastal-optimized Level 2 data [25; 26] were analyzed. Because this study was focused primarily on CYGNSS, only the ASCAT wind speed measurements were used, not wind direction. ASCAT is included in this study in order to examine the sensitivtiy of the results to the choice of wind instrument.  
ASCAT winds were matched to CYGNSS tracks in the following way. All CYGNSS tracks that overlapped the time period of a particular ASCAT orbit (approximately 90 min) were examined for that granule. The closest-in-distance ASCAT wind speed observation from either swath was matched to each CYGNSS specular reflection along a track; however, any ASCAT wind measurement more than 20 km away from the track was excluded, even if it was the closest. Only about 25% of CYGNSS tracks have matchups from either ASCAT-A or -B, because the CYGNSS track and ASCAT swaths often did not overlap spatially within the -90-min orbit window. Moreover, typically any overlap occurred only during a portion of the CYGNSS track's length. In sum, the number of matched samples with ASCAT was \(<\)10% of the entire track-based dataset. Matched ASCAT winds were included as a new field in each CYGNSS track file.
## 10 Conclusions  
The most crucial aspect of power system operations is the forecasting of short-term electrical loads. It provides the foundation for a multitude of power distribution and management decisions. Consequently, the accuracy and precision of such forecasts are of paramount importance to decision-makers in the power sector. This study presented a comprehensive analysis of various STLF models that were applied to databases from different geographic locations and connected to several distinct power system subsectors, including smart grids. Researchers have developed a variety of prediction models to improve the efficacy of the current forecasting techniques, given the necessity for regular and accurate assessments of fluctuating factors in order to accurately predict electrical load. To provide a comprehensive analysis of the most prominent STLF models, this research classified them into three general categories: statistical approaches, methods based on intelligent computing, and hybrid models. A comprehensive tabular review was initially presented on statistical methodologies and intelligent-computing-based methods. The two aforementioned categories were then subdivided into a specific analysis of cutting-edge hybrid models. Subsequently, the significance of STLF in the context of smart grids was evaluated. The advent of smart grids in modern power systems, their integration with smart meters, and innovations such as V2G and second-life batteries have introduced a novel dimension to STLF. V2G enables bidirectional energy flow between EVs and the grid, thereby enhancing flexibility and resilience. Furthermore, the incorporation of second-life batteries contributes to the stability and adaptability of the grid. As electric consumption patterns continue to evolve in the dynamic smart grid environment, these advancements present challenges for conventional machine learning models. A number of challenges, including meteorological and production-related factors, can affect the accuracy of load