esa-sceva/satcom-chunk-collection huggingface dataset:
# Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements
## 4 Results
### Evaluation of Filtering Based on Staring Measurements
#### 4.1.1 Data Availability
Figure 11 shows the error distribution of the velocity and the velocity standard deviation in dependency of the data availability on the basis of 10 min means. A high correlation of the general appearance of Figure 11a,b suggests a causal connection of the velocity and the velocity standard deviation error. While both standard deviation filters and the interquartile range filter mainly show error values above 80% data availability, the data distributions of the dynamic data filter and CNR-threshold based filters are widely scattered. We see a repeating pattern of data point clusters in Figure 11a,b that appears to be individually scaled for each of the dynamic and the combined filters. Although both dynamic data filters use the same normalised dataset, the observed differences in data availability appear for unknown reason. In this test case, the full potential of conservation of data availability by the kernel-based dynamic data filter cannot be seen. We assume that based on the behaviour shown (Figure 10), the data availability of the CNR-threshold based filters will drop significantly faster with increasing distances than of the dynamic data filters. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Avg.** & **Avg.** & **Abs. Avg.** & **RMS** & **Abs. Avg.** & **RMS** \\  & **Availability** & **Availability** & **Velocity** & **Velocity** & **Velocity Std.** & **Velocity Std.** \\  & **FINO1** & **All Ranges** & **Error** & **Error** & **Dev. Error** & **Dev. Error** \\ \hline Dyn. data histogram & 90.0\% & 90.4\% & 0.34 m/s & 2.38 m/s & 0.14 m/s & 1.82 m/s \\ Dyn. data Gauss. kernel & 75.1\% & 78.2\% & 0.30 m/s & 2.10 m/s & 0.18 m/s & 0.90 m/s \\ CNR threshold & 81.9\% & 87.6\% & 0.45 m/s & 3.02 m/s & 0.36 m/s & 2.24 m/s \\ Std. dev. two sigma & 96.2\% & 95.9\% & 0.49 m/s & 2.50 m/s & 0.73 m/s & 3.00 m/s \\ Iterative std. dev. & 98.1\% & 98.5\% & 0.54 m/s & 2.54 m/s & 0.79 m/s & 3.45 m/s \\ Quartile filter & 93.5\% & 94.0\% & 0.40 m/s & 2.42 m/s & 0.35 m/s & 2.77 m/s \\ Combined Wang & 77.5\% & 83.0\% & 0.40 m/s & 3.10 m/s & 0.00 m/s & 1.87 m/s \\ Combined Newman & 81.8\% & 87.5\% & 0.42 m/s & 3.02 m/s & 0.20 m/s & 2.14 m/s \\ No filter & 100\% & 100\% & 0.76 m/s & 2.58 m/s & 2.17 m/s & 4.10 m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of different filtering methods applied on staring mode measurements from 21.12.2013 15:35 h (UTC) till 19.01.2014 7:55 h (UTC) for all wind directions.
# Open Collaborative Platform for Multi-Drones to Support Search and Rescue Operations
## 3 Open Collaborative Platform
### Collaborative Searching Path Planning
#### 3.3.4 Dynamic Searching Path  
With an initial searching path assigned by the open platform, a Krypto drone follows the searching path until it sensed a Wi-Fi signal. To improve the accuracy of locating victims, Krypto will dynamically adjust its searching path. If it sensed a signal source while flying on the initial searching path, [PERSON] records the initial RSSI value (_Initial_R_) and continues to its path according to the initial searching path for \(d\) distance until the sensed signal becomes weaker than _Initial_R_+\(\alpha\).  
Next, [PERSON] will fly a \"Figure-Eight\" path with a diameter of each circle equal to \(d\) and perpendicular to the initial searching path, see Figure 13. The sweeping curve is used as our initial search path. The dynamically adjusted path will produce more quadrant planes of the sensing point for the signal source. As a result, better accuracy in the location estimation. The performance result of the dynamic searching path is shown in Section 4.1.1.  
To decide which half of the \"Figure 8\" path (e.g., top or bottom) to fly first, [PERSON] selects the part of the area that has not been previously scanned. Once it is finished the half of the \"Figure 8\" path, [PERSON] can decide to (\(a\)) finish scanning the remaining \"Figure 8\" path if most of the sensing points are on more than two quadrant planes or (\(b\)) continue the initial searching path (e.g., dotted arrow line in Figure 13) if [PERSON] has enough sensing points to estimate the location of the signal source.  
To guarantee for a drone is able to return home, we modify the Adaptive Return-to-Home Sensing (ARS) algorithm [45] to address additional power consumption due to the dynamic searching path. The additional distance (i.e., a circumference of the circle) from the dynamic searching path is added to the _Required Distance_ when deciding if it needs to return home.  
Figure 12: Required distance for distributed and hybrid approach.
# Advancing C-V2X for Level 5 Autonomous Driving from the Perspective of 3 GPP Standards
## 5 SA-Based 5G V2X Services
### System Support
#### 5.1.2 Architectural Enhancements Support  
The 5G-enabled concepts for V2X services are up-to-the-minute and require architectural enhancements in order to support V2X services. Apart from the architectural enhancements discussed in technical specifications (_TS 23.285_) and service requirements presented in technical specifications (_TS 22.185_ and _TS 22.186_), 5G-specific architectural enhancements are required for 5 GS to facilitate vehicular communications over 5G PC5 and Uu reference points (chosen independently when transmitting and receiving messages). In this regard, the technical specification (_TS 23.287 Rel-16_) provides architectural models and concepts i.e., reference model, functional entities, V2X application server, service parameters, reference points, and service-based interfaces. In addition, it specifies high-level functionality and features i.e., authorization and provisioning for V2X communications over PC5 and Uu reference points, V2X application server discovery, QoS handling for V2X communication, subscription to V2X services, and interworking between EPS-V2X and 5 GS-V2X services. To accomplish the stated enhancements, various procedures for service authorization and provisioning to UE; V2X communication over PC5 and Uu reference points, and service authorization to NG-RAN for V2X communications over PC5 referencepoint are provided under functional descriptions and information flows of the specification. These enhancements and procedures motivate the automotive research community to contribute to the realization of the next-generation V2X services.
# Wearable Plasma Pads for Biomedical Applications
## 2 Experimental Methods
### Detection of Nitric Oxide Generated by the Plasma Pad  
In order to detect exogenous nitric oxide (NO), an absorption-based Griess reagent method was used to determine total amounts of NO\({}_{x}^{-}\) generated by the plasma pad. Griess reaction solutions and processes were prepared as in a previous report [28]. Briefly, 2 samples (1 mL each) of Dulbecco's phosphate buffered solution (DPBS) and DMEM with 10% FBS were exposed to plasma for different treatment times (0 min, 1 min, 3 min, 5 min, and 10 min). Then, 100 uL of each plasma-treated sample was placed into the 96-well microplate and 100 uL of the two Griess reagents was added to each sample solution. The mixed solutions were left for at least 30 min to terminate this reaction. After mixing, the absorbance was measured at 540 nm using a multimode microplate reader (CYTATION 3, BioTek Instruments, Inc., Winooski, VT, USA). Calibration was performed using a sodium nitrate solution for each measurement.
# A Wideband High-Voltage Power Amplifier Post-Linearizer for Medical Ultrasound Transducers
## 3 Results and Discussion
### Experimental Performance Verification of the HVPA with Post-Linearizer
The setup to measure the gain as a function of frequency is identical to that depicted in Figure 5. A fixed 17 dBm input power from the arbitrary function generator (AFG3252C) was applied to the HVPA and the HVPA with post-linearizer to measure the output voltages via the oscilloscope (MSOX4154A) and subsequently, the gain and gain deviation of the devices were calculated. In Figure 8a, the measured gains of the HVPA with and without post-linearizer are 15.6 and 15.2 dB at 10 MHz, and 16.5 and 11.1 dB at 40 MHz, respectively. Thus, we confirm from these results that the gain of the HVPA with post-linearizer exhibited greater flatness than that of the HVPA without the post-linearizer. In Figure 8b, the gain deviation values were calculated based on how much the gain at each frequency (6 MHz to 40 MHz) deviated from the measured gain at 10 MHz frequency. The gain deviation of the HVPA with post-linearizer yielded values smaller than that of the HVPA without the post-linearizer throughout the mid-range frequencies, but was higher than that of HVPA without the post-linearizer at the highest measured frequency (with post-linearizer: 1.01 dB and 0.90 dB at 26 MHz and 40 MHz, respectively, and without the post-linearizer: 1.60 dB and \(-3.12\) dB at 26 MHz and 40 MHz, respectively). These measured results are significant because the operating frequency of the ultrasound transducer could vary depending on the cable effect and electrical impedances of the systems [7]. Therefore, an ultrasound transmitter such as the HVPA, which yields adequate operation over a wide bandwidth, is preferred for integration with ultrasound transducers.
## 3 Experimental Setup and Result Analysis
### Result and Analysis  
There are three basic indexes judging the performance of a tracking algorithm: accuracy, robustness, and real-time performance. The accuracy of the proposed algorithm in a VLC system means the accuracy of tracking and extracting the target LED that is used as a signal source. The image processing of the tracking part is mainly conducted for selecting the transmitter, tracking the target transmitter, and extracting the signal source area from images. Extracting the target area accurately and completely is the premise of communication. Robustness is that the performance of the proposed algorithm when interference occurs, such as the sun, background light-emitting source, shielding effect, etc., in an OVLC system. Moreover, real-time performance determines whether the tracking algorithm can be applied into a practical VLC system or not; in other words, tracking and communicating would lose its meaning if the processing time of the tracking algorithm is too long, especially in a VLC system. Therefore, to demonstrate the superiority of the proposed algorithm and reflect its performance in the round, it is judged using the three aspects in this paper.
# Unmanned Surface Vehicle Using a Leader-Follower Swarm Control Algorithm
## 5 Performance Test and Actual Sea Area Test of the Swarm USV
### Sensor Performance Test
#### 5.1.2 GNSS Performance Test  
Among the sensors installed in the USV, the most important for navigation is the GNSS. It provides the variable values necessary for the calculation to control each USV, such as the current position, target position, current direction angle, target azimuth, and distance error, and simultaneously creates the target virtual point necessary for maintaining the swarm formation. Therefore, determining whether the sensor data outputted from the GNSS are reliable is crucial. Hence, in this study, two cases were considered to verify the performance of the GNSS:  
1. Static performance testing; and
2. Dynamic state performance testing.  
The static performance test was conducted at the U-Busan-14 point of the unified reference coordinates announced by the Korea Geographical Information Service. The experiment was conducted by placing one USV at a point 6.5 m north of the integrated reference coordinates and the other at a point 3.2 m west of the integrated reference coordinates to acquire data. At this time, the data output period of the GNSS was 10 Hz, and the data were acquired for 1 h. Figure 17 depicts the experimental process, and Figure 18 shows the experimental results.  
Figure 16: The test result of communication speed test 2.  
Figure 17: Photo of the static state performance test.  
As shown in the results, the average value of each GNSS position output almost coincides with the target coordinate, and the position error occurring during the test had a value within about \(\pm\)0.5 m. Therefore, in the static state, it is confirmed that the position data outputted from the GNSS are reliable.  
The GNSS installed in each USV outputs not only the position data but also the heading data, and the heading data are essential to control the target heading of each USV. Therefore, the precision of the direction angle data outputted from the GNSS was confirmed. The experiment was conducted simultaneously in the same place as the static state performance test. Furthermore, it was arranged to face the true north (0\({}^{\circ}\)) direction indicated in U-Busan-14 to acquire data for 1 h and  
As a result, it was confirmed that the created pattern had a constant direction angle for each point through both rotations of each point. Therefore, it was confirmed through the test that the data of the GNSS were reliable even in the dynamic state.
# A Classification Method for Incomplete Mixed Data Using Imputation and Feature Selection
###### Abstract
PSOFI effectively combines the benefits of feature selection, data imputation, and PSO. Specifically, PSOFI incorporates a mixed data imputation method that classifies features into quantitative and categorical types and uses different imputation strategies for each. Additionally, it implements feature selection to choose promising features that can enhance its performance. By employing PSO to simultaneously optimize the parameters of the imputation strategies and select features in a wrapper manner, both feature and label information can be utilized, thereby further improving its effectiveness and performance. Moreover, a legacy learning mechanism is introduced to store and utilize the historical optimal solutions during iterations, thus enhancing the performance of the method. This paper makes several contributions to the field, which are specifically as follows:  
* We propose a novel data imputation method based on PSO to address the challenges of mixed data imputation. Our method exploits different types of missing features and labels to generate suitable imputation values. Furthermore, we integrate the feature selection technique to enhance its overall performance. * To further enhance the proposed method, we design a legacy learning mechanism that allows the method to utilize historical optimal solutions to guide the search process. * We conduct experiments using seven comparison algorithms and twelve datasets to evaluate the effectiveness and superiority of our proposed method. Empirical results demonstrate that our method outperforms the other algorithms. The remainder of this paper is organized as follows. The proposed method is elaborated in Section 2. The results of the experimental evaluation are presented in Section 3, followed by the conclusions in Section 4.
## 7 RNN-Based Optimization of Mirror Placement for Maximizing SNR in ADRs
### RNN Methodology
#### 7.2.4 Loss Function  
The RNN is trained to maximize the SNR by minimizing the following loss function:  
\[\mathcal{L}=\frac{1}{T}\sum_{t=1}^{T}\bigg{(}\frac{1}{\text{SNR}_{\text{ combined}}(t)}+\lambda\|\mathbf{o}(t)-\mathbf{o}(t-1)\|^{2}\bigg{)}, \tag{48}\]  
where \(\frac{1}{\text{SNR}_{\text{combined}}(t)}\) represents maximizing the SNR, \(\lambda\|\mathbf{o}(t)-\mathbf{o}(t-1)\|^{2}\) is the Regularization term to ensure smooth adjustments in mirror positions, and \(\lambda\) is a Regularization weight.  
The illustrative diagram of the RNN structure used, together with its reference elements, can be seen in Figure 2. The input component corresponds to the signal \(\mathbf{P}(t)\) received in the ADR, integrated by the signals received from each of the photodiodes \(\mathbf{P}_{r,\text{PD}_{N}}(t)\). The second component corresponds to the hidden RNN state \(\mathbf{h}(t)\), which encodes temporal dependencies, and finally, the third component corresponds to the obtained outputs o(t), which represent the optimized positions of the mirrors.
## 2 Material and Methods
### Methods  
#### 2.3.1 Technological Routes  
In this research, the RUSLE model, terrain elevation, rain, NDVI, soil type, and LUCC data were used to investigate soil erosion in the research domain between 2000 and 2020. In addition to performing future soil erosion studies utilizing constant and variable factors to forecast soil erosion in the research region for the years 2025-2050, the PLUS model was also used to simulate land use change in the study area. The various land uses in the research area, the main drivers of soil erosion, and their interactions were investigated using the geographical detector. Figure 2 provides a summary of the study's approach. The subsections provide a thorough discussion of the datasets and methods.  
Figure 2: Process and research approaches. RUSLE, or the revised universal soil loss equation, is a digital elevation model. The degree of soil erosion is a factor, vegetation cover factor, or C factor. The R component stands for rainfall erosivity; LS for topography; K for soil erodibility; and P for support practice.
# Clustering for Lifetime Enhancement in Wireless Sensor Networks
## 3 Advanced Clustering Approach
### Parameters That Affect Clustering Efficiency  
According to the literature, there exist many parameters that affect the clustering performance. However, the starting point is the LEACH energy model, which is the root of them all. It resumes the main communication operations in a WSN. Therefore, we start by analyzing this model, then we discuss other parameters that have been used by researchers in order to enhance their routing protocol's performance.
## 4 Proposed Hybrid FVIMDE Algorithm  
In this section, we present the Hybrid Four Vector Intelligent Mechanism with Differential Evolution (FVIMDE) algorithm, a novel optimization technique designed to effectively balance exploration and exploitation within the search space. The proposed algorithm integrates the strengths of Differential Evolution (DE), known for its robust exploration capabilities, with the Four Vector Intelligent Mechanism (FVIM), which excels at fine-tuning solutions in promising regions of the search space.  
The FVIMDE algorithm operates in two distinct phases. The first phase involves the application of Differential Evolution to explore the global search space, identifying potential areas where optimal or near-optimal solutions may reside. During this phase, the population undergoes initialization, mutation, crossover, and selection processes to iteratively improve the quality of solutions. The second phase transitions into the FVIM, where the best solution obtained from the DE phase is refined through a strategic position update mechanism involving the Alpha, Beta, Gamma, and Delta particles.  
By combining these two powerful optimization techniques, the FVIMDE algorithm leverages the global search capability of DE and the local search efficiency of FVIM, en suring a comprehensive search process that effectively avoids premature convergence and improves the likelihood of finding the global optimum. The following subsections provide a detailed explanation of the steps involved in each phase of the proposed algorithm.
# Enhanced Sensitivity of FeGa Thin-Film Coated SAW Current Sensor
## 1 Introduction
In addition, the FeGa thin film applied for microsensor and microsystem integration is conducive to the miniaturization and intelligence of the sensor, and it significantly improves the performance of the device. In this contribution, a new design of SAW device for sensing current is proposed by employing the patterned FeGa thin film as the sensitive interface, as depicted in Figure 1. A SAW chip with a delay-line pattern was fabricated photolithographically on 128\({}^{\circ}\) YX LiNbO\({}_{3}\) substrate to operate at 150 MHz. To improve the sensitivity and suppress the hysteresis error, the patterned design was performed to the FeGa thin film. The proposed current sensing device was built by sputtering the FeGa thin film to the SAW chip and characterized by connecting it into the differential oscillation loops. Larger current sensitivity, low hysteresis error, excellent repeatability and stability were achieved experimentally.
# A Novel Indoor Localization Method Based on Image Retrieval and Dead Reckoning
## 4 Vision-Aided PDR System Design
### Visual Place Recognition
#### 4.1.3 Location of VPR  
From the input image with size of 256 \(\times\) 256, images are extracted with size of 224 \(\times\) 224, flipped horizontally and then inputted into the trained network to get 4096-dimensional feature vectors. Before VPR is used, it is necessary to build an image vector dataset. When obtaining the images used for testing V-PDR, we extracted these images into the feature vector dataset. The vector dataset consists of the feature vectors of images and their labels. The labels are built for where the image is obtained. The images with the highest similarity are found by comparing the Euclidean distances between feature vectors and vector dataset. The location of the image is obtained by weighing the recognition results of these inputs. If the location of Recall@1 is not searched in the image from other angles in Recall@10, such locations are ignored. The most probable location is the VPR result. As for heading direction, the VPR direction is the most probable heading direction.
# Defining the Ideal Phenological Stage for Estimating Corn Yield Using Multispectral Images
## 5 Conclusions  
The spectral yield estimation models show that the following:  
* For estimating yield of the maize crop utilizing the spectral models based on multispectral images and machine learning algorithms, the reproductive phenological phase of R2 was found to obtain the best RMSE% and MAPE% values of 9.17% and 7.07%, respectively;
* Due to the influence of ground spectral response from images taken at the stadium
* VE, it is possible to estimate the yield with satisfactory levels of accuracy;
* The composition of the predictor variables and the accuracy and precision of the models are associated with the phenological stage of development; that is, the architecture of the model is variable for different areas and stages of senescence of the plant.