esa-sceva/satcom-chunk-collection huggingface dataset:
# Thermal, Multispectral, and RGB Vision Systems Analysis for Victim Detection in SAR Robotics
## 3 Materials and Methods
### Algorithms and Evaluation Metrics  
A system was devised to evaluate the proposed method, which stems from developing a series of missions for processing the proposed victim detection system through the three proposed vision modes. The outcome aims to maximize the **accurate** identification of  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Item** & **Component** & **Description** \\ \hline \multirow{4}{*}{4} & \multirow{4}{*}{MicaSense Altum} & **Multi-spectral Camera** \\  & & B = [440–510 nm] \\  & & G = [520–590 nm] \\  & & R = [630–685 nm] \\  & & Red Edge = [690–730 nm] \\  & & NIR = [750 nm–2.5 μm] \\ \hline \hline \end{tabular}
\end{table}
Table 2: _Cont._  
Figure 4: Experimental environments developed indoors and outdoors. Source: authors.  
victims in an explored area, placing potential \"refined\" locations on an **informed environmental map**. In this manner, the markings serve as reference points for first responders to prioritize their attention to those areas.
## 3 Results and Discussion  
### D-scanned Plant Model  
The leaf areas of the scanned data were approximately 12% larger than those of the measured data (Figure 3). This result conflicts with previous studies that found that the image-based model of plant leaves was well matched to actual ones [18; 22]; nevertheless, from our perspective, leaf area acquired by 3D scanning was more adjacent to actual values in this case. Leaf area is generally measured from projection area, and this assumes that leaves have flat structure. However, leaf morphology tends to be curled under LED lights with high blue content [27], and the lettuce used in this study also had convex and curled leaf shapes (Figure 1A), indicating that the projection area underestimated the actual leaf area in this case. Meanwhile, the 3D scanner collects the coordinates of the leaf surface converted to a 3D mesh, which can accurately reflect the leaf curvature and structure. Therefore, further analysis and calculation of light interception and photosynthesis were conducted using scanned leaf areas.  
Figure 3: Comparison between measured and scanned leaf areas. The total number of leaves was 14, and the continuous line was a 1:1 match line.
# Blind Recognition of Forward Error Correction Codes Based on a Depth Distribution Algorithm
## 4 Depth Distribution Algorithm Based on Linear Block Codes
### Convolutional Code Recognition Algorithm
When parameters such as \(D_{e}\), \(n_{e}\) and \(v_{e}\) are not correct, there is no specific constraint relationship inside the codeword group, and the depth distribution value is:  
\[S=(k_{e}+1)(D_{e}+1) \tag{31}\]  
When all parameters are recognized correctly and there is no error code, at least \(k+1\) bits in Equation (30) must satisfy the constraint relationship in Equation (28), which ensure that linear correlation vectors appear in the codeword group \(C_{t}(n_{e},v_{e},D_{e})\). When \(D_{e}\) is bigger than \(D\) with a correct code rate, there exists \((D_{e}-D)\) linearly independent vectors can satisfy the constraint relation of Equation (28). The depth distribution value of the accepted codeword group is solved by the following relations:  
\[S(C_{t}(n_{e},v_{e},D_{e}))=k(D_{e}+1)+D \tag{32}\]  
Using Equation (31) to normalize the depth distribution function in Equation (32), we can get the equation as follows:  
\[\rho(S(C_{t}(n_{e},v_{e},D_{e})))=1-\frac{1}{k+1}+\frac{D}{(k+1)(D_{e}+1)} \tag{33}\]  
In order to realize the blind recognition of code types, a unified processing method must be used to process the code word matrices of various codes. For the subcode groups presented in this section, they can be equivalent to \((nD+n,nD+k)\) linear block codes. For each subcode in the subcode set, the number of check bits in the codeword matrix is \((D_{e}-D+1)\). Therefore, the actual equivalent length of the code word is \(n(D_{e}+1)\). According to Equation (25), the depth distribution characteristics of the convolutional code codeword matrix under a different parameter estimation are calculated. So far, through the reconstruction of convolutional codes, the recognition process of the convolutional code word matrix has been equivalent to the recognition overpass of linear block code depth distribution matrix, and the equivalent normalized depth distribution of a convolutional code word matrix is shown as follows:  
\[\rho(S)=\left\{ \begin{array}{cl}1&(n_{e}\
eq n(D+1)\\ &(n_{e}=n(D_{e}+1)\\ 1-\frac{n-k}{n}(\frac{a+1}{a+1+D})\leq\rho\leq 1\\ 1-\frac{n-k}{n}(\frac{a+1}{a+1+D})\end{array} \right.\&\&d_{e}\
eq d)\\ (n_{e}=n(D_{e}+1)\\ \&d_{e}=d)\end{array}\right. \tag{34}\]With the equivalent normalized depth distribution function of convolutional code word matrix, blind recognition of linear block codes and convolutional code types can be realized.
## 3 Results
### Extension of Trackable Axial Range  
To extend the imaging axial range, the objective lens was required to oscillate in vertical direction. To achieve this, the objective lens used in the imaging was mounted on a piezo actuator (FL201V-C, THK Precision Co., Ltd., Tokyo, Japan), the oscillation of which was programmed using a Power Lab system (ADI Instruments, Sydney, Australia). Since the imaging position step, \(d\), was determined to be 500 nm for a desired calibration resolution of \(\leq\)2 nm, as shown in the previous section, the objective lens oscillation amplitude was set to 500 nm. With this amplitude, the piezo actuator was designed to move according to a two-stage step function, as shown in Figure 5D. The frequency of the shutter controlling the incident laser was adjusted to only collect the light when the objective lens was stable at the desired positions. Considering the response time between the piezo input and sensor monitor, the frequency of imaging (\(1/t_{P}\)), was set to 10 Hz, and the shutter exposure time \(t_{I}\) was set to 15 ms.
## 4 The DSEC Method for TSSP
### Flowchart of the DSEC Method for TSSP  
The proposed framework of the DSEC method for TSSP involves two stages: (1) Offline training and (2) online application, which are described respectively below:  
1. Offline Training: The classification database is randomly divided into three parts, as follows: Training set, validation set, and testing set. The training set is split by the data segmentation strategy and then utilized for training multiply AdaBoost classifiers. The validation set is used for selecting the optimal parameter of the classifier and the testing set is utilized for evaluating the classification performance of the DSEC model for TSSP.
2. Online Application: The monitoring information of power systems is utilized to judge whether the system is subjected to a disturbance or not. If a disturbance occurs, the measured data is adopted to construct the input features and the stability status is predicted immediately after feeding these features into the DSEC model. If the system is predicted to have an unstable status, the system operator is immediately alerted to take proper control strategies to prevent large-scale outages. During online application, if significant changes happen to the operating condition or grid topology, the classification database should be updated immediately so that the DSEC model can be retrained for robustness improvement.  
The flowchart of the proposed DSEC method for TSSP is shown in Figure 4:
## 2 Materials and Methods  
### Gantry Timestamp Sequences from Traffic Data Collection System (TDCS)  
The timestamp gantry sequences in this study are extracted from the raw data \"TDCS_M06A\" of Traffic Data Collection System (TDCS) ([[http://tisvcloud.freeway.gov.tw/history/TDCS/](http://tisvcloud.freeway.gov.tw/history/TDCS/)]([http://tisvcloud.freeway.gov.tw/history/TDCS/](http://tisvcloud.freeway.gov.tw/history/TDCS/))), one of the Taiwan government open data platforms. Generally, each of the vehicles is usually attached with one electronic tag, as shown in Figure 4, on the front light of one car, as its identification for fee-charging: a timestamp associated with one gantry is recorded when that vehicle passes one gantry. Figure 5 shows one gantry \"01F-196.0N\", for example, located at (N24.085622, E120.52785) in the northern direction of Freeway No. 1. Experimental resources, as shown in Table 1, that includes five months of gantry timestamp sequences collected from 2016/11 to 2017/3; the total size of 3624 files is about 103.0 GB. Table 2 shows some of records in the file \"TDCS_M06A_20161101_00000.csv\" collected at 2016/11/1, for example, and the field \"TriPInformation\" is for storing the contents of timestamp gantry sequences of vehicles.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Date Set** & **The Number of Files (24 files/per day)** & **The total Sizes of Files (GB)** \\ \hline
2016/11 & 720 & 20.1 \\
2016/12 & 744 & 21.1 \\
2017/01 & 744 & 21.6 \\
2017/02 & 672 & 19.2 \\
2017/03 & 744 & 21.0 \\ Total & 3624 & 103.0 \\ \hline \end{tabular}
\end{table}
Table 1: Experimental resources extracted from Traffic Data Collection System (TDCS).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Vehicle Type** & **Detection Time, O** & **Canty ID, O** & **Detection Time, D** & **Canty ID, D** & **Trip Length** & **Trip End** & **Trip Deformation** \\ \hline
42 & 11/1/2016 0.21 & 01F1292N1 & 11/1/2016 0.21 & 01F132N1  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline
**Vehicle Type** & **Detection Time, O** & **Gantry ID\_O** & **Detection Time, D** & **Gantry ID\_D** & **Trip Length** & **Trip End** & **Trip Information** \\ \hline
32 & 11/1/2016 0:32 & 03F1992N & 11/1/2016 1:52 & 03P0447N  
To make the contribution of this paper solidly, this study takes the longest trip, containing six gantries, from \"05F-000.0S\" to \"05F-049.4S\" (southern) or from \"05F-052.8N\" to \"05F-000.1N\" (northern) within the trips of Freeway No. 5 (Chiang Wei-shui Memorial Freeway), where that trip has been reported for a long time with serious traffic jam in the weekend or national holidays. Table 3 gives the details of these six gantries in both directions, the southern (S) from \"05F-000.0S\" to \"05F-049.4S\" and the northern (N) from \"05F-052.8N\" to \"05F-000.1N\".
###### Abstract  
Background: Laparoscopic pancreatic surgery demands high precision and minimal invasiveness, yet conventional robotic systems often face challenges due to complex anatomical environments and uncertainties inherent in surgical procedures. Optimizing key design parameters such as the Remote Center of Motion (RCM) and robotic link lengths is critical for enhancing workspace accessibility and instrument maneuverability. Methods: An integrated optimization framework combining genetic algorithms (GA) with fuzzy logic was developed to determine the optimal RCM position and the ideal lengths of crucial robotic links in a 3-DOF parallel robotic system. The GA explored a large design space based on 6951 tracking points recorded during manual instrument manipulation, while the fuzzy logic system refined fitness evaluations by incorporating expert-defined membership functions and heuristic rules to manage uncertainties and ensure robust performance. Results: Simulation studies demonstrated that the optimized RCM position shifted from an initial [100, 0, 300] to [119.003337, \(-\)146.610801, 269.07376], yielding improved workspace coverage and enhanced instrument maneuverability. The GA further determined optimal link lengths of approximately 213.5 mm, 248.5 mm, and 48.6 mm for the primary, tertiary, and minimum secondary links, respectively, which were rounded to practical values of 215 mm, 250 mm, and 50 mm. The optimized design exhibited significant improvements in workspace reachability, precision, and operational stability, as validated by detailed 3D workspace plots and time history diagrams of the instrument tip and joint trajectories. Conclusions: The integrated GA-fuzzy optimization approach effectively enhances the design of a 3-DOF parallel robot for laparoscopic pancreatic surgery by achieving superior kinematic performance. The optimized parameters contribute to improved surgical precision and workspace accessibility, indicating strong potential for clinical application and further experimental validation.  
parallel robot; design optimization; minimally invasive pancreatic surgery; fuzzy logic; genetic algorithm +
Footnote †: journal: Applied sciences  
Article
## 1 Introduction
### Mechanism Analysis of Situation Awareness Loss in Remote Tower Controllers  
[PERSON] [15] first presented the concept of situation awareness (SA), defining an extensive SA model that outlined the interplay between people and automation, along with the various aspects that influence this interaction. In this method, perception, understanding, and projection are the fundamental elements of situational awareness (SA).  
Perception is the first stage in attaining situational awareness. It entails identifying the conditions, characteristics, and pertinent components of the environment and establishing a basis for later phases of understanding and forecasting. [PERSON]'s study demonstrated that people's risk perception increases with an increase in danger levels [12]. In remote tower control operations, controllers reach the perception phase of situational awareness by keeping an eye on changes in interface components and physiological signals like heart rate variability.  
The comprehension phase is the cognitive process that follows information perception, with the objective of grasping the significance of pertinent items [16]. [PERSON] and colleagues discovered that as controllers' knowledge and experience increase, their situational awareness increases. Even with inadequate information, seasoned controllers may discern probable correlations among data points [17]. Consequently, the understanding phase is significantly dependent on the considerable work experience of controllers. The cognitive processes of the preceding two phases form the foundation of the projection phase. Controllers use this information to anticipate fluctuations in air traffic during a certain duration, consequently impacting their ensuing decision making. The three stages of SA intertwine, with the successful completion of one level serving as a prerequisite for the next stage [18].  
Remote tower controllers must accumulate information from the human-machine environment pertinent to their jobs or work goals, thereby enhancing situational awareness over time. Furthermore, this state information may vary across geographical dimensions. In summary, situational awareness is an essential precondition for controllers' decision-making processes, directly impacting their choices. Controllers with elevated situational awareness can effectively evaluate the present circumstances and make optimum judgments, while those with diminished situational awareness are susceptible to misinterpreting the environment, resulting in erroneous decisions.
# Therapeutic Applications of Halloysite
## 4 Recent Trends in HNT Research
### HNTs in Coatings and Films  
[PERSON] et al. [77] used the solution casting process to fabricate nanocomposite films with HNTs. In this study, the various concentrations of HNTs were loaded into the nanocomposites films. As expected, mechanical properties and water absorption were improved, and the films were cytocompatible. [PERSON] et al. [78] used HNT for preparing nanocomposite films based on chitosan for biomedical applications. The film exhibited decent mechanical properties, and dosages of HNT had a direct effect on this property because of creating a three-dimensional network.  
Additionally, water-resistance and the nanocomposite films' thermal stability were improved meaningfully. In 2019, [PERSON] et al. [79] ternary nanocomposite films were fabricated using solution casting. This film used chitosan, starch, and HNTs to improve mechanical properties, water absorption capacity, water solubility, and water vapor transmission rate. Furthermore, these hydrophilic films were non-hemolytic and impermeable to bacteria. Mo  
Figure 3: Schematic of the mechanism of release when hydrogel under pathological concentration. Reprinted by permission of the publisher.  
[PERSON] et al. [80] prepared elastomeric nanocomposites based on HNT surface modification using APTES and then a polyvinyl alcohol (PVA) coating applied through the layer-by-layer assembly. As a result, the nanocomposite film exhibited antibacterial properties and had a synergistic anti-infection effect (Figure 4). Furthermore, drug release was sustained and controllable.
# Artificial Neural Networks for Navigation Systems: A Review of Recent Research
## 6 Future Challenges
* The vehicle's movement depends on the capabilities of the vehicle and the layout of the route; an NN trained with measured data with a straight or curved road segment can provide a good location. * Future research should enhance the series of twisting roads for practice and modify the initial input values of the ANN model to address the issues. * Deep learning is a breakthrough in inertial navigation technology and can acquire more stability by using the trending ML technology.
## 3 Results and Discussion
### Computational Efficiency of the Proposed Algorithm  
Finally, experiments were conducted to measure the computational efficiency of the proposed method for apple segmentation and ripening estimation. The speed of the algorithm is an important factor in agricultural applications, e.g., enabling that the process can be applied in real time on the videos captured by the drones. Moreover, if the drone has enough computational power, the algorithm could be applied in the drone itself. For this reason, the algorithms that are used in machine vision systems, in addition to the precision, must have a fast processing speed.  
In this study, a laptop computer with an Intel Core i3 processor CFI, 330M at 2.13 GHz, 4 GB of RAM-4 GB, and MATLAB 2015b was used to execute the proposed algorithms. The average CPU times to perform apple segmentation, feature extraction, and ripening classification for each frame were 0.591, 0.296, and 0.198 s, respectively. The total processing time was around 1 s per frame. In the case of a compiled and optimized C/C++ implementation, this time could be greatly reduced.
## 7 Results
### Current State of Research in CLC for C-ITS
The majority of the remaining found research that included performance analysis did not have customized implementations but instead referred to other articles and used their results to theoretically compute their own protocol's execution time. This would yield a CLC-TRL not higher than 3. We also examined the trend of published CLC-based solutions in C-ITSs. We conducted a second literature search for articles between 2016-2023 and used a lightweight filtering process where we did not classify articles into sub-fields (authentication, signcryption and so on). The publication trend of these papers is presented in Figure 4, and we see clearly that the number of publications in this area increases over time. The articles in 2023 were collected up to and including Q1 of 2023. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Subfield** & **PoC Lab** & **PoC Industry** & **Reference Data** & **CLC-TRL** \\ \hline
2023 Q1 & & & & \\ Authentication & 33\% & 0\% & [41, 42, 43, 44, 45, 46] & 3 \\ Aggregated signatures & 66\% & 0\% & [59, 60, 75] & 3 \\ Key agreement & 0\% & 0\% & [65] & 1 \\ Signcryption & 0\% & 0\% & [67] & 2 \\
2022 & & & & [47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76] & 3 \\ Augmented signatures & 60\% & 0\% & [61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76] & 3 \\ Key agreement & 0\% & 0\% & [66] & 1 \\ Signcryption & 100\% & 0\% & [68, 69, 70, 71, 72, 73, 74, 75, 76] & 4 \\ \hline \hline Surveys & & & [32, 78, 79] & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Overview of the current subfields of CLC for C-ITS-related areas, separated into level of PoC implementations, year and Technical Readiness Level (TRL) score.
## 3 Results and Discussion
### Flow Control Effect by AC-DBD Plasma Actuation
#### 3.2.6 General Discussion of Results  
In Section 3.2.2 through to Section 3.2.5, the effects of using the linear AC-DBD plasma actuation at the trailer rear end in attempts to alter the flow characteristics along the wake region have been  
Figure 19: Frequency characteristics of the first three modes of the wake region at different AC-DBD plasma actuation frequencies. (**a**) Baseline (0 kHz), (**b**) 7 kHz, (**c**) 8 kHz, (**d**) 9 kHz, (**e**) 10 kHz, and (**f**) 11 kHz.  
discussed. This section aims to summarise the results that are already presented, and provide a general discussion about the way the flow control could be achieved by implementing the linear AC-DBD plasma actuation at the rear end of a square-back trailer. From the data shown previously about the velocity field downstream of the trailer rear end, it could be seen that using the linear AC-DBD plasma actuation at the rear end of a square-back trailer model shows no observable effects in altering the size and shape of the recirculating bubble. In addition, the linear AC-DBD plasma actuation shows nearly no effects in changing the flow velocity in the x-direction along the wake region although it does affect the flow velocity in the y-direction. In contrast, it is clear that compared to the baseline trailer, the linear AC-DBD plasma actuation at the trailer rear end generally increases the turbulence kinetic energy levels along the upper shear layer. It also increases the fluctuation in the turbulence kinetic energy and z-vorticity along the wake region. However, the use of the AC-DBD plasma actuation exerts no significant effect on the frequency response of the flow in the wake region. In fact, from the results obtained, it is deduced that although the use of the AC-DBD plasma actuation increases vorticity and turbulence kinetic energy fluctuation, the effect to drag due to these increased fluctuations is negligible. In contrast, it is believed that the AC-DBD plasma actuation was actually unable to generate any impact on the flow. To be exact, the plasma actuation cannot generate any effect to manipulate the flow characteristics in the wake region. The reason which led to the drag increase or reduction that was observed in some cases in the present study was due to the occurrence of random errors in the wind tunnel measurement and was not due to the flow control effect provided by the AC-DBD plasma actuation.