esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Robot Control System
### Task Management  
The inspection of a large-scale rotor blade is a long-running task. Therefore, we designed a task management system in form of a nested state machine. The state machine uses the SMACH framework [46]. The _main_ state machine includes two nested state machines managing the manipulation and the navigation task. Figure 10 shows the task management system.  
The main state machine requires as input the model of the workpiece (CAD File), the desired distance between the radar sensor and the surface of the workpiece (\(\mathrm{Distance_{SR}}\)) and the characteristics of the used manipulator (\(\mathrm{Manipulator_{Info}}\)). The \(\mathrm{Manipulator_{Info}}\) consists of the suitable area of the manipulator workspace, including the corresponding maximum reach of the manipulator.  
The workpiece segmentation and the position determination of the platform at the local scan subsegments is executed a priori. The workpiece segmentation provides the local scan areas (\(\mathrm{ScanArea_{Info}}\)). Each \(\mathrm{ScanArea_{Info}}\) consists of the area center (\(\mathrm{ScanArea_{Center}}\)) and the dimensions (\(\mathrm{ScanArea_{Dim}}\)) in reference to the workpiece coordinate system. The _platform positioning_ converts the centers of the local scan areas into 2D navigation goals (\(\mathrm{Poses_{Goal}}\)) in reference to the world coordinate system.  
The navigation and manipulation state machines are triggered sequentially for each pair of navigation goal (\(\mathrm{Pose_{Goal}}\)) and local scan area (\(\mathrm{ScanArea_{Dim}}\)). The navigation state machine takes care about the path planning and path execution processes. The move base flex [47] framework is implemented to change between different motion behaviors of the mobile platform depending on the current navigation zone. The manipulation state machine is divided into three process steps. The _point cloud executor_ gathers the point cloud data at the local scan pose (\(\mathrm{Poses_{Scan}}\)) and crops it accordingly to the dimensions of the local scan area. The waypoint generator determines the 6D end-effector waypoints (\(\mathrm{Waypoints_{EE}}\)), orthogonal oriented to the surface of the workpiece. The manipulation executor controls the motion of the manipulator.
## 5 In-Line Application of LIBS in Industrial Production
### Metal Scrap  
Metal scrap is an important resource material for various industries. In 2017, approx. 57 million metric tons of selected metals were recycled from scrap in the U.S. (an amount equivalent to about 47% of the apparent supply of those metals). Iron and steel accounted for about 89% of the recycled metal and about 88% of the apparent supply [456]. The use of recycled scrap metals in place of virgin iron ore is beneficial to the environment (e.g., energy savings by 75%) and for every ton of new steel made from scrap steel approx. 1.1 tons of iron ore and 0.6 tons of coal are saved [457]. Aluminum is one of the few materials that is completely recyclable. The production of recycled aluminum is 92% more efficient than the production of new aluminum. Per year, 5 Mt of aluminum are recycled in the U.S. and Canada [458].  
LIBS was applied for the in-line monitoring of steel scrap on a conveyor belt transporting the scrap metal to an electrical arc furnace (EAF) in a steel plant [102]. The real-time measurement of the content of key elements in the scrap allows stabilizing the process of furnace charging and steel making. The optical unit of the developed LIBS system was installed over the conveyor line (Figure 12a). The main components included a 3D scanner and a laser light section sensor to measure the geometry of the scrap pieces. The scanner optics directed and focused the laser beam onto the scrap in a wide field of the cross-section of the conveyor (1.2 m \(\times\) 2 m, varying filling level). The charging operation of the EAF is controlled by determining the mass flow of key elements (e.g., Si) from the element concentration measured by LIBS (Figure 12b). Parameters such as the belt speed, the filling height, and the average scrap density have to be taken into account to determine the element mass flow. The availability of data in real-time enables to adjust the charging process before the charging is finished. LIBS analysis of steel scarp was reported by other groups as well [215].  
The LIBS technique has been applied also to the inspection of aluminum scrap for metal recycling [6; 209; 210; 214]. Wrought and cast Al alloy pieces have been identified using a belt conveyor sorting system by measuring the LIBS signals for Al, Ti, and Si achieving a mass throughput of up to 4 tons/hour [6]. In another study, scrap of Al alloys containing different amount of Mg and Si was sorted by LIBS. For Al scrap pieces of 40-110 mm size the sorting throughput was 3-5 metric tons per hour and the sorting purity was 98% [209].  
Figure 11: LIBS in-line measurement of phosphate minerals. (**a**) Photograph of LIBS sensor installed above a conveyor belt in the Four Corners Mine, Florida (USA). (**b**) MgO, Fe\({}_{2}\)O\({}_{3}\), BPL, and MER measured by LIBS over several days. Adapted from [233].
# Analysis and Research on Secondary LT Coding Anti-Eavesdropping Scheme Based on LT Code Degree-1
## 3 Program Performance Analysis
### Decoding Complexity
For the SLT-LT fountain code [22], the same twice fountain code cascade coding is performed with the length of the participating decoded symbols as \(k+n\) and \(P_{AB}=0\). The decoding complexity is expressed as follows:  
\[ \begin{array}{ll}E_{SLT\_LT}&=E_{SLT}\cdot E_{LT}\\ &=k\left(\overline{d_{SLT}}-1\right)\cdot k\left(\overline{d_{LT}}-1\right) \end{array}  \tag{21}\]  
\(k\left(\overline{d_{SLT}}-1\right)\) denotes the SLT coding complexity. With the same principle of Equation (19), the number of XOR calculations is much larger than 2. From Equation (18), it is then:  
\[ \begin{array}{ll}E_{SLT\_LT}&=E_{SLT}\cdot E_{LT}\\ &\gg 2\cdot k\left(\overline{d_{LT}}-1\right)\end{array}  \tag{22}\]  
From Equations (20) and (22), it follows that, \(E_{double\_LT}\ll E_{SLT\_LT}\). From the above analysis, it can be seen that the complexity of double LT is much lower than that of the other two methods for the same twice fountain code encoding.
# Fusion of Computer Vision and AI in Collaborative Robotics: A Review and Future Prospects
## 7 Human Related Challenges and Trends
### Socio-Cognitive Models and HRI  
Collaborative robots are increasingly expected to operate with socio-cognitive awareness, enabling them to interpret human intentions, gestures, and mental states [146]. Cognitive architectures such as DAC-HRC integrate modules for memory, perception, and decision-making to support mechanisms like joint attention, turn-taking, and social responsiveness [6; 122]. These systems fuse visual inputs--such as gaze tracking, body pose estimation, and facial expression detection--with AI-based reasoning to infer user goals and adapt their assistance strategies accordingly [147; 148].  
Studies demonstrate that incorporating Theory of Mind capabilities--where the robot models human beliefs, emotions, or expectations--significantly enhances user trust andtask fluency, especially when robots apply trust-repair behaviors (e.g., explanations and apologies) tailored to situational and cultural contexts [25; 27; 149; 150].  
For example, in surgical robotics, cognitive architectures enable assistance robots to interpret eye gaze and verbal cues from surgeons, thereby anticipating required instruments or imaging views without explicit commands [151]. This contributes to a smoother workflow, reduced cognitive load, and enhanced sterility compliance [152]. In collaborative manufacturing, socio-cognitive models support dynamic negotiation and role allocation [153]: a robot can infer that a human worker is uncertain based on hesitations or gaze aversion, prompting the robot to take initiative or offer guidance in real time [154].  
Furthermore, systems capable of turn-taking and shared intention modeling have shown measurable gains in cooperative task performance [122]. These are especially relevant in education or training environments, where cobots serve as instructional assistants that modulate their responses based on learner engagement or confusion signals [155].  
Future developments will likely integrate multimodal signals--such as voice tone, pupil dilation, and micro-gestures--into unified user models. These models will drive more fluent, context-sensitive interaction styles, contributing to safe, efficient, and personalized human-robot collaboration in high-stakes environments.  
Figure 3 illustrates the integration of real-time adaptation and socio-cognitive interaction in vision-based collaborative robotics.
## 1 Introduction
The authors in [17] have presented a fuzzy-back stepping control strategy for battery/UC based on real-time evaluation of six on-off operational modes to reduce battery power fluctuations. The simulation platform via Cruise and MATLAB software has been presented to validate the results [18]. The authors in [19] have presented a fuzzy logic control method combined with genetic algorithm for FCHEV in a simulation platform of ADVISOR. The genetic algorithm has been used to optimize the membership function parameters of FC output power to reduce equivalent hydrogen consumption and improve battery lifetime [19]. In the field of multi-level EMS methods, the authors in [20] have proposed an EMS based on SMCS and ECMS for FC/UC hybrid tramway to increase the operating time of FC in a high-efficiency range and decrease the FC power fluctuations. The combination of SMCS with droop control has been presented in [21] to coordinate multiple ESSs based on their natural characteristics, aiming to enhance high-efficiency performance range and satisfy fast changes in the demand power. Generally, in the reviewed energy management strategies [16; 17; 18; 19; 20; 21], the HESS energy efficiency, battery or UC state division, and movement conditions of the vehicle have not been analyzed mathematically. Moreover, the fluctuations of FC and battery power can be reduced efficiently, and driving conditions can be detected without comparing driving cycles. To address the mentioned problems, the following contributions are proposed in this paper:  
* The vehicle movement states are formulated based on the vehicle's speed, acceleration, and power. * Battery and UC state divisions are presented based on the mathematical formulation of the proposed combined energy efficiency concept. * FLC and OMC methods are used to reduce FC and battery power fluctuations. Moreover, the operational modes of FC are selected based on efficiency and ECM concept. Finally, an online multi-mode energy management strategy based on SMC, FLC, ECM, and OMC methods is presented for power management of the FC/battery/UC vehicle. The rest of the paper is organized as follows. In Section 2, the framework of hybrid energy storage systems is presented. The proposed EMS, which includes SMC, ECM, FLC, and OMC power contribution, is proposed in Section 3. The MATLAB simulation results of the proposed EMS are given in Section 4. Finally, the conclusions are discussed in Section 5.
# Assessing Spatiotemporal LST Variations in Urban Landscapes Using Diurnal UAV Thermography
## 3 Methods
### Outlier Management  
Prior to statistical analysis, we implemented data filtering to ensure statistical robustness and reliable thermal pattern characterization. The initial dataset consisted of 500 sampling points, with 100 points collected for each of the five LULC classes. To mitigate the influence of statistical outliers that could potentially distort the interpretation of thermal behaviors, we applied the Interquartile Range (IQR) method to identify anomalous observations.  
Figure 2: Spatial distribution of sample points within the Harran University Osmanbey Campus study area. Sample points are color-coded by LULC class (depicted in the legend) and presented for zoomed-in locations representing the following: bare soil, buildings, grassland, paved roads, and water bodies.  
The IQR-based outlier detection was implemented independently for each LULC class and temporal period, identifying observations that fell outside the boundaries defined in Equation (1) by  
\[[Q_{1}-1.5\times\text{IQR},Q_{3}+1.5\times\text{IQR}] \tag{1}\]  
where \(Q_{1}\) represents the first quartile, \(Q_{3}\) represents the third quartile, and IQR = \(Q_{3}-Q_{1}\).  
This method defined outliers as observations that deviated significantly from the expected range, specifically those falling outside the 1.5 \(\times\) IQR of the first and third quartiles for each categorized dataset and time period. Observations falling outside these thresholds were classified as outliers and subsequently removed from the dataset. By applying this statistical threshold, we ensured that extreme values, which could unduly influence analysis outcomes, were systematically identified and eliminated. This approach enhances data integrity and allows for a more accurate representation of underlying patterns while accommodating variations within different LULC classes and temporal conditions.  
The IQR method was chosen because it provides a non-parametric approach to outlier detection that does not require assumptions about the underlying data distribution, making it suitable for potentially non-normal thermal data distributions [46; 47].
## 2 Figure of Performance for Analysis
### Visible Satellite Determination by Reflecting Geometric Structure  
The geometric structure between the GNSS satellite and receiver should be taken into consideration to analyze the characteristics of GNSS signals at the geostationary orbit. Figure 2 shows the geometrical structure of the receiver located in the geostationary orbit, the earth, and GNSS satellites. The reference vector \(\mathbf{v}_{\text{ref}}\) represents the direction vector towards the center of the earth at each GNSS satellite by a red line. The blue line represents the line-of-sight vector between the GNSS satellite and the earth's surface perpendicular to the earth. The angle \(\theta_{\text{tan}}\) between of the two vectors is calculated as follows:  
\[\theta_{\text{tan}}=\sin^{-1}\frac{Earth\;Radius}{\mathbf{v}_{\text{ref}}} \tag{1}\]  
Given that the altitude of the GNSS satellites, located in the MEO, is about 20,000 km, \(\theta_{\text{tan}}\) is about 14\({}^{\circ}\). The direction vector \(\mathbf{v}_{\text{xx}}\) towards the receiver located in a GEO from the GNSS satellites is depicted by a green line, where \(\theta_{\text{xx}}\) represents the angle between \(\mathbf{v}_{\text{ref}}\) and \(\mathbf{v}_{\text{xx}}\).  
\[\theta_{\text{xx}}=\cos^{-1}\left(\frac{\mathbf{v}_{\text{xx}}^{\text{T}} \mathbf{v}_{\text{ref}}}{\mathbf{v}_{\text{xx}}\mathbf{v}_{\text{ref}}}\right) \tag{2}\]If \(\theta_{\rm{xx}}>\theta_{\rm{tan}}\), the GNSS signal can reach the other side beyond the earth's surface. In this paper, the half-beam width of the main lobe of GNSS satellites is set at 23.5\({}^{\circ}\), identical to the GPS signal, to determine the visibility of GNSS satellites. Moreover, some of the transmission signals from GNSS satellites are blocked by the earth and cannot reach the other side.  
It is also possible to receive a signal from the region of the side lobe. The side lobe half-beam width of GPS is set at 27\({}^{\circ}\) to 39\({}^{\circ}\). Therefore, as shown in Figure 2, the area in which satellite visibility is obtained can be represented for \(\theta_{\rm{xx}}\) as follows:  
\[\theta_{\rm{tan}}<\theta_{\rm{xx}}<23.5^{\circ}\ \rm{or}\ 27^{\circ}<\theta_{\rm{xx}}< 39^{\circ} \tag{3}\]  
\[(\theta_{\rm{tan}}\ \approx 13.5^{\circ}\ :\ \rm{tan}\ \rm{g}\rm{ential}\ \rm{angle}\ \rm{from}\ \rm{GNSS}\ \rm{SV}\ \rm{to}\ \rm{Earth})\]
# Assessment of MicroPET Image Quality Based on Reconstruction Methods and Post-Filtering
## 4 Discussion  
We confirmed data credibility by comparison to previously reported data [6]. Our recovery coefficient values reconstructed to FBP were similar to those reconstructed to FBP in another study.  
In iterative reconstruction methods, image uniformity decreased depending on the number of iterations, because image noise increased along with number of iterations (Figure 3A). However, the increased range of OSEM3D was lower than that of OSEM2D. This disadvantage could be overcome by Gaussian filtering (Figure 3B). However, a filter size larger than 2 mm resulted in image distortion due to high blurring. The reconstruction time of 3 DRP was the longest (10:04:40, 43 frames, Table 1).  
The SOR effect in water was stronger than that in air. The SOR effect of the iterative reconstruction methods trended lower than that in the analytic reconstruction methods. However, it increased with increased iteration numbers and post-filtering was crucial for myocardial blood flow (MBF) evaluation in cardiac PET imaging.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Reconstruction Methods** & **Total Time** & **Average Time** \\  & **(Per Frame)** \\ \hline
3 DRP\_non & 10:04:40 & 00:14:03 \\
3 DRP\_Hann & 10:04:46 & 00:14:03 \\ FBP & 00:09:49 & 00:00:13 \\ OSEM2D\_14 & 00:11:33 & 00:00:16 \\ OSEM2D\_16 & 00:13:02 & 00:00:18 \\ OSEM2D\_18 & 00:16:10 & 00:00:22 \\ OSEM3D\_14 & 04:08:07 & 00:05:46 \\ OSEM3D\_16 & 05:54:34 & 00:08:14 \\ OSEM3D\_18 & 07:41:45 & 00:10:44 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Reconstruction times (hh:mm:ss) measured by embedded Inveçon computer.  
Figure 4: The spillover ratio (SOR) was estimated as the mean of uniform area to the mean of water or air area. (**A**) The SOR in water was significantly higher than that in air in the reconstruction methods without Gaussian post-filtering (_p_ < 0.001). (**B**) In the iterative methods, SOR decreased with increasing iteration number and increased with increasing Gaussian filter size (_p_ < 0.05).  
In the PET images, the reconstruction method and post-filtering affected the image quality, and activity correction of the target (tumor, brain, or heart) was positively necessary for accurate analysis. Tumor activity should be corrected by the RC value of actual tumor size. In cardiac PET image analysis, the TAC of blood pool and myocardium needs correction because of the spillover effect from myocardium to blood pool or from blood pool to myocardium.  
A limitation of this study is that we did not compare new, recently developed reconstruction algorithms. If the reconstruction algorithms of the Inveon system are updated, we will perform further study.
## 2 Principles and Experiment  
As is known to all, the surface form of a measured workpiece can be modeled and reconstructed by coordinates. In this paper, the coordinates of the cross-sectional circle of a small cylindrical workpiece are divided into several equal arcs to be scanned by a profilometer. The diameter and center coordinates of these arcs can be fitted by the least square method. The arc profiles can be characterized according to the arc data. The roundness profile of the small cylindrical part can be obtained by stitching these arc profiles one by one in sequence.  
As shown in Figure 1a, a small cylindrical workpiece with a diameter of 1.5 mm and length of 7.8 mm is attached to the circular magnetic jig, which is marked by eight equal lines on the surface. This kind of combination is mounted on the V-groove. As shown in Figure 1b, the stylus of the profilometer is brought to scan on the surface of the small cylindrical workpiece and returned to the initial position, after which the first arc coordinate data can be obtained. The small cylindrical workpiece is rotated by 45\({}^{\circ}\) by rotating the circular magnetic jig by one equal part. The stylus scans on the surface of the workpiece again and returns, then the second set of arc coordinate data can be obtained. We repeat the procedure seven times, then eight sets of arc coordinate data can be obtained. Since the maximum measuring inclination of the stylus is \(\pm 45^{\circ}\), only \(85^{\circ}\) arc coordinate data are extracted for the following data processing.  
Figure 1: (**a**) Linear scan with a stylus; (**b**) small cylinder mounted on the V-groove and scanned linearly.  
The eight arc profiles with respect to the arc coordinate can be characterized by a series of data processes. An inaccurate roundness profile can be formed by stitching these eight arc profiles together. Regarding the stitching procedure, as shown in Figure 2a,b, arc profiles 1 and 2 are at the initial position. As shown in Figure 2c, the first arc profile is kept static and the second arc profile is rotated by 45\({}^{\circ}\), since there is a 45\({}^{\circ}\) angular displacement between adjacent arc profiles according to the measuring procedure. As shown in Figure 2d, the second arc profile is kept static, and the third arc profile is rotated by 45\({}^{\circ}\), while the rest of the arc profiles are stitched in this manner. However, there are some stitching angle errors between adjacent arc profiles, since the small cylindrical workpiece is rotated manually, namely, the angular displacement between adjacent arc profiles is not always 45\({}^{\circ}\). Therefore, stitching angle error compensation is necessary. An accurate, integral, smooth, continuous roundness profile can be obtained after stitching error compensation, overlap part combination, and low-pass filtering.
# Effect of P+ Source Pattern in 4H-SiC Trench-Gate MOSFETs on Low Specific On-Resistance
## 4 Conclusions  
In this paper, 1.7-kV 4H-SiC trench-gate MOSFETs with a P+ source pattern are investigated in terms of P+ source dimension (length and width) on static characteristics, such as output characteristic and forward blocking voltage by 3-D numerical device simulation. It is demonstrated that P+ source pattern is the important factor in TMOSFETs due to cell-pitch and P+/N+ junction curve effect. The proposed TMOSFETs with P+ source with grid pattern reduced the cell-pitch approximately 35% and decreased the p-n junction effect. As a result, the grid P+ source TMOSFETs has a low specific on-resistance of 2.73 mOhm-cm\({}^{2}\) and a high breakdown voltage of 2061 V. Compared with the conventional P+ source pattern TMOSFET structures, simulation results show that the Figure-Of-Merit (FOM, BV\({}^{2}\)/R\({}_{\text{on,SP}}\)) of the proposed grid pattern TMOSFET is improved by approximately 33%.  
Conceptualization, [PERSON], [PERSON] and [PERSON]; data curation, [PERSON] and [PERSON]; formal analysis, [PERSON], [PERSON] and [PERSON]; investigation, [PERSON]-[PERSON] and [PERSON]; project administration, [PERSON]-[PERSON]; writing--original draft preparation, [PERSON]-[PERSON]; writing--review and editing, [PERSON], [PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This study was supported by a Korea Institute for Advancement of Technology (KIAT) grant funded by the Korean Government (MOTIE) (P0012451, The Competency Development Program for Industry Specialists). And the EDA tool was supported by the IC Design Education Center (IDEC), Republic of Korea.  
Not applicable.  
All data are presented in this paper in the form of figures.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Simulation Results**} \\ \cline{2-4}
**Device Structure** &
\begin{tabular}{c} **Specific On-Resistance** \\ **(R\({}_{\text{on,sp}}\))** \\ **[mOhm-cm\({}^{2}\)]** \\ \end{tabular} & **Forward Blocking (BV)** & **Figure-of-Merit (FOM)** \\ \multicolumn{4}{c}{**[V]**} \\ \hline Square pattern (CP = 4.6 \(\mu\)m) & 2.86 & 1830 & 1172 \\ \hline Stripe pattern (CP = 4.0 \(\mu\)m) & 2.80 & 1998 & 1426 \\ \hline Grid pattern (CP = 3.0 \(\mu\)m, L\({}_{\text{PP}}\) = 0.5 \(\mu\)m)