esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description
### Limitations of AODV Protocol  
As a typical on-demand routing protocol, AODV does not need periodic maintenance of routing table [33,34], and has the characteristics of low latency, scalability, and non-centralization [35]. Although it has been widely used in unmanned cluster networks in recent years, there are still many problems with it, which can be summarized as follows:  
1. As a passive routing protocol, AODV only initiates the routing process when nodes have communication requirements, and the process of establishing new routes requires a series of route discovery and maintenance operations [36].
2. In the process of AODV routing discovery, each node forwards the RREQ data frame to all its neighbor nodes within one hop [37]. However, when the number of nodes in the network is large or the density of nodes is substantial, this flooding data forwarding mode will lead to a significant increase in the number of data frames forwarded in a short time, which not only leads to the waste of network resources but also increases the risk of network congestion.
3. AODV protocol only takes \"minimum hops\" as the basis for route selection [38], and the data frames such as RREQ and RREP defined by AODV only store the information of source node, destination node and hop counts [39], without considering factors such as node mobility, link quality and node remaining energy. Therefore, AODV lacks the perception of the actual conditions of the network, and, in many cases, the selected shortest path may not be the optimal path for data transmission, thereby affecting the data transmission efficiency of nodes.
4. In AODV protocol, the key nodes in the network undertake more data transmission and forwarding tasks than the edge nodes due to their higher node degree, so key nodes experience faster energy consumption and a shorter lifetime. The failure of key nodes in the network may lead to widespread link disruptions, significantly reducing the network's reliability and stability.  
To solve the above problems, this paper proposes an intelligent unmanned cluster network routing protocol based on node energy consumption and mobility optimization to mitigate the drawbacks of traditional AODV protocol in applications and improve the performance of unmanned cluster networks.
## 3 Results
### Current-Assisted Tensile Testing and Thermal Simulation Conditions  
The result of the current-assisted tensile test is shown in Figure 21. At 0.10 A (\(\approx\)63 \({}^{\circ}\)C), the wire shows a modest rise in total elongation (TE), while the ultimate tensile strength (UTS) remains essentially unchanged relative to the cold-drawn baseline. Although 63 \({}^{\circ}\)C is far below the recrystallization temperature for 631 stainless steel, the cold-drawn wire retains a high dislocation density and stored energy due to the severe drawing. A small thermal increment enables slight dislocation rearrangement and dynamic recovery during the short (\(<\)30 s) tensile-loading window [21]. Increasing the current to 0.20 A (\(\approx\)202 \({}^{\circ}\)C) produces a modest (\(\sim\)5%) reduction in ultimate tensile strength (UTS), whereas total elongation (TE) is higher than at the 0.10 A level.  
Elevating the current to 0.25 A raises the wire temperature to \(\approx\)308 \({}^{\circ}\)C. At this temperature, recovery occurs, sharply reducing dislocation density and work-hardening. As a result, both YS and UTS dropped by roughly 50%. Concurrently, oxide growth produces surface grooves and microvoids that trigger early necking; the total elongation (TE) falls by a comparable amount. At 0.30 A (445 \({}^{\circ}\)C), the wire fails in a brittle manner during electrically assisted tension because the temperature falls within the thermal-softening region.  
Figure 20: Surface morphology and tensile fracture surface after electrical fatigue: 0.37-C20, 0.39-C20, and 0.41-C20.
# Complex-Valued CNN-Based Defect Reconstruction of Carbon Steel from Eddy Current Signals
## 4 Results  
### Measured Signal Results  
As a presentation of the results of the measured signals, Figures 4-6 present the centerline scan results from the eddy current testing on a carbon steel specimen. The flaw was a 10 mm long EDM notch with depths of 0.5 mm, 1.0 mm, 2.0 mm, and 3.0 mm. A total of 46,000 complex-valued signal samples were collected from eddy current testing on carbon steel plates using four excitation frequencies: 5 kHz, 20 kHz, 80 kHz, and 320 kHz. The dataset was subsequently divided into training, validation, and test sets in a 7:2:1 ratio.  
Figure 4 shows how the resistance (real part of impedance) changed with the probe position X at different frequencies. A clear increase or peak in resistance appeared when the probe scanned across the flaw region (approximately 40-60 mm). Low-frequency signals (5 kHz) resulted in broader, smoother responses, while high-frequency (320 kHz) signals produced sharp and localized peaks, suitable for surface flaw characterization.  
Figure 5 shows the variation in inductance (imaginary part of impedance) with position. The flaw caused a localized reduction in inductance, appearing as a clear dip in the response. At higher frequencies (e.g., 320 kHz), the change was more focused and accurate for flaw localization, while lower frequencies (e.g., 5 kHz) provided broader responses that better penetrated deeper flaws.  
Figure 6 uses \((R-R_{0})/\omega\) as the horizontal axis and \(L\) as the vertical axis to illustrate the phase trajectory of complex-valued impedance during the scan. where R0 is the carbon steel resistance when no bias magnetic field was applied. Different frequencies formed  
Figure 4: Resistance changes with probe position.  
Figure 5: Inductance changes with probe position.  
Figure 6: Impedance loci.  
distinct trajectory shapes. High-frequency traces were shorter and steeper, indicating higher sensitivity to surface-breaking flaws, whereas low-frequency curves were smoother, offering better penetration and depth characterization.
## 3 Results and Discussion
### The Hydrological Forecast  
The Flood-PROOFS' stream flow forecast is obtained using the WRF-OL meteorological forecast as input. The run goal is to support decision makers in issue warning before the event. In Flood-PROOFS, the WRF-OL forecast rainfall is operationally coupled to the hydrological model through the Rainfall Filtered Autoregressive Model (RainFARM) stochastic downscaling procedure [40] that allows to provide a probabilistic hydrological forecast.  
Figure 5 shows the hydrological forecast of 24 October 2021; red dots represent critical sections in which the expected discharge overcame the warning thresholds and can lead to flooding. Figure 6 shows the corresponding probabilistic hydrographs in three river sections in Calabria and Sicily. The yellow horizontal line corresponds to the alarm level and the orange one to the alert level. The event was therefore expected as an event of great intensity that could lead to widespread flooding, in particular in Calabria, and on the eastern coast of Sicily (red rectangle in Figure 2).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline  & **CEN** & **ANG** & **FCST** & **OBS** & **INTERS** & **UNION** & **SYMM** & **FCST** & **OBS** & **FCST** & **OBS** & **TOT** \\  & **DIST** & **DIFF** & **AREA** & **AREA** & **AREA** & **AREA** & **DIFF** & **INT50** & **INT50** & **INT90** & **INT90** & **INT90** \\ \hline
**150 mm** & 3.41 & 3.12 & 1943 & 715 & 570 & 1638 & 1086 & 228 & 194 & 360 & 275 & 0.95 \\
**200 mm** & 12.49 & 7.33 & 941 & 240 & 199 & 982 & 783 & 274 & 248 & 398 & 334 & 0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 2: WRF-OL forecast validation through the MODE tool.  
Figure 5: FloodPROOFS forecast over southern Italian rivers sections. Visualization on MyDewetra platform. The different sections’ colors refer to the magnitude of the peak discharge with respect to the fixed threshold for alarm (yellow) and alert (red). Green and white colors refer to discharge increasing but still below thresholds and discharge not increasing or decreasing, respectively.  
Figure 6: Hydrograph probabilistic forecast obtained by clicking on red sections represented in Figure 5 on MyDewetra platform Ponte Giaretta above and Lentini.
# Misalignment-Resilient Propagation Model for Underwater Optical Wireless Links +
## 2 Underwater Photon Propagation Model
### Sensor Architecture  
In this work, different sensor architectures are studied, and all are composed of a different number of lens-sensor pairs located at different positions inside a circle. The total output signal power is obtained by adding the powers at each detector. Aside from providing additional sensitivity, this architecture gives additional information about the lateral offset between the transmitter and receiver (see Figure 6), which will be explored later in Section 4, for prediction of the misalignment.
## 5 Simulation Examples
### Examples with No Consideration of Uncertainties  
In this part, the focus will be on using the algorithm without respecting multiple uncertainties at once. The first example covers the required network load, i.e., convergence to the optimal solution. The second example focuses on incorporating a BESS into a grid topology while considering a variable cost function. The third example combines both of the above using real weather forecast data. The learning gains \(\gamma\) will be represented as a constant in examples without uncertainties. Its value is calculated according to (27) at the beginning.
# Robotic Grasping of Unknown Objects Based on Deep-Learning Based Feature Detection
## 2 Methodology
### Abstract Shape Determination and Image Segmentation  
The abstract shape determination is achieved by creating a concave hull using the center points of all feature detections. That is, the abstract shape \(S_{abstract}\) of the object is defined as:  
\[S_{abstract}=Conc(P_{E}\cup P_{C}) \tag{3}\]  
Figure 4: Labeled data for feature learning: examples of training data labelled for feature learning. Green bounding boxes represent edges and orange bounding boxes represent corners.  
Figure 5: Training losses, precision, and recall of the feature learning.  
where  
\[P_{E} =\{p_{E_{1}},p_{E_{2}},...,p_{E_{m}}\}, \tag{4}\] \[P_{C} =\{p_{C_{1}},p_{C_{2}},...,p_{C_{m}}\}. \tag{5}\]  
This refers to the smallest geometrical shape formed which encloses the set of the feature points to give a general shape. Figure 6 shows the illustrations of the abstract shapes of two objects with the features determined. The abstract shape is the yellow line which gives the estimated perimeter \(P_{e}\). The estimated area is \(A_{e}\) and the estimated centroid \((x_{ce},y_{ce})\) is represented by the yellow circle. If there are less than three detections such that \(|E\cup C|<3\), this step is bypassed.  
An edge detection algorithm is also used to determine the borders of the object in the image \(X\). This is the perimeter \(P_{g}\), which is the blue line that outlines the object, as illustrated in Figure 7. The area enclosed by \(P_{g}\) is \(A_{g}\), and averaging all the points on \(P_{g}\) gives the geometric centroid of the object \((x_{cg},y_{cg})\) represented by the blue circle. Comparing \(A_{g}\) and \(A_{e}\) provides an idea of how closely the features represent the object. If \((x_{ce},y_{ce})\) is also close to \((x_{cg},y_{cg})\), it also shows its suitability in substituting \((x_{cg},y_{cg})\) should image segmentation fail.
## 2 Methodology  
To ensure a comprehensive and methodologically rigorous review, relevant literature was collected from major scientific databases, including Scopus, Web of Science, IEEE Xplore, ScienceDirect, and the NASA Technical Reports Server (NTRS). Google Scholar was also used for supplementary searches and cross-referencing. Searches were conducted using combinations of the following keywords: \"HAPS\", \"High-Altitude Pseudo-Satellites\", \"Stratospheric Aircraft\", \"Propeller Design\", \"Low Reynolds Number Propellers\", \"Electric Propulsion\", \"Hybrid Propulsion\", \"High-Altitude UAVs\", \"HALE\", \"Propulsion systems\", \"Experimental testing\". The time span of the literature spans from early founda  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year** & **Aircraft Name** & **Nominal Thrust [N]** & **Propeller Diameter [m]** \\ \hline
1987 & Egrett [48] & 2773 & 3.04 \\
1988 & Condor [27] & 1129 & 4.90 \\
1993 & Pathfinder [27,46] & 23 & 2.01 \\
1994 & Perseus [21] & 388 & 4.40 \\
1995 & Strato2C [46,47] & 2500 & 6.00 \\
1996 & Theseus [50] & 409 & 2.74 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of aircraft nominal thrust and propeller diameter from various high-altitude UAV platforms. Adapted from ref. [51].  
Figure 6: Illustration of the Centurion propellers (AeroVironment, Inc., Simi Valley, CA, USA). Reprinted from ref. [46].  
tional contributions to propeller theory (e.g., [PERSON] and [PERSON], mid-19 th century) to the most recent advancements published up to 2025. This wide temporal coverage allows for both a historical perspective and a focus on emerging high-fidelity and data-driven design methodologies. Inclusion criteria required that documents (i) be peer-reviewed or published by reputable institutions; (ii) address HAPS propulsion or propellers working in low-Reynolds-number environments; and (iii) present theoretical, computational, or experimental contributions. Sources that lacked methodological clarity, quantitative rigor, or were only tangentially related to the scope of the review were excluded. This ensured both relevance and reliability across the selected literature corpus. This approach ensured the inclusion of both historical developments and cutting-edge contributions to the field, providing a comprehensive and balanced overview of the state-of-the-art in HAPS propulsion and propeller technology.
## 1 Introduction  
Due to the production of three-dimensional (3D) images that satisfy all physiological factors of human depth perception [1, 2], holographic displays are ideal 3D displays devoid of visual fatigue caused by the vergence-accommodation conflict [3]. However, conventional techniques to construct holographic displays require that spatial light modulators (SLMs) exhibit a sub-micron pixel pitch and an ultra-high resolution to provide a wide viewing zone and a large screen size. This article reviews techniques to enlarge the viewing zone and screen size of holographic displays using microelectromechanical systems (MEMS) SLMs combined with spatial scanning systems.  
For the conventional holographic display techniques, when hologram patterns are displayed using the SLM with a resolution of \(N\times M\) and a pixel pitch of \(p\), the screen size (i.e., the 3D image size) is given by \(Np\times Mp\), and the light diffraction determines the viewing zone angle given by \(2\sin^{-1}\left(\lambda/2p\right)\), where \(\lambda\) is the wavelength of light. For instance, to obtain a viewing zone angle of \(30\lx@math@degree\) and a screen size of 40 inches, the resolution should be 886,600 \(\times\) 498,000, and the pixel pitch should be 0.97 \(\upmu\)m when \(\lambda\) = 0.5 \(\upmu\)m. SLMs with such a high resolution and a small pixel pitch are difficult to develop. Although many efforts have been made to develop SLMs with ultra-high resolution and a sub-micron pixel pitch [4, 5, 6, 7, 8, 9], substantial time is required to develop SLMs that can provide an effective practical viewing zone angle and screen size.  
Several techniques that do not depend on pixel pitch reduction and an increase in SLM resolution have been developed for enlarging the viewing zone angle and screen size. One method is the spatial-multiplexing technique that employs multiple SLMs [10; 11; 12]. This technique requires optical systems that combine many SLMs. Another method is the time-multiplexing technique, which employs a high-speed SLM combined with one-dimensional (1D) [13], two-dimensional (16) [14; 15], and circular scanning systems [16; 17; 18].  
Among several types of high-speed SLMs, MEMS SLM has the highest frame rate with moderate resolution. This article reviews the holographic displays developed by our research group, which are based on the time-multiplexing technique using MEMS SLMs combined with various scanning schemes, such as the screen [13], viewing zone [19], circular [20], and RGB scanning [21]. The measurements of the accommodation responses of the eyes to the scanning holographic display system [22] are also explained.
## 4 Discussion
### Crust Thickness Measurements
#### 4.3.2 Effect of Biopolymer Concentration  
_Medium-grained sand_. The results of crust thickness measurements revealed that doubling the biopolymer concentration significantly affected crust thickness (Figure 9 and Table 5). For most polysaccharides, crust thickness only increased slightly or even decreased upon doubling the concentration, which corresponds with the published literature (Table A9). This is likely because the increased viscosity limited the infiltration depth, resulting in a thinner crust. By contrast, NLS and POS treatment benefitted significantly from doubling the concentration, which increased the inter-particle cohesion sufficiently to form a thick crust.  
In contrast to the polysaccharide treatments, doubling the concentration significantly increased the thickness of protein-induced crusts. This is because doubling the concentration increased inter-particle cohesion without limiting the infiltration depth. While most polysaccharides formed relatively thick crusts at 1 wt%, most proteins required concentrations of 2 wt% to achieve crusts of similar thickness. Thus, proteins must be applied at higher concentrations to achieve similar crust thicknesses as polysaccharide treatments.  
_Fine-grained silica sand_. Results showed that doubling the concentration had no significant effect on the resulting crust thickness, leading only to slight increases or decreases (Figure 12 and Table 5). It appears that concentrations of 1 wt% are already sufficient to form a thick, stable crust, so doubling the concentration mainly resulted in adverse effects caused by the increase in viscosity.
## 5 Evolution of Opportunistic Routing in Asynchronous WSN
### Evolution of Asynchronous Duty-Cycled MAC Protocols
#### 5.1.9 Rix-Mac  
The RIX-MAC [36] protocol combines the advantages of X-MAC [30] and PW-MAC [34]. X-MAC avoids collisions via other transmitter frames. In situations with multiple senders, TX-nodes (transmitting nodes) can prevent data collision by listening to the short preambles from other nodes and the early-ACK frame of a receiver (RX-node). PW-MAC reduces the TX-node's power consumption by predicting the RX-node's wakeup schedule. The wakeup state is divided into two periods: the Sched-wakeup (scheduled wakeup) and Synch-wakeup (synchronized wakeup) periods; the Synch-wakeup period is optional when a node wants to transmit data frames. RIX-MAC [36] transmits short preambles, as X-MAC does, and wakes up twice as much as PW-MAC. RIX-MAC handles the CA with a back-off time procedure. Even though senders cannot recognize each other, which is the case in the hidden node problem, involved neighbors can perform the same operation with a value in the duration field of the early-ACK frame. Although a random back-off decreases the probability of a collision, in the case of multiple senders, the RIX-MAC protocol uses a time-out approach when collision occurs. In the case of two or more TX-nodes that want to transmit data to an RX-node, the nodes wake up at the same time owing to the same information about the destination, try to transmit short preambles, and set the timer k just before transmission. If two or more of them choose the same random back-off, their short preambles collide. When short preambles collide, the RX-node is unable to decode and respond to them, and the TX-node continues to transmit short preambles until the RX-node's early-ACK frame arrives, or the timer k expires. When the timer expires, the TX-nodes attempt retransmission with new random back-off values.  
In RIX-MAC (Figure 13), node A has a packet to send; it therefore wakes up and sends short preambles with a pause after each preamble. During each pause, the node waits for a preamble-ACK from the receiver. After preamble data is received by receiver B, B will send a preamble-ACK with the information of its wakeup schedule. After receiving the preamble-ACK, the sender stops sending the preamble and sends the data packet instantly. Later, since A now predicts [PERSON]'s next wakeup schedule, it will wake up before [PERSON] and send a small preamble when [PERSON] wakes up.  
However, the TX-node not only has its own schedule, but also the temporary schedule of RX-node. The TX-node in RIX-MAC wakes up twice in one cycle. In the TX-node, the first wakeup occurs, according to its own schedule, to receive data frames (Sched-wakeup), and the second wakeup is  
Figure 12: Packet transmission in Mic-MAC [35].  
Figure 13: Packet transmission in RIX-MAC [36].  
used to transmit data to the RX-node (Synch-wakeup). This increases the active time and affects battery lifetime.
## 3 Results
### Edge Height of Cells on Moderately Positively Charged PPAAm  
The edge height at the border of cells can be helpful to judge adhesion performance. At the edge of the cell, the height inheres in strongly curved or folded membrane regions and adjustment of the cleft distance between the basal membrane and the materials surface. An interesting parameter is the cell height, which represents the sum of the cleft and the curved region of the membrane, including the extracellular matrix (ECM). This height is directly accessible by SICM, a scanning probe microscopy method, largely avoiding forces on cells. As we pointed out earlier [47], the cell edge height is a strongly varying quantity (between 100 nm and 1 \(\upmu\)m) when measuring around the whole perimeter of the cell. Therefore, we took average values for each cell. For cells after only 1 h culturing on the substrate, the average cell edge heights were 227 nm \(\pm\) 18 nm on glass and 164 nm \(\pm\) 7 nm on PPAAm, i.e., the cell edge is significantly \(\approx\)30% smaller on PPAAm, indicating a higher cell-substrate contact (Figure 7). Upon culturing for 3 h on the surfaces, this difference has already vanished (on glass 171 nm \(\pm\) 11 nm, on PPAAm 150 nm \(\pm\) 8 nm). However, if we consider regions of peripheral ruffles, we find again a significant difference between 246 nm \(\pm\) 17 nm on glass and 174 nm \(\pm\) 10 nm on PPAAm. Note: This type of ruffle is a loosened and folded-back membrane and it can originate from membrane excess, which was more pronounced on glass [47]. The ruffles need to be excluded for this analysis, because they would lead to overestimation of the edge height in the case of glass.