esa-sceva/satcom-chunk-collection huggingface dataset:
# Investigations on Factors Affecting 3D-Printed Holes Dimensional Accuracy and Repeatability
## 2 Materials and Methods  
Two sets of experiments were conducted, for which two types of test parts were manufactured (Figure 1).  
The first experiment focused on evaluating the influence of three 3 DP process parameters on the straight thru-holes dimensional accuracy, and on determining the most influential factor and the optimal set of parameters. The test parts were built in different manufacturing sites using two PLA materials from different producers (denoted PLA 1 from Devil Design Sp. J, PL, and PLA 2 from Formfutura BV, NL) and ABS material (Stratasys Inc., Austin, TX, USA) on five 3D printers by using four slicing software (Prusa Replica/Prusa extruder/Prusa slicer/PLA 1; Original Prusa i3 mk3s+/direct drive extruder/Prusa slicer/PLA 1; Creality CR10/MK8 extruder/Cura Ultimaker slicer/PLA 1; Creality Ender 3/BIQU H2 extruder/Simplify3d slicer/PLA 2; Mojo 3D printer/Print Wizard slicer/ABS). The purpose was to investigate if the selection of a certain material, equipment or slicer is influencing the dimensional accuracy of 3D-printed straight holes. Thus, the first experiment was meant to answer two research questions: What is the most influential process parameter for the 3D-printed straight holes dimensional accuracy? Does the use of different 3D printers, materials and slicers impact the holes dimensional accuracy?  
The results of the first experiment were used in the second one in which 3D-printed PLA test parts mimicking a surgical drill guide with inclined holes were manufactured. The dimensional accuracies of the guide's holes before and after a post-processing process typical for the medical applications (i.e., decontamination by cleaning, washing and cold plasma sterilization) were compared. This experiment answered the following question: Are the accuracy and repeatability of the directly 3D-printed holes preserved after the medical decontamination process?
# Airspace Geofencing and Flight Planning for Low-Altitude, Urban, Small Unmanned Aircraft Systems
## 3 Definitions and Algorithms
### UAS Flight Planning in a Geofenced UTM Airspace
For the turn solution, a Rotational Plane Sweep (RPS) algorithm is used to find all straight-line segments connecting line-of-sight vertices to form a visibility graph map. Then, [PERSON]'s algorithm finds the minimum distance path from departure to destination point. For constant altitude climb and terrain follower solutions, points of intersection between a straight line solution path and obstacles are found using a polygon-line intersection operator. Then, obstacle height at the intersection points are extracted from keep-out geofence data. Three-dimensional flight trajectory \"turn\", \"constant cruise altitude\", and \"terrain follower\" solutions are wrapped with geofences using Algorithm 1. The best solution is the minimum cost module based on Equation (2). Note that geofence segment duration is not explicitly considered in this paper. Instead, it is assumed the flight trajectory keep-in geofence generated using Algorithm 4 remains active from UAS launch to landing. Figure 6: Three candidate flight planning solutions respecting keep-out airspace geofence and obstacle “no-fly” volumes. A turn solution uses a visibility graph to define a constant-altitude path around no-fly zones (**left**). A cruise altitude solution climbs to an altitude greater than the highest building enroute to the destination (**center**). The terrain follower defines an altitude profile maintaining minimum safe clearance or greater from no-fly zones (**right**). ```
1:Inputs: Departure Point \(\mathcal{R}_{start}\), Destination Point \(\mathcal{R}_{end}\), Cruise Altitude \(h_{cruise}\), Keep-out Goefence Boundaries \(\mathcal{S}_{geo}\), Aircraft Velocity \(\mathcal{V}\), Time to Climb \(t_{climb}\), Time to Descend \(t_{desc}\), Number of Geofences \(\mathcal{N}_{geo}\), UAS Safety Buffer \(\delta_{vehicle}\)
2:Planned Flight Trajectory \(\mathcal{P}_{traj}\), Trajectory-wrapping 3-D Geofence Volumes \(\mathcal{G}\)
3:Algorithm:
1://turn solution module
2:\(\mathcal{R}_{VG}\leftarrow[\mathcal{R}_{start};\mathcal{S}_{geo};\mathcal{R}_ {end}]\)\(\lhd\) Vertices of Visibility Graph
3:\([edges,vert\_ID]\gets RPS(\mathcal{R}_{VG})\lhd\) get visibility graph edges on the map using RPS
4:\([R_{turn}]\gets dijkstraPath(\mathcal{R}_{start},\mathcal{R}_{end},edges,vert\_ID)\lhd\) get min.
## 4 Results
### Organ-Level Segmentation
#### 4.1.3 Experiments on Image-Generated Point Cloud
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **OA** & **mACC** & **mIoU** & \multicolumn{2}{c}{**Stem**} & \multicolumn{2}{c}{**Leaf**} & \multicolumn{2}{c}{**Fruit**} \\ \cline{4-10} PointNet & 0.812 & 0.559 & 0.450 & 0.324 & 0.384 & 0.796 & 0.943 & 0.230 & 0.351 \\ PointNet++ & 0.823 & 0.504 & 0.426 & 0.366 & 0.435 & 0.806 & 0.945 & 0.101 & 0.132 \\ PointMLP & 0.806 & 0.721 & 0.466 & 0.431 & 0.573 & 0.782 & 0.874 & 0.185 & **0.716** \\ DGCNN & 0.852 & 0.639 & 0.538 & 0.490 & 0.603 & 0.829 & 0.931 & 0.296 & 0.382 \\ PointCNN & 0.861 & 0.662 & 0.567 & 0.512 & 0.620 & 0.839 & 0.937 & 0.351 & 0.427 \\ PAConv & 0.855 & 0.687 & 0.563 & **0.525** & **0.678** & 0.831 & 0.912 & 0.335 & 0.469 \\ CurveNet & 0.842 & 0.592 & 0.502 & 0.454 & 0.554 & 0.821 & 0.934 & 0.230 & 0.289 \\ PT & 0.856 & 0.708 & 0.629 & 0.464 & 0.546 & 0.835 & 0.949 & **0.588** & 0.628 \\ ST & **0.869** & **0.741** & **0.643** & 0.504 & 0.573 & **0.850** & **0.950** & 0.576 & 0.693 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Segmentation results on tomato point clouds derived from DSLR images. The bolded text indicates optimal performance, while the underlined text denotes the second-best results. Figure 6: Segmentation results of tomatoes reconstructed by close-range DSLR camera RGB images. The circled areas indicate incorrectly segmented regions; GT represents the ground truth. CurveNet exhibited the poorest performance, with an mIoU of 0.608, indicating significant degradation compared to data collected from other platforms. Moreover, PointMLP's performance was notably impacted, falling below that of its predecessor, PointNet. In contrast, DGCNN showed relative performance improvements over previous datasets. Both ST and PT maintained their positions as the top-performing and second-best models, respectively.
# Study of Marine Particles Using Submersible Digital Holographic Camera during the Arctic Expedition
###### Abstract  
The paper presents the results of in situ studies of marine particles of different nature using a submersible digital holographic camera (DHC) during the Arctic expedition. It also describes the features, performance specifications, and possibilities of the DHC and the DHC technology. The DHC technology can be used for noninvasive automatic evaluation of spatial and temporal characteristics of plankton, including the distribution of plankton concentrations. The comparison of quantitative analysis of zooplankton net samples and classification results using the DHC revealed that the error of the DHC classification of mesoplankton at the level of the main systematic orders was about 30%. The results of determining the data on the medium, such as water turbidity, according to the radiation shielding factor (degree) by the particles of the _Suspension_ taxon using the DHC technology are presented; the prospects for studying the size of gas bubbles and their volume content according to the _Bubble_ taxon data are shown. The use of holographic data for in situ point estimates is considered.  
digital holography; submersible digital holographic camera; marine particles; arctic zooplankton; marine snow; turbidimetry; methane release; floating bubbles +
Footnote †: journal: Applied Sciences
## 5 Experimental Results
### Multiple Access Interference  
In this experiment, two FM-ACSK SDR transmitters and one FM-ACSK SDR receiver were used. Both transmissions were carried out at the same center frequency of 867 MHz using 27 cm long coil whip antennas. The distance between the transmitting antennas and receiving antenna was 1 m. The second transmitter's master chaos generator's coefficients, provided in Table 4, were changed to make synchronization between the second transmitter and receiver impossible. In the tests, the first transmitter's and receiver's power were kept constant while the second transmitter's power was varied. The result of this experiment is depicted in Figure 15. From the plot, it can be seen that a second transmitter on the same carrier frequency has a devastating impact on the quality of the communication. Therefore, it can be concluded that the given modulation scheme does not provide good multiple access opportunities out-of-the-box.  
Figure 14: BER versus relative CFO with the strobe-based detector in the single model scenario. DEV—FM deviation. The receiver gain is 10 dB, and the transmitter gain is \(-26\) dBm.  
Figure 15: BER versus transmit power of the malicious transmitter. DEV—FM deviation. The receiver gain is 10 dB and the first transmitter power is \(-36\) dBm. The receiver uses a strobe-based detector.
# Microencapsulation of Natural Food Antimicrobials: Methods and Applications
## 2 Encapsulation Methods
### Vibrating Technology  
Vibrating technology has attracted great attention from various industries for producing homogenous and well-characterized capsules because of its simplicity. Laminar jet break up constitutes the main base of the vibrating-jet (nozzle) method, and a vibrational frequency with known amplitude is applied to the extruded jet. Extruding a liquid via a nozzle can cause a laminar jet to break up into short lengths, which then produce spherical droplets as a result of the surface tension force [78]. Parameters including nozzle diameter, surface tension, viscosity of fluid, jet velocity and disturbance frequency have significant effects on droplet size. However, limitations in production of a large quantity of microcapsules, along with using highly viscous polymers, are considered as the main disadvantages of this technique. Vibrating technology has been developed and used for the microencapsulation of different bioactive compounds [35; 79; 80]. Nisin has been encapsulated in alginate beads by vibrating technology for the first time. The formed microcapsules were homogeneous in shape and size with an average diameter of 118 \(\pm\) 3 \(\upmu\)m. The strong electrostatic interactions between negatively charged alginate and positively charged nisin in a feeding solution (pH 5.0) could result in a high EE (75%). A higher EE of nisin was achieved in alginate beads by applying vibrating technology other than matrices and methods used in different studies to nisin (EE 35-60%) [35].
# On the Performance of LDPC-Coded Massive MIMO Schemes with Power-Ordered NOMA Techniques
###### Abstract  
This article studies the power-ordered Non-Orthogonal Multiple Access (NOMA) techniques associated with Low-Density Parity-Check (LDPC) codes, adopted for use in the fifth generation of cellular communications (5G). Both conventional and cooperative NOMA are studied, associated with Single Carrier with Frequency Domain Equalization (SC-FDE) and massive Multiple-Input Multiple-Output (MIMO). Billions of Internet of Things (IoT) devices are aimed to be incorporated by the Fourth Industrial Revolution, requiring more efficient use of the spectrum. NOMA techniques have the potential to support that goal and represent strong candidates for incorporation into future releases of 5G. This article shows that combined schemes associated with both conventional and cooperative LDPC-coded NOMA achieve good performance while keeping the computational complexity at an acceptable level.  
NOMA; LDPC; massive MIMO; SC-FDE; 5G +
Footnote †: journal: Applied Sciences  
1
## 8 Experimental Evaluation
### Parsers  
Lastly, we evaluate our implementation on more complex programs: automatically generated parsers. For the generation, we use a tool Bison. It reads a language specification in the form of context-free grammar and produces a C parser that accepts the language. In the benchmarks, we generate two such parsers. The first one accepts a language of numerical expressions (mathematical expressions that consist of numbers and binary operators). The second parser is of a simple programming language with variables and branching. We present a evaluation for both parsers in Table 6. As with the previous benchmark sets, the M-String inputs with a smaller number of segments outperformed other analyses. In these benchmarks, we use specifically hand-crafted M-String inputs for parsers. For parsing of mathematical expressions, it was: addition input had a form of two arbitrary numbers with a plus sign between them, ones was a simple input of a single digit sequence, and lastly, alternation was input that produced complicated M-Strings by alternating digits inside of expressions. The other parser of simple programming language was evaluated on: value was in input that created a variable and assigned a constant to it, loop was a short program with some control flow and wrong was a program that contained a syntax error.  
\begin{table}
\begin{tabular}{r r r r r r} \hline \hline  & \multicolumn{2}{c}{**Correct**} & \multicolumn{2}{c}{**Error Found**} \\ \cline{2-5}  & **Tests** & **Time(s)** & **Tests** & **Time(s)** & **Timeout** \\ \hline apache & 0 & – & 26 & 384.26 & 24 \\ openser & 43 & 234.13 & 45 & 105.93 & 6 \\ wu-ftpd & 8 & 35.78 & 14 & 2461.27 & 19 \\ libgd & 4 & 9.01 & 4 & 1.85 & 0 \\ madwifi & 5 & 0.51 & 5 & 0.55 & 0 \\ gine & 1 & 0.53 & 1 & 0.25 & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Veriabs overflow benchmarks depict a few categories of programs exhibiting an overflow error and their fixed variants. The table shows the number of solved benchmarks (_tests_) and accumulated time for each category. For each category, the table depicts correctly verified benchmarks, benchmarks where the verifier was able to find an error and number of timeouts.
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
## 4 Real Experiments
### Time Series Deformation Monitoring Based on SBAS-InSAR Technique  
In this paper, one-dimensional deformation monitoring was first carried out using SBAS-InSAR technology, with spatiotemporal baseline thresholds set to 180 days and 140 m, respectively. Through baseline estimation, pre-differential interferometric processing, phase unwrapping, deformation rate estimation in the LOS direction, and spatial-temporal filtering, the interferograms with poor unwrapping effect were removed, and 25 unwrapped differential interferograms were acquired, and the 12 LOS time series deformation results were finally obtained for the period of 13 November 2015 to 19 November 2016; the time series deformation results are shown in Figure 8. During the period from 31 December 2015 to 2 October 2016, the whole mine area was from blue to light yellow, with a general trend of slow subsidence, and with the continuous advancement of mining, the mining area was intensified from light yellow and gradually changed to red; as of 19 November 2016, the LOS direction time series maximum deformation value reached 147 mm.  
Figure 8: LOS direction time series deformation (reference time: 13 November 2015).  
Figure 7: Spatio-temporal baseline.
# Visual Enhancement Capsule Network for Aspect-Based Multimodal Sentiment Analysis
## 3 Methodology
### Problem Formalization  
The multimodal aspect-based sentiment analysis problem is defined as follows. Suppose that \(T\) and \(I\) represent a text sample space and an image sample space, respectively, where the \(i\)th document is \(T^{i}=\{w_{1},w_{2},\ldots,w_{n}\}\), and its corresponding image set is \(I^{i}=\{I_{1},I_{2},\ldots,I_{m}\}\), where \(n\) and \(m\) represent the number of the words and images, respectively. The document \(T^{i}\) and its corresponding image sequence \(I^{i}\) constitute an instance, and the instance set is expressed as \(Ins=\{<T^{1},I^{1}>,<T^{2},I^{2}>,\ldots,<T^{u},I^{u}>\}\), where \(u\) is the size of the text-image set pairs in the training set. Given an aspect set \(A=\{a_{1},a_{2},\ldots,a_{l}\}\) and the aspect phrase sequence of \(a_{t}\) is represented as \(A_{t}=\{w_{1}^{a},w_{2}^{a},\ldots,w_{s}^{a}\}\), where \(l\) is the number of aspects and \(s\) is the number of aspect phrases of \(a_{t}\).  
Each instance is associated with a sentiment polarity label \(L_{s}\) on each aspect \(a_{t}\), where the sentiment label \(L_{s}\in\{Positive,Neutral,Negative\}\). The goal of multimodal aspect-based sentiment analysis is to learn a mapping \(f:T\times I\times A\to L\) from the quadruple sequence of multimodal training dataset \(\{<T^{i},I^{i},a_{j},L_{s}>|1\leq i\leq u,1\leq j\leq l\}\), where \(T\times I\times A\) is the cartesian space consisted of three sets, \(T\) and \(I\) and \(A\), and \(L\) is the label set composed of \(L_{s}\), that is \(L=\{Positive,Neutral,Negative\}\). Table 1 provides an overview of the basic notations used in the paper.  
Figure 1: The framework of the VECapsNet model for multimodal aspect-based sentiment analysis. The model is composed of three parts: MemCapsNet based on text features, AgCapsNet based on image features, and the multimodal fusion module based on interactive learning.
# A Thermal Transport Study of Branched Carbon Nanotubes with Cross and T-Junctions
###### Abstract  
This study investigated the thermal transport behaviors of branched carbon nanotubes (CNTs) with cross and T-junctions through non-equilibrium molecular dynamics (NEMD) simulations. A hot region was created at the end of one branch, whereas cold regions were created at the ends of all other branches. The effects on thermal flow due to branch length, topological defects at junctions, and temperature were studied. The NEMD simulations at room temperature indicated that heat transfer tended to move sideways rather than straight in branched CNTs with cross-junctions, despite all branches being identical in chirality and length. However, straight heat transfer was preferred in branched CNTs with T-junctions, irrespective of the atomic configuration of the junction. As branches became longer, the heat current inside approached the values obtained through conventional prediction based on diffusive thermal transport. Moreover, directional thermal transport behaviors became prominent at a low temperature (50 K), which implied that ballistic phonon transport contributed greatly to directional thermal transport. Finally, the collective atomic velocity cross-correlation spectra between branches were used to analyze phonon transport mechanisms for different junctions. Our findings deeply elucidate the thermal transport mechanisms of branched CNTs, which aid in thermal management applications.  
ballistic; phonon; branched CNT +
Footnote †: journal: Applied Physics Letters  
1
Footnote 1: email: {[[https://doi.org/10.3390/app11139933](https://doi.org/10.3390/app11139933)]([https://doi.org/10.3390/app11139933](https://doi.org/10.3390/app11139933))}
# CrossDeFi: A Novel Cross-Chain Communication Protocol
## 6 Analysis
### Efficiency Assessment
#### 6.1.2 Proof-of-Characteristics with Existing Cross-Chains  
Traditional mechanisms, such as hash-locking, multi-token standards, notary services, and sidechains typically achieve only low to medium interoperability and often lack robust finality guarantees. This comparison underscores the advanced capabilities of CrossDeFi, particularly in its unique approach to ensuring transaction finality. Table 8 provides a comparative analysis of the proposed CrossDeFi with existing cross-chain solutions using a comprehensive set of parameters.  
\begin{table}
\begin{tabular}{l l  
CrossDeFi distinguishes itself by employing a transfer confirmation strategy that is integral to its design, ensuring a higher level of finality guarantee compared to existing solutions. Unlike traditional models that often rely on simplistic asset-locking mechanisms, which do not prevent asset loss or double-spending effectively, CrossDeFi incorporates an asset-specific bridge selection model. This model leverages multi-criteria information to enhance bridge selection, significantly improving the system's performance, speed, and cost-efficiency.  
The asset-specific bridge selection (ABS) mechanism of CrossDeFi assigns significant weight to crucial parameters such as security, speed, cost, and adaptability, which greatly enhances the accuracy and efficiency of cross-chain asset transfers. This strategic focus not only reduces potential bottlenecks associated with bridge performance but also aligns with the diverse requirements of various DeFi applications, facilitating tailored solutions for different use cases.  
Despite its complexity, the implementation of CrossDeFi offers substantial improvements over existing systems by eliminating issues associated with asset-locking and double-spending. This makes it exceptionally suitable for various DeFi applications, from decentralized exchanges (DEXs) to complex financial products involving lending, borrowing, and yield farming. The system's design, centered around its unique bridge selection and transfer confirmation processes, ensures that CrossDeFi can adapt to and effectively support a broad spectrum of DeFi activities, thereby enhancing overall user experience and system reliability.
## 6 Offline Optimization
### Offline Optimized PID Gain-Scheduled Controller
Note that for an EGR valve it is desirable to have no overshoot; however, for the small opening case (Case 1), the fixed-gain PID controller has an 8.2% overshoot, compared with 3.8% for the optimal initial gain-scheduled PID controller. In order to find the root cause of this result, a detailed comparison is shown in Figure 12.  
The control inputs of both the gain-scheduled and fixed-gain controllers are shown in the middle plot of Figure 12, with their associated responses shown in the upper plot. With a large fixed proportional gain, the fixed-gain PID controller provides a fast response at the beginning; however, when the valve closes to its setpoint, the response slows down more quickly than that of the gain-scheduled controller, leading to a larger steady-state error for the fixed-gain controller. Note that the gain-scheduled controller increases the control gain significantly within the small error region to fight the static friction. Based on the control effort, although the fixed-gain PID controller has a fairly large \(P\) gain, it is not large enough to overcome the static friction when the valve is closed to its setpoint. On the other hand, the gain-scheduled PID controller increases the proportional gain significantly when the valve is close to its setpoint in order to overcome the large static friction; see the bottom plot in Figure 12. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case Number** & \multicolumn{2}{c}{**Case 1**} & \multicolumn{2}{c}{**Case 2**} \\ \hline Controller Type & G-S & F-G & G-S & F-G \\
2\% Settling Time & N/A & N/A & 58.5 ms & 55.3 ms \\ Steady State Error & 3.8\% & 8.2\% & 0.1\% & 0.34\% \\ Overshoot & 0\% & 0\% & 0\% & 1\% \\ \hline
**Case Number** & \multicolumn{2}{c}{**Case 3**} & \multicolumn{2}{c}{**Case 4**} \\ \hline Controller Type & G-S & F-G & G-S & F-G \\
2\% Settling Time & 79 ms & 75.5 ms & 29.5 ms & 34.7 ms \\ Steady State Error & 0.06\% & 0.19\% & 0.45\% & 0.9\% \\ Overshoot & 0\% & 1.04\% & 1.3\% & 3.35\% \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Offline optimized controller performance summary. Figure 11: Step responses of optimized initial controller.