esa-sceva/satcom-chunk-collection huggingface dataset:
# FOXS-GSC--Fast Offset Xpath Service with HexagonS Communication
## 3 Proposed Solution  
This section describes the proposed distributed communication protocol, which we call Fast Offset Xpath Service with hexaGonS Communication (FOXS-GSC). This protocol selects relay nodes based on message retransmission to disseminate data in an urban VANET scenario. FOXS-GSC considers the destination node's geographic position and the position of neighboring nodes concerning the (re)transmitting node. For this, vehicles must maintain local knowledge of their neighbors obtained using beacons sent by vehicles.  
Our protocol discretizes the scenario in sectors with a hexagonal shape (Figure 1). Using this division, each vehicle populates its adjacent sectors with vehicles in its neighborhood. The messages are carried in multiple hops between adjacent sectors that contain at least one vehicle (relay) until reaching the destination (the red hexagons in Figure 1). Note that while there may be several paths between the origin and the destination, the algorithm chooses the path with the fewest hops. If desired, however, another metric for the graph weight, such as network or sector occupation, can easily be implemented in the presented solution.
# Stability of Running Stride Biomechanical Parameters during Half-Marathon Race
## 3 Results
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Factors** & **Race Segment** & **Mean** & **SD** & **95\% CI** & **SE** & **CV (\%)** \\ \hline GCT (ms) & 1 & 208.6 & 17.4 & 194.1–223.1 & 6.14 & 8.3 \\  & 2 & 208.5 & 16.1 & 195.0–222.0 & 5.71 & 7.7 \\  & 3 & 210.0 & 14.6 & 197.8–222.2 & 5.15 & 6.9 \\  & 4 & 209.6 & 14.1 & 197.8–221.4 & 4.98 & 6.7 \\ LSS (kN/m) & 1 & 11.28 & 1.31 & 10.19–12.39 & 0.47 & 11.6 \\  & 2 & 11.21 & 1.31 & 10.11–12.31 & 0.47 & 11.7 \\  & 3 & 11.11 & 1.23 & 10.09–12.18 & 0.43 & 11.0 \\  & 4 & 11.10 & 1.28 & 10.04–12.16 & 0.44 & 11.5 \\ VO (cm) & 1 & 7.40 & 0.58 & 6.92–7.88 & 0.20 & 7.8 \\  & 2 & 7.41 & 0.58 & 6.92–7.90 & 0.21 & 7.9 \\  & 3 & 7.38 & 0.58 & 6.89–7.87 & 0.21 & 7.9 \\  & 4 & 7.36 & 0.59 & 6.86–7.86 & 0.21 & 8.1 \\ SL (m) & 1 & 1.42 & 0.17 & 1.29–1.56 & 0.06 & 11.4 \\  & 2 & 1.43 & 0.16 & 1.30–1.56 & 0.06 & 11.0 \\  & 3 & 1.41 & 0.14 & 1.30–1.54 & 0.05 & 9.9 \\  & 4 & 1.43 & 0.13 & 1.31–1.53 & 0.05 & 9.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of biomechanical characteristics as within-subjects factors across four race segments: GCT, LSS, VO, and SL measurements. The Bonferroni post hoc test reveals a statistically significant difference between the first and second race segments for LSS, with a mean difference of 0.07 kN/m and a \(p\)-value of 0.015. This indicates that the difference in LSS between these two segments is likely not due to random chance and is statistically significant after adjusting for multiple comparisons. The RM ANOVA analysis suggests that while most of the measurements did not significantly vary across race segments, LSS did show significant variability. However, the Bonferroni-corrected post hoc analysis specifies that this significant variability is only present between the first and second segments. The eta squared (\(\eta^{2}\)) values, which represent the effect size, are relatively small for all measures, indicating that even where significant effects are found, the proportion of variance explained by race segment differences is small.
## 5 High Capacity 2 \(\times\) 2 OPS Node with Contention Resolution and Fiber Transmission
### Packet Switching, Buffering and Transmission Results and Discussion  
BER measurements of back-to-back, after the packet dropping process (named DROP in Figure 15) and after the packet forwarding process (named FORWARD) are shown in Figure 16a-c, respectively. The BER for every individual packet was averaged over 10 captured packets. Discontinuities in the plots of back-to-back arise from error-free measurements. Deterioration of BER after the packet dropping was caused by a large insertion loss of the PLZT switch and couplers (about 18 dB). The BER after the forwarding process was kept below the FEC limit of 3 \(\times\) 10\({}^{-3}\). However, due to crosstalk between output ports of the optical switch, residual packets were output in the undesired delay lines. As a result of the interference between signals and residual signals after the couplers, a severe deterioration occurred. In contrast, the drop process without the buffering had no such interferences, and, consequently, BER was much lower than in the forward process. The improvement of the OPS node crosstalk characteristics is still an issue to be tackled in future work. These results successfully demonstrate the operation of a 12.8 Tbit/s optical packet switching node with buffering after 50 km of fiber transmission. Even after the signal propagation, DWDM PDM-16 QAM optical packets could be correctly handled by the 2 \(\times\) 2 OPS node consisting of low polarization dependent, wideband PLZT optical switches.
## 2 Materials and Methods
### Procedures  
#### 2.3.1 Myoton Measurements  
A Myoton device (MyotonPRO, Myoton Ltd., Tallinn, Estonia) was used to assess the passive muscle and tendon stiffness of both the calf and anterior thigh MTUs. All the assessments were performed by one investigator experienced in human anatomy, with two months of training on the Myoton device especially. For the assessment of the triceps surae MTU (i.e., Achilles tendon, gastrocnemius medialis, and gastrocnemius lateralis), the participant was asked to remain in a resting position, lying prone, with their foot hanging freely off a physiotherapy bed [21]. For the anterior thigh muscles (vastus lateralis, vastus medialis, rectus femoris), the participant was asked to change into a supine position, with their hips and knees fully extended [22]. For the patellar tendon, the participant was asked to remain in a sitting position, with both hip and knee joints at 90\({}^{\circ}\)[22].  
For the stiffness assessment, the probe of the Myoton device was applied perpendicular to the tissue, as proposed by the manufacturer. The measurement sites of the respective muscles were defined in accordance with SENIAM guidelines [23] for electrode placement during surface electromyography measurements. For the Achilles tendon, the assessment was at the level of the medial malleolus [24], and for the patellar tendon, it was at the midway point between the distal patellar rim and the tuberosity of the tibia [22].  
In total, 3 consecutive mechanical impacts at 15 ms and a force of 0.3-0.4 N were applied from the Myoton device. Moreover, this was repeated two times, which resulted in nine impacts per muscle and tendon. Consequently, the muscle and tendon stiffness of every single mechanical impact was calculated as the force applied relative to the deformation of the tissue. The average value out of the three \(\times\) three mechanical impacts was taken for the statistical analysis [25].
## 3 Traffic Demand
### Route Generation and Dynamic Equilibrium  
[PERSON]'s shortest path algorithm [14] is commonly used to find the shortest path to all nodes from a single source node in a network. In principle, the Dijkstra algorithm measures each possible path from the departure point to all the points around it at each step and finds the shortest path to the destination of the whole network in every step. In SUMO, we use \"duarouter\" [3] to compute routes for each vehicle. However, if all vehicles follow the shortest path generated by duarouter, vehicles with similar departure and arrival roads/edges will experience severe congestion on their shortest road, while no vehicles are on the second shortest road. In real-world, drivers certainly like to drive a shorter distance, but the current traffic situation is also an important consideration in route selection. An example can be found in car navigation; some drivers prefer the shortest distance route, while some others prefer the shortest time route. What is more, if a driver knows a special path from work to home, which avoids traffic jam on the main road during the peak hours, he would like to choose that despite a possible detour.
# AR Displays: Next-Generation Technologies to Solve the Vergence-Accommodation Conflict
## 4 Overview of Proposed Next-Generation 3D Display Solutions
### Holographic Displays  
True holographic displays in practical form for wearable displays really is a thing of the future, as there are multiple technical challenges associated with this type of display. On one hand, there is an image source problem. To reconstruct a wavefront, phase information has to be recorded, which is very challenging considering it requires coherent light. On a small-scale it can be accomplished but vast vista-type scenes are not feasible. Nonetheless, to partly solve this, integral 2D imaging methods are suggested as a viable alternative from which the corresponding holograms with greater or lesser approximations can be calculated [26; 27; 28; 29]. Similarly, for the computer-generated 3D content there are no principal obstacles for calculating a corresponding hologram. Though, often it is considered, that calculation of computer-generated holograms is a resource-intensive process, which hinders the real-time capability. It must be said, that in recent years advances in dedicated graphics processing equipment as well as developments in methodology have proven this to become substantially less relevant. For example, [PERSON] (Microsoft research) has demonstrated a fully real-time capable holographic image rendering pipeline [6]. On the other hand, there are technological challenges with the physical reconstruction of the hologram. One of the still existing issues is minimization of laser speckle-caused image artefacts. While, an even more inhibiting issue is the absence of really ultra-high-resolution spatial light modulators, limiting viewing angles as well as the size of the eye-box in case of a wearable display. In spite of existing challenges, holographic displays have many inherent advantages--one being capability of conveying all the important depth cues--such as parallax and monocular focus cues, but also the computational nature of these displays allows for implementation of optical aberration correction. Consequently, the envisioned advantages are the driving force behind constant progress in respect to computational methods for computer-generated holograms, solutions oriented towards expansion of the effective eye-box, miniaturization, etc. [6; 7].
# BIM Application in Multi-Criteria Assessment of
## 5 Discussion  
The final results of the G\({}_{1}\) (preparation for production) group of criteria show that the third alternative A\({}_{3}\) gained the first rank, the alternative A\({}_{1}\) gained the second rank, and the third rank was gained by alternative A\({}_{2}\). The results of the G\({}_{1}\) group show that A\({}_{3}\) exceeds A\({}_{1}\) by 26.88% and A\({}_{2}\) by 39.78%. Next, the final results of the G\({}_{2}\) (production) group of criteria show that A\({}_{1}\) gained the first rank by a very small margin, A\({}_{3}\) gained the second rank, and A\({}_{2}\) gained the third rank. The percentage ratio in the G\({}_{2}\) group is as follows: alternative A\({}_{1}\) is above A\({}_{3}\) with a lead of only 1.02% and above A\({}_{2}\) by 20.62%. Finally, the results of the G\({}_{3}\) (logistic) group of criteria show that A\({}_{1}\) gained the first rank, being above alternative A\({}_{3}\) in the second rank by a small lead of 2.47% and above alternative A\({}_{2}\) in the third rank by 7.41%.  
The final assessment and the overall ranking of the suspended brickwork facade installation methods at the production stage by considering the weights of each group of criteria are as follows: alternative A\({}_{3}\) (installation of prefabricated suspended brickwork masonry panels) is ranked first, with alternative A\({}_{1}\) (installation of suspended brickwork and mounting of brickwork lintels up to two meters) in the second rank, being 6.59% behind the first position, and alternative A\({}_{2}\) (installation of suspended brickwork and pre-prefabricated stainless-steel components that were manufactured offsite completed with adhesively bonded brick slips) in the third rank, being 23.08% behind the first position.  
The literature review and practical experience showed that offsite production could improve construction quality and save time and money, but current implementation of BIM in prefabricated construction is far from perfect and correct.  
In this research, a BIM brickwork facade model was prepared based on survey data and analyzed design solutions. The designed building object model elements did not have sufficient level of detail for the production stage, but the masonry bricks and brickwork support elements emulated the geometric and non-geometric data in the BIM model. Nevertheless, the model data of the building object model elements was used for production and the BIM model was applied for the calculation of elements, such as masonry bricks, mortar, reinforcement, stainless-steel wall ties, stainless-steel masonry support elements, and stainless-steel anchor rods. Moreover, in terms of cost savings, the BIM application showed benefits in terms of shareholder collaboration, reduction in errors, and optimization of production process for better construction results.
# ICRP: Internet-Friendly Cryptographic Relay-Detection Protocol
## 2 Internet Latency
### RTT Measurements without Adversary  
#### 2.3.1 Stability Over a Short Period  
We present the result of short-period (i.e., a few minutes) experiments in Figures 1-3. Figure 1 shows 6 graphs, in which each \"+\" represents the value of one RTT in milliseconds (readable in the \(y\)-axis). Each graph is a plot of 7000 RTTs between two end-points collected in a row. The dates and times of the start and end of the measurements are given on each individual graph. Figures 2 and 3 display the same samples on a more zoomed-in scale along with their statistic distribution.  
Regarding the stability of the measurements, the distributions show that the majority of the measures are concentrated in one dense interval. Depending on the sources and destinations, the samples appear in different shapes. Noticeably, samples from Germany-Oregon or France-Oregon seem to be formed of several layers. When this is the case, it seems that one layer always outstands the others. Indeed, for the sample gathered from France to Oregon, 93% of its measures are in \([200,210]\) and for Germany to Oregon, 81% are in \([150,164]\).
###### Abstract  
Pulse signals refer to electromagnetic waveforms with short duration and high peak energy in the time domain. Spatial electromagnetic pulse interference signals can be caused by various factors such as lightning, arc discharge, solar disturbances, and electromagnetic disturbances in space. Pulse disturbance signals appear as instantaneous, high-energy vertical-line pulse trains (VLPTs) on the spectrogram. This paper uses computer vision techniques and unsupervised clustering algorithms to process and analyze VLPT on very-low-frequency (VLF) waveform spectrograms collected by the China Seismo-Electromagnetic Satellite (CSES) electric field detector. First, the waveform data are transformed into time-frequency spectrograms with a duration of 8 s using the short-time Fourier transform. Then, the spectrograms are subjected to grayscale transformation, vertical line feature extraction, and binarization preprocessing. In the third step, the preprocessed data are dimensionally reduced and fed into an unsupervised K-means++ clustering model to achieve automatic recognition and labeling of VLPTs. By recognizing and studying VLPT, not only can interference be recognized, but the temporal and spatial locations of these interferences can also be determined. This lays the foundation for identifying VLPT sources and gaining deeper insights into the generation, propagation, and characteristics of electromagnetic radiation.  
China Seismo-Electromagnetic Satellite (CSES); very low frequency (VLF); vertical-line pulse train (VLPT); k-means ++; automatic recognition 2023
## 4 Switching Adaptive Notch Filter Vibration Control Strategy Based on Fuzzy Control
### Fuzzy Control Based on Adaptive ANF
The system employs a control strategy that combines a fuzzy controller with a notch filter to achieve precise control of the AMB rigid rotor system. The working principle of AFC-ANF is illustrated in the control block diagram shown in Figure 6: the rotor vibration displacement signal \(x(t)\) is first processed by the notch filter to remove periodic interference components at specific frequencies, resulting in the filtered displacement signal \(c(t)\). This signal is then compared with the reference displacement signal \(r(t)\) to generate the error signal E and its rate of change EC, which serve as the input variables for the fuzzy controller. Based on a predefined fuzzy rule base, the fuzzy controller performs fuzzification, rule evaluation, and defuzzification using the Mamdani inference method to produce a continuous control signal \(u(t)\). This control signal acts on the AMB rigid rotor system, driving the system output \(x(t)\) to track the reference signal \(r(t)\). Throughout the control process, the fuzzy controller dynamically adjusts the amplitude and direction of the control signal \(u(t)\) to achieve robust control against system nonlinearities and external disturbances. Simultaneously, the notch filter effectively suppresses periodic interference components, further enhancing the system's control accuracy and stability.
# Cross-Lingual Cross-Domain Transfer Learning for
## 4 Experiments
### Performance on Auxiliary Tasks--Stage 1
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**ALEN**} & **Sampling** & **English** & \multicolumn{2}{c}{**Spanish**} \\  & **Strategy** & **Best Macro-F1** & **Accuracy** & **Best Macro-F1** & **Accuracy** \\ \hline
5 & tail & 0.931 & 0.932 & 0.860 & 0.860 \\
5 & head & 0.958 & 0.958 & 0.899 & 0.899 \\
5 & head+tail & 0.947 & 0.947 & 0.916 & 0.916 \\
15 & tail & 0.966 & 0.966 & 0.921 & 0.922 \\
15 & head & 0.947 & 0.947 & 0.911 & 0.911 \\
15 & head+tail & 0.954 & 0.954 & 0.922 & 0.922 \\
**25** & **tail** & **0.970** & 0.970 & 0.893 & 0.894 \\
25 & head & 0.939 & 0.939 & 0.927 & 0.927 \\
25 & head+tail & 0.947 & 0.947 & 0.899 & 0.899 \\
50 & tail & 0.951 & 0.951 & 0.893 & 0.894 \\
**50** & **head** & 0.950 & 0.951 & **0.933** & 0.933 \\
50 & head+tail & 0.947 & 0.947 & 0.893 & 0.894 \\
75 & tail & 0.939 & 0.939 & 0.870 & 0.872 \\
75 & head & 0.932 & 0.932 & 0.927 & 0.927 \\
75 & head+tail & 0.958 & 0.958 & 0.888 & 0.888 \\
100 & tail & 0.943 & 0.943 & 0.876 & 0.877 \\
100 & head & 0.935 & 0.935 & 0.899 & 0.899 \\
100 & head+tail & 0.958 & 0.958 & 0.887 & 0.888 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Bot detection performance for English and Spanish languages. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**MAXLEN** & **Seed** & **Best Macro-F1** & **Accuracy** \\ \hline
512 & 37,837 & 0.578 & 0.756 \\
512 & 20,713 & 0.570 & 0.708 \\
512 & 22,180 & 0.559 & 0.775 \\
**512** & **1261** & **0.605** & **0.780** \\
512 & 1878 & 0.542 & 0.744 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Stance classification performance, for English language. based on _heads_. However, for simplicity, the same value for ALEN and the strategy of constructing CLMs were chosen. In the results (Table 4), Macro-F1 and the accuracy metrics are presented, since the accuracy is almost the same value; this is because the human and bot classes are balanced.
# Finger-Gesture Recognition for Visible Light Communication Systems Using Machine Learning
## 4 System Implementation
### Gesture Waveform Capture  
As a proof of concept, the system was trained with five gestures with an increasing number of fingers as follows:  
* Reference Rx signal (absence of movement),
* pointing up-down with 1 finger,
* pointing up-down with 2 fingers,
* pointing up-down with 3 fingers, and
* pointing up-down with 4 fingers.  
The hand was moved up and down over a period of two seconds at a steady-rate corresponding to a natural hand gesture. As the separation between each finger is only about 3-5 mm, the sampling rate needs to be sufficiently high to capture the correspondingly short duration of light. The Rx signal is first down-sampled as the sampling rate is higher than the modulated light signal. The modulation is removed by finding the signal maxima and the resultant signal corresponding to 1-4 fingers present is shown in Figure 6 (top) to (bottom). The small peaks at the start of each cycle are due to the combined filtering response of the analogue and sample and hold circuitry in the digital storage oscilloscope. The response quickly decays and does not affect the operation of the system. The blocking of light by each finger results in low amplitudes and can be seen in each capture. In part, the accuracy can decrease as the number of fingers increase due to the re  
Figure 5: Photograph of optical component section.  
duced clarity of the raw signal. This reduction is partly offset however as classification improves when a signal has more unique features.
# A Model-Driven Approach for Conducting Simulation Experiments
## 5 Evaluation
### Increasing Productivity and Quality for Complex Experiments
#### 5.2.3 Sensitivity Analysis of an Electric Fields Model  
The third simulation study aims to compute the electric field distribution in a specific chamber [50]. Based on the computed field distributions, the biological response of the stimulated biological sample can be linked to certain specifications of the electrical stimulation setup. A global SA was used to evaluate the influence of the dielectric parameters on the electric field strength at specific locations of the cells (the model, the described SA experiment, and the analysis results are available at [[https://github.com/j-zimmermann/EMStimTools/tree/master/examples/experimentSchemas](https://github.com/j-zimmermann/EMStimTools/tree/master/examples/experimentSchemas)]([https://github.com/j-zimmermann/EMStimTools/tree/master/examples/experimentSchemas](https://github.com/j-zimmermann/EMStimTools/tree/master/examples/experimentSchemas)) (accessed on 26 July 2022)).  
Following the structure of the meta model, the modeler enters all the required information into the GUI (i.e., various material properties as factors and their value ranges) and assumes uniform distributions. A PC was chosen as the sampling method, as it is an often applied method in FEA, where simulation runs are computationally expensive. Figure 4C shows the generated code, which combines the Python package EMStimTools [68] with Uncertainy [67]. Again, the structured approach of this framework allows the user to quickly specify and execute the desired simulation experiment, even without prior knowledge of the Uncertainty package.  
The first- and total-order Sobol indices are shown in Figure 5C. The results suggest that a change in the permittivity of the medium does not influence the field strength. Hence, the user concluded that the permittivity can be neglected in future uncertainty analyses for this kind of problem and similar input parameters.
# A Mathematical Model for Efficient and Fair Resource Assignment in Multipath Transport
## 3 Mathematical Model
### Extension of the Objective Function for Fairness
#### Bff-Ii  
\[\text{maximize}:\quad Alloc+\beta\cdot M, \tag{11}\]  
where \(Alloc\), \(\beta\) and \(M\) are defined in the same way as for BSF.  
The constraints which have to be applied specifically for BFF are:  
* All flows on a shared bottleneck link should get the same allocation (Equations (A44) and (A45)).
* Optionally, all subflows of a multipath flow which share the same bottleneck link are assigned the same allocation (Equations (A46) and (A47)).  
After performing the aforementioned operation, BFF optionally takes a second step if there are flows which are represented by more than one subflow on a particular link. For each of these flows, BFF tries to share the capacity assigned to the respective flow equally among the subflows on that link.