esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Experiment of Machining Error Decomposition from Coarse to Fine
### Residual Systematic Error Decomposition by the Wavelet Thresholding Method  
During the decomposition of the simulation processing error using the VMD algorithm, the components IMF2, IMF3, IMF4, IMF5, IMF6 and IMF7 did not meet the filtering conditions and were weakly correlated IMF components. During the decomposition of the coarse systematic error \(X_{1}\) using the EMD algorithm, component IMF2 did not meet the filtering conditions and was a weakly correlated IMF component. Since the weakly correlated IMF components of the two decomposition processes may also contain a small systematic error, to ensure the accuracy of the decomposition results, they were reorganized and decomposed using the wavelet thresholding method to obtain the systematic error \(X_{3}\).  
The systematic error \(X_{3}\) decomposed by the wavelet thresholding method and the systematic error \(X_{2}\) decomposed by the EMD algorithm were reorganized to obtain the fine systematic error \(X_{4}\), which was the part of the systematic error decomposed from coarse to fine from the simulated machining error, and its value and distribution are shown in Figure 8.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 6 Computing the Minimum Frame with a Genetic Algorithm
### Results Obtained  
We have implemented the GA using standard C code running on a notebook with a i5 CPU, 4 GB RAM and Ubuntu 16.04 version as operating system. In all cases we use a 20 random arrays to produce the initial slot allocations, with PMX crossover technique and 0.3% probability of mutation. At the same time, we use the GLPK solver [65] to compute the minimum frame length using the ILP model. We choose small and medium size networks with different topology configurations. The layouts were thought for different applications since the distribution of nodes change significantly. In the majority of the cases, the GA achieved the same result as the ILP method, but consuming much less time. Table 7 shows the results for the configurations presented in Figure 4. What is more important is that using the GA can facilitate the overlapping mechanism to reduce the effective period of the network. All the computation times (CT) in the Table are given in milliseconds and the frame length in slots.
# Research on Decision-Making Methods for Autonomous Navigation in Inland Tribitary Waterways
## 5 Discussion
### Comparison Analysis  
This study develops an autonomous navigation model for inland tributary waterways through the integration of a ship collision avoidance model utilizing exhaustive course-speed maneuver combinations and a ship trajectory tracking model. Although maritime collision avoidance has been extensively investigated [43], the existing research predominantly examines open-water scenarios and single-vessel encounters, often overlooking the critical influence of environmental factors on navigational decision-making [45; 46]. Conversely, the development of autonomous navigation systems for inland tributary environments remains significantly understudied, particularly regarding the unique challenges posed by narrow, dynamic waterways with complex hydrological characteristics. Multi-vessel collision avoidance demonstrates significantly greater complexity compared to single-ship scenarios. While prior studies on multi-vessel collision avoidance often oversimplify ship dynamics [34], our MMG-integrated trajectory predictor accounts for inertial drift and propulsion latency, enabling evasion paths that align with real-world vessel maneuverability. The failure to account for the dynamic maneuvering behaviors of TSs may render collision avoidance strategies vulnerable to abrupt course alterations or speed adjustments [20], ultimately compromising the reliability of evasive actions. While collision avoidance strategies leveraging propeller speed modulation remain underexplored--particularly the hydrodynamic coupling between variable-speed dynamics and evasion trajectories [12]--our hybrid control framework ensures safer clearance margins through synchronized propeller RPM adjustments, enabled by high-fidelity prediction of the OS's transient states during maneuvers.  
To address the existing disconnect between theoretical collision avoidance algorithms and real-world maritime operational requirements, this study develops an improved MMG framework for simulating high-fidelity ship dynamics. The proposed integrated autonomous navigation method incorporates comprehensive considerations of inland waterway environmental factors and navigation rule constraints, enabling robust multi-vessel collision avoidance and coordinated path following in complex inland river scenarios.
# Searching Open-Source Vulnerability Function Based on Software Modularization
###### Abstract  
Vulnerable open-source component reuse can lead to security problems. At present, open-source component detection for binary programs can only reveal whether open-source components with vulnerabilities are reused, which cannot determine the specific location of vulnerabilities. To address this problem, we propose BMVul, an open-source vulnerability function detection based on the software modularization method, which is oriented to binary programs. BMVul performs binary modularization by the overlapping clustering method DBM based on directed graph, then uses feature comparison technology to carry out modular software component analysis. After creating open-source component vulnerability function set through function signature, BMVul detects vulnerability function in the binary modules reusing open-source components. The experimental results show that compared with the component detection based on Louvain modularization and B2 SFinder, BMVul improves the precision by 3.16% and 59.57%, respectively. Moreover, the precision of unique binary module matching is improved by 39.43% compared with the Louvain method. The F1 score is improved by 8.45% compared to B2 SFinder. Module-level detection narrows the search space of vulnerability functions, thereby reducing the workload of open-source vulnerability detection, which is of great significance for software security analysis.  
open-source vulnerability; binary-to-source matching; program modularization; community detection; software security +
Footnote †: journal: Applied Sciences
## 3 Results
### Jensen-Shannon Divergence  
Another method of measuring the similarity of two probability distributions is the so-called Jensen-Shannon divergence (JSD). The JSD is based on the KL divergence, however it exhibits a few but important differences with it, namely it is symmetric and it always has a finite value. The square root of JSD, also defined as the JS distance, is given by [23],  
\[JSD(P||Q)=\frac{1}{2}D(P||M)+\frac{1}{2}D(Q||M) \tag{18}\]  
where \(M=(P+Q)/2\), and \(D(P\mid M)\) and \(D(Q\mid M)\) are the KL divergences as defined by Equation (11). Provided that at least one of the two measured distributions uses base 2 logarithm, JSD is upper bounded by 1, namely  
\[0\leq JSD(P||Q)\leq 1 \tag{19}\]  
The JSD, also called the _capacity discrimination_, can be interpreted as the total KL divergence to the average distribution, \(M\)[22]. Mathematically, it can also be defined as a Jensen divergence of the Shannon information h [22],  
\[JS(p;q)=h\bigg{(}\frac{p+q}{2}\bigg{)}-\frac{h(p)+h(q)}{2} \tag{20}\]  
where the notation \";\" is used instead, to emphasize its symmetry. Other symmetrizations of the KL divergence can be found in the literature, including the extrinsic Jensen-Shannon divergence, the Jeffreys divergence and the extended Kullback-Leibler divergence [24; 25], among others. The JSD can also be defined as the mutual information between a random variable \(X\) and the binary indicator variable \(Z\), used to switch between \(P\) and \(Q\), whose mixture distribution is associated with \(X\).  
In order to calculate the JSD of each theoretical distribution against the observed RSSI data we employed Equation (14). The \(M\) parameter was deduced by taking the average between each model and the RSSI data, a total of five. Following that, we calculated the KL divergence between each distribution, including the RSSI data distribution, and the corresponding \(M\) parameter, which is a total of ten distinct KL values. Finally, the JSD is calculated and the results are shown in Figure 7.  
Figure 6: Probability plot for Weibull distribution against RSSI data.
# Recent Progress in On-Chip Erbium-Based Light Sources
### Heterogeneous III-V Light Sources  
Heterogeneous integration is the process which combines III-V devices with silicon-based optoelectronic devices via wafer-bonding, including direct bonding, molecular bonding, metal bonding and polymer bonding [27; 28]. Compared with direct epitaxial growth, heterogeneous integration is more versatile and flexible to integrate different materials from the perspective of the materials system, size, and processing simplicity.  
In 2005, [PERSON] et al., demonstrated the first optically pumped heterogeneous laser on Si [29]. One year later, the research group realized the first electrically pumped heterogeneous laser [30]. Since then, the performance of heterogeneously integrated III-V on silicon components has continuously improved over the past decade. In 2015, [PERSON] et al., realized a heterogeneous InP laser with sub-kHz quantum noise-limited linewidth [31]. In 2020, [PERSON] et al., achieved a heterogeneously integrated InP DFB laser with threshold currents as low as 32 mA [32]. Heterogeneous integrated light sources have been used in commercial products by Intel since 2016. In 2020, a 400 Gbps PAM-4 fully integrated DR4 silicon photonics transmitter with four heterogeneously integrated DFB lasers has been demonstrated for data center applications [33].  
Heterogeneous III-V light source has since developed rapidly in recent years due to the improved fabrication processes. However, it is limited by the complicated fabrication procedures, smaller yields, and incompatibility with traditional CMOS production line. For wider application in the future, the processing technology still needs to be further improved, and the processing cost needs to be reduced.
# The Influence of Binaural Room Impulse Responses on Externalization in Virtual Reality Scenarios
## 3 Experimental Results
### Results for Experiment II  
Figure 5 shows the externalization ratings for the VR-based experiment with box plots. Each column shows the results for a different source direction (left: 0\({}^{\circ}\); right: 45\({}^{\circ}\)). The top and bottom rows denote the externalization results for virtual rooms B and C, respectively. Similar to the results obtained in Experiment I, the Friedman test confirmed the significant effect of BRIR types on externalization for each source direction and virtual room (\(p\ll 0.05\)). Additionally, the results for the condition \"BRIR-C\" are significantly lower than for the other two conditions (a series of Wilcoxon tests: \(p<0.02\)). Although the median values of externalization are slightly higher for the condition \"BRIR-A\" than for the condition \"BRIR-B\", the difference becomes smaller compared to that in Experiment I ([PERSON] tests: \(p>0.5\) for both directions and virtual rooms). When listening in these two visual-only virtual environments, the externalization ratings of binaural sounds rendered by BRIR-B increase, but the sounds generated by HRIRs (BRIR-C) can still not be perceived as well externalized. It seems that the auditory information provided by the local listening environment does not play a dominant role for determining externalization in VR scenarios, as ratings for the conditions \"BRIR-A\" and \"BRIR-B\" are both high. Furthermore, regardless of the visual impression of the virtual rooms, subjects consistently gave higher externalization ratings for reverberant sounds (BRIR-A and BRIR-B) than for anechoic sounds.  
Figure 4: Box plot of externalization ratings for stimuli generated with different BRIR types in Experiment I. The left and right panels show the externalization results for 0\({}^{\circ}\) and 45\({}^{\circ}\) sound sources, respectively. The median externalization ratings (red lines), 25 th and 75 th percentiles (bottom and top edges of the boxes), whiskers (\(1.5\times\) interquartile range) and outliers (red crosses) are shown in the figure.
###### Abstract  
The \"bare runway\" principle aims to ensure passenger and employee safety by making runways more usable during winter conditions, allowing for easier removal of contaminants like snow and ice. Maintaining runway operations in winter is essential, but it involves considerable cost and environmental impacts. Greater knowledge about the de-icing and anti-icing performance of runway de-icing products (RDPs) optimizes operations. The ice melting test, as per the AS6170 standard, gauges the rate at which an RDP dissolves an ice mass to determine RDP effectiveness. Here, we introduce a novel integrated methodology for assessing RDP-related ice melting. We combine laboratory-based procedures with infrared thermography and Raman spectroscopy to monitor the condition of RDPs placed on ice. The plateau of maximum efficiency, marked by the most significant Raman peak intensity, corresponds to the peak minimum temperature, indicating optimal RDP performance. Beyond this point, RDP efficacy declines, and the system temperature, including melted contaminants and RDP, approaches ambient temperature. Effective RDP performance persists when the ambient temperature exceeds the mixture's freezing point; otherwise, a freezing risk remains. The initial phases of RDP-ice contact involve exothermic reactions that generate brine, followed by heat exchange with surrounding ice to encourage melting. The final phase is complete ice melt, leaving only brine with reduced heat exchange on the surface. By quantifying these thermal and chemical changes, we gain a deeper understanding of RDP-related ice melting, and a more robust assessment can be provided to airports using RDPs.  
ice melting; Raman spectroscopy; infrared thermography; runway de-icing products; winter maintenance
# Weld Defect Detection of a CMT Arc-Welded Aluminum Alloy Sheet Based on Arc Sound Signal Processing
## 3 Experiment and Result
### Welding Experiment and Analysis
#### 3.2.3 Time-Frequency Analysis of the Arc Sound Signal  
Figure 7 shows the time domain waveform, frequency spectrum, and time-frequency spectrum. The arc sound signal within the red rectangle in Figure 7a corresponds to one CMT cycle, where three sound pulse signals correspond to three power changes in one CMT cycle. After the sound pulses, the sound signal still maintains a certain amplitude of vibration, and this can be explained as the change in arc shape and length and melt pool vibration during the CMT cycle. In Figure 7b, it can be seen that the frequency band of the CMT arc sound is wide and contains complex frequency components. This also illustrates the complexity of the CMT welding process. As seen in Figure 7c, the time-frequency spectrum shows periodic variation in the time axis, corresponding to the period of the CMT, and the spectrum of each arc sound frame is shown on the frequency axis. The time-frequency spectrum contains both the frequency spectrum of each period's arc sound and the changes in the frequency over time.  
Figure 6: CMT cycle.  
Figure 7: Time domain waveform, frequency spectrum, and time–frequency spectrum of arc sound. **(a)** Arc sound. **(b)** Frequency spectrum of arc sound. **(c)** Time-frequency spectrum of arc sound.  
The arc sound signal of the GTAW welding process was analyzed in the literature [16]. The GTAW arc sound signal consists of a pulse phase and a random variable phase. The frequency amplitude of the pulse phase is more active and is mainly concentrated in the low-frequency part. In comparison with GTAW, the arc sound signal of the CMT welding process has a wider frequency domain and contains more complex frequency components.  
Figure 8 shows the time-frequency spectrum of the three weld seam states. Figure 9 compares the average amplitude of the three arc sounds at different frequency bands. In Figure 8b, along the time axis in the 2-6 kHz bands, multiple peak amplitudes occur. In Figure 9, the frequency amplitude of burn-through state is larger than that of the normal and lack of fusion states in the 2-6 kHz bands. This is because the weld gap becomes larger, resulting in burn-through, a low-frequency sound, and burn-through discontinuity. The frequency amplitude of the lack-of-fusion state is lower than that of the normal and burn-through states in most frequency bands. Since the weld center is offset, the molten pool shape changes, and there is power leakage.
# Globally Optimal Facility Locations for Continuous-Space Facility Location Problems
## 3 Location Estimation Based on the Manhattan Distance
### Determination of the Globally Optimal Facility Locations
#### 3.5.3 Model for the Capacitated Multi-Facility Problem with Fixed Cost  
Generally, the clustering-based method is applied to solve the uncapacitated continuous-space multi-facility location problem. Even though some studies have applied the adjusted clustering-based algorithms to solve the capacitated multi-facility problem [30; 31], it is still impossible to guarantee the global optimum of the solution. In this sense, obtaining the globally optimal locations for the facilities is warranted when the practical factor of the limited capacities is considered. Based on the constructed candidate locations for the facilities, the globally optimal locations for the capacitated facilities also can be selected from the mesh points. Let \(r_{i}\) be the quantity of demand at demand point \(i\) and \(Cap_{l}\) be the capacity of the facility \(l\). Note that \(r_{i}\) can be considered as the weight of demand point \(i\). Then, the capacitated continuous-space multi-facility problem can be formulated as the following mathematical model \(\mathcal{M}3\).  
\(\mathcal{M}3\):  
\[\text{Min Obj}_{3}=c\sum_{l=1}^{\text{E}}\sum_{i=1}^{n}r_{i}D_{ll}t_{il}+ \text{F}\sum_{l=1}^{\text{E}}g_{l} \tag{49}\]  
subject to  
\[\sum_{l=1}^{\text{E}}g_{l}\leq n \tag{50}\]  
\[\sum_{l=1}^{\text{E}}t_{il}=1\ \forall\ i\in I \tag{51}\]  
\[t_{il}\leq g_{l}\ \forall\ i\in I,\ l\in L \tag{52}\]  
\[\sum_{i=1}^{n}r_{i}t_{il}\leq Cap_{l}\ \forall\ l\in L \tag{53}\]  
The objective function in (49) minimizes the total cost including the travel cost and fixed cost. The first constraint in (50) guarantees the maximum number of facilities. Constraints (51) and (52) are the same as the Constraints (43) and (44). Constraint (53) restricts the facility capacity.
# Investigation of Energy Harvesting Capabilities of Metglas 2605 SA1
## 4 Conclusions  
Due to their robustness and potential electromechanical coupling, magnetostrictive materials are a competitive solution for vibrational energy harvesters. Yet, their realistic implementation, mainly due to the material price, as well as the deep understanding of their conversion efficiency, is still an open question.  
In this work, an investigation of a magnetostrictive material Metglas 2605 SA1 was proposed based on a theoretical framework and experimental data because of its low price (thirty times lower than Galfenol and Terfenol-D) and soft magnetic behavior. The energy conversion capability was assessed under high levels of mechanical stress and excitation fields. Results showed that for low magnetic excitation, which is typical in realistic self-powered implementation using magnets, Metglas 2605 SA1 is a premium choice, showing relatively high converted energy in the range of hundreds of \(\mu\)J/cm\({}^{3}\)/cycle through thermodynamic cycles, and reaching up to 1.2 mJ\(\cdot\)cm\({}^{-3}\) per cycle for a magnetic field of 4 kA/m and a compressive mechanical stress of 30 MPa. In addition, in the case of low-level energy harvesting under magnetically biased conditions, the magnetoelastic coefficient \(\Delta B/\Delta\sigma\) was found to be at maximum under a very low excitation field of 500 A/m, and with a similar value compared to Terfenol-D.  
Hence, while not as widely used as a high magnetostriction material, Metglas 2605 SA1 appeared to exhibit a very attractive trade-off between cost and performance while being highly suitable in realistic applications using low magnetic excitation.  
Conceptualization, G.S. and M.L.; Methodology, M.L.; Investigation, Y.L., B.D. and G.S.; Data curation, Y.L.; Writing--original draft, Y.L.; Writing--review & editing, B.D., G.S., K.M. and M.L.; Supervision, M.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors would like to thank Metglas(r) Inc. for supplying Metglas 2605 SA1 materials. This work has been performed in the framework of the MATSURI project of the ELyT Global IRN.  
The authors declare no conflict of interest.  
Figure 12: Comparison of variation of \(\Delta B/\Delta\sigma\) of Metglas 2605 SA1 with the model (**a**) in the range of 0–4 kA/m and (**b**) zoomed in on the range of 0–1 kA/m.
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
## 4 Experiments and Discussion  
### Datasets  
**PALVIO dataset.** The public panoramic visual odometry dataset PALVIO [30] was adopted to verify the effectiveness of the proposed method. The dataset was acquired using two PAL cameras operating at 30 Hz, providing an FoV of \(360^{\circ}\times(40^{\circ}\sim 120^{\circ})\) supplemented by angular velocity and acceleration measurements recorded at 200 Hz. Note that the PAL camera has a central blind spot, and the optical axis is perpendicular to the direction of motion, capturing most environmental data in equatorial regions, as shown in Figure 9. According to the camera moving speed, the scenarios in the dataset were separated into fast-moving scenarios (ID01, ID05, and ID07) and slower-moving scenarios (ID02, ID03, ID04, ID06, and ID08).  
**SCP-Image dataset.** In addition to the PALVIO dataset, the custom SCP-Image dataset was created to evaluate the generalizability of the proposed method. Unlike the PALVIO dataset, images in the SCP-Image dataset lack the central blind spot, and the optical axis is aligned parallel to the movement direction. Therefore, the environmental information would be captured across both pole and peripheral regions. Specifically, the SCP-Image dataset includes panoramic images with a resolution of \(800*848\) captured at 30 Hz by the Intel RealSense T265 (Intel. Co., Santa Clara, CA, USA) with a \(360^{\circ}*165^{\circ}\) field of view. Bilinear interpolation is applied to enhance the resolution to \(1600*1696\), and a 17-level spherical uniform grid is constructed accordingly. Simultaneously, the dataset also contains the IMU data that were recorded at 200 Hz. The dataset scenario involves a \(6.5\,\mathrm{m}*10\,\mathrm{m}\) indoor environment, yielding 12 distinct sequences with ground truth trajectories captured by a NOKOV motion-capturing system. We open sourced the SCP-Image dataset using the following link: [[https://github.com/[PERSON]/SCP_VIO_dataset](https://github.com/[PERSON]/SCP_VIO_dataset)]([https://github.com/[PERSON]/SCP_VIO_dataset](https://github.com/[PERSON]/SCP_VIO_dataset)) (accessed on 10 April 2025).  
Figure 9: The PALVIO and SCP-Image datasets exhibit distinct feature movement patterns due to differences in camera motion: features in the PALVIO dataset are concentrated in the equatorial region, while features in the SCP-Image dataset are transformed from the poles to the equator.