esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Relevant Features of Avionics and Test Benches
#### 2.1.2 Components of an Avionics System  
All avionics systems have one thing in common. They all implement a power supply, a control unit, input and output interfaces, and access to the central on-board data network [1] (p. 260). These components are relevant for functional testing.  
Figure 4 shows the generic architecture of an avionics computer as an example. The components shown are available in the SFCC. The central processor unit and the bus interface to the ARINC 429 network are relevant for the present work. The other components are shown for the sake of completeness.  
Control unit: The control unit executes the programmed instructions in the airplane. In its additional memory, the signal values are stored for the subsequent processing and generation of output values for the actuators. For this, the programmed instructions are implemented in software. Thus, the software is the basis for the (flight) control system. It implements the mathematical functions, algorithms, tasks for data transformation or  
Figure 4: Rough architecture of a control computer.  
Figure 3: Model of a real-time system.  
calculation. The software represents the core of the device under test (DuT) for functional testing and for the certification. The goal of the verification is to prove the correctness of the software running here.  
Connection to the on-board data network: The aircraft's internal on-board data network transmits data for the exchange between the individual systems. This includes data from velocity sensors, attitude sensors and others. This data is generated by the air data inertial reference system (ADIRS) and exchanged via the ARINC 429 or ARINC 664 bus.  
During the execution of a test, the components outside the boundary of the system under test, such as the ADIRS, are simulated. The simulation generates the data and puts it on the bus system via the I/O interfaces to supply the DuT with the data. The data sent by the DuT to the on-board network is recorded and continuously checked for correctness. Thus, the on-board network represents one access point for the ARINC 429 I/O of the test bench.
###### Abstract  
At present, the automatic generation of tolerance types based on rule-based reasoning has an obvious characteristic: for the same assembly feature, tolerance items are recommended that satisfy all feature characteristics, with a large number of recommendations. For this reason, automatically selecting tolerance types and reducing designer autonomy remains a challenging task, especially for complex mechanical products designed using heterogeneous CAD systems. This article proposes a tolerance specification design method for the automatic selection of assembly tolerance types. Based on the construction of a hierarchical representation model of assembly tolerance information with tolerance-zone degrees of freedom (DOFs), a semantic model of geometric tolerance information with tolerance-zone DOFs and a meta-ontology model of assembly tolerance information representation are constructed. Descriptive logic is used to express the attribute relationships between different classes in the assembly tolerance information meta-ontology model, and screening inference rules are constructed based on the mechanism for selecting assembly tolerance types based on tolerance-zone DOFs. On this basis, a process for selecting assembly geometric tolerance types based on the ontology of tolerance-zone DOFs is formed. Finally, the effectiveness and feasibility of this method were verified through examples.  
tolerance specifications; assembly body; screening; degree of freedom; ontology Article
# Analysis of TDMP Algorithm of LDPC Codes Based on Density Evolution and Gaussian Approximation
## 5 Simulation Results
### The Actual Algorithm Simulation of the BP Algorithm and the TDMP Algorithm  
In this section, we simulate the LDPC codes with the code length 2304. With the code rates 2/3B and the SNR 3.0 (dB), the relationship between BER and iterations is shown in Figure 12. Here, SNR was not the same as that in Sections 5.1 and 5.2, because in the actual algorithm simulation, it cannot reach to the expected BER when SNR is 2.0 (dB). Though SNR was different, it did not influence the analysis of the convergence trend of the BP algorithm and the TDMP algorithm. As shown in Figure 12, the BER convergence speed of the TDMP algorithm in the actual algorithm simulation was also faster than the BP algorithm. When the actual algorithm can decode correctly at the code rate 2/3B under different SNRs, the iterations needed for the BP algorithm and the TDMP algorithm are shown in Figure 13.  
Figure 11: The relationship of BER and iterations: (**a**) the 802.16 standard; (**b**) DTMB standard.  
Figure 12: The relationship between BER and iterations.  
The analysis of Figure 13 was similar to that of Figure 4 in Section 5.1. The analysis of the results from the actual algorithm simulation can prove the correctness of the density evolution and Gaussian approximation analysis of the TDMP algorithm. Table 1 shows the number of iterations required for the BP and TDMP algorithms in DE, GA, and actual simulation when the code rate was 2/3 under the 802.16 standard.  
The relationship between the number of iterations required by BP and TDMP algorithm can be seen in Table 1 when the desired BER was reached under three simulations. When the SNR was greater than 2 dB, the number of iterations of the BP algorithm was about twice that of the TDMP algorithm.
## 4 Parametric Study
### Pipeline Design Size Effect
#### 4.2.1 Virtual Anchor Segment (VAS) Length Effect  
For pipelines with no installed triggers, the virtual anchor segment length \(L_{\text{S}}\) depends on the soil resistance. For pipelines that have triggers installed to control lateral buckle, the \(L_{\text{S}}\) is the space between the two triggers. The buckling deformation and amplitude of bending stress under different virtual anchor section lengths are shown in Figure 8a,b.  
When the length of the anchor section of the pipeline becomes larger, the amplitude of the buckle along the corresponding point of the pipeline increases, and the buckling extreme point will also move in the positive direction of the \(x\)-axis. This is followed by a decrease in the sliding segment distance when the trigger spacing is unchanged. As can be seen from Figure 8b, as the anchor segment lengths \(L_{\text{S}}\) of the pipeline keeps growing, the maximum bending stress increases, and the wavelength of the overall bending stress \(d\sigma_{\text{Mm1}}\) becomes shorter. This leads to more drastic changes in the bending stress over a local area. At the same time, as the anchor segment lengths \(L_{\text{S}}\) becomes longer, the bending stress waveform moves toward the fixed point position. Therefore, the sensitivity of the  
Figure 8: Effect of the virtual anchor segment length on the buckling configuration (\(\Delta\)T = 70 \({}^{\circ}\)C): (**a**) deformed shapes; (**b**) bending stress.  
bending stress increases with the design length of the pipe, and the external bending stress is higher. This phenomenon means that under the fixed-fixed beam model, the probability of buckling instability is greater. Therefore, increasing the spacing of the triggers increases the risk of the pipes failure.
## 5 Performance Evaluation  
### Simulation Environment  
We evaluate the performance of two protocols: MTARP and EMTARP, using the network simulator OPNET modeler version 18.7. Even though M-ATTEMPT and MTARP are the only comparative protocols with EMTARP in the aspects of mobility and temperature aware properties, the comparison with M-ATTEMPT is not observed since MTARP shows better performance than M-ATTEMPT already. The simulation parameters are summarized in Table 9. The network size is set to 2 m \(\times\) 2 m. The number of nodes constituting the network is 25, the number of mobile nodes is 10, and the number of sinks is 1. All nodes are deployed in the network while considering the human body. Moreover, the sink is placed at the center of the network. The transmission range of nodes is set to 30 cm. The temperature estimation model of TARA is used to estimate the temperature of nodes. The temperature threshold is set to 43 \({}^{\circ}\)C [20]. We define a node which exceeds the threshold as a hot spot. The mobile node moves every 3 s as to describe slow walking. HELLO packets are propagated to neighboring nodes every 5 s. We performed the simulation 100 times with 95% confidence interval.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \(Id\) & \(H\) & \(Temp\) & \(Link\) & Final Score & Rating \\ \hline N1 & 0.082 & 0.485 & 0.178 & 0.745 & 3 \\ N2 & 0.164 & 0.342 & 0.118 & 0.624 & 4 \\ N3 & 0.109 & 0.539 & 0.148 & 0.796 & 1 \\ N4 & 0.082 & 0.393 & 0.297 & 0.772 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Final score and rating.
# Ka-Band Doppler Scatterometry: A Strong Wind Case Study
## 3 Sea Surface Cross-Section
### NRCS Decomposition
It is usually postulated that \"modulating\" waves are separated from \"short-scale\" waves by a so-called dividing wavenumber, \(k_{\rm d}=k_{\rm r}/d\), with \(d\approx 3\) to 4. To account for uncertainties of the \(\overline{\zeta^{2}}\), several estimates were employed. These include the [PERSON] spectrum model [66], \(\overline{\zeta^{2}}=4.6\cdot 10^{-3}\ln(k_{\rm d}/k_{\rm p})/2\), with equal up- and cross-wind mss components and peak wavenumber, \(k_{\rm p}=g/U^{2}\) or \(k_{\rm p}=1\) rad m\({}^{-1}\) (the latter was observed in fetch-limited conditions at northerly offshore wind), as well as the Cox and Munk [67] clean surface and slick-covered empirical mss parameterizations. Excluding the Cox and Munk clean surface parameterization, which apparently overestimates the mss of modulating waves, the above range of mss estimates results in only \(<\)0.3 dB uncertainty in the final curvature spectrum estimates. For \"typical\" radar measurement conditions, the NRCS (PD) error is about 1-2 dB and thus it dominates the uncertainty of wave curvature spectrum estimate (Figure 9a).
# The Effect of Depth Information on Visual Complexity Perception in Three-Dimensional Textures
## 3 Results  
Table 3 shows the mean and standard deviation of the visual complexity score for each texture. We analyzed the relationship between texture and depth in determining visual complexity using two-way, repeated-measures ANOVA.  
The analysis of the differences in the perceived complexity of the textures revealed significant key impacts of depth (\(F(1,\,24)=22.41\), \(p<0.001\), partial \(\eta^{2}=0.48\)) and texture (\(F(1.58,\,38)=10\), \(p=0.001\), partial \(\eta^{2}=0.29\)), and a significant interaction between depth and texture (\(F(2,\,48)=5.13\), \(p=0.01\), partial \(\eta^{2}=0.18\)) (Table 1).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Test** & **Statistic** & **Confidence** \\ \hline \multirow{6}{*}{Complexity between depth and texture factors} & \multirow{6}{*}{Two-way repeated measures ANOVA} & depth: & \(p<0.001\), _partial \(\eta^{2}=0.483\)_,_ \\  & & & \(p=\text{Nothing}\), \(\varepsilon=1\); & power = 0.995, \\  & & \(F(1,\,24)=22.41\); & & corrected by [PERSON]; \\ \cline{2-3}  & & & \(\text{texture}\): & \\ \cline{2-3}  & & & \(\text{texture}\): & \(p<0.001\), _partial \(\eta^{2}=0.29\)_,_ \\ \cline{2-3}  & & & \(p=0.01\), \(\varepsilon=0.792\); & power = 0.953, \\ \cline{2-3}  & & \(F(1.58,\,38)=10\) & & corrected by [PERSON]; \\ \cline{2-3}  & & & \(\text{interaction}\): & \(p<0.01\), \\ \cline{2-3}  & & & \(\text{Mauchly’s Test}\)\(\chi^{2}(2)=0.84\), & \(\text{ }\  
This indicated that the characteristics of the texture and depth information affected visual complexity perception. Further, depth information had different degrees of impact on visual complexity perception for different textures.  
We indicated the pairwise comparison for interaction between the depth and texture factors, corrected using a Bonferroni adjustment. As shown in Table 2, there was a statistically significant difference between the complexity scores for each texture in 2D and 3D. The results revealed that the participants seemed to recognize the complexity in the 3D textures more (\(M=3.67\), \(SD=\pm 1.54\)) than in the 2D textures (\(M=2.61\), \(SD=\pm 0.94\)) ((\(t(74)=4.732\), \(p<0.001\), [PERSON]'s \(d=0.546\))).  
Figure 3 shows the result of the pairwise comparison for mean complexity between 2D and 3D textures. For all textures, the mean complexity scores in 3D were significantly higher than those in 2D.  
In our results, for _door_, a significant difference (\(p<0.001\), [PERSON]'s \(d=0.934\)) was observed between the complexity scores in 2D (\(M=2.60\), \(SD=\pm 0.866\)) and 3D (\(M=3.88\), \(SD=\pm 1.301\)). For _dot_, a significant difference (\(p=0.001\), [PERSON]'s \(d=0.769\)) was observed between the complexity scores in 2D (\(M=2.16\), \(SD=\pm 0.688\)) and 3D (\(M=2.88\), \(SD=\pm 1.054\)). For _stone_, a significant difference (\(p<0.001\), [PERSON]'s \(d=0.863\)) was observed between the complexity scores in 2D (\(M=3.08\), \(SD=\pm 1.038\)) and 3D (\(M=4.24\), \(SD=\pm 1.855\)).
###### Abstract  
In this paper, we propose a non-localization routing protocol for underwater wireless sensor networks (UWSNs), namely, the triangle metric based multi-layered routing protocol (TM2 RP). The main idea of the proposed TM2 RP is to utilize supernodes along with depth information and residual energy to balance the energy consumption between sensors. Moreover, TM2 RP is the first multi-layered and multi-metric pressure routing protocol that considers link quality with residual energy to improve the selection of next forwarding nodes with more reliable and energy-efficient links. The aqua-sim package based on the ns-2 simulator was used to evaluate the performance of the proposed TM2 RP. The obtained results were compared to other similar methods such as depth based routing (DBR) and multi-layered routing protocol (MRP). Simulation results showed that the proposed protocol (TM2 RP) obtained better outcomes in terms of energy consumption, network lifetime, packet delivery ratio, and end-to-end delay.  
link quality; pressure sensors; residual energy; sink node; supernodes; triangle metric; underwater wireless sensor network +
Footnote †: journal: Sensors
## 2 Materials and Methods
### Spectral Analysis  
The power spectral density (PSD) of the entire signal for each of the bipolar montages was calculated using EEGLAB's spectopo() function. This function makes use of the FFT algorithm to extract and plot the PSD. The signal was subsequently divided into five frequency bands: delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (36-44 Hz) and the mean power in each band was computed. These ranges were selected according to [28] and were also used in the connectivity analysis. A Shapiro-Wilk test was applied to the data to check for normality and subsequently, a Mann-Whitney U-test was used to compare band power medians between the AD and HC groups.
# Personalized Contextual Information Delivery Using Road Sign Recognition
## 2 Related Work
### Chain of Thought (CoT)  
Chain of Thought reasoning is a technique in artificial intelligence that breaks down complex problems into intermediate steps, enabling models to perform step-by-step reasoning. This approach mimics human cognitive processes and has been particularly useful in addressing tasks requiring logical inference and contextual understanding [8].  
CoT is significant for enhancing systems that require the integration of multiple data sources, such as road sign recognition systems. Traditional systems often stop at detecting objects or recognizing text, providing only raw outputs. CoT, however, enables the system to link these outputs sequentially, producing meaningful insights. For example, a system using CoT can combine detected road sign objects such as \"Mokpo\" and \"27 km\" with contextual user data, such as current speed or destination, to deliver a more actionable output [11].  
The application of CoT in multimodal systems represents a key advancement in generating contextualized, user-specific information. By structuring the reasoning process, CoT facilitates the development of systems that go beyond static outputs, creating insights tailored to complex, real-world scenarios [8; 11].
# Voice Maps as a Tool for Understanding and Dealing with Variability in the Voice
## Appendix A Implementations  
At the time of writing, there are several commercially available systems that construct voice range profiles, most of which render and analyze the contour only, although a few also map the density and a small selection of other voice metrics, but without deeper analysis such as outlined in the present article. They can be found with an internet search for \"voice range profile\" or \"phonetogram\" and have not been listed here.  
Most of the studies here used as examples were made using the then current versions of the freeware FonaDyn [46], which was first written by [PERSON] at KTH in 2015 for his M.Sc. project in computer science [55]. Author [PERSON] has continued to develop FonaDyn, with incidental contributions from students. FonaDyn is released to the public domain ([[https://www.kth.se/profile/stern](https://www.kth.se/profile/stern)]([https://www.kth.se/profile/stern](https://www.kth.se/profile/stern)), accessed on 1 November 2022), as an open-source, proof-of-concept workbench for analyses based on voice mapping; not as a commercial, supported product. It is in continuing development.  
For clinical use, [PERSON] has written RecVox, which is freely downloadable from www.tolvan.com (accessed on 1 November 2022). It maps six metrics and is currently the standard acquisition system for clinical voice recording at the Karolinska University Hospital at Huddinge, Stockholm, Sweden.  
Commercial systems that perform mapping of metrics over the voice field include _Voice Profiler_ (by author [PERSON]), which is no longer commercially available but still in use, especially in The Netherlands. Voice Profiler maps from 4 to 26 metrics, depending on the version. Another commercial system is _Phog_, which is part of the VoiceJournal system from Neovius Signalsystem, www.neovius.se (accessed on 1 November 2022) and reportedly is still available. _Phog_ maps only the metrics density, crest factor and a perturbation metric.
## 2 Data  
### Study Area  
The study area is the Basilicata region located in Southern Italy (Figure 1). The regional territory is about 10,000 km\({}^{2}\) mostly characterized by mountains (47%) and hills (45%) with a small flat percentage (8%). The climate is prevailing Mediterranean humid in the Apennine Mountain range and Mediterranean dry in the hilly and flat areas. The Regional Forest Atlas reports for the forest area of the Basilicata an extent of around 355,409 hectares, in full accordance with the National Inventory being drawn up by Forests and Carbon Reservoirs which attributes approximately 345,000 hectares of wooded area to the Basilicata region. Deciduous oak forests occupying 51.8% of the territory represent the majority of the forest cover, followed by beech forest (8.4%), Mediterranean macchia (7.9%), shrublands (6.9%), and other deciduous broad-leaved forests (5.5%); the remaining 19.5% are mainly made up of evergreen broad-leaved forests, Mediterranean pine forests, chestnut stands, hygrophilous forests, and garrigue. The rest of the territory is mainly devoted to agricultural activities.  
Figure 1: Spatial distribution of fires. Each circle represents the location of events within a specific municipality within Basilicata region. The size of the circles is proportional to the average burned area, while the color of the circles represents the number of fires that occurred in the event location. Larger circles indicate a greater average area, and the color gradient, ranging from blue to red, indicates an increasing number of events.
# Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns
## 7 Experiments and Results  
The experiments was executed 30 independent time then the average of the results was calculated. The experiments will be conducted in three steps:  
* The best number of clusters is experimented for each dataset, and the best model for each cluster is found.
* The proposed approach is experimented based on utilizing simple and common classifiers (NB, \(k\)-NN, and DT).
* The proposed approach is experimented based on utilizing powerful ensemble classifiers (Bagging, AdaBoost, RF, and XGB).  
For \(k\)-NN, the number of neighbours is set to 3 as this value showed best performance based on the training data compared to other values (i.e., 1, 5, 7, and 9). For the ensemble classifiers, the selected base classifier is decision tree, and the ensemble size is set to 100. The latter settings yield the best performance based on the training data with the least computation effort.
# A Sensitivity Study of POD Using Dual-Frequency GPS for CubeSats Data Limitation and Resources
## 3 Orbit Determination under Different Scenarios
### Length of Arc
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Arc Length/Products** & **IGS Final [cm]** & **IGR [cm]** & **IGC [cm]** \\ \hline
24 h & – & 4.8 & 3.2 \\ \hline
12 h & 0.6 & 5.1 & 3.1 \\ \hline
6 h & 1.0 & 5.6 & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: 3D RMSE of the phase-based reduced-dynamic orbits with different arc lengths and using different IGS products. 10 s observation data with a duty-cycle of 100% and a mean satellite number of 9 were used for the processing. When shortening the arcs to 6 h and applying worse IGS products, a larger increase in the RMSE can be observed. Figure 14 shows the 3D RMSE when processing with the arc lengths of 24 h, 12 h, and 6 h applying different IGS products. The x-intervals between each two vertical dashed lines represent different cases having the same sampling interval, which are sorted from the highest duty-cycle of 100% to the lowest duty-cycle of 20% first for the mean satellite number of 9, and then similarly for the mean satellite numbers of 7 and 6, respectively. The peaks observed in Figure 14 are thus mostly caused by the low duty-cycles of 40% and 20%. From Figure 14 it can be observed that when shortening the arcs from 24 h (green) to 12 h (blue) or 6 h (red), the increase in the 3D RMSE could be dramatic under bad data conditions, especially when using the IGR and IGC products. While this increase is still limited to around 1 dm when shortening the arcs from 24 h to 12 h, cases with increase above 2 dm appear when shortening the arcs to 6 h and when applying the IGR or IGC products. In cases that the duty-cycle is not lower than 40% and the sampling interval is not larger than 60 s, the 3D RMSE is generally within or around 1 dm when applying the IGS final products or the IGC products, even when processing with arcs with a length of 6 h. Due to the large sampling interval of the IGR satellite clocks, reaching a 3D RMSE of this level generally requires a duty-cycle not lower than 60% when processing 6 h arcs applying the IGR products. Figure 14: 3D RMSE of the reduced-dynamic orbits when processing with the arc lengths of 24 h, 12 h, and 6 h applying (**a**) the IGS final products, (**b**) the IGS rapid (IGR) products, and (**c**) the IGS real-time (IGC) products. Between each pair of the dashed lines, the cases are sorted from the highest duty-cycle of 100% to the lowest duty-cycle of 20%, first for the mean satellite number of 9, and then similarly for the mean satellite numbers of 7 and 6, respectively.