esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Maximum Tangential Stress and the Mean Stress Criteria  
Regarding the analysis of brittle fracture in keyhole notched specimens under different loading conditions, a broad diversity of failure criteria has been employed in the literature. Herein, two of the most well-known fracture models, namely, the maximum tangential stress (MTS) and the mean stress (MS) criteria, are employed.  
Based on [44], the MTS criterion postulates that, in a brittle medium, fracture (or cracking) takes place once the tensile tangential stress at a specific critical distance (\(r_{c}\)) reaches the value of the critical stress (\(\sigma_{c}\)). In the case of brittle and quasi-brittle materials, this critical stress (\(\sigma_{c}\)) is usually considered to be the same as the material ultimate tensile strength (\(\sigma_{u}\)) [45]. In addition, according to [45], the critical distance of the material can be calculated by:  
\[r_{c}=\frac{1}{2\pi}\left(\frac{K_{lc}}{\sigma_{u}}\right)^{2} \tag{4}\]  
where \(K_{lc}\) is the material plane-strain fracture toughness.  
Another brittle fracture criterion utilized in the present investigation is the MS criterion. This was first proposed by [PERSON] [46] to estimate brittle fracture in cracked samples. The MS criterion proposes that fracture occurs when the average tensile tangential stress over a particular critical distance (\(d_{c}\)) from the notch tip meets the material critical stress (\(\sigma_{c}\)). [PERSON] [47] suggested Equation (5) for computing the critical distance (\(d_{c}\)):  
\[d_{c}=4r_{c}=\frac{2}{\pi}\left(\frac{K_{lc}}{\sigma_{u}}\right)^{2} \tag{5}\]  
When dealing with crack-like defects, under negative mode I loading conditions the crack faces are in contact with each other, so the pre-existing crack does not propagate and instead, the failure of the cracked specimen occurs through different mechanisms. Hence, the parameter \(K_{lc}\) becomes physically meaningless and, subsequently, Equations (4) and (5) (\(r_{c}\) and \(d_{c}\)) seem revoked under such loading conditions. Thus, another approach should be used for the determination of both \(r_{c}\) and \(d_{c}\).  
In this sense, [PERSON] et al. [32] proposed a new method based on experimental calibration, successfully estimating the compressive fracture toughness of round-tip V-notched PMMA samples. In this work, a similar calibration process is followed by performing finite element (FE) analyses of the tested specimens for the calculation of the existing compressive tangential stress distributions, from which the critical stress (\(\sigma_{c}\)) and the critical distances (\(r_{c}\) and \(d_{c}\)) may be inferred. Here, it should be noted that the critical stress will thus be obtained by two different methodologies: the direct application of the EMC, and the above-mentioned calibration process.
# Concert Halls as Nearly Adaptive Spaces
## 2 Main Adaptive Factors for Room Acoustic Quality in Concert Halls
### Electracoustic Systems and Lighting Equipment  
Nowadays, concert halls of medium or large size are provided with electroacoustic systems. Whatever their type and purpose is, there is a close interaction between the system and the room, and a concert halls' performance depends on the natural acoustical properties of the enclosure itself. Therefore, the installation and use of such a system requires also a careful acoustic design. Furthermore, without a knowledge of the acoustic factors responsible of sound reflections, reverberation, and other sound effects, it would hardly be possible to plan, install, and operate electroacoustic systems with optimal performance without considering the natural acoustic response of the room.  
Often, this equipment is installed in concert halls as a support for speech and electroacoustic music. Originating around the middle of the 20 th century, electroacoustic music was followed by the incorporation of electric sound production into compositional practice. The initial developments in electroacoustic music composition to fixed media during the 20 th century are associated with the activities of the Groupe de Recherche's musicales at the ORTF in Paris, the home of musique concrete; the Studio for Electronic Music in Cologne, where the focus was on the composition of elektronische Musik; and the Columbia-Princeton Electronic Music Center in New York City, where tape music, electronic music, and computer music were all explored. Practical electronic music instruments began to appear in the early 20 th century, followed by \"immersive audio systems\" which were designed to produce a fully immersive experience wherein the sound is perceived as coming from all around the audience seats. Electroacoustic music may require a shorter reverberation time than natural classical music.  
In the early 20 th century, new lighting systems also began to appear, which used the new LED lamp technology. Because of their low operating temperature, LED lamps do not lose as much heat as previous lamp systems, reducing their effects on the thermo-hygrometric conditions, especially when on stage for illuminating musician scores during concerts, a scenario that cannot be neglected in any case.
# A Framework for Market State Prediction with Ontological Asset Selection: A Multimodal Approach
## 8 Future Work  
Future research could explore several directions to address the current limitations and enhance the proposed framework's scalability, interpretability, and applicability:  
* Threshold Optimization: Building upon the dynamic thresholds established using quartiles, future research can investigate optimization techniques including machine learning models, clustering algorithms, or adaptive strategies like genetic algorithms.
* Integration of Exogenous Variables: Incorporating additional exogenous variables, such as geopolitical events or macroeconomic indicators, to capture broader market dynamics and enrich the framework's predictive capabilities.
* Adoption of Computational Optimization Tools: Employing tools like _TensorRT_ and _ONNX Runtime_ for model deployment could significantly enhance computational efficiency. These tools have demonstrated the potential to reduce latency and optimize hardware utilization, as shown by [PERSON] (2024) [43]. For instance, converting models to the _ONNX_ format has achieved performance gains, including frame rate improvements of up to 43 fps on devices like the Jetson Nano [42]. Applying these techniques in future iterations would ensure scalability in real-time trading applications with stringent computational constraints.  
* Explainable AI (XAI): Adopting explainable AI techniques, as proposed by [PERSON] (2023) [28], could improve the framework's interpretability, fostering greater trust and adoption among financial practitioners.
* Exploration of Alternative Data Sources: Expanding model inputs to include alternative data, such as social media sentiment and ESG (Environmental, Social, and Governance) indicators, could provide a more holistic view of market dynamics and improve predictive accuracy.  
This study lays a strong groundwork for enhancing quantitative trading methods while tackling major limitations. The suggested framework shows powerful predictive abilities and flexibility, offering substantial potential for further refinement and application in various financial settings.  
Methodology, I.F.C.B.; Writing--original draft, I.F.C.B.; Writing--review & editing, I.F.C.B., C.M.d.O.R. and J.F.L.d.O.; Supervision, C.M.d.O.R. and J.F.L.d.O. All authors have read and agreed to the published version of the manuscript.  
This paper was financed in part by the Coordenacao de Aperfeicoamento de Pessoal de Nivel Superior--Brazil (CAPES)--Finance Code 001, Fundacao de Amparo a Ciencia e Tecnologia do Estado de Pernambuco (FACEPE), the Conselho Nacional de Desenvolvimento Cientifico e Tecnologico (CNPq)--Brazilian research agencies.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
Authors were employed by the company FITec--Technological Innovations.
## 6 Current Problems and Research Difficulties
### Unstable Links  
In UWSNs, the sensor nodes are moving all the time because of the sea waves. As a result, the routing connection is quite unreliable and the architecture of UWSNs seems to be very dynamic. Links that are unreliable frequently lose packets and have limited throughput, which significantly increases energy usage. In order to provide reliable communicating data packet forwarding, it is crucial to design a steady link quality.
## 8 Issues and Recommendations for Future Research
### Architecture and Network Heterogeneity Issues  
Designing a universal network architecture that encompasses the heterogeneity of IoV is a challenging task. Mobile cloud services can be integrated with the ICN-based IoV to provide access to the information. However, this creates several challenges. Edge nodes execute a considerable amount of computational tasks, including content caching and networking. Using edge nodes, the processing burden and latency are significantly decreased. Furthermore, future smart vehicles will be equipped with various wireless interfaces that should be supported, including WAVE, LTE, 5G, and even satellite-based ones. For efficient content retrieval and communication, the network stack should exploit these multiple wireless interfaces. Interoperability between different variants of ICN, and with existing TCP/IP architecture, must be maintained as IP is currently the dominant protocol on the Internet. Supporting heterogeneity and interoperability is the major challenge.
## 2 Materials and Methods
### SEM Measurements  
Scanning electron microscope (SEM) measurements were performed on the _Phenom Pro SEM_ with a magnification range of 160-350 000\(\times\), a maximum theoretical resolution of around 8 nm, and acceleration voltages of 5, 10, and 15 kV. High-resolution images were also obtained with the _Thermo Fisher Scientific Helios 5 UX SEM_ with a magnification up to 350 000\(\times\) and a maximum theoretical resolution of 0.7 nm.
## 3 Model Validation
### Aggregate System Performance
#### 3.2.1 Synthetic (Vehicular) Mobility Model
Furthermore, we note that the offloading efficiency is weakly dependent on the node density, and only low values of \(N\) lead to a slightly worse offloading efficiency and content latency. This can be explained by observing that the number of content copies stored in the local caches of mobile nodes depends mostly on the number of nodes that have already requested the content. The results depicted in Figure 8b confirm that our model accurately captures the content latency and that the higher the offloading efficiency, the lower the content latency. Finally, Figure 8c shows that our model slightly underestimates the number of copies stored in the devices' local caches when the sharing timeout is high. Figure 7: Vehicular mobility model: system performance vs. content request rate for \(T_{c}=5\) s and \(N=100\). **(a)** Offloading efficiency (\(\epsilon\)); **(b)** Content latency (sec); **(c)** Number of copies.
## 4 Experiments and Results
### Parametric Analysis
#### 4.3.3 The Effect of Weight Factors \(\lambda_{1}\) and \(\lambda_{2}\)  
The weight factors \(\lambda_{1}\) and \(\lambda_{2}\) are used to control the weight of sparsity and intra-class divergence, respectively. As presented in Table 6, we set four different cases to analyze the effect of weight factors \(\lambda_{1}\) and \(\lambda_{2}\). On this basis, we conducted several experiments in Scenario_A, and the recognition accuracy of several cases in Scenario_A is shown in Figure 8.  
In addition, it should be noted that, in this experiment, we set \(iteration=45\), and the number of atoms in the class-specific dictionary is set to 40.  
On the whole, the recognition accuracy of each task in Case_D is the highest, and the result in Case_A is second only to that in Case_D. The results in Case_B and Case_C are worse. It can be found that, in Case_B and Case_C, intra-class divergence takes a larger proportion in the objective function, while sparsity takes a larger proportion in Case_A and Case_D. Based on the above phenomenon, we think that sparsity has a greater impact on the recognition accuracy than intra-class divergence. In addition, in the objective function, the intra-class divergence with a small proportion can improve the recognition accuracy, while the intra-class divergence with a large proportion will have a negative impact on the experimental results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Case\_A** & **Case\_B** & **Case\_C** & **Case\_D** \\ \hline \(\lambda_{1}\) & 0.1 & 0.1 & 0.1 & 0.1 \\ \(\lambda_{2}\) & 0 & 0.1 & 0.02 & 0.01 \\ \(\lambda_{1}/\lambda_{2}\) & - & 1 & 5 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Combination of \(\lambda_{1}\). and \(\lambda_{2}\) in four different cases.  
Figure 8: The recognition accuracy of several cases in Scenario_A.  
Figure 7: The recognition accuracy under different atom numbers (Scenario_A).
# Study of Deformation and Fault Mechanisms in Bedding-Locked Rock Slope
## 4 Data Analysis  
The DH3821 data acquisition system collects real-time displacement data at a frequency of 0.5 s. Figure 16 displays the horizontal displacement of various parts of the slope for different loading intensities.  
Figure 16 displays displacement sensors DH1, DH3, and DH5 located at the bottom, middle, and top of the slope sliding surface, respectively. The curve illustrates that an increase in the strength of the vertical load on the top of the model results in an increase in the lateral displacement on the side slope.  
During the initial loading stage, shear deformation primarily occurred at the top and middle sections of the slope, while the foot of the slope experienced minimal to no deformation. Under increased load, the slope experienced a sudden small change in shear deformation due to its low shear strength. This deformation gradually increased. In the middle of the slope, deformation increased steadily, but it remained smaller than that of the upper part. At the foot of the slope, deformation was minimal or nonexistent.  
When the load strength increased beyond a certain point, the slope's sliding surface experienced significant shear deformation due to the absence of retaining on the free surface. Figure 4 shows that as the load strength increased step by step, DH5 at the top of the slope underwent large-scale shear deformation and fault at a load strength of 0.786 MPa, followed by DH3 in the middle of the slope at 0.857 MPa. Finally, DH1, located at the locking section of the slope foot, experienced abrupt lateral displacement at a load strength of 0.929 MPa. As the load increased, the displacement at the sliding surface of the slope decreased until the slope completely destabilized.  
Figures 17 and 18 show the analysis of signal strength and energy at S1-S7 during the test based on real-time photography and video recording, which was performed using a 3D laser scanner. The analysis is based on the increase in load and the passage of time during the test.  
Figure 16: Change in horizontal displacement of slope.  
Figure 17: Signal intensity comparison on slope.  
Figure 18: Energy comparison on slope.
## 3 Results
### Seasonal Variability of Field Observations and Remote Sensing Data
Gcc behaves opposite to EVI, as it tends to increase in the dry season and decrease in the wet season. Figure 2: Temporal pattern of variables for the study series, RJA site. (**A**) Precipitation in black and evapotranspiration in red; (**B**) Water table depth in black and soil moisture in red; (**C**) Net radiation in black and temperature in red; (**D**) SPI6 index; (**E**) Total litter; (**F**) Stem growth; (**G**) EVI index in black and long-term EVI index in red; (**H**) Gcc index in black and long-term Gcc index in red. The stem growth (Gr) and EVI presented a relationship (\(R^{2}\) = 0.32, \(p\)-value = 0.006), being observed that stem growth increases when the photosynthetic activity also presents an increase (Figure 3A). When observing the EVI and Gr long-time series (average monthly), the relationship was \(R^{2}\) = 0.70 (\(p\)-value = 0.001) (Figure 3B). The Gcc and Gr long-term averages showed \(R^{2}\) = 0.31 (\(p\)-value = 0.058, Lag 1) (Figure 3C), and \(R^{2}\) = 0.18 (\(p\)-value = 0.063, Lag 3) during the period of this research (2015-2017) (Figure 3D). Precipitation is the climatic factor that best explains the variability in long-term EVI, with \(R^{2}\) = 0.58 (\(p\)-value = \(<\)0.001), compared to net radiation (\(R^{2}\) = 0.30, \(p\)-value = 0.002). To the Gcc, precipitation explains 32% (\(p\)-value = 0.002) of variance, and net radiation explains 21% from Gcc (\(p\)-value = 0.015). The analysis of the historical averaged monthly Gcc and total litter production, Gcc explained 45% of the variation of the total litter with one month lag (\(p\)-value = 0.0169) (Figure 4A); however, this relationship is weakened and not significant when tested month by month in the study period (2015 to 2017) (\(R^{2}\) = 0.01; \(p\)-value = 0.68). Average monthly EVI does not follow changes in canopy structure associated with monthly leaf flush average by Gcc; on the contrary, an out-of-phase trend was observed between these two indices (Figure 2G,H). EVI \(\times\) Gcc was \(R^{2}\) = 0.03 \(p\)-value = 0.61 (Figure 4B), and EVI \(\times\) total litter was \(R^{2}\) = 0.14 \(p\)-value = 0.22 (Figure 4C). However, EVI with 3 months lag had \(R^{2}\) = 0.64 to total litter (\(p\)-value \(<\) 0.001) (Figure 4D).
## 3 Results and Discussion
### Xps
#### 3.1.2 Plots of XPS Intensity of Core Spectra Against Scratching Environments
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Environments** & **Temperature** & \multicolumn{3}{c}{**Binding Energy of the Components (eV)**} \\ \cline{3-5}  & **(\({}^{\circ}\)C)** & **O\({}^{2}\)-** & **OH\({}^{-}\) and OH\({}^{\delta-}\)** & **H\({}_{2}\)O** \\ \hline \multirow{4}{*}{Air} & 25 & 530.6 & 532.3 & 533.4 \\  & 200 & 530.7 & 532.5 & 533.9 \\  & 339 & 530.6 & 531.9 & 533.8 \\  & 25 & 530.5 & 532.0 & 533.2 \\ Benzene & 200 & 530.6 & 531.8 & 533.0 \\  & 339 & 530.6 & 531.8 & 533.1 \\  & 25 & 530.5 & 532.3 & 534.2 \\ Cyclohexane & 200 & 530.5 & 531.7 & 533.2 \\  & 339 & 530.7 & 532.0 & 533.4 \\  & 25 & 530.6 & 531.9 & 533.4 \\ Water & 200 & 530.7 & 532.2 & 533.6 \\  & 339 & 530.9 & 531.9 & 533.5 \\  & 25 & 530.8 & 532.2 & 533.5 \\ Methanol & 200 & 530.7 & 531.9 & 533.3 \\  & 339 & 531.1 & 532.2 & 533.6 \\  & 25 & 530.7 & 532.2 & 533.8 \\ Ethanol & 200 & 530.8 & 532.2 & 533.3 \\  & 339 & 530.9 & 532.1 & 533.3 \\  & 25 & 530.7 & 532.3 & 533.5 \\ Acetone & 200 & 530.7 & 532.0 & 533.3 \\  & 339 & 530.7 & 531.6 & 533.3 \\ \hline \hline \multicolumn{5}{c}{Average} & 530.7 & 532.1 & 533.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Temperature dependence of binding energies of three components of O\({}^{2}\), OH\({}^{-}\) and OH\({}^{\delta-}\), and H\({}_{2}\)O in the O 1\(s\) spectrum for iron surfaces scratched in different environments. Figure 6: Dependence of the number of electrons counted, XPS intensity of the O\({}^{2-}\) component in the O 1\(s\) spectra after the PE measurement at 25, 200, and 339 \({}^{\circ}\)C for real iron surfaces on the scratching environments: (1) air, (2) benzene, (3) cyclohexane, (4) water, (5) methanol, (6) ethanol, and (7) acetone.
# A New Cascade-Correlation Growing Deep Learning Neural Network Algorithm
## 4 Experimental Results
### Classification Problems at Real-World Data Sets
Sensitivity, specificity and accuracy were determined using the ROC curve threshold which was the closest point to the upper left corner of the ROC plot (\(0,1\)). However, based on the Delong test, this difference in AUC values was statistically significant (\(p\)-value \(<0.05\)). The value of AUC obtained for the CCG-DLNN algorithm was significantly greater than that for the GP-DLNN method (Delong test \(p\) values \(<0.05\)). The significance level and Z statistic between the AUC of CCG-DLNN and GP-DLNN algorithms for all data sets is presented in Table 7.  
To evaluate the speed of the three algorithms (CCG-DLNN, GP-DLNN, and CCNN), we compared the runtime of these algorithms when solving the LIDC-IDRI problem. Figure 14 shows that our proposed algorithm CCG-DLNN is not faster than the CCNN algorithm at the beginning steps and it is faster at the final stages. GP-DLNN is the slowest algorithm compared to the other two algorithms. Figure 11: Curves of loss obtained by CCG-DLNN, GP-DLNN and CCNN on the LIDC-IDRI problem. Figure 10: Curves of testing rate obtained by CCG-DLNN, GP-DLNN and CCNN on real-world classification problems. Figure 12: Curves of loss obtained by CCG-DLNN, GP-DLNN and CCNN on the LIDC-IDRI problem. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Data Sets** & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \hline  & **CCG-DLNN** & **CCNN** & **GP-DLNN** \\ \hline LIDC-IDRI & **95** & 93.6 & 92.3 \\ \hline DH & **84** & 77 & 82 \\ \hline HCV & **94** & 83 & 90 \\ \hline HD & **90** & 83 & 86 \\ \hline BCW & **93** & 92 & 90 \\ \hline BCC & **100** & 90 & 91 \\ \hline LD & **87** & 69 & 73 \\ \hline AI & **100** & 92 & **100** \\ \hline PID & **80** & 65 & 74 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Summary of simulation results. Figure 13: The ROC curves for CCG-DLNN, GP-DLNN and CCNN algorithms on the LIDC-IDR data set. Figure 14: Runtime of GP-DLNN, CCG-DLNN and CCNN algorithm on the LIDC-IDRI data set. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Data Sets** & \multicolumn{2}{c}{**AUC**} & \multicolumn{4}{c}{**Sensitivity (Avg. )**} & \multicolumn{4}{c}{**Specificity (Avg. )**} \\ \hline  & **CCG-DLNN** & **CCNN** & **GP-DLNN** & **CCG-DLNN** & **CCNN** & **GP-DLNN** & **CCG-DLNN** & **CCN** & **GP-DLNN** \\ \hline LIDC-IDRI & **0.92** & 0.84 & 0.86 & **0.93** & 0.89 & 0.87 & **0.79** & 0.67 & 0.71 \\ \hline DH & **0.9
# Research on Path Planning Based on the Integrated Artificial Potential Field-Ant Colony Algorithm
## 4 ACA Integrated with the APF Method
### Pruning Method for Path Optimization and Update  
To further reduce unnecessary turning points and shorten the path length, this paper adopts the pruning method to cut off redundant turning points for path optimization and update. Its principle is shown in Figure 2.  
The unpruned path is a\(\rightarrow\)b\(\rightarrow\)c\(\rightarrow\)d. Since there is no obstacle between a and c, the path a\(\rightarrow\)c is used to replace a\(\rightarrow\)b\(\rightarrow\)c, and the redundant turning point b is pruned, resulting in the pruned path a\(\rightarrow\)c\(\rightarrow\)d. As there is an obstacle between points a and d, the path flows from point a to point c to point d, and keeps the inflection point c unchanged. In this way, the length of the path can be reduced and the path can be further optimized.