esa-sceva/satcom-chunk-collection huggingface dataset:
# Multi-Path Routing Algorithm Based on Deep Reinforcement Learning for SDN
## 4 Trust-Based Proximal Policy Optimization (TBPPO)
### Reward Module  
The Reward Mechanism's role is to assign rewards to the network with respect to multiple objectives. By adjusting the weighting factors \(\alpha_{o}\) and the rewards \(r_{o}\) associated with different objectives, the system can guide the network's learning process. The formula for calculating the reward \(R(\tau)\) obtained by the network at the \(\tau\)-th time window is represented as:  
\[R(\tau)=\sum_{o\in O}\alpha_{o}r_{o}(\tau) \tag{13}\]  
here \(\alpha_{o}\) represents the weight of reward type o, and \(r_{o}(\tau)\) represents the reward value of type o obtained during the \(\tau\) time window. O represents the set of reward types, including the delay reward, delay variation reward, KL trust value reward, node diversity reward, and node redundancy reward. These can be expressed as:  
\[O=\{delay,variation,diversity,safety,repeat\} \tag{14}\]  
Here, \(delay\) represents the average delay, \(variation\) represents the delay variation, \(diversity\) represents the node diversity, \(safety\) represents the route trust value, and \(repeat\) represents the node redundancy. The delay reward is defined as the average delay of the current path group \(A(\tau)\) and it is defined as:  
\[r_{delay}(\tau)=\frac{1}{L}\sum_{l=0}^{L}D_{M(\tau)}(a_{l})=\frac{1}{L}\sum_{l =0}^{L}\sum_{e\in a_{l}}d_{M(\tau)}(e) \tag{15}\]  
Here, \(r_{delay}(\tau)\) represents the delay reward at time \(\tau\), \(D_{M(\tau)}(a_{l})\) is the delay of route \(a_{l}\) under \(M(\tau)\), and \(d_{M(\tau)}(e)\) is the delay of edge \(e\) under \(M(\tau)\). The delay variation reward is expressed as the average delay variation for each path in the current path group \(A(\tau)\) and is formulated as:  
\[r_{variation}(\tau)=mean\Big{[}\big{|}D_{M(\tau)}(a_{i})-D_{M(\tau)}\big{(}a_{ j}\big{)}\big{|}\;\Big{|}\;i<j\;\wedge\;i,j<L\Big{]} \tag{16}\]  
Here, \(r_{variation}(\tau)\) represents the delay variation reward at time \(\tau\), and \(mean\) indicates the calculation of the mean value. The KL trust reward is defined as the mean of the minimum safety node trust values for each path in the current path set \(A(\tau)\) and it can be expressed as:  
\[r_{safety}(\tau)=\frac{1}{L}\sum_{l=0}^{L}\max_{r_{i}\in a_{l}}(\textit{ Trust}(\tau,r_{i})) \tag{17}\]  
Next, the node diversity reward is defined as the average of the variance of the number of nodes of each type in each path within the current path group \(A(\tau)\) as follows:  
\[r_{t}(\tau)=\frac{1}{L}\sum_{l=0}^{L}var(a_{l})=\frac{1}{L}\sum_{l=0}^{L}\sum _{l\in T}\big{(}Count_{a_{l}}(t)-\overline{Count_{a_{l}}}\big{)}^{2} \tag{18}\]  
where the node redundancy reward is represented by the number of nodes that appear repeatedly in the current routing group \(A(\tau)\).
# Effect of Axial Modification on the Meshing Performance of Involute Developid Gear Pair
## 4 Meshing Performance Analysis of the Beveloid Gear Pair with Axial Modification
### Influence of Different Modification Amounts on Meshing Performance  
Figure 9 illustrates the distribution of equivalent stress of the beveloid gear under the condition of the same maximum modification length but different modification amounts. Figure 10 depicts the variance of the maximum equivalent stress of the tooth flank under varying modification amounts. The parameters of each modification scheme are presented in Table 2.  
Figure 8: Equivalent stress distribution on the tooth flank of the beveloid gear without modification.  
Figure 9: _Cont._  
The results demonstrate that the equivalent stress distribution on the beveloid gear tooth flank prior to and following modification is essentially unchanged. As the mod  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Schemes** & **Modification Parameter** & **Pinion** & **Gear** \\ \hline \multirow{2}{*}{Scheme 1} & Amount (\(\upmu\)m) & 5.0 & 5.0 \\  & Length (mm) & 5.2 & 4.8 \\ \hline \multirow{2}{*}{Scheme 2} & Amount (\(\upmu\)m) & 10.0 & 10.0 \\  & Length (mm) & 5.2 & 4.8 \\ \hline \multirow{2}{*}{Scheme 3} & Amount (\(\upmu\)m) & 15.0 & 15.0 \\  & Length (mm) & 5.2 & 4.8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Modification scheme group (1).  
Figure 10: Maximum equivalent stress under varying modification amounts.  
Figure 9: Equivalent stress distribution under varying modification amounts. (**a**) Scheme 1; (**b**) Scheme 2; (**c**) Scheme 3.  
ification amount increases, the contact area of the tooth flank gradually decreases and concentrates in the middle of the tooth flank of the beveloid gear. The maximum equivalent stress on the tooth flank exhibits a tendency to decrease.  
In conjunction with Figures 9 and 10, it can be observed that in Scheme 1, edge load persists on both sides of the tooth face, albeit with a diminished range and maximum equivalent stress compared to that of the non-modification tooth face. In Scheme 2, the edge load has been eliminated, and concurrently, the stress distribution at the edge position has become more concentrated, resulting in a further reduction in the maximum equivalent stress compared to that observed in Scheme 1. In Scheme 3, there is no discernible stress distribution at the edge position, and the reduction in maximum equivalent stress is not significant when compared to that observed in Scheme 2.  
The above analysis indicates that the edge load phenomenon will be enhanced following an axial modification. Furthermore, the stress change is not significant when the modification amount exceeds 10 \(\upmu\)m. Consequently, the modification parameter of Scheme 2 is more effective in improving the meshing performance of the tooth.
## 4 Discussion
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12083712/s1](https://www.mdpi.com/article/10.3390/app12083712/s1)]([https://www.mdpi.com/article/10.3390/app12083712/s1](https://www.mdpi.com/article/10.3390/app12083712/s1)), Figure S1: Cimatec_XCOV19 normal/abnormal classification model block diagram. Figure S2: Cimatec_XCOV19 COVID-19/abnormal classification model block diagram. **Author Contributions:** Conceptualization, A.F. and E.G.S.N. ; data curation, A.F., L.A. and T.M. ; formal analysis, D.F. and E.G.S.N. ; funding acquisition, A.F. and E.G.S.N. ; investigation, A.F. and L.A.; methodology, A.F., D.F. and E.G.S.N. ; project administration, A.F. and E.G.S.N. ; resources, T.M. ; software, A.F. and L.A.; supervision, A.F. and E.G.S.N. ; validation, R.B. and E.G.S.N. ; writing--original draft, A.F. ; writing--review and editing, A.F., L.A., D.F., T.M., R.B. and E.G.S.N. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was funded by ABDI, SENAI, EMBRAPII, REPSOL SINOPEC BRASIL grant \"Missao contra a COVID-19 do Edital de Inovacao para a Industria\". **Institutional Review Board Statement:** The retrospective study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of \"Escola Superior de Ciencias da Santa Casa de Misercordia de Vitoria--EMESCAM\" (STU# 34311720.8.0000.5065 07/08/2020) and was granted a waiver of written informed consent. **Informed Consent Statement:** Patient consent was waived in accordance with the evaluation of the Institutional Review Board considering that researchers undertake to maintain confidentiality, not disclosing the names of the participants and, using codes to identify the data generated by them to avoid violating participant privacy. **Data Availability Statement:** The model's source code, the external validation dataset with 1158 CXR images, and a complementary file sheet with the radiologists' analysis are freely available on the research group GitHub page at [[https://github.com/CRIA-CIMATEC/covid-19](https://github.com/CRIA-CIMATEC/covid-19)]([https://github.com/CRIA-CIMATEC/covid-19](https://github.com/CRIA-CIMATEC/covid-19)) (accessed on 20 February 2022). **Acknowledgments:** We gratefully acknowledge the support of SENAI CIMATEC AI Reference Center and the SENAI CIMATEC/NVDIA AI Joint Center for scientific and technical support, the SENAI CIMATEC Supercomputing Center for Industry Innovation for granting access to the necessary hardware and technical support, Repsol Sinopec Brasil, ABDI, SENAI and EMBRAPII for providing the funding for this research, HP Brazil for providing support, and Hospital Santa Izabel, MedSenior and HM Hospitals for providing data for this research. **Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 2 Materials and Methods
### Test Samples
#### 2.2.4 Determination of Friction Coefficients  
The static friction coefficient measurement setup is illustrated in Figure 2. To determine the static friction coefficient between the material and steel plate, the test sample was placed on the steel plate and lightly compacted to ensure full contact. The plate was initially positioned at 0\({}^{\circ}\), and then gradually inclined until the onset of sliding motion. The tilt angle (\(\theta\)) at this threshold was recorded. Ten replicate trials were conducted, and the average value was calculated. The static friction coefficient (\(\mu\)) was derived using the following formula:  
\[\mu=tan\theta, \tag{3}\]  
where \(\mu\): Coefficient of friction (dimensionless); \(\theta\): Tilt angle of the inclined plane apparatus (\({}^{\circ}\)).  
To determine the static friction coefficient between wheat straw feedstock particles, the material was evenly spread and adhered to a steel plate, forming a cohesive wheat straw layer. The aforementioned tilt table test was repeated, and the critical inclination angle (\(\theta\))was recorded. Using Equation (3), the static friction coefficients were calculated as 0.52 (straw-straw) and 0.47 (straw-steel).  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Sample No** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Density (kg/m\({}^{3}\)) & 45.18 & 45.72 & 47.40 & 44.77 & 45.17 & 46.32 & 45.38 & 46.55 & 45.85 & 11.21 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Density of wheat straw feedstock.  
The rolling friction coefficient was determined using the same instrumentation and methodology as described for static friction measurements. The inclination angle at the onset of rolling motion was recorded using the tilt table apparatus, with ten replicate trials conducted to calculate the mean value. Applying Equation (3), the rolling friction coefficients between wheat straw feedstock particles (straw-straw) and between wheat straw and steel (straw-steel) were determined to be 0.18 and 0.19, respectively.
# Urban Road Anomaly Monitoring Using Vision-Language Models for Enhanced Safety Management
###### Abstract  
Abnormal phenomena on urban roads, including uneven surfaces, garbage, traffic congestion, floods, fallen trees, fires, and traffic accidents, present significant risks to public safety and infrastructure, necessitating real-time monitoring and early warning systems. This study develops Urban Road Anomaly Visual Large Language Models (URA-VLMs), a generative AI-based framework designed for the monitoring of diverse urban road anomalies. The IntemVL was selected as a foundational model due to its adaptability for this monitoring purpose. The URA-VLMs framework features dedicated modules for anomaly detection, flood depth estimation, and safety level assessment, utilizing multi-step prompting and retrieval-augmented generation (RAG) for precise and adaptive analysis. A comprehensive dataset of 3034 annotated images depicting various urban road scenarios was developed to evaluate the models. Experimental results demonstrate the system's effectiveness, achieving an overall anomaly detection accuracy of 93.20%, outperforming state-of-the-art models such as IntemVL2.5 and ResNet34. By facilitating early detection and real-time decision-making, this generative AI approach offers a scalable and robust solution that contributes to a smarter, safer road environment.  
urban road safety; vision large language model; road anomaly; real-time monitoring; safety management Article
## 4 DE-KELM-Based Gearbox Fault Classification
### Gearbox Fault Diagnosis Experimental Data
#### 5.2.1 Vibration Signals Decomposition through GWO-VMD and VMD, Respectively
From the above two contrast experiments, it can be seen that when GWO-VMD was used to decompose the signal, after which the same signal reconstruction and feature extraction were conducted, the obtained fault features can improve the fault diagnosis accuracy under the same fault classification method. Contrast Experiment II--Fault Classification by DE-KELM and KELM, Respectively, on the Same Fault Feature Vectors  
To demonstrate that the proposed DE-KELM fault classification method can achieve higher diagnosis accuracy than KELM, in this section, the same fault features obtained in Section 5.2 through GWO-VMD decomposition were used to test DE-KELM and KELM, respectively. The contrasting experimental process is illustrated in Figure 22.  
The training and testing sets were divided in the same way as in the previous experiment. The classification results by DE-KELM are presented in Figure 20, the results by KELM on the same training and testing sets are shown in Figure 23, and the accuracy of the two classification methods is summarized in Table 14.  
As can be seen from Figure 23 and Table 14, for the 160 testing samples, 4 of them were misclassified by KELM, and the classification accuracy on the testing set was 97.5%, which is lower than 98.125%, the accuracy obtained by DE-KELM. The above experimental results show that the proposed DE-KELM method can achieve higher classification accuracy on the same fault features than KELM. Figure 21: Fault classification results by DE-KELM on the feature matrix calculated through VMD-based decomposition method. Figure 22: Process of contrast experiment II.
## 3 Laboratory Testing
The decrements of echo SNR at 45 kHz and 50 kHz are 2.7 dB (FE)/3.7 dB (BE) and 5.3 dB (FE)/5.4 dB(BE) compared to the processed signal at 40 kHz, respectively. The average sidelobe levels at 45 kHz and 50 kHz are increased by 1.5 dB (FE) and 4.7 dB (FE) compared to the processed signal at 40 kHz, respectively. The intensities of the signal echos from the front and back pipe ends are significantly increased after the echos are processed by fitted matched filter. The processed echo signal changes significantly at different frequencies by observing the echo signals, as indicated in Table 4.  
Figure 10 shows the comparison of SNR and PSL results which contains all codes designed in the experiment. Figure 10a shows the comparison among the theoretical SNR results, the FE SNR results and the BE SNR results. Figure 10b shows the comparison among the theoretical PSL results, the FE PSL results and the BE PSL results. The changing trend of the experimental results is consistent with the theoretical results. The proposed code method has a higher SNR in experiment results than theoretical results (especially at the frequency of 40 kHz). The main reason is that the device sensor's operational frequency is around 40 kHz, so the performance of exciting and receiving signals at this frequency is better than at other frequencies at a frequency range of interest. Meanwhile, From Figure 10, the all results show that theoretical calculations are close to experimental ones, especially the theoretical and experiential testing results of the SNR have a better agreement at 3.5 and 7-bit Baker codes 8 and 16 bit Golay code pairs, and 30 kHz b3g4 code pair when compared to others (see Figure 10a). In Figure 10b, the PSL results of 3.5 and 7-bit Barker codes due to their shorter code lengths, and b3g4 code pair at 35 kHz and 45 kHz have a better agreement than others. However, it also shows that there is a distinct difference (up to 10 dB) for the b3g4 code pair at 30 kHz and 40 kHz. The reason of that the higher order wave modes appearance as a coherent noise level leads to the non-dispersed travelling wavefield propagated with following some dispersive waves. The influenced effect for the PSL SNR could be more obvious when the echo signal is received from a longer distance at the operating frequency of 40 kHz (see Figure 10b) The relevant research and improvements were investigated in [23,28]. Overall, the BCG code has higher intensity of main lobe and lower intensity of peak sidelobe level than other coded excitation methods according to the signal quality criterion. Based on these results, there is a good agreement between theoretical and experimental results. From Figure 9, we can easily find that the developed BCG coded excitation method for a pure wave mode T(0,1) generation and prorogation has more effective behaviour in the circumferential direction.
# A Learning-Based Methodology to Optimally Fit Short-Term Wind-Energy Bands
## 2 Wind-Power Uncertainty and the Use of Forecasts
### Additional Accuracy Coming from Numerical Forecasts  
This section experimentally analyzes the benefits of using short-term wind-power forecastings based on numerical simulations of atmosphere's wind flows. As a measure of the error incurred we use the \(||.||_{1}\) distance between forecasts and actual realizations. Hence, the total energy error for the period starting on day \(d\) (denoted \(err^{d}\)) is computed as: \(err^{d}=\sum_{t=1}^{T}|w_{t}^{d}-p_{t}^{d}|\), being \(w_{t}^{d}\) and \(p_{t}^{d}\), respectively, the actual power (PLF) for the day \(d\) as seen \(t\) hours ahead and its corresponding forecasted value. On the other hand, \(T\) is the time horizon of the forecasts: 72 h in our examples. In Section 3 we explain the convenience of using such an error measure.  
This work used information for the Uruguayan grid, which was of public domain by the time computations were realized. Two independent forecast providers are considered: Garrad Hassan and Meteologica. Their common samples span around 300 days, starting in early 2016. A third forecast -about which we elaborate later on--that uses a purely statistics analysis (following PSF ideas [16]) is built to benchmark statistical and numerical forecast. In spite of its lower performance as an isolated technique, we see in Section 4 that this final forecast (referred to as PSF44) increases the overall quality of a convex combinations of filtered forecasts.  
Regarding statistical moments of the series, the mean value of the PLF error samples is 6.80 for Garrad Hassan and 5.99 for Meteologica, with respective variances of values 2.48 and 2.21. On the other hand, those figures for the PSF44 are: 13.99 and 4.52; notoriously worse than numerical forecasts. Complementarily, histograms in Figure 5 represent error distributions for each case, reinforcing the idea that Meteologica's forecast slightly outperforms Garrad Hassan's, while both are much better than PSF44.  
Figure 4: Histogram of daily wind energy samples [daily PLF upon the leftmost] and 30% most atypical realizations for Uruguayan wind-power over a year [rightmost].
## 6 Conclusions  
This work improves [PERSON] LRSV estimator by proposing an adaptive \(\kappa\) estimator. We differentiate between the direct sound presence/absence hypotheses, and derive the frame conditional direct sound presence probability \(p(l)\) using Bayes rule. Under the direct sound absence hypothesis, the estimated \(\kappa\) in the \(l\)th frame \(\widehat{\kappa}(l)\) is given under the assumption of \(\{\lambda_{z}(k,l)=\lambda_{r}(k,l)\}|H_{0}(l)\). Finally, \(\kappa(l)\) is recursive averaged with a time-varying smoothing parameter \(\widetilde{\alpha}_{\kappa}(l)\) which is adjusted by the frame conditional direct sound presence probability \(p(l)\).  
The proposed \(\kappa\) estimator has been evaluated and compared to conventional \(\kappa\) estimator and a recursive MSPP method proposed in recent years. Experimental results show that the LRSV estimator using the proposed \(\kappa\) estimator outperforms other methods. It is also found that the ground truth \(\kappa\) calculated using measured DRR is not the optimal \(\kappa\) for the LRSV estimator since the optimal \(\kappa\) may be affected by speech signals. It suggests us estimate \(\kappa\) directly and adaptively rather than using the blind DRR estimation algorithm to obtain \(\kappa\), which may be a less effective approach. However, further research is needed to prove this hypothesis.  
Conceptualization, Z.Z. and Y.S.; methodology, Z.Z.; software, Z.Z.; validation, Z.Z. and X.F.; formal analysis, Z.Z.; investigation, Z.Z.; resources, Y.S.; data curation, Z.Z.; writing--original draft preparation, Z.Z.; writing--review and editing, X.F. and Y.S.; visualization, Z.Z.; supervision, Y.S.; project administration, X.F.; funding acquisition, Y.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Measured\({}_{\text{full}}\)** & **Measured\({}_{\text{sub}}\)** & **Optimal\({}_{\text{scan}}\)** & **Proposed** & **Conventional** & **MSPP** \\ \hline \(\Delta\)SRR & 7.81 & 7.73 & 7.80 & 8.61 & 6.80 & 7.60 \\ \(\Delta\)LSD & \(-3.47\) & \(-3.46\) & \(-3.57\) & \(-3.77\) & \(-3.29\) & \(-3.82\) \\ \(\Delta\)STOI & 0.0678 & 0.0693 & 0.0765 & 0.0785 & 0.0676 & 0.0362 \\ \(\Delta\)PESQ & 0.18 & 0.17 & 0.20 & 0.24 & 0.16 & 0.04 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Improvement of objective speech quality measures.