esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
We examined the AE evolution of 75 red sandstone specimens submitted to UCLU tests with different prescribed stress levels in the first loading cycle (\(\sigma_{\rm A}\)) and loading strain rates (\(l_{\rm sr}\)). The stress level of the Kaiser effect (\(\sigma_{\rm AE}\)) during the re-loading stage was assigned. Influences of \(\sigma_{\rm A}\) and \(l_{\rm sr}\) on the rock Kaiser effect were revealed. The main conclusions are:  
1. The Kaiser effect is greatly influenced by \(\sigma_{\rm A}\). Internal damage of the rock was gradually intensified as \(\sigma_{\rm A}\) grows. Consequently, the Felicity ratio (\(FR\)) reduces and the Kaiser effect slowly vanishes, followed by appearance of the Felicity effect. The \(FR\) increases as the loading strain rate increases, and this, in turn, increases the possible appearance of the Kaiser effect.
2. For the red sandstone specimen, the [PERSON] effect is always observed when \(\sigma_{\rm A}\) is between \(0.5\sigma_{\rm c}\) to \(0.7\sigma_{\rm c}\), corresponding to the stable crack grow stage in the stress-strain relationship.
3. When \(\sigma_{\rm A}\leq 0.5\sigma_{\rm c}\), a large number of AEs appear in the rock specimen during the re-loading stage only after \(\sigma_{\rm A}\) is exceeded, suggesting an apparent Kaiser effect. When \(\sigma_{\rm A}=0.6\sigma_{\rm c}\), the appearance of the Kaiser effect is closely related to the loading strain rate. For \(l_{\rm sr}<10^{-4}\) s\({}^{-1}\), the Felicity effect appears in the re-loading stage, whereas the [PERSON] effect appears in the re-loading process if \(l_{\rm sr}\geq 10^{-4}\) s\({}^{-1}\). When \(\sigma_{\rm A}\geq 0.7\sigma_{\rm c}\), remarkable AEs are generated in the initial re-loading stage, indicating that the [PERSON] effect vanishes and the Felicity effect occurs.
4. The \(FR\) ascends and the [PERSON] effect appears readily as \(l_{\rm sr}\) is increased. The \(FR\) grows quickly when \(l_{\rm sr}\) increases, but is no larger than \(10^{-4}\) s\({}^{-1}\), whereas the increase rate of the \(FR\) becomes less remarkable after \(l_{\rm sr}\) exceeds \(10^{-4}\) s\({}^{-1}\). Therefore, a relative high loading strain higher than \(10^{-4}\) s\({}^{-1}\) possibly favors the determination of the stress level of the [PERSON] effect.  
**Author Contributions:** Conceptualization, Y.C. and Q.M.; methodology, Y.L.; data curation, H.P.; writing--original draft preparation, H.P and K.Z.; writing--review and editing, Y.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The research presented in this paper is funded by the National Natural Science Foundation of China (51974296, 51704280, 51974295, U1803118).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Fiber Sensing Technologies
### Temperature Measurement with Brillouin Scattering  
Distributed Brillouin sensing was proposed in the late 1980s as an alternative technique to the classic optical time-domain reflectometry to measure local attenuation along an optical fiber, but as soon it was realized it had many more potentialities for sensing, since Brillouin scattering is intrinsically sensitive to temperature and the deformations experienced by the fiber. Physically, distributed fiber optic sensing is based on the scattering of photons due to density fluctuations in the fiber. When the density fluctuations are dynamic, the scattered photons experience a frequency shift with a magnitude equal to the frequency of the density fluctuations. Through direct proportionality, the frequency shift is representative of the corresponding energy difference between the incident and scattered photons. This energy difference corresponds to the flux of acoustic phonons (sound waves) generated through electrostriction initiated by the strong input light waves entering the fiber, and the resulting scattering process is called Brillouin scattering. Brillouin scattering temperature based systems detect temperature changes by measuring the Brillouin backscattered intensity and its frequency shift as a function of temperature [41].  
Although Brillouin scattering yields signals that are approximately \(10^{5}\) to \(10^{6}\) times weaker than the incident light, they may be enhanced through a process known as Stimulated Brillouin Scattering (SBS). This can be achieved by sending light from two lasers into opposite ends of the sensing fiber. Through modulation of a pulsed laser, its amplification will occur at the expense of the second continuous wave laser. This amplification process allows the fiber sensing length to be significantly increased due to an improved signal-to-noise ratio resulting from nonlinear amplification in the fiber. This approach to Brillouin sensing is known as Brillouin Optical Time-Domain Analysis (BOTDA) and constituted a breakthrough in distributed sensing. Concerning temperature measurement, this technique permitted a measurement length in excess of 100 km with sub-meter spatial resolution [42].  
For distributed temperature measurement of the coal waste pile, Brillouin Optical Time-Domain Analysis was proposed with a single-mode fiber layout extended over the whole area of the waste pile in combustion. Details are presented in Section 5.1.
# Frequency-Separated Attention Network for Image Super-Resolution
## 2 Related Work
### Attention Networks  
The attention mechanism offers a new perspective for task resolution, learning crucial information from inputs and demonstrating superiority in various computer vision tasks, such as object detection, image segmentation, and action recognition. The authors who proposed SCA-CNN [33] argued that previous spatial attention models only considered information within the CNN's spatial and multilayer contexts; hence, they introduced channel attention to discern which channels are most important for generating image captions. DANet [34] enhances context capture by adding positional and channel self-attention modules to model semantic relations in both the spatial and channel dimensions, resulting in finer scene segmentation outcomes. [PERSON] et al. [35] employed an attention mechanism for the selective integration of multi-level features, reducing background interference and enhancing object detection performance.  
[PERSON] et al. [36] used an attention mechanism to distinguish and enhance high-frequency information, improving detail restoration. LR images contain both low- and high-frequency information treated equally, thus diminishing detail restoration. Hence, RCAN [37] combines channel attention with residuals, allocating more computation to high-frequency information as network depth increases. [PERSON] et al. [38] employed recursive units with channel attention to extract significant features from channels, using multi-level feature fusion techniques for feature enhancement. In CSFM [39], spatial and channel attention mechanisms, along with dense connections, are used to distinguish critical features across dimensions and mitigate the weakening of information flow in deep networks. The aforementioned works utilized attention mechanisms in super-resolution tasks but did not consider feature separation to alleviate the learning difficulty of attention mechanisms. By separating high- and low-frequency features in images and processing them differently, our approach enables attention mechanisms to focus on learning distinct features for backgrounds (low-frequency features) and complex texture features (high-frequency features) separately. This reduces the network's learning burden while enhancing its ability to distinguish between these two types of features.
## 3 Results
### Model Identifiability Analysis  
For Model 1 and Model 2, a posteriori identifiability has been evaluated from the estimated asymptotic variance-covariance matrix of the model parameter vectors \(\theta_{1}\) and \(\theta_{2}\). An approximation to the variance-covariance matrix of the model parameter vector has been computed as \(V=s^{2}(J^{T}J)^{-1}\), where \(s^{2}=S(\bar{\theta})/(N-q)\). \(S(\bar{\theta})\) is the sum-of-squares loss function evaluated at the optimal \(\theta\) vector; \(N\) is the number of observations points and \(q\) is the number of free parameters. The errors are assumed i.i.d. \(\sim\mathcal{N}(0,\sigma^{2})\), with \(s^{2}\simeq\sigma^{2}\).  
The optimal values for the parameters are reported in Tables 2 and 3, and the results of the identifiability analysis are reported in Table 5.  
For the zoledronate kinetic model parameters (\(k_{ZY}\) and \(\rho_{ZY}\)), the standard deviation (SD), the variation coefficients (CV) and the lower (LLC) and upper (ULC) confidence limits for the parameter suggest that the sub-model is identifiable. The parameter \(k_{XY}\), being highly correlated with the couple \((k_{ZY},\rho_{ZY})\), was excluded from the analysis, and the parameters \(Y^{*+}\) and \(k_{XZ}\), estimated at extremely small values, were fixed at zero.  
Figure 5: Model 2 simulation trend in the pulse experiment. Panel (**a**) shows the simulated zoledronate (\(ZA\)) trend over time; panel (**b**) shows the simulated antigen (\(IPP\)) trend over time. The continuous blue line represents the model forecast, while red asterisks represent the experimental data obtained by [39] and red circles represent the experimental data obtained by [40].  
For the antigen dynamic Model 1, the parameters \(K_{G}\) and \(k_{XG}^{tot}\) are determined (functions of other parameters) and were not considered in the analysis. Similarly, the parameters \(k_{XGZ}\) and \(G_{0}\), highly correlated with the remaining free ones, were excluded. The identifiability results for the considered parameters of Models 1 and 2 are also shown in Table 5.  
The analysis results suggest that the data sets available from the literature are not sufficiently informative for reliable identification of the model parameters, in particular regarding the antigen sub-models.
## 1 Introduction
The signal at a connection of artificial neurons is a real number, and the output of each artificial neuron is computed by a nonlinear function. During the training process, the weights and biases of artificial neurons are repetitively adjusted based on the difference between the processed output (i.e., the prediction) and the target output (i.e., the known solution). The training continues until a sufficient number of these adjustments are made, and subsequently, the ANN model is applied to solve the actual problems. The applications of ANN for predicting fracture toughness include studies by [PERSON] et al. [16] on polymethyl methacrylate (PMMA), [PERSON] et al. [17] on polymer nanocomposites (PNCs), [PERSON] et al. [18] on rocks, and [PERSON] et al. [19] on Nb-Si alloys. On the other hand, the applications of ANN for predicting stress intensity factors include studies by [PERSON] et al. [20] on unbalanced rotating cracked shafts, [PERSON] et al. [21] on cracked pavements under traffic loading, and [PERSON] et al. [22] on through-thickness cracks in bending tubes. Although ANN has been previously applied for predicting stress intensity factors in cracked tubes [22], it was specifically designed only for through-thickness cracks. However, the application of ANN for predicting stress intensity factors of propagating cracks in pipes (which require periodic observation and life assessment to prevent crack propagation through the thickness of the pipe) has not been investigated yet. Due to the complexity of FEA, the impact of linear interpolation on the accuracy of the geometry function from API 579-1/ASME FFS-1, and the absence of an ANN model for predicting stress intensity factors of propagating cracks in pipes, it is the objective of this work to investigate the applicability of the ANN model for predicting \(K_{\rm I}\) in a pressurized pipe with an infinitely long internal surface crack in the longitudinal direction. The ANN model consists of two input factors: the ratio between crack size and pipe thickness (\(a/t\)) and the ratio between pipe thickness and internal radius (\(t/R_{\rm i}\)), and an output factor being \(K_{\rm I}\). The \(K_{\rm I}\) solutions from API 579-1/ASME FFS-1 were used for training, while those from FEA were used for validation. Subsequently, the ANN model was applied to predict \(K_{\rm I}\) for actual problems, involving cracked pipes with geometry different from those provided in API 579-1/ASME FFS-1. The predicted \(K_{\rm I}\) values were compared with the FEA solutions for accuracy assessment. The applicability of the ANN model for predicting \(K_{\rm I}\) in a pressurized pipe with an infinitely long internal surface crack in the longitudinal direction was then discussed.
## 2 sRNA as Orchestrators of Bacterial Pathogenesis
### Invasion of the Host
#### 2.2.1 StxS  
Shiga toxin 1 (Stx1) expression is regulated through three pathways: (1) the phage late promoter (PR'), activated by phage induction and the SOS response, integrates signals from quorum sensing, antibiotics, and inhibitors [93]; (2) a Fur-responsive promoter specific to Stx1, modulating expression based on iron and nitric oxide levels [94]; and (3) posttranscriptional regulation by sRNAs [43]. During the lytic cycle, toxin genes are embedded within the late transcript of the phage and are regulated via anitterumination of the PR\({}^{\prime}\) late promoter. In the lysogenic state, transcription from PR\({}^{\prime}\) is prematurely terminated at tR\({}^{\prime}\), resulting in the production of a truncated 74-nt RNA, StxS as a byproduct of antitermination control. StxS represses Stx1 toxin production by binding to the ribosome binding site of \(\mathit{stx1B}\), silencing its translation [43] (Figure 1c). Interestingly, StxS does not regulate \(\mathit{stx2 AB}\), which is exclusively induced during the lytic cycle, suggesting that other Hfq-dependent sRNAs may modulate Stx2a expression. This posttranscriptional regulation ensures minimal Stx1 production during lysogeny, acting as a safeguard. In addition to repressing Stx1, StxS activates the general stress response regulator RpoS by binding to its 5\({}^{\prime}\) untranslated region (UTR), relieving repressive secondary structures and preventing Rho-dependent termination (Figure 1c). This activation enhances bacterial stationary phase survival, increasing cell density under nutrient-limited conditions by approximately 20%. Another sRNA, DicF, was shown to regulate Shiga toxin. As DicF responds to changes in oxygen level (the role of DicF is discussed in detail in the bacterial stress response section), the pathogen can modulate the expression of the toxin by sensing the oxygen tension across different regions of the gut [95].
# Mechanical Properties Analysis of WAAM Produced Wall Made from 6063 Alloy Using AC MIG Process
###### Abstract  
Wire and arc additive manufacturing (WAAM) is a promising method of producing medium- and large-sized aluminum alloy structures, though it faces challenges such as porosity, residual stresses and inconsistent mechanical properties. This study investigates the effect of current type (AC and DC MIG welding) and polarity balance (influencing the duration of the positive/negative period of the cycle) on the mechanical and microstructural properties of 6063 aluminum alloy walls produced by WAAM. A TiB\({}_{2}\)-refined Al-Mg-Si (6063) filler wire, specifically developed for arc-based processing, was used. Tensile tests, Vickers hardness measurements (HV5), optical microscopy and X-ray diffraction based on cos\(\alpha\) method were used to evaluate performance in terms of strength, ductility, hardness, grain structure, porosity and residual stress. The results showed that the balance of AC polarity significantly affects wall geometry, porosity and grain structure. Increasing the negative polarity period resulted in taller and narrower walls, while the widest walls were produced with increased positive polarity. Residual stress measurements revealed a tensile-compressive-tensile distribution, with the DC-MIG samples showing the highest surface stress values. The highest tensile strength (172 MPa) was measured in the lower region of the DC-MIG sample, suggesting that areas near the substrate benefit from faster cooling.  
AC MIG; WAAM process; 6063 alloy; residual stress; XRD method; mechanical properties 2020 IEEE/RSJ International Conference on Robotics and Automation (ICRA), Vol., 2008, pp. 1-10. IEEE, 2008.
# Safe Approximation--An Efficient Solution for a Hard Routing Problem
## 2 Previous Results
For example, [PERSON], [PERSON] and [PERSON] [4] provide a 2-approximation for trees with parallel edges. [PERSON] and [PERSON] [10] gives an \(O(\log n)\)-approximation for the 2-dimensional mesh. [PERSON] and [PERSON] [11] present an \(O(\log n)\)-approximation for a larger subclass of planar graphs, they call \"nearly Eulerian, uniformly high-diameter planar graphs.\"  
On the other hand, finding a constant factor, or at least a logarithmic factor approximation for general directed graphs is hopeless--[PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] [12] showed that on directed graphs it is **NP**-hard to obtain an \(O(n^{1/2-\epsilon})\) approximation for \(n\)-vertex graphs for any \(\epsilon>0\). For the general case the approximation factor of \(\min\{\sqrt{m},m/opt\}=O(\sqrt{m})\) is known to be achievable ([PERSON] [13]), where \(m\) is the number of edges and \(opt\) is the optimum, that is, the maximum number of disjoint connecting paths between the source-destination pairs. Similar bounds apply for the Unsplittable Flow problem, as well. Bounds have been also found in terms of special (less trivial) graph parameters. For example, [PERSON] and [PERSON] [14] proves that an efficient \(O(F)\) approximation exists, where \(F\) is the so called _flow number_ of the graph. Although the flow number can be computed in polynomial time [14], it is a rather indirect characterization of the graph. Numerous further related results are reviewed in the Handbook of Approximation Algorithms and Metaheuristics [15]. It is worth noting that in the capacitated version (Unsplittable Flow problem) the approximation does _not_ mean approximating the total carried flow. It is meant with respect to the number of source-destination pairs connected, such that for each pair their entire demand is carried. For example, if there are 30 source-destination pairs, each with flow demand four units, then a 2-approximation means routing at least 15 of these flows, each path carrying four units of flow. In other words, not only the paths are unsplittable, but also the flow demands. That is, if we connect all 30 source-destination pairs, but each route can carry only two units of flow, it would not count as a 2-approximation. In summary, the landscape of the Disjoint Connecting Path and the Unsplittable Flow problems can be characterized by the following facts:  
* The decision problems are **NP**-complete for the general case. * The approximation versions are **NP**-hard for the general case, even within a large approximation factor. * Polynomial time solutions or better approximations exist only for special cases. * Even in those cases when a polynomial time solution exists, it is either far from practical, or else it applies to a graph class that is too special to satisfy the demands of network design applications.
# Intelligent Traffic Model for Unmanned Ground Vehicles Based on DSDV-AODV Protocol
## 1 Introduction
In addition, the network topology was altered in this study based on the road conditions at the junctions to investigate the delay, network QoS, and throughput measurement parameters of AODV and the improved DSDV-AODV routing protocol. It is intended that the findings will be utilized as a basis for future studies on the use of VANET to intelligent traffic signals. The aim of this research is to enhance data transmission in VANET used in intelligent traffic signals. Specifically, the study will focus on the development of a new communication protocol that can reduce delays and increase the efficiency of data delivery. The contribution of this study suggests that combining DSDV and AODV routing protocols can improve the performance of AODV in terms of packet delivery ratio, average end-to-end delay, and average control overhead. The study also shows that the proposed combination of DSDV and AODV routing protocols yields better results in terms of throughput and the packet delivery ratio compared to AODV or DSDV alone. Furthermore, the proposed approach can be used to improve the performance of various routing protocols in Vehicular Ad Hoc Networks (VANETs). The remainder of the paper is structured as follows: The approach is presented in Section 2. Section 3 contains the results and discussion. Finally, in Section 4, the conclusions are presented. Table 1 represents the abbreviations used in the proposed study.
## 4 Results and Discussion
### System Coverage Results for Different Students  
This paper takes junior high-school, high-school, and university student groups as the research objects, and compares different models. The coverage results of each model for different student groups are shown in Figure 4.  
Figure 4: Coverage results of different student groups for each model.  
Figure 3: Recall results of each model.  
Figure 2: Accuracy results of each model.  
Overall, the model performed best among the three student groups (middle school, high school, and college), especially in the college group, where the coverage of the model in this paper reaches the highest level. The model proposed in this paper can provide higher-quality learning path recommendations for students of different grades, covering more learning content and with a higher degree of personalization. Among junior high-school students, the performance of GPT-3 and the model proposed in this paper is relatively close, with a coverage difference of less than 20%, but the coverage is still not as good as the model proposed in this paper. The BERT model performed the most mediocre, with a coverage rate of only 87%. The results show that in this group, the recommendation system in this paper can provide students with learning path options with wider content coverage. In the high-school group, the coverage rate of all models is significantly lower than that of the other two groups. This shows that although the recommendation system based on the LLM has the advantages of personalization and high accuracy, the task and content coverage of the recommended path in the second-class learning of high-school students still needs to be improved. Among college students, the performance of this model is the most outstanding, with a coverage rate of 94.4%, which exceeds other models and is significantly higher than BERT. This shows that as students' learning level improves, the model in this paper can still meet the learning needs in complex environments and provide richer and more customized learning paths.  
Overall, the model in this paper can simultaneously process students' multimodal information, thereby providing more personalized recommendations. Especially in the university population, students' learning content is more diverse and complex, and the model in this paper can provide more accurate and diverse learning paths. The system can adjust the recommendation strategy based on students' real-time feedback to ensure that students always receive the learning content that best suits their current needs. This dynamic adjustment capability enables the system to maintain a high coverage rate in different groups. By introducing GAN technology, the model in this paper can generate innovative and exploratory learning paths to avoid the monotony of recommended content. In different student groups, the system's recommended content is both rich and in-depth, and the content coverage is more extensive.