esa-sceva/satcom-chunk-collection huggingface dataset:
# Long-Term Creep Mechanical and Acoustic Emission Characteristics of Water-Immersed Coal Pillar Dam
## 3 Results and Discussion
### Time-Dependent Axial Strain
#### 3.1.2 Natural Sample  
The long-term creep deformation results of the coal sample under natural conditions are shown in Figure 5. The results indicate that compared to water-immersed coal-rock, this specimen under natural conditions experienced a longer primary creep stage before entering the steady-state creep stage. The primary creep stage lasted approximately 36 days, with a strain rate of about 0.0127%/d, after which it entered the steady-state creep stage. During this stage, minor strain fluctuations occurred, but the overall strain variation was minimal, with a strain rate of approximately 3.0 \(\times\) 10\({}^{-6}\)/d.  
Comparing the long-term creep behavior between natural and immersed conditions, it was revealed that higher strain magnitudes are needed in natural condition coal samples for entering the steady-state creep stage. This phenomenon arises from two factors: On the one hand, as heterogeneous geological materials, coal specimens inherently exhibit strength variations. On the other hand, natural specimens lack bound water interactions. Being typical clay minerals, dehydration shrinkage generates contraction stresses that trigger  
Figure 5: Long-term creep deformation characteristics of coal under natural conditions.  
microcrack network propagation. Previous studies using CT scanning [30; 31; 32] have investigated meso-structural differences between natural and saturated coal samples, finding increased mesopore proportions in drier specimens. These pores create stress concentration points that become prioritized failure paths under compression, significantly amplifying deformation. Concurrent research [33] demonstrates higher internal friction coefficients in drier coal specimens, accelerating elastic strain energy accumulation during loading. When reaching critical energy thresholds, abrupt energy releases manifest as \"rockburst\" failures, exhibiting stepwise strain jumps. In contrast, water-saturated specimens exhibit smoother deformation curves due to water's viscous damping effect, which moderates energy release. This fundamentally explains the more pronounced fluctuation characteristics observed during creep experiments for natural specimens.
## 3 Optical Indices
### Telescope Aperture and Overall Throughput
#### 3.3.1 Stellar Signal  
Spectral radiant emittance of target stars \(M\) is a function of observed wavelength \(\lambda\), star magnitude \(m\), and absolute temperature \(T\):  
\[M(m,\lambda,T)=K_{0}(m,T)B(\lambda,T)=K_{0}(m,T)\frac{2 hc^{2}}{\lambda^{5}( \exp(hc/kT\lambda)-1)}, \tag{11}\]  
where \(B(\lambda,T)\) is the Planck black-body radiation formula, \(h\) is the Planck constant, \(c\) is the speed of light in vacuum, and \(k\) is the Boltzmann constant. \(K_{0}(m,T)\) is the calibration parameter, which is established corresponding to the observed star by defining the magnitude zero with \(A_{0}\) star. When the target star is given with a fixed magnitude and temperature, \(M(\lambda)\) is a function of wavelength [26].  
The relationship between emittance from the star surface and spectral irradiance in the primary telescope mirror is denoted as \(E_{tel}(\lambda)\), while the distance from optical system to star is \(D\):  
\[E_{tel}(\lambda)=M(\lambda)\Big{(}\frac{r_{s}}{D}\Big{)}^{2}. \tag{12}\]  
The light energy received by the telescope has certain attenuation after passing through optical elements. The overall throughput of the optical system, \(\tau(\lambda)\), is the product of each optical element, which should consider the influence of lens transmittance, mirror reflectivity, and lens material absorption, expressed as  
\[\tau(\lambda)=\prod_{i}t_{i}(\lambda)\prod_{j}p_{j}(\lambda)\prod_{k}(1- \alpha_{k}(\lambda))^{d_{k}}, \tag{13}\]  
where \(t_{i}(\lambda)/p_{j}(\lambda)\) is the transmittance or reflectivity of the \(i\)-th/\(j\)-th transmission or reflection surface, and \(\alpha_{k}\) and \(d_{k}\) are the material absorption coefficient and element thickness of the \(k\)-th transmission element, respectively.  
The photoelectron count caused by the stellar signal in each spectral bin per exposure during the OOT period can be approximately calculated by  
\[S_{OOT}(\lambda)=A_{tel}E_{tel}(\lambda)\tau(\lambda)tQE(\lambda)\delta \lambda\bigg{(}\frac{hc}{\lambda}\bigg{)}^{-1}, \tag{14}\]  
where \(A_{tel}\) is the collection area of the primary mirror, \(QE(\lambda)\) is the quantum efficiency of the detector pixels for each wavelength, and \(\delta\lambda\) is the span of each spectral resolution element [15].
## 4 Experimental Results
### Visualization Experiment  
In this section, we aimed to visually demonstrate the impact of environmental perception attention (EPA) on the model's attention mechanism. To achieve this, we utilized GradCAM [58] to generate a visual heat map for the output feature map of the backbone network to show the model's attention area before and after the application of EPA, as shown in Figure 5. In the heat map, warm and cold tones are used to distinguish the contribution of position pixels to the prediction results. The warmer the color, the greater the contribution of the position to the final prediction result of the model, indicating more important position information and greater attention paid by the model. The colder the color, the opposite is true. As seen in Figure 5, the attention distribution of the baseline model was relatively scattered, and it also produced large responses to many unimportant areas. Compared with the baseline model that does not use EPA, EPA could effectively organize the model's attention area, avoid distracting attention to useless locations, and significantly reduce the feature response to irrelevant areas. This shows that the EPA module we proposed could effectively guide the entire network model to focus more accurately on the area where the target to be detected is located.  
Next, we visualized the detection results of the baseline model (left) and the detection results after applying the proposed method (right). As shown in Figure 6, it can be seen that in different scenes affected by factors such as time and lighting, the baseline model had certain false detections and misdetections, while our method effectively improved the detection effect and effectively improved the adaptability and robustness of the model to diverse and complex backgrounds.  
\begin{table}
\begin{tabular}{c c c} \hline
**Model** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** \\ \hline Baseline & 94.5 & 76.3 \\ Ours & 95.1 & 76.8 \\ \hline \end{tabular}
\end{table}
Table 6: Results of our method and baseline on KITTI dataset.  
Figure 5: Heatmap visualization results before and after adding environmental perception attention.
## 3 Approach and Methodology
: _The test suite, where \(j\) is the current version of the test suite \(\mathcal{T}\) used to test program \(\mathcal{P}_{j}\)_  
Run \(\mathcal{P}_{j}(T_{j})\) and evaluate every \(t_{i}\in\mathcal{T}_{j}\)  
Build and maintain a dataset \(\mathcal{D}_{j}\) from testing program \(\mathcal{P}_{j}\) with test suite \(\mathcal{T}_{j}\)  
\(\triangleright\)_Phase#1: Build datasets by collecting execution traces for each test case in the test suite. \(\triangleright\)\(L_{1}\): represents the top level; all events are grouped into one of 4 different categories; see Section 4.1. \(\triangleright\)\(L_{2}\): represents the middle level; all events are grouped into one of 16 different subcategory; see Section 4.2. \(\triangleright\)\(L_{2}\): represents the lower level; each event is encoded in one of 68 distinct event type; see Section 4.3. for each passed \(t_{i}\in\mathcal{T}_{j}\)do  Collect execution trace \(ETR_{i1}\) of type \(\mathcal{L}_{1}\)  Add \(ETR_{i1}\) to \(\mathcal{D}_{j1}\)  Collect execution trace \(ETR_{i2}\) of type \(\mathcal{L}_{2}\)  Add \(ETR_{i2}\) to \(\mathcal{D}_{j2}\)  Collect execution trace \(ETR_{i3}\) of type \(\mathcal{L}_{3}\)  Add \(ETR_{i3}\) to \(\mathcal{D}_{j3}\) endfor \(\triangleright\)\(\emph{Phasel2}\): Analyze datasets for each of the test suites, and build a similarity matrix \(SM_{jk}\). for each\(ETR_{i}\in\mathcal{D}_{k}\), where \(1\leq i\leq n\) and \(k=\{1,2,3\}\)do for each\(ETR_{x}\in\mathcal{D}_{jk}\), where \(1\leq x\leq n\) and \(k=\{1,2,3\}\)do if\(i\
eq x\)then  Find similarity for \(ETR_{i}\) and \(ETR_{x}\)  Build similarity vector \(SV_{i}\) for \(ETR_{i}\) and \(ETR_{x}\)  Add \(SV_{i}\) to \(SM_{jk}\)  endif endfor endfor for for each\(SM_{jk}\), where \(k=\{\mathcal{L}_{1},\mathcal{L}_{2},\mathcal{L}_{3}\}\)do  Analyze and evaluate the \(SM_{jk}\)  Rank/cluster test cases based on their similarity score \(\triangleright\)\(\emph{Phasel3}\): Optimize the test suites based on the similarity matrix  Remove redundant test case in \(\mathcal{T}_{j}\) \(\triangleright\) any two test case with similarity score \(=1\)  Select a subset of the test cases in future testing. endfor ```  
Figure 2: AbstractTrace’s hierarchical execution trace: the top level with 4 classes, the mid-level with 16 classes, and the lowest level captures 68 kind of source-code events.
## 4 Discussion  
This research aims to verify whether we can use the OBIA labels to replace the Manual labels with intermixing strategies (Scenario I), and, if the intermixing strategies fail, whether we can use a smaller number of Manual labels to reduce the manual annotation work (Scenario II) to achieve similar performance to a large number of Manual labels.  
[PERSON] and [PERSON] [44] collected building footprints from open data web portals from 15 cities in America as label data for a semantic segmentation model (DeepLab-v3+). The collected labels, similar to the OBIA labels, include many annotation errors. The models trained with mixed datasets from 15 cities can produce a mean IoU value of 0.89. The finding shows the high value of using mixed open datasets from multiple sources to replace manually annotated labels for extracting building footprints. However, the performance of the OBIA labels was slightly improved only by the TCTS intermixing strategy. The other two intermixing strategies even slightly harmed the models. This is perhaps because we selected only two refugee camps. It may be worthwhile to intermix data from more refugee camps to verify whether the intermixing strategy can help improve this extraction task.  
As mentioned in the Section 1, many techniques have been applied to handle label noise in deep learning. In this research, we found that the improvement of intermixing data from multiple sources is limited under the initial set up, but fine-tuning based on the models trained with intermixing data has very high potential.  
In terms of the selection of networks, even though it is not the main focus of this research, we can nevertheless find that the selected model performs quite well compared with other models in previous research. [PERSON] et al. [34] selected a Mask-RCNN model with ResNet-101 as a backbone to extract refugee dwellings in Cameroon. They used WorldView-3 and WorldView-2 images with a spatial resolution of 0.5 m, which is the same as that used in this research. The Mask-RCNN model produced the mean IoU value up to 0.669 which is lower than the outputs of all experiments here, as shown in Table A2 in Appendix A. These comparison results are consistent with the results from [PERSON] and [PERSON] [35]. In addition, [PERSON]. [54] compared multiple semantic segmentation models, including three classical deep learning architectures (U-Net, LinkNet, and Feature Pyramid Network (FPN)) and twelve backbones (VGG16, VGG19, ResNet-18, ResNet-34, DenseNet-121, DenseNet-169, Inception-v3, InceptionResnet-v2, MobileNet-v1, MobileNet-v2, EfficientNet-B0, and EfficientNet-B1) to test the validity of the OBIA labels compared with the Manual labels by using the same training and testing data of the Kule refugee camp during the dry and the wet seasons. The results show that these models perform similarly, in general. Therefore, it is necessary to undertake more state-of-the-art techniques in the future to discover more suitable workflows for this task.
# The Current State and Future Potential of Microbial Control of Scarab Pests
## 10 Future Advancements  
The management of insect pests through biocontrol is an incredibly complicated endeavor. It not only requires a thorough understanding of the pest and its potential pathogens, but also how other ecosystem factors affect the host, the pathogen, and host-pathogen interactions; a level of understanding is often lacking for microbial entomopathogens, as their life cycles and pathologies are complex and often difficult to study. This is reflected in the many limitations that microbial pesticides possess, particularly when compared to chemical controls. Many are slow-acting, environmentally unstable, and exhibit variable efficacy. However, new technologies may be able to overcome some of these obstacles by improving their spread, infectivity, reliability, and persistence in the environment. While many of these technologies are still in the developmental phase, they show great promise for future formulations.
# Biological Properties of Sandalwood Oil and Microbial Synthesis of Its Major Sesquietrepenoids
## 2 The Source of Sandalwood Essential Oil, Its Main Components, and Its Applications
### Sedative and Anxiolytic Effects of Sandalwood Oil  
Sandalwood oil has sedative and anxiolytic properties and serves protective functions against cerebral damage by inhibiting oxidative stress and inflammatory pathways [43]. The \(\alpha\)-santalol has been shown to exhibit anxiolytic-like action, lowering locomotor activity following stress exposure. It can also transfer to the brain, adding to its calming effects [44]. Sandalwood oil has also been used to treat neurological complications by influencing neurotransmitter systems such as gamma-aminobutyric acid (GABA), dopamine, serotonin, and acetylcholine, which can regulate physiological effects in the brain and possibly enhance memory function [45]. Also, santalol exhibits neurootherapeutic activities and psychotropic drug characteristics. Research indicates that when inhaled at specific concentrations, santalol can significantly impact the sleep-wake cycle by decreasing total waking time and increasing non-rapid eye movement sleep time [46]. Moreover, sandalwood extracts are essential to the fragrance industry due to their warm, woody scent. They are used in various cosmetics, fragrances, flavors, and pharmaceutical industries [16; 30; 47].
## 5 Conclusions  
The ability to identify areas in rangelands that are most productive is of great value for land users, as it may prevent overgrazing and assure the long-term sustainability of livestock farming. Without such information, selective grazing activities can unintentionally lead to a mosaic of overgrazed patches [105], especially under low stocking rates. Given the threat of climate change and desertification, unintentional overutilization of productive drainage lines can lead to the formation of bare patches and/or encroachment of alien species. Mapping of these productive areas and understanding their use by small stock could assist in the sustainable use of these areas through improved management strategies. Historically, the process of determining the grazing patterns of sheep in the semi-arid ecosystems of the Upper Karoo was extremely time-consuming and costly. This study demonstrated that high-resolution NDVI data can be used as a grazing management tool to determine the spatial variability of productive areas across the Upper Karoo rangelands and for identifying preferred grazing areas.  
This study is the first to use a dense time series of Sentinel-2 data for rangeland monitoring in the semi-arid Karoo. The Sentinel-2 satellite constellation offers an unprecedented opportunity for frequent and long-term monitoring of vegetation conditions in the Upper Karoo and similar rangelands. By combining RS and GPS data, we can improve our understanding of sheep grazing patterns, which can ultimately lead to the optimization of production through precision farming.  
Conceptualization, C.J.H. and A.v.N.; methodology, C.J.H.; formal analysis, C.J.H.; investigation, C.J.H.; writing--original draft preparation, C.J.H.; writing--review and editing, A.v.N., H.G. and C.J.H.; visualization, C.J.H.; supervision, A.v.N. and H.G.; project administration, C.J.H.; funding acquisition, A.v.N. and H.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors thank the Northern Cape Department of Agriculture, Land Reform and Rural Development for the maintenance and continuous monitoring of this valuable long-term grazing trial. The care and management of the trial by the farm manager, [PERSON], is also recognised. The authors also thank [PERSON] (www.linguafix.net, accessed on 10 November 2021) for language editing an early version of this manuscript.  
The authors declare no conflict of interest.
# Denoising of Wrapped Phase in Digital Speckle Shearography Based on Convolutional Neural Network
## 3 Experiments
### The Denoising Effect on Simulated Images
Conversely, in Figure 8d, DNCNN achieves remarkable denoising effects but introduces noticeable boundary blurring, impacting visual quality. As evident from Figure 8e, images processed by SDCNN not only attain outstanding denoising results but also exhibit clearer edges and textures, preserving a greater amount of image detail.
## 2 Related Work
### Heuristic and Optimization-Based Resource Allocation  
Traditional service provisioning techniques in IoT and edge-cloud environments have predominantly relied on static heuristics or mathematical optimization models. These methods aim to reduce latency and optimize resource utilization under fixed constraints, but they often fail to adapt in real-time to fluctuating service demands and network dynamics. [PERSON] et al. [14] proposed a tensor-based resource mapping mechanism to assign service requests to cloud and edge servers using predefined latency thresholds. Although the method effectively reduces bandwidth overhead, its reliance on tensor decompositions introduces computation latency, limiting scalability in dynamic environments. Similarly, [PERSON] et al. [12] utilized a mixed integer linear programming model to optimize crowdsensing-based service placement. Although their model demonstrates efficient workload distribution under static conditions, it lacks predictive adaptability and incurs significant computation time for larger systems.  
Other linear programming approaches have tackled multi-objective service optimization. For example, [PERSON] et al. [15] formulate service provisioning as a latency-bounded function chain placement problem. However, the model neglects real-time delivery rate optimization, making it unsuitable for data-intensive services. [PERSON] et al. [16] addressed deployment efficiency in fog computing infrastructures for healthcare, proposing a resource-aware placement scheme that reduces operational cost. However, the model operates under fixed network assumptions and does not take advantage of learning techniques, limiting its effectiveness under unpredictable service bursts.  
These optimization and heuristic solutions offer analytical clarity and performance guarantees but suffer from three major drawbacks: (i) lack of real-time adaptability, (ii) inability to jointly model latency and throughput constraints, and (iii) reliance on static thresholds that fail to generalize across heterogeneous workloads. These limitations require more adaptive, learning-enabled architectures that can anticipate traffic surges, react dynamically to network variations, and jointly optimize key QoS parameters.
## 4 Experimental Results
Similar measurements are also reported in Figure 12b,c with the same conclusion when using tellurite step-index fibers with different core diameters (i.e., distinct dispersion properties at the pump wavelength). It is worth mentioning that the optimized SC length for the latter fibers was also found to be around 12 cm. Finally, Figure 13 summarizes the largest SC spectra (maximum power of 3 kW and optimized length of 12 cm) obtained with the three different tellurite step-index fibers when pumping at 2300 nm. Those results are directly compared to numerical simulations taking into account the corresponding fiber properties. The noticeable agreement shows that the largest SC spectrum extending from 1.5  
Figure 11: Evolution of experimental SC spectra recorded at the output of the tellurite step-index fiber with a core diameter of 3.5 \(\upmu\)m (i.e., with two ZDWs) when pump wavelength is fixed to 2300 nm. (**a**) Evolution as a function of fiber length for an input peak power of 2.2 kW. (**b**) Evolution as a function of input peak power for a fiber length of 12 cm. Figure 12: Evolution of experimental SC spectra recorded as a function of pump wavelength for a fixed input peak power of \(\sim\)2.5 kW for 12-cm-long segments of tellurite step-index fiber with a distinct core diameter, namely (**a**) 3.5 \(\upmu\)m, (**b**) 3\(\upmu\)m, and (**c**) 4 \(\upmu\)m.  
to 4.5 \(\upmu\)m is obtained for a fiber core diameter corresponding to dispersion profiles with two ZDWs (see Figure 13), and this also requires pumping into the anomalous dispersion regime (i.e., between the ZDWs). From previous numerical simulations, the full coherence of such a mid-IR SC configuration is only preserved for a moderate input pulse energy (about a few hundreds of pJ). The same analysis is also valid when using the tellurite fiber with a single ZDW and a core diameter of 4 \(\upmu\)m. However, in that case, the mid-IR SC edge does not reach wavelengths beyond 4000 nm (see Figure 13). In contrast, for small core tellurite fibers that provide ANDi profiles, coherent mid-IR SC could still be investigated with higher input powers than in Figure 13, in order to reach larger bandwidths. In spite of this limited SC bandwidth, in the present SC configuration, higher power spectral density and the single-pulse feature of the SC remain some of the main advantages when compared to previous fibers.