esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluation of Low-Temperature Performance of Recycled
## 3 Results and Discussion
### Prediction of Low-Temperature Cracking Temperature  
In this study, it is demonstrated that the Burgers model is capable of accurately characterizing the rheological properties of asphalt mixtures at low temperatures, within a specific temperature range [37, 38]. Comprising two spring units and two damper units, the Burgers model is a four-parameter rheological model. The experimental analysis is conducted using the bending beam creep tests on RAMs at temperatures of \(-20\)\({}^{\circ}\)C, \(-10\)\({}^{\circ}\)C, and \(0\)\({}^{\circ}\)C. The software 1 stOpt curve fitting is utilized to determine the parameters required for the Burgers model creep Equation (10). \[\varepsilon\ (T,\ t)=\sigma_{0}\left[\frac{1}{E_{1}}+\frac{1}{\eta_{1}}+\frac{1}{ E_{2}}\left(1-\mathrm{e}^{-(\frac{E_{2}}{\eta_{2}})^{t}}\right)\right] \tag{10}\]  
where \(E_{1}\) is the instantaneous elastic modulus; \(\eta_{1}\) is the steady flow viscosity coefficient; \(E_{2}\) is the delayed elastic modulus; and \(\eta_{2}\) is the delayed flow viscosity coefficient. In conjunction with the temperature shrinkage strain test findings, consider that the temperature shrinkage strain rate in seasonally frozen regions is \(4\times 10^{-4}/\mathrm{s}\). Assuming a local cooling rate of \(10\)\({}^{\circ}\)C/h and a starting temperature of \(10\)\({}^{\circ}\)C during winter, then, \(\sigma(0)=0\), \(\dot{\sigma}_{\mathrm{t}=0}=E_{1}\dot{\varepsilon}\). We can substitute these values into the constitutive equation (Formula (10)) to calculate the temperature stress of the asphalt mixture during the cooling process, as shown in Expression (11). \[\sigma(t)=\frac{\dot{\varepsilon}\eta_{1}\eta_{2}}{E_{1}E_{2}\sqrt{\Lambda}} \left[\left(E_{1}-\frac{E_{1}E_{2}}{2\eta_{2}}\Big{(}M+\sqrt{\Lambda}\Big{)} \right)e^{-\frac{M-\sqrt{\Lambda}}{2\eta_{1}E_{2}}t}-\left(E_{1}-\frac{E_{1}E_ {2}}{2\eta_{2}}\Big{(}M-\sqrt{\Lambda}\Big{)}\right)e^{-\frac{M+\sqrt{\Lambda }}{2\eta_{1}E_{2}}t}\right]+\eta_{1}\dot{\varepsilon} \tag{11}\]  
where \(M=\frac{\eta_{1}}{E_{1}}+\frac{\eta_{1}+\eta_{2}}{E_{1}E_{2}}\), \(\Lambda=\left(\frac{\eta_{1}}{E_{1}}\right)^{2}+\left(\frac{\eta_{1}+\eta_{2}} {E_{2}}\right)^{2}+\frac{2\eta_{1}\left(\eta_{1}-\eta_{2}\right)}{E_{1}E_{2}}\). The viscoelastic parameters in the formula above are derived from bending creep experiments conducted at specific temperatures. For viscoelastic materials, the time-temperature equivalence principle can be applied to address variations in material parameters during continuous cooling [39]. The Arrhenius displacement factor is utilized in the calculation process, as shown in Formula (12).
# A Precise and Stable Space-Based Time System for Navigation in Smart Cities
## 3 Atomic Clocks and Time Scale Metrics  
An atomic clock serves as a precise frequency source, producing a sinusoidal signal with a stable frequency. The output signal of each atomic clock, treated as a time scale, undergoes disturbances--both deterministic and random--resulting in phase and frequency deviations, thereby affecting its stability. This section introduces the output signal model of atomic clocks and two key performance metrics for the assessment of time scales.
## 3 Experiment
### Impact Experiment Setup  
During the operation of aircraft, impacts may come from a range of sources, both natural and man-made. Among the natural sources, bird strikes and hailstorms pose a significant risk and are relatively frequent events in the aviation industry. These impacts may result in significant damage [41]. In terms of man-made sources, debris on runways also poses a significant risk. The debris can be propelled by the engines, causing potential damage to the fuselage or wings [42]. Of these sources, certain types of runway debris may  
Figure 3: Impact experiment setup.  
Figure 2: Structure of random forest regression model.  
exhibit some degree of constant impact energy, assuming a degree of consistency between the type of debris, its size, and the speed of the aircraft during take-off or landing [43; 44]. However, it is important to note that the inherent randomness and unpredictability of these phenomena often result in large variations in the associated impact energies. In order to simplify the impact scenario, the present paper only locates impacts of constant energy.  
The impact experiment setup is shown in Figure 3. An experimental setup involving a steel sphere impacting the elevator specimen was employed to assess the proposed impact localization system. The steel sphere, with a diameter of 0.013 m and a weight of 8.4 g, was consistently positioned at 0.61 m from the elevator's surface for all impacts. The impact energy was maintained at 0.05 J, with all variables remaining constant except the impact location. A guide tube was utilized to regulate the location and height of each impact. The elevator featured 20 ribs, with one distinct impact point on each rib. An acoustic emission (AE) sensor was affixed to the elevator's spar near the location of the vertical stabilizer. This location was chosen to minimize cabling in the aircraft. Each impact location was subjected to 60 impacts, resulting in a total of 1200 impacts. AE signals were recorded by an acquisition system throughout the experiment. The dimensions, material of the elevator, and the locations of the sensor and impact are presented in Figure 4. It should be noted that in the rest of the paper, \"rib number\" is used as the label for linear regression and random forest models. This \"rib numbering\" refers to the listing of the ribs in Figure 4, starting from left to right.  
In this study, the impact of aircraft elevator panels was not investigated. As an integral part of the fuselage, the ribs are responsible for maintaining the aerodynamic shape of the control surfaces. Damage to the ribs due to impact (e.g., gradual reduction in stiffness due to repeated impacts) can therefore have serious aerodynamic and structural effects that may endanger flight safety. This investigation, therefore, prioritizes the monitoring of impacts to the ribs to facilitate a detailed understanding of their long-term structural integrity under repeated low-velocity impacts.
## 3 Method  
### The SPDSAN Diagnostic Process  
The SPDSAN model process proposed in this paper is shown in Figure 4. It includes the time-frequency image generation, the domain-shared feature extractor, subdomain adaptation, and fault classification.  
The diagnostic process was carried out as follows: firstly, the vibration signal was transformed into a time-frequency image as the network learning object. Secondly, the ResNeSt-50 was used to extract image signal features. By assigning different weights to channels, the ResNeSt-50 integrated channel attention mechanism improved the weight of fault features in the sample population, thus enabling, the network to learn more about the fault features in the sample. To reduce the training time and accelerate the model convergence, the ResNeSt-50 model was pre-trained using the ImageNet 2012 data for the general feature extraction. Then, the LMMD was used to measure the distribution differences of related subdomains in the subdomain adaptation layer, which was used as the optimization target \(Loss\mathcal{B}\) of the SPDSAN model. Finally, the error between the true \(y_{i}^{s}\) of \(D_{s}\) and the classifier-predicted label \(\hat{y}_{i}^{s}\) was assumed as the optimization objective \(Loss\mathcal{A}\).  
In sum, the training goal of the SPDSAN is to minimize \(Loss\mathcal{A}\) and \(Loss\mathcal{B}\) to achieve higher diagnostic accuracy in the final diagnosis.  
Figure 4: The structure diagram of the SPDSAN network model.
# In-Situ Testing of Methane Emissions from Landfills Using Laser Absorption Spectroscopy
## 4 In Situ Testing at Xiaping Landfill  
### Site Description  
The in situ testing of methane emissions was carried out at Xiaping landfill. Xiaping landfill is located in Shenzhen City, southeastern China. It was designed and constructed in the manner of modern sanitary landfills. It is a valley-type landfill, high in the west and low in the east. The elevations of the west and east were +230 m and +140 m (coordinate system of WGS 84/UTM zone 55 N). The elevations of the surrounding mountains are  
Figure 8: SNR for the gas sample with methane concentration of 5000 ppm.  
Figure 7: Relationship between CH\({}_{4}\) mass concentration and absorption spectrum amplitude.  
approximately in a range from +286 m to +314 m. As shown in Figure 9, this landfill was divided into two parts. The first part covers an area of 23.4 ha and was put into operation in 1997. It has a designed capacity of 14.93 million m\({}^{3}\) and a service life of 12 years. The average mass of landfilled MSW was nearly 3000 tons per day. By the end of May 2012, the first part was closed with a total landfilled MSW of 15 million tons. The second part lies on the west of the first part, which covers an area of 55.8 ha. It has a design capacity of 12 million m\({}^{3}\) and a service life of 10 years. With the continuous increase of MSW production in Shenzhen City, the daily amount of the landfilled MSW in the second part was much larger than that of the first part. At present, the second part received approximately 5700 tons of MSW per day. The accumulated landfilled MSW was about 5.67 million tons in the second part. The maximum thickness of the waste body waste was about 60 m.  
The surface of the landfill was covered to avoid methane emissions and rainfall infiltration. As the first part was closed, it was covered with soil. From bottom to top, the final cover of the first part was composed of 0.7 m thick clayey soil, 0.5 mm thick linear low-density polyethylene (LLDPE) geomembrane, 1.3 m thick clayey soil, and plants. The surface of the second part was temporarily covered using a 1.5 mm thick high-density polyethylene (HDPE) geomembrane. Below the temporary cover, the gas produced by waste biodegradation was collected using pipes for power generation. The gas collection volume was about 41,000 m\({}^{3}\)/h for the whole landfill. The gas collection efficiency, which is defined as the ratio of the collected amount of gas to the amount generated, was calculated to be approximately 90% for this landfill.
## 4 Discussion
The connection, or lack thereof, between the presence of GP particles in apical tissues and biofilm accumulation on GP points has not been sufficiently studied. Outcome studies showing associations between extruded GP and delayed apical tissue healing do not refer to this issue [2; 3; 4; 5; 6]. However, some studies state that over-instrumentation and apical transportation of contaminated debris might cause this adverse effect [2; 10; 11; 12]. This might also be the result of an unusual root apex form or apical zipping that might leave residual bacteria around the apex. The most common cause of failure is persistent intraradicular infection. Therefore, retreating failed teeth prior to surgery is recommended to exclude this possibility [10]. Retreatment of a failed root canal treatment requires the complete removal of the root canal filling. Poorly condensed GP fillings can be easily removed, but GP fragments may remain in the periapical tissue during attempts to remove overextended fillings [28]. Our findings suggest that healing was noticed in the non-infected and infected groups, even when GP particles remained in the apical tissue. Therefore, clinicians should not initiate surgical procedures as long as the tooth is functional, with no radiographic evidence of enlargement of apical rarefication. Note that the biofilm in this study was formed by two strains of Gram-positive facultative anaerobic bacteria (E.F. and S.S.) and one Gram-negative anaerobic bacterium (P.G.) that were capable of colonizing and forming extracellular matrices on GP. To the best of our knowledge, this is the first study on the biofilm-forming ability of these species on GP points. E.F. is the most frequently isolated strain from root-filled teeth with apical periodontitis [29]. Few species can overcome host defense mechanisms, flourish in the inflamed periapical tissues, and establish an extraradicular infection. Actinomyces and Propionibacterium propionicum participate in extraradicular infections that cause apical actinomycosis, which can be successfully treated only by periapical surgery [30; 31; 32]. Earlier studies suggested that some root canal treatments fail because of non-microbial factors, such as extruded root canal filling materials, which may cause foreign body reactions. In this study, healing was noticed in both the non-infected and infected groups, suggesting that overextension should not be considered an indication for endodontic surgery.
## 3 Retrieval
### Inversion Experiments
#### 3.2.1 Retrieval Setups  
The baseline retrieval setup is described in Section 3.1. As this setup accounts for scattering with three scattering related state vector elements (pressure, i.e., height of the scattering layer \(p_{s}\), scattering optical thickness \(\tau_{s}\), and Angstrom exponent \(\AA\) ), it is referred to as _3-Scat_ setup in the following. All other tested retrieval setups are descendants of this setup. The _4-Scat_ setup has an extended state vector, additionally fitting the fraction of radiation scattered into the hemisphere in forward direction (_f_ in Equation (15)). The _0-Scat_ setup equals an absorption only retrieval; this means, the state vector does not include any scattering related parameters and the fit is limited to the CO\({}_{2}\) fit windows. The _3-Scat_-O\({}_{2}\) setup equals the baseline setup except for scattering parameter derivatives which have artificially been set to zero in the CO\({}_{2}\) bands in order to ensure that the scattering information solely comes from the O\({}_{2}\) band. Accordingly, the _3-Scat_-CO\({}_{2}\) setup ensures that the scattering information solely comes from the CO\({}_{2}\) bands. The scenarios _3-Scat_-_synth_ and _0-Scat_-_synth_ use a synthetic a priori error covariance matrix for the CO\({}_{2}\) profile as proposed by [PERSON] et al. [30] but with a correlation length of 1.0 \(p_{0}\) instead of 0.3 \(p_{0}\). The scenarios _3-Scat_-_stiff_ and _0-Scat_-_stiff_ use a similar synthetic a priori error correlation matrix but computed with a correlation length of 100 \(p_{0}\). This \"stiffens\" the a priori error covariance matrix so that the departure from the a priori profile becomes basically proportional to the uncertainty profile. For these scenarios, the a priori error covariance matrix of the H\({}_{2}\)O profile has been stiffened in the same way.
## 4 Discussion  
Biochar is known to increase crop production through enhancing soil microbial activities. [PERSON] et al. [12] documented a biochar-induced result at the family level via the terminal restriction fragment length polymorphism (TRFLP) method, in which the pine-derived biochar showed a positive effect on the growth of Bradyrhizobiaceae, Hypphomicrobiaceae, Streptococcusporangineae, and Thermomonosporaceae, but a negative effect on Streptomycesetaceae and Micromonosporaceae. However, little information is currently available on how biochar influences the growth and activities of soil biota at the species level [1]. Here, the effect of biochar addition on the growth of 12 soil bacterial strains belonging to different genus or species was investigated. To exclude possible interferences that soil may produce, the artificial medium added with biochar samples was applied. As a result, different species belonging to the same genus or not showed disparate responses to the same biochar sample. Moreover, such an alterable influence on bacterial growth was observed even in different strains from the same species (_B. megaterium_ and _B. pumilus_ in Figure 1), suggesting that the influence of biochar was strain-dependent (Figure 1). All three biochar samples greatly supported the growth of _B. mucilaginous_ AS1153, whereas the growth of _Kitasatospora viridis_ was significantly blocked by biochar, indicating the potential activation/toxicity of organic compounds absorbed to biochar towards bacteria [22]. Previous studies have demonstrated that biochar generated from different feedstocks lead to different disturbances in soil biota community structure [15]. Similarly, Figure 1 revealed that biochar samples derived from rice husk, corncob, and bamboo exhibited different impacts on the growth of the same bacterial strain. Previous researches have suggested that changes in microbial community were mainly due to the large surface area and porous structure of biochar [19]. Both the surface area and average pore size varied considerably among rice husk biochar, corncob biochar, and bamboo biochar (Table 1), which might contribute to different responses of the same strain. Compared to rice husk biochar and bamboo biochar, corncob biochar had an excellent performance in promoting the growth of most bacterial strains, which might be due to the favorable physico-chemical properties of corncob biochar, such as high contents of TN, TOC, and volatile matter, and moderate pH value and surface area. In general, biochar supplies a favorable habitat for bacteria growth through cell sorption [6]. However, the direct relationship between cell adsorption rate and growth stimulation has not been investigated, although biochar could adsorb all the tested strains at different extents (Figure 4). The maximal adsorption was observed on _B. thuringiensis_ but the greatest cell growth promotion was detected on _B. mucilaginous_. Interestingly, the growth of _B. mucilaginous_ could also be significantly improved after the inoculum was absorbed by the washed biochar (Figure 3). The similar phenomenon
## Materials and Methods
### Evaluation of DBF_SOSsat against Bud Break Observations  
As described in the previous section, the method for the detection of DBF_SOSsat was validated earlier in Siberia [70] for boreal forests with a different species composition than in Finland. As described in Section 2.5.2, we made small changes to the original method. Therefore, we used bud break observations of birch from phenological sites (Section 2.3) for evaluation of DBF_SOSsat in Finland. The DBF_SOSsat at the coarse grid for the evaluation of JSBACH model results is not appropriate for comparison with ground observation. Thus, the original MODIS NDWI time series with a pixel size of 0.005\({}^{\circ}\) by 0.005\({}^{\circ}\) were aggregated to 0.05\({}^{\circ}\) by 0.05\({}^{\circ}\) taking into account pixels with a dominance of deciduous and mixed forest (>50% coverage, according to National CORINE Land Cover) and excluding pixels that were partially (>10%) covered by water bodies. DBF_SOSsat was extracted from the nearest grid cell (at 0.05\({}^{\circ}\) resolution) to the phenological observation sites. The number of MODIS pixels that were used in the averaging of the NDWI time series is given in Table 1. The difference between the median of elevation within the grid cell and the elevation of the phenological site was less than 50 m (Table 1). For only two sites, Kevo and Koli, the variation in elevation within the pixel was larger than 50 m.  
We analysed the correspondence between satellite and ground observations for all site-years, for each site and for the southern, middle and northern boreal zone, separately. Furthermore, we investigated the correspondence of the year-to-year anomalies between DBF_SOSsat and the bud break observations. The statistical metrics used in the comparison were the coefficient of determination (R\({}^{2}\)), the root mean square error (RMSE) and the bias. The equations are given in Appendix B (Table 1). We excluded year 2002, because of missing data in the MODIS archive during the spring period. In addition, due to long periods of cloud cover, the DBF_SOSsat observations were not available for the following site-years: Aulanko 2001, Kolari 2005, Saariselka 2001 and Kevo 2007 and 2008.
## 5 Results
### Main Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{6}{c}{**FB15K-237**} & \multicolumn{6}{c}{**WN18 RR**} \\ \cline{2-9}  & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** & **MRR** & **Hits@1** & **Hits@3** & **Hits@10** \\ \hline \multicolumn{9}{c}{**Embedding-based**} \\ \hline TransE & 0.279 & 0.198 & 0.376 & 0.441 & 0.243 & 0.043 & 0.441 & 0.532 \\ TuckER & 0.358 & 0.266 & 0.394 & 0.544 & 0.470 & 0.443 & 0.482 & 0.526 \\ NBFNet & 0.415 & 0.321 & 0.454 & 0.599 & 0.551 & 0.497 & 0.573 & 0.666 \\ SimKGC & 0.338 & 0.252 & 0.364 & 0.511 & 0.671 & 0.595 & 0.719 & 0.802 \\ \hline \multicolumn{9}{c}{**LLM-based**} \\ \hline ChatGPT\({}_{\text{zero-shot}}\) & – & 0.237 & – & – & – & 0.190 & – & – \\ ChatGPT\({}_{\text{one-shot}}\) & – & 0.267 & – & – & – & 0.212 & – & – \\ KICGPT & 0.412 & 0.327 & 0.448 & 0.581 & 0.564 & 0.478 & 0.612 & 0.677 \\ CP-KGC & 0.329 & 0.243 & 0.353 & 0.503 & 0.648 & 0.580 & 0.683 & 0.773 \\ LLaMA \(+\) TransE & 0.232 & 0.080 & 0.321 & 0.502 & 0.202 & 0.037 & 0.360 & 0.516 \\ LLaMA \(+\) SimKGC & 0.236 & 0.074 & 0.335 & 0.503 & 0.391 & 0.065 & 0.695 & 0.798 \\ LLaMA \(+\) CoLE & 0.238 & 0.033 & 0.387 & 0.561 & 0.374 & 0.117 & 0.602 & 0.697 \\ DIFT \(+\) TransE & 0.389 & 0.322 & 0.408 & 0.525 & 0.491 & 0.462 & 0.496 & 0.560 \\ DIFT \(+\) CoLE & 0.439 & 0.364 & 0.468 & 0.586 & 0.617 & 0.569 & 0.638 & 0.708 \\ GLR (ours) & \(0.507_{\pm 0.018}\) & \(0.402_{\pm 0.011}\) & \(0.511_{\pm 0.009}\) & \(0.643_{\pm 0.008}\) & \(0.679_{\pm 0.014}\) & \(0.621_{\pm 0.012}\) & \(0.705_{\pm 0.007}\) & \(0.796_{\pm 0.009}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of different models on FB15K-237 and WN18 RR for KGC. The best scores for each metric are shown in underline. The results of SimKGC are adopted from [19], while those of ChatGPT\({}_{\text{zero-shot}}\) and ChatGPT\({}_{\text{zero-shot}}\) are taken from [14]. Performance metrics of the remaining baselines were retrieved from the corresponding published sources. This is primarily attributed to its explicit integration of graph-structured reasoning via Graph-CoT and confidence calibration through the P(True) mechanism, which together enable the model to capture nuanced relational patterns and filter out less reliable candidates, thereby improving the robustness of final predictions.
## 3 Experimental Results
### Coupled Three-Rod Waveguides, \(N=3\)
\right), \tag{17}\]  
where \(\phi_{12}=\phi_{1}-\phi_{2}\), \(\phi_{23}=\phi_{2}-\phi_{3}\), and for the sake of simplicity we assume \(\phi_{2}=0\). From Equation (16) and (17) it is clear that if \(B\) is a real number (which could be normalized to one), \(A\) has be a complex. For the specific excitation amplitude ratio, we find, \(\arg(A)\approx\frac{7}{8}\pi\), with an estimated experimental uncertainty of \(\frac{1}{12}\pi\). The complex coefficients arise naturally due to the Lorentzian character of the resonance modes as will be shown in the numerical modeling of Section 3 and a theoretical model  
Figure 6: Variations of the: (**a**) transmission amplitudes of each rod and (**b**) phase differences, \(\phi_{ij}\); \(i,j=1,2,3\) and \(i\
eq j\), between rods of the coupled three waveguides as a function \(r=|F_{1}/F_{2}|\) (\(F_{1},F_{2}\): excitation amplitudes of rod 1 and 2). Driving frequency \(\omega=33.25\) kHz. (see Supplementary Materials). Therefore, the \(\arg\left(A\right)\) and, hence, the Bell state coefficients \(A\) and \(B\) depends on the amplitudes \(\left(C_{i}\right)\) and phases \(\left(\phi_{i}\right)\) of each detecting rods, which also depends on the excitation amplitudes of each rods as shown in Figure 6. Therefore, Equations (16) and (17) shows how to tune the eigen mode superposition, i.e., the Bell state. In summary, by manipulating a single input, the relative driving amplitudes of the two rods (rods 1 and 2), the Bell state coefficients can be tuned, which allows to navigate a portion of the Hilbert space. Further, we anticipate that exploration of the Bell state Hilbert space can be enlarged by choosing other choices of input parameters of the drivers.