esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Comparison between Calculation Models and Full-Scale Measurements
### Sound Pressure Level along the Street  
Figure 6 presents the results of the SPL attenuation along the street, where the ISM and IMCRI show good accuracies. In most receiver positions, the differences between these two models and measured data are smaller than 2 dB. Meanwhile, the ARM yields larger deviations, especially in streets with very smooth surfaces (Street 1). In the street with a smooth ground surface, the received sound energy is mainly determined by the direct sound and the first reflected sound from the ground. The late reflected sound energy, which is significantly influenced by the reflection modeling scheme, is only a small proportion of the overall sound energy. In ARM, the reflections on all surfaces are modeled as perfect diffuse reflections. However, this is very different from the situations on the actual facade, especially for the smooth ground surfaces. Meanwhile, in ISM, the ground surface is assumed to produce perfect specular reflection, which is relatively close to the real situation. In IMCRI, a small scattering coefficient (0.02) is assigned according to the scattering characteristics of the ground surface. Therefore, ISM and IMCRI achieve significantly higher accuracies than ARM in the SPL simulation. As expected, the error in the Hopkins-Stryker equation is significant when the facades are very smooth (Street 1 and Street 2, scattering coefficient \(<\)0.2). However, it shows relatively good performances in streets with fluctuated facades (Street 4 and Street 5), because the sound field is more diffuse in these streets.  
Variations in the measured SPL attenuation are observed, as shown in Figure 6, especially in the far field (D \(>\) 25 m). These abnormal points only appear at a few positions in the specific frequency. Moreover, at a different frequency in the same position, the SPL attenuation curve is smooth. This indicates that the variations are probably due to accidental noise. Although the measurements were carried out late at night to achieve a relatively quiet environment, there were inevitable disturbances in the outdoor environment, i.e., wind sounds and accidental noises from surrounding areas. However, the differences among different models are much more significant than the variations. The performances of ISM and IMCRI are much better than that of ARM.
## 2 Routing Protocols in Large-Scale WSNs
### Energy Consumption Mitigation Algorithms
Sensor nodes running GMR use the position of their neighbors to select the subgroup which is the best one to deliver the message towards the destination, and the selected neighbors can reduce most the total route to destination. When no neighbor of the current node can reduce the route to the destination, face routing is used to circuitously search the path to the destination. In HGMR, the geographic hashing algorithm makes the membership management very simple with almost zero cost. According to the number of the nodes which play the different roles, HGMR selects the transmission methods for different hierarchies in reason, which makes the routing energy-efficient and scalable. However, the RP is in charge of too much missions in HGMR, which may bring the problem of rapid energy consumption and make the entire network collapse.
## 4 Conclusions  
In this paper, four types of asphalt mixes (CAM, DMAM, LFMAM, and DLFAM) have been evaluated under low temperatures and water effect. From the experimental results of three points bending, Marshall immersion and freeze-thaw splitting tests, the following main conclusions can be outlined:  
* The addition of diatomic and lignin fiber increase the low-temperature performance of asphalt mixes compared to the control mix. Moreover, the lignin fiber has a greater improvement in the bending strength and failure strain than the diatomic.
* Diatomite and lignin fiber improve water damage resistance of asphalt mixture significantly. However, the influence of diatomic on water damage resistance property is more significant than that of lignin fiber.
* The composite asphalt mix of diatomic and lignin fiber (DLFAM) is the best alternative to resist low-temperature cracking and moisture sustainability in asphalt pavements since it has the highest low-temperature performance, MSR, and TSR than other mixes.
* The Asphal mixes modified with single additives cannot enhance the overall properties of asphalt mixes significantly, while the double-adding technology can improve the overall asphalt mixes performance at the same time.
* Using the composite asphalt mix of DLFAM in construction of pavement will have a greater advantage in enhancing the service life and ride quality than the usage of single additives in pavements constructed.  
Figure 9: Results of the indirect tensile test.  
In summary, the pavement constructed with composite asphalt mix of DLFMAM will have a better traveling performance than that constructed with a single additive of diatomic or lignin fiber. A trial section of DLFMAM should be constructed in the future to determine its applicability in cold regions.  
Conceptualization and design of experiment, M.A., and A.K.; experiment, validation, and formal analysis, M.A. and A.K. Project administration, Y.Y.; writing-original draft preparation, M.A. and A.K.; writing-review and editing, M.A., A.K., and J.M.; supervision, Y.Y., D.L., and X.Q. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Central University's special research funding special fund cross-disciplinary project (xj2017174). This research was also funded by Natural Science Foundation Research Program of Shaanxi Province-Youth Talents Project (51408481).  
A financial support from CSC (Chinese Scholarship Council) is gratefully acknowledged.  
The authors declare no conflict of interest.
## 3 Results
### Printing Resolution Effects
#### 3.4.3 Printing Resolution and Permeability  
In this section, we discuss the variation of permeability with respect to grain size or printing resolution, assuming that the grain array is printed in the first type of arrangement. The variation trend of permeability for the medium arranged by the second style is very similar, but with a slight difference in absolute value, as indicated by the above analysis. The maximum print resolution achievable with 3D printing technology is approximately 100 nm, while the present resolution is 100 \(\upmu\)m. Therefore, we calculated the permeability of the printed medium with an effective pore radius ranging from 10 nm to 1 mm based on Equations (17) and (18). The results presented in Table 5 demonstrate the sensitivity of permeability to pore radius. Advanced 3D printing techniques, with a high resolution of 10 nm, can be used to simulate rocks with very low porosity, such as granite, physically. [PERSON] noted that the permeability K is an intrinsic physical property of rocks independent of the fluid viscosity, while the permeability coefficient k is dependent on the fluid viscosity. The relationship between these two concepts is shown in Equation (19) [74].  
\[k=K\frac{g}{\mu} \tag{19}\]
## 4 Discussion
The material is prepared by the silicate-based cement technique with some adjustments aimed to improve its physical properties and handling. It had been claimed that Biodentine has a fast setting time due to its increased particle size, the addition of calcium chloride to its liquid constituent, and its decreased liquid content [35; 37]. However, its bigger particle size may also explain its shallower penetration depth into the dentinal tubules compared with the other materials and its lesser ability to prevent bacterial invasion deep into the dentinal tubules as compared to the other evaluated materials. Generally, in the presence of a filling material, significantly more dead bacteria than live bacteria were detected within the dental tubules (\(p=0.003\), Figure 3), without significant differences between the filling materials (\(p=0.087\)). Nevertheless, deeper filling penetration was associated with shallower penetration depths of live bacteria (\(p=0.015\)) (Figure 4). The fact that in the present investigation the viability of the proliferating bacteria was influenced by the filling material penetration depth, rather than by the filling material type, may seemingly contradict the results of other studies that focused on the direct antimicrobial properties of different retrograde filling materials by assessing their spectra of affected bacteria, effectiveness periods, and antimicrobial mechanisms [37; 38; 39; 40; 41]. [PERSON] et al. [38]reported that MTA has no effect on any of the strict anaerobic bacteria and some antibacterial effect on several of the facultative bacteria, whereas other materials such as IRM possess antibacterial properties on both groups of bacteria [38]. Additional studies have reported that immediately after its setting, IRM has antibacterial effects on _E. faecalis_ and sustains these capabilities for at least one day [39], while others [40] reported identical effects in root-end fillings. Others claimed that Biodentine possesses antibacterial capabilities because of its relatively high pH that has an inhibitory effect on bacteria, and because of alkalization of the ecosystem that leads to the decontamination of neighboring soft and hard tissues [37; 41]. However, in addition to the direct antibacterial effect of the retrograde filling material, its capability to penetrate and to entomb the bacteria is responsible for the prevention of bacterial invasion [1]. Our findings suggest that the antibacterial properties of the retrograde filling material are related to its penetration capacity deep into the tubuli. In the current study we used an ex vivo model to simulate an in vivo clinical scenario. It should be noted that the experimental methods used to assess biofilm formation do not accurately simulate true in vivo conditions; therefore, the methods' ability to provide clinically relevant information is limited. However, in the current study, we used representative surfaces to grow the biofilm--ex vivo dentin tissue samples--which are more likely to provide information that is relevant to true in vivo endodontic infections [42].
# FT-IR Characterization of Antimicrobial Hybrid Materials through Sol-Gel Synthesis
###### Abstract  
Silica/polycaprolactone and titania/polycaprolactone hybrid organic/inorganic amorphous composites were prepared via a sol-gel method starting from a multi-element solution containing tetramethyl orthosilicate (TMOS) or titanium butoxide (TBT), polycaprolactone (PCL), water and methyltylketone (MEK). The molecular structure of the crosslinked network was based on the presence of the hydrogen bonds between organic/inorganic elements as confirmed by Fourier Transform Infra-Red (FT-IR) analysis. In particular, the structure of crosslinked network was realized by hydrogen bonds between the X-OH (X = Si or Ti) group (H donator) in the sol-gel intermediate species and ester groups (H-acceptors) in the repeating units of the polymer. The morphology of the hybrid materials; pore size distribution, elemental homogeneity and surface features, was studied by scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS) and by atomic force microscopy (AFM). The bioactivity of the synthesized hybrid materials was confirmed by observing the formation of a layer of hydroxyapatite (HAP) on the surface of the samples soaked in a simulated body fluid. The antimicrobial behavior of synthetized hybrids was also assessed against _Escherichia coli_ bacteria. In conclusion, the prepared hybrid materials are proposed for use as future bone implants.  
hybrid composite; PCL; TiO\({}_{2}\); SiO\({}_{2}\); FT-IR spectroscopy; antibacterial behavior +
Footnote †: journal: Applied Physics Letters
# Probabilistic Modeling of Dust-Induced FSO Attenuation for 5G/6G Backhaul in Arid Regions
###### Abstract
In a related study [17], the authors employed radiation propagation theory and the small-angle approximation method to model FSO signal attenuation, concluding that the attenuation follows a negative exponential distribution. While these studies represent important initial steps, they also underscore a significant research gap, highlighting the need for deeper understanding and more comprehensive modeling of the FSO channel under dusty conditions. Probabilistic models are vital, capturing the inherent variability and uncertainty in real-world FSO performance. Unlike deterministic models, which yield average estimates, probabilistic approaches enable system designers to assess risk and design for robust performance under varying atmospheric conditions. We propose developing a probabilistic model based on data collected under controlled environmental conditions. Similar methodologies have proven effective in other domains--such as underwater optical communication and turbulence modeling--where controlled experimental setups were used to establish probabilistic frameworks [18; 19], further validating the soundness of our approach. The main contribution of this work is the development of a probabilistic attenuation model for FSO links operating under dust storm conditions. Analyzing empirical histograms and fitting metrics reveals that signal attenuation exhibits statistical variability across different visibility levels. The key contributions are as follows:  
* Development of a probabilistic model for dust-induced attenuation in FSO communication links, applicable to varying dust storm intensities. * Comprehensive performance evaluation of multiple statistical distributions, showing that the Johnson SB distribution achieves the best fit under dense, moderate, and light dust conditions. The remainder of the paper is organized as follows: Section 2 presents the experimental setup and outlines the data collection procedure. Section 3 discusses the modeling results and provides statistical insights. Section 4 concludes the paper by summarizing the main findings and outlining directions for future research.
## 4 Experiments
### Implementation Details  
Our model is implemented by Python and TensorFlow. ResNet-50 pre-trained on ImageNet is used to extract visual features, and the input images are of size \(224\times 224\times 3\). The output from the _pool5_ layer is used as \(\sigma\) and \(F\) is 2048. The feature map from _block3_ is used as \(r\), which is of size \(7\times 7\times 1024\), hence \(n\) is 7 and \(L\) is 1024. The outputs from _pool5_ and _block3_ layers are often used to represent image features. For hyperparameters, \(K\) is 100, \(K^{\prime}\) is 512 and \(D\) is 10. \(M\) is 2 or 3. \(\alpha\), \(\gamma\) and \(\delta\) are equal to 1. \(\beta\) is 1 for label-given task, and 0 for non-label-given task in most cases. \(\mu\) is tuned according to the dataset. \(\lambda_{1}\) is \(10^{-4}\), and \(\lambda_{2}\) is 1. We perform hyper-parameter tuning carefully for all of the models, and the values of hyperparameters are chosen according to the tuning results. For training, the maximum training epoch is 150, and early stopping is adopted once we detect overfitting. The learning rate is \(10^{-4}\), and the batch size is 64. The model converges within 4 h when trained on a single GPU machine (RTX2080 Ti).
## 9 Discussion and Conclusions
This approach is applicable to any lagged cross-correlation structure for the noise terms that might be chosen. It is important to note, however, that the computational cost of this non-recursive algorithm, in terms of time and memory, scales significantly with the length of the time series being modelled. For this reason, an approximative recursive algorithm has been proposed that was obtained by merging the recursive Kalman filter algorithms for the contemporaneous-form and future-form state space models; no smoother backward pass was included in this algorithm. The resulting generalised Kalman filter algorithm was obtained using a purely heuristical approach. Nevertheless, it has been demonstrated that, both in a simulation study and in an example of the analysis of real-world data, this approximative recursive algorithm performs almost as well as the optimal non-recursive algorithm. A further potential benefit of formulating state estimation problems as weighted least-squares problems is given by the possibility of imposing constraints and applying regularisation. If one is willing to perform state estimation using non-recursive algorithms, as suggested by [PERSON] and [PERSON] [PERSON] and [PERSON] (2014), the considerable repertoire of methods and experience that have been accumulated in the least-squares field can be utilised. Future research directions in this field include the design of approximate recursive smoothers for the case of both lag-zero and lag-one cross-correlations being present simultaneously. Additionally, the development of square-root variants of the information filter and RTS smoother algorithms presented in this paper could be explored. Preliminary experience with modelling real-world data sets using state space models with correlated noise terms indicates that the covariance matrix of the filtered state estimate, given by Equation (104), exhibits a propensity to lose the property of positive definiteness, potentially hindering parameter estimation via maximum likelihood methods; this is a well-known potential effect of forming differences in matrices ([PERSON] and [PERSON], 2016). The same problem may arise with the definition of the modified dynamical noise covariance matrix for the future-form Kalman filter; see Equation (28). Square-root filtering algorithms would provide a convenient solution to these kinds of numerical problems. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The simulated data will be provided by the author upon request. **Acknowledgments:** This work has benefitted significantly from discussions with [PERSON]. The comments of two anonymous reviewers have contributed to improving the paper. **Conflicts of Interest:** The author declares no conflicts of interest.
## 2 Aims and Objectives
### Power System Aims and Objectives  
The objective of the craft's power subsystem is to generate, store, control and distribute electrical energy to the craft's sub-systems [3]. Typically, the propulsion system, Altitude Determination and Control System, Payload, Communication system and On-Board Computer will all require an electrical power source to operate for conventional designs [7]. It is necessary to specify the amount of power generated throughout the CubeSat's orbit, the consumption of the craft's systems, maximum storage capacity and other key electrical equipment such as DC to DC converters to step down supplied voltage incoming power from the solar cells. The power system is critical to the mission's success as the craft cannot operate without a suitable power supply [3].  
Power System Objectives
* Research suitable propulsion systems for a 3U CubeSat gathering performance data and operational complexity data.  
* Research existing Power systems used for previous missions.
* Identify, select/design and analyze means of power generation, storage, control, and distribution.
* Verify the selected system can provide power to the fed subsystems throughout orbit.
* Create an electrical layout, noting mass, volume, performance, and cost of each component.
## 2 Materials and Methods
### Statistical Analysis  
We assumed the plants conditions in each eight-day interval was the same as many previous studies assumed [33; 40; 42]. Thus, all tower-based flux, meteorological and soil measurements were averaged in eight-day time steps consistent with the remote sensing products. We also put ground vegetation measurements and chamber-based Re estimations into this consecutive eight-day time step measuring series from 2009 to 2011 according to the specific measuring date. We employed linear regression and a paired \(t\)-test (\(\alpha=0.05\)) to investigate the performance of different models for GPP estimations as compared to GPP_EC [30]. In addition, two indices, root mean square error (_RMSE_) and relative predictive error (_RPE_) (Equations (21) and (22)), were used to evaluate the model agreement and bias from GPP_EC [30; 33]. After tests of the normality (Shapiro-Wilk test) and homogeneity of variance (Bartlett test) (\(p>0.05\)), we employed the one-way analysis of variance (ANOVA) and Tukey's honest significant difference (HSD) to evaluate the accuracy of the cumulative growing season GPP estimations.  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}{(x_{i}-y_{i})^{2}}}{n}} \tag{21}\]  
\[RPE=(\frac{\overline{y}-\overline{x}}{\overline{x}})\times 100\% \tag{22}\]  
where \(x_{i}\), \(\overline{x}\), \(y_{i}\) and \(\overline{y}\) represents the satellite-based GPP (Re) estimations, the mean value of satellite-based _GPP_ (Re) estimations, the GPP_EC (GPP_Re) time series, and the mean value of GPP_EC (GPP_Re), respectively.  
Both those satellite-based GPP estimations that accurately represented GPP_EC (\(p>0.05\) and _RPE_ \(<10\%\)) and GPP_EC were used to evaluate satellite-based Re estimations during the growing season. Satellite-based Re estimations were also compared with the EC-based Re (Re_EC) using identical methods to satellite-based GPP comparison with GPP_EC. The best satellite-based Re estimation was used to partition Re into Ra and Rm. Linear regression and paired \(t\)-test (\(\alpha=0.05\)) were used to validate the accuracy of satellite-based _Re_ partition ratios based on the chamber-based _Re_ components measurements. In addition, we test the effect of SWC on seasonal variation of the _Re_ partition ratio, and the missing values of SWC in 2010 were filled by the quadratic regression relationship between SWC and LSWI. Using the exponential relationship between Re and _Ts_ (Equation (2)), we estimated the temperature sensitivity (\(Q_{10}\)) of Re and respiration components (Equation (23)) [8].  
\[Q_{10}=e^{(10\times b)} \tag{23}\]  
where \(Q_{10}\) is used for representing the change in _Re_ and respiration components rate over a 10 \({}^{\circ}\)C change in soil temperature, and \(b\) is regression parameter from Equation (2). All statistical and modeling procedures were performed in the \(R\) statistical computing packages (Version 3.2.0).