esa-sceva/satcom-chunk-collection huggingface dataset:
# Ecosystem Water Use Efficiency in the Three-North Region of China Based on Long-Term Satellite Data
## 4 Discussion
### Potential Link to Sustainable Management of Environment  
Ecosystem WUE is critical for the sustainable management of environment for water-limited areas and irrigation-dependent areas. For example, [PERSON] et al. [53] found that excess water extraction has depleted the water table to an alarming level, threatening the water supply of other sectors of agriculture, posing a question on the sustainable use of water in Mexico. [PERSON] et al. [54] pointed out that, in dry land areas, it is more urgent to improve water use efficiency to increase crop production per drop. Hence, it is important to prioritize environmental sustainability by increasing water use efficiency in agriculture [55; 56; 57]. Furthermore, WUE is also useful for sustainable environmental management in water supply-limited areas. [PERSON] et al. [58] found that the seasonal variations of WUE in a desert riparian forest are correlated to the irrigation conditions. [PERSON] [59] discovered that ecosystem WUE in semiarid shrubland and grassland communities have some relationships with the vegetation types. Furthermore, ecosystem WUE is found to keep stable at slight drought conditions and decrease greatly at moderate to severe drought conditions [60], having some implications for ecosystem sustainable development.  
Figure 10: Spatial distributions of human activity and climate factors on WUE.  
Therefore, WUE at leaf scale, field scale, and ecosystem scale is a useful indicator to measure the trade-off between carbon gain and water loss, helping people to keep a balance between these two factors and continue environmentally sustainable development.
## Materials and Methods
### Complex Permittivity Modeling  
The complex permittivity of the prepared samples was modeled through a Maxwell-Garnett (MG) formulation [19]. Such a model can be applied to samples containing a filler volume fraction below the percolation threshold. The MG model was derived from a quasi-static approximation regarding the polarization of the whole sample.  
According to the MG model, the effective relative permittivity of a multiphase mixture (isotropic, linear, and with inclusions small compared to the wavelength) can be calculated through the following equation [19; 20]:  
\[\varepsilon_{\text{eff}}=\varepsilon_{b}+\frac{\frac{\frac{1}{3}\sum\limits_{i= 1}^{n}f_{i}(\varepsilon_{i}-\varepsilon_{b})\sum\limits_{k=1}^{3}\frac{ \varepsilon_{b}}{\varepsilon_{b}+N_{ik}(\varepsilon_{i}-\varepsilon_{b})}}}{1- \frac{1}{3}\sum\limits_{i=1}^{n}f_{i}(\varepsilon_{i}-\varepsilon_{b})\sum \limits_{k=1}^{3}\frac{N_{ik}}{\varepsilon_{b}+N_{ik}(\varepsilon_{i}- \varepsilon_{b})}} \tag{1}\]  
where \(\varepsilon_{b}\) is the relative permittivity of the polymer matrix (base dielectric), \(\varepsilon_{i}\) is the relative permittivity of the inclusions of type \(i\), \(f_{i}\) is the fraction volume occupied by the inclusions of type \(i\), \(N_{ik}\) is the depolarization factor of the inclusions of type \(i\), \(k\) is each of the Cartesian coordinates \((x,y,z)\), and \(n\) is the number of inclusion types.  
The depolarization factors of canonical and oblate spheroids can be theoretically calculated. For instance, in the case of a needle. Like MWCNTs, the depolarization factors read [20]:  
\[N_{i1}=0,N_{i2}=N_{i3}=\frac{1}{2} \tag{2}\]  
The electrical permittivity of the inclusions can be estimated from a theoretical model [19] by assuming that the conductive losses predominate over the dielectric relaxation:  
\[\varepsilon_{i}(j\omega)=\varepsilon_{i}t-j\varepsilon_{i}{}^{\prime\prime}= \varepsilon_{i}t-j\frac{\sigma_{i}}{\omega\varepsilon_{0}}\approx-j\frac{ \sigma_{i}}{\omega\varepsilon_{0}} \tag{3}\]  
where \(\sigma_{i}\) is the bulk conductivity of the i-th filler.
## 3 Simulations and Discussions
### Constant Damping Model  
When the aforementioned three kinds of beams are studied, firstly constant damping and elastic properties are considered. The material loss factor is given as 0.6 and the shear modulus is set as 1 MPa. The loss factors and shear moduli of the damping material are calculated by the proposed method based on the modal data of the aforementioned three sandwich beams. The comparison is shown in Figure 3.  
Figure 3 shows that accurate results of modal loss factors and shear moduli can be evaluated using the two kinds of asymmetrical beams (beam 1 and beam 2), just as the traditional symmetrical beam (beam 3) does. It can also be observed that the three kinds of cantilever sandwich beams have similar modal frequencies and similar modal dynamic mechanical properties. The reason lies in that beams in this analysis have similar flexural rigidities, structure loss factors as well as identical beam length.
## 3 Experiment Results and Analysis of Dynamic Characteristics
### Analysis of Influence of Reinforced Layer on Dynamic Characteristics of Gravel Soil
#### 3.1.5 Reinforcing Effect Coefficients of Different Reinforcing Schemes  
The following reinforcing effect coefficients are used to compare the efficiency of different reinforcing schemes:  
\[\eta_{dd}=\frac{(\epsilon_{r}-\epsilon_{0})}{\epsilon_{0}} \tag{3}\]  
\[\eta_{Ed}=\frac{(E_{r}-E_{0})}{E_{0}} \tag{4}\]  
\[\eta_{ud}=\frac{(u_{r}-u_{0})}{u_{0}} \tag{5}\]  
\[\eta_{vd}=\frac{(\epsilon_{vr}-\epsilon_{v0})}{\epsilon_{v0}} \tag{6}\]  
where \(\eta_{dd}\), \(\eta_{Ed}\), \(\eta_{ud}\), \(\eta_{pd}\) are reinforcing effect coefficients in respect of axial cumulative strain, resilience modulus, excess pore water pressure, and volumetric accumulative strain respectively; \(\epsilon_{0}\), \(E_{0}\), \(u_{0}\), and \(\epsilon_{v0}\) are the axial cumulative strain, resilient modulus, excess pore water pressure, and volumetric accumulative strain of the unreinforced sample; and \(\epsilon_{r}\), \(E_{r}\), \(u_{r}\), and \(\epsilon_{vr}\) are the axial cumulative strain, resilient modulus, excess pore water pressure, and volumetric accumulative strain of the reinforced sample. Since all the tests finished after 10,000 cycles, the values are calculated using the test results of the last loading cycle.  
The reinforcing effect coefficients of the three reinforced samples loaded under 1 Hz are compared in Table 4. The results show that, with the inclusion of reinforcement,  
Figure 10: Relationship between volumetric strain and number of loading cycles.  
the accumulated axial strain and the accumulated excess pore water pressure decrease significantly. When including two layers of reinforcement, the accumulated axial strain reduced about 64.3%, which is slightly lower than that of the sample with three layers of reinforcement (65.5%), but the reduction in excess pore water pressure is about 31.3%, which is the highest of the three samples. The reinforcing effect coefficient in respect of resilient modulus increased from 8.8% to 14.1% and 16.4% by including 1, 2 and 3 layers of reinforcement, comparing to the sample with no reinforcement. Considering the combined effect, the inclusion of two layers of reinforcement is the most optimum in terms of cost and improvement of performance. The addition of geogrid can reduce the excess pore water pressure of the sample and increase slightly the embedding effect of the sand sample. Furthermore, the embedding effect gradually weakens as the loading frequency increases.
# Research on the Self-Drilling Anchor Pull-Out Test Model and the Stability of an Anchored Slope
## 3 Self-Drilling Anchor Bolt Anchorage Mechanism Simulation Study
### Model and Parameter Selection
#### 3.2.2 Selection of Calculation Parameters  
The formulas for calculating the grout cohesion and grout stiffness, which are essential for the numerical simulation, are as follows. These are defined as the tensile force per unit length applied to the anchor bolt and the slope of the force-displacement curve per unit length of the anchor bolt, respectively:  
\[C_{g}=\frac{P_{a}}{L}=\pi dr \tag{6}\]  
\[K_{g}=\frac{C_{g}}{u} \tag{7}\]  
\(C_{g}\) is the grout cohesion, in N/m;  
\(K_{g}\) is the grout stiffness, in N/m\({}^{2}\);  
Figure 8: Numerical simulation model diagram.  
\(P_{a}\) is the maximum uplift force applied to the anchor bolt during uplift, in N;  
\(L\) is the anchorage length, in meters;  
\(D\) is the anchor bolt diameter, in meters;  
\(\tau\) is the bond strength between the anchor bolt and the grout, in Pa;  
\(u\) is the displacement length during the uplift of the anchor bolt, in meters.  
The parameters of the anchorage system can be calculated using Equations (6) and (7), where the anchorage body diameter is D = 100 mm, the anchorage length of the self-drilling anchor bolt is L = 5.75 m, and the displacement length during anchor bolt uplift is u = 26.1 mm. The parameter values for the uplift simulation model of the self-drilling anchor bolt are shown in Table 2.
## 3 Theoretical Analysis
### Imaging Characteristic between MBS and EGT
Moreover, the FBP's computational cost of projection calculation in the imaging area on the compressed data is \(\mathrm{N}_{a}\times\sqrt{\mathrm{M}}\times\sqrt{\mathrm{N}}\), the computational cost of phase interpolation is also \(\sqrt{\mathrm{N}_{a}}\times\sqrt{\mathrm{M}}\times\sqrt{\mathrm{N}}\), and the cost of coherent superposition is \(\sqrt{\mathrm{M}}\times\sqrt{\mathrm{N}}\). Therefore, the total computational cost is the sum of the above three computational costs, i.e., \((\sqrt{\mathrm{N}_{a}}+\mathrm{N}_{a}+1)\cdot\sqrt{\mathrm{M}}\times\sqrt{ \mathrm{N}}\), and it is much smaller than the traditional computational cost of BP. Through the analysis of MBS, this FBP algorithm in the time domain based on the division of aperture and image area can be applied to avoid the coupling analysis in the frequency domain and to reduce the large computational cost of the BP algorithm.
# An Intrusion Resistant SCADA Framework Based on Quantum and Post-Quantum Scheme
## 4 Proposed Scheme
### Key Generation  
This subsection explains the first phase of our proposed algorithm. It explains the mechanism of the B92 protocol to generate a key for encryption. For a better and easier understanding of the B92 protocol, we have included the key sifting phase. However, key sifting of B92 is slightly different from that in BB84.  
We assume that the Remote Terminal Unit (RTU) is the sender, and Master Terminal Unit (MTU) is the receiver. The key generation phase exhibits the B92 protocol, which comprises of the following sub-phases to generate a secret key, using quantum and public channel [11; 34].
# Constructing Condition Monitoring Model of Harmonic Drive
## 2 Related Work
### Anomaly Detection  
The fault detection task of the harmonic drive can be regarded as a kind of rotating machinery fault detection task. The fault in the rotating machinery can be detected through temperature, vibration, image, and sound. An industrial environment requires a fast and accurate detection method with noise processing.  
In terms of rotating machinery fault detection, the current mainstream research direction is to use a Support Vector Machine (SVM) or CNN with various preprocessing methods for training. The SVM method can obtain an excellent model but requires a long computing time [10], while the latter has excellent feature mining ability but requires more samples to train a high-performance model [11].  
[PERSON] et al. [12] built a wind turbine scale-down model and categorized blade failures into three types. Data were collected through an accelerometer, and the vibration signal was passed through a total of 12 statistical methods, such as standard deviation, Root Mean square, and Kurtosis, before being processed as input parameters of the prediction model. The team compared different rotational speeds and different architectures. In this study, the data of different rotational speeds were passed through Decision Tree, K-Nearest Neighbors (KNN), and SVM for blade fault detection. [PERSON] et al. found that the accuracy of the fault detection model increased with the increase in rotational speed. Since noise was not processed, the model accuracy of this study was estimated to be 87%, which was slightly lower than that of other studies.  
[PERSON] et al. [10] preprocessed the samples by using an improved version combining wavelet transforms and Empirical Mode Decomposition, and used Particle Swarm Optimization to select hyper-parameters of SVM; the accuracy of the fault detection for many types of bearings exceeded 99%.  
[PERSON] et al. [1] proposed a harmonic drive fault detection model using the multiscale convolutional neural network (MSCNN). MSCNN includes the coarse-grained layer, the classification layer, the multiscale feature learning layer, and the multisensor data fusion layer. After the fusion of multiple sensor data, the original signal data were decomposed into four layers, and the processed data were subjected to feature learning through a multiscale feature learning layer. Their research obtained 96.79% for the normal-anomaly binary classification. The method proposed in this study did not include noise processing but reduced the negative impact of noise through data from multiple sensors.
## 8 Conclusions  
This paper presents an SD-scheme for mode selection, channel assignment, and power allocation in D2D communications underlaying cellular networks. The primary goal of this research is to enhance D2D performance while maintaining the QoS demands of CUs.  
Figure 14: Number of disconnected pairs versus number of D2D pairs.  
Figure 13: Probability of D2D connections versus max distance between D2D pairs.  
Furthermore, the sub-channel of each single CU can be reused across several D2D pairs, and the detrimental effects of the interference that occurs across D2D pairs are considered during the process of resource allocation. The joint problems of mode selection, channel assignment, and power allocation are MINLP and NP and difficult to resolve. Therefore, we designed a hybrid scheme: a centralized mode selection and channel assignment approach, followed by a distributed power management approach. The initial process involves the concept of a centralized greedy-based mode selection and two-sided preference lists channel assignment approach, followed by the implementation of a distributed power control approach in the subsequent phase. Moreover, an SARSA-based RL power control method has been proposed to iteratively update the transmission power for each D2D pair utilizing the same assigned sub-channel of individual CUs with the goal of improving the EE of the D2D communications in the network. The simulation findings demonstrated that the introduced scheme yields better performance with low complexity and outperforms traditional and Q-learning schemes in terms of data rate, SE, and EE. Future research may include examining the influence of other networking factors such as users' mobility as well as including modern equipment like unmanned aerial vehicles and satellites to the network.  
Conceptualization, N.M.M.; Data curation, N.M.M.; Formal analysis, I.S.A.; Investigation, I.S.A.; Methodology, I.S.A. and M.H.D.N.H.; Project administration, N.M.M.; Software, I.S.A.; Supervision, N.M.M.; Validation, M.H.D.N.H.; Writing--original draft, I.S.A.; Writing--review and editing, M.H.D.N.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
No new data were created in this study. All relevant data are included within the paper.  
The authors declare no conflicts of interest.
# The Impact of Cell Phone Usage on Fall Risk and Postural Control During Free-Standing
## 5 Limitations and Future Research Directions  
While this study provides valuable insights, several limitations must be considered. The sample size was relatively small, and all participants were young, healthy adults, which limits the generalizability of the findings to other age groups or individuals with balance impairments. Future research could examine the effects of smartphone use on postural control in older adults or individuals with known balance disorders to better understand the broader implications of phone use on fall risk.  
Additionally, the study focused on a specific type of cognitive-motor dual task (texting), and future studies could explore other common smartphone-related tasks, such as browsing or watching videos, to determine how these activities affect postural control. The influence of environmental factors, such as different types of surfaces or levels of instability, should also be considered in future investigations to better replicate real-world conditions.  
Furthermore, while we acknowledge the potential impact of factors such as hand dominance on phone use and balance control, we were unable to incorporate additional experiments within the scope of this study. However, we have included these aspects as potential directions for future research. Investigating whether using the dominant versus the non-dominant hand influences postural stability could provide further insights. Similarly, future studies could explore ergonomic interventions aimed at reducing the fall risk associated with smartphone use, offering practical recommendations based on experimental validation.
# Polymethylmethacrylate (PMMA) Hosts Microbiome Associated with Oral Malodor
###### Abstract  
Previous study suggested that fixed dental prostheses (crowns) increase oral malodor production. There might be a role to temporary acrylic crowns contributing to oral malodor. In the current study, we analyzed the microbiome associated with malodorous temporary dental crowns. The study population comprised nineteen patients (mean age 45.8 \(\pm\) 10.9, 8 females) who visited the Tel Aviv University dental clinic. Temporary crowns were scored by an odor judge using a 6-point malodor organoleptic scale (0-5) and temporary crowns that were scored 2 and above were assigned as malodor positive. Microbial DNA was extracted from the temporary dental crowns and analyzed using next generation 16S rDNA sequencing. Taxa identified could be classified into 11 phyla, 50 genera and 119 core species. Malodor positive samples demonstrated higher abundance of the phyla _Protebucateria_ and _Actinobacteria_ and the genera _Tannerella_, _Allopvreodella_, _Truponna_, _Olsenella_ and _Bifidobacterium_. Malodorous samples showed higher bacterial diversity and significant differences in microbial population. Taken together these results suggest a difference between the microbial populations of malodorous and non-malodorous temporary dental crowns both in composition and diversity.  
malodor; temporary dental crowns; microbiome; 16S deep sequencing +
Footnote †: journal: Applied Sciences  
bacteria utilize oral glycoproteins for energy and growth yielding malodorous metabolites comprising of volatile sulfide compounds (VSC), diamines, volatile fatty acids and indoles that are felt during speech and exhalation [12; 13; 14].  
According to data reported by several multidisciplinary breath odor clinics (involving various medical fields such as dentistry, E.N.T., internal medicine and psychology) in various medical and academic centers, faulty restorations (e.g., leaking crowns and bridges) are considered one of the causes for oral malodor [15; 16; 17]. Furthermore, a recent study conducted on 96 subjects measured oral VSC levels in patients with and without permanent dental crowns and concluded that the presence of final fixed partial dentures significantly contributed to patients' oral malodor [18].  
Since temporary dental crowns are faulty and leaky by definition, the aim of the current study was to characterize the microbial population (i.e., microbiome) associated with temporary dental crown malodor using next generation 16S microbial identification techniques. Given the microbial nature of this condition, we hypothesize that the microbial population will vary between malodorous and non-malodorous temporary acrylic crowns.
# The Biocatalytic Degradation of Organic Dyes Using Laccase Immobilized Magnetic Nanoparticles
## 3 Results and Discussion
### The Stability of Free and Immobilized Laccase
#### 3.2.1 Storage Stability of Free and Immobilized Laccase  
Fe\({}_{3}\)O\({}_{4}\) and CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase, as well as free laccase, were kept for 20 days at a temperature of 4.0 \({}^{\circ}\)C, in a buffer solution of sodium citrate, to study their storage stability, as shown in Figure 3a. According to Figure 3a, the free laccase lost 71.0% of its initial activity after the storage for 20 days, while the Fe\({}_{3}\)O\({}_{4}\) and CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized lacases lost only 27.0% of their initial activity within the same period. The nanoparticles of Fe\({}_{3}\)O\({}_{4}\) and CuFe\({}_{2}\)O\({}_{4}\), as support for laccase, made the enzyme retain its activity and resist the changes in its structure. The enzymatic active sites' distortion that causes the conformational modification was prevented in the immobilized lactose, due to the contact between laccase and Fe\({}_{3}\)O\({}_{4}\) or CuFe\({}_{2}\)O\({}_{4}\) nanoparticles, by covalent linkages. Also, the CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase reserves its activity more than the Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized laccase, which indicated the great effect of copper ions on the laccase activity. This laccase stimulation occurs by the binding of copper ions to laccase enzymes via the Cu-binding sites of type-2.  
Figure 2: SEM images of Fe\({}_{3}\)O\({}_{4}\) nanoparticles (**a**), CuFe\({}_{2}\)O\({}_{4}\) nanoparticles (**b**), Fe\({}_{3}\)O\({}_{4}\) nanoparticle-immobilized laccase (**c**), and CuFe\({}_{2}\)O\({}_{4}\) nanoparticle-immobilized laccase (**d**).