esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Analysis of Hardness and Elastic Modulus Results
#### 3.3.1 Comparative Analysis of Hardness and Elastic Modulus Under Different Treatments
Heating leads to moisture loss on the sample surface, resulting in hardening and subsequent increases in the average values of \(H_{\text{IT}}\) and \(E_{\text{IT}}\). Under identical controlled conditions, moisture exerts a more pronounced effect on the coal damage than temperature. After the hygrothermal coupled treatment, the degree of damage falls between that observed under humidification and heating alone, suggesting that the elevated temperature may partially offset moisture-induced damage. The results of the nanoindentation test show that the increase in the water content significantly reduces the hardness and elastic modulus of the coal sample surface. This trend is highly consistent with the research results of [PERSON] et al. [26] on the reduction in the macroscopic compressive strength and elastic modulus of coal-rock composites under different water content conditions. Although this study mainly focused on overall mechanical properties, the current experiment provides direct evidence at the microscale, revealing the water-induced softening effect and its structural response at the mineral level. The above results not only verify the mechanical changes at the macroscopic level but also show that microstructural degradation can serve as a precursor to the weakening of the mechanical properties of the coal body.
## 5 Methods and Models
### Mine Warning Index (MWI) and Ambient Intelligence
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**MWI**} & \multirow{2}{*}{\begin{tabular}{c} **Serving** \\ **State** \\ \end{tabular} } & \multicolumn{5}{c}{**Variables**} \\ \cline{3-8}  & & **HI** & \begin{tabular}{c} **Temperature (\(T_{P}\))** \\ \(T_{2}\)**, \(\ldots\)**, \(T_{4}\)**)** **(C)** \\ \end{tabular} & \
\begin{tabular}{c} **Humidity (\(H_{P}\)** \\ \(H_{2}\)**, \(\ldots\)**, \(H_{4}\)**)** **(C)** \\ \end{tabular} &
\begin{tabular}{c} **Gases Concentrations (ppm)** \\ \(\mathbf{CH_{4}}\) \\ \end{tabular} \\ \hline
0–3.5 & Normal & \textless{}90 & \(T_{i}\leq 28\) & \(H_{i}\leq 70\) & \(G_{n}\leq 2000\) & \(G_{n}\leq 15\) & \(G_{n}\leq 2000\) \\ \hline
3–5–7 & Warning & \(90<H_{i}<103\) & \(28<T_{i}<40\) & \(70<H_{i}<80\) & \(2000<G_{n}<4000\) & \(15<G_{n}<25\) & \(2000<G_{n}<5000\) \\ \hline
8–10 & Alarming & \(103<H_{i}<124\) & \(T_{i}\geq 40\) & \(H_{i}\geq 80\) & \(G_{n}\geq 4000\) & \(G_{n}\geq 25\) & \(G_{n}\geq 5000\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Threshold limit values of temperature, humidity, and toxic combustible gases based on the effect on humans. and whenever skewness is zero the distribution will be uniform around its mean. Non-zero values of skewness are a clear indication of an event in that particular region. In addition, a comparison of the statistical values of different regions allows ambient intelligence to answer following questions:  
1. _What is the most well-known range of gas concentration?_
2. _What is the safety state in each region of the mine?_
3. _What are the variations from the norm and why? Are they detected?_
4. _What is the relationship between the different monitoring parameters?_
## 4 Impacts on the Range Delay
### Ionospheric Delay  
An additional delay suffers the signal along the propagation path due to the electron content within the ionosphere. This ionospheric delay is estimated by multiplying half the speed of light with the frequency dependent range offset from Equation (7) taking into account the carrier frequency \(f\) of the radar. In order to determine the electron content in slant range, the vertical total electron content (VTEC) has to be divided by the cosine of the incidence angle (\(\theta\)):  
\[\delta\,r_{ionosphere}\,=\,\frac{40.28\,m^{3}\,s^{-2}}{f^{2}}\,\cdot\,\frac{ VTEC}{cos\,\theta}. \tag{7}\]  
Several models exist for retrieving VTEC values for a given time and geographic position (latitude, longitude) using the measurement of a ground based station network. For the current study, an empirical electron density model called International Reference Ionosphere (IRI) 2012 has been used [22]. The model output to a given time and location is available via website, e.g., at [[https://omniweb.sci.gsfc.nasa.gov/vitmo/iri2012_vitmo.html](https://omniweb.sci.gsfc.nasa.gov/vitmo/iri2012_vitmo.html)]([https://omniweb.sci.gsfc.nasa.gov/vitmo/iri2012_vitmo.html](https://omniweb.sci.gsfc.nasa.gov/vitmo/iri2012_vitmo.html)). Based on these VTEC values, the ionospheric delay has been derived for each overpass according to Equation (7).  
It is found that the impact of the ionosphere is slightly different for the two observation periods. For S-1A, the delays vary from 0.6 ns to 1.4 ns with an average value of 1.0 ns and a standard deviation of 0.3 ns. For S-1B, the ionospheric activity is lower; delays between 0.14 ns and 0.56 ns have been detected with 0.3 ns in average and a standard deviation of 0.1 ns. The contribution of the ionosphere to the range offset is estimated to be 1 cm for S-1A and 0.4 cm for S-1B with a small uncertainty of 0.3 cm and 0.15 cm for S-1A and S-1B, respectively.  
Note that, for low orbit satellites, the SAR pulse does not travel through the full ionosphere. This fact leads to an overestimation of the ionospheric delay with Equation (7). However, this additional correction is neglected in our study due to the small impact of the ionospheric delay for the C-band.
## 6 Conclusions  
In this paper, an all-fiber symmetrical two-mode coupler with a 1 \(\upmu\)m band that can realize the conversion of the LP\({}_{01}\) mode to the LP\({}_{11}\) mode with high purity is designed and produced, and a feasible solution for CVB all-fiber lasers is provided. Finally, we obtain a CVB output with working central wavelengths of 1038.97 nm/1067.72 nm, a repetition of 8.78 MHz, pulse widths of 660 ps/656 ps, and maximum output average powers of 5.25 mW/5.2 mW. By adjusting PC3, we can obtain TM\({}_{01}\) and TE\({}_{01}\) modes with mode purity values of 97.18%/97.07%, respectively. To our knowledge, this is the first time that a symmetrical two-mode coupler has been obtained using two identical commercial Corning SMF-28e fibers, and the highest mode purity 1 \(\upmu\)m all-fiber CVB output was  
Figure 10: (**a**) TM\({}_{01}\) mode; (**b–e**) mode distribution after TM\({}_{01}\) mode passes through polarizers in different directions; (**f**) TE\({}_{01}\) mode; (**g–j**) mode distribution of the TE\({}_{01}\) mode passes through polarizers in different directions.  
obtained, which minimizes the manufacturing process of mode selection couplers and reduces manufacturing costs. In future work, the process of taper excitation of the LP\({}_{11}\) mode should be simulated and analyzed to investigate the factors affecting the mode conversion efficiency of the STMC, and special optical fibers should be designed to improve the mode conversion efficiency of the STMC.  
Conceptualization, B.Y. and X.L.; methodology, B.Y., X.L., Y.L. and Z.W.; formal analysis, H.H., K.Z. and Y.Z.; investigation, S.P. and T.Z.; data curation, B.Y.; writing--original draft preparation, B.Y.; writing--review and editing, B.Y.; supervision, T.L. and A.Y.; project administration, X.L.; funding acquisition, X.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Beijing Municipal Natural Science Foundation(1212009); the Basic Scientific Research(JCKY2021110B175); the Rapid Support Programme(80914020105); and the Open Fund for Key Laboratory of Solid-State Laser Technology.  
Not applicable.  
Data Availability StatementData underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.  
The authors declare no conflict of interest.
## 4 Conclusions  
In this paper, based on joint atmospheric turbulence, we established a mathematical model for electromagnetic vortex wave transmission in free space communication systems in Ku band. Initially, through theoretical analysis and numerical simulation validation, the intensity and phase of electromagnetic vortex waves were easily distorted by atmospheric turbulence after it was transmitted through a free space channel. Simultaneously, mode crosstalk was obvious, and its own part of the energy was transferred to another OAM mode, making this other OAM mode  
Figure 11: Variance of channel capacity with different OAM modes.  
Figure 10: Signal-to-crosstalk ratio with transmission path after adaptive compensation.  
carry information that was not pure. Then, moderate-to-strong turbulence induced severe intensity and phase disturbances, which were significantly reduced by the applied adaptive compensation. In addition, with the compensation method, the probability of crosstalk of the OAM mode to the neighboring mode increased; however, the standard mode of receiving was still the main part, which could be accepted. Finally, we used OAM multiplexing technology to realize the multiplexing transmission of information, and the intensity and phase distortion of multiplexing signals caused by the atmospheric turbulence were improved by an adaptive compensation technique. This method can effectively improve the mode crosstalk generated by OAM mode multiplexing, which provides a foundation for the application of module division multiplexing technology. The channel capacity of OAM-based wireless communication links, under the impact of weak-to-strong atmospheric turbulence, is evidently promoted by the increasing number of OAM modes. This study provides a theoretical support for future research on microwave propagation in wireless communication links through weak-to-strong turbulence.  
Methodology, J.Y.; Software, H.Z.; Validation, X.Z.; Formal Analysis, J.Y.; Resources, X.Z. and H.Z.; Writing-Original Draft Preparation, J.Y.; Funding Acquisition, X.Z., L.X. and H.Z.; Writing-Review & Editing, J.Y. and H.L.  
This work was supported by the National Natural Science Foundation of China (NSFC) (61571057, 61527820, 61575082).  
The authors declare no conflict of interest.
# Does Intensive Land Use Contribute to Energy Efficiency?--Evidence Based on a Spatial Durbin Model
## 3 Data and Methods
### Data
#### 3.1.2 Data Source  
The quantity of patent applications in this article was manually compiled on the CNKI platform. The rest of the data come from the \"China Statistical Yearbook\", \"China City Statistical Yearbook\", Statistical Yearbooks of Various Provincial Administrative Units, and \"China Energy Statistical Yearbook\". The night light data are from the Visible Light Imaging Linear Scanning Service System (DMSP/OLS) in the U.S. Defense Weather Satellite and Visible Near Infrared Imaging Radiometer (NPP/VIIRS) from the National Polar Orbit Satellite. The sample selected in this paper is 280 cities as the data availability, and the time period is from 2009 to 2018. The missing data are filled in by the difference method (if a city has a missing year, we use the temporal trend of the variable to make it up).
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## 3 Dynamic and Adaptive Routing Algorithm (DARAL)
### Sub-Network Routing  
The routing between different sub-networks is strictly necessary for the right functioning of _DARAL_. Thus, and as can be seen in Appendix A, there exist a specific field called _routing type_ in the protocol header, which defines the three different routing methods in _DARAL_:  
* Gateway: It represents a message going up in the network tree, using the link with the upper VC. This routing type is based on the sub-network identification or vID to route packets.
* Forwarding: It means that a message goes down in the network tree until it reaches its destination sub-network. This routing type is also based on the vID to route packets.
* Parsing: It is the last step for a message transmission. This type is used when a message reaches the destination sub-network, and it is going to be delivered to the destination node, using from now on the node address to route.  
Figure 10 shows examples of these kinds of messages graphically. The nodes represented as a triangle show the VCs, which manage a virtual sub-network. Cases Figure 10a,b show a communication between different clusters. In both cases, the node \(EN_{2F}\) generates a packet with destination \(EN_{3D}\) in Figure 10a and \(EN_{1B}\) in Figure 10b. As long as \(EN_{2F}\) is an EN without routing tables, it needs to send the message with the routing type _Gateway_ to its VC. For this example, the VC from the sub-network 2 does not know the destination node, so it also routes the message to its VC (in this case, the _Network Root_) using also the routing type _Gateway_. As the _Network Root_ knows the complete routing tree, it can find a route to forward the packet using the routing type _Forwarding_ Figure 10a, or find the destination node using the routing type _Parsing_ Figure 10b.  
Figure 10: Example of different routing types in _DARAL_: (**a,b**) inter-cluster communication; and (**c**) intra-cluster communication.  
Another example is shown in Figure 10c, and it shows an intra-cluster communication. As long as a node sends a message to another node in the same sub-network, the VC can route it directly without external intervention i.e., the \(EN_{2F}\) sends a message to \(EN_{2C}\). The message goes firstly to its VC, which finds the node in the sub-network. Then, the VC sends the message to \(EN_{2C}\) with the routing type _Parsing_.
## 6 Antioxidant and Antimicrobial Properties of _Camelia sinensis_
BHA, at non-inhibitory concentrations, increased the microbicidal activity of green tea against _S. mutans_, non-susceptible _E. coli_, and _C. albicans_. On the other hand, green tea in combination with BHA reduced _S. mutans_ hydrophobicity and greatly inhibited _C. albicans_ hyphae formation. The mechanism involved in the increased antimicrobial activity of green tea seems to be related to an impairment of the barrier function in microorganisms and a depletion in thiol groups. Tea chloroform and petroleum ether extracts showed strong antibacterial activities against _B. subtilis_ and _P. aeruginosa_, while other extracts were less active [65]. The MIC value of tea chloroform extract was found to be 25 ug/mL. The antibacterial activities of Kenyan and Nigerian tea bag extracts were tested against _S. aureus, P. aeruginosa, V. cholerae, E. coli_, and _Salmonella_ and _Proteus_ species using agar-gel diffusion method [9]. Results obtained showed that 20% extract of both teas exerted antibacterial activities against _S. aureus, E. coli, V. cholerae_, and _Proteus_ species. [PERSON] and [PERSON] [69] evaluated and compared _C. sinensis_ fresh leaves, commercial green tea leaves, and dust tea antimicrobial effects against enteropathogens and specific fungi, using well diffusion and paper disk diffusion assays, and MIC determination. Synergistic activity of green tea and commercial antibiotic chloramphenicol was also analyzed. Catechin (the major green tea polyphenols) antibacterial effects were studied using _Clostridium_ and _Bacillus_ species spores [63]. _Clostridium botulinum_ and _Bacillus cereus_ vegetative growth was inhibited by catechins' crude extracts. Low catechins concentrations, although requiring a long exposure time, markedly inhibited bacterial spores' growth. Indeed, spores are generally resistant to many disinfectants, but here were sensitive to catechins. [PERSON] et al. [76] employed tested alkaloids, carbohydrates, flavonoids, saponins, steroids, tannins, and terpen in tea. The antimicrobial effects of tea methanolic and aqueous extracts on 4 standard _E. coli, P. aeruginosa, S. aureus,_ and _Bacillus subtilis_ strains were also determined by standard methods. Studied tea extracts evidenced antimicrobial effects against _E. coli_, _S. aureus_, _B. subtilis_, and _P. aeruginosa_, besides also containing tannins and reducing sugars. Black and green tea extracts also exerted growth inhibitory effects on _C. jejuni_ and _Campylobacter coli_ clinical isolates [77]. Tea extracts studied were able to kill _C. jejuni_ and _C. coli_ within 4 h. As a defense response against herbivores attacks, such as the smaller tea tortrix (_Adoxophytes hounnai_ Yasuda), _C. sinensis_ leaves exhales numerous volatiles at higher doses ((Z)-3-hexen-1-ol, linalool, \(\alpha\)-farnesene, benzyl nitrile, indole, nerolidol, and ocimenes) [64].
## 4 Shannon Entropy and Kullback-Leibler Divergence
### Gaussian BNs
Formally, this is equivalent to \(\widetilde{\Sigma}_{\mathcal{B}}^{*}=P\widetilde{\Sigma}_{\mathcal{B}}P^{ \mathsf{T}}\) where \(P\) is a permutation matrix that imposes the desired node ordering: since both the rows and the columns are permuted in the same way, the diagonal elements of \(\widetilde{\Sigma}_{\mathcal{B}}\) are the same as those of \(\widetilde{\Sigma}_{\mathcal{B}}^{*}\) and the trace is unaffected. 2. We have \(\widetilde{\Sigma}_{\mathcal{B}^{\prime}}=C_{\mathcal{B}^{\prime}}C_{ \mathcal{B}^{\prime}}^{\mathsf{T}}\). 3. As for \(\widetilde{\Sigma}_{\mathcal{B}}^{*}\), we can write \(\widetilde{\Sigma}_{\mathcal{B}}^{*}=P\widetilde{\Sigma}_{\mathcal{B}}P=(PC_ {\mathcal{B}})(PC_{\mathcal{B}})^{\mathsf{T}}=C_{\mathcal{B}}^{*}(C_{ \mathcal{B}}^{*})^{\mathsf{T}}\) where \(C_{\mathcal{B}}^{*}=PC_{\mathcal{B}}\) is the lower triangular matrix \(C_{\mathcal{B}}\) with the rows re-ordered to match \(\widetilde{\Sigma}_{\mathcal{B}^{\prime}}\). Note that \(C_{\mathcal{B}}^{*}\) is not lower triangular unless \(\mathcal{G}\) and \(\mathcal{G}^{\prime}\) have the same partial node ordering, which implies \(P=\operatorname{I}_{\operatorname{N}}\). Therefore \[\operatorname{tr}(\Sigma_{\mathcal{B}^{\prime}}^{-1}\Sigma_{\mathcal{B}})= \operatorname{tr}\Bigl{(}(C_{\mathcal{B}^{\prime}}^{-1}C_{\mathcal{B}}^{*})^{ \mathsf{T}}(C_{\mathcal{B}^{\prime}}^{-1}C_{\mathcal{B}}^{*})\Bigr{)}=\|C_{ \mathcal{B}^{\prime}}^{-1}C_{\mathcal{B}}^{*}\|_{F}^{2}\] (17) where the last step rests on [PERSON] [53] (Result 4.15). We can invert \(C_{\mathcal{B}^{\prime}}\) in \(O\bigl{(}N^{2}\bigr{)}\) time following [PERSON] [54] (Algorithm 2.3). Multiplying \(C_{\mathcal{B}^{\prime}}^{-1}\) and \(C_{\mathcal{B}}^{*}\) is still \(O\bigl{(}N^{3}\bigr{)}\). The Frobenius norm \(\|\cdot\|_{F}\) is \(O\bigl{(}N^{2}\bigr{)}\) since it is the sum of the squared elements of \(C_{\mathcal{B}^{\prime}}^{-1}C_{\mathcal{B}}^{*}\). * _Computing the determinants_\(\det(\Sigma_{\mathcal{B}^{\prime}})\)_and_\(\det(\Sigma_{\mathcal{B}})\). From (13), each determinant can be computed in \(O(N)\). * _Computing the quadratic term_\((\mu_{\mathcal{B}^{\prime}}-\mu_{\mathcal{B}})^{\mathsf{T}}\Sigma_{\mathcal{B} ^{\prime}}^{-1}(\mu_{\mathcal{B}^{\prime}}-\mu_{\mathcal{B}})\). Decomposing \(\Sigma_{\mathcal{B}^{\prime}}^{-1}\) leads to \[(\mu_{\mathcal{B}^{\prime}}-\mu_{\mathcal{B}})^{\mathsf{T}}\Sigma_{\mathcal{B }^{\prime}}^{-1}(\mu_{\mathcal{B}^{\prime}}-\mu_{\mathcal{B}})=(C_{ \mathcal{B}^{\prime}}^{-1}(\mu_{\mathcal{B}^{\prime}}^{*}-\mu_{\mathcal{B}}^{*} ))^{\mathsf{T}}C_{\mathcal{B}^{\prime}}^{-1}(\mu_{\mathcal{B}^{\prime}}^{*}- \mu_{\mathcal{B}}^{*}),\] (18) where \(\mu_{\mathcal{B}^{\prime}}^{*}\) and \(\mu_{\mathcal{B}}^{*}\) are the mean vectors re-ordered to match \(C_{\mathcal{B}^{\prime}}^{-1}\). The computational complexity is still \(O\bigl{(}N^{2}+2N\bigr{)}\) because \(C_{\mathcal{B}^{\prime}}^{-1}\) is available from previous computations.
# Photonics-Based Microwave Image-Reject Mixer
## 2 Photonic IRMs Based on Pre-Filtering
In addition, because the multiple-stage frequency conversion needs multiple LO sources, the system always has a complex structure with large volume and high cost. Figure 4: Principle of a photonic IRM based on multiple-stage frequency conversion: **(a)** the received RF signal and the location of LO1; **(b)** the downconverted signal using LO1 and the image to be rejected by the bandpass filter (BFF); **(c)** the signal downconverted to the desired output intermediate frequency (IF) band using LO2. Figure 3: Schematic diagram of photonic an IRM based on multiple-stage frequency conversion. For photonic IRMs based on pre-filtering, one trend has been to involve microwave photonic filters [44; 45], which are designed to undertake equivalent tasks to those of microwave filters. However, it is hoped that these IRMs will introduce advantages brought by photonics, such as low loss, high bandwidth, EMI, tunability, and reconfigurability.
# High-Efficiency Triple-Band RF-to-DC Rectifier Primary Design for RF Energy-Harvesting Systems
In 2017 [16], a triple-band rectifier operating at 925 MHz (GSM-900), 1820 MHz (GSM-1800), and 2170 MHz (UMTS), respectively, was proposed. Numerical results indicated that the peak efficiency was 41%, 32%, and 26% for an input power of \(-\)10 dBm and for the three aforementioned bands, respectively. In 2018, [PERSON] et al. [17] designed a triple-band rectifier with an impedance-matching network comprised of meander lines and open- and radial-stubs. A peak efficiency of 68% was achieved for a three-tone signal and input power of \(-\)7 dBm, whereas the output load was equal to 1.1 kOhm. The same year, [PERSON] et al. [18] introduced a rectifier design with a triple-band impedance-matching network. The system resonated at 0.85 GHz, 1.77 GHz, and 2.07 GHz, whereas the well-known Schottky diode HSMS2850 from Avago Technologies was selected due to its low biasing voltage. They achieved maximum efficiencies of 61.9%, 71.5%, and 60.5% at the three frequencies of interest. In 2020 [19], the authors presented a triple-band system that simultaneously harvested ambient RF energy at relatively low and medium input power densities. For an input power level of \(-\)10 dBm, they achieved a power conversion efficiency of 33.7%, 21.8%, and 20% at 0.9 GHz, 1.8 GHz, and 2.45 GHz, respectively. For an input power of 4 dBm, a maximum efficiency of 54%, 51%, and 48% was achieved for the above frequencies. Finally, the same year, [PERSON] et al. [20], to overcome the challenge of battery recharging and/or replacement in industry, proposed a triple-band high-gain multibeam ambient RF-to-DC rectifier for the GSM-1800, UMTS-2100, and WiFi 2.4 GHz frequency bands. They managed a maximum power conversion efficiency of 42%, 45%, and 38% for an input power of \(-\)10 dBm and an output load of 5.0 kOhm at 1.85 GHz, 2.15 GHz, and 2.48 GHz, respectively. In this work, a novel triple-band rectifier that operates in the frequency bands of the LoRaWAN network, the GSM-900 mobile communication network, and the WiFi 2.4 GHz wireless network is presented. It is an extended version of the work presented in [21]. The RF-to-DC rectifier resonates at three different frequencies, i.e., 866 MHz, 948 MHz, and 2.423 GHz, which fall into the previously mentioned frequency bands. For the rectification unit, a single-layer voltage-doubler circuit was utilized that operates in the previously mentioned frequencies. A series of shunt stubs was applied to the initial design of the RF-to-DC rectifier to form the proposed triple-band system. A feasible solution of the proposed system was obtained by the utilization of a powerful genetic algorithm optimizer. The remainder of the paper is as follows. In Section 2, a detailed description of the proposed radio frequency energy-harvesting system is presented, indicating the Schottky diode-based dual- and triple-band rectifying circuits. Section 3 summarizes the main results of the proposed rectifier and evaluates its performance. Finally, Section 4 concludes with the findings of the paper.