esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Simulation Results and Analysis
### Evaluation of Turbulence Layer Outer Scale Based on TILAS Measurements  
In this work, the possibility of direct evaluation of the atmospheric-turbulence outer scale \(L_{0}\) using TILAS measurements and autocorrelation function analysis is investigated. This possibility can help to resolve a long-standing problem of sensing of the so-called piston phase (optical path difference) [23], which, within the framework of the Kolmogorov turbulence theory, is associated with the outer scale. This is essential, e.g., for long-range electro-optical systems based on coherent detection techniques [24].  
Typical autocorrelation functions \(B(\tau)\) computed in the presence of a single turbulence layer of strength \(C_{n}^{2}\ =\ 10^{-15}\ \mathrm{m}^{-2/3}\) (\(r_{0}\) = 16.3 cm) characterized by the turbulence outer scale values \(L_{0}\) varied from 0.25 m (dotted lines) to 16 m (solid lines) are shown in Figure 6. Here, the TILAS transceiver aperture diameter is fixed at \(d_{\mathrm{TILAS}}\) = 7 cm; for the TILAS aperture size \(d_{\mathrm{TILAS}}\) = 3.3 cm considered in previous sections, the functions dependence on the outer scale is less evident. Analysis of shapes of these functions reveals that the \(B(\tau)\) decline rate increases with the decrease of \(L_{0}\). Besides, if the outer scales are small enough, the negative correlation may be observed. The impact of \(L_{0}\) seems to be more pronounced on the functions computed for layers located closer to the TILAS transceiver (compare Figure 6a for \(z\)/L = 0.1 (near TILAS) and Figure 6b for \(z\)/L = 0.3); for turbulence layers that are closer to the target, the width of the autocorrelation functions (i.e., the correlation time \(\tau_{\mathrm{cor}}\)) becomes nearly indifferent to the turbulence outer scale. To increase the sensitivity of \(\tau_{\mathrm{cor}}\) to \(L_{0}\), the correlation time can be re-defined, e.g., by the drop in the intensity level by a factor \(\exp(-2)\approx 0.14\). Alternatively, the size of the transceiver aperture diameter can be further increased; as mentioned in Section 4.2, the values of \(\tau_{\mathrm{cor}}\) increase with the increase of \(d_{\mathrm{TILAS}}\), which helps to differentiate the curves with different values of \(L_{0}\). Thus, the correlation time can be used to estimate the turbulence outer scale if the layer position and strength are already known.  
Figure 6: Autocorrelation function \(B(\tau)\) computed for a single turbulence layer of strength \(C_{n}^{2}\ =\ 10^{-15}\,\mathrm{m}^{-2/3}\) (\(r_{0}\) = 16.3 cm), different outer scale values, \(L_{0}\) = 16 m (solid lines), \(L_{0}\) = 1 m (dashed lines), and \(L_{0}\) = 0.25 m (dotted lines), and different turbulence layer positions: (**a**) \(z\)/L = 0.1; (**b**) \(z\)/L = 0.3. Here, the diameter of the TILAS transceiver aperture \(d_{\mathrm{TILAS}}\) = 7 cm; other parameters are the same as in Figure 3.
# Inter-Beam Co-Channel Downlink and Uplink Interference for 5G New Radio in mm-Wave Bands +
## 5 Simulation Results
### Exemplary Comparison of MPM with 3 GPP Approach for DL Scenario
With regard to the presented above comparative analysis, it is also worth providing the mean values of the standard deviation, \(\overline{\sigma}_{LOS/NLOS}^{Model}\), obtained for the MPM: \(\overline{\sigma}_{LOS}^{MPM}=0.58\) dB and \(\overline{\sigma}_{NLOS}^{MPM}=1.69\) dB, and 3 GPP model: \(\overline{\sigma}_{LOS}^{3 GPP}=0.97\) dB and \(\overline{\sigma}_{NLOS}^{3 GPP}=7.51\) dB under LOS and NLOS conditions, respectively. The differentiation of the obtained results is related to the different spatial nature of scatterings (i.e., spatial dispersion) in the two analyzed propagation models. In the MPM, we use a multi-ellipsoidal geometric structure which is defined based on the TDL of the 3 GPP standard [14]. On the other hand, the 3 GPP statistical model has greater flexibility in the spatial location of potential scatterers. A more detailed comparison of the MPM and 3 GPP model will be presented in the prepared next paper focusing on the 3.5 GHz band used in 5G massive-MIMO systems. A more detailed description of the 3 GPP model and simulator will be presented there.
# A Survey of Wireless Sensor Network Based Air Pollution Monitoring Systems
## 3 Air Pollution Monitoring Equipment
### Gas Sensor
#### 3.1.1 Solid-state Gas Sensor [80]  
The working principle of the solid-state ambient gas sensors was discovered when researchers were dealing with the semiconductor p-n junctions, which are sensitive to environmental gases.  
A solid-state sensor consists of one or several metal oxides like tin oxide or aluminum oxide (the type of metal oxide being used depends on the target ambient gas the sensor aims for), and a heating element. The metal oxides can be processed into a paste, which is called bead-type sensor (see Figure 2). The metal oxides can also be deposited onto a silica chip similar to making semiconductors, which is called chip-type sensor (see Figure 3). When the metal oxides are exposed to the ambient gases, the gases will dissociate into charged ions or complexes that make the electrons accumulate on the surface of the metal oxides. The accumulation of electrons changes the conductivity of the metal oxides. By measuring the conductivity change, researchers are able to deduce the concentration of a specific kind of ambient gas.  
In order to increase the reaction rate that results in a strong electrical signal, a heating element is used inside the solid-state ambient gas sensor. The heating element is also used to regular the temperature because the response (conductivity change) of a specific kind of ambient gas is different in different temperature ranges.
## 1 Introduction
### Literature Review
Currently, data mining (DM) and machine learning (ML) based artificial intelligence (AI) methods have gained huge attention for fault detection, classification, and location in HVAC systems [41; 42; 43; 44; 45], as they are known to be the most effective tools for solving complex problems. However, the application of the data-mining approach in the HVDC system has seen limited study in the past literature. In this regard, the artificial neural network (ANN) is one of the most popularly used AI methods in the protection of HVDC systems [46; 47; 48; 49; 50; 51]. In addition to this, some other AI methods for application in HVDC protection lines are as follows: a fuzzy logic theory based approach [52], a support vector machine (SVM) based approach [26; 53], an extreme learning machine [54], and a stacked autoencoder [55]. The ML-based fault detection schemes pose a fast execution period and have good sensitivityand better reliability. Therefore, this work has tried to explore the applicability of different data-mining techniques in HVDC fault detection, classification, and location.
# Lead Monoxide Nanostructures for Nanophotonics: A Review
## 2 Synthesis of PbO Nanostructures
### Zero-Dimensional PbO NPs  
NPs (with a size range from 1 nm to 100 nm) have attracted extensive attention because of their versatile applications in (opto)electronics, catalysts, sensors, and biomolecules [3; 5; 6; 7; 20; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41]. In 2019, [PERSON] et al. [42] reported the formation of PbO NPs by laser ablation in hot water using Pb as a model metal. The PbO nanoparticles in a classical spherical shape can be observed immediately after only the laser ablation treatment (Figure 2a), and the spherical NP shape rapidly changes to octahedra, rods, or plates when heated by laser ablation in water. Additionally, in 2018, our group successfully fabricated PbO quantum dots (QDs) with an LPE method [20]. The transmission electron microscopy (TEM) of the as-prepared PbO QDs showed an average lateral size of 3.2 \(\pm\) 0.9 nm and an average thickness of 2.5 \(\pm\) 0.5 nm, which corresponds to 4 \(\pm\) 1 layers (Figure 2b). The high-resolution TEM (HRTEM) image of the PbO QDs displays a clear lattice fringe of 0.20 nm (Figure 2b inset), which is well assigned to the (200) plane of the \(\beta\)-PbO crystal [43]. Moreover, [PERSON] et al. [44] demonstrated that PbO NPs were successfully synthesized at the water/air interface in the condition of the Langmuir films of poly(N-vinylcarbazole). A large quantity of round PbO NPs with a diameter of several nanometers can be observed (Figure 2c). It should be pointed out that, if the temperature rises up to 40-50 \({}^{\circ}\)C, the size of the as-prepared NPs sharply increases and the crystallinity also improves [44].
# Effect of Structural Materials on Monopropellant Thruster Propulsion Performance in Micro Scale
## 3 Numerical Comparative Performance Analysis
### Gas and Materials Properties  
The decomposed gas mixture entering the chamber and the nozzle was assumed to be homogeneous and to follow the ideal gas law, as the adiabatic decomposed temperature was sufficiently high. The performance of the microthrusters operating with gas mixtures created from the decomposed hydrogen peroxide of 85, 90, and 95 wt% was assessed. The mole fractions of O\({}_{2}\) and H\({}_{2}\)O were 0.273 and 0.727 for the 85 wt% hydrogen peroxide decomposed gas mixture, 0.292 and 0.707 for the 90 wt%, and 0.313 and 0.687 for the 95 wt%. In order to attain a higher level of accuracy, 6-degree polynomials were employed for the properties considered in the simulation as follows:  
\[C_{P,mixture}=\sum_{i}x_{i}C_{p,i} \tag{7}\]  
Figure 2: Microthruster computational domain for conjugate heat transfer analysis. (**a**) Left side view. (**b**) Right side view. \[k_{mixture} = \frac{\sum x_{i}k_{i}(M_{i})^{1/3}}{\sum x_{i}(M_{i})^{1/3}} \tag{8}\] \[\mu_{mixture} = \frac{\sum x_{i}\mu_{i}(M_{i})^{1/2}}{\sum x_{i}(M_{i})^{1/2}} \tag{9}\]  
where \(x_{i}\) is the mole fraction, \(C_{p}\) is the specific heat capacity, \(\mu\) is the dynamic viscosity, and \(M\) is the molar mass. The polynomials were calculated from temperature-dependent gas properties of O\({}_{2}\) and H\({}_{2}\)O, ranging from 400 to 1000 K, with the NIST Chemistry Webbook [36] as a reference. Table 2 summarizes the polynomial coefficients obtained from the curve fitting of a 6-degree polynomial equation to the property dataset.
# A Scheme for Controlled Cyclic Asymmetric Remote State Preparation in Noisy Environment
## 4 Four-Party Controlled Cyclic Asymmetrical RSP Protocol in Noisy Environments
### Phase Damping Noise  
The phase damping noise can be expressed by Kraus operators given in Formula (31).  
\[E_{0}^{p}=\sqrt{1-p_{P}}\begin{bmatrix}1&0\\ 0&1\end{bmatrix},E_{1}^{p}=\sqrt{p_{P}}\begin{bmatrix}1&0\\ 0&0\end{bmatrix},E_{2}^{p}=\sqrt{p_{P}}\begin{bmatrix}0&0\\ 0&1\end{bmatrix}, \tag{31}\]  
where \(p_{P}\in[0,1]\) is the decoherence rates for phase damping noise.  
If the scheme is implemented in phase damping noisy environment, then the concrete density matrices of the output states can be obtained as follows.  
\[(\rho_{out}^{p})=\left\{\begin{array}{l}\left(1-p_{P}\right)^{10}\{\left[c_{0 }e^  
Then, the fidelity of the output state in phase damping noisy environment can be calculated as:  
\[\mathrm{F}^{P}\!\!=\!\!\langle\zeta|\rho_{out}^{P}|\zeta\rangle\!\!=(1\!-\!p_{P}) ^{10}+p_{P}^{10}c_{0}^{4}b_{0}^{4}a_{0}^{4}+p_{P}^{10}c_{1}^{4}b_{1}^{4}a_{1}^{4}. \tag{33}\]  
It can be seen from the above formula that the fidelity of the output quantum state is related to the coefficient of the desired quantum state and the decoherence rate in phase damping noise.
## 3 Results
### Effect of JGTW on MMP-9 Activity  
BEAS-2B cells were pretreated with various concentrations of JGTW and then stimulated with IT. The activities of MMP-9 and MMP-2 in cells increased after treatment with increased concentrations of IT (Figure 4A). Pretreatment with JGTW markedly reduced MMP-9 activity in a dose-proportional manner. Again, the ratio of MMP-9/MMP-2 was much higher in IT-stimulated cells as compared to that in vehicle-treated cells (\(p<0.01\)) and JGTW pretreatment effectively decrease the ratio of MMP9/MMP-2 in IT-treated cells (\(p<0.01\), Figure 4B).  
Figure 3: Effect of JGTW on the production of chemokines in BEAS-2B cells. The levels of h-eotaxin-3 (**A**) and h-eotaxin-1 (**B**) and h-RANTES (**C**) after 48 h IT (50 ng/mL) treatment with or without JGTW pretreatment in culture supernatants were measured. The values are presented as mean \(\pm\) SEM. \(\mathbf{{}^{\#}}\)\(p<0.01\) versus vehicle-treated cells; and \(\mathbf{{}^{**}}\)\(p<0.01\) versus IT-treated cells.  
Figure 2: Cytotoxicity effect of JGTW in BEAS-2B cells. Cell viability after 24 h JGTW treatment was assessed and the values are presented as mean \(\pm\) SEM.
# Error Analysis of Integrated Absorbance for TDLAS in a Nonuniform Flow Field
## 4 Discussion and Application
### Effect of the Absorption Line Overlap

Table 1: Physical parameters of the flow field under two working conditions. Figure 8: Schematic diagram of the absorption signal simulation and fitting. Figure 8 shows that the multiple absorption lines in the four wavenumber ranges overlap to form a blended spectrum. The right wing of the absorption line at 6807.8 cm\({}^{-1}\) has two peaks that formed through the overlapping of several absorption lines. There is a small absorption peak that formed through the overlapping of several absorption lines on the left wing of the absorption line at 7185.3 cm\({}^{-1}\). The absorption peak at 7444.3 cm\({}^{-1}\) formed through the overlapping of three approximate strength absorption lines, and there are two absorption peaks on its right wing. The absorption peak at 7466.3 cm\({}^{-1}\) formed through the overlapping of four absorption lines. Among the four lines, the line at 7185.3 cm\({}^{-1}\) has the best \"isolation\" and is followed by the line at 6807.8 cm\({}^{-1}\). The isolation of the absorption peak at 7444.3 cm\({}^{-1}\) is poor and that of the absorption peak at 7466.3 cm\({}^{-1}\) is the worst. Figure 9a,b presents the calculation results of IA error under two conditions. The red and black lines show IA errors of isolated and blended peaks, respectively. Overall, the trends of the black and red lines are the same for the same absorption line and the same working condition. The isolated peak error better characterizes the IA error when the isolation of the absorption line is better, and the error generated by the nonuniformity is larger. The IA error is more affected by the absorption line overlap when the isolation of the absorption line is worse or the error for the isolated peak is smaller. In the case of the shock wave, the oblique shock wave generates a pressure and velocity gradient along the optical path, resulting in a frequency shift \(\
u_{c}\) at the center of the peak. According to a calculation based on spectral parameters, the frequency shift generated by the pressure and velocity is \(\
u_{c}<0.001\) cm\({}^{-1}\), which can be ignored in the analysis. The effect of the frequency shift can be added following this work if needed.
## 2 Related Work
Researchers used trust information to improve prediction accuracy and overcome cold-start and sparsity problems. In [38], two approaches were presented for calculating user trust in items according to their ratings. Although it is challenging to obtain trust information between users and objects, the proposed method performed better than tested methods in addressing the specific problems. The accuracy and quality of recommender systems diminish when entity information is insufficient [21]. Therefore, various solutions have been proposed to solve the sparsity and cold-start problems. The study in [39] introduced a hybrid approach for solving sparse data by determining user interests. The movie feature was used to create a vector of movie attributes and was merged with the user rating vector to generate a user interest vector to calculate user similarity. As a result, this method outperformed specific existing recommendation algorithms in terms of accuracy. The authors of [40] enhanced prediction accuracy by using auxiliary data on a user's demography and item characteristics. The model incorporates supervised label terms to make feasible recommendations for new entities using a matrix factorization framework. In [41], a method for resolving the cold-start status using a clustering approach was proposed. This approach was constructed using a user's preferred item ratings and genres to determine similarity. The results demonstrated that the suggested model could significantly improve the cold-start solution and outperform comparable approaches in standard recommendations and cold-start situations. However, new users may abandon the system due to flaws in the suggestions during the initial phases when users lack the necessary ratings to needed by the CF in RS to calculate their user similarity. To address this issue, the authors of [42] proposed implementing a neural learning-based user similarity measure that would deliver favorable recommendations to users with few ratings (2 to 20 ratings). The experimental results demonstrated that this system was more precise, recallable, and accurate than its predecessors. Unlike algorithms that only rely on user ratings, we seek to develop a similarity measure that incorporates user interest and identifies user-suitable neighbors, even when they leave very few ratings. Since specialists in the relevant item fields handle item genre information, the genre is regarded as reliable information that can be used to identify user similarities. The proposed technique computes users' average preferences for each item genre. Then, it identifies nearest neighbors in each genre, based on a similarity threshold value. Finally, by intersecting user preferences per genre, it determines neighbors who share the same tastes across all genres and considers them the ultimate neighbors for the current user. We applied our method to real datasets and performed different experiments to verify the suggested method's accuracy and efficacy in picking correct neighbors. Our novel approach consistently generated superior results, even for users with a few ratings compared to past baseline procedures.
## 3 Results
### PAW Modulates the Root Hair Density through Root Developmental Genes
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **DW** & **PAW5** & **PAW7** & **PAW12** & **PAW19** \\ \hline Root length (mm) & 9.75 \(\pm\) 0.96 & 10.07 \(\pm\) 0.75 ns & 10.10 \(\pm\) 0.63 ns & 9.78 \(\pm\) 0.95 ns & 10.08 \(\pm\) 0.66 ns \\  & (n = 15) & (n = 15) & (n = 15) & (n = 15) & (n = 15) \\ Root hair number & 14.50 \(\pm\) 1.37 & 40.66 \(\pm\) 5.71 *** & 33.16 \(\pm\) 3.6 *** & 31.00 \(\pm\) 3.84 ** & 27.50 \(\pm\) 5.54 ns \\  & (n = 6) & (n = 6) & (n = 6) & (n = 6) & (n = 6) \\ Root hair length (\(\mu\)m) & 261.21 \(\pm\) 100.37 & 138.35 \(\pm\) 11.11 *** & 108.25 \(\pm\) 11.54 *** & 99.50 \(\pm\) 13.65 *** & 85.05 \(\pm\) 11.65 *** \\  & (N = 60) & (N = 60) & (N = 60) & (N = 60) & (N = 60) \\ \hline \hline \end{tabular} For root length and root hair number, \"n\" indicates the number of individual plants as biological replicates measured; for root hair length, \"N\" indicates the individual root hair length measured from a total of 6 samples. The results are the mean \(\pm\) SD (Student’s \(t\)-test compared with D.W. \(p\) \(>\) 0.05 ns, \(p\) \(<\) 0.05 \(\lx@math@degree\), \(p\) \(<\) 0.01 \(\lx@math@degree\), \(p\) \(<\) 0.001 **).  
\end{table} 
Table 4: Root hair number and length of _Arabidopsis_ (_WER:GFP_/_cob19_-1_) at 5 days after planting. Figure 5: PAW regulates the number of root hairs and epidermal cell size in the _Arabidopsis_ root (_WER:GFP_/_cob19_-1_). _Arabidopsis_ seedlings were grown in solid media for 7 days. (**a**) DW, (**b**) PAW5, (**c**) PAW19. The white arrows indicate the individual root hairs that were used in the quantification. Bar = 100 \(\mu\)m.  
cell growth [35]. _AUX1_ and _LAX3_ both had up-regulated expression patterns in PAW5 while _AUX1_ was down-regulated in the PAW19 treatment condition. For _OBF BINDING PROTEIN 4_ (_OBP4_), its expression was down-regulated in the PAW5 and PAW19 treatment conditions. _OBP4_ is known as a negative regulator of root growth and differentiation including the root hairs [33; 34], and it also functions as a negative regulator in PAW. Furthermore, _XYOLOGICAL ENDOTRANSGLUCOSYLASE/HYDROLASE_ (_XTH_) gene family members are factors involved in cell wall modification [36; 37; 38]. Interestingly, two genes, _XTH9_ and _XTH17_, showed high expression for PAW5 but low expression for the PAW19 treatment condition (Figure 6). We concluded that the root architecture under PAW is modulated by root developmental genes including _COBL9_, _XTH9_ and _XTH17_.
# Towards Hybrid Energy-Efficient Power Management in Wireless Sensor Networks
## 2 Survey of Energy Optimization Approaches in WSNs
### Processing Layer  
The data processing unit consists of a memory, microcontroller, and specific operating system. Due to memory limitations, packets can be deleted before the node can send them to the destination node. Connectivity problems, therefore, arise over time, which can lead to a high latency and subsequently low Quality of Service (QoS). As a result, the power cost of the activation of its processing unit during the operations performed or when reading/writing in memory must be optimized [2].  
[PERSON] [26] studied the relationship between energy consumption, resource utilization, and performance (throughput). The author assumed that the model is more realistic when the usage rates are evenly distributed. The analytical models considered assume that the electrical consumption of a workload can be known in a deterministic way. However, even if a deterministic relationship exists between workload and energy consumption, predicting this workload is tedious. Similarly, the experimental results developed by [27] indicate that there is no significant relationship between the power, CPU utilization, and performance when the server runs with two different workload scenarios, and it is not possible to conclude whether it is because of the DVFS problem or in the workload estimation.
## 2 Experimental Setup  
### Rock Preparation  
The material chosen is medium-grained white sandstone sourced from Shandong Province, China. This sandstone exhibits an average density of 2.41 g/cm\({}^{3}\) and a porosity of 6.04%. Comprehensive X-ray diffraction (XRD) analysis, detailed in Figure 1, revealed that the primary mineral composition of the sample was 56.6% quartz, 22.2% feldspar, and 21.2% mica.  
Initially, the rock sample was cut to form a complete cube with dimensions of 50 mm \(\times\) 50 mm \(\times\) 100 mm. Subsequently, the central portion of the sample was pre-fabricated using a wire-cutting method, as illustrated in the diagrammatic representation of the hole-fissure arrangement in Figure 1b. The hole has a diameter D = 10 mm, and two fissures, symmetrically positioned about the center, are prefabricated in the top and bottom areas of the aperture. Each fissure has a length a = 10 mm, a width b = 2 mm, and a tilt angle \(\beta\) = 45\({}^{\circ}\). The vertical projection length c between the centers of the crack and the hole is set at 25 mm. Additionally, the horizontal projection distance L is varied, with values of 0, 5, and 10 mm, respectively.