esa-sceva/satcom-chunk-collection huggingface dataset:
# Backscatter Assisted NOMA-PLNC Based Wireless Networks
## 2 System Model  
Consider a proposed wireless network which consists of an access point (AP), one near node pair and one far node pair as shown in Figure 1. It is assumed that an ABS transmitter exists in this model near to the far node pair which will help to improve the signal strength of the far node pair. The network model is designed using the PLNC scheme such that nodes can exchange their information with three time slots. The detailed time slot management of the proposed network is shown in Figure 1(a). Using orthogonal multiple access channel (OMAC), the near node pair and far node pair send their data to the AP in first time slot and second time slot, respectively. At the AP, nodes data will be detected, decoded and then XORed using the PLNC mapping. Then, the resulting PLNC encoded signals are combined with different power levels using superposition coding as shown in Figure 1(b). Then, the resulting NOMA signal is transmitted back to the same node pairs simultaneously over non-orthogonal broadcast channel (NOBC) in the third time slot. In this model, far node pair acting as an IoT reader using suitable SIC operation as shown in Figure 1(c). Far node pair first decode the PLNC signal then performing the PLNC demapping to obtain the original transmitted signal. Then using SIC, it removes its own message then decode backscatter message.  
The data transmission between the nodes in this wireless network are performed over the two phases such as OMAC phase and NOBC phase.
# Fungi of the _Trichoderma_ Genus: Future Perspectives of Benefits in Sustainable Agriculture
## 5 Application of _Trichoderma_ Fungi in Promoting Plant Growth
### Regulation of Growth, Physiological and Biochemical Processes of Plants  
Fungi of the genus _Trichoderma_ regulate the hormonal, water, and physiological processes of plants, such as gas exchange, photosynthesis, transpiration, or stomatal conductance. In addition, they modify their morphological characteristics [34], thus leading to increased yields and crop biomass [21; 55; 58].  
[PERSON] et al. [184] documented that _Trichoderma_ sp. had a positive effect on the physiology and morphology of rice. The results of the studies of the above-mentioned authors showed that inoculation of plants with the microorganisms enhanced the stomatal conductance, thus leading to an increase in the concentration of carbon dioxide and water in rice. In turn, the increase in internal CO2 levels and H2O use efficiency resulted in enhancing the net photosynthetic rate to 8.66 umol CO2/m2s. In addition, the treatment of seeds with _Trichoderma_ isolates contributed to significant plant height, length, and fresh weight of roots and the number of leaves and tiller of rice. [PERSON] et al. [24] proved that _T. viride_ induced wheat height, root weight, leaf length, and the number of grains, thus leading to a 36.5% increase in grain yield. In turn, [PERSON] et al. [185] reported that _T. harzianum_ enhanced the nutritional and functional quality and yield of plum tomato crops. Treatment of plant roots with analyzed microorganisms increased the number and weight of fruits. There was also a 40.1% enhancement in tomato yield and an increase in lycopene, asparagine, gamma-aminobutyric acid, and monoethyl amine in plants by 49%, 37%, 87%, and 102%, respectively, compared to the control. Similarly, analyses by [PERSON] et al. [186] showed that two strains of _T. harzianum_ increased total strawberry yield by 35% and 38% and the number of fruits by 17% and 39%, respectively. In contrast, the _T. virens_ induced root length and fresh and dry weight by 11%, 17%, and 21%, respectively. In addition, the treatment of plants with these fungi had a positive effect on the accumulation of anthocyanins in fruits and the regulation of proteins associated with metabolism, nutrient uptake, and plant stress response. By carrying out further studies, the above-mentioned authors found that the physiological and morphological changes occurring in strawberries are probably the result of the action of harzianic acid, 6-pentyl-\(\alpha\)-pyrone, and hydrophobins produced by _Trichoderma_ sp. [187]. Similarly, [PERSON] et al. [188] showed that harzianic acid, 6-pentyl-\(\alpha\)-pyrone, and hydrophobins induced soybean growth. Treatment of plants with metabolites significantly increased stem length, fresh and dry weight of soybean, and the fatty acid concentration in seeds by 17%. The ability of plants to absorb sodium, aluminum, and lithium was also enhanced by 58%, 89%, and 150%, respectively.  
Impact of Environmental Conditions on Growth, Spores, and Phytosanitary Properties of _Trichoderma_ Fungi
## 1 Introduction
### Related Works
#### 1.1.6 Spatial-Temporal Fusion Methods  
In summary, in recent years, infrared small target detection methods have primarily been categorized into spatial-driven and temporal-driven approaches. Spatial methods distinguish targets from backgrounds through local contrast enhancement (e.g., NTLLCM NTLLCM (2018)), low-rank sparse priors [PERSON] et al. (2018), or end-to-end networks [PERSON] et al. (2018). However, they are prone to false alarms in complex scenarios such as dynamic clouds and sea clutter. Temporal methods detect targets by leveraging the continuity of their motion trajectories [PERSON] et al. (2019), but they perform poorly when the image shift is significant or the target moves rapidly.  
Recent studies have attempted to fuse spatiotemporal features. For instance, SSTLCM [PERSON] et al. (2018) is a proposed detection method combining temporal sampling with multi-layer contrast, while [PERSON] et al. [PERSON] et al. (2018) introduced a detection algorithm based on spatial and temporal contrast, designing a spatiotemporal filter called the Spatial-Temporal Local Contrast Filter (STLCF). [PERSON] et al. [PERSON] et al. (2018) further enhanced this approach with an improved Spatial-Temporal Local Contrast Map (STLCM). These spatial-temporal-based methods have indeed demonstrated robust detection capabilities. However, they rely solely on temporal sampling points to analyze temporal profile features, potentially overlooking critical local gray intensity difference information of small moving targets across consecutive frames. Additionally, the spatial feature analysis tends to homogenize with the temporal domain, failing to provide new information. The core limitations of these methods lie in: (1) insufficient interaction between spatial and temporal features, which hinders the synergy between dynamic background suppression and weak target enhancement, and (2) the lack  
Figure 1: Temporal profile diagram, blue-bordered rectangles denote image frames; red-bordered hollow rectangles mark regions of interest; red arrows indicate target movement direction; multi-colored circles represent targets at different time points.  
of effective detection of spatiotemporal target features, which limits the cross-scenario generalization capability of the detection methods.
# Construction Progress and Aviation Flight Test of BDSBAS
## 2 Overview of BDSBAS
### Tropospheric Correction
#### 2.3.5 Carrier Smoothing Pseudo-Range  
In order to reduce the code measurement noise and improve the ranging accuracy, the DO-229E standard recommends, the following \(\alpha\)-\(\beta\) filter is used to smooth the raw pseudo-range observations:  
\[\left\{\begin{array}{c}P_{n}=\alpha\rho_{n}+(1-\alpha)P_{proj}\\ P_{proj}=P_{n-1}+\frac{\lambda}{2\pi}(\varphi_{n}-\varphi_{n-1})\end{array}\right. \tag{22}\]  
where \(P_{n}\) and \(P_{n-1}\) are the smoothed pseudo-ranges of the current epoch \(n\) and the previous epoch \(n-1\), respectively. \(P_{proj}\) is the projected pseudo-range; \(\varphi_{n}\) and \(\varphi_{n-1}\) are the accumulated carrier phase measurements of the current epoch \(n\) and the previous epoch \(n-1\), respectively. \(\lambda\) is the wavelength. \(\rho_{n}\) is the raw pseudo-range measurement. \(\alpha\) is the weight parameter in the filter (\(\alpha<1\)).  
Using the smoothed pseudo-range \(P_{n}\), the final corrected pseudo-range \(\hat{P}_{n}\) for user position calculation can be further obtained by:  
\[\hat{P}_{n,i}=P_{n,i}+PRC_{i}+B_{i}-IC_{i}-TC_{i} \tag{23}\]  
where the positive and negative signs of different corrections should be paid attention to.
## 6 Algorithm Details
### Algorithm Scalability Analysis  
The uplink satellite reselection strategy in the previous section is an exploration of the scalability of the algorithm in this article. Since the ground station can be connected to the space segment of the LEO satellite network through multiple feeder links with different access points, new routing problems arise. The result of routing selection here affects the traffic input of this algorithm. Therefore, we comprehensively select the best uplink satellite by monitoring the load of the feeder link and the uplink satellite, combined with the positional relationship between the destination satellite and the uplink satellite, thereby improving the performance of this algorithm. Similarly, in a multi-layer LEO satellite network and a satellite network that combines low orbit, medium orbit and high orbit, there are also multiple access points between satellites in different altitude orbits, and the same method as in the previous section can be adopted: first select a suitable uplink or downlink satellite for the traffic from satellites orbiting at different altitudes, and then use the centralized routing algorithm in this article to calculate the route during sun outage.  
For large-scale constellations, first, the number of hops \(n\) in the limited area in the algorithm needs to be increased. According to the number of satellite node hops experienced by the main services in the network, the appropriate limited area range is selected. In addition, in large-scale satellite networks, adjacent inter-satellite links may fail at the same time. In this case, multiple limited areas can be combined to jointly calculate the routing path during the sun outage. The merged limited area is shown in Figure 10. The overlapping portion of different limited areas is called a congestion area. For path calculation in the congestion area, the high-load link threshold \(\alpha\) can be appropriately reduced and more links can be marked as congested links to obtain larger-scale detour paths.  
Figure 10: The merged limited area in large-scale satellite networks.  
Figure 9: Reselect the satellite for transmission.
## 3 Results and Discussion
### Metamodel and Multi-Objective Optimization
#### 3.2.1 Design Variables, Objective Function, and Constraint  
Two conflicting objective variables were optimized in this MOOP, which are the permanent displacement (\(\delta_{p}\)) of the ASP back face and specific energy absorption (SEA) of the auxetic core. The \(\delta_{p}\) was obtained by averaging the displacement-time history in the area where the displacement had already converged, while the SEA of the core was calculated by dividing the internal energy \(E\) by the core mass \(m\). The objective functions are to minimize the \(\delta_{p}\) and \(-\)SEA.  
The design optimization focused solely on the independent geometric parameters of each auxetic geometry. The two face sheets, material types, and other variables were not optimized since this study focuses only on the influence of auxetic geometry on blast responses. The independent variables \(w\) and \(h\) were converted into the number of cells \(N_{y}\) and \(N_{z}\), respectively, due to the constraint of fixed ASP dimensions. The design variables for the MOOP for REH, DAH, and SH are specified as follows:  
\[\begin{split}&\min\big{(}\delta_{p},\ -\text{SEA}\big{)}\\ &\text{s.t.}\ \begin{cases}5\leq N_{y}\leq 25\\ 2\leq N_{z}\leq 6\\ 0<\theta<\theta_{max}\\ 0.5\leq t\leq 2\end{cases}\\ \end{split} \tag{13}\]  
Figure 9: Comparison of deformation (**left**) and cross-sectional displacement profiles of face sheets (**right**) between numerical simulations and experimental results [14] for specimens (**a**) TZ-12, (**b**) TZ-2, and (**c**) TZ-13. The red and green dashed lines represent plastic buckling and local indentation after blast impact, respectively.  
and for the CH, the variable \(\theta\) is changed with the \(r\):  
\[\begin{split}\min\big{(}\delta_{p},\ -\text{SEA}\big{)}\\ \text{s.t.}\begin{cases}5\leq N_{y}\leq 25\\ 2\leq N_{z}\leq 6\\ 2<r<r_{max}\\ 0.5\leq t\leq 2\end{cases}\end{split} \tag{14}\]  
Design constraints for the MOOP are listed in Table 6, taking into account the geometric limitations. Based on the design variables and constraints, the design space with the corresponding normalized relative density (\(\rho_{r}\)) for each auxetic model is presented in Figure 10. As shown in Figure 10, higher input variables result in a higher \(\rho_{r}\) of the model.
## 3 Results and Discussion
### Bioactive Compounds Composition
[33] reported that, in yellow onion, total phenolic content (TPC) was in the range of 1.64 mg GAE/g of fresh matter (FM), while [PERSON] et al. [34], in their studies on yellow onion varieties, determined TPC in the range of 1.17 to 1.62 mg GAE/g FM, depending on the harvesting season. The concentration of phenolic compounds in fresh and frozen kale, as determined by [PERSON] and [PERSON] [35], was 12.69 and 21.30 mg GAE/g DW, respectively, and [PERSON] et al. [36] found that TPC in kale ethanolic extract ranged between 13.49 and 28.54 mg/g DM. Analysis of carotenoid concentrations in sweet potato samples revealed that the drying method and functional addition had a significant effect on the total carotenoid content of the tested samples (Table 1). The addition of vegetable juices increased the concentration of total carotenoids (TC). The highest concentration of TC was determined in samples with the addition of onion and kale juices (mean of 173.79 mg/g DM). This is most likely due to the enrichment of the samples with carotenoids derived from onions and kale, as the presence of carotenoids in both these materials was noted [36; 37]. In the case of drying without the addition of a functional ingredient and with the addition of sodium chloride, a reduction in carotenoid content was observed (on average by 19.2% and 13.8%, respectively). The samples obtained with the VD method had a 19.2% lower TC content compared to the freeze-dried samples. Chlorophyll content in samples after drying without additives and with the addition of sodium chloride slightly decreased; however, these changes were not statistically significant (Table 1). The addition of vegetable juices increased the content of chlorophyll; the highest values were determined in samples with the addition of kale (2.2-fold) for FD and 2.1-fold for VD. Increasing the content of chlorophyll in dried samples is related to their enrichment with kale juice. The literature indicates that the chlorophyll content in kale varies between 1624 and 13,543 mg/kg DM [36; 38]. The results show that the concentration of bioactive compounds in dried plants is dependent on the conditions during the drying process. The variability of bioactive compounds in plant tissues depends on many factors, such as process temperature, UV light, the presence of oxygen, and the presence of protective additives [39; 40; 41].
# Modification of Nonlinear Controller for Asymmetric Marine Vehicles Moving in Horizontal Plane
## 2 Marine Vehicle Model
### Additional Information Obtained by Using QV  
If QV is employed, then additional ones can be obtained that are not available in the classical equations of motion. The set of quantities and indexes was presented for fully actuated 6 DOF underwater vehicles, e.g., in [50,51]. However, in this paper, they are used to study the dynamics of a 3 DOF underactuated vehicle and at relative velocities. The following measures were adopted to evaluate the performance of the algorithm:  
1. Norm of the matrix \(\left\|\mathrm{Y}^{-1}\right\|\) representing the normalized value of the couplings in the dynamic vehicle model. The matrix \(\mathrm{Y}\) is calculated from the symmetric matrix \(M\) and depends on the dynamic and geometric parameters.
2. The kinetic energy dissipated by each vector variable \(\zeta\) (and its sum concerning the whole vehicle) following the formula: \(K{=}\frac{1}{2}v_{r}^{T}Mv_{r}{=}\frac{1}{2}\zeta^{T}N\zeta{=}\frac{1}{2}\sum _{i=1}^{3}N_{i}\zeta_{i}^{2}{=}\sum_{i=1}^{3}K_{i}\).
3. The mean value of kinetic energy related to each variable and the sum of the mean kinetic energy: \(K_{m}=\mathrm{mean}\left(K\right)=\sum_{i=1}^{3}\mathrm{mean}\left(K_{i}\right)\).
4. The distortion of each velocity due to couplings resulting from the formula: \(\Delta\zeta_{i}=\zeta_{i}-v_{ri}\). The QV \(\zeta_{i}\) includes couplings between itself and the other velocities because \(\zeta_{i}=\mathrm{Y}_{ii}^{-1}v_{ri}+\sum_{j=i+1}^{3}\mathrm{Y}_{ij}^{-1}v_{rj}\).
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 4 WSN Systems in Different Applications: Aerospace, Bridge Structures, and Wind Turbines
### WSN in Wind Turbines
#### 4.3.2 Advantages of Wireless Communications for SCADA Systems  
Wide Area Networking (also known as WAN) solutions provide a number of advantages in assisting SCADA systems and important infrastructure. The advantages are as follows [89]:  
* **Simple implementation**. In the locations which do not have wire lines installed, the wireless connections are cheap to deploy, allow faster installation, and are simpler to operate. Building a new wire line is not competitive. Nowadays, cellular networks today cover up to 99 percent of the whole world population. Therefore, its availability is granted everywhere the customers wish to have. In many areas of the world, there are several cellular networks operating now. In order to deploy wireless equipment, the utilities have a choice and can select the best cellular network available at the particular location.
* **Reduced costs of operation**. Some SCADA operators own and maintain their private radio networks. Changing to cellular operators, they can eliminate maintenance problems. They can rely on mobile operators providing the network. Moreover, having many locations of wind turbines, it is possible to choose one supplier of the network for all locations. This may eliminate other connections, typically controlled by regional providers.
* **Fully reliable technology.** More than 20 years ago, a new wireless technology emerged and started to replace traditional wire line structure. Huge investments in the wireless infrastructure, constant monitoring and protecting against security threats pushed the development of these systems to a very high level. Nowadays, cellular networks seem to be more secure in comparison with privately operated infrastructure. Therefore, they are able to support critical infrastructure such as SCADA systems. It is estimated that SCADA applications are supported by one billion machines using cellular networks nowadays. Sensitive financial transactions and critical government communications are good examples proving the reliability of wireless systems using this technology. Now, up to 20 percent of all cellular communications worldwide concern data transfer (typically IP-based).
* **Market perspective.** Fast development of IT technologies and equipment causes the cost of cellular services to decline. Providers must compete more fiercely for utility  
Figure 13: Block diagram of wireless SCADA [88].  
customers. Thanks to this, the provided technology and services are considerably improved and less expensive.
# Numerical Study on Dynamic-Stall Characteristics of Finite Wing and Rotor
## 3 Analyses and Discussion
### Finite-Wing Dynamic Stall with Spanwise Flow
As a result, \(C_{m}\) variations near the wing root are much gentler than those of the wing tip. From the comparison of \(C_{d}\) curves at different wing sections in Figure 13, it is illustrated that \(C_{d}\) peaks near the wing root are smaller than those of the wing tip due to the restriction of LEV. As a result, it is indicated that the vorticity collection induced by the spanwise flow could significantly affect the dynamic-stall characteristics of a finite wing.
## 3 Methodology  
To utilize the diversity and complementation of denoised images generated from DIP to obtain better denoising results, we propose certain preprocessing strategies to generate a set of intermediate denoised instances of the noisy image and then fuse them into the final denoised image. An overall flowchart of the proposed method is shown in Figure 4. As shown in Figure 4, the proposed method comprises two main steps. In the first step, we propose a two-target DIP learning strategy to produce an intermediate denoised image, \(d_{i}\). Specifically, we first used the given noisy image and preliminary image \(p_{i}\) as the learning targets of the DIP network. Using two targets in DIP learning can constrict the image search space and improve the denoised result. Then, we repeat the two-target DIP optimization process \(n\) times with different network inputs and different preliminary images to generate \(n\) intermediate denoised images \(\{d_{1},d_{2},\ldots,d_{n-1},d_{n}\}\). To generate the preliminary images, the given corrupted image \(y\) is fed into a pre-trained denoising model, such as FFDNet, to generate a cleaner image \(p\). Then, we employ an up- and down-sampling strategy to generate diverse sampled images \(\{p_{1},p_{2},\ldots,p_{n-1},p_{n}\}\) of \(p\). In the second step, we develop an unsupervised image fusion network to fuse intermediate denoised images with a single denoised image \(x_{\text{f}}\). These intermediate denoised images, which have similar image quality and different local details, complement each other. Fusing these into a denoised image can further improve the denoising effect.
## 3 Results
### Performance of ML Methods
In Figure 2B, the patient examination features were found relatively important only during the prediction of cardiovascular scenarios, including CABG, PCI, VS, stroke, and MI, but not for the prediction of only PCI or CABG treatments. Further, these examination features had higher importance than the demographic information and patient medical history features in this risk estimation model, and the HDL was the most important feature. Since the cardiovascular risk was assessed within 90 days, the recent physical conditions of the patients would be helpful in identifying the high-risk patients. In the PCI treatment prediction, the history of diabetes feature was uniquely found as an important feature that was supported by other studies [24; 25]. In contrast, the CABG treatment predictions are more affected by the demographic information (age, race), also evident in Figure 2A. Based on the results from Tables 2, 4 and 6, the overall importance of CR-related features was higher than the others, which are also visually supported by the colored heatmaps (in Figure 2). Among these CR features, the NUMDZV (the number of significantly diseased vessels) feature was important in all three prediction cases. This feature can be accessed via noninvasive techniques, such as noninvasive CT angiograms [26; 27]. Among the non-CR related features, CBRUITS (carotid bruits), S3 (the third heart sound), YRCATH_G (year of cardiac catheterization), and HXPEPULC (history of peptic ulcer disease) were removed. After combining the NUMDZV with other non-CR-related features, the performances of all three models were improved (listed in Table 7). For estimating high-risk conditions within 90 days, the RFC achieved 88.17% accuracy, 89.72% sensitivity, 86.98% specificity, and an AUROC of 91.68%. For predicting the PCI treatments within 90 days, the GBC achieved 89.21% accuracy, 90.20% sensitivity, 88.74% specificity, and 94.16% AUROC. For predicting the CABG treatments within 90 days, the GBC achieved 93.86% accuracy, 77.57% sensitivity, 96.23% specificity, and 96.47% AUROC.