esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Proposed Method
### Rasan-W  
The target loss function of the RASAN-W proposed in this article consists of the following four parts:  
1. Cross-entropy loss function \(L_{cls}\);
2. Local maximum mean discrepancy function \(L_{lmmd}\);
3. Pseudo label loss function \(L_{pseudo}\);
4. Wasserstein loss function \(L_{w}\).  
Where (1)-(3) are used as the loss function in the label classifier and (4) is used as the loss function in the domain discriminator. Combined with the above loss functions, the target loss function of RASAN-W is as follows:  
\[L=L_{y}-\alpha L_{w} \tag{10}\]  
where \(\alpha\) is a trade-off parameter against loss of \(L_{w}\). Therefore, the optimization objective of the model can be given by Equations (10)-(12):  
\[\left(\hat{\theta}_{f},\hat{\theta}_{y}\right)=\arg\min_{\theta_{f},\hat{ \theta}_{y}}L\left(\theta_{f},\theta_{y},\theta_{d}\right)indent \tag{11}\]  
\[\left(\hat{\theta}_{d}\right)=\arg\max_{\theta_{d}}L\left(\theta_{f},\theta_{y },\theta_{d}\right) \tag{12}\]  
By optimizing Equations (10)-(12), when the model converges, a set of saddle point parameters \(\hat{\theta}_{f},\hat{\theta}_{y},\hat{\theta}_{d}\) will be obtained. At the saddle point, the loss of the label classifier is minimized and the loss of the domain discriminator is maximized. The training strategy of RASAN-W is shown in Algorithm 1, and the network parameters \(\theta_{f},\theta_{y},\theta_{d}\) are updated through the SGD optimizer.  
```
1: Initialization: Input samples \(D_{s}\) and \(D_{t}\). Initialize the parameters \(\theta_{f},\theta_{y},\theta_{d}\). Learning rate is \(l_{r}\). Batchsize is M, tradeoff parameter is \(\{\lambda,\quad\mu,\quad\epsilon\}\).
2: Training:
3:for each epoch do
4: Forward propagation: Random samples \(x_{s}=\{(x_{i}^{s})\}_{i=1}^{M}\), \(x_{t}=\{(x_{i}^{t})\}_{j=1}^{M}\). Extract features from source and target domain samples layer by layer using formulas (1) to (7). calculate input sample features \(H^{s}\) and \(H^{t}\). calculate subdomain features \(\mathbf{z}_{i}^{sl}\) and \(\mathbf{z}_{j}^{ij}\). Calculate the classification labels of the samples. Calculate the domain labels of the samples.
5: Back propagation: Calculate the cross-entropy loss using formula (1), LMMD loss using formula (2), pseudo-label loss using formula (4), and Wasserstein metric loss using formula (9). Utilize the SGD optimization algorithm to update model parameters layer by layer.
6:endfor
7: Testing: Store all RASAN-W parameters, and predicting labels for target domain sample \(\mathrm{x}^{t}\).
```  
**Algorithm 1** Training algorithm for the proposed RASAN-W.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 4 Methodology
### Rock-Fall Risk Assessment  
The rock-fall risk is characterized as the possibility of a rock-fall phenomenon occurring in a particular space and time causing a specific degree of harm to road infrastructure, vehicles, and pedestrians [41]. Vehicles are known to be the targeted priorities on the highway in this analysis. The risk was calculated by integrating the likelihood that the fallen rocks will hit the vehicle, the temporal-spatial probability, and the susceptibility. In this paper, the likelihood of rocks falling is calculated based on the possibility that vehicles are presenting a specific position and period affected by rocks falling [42]. The rock-fall risk is determined utilizing a probability equation that combines the probability of the occurrence, the temporal and the spatial probabilities, and the vulnerability. The value of the risk probability is presented in Equation (1).  
\[P_{(Risk)}=f_{h}\times P(r)\times P(S:T)\times V(D:T) \tag{1}\]  
where \(P_{(Risk)}\) is the risk probability of rock-fall, \(f_{h}\) represents the rock-fall frequency, and it is the likelihood of a rock-fall incident occurring during one hour. \(P(r)\) is the possibility  
Figure 2: Methodology flowchart.  
that the fallen rocks will hit the vehicle on the road. _V(D:T)_ is the vulnerability of the vehicle regarding rock-fall incidents. It takes two values: 1 in the case of a rock hitting the vehicle or 0 otherwise. _P(S:T)_ is the temporal-spatial probability, which is the possibility that vehicles are present in a specific position and time.  
It is a probability that a vehicle occupying the length of the path is affected at the time of effect (temporal-spatial probability). This is measured according to Equation (2) [43]:  
\[p_{(S:T)}=\frac{N_{V}}{24}\times\frac{L_{v}}{1000}\times\frac{1}{V_{v}} \tag{2}\]  
where \(N_{v}\) = is the average number of vehicles per day, \(L_{v}\) = is the average vehicle length in meters, and \(V_{v}\) = is the average vehicle speed (km/hour).
## 2 Types and Characteristics of Ground Motions  
The ground motions used in this study were referenced from the set of ground motions used in FEMA P-695 [24]. In FEMA P-695, ground motions are classified as \"Far-Field\" if the epicentral distance is 10 km or more and \"Near-Field\" if it is less than 10 km. \"Near-Field\" motions are further divided into \"Pulse\" and \"Non-Pulse\" types. The data from the PEER NGA Database were used; 400 ground motions were selected, considering the \"Pulse\" and \"Non-Pulse\" types of \"Near-Field\" motions and \"Far-Field\" motions, per the conditions specified in FEMA P-695. The 400 ground motions consist of 200 near-field motions and 200 far-field motions. The selection criteria are followed: (1) magnitude ranging from M6.5 to M8.0 for both the near-field and the far-field ground motions and (2) site-source distance, i.e., Rrup and Rjb, ranging from 0 km to 10 km for the near-field and from 10 km to 1000 km for the far-field ground motions. Figure 1 illustrates the distribution of the 400 ground motions, with an average PGA of 0.2547 g. Table 1 summarizes the range of characteristics of the ground motions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Min. Value** & **Max. Value** & **Avg. Value** \\ \hline Magnitude, Mw & 6.5 & 7.9 & 7.11 \\ Peak Ground Acceleration, PGA (m/s) & 0.01 & 1.49 & 0.25 \\ Arias Intensity, IA (m/s) & 0.1 & 26.10 & 2.33 \\ Soil preferred shear-velocity, Vs30 (m/s) & 133.11 & 2016.13 & 408.30 \\ Joyner-Boore distance to rupture plane, Rjb (km) & 0.07 & 349.57 & 70.13 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Min/Max values of ground motion parameters.
# Surface Quality of Wood Plastic Composites as Function of Water Exposure
## 3 Results and Discussion
The mean R\({}_{\text{a}}\) differences of samples soaked in water for 4 weeks were 2.3797 \(\upmu\)m, 0.7877 \(\upmu\)m, 0.6376 \(\upmu\)m, and 0.2026 \(\upmu\)m larger than those of samples soaked in water for 0 weeks, 1 week, 2 weeks, and 3 weeks, respectively. One week of water soaking created a difference of 1.5920 \(\upmu\)m with a _p_-value of \(<\)0.0001 when compared to the control group, which was not exposed to water. A 0.2026 \(\upmu\)m difference was observed between means of samples soaked in water for 3 weeks and for 4 weeks. This difference was also statistically significant at the same alpha level, with a _p_-value of \(<\)0.0001, as can be seen in Table S2. The main effects plot given in Figure 4 also supports the findings discussed above. Increasing water exposure time resulted in rougher surfaces for all WPC types, with an exception for Type C WPC samples since the surface roughness of those samples did not change much as a function of 1 week of soaking, compared to aprolonged exposure (3 weeks to 4 weeks), as can be observed in Figure 4. This finding was also in conjunction with the idea that increased levels of nano-clay may be considered as a successful water repellent. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multicolumn{10}{c}{**Tests of Between-Subject Effects**} \\ \hline
**Dependent Variable:** & \multicolumn{6}{c}{**Average Roughness (Ra)**} \\ \hline
**Source** & \begin{tabular}{c} **Type III** \\ **Sum of** \\ **Squares** \\ \end{tabular} & **df** & \begin{tabular}{c} **Mean** \\ **Square** \\ \end{tabular} & **F** & \begin{tabular}{c} **Sigma** \\ **(_p-Value_)** \\ \end{tabular} & \begin{tabular}{c} **Partial** \\ **Eta-** \\ **Squared** \\ \end{tabular} & \
\begin{tabular}{c} **Noncent.** \\ **Parameter** \\ \end{tabular} &
\begin{tabular}{c} **Observed** \\ **Power b** \\ \end{tabular} \\ \hline Corrected Model & 200.136 \({}^{\text{a}}\) & 14 & 14.295 & 2957.492 & \(<\)0.0001 & 0.994 & 41404.888 & 1.000 \\ \hline Intercept & 2830.306 & 1 & 2830.306 & 585544.489 & \(<\)0.0001 & 1.000 & 585544.489 & 1.000 \\ \hline Exposure Time & 196.898 & 4 & 49.225 & 10183.756 & \(<\)0.0001 & 0.993 & 40735.025 & 1.000 \\ \hline WPC Type & 0.144 & 2 & 0.072 & 14.870 & \(<\)0.0001 & 0.100 & 29.740 & 0.999 \\ \hline Exposure Time * WPC Type & 1.371 & 8 & 0.171 & 35.445 & \(<\)0.0001 & 0.515 & 283.558 & 1.000 \\ \hline Error & 1.291 & 267 & 0.005 & & & & & \\ \hline Total & 3030.052 & 282 & & & & & & \\ \hline Corrected Total & 201.427 & 281 & & & & & & \\ \hline \end{tabular} \({}^{\text{a}}\) R Squared = 0.994 (Adjusted R Squared = 0.993). \({}^{\text{b}}\) Computed using alpha = 0.05.  
\end{table} 
Table 3: Two-way ANOVA results for Surface Roughness (R\({}_{\text{a}}\)), with wood plastic composite (WPC) samples based on their Type and Water Exposure Time as dependent variables.
# Computational Study of Inertial Flows in Helical Microchannels
## 4 Results--Effects of the Simulation Parameters on the Cell's Trajectory
### Dimensionless Reynolds Number and Dean Number  
Instead of varying the individual parameters of the simulation to study the resulting effect on the cell's trajectories, one can modify the dimensionless numbers that characterize the simulation setup. We tested whether the constant \(Re\) gives us the same results even with different physical parameters. We found that varying \(U_{max}\) gives the same results (or at least very similar) as varying the fluid viscosity while keeping the same \(Re\). Since the fluid velocity enters the Reynolds number in the numerator, and the kinematic viscosity enters in the denominator, doubling the velocity gives the same effect on \(Re\) as halving the viscosity and vice versa. Comparing the results for this doublet of simulations gave almost identical trajectories of the cells; see Figure 5. Therefore, in further analyses, we restricted ourselves to the modification of \(U_{max}\) only.  
On the other hand, we tested whether the same was applicable to the Dean number. We modified the geometry from the reference simulation in two ways: First, we doubled the curvature radius \(R_{c}\), and second we multiplied \(D_{h}\) by 0.8, which, in the third power, gives again approximately 1/2 in the fraction under the root in expression (6). In Figure 6, we clearly see that the red and green lines are different, leading to different stabilized positions. This can be explained by the fact that the inertial effects resulting from curved channels are influenced not only by the curvature radius and tube radius but also by the pitch of the helix. In this view, a helix is quite a unique geometry.
# New Insights into the Symbiotic Relationship between Orchids and Fungi
## 1 Introduction  
All living organisms essentially interact and communicate with their surrounding organisms for their survival. In the case when all of the implied partners benefit from their interactions, the interaction is called symbiosis [1]. Over 90% of plants form a symbiotic relationship with a variety of environmental microorganisms and the associations, especially with fungi, occur in mycorrhizizs, which are the parts located in plant roots [1; 2; 3]. One of the well-known benefits of mycorrhizas to both plants and fungi is nutrient acquisition. For example, fungi mainly obtain carbon (C) sources from their coexisting plants, and in return, enhance a plant's ability to take up minerals or water, subsequently leading to improvement of plant growth and reproduction [1]. Furthermore, fungi are also known to contribute to the tolerance of their symbiotic plants to abiotic stresses including drought, salinity, too high and low temperature, and biotic stresses including pathogens and insects [4; 5]. In nature, more than 6,000 fungal species are known to be capable of establishing mycorrhizal associations with about 240,000 plant species [6]. Mycorrhizizs are commonly classified into the following groups based on the anatomical aspects, such as the presence of internal hyphae of mycorrhizal fungi in the host plant cells: (1) Etomycorrhiza: about 5,000 species of fungi do not enter the cells, but create a mycelialsheath, which functions as a plant-fungus interface, surrounding the epidermal and outer cortical cells. The interface is used for the bidirectional nutrient transfer between a plant and its fungal partner [7]. (2) Endomycorrhiza: the fungi possess specific types of hyphae such as arbuscules and coils to penetrate and grow inside of the root cells of host plants [3]. Endomycorrhizas can be further classified as ectendomycorrhizas, ericoid, arbutoid, monotropoid, orchid and arbuscular mycorrhizalas based on the specificity of plant families and the type of internal hyphae [3,8]. Ectomy Schizas are also alternatively represented as arbuscular mycorrhizalas because the class is the most prevalent type of mycorrhizalae, as shown in approximately 71% of all vascular plant species [1,9].  
The Orchidaceae is the largest family of flowering plants including over 700 genera and about 30,000-35,000 species and still hundreds of new species displaying variable floral features, lifestyles, habitat distributions and trophic patterns are being discovered and developed every year [10,11]. Mycorrhizas in orchid species are highly important throughout their whole life including germination and further development. In this article, we provide a brief review of the roles of mycorrhizalas in orchid growth and development and the current understanding of the mechanisms in nutrient exchange between orchids and their fungal partners.
# Power Sources for Unmanned Aerial Vehicles: A State-of-the Art
## 2 Unmanned Aerial Vehicle (UAV)
### Battery-Based Supply Techniques
#### 2.2.4 Tethered UAVs  
Tethered UAVs offer a unique solution for achieving unlimited autonomy by connecting to a power supply station through tethering lines. This eliminates the need for  
Figure 5: A laser-powered UAV inflight charging system [54; 58; 68].  
-frequent recharging or battery replacement, enabling long-duration missions. Typically located on the ground, the power supply station continuously provides electricity to the UAV through the tethering lines, ensuring uninterrupted operation. Traditionally, copper wires have been used for the tethering lines of UAVs. However, there is a growing trend toward using fiber optic technology in this domain. Fiber optic cables allow for the transfer of kilowatts of power using high-intensity light, offering several advantages over copper wires. Optics-based power transfer reduces detectability by eliminating electrical signatures and significantly reduces both the payload weight of the UAV and the weight of the power lines compared to copper wires. In fact, fiber technology can reduce weight by up to eight times. Furthermore, power losses in fiber optic cables are minimal, making them more efficient for high-altitude operations.  
Tethered UAVs have found applications in various fields. For instance, in maritime pollution monitoring, a tethered UAV can be deployed on a ship to detect oil spills in the sea, preventing contamination of the shoreline [57]. In data-gathering applications, tethered UAVs equipped with sensors can be utilized to collect information in specific areas [58]. However, one limitation of tethered UAVs is the restricted operating area imposed by the length of the connecting cable, which prevents the UAV from flying far from its ground station. To overcome this limitation, in some cases, a moving vehicle is used to carry the prime power source, allowing the UAV to cover a larger area. In missions requiring extremely long endurance, such as monitoring nuclear power plants, tethered UAVs can be deployed to provide continuous aerial surveillance for days or even months, as long as the tethered cable provides a continuous power supply [55]. Prototypes of tethered UAVs have been designed and successfully demonstrated in outdoor environments, showcasing the feasibility and potential of this technology. The use of fiber optic cables in tethered UAV systems presents unique advantages in terms of weight reduction, power efficiency, and reduced detectability, making them an attractive option for extended-duration missions in various applications.
# Zero-Shot Recognition Enhancement by Distance-Weighted Contextual Inference
###### Abstract  
Zero-shot recognition (ZSR) aims to perform visual classification by category in the absence of training samples. The focus in most traditional ZSR models is using semantic knowledge about familiar categories to represent unfamiliar categories with only the visual appearance of an unseen object. In this research, we consider not only visual information but context to enhance the classifier's cognitive ability in a multi-object scene. We propose a novel method, _contextual inference_, that uses external resources such as knowledge graphs and semantic embedding spaces to obtain similarity measures between an unseen object and its surrounding objects. Using the intuition that close contexts involve more related associations than distant ones, distance weighting is applied to each piece of surrounding information with a newly defined distance calculation formula. We integrated contextual inference into traditional ZSR models to calibrate their visual predictions, and performed extensive experiments on two different datasets for comparative evaluations. The experimental results demonstrate the effectiveness of our method through significant enhancements in performance.  
zero-shot recognition; similarity measures; distance-weighting; knowledge graph; semantic embedding +
Footnote †: journal: Applied Sciences
## 2 Related Work
A uniform pixel distribution is expected from an encrypted image, and the unusual pixel distribution in the image due to the data-hiding process will lead to security concerns. In this research, a novel \(MPSA\) RDH scheme is disclosed with an acceptable embedding capacity that does not change the distribution of the pixel in the encrypted images during the data-hiding process. The advantages of the proposed \(MPSA\) RDH scheme are as follows:  
1. Most of the existing schemes modify the entropy of the encrypted image after hiding the additional information, while the proposed scheme does not. Thus, the proposed scheme comes under the banner of a secured RDH scheme from the encryption algorithm purview;
2. A slight change in the entropy of the fully encrypted image is an indication of the presence of information. As we retain the same entropy of the encrypted image, the proposed scheme does not expose the presence of hidden information;
3. The properties of the proposed \(MPSA\) RDH scheme are very much suitable for highly secure and sensitive applications, such as medical image communication, military applications, satellite image communication etc.
# Direct Multistep Wind Speed Forecasting Using LSTM Neural Network Combining EEMD and Fuzzy Entropy
## 5 Case Study  
The three forecasting cases of wind speed series selected in this paper are from three anemometer towers in mountainous area, plain area and coastal area, respectively. Then the EEMD-FuzzyEn-LSTMNN model was established to forecast the wind speed series for the future four hours. Furthermore, the applicability and effectiveness of the EEMD-FuzzyEn-LSTMNN model were verified by comparing with the BPNN model, SVM model and LSTMNN model.
## 3 Research Methodology
### Data Fusion  
Previous studies have confirmed that the coupling relationship between POI data and nighttime lighting data is better, and many scholars have studied the urban spatial structure based on the fusion data of POI and nighttime lighting [57]. In this study, the data fusion is based on the theory that the gray value of nighttime lighting and the numerical weight of POI kernel density are the same to construct the NPP&POI composite index [58]. [PERSON] et al., constructed the PLANUI based on the same idea to fuse the POI and nighttime lighting data, and then fused them together [57]. The averaging method was utilized to eliminate the noise effect of nighttime lighting data to improve the problem of missing urban information of weak light signals [59]. Based on the fusion of POI and nighttime lighting data, [PERSON] et al. constructed a comprehensive NPP&POI index based on the averaging method, which not only retains the continuous advantage of using nighttime remote sensing to identify the results but also reduces the \"saturation\" and \"spillover\" effects [59]. In order to prevent too many 0 values from appearing in the study of urban  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Unit** & **Mean Value** & **Standard Deviation** & **Min** & **Max** \\ \hline Labor force & 10,000 persons & 360.12 & 165.78 & 84.7 & 722.6 \\ Energy & 100 million kw \(\times\) h & 352.49 & 236.29 & 102.4 & 1240.5 \\ Water resource & 100 million cubic meters & 12.7 & 5.99 & 2.8 & 23.5 \\ Land & hectare & 175,240.76 & 76,938.46 & 39,567 & 354,460 \\ GDP deflator & 100 million yuan & 1089.97 & 662.61 & 198.97 & 2977.31 \\ Wastewater & 10,000 t & 8893.15 & 5304.19 & 1210 & 27,101 \\ SO\({}_{2}\) & t & 35,706.33 & 37,503.44 & 2692 & 184,431 \\ Economic development level & 10,000 yuan/person & 1.95 & 0.51 & 0.808 & 3.0499 \\ Industrial structure & \% & 46.36 & 6.77 & 30 & 61.7 \\ Cultural and educational level & \% & 2.5 & 0.7 & 1.3 & 4.2 \\ Scientific and technological & \% & 0.23 & 0.14 & 0.5 & 0.9 \\ development level & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Data description table.  
spatial structure and affecting the results of the study, the DN values of nuclear density data and nighttime lighting data are added on the basis of the NPP&POI composite index constructed by [PERSON]. It retains the advantage of the original form to retain the characteristics of various types of data and also meets the needs of this study.  
\[POINTL=P_{i}+N_{\rm i}+\sqrt{P_{i}*N_{i}} \tag{1}\]  
POINTL is a composite index of POI and nighttime lighting data; \(\rm P_{i}\) is the value of POI kernel density in year \(\rm i\); \(\rm N_{i}\) is the value of NPP nighttime lighting luminance at point \(\rm i\).
## 3 Materials and Methods
### Experimental Set-Up
#### 3.2.4 Real Samples Assays  
To evaluate the performances of the HS-SPME(PDMS/DVB) and HS-BA\(\mu\)E(R) methodologies on real matrices, BVOCs analysis was performed on the sampled leaves (Figure 2a,b). Before each analysis, fresh leaves of _E. globulus_ Labill. and needles from _P. pinaster_ Aiton trees were cut and weighed in 40 mL glass flasks, using amounts of 2 and 5 mg, respectively, and for the HS-SPME(PDMS/DVB) assays, 1 \(\mu\)L of the IS solution (20 mg L\({}^{-1}\)) was introduced into the flask, which was conditioned over 30 min (30 \({}^{\circ}\)C), followed by HS extraction under previously optimized experimental conditions. Assays were performed in duplicate for the leaves of each tree species from each sampling. The amounts of \(\alpha\)-pinene, \(\beta\)-pinene, myrrene, limonene and 1,8-cineole emitted by the leaves of both tree species were determined through the previously obtained calibration plots. For quantification purposes, the dry weight (DW) leaves were determined by volatilization gravimetry. Thus, fresh leaves from all samplings were weighed to determine their fresh weight (FW) and then dried in a previously heated oven (80 \({}^{\circ}\)C) until their weight was constant (approximately 48 h) to eliminate the leaf moisture [27]. This allowed for the determination of the mass of each compound released by DW leaves (\(\mu\)g g\({}^{-1}\)).