esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It employs random forest for trust determination, benefiting from its ability to mitigate the impact of noisy data generated by underwater acoustic channels and reduce the risk of overfitting by combining multiple decision trees for classification or regression. Deep reinforcement learning is harnessed to make adaptive decisions, a versatile technique with broad applications in cyber security [10]. In our proposed approach, we harness deep reinforcement learning to formulate an optimal trust update policy. The primary contributions can be outlined as follows:  
* To enhance the precision of the assessment, the influences that underwater sensor nodes are susceptible to when judging trust are extensively analyzed, and the environmental trust indicators are refined to provide reliable trust evidence for the next step of trust evaluation. Simultaneously, we account for the impact of the underwater environment on node mobility and dynamically update the network topology, rendering the simulation scenario more akin to real-world underwater conditions. * We propose a novel dynamic trust evaluation model for underwater acoustic sensor networks (DRFTM), which fuses deep reinforcement learning and random forest algorithms to assess the trustworthiness of sensor nodes within UASNs. The DRFTM exhibits excellent trust determination capability and detection accuracy when subjected to single and hybrid attacks. * Regarding trust updates, this paper introduces a trust update mechanism rooted in deep reinforcement learning. Leveraging the training and learning capabilities of deep reinforcement learning algorithms, this adaptive trust update strategy excels in identifying and excluding potentially malicious nodes. It ensures the timely and precise representation of trust relationships between nodes, concurrently enhancing the trust model to bolster detection accuracy. The rest of this paper is organized as follows: In Section 2, related work on trust models is analyzed. Section 3 describes the network architecture, the attack model, and the assumptions on which this research is based. Section 4 details the design of the DRFTM model. Section 5 performs experimental simulation and performance analysis of the DRFTM. Section 6 draws conclusions and provides an outlook on future research directions.
## 2 Materials and Methods
### Memory Allocation for Non-Real-Time Tasks
#### 2.2.4 Establishing State Transition Equations  
In stage k, there are four possible decisions:  
1. Allocating the available memory block \(k\) to \(\mathit{Req}_{1}\) when the decision for \(\mathit{Req}_{0}\) has been made.
2. Allocating the available memory block \(k\) to \(\mathit{Req}_{0}\) when the decision for \(\mathit{Req}_{1}\) has been made.
3. Allocating the available memory block \(k\) to both \(\mathit{Req}_{0}\) and \(\mathit{Req}_{1}\).
4. Not allocating the available memory block \(k\) to either \(\mathit{Req}_{0}\) or \(\mathit{Req}_{1}\), and continuing with the previous decision.  
Figure 4: Memory allocation in the non-real-time zone.  
When both memory requests are allocated to the same available memory block \(i\), we combine the two requests into one by creating a new temporary request with a temporary identifier 2, referred to as \(Req_{2}\). If the decision is made to allocate both memory requests to the same available memory block, a split operation is required and, thus, an additional group of memory header information needs to be considered. Size of \(Req_{2}\) can be represented as:  
\[ReqSize_{2}=ReqSize_{0}+ReqSize_{1}+HeadSize \tag{3}\]  
According to the four possible decision scenarios, the state transition equation for the maximum allocation value \(S_{(1,1)}^{k}\) when both memory requests make decisions among all available memory blocks with a number not greater than \(k\) is expressed as:  
\[S_{(1,1)}^{k}=max\{S_{(0,1)}^{k-1}+Value_{i}^{0},S_{(1,0)}^{k-1}+Value_{i}^{1}, Value_{i}^{2},S_{(1,1)}^{k-1}\} \tag{4}\]  
The maximum allocation value \(S_{(1,0)}^{k}\) when only \(Req_{0}\) makes decisions among all available memory blocks with a number not greater than \(k\) is as follows:  
\[S_{(1,0)}^{k}=max\{Value_{i}^{0},S_{(1,0)}^{k-1}\} \tag{5}\]  
Similarly, the maximum allocation value \(S_{(0,1)}^{k}\) when only \(Req_{1}\) makes decisions among all available memory blocks with a number not greater than \(k\) is as follows:  
\[S_{(0,1)}^{k}=max\{Value_{i}^{1},S_{(0,1)}^{k-1}\} \tag{6}\]
# A Novel SDWSN-Based Testbed for IoT Smart Applications
## 3 System Model
### Physical Network Setup for WSN-Based Clustering
#### 3.2.3 Mobility Scenario Using Rechargeable Battery  
The RSSI sensing data collected from the physical network are of high importance to dynamic networks as they indicate a node's possible departure when it ventures outside the communication range of the other nodes. The collected RSSI data can be analyzed to observe any variations in the sensed values. A test scenario based on the TI CC2538 network was performed to collect RSSI data from a mobile coordinator node in relation to its leaf node. Herein, the coordinator node was powered using a rechargeable battery to flexibly move away from the leaf node. When the coordinator node moves outside the testing room, the RSSI values range from \(-72\) to \(-94\) dBm, indicating that the node signal strength is degraded. It is worth mentioning that the RSSI-sensed values collected from the physical network can be fed into the virtual network to test any potential network rerocketration. Table 2 shows the RSSI values of the departing coordinator node, which are equivalent to the departed node's distance in meters.
## 3 Lens Construction
### Monolithic Lens
(**b**) Hemispherical DEA-driven liquid double-cavity lens: (**b1**) top view of the lens object; (**b2**) schematic diagram of the lens structure; (**b3**) lens zoom effect diagram, the upper and lower diagrams, respectively, before and after the application of the voltage, the focal position changes after the laser passes through the lens [60]. (**c**) Hemispherical DEA-driven active-passive membrane structure: (**c1**) schematic diagram of the lens structure; (**c2**) lens zoom schematic diagram, the upper and lower diagrams, respectively, before and after the application of the voltage, the focal position changes; (**c3**) lens zoom effect diagram, the upper and lower diagrams are the imaging pictures of the lens before and after the voltage change, respectively [64]. (**d**) Spherical DEA-driven active-passive membrane structure (**d1**) schematic diagram of the lens structure; (**d2**) lens zoom principle diagram [66]. Tapered DEA  
[PERSON] et al. [68] developed a conical dielectric elastomer-driven tuneable lens, shown in Figure 9a, which achieves zoom by squeezing the intermediate PDMS deformer with a conical DEA. The focal length was at a minimum when no voltage was applied, and the axial squeezing force on the deformer decreased after the voltage was applied, leading to an increase in the focal length and a zoom ratio of up to 178.2%. [PERSON] et al. [69] introduced a self-recovering paper folding mechanism (Figure 9b), which achieves a zoom ratio of 117% at a driving voltage of 3.5 kV using the axial-radial force conversion mechanism of the tapered DEA. The DETLs based on a conical DEA drive with a predominantly soft solid lens demonstrate better stability. Additionally, they may exhibit a faster response time due to the force exerted on the DEA by the soft solids or springs in the initial conditions. However, the lenses associated with this configuration are susceptible to errors during the preparation process, which makes them more prone to aberrations during the operation process.
## 4 Numerical Analysis
### Numerical Analysis Summary
#### 4.1.2 Track and Trackbed Modeling  
An accurate track and tracked model is needed to simulate the track settlement due to the different support stiffness. In this study, a 3D solid model of all track elements was created, and the model was composed of rail, railpad, sleeper, track concrete layer (TCL), Hydraulically stabilized base (HSB), reinforced trackbed, upper trackbed, and lower trackbed. An elastic model was applied to the rail, sleeper, TCL, HSB, reinforced trackbed, and the Mohr-Coulomb model was applied to the upper and lower tracked. The rail was applied by UIC 60 kg and was modeled as a cuboid without considering rail surface defects. The Hertzian nonlinear rigidity was applied to the wheel-rail contact, so the vehicle and track were considered as a monolithic system in the analysis. Size and spacing of the sleeper was modeled by referring to the KTX mainline standard and UIC 713 R Concrete sleeper [19]. The railpad was modeled as a combination of massless spring-damper couple [19; 20; 21; 22; 23]. It was modeled with an EVA pad, which has the spring factor \(3.032\times 10^{5}\) kN/mm and the damping coefficient 29 kN/m [13]. Slide line was used to move while the wheel maintained contact with the rail. Damping ratios in the analysis were assumed to be 2% for the rail, sleeper, TCL, and HSB, and 5% for the reinforced trackbed, upper, and lower trackbed [24]. The model was composed of a very fine mesh, the aspect ratio of which did not exceed 1 to 4, to simulate a rail vertical deflection in the range of millimeters. Figure 15 shows the model used for the analysis and the material properties are summarized in Table 2.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classification** & **Material Properties** & **Classification** & **Material Properties** \\ \hline Speed (km/m) & 300 & Wheel diameter (mm) & 460 \\ Car body weight (kN) & 269.58 & Wheel elastic modulus (kN/m\({}^{2}\)) & 2.1 \(\times\) 10\({}^{8}\) \\ Bogie weight (kN) & 11.87 & Boøig distance (m) & 14.0 \\ Wheel weight (kN) & 10.05 & wheelbase (m) & 3.0 \\ Primary suspension spring coefficient (kN/m) & 1.252 \(\times\) 10\({}^{3}\) & Secondary suspension spring coefficient (kN/m) & 1.26 \(\times\) 10\({}^{3}\) \\ Primary suspension damping factor (kN-s/m) & 16.0 & Secondary suspension damping factor (kN-s/m) & 28.5 \\ Number of bogies & 4 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Material properties of vehicle model.  
Figure 14: Diagram of vehicle model.  
An infinite element was used to set lateral and bottom boundaries in order to eliminate any reflected forces and dynamic waves from the boundaries. The stress caused by the vehicle load was reduced by up to 85% when the depth was 3 m or more [25], and thus, modeling was carried out with the 3 m of the upper tracked and 2 m of the lower trackbed.
# Design and Kinematic Analysis of Deployable Antenna for Bionic Bird Tail Feather
###### Abstract  
The application field of space deployable antennas covers mobile communication, navigation, deep space exploration, etc. The traditional space deployable antenna mechanism deploys in a fixed way and mostly in a circular direction in order to expand the space deployable antenna configuration; this paper summarizes the bionic principle that can be used for antenna structure design by studying the tail feather deploying behavior of birds such as peacocks and proposes a new parabolic antenna configuration that imitates the tail feather deploying of birds. The kinematic model of the deployable mechanism is established, and kinematic analysis of the deployable process is carried out on the key rods. The simulation of the rod motion is carried out using ADAMS 2020 software, and the angle change results obtained from the simulation are compared with MATLAB 2020b to verify the correctness of the kinematic equations. The deployment trajectories of the innermost and outermost rib endpoints are analyzed, and the spatial arrangement of the antenna is determined to be in the range of 4 m \(\times\) 3.5 m. This is a solid foundation for the development of spatially deployable antennas.  
bird tail feathers; deployable mechanism; motion analysis; bionics Article  
Design and Kinematic Analysis of Deployable Antenna for Bionic Bird Tail Feather  
20231312598132598132598
## 2 Materials and Methods
### Beneficial Characteristics (Tolerance to Oxgall/Bile, Low pH, Postbiotic Activity)  
For further application, the properties such as tolerance to bile and to low pH and/or postbiotic potential via bacteriocin activity, are recommended for testing. Tolerance to oxgall/bile in the medium was tested in M17 broth (Difco, Sparks, MD, USA) supplemented with 3% and 5% bile/oxgall (Difco, Sparks, MD, USA), as previously described by [PERSON] et al. [26]. Viable cell count was counted on M17 agar at time 0 and at 24 h after the standard dilution of samples in Ringer solution (1:9). The cell count was expressed in colony-forming units (CFUs) per mL.  
Tolerance to pH 3 was controlled in simulated gastric juice containing pepsin and not containing pepsin (Sigma-Aldrich Chemie GmbH, Schnelldorf, Germany), as previously described [PERSON] et al. [26]. Tubes with pepsin and those not containing pepsin were inoculated with 0.1% culture of the strain MK1/3 in M17 broth. Surviving cells were counted on M17 agar (Difco, Sparks, MD, USA) at time 0 and after 180 min. After the standard microbiological dilution, the cells count was performed and expressed in CFU/mL.  
Postbiotic activity (bacteriocin activity) of the strain MK1/3 was tested using the qualitative method [7] against the principal indicator (the most susceptible strain) _Enterococcus avium_ EA5 (feces of piglet, our strain). Then, the concentrated substance (CS) was prepared as reported by [PERSON] et al. [7]. _L. lactis_ MK1/3 was inoculated in 40 mL MRS broth (Merck, Darmstadt, Germany) and incubated overnight at 37 \({}^{\circ}\)C to achieve an absorbance of A\({}_{600}\) up to 1.0. The broth culture was centrifuged (10,000\(\times\)\(g\)) for 30 min. The pH of the supernatant was adjusted to 5.5. The cell-free supernatant was treated by adding Chelaton III-EDTA (Sigma-Aldrich, Muenchen, Germany). This followed heating at 80 \({}^{\circ}\)C for 10 min, to eliminate the organic substances effect. The next step included concentration of supernatant using Concentrator Plus (Eppendorf, Hamburg, Germany), to obtain the concentrated substance (4.0 ml, CS). The inhibitory activity was analyzed by the agar spot method [27] against the EA5 strain, and expressed in arbitrary unit per ml (AU/mL). It corresponds to the highest dilution of CS (ratio 1:1 in phosphate buffer, pH 6.5) which inhibited the growth of the indicator strain. CS was stored at \(-\)20 \({}^{\circ}\)C in the fridge. Besides the EA5 strain, 22 different _Enterococci_ (our isolates from fresh ewe-milk lump cheeses) were used as indicators.
## 2 Materials
### Satellite Imagery  
We considered the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), near-infrared reflectance of vegetation (NIR\({}_{\rm V}\)), and solar-induced chlorophyll fluorescence (SIF) for crop yield prediction in this study. Both NDVI and EVI were extracted from the 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) MOD13A2 product with a spatial resolution of 1 km. NIR\({}_{\rm V}\) was calculated using the MODIS NIR reflectance (NIR\({}_{\rm T}\)) and MODIS NDVI from the MOD13A2 product [38].  
\[{\rm NIRV}={\rm NDVI}\times{\rm NIR} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Variables**} & \multicolumn{2}{c}{**Related**} & \multirow{2}{*}{**Spatial Resolution**} & \multirow{2}{*}{**Source**} & \multirow{2}{*}{**Temporal Resolution**} \\  & & **Crop Properties** & & & & \\ \hline Satellite imagery & NDVI & Plant vigor & 1000 m & MODIS & 16-day \\  & EVI & & & & \\  & NIR\({}_{\rm V}\) & & & & \\  & SIF & & 0.05 degree & CSIF & 4-day \\ Climate & Tmax & Heat stress & 0.5 degree & CRUNCEP & 1-day \\  & Tmin & & & & \\  & Srad & & & & \\  & Pr & Water stress & 0.05 degree & CHIRPS & 1-day \\ Others & Historical average yield (t/ha) & & & District-level & N/Å \\  & Crop area & & 500 m & MODIS & Yearly \\ \hline \hline \end{tabular} Abbreviations: NDVI, normalized difference vegetation index; EVI, enhanced vegetation index; NIR\({}_{\rm V}\), near-infrared reflectance of vegetation; SIF, solar-induced chlorophyll fluorescence; Tmax, daily maximum temperature; Tmin, daily minimum temperature; Sard, solar radiation; Pr, daily total precipitation.  
\end{table}
Table 1: Summary of the input features for model development.  
Besides, the satellite-based SIF data were extracted from a global spatially contiguous SIF (CSIF) dataset product, which provides 4-day data with 0.05\({}^{\circ}\) spatial resolution [39]. By training a neural network, [PERSON] et al. [39] generated the CSIF dataset using surface reflectance from the MODIS and SIF from the Orbiting Carbon Observatory-2 (OCO-2). SIF has been proven effective in monitoring the photosynthetic activity of terrestrial ecosystems and is widely used in predicting crop yield [19].
# Fuzzy Multi-Criteria Based Trust Management in Heterogeneous Federated Future Internet Testbeds
## 4 Evaluation
### Fuzzy VIKOR Evaluation
The first part of the dataset corresponds to experiments where the ratings are random with respect to SLA and monitoring values. As shown in Figure 3, in the first part, named RANDOM, the small fluctuations of the reputation score are due to the random difference between monitoring value and the user's opinion. The implementation of the credibility mechanism improves the reputation score by 1%. Then, the reputation score increases rapidly, almost 6%, because the users are honest and their opinions agree with the monitoring data, as in CASE2 of Algorithm 1. In CASE3, the decrease of the reputation score is not steep, only 2%, because contrary to the user's opinion, the monitoring value is higher than the SLA. This case reflects the behavior of malicious users, who try to damage the reputation of a testbed. In CASE1, the users are rightly unsatisfied with the testbed's performance; thus, its reputation score decreases 5%. The last part of Figure 3 corresponds to CASE4, and some biased users try to enhance the testbed's reputation unfairly. However, the increase of the reputation score is negligible due to the fact that the monitoring value violates the SLA value. Furthermore, Figure 3 indicates that the credibility mechanism plays a key role in the robustness of the reputation system. More specifically, CASE3 and CASE4 illustrate that the reputation system without the credibility mechanism is not adequate against the malicious users. In CASE3 and applying the credibility mechanism, the reputation score is 4% higher than without this mechanism. Similarly, in CASE4 and using Algorithm 1, the output of the reputation system is 5% higher than the opposite case. The most important parameter of the modified fuzzy VIKOR method is parameter \(\alpha\) of Step 5. Large values of \(\alpha\) mean that we penalize the poor testbed performance, while small values mean that we are lenient with the worse solution. It should be noted here that we compare our testbed performance with the ideal rating of a virtual user, which means that the experimenter's evaluation is always worse  
Figure 3: Credibility mechanism effect on the fuzzy VIKOR reputation system. than the virtual one. Figure 4 demonstrates the performance of the fuzzy reputation system for three different values of the \(\alpha\) parameter. Generally, the smaller values of \(\alpha\) produce higher reputation scores. Thus, for \(\alpha=0.5\), the produced reputation score is too small and does not encourage the experimenter to select a testbed even if it has actually good performance. On the contrary, in the case of \(\alpha=0.3\), the value is over-optimistic and cannot depict the real performance of the testbed. Furthermore, the reputation system ignores bad evaluations and is stiffer. Thus, the selected \(\alpha=0.4\) is a good trade-off that offers a realistic reputation score and enhances the sensitivity of the reputation system.
## 4 Experimental Study
### Evaluation Methods  
We structured the segmentation performance evaluation around two key aspects. First, we quantitatively evaluated the exactness of label assignment within object boundaries. To achieve this, mean intersection over union (MIoU) was used as the primary evaluation metric. MIoU is a widely adopted metric for segmentation tasks where precise boundary delineation is crucial, such as plane segmentation. MIoU is calculated using the following Equation (1):  
\[\text{MIoU}=\frac{1}{N}\sum_{i=1}^{N}\frac{|P_{i}\cap G_{i}|}{|P_{i}\cup G_{i}|} \tag{1}\]  
Figure 7: Examples of panoramic images captured at different construction sites.  
where \(N\) is the total number of segmented objects in an image, \(P_{i}\) represents the predicted segmentation mask, and \(G_{i}\) denotes the ground truth mask. The MIoU value ranges from 0 to 1, with values closer to 1 indicating higher segmentation accuracy.  
Since the evaluation was conducted across multiple images with varying environmental conditions, Mean MIoU was used as the final performance metric, calculated using the following Equation (2):  
\[Mean\ MIoU=\frac{1}{M}\sum_{i=1}^{M}MIoU_{i} \tag{2}\]  
where \(M\) is the total number of images and \(MIoU_{i}\) is the MIoU of the i-th image. A higher Mean MIoU indicates that the model consistently achieves high segmentation performance across diverse indoor construction environments.  
Second, we qualitatively evaluated whether the assigned labels preserved the correct geometric information. This evaluation was based on (1) a visual comparison of the plane segmentation masks and (2) a visual inspection of the 3D models reconstructed from the predicted planar information. While the previous quantitative metric provides numerical insight, it has inherent limitations in directly verifying whether the labels are correctly assigned to the corresponding planar regions. To overcome these limitations and conduct a more detailed analysis, qualitative evaluation was additionally conducted.  
We visualized the predicted plane segmentation masks by substituting the labels of each plane with distinct colors. Furthermore, we reconstructed the planar regions in 3D space using the geometric information inferred from the segmentation masks and visualized the resulting 3D models. If the predicted planar objects are labeled correctly, the plane segmentation mask should represent each plane with a consistent color, and the reconstructed 3D model should accurately reflect the spatial structure of the indoor environment, including the ceiling, walls, and floor.
# Heatwave Damage Prediction Using Random Forest Model in Korea
## 2 Methodology  
### Test Area  
South Korea was selected as the test bed, and its heatwave characteristics were investigated to establish the range and duration of the collected data for model training. The typical weather pattern that causes heatwaves in South Korea is a significant rise in temperature during the daytime, owing to stagnant high atmospheric pressure, which is a widespread occurrence across the country [13]. Although heatwave standards vary by country, a heatwave warning in South Korea is issued when the daily maximum temperature is expected to be above 33 \({}^{\circ}\)C for at least 2 consecutive days. Alerts are concentrated mainly from June to August. Heatwave occurrences in South Korea exhibit substantial interannual variability, but recently, they have become more frequent in late May and early September, and their frequency and intensity have increased [14; 15]. In particular, record-breaking heatwavesoccurred in 2016 and 2018, causing many casualties [16]. The Korea Disease Control and Prevention Agency (KDCA; formerly the Korea Centers for Disease Control and Prevention) has been operating a nationwide thermal disease monitoring system since 2011 to determine the weekly health damage caused by heatwaves from late May to early September every year. South Korea has 17 administrative districts composed of 8 municipalities and 9 provinces. In accordance with the characteristics of these test beds, we set the range resolution to match the 17 administrative divisions, and the temporal resolution was set to a 1-week period to match the disease monitoring system data from the KDCA.