esa-sceva/satcom-chunk-collection huggingface dataset:
# New Insights in the Ion Beam Sputtering Deposition of ZnO-Fluoropolymer Nanocomposites
## 3 Results and Discussion
### Surface Analytical Characterization
The presence of such broad signals could be explained based on the coalescence of the fine transition structure that could be observed in the Auger spectrum of metallic zinc into two large, merged signal components [47]. Figure 3: F1s XP-high resolution regions of ZnO-CF\({}_{x}\) nanocomposites having an inorganic phase volume fraction \(\phi\) = 0 (**a**), \(\phi\) = 0.05 (**b**), \(\phi\) = 0.10 (**c**), and \(\phi\) = 0.15 (**d**). Separation between the two peaks was quite constant, and approximately equal to 3.3 \(\pm\) 0.1 eV. The position of the major component was used to calculate modified auger parameters (\(\alpha\)) in order to assess zinc chemical speciation [48]. At increasing \(\phi\) values, the formation of zinc fluoride (ZnF\({}_{2}\)) could be observed. Such a phenomenon has already been proved in other composite fluoropolymeric films containing transition metal clusters [13; 14; 46]. This was confirmed by a shift in the modified Auger parameter from values typical for ZnO, of about 2010.6 \(\pm\) 0.2 eV, to values attributable to the formation of the inorganic halide: 2008.4 \(\pm\) 0.3 eV [49]. To facilitate the comparison of all spectra, the Auger signals for a pure, IBS-deposited, ZnO thin film and for a standard ZnF\({}_{2}\) powder are reported in Figure 4. On the one hand, the presence of the halide appeared to be predominant in the samples  
Figure 4: ZnL\({}_{3}\)M\({}_{45}\)M\({}_{45}\) XP-high resolution regions of ZnO-CF\({}_{x}\) nanocomposites having an inorganic phase volume fraction of \(\phi\) = 0.05 (**b**); \(\phi\) = 0.10 (**c**); and \(\phi\) = 0.15 (**d**). The same region for a pure ZnO film (**a**) and a ZnF\({}_{2}\) standard (**e**) is also reported, for comparison. with \(\phi\geq\)0.10, so that the position of the analyzed signals did not significantly differ from that of ZnF\({}_{2}\). On the other hand, the Auger region of the composite with \(\phi=0.05\) was comparable to that of pure ZnO. The corresponding Zn2p regions did not show significant differences, as expected, despite the significant variation in the chemical speciation of the aforementioned element, and therefore they are not shown here.
# Monolitic Hybrid Transmitter-Receiver Lens for Rotary On-Axis Communications
## 4 Discussion
### Suitability for Rotary Scenarios  
[PERSON] et al. [19] already reported data rates of up to 10 Gbit/s. However, what data rates are generally possible compared to FORJ? From an electrical point of view, OWC is able to achieve similar data rates as single-FORJ. In contrast to FORJ, the PD has typically a larger area and thus a larger capacitance, which limits the bandwidth. Choosing a smaller PD, will reduce the maximum tolerable misalignment. The hybrid lens from this work exhibits 14.5 dB link loss at \((x,y)=(0\,\text{mm},0\,\text{mm})\). This link loss is a part of the active link concept. The signal is directly recovered at RX by amplification and optimally by subsequent analog-to-digital conversion. The magnitude of the OWC link loss depends on the magnitude of tolerable misalignment. Thus, a higher data rate requires a smaller FOV. In summary, the data rate of OWC links might be slightly below single-fiber FORJ due to a higher link loss.  
With regards to the communication distance, the hybrid lens is flexible and not restricted to \(z=12.5\) mm. If the lens is designed for larger ranges, the TX beam exhibits a lower divergence and RX should be designed for smaller angles of incidence. Thereby, the hybrid lens can be tailored to the ideal distance of the rotary system.  
As mentioned in Section 2.1.3, the hybrid lens approach has the potential to be very cost effective: in contrast to fully separated TX and RX, only a single lens has to be fabricated, potentially coated and assembled.  
The proposed hybrid lens works in on-axis configuration like FORJs. Future work will deal with off-axis optical links to improve flexibility. Only data rates in the range of kbit/s to a few Mbit/s have been demonstrated [2; 3], which cannot compete with modern capacitive links [6]. Another interesting field of research is the realization of multi-channel optical wireless links similar to multi-FORJs. In nonrotary scenarios, data rates of several hundreds of Gbit/s have already been demonstrated [55].
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 1 Introduction
### Proposed Scheme and Contributions  
In this study, we adopted the Stackelberg game theory model, in which we consider a black hole an attacker and a UAV a defender. The proposed scheme considers two scenarios: first, where attackers try to attack all resources of the UAV, and the attacker tries to find weak resources by using a probability function. To address these issues, we proposed the Stackelberg ad hoc on-demand distance vector (SBAODV) scheme, which offers the best solution to overcome these challenges. In the below table, we have mentioned benchmark routing schemes for UAVs in the IoMT network.  
In summary, the main contributions of this paper are as follows:  
* Our proposed method can detect malicious devices that attack IoMTs. Specifically, UAVs are more vulnerable to attackers due to a lack of advanced algorithms.
* We introduced the Stackelberg game theory model to allow healthcare networks to identify potential security threats before they occur. In case the data are affected, defenders can take proactive measures to secure their networks, prevent attacks, and recover the data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Abbreviations** & **Definitions** \\ \hline IoT & Internet of things \\ IoMT & Internet of Medical Things \\ UAVs & Unmanned aerial vehicles \\ AODV & Ad hoc on-demand distance vector \\ SBAODV & Stackelberg ad hoc on-demand distance vector \\ DR & Doctor layer \\ MAC & Media access control \\ UDP & User datagram protocol \\ TCP & Transmission control protocol \\ HTTP & Hypertext transfer protocol \\ NRL & Network routing load \\ SSE & Strong Stackelberg equilibrium \\ PDR & Packet delivery ratio \\ QOS & Quality of service \\ RREQ & Route-request \\ RREP & Route reply \\ FRFEP & Fake route reply \\ N2S & Network simulator two \\ MANETs & Mobile ad hoc networks \\ LPNRP & Low-power and noisy routing protocol \\ TPH & Tree protocol for healthcare \\ RPH & Routing protocol for healthcare \\ FDTRP & Fuzzy dynamic trust-based routing protocol \\ DADRP & Dual attack detection routing protocol \\ CBR & Constant bit rate \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of Abbreviations.  
* The proposed scheme allows healthcare networks to quickly adapt to changing security threats. By constantly monitoring the network and modeling the interactions between attackers and defenders, healthcare networks can adjust their security measures to respond to new threats as they arise.  
This paper is organized as follows: Section 2 summarizes related work. Section 3 describes our approach to securing the IoMT network. Further, this section details the algorithm and its implementation. Section 4 describes the simulation and experimental setup to compare our suggested approach to the current algorithm. The analytical results and implications are presented here. Section 5 reviews the paper's key findings and discusses further research. This paper concludes with its references.
# Study on Early Warning Methods for Shipping Input Risks Under Consideration of Public Health Events
## 4 Cross-Border Risk Transmission Model in the Global Shipping Network
### Construction of a Coupled Global Air and Sea Transport Network
#### 4.1.2 Airline Network  
Global flight data primarily come from customs statistics, which include information about the origin airport and the number of flights for each flight segment. The original data table contains a total of 3318 different airport points. Since multiple airports exist within the same country, we considered the city with the highest frequency of passenger flow to represent each country and region as a node, resulting in 241 city nodes. The number of flights was used as the weight of the edges to connect the nodes and construct the international flight network.  
Through the method of network analysis, the international flight network can be thoroughly analyzed. Node degree centrality can be used to measure the influence of nodes within the network; betweenness centrality can be used to analyze nodes' bridging roles within the network; and network clustering can help us understand the groups and community structures within the international flight network.  
In the complex international flight network constructed in this paper, there are a total of 241 nodes, of which 28 have a node degree of over 100. Among them, five nodes have a degree surpassing 200. The node representing Marseille has the highest degree at 236. The average degree \(<k>\) of this network is 46.62, indicating that the international flight network is densely connected among nodes, and from an overall perspective, the network is relatively dense. The degree of the node representing Shanghai is as high as 166, significantly higher than the network's average degree, meaning Shanghai has more flights connecting it with other nodes.  
According to calculations, the average clustering coefficient of the international flight complex network constructed in this paper is 0.60, which is relatively high, indicating that the network exhibits a clustered state. The clustering coefficient for the node representing Shanghai is 0.33, suggesting that although the connections between the Shanghai node and other nodes in the network are very dense, the clustering among its neighboring nodes is relatively low.  
The closeness centrality of the node representing Shanghai is 0.29, which is greater than the entire international flight network's average closeness centrality of 0.22, making it one of the nodes with shorter average distances to other nodes and higher communication efficiency in this network.  
The following Figure 11 illustrates the interconnections between city nodes across different national aviation networks, visualized using SCImago Graphica Beta 1.0.49 software:  
Figure 11: Aviation network diagram.
# Extracting Crop Spatial Distribution from Gaofen 2 Imagery Using a Convolutional Neural Network
## 2 Study Area and Data
### Establishment of Image-Label Dataset  
There are eight main land cover types in the images described in Section 2.2: developed land, bare fields, farmland, agricultural buildings, roads, water bodies, winter wheat, and woodland. Of these, developed land, bare fields, farmland, agricultural buildings, roads, and water bodies can be directly distinguished by visual interpretation in the ENVI software. In order to accurately distinguish between winter wheat and woodland, 391 sample points were located on the images, as shown in Figure 2. Of these, 135 sample points were from woodland areas and 256 were from winter wheat areas. As can be seen in Figure 2, the texture of the woodland area is coarse, the color changes greatly, and the shape is irregular. The texture of the winter wheat area is fine, relatively smooth, and the shape is generally regular. Awareness of these characteristics can help improve the accuracy of visual interpretation.  
In total, 537 non-overlapping sub-images were selected from the fused GF-2 remote sensing images described in Section 2.2 to establish the image-label dataset to train the CEM model and test it. The size of each image was \(960\times 720\) pixels. The dataset covered all land cover types in Feicheng county during the period corresponding to the GF-2 remote sensing images we selected.  
We created a label file for each image to record its category number. Each pixel was assigned a code value in the label file. Codes 1-8 were used to represent each land type in the label files. The task of labeling each pixel was performed using the visual interpretation function of ENVI. Figure 3 shows an image and its corresponding label.  
Figure 2: The distribution of sampling points acquired via ground investigation.
## 6 Experiments and Results
### Data Preprocessing and Visualization  
Before applying the machine learning models on the dataset, we performed data preprocessing to ensure that the data was in a suitable format for analysis. Data preprocessing involved cleaning the dataset by removing any inconsistencies or missing values, encoding categorical variables, and scaling the numerical features. Scaling was a critical step, as it
## 2 Numerical Details and Layouts for Analysis  
### Computational Domain  
To simulate the convective flow and heat conduction in new introduced cooling configurations, a 3D solid-fluid conjugate heat transfer model is made. The simulations are conducted using finite element method based on the commercial package--Comsol Multiphysics 5.4. This software provides a convenient and robust graphical user interface. This easy-to-work interface facilitates to set up a simulation model, to run it and to visualize the results.  
To model the heat sink, the conjugate heat transfer module is used. Continuity, Navier-Stokes and energy equations for both the fluid and solid subdomains are solved, simultaneously. In addition, conjugate heat transfer module provides necessary tools to do analysis of heat sink model in conduction and convection heat transfer.  
An Insulated-Gate Bipolar Transistor (IGBT) module is placed on a heatsink. IGBT chip and diode chip on the top and other metal and isolation layers at the bottom are illustrated in Figure 2(a),b. It should be noted that in this model (Figure 2(b)) larger chips (IGBTs) and smaller chips (diodes) produce 50 W and 30 W, respectively. Thermal characteristics of the materials used in the IGBT module under study are given in Table 1. Also, the size of these layers can be seen in Table 2.  
To study the effectiveness of this new design, results are compared with conventional version of the offset strip fin heatsink. Figure 4 shows the conventional offset strip fin along with the different parameters used to describe the geometry. Table 3 lists the different dimensions in Figure 4a,b. Schematics of the new offset design can be seen in Figure 4c.  
It should be noted that the coolant is water with an inlet temperature of 20 \({}^{\circ}\)C and inlet mass flow rate in the range of 3-18.5 cm\({}^{3}\)s\({}^{-1}\). The flow regime is laminar in this mass flow range. The geometric parameters of \(A_{1}\), \(A_{2}\) and \(A_{3}\) have been chosen based on some preliminary simulations to obtain the highest performance from a heat transfer point of view.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Description** & **Length \(\times\) Width \(\times\) Height** \\ \hline Diode & \(5.5\times 4.78\times 0.13\) mm\({}^{3}\) \\ Solder (under Diodes) & \(5.5\times 4.78\times 0.025\) mm\({}^{3}\) \\ IGBT & \(7.53\times 7.6\times 0.13\) mm\({}^{3}\) \\ Solder (under IGBTs) & \(7.53\times 7.6\times 0.025\) mm\({}^{3}\) \\ Copper & \(20.5\times 28\times 0.3\) mm\({}^{3}\) \\ Ceramic & \(22.5\times 30\times 0.63\) mm\({}^{3}\) \\ Solder (between copper and baseplate) & \(7.6\times 0.025\times 0.025\) mm\({}^{3}\) \\ Baseplate & \(60\times 36\times 1.5\) mm\({}^{3}\) \\ Thermal grease & \(60\times 36\times 0.01\) mm\({}^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometric values of layers of IGBT module.  
Figure 4: Geometries of (**a**) 3D Offset strip fin heatsink, (**b**) Detailed dimensions of a channel with fins, (**c**) New offset design (Blue and red arrows show inlet and outlet flow, respectively).
## 3 Inertial Navigation and IMU Error Model
### Outlier Removal by RANSAC  
Based on the primary principle of epipolar geometry, all corresponding image points satisfy the coplanarity equation:* x''Fx=0  
where F is a 3 \(\times\) 3 fundamental matrix which geometrically describes the projection between two corresponding points in a pair of images representing the same 3D object; x and x'' are the coordinates of the points in image planes, respectively. It is supposed that RANSAC algorithm estimates the fundamental matrix F which optimally fits all the inliers. However, RANSAC may miss out a few outliers and estimate a biased F matrix due to the improper number of iteration operations and a relatively large number of outliers. Consequently, a multilayer RANSAC scheme [29] is proposed and implemented as follows:  
1. arbitrarily selecting a subset samples from all pre-selected matching features;
2. reconstructing epipolar geometry constraint satisfying Equation (12) and computing the fundamental matrix F based on the selected samples;
3. using the same constraint and the calculated matrix F to determine the error from all features;
4. with a proper threshold, categorizing all feature correspondences as inliers or outliers by appraising the error statistics;
5. iterating the first three steps multiple times (refer to [28] for a favorable number of iterations);
6. finding the best-fit matrix F which yields the optimal error estimates and removing the outliers discriminated by this matrix F;
7. repeating the first six steps with a gradually shrunken inlier/outlier determination threshold.  
To test the multilayer RANSAC scheme, we use a sequence of images (2.5 Hz rate) which will be elaborated in Section 7. Figure 3 shows the number of originally matched features as well as that after implementing multilayer RANSAC. It is clearly seen that after the fourth layer of RANSAC processing, there are still hundreds of matched features for every moment to maintain observable redundancy for motion estimation.  
Figure 4 intuitively illustrates a demonstration of feature matching and outlier removal between two adjacent image frames. The upper two subfigures are the original images for analysis showing a van overtaking the host vehicle. The middle ones present the feature matching after SIFT processing, and each cyan line indicates a pair of feature correspondence. It can be noted that the false matching arises and the images thus require outlier removal algorithms in order to retain only the static inliers. The  
Figure 3: Number of matched features after implementing multilayer RANSAC.  
lower subplots present the refined matching results by applying multilayer RANSAC scheme. As it has shown, all the remainder feature correspondences are recognized as inliers and no feature locates on the moving van any more.
## 4 Discussion
### Limitations  
There are several limitations to this study. First, due to the limited size of the research staff, only 242 valid questionnaires were collected, which is somewhat small. Second, this study did not stratify the elderly into different groups and set corresponding proportions of samples. Finally, although the data covered 24 subdistricts, full coverage of the core area of Beijing would have required 32. Therefore, follow-up studies need to expand the scope of sample collection, increase the sample size for the proportion of each type of elderly group, and maintain a balanced sample size in different subdistricts.
# Magnetic Communication Using High-Sensitivity Magnetic Field Detectors
## 3 Sensitivity Analysis
### Communication Range for Lossless Transmission Mediums
#### 3.2.4 Transmission Range for Different Coil Radii  
Equations (3) and (8) show that the coil radii have a direct influence on the maximum transmission range. In the case of coil-to-coil transmission, the radii of the transmitter coil and the receiver coil can be adapted. For coil-to-AMR communication, only the coil radius at the transmitter side is scalable.  
In the following, the influence of the transmitter and receiver coil radii on the coil-to-coil and the transmitter radius on the coil-to-AMR communication is investigated. Let us start with air coils, i.e., \(\mu_{\mathrm{r}}\approx 1\). Figure 5a shows the transmission range as a function of the transmitter coil radius \(r_{\mathrm{T}}\) for coil-to-coil and coil-to-AMR communications. As can be seen, the maximum transmission distance increases strictly monotonically with the transmitter coil radius for both communication techniques. It is remarkable that the transmitter coil radius has a stronger influence for coil-to-AMR than for coil-to-coil communications. Furthermore, the figure indicates that the correction term (5) is of negligible influence.  
Figure 4: Measured noise signal for different bandwidths.  
In the next investigation, the receiver side of both communication approaches is examined. For this purpose, the receiver coil radius for coil-to-coil transmission is varied. Figure 5b shows the range of coil-to-coil communication as a function of the receiver coil radius. As expected, the transmission range of the coil-to-coil system increases with the receiver coil radius. The maximum transmission distance for the coil-to-AMR system is determined by \(B_{\text{min}}\) of the magnetic field detector and is shown in the figure as reference values for the three different detection thresholds. For the given system parameters and e.g., \(B_{\text{min}}=5\) nT, the range of coil-to-AMR communication is outperformed by coil-to-coil communication beyond a receiver coil radius of \(r_{\text{R}}\approx 15\) cm.  
The results indicate that the range for coil-to-coil communication is severely restricted when small receiver coil radii are required. This is necessary for mobile applications such as AUVs and sensor networks. For these requirements, coil-to-AMR communication can serve much longer distances.
## 4 Multi-Antenna Frequency-Selective Channels
### Rank-Estimation for LSTD-Based Prediction  
The number of total features \(K\) for LSTD-based predictions depends on the rank of the unknown space-time signature matrix \(T_{f}\) as discussed in Section 4.2. This rank can be estimated by using available channels from previous frames if we assume that the number of total features does not change over multiple frames. This can be achieved via one of the standard methods, [PERSON]'s information theoretic criterion (AIC) (Equation (16) in [62]), which is applicable for all the proposed LSTD-based techniques. However, as the AIC-based rank estimation generally tends to be overestimated [62; 63], we propose a potentially more effective estimator for meta-learning, which utilizes a validation dataset.  
To this end, we first split the available \(F\) frames into \(F^{\text{tr}}\) meta-training frames \(f=1,\dots,F^{\text{tr}}\) and \(F^{\text{val}}\) meta-validation frames \(f=F^{\text{tr}}+1,\dots,F\). Then, we compute the sum-loss as (cf. (49))  
\[\sum_{f=F^{\text{tr}}+1}^{F^{\text{tr}}+F^{\text{val}}}\left\|X_{f}^{\text{te}} V^{(k),*}(\mathcal{Z}_{f}^{\text{tr}}|\{b^{k^{\prime},\text{meta}},\varrho^{k^{ \prime},\text{meta}}\}_{k^{\prime}=1}^{k})-Y_{f}^{\text{te}}\right\|_{F^{ \prime}}^{2}, \tag{56}\]  
where the hyperparameters \(\{\{\beta^{k^{\prime},\text{meta}},\varrho^{k^{\prime},\text{meta}}\}_{k^{ \prime}=1}^{k}\}\) are computed by using the \(F^{\text{tr}}\) meta-training frames, as explained in the previous section. The rank-estimation procedure sequentially evaluates the meta-validation loss (56) in order to minimize it over the selection of \(k\). In this regard, it is worth noting that an increase in the total number of features \(k\) always decreases the meta-training loss in (49), whereas this is not necessarily true for the meta-validation and meta-test losses.