esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
In conclusion, we have presented a simple and inexpensive method for synthesizing a uniform nanostructured SERS active substrate with tunable nanosphere diameter and interparticle gaps. The SERS active substrate was fabricated through hot embossing and sputtering deposition. The nanosphere diameter and interparticle gaps were controlled by adjusting the sputtering duration to obtain a uniform and a densely packed silver nanosphere array substrate. According to the SERS experiments, the nanostructured polycarbonate substrate combined with uniformly distributed and closely packed silver nanospheres showed a strong SERS enhancement factor up to \(1.12\times 10^{7}\). The developed SERS substrate has been further applied to the sensitive detection of DEHP molecules  
Figure 11: SERS spectra of different concentrations of DEHP (from 1000 ppm to 2 ppm) on the nanostructured SERS substrate. The Raman band assignment of the main peaks of DEHP is given in the figure.  
Figure 10: (**a**) The SERS spectra of R6G measured from 10 random positions of the SERS substrate. (**b**) The SERS intensities of the Raman characteristic peak of the 1503 cm\({}^{-1}\) band from these locations.  
and demonstrated its practical application potential in food contamination detection. Therefore, the developed SERS active substrate is highly promising for use in rapid chemical and biomolecular detection applications.  
Conceptualization, C.-Y.H.; methodology, C.-Y.H.; investigation, M.-S.T.; data curation, M.-S.T.; writing--original draft preparation, C.-Y.H.; writing--review and editing, C.-Y.H.; supervision, C.-Y.H.; project administration, C.-Y.H.; funding acquisition, C.-Y.H. This research was funded by Ministry of Science and Technology, Taiwan, grant number MOST 107-2221-E-005-045.  
The authors would like to thank the Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract No. Grant MOST 107-2221-E-005-045. We would also like to thank Vida Biotechnology Co., Ltd. for their help in the fabrication of PC substrates.  
The authors declare no conflict of interest.
# Analysis of Maritime Wireless Communication Connectivity Based on CNN-BiLSTM-AM
### Contributions  
However, the existing solutions have often relied on instantaneous and precise channel state information (CSI) and have primarily focused on relatively simple and small terrestrial wireless networks. However, there are fundamental differences between terrestrial and maritime environments: the channel characteristics are influenced by the coupling of multidimensional dynamic hydrometeorological factors, such as wind speed, wave height, and air humidity, resulting in highly time-varying and uncertain channel conditions. Existing channel models have not adequately considered the inherent dynamics of the maritime environment, making it difficult to accurately simulate real-world oceanic conditions. Although LSTM networks have shown potential in time-series prediction [17], a single LSTM model lacks the ability to capture the multi-scale characteristics of maritime channels, leading to poor prediction accuracy.  
In response to these challenges, this paper puts forward a dual-hop communication connectivity prediction framework for dynamic oceanic environments, and its main contributions are as follows:  
* A relay-assisted terrestrial-maritime collaborative network coverage enhancement scheme is proposed, which characterizes the dynamic characteristics of the maritime wireless environment by permitting the channel parameters to vary randomly in the channel modeling.
* In the analysis of maritime communication connectivity, the selection of the input features incorporates the ship navigation trajectories and real-time hydrometeorological parameters, fully considering the complexity and time variability of the maritime environment.
* A CNN-BiLSTM-AM cascade scheme is designed, where a CNN is used to extract the local features, BiLSTM is employed to model the long-term dependencies of the channel states, and an attention mechanism (AM) is introduced to adaptively focus on key node information. This approach achieves a high prediction accuracy.
# UAV-Derived Himalayan Topography: Hazard Assessments and Comparison with Global DEM Products
## 2 Study Sites and Methods
### Applications
#### 2.4.2 Landslides  
A large landslide that partially blocked the Marsyangdi River following the 2015 Gorkha earthquake was manually digitised with reference to the UAV orthophoto and a DEM of difference (UAV April 2018 and HMA March 2014 DEM). The landslide volume was derived from the DEM of difference between two HMA DEMs (May 2015 and March 2014). Additionally, we simulated a complete river blockage by dams with heights of 5, 10, and 15 m close to the village of Lower Pisang. We selected a reach of the Marsyangdi River below the 2015 landslide so that the channel topography was not preconditioned by this river-damming event. For each DEM and dam height, we derived the area and volume of the lake that would form upstream if the dam was not downcut by the river (i.e., the lake was in equilibrium with the dam crest).
## 3 Results
### System Configurations to Be Tested  
The main purpose of the reported experiments is to validate that the haptic system facilitates the control of the impact hammer to perform the task of positioning the tip of the end-effector above the rock, without colliding. That means that it improves the tele-operation experience.  
Several tests are recorded doing this task with different configurations of the haptic system in order to study the impact of this feature. In particular, three different configurations of the system are tested. The configuration combines a different feature of the haptic system, which are:  
1. Type of control: Determine how the machine will control the arm (impact hammer). In normal tele-operation, the user directly controls each joint of the arm. In the proposed system, the user directly controls the pose of the end-effector;
2. Use of the 3D reconstruction of the environment: This feature considers the use of the 3D reconstruction to get a multi-view of the operational environment. In normal tele-operation of the impact hammer, just one or two views are used, which are obtained directly by the cameras;
3. Use of haptic feedback: This feature will give the operator the feedback of the joystick to avoid colliding with the grill or other rocks  
Finally, these features are combined in different configurations, A to D, which are defined in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Configuration Id** & **Type of Control** & **Use 3D Reconstruction** & **Use Haptic Feedback** \\ \hline Configuration A & Joints & No & No \\ Configuration B & End effector & No & No \\ Configuration C & End effector & Yes & No \\ Configuration D & End effector & Yes & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 1: Types of system’s configurations used in tests.  
Figure 17: User operating the UR5 arm, using the haptic controls (installed on a chair), and the user interface.
## 3 Algorithm Description  
In this section, the current algorithm is described in the order of LUT construction with aerosol model and retrieval process based on OE with a priori. The aim of this algorithm is the retrieval of AOD and SSA. To improve retrieval accuracy and to make the algorithm standalone, ALH is simultaneously retrieved with the AOD and SSA by fitting the reflectance spectrum from simulations and observations.  
The basic assumption of this algorithm is that the measured spectrum consists of wavelength-dependent Rayleigh scattering and aerosol effects and therefore the aerosol information can be retrieved by fitting the spectrum. Thus, the OE method, which optimizes state variables to represent the measured radiance spectrum, is adopted here.  
Figure 3 presents a schematic of the UV-visible aerosol algorithm. LUTs for the three aerosol types are calculated using VLIDORT (see Sections 3.1 and 3.2). One value from the LUTs is selected according to the aerosol type and geometry for each pixel (see Section 3.3). Before the retrieval, cloud pixels are removed using a reflectance threshold and standard deviation of reflectance within a 3 pixel \(\times\) 3 pixel window. The thresholds for the cloud mask and surface reflectance dataset will be optimized after the launch of GEMS. After the aerosol type selection and cloud masking, a priori values of AOD and SSA are estimated based on the two-channel method; a priori ALH are assumed to be the grid-averaged \(\text{Z}_{\text{aer}}\)  
Figure 2: Annual average value of the aerosol layer height (ALH) (km) for 2007 from CALIOP-measured attenuated backscatter profiles at 1064 nm (1.5\({}^{\circ}\)\(\times\) 1.5\({}^{\circ}\) resolution). By definition, the ALH is equal to the attenuated-backscatter-weighted height.  
shown in Figure 2 (see Section 3.4). Those a priori states are applied to solve the Levenberg Marquardt equation and are optimized to fit the measured spectrum (see Section 3.5).
## 3 Deep Learning Architecture and Signal Reconstruction
### Autoencoder  
The AE models were implemented in several variants; the first two versions can be distinguished based on the size of the model, with the small version (17,121 parameters) and the big version (188,193 parameters). Both models were then implemented in one of two versions, with the last activation function of ReLU and Sigmoid. That was conducted since ReLU might perform better for data with no specified upper bound. Figure 2 shows the general diagram of the autoencoder network used during the experiments.  
In contrast, for the data in the range between 0 and 1, Sigmoid might be more beneficial since the model output will be restricted to the expected range of values, producing more natural representations.
# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
###### Abstract
Additionally, for positioning noise caused by overlapping objects, we found that as the degree of overlap increases, the mutual interference also increases, resulting in greater uncertainty. Therefore, we calculated the overlap rate between objects and adjusted the measurement noise covariance to be inversely proportional to this overlap rate. By updating the measurement noise covariance in this way, we improved tracking performance. Data association is another critical subtask in TBD-based MOT. Association methods can be based on motion models, deep visual feature models, or a combination of both [30]. When tracking objects exhibit complex motion, non-linear movement patterns can arise, posing significant challenges for matching algorithms that use the Intersection over Union (IoU) distance as the affinity matrix, as IoU is based on area calculations. We observed that compared to area-based IoU, using height as a feature provides better distinctiveness for tracking objects. Height is less prone to change compared to other simple positional features, offering greater stability and compensation ability. Therefore, we used the height feature to adjust the IoU distance, which enhances the stability of associations and reduces the occurrence of identity switch (IDSW). To sum up, our key contributions can be outlined as follows:  
* To improve the quality of detection boxes and reduce the impact of detection results on tracking performance, we proposed a method that utilizes predictive information to filter candidate boxes. NMS is not applied during the detection phase but is instead delayed until the tracking phase to better support the tracking task, significantly enhancing the overall performance of MOT. * To mitigate the impact of localization errors on the motion model, we proposed an adaptive measurement noise method that dynamically adjusts the ratio between prediction and measurement weights, significantly enhancing overall tracking performance. * To mitigate the impact of complex motion on tracking stability, we proposed a method that adjusts the IoU distance using height information to enhance the overall association stability. Our proposed method underwent comprehensive experimental validation on the DanceTrack dataset, and competitive results were also achieved on the MOT20 dataset.
# Path Planning Based on Artificial Potential Field with an Enhanced Virtual Hill Algorithm
## 5 Discussion  
In order to compare with the previous virtual hill algorithm, simulations are performed on the same environment for both _previous_\(\mathbf{e}_{b}\) and _new_\(\mathbf{e}_{b}\). Also, the dead-end algorithm is applied when entering a blocked road, and how the path changes is checked. The circle mark is displayed at the position of \(\mathbf{P}(\mathbf{t}_{0})\) for visual confirmation at the moment the blocked path is recognized. All simulations are saved as pictures, data files, and videos.  
In Figures 12-19, each (a) is the result when _previous_\(\mathbf{e}_{b}\) is applied, and each (b) is the result when _new_\(\mathbf{e}_{b}\) is applied. They show that the moving distance can be significantly reduced when determining the driving direction by comparing the degree of openness of the two-way route regardless of the relative position of the robot and the goal. (c) Shows the result of applying the dead-end algorithm when a route including a blocked road is selected. It is possible to reduce the moving distance by more than about 4 m by coming back without entering deep into the dead end. In the service environment, if the mobile robot travels at a low speed of less than 0.2 m/s, it can save more than about 20 s. The saving distance and time can vary depending on the measurable distance of the sensor and control techniques. Very occasionally, the path along the _new_\(\mathbf{e}_{b}\) is more inefficient, as shown in Figure 20. The maximum measurement distance of the sensor is 4 m. Since the distance from the robot position in Figure 20 to the obstacle CD is about 4 m, it is difficult for the robot to recognize the dead end road in the \((+)\) direction. Unless the robot has map information in advance or creates a map while moving, it may encounter dead ends when moving through an unknown space. The dead-end algorithm prevents the robot from having to drive unnecessarily deep into dead ends.
# An IoT-Based Fog Computing Model
## 6 Experiment and Discussion of Results
### Simulating the Terminal Layer
#### 6.2.2 Fault-Tolerance/Robustness  
It is important to note that the energy savings and the number of sink nodes in Table 4 are not linearly related. This is because LIMP does not have to use every sink node especially if the chosen sink node is not suitable. Such is the case of the 5 sink nodes network and the three sink nodes network, where some of the sinks are orphan nodes with no children. However, though being found inadequate by the algorithm during resource allocation, the orphan nodes can be used as recovery sinks upon failure due to the multi-sink deployment. They can thus be used to improve the robustness and fault-tolerance of the terminal layer. In the simulation, we calculated the longest distance from leaf nodes to each sink nodes in different multi-sink network configurations with the goal of assessing if the distance in number of hops between two nodes can affect the delay. We also evaluated the fault-tolerance capability of the LIMP protocol by measuring the recovery time of the network upon failure of a sink node. In our case, the same sink node 1 was set offline to mimic a failure and the recovery was triggered by LIMP to migrate the orphaned nodes to alternate sink node(s). The results are shown in Table 5 reveal that multi-sink deployment does not necessarily reduces maximum distances in terms of number of hops in the terminal layer as this depends on the topology of the sensor network and the design of the LIMP protocol. However, multi-sink deployment using the LIMP protocol reduces the recovery time upon a single sink node failure.
## 4 The Proposed Key Distribution Framework
### A Spatiotemporal Diversification-Based Multipath-Multi-Key Distribution
#### 4.1.2 Execution Phase  
Keys are distributed across the selected paths with spatiotemporal diversification, as described in Section 4.2.2. In this phase, multiple key pairs are generated and transmitted through different routes at each time step. A flexible key reconstruction scheme is used to optimize the security and efficiency of the end-to-end key distribution, allowing for dynamic adjustments based on varying security requirements. This approach is analogous to multipath-based quasi-real-time QKD schemes in SDNs, enhancing the resilience of the key distribution process against potential eavesdropping.
## 4 Deploy chosen solution  
Once a solution has been chosen that reflects the network requirements desired by the application it must be deployed to all nodes to take effect. The network manager maintains the state of the network with regard to the currently implemented solution. If the chosen solution identifies the currently implemented solution no further action is necessary, otherwise the network manager must take steps to implement a new solution. Once a solution has been implemented the _network state_ is updated. Two techniques are discussed in Section 6.3 which may be used to implement the chosen solution:  
* Reprogramming each node with a new binary should the network require a change of solution. This solution carries extensive communications overhead.  
Figure 1: The _network manager_ residing on a device capable of higher level processing power either on or connected to the root of the network.  
* Switching between solutions with the implementation of each solution present on all nodes at all times. This solution requires increased memory overhead and is less flexible to the introduction of new solutions.  
At the outset of the network deployment a predetermined solution from the possible solutions list is deployed in the network for the purpose of network probing.
## 4 Conclusions  
In this paper, an OAM mode recognition technique based on AP-CNN is proposed. Utilizing ResNet18 as the backbone of the AP-CNN, a dual-path algorithm structure, including a top-down feature path and a bottom-up attention path, is added. Based on the dual-path algorithm structure combined with the attention pyramid, low-level detailed information of the similar light intensity map is fully utilized. In our simulated experiments, the size of the light intensity distribution map of the OAM beam was set to 128 \(\times\) 128 and was input into the ResNet18 network for training. Then, the output feature maps of the third, fourth, and fifth layers of ResNet18 were selected to build a pyramidal hierarchy. After supervised training with a large OAM communication dataset with different turbulence conditions, the recognition accuracy and the BER were numerically determined. The simulation results showed that the AP-CNN achieved greatly improved OAM mode detection accuracy and demodulation performance compared with the ResNet18 network. When the turbulence was weak, the optimization effect of AP-CNN was not obvious, i.e., a 0.6% improvement, while when the turbulence was strong, the optimization was clear, with an improvement of about 2.3%. The OAM detection accuracy of the AP-CNN was up to 5.5% higher than that of ResNet18 at 2 km with strong turbulence. This technique has significant applications in communication, target detection, and radar imaging. Due to the limitations of experimental conditions, our research was only based on a simulated intensity distribution dataset, and light intensity information was collected without phase information. The training and analysis of the real turbulence OAM communication data under different conditions, as well as the phase information, will be the focus of future work by our team.  
Conceptualization and methodology, T.Q. and Z.Z.; software, Z.Z.; validation, T.Q. and Y.Z.; formal analysis, Y.Z. and J.W.; editing, Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (62071359), Scientific Research Program Funded by Shaanxi Provincial Education Department (19 JK0673), the Open Foundation of Laboratory of Pinghu, Pinghu, China, Postdoctoral Science Foundation in Shaanxi Province and the Fundamental Research Funds for the Central Universities.  
Not applicable.  
The authors declare no conflict of interest.
## 4 Numerical Simulations and Discussions  
As shown in Figure 2, we apply the following coupled complex Lorenz system to the transmitter \(L1\) at the sensor node and the receiver \(L2\) at the sink node for secure communication.  
\[L1:\left\{\begin{array}{rcl}\dot{y}_{1}&=&35(z_{1}-y_{1}),\\ \dot{z}_{1}&=&55y_{1}-z_{1}-y_{1}z_{2},\\ \dot{z}_{2}&=&-8/3z_{2}+(1/2)(\dot{y}_{1}z_{1}+y_{1}\dot{z}_{1}).\end{array}\right. \tag{8}\] \[L2:\dot{x}_{1}=35(z_{1}-x_{1})+v_{1},\]where \(y_{1}\), \(z_{1}\), \(z_{2}\), and \(x_{1}\) are state variables (\(q=1\)). The overbar \(\bar{y}_{1}(z_{1})\) represents the complex conjugate of \(y_{1}(z_{1})\). The designed controller is \(v_{1}\). The Lyapunov exponent of the \(L1\) system is \(LE1=1.1019,\ LE2=0.6602,\ LE3=-0.1350\approx 0,\ LE4=-0.6355\), and \(LE5=-2.0331\), which includes a positive Lyapunov index at least. It indicates that the \(L1\) system is chaotic.  
According to Equation (5), we construct the following controller:  
\[v_{1} = \mathbf{s}^{\prime}-A\varepsilon(t)-\mathbf{f}(\mathbf{x}, \mathbf{z})+\mathbf{k}\mathbf{e} \tag{9}\] \[= \dot{H}+D35(z_{1}-y_{1})-35(z_{1}-x_{1})+k_{1}(x_{1}-\int_{0}^{t} \left(s^{\prime}-A\varepsilon\right)dt),\]  
where \(s^{\prime}-A\varepsilon\) can be processed through a filter bank.  
The noise \(\varepsilon(t)\) is described by the probability density function of Gaussian distribution  
\[w(\varepsilon)=\frac{1}{\sqrt{2\pi\sigma}}exp(-\frac{(\varepsilon-\varepsilon _{0})^{2}}{2\sigma^{2}}). \tag{10}\]  
where \(\sigma=1\) and \(\varepsilon_{0}=0\) are the variance and mean value [34], respectively.  
The following initial conditions were selected: \(\{y_{1},z_{1},z_{2}\}(0)=\{1+2j,3+4j,1\}\), \(x_{1}(0)=\{-1-2j\}\). The fourth-order Runge-Kutta method was used with \(\Delta t=10^{-3}\) and was implemented with MATLAB software. We take the following signal as an example:  
\[H(t) = 20 sin^{2}(0.5\pi t)+jcos(\pi t) \tag{11}\]  
Then, we have  
\[\left\{\begin{array}{rcl}\dot{H}^{r}(t)&=&20\pi sin(0.5\pi t)cos(0.5\pi t)\\ H^{i}(t)&=&-\pi sin(\pi t)\end{array}\right. \tag{12}\]  
where the derivative of \(H(t)\) is continuous during the computer sampling period. Continuous range of general signal is much larger than the sampling period. If the continuous range is smaller than the computer sampling period, we can reselect a smaller sampling period.  
In this section, the same control strength matrix, \(k=-500\), was utilized for a better comparison.
## Abbreviations  
* \(x_{C}\), \(y_{C}\); \(x_{S}\), \(y_{S}\) coordinates of the centers of mass of the robot and the trailer, respectively \(\psi\), \(\chi\) head angles of the robot and the trailer, respectively \(2\mathit{l}_{C}\), \(2\mathit{l}_{S}\) track widths of the robot and the trailer, respectively \(2\mathit{\rho}\) the wheelbase of the four-wheeled robot \(R_{C}\), \(R_{S}\) radii of the wheels of the robot and the trailer, respectively \(b_{C}\), \(b_{S}\) distances between the connection point of the rods and the centers of mass of the robot and the trailer, respectively \(\delta\) angle between the roller axis and the Mecanum wheel plane \(\mathit{\varphi}_{i}\), \(\theta_{i}\) angles of rotation of the wheels of the robot and the trailer, respectively \(\alpha\) angle of rotation of the rods relative to each other \(\boldsymbol{V}_{C}\), \(\boldsymbol{V}_{S}\) vectors of the velocities of the centers of mass of the robot \(\boldsymbol{\Omega}\) vector of the angular velocity of a rigid body \(\boldsymbol{\omega}\) vector of the wheel's angular velocity \(\boldsymbol{\tau}\) unit vector tangent to the wheel \(\boldsymbol{\gamma}\) unit vector of the roller axis