esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characteristics of the Materials
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Parameter/Material** & \begin{tabular}{c} **Specific Surface** \\ **(m\({}^{2}\)/g)** \\ \end{tabular} & \begin{tabular}{c} **Mesopores Volume** \\ **(Micropore Volume) (cm\({}^{3}\)/g)** \\ \end{tabular} & \
\begin{tabular}{c} **Mesopores Surface** \\ **(m\({}^{2}\)/g)** \\ \end{tabular} &
\begin{tabular}{c} **Average Pore Size** \\ **(nm)** \\ \end{tabular} \\ \hline Activated carbon & 782.67 & 0.510 (0.162) & 177.10 (366.41) & 3.65 \\ Diatomite & 29.32 [1] & 0.055 & 20.05 & 7.56 \\ Zeolite & 33.34 [2] & 0.09 & 14.01 & 17.54 \\ Limestone sand & 0.91 [3] & 0.003 & 0.33 & 13.34 \\ Halloysite & 51.41 & 0.193 & 51.18 & 13.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Surface characteristics of the reactive materials. Cation exchange capacity (CEC) indicates the number of negatively charged sites on the material surface able to exchange cations (Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\) and Mg\({}^{2+}\)). Zeolite was found to have the largest CEC, followed by diatomite, halloysite, activated carbon and limestone sand (Table 2). [PERSON] and [PERSON] [58] demonstrate that this characteristic not only presents an evaluation of the ion exchange processes responsible for contaminant removal, but also indirectly properties such as water adsorption, ability of water transfer and contaminant migration.
## 2 Radiation in Space and Its Effects on Electronic Components
### Radiation Effects on Electronic Components  
Electronic components are critical to the operation of spacecraft systems, but they are also vulnerable to damage from exposure to radiation in space. The effects of radiation on electronic components can range from minor upsets to permanent failure, which may result in reduced performance and increased operational risk. In this chapter, we will summarize the main effects of radiation on electronic components in an LEO.  
* Single Event Effects: SEEs are caused by high-energy particles, such as cosmic rays or solar particles, that collide with electronic components and cause temporary or permanent damage. Single event upsets (SEUs) can result in incorrect data or system reset, while single event latch-ups (SELs) can cause permanent damage to the component.
* Total Ionizing Dose: TID is the accumulation of ionizing radiation that damages electronic components over time. It can cause changes in the electrical characteristics of electronic components, leading to a degradation in performance and increased risk of failure. This study focuses on the total ionizing dose in the Earth's orbit, using dosimetry data and predictive models.
* Displacement Damage: DD is caused by the displacement of atoms in the material structure of electronic components caused by high-energy particles, which can cause permanent structural damage. In turn, this can result in a loss of performance or in the failure of the component.
* Single Event Transients: SETs are short-lived electrical signals that can occur in electronic components when exposed to radiation. SETs can cause false signals, generate electromagnetic interference, or cause damage to other components in the system. It is important to consider these effects in the design and testing of spacecraft systems to ensure a reliable and safe operation in the harsh space environment. It is crucial to implement mitigation strategies and use components tested in radiation environments to guarantee their robustness.
## 5 Test Experiments
### Length Measurement Experiment
#### 5.5.1 Experimental Setup  
In the process of length measurement experiment, we used a laser tracker to measure the coordinates of two endpoints (A and B) of a scale bar. The laser tracker uses retroreflectors for measurements. The retroreflectors we used in this experiment had a diameter of \(19.05\pm 0.001\) mm. By moving the retroreflector from HOME to the target points A and B of the scale bar, we were able to measure the coordinates of the target points A and B of the scale bar. HOME is a fixed point on the laser tracker, and the distance from HOME to the center of rotary head of laser tracker is known. The experimental setup is shown in Figure 13. The experimental temperature was 20.2 \({}^{\circ}\)C, the humidity was 40%, and the pressure was 101.325 kPa.
## 2 Development of Beam Element-Based Simulation
### Model Description
To obtain Equation (3), the standard shape functions for an Euler-Bernoulli beam denoted by \(N_{ax}\) and \(N_{b}\) for axial and bending, respectively, are substituted into Equation (2) for the corresponding displacements, virtual displacements and their derivatives. Further, applying \(\sigma=E(\epsilon-\epsilon_{T})\) where \(\epsilon_{T}\) is a thermally induced strain and letting \(\epsilon=u\prime\) from standard small strain kinematics produces  
\[\int_{0}^{l}EIN_{b}^{\prime\prime}U^{el}N_{b}^{\prime\prime}\delta U^{el}dx+ \int_{0}^{l}AEN_{ax}^{\prime}U^{el}N_{ax}\prime\delta U^{el}dx+\int_{0}^{l}kN_ {b}U^{el}N_{b}\delta U^{el}dx-\int_{0}^{l}qN_{b}\delta U^{el}dx-\int_{0}^{l} AE\epsilon_{T}N_{ax}\prime\delta U^{el}dx=0 \tag{3}\]  
Simplifying and combining like terms produces Equation (4),  
\[\delta U^{el}{}^{T}\bigg{(}\bigg{(}\int_{0}^{l}EIN_{b}^{\prime\prime}N_{b}^{ \prime\prime}dx+\int_{0}^{l}AEN_{ax}^{\prime}{}^{T}N_{ax}rdx+\int_{0}^{l}kN_{b }^{T}N_{b}dx\bigg{)}U^{el}-\int_{0}^{l}qN_{b}{}^{T}dx-\int_{0}^{l}AE\epsilon_{T }N_{ax}\prime^{T}dx\bigg{)}=0 \tag{4}\]  
Because the virtual displacements are arbitrary, Equation (5) must hold. \[\bigg{(}\int_{0}^{l}EIN_{b}^{\prime\prime}N_{b}^{\prime\prime}dx+\int_{0}^{l} AEN_{ax}^{\prime}{}^{T}N_{ax}rdx+\int_{0}^{l}kN_{b}^{T}N_{b}dx\bigg{)}U^{el}= \int_{0}^{l}qN_{b}dx+\int_{0}^{l}AE\epsilon_{T}N_{ax}\prime^{T}dx \tag{5}\]  
Equation (5) is the discretized version of the weak form for a single element which, in conjunction with the multi-point constraints detailed later, drives the model. The terms multiplying the nodal displacements are the bending, axial and foundation contributions to the element stiffness matrix. The axial and bending stiffness matrices are the standard ones for an Euler-Bernoulli beam. The foundation contribution is non-zero only for the bottom layer of elements, and the treatment of this is detailed in Section 2.1.3.
# A Novel Spaceborne Sliding Spotlight Range Sweep Synthetic Aperture Radar: System and Imaging
## 7 Conclusions  
This study investigated the system design and imaging method for a new SSS-RSSAR that continuously steers its beam in azimuth and in range to generate a ROI-matched BIS. Comparing to the fixed azimuth pointing of the SS-RSSAR, the SSS-RSSAR is capable of implementing a wider synthetic aperture and achieving a higher azimuth resolution. The continuous range-azimuth beam steering of the SSS-RSSAR is to cause a wide range of slant range variation and hence lead to the problem of transmission blockage. Aiming at solving this problem, a new CVPI technique was used to mitigate the slant range variation by continuously adjusting the PI based on the instant data acquisition geometry. An MPFA was derived to focus the seriously coupled echo received in a highly squinted geometry due to the perpendicular-to-BIS CBP. Based on the CVPI technique and the MPFA method, an integrated system parameter design flow for the SSS-RSSAR has been suggested, including a new PA technique to suppress the spatial range wavenumber variation. The total data amount and the total computational load between the SSS-RSSAR and the traditional SSS-SAR have also been compared. Based on the simulation experiments, the advantages of the SSS-RSSAR in imaging a moderate-swath and tilted ROI in a high resolution over the traditional SSS-SAR have been demonstrated. Future research may include the development of an airborne prototype of the SSS-RSSAR for real-data based experiments.  
This work was supported in part by the National Natural Science Foundation of China (No. 61601257 and No. 61371133), in part by the Major Project of China High-resolution Earth Observation (No. 30-Y20A12-9004-15/16), in part by the China Postdoctoral Science Foundation (No. 2016M591179) and in part by a special project with No. 30402010203.  
The work presented here was carried out in collaboration between all authors. [PERSON], [PERSON], [PERSON] and [PERSON] defined the research theme. [PERSON] designed the methods and experiments, carried out the laboratory experiments, analyzed the data, interpreted the results and wrote the paper. All authors have contributed to, seen, and approved the manuscript.  
The authors declare no conflict of interest.
# Virtual Streamline Traction: Formation Cooperative Obstacle Avoidance Based on Dynamical Systems
## 2 Obstacle-Avoidance Formulation
### Stationary Points  
As shown in Figure 2, when the target point of the USV lies directly behind an obstacle, the USV navigates straight toward the center of the obstacle. In this case, there is collinearity among the USV, obstacle center, and target point. Upon reaching the edge of the obstacle, the USV cannot cross the obstacle in a two-dimensional plane. Therefore, the velocity vector becomes zero on the edge of the obstacle, causing the USV to stall at the edge, creating a stationary point.  
It is evident that even minor deviations in heading can enable the USV to evade stationary obstacles effectively. In practice, as illustrated in Figure 2, it is uncommon for a route to maintain a consistent heading; slight alterations in the heading of the USV often occur because of natural influences such as waves and wind during navigation. To mitigate unnecessary risks and building on the aforementioned observations, the disturbance formula was modified by incorporating an offset vector to the surface normal vector of  
Figure 2: Illustration of the stagnation point.  
the obstacle. A tangent vector \(\tau_{k}=\left[\frac{\partial\tau_{k}}{\partial y}\quad-\frac{\partial\tau_{k}}{ \partial x}\right]^{T}\) perpendicular to the obstacle's surface normal vector \(n^{k}\) was selected and incorporated into Equation (5), yielding  
\[M_{k}=I-\frac{n_{k}n_{k}^{T}}{\left|\Gamma_{k}\right|^{1/\rho_{k}}n_{k}^{T}n_{k }}+\frac{i\tau_{k}n_{k}^{T}}{\left|\Gamma_{k}\right|^{1/\alpha_{k}}n_{k}^{T}n_ {k}\mathcal{C}_{k}}, \tag{8}\]  
where the newly added matrix \(\frac{i\tau_{k}n_{k}^{T}}{\left|\Gamma_{k}\right|^{1/\rho_{k}}n_{k}^{T}n_{k} \mathcal{C}_{k}}\) is the disturbance-deviation matrix of the obstacle. By introducing this matrix, the sum of the tangent vector \(\tau_{k}\) and the normal vector \(n^{k}\) acts as a disturbance to the initial flow field. Here, \(i\in[-1,1]\) can adjust the deviation direction. Proper selection of the value of \(i\) enables the USV to deviate right when approaching the right boundary and left when approaching the left boundary. In Equation (8), \(\sigma_{k}\) is the deviation coefficient of the \(k\)-th obstacle. A larger deviation coefficient leads to earlier avoidance of stationary points. To ensure that the deviation matrix does not affect the obstacle-avoidance effect of the disturbance matrix, a constant \(\mathcal{C}_{k}\) is introduced. When this parameter is reasonably set, the norm of the deviation matrix \(\frac{i\tau_{k}n_{k}^{T}}{\left|\Gamma_{k}\right|^{1/\rho_{k}}n_{k}^{T}n_{k} \mathcal{C}_{k}}\) should be far smaller than that of the disturbance matrix \(\frac{n_{k}n_{k}^{T}}{\left|\Gamma_{k}\right|^{1/\rho_{k}}n_{k}^{T}n_{k}}\).
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 6 Conclusions  
In this paper, an energy-efficient DS-SLAM based semantic SLAM system is proposed on the HERO heterogeneous platform. The model of SegNet for extracting semantic information is first quantized to the 8-bits dynamic fixed-point number. The proposed quantization strategy reduces the storage requirements and computational complexity, and only loses 0.8% of the global accuracy on the Pascal VOC dataset. Meanwhile, as the most time-consuming part, the semantic segmentation thread is accelerated on the FPGA to reduce the latency. A function configurable pipeline architecture with expansibility is proposed. By designing and replacing kernels, other convolutional neural networks can be implemented flexibly. Compared with Intel i7 CPU, our DS-SLAM accelerator on HERO platform achieves up to 13\(\times\) frame rate improvement and 18\(\times\) energy efficiency improvement. Moreover, there is a minute localization accuracy loss on the TUM dataset. The proposed energy- efficient DS-SLAM accelerator fundamentally solves the dependence of the algorithm on high energy consumption GPU. It increases the working time of battery-powered robots, which can be utilized as a functional module to provide mobile robots with autonomous localization.  
Figure 8: Visual experimental results: (**a**) ATE and RPE from ORB-SLAM2, (**b**) ATE and RPE from our work.  
**Author Contributions:** Conceptualization, Y.W., M.Y., H.H. and F.Q.; data curation, Y.W., H.H. and M.Y.; formal analysis, Y.W.; funding acquisition, F.Q., Q.W. and X.L.; investigation, Y.W. and M.Y.; methodology, Y.W., M.Y., H.H. and X.S.; project administration, F.Q., S.Y. and L.L.; resources, X.S., F.Q., Q.W. and X.L.; software, M.Y. and X.S.; supervision, F.Q., S.Y. and L.L.; validation, Y.W. and X.S.; visualization, Y.W.; writing--original draft, Y.W.; writing--review & editing: M.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Key R&D Program of China under grant No. 2018 YFB1702500.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Informed Consent Statement: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors would like to acknowledge supports from National Key R&D Program of China under grant No. 2018 YFB1702500. The authors would also acknowledge support from Beijing Innovation Center for Future Chips, Tsinghua University.  
The authors declare no conflict of interest.
# MSC-Net: Multitask Learning Network for Retinal Vessel Segmentation and Centerline Extraction
## 2 Materials and Methods
### Channel and Atrous Spatial Fusion Block  
Inspired by [33; 34], CAS-FB is designed to fuse and calibrate the features from the two branches, enhance the useful features for segmentation and centerline extraction, and suppress noise. As shown in Figure 3, CAS-FB includes a dual-channel attention module [35] and an atrous spatial attention module, which can realize attention to the channel and the spatial context of different scales, and the fused feature map is obtained by convolution and ReLU.  
Assume that we have two feature maps, \(F_{S}\) and \(F_{C}\), from two branches as input to CAS-FB. \(F_{S}\) and \(F_{C}\) are first input to the dual-channel attention modules to achieve the adaptive correction of features. The channel of interest can be emphasized, and useless channel features can be suppressed through channel attention. The outputs of the dual-channel attention modules are  
\[F_{ca1}=W_{ca1}\otimes F_{S} \tag{1}\]  
\[F_{ca2}=W_{ca2}\otimes F_{C} \tag{2}\]  
\[W_{ca1}=\sigma(k^{1\times 1}\alpha(k^{1\times 1}AvgPool(F_{S}))) \tag{3}\]  
\[W_{ca2}=\sigma(k^{1\times 1}\alpha(k^{1\times 1}AvgPool(F_{C}))) \tag{4}\]  
where \(F_{ca1}\) and \(F_{ca2}\) are the output feature maps of the dual-channel attention modules, \(W_{ca1}\) and \(W_{ca2}\) are the channel-wise attention weights, and \(\otimes\) is the element-wise multiplication, while \(k^{a\times b}\) refers to a convolutional layer with a kernel size of \(a\times b\), and \(\alpha\) and \(\sigma\) is a ReLU layer and Sigmoid activation, respectively. Then, concatenate \(F_{ca1}\) and \(F_{ca2}\), and the result \(F_{concat}\) is  
\[F_{concat}=concatenate(F_{ca1},F_{ca2}). \tag{5}\]With reference to the design of the ASPP [36] module, we design the spatial attention module using atrous convolution with different dilation factors. This atrous spatial design enables CAS-FB to pay more attention to the morphological characteristics of retinal blood vessels at different scales (from thick to thin) in the spatial context. The output of the atrous spatial module is as follows:  
\[F_{as}=W_{as}\otimes F_{concat} \tag{6}\] \[W_{as}=\sum_{r\in 1,3,5,7}k_{r}^{1\times 1}(\alpha(\beta(k_{r}^{3 \times 3}F_{concat}))) \tag{7}\]  
where \(F_{as}\) is the output feature map of the atrous spatial module, \(W_{as}\) is the element-wise spatial weight, \(k_{r}^{\times b}\) is defined as the atrous convolution with an expansion rate of \(r\) and a kernel size of \(a\times b\), and \(\beta\) refers to a BN layer. Finally, the output of CAS-FB is  
\[F_{out}=\alpha(\beta(k_{out}^{3\times 3}F_{as})) \tag{8}\]
# An Experimental Study on the Reflection Characteristics of Laser Echo Light Waves
## 4 Analysis and Discussion
### Atmospheric Refractive Index Structure Constant
At the same time, the experimental samples of different link lengths show that the \(C_{n}^{2}\) values on sunny days, snowy days, lightly rainy days, and moderately rainy days are significantly higher than the \(C_{n}^{2}\) values on heavily rainy days and cloudy days. At the same time, we found that under the same weather conditions, the higher the humidity, the smaller the value of \(C_{n}^{2}\). According to [PERSON] inequality, the intensity of atmospheric turbulence during the test period was divided into two categories. In the Link 1 measurement samples, 66 \(C_{n}^{2}\) values belonged to the moderate-turbulence category (accounting for 1.01% of the total samples), and 5934 \(C_{n}^{2}\) values belonged to the weak-turbulence category (accounting for 98.99% of the total samples). There was no strong turbulence. In the Link 2 measurement samples, 12 values belong to the category of strong turbulence (accounting for 0.24% of the total samples), and 4988 \(C_{n}^{2}\) values belong to the category of moderate turbulence (accounting for 99.76% of the total samples).
## 3 GAs for Multi-Objective Routing Optimization
### Pareto Dominance and Non-Dominated Sorting  
In multi-objective optimization for EnFVs, Pareto dominance is a fundamental concept used to evaluate and compare different solutions [14]. A solution is considered superior to another if it performs better in at least one objective without sacrificing performance in any other objective. This concept allows for the identification of trade-offs between conflicting objectives and helps in finding optimal solutions.  
Non-dominated sorting is the process of categorizing solutions based on Pareto dominance as depicted in Figure 2. This involves dividing the population into different fronts, where each front contains solutions that are not dominated by any other solution. Non-dominated sorting helps identify the Pareto-optimal solutions, which cannot be improved in any objective without worsening another objective.  
Non-dominated sorting is crucial in GA-based approaches for multi-objective message routing in EnFVs. It enables the selection of promising solutions for the next generation, allowing the GA to converge towards the Pareto-optimal front. By exploring trade-offs between objectives, non-dominated sorting aids decision making and helps stakeholders make informed choices based on their preferences. To improve efficiency and performance, researchers have proposed various variations and enhancements to non-dominated sorting, including adaptive methods, elitism strategies, and diversity preservation techniques. Continuous research aims to develop novel algorithms to address challenges posed by large-scale optimization problems in multi-objective message routing for EnFVs.
## 5 Conclusions  
In this paper, we proposed a model, the DCTransformer, to classify HSIs. The DCTransformer includes a base feature extractor and detail feature extractor, to capture the local features, restore high-frequency information, and analyze global information in HSIs, separately. The BFE employs the DCT method to distinguish high-frequency and low-frequency information and weighs them in a cross-channel way using channel attention. The BDE module employs convolution networks to capture local information, whilst the DFE stack utilizes blocks with dilation convolution to extract detailed features. A dynamic focus mechanism further refines the resulting embedding features from both modules. Classification is achieved using the transformer. Comparative experimental results on four popular HSIs demonstrate that our framework outperforms current state-of-the-art (SOTA) algorithms, especially in the case of few samples, and also improves in terms of efficiency due to the abandonment of 3D convolution in favor of 2D convolution, where the parameters of the model are greatly reduced. The results of experiments and analyses on four datasets substantiate the method's efficacy in improving the classification performance while concurrently reducing the count of parameters. Yet, the HSI classification still encounters overfitting. Further, we strive to discover a more efficacious technique and concise architecture to amplify the efficacy and exactness of categorization.  
Conceptualization, Y.D. and X.Z.; Methodology, X.Z.; Software, B.L.; Validation, H.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data are contained within the article.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} HSIs & Hyperspectral Images \\ CNN & Convolution Neural Network \\ CAM & Channel Attention Mechanism \\ DCT & Discrete Cosine Transform \\ SVMs & Support Vector Machines \\ ViT & Vision Transformer \\ SA & Self-Attention \\ DC & Dilation Convolution \\ DSC & Depthwise Separable Convolution \\ CLS & Class Token \\ GAP & Global Average Pooling \\ PU & Pavia University \\ CASI & Compact Airborne Spectrographic Imager \\ MPP & Meters Per Pixel \\ GSD & Group Sampling Distance \\ IP & Indian Pines \\ UH & University of Houston \\ POSIS & Reflective Optics System Imaging Spectrometer \\ OA & Overall Accuracy \\ AA & Average Accuracy \\ \(\kappa\) & Kappa Coefficient \\ \end{tabular}
## 5 Performance Evaluation
### Experimental Analysis
The DQN algorithm demonstrated relative stability in both delay and energy consumption; however, its learning efficacy was marginally outpaced by the A2C and PPO algorithms. After \(300\) steps, the A2C algorithm's performance in the delay reduction closely approximated that of the PPO algorithm, yet it lagged slightly behind in energy consumption efficiency. Overall, when evaluating the learning outcomes, the PPO algorithm emerged as the superior performer in comparison to the DQN and A2C models, particularly in terms of optimizing the trade-off between the delay and energy consumption. Figure 6: (**a**) Comparison of average reward values across different schemes; (**b**) comparison of average costs across different schemes.