esa-sceva/satcom-chunk-collection huggingface dataset:
Existing studies often utilize deformation information obtained from single or differential InSAR methods but frequently overlook issues such as incomplete deformation information and limited coherence caused by topographic variations. Furthermore, most studies neglect the deformation magnitude information when incorporating InSAR deformation rates into assessment models, failing to fully leverage the detailed information contained in the deformation data. This study focuses on Xiaojin County, a region frequently affected by geological disasters in Sichuan Province. A total of 23 Sentinel-1A satellite ascending and descending orbit SAR images covering the area were acquired. Time-series InSAR technology was used to obtain surface deformation data from January to December 2021. The deformation data from both ascending and descending orbits were incorporated as two distinct evaluation factors into the geological hazard risk assessment model. Hazard zoning and risk evaluation were then conducted for Xiaojin County, with the results validated through accuracy assessment using the ROC curve.
# Improved Adversarial Transfer Network for Bearing Fault Diagnosis under Variable Working Conditions
## 3 IATN for Bearing Fault Diagnosis under Variable Working Conditions
### Preprocess  
Compared with the raw time-domain signal, the time-frequency image has both time and frequency domain information and contains more fault information. In addition, the features of time-frequency images are more prominent and require lower feature extraction capabilities of the network. In fault diagnosis research, relevant studies have proved that time-frequency domain input is better than time-domain input [58]. By converting the raw 1D time-domain signal into a 2D time-frequency diagram, the features of the signal are enhanced, allowing us to use lighter downstream networks for classification.  
Therefore, this paper performs STFT on the collected raw signals. STFT selects a time-frequency localized window function, assumes that the signal is stationary (pseudo-stationary) within a brief time interval and moves the window function to calculate the frequency spectrum at different moments [59]. STFT is defined as follows:  
\[STFT_{x}(t,f)=\int_{-\infty}^{+\infty}x(\tau)g^{*}(\tau-t)e^{-\beta\pi f\tau}d\tau \tag{3}\]  
where \(x(t)\) is the raw signal, and \(g^{*}(\tau-t)\) is the window function.  
Ideally, STFT can provide time and frequency information at the same time so that the frequency distribution of the signal over time can be observed, which is crucial for fault diagnosis. By converting a signal into its time-frequency representation, STFT can reveal structural features that may not be apparent in the original time-domain signal. This conversion can improve the separability between different categories, such as inner rings, outer rings, rolling elements and different degrees, such as standard, minor and major faults, so shallow neural networks can achieve high-accuracy classification.  
However, in actual scenarios, various factors often interfere with bearing signals, including background noise, structural resonance, amplitude modulation, etc. At this time, the classification of time-frequency diagrams may involve highly nonlinear and complex data structures, especially when there are multiple fault types and changing work conditions. Shallow neural network performance cannot model such complexity and nonlinearity. In contrast, deep neural networks could be better at extracting high-level features from complex data. Deep neural networks extract abstract features that are difficult to observe directly through multi-layer nonlinear transformations to achieve high-precision classification of time-frequency images.
## 5 Simulation and Evaluation Results
### DRTO Algorithm Performance Evaluation
#### 5.1.2 The Offloading Cost of Decision Location Given by DRTO  
Regarding the offloading cost performance, we compare our DRTO algorithm with other five representative benchmarks to demonstrate its superiority:  
* Distributed deep learning-based offloading (DDLO) [41]. Multiple DNNs take the duplicated channel gain as input, then each DNN generates a candidate offloading location. Then, the optimal offloading location is selected with respect to the minimum offloading cost. In the comparison with DRTO, we assume that DDLO is composed of \(N\) DNNs.
* Coordinate descent (CD) [42]. The CD algorithm is a traditional numerical optimization method, which iteratively swaps the offloading location of each ST that leads to the largest offloading cost decrement. The iteration stops when the offloading cost cannot be further decreased by swapping the offloading location.
* Enumeration. We enumerate all \(2^{N}\) offloading location combinations and greedily select the best one.
* Pure TC computing. The LEO access satellite forwards all the tasks to TC for execution.
* Pure SatEC computing. The LEO access satellite locally executes all the tasks.  
We consider \(N=5\) STs attaching to the same access satellite. In Figure 7, we compare the performance of the average offloading cost per time frame achieved by different offloading algorithms. As we can see, DRTO achieves similar performance as the greedy enumeration method, which verifies the optimality of DRTO. Since the optimal offloading location combination is unique, any other random combinations will lead to a higher offloading cost. In addition, we see that DRTO achieves lower offloading cost with about 17.5% and 23.6% reductions compared to the pure TC computing and pure SatEC computing methods, which indicates the necessity of cooperation between SatEC servers and TCs to provide satisfying computing service.  
Figure 6: Normalized offloading cost with \(\Delta=64\).
# A Behavioral Robotics Approach to Radiation Mapping Using Adaptive Sampling
## 2 Methodology
### System Overview  
Executing adaptive sampling requires a robot capable of traversing a diverse range of environments, which one may find in nuclear facilities. The Spot quadruped platform by Boston Dynamics, shown in Figure 1, was chosen. It comes equipped with six depth cameras, a six-degree-of-freedom manipulator, and an onboard Spot CORE computer. A custom 3D-printed mount was created to add a Kromek GR1 gamma radiation sensor to the jaw of the manipulator.  
With this setup, the robot localizes itself in space, executes programs from the onboard computer, and starts creating a gamma intensity map by moving its arm close to the points of measurement. To accomplish the necessary movement, software libraries are used, primarily Robot Operating System 2 (ROS2) for unifying data communication through standardized message types and community-developed software. The Spot Python client library is used to interface with the robot's services and motion planners. A ROS2 wrapper (Humble) for the Python API (4.1.1) by the Boston Dynamics AI Institute (Cambridge, MA, USA) is used to integrate the library with all the software used. The Python client library also handles the running of the motion planners required for moving the body while avoiding obstacles and planning combined arm-body trajectories for moving the end effector to reach specified points in space. Robot localization is required to construct a map, and it typically involves an odometry frame for real-time movements and a global frame for correcting errors in the odometry frame that accumulate without disturbing the real-time execution of movement. The visual odometry that comes with the Spot platform and is derived from its depth cameras is used as the odometry source, and RTAB-map [27], a simultaneous localization and mapping (SLAM) program, is used for global positioning. The SLAM program creates a map by remembering key features from depth camera data,and it corrects accumulating errors when it sees the key features again. While measuring gamma radiation with a Kromek GR1 sensor, ROS2 interfaces such as \"actions\" are used to manage message communication between the different software nodes. Actions are a communication method in ROS2 that allows for the running of specific code upon request and with feedback, rather than having the code continuously running. In this system, the action server sets up the radiation sensor and awaits the command to start measuring in counts per second (cps). Once a separate program sets up the action client and calls it, the device will begin measuring for a specified time interval and then return the average counts per second to the client program. To manage the Bayesian optimization modeling, the Python libraries Sklearn, Scipy, and Numpy are used.
## 3 Detailed Analysis of Maximum Power Point Tracking (MPPT) Methods
### RL (Reinforcement Learning)-Based MPPT Method
#### 3.2.1 Algorithm and Working Principle  
The RL-based MPPT algorithm typically uses the Q-learning method. The main steps are as follows:  
State: represents the current state of the PV system (e.g., solar irradiance and temperature).  
Action: represents the decision to adjust the operating point of the PV system (e.g., voltage or current adjustment).  
Reward: a positive reward is given when the maximum power point is approached.  
Q-value update: The agent updates the Q values according to the reward and the new state, as in Equation (4):  
\[Q(s,a)=Q(s,a)+a\big{(}r+\gamma\text{max}Q\big{(}s^{\prime},a^{\prime}\big{)}-Q (s,a) \tag{4}\]  
where \(\alpha\) is the learning rate, \(r\) is the reward, \(\gamma\) is the discount factor, \(s\) is the current state, \(s^{\prime}\) is the next state, and \(a^{\prime}\) is a possible next action.  
The RL algorithm selects the best action based on the Q-values and ensures that the PV system operates at the maximum power point.
# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 3 Kubernetes Architecture  
Although virtualization transformed IT infrastructure by allowing multiple operating systems to operate on a single physical machine, it did not provide inherent support for rapid scaling, lightweight deployment, and application-centric management. Kubernetes enhances the containerization paradigm by providing an elevated level of abstraction aimed at orchestrating application containers within a distributed cluster. It underscores portability, automation, and resilience--essential attributes lacking in conventional virtualization.  
Kubernetes is a robust, open-source framework for automating the deployment, scaling, and administration of containerized applications. It is intended to oversee clusters of physical or virtual machines, facilitating the orchestration of containers as a cohesive infrastructure layer.  
The basics of Kubernetes architecture are shown in Figure 1:Kubernetes' declarative configuration model and automation features have established it as the foundation of contemporary cloud-native systems. Comprehending Kubernetes architecture is crucial for utilizing its functionalities securely and effectively. This section delineates the principal architectural components, their interrelations, and their functions in facilitating scalable, resilient, and dynamic application management.
# Deep Learning Based Semantic Image Segmentation Methods for Classification of Web Page Imagery
## 5 Discussion  
Our findings demonstrate that both UNet and FCN models offer high segmentation performance, particularly for webpage images that completely match the segmentation structural assumptions. The highest accuracy was attained using the proposed Seg-UNet model, at 95%. The suggested semantic segmentation method's overall effectiveness is demonstrated by a comparison with different optimizer functions (Figures 6-11).  
There has been more effort made to improve the visual comprehension of online pages. In many instances, the document object model (DOM) tree is used to gain visual evidence rather than a picture of the displayed page [43]. This is difficult to generalize to other systems and does not have implementation independence. A system that uses images of webpages has been described by [PERSON] et al. [44], who proposed a block-based conceptual model for segmentation that takes the content as well as the geometry of blocks into account to evaluate the correctness of a segmentation according to a predefined ground truth. Our suggested webpage segmentation mechanism is more complete since it is independent of the technology used for implementation. The results of our studies demonstrate that the method is effective in terms of expansibility and performance for segmenting web pages.  
A high-quality segmentation of a web page can facilitate higher-level parsing of the page based on its visual features; this has significant applications, especially in the inspiring application field of assistive technology. Magnification and clearing are two uses for segmentation. Users who suffer from cognitive disorders such as attention deficiencies or older users may benefit from decluttering. Users with vision impairments may find benefits through magnification.
## 3 Results
### Performances of SIF in Grassland GPP Monitoring under Drought Conditions
Under drought, the R\({}^{2}\) between SIF and GPP reached 0.90, representing a 47.54% increase compared to SIF\({}_{\text{yield}}\) and a 45.16% increase compared to NDVI. This further confirmed the strong capability of SIF to track and monitor GPP variations under drought conditions. Figure 9: Relationships of (**a**) SIF, (**b**) SIF yield, and (**c**) NDVI with GPP under drought and no-drought scenarios in the Xilingol grassland. Each point represents a monthly average value for the entire study area. There are 12 (months/year) \(\times\) 9 (years) = 108 points from 2007 to 2015. The red dots represent the monthly values under drought conditions, while the green dots represent the monthly values under non-drought conditions.
# Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges
## 3 Componential Classification of Blockchain Privacy Protecting Techniques
### Effect of Blockchain's Secondary Cryptography on Privacy
#### 3.2.4 Zero-Knowledge Proofs for Achieving Blockchain Privacy  
Zero-Knowledge Proofs (ZKPs) are the most widely used cryptographic methods enabling transfer of assets across a decentralized, distributed, peer-to-peer blockchain network with improved privacy. The objective of zero-knowledge proofs is to attest the legitimacy of a transaction with zero knowledge offered to the verifier related to the transaction. The notion of ZKPs involve the prover to articulate a formal proof as an evidence of a particular assertion being true without provision of any extended and useful information to verifying party [74]. In blockchain networks, a variant of ZKP, known as Non-Interactive Zero-knowledge Proof (NIZK proof), is extensively utilized as it drastically reduces communication complexity. It is not desirable to deploy the extensive communication requirements of simple ZKPs. NIZK proofs must meet the following three properties:Completeness: Everything that is true has a proof.  
Soundness: Everything that can be proved is true.  
Zero knowledge: Only the proven statement is revealed.  
A commonly known application of NIZK is Zerocoin [75]. It utilizes NIZK for provision of user anonymity by involving mechanism of preventing transaction graph analysis, i.e., breaks the traces of coins. However, it is unsuccessful in achieving so because of several reasons including fixed coin denominations, conversion of anonymous coins into non-anonymous before payments, and unconcealed transaction amounts. To overcome these limitations, another application of NIZK named Zerocash [76] was introduced. Zerocash provided data confidentiality as well as user anonymity. Additionally, transaction size and verification time were also considerably reduced. Zerocash uses ZK-SNARKS. However, the NIZK protocol experiences high computation outlays specially in the proof generation phase of ZK-SNARKs protocol used in Zcash.
## 4 Experimental Validation
### Validation of the Proposed Power Loss Model
#### 4.2.1 The Accuracy Validation of the Junction Temperature Prediction  
In order to realize the accurate losses calculation, the accurate junction temperature information is needed. Under different step-rise currents and step-drop currents, IGBT junction temperature and coolant temperature is estimated through the Foster thermal model, and the results are compared with the measured temperature from the infrared camera (IGBT) and temperature sensors (coolant). The temperature prediction error is within 2 \({}^{\circ}\)C with good temperature estimation effect shown in Figure 12. Thus, the used Foster thermal model can provide a better temperature reading for the proposed losses model.
###### Abstract
The temporal correlation means that the departure call of the current cell and the new call of the adjacent cell occur at the same time. The spatial correlation means that the adjacent cell and the current cell have a neighbor relationship in space. We made full use of the temporal-spatial correlation of handoff calls in LEO satellite networks to propose an adaptive DCA algorithm based on RL. This algorithm not only considered the problems caused by a frequent handoff, but also improved the total performance of the network. The main efforts of this paper were:  
* The temporal-spatial correlation of handoff calls was analyzed and the Markov decision process (MDP) was used to formally describe the channel allocation process so that the channel allocation could be dynamically adjusted according to the environment. * A policy for different call events was designed. Afterwards, an online RL algorithm--namely, SARSA--was used to solve the DCA problem. SARSA iteratively updated the policy from the performed actions so that the channel allocation could be adjusted in real-time according to the environment. * The effectiveness of the proposed algorithm was verified by simulation experiments under different traffic distributions and different traffic intensities. The remainder of this paper is structured as follows. Section 2 introduces the related technologies. The proposed algorithm is presented in Section 3. Section 4 presents and discusses the simulation results. Finally, conclusions are drawn in Section 5.
# An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
## 3 Efficient Dynamic Engineering Implementation Architecture for MIMO Radar Systems
### Engineering Implementation Process  
We have deployed both architectures on an FPGA-based platform to evaluate the performance of the efficient dynamic engineering implementation architecture for a MIMOradar system. This section outlines the implementation flow on the FPGA platform and elaborates on the novel key steps involved in data management.  
Significantly, to facilitate rapid prototyping and expedite the testing process for validating the effectiveness of the new architecture, we selected the well-acquainted FPGA platform as the core processing unit. Although the proposed architecture presented in this paper is experimentally validated on an FPGA platform, the architecture is optimized by refining the signal processing workflow and employing a modular design approach. Significantly, this optimization is not constrained to any specific hardware platform; hence, the foundational concepts and methodologies are universally applicable. They can be adapted and optimized for different processing platforms according to specific requirements.
## 3 Results  
### Classifier Training with Different Delays and Window Lengths  
Applying different \(\text{D}_{\text{HR}}\) and \(\text{L}_{\text{FW}}\) affected classification performance. Figure 3 shows the \(\text{MCC}_{\text{RS}}\) and detection latency maps. Depending on the applied \(\text{D}_{\text{HR}}\) and \(\text{L}_{\text{FW}}\), classification accuracy rose from 0.6489 up to 0.7880 (a 21.42 % increase) in the \(\text{MCC}_{\text{RS}}\). The \(\text{D}_{\text{HR}}\) and \(\text{L}_{\text{FW}}\) were positively related to each other, as evidenced by the antidiagonal direction of the map. Longer \(\text{D}_{\text{HR}}\) with longer \(\text{L}_{\text{FW}}\) tended to generate better classification accuracy. However, after \(\text{D}_{\text{HR}}\) reached 9 s and \(\text{L}_{\text{FW}}\) reached 13 s,  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Label**} & \multicolumn{3}{c}{**Composition**} & \multirow{2}{*}{**Dimension**} \\ \cline{2-2} \cline{4-5}  & **Channel** & **[Hb]** & & **Average** \\ \hline FC-1 & & both & - & \(36\times 1\) \\ FC-2 & All & [HbO] & - & \(18\times 1\) \\ FC-3 & & [HbR] & - & \(18\times 1\) \\ \hline FC-4 & & both & - & \((\text{N}_{\text{CNR-sel}}\times 2\times 2)\times 1\) \\ FC-5 & CNR-selective & [HbO] & - & \((\text{N}_{\text{CNR-sel}}\times 2)\times 1\) \\ FC-6 & & [HbR] & - & \((\text{N}_{\text{CNR-sel}}\times 2)\times 1\) \\ \hline FC-7 & & both & y & \(4\times 1\) \\ FC-8 & All & [HbO] & y & \(2\times 1\) \\ FC-9 & & [HbR] & y & \(2\times 1\) \\ \hline FC-10 & & both & y & \(4\times 1\) \\ FC-11 & CNR-selective & [HbO] & y & \(2\times 1\) \\ FC-12 & & [HbR] & y & \(2\times 1\) \\ \hline \end{tabular}
\end{table}
Table 1: The Feature Vector Compositions. For channel, ‘all’ means that all channels were included in a feature vector. ‘CNR-selective’ means only the channels meeting CNR-criteria were included. For [Hb], ’both’ indicates that both [HbO] and [HbR] were used. If ‘Average’ is ‘y (yes)’, [Hb]s were averaged and the features were calculated from the averaged [Hb]. Note that averaging was done with only [Hb]s of the same type, i.e., [HbO] or [HbR].  
classification accuracy decreased. The combination of long D\({}_{\text{HR}}\) and short L\({}_{\text{FW}}\), or vice versa resulted in a relatively poor classification accuracy of around 0.65.  
Detection latency also depended on the D\({}_{\text{HR}}\) and L\({}_{\text{FW}}\), ranging between 0.1640 s and 4.7761 s. The detection latency map in Figure 3 was obtained based on the introduced D\({}_{\text{HR}}\). To obtain the true detection latency value, the corresponding D\({}_{\text{HR}}\) must be added to the value of each pixel.