esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methods
### Fusion of Path Planning Method  
The combination of the global path planning method and local path planning method is used to realize the path planning of the vehicle. The specific steps are as follows:  
Step 1: The initial environment map is obtained.  
Figure 10: Simulation experiment results of Scenario 3. (**a**) The resulting graph of the path comparison experiment; (**b**) output variation chart.  
Figure 9: Simulation experiment results of Scenario 2. (**a**) The resulting graph of the path comparison experiment; (**b**) output variation chart.  
Step 2: The coordinate sequence of the global critical path nodes of the improved A* is recorded, and the global key nodes are regarded as local target points.  
Step 3: The distance between the current position of the vehicle and the local target point is determined, if the distance is less than the threshold, it is considered that the local target point has been reached, and the local target point is updated to continue local planning.  
Step 4: When the local target point is not the last node in the global target point sequence, the next local target point is updated, and Step 3 will be repeated.  
Step 5: When the local target point is the last node of the global sequence, the entire path planning is completed.  
As shown in Figure 11, when the vehicle moves along the global path from the start point S, the key nodes \(\{n_{1},n_{2},n_{3},n_{4}\}\) guide the vehicle separately to perform local planning and move towards the target point T.
## 5 Numerical Results  
### Performance Analysis  
To evaluate how various parameters affect the system's performance, numerical experiments are conducted to obtain various performance index figures. In the numerical experiments, the fixed parameters are displayed in Table 3. Other dynamic parameters are displayed in the legends, while the wakeup threshold \(H\) is set as the horizontal axis parameter.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Fixed Parameters** & **Symbols** & **Values** \\ \hline Cache capacity & \(K\) & 10 \\ Non\(-\)emergency packets arrival rate at CH & \(p_{2}\) & 0.10 \\ Non\(-\)emergency packets’ service rate at CH & \(s_{2}\) & 0.15 \\ Energy consumption for keeping CH in sleep state & \(E_{1}\) & 0.20 (W) \\ Energy consumption for changing CH from sleep state to & \(E_{2}\) & 2 (W) \\ Energy consumption for changing CH from working state to & \(E_{3}\) & 1.50 (W) \\ sleep state & \(E_{4}\) & 1 (W) \\ Energy consumption for keeping CH in working state & \(E_{4}\) & 1 (W) \\ \hline \hline \end{tabular}
\end{table}
Table 3: The fixed parameters of the experiments for performance analysis.
# Wavelet-Based, Blur-Aware Decoupled Network for Video Deblurring
## 4 Experiments
### Training Details  
During the training process, we employ data augmentation to enhance the diversity of our training data. This includes techniques such as horizontal or vertical flipping, 90 rotation, and random cropping of the images. Our batch size is set to 8 to optimize the training process, and the network extracts image patches with dimensions of \(160\times 160\) for training. Our model is trained by the Adam optimizer [44] with \(\beta_{1}=0.9\), \(\beta_{2}=0.999\), and \(\epsilon=10^{-8}\). The initial learning rate is \(10^{-4}\). All experiments were conducted on two NVIDIA RTX 2080 Ti GPUs using PyTorch ([[https://pytorch.org/](https://pytorch.org/)]([https://pytorch.org/](https://pytorch.org/))). We train the network end-to-end by minimizing L1 loss between the generated sharp frames and the ground-truth frames.
# Anticipatory Troubleshooting
## 4 Anticipatory Troubleshooting
### The Fix or Replace Dilemma  
The first dilemma arises when there is more than one way to repair a faulty component. In particular, consider having two possible repair actions:  
* _Replace(C)_, which means the troubleshooting agent replaces \(C\) with a new one.
* _Fix(C)_, which means the troubleshooting agent fixes \(C\) without replacing it.  
Both fix and replace are repair actions, in the sense that after performing them the component is healthy and the agent knows about it, i.e., replacing \(\
eg h(C)\) with \(h(C)\) in both the system state and the agent's belief. We expect fix and replace to differ in two important aspects.  
First, the cost of replacing a component \(C\) is likely to be different than the the cost of fixing an existing component--i.e., \(cost(Fix(C))\
eq cost(Replace(C))\). Second, we expect the probability that a component \(C\) will fail in the future changes after it is fixed. Formally, we define the _AfterFix_ curve of a component \(C\) for time \(t_{Fix}\), denoting \(S_{C}(t,t_{Fix})\) as  
\[S_{C}(t,t_{Fix})=Pr(T_{C}\geq t|C\text{ fixed at age = }t_{Fix}) \tag{2}\]  
and expect that  
\[\forall C\in COMPS\ \ \exists t,t_{Fix}>0:S_{C}(t,t_{Fix})\
eq S_{C}(t) \tag{3}\]  
A common setting is that (1) getting a new component is more costly than fixing it, and (2) a new component is less likely to fail compared to a fixed component. This setting is captured by the following inequalities:  
1. \(cost(Fix(C))\leq cost(Replace(C))\)
2. \(S_{C}(t,t_{Fix})\leq S_{C}(t)\)  
This setting embodies the fix-replace dilemma in anticipatory troubleshooting: weighing current troubleshooting costs (according to which a fix is preferable) against potential future troubleshooting costs (according to which a replacement is preferable). All the algorithms proposed in this work are applicable beyond this setting, e.g., for cases where replacing a component is cheaper than fixing it.
## 2 Research Methodology
### Heat Source Modeling  
In the simulation and computation of SLM additive manufacturing, a common approach is to employ a heat source model to simulate the material's absorption of energy instead of directly modeling the laser. One frequently used model for the laser heat source is the Moving Heat Source (MHS) model [32]. This method is based on the heat conduction equation for numerical computation. The MHS approach can capture the non-uniform and variable nature of the actual laser scanning path while considering factors such as laser irradiation time, power, scanning speed, and their effects.  
\[T=\frac{Q}{2\pi\rho c\omega(t)}exp\left[-\frac{\left(x-x_{0}(t)\right)^{2}+\left(y -y_{0}(t)\right)^{2}}{2\omega^{2}(t)}\right]exp\left[-\frac{\left(t-\tau\right)^ {2}}{\alpha^{2}}\right] \tag{4}\]  
In the equation provided, \(\rho\) represents the density of the metal; \(c\) denotes the specific heat capacity of the metal; \(k\) stands for the thermal conductivity of the metal; \(Q\) signifies the laser power density absorbed by the metal per unit time; \(T\) represents the temperature of the metal; \(t\) is the time; and \(S\) corresponds to the laser power density.  
In the context of the MHS (Moving Heat Source) method, the parameter \(\alpha\) in the formula represents the time constant of the laser heat source. It holds crucial significance as a control parameter governing the time response of the laser heat source. Specifically, the value of \(\alpha\) is often determined by the laser's pulse width and Full Width at Half Maximum (FWHM). A smaller \(\alpha\) indicates a faster response of the laser heat source, leading to greater variations in the heat source power density. Conversely, a larger \(\alpha\) results in a slower response of the heat source, causing smoother changes in the heat source power density.
# Design of a Technique for Accelerating the WSN Convergence Process
## 2 Related Works  
This chapter presents what solutions other authors bring to fault tolerance for WSNs. FRR mechanisms can calculate a backup route before a line or node failure is detected. In order to minimize packet loss during the site convergence process, the backup path is calculated locally on the router [27; 28].  
The ongoing development of FRR mechanisms leads to the creation of two types of FRR solutions--reactive and proactive. Approaches proactively precalculate alternative FRR paths [29; 30]. This precalculation will ensure the achievement of a quick change in route to an alternative route.  
Proactive IP FRR mechanisms include the Loop-Free Alternate (LFA) [31] and Multiple Routing Configuration (MRC) [32]. The LFA mechanism uses next-hop selection without a loop. MRC uses multiple routing configurations on routers. The source router inserts the configuration number into the IP packet header, and the receiving router selects the correct routing configuration based on this number.  
The IP Fast Reroute LFA mechanism has several proposed improved versions, such as directed LFA [33] and Topology-Independent LFA (TI-LFA) [33; 34].  
Only a few existing FRR solutions, such as LFA and R-LFA, are implemented in current routers. The FRR technology itself was also implemented on programmable switches [35].  
MPLS-TE (MultiProtocol Label Switching-Traffic Engineering) was developed for MPLS networks [36].  
Alternative distribution trees reconstruct another possible mechanism in the field of FRR [37; 38; 39]. The most widely used mechanism based on distribution trees is Maximum Redundancy Trees (MRTs) [40; 41].  
Another option is reactive IP Fast ReRoute mechanisms that do not calculate alternative routes in advance. These include Multicast Repair (M-REP) [42] and Enhanced M-REP [43]. Table 1 shows the evaluation of the properties of individual FRR solutions.
# Application of Collision Warning Algorithm Alarm in Fishing Vessel's Waterway
###### Abstract  
The aim of this study is to apply a collision warning algorithm for a small fishing vessel in a fishing waterway to verify its alarm operation and to validate its feasibility. For this purpose, a scenario-based real ship test was conducted, and cases extracted from real sea data (Vpass data) were applied. Moreover, zones with frequent alarms and high-risk waters were compared. First, we installed millimeter-wave communication terminals in three small fishing vessels and applied our algorithm based on two scenarios. Furthermore, we applied the collision warning algorithm by extracting two cases encountered by multiple ships from the Vpass data. The results show that the algorithm triggered alarms continuously under risky situations. This study also compares waterway risk levels as assessed by maritime risk-assessment tools (potential assessment of risk model, environment stress model, and International Association of Marine Aids to Navigation and Lighthouse Authorities Waterway Risk Assessment Program MkII) and the locations having frequent alarms based on Vpass data collected for 7 days. Not only did the eastern sea of Yeongheung Island indicate that more alarms were triggered, but we found high-risk results from the risk-level assessment, indicating that the risky zones and the frequent alarm zones were identical. Additional research is necessary to develop an algorithm based on qualitative evaluation by actual ship operators. In addition, since fishing vessels navigate differently from general navigation methods during fishing, it is necessary to develop additional algorithms for this.  
collision warning algorithm; small fishing vessel; potential assessment of risk model (PARK); environment stress model (ES); IALA waterway risk assessment program MkII (IWRAP) Article  
Application of Collision Warning Algorithm Alarm in Fishing Vessel's Waterway  
1
## 2 Materials and Methods
### E-Nose Measurements  
An E-nose is a piece of miniaturized electronic equipment composed of 11 sensors [15]. It has low power consumption and is the size of a hockey puck, thereby making it easily portable and convenient. The device contains novel metal oxide semiconductor (MOX) chip gas sensors which exhibit global selectivity to odor patterns for each sample: (i) BME680 (Bosch Sensortech GmbH, Reutlingen, Germany): temperature (\({}^{\circ}\)C), pressure (hPa), humidity (\({}^{\circ}\)RH), and gas measurement (\(\Omega\)); (ii) SGP30 (Sensirion AG, Stafa, Switzerland): eCO\({}_{2}\) (ppm), TVOC (ppb), H\({}_{2}\)(2), and ethanol; (iii) CCS811 (ScoSense B.V., Eindhoven, The Netherlands): eCO\({}_{2}\) (ppm), TVOC (ppb), and sensor resistance (\(\Omega\)); and (iv) iAQ-Core (ScoSense B.V., Eindhoven, The Netherlands): eCO\({}_{2}\) (ppm), TVOC (ppb), and sensor resistance (\(\Omega\)).  
The electronic analysis required 5 g of green coffee beans to be placed into standard tasting glasses, covered with a watch glass, and placed on a block kept at 25 \({}^{\circ}\)C. Eleven measurements were taken per sample. The E-nose was placed on top of the cup and the adsorption phase was carried out to adsorb the headspace of the sample for 60 s. Once this time had elapsed, the E-nose was moved to another empty cup to carry out the desorption phase for 30 s to obtain the baseline. The E-nose received data in one second intervals and eight measurements were carried out for each roasted bean sample. After the analyses were conducted, the microprocessor sent the values obtained to a specific application in an intelligent device (mobile phone) via Bluetooth, and a multivariate analysis was completed with a computer. The algorithm used to characterize the sensor response curves to obtain the raw data for the E-nose was maximum signal value minus minimum 1, plus 100, minus 1. As a result, a vector of data with 11 rows (sensors) for each sample was obtained and a multivariate analysis was performed with the data obtained. The E-nose also had temperature and humidity sensors that enabled the monitorization and correction of the data with compensation algorithms. The results of both analyses were analyzed together to obtain more comprehensive information.
# Toward Integrated Satellite Operations and Network Management: A Review and Novel Framework
## 5 Framework Evaluation
### Constraint Satisfaction Programming  
The core scheduling engine is built on CSP, which provides significant flexibility in solving optimization problems. Unlike other formulations (e.g., linear programming), CSP is not restricted by specific solver limitations, allowing for the modeling of complex constraints. This capability makes it particularly effective in addressing integrated management challenges since CSP enables the representation of intricate constraints in mobility management, such as handovers and scenario uncertainty. Additionally, it enables the simultaneous modeling of network constraints and optimization criteria alongside satellite operations constraints and optimization functions, supporting a truly integrated operations model. Finally, within the domain of operational dynamics, CSP is useful in handling constraints related to space routing, providing key information to routing algorithms such as node availability.
# Intelligent Environment Monitoring System for University Laboratories
## 3 System Software Design  
The system uses several development tools including Proteus (LCE, London, England), Protel99 SE (Altium, Sydney, Austrilia), Keil-uVision4 (ARM, London, England) and a firing machine. It combines wireless communication protocol, system control instruction and a sensor manual to design the module function of our proposed system.  
We build the project under Keil-uVision4's C language development environment and design the source code of the module function. We use Keil-uVision4 and Proteus to realize the module simulation in process of the hardware circuit design. Protel99 SE, a design tool, is used to finish hardware circuit schematic and PCB board. At last, we confirm that the whole source code debugging is correct, and run the program under the condition that partial hardware circuit simulation is completed. The flow chart of the system program is shown in Figure 3.  
As shown in Figure 3, the first step is the system initialization when the program is starting. After that, there will be the voice prompt \"system boot success\" which means the system initialization is successful. Then, there are four branches for different monitoring objects including light monitoring, body infrared detection, temperature and humidity detection, and the fire alarm. For light monitoring, based on light collection, if the collected data are higher than 1000 lux, the relay causes the light to go off. For the body infrared detection, when a body is detected, a message is sent. For temperature and humidity detection, we set up the threshold of humidity and temperature. When the detected data of humidity and temperature exceeds the predefined threshold, it will call an alarm. For the fire alarm, voice prompts such as \"fire alarm\" are launched when the acquired signal is higher than the predefined level, and the corresponding messages are sent.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Module** & **Model** \\ \hline Temperature and humidity acquisition module & DHT11 (Aosong, Guangzhou, China) \\ Light intensity acquisition module & GY-30 (Dianyingpu, Shenzhen, China) \\ Fire monitoring module & MQ-9 (Dashidai, Shenzhen, China) \\ Human body Infrared sensor module & HC-SR501 (Quanyuantong, Shenzhen, China) \\ \hline \hline \end{tabular}
\end{table}
Table 1: The selection process of data acquisition sensor module.
## 4 Discussion of Academic Literature and Council Uses
### Social and Community  
In capturing community events, councils are already utilising drones to offer a new perspective and enhance the documentation of local activities. Beyond these, drones, integrated with AI, have the potential to revolutionise aerial journalism, aiding in investigative reporting and providing live coverage of news and large-scale events. Moreover, in educational contexts, drones can significantly augment learning, particularly in developing children's spatial visualisation and sequencing skills through after-school drone-flying programmes. Furthermore, drones are instrumental in enhancing road safety around schools, by monitoring traffic conflicts and capturing vital information about driving behaviours and the positions of road users.
## 3 Target Tracking Method
### Mutual Observation Information Based on a Cooperative Platform  
#### 3.1.1 Mutual Observation Information  
The mutual observation information among UAVs is obtained by approximating the observation information of UAVs on a cooperative platform. The cooperative platform is treated as a fusion center. The positions of the UAVs, the cooperative platform, and the maritime target are unified into a local geographic reference system under the fusion center.  
The position of the cooperative platform under the local geographic coordinate system centered on platform \(A\) is  
\[\mathbf{X}_{AC}=\mathbf{T}_{uts}(\mathbf{\mu}_{A})\mathbf{h}_{ctr}(\mathbf{Z}_ {AC}) \tag{7}\]  
where \(\mathbf{T}_{uts}(\mathbf{\mu})\) is the transformation of the unstable carrier coordinate system into the stable carrier coordinate system and the expression is  
\[\mathbf{T}_{uts}(\mathbf{\mu})=\begin{bmatrix}\cos\psi&-\sin\psi&0\\ \sin\psi&\cos\psi&0\\ 0&0&1\end{bmatrix}\begin{bmatrix}1&0&0\\ 0&\cos\theta&-\sin\theta\\ 0&\sin\theta&\cos\theta\end{bmatrix}\begin{bmatrix}\cos\phi&0&\sin\phi\\ 0&1&0\\ -\sin\phi&0&\cos\phi\end{bmatrix} \tag{8}\]\(\mathbf{h}_{ctr}(\mathbf{Z})\) is the conversion from spherical to Cartesian coordinates and the expression is  
\[\mathbf{h}_{ctr}(\mathbf{Z})=\begin{bmatrix}x\\ y\\ z\end{bmatrix}=\begin{bmatrix}r\cos b\cos e\\ r\sin b\cos e\\ r\sin e\end{bmatrix} \tag{9}\]  
Due to the close proximity of the cooperative platform and the UAV, the influence of the curvature of the Earth on the coordinate transformation can be ignored, so the position coordinate of UAV \(A\) in the local geographic coordinate system under the fusion center is approximated as  
\[\mathbf{X}_{CA}=-\mathbf{X}_{AC}=-\mathbf{T}_{uts}(\mathbf{\mu}_{A})\mathbf{ h}_{ctr}(\mathbf{Z}_{AC}) \tag{10}\]  
Similarly, the position coordinate of UAV \(B\) in the local geographic coordinate system under the fusion center is  
\[\mathbf{X}_{CB}=-\mathbf{X}_{BC}=-\mathbf{T}_{uts}(\mathbf{\mu}_{B})\mathbf{ h}_{ctr}(\mathbf{Z}_{BC}) \tag{11}\]  
So far, the positions of UAVs \(A\) and \(B\) have been unified into the same coordinate system.  
Combining Equations (10) and (11), the position of UAV \(B\) in the local geographic coordinate system of UAV \(A\) can be approximately obtained  
\[\mathbf{X}_{AB}=\mathbf{X}_{CB}-\mathbf{X}_{CA} \tag{12}\]  
Therefore, the observation information from UAV \(A\) to UAV \(B\) is  
\[\mathbf{Z}_{AB}=\mathbf{h}_{trc}(\mathbf{T}^{T}_{uts}(\mathbf{\mu}_{A}) \mathbf{X}_{AB}) \tag{13}\]  
where \(\mathbf{h}_{trc}(\mathbf{X})\) is the conversion from Cartesian to spherical coordinates and the expression is  
\[\mathbf{h}_{trc}(\mathbf{X})=\begin{bmatrix}r\\ b\\ e\end{bmatrix}=\begin{bmatrix}\sqrt{x^{2}+y^{2}+z^{2}}\\ \arctan(\frac{y}{x})\\ \arctan(\frac{-z}{\sqrt{x^{2}+y^{2}}})\end{bmatrix} \tag{14}\]
# A Systematic Approach to Studying Quark Energy Loss in Nuclei Using Positive Pions +
## 3 Systematic Uncertainties  
In the present analysis, systematic uncertainties were evaluated to quantify the impact of our assumptions and methodological choices on the final results. A key source of systematic uncertainty arises from the granularity of the energy step size used in the Kolmogorov-Smirnov (KS) test. For this study, the energy interval per iteration was set to \(1\) MeV, ensuring manageable computational costs while maintaining a sufficient resolution to capture the variations in the nuclear spectra.  
The choice of a 1 MeV step introduces potential biases into the estimation of the quark energy loss, as finer or coarser resolutions could alter the sensitivity of the KS test to subtle differences between the experimental and theoretical distributions. To estimate the corresponding systematic uncertainty, varying the step size allows for an evaluation of the magnitude of this systematic effect, for example, including steps of 0.1 MeV and 0.01 MeV.  
Furthermore, the systematic uncertainty due to the energy step size propagates into the estimation of the total induced energy loss and its scaling with the medium size \(L\) and parton energy \(E\). This uncertainty may be related to the KS test results across different step sizes in order to analyze their influence on the extracted scaling exponents and nuclear spectra parameters.  
In addition to the energy step size, other sources of systematic uncertainty include corrections for Coulomb effects and acceptance in the experimental data.
## 3 Experiments and Analysis
### Real-Time Measurement  
To show the real-time measuring practicality of the proposed method, many real-time measuring experiments were also carried out. Here, the real-time measurement of the moving gloved hand is taken as an example. Figure 9a-c shows the deformed patterns at States A-C from the real-time recorded video. Figure 9d-f shows the deformed patterns of G component after correction. The corresponding reconstruction results of the gloved hand are shown as Figure 9g-i, respectively. It is observed that the different states of the moving gloved hand can be well restored by the proposed method. The real-time experimental results prove the practicality of the proposed method in real-time measurement process. In the real-time measurement, the frame rate of the recorded video is only 30 fps with the video resolution of 640 \(\times\) 480 pixels due to the performance limitation of the camera. Because of its single-shot feature, if a higher performance DLP and camera are adopted, the frame rate can be higher.  
Figure 8: Comparison height error distributions: (**a**) traditional color PMP; and (**b**) the proposed method.