esa-sceva/satcom-chunk-collection huggingface dataset:
# SVDC: Synchronous Update with the Division and Combination of SRv6 Policy
## 5 Performance Evaluation
### Result and Analysis
#### 5.2.3 Propagation Time of Policies in the Network  
The period from when the controller initiates new policy distribution until all policies are received by their corresponding target nodes is referred to as the propagation time of policies.  
For ordered updates, each SRv6 policy is distributed to the destination router along the path with the smallest transmission delay. The propagation time for ordered updates  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of** & **SUDC** & **Ordered** & **Reduction** & **Two-Phase** & **Reduction** \\
**Policies** & **SUDC** & **Update** & **Ratio** & **Update** & **Ratio** \\ \hline
5000 & 0.131 s & 5.018 s & 97.39\% & 0.949 s & 86.20\% \\
10,000 & 0.131 s & 9.99 s & 98.69\% & 1.961 s & 93.32\% \\
25,000 & 0.131 s & 24.982 s & 99.48\% & 4.905 s & 97.33\% \\
50,000 & 0.131 s & 50.01 s & 99.74\% & 10.005 s & 98.69\% \\
100,000 & 0.131 s & 99.989 s & 99.87\% & 19.952 s & 99.34\% \\
250,000 & 0.131 s & 249.999 s & 99.95\% & 49.878 s & 99.74\% \\
500,000 & 0.131 s & 500.022 s & 99.97\% & 99.894 s & 99.87\% \\
1,000,000 & 0.131 s & 1000.01 s & 99.99\% & 200.113 s & 99.93\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Coexisting time of new and legacy policies.  
Figure 6: Coexisting time of new and legacy policies.  
is the time lag between the moment when the first policy is distributed and the moment when the last policy is received.  
For a two-phase update, each SRv6 policy whose target node is an internal switch is distributed sequentially along the path with the smallest transmission delay, and then each SRv6 policy whose target node is an ingress switch is distributed sequentially along the path with the smallest transmission delay. The propagation time for a two-phase update is the time lag between the moment when the first policy is distributed to one internal switch in phase 1 and the moment when the last policy is received by one ingress switch.  
For SUDC, each policy distribution SID block is multicast to the set of target routers along the transmission delay minimizing spanning tree. In addition, this metric of SUDC is the time lag between the moment when the first multicast SID block is multicast to its set of target routers and the moment when the last multicast SID block finally arrives at one router in its set of target routers.  
Table 5 and Figure 7 demonstrate the propagation time for an ordered update, two-phase update, and SUDC. In both ordered updates and two-phase updates, the controller distributes policies to the target routers one by one. Therefore, this metric is nearly the same for both schemes. Compared with ordered updates and two-phase updates, SUDC can improve the policy transmission speed in the network and shorten the propagation time by 46.6% on average.  
As observed from Table 5 and Figure 7, it is evident that with the increase in the size of the test set
## 5 Results and Discussion
### The Simulated Precipitation
The top-left panel represents the total simulated precipitation during the period 11 to 17 October 2000. Letters of panels and on the cumulated precipitation plot refer to Table 1. The white dot in the upper-left panel indicates the position of Croso stream bridge. The Alpe Veglia station was not yet installed. WRF forecasted the event quite well (Figure 10), catching the timing as well as the total precipitation at station locations. The more significant discrepancy was registered at San Domenico (Figure 10A), where WRF simulated 544.9 mm (difference of 132 mm). Meanwhile, the best performance of WRF was registered at Domodossola, where the discrepancy was 22.3 mm. Even with the high-spatial-resolution of the applied WRF set-up, the obtained discrepancies at San Domenico station during the October 2000 event highlight the very high complexity of this site and suggest the importance of a widespread analysis that should also consider the neighbouring areas and the general pattern of precipitation. In this simulated event, San Domenico station is partly sheltered by the mountains with respect to south-easterly advection. Again, the resolved topography compromised the simulation results, even if the discrepancy between observed and simulated precipitation was only 20% while in August 2019 was 60%. The up-left panel in Figure 10 shows that the total precipitation simulated by WRF was entirely above 400 mm with a small area characterised by values comprised between 300 and 400 mm. The relative discrepancy between observed and simulated values are lower during the October 2000 episode rather than during August 2019 one, as a consequence of the meteorological event that, in this second case, is easily captured by WRF. The main difficulties of WRF in mountainous terrain lie in the localisation and description of the convection, and for this reason, the less convective event (October 2000) was better simulated.
## 2 Battery Aging Following Frequency Regulation Cycles
Hence, the circuit equivalent parameters can be obtained by solving the system below, consisting of the characteristic equations of the circuit shown in Figure 4, for different currents, SoCs and temperatures. \[\left\{ \begin{array}{l}R_{0}=\frac{V(t_{0})-V(t_{1})}{t_{i}}\\ \frac{dV_{R_{0}C}}{dt}=\frac{I}{C_{n}(S\circ C,\theta)-C_{n}(S\circ C,\theta)} \\ dV=E_{1}(1-e^{-\frac{t_{1}}{t_{1}}})+E_{2}(1-e^{-\frac{t_{2}}{t_{2}}})\\ \tau_{n}=C_{n}R_{n}\end{array} \right. \tag{1}\]  
Figure 4: Battery voltage measurement (left) from which to derive the battery equivalent electrical model (right). Figure 3: Battery capacity decrease during the aging test. On the basis of the instantaneous values of current, SoC and temperature during the simulations, the correct battery parameters \(R_{0}(SoCi,\#)\), \(C_{n}(SoC,i,\#)\), \(R_{n}(SoC,i,\#)\) with \(n=1,2\), are automatically computed by means of advanced interpolation functions [19]. The instantaneous battery SoC during the simulation is computed as:  
\[SoC\%=\frac{Q-\int_{t_{0}}^{t_{i}}i(t)dt}{Q}\cdot 100 \tag{2}\]  
where \(Q\) is the battery maximum capacity. Hence, the instantaneous battery voltage \(V_{b}\) can be computed as:  
\[V_{b}=OCV(SoC)-R_{0}\cdot I-V_{R_{1}C_{1}}-V_{R_{2}C_{2}} \tag{3}\]  
where OCV is the battery open circuit voltage. Figure 5 shows a screenshot of the battery Simulink model.
# Salient Region Detection Using Diffusion Process with Nonlocal Connections
## 3 Experiment  
To show the effectiveness of our proposed algorithm, we evaluated the proposed method on four datasets: ASD [15], ECSSD [41], DUT-OMRON [23], PASCAL-S [42]. ASD dataset is the most widely used benchmark and it contains 1000 images selected from the MSRA dataset. Compared to other datasets, it can be detected relatively easily. ECSSD dataset contains 1000 semantically meaningful but structurally complex images with pixel-wise ground truth. DUT-OMRON dataset is used to compare models on a large scale, and it consists of 5168 images with complex background. PASCAL-S selects 850 natural images from the PASCAL VOC 2010 segmentation challenge.  
We compared our method with 21 state-of-the-art methods, these methods can been roughly divided into local contrast-based approaches (SR [13], AC [14]), global contrast-based approaches (RC [7], FT [15], HC [7]), background-based approaches (GS [10], RBD [11], DSR [12]), compactness-based approaches (SF [8]), multiple visual cue integration approaches (HS [41]), diffusion-based approaches (GBMR [23], BSCA [30], MC [24], IDCL [9], TLSG [16]), and other approaches (GC [43], GR [44], SWD [39], GU [43], MSS [45], FES [46]).  
Figure 8: Main phases of pixel-wise refinement process. (**a**) Original image. (**b**) \(S_{unite}\) saliency maps. (**c**) \(S_{init}\) saliency maps. (**d**) Saliency maps after single-layer updating process. (**e**) Saliency maps after multi-layer integration process. (**f**) Ground truth.
# Design and Analysis of Adaptive Hierarchical Low-Power Long-Range Networks
## 2 A Hierarchical Communication Scheme
### Self-Organizing Sub-Network Formation
Devices receiving these invitation messages respond by joining the sub-network by sending back to its  
Figure 4: Asynchronous cycling in a five-device network—gray and white boxes show different epochs during which a device re-initiates the controller selection mechanism. controller a message declaring their join decision. This breadth-first process is expected to take \(O(d)\) rounds so that all messages are properly received. After the sub-network is established, any device that wishes to forward traffic through the LPWAN transmits to the coordinator using a routing protocol such as RPL [34]. In this way, only one device for each \(d-hop\) sub-network accesses the LPWAN, thus improving the overall scalability. The network structure that operates to maintain the self-stabilizing properties is analysed in [35]. The self-organizing mechanism presented here for forming sub-networks operates to maintain the self-stabilizing properties analysed in [35]. The experimental evaluation of the algorithm presented in Section 5 indicates that the algorithm always succeeds in selecting a controller and all devices end up joining a sub-network. The theoretical analysis of the correctness and required convergence time is based on the arguments used in [35]. Given the self-stabilizing properties of the protocol, the protocol does not require any specific initialization phase. Each device can start from any arbitrary configuration. Some devices may consider themselves as members of a sub-network, others as members of non-existing sub-networks, etc.. Regardless of this initial arbitrary state, within a bounded number of \(O(d)\)steps, the network structure converges to a stable configuration, i.e., a configuration where all devices of the network participate in a valid sub-network of \(d-hop\) diameter. This is done regardless of the way that the devices are positioned in the network area. In fact, as explained in the following section, the network structure will suitably adapt its operation to the physical topology of the network.
# A Transfer Learning Method for Meteorological Visibility Estimation Based on Feature Fusion Method
## 4 Discussion and Summary
This method did not need to define a large-scale visual annotation set, and had high robustness and effectiveness. This method alsoeliminates the interference of invalid regions and reductant features on the visibility estimation, and reduced the complexity and operations of the estimation process.
# A Low-Cost Automated Digital Microscopy Platform for Automatic Identification of Diatoms
## 3 Results and Discussion
### Software Integration and User Interface  
Python was the main program language chosen for software development because of its versatility, the availability for many operating systems and platforms, and the numerous publicly available modules and libraries. Moreover, MATLAB was employed for the experiments and C++ when better code performance was preferred (e.g., the YOLO network). The most important modules and libraries used with Python were: OpenCV (for image processing), PyQt (for GUI development), NumPy (for scientific computing), and pySerial (for communications).  
A graphical user interface (GUI) was developed as a friendly way to provide access to our automated microscopy platform and its functionality. The GUI provides an intuitive way to set the main configurations and microscope functions through three panels with the following controls (see Figure 9):  
* Configuration panel: enables camera view (show button), setup for home position (setup and home buttons), switch between color and grey scale images (RGB check box), switch between brightfield and eccentric oblique illumination (dark-field check box), display a scale bar (scale bar check box),  
\begin{table}
\begin{tabular}{r c c c} \hline \hline
**Dataset** & **Images/Class** & **Average Accuracy (\%)** & **Standard Deviation** \\ \hline Original & 1000 & 99.24 & 0.09 \\ Segmented & 1000 & 98.81 & 0.15 \\ Normalized & 1000 & 98.84 & 0.15 \\ Original+Normalized & 2000 & 99.51 & 0.048 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of the AlexNet results after fine-tuning with different datasets.  
autofocus (autofocus button), background correction (take background image button and background correction check box), live diatom detection (live detection check box), merge detected bounding boxes (merge box check box), and save image (save button).
* Scanning and processing settings panel: objective selection (objective radio buttons), color normalization (Reinhard check box), random scanning (random fields button), sequential scanning (X-size, Y-size dialog boxes and scan button), stitching (stitching button), focal stack acquisition (# images and step dialog boxes and take stack button), and multifocus fusion based on the extended depth of field technique [9] (EDF button).
* Motorized stage control panel: step size of motion (step size radio button), unit of motion (select unit radio button), XYZ motion (XYZ dialog boxes and move button), Z motion (Z\(-\) and Z\(+\) buttons), and X-Y motion (arrow buttons).
# Precision Agriculture: A Remote Sensing Monitoring System Architecture +
###### Abstract  
Smart Farming is a development that emphasizes on the use of modern technologies in the cyber-physical field management cycle. Technologies such as the Internet of Things (IoT) and Cloud Computing have accelerated the digital transformation of the conventional agricultural practices promising increased production rate and product quality. The adoption of smart farming though is hampered because of the lack of models providing guidance to practitioners regarding the necessary components that constitute IoT-based monitoring systems. To guide the process of designing and implementing _Smart farming monitoring systems_, in this paper we propose a generic reference architecture model, taking also into consideration a very important non-functional requirement, _the energy consumption_ restriction. Moreover, we present and discuss the technologies that incorporate the seven layers of the architecture model that are the Sensor Layer, the Link Layer, the Encapsulation Layer, the Middleware Layer, the Configuration Layer, the Management Layer and the Application Layer. Furthermore, the proposed Reference Architecture model is exemplified in a real-world application for surveying Safron agriculture in Kozani, Greece.  
Keywords:Wireless Sensor Networks; Internet of Things; precision agriculture; smart farming; communication technologies; Cloud Computing +
Footnote †: journal:
## 4 Propagation Characteristics and Phenomena of LMS Communication
### Shadowing Fading  
When the signal suffers from obstructions in the propagation path, its level decreases, and a shadowing effect occurrs because the LoS component is absorbed or dispersed. It is also referred to as large-scale fading. The attenuation of the direct path can be caused by different obstacles, such as roadside trees, buildings, hills, or mountains. The most dominant effect in this scenario is the shadowing that determines fading. This effect is strongly affected by elevation angles and by the frequency used in the system. The higher the frequency, the greater the effect due to obstructions such as trees. The degree of the signal attenuation shadowing effect, which is commonly characterized by a lognormal distribution, is characterized by the following PDF:  
\[p_{Lognormal}(a)=\frac{1}{a\sqrt{2\pi}\sigma}exp\left[-\frac{(lna-\mu)^{2}}{2 \sigma^{2}}\right], \tag{6}\]  
with \(\mu\) and \(\sigma^{2}\) being the mean and variance of the shadowed component of the received signal, respectively [24].
# Artificial Intelligence in Adaptive and Intelligent Educational System: A Review
## 2 Reinforcement Learning
### Deep Q-Network and Double Deep Q-Network  
DQN is a subordinate branch of deep reinforcement learning and is a value-based reinforcement learning algorithm [23], in which only the value function and not the policy network is involved. It is an upgrade to classical Q-learning algorithms. As tasks become increasingly complicated, classical Q-learning algorithms do not sufficiently perform these tasks because in classical Q-learning algorithms, if the Q-table is excessively huge, it is a challenge for the computer to store all the data, thus making computation difficult and an excessively large Q-table also makes the retrieval of Q-values sluggish. When convolutional neural networks are implemented, states and actions can be used as inputs to calculate the corresponding Q-values, thus sparing the space required to store the Q-table and the time required to retrieve the Q-values. DQN introduces a convolutional neural network as an approximation function to approximate the value function. The \(\varepsilon\)-greedy strategy is used in DQN to select the next action. \(\varepsilon\)-greedy is a greedy strategy, where the option with the highest reward is chosen for each selection.  
Next, the operation of the DQN is explained. First, the environment provides a state value, and the agent can determine all the states and actions currently available to the agent based on the value function. In order to determine the next action, an \(\varepsilon\)-greedy policy is used to determine what the next action will be, and after the selection is completed, the environment returns the corresponding reward value and the new state value. Up to this stage, it is a selection loop, and DQN makes a continuous loop of these to obtain the optimal policy [21].  
Unfortunately, similarly to Q-learning, the overestimation problem cannot be avoided by DQN, but an article published in 2016 proposed a new algorithm that provides an initial solution to the overestimation problem [12]. In this article, a function called Double DQN is defined, which solves the overestimation problem at its core by stopping the constant propagation of overestimation between states. In simple terms, after selecting the maximum Q-value for the first time, the maximum Q-value is picked again in another network according to the corresponding action value, and if the Q-value is the maximum value in both networks, then the probability of overestimation is reduced to a large extent, even though it still occurs sometimes. Double DQN is a significant improvement over classical Q-learning and classical DQN for the overestimation problem.
# A Network Analysis Approach to Detecting Social Issues with Web-Based Data
###### Abstract  
Social issues refer to topics that occur and become increasingly focused in various areas of society. Because of the evolutionary pattern of issues, detecting social issues requires monitoring various stories formed by members of society over time. Various studies related to issue detection have been preceded, but it is necessary to supplement in two aspects: presenting the time when issues occurred and prioritizing issues by urgency. As a remedy, the purpose of this study is to propose a new approach to detecting social issues from web-based data through network analysis. Since stories that form social issues are composed of various keywords and topics, this study detects social issues by monitoring keyword co-occurrence networks constructed with web-based data. Specifically, this approach uses network structure entropy to identify a time period at which social issues occur. Next, a community detection algorithm is used to extract social issue candidates in the identified time period. Finally, social issues are detected by deriving the priority of social issue candidates through the centrality index of keywords constituting the candidates. This study detected South Korean social issue topics that attract people's attention among the various topics of society. The proposed approach contributes to the existing literature by identifying when social issues occurred quantitatively based on the characteristics of issues. In addition, since the proposed approach detects urgent issues to be dealt with priority, it can support timely responses to social issues.  
social issue; web-based data; network analysis; network structure entropy; community detection algorithm +
Footnote †: journal: Medical  
and the period to be monitored. Since the dynamic patterns of social issues, it is hard to define the parameters mentioned above. This was addressed by suggesting an incremental approach that detects both existing issues and new ones as they arise [10; 11].
## 2 Optimization Model for Network Reconfiguration Problem of Ship Power System
### Mathematical Description of Network Reconfiguration for Ship Power Systems
#### 2.2.2 Objective of Minimizing Switch Operation Frequency  
The number of switch operations refers to the total count of switching actions necessary after a fault occurs within the ship's power grid. This count is established following the identification and isolation of the faulty area through a fault detection mechanism and involves comparing the states of the grid switches before and after their reconfiguration using intelligent algorithms. In the ship's power grid, the operational duration of various switches differs significantly. The frequency of switch operations has a direct impact on the speed of reconfiguration. Consequently, the cost associated with switch operations serves as a crucial metric for assessing the reconfiguration efficiency of the ship's distribution network. Shown as Equation (5)  
\[\min f_{2}=\lambda_{h}\sum_{h=1}^{p}s_{Mh}+\lambda_{i}\sum_{l=1}^{t}s_{Ai}, \tag{5}\]  
\(s_{Mh}=0\) or 1, indicating the transformation of Manual Transfer Switches (MBTs) from normal power supply to standby power supply; \(s_{Ai}=0\) or 1, indicating that the Automatic Transfer Switch (ABT) is switched from a normal power supply to a standby power supply. Because the operation time of the Automatic Transfer Switch is smaller than that of the manual one, it is necessary to give different weighting coefficients in order to better meet the requirements of the reconfiguration speed. \(p\) is the total number of MBTs in the reconstruction process, it takes 8-15 s for normal operation, and \(t\) is the total number of ABTs which can be switched within 1 s. In this paper, the weight coefficients of the two kinds of switches are set to \(\lambda_{h}=10\), \(\lambda_{i}=1\). The exact number of switches operated during the reconfiguration process cannot be accurately estimated, and the normalization needs to be performed taking into account the extreme case where all switches are operated. \(\max\!f_{2}\) as Equation (6)  
\[\max\!f_{2}=\lambda_{h}S_{Msum}+\lambda_{i}S_{Asum}, \tag{6}\]  
where \(S_{Msum}\), \(S_{Asum}\) represent the total number of MBTs and ABTs, respectively, and when \(\min\!f_{2}=0\) indicates no switching operation, the normalized model of \(f_{2}\) as Equation (7)  
\[U(f_{2})=\frac{f_{2}}{\max\!f_{2}}, \tag{7}\]