esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
In satellite remote sensing images, the existence of clouds has an occlusion effect on ground information. Different degrees of clouds make it difficult for existing models to accurately detect clouds in images due to complex scenes. The detection and extraction of clouds is one of the most important problems to be solved in the further analysis and utilization of image information. In this article, we refined a multi-head soft attention convolutional neural network incorporating spatial information modeling (MSACN). During the encoder process, MSACN extracts cloud features through a concurrent dilated residual convolution module. In the part of the decoder, there is an aggregating feature module that uses a soft attention mechanism. It integrates the semantic information with spatial information to obtain the pixel-level semantic segmentation outputs. To assess the applicability of MSACN, we compare our network with Transform-based and other traditional CNN-based methods on the ZY-3 dataset. Experimental outputs including the other two datasets show that MSACN has a better overall performance for cloud extraction tasks, with an overall accuracy of 98.57%, a precision of 97.61%, a recall of 97.37%, and F1-score of 97.48% and an IOU of 95.10%.  
cloud extraction; concurrent dilated convolution; multiscale convolutional neural network; soft attention mechanism; ZY-3 satellite images +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON]; [PERSON]; [PERSON];  
[PERSON] MSACN: A Cloud Extraction Method from Satellite Image Using Multiscale Soft Attention Convolutional Neural Network. _Appl. Sci._**2024**, 14, 3285. [[https://doi.org/10.3390/app14083285](https://doi.org/10.3390/app14083285)]([https://doi.org/10.3390/app14083285](https://doi.org/10.3390/app14083285))  
10.390/app14083285
## 4 Empirical Study and Result
### Analysis of Merging Behavior in Different Work Zones Layouts
#### 4.3.3 Speed and Speed Impact Differences
This leads to significant deceleration of merging vehicles and their followers, as reflected in Table 4. The standard deviation (Std) and range of speed changes for followers in the outer lane are greater at Site A3 compared to Site B4, indicating more variability in follower behavior. This suggests that in scenarios with restrictive road layouts like Site A3, merging maneuvers create more disruption to traffic flow, leading to greater speed differentials between merging vehicles and surrounding traffic. The scatter plots in Figure 11 further illustrate this relationship, showing that the finishing speed of merging vehicles has a direct impact on the speed reduction of their followers, with larger reductions observed in higher-density situations. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline  & **Lane** & **Count** & **Mean** & **std** & **min** & **25\%** & **Median** & **75\%** & **max** \\ \hline \multirow{3}{*}{A3} & Delta & 1216 & \(-9.51\) & 11.91 & \(-64.40\) & \(-14.97\) & \(-6.87\) & \(-1.88\) & 31.64 \\  & Std & 1216 & 3.55 & 3.53 & 0.04 & 0.99 & 2.39 & 4.84 & 24.51 \\  & Range & 1216 & 11.53 & 10.68 & 0.12 & 3.67 & 8.16 & 16.20 & 65.36 \\ \hline \multirow{3}{*}{B4} & Delta & 638 & \(-7.07\) & 8.57 & \(-50.05\) & \(-11.04\) & \(-4.97\) & \(-1.35\) & 17.07 \\  & Std & 638 & 2.62 & 2.64 & 0.05 & 0.85 & 1.74 & 3.56 & 16.10 \\  & Range & 638 & 8.47 & 7.91 & 0.17 & 2.82 & 5.63 & 12.12 & 50.05 \\ \hline \multirow{3}{*}{A4} & Delta & 154 & \(-13.16\) & 15.99 & \(-65.06\) & \(-21.12\) & \(-11.43\) & \(-3.83\) & 22.79 \\  & Std & 154 & 4.93 & 4.59 & 0.38 & 1.71 & 3.66 & 6.77 & 26.50 \\ \cline{1-1}  & Range & 154 & 16.53 & 13.31 & 1.33 & 6.23 & 13.40 & 22.13 & 67.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Descriptive statistics of the change in follower speed at Sites A3, B4, and A4. Figure 11: Scatter plots of the relationship between merging vehicle’s finish speed and follower’s speed reduction, over density. The finishing speed of merging vehicles has a direct impact on the speed reduction of their followers, with more significant reductions occurring in higher-density scenarios.
# Research on Named Entity Recognition Based on Gated Interaction Mechanisms
## 3 Results and Discussion
### Evaluation Indicators
#### 3.2.1 Precision (P)  
Precision refers to the proportion of entities correctly identified by the model out of the total number of entities predicted by the model. The calculation formula is as follows:  
\[P=\frac{TP}{TP+FP} \tag{7}\]  
where TP (true positives) represents the number of correctly recognized named entities, and FP (false positives) represents the number of incorrectly recognized entities. Precision reflects the ability of the model to avoid false positives, that is, to correctly identify only entities that are actually entities. A high precision indicates that the model has a low rate of false positives and is more reliable in identifying entities.
# A Scheme for Controlled Cyclic Asymmetric Remote State Preparation in Noisy Environment
## 1 Introduction
The different kinds of theoretical protocols for quantum communication and computation [32; 33; 34] have already been verified in the IBM quantum experience. IBM Quantum Experience has been utilized to perform many practical experiments on quantum chips, including quantum simulation, the development of quantum algorithms [35], testing quantum information theory tasks, quantum cryptography, quantum error correction [36], quantum applications [37], and so forth. In this paper, we put forward a novel (N + 1)-party cyclic RSP protocol for preparing sequentially increasing qubits among arbitrary number of players with a controller. To be specific, the i-th party prepare an i-qubit state for the (i + 1)-th party in a loop of N(1, 2,... i, i + 1,... n)-party, and the n-th party prepares the n-qubit state for the first party. Moreover, we demonstrate the special case of our scheme on the IBM quantum computer by designing appropriate quantum circuits using single-qubit and two-qubit quantum gates. For verifying the practicality of our scheme, we also discuss the variation of the fidelity of the scheme affected by different noises. This paper is structured as follows--in Section 2, we specifically introduce a controlled cyclic asymmetric RSP scheme in which [PERSON] remotely prepares an arbitrary single-qubit state for [PERSON], [PERSON] prepares an arbitrary two-qubit state to [PERSON], and [PERSON] prepares an arbitrary three-qubit state for [PERSON]. Then, we construct the quantum circuit simulation of this scheme in IBM's quantum simulator and analyse the mean probability of the output results. Furthermore, we generalize the scheme to (N + 1)-party controlled cyclic RSP protocol for sequentially increasing qubits in Section 3. In Section 4, we analyze and compare the fidelity of output state in four noisy environments (depolarization noise, amplitude damping noise, phase damping noise, and bit-phase flip noise). Finally, we give a brief comparison and conclusion of the scheme in Section 5.
# Experimental Evaluation of a MIMO Radar Performance for ADAS Application
## 3 Results
### Effective Probability of False Alarm
The set of echoes received is shown in Figure 5.  
We observed a concentration of false targets near the radar in a position sixty degrees to the left, as is marked with a dashed contour in the figure. This phenomenon of concentration of echoes where there is no real object did not happen in the other two experiments, and we do not know why they appear. It could be related to some kind of double reflection of the signal or, perhaps, with the reflection in the wheels, which can introduce an overlap with their own Doppler shift. The first analysis carried out in the experiments was to evaluate the number of false targets detected based on the PFA value used in the detection of targets for each of the selected speeds. That result is shown in Figure 6. It has been defined as follows:  
\[\mathit{PFA}_{eff}=\frac{n\;of\;false\;echoes}{n\;of\;received\;echoes}. \tag{1}\]  
In this calculation, the class of doubtful echoes has been omitted, assigning them to the set of valid echoes or false echoes in each case. The choice of a higher or lower value for the PFA parameter depends on the necessity of having enough echoes. So, in situations where the signal-to-noise ratio is low, even admitting an important number of false echoes, the choice could be \(\mathit{PFA}_{range}\sim 0.1\). The alternative is minimizing that number of false targets when the signal-to-noise ratio is not bad. In this situation, a value \(\mathit{PFA}_{range}\sim 0.01\) would be more appropriate. In our case, having enough echoes received per second and avoiding, as much as possible, the number of false echoes seems to be the best strategy. The behavior of the echoes in the three scenarios is slightly different. In scenarios 1 and 2, the number of false targets is lower than in the other, and with a value of \(\mathit{PFA}_{range}=0.01\) or \(\mathit{PFA}_{range}=0.005\), the detection of targets is quite clean. In scenario 3, however, there are a significantly larger number of false targets, and a value as low as \(\mathit{PFA}_{range}=10^{-3}\) should be used to sufficiently filter  
Figure 5: The graph shows the set of echoes received from a motorcycle (blue little boxes) occupying the center of the scene and doing some laps in front of the radar at an approximately constant speed. Red boxes mark the beginning and the end of their trip. A group of false echoes is enclosed with a dashed line. Arrows indicate the direction of the motorbike movement. out false alarms. It must be considered, however, that an unnecessarily small value of this parameter would also cause a significant loss of true echoes. On the other hand, we have fixed most of the time the other variable threshold in the velocity axis, with \(PFA_{vel}=0.2\), but it must be said that this value could also be relaxed.
# A 1D-2D Coupled Lattice Boltzmann Model for Shallow Water Flows in Large Scale River-Lake Systems
## 4 Simulation of Surge Waves in the Reservoir of a Run-of-River Hydropower Station
### Computational Efficiency Analysis  
The GPU card equipped with a large number of computing units and massive thread parallelism has shown powerful capability in non-graphical computations. The LB method has explicit and local features, which match the multi-thread parallel characteristics of GPU quite well. Thus, the 1D-2D coupled model could fully take advantage of the GPU to solve the computational intensive problem. The 1D-2D coupled model was implemented on a GPU platform, and the specific GPU implementation of the LB method could be found in [17]. All computations were carried out using double precision floating on an NVIDIA Geforce GTX TITAN X graphics card, which could deliver a peak performance of approximately \(7\times 10^{12}\) single-precision floating-point operations per second and a peak memory bandwidth of approximately 336.5 GB/s.  
To verify the strong ability of the 1D-2D coupled model in computational intensive problem, we compared the computational efficiencies of the proposed model in different mesh sizes. Four different lattice spacing 2.16 m, 4.32 m, 8.64 m, and 17.28 m were selected. As shown in Table 2, the consumed time referred to the computational time, given in hours, and the performance denoted the updated number of nodes per second, given in Millions of Node Updates Per Second (MNUPS). The speedup ratio represented the ratio of the computational performance of other mesh sizes to that of the coarsest mesh (\(\Delta x=17.28\)). As we could see that as the mesh became finer, the consumed time increased, but the performance and the speedup ratio increased, indicating that the proposed model could achieve better performances on finer meshes. Therefore, the 1D-2D coupled model had an outstanding advantage in simulating flows in large-scale river-lake systems.
# Model-Based Assessment of Energy Efficiency in Industrial Pump Systems: A Case Study Approach
## 3 Energy-Efficiency Model of the Electric Drive
### Power Loss Data for Motors and Converters  
Accurate data collection is fundamental to the development of a reliable energy efficiency model, as it directly influences the predictive accuracy of the model. To achieve this, we used ABB DriveSize (v. 5.8), a desktop/web application that assists in the selection of converters and motors for low-voltage AC drives. The DriveSize database provides de-tailed power loss characteristics over the entire speed-torque operating plane for motors and converters [49].  
The motor losses given are total losses including copper, iron, and stray losses. The converter is selected to complement the motor at the base operating point and its losses include switching, conduction, and standby losses. All loss data are expressed directly in watts (rather than efficiency), which is a more basic description of the phenomena and, more importantly, amenable to fitting.  
Once the user has entered the specific load requirements of the drive, a motor and a converter are automatically selected and a detailed loss characteristic is provided for both units. For example, if you select a pump/fan application with a certain base load power at base speed, you will receive loss data for 25 discrete operating points ranging from 0% to base torque (steps 4%, 16%, 36%, 64%, and 100% of base torque) and 20% to base speed (steps 20%, 40%, 60%, 80%, and 100% of base speed). This behaviour is slightly different for other load types in terms of the number and distribution of operating points.  
To obtain more detailed loss data for each drive combination, the query was repeated for different load types and base load conditions, keeping the selected drive combination. The motor operating plane was populated with additional data points in both the constant flux range and the flux weakening range from 0% to 150% of the base torque. Approximately 200 data points were collected for each drive combination, providing a solid foundation for the subsequent fitting process.
# Continuous Emotion Recognition with Spatiotemporal Convolutional Neural Networks
## 1 Introduction
Starting from pre-trained 2D CNNs, two types of DL models are fine-tuned with videos: (i) a 2D CNN combined with a long short-term memory (LSTM) structure; and (ii) a 2D CNN inflated into a 3D CNN. These models predict emotions through regression of valence and arousal values. Long video sequences are captured in the wild and split into several clips to augment training data and isolate unique facial expressions. For proof-of-concept, CNN architectures, such as VGG and ResNet, were pre-trained with ImageNet and RAF-DB datasets and fine-tuned with multiple video sequences from the SEWA-DB dataset to predict continuous values of valence and arousal. Experiments were conducted over different clip lengths, overlapping ratios, and strategies for fusing annotations. The main contributions of this paper are (i) spatiotemporal models, combining pre-trained 2D CNNs and LSTM units for continuous emotion recognition that achieve state  
Figure 1: The circumplex model. Adapted from [7]. of-the-art performance on SEWA-DB; (ii) a comparative study on the impact of the dataset used for pre-training and level of fine-tuning on the accuracy of different CNN models for discrete emotion recognition; and (iii) a novel spatiotemporal model based on an inflated pre-trained 2D CNN for continuous emotion recognition. This paper is organized as follows. Section 2 provides a review of DL models proposed for emotion recognition in videos captured in the wild, focusing on models that allow encoding the spatiotemporal relations in facial emotions. Section 3 presents an overview of DL models that are proposed for continuous emotion recognition, including pre-processing steps, model architectures, pre-training, fine-tuning procedures, and post-processing steps. Section 4 describes the experimental methodology (e.g., protocol, datasets and performance metrics) used to validate DL models for continuous emotion recognition, as well as the experimental results. Section 5 presents a discussion and a comparison with the state of the art. Conclusions are presented in the last section.
## 3 Circuit Design and Implementation
### Intermediate Matching Network  
The equivalent lumped-component structure of the input and interstage matching networks, along with their corresponding parameters, is shown in Figure 8. In the design of the interstage matching network, it is essential to match the output impedance of the preceding stage transistor with its optimal power impedance, ensuring sufficient power output from the preceding stage. Simultaneously, the right-side impedance must form a conjugate match with the input impedance of the subsequent stage to achieve adequate power gain.  
Figure 6: (a) Output harmonic tuning network; (b) simulated drain voltage and current waveforms.  
To accomplish this, an interstage matching network composed of an RC parallel network and an LC network is employed. To enhance the flexibility of the matching network, all components contribute to impedance matching. Through a combination of calculations and tuning, the impedance is precisely conjugately matched to the input impedance of the subsequent stage transistor and aligned with the optimal power impedance of the preceding stage transistor. The RC parallel network (highlighted in the red box in Figure 8) not only broadens the bandwidth of the matching network but also allows for simultaneous adjustments to the amplifier's stability and gain by tuning the value of the resistor. Notably, the RC parallel network between the second and third stages is placed after the LC matching network rather than directly on the gate of the transistor, as seen in conventional matching networks. This placement minimizes the resistive losses that could otherwise be used to reduce the power impedance of the network.  
Figure 8: Equivalent lumped-component schematic of the input and intermediate matching networks.  
Figure 7: Simulated impedance of the output matching network.  
negatively impact the saturated output power, further enhancing the saturation performance. Additionally, \(TL_{5}\) and \(TL_{9}\) serve as drain supply lines for the first and second stages, while \(TL_{7}\) and \(TL_{11}\) act as gate supply lines for the second and third stages. These lines participate in impedance matching while also functioning as RF chokes.
## 4 Experiments
### Discussion  
In this study, we propose the ICP-BGCN model, an innovative approach to disinformation detection, which integrates original text content, propagation text, and the structural information of message dissemination. Our experiments on the Twitter15, Twitter16, and Pheme datasets demonstrate that the fusion of semantic features extracted via BERT and propagation features learned through a bidirectional graph convolutional network leads to superior detection accuracy. Notably, the model achieves accuracies of 89.7% on Twitter15 and 91.7% on Twitter16, outperforming eight mainstream baselines by 1.1% and 3.7%, respectively, and maintains robust generalization with an 84.4% accuracy on the Pheme dataset. These results strongly support our original hypothesis that leveraging both text semantics and propagation structure can enhance disinformation detection.  
Our work makes several key contributions that push the field forward. First, by embedding interactive data, such as user comments and retweets, into a graph structure, ICP-BGCN captures global coupling features that traditional models often overlook. This methodological advancement addresses limitations identified in earlier studies [11,45] and opens new avenues for exploiting network topology in misinformation analysis. Second, the detailed analysis of propagation metrics--such as degree distribution, diameter, and average path length--provides fresh insights into the distinct dissemination patterns of disinformation versus non-disinformation. For example, our observation that disinformation tends to form low-density heterogeneous networks with several highly connected nodes not only explains the superior performance on the Twitter16 dataset but also suggests potential indicators for early detection in real-world applications.  
Despite these advances, our study has limitations that must be acknowledged. One notable limitation is that the current model does not account for the temporal decay of post influence, an aspect that requires further investigation.
## 5 Performance Evaluation  
Any proposed new protocol needs to be tested and validated against results from open sea experiments. While numerous testbed systems have been developed to allow for inexpensive small-scale testing, they still require abundant resources to prepare and perform the experiments. Therefore, simulators have been commonly adopted as an alternative approach to protocol testing [24]. However, many of the existing simulators are not all inclusive as they only focus either on the physical communication layer or the networking layers [24]. Furthermore, many of them use a generic underwater acoustic channel model and consequently the simulation results may not accurately reflect the real-world applications.  
After a thorough investigation, we chose Aqua-Sim [4,25] to conduct a series of simulations. Aqua-Sim was developed based on NS-2, a popular network simulator, and has the ability to simulate UWSNs. Compared with other wireless network simulators, Aqua-Sim offers many distinctive features such as underpinning to discrete-event-driven networks [26], support for mobile and 3D networks, simulation of high-fidelity underwater acoustic channels, and implementation of a complete protocol stack.  
We evaluated the performance of the GPNC protocol by comparing it with DBR [15] and VBF_NC[22] using Aqua-Sim as the routing protocol simulator. DBR greedily forwards data packets to any sink node on the sea surface without using any mechanism to transmit data at all. VBF_NC instead employs vector-based forwarding to determine the routes from a source node to a target sink node and uses full network coding to transmit data packets. We first compare GPNC with VBF_NC in terms of network delay and energy consumption and then compare GPNC with both DBR and VBF_NC in terms of packet delivery rate.  
We randomly deployed 800 sensor nodes in a 3D region of 2000 m \(\times\) 2000 m \(\times\) 2000 m and 64 sink nodes in a sea surface region of 2000 m \(\times\) 2000 m. The data transmission rate of the underwater acoustic modern is 2500 bps [27]. The acoustic signal propagation speed is 1500 m/s. The sensor nodes each has a transmission range of \(R=250\)\(m\), an equivalence factor of \(\partial=0.5\), an initial energy of \(E_{o}\)= 100 J, and an energy consumption rate of 60 uJ/bit. There are 1000 original data packets generated at the source node and the size of each packet is 64 KB.
## 2 Modeling and Research Methods
### Temporally Topological Measures
#### 2.2.2 Temporal Average Path Length  
The temporal average path length \(L(t)\) refers to the average number of edges that connect any two nodes \(i\) and \(j\) in a network at a given time \(t\). For pairs of nodes with no connection, their path length is defined as infinity. The following formula can be used to calculate \(L(t)\):  
\[L(t)=\frac{1}{N(N-1)}\sum_{i\
eq j}l_{ij}(t) \tag{3}\]  
where \(l_{ij}(t)\) symbolizes the shortest path length between nodes \(i\) and \(j\) at time \(t\). In an airline network, it represents the average number of transfers needed for any node to reach its final destination.  
Figure 1: Different forms of instantaneous undirected networks: (**a**) an aggregated network, (**b**) a snapshot-based temporal network with a time window of 1 based on graph (**a**), and (**c**) a snapshot-based temporal network with a time window of 2 based on graph (**a**).