esa-sceva/satcom-chunk-collection huggingface dataset:
# Length-Bounded Hybrid CPU/GPU Pattern Matching Algorithm for Deep Packet Inspection
## 2 Related Work
In other words, allocating all tasks to GPU may not achieve the best efficiency in certain cases, even with its powerful parallel processing. Thus, other studies began to concentrate on CPU and GPU collaboration design to improve the efficiency. For this purpose, we focused on designing effective cooperation methods based on CPUs and GPUs. Our previous work HPMA [10] divides the processing tasks into two parts: rapid packet prefiltering and full pattern matching, as shown in Figure 1. First, the incoming packets are delivered to the CPU host memory, and then the CPU accessed the packets and started the prefiltering according to the lookup tables (\(T_{1}\), _Base Table_, _Indirect Table_ and \(T_{2}\)). The table size is sufficiently small so that it can reduce the memory access latency to decrease the pre-filtering time. In addition, a high filtration ratio is also achieved so that the number of packets can be decreased to reduce the data transfer latency. Next, the prefiltered packets considered \"suspicious\" are sent to the GPU for the full pattern matching process. We adopted the AC algorithm into the GPU platform, and the AC lookup tables were stored in the GPU texture memory for the algorithm use, since the access latency of texture memory is much lower than that of device memory. The matched result is copied back to the CPU host memory after the full pattern matching is completed. By using the pre-filtering method in HPMA, the CPU can perform rapid pre-filtering and reach a high filtration ratio so that the GPU latency can be decreased and make the processing more efficient. HPMA outperformed the CPU-only and GPU-only pattern matching algorithms by 3.4 and 2.7 times, respectively, with input traffic of 1460-byte random payloads, indicating that the collaboration between the CPU and GPU can improve overall processing speed. Nevertheless, the HPMA performance may be restricted under the following conditions: (a) all incoming packets are finally delivered to the GPU, causing redundant operations rather than directly delivering packets to the GPU; and (b) the input traffic consists of varying length packets that cause execution time inconsistency of each thread in parallel processing. For these limitations, we have proposed another design, a capability-based hybrid CPU/GPU pattern matching algorithm (CHPMA) [29] to face the former condition. The CHPMA first estimates the CPU and GPU processing capability of the system, and, during runtime, the CHPMA can automatically perform the processing selection according to the historical filtration ratio and capability estimation result. To face the latter condition, we propose a length-bounded hybrid CPU/GPU pattern matching algorithm in this paper, as illustrated in the next section.
## 4 Classification of Lung CT Images
### Proposed 3D-CNN Architectures for Classification of Lung CT  
The main structures of both proposed 3D-CNN architectures, which are used for automatic feature extraction, contain six convolutional layers, four max pooling layers, and two fully connected layers. The kernel size in all convolutional layers and pooling layers is considered 3 \(\times\) 3 \(\times\) 3 and 2 \(\times\) 2 \(\times\) 2 respectively. Moreover, the number of applied kernels on feature maps in convolutional layers is 96, 128, 256, 324 and 512 respectively. For non-linearity, the Relu activation function with less computation cost is applied to the output of each convolutional layer and fully connected layers. In the first convolutional layer, 96 filters by 3 \(\times\) 3 \(\times\) 3 filter size are applied on the input images of 227 \(\times\) 227 \(\times\) 227 size. The max pooling layer is used to reduce the output size of the previous convolutional layer by applying 2 \(\times\) 2 \(\times\) 2 filters. Consequently, the downsampled feature maps go through the second and third convolutional layers. In order to operate downsampling on the output feature maps of the third convolutional layer, the second pooling layer is applied. Afterwards,  
Figure 3: Example of without cancer samples (**left**) and with cancer samples (**right**) of the dataset [18].  
the fourth and fifth convolutional layers are applied on the downsampled feature maps. The model continues by the rest of layers until two fully connected layers where all neurons are connected to all neurons of the previous layer. Consequently, the difference between the two 3D-CNN architectures is in their classification layers. Accordingly, in straight 3D-CNN architecture as commonplace CNN, Softmax classifier is used to classify with cancer and without cancer classes of CT scan images and, additionally, the RBF-based SVM classifier is used as a classification layer of hybrid 3D-CNN. Thus, SVM through RBF kernel can map the data to a high dimensional feature space which is considered as separable data. Furthermore, in both proposed architectures, outputs of the last fully connected layer after applying Relu activation function are fed to the classification layer as the input of classifiers. The architecture of both proposed 3D-CNNs is illustrated in Figure 4, where the classification layers are separated as two softmax and RBF-based SVM classifiers to depict both proposed architectures. Moreover, the summary of the layers is illustrated in Table 1. In addition, in this study, batch size (number of samples through one training cycle) and the learning rate of the network are determined to be 32 and 0.01, respectively, in both proposed 3D-CNN architectures. Furthermore, both proposed architectures are trained in 30 epochs as well.
###### Abstract  
With the rapid advancement of space technology, satellites are playing an increasingly vital role in fields such as Earth observation, communication and navigation, space exploration, and military applications. Efficiently deploying satellite missions under multi-objective, multi-constraint, and dynamic environments has become a critical challenge in the current aerospace domain. This paper integrates the concepts of game theory and proposes a distributed collaborative task model suitable for on-orbit satellite mission planning. A two-player impulsive maneuver game model is constructed using differential game theory. Based on the ideas of Nash equilibrium and distributed collaboration, multi-agent technology is applied to the distributed collaborative task planning, achieving collaborative allocation and countermeasure strategies for multi-objective and multi-satellite scenarios. Experimental results demonstrate that the method proposed in this paper exhibits good adaptability and robustness in multiple impulse scheduling, maneuver strategy iteration, and heterogeneous resource utilization, providing a feasible technical approach for mission planning and game confrontation in satellite clusters.  
g Game theory; distributed collaborative task; countermeasure strategies; satellite missions 2025  
[PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], D. Research on Distributed Collaborative Task Planning and Countermeasure Strategies for Satellites Based on Game Theory Driven Approach _Remote Sens._**2025**, 17, 2640. [[https://doi.org/10.3390/rs17152640](https://doi.org/10.3390/rs17152640)]([https://doi.org/10.3390/rs17152640](https://doi.org/10.3390/rs17152640))
# Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
## 3 Shortest Paths with Vertex Transfer Penalties
### A Note on the Number of Colours in a Graph  
As we might expect, the time requirements of a shortest path algorithm defined for use with edge-coloured graphs will depend on the number of vertices \(n\), edges \(m\), and colours \(k\). However, we find that it is expedient to avoid using \(k\) in any asymptotic analysis because it can lead to an overestimation of complexity due to a relationship with the graph colouring problem, which we now describe.  
Let \(G=(V,E)\) be an edge-coloured graph using \(k\) colours as previously defined. Now let \(G^{*}=(V,E^{*})\) be a copy of \(G\) with all edge directions removed. Finally let \(G^{*}(i)\) denote the subgraph formed from \(G^{*}\) using edges of colour \(i\) only. Note that if two such subgraphs \(G^{*}(i)\) and \(G^{*}(j)\) have no common vertices, then no transfers are possible between colours \(i\) and \(j\). In this case we have the opportunity to relabel all \(i\)-coloured edges with colour \(j\) (or vice versa), and potentially reduce the number of colours \(k\) being used in the graph.  
In more detail, consider a conflicts graph created using a single \(i\)-coloured vertex for each component of each subgraph \(G^{*}(i)\) (for \(i\in\{1,\ldots,k\}\)), with edges corresponding to any vertex pair representing differently-coloured overlapping components in \(G^{*}\). From a graph colouring perspective, note that the colours of the vertices in this conflicts graph define a _proper_\(k\)-colouring, in that pairs of adjacent vertices will always have different colours; however, it may be possible to colour this conflicts graph using fewer colours. If this is so, then an equivalent graph to \(G\) with fewer colours can also be created. An example of this process is shown in Figure 4. This illustrates that, while the number of different edge colours \(k\) could have any value up to and including \(m\), the _minimum_ number of colours needed to express this graph might well be smaller. However, identifying this minimum can be difficult since it is equivalent to solving the \(\mathcal{NP}\)-hard chromatic number (graph colouring) problem ([PERSON], 2018).  
Given these observations on \(k\), a better alternative for analysing complexity is to consider the number of colours entering and exiting each vertex (given by \(|C^{-}(v)|\) and \(|C^{+}(v)|\)) and, in particular, their  
Figure 4: (a) A graph \(G^{*}\) using \(k=3\) colours; (b) the underlying conflicts graph (and 3-colouring) prescribed by \(G^{*}\); (c) a 2-colouring of this conflicts graph; and (d) the original graph \(G^{*}\) using \(k=2\) colours.  
maximum values \(c_{\max}^{-}=\max\{|C^{-}(v)|:v\in V\}\) and \(c_{\max}^{+}=\max\{|C^{+}(v)|:v\in V\}\). These will be used in the next section.
# Study on Dynamic Splitting Properties of S-PP Hybrid Fiber Concrete after High Temperatures
## 3 Experimental Results and Analysis
### Analysis of Damage Variables Based on Dissipative Energy
It is more reasonable to define damage in combination with the relationship between material performance degradation and energy dissipation. Based on the previous research results [26], \(E_{\xi}\) is defined as the constitutive energy of the material, \(E_{\lambda}\) as the deformation energy, and \(D_{o}\) as the damage variable. \[E_{\xi}=E_{\lambda}+w_{d} \tag{11}\]  
\[D_{o}=w_{d}/E_{\xi} \tag{12}\]  
where \(w_{d}\) represents the dissipative energy density. \(E_{\xi}\) can be obtained by calculating the area surrounded by dynamic splitting stress and strain. \[E_{\xi}=\int\sigma_{T}d\varepsilon_{T} \tag{13}\]  
Table 5 shows the relevant parameters of constitutive energy, dissipated energy density, and damage variables during the dynamic splitting process of PC and S1 PP0.2 concrete under different temperature groups. It can be seen that the constitutive energy level of S1 PP0.2 concrete is significantly higher than that of PC, and the constitutive energy is the highest at 200 \({}^{\circ}\)C. At the same time, it is found that the dissipative energy density of PC at 400 \({}^{\circ}\)C is similar to that of S1 PP0.2 concrete at 600 \({}^{\circ}\)C, but the difference in constitutive energy is significant. According to the test failure diagram, the former is more serious, indicating that S1 PP0.2 concrete contains high releasable internal energy, which can be released through reflected and transmitted waves, showing toughness characteristics, which are closely related to the addition of steel fiber and polypropylene fiber. Most of the constitutive energy of PC is used for deformation and crack propagation, resulting in more serious damage [27]. It can be seen from the change law of the damage variable that with the increase of temperature, the value of the damage variable decreases slowly and  
Figure 9: Time history curve of dynamic splitting dissipation energy of concrete under different temperature groups:(**a**) PC; (**b**) S1 PP0.2. then increases, indicating that there is a temperature strengthening effect before 200 \({}^{\circ}\)C, and the damage degree does not change significantly at this stage. From 400 \({}^{\circ}\)C to 800 \({}^{\circ}\)C, the value of the damage variable increases significantly, indicating that higher temperature seriously degrades the overall performance of concrete. In S1 PP0.2 concrete, the level of the concrete damage variable is significantly lower than that of PC. Comparing the damage diagrams under different temperatures, it is concluded that the greater the value of the damage variable, the greater the damage and damage degree of concrete. In conclusion, from the perspective of energy analysis, it can be seen that adding an appropriate amount of steel polypropylene fiber can improve the dynamic splitting tensile toughness of concrete after high temperatures, and the damage degree of concrete under different states can be better characterized by defining the damage variable.
## 1 Introduction  
Named entity recognition (NER) is a core task in natural language processing (NLP) aimed at identifying and classifying specific entities within text, including the names of people, places, and institutions [1]. Traditional medical NER methods depend on rule-based approaches and dictionary matching, which require manual formulation and maintenance of medical dictionaries, making them difficult to adapt to diverse and evolving texts. Deep learning approaches, including recurrent neural networks (RNNs [2]), long short-term memory networks (LSTMs [3]), convolutional neural networks (CNNs [4]), and pre-trained models such as BERT [5], have demonstrated strong performance in medical NER. However, these approaches often struggle to capture contextual dependencies, particularly when processing long sentences, polysemous words, and homographs. To address these challenges, we propose a Chinese medical NER method that integrates BERT, RNNs, an attention mechanism, and a novel memory unit. The model leverages BERT to capture contextual information, enhances entity and relationship understanding via RNNs and an attention mechanism, and introduces novel memory units to improve long-distance dependency capture. The primary innovations and contributions of this study are:  
1. New memory unit introduction: The model integrates novel memory units (GLMU and RMN) to enhance its capacity for capturing long-distance dependencies. These memory units more effectively preserve and utilize contextual information, particularly in the processing of lengthy texts and complex sentences, significantly improving NER accuracy.
2. Fully connected layer optimization and application: The fully connected layer optimizes feature representation and classification performance in the model. By integrating and mapping high-dimensional features from earlier layers, it enhances classification accuracy, allowing for more precise identification of named entities.
3. Complex named entity structure handling: The model effectively processes complex named entity structures. It accurately captures internal entity structures and relationships through the combined efforts of the memory units and fully connected layer, improving recognition accuracy and robustness.
4. Enhanced recognition of new words and domain-specific terms: Through the integration of BERT, RNNs, attention mechanisms, novel memory units, and fully connected layers, the model's ability to recognize new words and domain-specific terms has been markedly enhanced.  
In summary, this study proposes a Chinese medical NER model integrating multiple advanced technologies, effectively improving performance in context-dependent perception, complex entity processing, and recognition of new words and domain-specific terms. The introduction and optimization of novel memory units and fully connected layers were particularly significant.
## 1 Introduction  
With gradually increased applications of three-dimensional (3D) components in the fields of semiconductor industry and nanotechnology, obtaining 3D information of these targets is critical. However, conducting 3D topography optical metrology of nanoscale targets for general optical microscopy is becoming increasingly challenging due to severe diffraction.  
Through-focus scanning optical microscopy (TSOM) is a novel and fast optical metrology. TSOM is not limited by the need to acquire an in-focus image in the conventional measurement. This method utilizes a TSOM image by scanning the target through the focus, and it extracts dimensional information of the targets by analyzing scattering intensity of the TSOM image and matching the TSOM image with the simulation results in the database [1]. Low resolution due to the diffractive limit in conventional optical metrology is consequently avoided. The lateral and vertical sensitivity of TSOM is quantified by the optical intensity range (OIR) of the TSOM image, which is defined as the absolute difference between the maximum and minimum intensity of the normalized TSOM image [2].  
The sensitivity of TSOM strongly depends on many mechanical and optical factors [3; 4; 5]. The critical dimension of a target is usually smaller than \(1/10\) of wavelength of the illumination light;thus, the interaction between incident light wave and target surface is complicated, and the spatial distribution of scattered light wave depends on illumination polarization and target structures [6]. TSOM images with different OIRs correspond to different spatial distributions of scattered light [7]. Consequently, the illumination polarization and structure characteristics of the target strongly affect the sensitivity of TSOM [8; 9]. To address the lacking systematic analysis of these effects, many works attempt to optimize illumination polarization according to different targets through pre-tests [10; 11]. The drawbacks are long preparation time and low inspection efficiency. To understand the light scattering around the target surface and conduct the inspection in the most optimized condition, the effects on TSOM sensitivity brought by illumination polarization and target structure are analyzed and summarized in this paper.  
The reminder of this article is organized as follows: the complete image-forming procedure, from incidence of illumination to the imaging plane of the charge-coupled device (CCD), is analyzed in the second section. The simulation of the system is presented in the third section. A few experiments are performed in the fourth section to verify the simulation results. The simulation and experimental results are interpreted in the fifth section. A conclusion based on the analyses and results is proposed in the sixth section.
# A Hybrid Neural Network Model for Predicting Bottomhole Pressure in Managed Pressure Drilling
## 2 Theory Background
### LSTM Neural Network  
Different from feedforward neural networks, recurrent neural network is a basic multilayer feedback neural network. In the network, each neuron and its output signal, as the input signal feedback to other neurons and LSTM in Figure 2, is a special kind of recurrent neural network [29]; on the basis of the original loop structure it introduces four interactive layers, including the cell state, input gate, forget gate, and the output gate. The network can be used more efficiently to extract long-term correlations in sequence data. Specifically, the cellular state acts as a pathway for information to travel through sequence connections, and gate structures are trained to learn which information to save or forget, which makes it able to forget useless information and learn correlations between data points that are far away from each other in sequence. Therefore, the LSTM constitutes a new method for building a new model for this type of prediction problem.  
Figure 1: Schematic diagram of BP neural network with one hidden layer where \(x_{n}\) is the current input; \(i\) is the input layer; \(h\) is the hidden layer; \(w_{ih}\) and \(w_{jk}\) are the weight matrix of the hidden layer; \(o\) is the output layer and \(y_{n}\) is the current output.  
Figure 2: Self-circulating structure in LSTM where \(x_{t}\) is the current input; \(h_{t-1}\) is the output at the previous moment; \(h_{t}\) is the current output; \(C_{t-1}\) is the previous cell state; \(C_{t}\) is the current cell state; \(f_{t}\) is the forget gate neuron in LSTM The output of the cell; \(i_{t}\) is the output of the input gate neuron in LSTM; \(\hat{C}_{t}\) is the new candidate values for cell status; \(O_{t}\) is the output of the output gate neuron in LSTM; \(\sigma\) is the sigmoid activation function and tanh is the tanh activation function.
# A Hybrid Energy Equating Game for Energy Management in the Internet of Underwater Things
## 5 Performance Evaluation
### Network Energy Consumption  
Figure 9a elaborates the comparison of energy consumption and the number of FNs in the network between ADDP [20], PRADD [21], and DEEP [14]. The presented results show that the proposed framework achieves a longer lifetime with less energy consumption as compared to the counterpart schemes. In the proposed model, the energy load on each node is balanced in a distributed manner. The FNs in the proposed model are forced to use the single-hop as well as the multi-hop transmission ranges to communicate with CHs depending on the situation. HEEG controls the repeated transmission by selecting a few nodes to perform the sensing activity in the overlapping region. The CHs are redundancy-aware in HEEG, so only selected information can travel toward the SS to save the available energy of the network. The counterpart schemes do not define a clear strategy to overcome the issues of redundant data and repeated information.  
Figure 9b demonstrates the comparison of energy consumption and the network size between ADDP [20], PRADD [21] and DEEP [14]. From the graph, we can see that the proposed framework has less energy consumption as compared to the counterpart schemes. The proposed model has strong CHs management, so increasing the size of the network cannot affect the network energy consumption. While in the case of ADDP, PRADD and DEEP the increasing network size cannot affect energy consumption to some extent, if we keep increasing the number of nodes these schemes face some serious problems.  
Figure 9c shows the comparison of energy consumption and data generation interval between ADDP [20], PRADD [21] and DEEP [14]. From the graph, we can see that the proposed framework achieves a longer lifetime with less energy consumption as compared to the counterpart schemes. In the case of ADDP, as the data generation interval increases the more AUVs move in the network to receive the same data, which increases the network energy consumption. This is the same in the case of PRADD; the ferry moves more frequently around the network to collect the data to attain the maximum data limit, which increases the energy consumption. In the case of DEEP, the data generation interval increases the energy consumption of the network because the data travels a long way to avoid collisions.