esa-sceva/satcom-chunk-collection huggingface dataset:
# The Use of Artificial Intelligence in eParticipation: Mapping Current Research
## 4 Results
### Mapping the Domain
#### 4.2.2 eParticipation Activities  
eParticipation initiatives revolve around various activities, with online political discourse maintaining a prominent position. The relevant research focuses on analyzing citizens' online discussions to discern their opinions [74; 67]; on introducing new platforms, technologies, or techniques for facilitating discussions [71; 78]; or both [51]. Theoretical investigations into the role of algorithms in online discussions [41], as well as design practices of online discussion platforms [79], constitute other research objectives.  
The second category of eParticipation activities pertains to citizens' requests, commonly known as ePetitions. Studies in this domain frequently involve analyzing existing requests to extract citizen opinions [56; 59] or derive other insightful conclusions [52]. Efforts are also made to develop methods facilitating citizens in drafting requests [61; 86]. Additionally, petitions serve as subjects for parliamentary debates and further analysis [44].  
The third category of eParticipation activities concerns online citizen assistance in policy formulation, aka online decision-making. Similar to previous categories, analyses  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Actors** & **Paper ID** \\ \hline Citizens & [41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 84; 85; 86] \\ \hline Government institutions & [41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 72; 74; 75; 76; 77; 78; 80; 81; 82; 84; 86] \\ \hline Politicians & [44; 52; 72; 75] \\ \hline Voluntary Organizations & [50] \\ \hline \hline \end{tabular}
\end{table}
Table 1: eParticipation Actors.  
Figure 6: Number of research papers per country.  
are conducted on platforms where citizen proposals are published [73]. In addition, new technologies or platforms are proposed to streamline policy formulation processes [50; 60; 75]. Challenges encountered in implementing online debate initiatives for policy shaping are also reported [81].  
Finally, the collection of citizens' opinions, termed eConsultation, constitutes another category of eParticipation activities that is present in the studies. Most studies in this cluster focus on data analysis from eConsultation platforms [43; 46; 47]. Additionally, exploration into game design principles to enhance eConsultation initiatives [55] and optimize AI utilization in governance through eConsultation platform data [45] is noted.  
eVoting, eCampaigning, and eActivism are eParticipation activities where the application of AI has not yet been investigated, therefore constituting interesting areas for further research (see Table 2). Interestingly, some articles do not mention any specific activity [70; 85].
# Sub-Pixel Displacement Measurement with Swin Transformer: A Three-Level Classification Approach
## 2 Related Work
### Swin Transformer  
The structure of the Swin Transformer [36] is shown in Figure 2. The size of the original images at the input is \(H\times W\times C\), where \(H\) is the height of the input image, \(W\) is the width of the input image, and \(C\) is the number of channels of the input image. For an original  
Figure 1: Sub-pixel search correlation principle of DSCM. (**a**) Speckle image before deformation; (**b**) speckle image after deformation.  
image, the Patch partition module splits it into \(4\times 4\)-sized patches, which are flattened and superimposed to obtain a feature map with the size of \(\frac{H}{4}\times\frac{W}{4}\times 16\)C. In \"Stage 1\", a Linear Embedding layer is applied on the feature map just obtained to project it to a feature map with dimensions of \(\frac{H}{4}\times\frac{W}{4}\times F\) (\(F\) is an arbitrary dimension), and it is then fed into the Swin Transformer block module for processing, where the Swin Transformer block module can be repeated \(n_{1}\) times, each time with two consecutive Swin Transformer blocks, whose structure is shown in Figure 2b. In \"Stage 2\", the feature image size obtained after the Patch Merging layer is \(\frac{H}{8}\times\frac{W}{8}\times 2F\), which is fed into the Swin Transformer block module for processing, and here the Swin Transformer block module can be repeated \(n_{2}\) times, each time with two consecutive Swin Transformer blocks, whose structure is shown in Figure 2b. Similarly to the process of \"Stage 2\", the sizes of the feature images obtained after \"Stage 3\" and \"Stage 4\" are \(\frac{H}{16}\times\frac{W}{16}\times 4F\) and \(\frac{H}{32}\times\frac{W}{32}\times 8F\), respectively. The \(n_{1},n_{2},n_{3}\), and \(n_{4}\) in the Swin Transformer model are configurable integers, such as 1, 1, 3, and 1 in the tiny version (Swin-T) in [36].  
The Swin Transformer introduces a novel architecture that diverges from the traditional vision transformer (ViT) by incorporating hierarchical feature maps and a shifted window-based attention mechanism. At its core, the Swin Transformer processes images by dividing them into non-overlapping patches, which are then linearly embedded to form the initial tokens. These tokens are passed through a series of Swin Transformer blocks, each comprising multi-head self-attention (MSA) and a two-layer feed-forward network (FFN) with GELU nonlinearity. The key innovation lies in the shifted window approach to MSA, where the image is partitioned into local windows, and attention is computed within these windows to reduce computational complexity. The window configuration is shifted in subsequent layers to facilitate cross-window connections and capture global context. This hierarchical and local-to-global attention mechanism is central to the Swin Transformer's design.
## 5 Experimental Results
### Implementation
#### 5.5.1 Web Application  
In this section, we present the interfaces with a description of the web application.  
Figure 9 illustrates the detailed data collection process and temporal analysis of customer interactions with products. In this step, customers engage with various products in the supermarket, and the time spent in front of each product is recorded. Customers' attention to each product can be inferred from this temporal analysis.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & \multicolumn{2}{c}{**Tools/Technologies**} & \multicolumn{2}{c}{**Purpose**} \\ \hline \multirow{4}{*}{**Development Languages**} & - & HTML, CSS, JavaScript, PHP & - & To create dynamic and interactive web \\  & - & Java (for mobile agent & applications \\  & & development) & - & For the development of mobile agents \\  & - & Python & - & To perform data processing and temporal \\  & - & PostgreSQL & - & To manage customer and product \\
**Database Management** & - & Google Maps API & & information \\  & - & OpenStreetMap API & - & For visualizing geographic data and \\  & - & Java Agent Development & - & To facilitate communication, autonomy, \\
**Mobile Agents** & - & Framework (JADE) & & and mobility among agents within the \\  & - & Matplotlib & - & To create graphs and visualizations \\
**Data Visualization Tools** & - & Plotly & - & For detailed data visualization and \\  & - & Tableau & & dashboard creation \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of technologies and their applications.  
Figure 8: Performance metrics for the recommendation framework.  
The integration of these temporal data into a virtual geographic map is shown in Figure 10. The location of a product in the supermarket is shown on the geographic map when a user clicks the \"View\" button for that particular product. It also indicates the average time customers spend interacting with this product. This visualization lets users comprehend how products are arranged in the actual supermarket space and how they draw in customers over time.  
Figure 11 shows how different stores are categorized according to how many products they sell to customers. It illustrates how retailers can be classified into various groups according to the range of goods they carry. Marketing researchers and store managers can use this classification to understand better how their store is positioned to the competition and to help them make strategic decisions about product offer  
Figure 10: Integration of temporal data into the geographic map.  
Figure 9: Data collection and temporal analysis of customer–product interactions.
## 3 Components of VPN Architecture
### IPsec  
IPsec is used to manage encrypted VPN tunnels at OSI Layer 3 [5; 6]. IPsec is part of a series of protocols and its architecture has been proposed as a standard by the IETF, an organization that is responsible for continuing the technical development of the Internet. It was created for the new version of the network protocol (IPv6) and later also for IPv4. It can be configured according to three functional implementations:  
* Transfer protocols : Encapsulating Security Payload (ESP) and Authentication Header (AH)
* Encryption Process Management: Internet Key Exchange (IKE) and Internet Security Association and Key Management Protocol (ISAKMP)
* Database: Security Policy Database (SPD) and Security Association Database (SAD)  
Using the AH and ESP protocols, IPsec makes it possible to guarantee the integrity and authenticity of the data sent. The AH protocol, via the Packet Accelerator Extension, authenticates the data source and protects against modification of packets during transmission. Finally, it adds to the header a sequence number for preventing packets from being sent.  
The ESP protocol guarantees, in addition to verifying the identity and integrity of the data, also the encryption of the pending data. It should be noted that ESP authentication does not consider the most external IP header and therefore it is not complete, therefore an additional encapsulation is required because the ESP contents are delivered correctly, traversing networks connected via Network Address Translation (NAT), as is generally the case in private xDSL networks. IPsec is configured to work in two modes: Tunnel and Transport.  
By using \"transport mode\", the corresponding transfer protocol is added between the IP packet header, which remains unchanged, and the data area. The protection starts on the outgoing computer and comes up until it reaches the target computer. After the package is received, the original data are decompressed and made available. Transport mode has a very low elaboration time, and guarantees data security only to the detriment of that source and destination addresses. This is used for \"Host 2 Host\" or \"Host 2 Router\" connections.  
By using \"tunnel mode\", the data packets receive a fresh complete IP header, then original address, destination address, and data are hidden. In addition to this the header of the respective transfer protocol is generated, creating the so-called \"encapsulated\" mode. The new outermost IP header defines the endpoints, encrypted and differentiated from the communication endpoints fixed in the actual IP header. Only when the packet has been unpacked on the encrypted endpoints it will be forwarded to the recipients. Data transmission in tunnel mode occurs at \"Site 2 Site\", \"Host 2 Site\", and \"Host 2 Host\".
# Enhancing Cybersecurity Through Comprehensive Investigation of Data Flow-Based Attack Scenarios
## 2 Background
### Threat Modeling  
By definition, a threat is a potential harm that may result from a weakness present in the development of software applications [9]. When this weakness is exploited, a threat forms and an attack happens. Identifying possible threats during the design phase of software development using threat modeling can enhance system security [9], helping to recognize potential threats and different mitigation techniques. In this way, it is possible to identify critical design elements that need to be protected. Among the various security threat classification tools and methods, STRIDE is widely used for threat modeling and provides the industry-standard approach for identifying threat scenarios [10, 11].  
STRIDE is a framework for modeling threats developed by Microsoft ([PERSON], WA, USA) [12] in 1999. The method provides threat identification in the design phase of any software or hardware, and as such provides understanding of possible attack scenarios. To apply the STRIDE framework, security experts first need to create Data Flow Diagrams (DFD) of the system, which show the communication patterns among the elements. Then, the method examines these diagrams to identify potential threats to the system. Based on the STRIDE model, threats are divided into six different categories, as shown in Figure 1: _Spoofing_, _Tampering_, _Repudiation_, _Information Disclosure_, _Denial of Service_, and _Elevation of Privileges_[13]. DFD diagrams can be investigated manually (brainstorming) or by using the Microsoft Threat Modeling tool. The methodology conducted in this paper applies the manual approach.
# Research on Chinese Nested Entity Recognition Based on IDCNNLR and GlobalPointer
## 4 Experiment
### Comparative Experiment  
The comparative experimental results of the model on the CMeEE dataset are shown in Table 3, and the comparative experimental results on the CLUENER2020 dataset are shown in Table 4. The analysis of the comparative experiments is as follows. In Tables 3 and 4, the bolded data represent the best experimental results for this metric.  
Comparing BERT-ATT-BILTAR-GlobalPointer with BERT-GlobalPointer, the addition of IDCNNLR and multi-position attention mechanism in the BERT-GlobalPointer model improves the \(F1\) score of the model by 1.012% on the CMeEE dataset and by 1.378% on the CLUENER2020 dataset. The improved performance indicates that the IDCNNLR module can extract deeper semantic information, and the multi-position attention mechanism can highlight important information, thereby enhancing the model's performance.  
In the comparative experiments on the CMeEE dataset, compared to the FLR-MRC [38] model, the \(F1\) score of our model improved by 3.097%. Compared to the GPa+RoBERTa-large+SoftLexicon [39] model, the \(F1\) score of our model improved by 1.487%. Compared to TPORE, the \(F1\) score improved by 4.677%. These experiments demonstrate that combining GlobalPointer with the IDCNNLR deep semantic feature extraction layer and the multi-position attention mechanism can achieve excellent results, surpassing the current baseline models.  
In the comparative experiments on the CLUENER2020 dataset, our model outperformed the BERT-CRF model by improving the \(F1\) score by 4.629%. Compared to the BERT-IDCNN-CRF model, our model improved the \(F1\) score of our model by 3.042%. When compared to the BERT-BILSTM-CRF model, our model improved the \(F1\) score by 4.591%. Furthermore, compared to the BERT-ATT-BILSTM-CRF model, our model improved the \(F1\) score by 4.929%. These experiments demonstrate that the BERT-ATT-IDCNNLR-ATTR-GlobalPointer model performs better than classical entity recognition neural network models.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compare Models** & **P\%** & **R\%** & **\(F1\%\)** \\ \hline BERT-ATT-IDCNNLR-ATTR- & **77.325** & **81.407** & **79.285** \\ GlobalPointer & 77.034 & 78.519 & 77.906 \\ BERT-CRF & 71.867 & 77.669 & 74.655 \\ BERT-BILSTM-CRF & 73.978 & 75.423 & 74.693 \\ BERT-ATT-BILSTM-CRF & 74.525 & 74.186 & 74.355 \\ BERT-IDCNN-CRF & 74.244 & 78.352 & 76.243 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Contrast experiments of CLUENER2020 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Compare Models** & **P\%** & **R\%** & **\(F1\%\)** \\ \hline BERT-ATT-IDCNNLR-ATTR- & **71.276** & **69.127** & **69.617** \\ \begin{tabular}{c} BERT-GlobalPointer \\ FLR-MRC \\ GPa-large+SoftLexicon \\ TPORE \\ \end{tabular} & 69.405 & 69.032 & 68.605 \\ \
\begin{tabular}{c} FLR-MRC \\ GPa-large+SoftLexicon \\ TPORE \\ \end{tabular} & 66.79 & 66.25 & 66.52 \\
\begin{tabular}{c} NULL \\ 63.73 \\ \end{tabular} & 66.25 & 64.94 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Contrast experiments of CMeEE dataset.
## 4 Conclusions  
In this study, a self-adhesive asphalt waterproofing membrane was subjected to a coupling aging test, and the low-temperature properties, tensile properties, and peeling properties of the membrane after aging were investigated. The degradation of the specimens due to coupled aging was analyzed using FTIR and SEM, leading to the following main conclusions:  
After 28 days of coupled aging, the low-temperature flexibility of the specimens decreased from -28.5 \({}^{\circ}\)C in the unaged condition to \(-15\)\({}^{\circ}\)C, indicating that aging had a significant impact on their low-temperature properties. Additionally, the peel strength decreased by 21%, reflecting the deterioration of surface adhesion properties, particularly in cases in which asphalt aggregation was prominent.  
FTIR analysis revealed a significant increase in oxygen-containing functional groups, such as carbonyl (C=O) and sulfinyl (S=O), during the coupled aging process. This indicates that oxidation reactions occurred in the asphalt, leading to a reduction in the light components and causing the hardening and embrittlement of the specimens. These chemical changes negatively affected the material's mechanical properties, particularly its low-temperature flexibility and adhesion performance.  
The self-adhesive asphalt waterproofing membrane demonstrated good mechanical properties after short-term aging (7 days), indicating its ability to adapt to moderate aging conditions. However, with prolonged aging (28 days), its mechanical properties significantly declined, particularly in extreme temperature environments, where its durability performance was notably poor. Therefore, for practical engineering applications, it is recommended to enhance the long-term stability and aging resistance of the membrane in harsh environments by improving the material formulation and optimizing structural design.  
Conceptualization, G.S.; methodology, C.Z., G.S., X.M. and H.D.; investigation, J.T. and C.Z.; resources, J.T.; writing--original draft, G.S., X.M. and H.D.; writing--review and editing, X.M. and H.D.; supervision, J.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China (No: 2022 YFC3801600).  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company China Electronics Engineering Design Institute Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
Figure 10: Peel strength of specimens with different aging times.
## 3 Results and Discussion
### Direct Heat Drying Results  
A substantial fraction (40%) of the environmental impacts associated with Direct Heat Drying fall into the resources category (Figure 3). The Heat-Drying process itself, which includes burning natural gas to produce hot air to dry the biosolids, contributes the most out of the three unit operations (dewatering, heat drying, and transportation) to all four damage categories. Transportation was a smaller contributor to all of the damage categories due to the very high percentage of solids in the end product. The lower water content of the biosolids means there is less mass to transport.  
The utilities/fuel category clearly accounts for the majority of the environmental impacts associated with Direct Heat Drying (Figure 5D). The utilities and fuel consumedin Direct Heat Drying include the electricity used in mechanical dewatering, the natural gas used in the rotary dryer, and the fuel required for transportation. The polymer used in mechanical dewatering accounts for a small amount of materials contributing to environmental impacts. A key assumption made in this analysis was that no volatile solids were destroyed during the Direct Heat Drying process (Figure 2) because generally, the temperatures achieved during the Heat Drying of biosolids are not high enough to achieve the oxidation of organic matter [33]. Therefore, in this study, it was also assumed that no biogenic emissions of carbon dioxide, methane, or nitrous oxide occurred during Direct Heat Drying. However, if a dryer were to generate temperatures that are sufficiently high to achieve the oxidation of organic matter, then it would be necessary to account for the environmental impacts of Direct Heat Drying in the climate change damage category. Therefore, the validity of the assumption regarding volatile solids destruction and biogenic emissions should be evaluated for each Heat Drying operation.
## 3 Method
### Graph Embedding Representations  
After generating the scene graphs, we use the GraphSAGE graph embedding method to transform these scene graphs into low-dimensional vector representations that can be processed by machine learning models to preserve the topological information and spatial relationships in the graph structure [12]. The process of graph embedding is illustrated in Figure 3. Through this graph embedding method, the nodes in the scene graph can be embedded into the low-dimensional space to ensure that the relationship between adjacent nodes is effectively preserved.  
To manage high-dimensional scene graph embeddings, Principal Component Analysis (PCA) is used to reduce dimensionality, as illustrated in Figure 4. This approach preserves the most critical features while minimizing redundancy. Specifically, the eigenvalue decomposition method is applied to the covariance matrix, selecting the top\(K\) eigenvectors with the highest eigenvalues. The data are then projected onto the subspace formed by these eigenvectors, achieving effective dimensionality reduction. By compressing the high-dimensional embeddings into a lower-dimensional space, the model retains representative features, providing streamlined and efficient input for subsequent machine learning tasks. This embedded graph representation not only reflects the spatial distribution within a region but also captures developmental trends across various economic zones, offering structured information for feature fusion and decision support.  
\[C=\frac{1}{n}X^{T}X, \tag{5}\]  
Figure 3: Graph embedding process. (**a**) The blue nodes represent the original scene graph embedding features. (**b**) The red nodes represent the low-dimensional embedding features.  
where \(X\) is the feature matrix of the scene graph embeddings, \(C\) is the covariance matrix, and \(n\) is the number of samples.  
\[Cv=\lambda v, \tag{6}\]  
where \(\lambda\) represents the eigenvalues of the covariance matrix \(C\), and \(v\) is the corresponding eigenvector.  
\[\mathbf{h}_{v}^{(k)}=\sigma\Big{(}\mathbf{W}^{(k)}\cdot\text{AGGREGATE}^{(k)} \Big{(}\{\mathbf{h}_{u}^{(k-1)}:u\in N(v)\}\Big{)}+\mathbf{b}^{(k)}\Big{)}, \tag{7}\]  
where \(\mathbf{h}_{v}^{(k)}\) is the embedding of node \(v\) at layer \(k\), AGGREGATE is the aggregation function (such as mean, LSTM, max pooling, etc.), \(\sigma\) is the activation function, and \(\mathbf{W}^{(k)}\) and \(\mathbf{b}^{(k)}\) are learnable parameters.