esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results
### Quantitative Assessment of Multi-Task Learning  
Table 4 lists the MTL results for ChangeStar and the proposed methods. The CD head was kept constant when testing the ChangeStar-based approach. The backbones for both approaches were first pre-trained on the BE task using the WHU-CD, as the comparisons in Section 4.1.2 have shown the advantages of the dataset.  
Our proposed MTL idea can significantly improve CD performance, with IoU improving from 72.44 to 81.86 and from 70.46 to 78.33 for our model and ChangeStar, respectively.
## 3 The Cross Time Slot Partial Interference Alignment  
Since the intra-cell interference from relay to CCU in sub-time slot 2 contains the desired signal from BS to CCU in sub-time slot 1, we extract and align this part of the interference to the desired signal subspace at the CCU in sub-time slot 2 (i.e., the cross time slot alignment). Then, a partial IA scheme is proposed to deal with the intra-cell and inter-cell interference at the CEUs. Taking Cell 1 as an example, the details are given as below.
# Comprehensive Document Summarization with Refined Self-Matching Mechanism
## 3 Method
### Sentence Selection Based on Pointer Network  
We make sentence selection based on the above encoding representation, and use another LSTM to train pointer network for extracting sentences recurrently. Given the sentence vector \((s_{1},s_{2},\ldots,s_{n})\), and the target sequential indices\((r_{1},r_{2},\ldots r_{m})\), \(\forall r_{j,j<n}\). \((e_{1},e_{2},\ldots e_{n})\) and \((d_{1},d_{2},\ldots d_{m})\) denote the hidden state of encoder and decoder respectively. The attention distribution of pointer network at each decoding step \(t\) can be calculated as following:  
\[u_{i}^{t}=v_{p}^{\top}tanh(W_{e}e_{i}+W_{d}z_{t}^{{}^{\prime}}) \tag{19}\]  
\[p(r_{t}|r_{1},r_{2},\ldots,r_{(t-1)})=softmax(u_{t})=\frac{exp(u_{i}^{t})}{ \sum_{k=1}^{n}exp(u_{k}^{t})} \tag{20}\]  
where \(z_{t}^{{}^{\prime}}\) represents the result of glimpse operation [32]:  
\[d_{t}=LSTM(d_{t-1},[W_{l}l(r_{t-1});e_{t}]) \tag{21}\]  
\[z_{i}^{t}=v_{g}^{\top}tanh(W_{e}e_{i}+W_{d}d_{t}) \tag{22}\]  
\[att_{t}=Softmax(z_{t}) \tag{23}\]  
\[z_{t}^{{}^{\prime}}=\sum_{i}att_{i}^{t}W_{g}e_{i} \tag{24}\]  
where \(v_{g}\),\(W_{e}\), \(W_{d}\) are automatically learned scalar weights. \(Softmax\) function normalized vector \(z_{j}\) into the attention mask over input. At each decoding step, the pointer network selects one vector with the highest probability from the \(n\) input vectors. \(d_{t}\) is the output of the added LSTM-based decoder. Pointer network performs two-hop attention at each time step: firstly, it pays attention to the encoder state \(e_{i}\) to obtain context vector \(z_{t}^{{}^{\prime}}\), secondly, it attends to \(e_{i}\) for extraction probabilities. Thus, the pointer network effectively takes the previous decisions as relative importance gain during each decoding step.
## 3 Artificial Intelligence as Enabler for Adoption of Nuclear Maritime Ships
### Electricity Supply at Ports  
Onboard nuclear reactors can also serve as a power source for port facilities while docked. This not only generates revenue for the ship owner but also supports decarbonization efforts. By connecting to port infrastructure, the nuclear ship can supply a steady and reliable energy flow directly from its reactor [101].  
Notably, even when docked, the ship still requires energy for critical onboard loads, such as refrigeration, lighting, and other essential systems, necessitating a constant powersupply [102]. Therefore, balancing the ship's own energy needs with the electricity supplied to the port requires the intelligent management of nuclear power generation to maximize efficiency. SMRs, expected to be installed on nuclear maritime ships, offer flexible operation, allowing their output to be adjusted in response to demand fluctuations. It is crucial to ensure that generation and consumption are properly aligned to prevent energy waste.  
AI systems have been successfully proposed and tested for managing power and energy systems. Tailored algorithms can optimize energy generation and consumption, ensuring efficiency [103]. Similarly, AI can regulate the output of SMRs, dynamically adjusting power production to match both the ship's and the port's energy demands. AI becomes even more valuable when the port incorporates renewable energy sources, as it can coordinate all available generation resources to minimize energy waste and enhance overall efficiency.  
Additionally, intelligent algorithms can be employed to forecast energy demand in advance [104]. Accurate forecasting is crucial for developing an effective management strategy, as it enables the pre-scheduling of SMR-based electricity supply. By integrating AI-driven forecasting with intelligent management algorithms, energy supply to ports can be optimized, ensuring efficiency and reliability. The use of AI as an enabler for utilizing nuclear ships as electricity suppliers to the port where they are docked is illustrated in Figure 6. From a wider point of view, AI is an enabler that further assists the creation of the so-called _Positive Energy_ in collaboration with the district's and the vessel's digital twins for optimization and decision support systems.
# Application Ranges of EPB Shield TBM in Weathered Granite Soil: A Laboratory Scale Study
## 3 Results and Discussion
### Optimal Mixing Ratio of Conditioning Agents: With Foam Only  
As mentioned in the previous section, if the water content in the working chamber is controlled within the allowable ranges proposed in Figure 5, the workability of the excavated soils will be satisfied by conditioning using foam only. Permeability and compressibility tests were performed on the soil samples conditioned with foam (Soil 1 through Soil 4), in which the water contents were controlled within allowable ranges. Since [PERSON] et al. [10] revealed in their preliminary study that soils conditioned with foam satisfy all the requirements when FIR can be controlled between 22% and 67% in the optimum water contents shown in curve A in Figure 5, in this study, all the tests were done under the same  
Figure 3: Slump values with variation in the water content and foam injection ratio (FIR) [10]: (**a**) Soil 1, (**b**) Soil 2, (**c**) Soil 3, (**d**) Soil 4.  
The measured permeability coefficient summarized in Table 3 is the initial measurement value; however, at the job site, it would be necessary to maintain this low coefficient for at least 90 min to provide good workability during ring assembly and other processes [3]. Therefore, the change of permeability coefficients with elapse of time was monitored up to 10 h; monitored results are shown in Figure 6. The figure shows that even though the permeability coefficients tended to increase with time, the final values were still far below 1 \(\times\) 10\({}^{-3}\) cm/s.  
In summary, in weathered granite soil with fine particles at least 1.2%, the excavated soils can be conditioned with foam alone as long as the water contents are controlled within the allowable values (and preferably the optimal values) shown in Figure 5 and the conditioned FIR is controlled within the range of 22% to 67%.
# Experimental Research on In Situ Uniaxial Tensile Response of Silica-Based PLC Optical Splitters
## 2 Experimental Work  
### Structure of PLC Optical Splitter  
In this research, the same batch of silica-based 1 \(\times\) 8 PLC optical splitters that were produced by company A was selected as the research object. Figure 1 shows the typical structure of 1 \(\times\) 8 PLC optical splitters, where the three components, that is, 1-channel input and 8-channel output fiber arrays and a PLC (splitter) chip were aligned precisely (optical axis alignment) and then connected together with a UV-curable adhesive for optical and mechanical interconnection [19]. The fiber array and PLC chip faces were polished at an angle of 8\({}^{\circ}\), which is designed to increase the return loss and ensure stable optical power output [15]. Both the fiber array and the PLC chip structure contained a corresponding glass lid (see Figure 1) with a height of 1 mm, which plays the role of fixing the fiber and protecting the PLC chip, respectively. The total length of the PLC optical splitter module was about 42 mm, the rectangular cross-sectional dimension of the fiber array was W \(\times\) H (width \(\times\) height) = 2.5 \(\times\) 2.5 mm\({}^{2}\), and the coverage area of the UV-curable adhesive at the adhesively-bonded joint was similar than the cross-sectional area of the fiber array.  
Silica PLCs were invented in the 1980s by using silica-based materials and large-scale integrated circuit process technologies on a si substrate [7]. The waveguide consists of a central core, about 8 \(\upmu\)m square, and surrounding cladding. In terms of size and materials, this structure is exactly the same as that of conventional optical fiber. Since PLC chips and fiber arrays of silicon-based/quartz glass materials are chemically and physically stable, it is important to check the reliability of fiber connection parts, which include UV-curable adhesives. In this study, the UV-curable adhesive that was used to interconnect the fiber array to the PLC chip was an acrylate-based adhesive, which is a viscoelastic polymer adhesive [23].
## Appendix A
### NP-VAE Decoder Process  
In the NP-VAE model, the decoder generates a compound structure from the input latent variable \(z_{t}\) by sequentially building a tree structure using a depth-first algorithm. The decoding process comprises seven key stages: Root label prediction, Topological prediction, Bond prediction, Label prediction, Latent variable update (\(z\)), Conversion to compound structure, and Chirality assignment.  
1. Root label prediction: Predicts a substructure label for the root node by applying \(L_{r}\) fully connected layers to the latent variable \(z\). This multi-class classification selects a substructure label from those generated during preprocessing. Specifically, transformations are applied to \(z\) through fully connected layers, with a softmax operation at the final layer to determine the most likely root label.
2. Topological prediction: Determines whether a child node should be generated under the current node. Binary classification is performed to decide on generating a child node. If a child node is created, bond and label prediction steps follow. Otherwise, the process either terminates (at the root) or backtracks to the parent node for further structure generation.
3. Bond prediction: Predicts the type of bond (single, double, or triple) between the current node's substructure and the child node's substructure. A ternary classification is applied through \(L_{b}\)-layer fully connected transformations to \(z_{t}\).
4. Label prediction: Predicts the substructure label for the newly generated child node. \(L_{s}\)-layer fully connected layers are applied to \(z_{t}\) for multi-class classification. The predicted substructure label is validated for chemical plausibility. If invalid, bond prediction attempts are adjusted until a valid connection is achieved.
5. Latent variable update (\(z\)): Updates the latent variable \(z_{t}\) to \(z_{t+1}\) after label prediction or backtracking. \(z_{t}\) is updated using a fully connected layer that integrates the feature vector \(h_{i}\), derived from Child-Sum Tree-LSTM. Node-specific features propagate to enrich feature representation.
6. Conversion to compound structure: Constructs the compound structure from the generated substructure labels. The tree structure is converted to the final compound structure by linking substructure labels, with bonding information uniquely defining the compound.
7. Chirality assignment: Assigns stereochemistry to ensure correct 3D structural representation. \(L_{c}\)-layer fully connected transformations produce an ECFP value, and stereoisomers are generated. The final structure is chosen based on the smallest Euclidean distance between predicted and calculated ECFP.
# Advanced Persistent Threats and Their Defense Methods in Industrial Internet of Things: A Survey
## 3 APTs Faced by IIoT and Its Defense Methods
### Perception Layer  
The perception layer of IIoT, often referred to as the equipment layer, is composed of physical equipment such as industrial equipment and sensors that transmit data to the upper-level control system through the Internet [46; 47]. APT attacks on the equipment layer may occur through malicious software or hardware intrusion, physical attacks, or equipment firmware vulnerabilities, among others, in order to gain control of equipment or steal equipment data.  
* Physical attacks [48; 49]: Attackers can physically attack equipment, for example, by disassembling it to obtain confidential information or by destroying it to paralyze factory production. This attack method requires attackers to have the opportunity to physically touch the equipment, but once successful, the consequences are usually severe;
* Human-based attacks [50]: Attackers can directly invade equipment or obtain access permissions through social engineering, among other methods. This attack method requires attackers to have certain technical capabilities and knowledge, but once successfully invaded, attackers can control the equipment for an extended period without being detected;
* Malware attacks [51; 52]: Attackers can invade equipment by infecting them with malware and controlling them for the attack. This attack method is particularly dangerous because attackers can control equipment for a long time without being detected;
* Zero-day vulnerability exploitation [53; 54]: Attackers can exploit unknown vulnerabilities to invade equipment and control them for the attack. This attack method is usually difficult to detect and prevent since the vulnerabilities have not yet been discovered.  
In summary, the APTs that the IIoT perception layer faces are very serious, and comprehensive measures are needed to strengthen the security of equipment, such as using antivirus software, regularly updating system patches, and using network isolation. Additionally, regular security assessments and vulnerability scans are required, as well as raising employee awareness of security, and strengthening security training and education.  
Figure 1: IIoT architecture.
## 3 Results and Discussion
### Principal Component Analysis (PCA)  
The obtained results of analyses of physico-chemical, sensory, and microbiological properties were submitted to statistical inference using the main components method with classification (PCA). The purpose of the application was to demonstrate which of the heat treatment techniques was used to shape the selected culinary properties of chicken breast samples. Quality characteristics such as tenderness, juiciness, tastiness, and cutting force (CF) were active variables and were involved in the formation of the main components assessed for chicken breast fillet. The other indicators were additional variables, which were not taken into account for the calculation, but were 'dropped' in the main components generated by the active variables. The sum of both components was 97.23% (PC1 92.92% and PC2 4.28%) of variability (Figure 4a).  
Figure 4: Similarities and differences (PCA) in the sensory quality of chicken breast fillet samples: (**a**) projection of the assessed sensory characteristics, (**b**) projection of differences and similarities in the sensory quality of chicken breast fillet, depending on the heat treatment used and the heating group. Code designations as in Table 1. TMC—total microbial count, Aw—water activity, BI—browning index.  
Figure 3: Sensory quality of culinary chicken breast samples. Code designations as in Table 1.  
The water content strongly positively correlated with the flavor, and negatively with the palatability and browning ratio. Moderately average correlation showed between SFAs, PUFAs, TMC, and color--both external color desirability and internal color. There was also a correlation between some sensory characteristics--smell, taste, and coloring. The latter characteristic was strongly positively correlated with the browning coefficient, water activity, and water content. In contrast, the browning coefficient correlated highly with the meat smell, which resulted from changes that took place during heat treatment and the formation of sensory values as a result of the action of high temperature.  
The most similar properties were shown for samples of chicken fillet baked and fried with both techniques A and B (Ba-a, Ba-B, F-a, F-B) (Figure 4b). Similar properties showed samples of chicken breast cooked using the sous-vide technique. However, samples cooked with techniques A and B and stewed with technique A significantly differed from other analyzed samples.
#### Research Contributions  
* First, based on our trust model, we propose on-demand trust enhanced routing (TER) among vehicles in VIoT, which extends trust-based source routing mechanisms by combining with the TW algorithm. Our proposed TER scheme predicts the next route-finding and efficient route discovery based on the current node threshold trust value and the TW algorithm for transmitting the information.
* Second, for the performance evaluation of the proposed TER, we compare it with the performance of two other routing protocols even though they do not have trust mechanism among communicating nodes. One is an authenticated anonymous secure routing (AASR) scheme that uses a cryptographic operation, and the other is standard AODV routing. Based on the analytical results, it is shown that the proposed TER is a promising approach in enhancing packet delivery, minimizing E2E delay, and resisting against malicious threats.
* Third, we argue that the proposed TER can resist certain malicious attacks by the continuous update of route trust by taking the latest interactions among peers. The TER helps us to cope with malicious attacks, e.g., black hole and zigzag.
* Finally, taking advantage of the TW algorithm, we can efficiently assess trust in large scale vehicular networks. Therefore, it can be seen that the proposed TW algorithm can also be applicable to interpersonal trust assessment in online social networks.  
The rest of the paper is organized as follows : Section 2 explains the related work in two parts, i.e., a brief history of the proposed trust models and trust-based routing schemes in VIoT. In Section 3, we define a system model and trust assumptions along with the design goals for the proposed scheme. In Section 4, fundamentals on the trust model for the proposed scheme are described in details. Section 5 expresses the framework for the proposed TER. Section 6 provides details about the TW algorithm and its related operations in the TER. In Section 7, the route maintenance operations related to route error, route hand-off, and node mobility are explained. In Section 8, the simulation setup is carried out, and results are obtained. The conclusions are provided in Section 9.
# Development of Multimodal Physical and Virtual Traffic Reality Simulation System
## 4 Integration of Co-Simulation Components
### Design Challenges and Solutions of PTV Vissim--CARLA Integration
#### 4.1.2 Real-Time Simulation and Synchronization of Calculation Frequencies  
PTV Vissim provides a built-in add-on module called the Driving Simulator Interface, which facilitates real-time data exchange with external driving simulation software. This module allows PTV Vissim to visualize and simulate vehicles controlled by the driving simulator. To establish communication, the Driving Simulator Interface must be activated in PTV Vissim's network settings, and the driving simulation software must be connected to the provided _DrivingSimulator.dll_ file.  
CARLA operates on a different update frequency by default, requiring synchronization with PTV Vissim to ensure a seamless simulation experience. In this study, CARLA's _run_synchronization.py_ script was modified so that both platforms operate on a shared simulation timestep of 0.05 s, enabling real-time coordination between the vehicle movements, traffic signals, and user inputs. This ensures that both systems progress frame-by-frame in a lockstep execution model, maintaining strict temporal consistency across the co-simulation loop.  
While this synchronous approach is essential for maintaining temporal consistency across the simulation platforms, it may lead to a reduction in the frame rate (FPS) under certain conditions. This is a common characteristic of synchronous simulation environments, where the rendering of each frame depends on all components completing their updates. Factors such as detailed 3D environments or increased vehicle counts can lengthen the simulation steps, affecting the runtime performance. Therefore, achieving optimal performance requires carefully balancing the background traffic volume, scene complexity, and hardware capabilities to ensure both realism and responsiveness.
## 2 Materials and Methods
### Taxonomy  
The prior work [6] described a taxonomy based on three classifications in the literature: direct vs. indirect, dense vs. sparse, and classic vs. machine learning.  
* **Direct vs. indirect.** Indirect methods refer to those algorithms that implement preprocessing steps, like feature extraction or optical flow estimation, before their pose and map estimation processes; so, the amount of information that moves into the following steps is considerably reduced, requiring less computational power but also reducing the density of the final 3D reconstruction [21]. Indirect methods typically perform their optimization steps by minimizing the reprojection error due to the feature type of information that the preprocessing step outputs [41]. On the other hand, direct methods work directly on the pixel intensity information without requiring preprocessing steps, implying that the algorithm has more information for estimation tasks allowing one to obtain denser reconstructions of the scene, requiring more computational power [41]. In addition, direct methods typically perform their optimization steps based on the photometric error due to the direct pixel availability information.
* **Dense vs. sparse**. Dense vs. sparse classification refers to the amount of information recovered in the final map as a 3D reconstruction [21]. Denser reconstructions have more definition and continuity in the reconstructed objects and surfaces. In contrast, sparser reconstructions are typically represented as largely separated point clouds, where the edges and corners are commonly the only objects that can be recognized clearly [7].
* **Classic vs. machine learning.** Classic methods have been proposed in the last three decades, typically basing their formulation on geometric, optimization, and probabilistic techniques without machine learning. However, in recent years, due to the impressive advances in artificial intelligence, especially in Convolutional Neural Networks (CNN), many techniques have been applied to improve the SLAM or VO estimation tasks [11; 32; 36; 42]. The methods based on classic formulations enhanced with machine learning are called Machine-learning-based approaches (ML).  
Combining these three classifications in all their possible configurations [9] establishes the taxonomy: Classic + Dense + Direct, Classic + Sparse + Direct, Classic + Dense + Indirect, Classic + Hybrid, ML + Dense + Direct, ML + Sparse + Direct, ML + Dense + Indirect, ML + Classic + Sparse + Indirect, and ML + Hybrid. It must be mentioned that the hybrid category was added for those methods that efficiently combine the direct and indirect principles to estimate ego-motion and scene geometry, like SVO [13] and CNN-SVO [11]. Figure 1 depicts the monocular RGB taxonomy for the SLAM, SFM, and VO algorithms.