esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Multi-Agent Particle Swarm Optimization (MAPSO) Approach
### Mapso
(5) Implementation of MAPSO  
Through the introduction to MAPSO, we can see that the combination of MA and PSO provides a more effective method for solving problems. The concrete steps of the MAPSO algorithm are as follows:  
Figure 2: Structure of global environment. * Define the global environment \(a\times b\). * Define the solution space, the maximum number of iterations, the inertia weights \(w\), the acceleration constants \(c_{1}\) and \(c_{2}\). Specify the lower and upper boundaries of all the variables. * Initialize the location and speed of an agent in the solution space. * Calculate the location coordinates of each agent's neighbor according to Equation (15). * Update the time counter. * Calculate the fitness value of each agent respectively based on Equations (2) and (3). At the same time, find the initial individual optimal fitness value and the initial global optimal fitness value. * Perform the neighborhood competition and cooperation operation of each agent on the basis of Equations (16) and (17). Suppose there is an agent \(A_{m,n}\) and \(M_{m,n}\) is the agent with the minimum fitness value among eight neighbors of \(A_{m,n}\). If \(A_{m,n}\) and \(M_{m,n}\) satisfy Equation (16), the position of \(A_{m,n}\) remains unchanged, otherwise changing according to Equation (17). * Update the position and speed of each agent according to Equations (11) and (12). * Calculate the fitness value of each agent and then update the individual optimal fitness value and the global optimal fitness value. * Determine whether the termination conditions are satisfied. If one of the stopping criteria is not satisfied, then go to Step 5. Otherwise, go to Step 5. * Output the agent with the optimal fitness value in the last generation.
###### Abstract  
Semantic segmentation of rural roads presents unique challenges due to the unstructured nature of these environments, including irregular road boundaries, mixed surfaces, and diverse obstacles. In this study, we propose an enhanced PP-LiteSeg model specifically designed for rural road segmentation, incorporating a novel Strip Pooling Simple Pyramid Module (SP-SPPM) and a Bottleneck Unified Attention Fusion Module (B-UAFM). These modules improve the model's ability to capture both global and local features, addressing the complexity of rural roads. To validate the effectiveness of our model, we constructed the Rural Roads Dataset (RRD), which includes a diverse set of rural scenes from different regions and environmental conditions. Experimental results demonstrate that our model significantly outperforms baseline models such as UNet, BiSeNetv1, and BiSeNetv2, achieving higher accuracy in terms of mean intersection over union (MIoU), Kappa coefficient, and Dice coefficient. Our approach enhances segmentation performance in complex rural road environments, providing practical applications for autonomous navigation, infrastructure maintenance, and smart agriculture.  
semantic segmentation; rural roads; bottleneck attention; strip pooling; feature fusion Article
# Haptic Soft-Keyboard for Tablet-Sized Touchscreens
## 3 Materials and Methods
### Perception Study to Estimate the Sensitivity to Time Difference of Tactile Key Click  
Understanding the human perception of key click tactile feedback on touchscreens by piezoelectric actuators is a crucial step to designing the haptic soft keyboard due to the limited tablet power to consistently drive multiple piezoelectric actuators. Therefore, our strategic approach was to drive piezoelectric actuators more efficiently (saving power) while maximizing human perceptibility on touchscreens by utilizing temporal discrimination thresholds of the piezo bending effect. It was learned through a pilot experiment that humans perceive a stronger key click effect when subsequent double bending imperceptibly occurs. This double bending effect of piezoelectric actuators can be achieved by driving with a pulse train that has two peaks with an imperceptible interval, which should be smaller than a temporal discrimination threshold. To estimate the temporal touch discrimination thresholds on the touchscreen of a 10.1 inch tablet device (Samsung Galaxy Tab), a psychophysical experiment was designed and conducted using an adaptive psychophysical method for two conditions, haptics only and haptics combined with audio (key click sound with the same 500 Hz square wave). The combined condition was also tested to see the increased sensitivity by adding audible feedback, which is commonly used for typing on mobile touchscreens.  
Figure 1: Results of bending simulations for a piezoelectric plate [(H) 40 \(\times\) (W) 8 \(\times\) (T) 0.4 mm)] with COMSOL Multiphysics: displacement (\(\upmu\)m) in the z axis from a single layer to three layers (left) and the optimal ratio (Pi/Pe) for a three-layer bender to be bent most (right). A square wave (500 Hz, 20 Vpp) was applied to the piezo plate for simulations.  
Figure 2: Final design (**a**–**c**) of a three-layer piezoelectric actuator based on the results of simulations and (**d**) is a manufactured actuator. Pe and Pi are 0.1 mm and 0.14 mm, respectively. A and B are positive and negative electrodes, and G is the ground, respectively. The final thickness (0.39 mm) was estimated including bonding.
# Generating Process Models by Interacting with Chatbots--A Literature Review
## 5 Reporting of the Systematic Literature Review
### How Are Chatbots Included in the Processes of RQ1-RQ3 (RQ4)?  
Among the 18 studies gathered for analysis, a mere two integrated chatbots into their methodologies (cf. Table 10). Surprisingly, none of these studies effectively harnessed the potential of chatbots for generating process models from natural language text, nor for the conversion of process models into natural language text. Studies S4 [15] and S7 [18] were the only two studies we found with our search string including a chatbot. Study S4, 'Comparing Generative Chatbots Based on Process Requirements: A Case Study' [15], evaluates chatbots to determine their effectiveness in executing and managing predefined business processes. The chatbots are not used for process modeling or generating natural language text from process models itself, but rather for the execution and adherence to business process tasks. Study S4 assesses how well these chatbots can follow BPMN standards, manage task sequences, and handle decision gateways, providing insights into their practical applicability in supporting business process execution and management. Study S7, 'Quantifying Chatbots' Ability to Learn Business Processes' [18], focuses on evaluating the capability of chatbots to learn and execute business processes. Study S7 develops a method to measure this proficiency using process mining metrics. By analyzing a large dataset of customer service interactions, the paper assesses how well chatbots can adhere to process models, execute tasks correctly, and improve over time. The evaluation provides insights into the effectiveness of chatbots in supporting and automating business processes but not for process modeling and generating natural language text from process models.  
\begin{table}
\begin{tabular}{l l l} \hline
**Technologies** & **Studies** & **\# of Studies** \\ \hline NLP & S18 [29] & 1 \\ \hline \end{tabular}
\end{table}
Table 9: Distribution of the studies for generating natural language text input from business process models and backwards (RQ3).
# Research on Rock Strength Test Based on Electro-Hydraulic Servo Point Load Instrument
## 4 Results and Discussion
### Effect of Loading Rate and Rock Sample Size on \(I_{\text{s(50)}}\)
#### 4.3.3 Reasonable I\({}_{\text{s(50)}}\) Evaluation Method  
Figure 10 shows the curves of I\({}_{\text{s(50)}}\) versus \(D_{\varepsilon}^{2}\) for three rock samples solved by different methods at different loading rates. The analysis shows that I\({}_{\text{s(50)}}\), I\({}_{\text{s(50)}}\), and I\({}_{\text{s(50)}}\) show an overall increasing trend with the increase in rock sample size, while I\({}_{\text{s(50)}}\) shows fluctuating changes with the increase in rock sample size and maintains an overall horizontal change trend. It indicates that the I\({}_{\text{s(50)}}\) values solved using methods I, III, and IV still have an obvious size effect, while the I\({}_{\text{s(50)}}\) solved using method II does not have an obvious size effect. Therefore, considering the effect of rock sample size on I\({}_{\text{s(50)}}\) for the different methods, it is more reasonable to use I\({}_{\text{s(50)}}\) solved by method II to predict the uniaxial compressive strength of rocks in the following analysis. It should be noted that even at the same loading rate, the degree of I\({}_{\text{s(50)}}\) for the three rock samples is affected by the differing size of the rock sample, and in practical engineering, specific analysis should be performed for different rock samples.
# Survey of Blockchain-Based Applications for IoT
## 4 Identity and Access Control
### DIDs--Decentralized Identifiers  
Decentralized identifiers (DIDs) are a new type of identifier that enable verifiable, decentralized digital identities without relying on centralized registries, identity providers, or CAs. DIDs empower individuals and entities to create and control their own unique identifiers, enhancing privacy, security, and self-sovereignty in digital interactions. Unlike traditional identity systems, where a third party manages identifiers, DIDs operate in a decentralized manner, allowing users to manage authentication, verification, and secure data exchange without requiring approval from external authorities [31]. The main components of DID architecture are as follows:  
1. DID: A URI with three components: a scheme, method identifier, and unique identifier. They resolve to DID documents.
2. DID URLs: A DID URL extends a DID with paths, queries, and fragments, allowing access to specific resources like cryptographic keys or external data.
3. DID subject: The entity identified by the DID, which can be a person, organization, device, or concept.
4. DID controller: An entity that manages the DID document, typically using cryptographic keys. It can be the same as the DID subject or different.
5. Verifiable data registry: A system that records and resolves DIDs. It can be a blockchain, decentralized file system, peer-to-peer network, or database, ensuring trust and security for DID storage and retrieval.
6. DID documents: These contain data about a DID, including public keys, verification methods, and service endpoints. They enable secure interactions with the DID subject.
7. DID resolvers and DID resolution: A DID resolver takes a DID as an input and returns a DID document. This process is called DID resolution.  
DID URL dereferencers: A DID URL dereferencer converts a DID URL into a specific resource, such as a cryptographic key or service endpoint.  
Figure 5 below shows a sample DID and DID document based on decentralized identifiers (DIDs) v1.0 W3C Recommendation, dated 19 July 2022 [31]. The figure illustrates the relationship between key components involved in the DID ecosystem, highlighting how different elements interact to establish secure and verifiable identities.  
DIDs can replace traditional certificate-based authentication [32]. This approach mitigates risks such as identity spoofing, unauthorized access, and reliance on centralized authorities, ensuring a more secure and scalable IoT authentication framework.  
Hyperledger Indy [33] is a blockchain framework for decentralized identity (DID) management. It offers tools to create self-sovereign identity (SSI) systems, allowing individuals and organizations to manage digital identities without centralized authorities. It is part of The Hyperledger Foundation [15], now under LF Decentralized Trust, the Linux Foundation's main organization promoting innovation in decentralized technologies.
## 4 Parameter Analysis
### Effect of Power Exponent on Bandgap Characteristics
When the power exponent \(m\) is greater than 2.4, this indicates that the change in stiffness and the LR effect jointly affect the change in energy bands. As for the longitudinal BG, it can be viewed from Figure 10. With the growth in the power exponent \(m\), the flatness of the energy bands of the first and second longitudinal BGs also gradually increases, while the energy bands do not move to the lower frequencies. This means that the change in stiffness has no effect on the first and second longitudinal BGs. The LR effect is the dominant factor in the longitudinal BG change. For this reason, the positions of the first and second longitudinal BGs do not change significantly with increasing power exponent \(m\). Therefore, the increase in the power exponent \(m\) can effectively improve the BG characteristics of the AREM, but it does not mean that a larger power exponent \(m\) is better. On the contrary, when the power exponent \(m\) is too large, the smoothing criterion of ABHs will no longer be satisfied and wave scattering may be generated, impairing the ABH effect. Therefore, the LR effect may also be weakened, and the LR BG may also be weakened [6,29]. Figure 10: Dispersion relation graph of AREM with different power exponent \(m\). The green lines indicate the curved dispersion relation curves, and the blue lines indicate the longitudinal dispersion relation curves. Figure 9: Effect of power exponent \(m\) on the BG characteristics between the AREM energy bands: (a) flexural BG; (b) longitudinal BG.
# Multiscale Content-Independent Feature Fusion Network for Source Camera Identification
## 3 Experiment and Evaluation  
### Dataset  
All experiments in this paper are based on the Dresden Image Database [42], which is the most commonly used database in the field of image source forensics and has the most complete types of cameras. Under controlled conditions, more than 14,000 indoor and outdoor scene images were collected from 73 digital cameras covering different camera settings, environments and specific scenes. It is helpful to strictly analyze the characteristics of manufacturers, models, and equipment and their relationship with other influencing factors.
# A Searchable Encryption with Forward/Backward Security and Constant Storage
## 4 Our Proposed Algorithm
### Key Generation (KeyGen)  
PKG calculates the public/private key for the user with the identity \(ID_{i}\).  
The system calculates \(h_{i_{1}}=H_{1}(ID_{i})\), \(h_{i_{2}}=H_{2}(ID_{i})\), since any integer greater than \(pzq-p-zq\) (Frobenius number) can be expressed as a linear combination of \(p\), \(zq\). Therefore, it can also be expressed as: \(h_{i_{1}}=y_{i_{1}}p+k_{i_{1}}zq;h_{i_{2}}=y_{i_{2}}p+k_{i_{2}}zq\). The inverse solution yields the public key \(pk_{i}=(y_{i_{1}},y_{i_{2}})\):  
\[y_{i_{1}}=p^{-1}h_{i_{1}}\text{mod}zq=p^{-1}(y_{i_{1}}p+k_{i_{1}}zq)\text{mod} zq;=y_{i_{1}}pp^{-1}\text{mod}zq\]  
\[y_{i_{2}}=p^{-1}h_{i_{2}}\text{mod}zq=p^{-1}(y_{i_{2}}p+k_{i_{2}}zq)\text{mod} zq=y_{i_{2}}pp^{-1}\text{mod}zq\]  
PKG generates the private key \(sk_{i}\) of user \(ID_{i}\):  
\[sk_{i}=(y_{i_{1}}s_{1}+y_{i_{2}}s_{2})^{-1}\text{mod}zq\]
## 3 Methods  
### Fuzzy Weights-of-Evidence (FWoffE)  
[PERSON] and [PERSON] (1999) improved upon the conventional weights-of-evidence model by introducing the FWoffE model, which is a logarithmic, linear model under the Bayesian probability framework ([PERSON] and [PERSON], 2016). It updates prior probabilities by calculating posterior probabilities under certain conditions, enabling the superposition and analysis of predictor variables for a target variable to generate posterior probability distribution maps. Compared to the traditional weights-of-evidence model, the FWoffE model addresses the loss of information caused by discretized layers, effectively characterizing the spatial relationships between evidence layers and targets such as mineral deposits or crops. This results in improved prediction accuracy ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016). In recent years, the FWoffE model has been widely applied in fields such as mineral prospecting and crop planting planning, achieving promising results (e.g., [PERSON] et al., 2016; [PERSON] et al., 2023; [PERSON] et al., 2023) ([PERSON] and [PERSON], 2016; [PERSON] et al., 2023).  
The FWoffE model defines evidence layers as fuzzy sets through a multivalued fuzzy membership function (MSF) and quantifies their fuzziness by calculating membership degrees. This approach enables the processing of multi-class evidence layers. Using MSF to fit training samples, the evidence weights are computed. The computational workflow of the FWoffE model involves the following steps: preliminary screening of evidence layers, construction of the FWoffE model, calculation of weights for evidence factors, analysis of evidence factor weights and contrasts to determine which evidence layers to retain, plotting the ratio of evidence factor contrast C to its standard deviation S(C), calculation of multi-value membership functions for evidence factors, derivation of fuzzy weights and posterior probabilities for evidence factors ([PERSON] and [PERSON], 2016; [PERSON] and [PERSON], 2016).
## 1 Introduction  
More and more contemporary companies are shifting their business paradigm from design and manufacture of artefacts to the provision of through-life support, leading to a knowledge intensive product, covering a large area, not only engineering issues, but human and business related, spanning the whole product life cycle [1,2]. In addition to this, the global economy has increased the amount of collaboration that happens within modern engineering design practices. With designers located in disparate locations, it is becoming particularly important (and more difficult) to capture, exchange and reuse the information and experiences generated during these design activities.  
Engineering design is the use of scientific principles, technical information and imagination in the definition of a mechanical structure, machine or system to perform pre-specified functions with the maximum economy and efficiency [3]. Generally, the design process can be regarded as a series of activities, which aim to create a desired artefact. Included in this process are: decision-making, problem-solving, rationale related to decisions, and various information resources (such as catalogues, design/factory guidelines, and properties databases). During the last decades, there has been a lot of work carried out to support engineering design in both the academic and commercial communities. Recent examples have been a number of Computer Aided Design (CAD) systems [e.g. 4-6], various design process models [e.g. 7-9] and rationale tools [e.g. 10,11], and several techniques for Computer Supported Cooperative Work (CSCW) [e.g. 12,13]. However, in today's industry, such traditional supporting tools are still insufficient due to various limitations, e.g. lack of engineering context and semantics in CAD models, extra manual work needed, _etc._  
Recently, with the rapid development and adoption of digital technologies, annotation and markup are attracting more and more attentions for information communication, retrieval and management. Correspondingly, some critical research work has already been conducted on supporting and capturing engineering design process. This paper presents a state-of-the-art review of recent research in markup for engineering design aiming to provide a complete view of markup on requirements, technique status, applications and future directions. The subsequent content is organized as follows. Section 2 introduces the definition and classification of annotation and markup. Section 3 and section 4 review the current core markup languages and markup strategies, respectively. Their applications and future utilization in engineering design, including engineering document management, support of multi-viewpoint, product lifecycle information support, design communications in collaborative environment, and integration of engineering design processes, are comprehensively discussed in section 5. Finally, conclusions are drawn in section 6 and future research directions are suggested based on the discussion in section 7.
# An AC/DC LED Driver with Unity Power Factor and Soft Switching
## 2 Proposed Circuit Topology and Operation Analysis
### Operation Analysis
#### 2.2.4 Mode IV (\(t_{3}<t<t_{4}\); in Figure 3d)  
The current \(i_{p}\) reach a peak value of each high-frequency cycle at the instant of turning off \(S_{2}\). The current \(i_{p}\) will be diverted from \(S_{2}\) to discharge the parasitic capacitance (\(C_{DS1}\)) between the drain and source of \(S_{1}\). As soon as the voltage across \(C_{DS1}\) decreases to \(-0.7\) V, \(D_{S1}\) is forward biased to conduct \(i_{p}\). The gated voltage \(v_{GS1}\) becomes high level after the short deadtime and \(i_{p}\) diverts from \(D_{S1}\) to flow through \(S_{1}\) from its source to drain. By this way, the voltage across \(S_{1}\) is clamped at near zero voltage. Now, the current loop of \(i_{p}\) is \(L_{p}\)-\(S_{1}\)-\(C_{dc}\)-\(D_{5}\) and the DC-link capacitance \(C_{dc}\) is charged. The voltage across \(L_{p}\) is equal to \(-V_{dc}\) and hence, \(i_{p}\) decrease linearly.  
\[v_{p}(t) = -V_{dc} \tag{5}\] \[i_{p}(t) = i_{p}(t_{3})-\frac{V_{dc}}{L_{p}}(t-t_{3}) \tag{6}\]  
On the other hand, the current \(i_{b}\) starts to increase from zero with the current loop: \(L_{p}\)-\(D_{6}\)-\(L_{b}\)-\(C_{o}\)-\(v_{rec}\). The voltage and current equations for the buck inductor can be expressed as:  
\[v_{b} = v_{rec}(t)+V_{dc}-V_{o},\]\[i_{b}(t)=\frac{V_{m}|sin(2\pi f_{L}t)|+V_{dc}-V_{o}}{L_{b}}(t-t_{3}). \tag{8}\]  
In this mode, \(i_{p}\) is higher than \(i_{b}\). There are two current loops for \(i_{p}\). Parts of \(i_{p}\) flow through \(S_{1}\) to charge \(C_{dc}\), while its residual is equal to \(i_{b}\) and flows into the buck converter. This mode ends when \(i_{b}\) becomes higher than \(i_{p}\).  
Figure 3: Equivalent circuit of operation modes: (**a**) Modes I–(**f**) Modes VI.
## 3 Results
Figure 3: Scanning electron microscope (SEM) images of 2-chlorotriyl chloride (CTC) resin: (**A**) diethyl ether (DEE) (dimples); (**B**) tert-butyl methyl ether (MTBE) (fracture); (**C**) 2-methyltetrahydroofuran (2-MeTHF) (denoted tracks); and, (**D**) cyclopentyl methyl ether (CPME) (intact). Osmotic shock results when polymer chains gather as a result of restoring their intermolecular stickiness [12], which appears as cracking and sometimes fracturing of the resin's beads [9; 37]. As a result of a gap in the solubility parameters of DEE and/or MTBE with that of CTC resin, severe morphological damage was observed when introducing any of the ethers in the final precipitation step. In the case of DEE, clear dimples were observed in the resin's beads (Figure 3A). Whereas, a fracture of the beads was observed in the case of MTBE (Figure 3B). 2-MeTHF, which has a close solubility parameter to that of CTC resin, was not expected to affect the morphological structure of the CTC resin, just like CPME; however, unexpectedly it did affect the morphological structure of CTC resin, but to a lower extent than DEE and MTBE. Thus, the resin did not experience dimples or fractures in its beads; in contrast, only dented tracks were observed, which is considered to be a mild effect (Figure 3C). However, this unexpected behavior of 2-MeTHF was explained by a computational study (see the corresponding section). Contrary to the previously mentioned three ethers, CPME has proven to be an optimum ether and compatible with the PS resins for the precipitation of peptides after the final global deprotection step and for washings/dryings during the synthesis. Thus, no osmotic shock was observed, and the morphological structure of the CTC resin remained intact after the treatments (Figure 3D). After treatment with CPME, the cleaved CTC resin was regenerated using the activation mixture (thionyl chloride- CH\({}_{2}\)Cl\({}_{2}\), 1:1), and the SEM images also proved that the morphological structure of its beads remained intact (Supplemental Figure S2D), which means that the resin is now ready for new syntheses to be carried out.