esa-sceva/satcom-chunk-collection huggingface dataset:
# Evaluating the Efficiency of Physical and Cryptographic Security Solutions for Quantum Immune IoT
## 6 Results  
In this section, the results are presented. First, we describe the computational cost results from the simulator. Then, we complement the simulated results of computational costs with an analysis that considers the costs of radio transmissions.
## 5 Discussion  
### Advantages of the MM-G2 SFCA Method and the Standardized Gravity Model  
In this paper, building on the MM-2 SFCA method, the MM-G2 SFCA method is proposed in combination with the gravity model. Meanwhile, the standardized gravity model, derived from the traditional gravity model, is proposed to overcome the shortcomings of the gravity model when applied to the 2 SFCA method.  
(1) In the MM-2 SFCA method, the supply-demand ratio and accessibility intensity are independent of the distance between the point and the destination within each travel mode catchment area, while the MM-G2 SFCA method, which introduces the gravity model as a distance decay function, solves this problem by ensuring that the supply-demand ratio and accessibility intensity decrease with the increase in distance between the point and the destination. This approach is theoretically closer to the actual situation and more in line with the First Law of Geography. (2) Example experiments show that the MM-G2 SFCA method leads to a decrease in the overall accessibility level due to the increased constraint of distance cost on accessibility. However, while the accessibility level around hotspots decreases, the accessibility level of remote areas increases relatively, which demonstrates that the MM-G2 SFCA method helps to correct the influence of local neighborhoods and makes the accessibility distribution more reasonable. (3) The traditional gravity model  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**District** & **Gini Coefficient** & **District** & **Gini Coefficient** \\ \hline Yantian & 0.277 & Bao’an & 0.307 \\ Nanshan & 0.207 & Dapeng & 0.308 \\ Luohu & 0.198 & Pingshan & 0.200 \\ Longhua & 0.229 & Futian & 0.171 \\ Longgang & 0.235 & Guangming & 0.156 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Gini coefficient of accessibility of public hospital in each district.  
Figure 14: Lorenz curve of public hospital in Shenzhen.  
faces the issue that when the distance is close to 0, the decay function value tends to infinity. The standardized gravity model regulates the upper and lower limits of the distance decay function value range, ensuring the distance decay characteristics and addressing the problem of extremely large value points. Consequently, the results of the accessibility analysis are more reasonable and aesthetically pleasing.
# Systematic Review: Malware Detection and Classification in Cybersecurity
## 2 Methodology
### Limitations of Current Approaches and Opportunities  
Despite significant progress in malware detection and classification techniques, current approaches present significant limitations that must be addressed to strengthen their applicability in real-world, dynamic scenarios. First, many models rely on specific datasets widely used, do not always reflect the diversity and complexity of threats present in production environments [44]. This poses a challenge in terms of generalization capabilities, especially for malware variants that employ evasion techniques such as polymorphism or metamorphism.  
Furthermore, while models based on deep learning and hybrid techniques have demonstrated high levels of accuracy, their practical implementation faces barriers related to computational consumption, model explainability, and vulnerability to adversarial attacks. In particular, techniques such as adversarial machine learning reveal that many current classifiers can be fooled by small perturbations in the inputs, compromising the system's robustness [45].  
Another critical aspect is the lack of standardization in evaluation metrics and the limited use of cross-validation, which makes objective comparisons across studies difficult [39]. Furthermore, there is little discussion about the resilience of models to noisy, unbalanced, or heterogeneous data sources, such as IoT devices or distributed industrial environments.  
Given these limitations, several opportunities for further investigation are identified, such as the development of standardized and replicable evaluation frameworks to enable consistent benchmarking; the integration of explainable artificial intelligence to enhance transparency and support forensic decision-making; the exploration of lightweight and decentralized architectures, to facilitate scalable and real-time detection in distributed environments and the generation of updated datasets that reflect the complexity of contemporary malware behaviors across diverse platforms. Advancing these lines of research  
Figure 6: Frequency of ML/DL, Algorithms.  
will contribute to the development of more effective, adaptable, and sustainable detection systems in the face of emerging cyber threats.
## 2 Overview of Research Area
### TRHR Climate Change  
Because the average altitude of the TRHR is greater than 4500 m, this area is characterized by a cold and dry climate with an annual mean temperature of \(-\)3.03 \({}^{\circ}\)C. Temperature varies spatially between \(-\)13.34 and 4.98 \({}^{\circ}\)C, decreasing along a gradient from east-to-west, while annual precipitation is 428 mm, varying spatially between 144.16 and 728.51 mm and decreasing along a gradient from southeast-to-northwest. Between 2000 and 2012, the climatic conditions of the TRHR were characterized by a trend of warming and wetting, as revealed by time series data. Over the course of our study period, both precipitation and temperature increased annually by averages of 11.32 mm year\({}^{-1}\) (\(p<0.1\)) and 0.059 \({}^{\circ}\)C year\({}^{-1}\) (\(p<0.1\)), respectively (Figure 2). Data show that overall slope of annual mean temperature diminished from the southwest to the northeast, while in different basins and counties, the Lancang River basin, including Nangqian, Yushu, Zaduo and Zhiduo counties, experienced the largest annual temperature increase within the TRHR, followed by the Yangtze and Yellow river basins. The spatial distribution of wetting trends is characterized by a decrease from east-to-west, while the linear trend line slope of annual precipitation is 12.38 mm year\({}^{-1}\) in the Yellow River basin, including Zeku, Henan and Chengduo counties, 10.23 mm year\({}^{-1}\) in the Yangtze River basin and 8.72 mm year\({}^{-1}\) in the Lancang River basin.  
Figure 1: Map to show the geographic location of the Three-Rivers Headwaters region (TRHR). This natural reserve contains three regions encompassing core, buffer and trial areas; of these, the first is strictly managed with no grazing allowed and the implementation of measures to protect endangered species. Thus, all development and land use within the core zone are prohibited; this is a “no man zone”, and all former residents have been ressettled elsewhere. Conservation is promoted in the buffer area alongside limited and rotational grazing, while the trial area is used for scientific investigations, eco-tourism and other green industries.
## 3 Results and Discussion
#### VCCT-Based Fatigue Crack Growth
Furthermore, comparing the effects of the stress ratio in the predicted fatigue crack propagation of both sheets of steel. The VCCT can explain this increase in fatigue crack propagation rate using the similarity concept. Overall, these results indicate that the adaptive remeshing strategy with VCCT in MSC Marc/Mentat is capable of the conservative prediction of fatigue crack propagation.
# Effects of Sinusoidal Model on Non-Parallel Voice Conversion with Adversarial Learning
## 4 Modified Generative Adversarial Networks  
Our proposed algorithm is adopted from the StarGAN approach [33], which was proposed for multi-domain image-to-image translation. It differs from the method in [25] by introducing constructive changes in terms of training architecture to be able to adapt the proposed sinusoidal framework. Our aim is to use a single generator \(G\) that can learn mappings between a group of speakers. To achieve this, we train \(G\) to convert the attribute of source \(x\in R^{Q\times T_{x}}\) speaker domain into target \(y\in R^{Q\times T_{y}}\) speaker domain conditioned on the target domain label \(c\in\{1,\dots,N\}\) to generate a new acoustic feature sequence \(\hat{y}=G(x,\;c)\), where \(N\) is the number of domains, \(Q\) is the feature dimension, and \(T\) is the sequence length. An auxiliary classifier is also introduced as with [34] which allows a real/fake discriminator \(D\) to learn the best decision boundary between the converted and real acoustic features. Hence, our \(D\) produces a probability \(D(\hat{y},\;c^{\prime})\) over both sources and domain labels that \(c^{\prime}\) (of the input source speech) designed to produce class probabilities \(p(c^{\prime}|y)\). Figure 4 displays the training process of the suggested approach.  
Figure 4: Overview of the developed VC based on the sinusoidal model, consisting of two training modules: a discriminator D and a generator G. D learns to distinguish between real and fake data and classify the real data into its corresponding domain. G tries to generate data indistinguishable from real data and classifiable as a target domain by D.  
Converted speech can also be clipped (which inevitably changes the spectrum of speech signals), depending on the input gain. This will partially distort the speaker information contained in the signal. To stabilize the training procedure, we applied three preservation losses (adversarial, classification, and reconstruction) in the objective function to alleviate the issues of the over-smoothing caused by statistical averaging. The smaller these losses are, the closer the converted data distribution is to a normal speech distribution.
# Energy and Distance Based Multi-Objective Red Fox Optimization Algorithm in Wireless Sensor Network
## 6 Results and Discussion
### Performance of End-to-End Delay  
ED-MORFO and existing methodologies were used to compare the performance of the lifetime of a network. Figure 7 depicts the existing MCH-EOR [24] and RDSAOA-EECP [25] assessments. Figure 7 illustrates that, when compared to existing approaches, ED-MORFO produces better outcomes. In contrast to the existing MCH-EOR [24] and RDSAOA-EECP [25], there is a decrease in the end-to-end delay of ED-MORFO. Figure 7 clearly illustrates that ED-MORFO achieves the least delay, and continues to try to transmit the data packet to the best intermediary node, satisfying the optimality feature. On the contrary, the traditional MCH-EOR [24] and RDSAOA-EECP [25] methods take a long time to determine the end point node. Furthermore, it initiates the prolonged return after the end point node. As a result, the related system's typical end-to-end delay is longer than the ED-MORFO approach. Table 6 tabulates the comparative analysis of end-to-end delay performance. Table 6 shows that the proposed ED-MORFO achieves a time delay of 11 s when compared with the existing techniques of MCH-EOR and RDSAOA-EECP, which attain 15 s and 14 s, respectively.
## 3 Results and Discussion
### Thermal Cycle during the AFSD Process
The AFSD thermal cycles of the DPs were plotted against the deposition time at the recorded downward force for all the applied deposition rates (1-4 mm/min) and a constant 400 rpm rotation speed, as given in Figure 5:6 Starting from the earliest contact between the bottom surface of the rotating rod and the upper surface of the substrate, along with the continuously increasing deposition rate, heat is generated due to the exerted downward force, and the temperature within the materials increases. The generated temperature is found to be adequate to cause the initial softening of the rubbed materials. It is found to reach its peak value at the onset of the first layer deposition of the AA7075 on the substrate (i.e., rod material plasticization stage as shown in Figure 7), where the plasticization temperatures were 339, 375, 386 and 407 \({}^{\circ}\)C at deposition rates of 1, 2, 3 and 4, respectively. With deposition continuation, a temperature plateau is established at the peak temperature that is sufficient to deposit more layers of the rod material, creating the third stage. At the end of the AFSD process, the temperature of the DP is gradually decreased to room temperature during the air-cooling stage. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Deposition** & **Rotation** & **Axial Force,** & **Heat Input,** & **T\({}_{1}\):** & **T\({}_{2}\): Deposited** \\
**Rate** & **Speed,** & **Kg** & **Wait** & **Substrate** & **Layer** \\
**(mm/min)** & **RPM** & **Kg** & **T\({}_{2}\)** & **T\({}_{3}\)** & **T\({}_{2}\)** \\ \hline
1 & & 976 & 7173.60 & 330 & 339 \\
2 & & 1020 & 7497.00 & 352 & 375 \\
3 & & 1144 & 8408.40 & 375 & 386 \\
4 & & 1225 & 9003.75 & 395 & 407 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The axial force, heat input, and temperature during the FSD of AA7075-T6. The plasticizing temperature increases with increase in the deposition rate based on FSW principles [46; 47]. At a higher deposition rate, the compressive force of the rotating rod on the substrate and the generation of more frictional heat increase, so that the deposition temperature increases. The highest deposition temperature of 407 \(\pm\) 4 \({}^{\circ}\)C was generated at a deposition feed rate of 4 m/min; meanwhile, the lower deposition feed rate of 1 mm/min generated the lowest temperature of 339 \(\pm\) 5 \({}^{\circ}\)C. It was observed that, at the constant rotation speed (400 rpm), the axial force, heat input, and bulk and substrate temperatures increased with increasing deposition feed rates. Moreover, the rise in temperature of the substrate was less than that of the DP because of the larger heat sink.
## 6 Discussion
### The Role of the YAPS Manager in Ensuring Reliable Data Exchange  
A key enabler of the YAPS Parameter Server's success is the dedicated role of the YAPS Manager. This role is vital for establishing consistent and reliable data, which, in turn, supports early design optimization by relieving planners of manual data management burdens. The YAPS Manager is responsible for the following:  
* Creating and Curating Libraries.
* Developing Default and Threshold Libraries: default libraries provide pre-validated, ready-to-use data for early design phases, while threshold libraries define critical performance benchmarks (e.g., energy efficiency limits) used for simulation verification and certification.
* Ensuring Data Consistency and Reliability: by overseeing the creation and maintenance of these libraries, the YAPS Manager ensures that data are consistent, accurate, and up-to-date. This reliability is crucial for seamless information transfer from the YAPS server through BIM to BEM simulation tools.
* Facilitating Collaboration and Standardization: public libraries, ideally created and maintained by reputable software companies or industry committees, set standardized baselines that can be adopted across multiple projects. This not only increases trust in the data exchange process but also streamlines interoperability among different tools and stakeholders.  
In summary, the YAPS Manager is the cornerstone that guarantees the success of the overall BIM-to-BEM integration process. Their expertise and oversight transform raw parameter data into a structured, reliable asset that supports efficient early design optimization and reduces the repetitive workload for planners. The establishment of public libraries further amplifies these benefits by providing standardized, validated parameter sets that enhance collaboration and promote industry-wide best practices.
# Calculation Method of Rotational Constraint Stiffness for a New Tower-Pier Connected System
## 1 Introduction
The previous studies demonstrate that the boundary conditions have a great impact on the structure performance [25; 26]. Therefore, UHS cannot be considered as an ideal hinged boundary condition in structural calculations, and the real boundary parameters of UHS need to be known. The friction behaviors in engineering applications are mostly calculated through a numerical method [27; 28; 29]. The structural response can be accurately obtained by establishing a multi-scale finite element (FE) model [30; 31; 32; 33; 34; 35]. [PERSON] et al. also tried to use artificial neural networks to identify structural boundary conditions [36]. In general, the construction parameters are determined by repeatedly updating the relevant parameters according to the structural state of the construction process, which generally need so much time for each iteration of the multi-scale modeling calculation method, that it is unacceptable for large-scale bridge building construction. In addition, the method using artificial neural networks could not directly obtain the stiffness parameters of UHS. To solve these problems, this paper proposed a calculation model to quickly and accurately determine the rotational constraint stiffness (RCS) of UHS to optimize the structural calculation procedure, so that engineers could easily obtain the true response of the structure of UHS using the model. The proposed calculation model uses the finite element method and theoretical analysis to study the boundary parameters of UHS. At first, a theoretical mechanical model of the plane rotation of UHS was established to compute the critical torque. Secondly, the refined FE model of the UHS was built and its effectiveness was verified by the above theoretical model, and we further established the RCS calculation model of UHS by analyzing the influence of vertical force, the friction coefficient between the components, and the geometry parameters on RCS. This study can provide reference for the design and construction of practical projects using UHS, and shows promise for the application of special-shaped bridge structures.
# Detection and Analysis of Ethereum Energy Smart Contracts
## 2 Background
### Energy Smart Contract  
In recent years, much attention has been given to blockchain platforms supporting smart contracts for the development of decentralized applications. Smart contracts have been designed and deployed in a broad array of applications, from fund management to the power grid. Given that smart contracts are deployed on a blockchain that was primarily intended to store financial transactions, the most instinctive applications of smart contracts involve trading assets between contracting parties [18]. Coupled with increasing progression toward a more decentralized and dynamic energy system, the viability of blockchain-enabled smart contracts in transactive energy systems has become prominent. Energy smart contracts are employed for automatic execution and monitoring of energy trading events [19; 20]. An energy smart contract is implemented with instructions to retrieve the capacity and price offered by generators. The offer is then designed to the prospective buyers for the bidding process to begin. A double auction is a commonly used mechanism to settle the price. Once the price has been cleared, the grid power flow is examined to ensure the feasibility of the allocations.  
According to [PERSON] et al. [19], the information is transmitted through six different layers in the energy smart contract workflow, as illustrated in Figure 1. The first layer involves the transmission of input data in the form of bids, offers, voltage levels, or availability signals from any agent involved in the peer-to-peer transaction for the demand side to be automatically triggered.  
The second layer implements innovative optimization and energy management algorithms to address potential inconsistencies within contracted and delivered energy. Any computationally intensive control algorithms should be placed off smart contracts to minimize extra computational expenses imposed by implementing them in this layer. The smart contract is coded in the third layer using solidity. This allows the preceding layers to incorporate different programming languages. This layer handles all financial transactions, including gas expenditure and agent registration. Layer 4 controls the verification and encryption requirements for the smart contract's placement into the block. Layer 5 is where implementation and execution occur, which require interaction with virtual machines, namely the Etheruem Virtual Machine (EVM). Subsequently, the data are transmitted across communication protocols using wired or wireless communication means.
## 1 Introduction
### Related Work
While these models have shown promise, their effectiveness in terms of computational efficiency, robustness, and extraction accuracy remains an open area of research. These issues become even more significant in FSO communication systems, which are highly susceptible to harsh environmental conditions like heavy fog and dry snow. In such dynamic and challenging scenarios, ensuring reliable and adaptive performance monitoring is vital to maintaining link stability and data integrity.
# Earthquake Damage Index and Fragility Analysis of Steel Damper for Seismic Isolation Bridge
## 2 Steel Damper Experiment  
### Specimen Parameters  
This study chooses cylindrical and E-shaped steel dampers as the research subjects. These two types of steel dampers exhibit clear mechanical behavior, simple construction, and stable performance. They are less influenced by factors such as processing accuracy and installation errors. Specifically, cylindrical dampers allow for bidirectional vibration control and are widely used in seismic isolation bridges, making them highly representative choices for investigation. The common cylindrical steel damper device is shown in Figure 1a. In practical engineering applications, the bottom plate and upper plate are connected to the pier and girder, respectively. When a relative displacement occurs between the pier and the girder, the upper plate induces the movement of the spherical head, causing deformation in the cylindrical damper and generating horizontal damping force. The typical E-shaped steel damper device is shown in Figure 1b. The upper connecting plate is connected to the middle limb, while the lower connecting plate is connected to the two side limbs. The upper and lower connecting plates are installed on the pier and girder, respectively. When a relative displacement occurs between the girder and the pier, the upper connecting plate drives the movement of the middle limb, causing deformation in the E-shaped damper and generating horizontal damping force.  
The cylindrical and E-type steel dampers were designed with the same mechanical parameters. The yield stress of the steel damper is 345 Mpa, the design yield force is 150 KN, and the design displacement is \(\pm\)300 mm. Figure 2 shows the steel damper dimensions. The number of each type of damper is two. The dimensions of Cylindrical Specimens #1 & #2 and E-shaped specimens #1 & #2 are identical.
## 2 Materials and Methods
### Data Processing and Analysis
#### 2.4.3 Comparison of Video Recording and Sensor Detection  
The data analysis also included a comparison of video recordings with the 15 s sensor data to evaluate sensor responses to pig behaviors within the 5 min observational windows, most of which were not captured by the visual observation data. The video files, saved in \"264\" format files with Zviewer, were reviewed using Zplayer. Video clips featuring transitions in sow behaviors (e.g., from lying to standing), totaling 240 min, were selected. The 15 s sensor data corresponding to the times and pens featured in these selected video clips were visualized through time-series graphs. This approach enabled a synchronized review of pig activities in the video recordings against the sensor outputs in the graphs, providing a comprehensive evaluation of the observed behaviors.