esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Network Overhead  
In this section, the network overhead is evaluated of the proposed DR-UAVIoT protocol with existing AODV and GyTAR protocols in the presence of different numbers of IoT devices. In Figure 10, it is observed that the proposed DR-UAVIoT had less overhead in the number of rounds versus the number of IoT devices. The performance of the proposed DR-UAVIoT was better due to its RREQ and RREP packet strategies. The other existing protocols including AODV and GyTAR also suffered from overhead issues in the network because the AODV is designed for MANET networks whereas the GyTAR is designed for vehicular networks and is not suitable for IoT devices. It is also observed that the overhead gradually increased with the number of IoT devices. The GyTAR protocol is based on control messages which are broadcasted in the network and cause more overhead in the network whereas the AODV protocol generates more overhead due to its static nodes' handling strategy. Figure 10 shows the overhead analysis of the proposed protocol in the presence of different numbers of IoT devices.  
Figure 10: Overhead Analysis in the Presence of Different Numbers of IoT Devices.  
Figure 9: End-to-End Delay in the Presence of Different UAVs Velocities.  
In the second result, the network overhead is evaluated of the proposed DR-UAVIoT protocol with existing AODV and GyTAR protocols in the presence of UAVs' velocities. This experiment is evaluated among UAV nodes when they transmit and receive data packets from each other. In Figure 11, it is observed that the proposed DR-UAVIoT had less overhead in the number of rounds versus UAVs' speed or velocity. The performance of the proposed DR-UAVIoT was better due to its RREQ and RREP packet strategies. The other existing protocols including AODV and GyTAR also suffered from overhead issues in the network because the AODV was designed for MANET networks whereas the GyTAR was designed for vehicular networks and is not suitable for IoT devices. It is also observed that the overhead gradually increased with the number of IoT devices. The GyTAR protocol is based on control messages which are broadcast in the network and cause more overhead in the network whereas the AODV protocol generates more overhead due to its static nodes' handling strategy. Figure 11 shows the overhead analysis of the proposed protocol in the presence of different UAVs' velocities or mobility.  
The proposed protocol is designed to reduce the overhead and delay issues in the network although the complexity of the proposed protocol is observed in large areas such as highway roads or covered areas such as tunnels or underground networks. This limitation is due to UAVs' node coverage issues and the unavailability of communication range due to obstacles.
# Data Fusion Based on Adaptive Interacting Multiple Model for GPS/INS Integrated Navigation System
## 3 System Architecture
### Interacting Multiple Model Filter Structure
#### 3.1.3 Combination  
Now, the prediction for the next step would be gotten according to the updated probability of each filter. Under the hybrid formula, the final integrated state estimates \(x_{m}\) and its covariance \(P_{m}\) can be computed as  
\[x_{m}=\sum_{i=1}^{r}\mu_{m}^{i}x_{m}^{i} \tag{17}\]  
\[P_{m}=\sum_{i=1}^{r}\mu_{m}^{i}\bigg{[}P_{m}^{+,j}+\left(x_{m}^{+,j}-x_{m} \right)\left(x_{m}^{+,j}-x_{m}\right)^{T}\bigg{]} \tag{18}\]
# Assessing the Value of Information in an Augmented Reality City Experience
## 2 Background  
### Value of Information: Consumer vs. Producer Perspective  
The concept of information value has transcended traditional consumer-centric perspectives. Researchers [7; 8; 9] assert that information is an experience good, with its value unfolding upon utilization. Recent research has expanded this notion by emphasizing the experiential dimension of producing digital information [10; 11]. For example, researchers have underscored the importance of exploring the impact of information production on value perception [1]. They revealed that the perception of information value is dynamic. After experiencing information, value perception tends to increase, mainly when individuals engage in producing information rather than solely consuming it. This dynamic nature of information value perception presents an intriguing area for investigation, particularly in the context of emerging technologies like AR.  
From an economic valuation perspective, the subjective perception of information creates a gap between the amount consumers are willing to pay (WTP) for a product and the payment sellers are willing to accept (WTA) as compensation for giving up an identical product. This phenomenon is also relevant in information contexts, where consumers tend to assign higher value to information in their possession than the same information owned by others [2]. This study examines the WTP and WTA of AR to understand how innovative information delivery is valued by both consumers and producers. Building on the existing literature, we propose that the perceived value of information (as measured by WTA and WTP) during an AR location-based experience will be higher for information producers than for information consumers (Figure 1; H1). This hypothesis is grounded in the idea that producing information in an AR environment may create a stronger sense of ownership and investment, leading to higher valuations. The findings may offer new perspectives on the interplay between technological innovations and information valuation in real-world contexts, potentially revealing how AR reshapes traditional economic valuation principles in the domain of information goods.
# Research and Development of a Large-Scale Axial-Flux Generator for Hydrokinetic Power System
## 1 Introduction
Water wheels are actually the focus of various research studies [19; 20; 21; 22; 23], including large-scale designs such as the presented one [24]. However, the specifics of large-scale electricity generators for such applications are not widely examined. The application of this solution  
Figure 1: Floating hydropower plant with water wheel: (**a**) general concept; (**b**) assembly of gearbox and generator. to a river stream requires certain parameters of the river stream, such as a water-flow velocity minimum of 2 m/s. Such conditions exist in rivers with average to high discharge volumes, some of them in areas with dominant tropical monsoon climates. The water-flow velocity has a nonlinear effect on the return on investment in this type of system, and artificial parallel channels could also be searched for to improve this parameter. Another option is to use tidal conditions to increase efficiency. Additional studies, mostly related to large-scale water-wheel concept development, would involve detailed feasibility analyses. The concept of the developed electricity generator is of the axial-flux permanent magnet (AFPM) type. Axial-flux permanent magnet generators are always less widely known than radial flux generators because of their drawbacks, which include manufacturing issues and difficulties maintaining air-gap uniformity [25]. However, an axial flux-type machine applies to the low-speed, high-power operation of a direct drive energy system [26]--such as wind or run-of-river systems [27]. The research and development of the current AFPM generator is based on the virtual prototyping of electromagnetic processes, as it is already a common practice in product development [28; 29; 30; 31]. Virtual prototyping offers the significant advantage of being able to explore certain products without the necessity of building their physical replication. This is very important in the case of heavy industrial product development as large-scale electricity generators. This type of prototype (or digital mock-up) allows for performing variations of different parameters in a search for better performance and even optimization [32; 33; 34; 35]. The evaluation of these conceptual design variants is achieved using electromagnetic simulations via the application of numerical analysis techniques.
## 3 Theoretical Approaches
### Correlation Analysis  
The data obtained allowed the use of correlation analysis. The gas permeability coefficient estimation of the PMPentene crystalline phase was performed using a correlation approach [12; 20] and available experimental data. Thus, the correlation of the diffusion coefficients (\(D\)) with the molecular cross-section is known for the noble gases and several polyatomic gases and is described by Equation (7) as follows:  
\[logD_{i}=K_{1}-K_{2}d_{efi}^{2} \tag{7}\]  
The values of the gas solubility coefficient (\(S\)) increase exponentially with the Lennard-Jones constant Equation (8) as follows:  
\[logS_{i}=K_{3}+K_{4}(^{z_{i}/k}) \tag{8}\]  
The general expression for estimating the gas permeability coefficient is described by the following Equation (9):  
\[logP_{i}=logD_{i}+logS_{i}=K_{1}-K_{2}d_{efi}^{2}+K_{3}+K_{4}(^{z_{i}/k}) \tag{9}\]  
These Equations (7)-(9) can be used in any combination. The gas permeability parameters were calculated for 3D surface, which includes \(\geq\)3 parameters for different gases (from the group \(P\), \(D\) and \(S\) values at a specified temperature) in coordinates of \(d_{efi^{\prime}}^{2}\) (\({}^{z_{i}/k}\)) and \(p\) with employing least squares method. This method was used for the cases where experimental information was lacking (e.g., butane in PMPentene and ethane permeability in PMPentyne).
## The Experimental Process and the FEM Model  
### Basic Principle of FBG  
The FBG working principle was explained in the paper [17; 18]. If FBG and the host material were bonded perfectly, the strain of host material would be transferred to FBG. Then the wavelength shift of FBG (\(\Delta\lambda_{\text{b}}\)) would be influenced by the load induced strain (\(\varepsilon_{1}\)). The temperature change (\(\Delta T\)). \(\Delta\lambda_{\text{b}}\) is given by Equation (1).  
\[\frac{\Delta\lambda_{\text{b}}}{\lambda_{\text{b}}}=\big{(}1-\text{p}_{\text{ e}}\big{)}\varepsilon_{1}+\big{(}1-\text{p}_{\text{e}}\big{)}\alpha_{\text{h}} \Delta T+\xi\Delta T \tag{1}\]  
The parameter \(\text{p}_{\text{e}}\) is the optical fiber photo-elastic coefficient, \(\alpha_{\text{h}}\) is the thermal expansion coefficients of the host material and \(\xi\) is the thermo-optic coefficient [18]. From Equation (1), it is clear that the stain measured by FBG will depend on the strain of host material, the thermal expansion of host material and the temperature influence on index of refraction of the fiber optic sensor. Since concrete is a composite material, \(\alpha_{\text{h}}\) varies at different locations. To simplify the calculation, thermal expansion of concrete was not considered in strain calculation. In order to separate fiber optic expansion caused by temperature from the monitoring strain, the temperature compensating method [19] was used. The temperature-compensating FBG (FBG-T-01, manufactured and calibrated by NingBo ShanGong Intelligent Security Technology, Co., Ltd., Ningbo, China) and strain FBG (SQ-FBG-GS01, manufactured and calibrated by NingBo ShanGong Intelligent Security Technology, Co., Ltd.) were calibrated in the laboratory before installing them in-site.
## 4 Discussion
As these systems are limited to capturing the spatial positions of implant abutments within the oral cavity, the supplementary use of conventional intraoral scanners remains necessary to obtain detailed soft tissue information, as previously discussed [6]. Nonetheless, recent reports in the literature have highlighted the use of open-source software in photogrammetry as a cost-effective approach for creating digital models. This strategy presents a viable option for practitioners at all levels of experience, particularly for dental students and early-career clinicians aiming to incorporate digital workflows into their clinical practice [54]. Future in vitro investigations could further examine how implant-related variables--such as angulation, insertion depth, alternative spatial configurations, and Scanbodies' supramucosal height and morphology--affect the trueness of digital impression techniques [39]. Additionally, research into the time efficiency of the photogrammetry process could offer clinically relevant insights for implant prosthodontics. The integration of photogrammetry with emerging technologies, including smartphone-based applications and artificial intelligence, may also enhance our understanding of its reliability and suitability for routine clinical workflows [55, 56]. Investigations based on larger sample sizes could help to validate and expand upon the findings of the present investigation. Furthermore, incorporating conventional impression techniques in future research would enable a more detailed comparative analysis of trueness. It should be recognized, however, that in vitro studies cannot fully replicate the complexity and dynamic conditions of the intraoral environment. Thus, in vivo studies are recommended to assess the performance of scanning systems under clinical conditions, including variables such as bone resorption, salivary presence, soft tissue mobility, and ambient lighting. Collectively, the present findings and future investigations may contribute to identifying the digital scanning approach that offers the highest level of trueness for full-arch implant digital impressions in clinical practice.
## 3 Methodology
### Effect of the Black Hole Attack in the IoT-Enabled AODV Network  
The AODV protocol was developed primarily for mobile ad hoc networks and is regarded as a flat-based routing mechanism. It can also be used for IoT-enabled mobile nodes. The AODV protocol is based on the distance vector method. It may be viewed as a development of the proactive DSDV method, which stands for \"Destination-Sequenced Distance-Vector Routing\". AODV is ad hoc on-demand routing protocol, which decreases the number of message broadcasts compared to DSDV. AODV adopts two procedures: \"RD\" for route discovery and \"RM\" for route maintenance. The AODV protocol defines three well-known messages, called route request (RREQ), route reply (RREP), and route error (RERR). During the crucial \"Route Discovery\" step of the AODV, \"RREQ\" messages are consistently sent out over the IoT-enabled network. When the respective node obtained an \"RREQ\" message, it immediately sent a reverse route \"RREP\" message. \"RERR\" messages are sent to the destination and source nodes immediately when the routes are broken. AODV operates well in low-density networks and mobile nodes [47].  
The black hole attack (BHA) lies in the category of the network layer attack. The BHA node drops all the data and affects the overall network traffic [48]. In the BHA, the malicious node waits for the RREQ message to come from the neighboring nodes. After receiving the RREQ messages, it immediately sends the fake copy of RREP messages with the maximum sequence number before the actual response of other nodes. A malicious node pretends one of the nodes has a suitable path to the destination. By doing so, the sender node acknowledges that the route discovery has been acquired and starts sending data to the malicious node. In this way, the malicious node drops all the data, which affects  
Figure 7: (**a**) CNC milling 1, (**b**) CNC milling 2, (**c**) CNC turning, (**d**) Pneumatic jet printer, (**e**) universal tensile testing machine, (**f**) spinning machine, (**g**) 3D laser scanner, (**h**) retrofitted FMC, (**i**) mobile industrial robot (MiRs), (**j**) 5-axis CNC milling machine, (**k**) selective laser sintering 3D printer, (**l**) binder jet printer, (**m**) precision router, and (**n**) indigenous FMC.  
the network traffic badly. This phenomenon is shown in Figure 9, \"H\" is the sender node, \"C\" is the BHA node, and \"E\" is the destination node.
## 4 Case Study
### Prefabricated Component Model Library and Model Data Processing  
#### 4.1.1 Prefabricated Component Model Library  
To better apply the standardized and modular design of prefabricated buildings, it was necessary to build a library of prefabricated house types and component products to achieve the standardization of house types and specification of components, thus reducing the rate of design errors and improving drawing efficiency. This project utilized the design institute's shared cloud drive to build a model database. Initially, components and house types that met the specific requirements of this project were selected from the existing model library. However, since the components and house types in the model library were prototypes, appropriate modifications based on this project were required. Meanwhile, this project's customized components and house types were uploaded to the shared platform, expanding the prefabricated component model library. Figure 2 shows the prefabricated component models, specialized models, and in-depth design models added to the shared platform in this project. The BIM and VR collaborative design platform ensures effective communication and coordination with prefabricated component manufacturers and assembly construction units. It guarantees the models' rationality, applicability, and accuracy, fulfilling factory-standardized production and assembly construction requirements.  
Figure 2: Cont.
# Multi-Authority Revocable Access Control Method Based on CP-ABE in NDN
## 4 Security Analysis
### Confidentiality of Data  
When the user's attributes meet the requirements of the access structure, it can use the key to implement the decryption process of the data. If the user's attributes cannot meet the requirements of the ciphertext access structure, then they cannot accept the ciphertext information during the decryption proxy process. Since the proxy decryption process can be decrypted by the cloud private key, the lack of the user agent key will not help the data decryption process. When a user is revoked, the cloud server must delete the proxy key it holds. When the proxy key is deleted and does not exist, user will not be able to accept the partially decrypted ciphertext.
# Real-Time Communication Support for Underwater Acoustic Sensor Networks +
## 4 System Model  
Although underwater sound propagation has been studied in the literature and the physics are well-known, actually establishing a link between two nodes continue being quite difficult. Next we present the models proposed to represent the physical propagation of the messages, the network (including the nodes synchronization), the real-time message transmission, and the message routing.
## 1 Introduction
These generators are shared within the system to guarantee cyclic consistency and enable efficient reconstruction of samples mapped across domains. Despite improved model performance, it is observed that the data generated by the original CycleGAN generator exhibit noise and poor quality, with a lack of detailed features. * Consequently, modifications are made to the original internal module of the generator by incorporating a multi-scale attention mechanism. This adjustment allows for an enhanced capture of subtle relationships between spectral bands, prioritization of salient spectral features, and consideration of the spatial distribution of spectral information. * The discriminator employs the PatchGAN [15] architecture for local feature analysis, enhancing detail and structure recognition in hyperspectral images. Meanwhile, the Wasserstein GAN with gradient penalty (WGAN-GP) [16] technique is utilized to stabilize the training process and improve both fidelity and quality of the synthetic hyperspectral data. * A hyperspectral data evaluation tool based on the ResNet architecture is subsequently created. This tool employs a complex structure consisting of ResBlocks [17] and DenseBlocks [18] to efficiently capture and interpret the rich spectral information in each hyperspectral data sample, ensuring its performance in processing hyperspectral data analysis tasks and its robustness in processing these tasks. In summary, our proposed Hyper-CycleGAN architecture harnesses the strengths of CycleGAN, along with custom modifications like the integration of multi-scale attention mechanisms [19], to enhance feature learning capabilities specifically tailored for hyperspectral data. This results in a more robust and efficient image transformation model capable of generating high-quality, realistic hyperspectral data while improving details and reducing noise. Experimental results validate the superiority of our Hyper-CycleGAN architecture over traditional approaches, demonstrating enhanced recognition performance across diverse scenarios from the perspective of hyperspectral data analysis.
# Combined Data Augmentation on EANN to Identify Indoor Anomalous Sound Event
## 2 Materials and Methods
### Dataset Generation  
We primarily combined two publicly available datasets, ESC-50 and UrbanSound8k, to obtain indoor anomalous sound data. ESC-50 is a dataset that comprises 50 different types of sounds, with 40 samples for each sound, resulting in a total of 2000 samples. It includes common sounds such as animal calls, urban noises, and indoor sounds. UrbanSound8k is a dataset consisting of 10 categories of urban environmental sounds, with a total of 8732 samples. These samples encompass sounds like vehicle noises, human voices, gunshots, air conditioning sounds, and children playing.  
Figure 1: Experimental framework for data augmentation methods of indoor anomalous acoustic events identification.  
To obtain indoor anomalous sound data samples, we selected data from these two datasets that were relevant to indoor anomalous sounds. We focused on eight categories of anomalies: coughing, cracking fire, crying baby, glass breaking, gunshots, sneezing, snoring, and screams. This dataset served as the input for our model. During the experiments involving Background Noise data augmentation, we acquired four categories of indoor-related background noises from freesound.org (accessed on 18 October 2023), such as air conditioning noise, rain noise, television noise, and fan noise. This not only expanded the dataset but also increased the diversity and representativeness of indoor anomalous sound data.