esa-sceva/satcom-chunk-collection huggingface dataset:
# Generic Digital Twin Architecture for Industrial Energy Systems
## 1 Introduction  
### Motivation  
Digitalization is changing the way business is conducted within industrial value chains, facilitated by the rapid development of communication and information technology [1]. This process is also referred to as the fourth industrial revolution or Industry 4.0. The goal is a highly optimized and customized production, as well as enhanced automation and adaption capabilities [2]. To realize these visions of Industry 4.0, the Digital Twin (DT) is one of the most promising enabling technologies [3].  
Industry 4.0 and the sustainable energy transition share important characteristics and can mutually benefit from each other [4]. Information and communication technology helps to increase energy efficiency and the interaction of industry with smart grids which facilitates the integration of renewable energy sources. DTs are also the key enabler for such applications, as their common functionality includes monitoring, diagnostic, prediction, and control [5].  
The concepts and capabilities of DTs are not clearly defined and sometimes hard to grasp. This is caused by the fact that DTs can be applied for various tasks in different life-cycle phases and industrial domains. Thus, different interpretations of a DT exist, driven by specific use cases. This leads to theproblem that concrete realizations of DTs follow a concrete goal without any architectural template [6]. For this reason, we present a novel generic architecture for a DT, which is in line with the Reference Architecture Model Industry 4.0 (RAMI 4.0), called Generic Digital Twin Architecture (GDTA). The GDTA facilitates a technology independent implementation of DTs and gives orientation for locating existing frameworks and technologies inside this architectural model. We also introduce a context-dependent View on the Virtual Entity inside the DT, consisting of special-purpose simulation models in combination with context information. Additionally, a prototypical implementation of the proposed GDTA is presented as a proof-of-concept, using Semantic Web technologies. It demonstrates how the context information of resources and services can be managed inside a DT, and how a View of the Virtual Entity can be based on a Simulation Service.  
The remainder of the paper is structured as follows: Section 1.2 gives a short overview of related work in the area of DT concepts, frameworks, and reference architectures. Section 2 describes the methods which are applied for carrying out the presented work. Afterward, the GDTA is introduced in Section 3. The proposed architecture is used for our proof-of-concept implementation, based on Semantic Web technologies and applied for a use case of a thermal energy storage system in Section 4. In the end, the presented GDTA is discussed with respect to other DT architectural frameworks, and an outlook on our future work is given.
## 5 Experiment  
### Experimental Settings  
**Experimental Environment.** In this study, we deployed and tested the algorithm using the ns-3 simulator, version 3.35. The experimental setup, illustrated in Figure 9, features a combination of wired and wireless links, with specific links configured to have a bandwidth of 100 Mbps and a latency of 10 ms. The wireless network operates on the 802.11 ac protocol, the most advanced version supported by ns-3. In this configuration, the server functions as the sender, while the stations serve as the receivers. For wireless loss, we used the LogDistancePropagationLossModel, and for propagation delay, the ConstantSpeedPropagationDelayModel. The IdealWifiManager algorithm was selected for wireless rate control.  
Figure 9: Wireless network topology in our ns-3 testbed.  
Each server and station pair maintains a TCP stream using the Reno [23] congestion control algorithm, with the application layer generating continuous data. In the experiment, the distances between the three Stas and the access point vary, with Sta1 being the closest, followed by Sta2, and Sta3 being the farthest. This distance variation leads the WiFi rate selection algorithm to assign different modulation and coding schemes (MCSs) to each Sta: MCS8 for Sta1, MCS6 for Sta2, and MCS4 for Sta3.  
**Airtime statistics in ns-3.** We record the timestamps of each data frame transmitted by the AP and Sta in the wireless network using the ns-3 API, correlating TCP data packets and acknowledgments by their sequence numbers. Figure 10 illustrates the airtime calculation method for a TCP stream, which includes \(t1+t2\). Since UDP streams lack transport layer acknowledgment packets, airtime for UDP is calculated using only \(t1\).  
**Evaluation Metrics.** We collect two key metrics: the algorithm's throughput and airtime fairness, measured using [PERSON]'s fairness index. The calculation methods are outlined below, with higher values of \(F\) indicating better fairness:  
\[F(x)=\frac{(\sum_{i=1}^{n}x_{i})^{2}}{n\times\sum_{i=1}^{n}x_{i}^{2}} \tag{14}\]
# UAV Deployment Using Two Levels of Stigmergy for Unstructured Environments
## 3 Methods  
This section will initially detail the swarm on which the deployment task is planned. Details of the types of base vehicles and the swarm simulator developed for this work will also be presented. Additionally, the process of generating unstructured environments using a cellular automaton will be described briefly. The two stigmergy-based swarm behaviors for deployment tasks will be described in detail.  
Stigmergy provides an indirect means of communication between individuals through the environment. There are several types of stigmergy depending on how the communication is achieved. If communication is established through the placement of markers in the environment, we call the approach marker-based stigmergy. If individuals base their actions on the state of the solution, we refer to the approach as sematectonic stigmergy. If we work with discrete signals, we refer to strategy as qualitative stigmergy. On the other hand, if the communicated signals are continuous, we refer to the approach as quantitative stigmergy.  
In the first behavior, an approximation of quantitative sematectonic stigmergy will be presented. In this case, some individuals will be deposited in areas of the environment and used as the main elements in the communication of pheromones; that is, the individuals themselves are defined as active markers of the environment.  
The second behavior will use a hybrid of sematectonic, marker-based, and quantitative stigmergy. In this case, a group of individuals will be deposited as markers, but these markers will form a local gradient that will indicate the direction of navigation. The gradient of a pheromone map is managed by this same group of individuals.
# A Secure and Efficient Three-Factor Authentication Protocol in Global Mobility Networks
## 1 Introduction
### Motivation and Contributions  
We have studied numerous user authentication schemes [6; 8; 15; 16] for roaming services and found that they had the following in common:  
1. Many authentication protocols [6; 8; 15; 16] are exposed to well-known attacks, such as masquerade, replay, mobile device theft, and session key disclosure attacks in global mobility environments.
2. Many authentication schemes must provide secure convenience for mobile users in the GLOMONET and must take into account all the security requirements specified in Section 1.2.
3. Secure and lightweight authentication schemes are essential, which take into account limitations for resource-constrained mobile devices relative to computing power, memory, and battery capacity.  
Recently, [PERSON] et al. [16] presented a secure and efficient user authentication scheme for GLOMONET using a mobile device. They claimed that their scheme could resist various attacksand could ensure secure mutual authentication and anonymity. However, our paper presents a brief review of [PERSON] et al.'s scheme [16], and we demonstrated that their scheme could not prevent various security threats. To resolve the security threats of [PERSON] et al.'s scheme, we proposed a secure and efficient three-factor authentication protocol. The proposed scheme demonstrated several advantages compared with previous related authentication schemes.  
First, the proposed scheme could prevent various attacks, such as mobile device theft, masquerade, session key disclosure, and replay attacks and also provided secure mutual authentication, user anonymity, and user friendliness. Second, the proposed scheme used the fuzzy extractor mechanism to improve the security level of the protocol. Even if two of the three factors were compromised, the proposed scheme was still secure. Finally, the proposed scheme provided better effective computation costs with related schemes as it only utilized the one-way hash function. Therefore, the proposed scheme was secure, efficient, and more suitable for practical mobile and wireless environments.
## 2 Formulation of the Proposed BMUA
### Parameterization of a Structural Model  
A linear structural model can be parameterized by model parameters based on the degree-of-freedoms (DOFs), \(N_{d}\), and defined model class, \(C\). A commonly used parameterization of the stiffness matrix, \(\mathbf{K}(\theta)\), and mass matrix, \(\mathbf{M}(\beta)\), could be described as [53]  
\[\mathbf{K}(\theta)=\mathbf{K}_{0}+\sum_{l=1}^{N_{\theta}}\theta_{l}\mathbf{K}_ {l}\quad\mathbf{M}(\beta)=\mathbf{M}_{0}+\sum_{m=1}^{N_{\beta}}\beta_{m} \mathbf{M}_{m} \tag{5}\]  
where \(\theta=\left[\begin{array}{cccc}\theta_{1},&\theta_{2},&\cdots,&\theta_{N_{ \theta}}\end{array}\right]^{T}\) is a stiffness parameter vector; \(\beta=\left[\begin{array}{cccc}\beta_{1},&\beta_{2},&\cdots,&\beta_{N_{ \beta}}\end{array}\right]^{T}\) is a mass parameter vector. The \(l\)th stiffness parameter forms the \(l\)th elemental stiffness matrix, \(\mathbf{K}_{l}=\partial K/\partial\theta_{l}\); similarly, the \(m\)th mass parameter forms the \(m\)th elemental mass matrix, \(\mathbf{M}_{m}=\partial M/\partial\beta_{m}\). In the proposed updating framework, \(\theta_{l}\) and \(\beta_{m}\) are updated to match the FE model with the real structure using measured data. Note that \(\mathbf{K}_{0}\) and \(\mathbf{M}_{0}\) in Equation (5) are defined as constant matrices that are not dependent on model parameters. For the sake of simplicity, \(\mathbf{K}_{0}\) and \(\mathbf{M}_{0}\) are set as zero.
# IoT-Driven Intelligent Scheduling Solution for Industrial Sewing Based on Real-RCPSP Model
## 2 Related Works
### Implementation Challenges and Future Directions
Therefore, researching the application of IIoT in the garment manufacturing industry will promote economic and social development in countries with a thriving garment manufacturing sector. In this study, we focus on investigating the application of IoT in the garment manufacturing industry to collect real-time production data, thereby supporting management, operation, and automated production planning. The specific contents of this study include:  
* Proposing a method for deploying IoT applications in garment manufacturing, synchronizing collected data to the cloud computing system, and then converting and integrating it into the enterprise's Management Information System (MIS), thereby facilitating the compilation and generation of timely management reports. * Based on garment contracts signed between enterprises and partners, this paper proposes a method for digitizing garment contract data into a standardized dataset for automated production planning. * To support automated production planning and scheduling, this paper utilizes the Real-RCPSP (Resource-Constrained Project Scheduling Problem), a complex problem with many constraints that align well with real-world production models, especially in the garment industry. The solution to this problem is a schedule that assigns resources (workers) to perform production stages according to signed garment contracts. Since Real-RCPSP is an NP-Hard problem, finding an optimal solution in polynomial time is impossible. Therefore, it is necessary to use metaheuristics (evolutionary algorithms) to find an approximate solution that satisfies the problem's constraints. This paper does not use an exact solution, so this study uses two evolutionary algorithms to find a simplification of Real-RCPSP, namely GA (Genetic Algorithm) and PSO (Particle Swarm Optimization). The found schedule can be used for automatic production planning in industrial production.
## 4 Results
### Parameter Sensitivity
#### 4.1.2 One-at-a-Time Sensitivity  
One-at-a-time sensitivity, as a method, involves assessing the sensitivity of a single parameter by altering its value while keeping all other parameters constant. This method was proposed by [PERSON] in 1991 and has a long history of application [32]. It enables a clear attribution of model output variations to specific parameter modifications. Consequently, the results are both quantitative and exclusive. In this analysis, the values of the other parameters were the fitted values established during the calibration period, while the altered parameter values spanned the parameter range established during that same calibration period, ranging from the minimum to the maximum values.  
In one-at-a-time sensitivity (Figure 5), most of the parameters and simulation results show sensitivity except GWQMN. The smaller the values of CN2, ALPHA_BF, SOL_K, HRU_SLP, ESCO, and BFLO_DIST within the provided parameter range, the better the simulation effect. On the contrary, the values of OV_N and EPCO and the simulation effect show a positive trend in the range. The point distribution of relation between GW_DELAY and NSE is discrete, which is consistent with the low sensitivity in our global sensitivity analysis. We have noticed that the change of GWQMN value does not affect the value of NSE, but its T-stat value is greater than 1 in global sensitivity. The high sensitivity of GWQMN in global sensitivity may be a false phenomenon caused by the influence of the SOL_BD parameter (Figure 4). In addition, we also noticed that the fitted value of one-at-a-time sensitivity analysis was close to the fitted value of global sensitivity simulation, which provided credible evidence for the results of calibration.
## 2 Materials and Methods
### Proposed UIM-VLC Channel Model
#### 2.4.3 Proposed Scattering and Absorption Model
If the optical link is contained in an environment where the particle concentration and size distribution are constant, then the dependence on the optical path can be relaxed (i.e., \(B_{\text{ext}}(\lambda,\ell)\equiv B_{\text{ext}}(\lambda)\), \(N_{p}(\ell)\equiv N_{p}\), and \(F(x_{p},\ell)\equiv F(x_{p})\)), and Equation (82) can be rewritten as  
\[T(\lambda)=\exp(-LB_{\text{ext}}(\lambda))\,. \tag{84}\]  
The Rosin-Rammler (R-R) distribution is widely used in the literature to model the particle size distribution of dust that results from the crushing of solids such as coal, rocks, and other materials and has been empirically validated numerous times [41]. The authors in [30] use the R-R distribution in their calculations of the extinction coefficient of coal dust in underground mines. The authors in [34] also argue that mine dust is best described by the R-R method. Assuming mine dust follows the R-R distribution, then \(F(x_{p})\) can be expressed as [30; 40]:  
\[F(x_{p})=1-\exp(-(x_{p}/x_{p})^{n_{p}})\,, \tag{85}\]  
where \(x_{p}\) and \(n_{p}\) are parameters found empirically. \(x_{p}\) is known as the characteristic size and \(n_{p}\) is the spread index of the distribution. The number of coal dust particles \(N_{p}\) can be derived from the mass per unit volume of the mine dust (\(\text{mg}/\text{m}^{3}\)), \(W_{p}\), which can be found empirically. Given \(W_{p}\), \(N_{p}\) can be expressed as [30]  
\[N_{p}=\frac{W_{p}}{\rho_{p}C_{p}\int x_{p}^{3}F(x_{p})\,dx_{p}}\,, \tag{86}\]  
where \(\rho_{p}\) is the density of the mine dust (\(\text{mg}/\text{m}^{3}\)) and \(C_{p}\) is the irregularity coefficient of the mine dust. The integration limits over the possible particle diameters are omitted for the sake of simplicity. Note that when considering a spherical particle, \(C_{p}=\pi/6\). Considering mine dust consisting of spherical particles and constant particle concentrations and size distributions over the optical link, and combining Equations (83) and (86), the extinction coefficient \(B_{\text{ext}}(\lambda)\) can be expressed as  
\[B_{\text{ext}}(\lambda)=\frac{3}{2}\frac{W_{p}}{\rho_{p}}\frac{\int x_{p}^{2}F (x_{p})Q_{\text{ext}}(x_{p},\lambda,m(\lambda))\,dx_{p}}{\int x_{p}^{3}F(x_{p} )\,dx_{p}}\,, \tag{87}\]and from Equation (84), it follows that the spectral transmittance of mine dust for the UM-VLC channel can be expressed as  
\[T(\lambda)=\exp\left(-\frac{3}{2}L\frac{W_{p}}{\rho_{p}}\frac{\int x_{p}^{2}F(x_{ p})Q_{\text{ext}}(x_{p},\lambda,m(\lambda))\,dx_{p}}{\int x_{p}^{3}F(x_{p})\,dx_{p}} \right). \tag{88}\]  
Using Equation (84) and \(L=d\) for the LOS component and \(L=d_{1,w}+d_{2,w}\) for the \(w\)th reflector NLOS component, \(\zeta_{0}(\lambda)\) can be expressed as  
\[\zeta_{0}(\lambda)=\exp(-dB_{\text{ext}}(\lambda))\,, \tag{89}\]  
and \(\zeta_{w}(\lambda)\) can be expressed as  
\[\zeta_{w}(\lambda)=\exp(-(d_{1,w}+d_{2,w})B_{\text{ext}}(\lambda))\,. \tag{90}\]
# Friction Factor Correlation for Regenerator Working
## 5 Results and Discussion
### Analysis of Phase Shift  
Phase angle between pressure and velocity oscillations is important in determining the hydrodynamic condition of porous media such as the regenerators. If the pressure and velocity are not in phase, the maximum pressure occurs at a certain phase lag compared to maximum velocity. Then, the amplitude data may not be accurate enough to represent the friction factor according to Equation (1).  
In other studies [10; 11; 18], the phase shift between pressures at location before and after the regenerator is observed when the operating frequency increases. The phase shift effect is observed even at a frequency as low as 5 Hz in the results presented by [PERSON] et al. [18]. [PERSON] and [PERSON] [17] reported that the phase shift between pressure and velocity in the experimental investigation appeared at a very low kinetic Reynolds number (calculated to be equivalent to 2.5 Hz).  
In the current experiment, it is not possible to measure the pressure drop and velocity amplitude within the regenerator. However, the numerical model is validated for several points within the rig. Thus, it is reasonable to expect that it will reliably predict the pressure drop and velocity within the regenerator.  
Figure 9 shows the pressure drop and velocity within the regenerator. The pressure drop was calculated using values obtained at locations P2 and P5, where pressure sensors are available. Note that vertical scales of the plots in Figure 9 vary. This was done deliberately to show more clearly the changes in phase difference between pressure and velocity. For fine regenerator mesh screens (#200 and #180), the pressure drop is in phase with the velocity within the regenerator. When the mesh screen is coarser (#30 and #94), the pressure drop becomes slightly out of phase from the velocity within the regenerator. The pressure drop tends to lead the velocity more visibly as the mesh becomes coarser. Theoretically, bigger porosity results in a lower flow velocity within the porous region, if set at a similar level of acoustic excitation. The shear stress and form drag is likely to be smaller too. However, due to the limitation of the experimental setup, data for coarse mesh number are only available for a relatively high Reynolds number. Similarly, data for fine mesh are only available for the low Reynolds number region. Therefore, it is possible that the phase shift between pressure and velocity seen within the coarse mesh is due to the relatively high velocity excited by the linear motor. This could be the reason for the time delay between pressure drop and velocity. It is likely that the phase shift between pressure and velocity is the feature of the inertial effect described by the Forchheimer model.  
Figure 8: Comparison between friction factor from experiment: (**a**) Swift and Ward correlation of [7]; and (**b**) current CFD-assisted correlation.
# Data-Driven Damage Classification Using Guided Waves in Pipe Structures
## 5 Training and Validation of the Neural Network
### Noise Effect  
For investigating the validity of the proposed method in the presence of noise, Gaussian noise was added to the guided wave signals with two different levels, SNR = 30 and 20 dB, respectively.  
Then, signals were trained and tested by the multi-layer neural network with ReLU hidden layers, which shows good results of almost 100% during the training process for 420 signals with both 20 dB and 30 dB SNR noises, but the test results of 100 samples, i.e., the recognition accuracy is, respectively, 88% for signals with 30 dB SNR noise and 82% for signals with 20 dB SNR noise. These results were unable to meet the requirement in the structural health monitoring (SHM) field, so it can be concluded that it is necessary for guided waves to be filtered before pattern recognition. The test results of the signals with 30 dB and 20 dB SNR noise are displayed in Figure 12, which also shows that confusion is easily made among the labels 1, 2, and 3--that is to say, the signals from no damage, extensive corrosion, and dent led by impact.  
Figure 10: Training and test confusion matrices of neural network with tanh hidden layers: (**a**) training confusion matrix; and (**b**) test confusion matrix.  
Figure 9: Training processes of the multi-layer neural network with different activation functions: (**a**) tanh; (**b**) ReLU.
# Personalized Online Live Video Streaming Using Softmax-Based Multinomial Classification
## 3 System Model and the Proposed Algorithm
### System Model  
The overall process and the overall streaming system architecture using the UAS method proposed in this paper are shown in Figures 4 and 5, respectively. As illustrated in Figure 5, the basic components  
Figure 3: Structure of the dynamic adaptive streaming over HTTP (DASH) method.  
in this system are similar to those in DASH methods (details are in Section 2.4). However, the proposed UAS method includes more components, which are used to re-order the arriving chunks from servers.  
The UAS method is implemented at the server side. Thus, the basic process of UAS works in the same way as DASH. However, UAS re-organizes the received order of the units of video (i.e., chunks). In other words, the request is generally not sequential, and the chunk priority is set per chunk and then chunks are streamed according to their priority. A detailed method of prioritizing chunks by each user is described in Section 3.2.