esa-sceva/satcom-chunk-collection huggingface dataset:
# Comparative Analysis of Producer Mobility Management Approaches in Named Data Networking
## 2 Related Work  
Producer mobility management in NDN has received extensive attention and investigation. There exists a wide range of surveys and reviews in the literature which have synthesized producer MM approaches with different objectives and goals, such as [9; 10; 11; 12; 13; 14; 15].  
Some of the related literature aimed to compare different producer MM solutions for a specified functional environment. The authors of [16] compared some producer MM schemes for cloud-based environments, while the authors in [17] focused on comparing a couple of producer MM schemes under different network topologies.  
We may classify producer MM approaches into two broad categories: anchor-based and anchorless.  
In anchor-based producer MM approaches, mobility is handled by a dedicated network entity (referred to as the anchor). This MM design approach works by binding the anchor content names to their locations.  
We may further distinguish two groups of the anchor-based approaches proposed in the literature: rendezvous point (RV)- and proxy-based MM approaches.  
In general, anchor-based MM approaches do not fit well with the essence of NDN, tying data to locations and requiring a dedicated network entity (the anchor) through which traffic passes. Aside from that, the anchor is a single point of failure and generates the great memory overhead required during the prefix look-up process.  
On the contrary to anchor-based approaches, in anchorless approaches, a mobile producer can announce its mobility to the network and regain connectivity without requiring special role nodes. Compared with anchor-based MM schemes, anchorless MM schemes are distributed and do not have special purpose entities or nodes which might become congestion points or single points of failure. Furthermore, anchorless MM schemes are transparent and do not require tampering the interest packets of the consumer or the data packets of the producer. This is important not only to ensure full compatibility to the NDN specification but also to maintain caching capabilities and avoid security vulnerabilities.  
The anchorless producer MM approaches proposed in the literature may be further classified into routing-based and data plane-based approaches.  
The taxonomy of producer MM approaches is shown in Figure 1.  
The variants of each approach are detailed next, and the advantages and disadvantages of the approaches along with the provided literature are summarized in Table 1.  
Figure 1: Producer mobility management in the literature.
# A Dynamic Assessment Methodology for Accident Occurrence Probabilities of Gas Distribution Station
## 3 Application of the Methodology
### Adapting Probabilities for Accident Cause Diagnosis  
Utilizing the dynamic BN model of the GDS, the prior probabilities of REs can be adapted given the occurrence of near-misses or incidents. Through continuous updating and adapting the RE probabilities, the reliability and accuracy of the assessment results generated by the dynamic prediction model can be enhanced [28]. Table 5 shows the adapted probabilities of the REs subsequent to a leakage event at the GDS. A comparison of the RE probabilities before and after the update reveals the most critical REs that significantly contribute to the occurrence of the GDS leakage accident. As depicted in Figure 8, the REs  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**RE** & **Cumulative Plant-Specific Failure Data** & **Number of Samples** & **Updated Probability** & **UE** & **Updated Probability** \\ \hline X\({}_{3}\) & 2 times within 56,160 h & 8 & 3.34 \(\times\) 10\({}^{-2}\) & CE & 6.18 \(\times\) 10\({}^{-2}\) \\ X\({}_{6}\) & 1 times within 17,520 h & 2 & 1.69 \(\times\) 10\({}^{-1}\) & OE\({}_{1}\) & 5.35 \(\times\) 10\({}^{-2}\) \\ X\({}_{7}\) & 0 times within 105,500 h & 8 & 2.42 \(\times\) 10\({}^{-2}\) & OE\({}_{2}\) & 8.22 \(\times\) 10\({}^{-7}\) \\ X\({}_{10}\) & 1 times within 73,270 h & 8 & 8.94 \(\times\) 10\({}^{-2}\) & OE\({}_{3}\) & 6.80 \(\times\) 10\({}^{-3}\) \\ X\({}_{11}\) & 1 times within 73,270 h & 8 & 7.17 \(\times\) 10\({}^{-2}\) & OE\({}_{4}\) & 1.34 \(\times\) 10\({}^{-3}\) \\ X\({}_{39}\) & 1 times within 87,640 h & 10 & 2.12 \(\times\) 10\({}^{-2}\) & OE\({}_{5}\) & 2.06 \(\times\) 10\({}^{-8}\) \\ X\({}_{40}\) & 1 times within 98,480 h & 10 & 6.38 \(\times\) 10\({}^{-2}\) & OE\({}_{6}\) & 1.70 \(\times\) 10\({}^{-4}\) \\ X\({}_{41}\) & 1 times within 121,040 h & 10 & 6.43 \(\times\) 10\({}^{-2}\) & & \\ X\({}_{43}\) & 1 times within 103,680 h & 12 & 2.45 \(\times\) 10\({}^{-2}\) & & \\ X\({}_{55}\) & 1 times within 141,080 h & 12 & 1.99 \(\times\) 10\({}^{-2}\) & & \\ X\({}_{56}\) & 1 times within 109,820 h & 10 & 4.11 \(\times\) 10\({}^{-3}\) & & \\ X\({}_{58}\) & 1 times within 165,600 h & 14 & 4.93 \(\times\) 10\({}^{-2}\) & & \\ X\({}_{59}\) & 0 times within 146,300 h & 14 & 2.22 \(\times\) 10\({}^{-2}\) & & \\ X\({}_{61}\) & 1 times within 107,900 h & 10 & 4.24 \(\times\) 10\({}^{-2}\) & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: The updated probabilities of REs and UEs in the BN accident model.  
X\({}_{13}\)-X\({}_{25}\), X\({}_{45}\)-X\({}_{53}\), and X\({}_{64}\) are identified as the most critical, indicating that they have the greatest impact on the likelihood of the GDS leakage accident. Consequently, these REs should be prioritized in the risk-management plan to mitigate the failure probability of the GDS.
## 4 Materials and Methods
### Case Study  
The case study used PV power-generation data obtained from a publicly accessible database maintained by the National Institute of Standards and Technology [38]. This database provides historical PV power generation data for two PV power plants that are located in close proximity. This dataset has been widely utilized in recent studies [39; 40; 41] and is divided into two subsets: D1 contains data for a larger farm with a rated power of 243 kW, and D2 contains data for a smaller farm with a rated power of 75 kW.  
In our study, we utilized dataset D2, with reference to the output power of inverter no. 2, measured in watts (W) during the second year of observations, as a numerical case study. We focused on the hourly generated power to reduce the data density. Days with missing values were filled with zero production values to maintain the integrity of the time-series. The original data provided timestamps and 24 h power production data for each day. Neither meteorological features nor NWP data were considered.  
The primary objective of this case study is to investigate the daily PV power generation, which exhibits diverse output shapes each day, as depicted in Figure 4. This figure shows a random row of five consecutive days from the dataset, and illustrates that the output patterns vary across different days. Daily PV power generation exhibits cyclic behavior during daytime hours and may have different peak values depending on weather conditions. Sunny days ensure maximum PV power generation capacity. In contrast, rainy or cloudy days may produce less or negligible power from the PV plants.  
Table 1 presents the hyperparameters to be optimized in the proposed LSTM model, including the number of units in the embedding and LSTM layers. A BO algorithm was employed to optimize the five parameters of the proposed LSTM model, as described in  
Figure 4: PV power outputs in in watts (W vertical \(y\)-axis) in a row of five days corresponding to 120 hourly observations ranging from hour no. 1047 to hour no. 1167 (horizontal \(x\)-axis).  
Section 3.3. The optimal set of values for these parameters along with the convergence time of the optimization algorithm are listed in Table 2.  
Table 2 presents the values for \(\alpha\), \(\beta\), and \(\gamma\) of the AHW method, which were obtained by applying the GA optimization to the error function MASE, as described in Section 3.1. The seasonal component of the AHW method was set equal to the value of \(s=24\), which corresponded to the cyclic fluctuations of PV power generation related to the cyclic behavior of alternating night and day times within the 24-h sampled for each day. As shown in Table 2, the GA optimization of the AHW method achieved the lowest configuration time, whereas the LSTM and HM methods presented higher configuration times, owing to the application of the BO algorithm.
## 1 Introduction
### Driving Simulator Role in Healthcare  
In-clinic assessments, such as the useful field of view (UFOV)--the area within the visual field where information can be extracted without having to move the eyes--has been associated with various driving outcomes [22; 23; 24]. UFOV evaluates visual field which has been shown to decrease with age and be associated with crash risk while driving [25]. However, the applicability of the assessment is limited and does not occur in a driving context. Given the dynamic and complex nature of driving, it may be valuable to explore other tools, such as driving simulators, that can be used in conjunction with the UFOV to examine functional visual abilities in the driving environment.  
Researchers have used driving simulators in research settings to investigate driving performance and in clinical settings for patient evaluation and rehabilitation [26; 27; 28; 29; 30]. Clinical observations indicate that difficulties experienced on the simulator often mirror those seen during on-road testing [29; 31; 32]. Simulators provide a safe environment for patients to learn how to operate vehicles equipped with new assistive technologies. Moreover, driving-related anxiety, which can negatively impact driving habits and satisfaction, may be mitigated through simulator-based interventions [30]. [PERSON] et al. examined hazard detection at night, comparing younger and older adults across simulator and on-road settings [27]. Their findings showed similar hazard detection capabilities between older and younger adults during on-road driving, supporting the use of simulators as a proxy for night driving evaluations. Additionally, [PERSON] et al. conducted a study aimed at training new driving rehabilitation specialists on using driving simulators to identify driver errors effectively [26]. Such evidence-based training methods enhance the process of training, leading to consistent clinical recommendations from healthcare practitioners.
## 2 Materials and Methods
### Data Processing and Statistical Analysis
#### 2.4.3 Principal Component Analysis  
PCA can be generalized as correspondence analysis (CA) to handle qualitative variables and as multiple factor analysis (MFA) to handle heterogeneous sets of variables. Mathematically, PCA depends upon the eigen-decomposition of positive semi-definite matrices and the singular value decomposition (SVD) of rectangular matrices. In this study, the PCA was employed to reduce the dimensionality of the high-dimensional data and extract the principal components that capture the essential features of the variance [52]. Research studies have demonstrated that biomechanical attributes, which were linked to both knee kinematics and kinetic, may play a role in the risk of sustaining an ACL injury [14]. In this study, the PCA was used to identify the differences in biomechanical characteristics that result from variations in ACL strain values when performing the same CM direction. In particular, for the knee joint, 9 matrices were generated [53, 54], encompassing 3 angles, 3 moments, and 3 contact forces. Within each of the matrices, the joint angle, moment, and contact force waveform data of the high ACL strain group was compared with those of the low ACL strain group in this study. This analysis was expected to reveal the particular biomechanical characteristics that are significantly affected when the ACL is subjected to high and low strains. In essence, our objective was to identify the primary biomechanical characteristics that exhibit a strong correlation with ACL strain through the utilization of PCA. The score plot of PCA employed two primary components to visually represent the joint angle, moment, and contact force datasets. The clustering criterion utilized for all PCAs in the score plot was a 95% confidence interval.
## 3 Results and Discussion
### Relating Samples to VOCs by CoDa Exploratory Analysis
#### 3.5.2 Relating Samples and VOCs by Clustering
VOCs included in cluster C3 were primarily associated with samples from industry A, including F282 and F283. C4 was composed mainly of VOCs from samples F282 and F283 (putrid) or industry B like F508 and F562, affected by sulfidic and putrid. C6 grouped compounds related to most samples, while VOCs included in C5 did not follow identifiable trends. Figure 7: Clustering of VOCs, based on _clr coefficients_, using the function WARD from easyCODA with the unweighted (**A**) and weighted (**B**) options. See Table S1 for the meaning of VOCs’ symbols. Concerning the weighted option (Figure 7B), apart from the three VOCs in C1, other associations with the ANOVA results were not straightforward. C2 mainly included VOCs linked by the ANOVA to F282 but only methyl propanoate (V8), pentanoic acid (V68), and octanoic acid (V85) in exclusive to this sample. Furthermore, several were also linked to F508 and others to the normal fermentation from industry A. C3 was a complex group hardly associated with any spoilage. C4 was formed by only (Z)-3-hexen-1-ol (V47) related to most samples (except F282). Finally, C5 consisted mainly of F562 and F592 (industry B) VOCs and several from industry B. Overall, the unweighted clustering agrees with the ANOVA models in grouping the VOCs related to spoilages and could represent a complementary tool for associating them with specific spoilages. In this case, the unweighted option, apparently, better represents the reality.
# Secure Encapsulation Schemes Using Key Recovery System in IoMT Environments
## 3 System Model
### Proposed Scheme-I(Key Recovery System Using Signcryption)  
This section describes additional elements of the key recovery system model using signcryption, excluding the common elements of the two models proposed in Section 3.1.
## 4 Conclusions  
By combining 3D reconstruction and advanced machine learning technology, a new model for predicting the remaining service life of turbine blades is proposed. Firstly, a 3D reconstruction model of a real high-pressure turbine blade was established using the scanning data. Then, through the coupled analysis of thermal-fluid-structure and the results of the blade strength calculation, the vulnerable checkpoints which are prone to failure were extracted. Based on the study of fatigue creep interaction, the life calculation results were obtained and the data set verified by the CEHM system was established. Finally, a surrogate model was created based on the data set to speed up lifetime assessment. The conclusions are as follows.  
1. Based on the scanning data, a 3D reconstruction model of actual high-pressure turbine blades was established. Then, through the coupled analysis of heat-flow-structure, six vulnerable checkpoints prone to failure were extracted, which were the places with the greatest stress-strain and temperature on the static blade and the rotational blade, and geometrical changes that may cause stress concentration and damage.  
2. The EOH calculation results of 12 groups of sample points were obtained by changing the finite element simulation boundary and repeating the work, and then the results were compared with the calculation results of the same working point in the CEHM system. The maximum error of the two methods was less than 1%, indicating that the calculation method has reference value.  
3. A dataset comprising 137 data sets was generated through finite element calculations and numerical simulations. These data sets were randomly split into training and validation groups. BP, DNN, and LSTM prediction models were built. LSTM had the best prediction effect, and the coefficient of determination, RMSE, and MAE were 0.9899, 0.051104, and 0.037223, respectively.  
4. The LSTM model was applied to the EOH increment prediction of actual units operating from July 2019 to November 2020, and the units showed different increases due to five starts and stops and different operating conditions. The EOH of the unit operating in low load condition increased slowly, and for the contrary the EOH increased significantly.  
Conceptualization, W.X., Y.C. and H.Z.; Methodology, W.X., Y.C. and D.S.; Software, W.X., Y.C. and D.S.; Investigation, Y.C. and D.S.; Writing--original draft preparation, W.X. and Y.C.; Writing--review and editing, H.Z. and D.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability StatementNot applicable.  
Figure 12: EOH variation trend of a gas turbine unit.
# Delay Bounded Multi-Source Multicast in Software-Defined Networking
## 6 Performance Evaluations
### Simulation Setup  
As we have shown in Sections 4 and 5, the efficacy of the proposed algorithms does not rely on specific network factors, such as system topologies, source or destination nodes placement and request patterns. IBM ILOG CPLEX Optimizer [37] is adopted to solve the MILP model presented in Section 4. We simulate the routing algorithms with randomized configurations. Through this experiment, we demonstrate the DBMS scheme routing with respect to total delay cost, execution time, multicast nodes and delay bound for each individual deployment of the network. We compared the performance of our DBMS scheme with the following algorithms: (1) slightly modified recover aware edge reduction algorithm (RAERA) [24], which we renamed to mRAERA, (2) the shortest-path tree algorithm (SPT), (3) a Steiner tree (ST) algorithm [38], (4) KPP and (5) Integer Programming solver CPLEX, which finds the optimal solution of the DBMS scheme by solving the MILP formulation presented in Section  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Tools and Technologies** & **Name** & **Version** & **Description** \\ \hline SDN Controller & Pox [34] & 2.0.2 & Python based open source OpenFlow/SDN Controller. \\ Simulator & Mininet [33; 35] & 2.2.1 & An emulator for deploying large networks on the limited resources of a simple single Computer or Virtual Machine. Mininet has been created for enabling research in SDN and OpenFlow. \\ Traffic Generator & Inet [36] & 3.0 & It generates random networks with characteristics similar to those of the Internet from November 1997 to February 2002, and beyond. The generator should be used to generate network of no less than 3037 nodes, which was the number of ASs on the Internet in November 1997. \\ Development tools & C++ & C++14 & A general-purpose programming language with a bias towards systems programming. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Software Used in implementation of the experiment.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Hardware parts** & **Specification** \\ \hline CPU & Intel Core i7-4710 HQ CPU 2.5 GHz \\ RAM & 10 GB DDRII 1600 MHz \\ Storage & 512 GB SAMSUNG SSD 6 Gbps \\ Operating System & Microsoft Windows 10 64-bit \\ \hline \hline \end{tabular}
\end{table}
Table 3: Hardware specification used for the experiment.  
4. RAERA was slightly modified by introducing multi-sources since it was mainly based on single source multicast.  
The source nodes set \(|S|\), destination node set \(|D|\) and delay bond (\(\delta\)) will be given as a priority for SDN deployment [39]. The destination and source nodes sets are selected randomly independent of traffic matrix similar to in [3]. Table 5 shows the detail of the experiment parameters used in the simulated scenario for this paper.
## 2 Mechanism Analysis
### Non-Parasitic Motion Analysis
#### 2.4.1 Analysis of Non-Parasitic Motion of the Mechanism in the 1T2R Motion Mode  
A branched chain that is composed of links and pairs in series is called a Single Opened Chain (SOC). Therefore, the topological structure of branched chain 1 is SOC\({}_{1}\){-P\({}_{11}\)-S\({}_{12}\)-},the topological structure of branched chains 2 and 3 is SOC\({}_{i}\)[-U\({}_{1i}\)-P\({}_{1i2}\)-S\({}_{i3}\)-] (\(i=2\), 3), and the topological structure of branched chain 4 is SOC\({}_{4}\)[-C\({}_{41}\)-P\({}_{42}\)-U\({}_{43}\)-]. The center point \(O_{1}\) of the moving platform is selected as the reference point.  
In accordance with Equation (13), the POC sets of the branched chains in the 1T2R motion mode are as follows:  
\[M_{b_{1}}=\begin{bmatrix}l_{r}^{1}\\ r^{3}\end{bmatrix};M_{b_{2}}=M_{b_{3}}=\begin{bmatrix}l_{r}^{3}\\ r^{3}\end{bmatrix};M_{b_{4}}=\begin{bmatrix}l_{r}^{3}\\ r^{2}\end{bmatrix} \tag{15}\]  
where \(t\) and \(r\) represent translation and rotation, respectively, and the superscript is the number of translational/rotational motion.  
In accordance with Equation (14), the POC set of the moving platform in the 1T2R motion mode is  
\[M_{Pa}=M_{b_{1}}\cap M_{b_{2}}\cap M_{b_{3}}\cap M_{b_{4}}=\begin{bmatrix}l_{ r}^{1}\\ r^{3}\end{bmatrix}\cap\begin{bmatrix}l_{r}^{3}\\ r^{3}\end{bmatrix}\cap\begin{bmatrix}l_{r}^{3}\\ r^{2}\end{bmatrix}=\begin{bmatrix}l_{r}^{1}\\ r^{2}\end{bmatrix} \tag{16}\]  
The number of degrees of freedom of the mechanism is 3. Since the number of independent elements of the POC set of the moving platform is not greater than the number of degrees of freedom of the mechanism, the POC set of the moving platform can have three independent elements. It can be seen from the calculation of \(M_{pa}\) that there is one independent translation and two independent rotations of the moving platform, and there is no non-independent parasitic motion.
# Advanced Hybrid Beamforming Technique in MU-MIMO Systems
## 4 Advanced Hybrid Beamforming System
Because the digital precoder can use ZF scheme in PCS hybrid beamforming system, \(\sum\limits_{l\
eq k}\left|\mathbf{H}_{k}\mathbf{V}_{RF}\mathbf{V}_{D_{l}} \right|^{2}\) has zero. Therefore, the sum rate of all users is as follows,  
\[R=\sum\limits_{l=1}^{k}\log_{2}\left(1+\frac{\left|\mathbf{H}_{l}\mathbf{V}_{ RF}\mathbf{V}_{D_{l}}\right|^{2}}{\sigma^{2}}\right). \tag{11}\]  
Figure 8 shows the flow chart for the advanced hybrid beamforming scheme. The flow chart contains the method for estimating the azimuth and elevation angles of the \(k\)-th user and contains the process calculating the average sum rate. The proposed scheme is based on the nonlinear codebook design and uses the information for the azimuth and elevation angle of the multi-path with the highest power. Thus, the proposed PCS hybrid beamforming scheme has candidates which have higher reliability than the conventional PCS hybrid beamforming system, and it leads performance improvements. \begin{table}
\begin{tabular}{c|c} \hline \hline
**Beam Pattern Index** & \(\left(\theta,\,\phi\right)\) \\ \hline
1 & \(\left(\frac{1}{6}\pi,\,-\frac{1}{3}\pi\right)\) \\
2 & \(\left(\frac{1}{6}\pi,\,\frac{1}{3}\pi\right)\) \\
3 & \(\left(\frac{1}{6}\pi,\,\frac{\sqrt{2}}{2}\pi\right)\) \\
4 & \(\left(\frac{\sqrt{2}}{2}\pi,\,-\frac{1}{3}\pi\right)\) \\ � & � \\
9 & \(\left(\frac{2}{3}\pi,\,\frac{\sqrt{2}}{2}\pi\right)\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Example of the beam patterns.