esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 ERPCAWF-Based OMA Method for LTV Structures
### Theory of ERPCAWF  
To avoid recalculating the PCs and eigenvectors by matrix decomposition, we use a rank-1 matrix to correct the error [39], and update the new PCs and eigenvectors according to the eigenvalues and eigenvectors from the previous step. This realizes the effective tracking of time-varying structures. The rank-1 correction can be described as:  
\[\mathbf{C}_{rank1}=\mathbf{D}_{rank1}+\varepsilon\vec{u}\vec{u}^{T} \tag{13}\]  
where \(\mathbf{C}_{rank1}\in\mathbb{R}^{N\times N}\) and \(\mathbf{D}_{rank1}\in\mathbb{R}^{N\times N}\) are diagonal matrices with diagonal elements \(c_{t^{\prime}}\) and \(d_{t^{\prime}}(k^{\prime}=1,2,...,N)\), respectively, and \(\varepsilon\) is a constant. \(\vec{u}\in\mathbb{R}^{N\times 1}\) is the newest data vector, and \(\mathbf{C}_{rank1}\in\mathbb{R}^{N\times N}\) is calculated by correcting \(\mathbf{D}_{rank1}\in\mathbb{R}^{N\times N}\) according to the diagonal matrix \(\vec{u}\vec{u}^{T}\). This is called rank-1 matrix correction because the rank of \(\vec{u}\in\mathbb{R}^{N\times 1}\) is 1.  
Let the covariance matrix at time step \(i\), \(\mathbf{C}_{\mathbf{X}^{(i)}\mathbf{X}^{(i)}}\) be able to be decomposed into \(\mathbf{C}_{\mathbf{X}^{(i)}\mathbf{X}^{(i)}}=\mathbf{W}^{(i)}\mathbf{\Lambda} ^{(i)}\mathbf{W}^{(i)T}\), where \(\mathbf{\Lambda}^{(i)}\) is a real positive diagonal matrix and \(\mathbf{W}^{(i)}\in\mathbb{R}^{N\times N}\) is the unit orthogonal matrix. Thus, Equation (13) becomes:  
\[\begin{split}\mathbf{C}_{\mathbf{X}^{(i+1)}\mathbf{X}^{(i+1)}}& =\mu\mathbf{W}^{(i)}\mathbf{\Lambda}^{(i)}\mathbf{W}^{(i)T}+(1- \mu)\vec{x}(i+1)\vec{x}^{T}(i+1)\\ &=\mathbf{W}^{(i)}[\mu\mathbf{\Lambda}^{(i)}+(1-\mu)(\mathbf{W}^{ (i)T})\vec{x}(i+1)(\mathbf{W}^{(i)T}\vec{x}^{T}(i+1))]\mathbf{W}^{(i)T}\\ &=\mathbf{W}^{(i)}(\mathbf{D}_{rank1}+\varepsilon\vec{u}\vec{u}) \mathbf{W}^{(i)T}\end{split} \tag{14}\]  
Let \(\mathbf{D}_{rank1}=\mu\mathbf{\Lambda}^{(i)}\), \(\varepsilon=1-\mu\), \(\vec{u}=\mathbf{W}^{(i)T}\vec{x}(i+1)\). Then, through rank-1 correction, we obtain:  
\[\mathbf{P}\mathbf{I}\mathbf{P}^{T}=\mu\mathbf{\Lambda}^{(i)}+(1-\mu)( \mathbf{W}^{(i)T}\vec{x}(i+1))(\mathbf{W}^{(i)T}\vec{x}(i+1))^{T} \tag{15}\]  
Equation (14) can be further simplified as:  
\[\mathbf{C}_{\mathbf{X}^{(i+1)}\mathbf{X}^{(i+1)}}=\mathbf{W}^{(i)}\mathbf{P} \mathbf{I}\mathbf{P}\mathbf{W}^{(i)T} \tag{16}\]  
where \(\mathbf{W}^{i+1}=\mathbf{W}^{(i)}\mathbf{P}\) and \(\mathbf{\Lambda}^{(i+1)}=\mathbf{\Gamma}\) is the diagonal matrix formed by the updated eigenvalues. Therefore,  
\[\mathbf{C}_{\mathbf{X}^{(i+1)}\mathbf{X}^{(i+1)}}=\mathbf{W}^{(i+1)}\mathbf{ \Lambda}^{(i+1)}\mathbf{W}^{(i+1)T} \tag{17}\]  
where \(\mathbf{W}^{(i+1)}\) is the updated eigenvector. Thus, the sub-vector of the updated PC is:  
\[\mathbf{Y}^{(i+1)}=\mathbf{W}^{(i+1)T}\mathbf{X}^{(i+1)} \tag{18}\]  
Therefore, the new PCs and eigenvectors will be updated according to the old eigenvalues and eigenvectors on every iteration.
## 3 System Architecture and Implementation
### Envisioned Core Activities
All the above are illustrated in Figure 12. Figure 12 describes the state machine of the data log phase, Figure 12 illustrates the required activities, while Figure 12c showcases the sequence of the messages between the actors (users and services). **Data Request Activity:** During the data request phase, a user accesses the marketplace and creates an account and applying appropriate filters (e.g., data types, geolocation, etc.) reveals data streams on  
Figure 12: UML diagrams regarding the data log phase. the map. The user can purchase data, in chunks of m SDL, selecting from specific streams, where m is the result of the PSK changes frequency for each data stream. The user can either purchase access of data already in the Tangle or pre-purchase access for future SDL by subscribing to a data stream for a specific amount of SDL chunks. After the user selects the appropriate data stream and accepts the exchange of tokens, the backend generates an IOTA address and an Oauth token by using a uniquely generated seed [39]. Consequently, the user acquires the IOTA address to deposit the requested amount and the Oauth token in the data_field. Upon the verification of the transfer, the backend searches the database for the appropriate _msgid_ and _PSKs_ combinations which are used to find the transactions in the Tangle, fetches the Bundles and decrypts the data. Data are served through the web interface to the end user as the corresponding diagrams depict. The above mentioned service is also offered through a REST API that can be used in lieu of the web interface. Figure 12(a) showcases the state machine of the data request phase, Figure 12(b) depicts the required activities, while Figure 12(c) illustrates the sequence of the messages between the actors (users and services). Figure 13: UML diagrams regarding the data request activity.
## 4 Discussion
### Limitations  
This study has several limitations. First, the small sample (n = 16) limited statistical power and prevented subgroup analysis. Future studies with larger cohorts will be neededto confirm and extend these findings. Second, although a 5- to 7-day interval was implemented to minimize carryover effects, we cannot entirely rule out residual stimulation effects influencing sham responses. A longer washout period or counterbalanced design may be more appropriate.  
Third, we did not include a standardized fatigue assessment, which may have influenced task performance--particularly during later time points. Including such measures in future studies would improve interpretability. Fourth, knee stabilization in the ankle apparatus may have restricted natural movement, potentially influencing tracking performance. More flexible apparatus designs could increase ecological validity. Finally, we did not assess functional outcomes of post-intervention, limiting our ability to evaluate real-world benefits. Incorporating functional mobility measures in future protocols would offer a more comprehensive view of rPMS efficacy. Moreover, this was a single-blinded study in which participants were blinded to the stimulation conditions; however, we did not assess participants' subjective perception of stimulation, which may have influenced motor performance or engagement. Future studies should include post-session questionnaires to assess perceived stimulation and verify the effectiveness of blinding procedures. Lack of blinding among assessors may have introduced bias, and future studies should consider double-blind protocols
# A Review of the Control Plane Scalability Approaches in Software Defined Networking
## 1 Introduction
Firstly, _topology_ based scalability where the relation between the network topology (e.g., controllers' number, locations, etc.) and the scalability issues is investigated. Secondly, _mechanisms_ based scalability where the relation between the controllers' optimization mechanisms and scalability issues is investigated. Each of these main categories is further classified as shown in Figure 2. The benefits and shortcomings of different scalability approaches are presented as motivation for future work into optimization of scalability approaches. Figure 1: Software Defined Networking (SDN) General Architecture. The aim of this survey paper is to--(1) provide a classification taxonomy of the existing approaches devoted to achieve higher control plane scalability; (2) survey the topology-based and the mechanism-based approaches and evaluate them by their strengths and weaknesses; (3) provide a discussion and analysis on how these approaches attempt to solve the problem; (4) identify gaps and opportunities and elaborate on the promising research trends and challenges. The rest of this paper is organized as follows. Section 2 provides a review of the recent work of different topology-based scalability approaches. Section 3 reviews and discusses the mechanisms-based optimization approaches. Section 4 provides a thorough discussion over control plane scalability approaches and discusses to inspire further research efforts addressing the scalability in SDN. Section 5 concludes the paper and identify some future work avenues.
# Determining the Optimal Configuration of the Multi-Ring Tree for Bluetooth Multi-Hop Networks
## 5 Conclusions  
In order to determine the optimal configuration in one dense area and many sparse areas, this study proposes a multi-ring tree formation algorithm. With local topology information, the algorithm designs two stages to form the multi-ring tree scatternet. In the first stage, a leader root is elected and the optimal multi-ring configuration can be determined by a min-path algorithm for various sized subnets. In addition, a leader root connects with its neighboring roots to build the multi-ring subnet for the dense areas. In the second stage, each root in the multi-ring subnet starts to build its corresponding tree-shaped subnet for the other sparse areas. Finally, an optimal multi-ring tree topology is generated for non-uniform distribution applications. In addition, a proposed self-routing protocol for both multi-ring and tree topologies efficiently delivers packets over the scatternet, and this study also designs a self-healing routing protocol to extend network routing capability. As a result, the optimal configuration of the multi-ring subnet can be determined, and the multi-ring tree method outperforms conventional BlueHRT and DRT in terms of network performances. Thus, the optimal multi-ring tree topology demonstrates superior network performance for Bluetooth Low Energy networks.  
This work was supported by the Ministry of Science and Technology, MOST 106-2221-E-216-002 and MOST 106-2221-E-009-050, Taiwan.  
[PERSON] designed and developed the multi-ring tree topology with the self-routing protocol. In addition, a min-path algorithm is introduced and simulated to determine the optimal MRT configuration for Bluetooth multi-hop scenario. He was also responsible for writing and revising the manuscript. [PERSON] helped the validation stage of performance evaluation and simulation.  
The authors declare no conflicts of interest.
## 5 Performance Evaluation
### Energy Efficiency  
The purpose of the next experiment is the energy utilization efficiency of the proposed algorithm, where the deadline is set to be 1 s, and the required packet delivery ratio (i.e., _P_) is set to be 0.85. Figure 7 illustrates the result of energy consumption.  
The primary energy consumption consists of communication energy and the energy for processing the video frames. Compared with M3 SPEED, both CDCS and DCSR require energy consumption for in-network processing (i.e., encoding local video frames). However, the primary problem for M3 SPEED is that it lacks control for redundant data, so there is much energy consumption in routing decisions. The proposed CDCS approach aims to reduce energy consumption by reducing the transmission of redundant information and selecting energy-efficient next hops. Hence, both CDCS and DCSR consume less energy in contrast to M3 SPEED. Without consideration of FoV correlation, DCSR has less number of nodes that observe an object in a cluster. Due to limited video compression, the energy consumption of DCSR is less than CDCS.
###### Abstract
Although air is rarely used as an oxidant in thermal regeneration, their study suggested it may offer economic advantages due to lower energy consumption and shorter processing times. [PERSON] et al. evaluated regeneration using acid washing followed by thermal treatment (650-950 \({}^{\circ}\)C), demonstrating that acid washing prior to thermal regeneration was critical for reactivating spent GAC [21]. [PERSON] et al. assessed thermal regeneration of spent commercial activated carbon (SAC) by N\({}_{2}\) pyrolysis (Pyro-RAC) and CO\({}_{2}\) activation (CO\({}_{2}\)-RAC) [22], indicating the order of Pyro-RAC \(>\) CO\({}_{2}\)-RAC \(>\) SAC based on their specific area values. [PERSON] et al. used CO\({}_{2}\) activation and chemical activation with K\({}_{2}\)CO\({}_{3}\)/KCl, showing that combining K\({}_{2}\)CO\({}_{3}\) and KCl under N\({}_{2}\) pyrolysis conditions yielded higher pore properties compared to CO\({}_{2}\) activation alone [23]. [PERSON] et al. showed that while N\({}_{2}\) pyrolysis at 900 \({}^{\circ}\)C could provide the highest surface area values [24], a simpler thermal regeneration process at (\(<\)350 \({}^{\circ}\)C) and 1 h using an oxidizing atmosphere (air) could effectively restore most of the pore properties of exhausted GAC. [PERSON] et al. regenerated spent GAC using N\({}_{2}\) pyrolysis (700-900 \({}^{\circ}\)C) followed by steam activation for 0.5-1.5 h [25], showing that the optimal conditions for pore property recovery of GAC were at 800 \({}^{\circ}\)C and 1 h.  
In the previous studies [26; 27], spent bleaching earth (SBE) was thermally regenerated at 500-800 \({}^{\circ}\)C under pyrolysis gas nitrogen (N\({}_{2}\)) and 800-900 \({}^{\circ}\)C under CO\({}_{2}\) activation. Compared to untreated SBE, the regenerated material exhibited enhanced pore properties due to new pore formation from the carbonization and activation of non-desorbed organics [28]. Although regeneration of spent GAC is a well-explored topic, this work uniquely focuses on using a combination of N\({}_{2}\) pyrolysis and CO\({}_{2}\) activation, making it different from most prior studies. In this context, proximate analysis, thermogravimetric analysis (TGA), and pore property measurements of a spent GAC from a local liquid crystal display (LCD) manufacturing plant were first determined to be used as baseline characteristics. The regeneration experiments were then performed under various conditions (i.e., 800, 850, and 900 \({}^{\circ}\)C with holding times of 0, 30, and 60 min, respectively), in compliance with Taiwan's official regulation for spent GAC regeneration (i.e., \(\geq\)800 \({}^{\circ}\)C). The resulting pore properties (i.e., surface area, pore volume, and pore size distribution), along with textural and chemical characteristics of GAC samples (i.e., spent and regenerated GAC products), were analyzed and discussed in relation to the thermal regeneration conditions.
# Optical Hyperspectral Image Cryptosystem Based on Affine Transform and Fractional Fourier Transform
## 3 Numerical Simulations  
In this section, various numerical simulations are given to validate the performance of the proposed hyperspectral image encryption scheme. As shown in Figure 2, one hyperspectral image named \"Sandiego\", having 189 bands, which was captured by AVIRIS (Jet Propulsion Laboratory, USA), was used in the following experiment. In fact, any other hyperspectral image or multiband data can be used to process the presented cryptosystem. To simplify the calculation, we selected the first 100 bands from the 189 bands in the original hyperspectral image. Therefore, the dataset was used in the following experiments having the size 256 \(\times\) 256 \(\times\) 100. The random function \(\psi\) and \(\theta\) in affine transform are the random functions having 256 \(\times\) 256 pixels in the range of \([0,1]\) and \([0,2\pi]\), respectively. To simplify the encryption process, the parameter of FrFT was set to 0.5 in our encryption scheme. In fact, the parameters can be different for each band to enhance the security. The hardware parameters of the computer used in the simulation experiment are Core 2, CPU 2.1 GHz, and 2048 Mb memory (T430, Thinkpad Lenove, Beijing, China). By using the conditions described above, the encryption results are displayed in Figure 3. As shown in Figure 3, the encrypted result is a noise-like image.  
Figure 3: Experimental results: (**a**) the encrypted image and (**b**) decrypted image.  
Figure 2: The (**a**) RGB color composite and (**b**) 88 th band data of the original hyperspectral image.  
The peak signal-to-noise ratio (PSNR) is considered and introduced to weight the discrepancy between the decrypted and original data. The mathematical definition of PSNR is expressed as follows:  
\[PSNR(I_{\text{d}},I_{0})=10\log_{10}\frac{255^{2}M\times N}{\sum\limits_{ \forall x,y}\left[I_{\text{d}}(x,y)-I_{0}(x,y)\right]^{2}}(\text{dB}), \tag{5}\]  
where the symbol \(I_{\text{d}}\) and \(I_{0}\) represent the decrypted data and original data, respectively. In addition, the sizes of the data can be represented by \(M\) and \(N\). Here, the value of \(M\) and \(N\) are fixed at 256.
# Robot Delay-Tolerant Sensor Network for Overhead Transmission Line Monitoring
## 4 Results
### Results for the Change of Routing Parameters  
The values of the routing parameters are changed and the number and speed of the robots are adjusted in Scenario 1. The alpha of DTMA and beta of Prophet V2 varies from 0.05 to 0.99, with an increment of 0.05 for each simulation run. The number of copies of Spray and Wait varies from 1 to 10, with an increment of 1 for each simulation run. The configuration of the number and speed of the robots in Scenario 1 are as shown in Table 2. In different scenarios, the robots' performance changes with the configured parameters, as presented in Figures 11-13.  
As can be seen in Figure 12, for alpha values of DTMA between 0.05 and 0.65, there are no significant differences in all variables except message delivery ratio. For alpha values from 0.7 to 0.9, the hop count remains steady but the network life time and the delivery probability decreases sharply. These results show that choosing an adequate alpha value is a critical step, and that the alpha value between 0.65 and 0.8 most likely provides the best trade-off between network life time and delivery probability.  
In Figure 13, for the beta values of Prophet V2 ranging from 0.2 to 0.4, significant differences exist in the message delivery ratio. For the beta values from 0.2 to 0.37, the delivery probability goes steadily up, and when beta values are greater than 0.37, delivery probability begins to fall. In the simulation, the network delay keeps declining with the increase of beta values.  
The results of the change in the routing parameters of Spray and Wait are shown in Figure 14. In multiple scenarios, with the change of number of copies, network performances show a common feature. As the number of copies increase, the delivery ratio and message delay rise steadily, until when the number of copies is greater than 8, delivery probability begins to drop sharply.  
To sum up, this paper selects the optimized routing parameters based on the change rules of network performance. The parameter alpha of DTMA energy consumption is set to 0.78, and the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Scenario 1.1** & **Scenario 1.2** & **Scenario 1.3** & **Scenario 1.4** & **Scenario 1.5** \\ \hline Numbers of robots & 1 & 5 & 10 & 5 & 5 \\ Movement rate (ms\({}^{-1}\)) & 3–5 & 3–5 & 3–5 & 1–3 & 5–8 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of Scenario 1.  
Figure 11: Statistical analysis data of delivery time and distance at message send and hop count, the blue scatter point is the result of data distribution, the red histogram is the statistical result of the data distribution.  
optimized transfer factor of Prophet V2 beta is 0.36. For Spray and Wait, binary-mode mode is used and the initial number of copies is set to 8.
## 3 Experimental Data and Strategy Selection  
### Dataset  
Seven GNSS stations, each equipped with an H-master clock, were selected for this study and are listed in Table 1. Their geographical distribution is depicted in Figure 1. The PTBB station serves as the central node. The observational data were collected over a 22-day period, ranging from DOY (Day of year) 165 to 187 in 2023. The precise orbit and clock products will be derived by integrating the broadcast ephemeris with the Galileo HAS corrections. The IGS final products were obtained by downloading from the ftp server at [[ftp://igs.gnsswhu.cn/](ftp://igs.gnsswhu.cn/)]([ftp://igs.gnsswhu.cn/](ftp://igs.gnsswhu.cn/)) (from DOY 165 to187 in 2023, accessed on 13 April 2025). The GPS PPP time transfer solutions, derived from IGS final products using a static model, are established as the benchmark reference.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Station** & **Receiver** & **Antenna** & **Clock** \\ \hline PTBB & SEPT POLARX5 TR & LEIAR25.R4 & UTC (PTB) \\ BRUX & SEPT POLARX5 & TRM59800.00 & UTC (ROB) \\ OP71 & SEPT POLAX5 TR & LEIAR25.R4 & UTC (OP) \\ NIST & SEPT POLARX5 TR & NOV750.R4 & UTC (NIST) \\ TWTF & SEPT POLARX4 TR & ASH701945C\_M & UTC (TL) \\ USN7 & SEPT POLARX5 TR & TPSCR.G5 & H-MASER \\ USN8 & SEPT POLARX5 TR & TPSCR.G5 & H-MASER \\ \hline \hline \end{tabular}
\end{table}
Table 1: The information on the selected stations.
## 6 Ranking between Binder MSCR and Mixture Testing  
A paired ranking method was used to determine if the binder MSCR test matched well with the mixture's fatigue test. The method follows three steps: compare the MSCR results between base asphalt and SBS modified asphalt at different temperature and aging conditions and obtain a ranking (base asphalt \(\geq\) SBS, or base asphalt \(<\) SBS, six comparisons in total); compare mixture testing between base asphalt and SBS modified asphalt at different aging conditions and obtain a ranking (base \(\geq\) SBS, or base \(<\) mixture with SBS modified asphalt base asphalt \(<\) SBS, 12 comparisons in total). Then, the binder and mixture's ranking results were compared.  
The percent difference criterion was used for the ranking purpose. It is suggested based on engineering experience that if the percent difference was within 20%, then there was no significant difference between the pair [20]. Otherwise, whichever mean value was higher indicated more/less crack resistance. Results are shown in Figure 8. As seen, four out of six MSCR pairs showed a percentage difference over 20%, sharing the same trend with mixture ranking in which 11 out of 12 pairs showed a percentage difference over 20%. In contrast, one pair from the MSCR test and two pairs from mixtures' test experienced a percentage difference less than 20%.
## 7 Results and Discussions
### Effect of Temperature Gradients (\(\Delta\)T) on Natural Frequencies for Different Crack Depths  
Natural frequencies of a slant-cracked EG rotor-bearing system have been computed for different thermal gradients at a normalised crack position, \(\overline{Lc}\) = 9.5. Here, the temperature of Stainless Steel (T\({}_{\text{m}}\)) is fixed at 300 K, and the temperature of ZrO\({}_{2}\) is varied accordingly. It can be noticed from Figure 7 that the natural frequencies of the system tend to decrease with an increase in temperature gradient, while the material properties, such as Young's modulus, decrease with an increase in temperature. Therefore, the stiffness of the EG shaft is reduced. The presence of slant crack in the EG shaft further affects the stiffness of the EG rotor-bearing system. Consequently, the natural frequencies of the system are decreased.