esa-sceva/satcom-chunk-collection huggingface dataset:
# A Reconfigurable SRAM CRP PUF with High Reliability and Randomness
## 5 Measurement Result
### Inter and Intra Hamming Distance  
Both inter and intra Hamming distance analyses were performed to comprehensively evaluate the uniqueness and stability of the PUF [30]. The inter-HD quantifies the Hamming distance between responses obtained from 40 different PUF instances. Conversely, the intra-HD measures the Hamming distance between responses acquired from the same PUF cell across 5000 trials. Figure 7b depicts the histogram, which shows that the measured inter-HD is 0.4949 with a standard deviation of 0.0445. This inter-HD value indicates a high level of distinctiveness among the PUF cells. Additionally, the measured intra-HD is 0.0167 with a standard deviation of 0.01, indicating consistently reliable responses from the same PUF cell over time. These results highlight the robustness of the PUF, making it a dependable and stable hardware security component for various applications.  
Figure 6: (**a**) Die microphotograph of the proposed SRAM CRP PUF and (**b**) Overall chip measurement environment.  
Figure 7: (**a**) Measured Hamming weight distribution and (**b**) measured Inter/Intra-PUF Hamming distance distribution.
# A Spectrum Management Platform Architecture to Enable a Sharing Economy in 6G
## 2 Literature Review
### Spectrum Sharing Platforms  
Several articles in the literature propose platforms to address the problem of spectrum resource scarcity for next-generation networks. The work in [14] presents a survey on spectrum sharing for next-generation networks discussing how several methods can be implemented in a general architecture. Moreover, the authors discuss techniques for spectrum sensing, network selection, channel allocation, and power optimisation in spectrum sharing together with the corresponding security issues. The authors in [15] present a survey on recent spectrum-sharing solutions for 5G networks and 5G-enabling technologies. Spectrum-sharing methods are presented in terms of network architecture, spectrum allocation, and spectrum access solutions. Furthermore, they provide a survey on cognitive radio (CR) technology for spectrum sharing in 5G networks. Additionally, the work in [16] proposes a solution based on CR in 5G networks. Specifically, it proposes the integration of non-orthogonal multiple access (NOMA) with CR into a holistic system, namely a cognitive NOMA network, for intelligent spectrum sharing. In [17] authors present a survey on spectrum-sharing solutions, interference models, and interference management for IoT technology. Work in [18] presents a green spectrum-sharing framework targeting Beyond 5G networks with the aim to improve spectrum utilisation through a cost-efficiency approach for IoT devices.  
Moreover, note that communication networks have recently witnessed a paradigm shift in the way data traffic and spectrum resources are managed. The introduction of novel softwarisation techniques such as Software Defined Networking (SDN) [19] and Network Function Virtualisation (NFV) [20] has enabled more scalable and efficient management of these networks. Although initially limited to wired networks, such techniques are now also used for wireless communications. The rise of Software Defined Wireless Networking (SDWN) [21] represents an extension of SDN specifically in this context. Spectrum programming [22] extends this concept further, from managing data traffic right down to the radio resources themselves. In this context, the Wi-5 spectrum programming architecture in [22] presents a system to manage radio resources for Wi-Fi networks.
# Mixed Alternating Projections with Application to Hankel Low-Rank Approximation
## 5 Mixed Cadzow Iterations  
### Convergence of Linearized Mixed Iterations  
Let us introduce a linearized analogue of the mixed Cadzow algorithm (6), using the matrix \(\mathbf{P}_{\mathsf{S}_{0},(\mathbf{L},\mathbf{R})}\) defined in (14). Instead of (6), we will study  
\[\mathsf{X}_{T}=\mathbf{P}_{\mathsf{S}_{0},(\mathbf{L}_{2},\mathbf{R}_{2})}^{T _{2}}\mathbf{P}_{\mathsf{S}_{0},(\mathbf{L}_{1},\mathbf{R}_{1})}^{T_{1}} \mathsf{X}_{0}, \tag{20}\]  
where \(T=T_{1}+T_{2}\). Since this formula uses only linear operators, the following theorem follows directly from Theorem 2.  
**Theorem 6**: _Let \(\mathsf{S}_{0}\) be a time series governed by the \(\text{GLRR}(\mathbf{a}_{0})\). The following relations are true for iterations (20), where convergence is in the operator norm:_  
1. \(\mathbf{P}_{\mathsf{S}_{0},(\mathbf{L}_{2},\mathbf{R}_{2})}^{T_{2}}\mathbf{P}_ {\mathsf{S}_{0},(\mathbf{L}_{1},\mathbf{R}_{1})}^{T_{1}}\to\mathbf{\Pi}_{ \mathcal{Z}(\mathbf{a}_{0}^{2}),\mathbf{L}_{1}+\mathbf{R}_{1}}\) _if_ \(T_{2}\) _is fixed and_ \(T_{1}\to+\infty\)_._
2. _There is a symmetric positive-definite matrix_ \(\mathbf{Q}\in\mathbb{R}^{N\times N}\) _such that_ \(\mathbf{P}_{\mathsf{S}_{0},(\mathbf{L}_{2},\mathbf{R}_{2})}^{T_{2}}\mathbf{P}_ {\mathsf{S}_{0},(\mathbf{L}_{1},\mathbf{R}_{1})}^{T_{1}}\to\mathbf{\Pi}_{ \mathcal{Z}(\mathbf{a}_{0}^{2}),\mathbf{Q}}\) _if_ \(T_{1}\) _is fixed and_ \(T_{2}\to+\infty\)_._  
Thus, to achieve fast and accurate estimates, the weights \((\mathbf{L}_{1},\mathbf{R}_{1})\) should correspond to more accurate but possibly slower-converging Cadzow iterations, and the weights \((\mathbf{L}_{2},\mathbf{R}_{2})\) should correspond to less accurate but faster-converging iterations. The convergence rate of the corresponding operators is explained by Proposition 1. As follows from the discussion in Section 4.4, the pair of matrices \((\mathbf{L}_{1},\mathbf{R}_{1})\) is natural to choose as an approximation of the optimal weight matrix \(\mathbf{W}\) with a small parameter \(\alpha\), and the pair \((\mathbf{L}_{2},\mathbf{R}_{2})\) corresponds to a larger value \(\alpha\).
# Digital Twins, Virtual Devices, and Augmentations for Self-Organising Cyber-Physical Collectives +
## 3 Motivation and Related Work
### Reference Example: Self-Organising Navigation and Exploration in Dynamic Environments
Devices that are located inside the channel represent safe locations that can be followed as waypoints to move from the current location to the PoI. Usually, the approach is implemented through repeated computation of the self-organising logic in asynchronous rounds (although some models promote reactivity [31]), interwoven by exchanges of the information processed locally with devices considered to be logical neighbours. Devices can be neighbours because of the physical structure of the network (e.g., because they are communicating through a direct channel, such as a managed Wi-Fi network), or, more commonly, because of a logical network layer. In principle, the programming model takes no stance on the nature of a device: it could be a piece of hardware as well as a digital twin corresponding to some situated physical device. The consequence of this flexibility has been partly explored in [11], where different architectural deployments, with very diverse performance and cost profiles, are shown to be viable when implementing the \"digital thread\" (synchronising sensor readings from physical sensors to digital twins, forwarding actuation commands to physical twins, and dealing with the network required to enable logical interaction). Logical decentralisation makes the proposed approach to crowd-aware navigation scalable. Additionally, it is agnostic to the specific network infrastructure: it can work with mobile, opportunistic ad-hoc networks, and thus be available even when cloud-based navigation services such as Google Maps are not available. The sole requirements are: the ability to communicate with other devices, the capacity to provide an estimation of the distance to neighbours, and the ability to locate the PoI when in-range. Of course, the approach also has limitations: on the one hand, reachability issues may arise if crowded areas are considered entirely inaccessible (the channel may not be formed); on the other hand, diverse device density across the area may result in spotty service quality, with areas less covered by devices getting underexploited during path computation. To mitigate the latter issue, we consider spawning virtual devices to support coverage for areas with a low density of physical nodes, improving the application performance and availability, potentially finding shorter paths, at the cost of some additional virtualisation overhead. See Figure 2 for a graphical example of the idea.
# Removal of Cu(II) from Aqueous Solutions Using Amine-Doped Polyacrylonitrile Fibers
## 2 Materials and Methods
### Adsorption Experiments  
Adsorption experiments were conducted in batch systems. The required concentrations of Cu(II) were prepared by diluting the stock Cu(II) solution (1000 mg/L). Cu(II) solutions of different concentrations (50, 100, 150, 200, 300, and 500 mg/L) were stirred with 1.0 g/L surface-modified PAN fibers at 100 rpm and 25 \({}^{\circ}\)C. Then, the samples were filtered and their residual Cu(II) concentrations were determined using inductively coupled plasma optical emission spectroscopy (ICP-OES, Agilent 5100, Agilent Technologies, USA). Kinetics experiments were conducted on the system and were obtained by adding 0.04 g adsorbent to 40 mL of 100 mg/L Cu(II) test solution at different reaction times (0.25, 0.5, 1, 2, 3, 6, 12, and 24 h). To analyze the effect of the pH on the adsorption process, the pH of the solution was adjusted to 2, 3, and 4 using 1 M HCl or 1 M NaOH solutions. For the adsorbent dosage experiments, 0.02, 0.04, 0.06, 0.08, and 0.1 g surface-modified PAN fibers was added to 40 mL of 100 mg/L Cu(II) solution and the mixtures were shaken for 24 h at 100 rpm. Regeneration experiments were also carried out to observe the removal of Cu(II) ions using the surface-modified PAN fibers after they were subjected to a few regeneration cycles. Adsorption experiments were performed using 0.04 g surface-modified PAN fibers and 40 mL of 100 mg/L Cu(II) solution at pH 4. After 2 h, the adsorbed Cu was desorbed using 50 mL of 0.1 M NaOH followed by washing with DI water. Further adsorption tests were carried out under the same conditions.  
The adsorption capacity (q) and removal percent of Cu (%) were calculated using the following equations:  
\[\mathrm{q}=(\mathrm{C_{i}-C_{f}})\frac{\mathrm{V}}{\mathrm{w}} \tag{1}\]  
and  
\[Removal\ (\%)=(\mathrm{C_{i}-C_{f}})\times\frac{100}{\mathrm{C_{i}}} \tag{2}\]  
where q (mg/g) is the amount of Cu(II) adsorbed per unit of adsorbent, \(\mathrm{C_{i}}\) and \(\mathrm{C_{f}}\) (mg/L) are the initial and final Cu(II) concentrations, respectively, V (L) is the volume of Cu(II) solution, and w (g) is the amount of adsorbent.
## 3 Results
### Silver Effects on Mussel Antioxidant System  
In-gel assays were used to evaluate the long-term effects (28 days) of silver exposure--either to the nitrate form or with 5 nm alkane-coated AgNPs--on the antioxidant enzyme systems of mussels in the digestive glands or gills. Figure 5 shows the effects on catalase activity. Indeed, silver nanoparticles evoked no significant changes at the exposure levels tested, whereas the effects of silver nitrate were statistically significant in both digestive gland and gills (Kruskal-Wallis one-way analysis of variance, \(p\) = 0.019 and 0.040, respectively), with enzyme activity increasing in the latter tissue in accordance with a dose-dependent trend.  
Figure 5: Effects of silver on CAT activity. Semiquantitative analysis of enzyme activity as evaluated by densitometric analysis of achromatic bands in the gel assay (boxplot). Data (arbitrary units) are only comparable within each individual panel. Values between the inner and outer fences are plotted with asterisks. Values beyond the outer fences, called far outside values, are plotted with empty circles. (**A**) Digestive gland, silver nitrate exposure; (**B**) digestive gland, silver nanoparticle exposure; (**C**) gills, silver nitrate exposure; (**D**) gills, silver nanoparticle exposure. \({}^{\circ}\) statistically different from control reference (0), \(p\) < 0.05; two-sample Kruskal–Wallis, \(n\) = 40. Data for 0.2 ug L\({}^{-1}\) silver nanoparticles were not available.  
A more pronounced dose dependence was observed for the activity of SOD in the gills, while in the digestive gland, the appearance of a threshold effect of silver nitrate is now more evident. Both effects were highly significant (Kruskal-Wallis one-way analysis of variance, \(p<0.001\)). In contrast, no significant effects were observed for exposure to AgNPs (Figure 6).  
In the case of GPX, the total glutathione-dependent peroxidase activity increased for the effects of silver nitrate, but only in gills at the highest concentration tested (20 \(\upmu\)g L\({}^{-1}\)) (Figure 7).
# A Mobility Management Using Follow-Me Cloud-Cloudlet in Fog-Computing-Based RANs for Smart Cities
## 5 Performance Analysis
### Probability of Packet Loss  
The simulation results of the probability of packet loss v. the UE moving speed, cache size, and CDF are shown in Figure 14a-c, respectively. As the \(UE\) moving speed increases, the probability of packet loss reduces. As shown in Figure 14a, the probability of packet loss can be reduced if the more number of handover events occurred. Figure 14a shows that the probability of packet loss is 22.5 (%) using the PMIPv6 with FMC under the case of the UE moving speed is 5 (m/s). However, the probability of packet loss of our proposed scheme with FML are 23 (%), 20.5 (%), 14.5 (%), and 8.5 (%) if the cache hit rate is 0, 10%, 40%, and 70%, under the case of the UE moving speed is 5 m/s, respectively. Figure 14a shows that the probability of packet loss of PMIPv6 with FMC was \(\approx\) that of our proposed scheme with FML (the cache hit rate = 0%) was \(>\) that of our proposed scheme with FML (the cache hit rate = 10%) was \(>\) our proposed scheme with FML (the cache hit rate = 40%) was \(>\) our proposed scheme with FML (the cache hit rate = 70%) was, under the different UE moving speed. Figure 14b shows that the simulation result of the probability of packet loss vs. the different cache size (is ranging from 100 M to 200 M). The higher the size of the cache is, the lower probability of packet loss will be. Figure 14b shows that the probability of packet loss is 21 (%) using the PMIPv6 with FMC under the case of the cache size is 150 (M). However, the probability of packet loss of our proposed scheme with FML are 20.5 (%), 15 (%), 11.5 (%), and 8.6 (%) if the cache hit rate is 0, 10%, 40%, and 70%, under the case of the cache size is 150 (M), respectively. Figure 14b also shows that the probability of packet loss of PMIPv6 with FMC was \(\approx\) that of our proposed scheme with FML (the cache hit rate = 0%) was \(>\) that of our proposed scheme with FML (the cache hit rate = 10%) was \(>\) our proposed scheme with FML (the cache hit rate = 40%) was \(>\) our proposed scheme with FML (the cache hit rate = 70%) was, under the different cache size. Finally, Figure 14c offers the result of the CDF vs. probability of packet loss.  
Figure 14: _Cont._
# Mobile Robot Path Planning Algorithm Based on NSGA-II
## 3 Smooth Path Planning
### Path Smoothing Algorithm  
A key step in using Bezier curves to smooth path planning is selecting control points. During path planning, it is essential to ensure the continuity of the planned path, meaning that the curvature needs to be continuous when two segments of Bezier curves are concatenated. Simultaneously, it is crucial to avoid intersections between the planned path and obstacles, which would render the path invalid. The selection of control points directly affects the curvature and shape of the Bezier curve, making the position of control points critical.  
The position of control points determines the curvature of the curve and the continuity of the fitted path. Using the path nodes directly from the planned path may result in the planned path overlapping with obstacles, leading to an invalid fitted path, as shown in Figure 3.  
Therefore, the fitted path must meet the following three requirements:  
1. The fitted path must not collide with obstacles, i.e., \[|q(t)-w|>\frac{\sqrt{2}}{2},\] (12) where \(||\) represents the distance from the line segment to the nearest obstacle, and \(w\) represents the obstacle coordinates.
2. The fitted path must satisfy the smoothness requirement. To ensure the continuous smoothness of the path, it is necessary to ensure that the curvature is continuous at the junction of two Bezier curves. Due to the properties of Bezier curves, the tangent direction at the starting and ending points of the Bezier curve and the first and last edges of the characteristic polygon, respectively, are tangential. Therefore, it is only necessary to ensure that the first and last edges of the characteristic polygons of two Bezier curves lie on the same straight line.
3. The fitted path should be shorter, i.e., the fitted path should be as close as possible to the source path, i.e., \[min|q(t)-D|,\] (13) where \(D\) is the planned path.  
To meet these three requirements, this paper combines geometric methods and optimization algorithms to design a point selection algorithm for selecting control points. Then, a third-order Bezier curve is used to fit the path, ultimately generating a collision-free  
Figure 3: The planned path coincides with the obstacle.  
smooth path. A flowchart of the path planning algorithm is shown in Figure 4. The specific steps of the path smoothing algorithm are as shown in Algorithm 2.  
```
1: Input: \(D\), \(G_{max}\), \(Gen\);
2: Determine the start and end positions of all line segments in the path;
3: Generate the initial control point position;
4: for \(Gen<G_{max}\)
5: Calculate gradient;
6: Update momentum;
7: Update control point location;
8: \(Gen=Gen+1\);
9: end
10: Generate third-order Bezier curve;
11: Output: Smoothed path;
```  
**Algorithm 2** Path Smoothing Algorithm
## 4 Results and Analysis
### WASN-Based Analysis in Real-Life Scenarios
#### 4.3.3 The Visualization of Monitoring Platform  
Finally, we developed a monitoring platform to display the noise map, noise change of sensor nodes, and environmental sound event information. As shown in Figure 11, the 50 sensor nodes are marked on the map in the middle of the platform according to the location of actual deployment. The noise value measured by each sensor node was visually represented by the color circle, and interpolation was performed between two adjacent sensor nodes so that it can form a linear distribution. On the bottom of the platform, a colorful histogram displayed the noise statistics of some important area in a day. We can choose a different area to see the corresponding variation of noise. On the left side of the platform, the column of sound event information displayed the sound event information, including location, event, and time. As shown in Figure 12, the landmark thrived briefly when the system detected an acoustic event of the gate, what is more, the corresponding event information was presented if we clicked it. At the same time, the event information is displayed and recorded in the information column on the left. As shown in Figure 13, a button of data query was set on the top left of the platform, and all the above data can be reviewed by selecting data and time. Users can access the monitoring platform through the URL to view real-time data and historical data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Metric** & **Result** \\ \hline TP & 71 \\ FP & 16 \\ FN & 3 \\ Recall & 95.9\% \\ Precision & 81.6\% \\ F1-score & 88.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Recognition result with audio steaming.  
Figure 11: The monitoring platform.  
Figure 12: The presentation of acoustic event information.  
Figure 13: The function of data query.
## 3 Results and Discussion
### Effect of Solvent Volume/Composite Mass Ratio  
In order to determine the effect of the solvent volume/composite mass ratio on the DRs obtained, a number of experiments were carried out using the following ratios: 223 \(\mathrm{ml_{solv}/g_{comp}}\) (P6), 301 \(\mathrm{ml_{solv}/g_{comp}}\) (P4), and 448 \(\mathrm{ml_{solv}/g_{comp}}\) (P5). The other operating variables remained unchanged: 300 \({}^{\circ}\)C, 130 bar, 60 rpm, 80 min, 5 wt.% IPA solution as solvent, and 0.1 M KOH as catalyst.  
Given that the specific surface area (S.S.) could not be kept constant for these tests, with a surface area of 850 \(\mathrm{mm^{2}/g}\) in the 223 \(\mathrm{ml_{solv}/g_{comp}}\) test, 973 \(\mathrm{mm^{2}/g}\) in the 300 \(\mathrm{ml_{solv}/g_{comp}}\) test, and 950 \(\mathrm{mm^{2}/g}\) in the 448 \(\mathrm{ml_{solv}/g_{comp}}\) test, the conclusions reached in Section 3.2 should be taken into account. According to those conclusions, a certain increase in the DR is to be expected when the specific surface area of the sample also increases.  
According to the DR values (%) that are shown in Figure 6, the test with the lowest solvent/mass ratio (223 \(\mathrm{ml_{solv}/g_{comp}}\)) presents a slightly lower resin removal rate than the tests with greater ratios of 301 and 448 \(\mathrm{ml_{solv}/g_{comp}}\), namely, 90% versus 95% and 94%, respectively.  
A slight DR increment can therefore be observed as the solvent ratio increases. However, as the experiments seem to have been performed using a substantial excess of solvent,  
Figure 5: Decomposition rates achieved when using different reaction times at 300 \({}^{\circ}\)C, 100–130 bar, 60 rpm, 300 \(\mathrm{ml_{solv}/g_{comp}}\), IPA 5 wt.%, KOH 0.1 M.  
we can conclude that, in this case, the solvent/mass ratio should not have a great impact on the resin decomposition rates to be expected.  
Moreover, the fact that the S.S. of the composite samples could not be kept constant for these tests may have had some influence on the results. It is a fact that the experiment run at 223 ml\({}_{\text{solv}}\)/g\({}_{\text{comp}}\), which had the lowest S.S., also produced the lowest DR, while the experiment run at 301 ml\({}_{\text{solv}}\)/g\({}_{\text{comp}}\), which was the highest S.S., resulted in the highest DR.  
[PERSON] et al. (2017) [33] studied the recycling of a composite material with hybrid reinforcement (glass fibers and carbon fibers) for which water was used at conditions close to its critical point. As part of the tests performed, the effect of the ratio (solvent volume/composite mass) was evaluated, and it was observed that for ratios between 0.8 and 1.96 ml\({}_{\text{solv}}\)/g\({}_{\text{comp}}\), the registered DRs consistently maintained levels in proximity to 95%, which indicates that, under these conditions, the solvent/mass ratio is not a variable with a substantial impact on DR, as similar resin degradation rates can be achieved by using much lower amounts of solvent.