esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 An Integrated Inductive-Deductive Framework for Data Mapping
### Distributed Self-Adaptation in AMA
#### 5.2.2 Adaptability Implementation in AMA  
To implement adaptability in AMA, the interpolation capability of the network is exploited to perform local training of the Map Generation service. Each node uses the readings of its surrounding nodes to predict its own reading value \((y^{\prime})\) using the _scale-based metric_ defined in Equation (14). Then, \(y^{\prime}\) is compared to the node measured value \((y)\). It is desirable that the estimate of \(y^{\prime}\) minimises the Standard Deviation \(\sigma\).  
Nodes modify the size of the support set to include the minimum number of nodes needed to predict \(y\) with a certain level of accuracy. Furthermore, in multi-dimensional applications, nodes will change the weight of each dimension to improve the prediction accuracy of \(y^{\prime}\). In fact, nodes will alter the relationships between different dimensions given in the initial domain model to reduce the effect of modelling inaccuracies or to adapt to emerging environmental changes. These model updates influence the estimation results, because \(y^{\prime}\) is calculated using the scaling metric \(m_{p}\) (Equation (14)). Finally, a prediction accuracy criterion, \(\Delta\), is defined as the average \(\sigma_{j}\) where  
\[\sigma_{j}=\sum_{i}\sqrt{\left(y_{i}-y^{\prime}_{i}\right)^{2}}\quad j\in[1,n ]\,\,\text{and}\,\,i\in[1,N] \tag{15}\]  
where \(n\) is the number of dimensions and \(N\) is the number of readings in dimension \(j\). Then, \(\Delta\) is written as  
\[\Delta=\frac{\sum\sigma_{j}}{n}\quad j\in[1,n] \tag{16}\]  
Likewise the one dimensional case, \(\Delta\) must always be minimised to achieve the best mapping results.  
However, when individual nodes alter their programmed domain model independently from the network, the Mapping Service may become unstable because of the inconsistency in the domain model defined on various nodes. Such inconsistencies may lead to inconsistent system states and conflicting differences in calculating mapping values. Furthermore, some of the detected environmental changes may result from sensing faults, which are hard to detect without collaboration with neighbouring nodes. To ensure mapping stability and overcome such concerns, we propose a _Virtual Congress Algorithm_ to manage global model updates locally.
# Development and Evaluation of Light-Weight Active Noise Cancellation Earphones
## 2 ANC Algorithms for Earphones
### Natural Sound for Secondary Path Modeling  
As shown in Figure 2, the adaptive feedback ANC algorithm requires the secondary path model \(\hat{S}(z)\) for synthesizing the reference signal \(x(n)\), and compensating the effects of secondary path \(S(z)\) for updating \(W(z)\). The most popular method for estimating the secondary path is adaptive system identification using a white noise as the excitation signal. Unfortunately, extra random noise is undesired for consumer electronics such as earphones. Therefore, this paper proposes to use natural sound instead of white noise for the secondary path modeling.  
Figure 3 shows the block diagram of using audio signal for the secondary path modeling. The natural sound signal \(a(n)\) stored in memory is used as the excitation signal to drive the loudspeaker, and it also served as the input signal for the adaptive filter \(\hat{S}(z)\). The desired signal \(b(n)\) is received by the error sensor.  
Figure 3: Block diagram of using natural sound for secondary path modeling.  
Figure 2: Feedback ANC algorithm.  
The modeling error signal is computed as  
\[e_{a}(n)=b(n)-c(n) \tag{4}\]  
where \(c(n)\) is computed as  
\[c(n)=\sum_{m=0}^{M-1}\hat{s}_{m}(n)a(n-m) \tag{5}\]  
In (5), \(\hat{s}_{m}(n)\) is the \(m\)-th coefficient of the secondary path estimation filter \(\hat{S}(z)\) at time \(n\). The adaptive filter is updated using the traditional least mean square (LMS) algorithm,  
\[\hat{s}_{m}(n+1)=\hat{s}_{m}(n)+\mu a(n-m)e_{a}(n),m=0,\,1,\cdots,M-1 \tag{6}\]  
Both the reconstruction and anti-aliasing filters are lowpass filters.  
It is important to note that Figures 2 and 3 can be combined to form the audio integrated ANC algorithm for on-line modeling of secondary path using the audio signal played by the ANC earphones [15].
# Application-Aware Network Traffic Management in MEC-Integrated Industrial Environments
## 5 SDN-eINP Interplay for Application-Aware Network Traffic Management  
This section lays out the architecture we designed to provide application-aware network traffic management through the SDN-eINP interplay. From a high-level perspective, the architecture consists of a control plane and a data plane (see Figure 2). In the following, we will break down those planes into fundamental components.
## 2 Theoretical Background
### Formability Evaluation Method  
In this study, the forming limit diagram (FLD) was used to evaluate the formability according to drawbacks, as shown in Figure 2. The FLD is a graph of the maximum deformation limit that the sheet metal can withstand during the deformation process. It plays an important role in predicting defects, such as wrinkling and tearing, that may occur in the forming process. As shown in Figure 2a, the FLD indicates the major strain and minor strain at the necking and fracture points based on each deformation state when the sheet metal is deformed. In the FLD, the area below the curve represents the safe range, and the area above the curve is the range where fracture may occur, as shown in Figure 2b. The designer must adjust the process parameters based on this data so that the specific deformation path can be located below the curve [15]. In Figure 2b, the lower left region of the FLD is an area where wrinkles may occur, and they are mostly generated when the sheet metal is compressed in the thickness direction. To ensure the safety of the forming process and improve the quality of the final product, it is important to evaluate formability using an FLD.  
Figure 1: Composition of the drawbead and force action schematic [8].
# Secure Authentication and Credential Establishment in Narrowband IoT and 5G
## 6 Experimental Results
### Runtime  
During the execution of the real-life experiments, the total elapsed time needed to perform a complete authentication process exchange was measured for each protocol. For each iteration of the experiment, all the packets detailed in Table 2 were exchanged for each protocol. First, a time mark with a precision of milliseconds was logged in the IoT device at the beginning of the procedure. Next, both the IoT device and the authentication server sent and received all the packets needed for the full exchange. Finally, when the IoT device received the last piece of information needed to complete the authentication process, an end time mark was logged. Note that this measurement includes all the real-life computational times of the IoT device, the intermediate routing elements, and the authentication server. Additionally, this measurement also includes any delays introduced by the real-life NB-IoT LTE network routing elements. Due to the high statistical skewness and kurtosis levels of the average results, the median was chosen instead as a better representation of this measurement. For the case of the LO-CoAP-EAP protocol, the measured value had a median of 1975 ms with a confidence interval of 1971-1982 ms over the median with a confidence level of 95%. Similarly, for the PANA protocol, the elapsed time had a median of 2857 ms with a confidence interval of 2849-2862 ms over the median, also with a confidence level of 95%. Figure 7 presents the aforementioned results.
# A Novel Strategy for VNF Placement in Edge Computing Environments
## 4 Challenges of VNF Placement in Edge Environments
### Decision Making for Deploying VNFs Considering VNF Requirements and SLA  
When deploying VNF instances into the edge tier, one should select the most suitable edge nodes so that SFC packets can traverse VNFs to meet the users' requests without violating the established SLAs. An SLA is defined per SFC and comprises the maximum end-to-end packet delay. The packet delay is the difference between the time a packet enters the first VNF and its processing in the last VNF of the corresponding SFC.  
Additionally, given the dynamism of the workload and the potentially high number of edge nodes in the network, the placement decision needs to be done automatically. Furthermore, this selection should consider the VNF requirements and optimize the use of service provider resources. Such requirements include CPU and memory for processing VNFs in edge nodes and bandwidth regarding the traffic between VNFs that compose an SFC. Several VNF placement solutions face these challenges [33; 34; 35; 36; 37; 38] and aim to overcome them to provide an optimal or sub-optimal approach.
From the electromyography (EMG) data, NMF analysis can be used to extract two components of the module: \"muscle weighting\", which indicates the coordination of multiple muscles (muscle synergies), and \"activation coefficient\", which determines the timing of the muscle synergy activity. For example, it was found that while the same number of muscle synergies were controlled in the jump landing of chronic ankle instability patients as in healthy participants, the synergies that control knee joint motion by the vastus lateralis and the lateral head of the gastrocnemius were deficient and a compensatory movement strategy by the gluteus maximus was used [16]. In addition, the module of ankle motion showed an increase in the muscle weighting of the tibialis anterior [16]. This was the basis for the demand for a new rehabilitation treatment for patients with chronic ankle instability. Recently, muscle synergy analysis has been applied to sports motion, and muscle synergies differ depending on performance level and the presence of injury [17; 18]. Given the expected increase in the number of WB players, the need for studies on performance improvement and injury prevention in WB will likely become even more pronounced. As one measure, understanding the muscle synergy comprising wheelchair propulsion enables the assessment of coordinated muscle activity during movement, leading to more specific and efficient training and injury prevention programs. In this study, we aimed to investigate the muscle synergy in the periarticulars muscles of the shoulder joint during WB wheelchair propulsion motion. We hypothesized that there are distinct muscle synergies for the PP and RP of wheelchair propulsion motion, with the PP constituting a synergy primarily with the shoulder flexor muscles and the RP constituting a synergy primarily with the shoulder extensor muscles. As a first attempt in this research project, the identification of muscle synergy during wheelchair propulsion motion in healthy participants unaffected by disability or pain will provide useful information as standard data to be compared with that of wheelchair basketball players in the future.
## 3 Proposed ATMALS-GSK Algorithm  
In this section, at first, the original GSK algorithm is described (Section 3.1), and then, the proposed ATMALS-GSK algorithm is presented in detail (Section 3.2). The proposed ATMALS-GSK algorithm has three innovations compared to the GSK for enabling it with auto-tuning of the controllable parameters, memory-based knowledge enhancement, and adaptive local search-empowered search strategies. These enhancements are proposed in Sections 3.2.1-3.2.5.
## 4 Experiments
### Ablation Study  
In addition to the method of weight initialization, two major improvements are introduced in the framework proposed in this paper to help train models on limited remote sensing images: the specific data augmentation strategy search and the regularization strategy. In this section, we explore the effectiveness of these two improvements. We investigated each component of GFSC by designing several controlled experiments on NWPU-RESISC45. For convenient performance comparisons, ResNet-50 initialized via SSL on NAP+ was used to explore the performance of the different components.
## 5 Open Challenges and Research Perspectives
### Optimal Service Placement  
Optimal placement of services that maximize performance, while minimizing resource utilization and latency, is a key factor in INC scenarios. Placement decisions must account for a complex interplay of factors, including application requirements, network conditions, and resource availability, while ensuring scalability and adaptability to dynamic traffic patterns [73].  
Centralized placement decision systems offer the advantage of holistic network visibility, enabling comprehensive optimization of in-network task placement. However, they require the real-time collection of extensive data from across the network, which can introduce communication overhead and potential bottlenecks. On the other hand, distributed placement decisions leverage localized information to reduce overhead and improve responsiveness. While these approaches are more scalable, they can lead to suboptimal placements if the global network state is not adequately considered.  
In both centralized and distributed approaches, the selection of input parameters is pivotal to the success of placement decisions. Input parameters may include application-specific requirements such as latency tolerance, bandwidth needs, and computational load, as well as network-specific factors like link capacity, node resource availability, and energy consumption constraints. Incorporating dynamic traffic recognition methods adds a critical layer of adaptability to the decision-making process. By classifying data flows into categories such as latency-sensitive or bandwidth-intensive, traffic recognition can guide service placement to dynamically adjust to current network conditions [74]. In parallel, ICN can support traffic-aware placement strategies, e.g., where popular or latency-sensitive services are proactively placed near end-users, minimizing delays and enhancing overall performance.  
Cross-layer technologies like Cognitive Radio (CR) could assist service placement by identifying nodes with optimal connectivity and channel availability, ensuring seamless communication between them [75]. Integrated with ICN, CR can dynamically manage spectrum access in highly variable 6G environments, enabling ICN nodes to operate across frequency bands that best meet the QoS requirements of applications. For instance, CR can complement ICN's content-based forwarding and caching by adapting to fluctuating network conditions, ensuring timely and reliable content delivery and computation, even in spectrum-constrained scenarios [76].  
Emerging 6G technologies such as Reconfigurable Intelligent Surfaces (RIS) can further enhance ICN's service placement strategies. For example, RIS can dynamically reconfigure the wireless environment by altering signal propagation characteristics, enabling ICN nodes to maintain optimized communication paths and reduce service provisioning latency. AI techniques can also complement placement strategies by dynamically identifying content and service popularity trends and predicting access patterns [77].
## 1 Introduction
In this study, we first used the EFAST method to screen out the sensitive ecophysiological parameters of the Biome-BGC model in these 10 sites and then assigned values to these sensitive parameters by means of a literature search. Sensitive parameters that could not be collected were calibrated to optimal values using the PEST method and measured values observed at the flux stations. Subsequently, the Biome-BGC model was driven to simulate the productivity (including GPP and NEP) of ten typical forest and grassland ecosystems in China. A detailed comparison of the model's simulation accuracy at these ten sites was conducted, leading to the derivation of a spatial pattern of the model's applicability across China. Furthermore, the study discusses potential improvements for the model in regions where its applicability was found to be limited. In this study, we quantitatively assessed the simulation accuracy of the Biome-BGC model across 10 typical forest and grassland ecosystems in China. Through parameter sensitivity analysis, a literature review, and parameter optimization, we developed a set of localized ecophysiological parameters for the Biome-BGC model, which will serve as a valuable reference for future users of the model in China. Furthermore, we strongly recommend that the application of the Biome-BGC model be preceded by thorough parameter sensitivity analysis and parameter optimization whenever possible.
# Frequency Division Multiplexing of Terahertz Waves Realized by Diffractive Optical Elements
## 3 Results  
### Experimental Setup  
The scheme of the setup used for the experimental verification of the DOEs is shown in Figure 5.  
Schottky diode based frequency multiplier with antenna (produced by Virginia Diodes Inc., Charlottesville, VA, USA--VDI) has been used as a source of terahertz radiation. The DOEs under measurement have been illuminated by a quasi-plane wave, collimated with a parabolic mirror. Intensities of the focal spots have been measured with a VDI Schottky diode with a hemispherical lens attached to it, placed on the motorized stage. All scans have been performed in the plane perpendicular to the optical axis in the 10 mm \(\times\) 10 mm matrices with a 1 mm step. The detector used has an angle of acceptance limited to about 15-20\({}^{\circ}\). The radiation with the frequency of 300 GHz is redirected at an angle of 32 \({}^{\circ}\) (according to Equation (1)). Therefore, to obtain the best possible information about the  
Figure 4: The photographs of DOEs with (**a**) \(f=200\) mm and (**b**) \(f=400\) mm manufactured by the SLS method from PA2200 (polyamide 12). The inset (**c**) shows the fragment of the 3D visualization of the \(f=400\) mm structure.  
Figure 5: Image depicting experimental setup. \(z_{1}\) was equal to 600 mm, \(z_{2}\) was established as optimal distance for 300 GHz for \(f=200\) mm and \(f=400\) mm separately. Distance of the scan in the measurement plane from optical axis \(y_{n}\) depended on frequency of reconstruction (details in Table 1).  
intensity and shape of the focal spots, it is advisable to rotate the detector in a way that it faces the middle of the DOE. All measurements have been performed with the detector aligned to the direction of propagation of the 300 GHz beam. The influence of the directivity and rotation angle of the detector has been discussed more specifically in our previous publications [41]. Each DOE has been tested with multiple frequencies according to their design: 240 GHz, 270 GHz, 300 GHz, 330 GHz, 360 GHz and additionally 390 GHz for DOE with \(f=400\) mm. The distance between DOE and the measurement plane (\(z_{2}\)) has been adjusted for the 300 GHz beam by maximizing the measured intensity in the scan performed along the optical axis and kept constant for a given DOE. The angle of the detector has been optimized for the 300 GHz beam and kept unchanged for a given focal length for the sake of comparison clarity. The distance \(z_{2}\) for DOE has been equal to 179 mm for \(f=200\) mm and 235 mm for \(f=400\) mm. The difference between the designed distances and the registered ones was caused by illuminating the structures by quasi-plane waves, which turned out to be slowly convergent.
# The Design and FPGA-Based Implementation of a Stream Cipher Based on a Secure Chaotic Generator
## 5 Conclusions  
In this paper, we studied and implemented on a Xilinx PYNQ-Z2 FPGA hardware platform using VHDL a novel chaos-based stream cipher (SCbSC) using a proposed secure pseudo-chaotic number generator (SPCNG). The proposed chaotic system includes some countermeasures against side channel attacks (SCAs) and uses a weekly coupling matrix, which prevents division and conquers attacks on the initial vector (IV). Next, we analyzed the cryptographic properties of the proposed SPCNG and evaluated the performances of its hardware metrics. The results obtained demonstrate, on the one hand, the high degree of security, and on the other hand, the good hardware metrics achieved by the SCPNG. After that, we realized the SCbSC system and asserted its resilience against cryptanalytic attacks. Further, we evaluated its hardware metrics and compared them to those of some chaotic and non-chaotic systems. All the results obtained indicate that the proposed SCbSC is a good candidate for encrypting private data. Our future work will focus on designing a chaos-based block cipher to secure IoT data and to check hardware implementations when using non-volatile FPGA technology, which reduces the side attack possibilities in real-field applications.  
Writing--original draft, F.D.; Writing--review & editing, F.D. and S.E.A.; Validation, W.E.H.Y., M.M. and R.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
**Data Availability Statement**: Not applicable.  
**Conflicts of Interest**: The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image** & **Horizontal** & **Vertical** & **Diagonal** \\ \hline Lena & 0.939403 & 0.971060 & 0.931085 \\ Lena encrypted & \(-\)0.003684 & \(-\)0.009015 & 0.002278 \\ Peppers & 0.959869 & 0.967869 & 0.940375 \\ Peppers encrypted & \(-\)0.005938 & \(-\)0.004665 & \(-\)0.001154 \\ Baboon & 0.877794 & 0.834230 & 0.788141 \\ Baboon encrypted & \(-\)0.006750 & \(-\)0.005998 & \(-\)0.002088 \\ Barbara & 0.907829 & 0.946119 & 0.883508 \\ Barbara encrypted & \(-\)0.008293 & \(-\)0.010526 & 0.004815 \\ Boats & 0.940837 & 0.953357 & 0.904555 \\ Boats encrypted & \(-\)0.002802 & \(-\)0.009909 & 0.002302 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Correlation coefficients of two adjacent pixels in the plain and ciphered images.
## 3 FEA Model and Validation
### Ground Surface Settlement  
Figure 9 shows the influence of the coefficients \(K_{g}\) and \(K_{s}\) on ground surface settlement. As shown in Figure 9a, the TBM tail of the second tunnel was parallel to ER60 of the first tunnel in a different case, the grouting pressure range from 0.6 to 1.4 \(G_{0}\) with an increment of 0.2 \(G_{0}\). The first tunnel construction results in a typical settlement trough which is consistent with the Gaussian distribution assumption [32]. Lower grouting pressures result in larger surface settlement above the second tunnel. Double-bottom settlement type can be observed during the tunneling process of the second tunnel. While the grouting pressure is 0.6 \(G_{0}\) (Case 1), the maximum settlement of 7.5 mm occurs above the center line of the second tunnel. Figure 9b shows the settlement curves under different face supporting pressure when the second tunnel face arrived at ER 60. The heading pressures of different cases range from 0.6 to 1.4 \(S_{0}\) with the gradient of 0.2 \(S_{0}  
the surface settlement of the first tunnel induced by the second tunnel advance is more sensitive to the grouting pressure than face supporting pressure.  
Figure 9: Surface settlement: (**a**) the cases with different grouting pressures; (**b**) the cases with different face supporting pressures.