esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Effectiveness of the Proposed Classification of Clusters  
The classification of clusters into four categories--one-to-one, one-to-many, many-to-one, and many-to-many--based on their density provides a structured framework for managing various cluster configurations. These categories effectively cover various scenarios, from direct correspondence to merging and outlier patterns. However, these predefined categories may not fully encompass all possible spatial or geometric patterns, such as highly irregular clusters or features with specific axial or circumferential alignments (e.g., single-path colonies [37]). Extending the classification to include additional scenarios or subcategories could further enhance the model's ability to accommodate unique feature configurations.
## 5 Discussion
### The Importance of Remote Sensing in Large-Scale NSP Intensity Mapping
#### 5.2.2 Potential for Large-Scale NSP Intensity Estimation from Remote Sensing  
At present, the research on NSP has been limited to the watershed. NSP mapping for a larger scale has been unable to break through [75]. The main reasons for this are as follows: First, the NSP itself. NSP has the characteristics of fuzziness, lag, randomness, universality, and latency. When estimating on a large scale, these characteristics make the estimation more difficult. Second, the problem of NSP model. In the existing NSP models, both physical models and empirical models have their own disadvantages, which makes large-scale application very difficult. The main problem lies in the acquisition of parameters. If we want to obtain more accurate estimates of NSP intensity, we need more and more accurate field data. However, the measurement of these data often takes a lot of time, energy, and money [76]. Remote sensing can solve this problem and obtain ground inversion and classification data in a short time. In addition, the NSP intensity estimation combined with remote sensing can overcome the problem of the low efficiency of the distributed model and the low accuracy of lumped model. Taking rich large-scale remote sensing data as input parameters can refine the method of over averaging in the lumped model. It not only makes the results more accurate but also avoids the problem of low calculation efficiency caused by the need to partition the large-scale region in the physical model. Secondly, the temporal and spatial characteristics of remote sensing can also provide long-term and time-effective data sources, which makes it possible to study the multi-year variation and lag of NSP [77].
# Secure and Dynamic Memory Management Architecture for Virtualization Technologies in IoT Devices
## 2 Security of IoT
* A lightweight hashing-based block-chain method described in [13] ensures confidentiality, integrity, and availability protection for IoT devices in smart homes. The advantage of this method was that it incurred less computing overhead for improved security, compared to the non-secure base model [13]. * A method based on a physical unclonable function (PUF), described in [14], is characterized by ultra-fast performance, ultra-low energy consumption, and small silicon footprint, and is hence suitable for low-energy devices. PUF ICs are embedded into each IoT device in the network to create a secure network protocol [14]. The network protocol relies on PUFs' challenge and response instead of cryptographic functions. * Virtualization technology is considered as a security solution by itself in embedded devices [15; 16] due to its ability to provide unique execution environments for each application. Another main benefit of virtualization technology is that it can provide a heterogeneous application environment on a single embedded platform with temporal and spatial separation. In other words, it can provide an isolated application environment for real-time applications, and another one for general-purpose applications, on the same embedded platform at the same time. Thus, virtualization technology helps users to load their applications on embedded devices without affecting the security or performance of machine-critical applications loaded in separate virtual environments [17]. An isolated heterogeneous application environment is a prime requirement in embedded devices like smartphones and smart TVs, where the original equipment manufacturer (OEM) applications and user applications can run simultaneously without affecting the security and performance of the device. This article focuses on virtualization technology security-based solutions, as they can provide an isolated heterogeneous application environment for real-time applications. The next section discusses virtualization technologies in embedded devices and their challenges.
## 4 Proposed Model of SACFIR and SAMCFIR
### Network Topology Management Phase of SACFIR and SAMCFIR  
Both proposed models adopt the topology manager functionality to maintain the global view of current network nodes at the central controller to compute flow configurations for the available resources. The SDN controller requires periodic updates about the network state to establish an efficient and reliable topology discovery mechanism, which is critical for the accuracy of every SDN-enabled system. Existing OpenFlow-enabled sensor devices do not support any dedicated functionality for topology discovery, which is why it is the sole responsibility of the controller to implement this service. The most widely-implemented SDN controller platforms are derived from the topology discovery of the conventional controllers POXand NOX [33; 34]. Initially, this mechanism was known as the OpenFlow Discovery Protocol (OFDP), and, furthermore, OFDP leveraged the Link Layer Discovery Protocol (LLDP) [35]. This makes the exchange of LLDP the de facto SDN topology discovery standard, and generically, we refer to it as Topology Discovery (TD) packets.  
In the case of our proposed models, the SDN controller iteratively broadcasts TD packets on the OpenFlow southbound channel simultaneously with existing forwarding instructions. These TD packets envelop basic information like the identity of the corresponding SDN controller in the case of multiple SDN controllers available at BS, residual energy resources and the distance range between the sensor node and the serving SDN controller. In static networking environments, all sensor nodes initially share neighborhood distance information, the RSSI level among neighbors, the density and their energy level at the Tier 1 level. Distance remains constant, while density and residual energy resources require the sharing of regular updates. In contrast, mobility scenarios require continuous sharing of the current distance to BS and the exponential increment or decrement of distance in the neighborhood. Because each sensor node periodically generates a TD packet containing its current list of neighbors and transmits it to BS, the SDN controller can notify the sensor node to clear its pervious list and recompute it. A higher rate of continuous generation of these TD packets affects the performance of the network with limited resources. As WSNs can afford small-scale topology information exchange due to the shortage of energy resources, the execution of SACFIR and SAMCFIR requires the overhead of the frequent generation of TD packets to be minimized. In default settings, the interval size of 5 s is considered to be the TD packet interval [34]. In the case of the proposed models, the SACFIR-Visor undertakes topology management according to the application-sensitivity and residual energy resources of active sensor nodes. Therefore, it can increase the TD packet interval value to avoid the overhead of extensive topology updates. The SACFIR and SAMCFIR routing protocols enhance the settling phase management because they have an efficient network topology from the viewpoint of the SACFIR-Visor.
## 5 Simulation Experiment
### Disturbance Suppression without Loading Force Command
#### 5.1.2 Robustness of Suppression  
When designing the controllers, the perturbations of some system parameters have been taken into consideration. The designed hybrid controller achieves a good disturbance suppression performance with the nominal values of perturbation parameters. The influence of each perturbation parameter on the disturbance suppression is studied separately, meaning that when a parameter is perturbed, the other parameters are taking their nominal values. The experiments are carried out with the designed hybrid controller.  
Figure 19a-c show the restraining results for different \(p_{\mathrm{s}}\), \(\beta_{\mathrm{e}}\) and \(m_{\mathrm{L}}\), respectively. It is observed that the perturbation of \(p_{\mathrm{s}}\) and \(\beta_{\mathrm{e}}\) causes a very small increase in the maximum value of the output force (less than 5 N), while there is almost no effect for the perturbation of \(m_{\mathrm{L}}\). It can also be known from Figure 19b that increasing fluid bulk modulus \(\beta_{\mathrm{e}}\) is beneficial to disturbance suppression.  
Therefore, we can draw that the disturbance force, including the extraneous force and the friction force, is effectively suppressed by the proposed hybrid control scheme and controller, and it has considerable robustness within the perturbation range of our study.  
Figure 18: Effect of friction on suppression, (**a**) friction model with Stribeck effect; (**b**) corresponding graph of friction force and \(F_{\mathrm{L}}\) in the time domain with disturbance 7 sin14\(t\) (mm/s).
# Evolution of the Complex Supply Chain Network Based on Deviation from the Power-Law Distribution
###### Abstract
[9] proposed a model with preference selection and geographic distance limitation. The dual power-law distribution generated by this model coincides with the data of the world aviation network. [PERSON] et al. [10] generated the Zipf degree distribution using the generation and deletion mechanism of consecutive edges. In summary, most of the current research on the power-law distribution of complex SCNs is mainly based on network dynamics, cooperation, and evolution. However, there are few domestic and foreign studies on the deviation of complex SCNs. Therefore, in this paper, the complex SCN conforms to the characteristics of the small-world and the scale-free network. Based on the entire network data set, the model is constructed, and the deviation of the power-law distribution is described by the model. It is characterized by standard statistical methods. In reality, the underlying model of the power supply law distribution and deviation characteristics of complex SCNs explores the phenomenon of power supply network deviations from the perspective of complex networks. Therefore, this paper addresses the SCN in a complex environment. Combining complex network theory, this paper analyzes the topological structure and evolution char acteristics of the SCN with small-world and scale-free characteristics and explains that the current network distribution in many studies does not describe an accurate phenomenon. On the basis of the three parameter settings of new nodes, new connecting edges, and connecting edges, an SCN evolution model based on the deviation of the power-law distribution is established. Model parameters are resolved. This research is conducive to accurately characterizing the node distribution of complex SCNs, exploring the actual evolution mechanism of the network, and providing theoretical and technical support for supply chain enterprise and network layer management and optimization.
## 4 Experimental Validation
### Experimental Setup
#### 4.1.2 Competing Methods  
We compare SHAT with the following three baseline methods, including the two existing asynchronous PS-based training methods and a variation of SHAT.  
* ASP (i.e., HOGWILD) [6]: this baseline is a widely-recognized data-parallel distributed training method, where each worker updates its local model by replacement with the global model (i.e., \(\alpha=1\)).
* ENSEMBLE: this baseline is an ensemble learning method, where each worker trains its local model only locally without receiving the global model (i.e., \(\alpha=0\)).
* SHAT\({}^{Root}\): this baseline is a variation of SHAT with \(\alpha^{i}=1-\frac{h^{i}}{\sqrt{n}}\) for each worker.
* SHAT: this method is the original version of SHAT, the asynchronous PS-based training with \(\alpha^{i}=1-\frac{h^{i}}{\log n}\) for each worker.
## 3 Motion Planning
### Trajectory Generation
#### 3.2.1 Constraints on Positions  
For \(n+1\) intermediate points, there are usually \(n\) trajectory sections, and in this case, each trajectory section is represented by a seventh-order polynomial. These polynomials lie between intermediate points, and the positions of these points are well defined. Therefore, these polynomials must pass through these well-defined positions of the intermediate points at various allocated time stamps. Mathematically, we can write this as follows:  
\[p_{i}(t_{i-1})=\xi_{i-1} \tag{60a}\] \[p_{i}(t_{i})=\xi_{i} \tag{60b}\]  
where \(i=1\cdots n\), \(t_{i-1}\) is the initial time and \(t_{i}\) denotes the final time for \(i\)th polynomial, while the terms \(\xi_{i-1}\) and \(\xi_{i}\) are the initial and the final positions for \(i\)th polynomial, respectively.
# Preliminary Impact Assessment of the Ad Hoc Separation Minima: A New Separation Mode
## 5 Discussion
Considering the total \(LOS\) (lateral and longitudinal) and both resolution maneuvers (FL change and horizontal vector), there is an average reduction of 0.03 NM/\(LOS\) in distance saved (0.03% of the flown distance in the sector), a delay of 0.03 min/\(LOS\) saved (0.2% of the flight time in the sector), 20 kg fuel/\(LOS\) saved, and 63 kg \(CO_{2}\)/\(LOS\) saved. It should be noted that, in these results, there may not be a correlation between the reduced distance indicator and fuel consumption (as occurs in LECMDGU), since when a \(LOS\) is resolved with FL change, there is fuel consumption but the difference in terms of the flown distance at resolution and the planned distance is considered negligible. The best results are obtained for the reference scenario LECMZMU. However, there are also benefits in the LECMTLU and LECMDGU sectors, albeit smaller. In conclusion, it could be stated that if the concept were implemented in a broader scenario (ACC) rather than in a single sector, the obtained benefits would be more significant. The improvements in flight distance, fuel, \(CO_{2}\) emissions, and delay are a result of minimizing ATCO interventions in aircraft trajectories by reducing the number of \(LOS\) through the Ad Hoc separation application. Given the compelling findings outlined above, it is suggested to pursue further development and research into this innovative separation mode. Future research on Ad Hoc separation would follow a stepwise process. Firstly, this would entail the technical development, validation, verification, and certification of the ASMT tool, all based on the ASMT tool prototype. The certification process of this system should be conducted in accordance with the EASA and European Commission regulations [47]. The validation of this concept would follow the Eurocontrol E-OCVM methodology [48]. Upon positive validation, the implementation of this concept could be extended to futuristic scenarios, such as free route scenarios. Finally, the concept could be generalized to 3D and applied to other scenarios, including Terminal Maneuvering Areas (TMAs).
## 4 Algorithm Implementation
### Push and Pull Phases  
The transition between the Push and Pull phases can be achieved either by manually setting the population update generations or by introducing parameters for dynamic switching. This dynamic switching mechanism can automatically adjust the search strategy based on the optimization state of the population, thereby achieving a better balance between global exploration and a local search.  
During the Push phase, the algorithm ignores the constraint conditions and leverages the efficient optimization capabilities of the MOEA/D algorithm. By decomposing the multi-objective optimization problem into multiple subproblems and employing a neighborhood search mechanism, the algorithm accelerates the convergence of the population. This design enables the algorithm to rapidly explore the search space without considering constraints, thereby providing a well-initialized population for the subsequent Pull phase.  
In the Pull phase, the algorithm first introduces a constraint handling mechanism to gradually guide infeasible solutions into feasible regions. Additionally, a dynamic adjustment strategy is implemented, allowing the algorithm to adapt more flexibly to varying constraints and objectives.
# On the Application of Machine Learning to the Design of UAV-Based 5G Radio Access Networks
## 2 Application Scenarios
### UAV-Based Cooperation  
Relaying is a way to improve transmission reliability over fading channels, by opportunistically taking advantage of the broadcast nature of wireless communications [47]. Due to practical constraints, i.e., limited mobility and wired backhauls, many relays in terrestrial RANs are placed in fixed locations, thus providing only static relaying functionalities. Using UAVs as relays (see Figure 2) introduces additional degrees of freedom: for instance, UAVs can move between a source-destination pair, by uploading data from the source and, then, downloading it to the destination (so-called data ferrying) [38].  
In addition to relaying, cooperation among BSs can be beneficial to system performances as well. Specifically, neighboring UAV-based BSs can cooperate among themselves and/or with terrestrial networks, with the aim of decreasing the number and the latency of handovers, optimizing power and resource allocation, and mitigating interference of terrestrial and air-to-ground (A2G) transmissions [48; 49].  
Figure 2: A practical scenario where UAVs act as relay stations (RSs).
## 2 Materials and Methods
### ECG Signal Denoising Based on Discrete Wavelet Transform Method  
ECG signals are often accompanied by noise and interference. Therefore, the denoising of ECG signals is necessary before performing the feature extraction. Wavelet analysis helps to remove the noises and improves the signal strength [20]. The discrete wavelet transform (DWT) is a widely used method to decompose the signal simultaneously into time and frequency domain information. In general, to denoise the signal using DWT, we need to decompose the signal into L levels and select the suitable mother wavelet function [21]. The types of mother wavelets are Daubechies, Haar, Symlets, Biorthogonal, Coiflets, etc. However, selecting a relevant wavelet is the most important task, as there is no universal method to choose a particular wavelet. The wavelets are derived from a single prototype wavelet \(y(t)\) called the mother wavelet by scaling and shifting the parameters, as expressed in [21] Equation (1).  
\[\psi_{ab}(t)=\ \frac{1}{\sqrt{a}}\ \psi\ \bigg{(}\frac{t-b}{a}\bigg{)} \tag{1}\]  
where \(a\) represents the scaling factor, and \(b\) represents the shifting factors.  
Daubechies wavelet is one of the most perspective discrete wavelets transforms, with the basic properties of orthogonality, normalization and compactness of support [22]. The process of signal denoising based on discrete wavelet transform consists of decomposition of the signal, thresholding and reconstruction of the signal [23]. In the hard thresholding method, the wavelet coefficients with absolute values below or at the threshold level are replaced by zero, and the others are kept unchanged. In the soft threshold, when the absolute value of the wavelet coefficient is less than the given threshold value, set it as zero. If the coefficient is larger than the given threshold value, let the wavelet coefficient subtract the threshold value [24].  
Soft thresholding provides smoother results than the hard thresholding method [25]. We used the Daubechies as the mother wavelet of order eight and decomposed the signals into four levels using a soft thresholding technique. The Daubechies wavelet of order 8 (db8) of the original and denoised ECG signal is shown in Figure 1.
# High-Frequency Artifacts-Resistant Image Watermarking Applicable to Image Processing Models
## 2 Preliminaries and Proposed Method
### Loss Functions  
As an image watermarking framework, the frequency perturbation generation network \(F\) is responsible for generating the frequency perturbation \(f\). This perturbation is subsequently added into the frequency domain of the carrier image \(C\), yielding the marked image \(C^{\prime}\). And \(C^{\prime}\) is input into the watermark extraction network \(E\), with the objective of extracting the watermark. It is crucial that, when \(E\) receives a clean image, it should generate a noise. To achieve these objectives, our design incorporates four key loss functions: the frequency restriction loss, watermarking loss, clean loss, and concealment loss. These losses play a pivotal role in optimizing the watermarking process and ensuring its effectiveness.
## 4 Discussion
### Blast Athletic Performance Monitor Device  
The Blast Athletic performance device is a video capture technology that analyzes performance, jump, and sprint. Blast automatically detects the jumps and their respective heights, later synchronized via Bluetooth with the Blast Athletic mobile app on the smartphone for further analysis.  
Only the study by [PERSON] and [PERSON] [19] used the Blast Athletic performance monitor in its investigation. The authors reported that Blast underestimated jump height and had limited ability to detect changes up to 5.0 cm after a volleyball season. In addition, a relative correction was observed that reduced but did not eliminate the measurement error. However, researchers should consider that this device has only been validated in a single study; thus, further studies must confirm and validate the results against other devices.
# Model Test on Effect of Ground Fissure on the Behavior of Oblique Two-Section Subway Tunnel
## 3 Results and Discussion
### Dynamic Response of Tunnel Strain  
Figures 23 and 24 show the maximum longitudinal additional strain at the crown and bottom of the tunnel under the 10 Hz excitation from 60 and 100 cm. Results show a neglected additional strain at the tunnel crown when the ground fissure stays in place (nearly 0), as shown in Figure 23. Moreover, after the hanging wall of the ground fissure descends from the original place, a negative additional strain develop at the tunnel crown in the hanging wall (compression), while a slight positive additional strain is identified at the tunnel crown in the footwall (tension). This is due to the existence of the tunnel section. The downward force of the soil only affects the local region that is near the ground fissure, and, thus, causes a relatively low additional strain. Test results at the tunnel bottom (Figure 24) show that the excitation-induced additional strain at the tunnel bottom is quite small when the hanging wall of ground fissure stays in place. When the hanging wall of the ground fissure descends from the original place, a positive additional strain is generated at the tunnel bottom in the hanging wall, and a small strain is measured at the tunnel bottom in the footwall.  
Figure 23: Distribution of the maximum longitudinal additional strain at the tunnel crown: (**a**) vibration exciter at 60 cm; (**b**) vibration exciter at 100 cm.