esa-sceva/satcom-chunk-collection huggingface dataset:
# Cooperative Power-Domain NOMA Systems: An Overview
## 3 Types of NOMA
### Power Domain NOMA
#### 3.2.2 NOMA with SWIPT Protocol
Second, the MISO approach was discussed in order to increase the data rate of users with strong channel conditions. Third, in order to explore practical applications, the SISO approach was utilised in the transmission strategy. _NOMA-EH:_ The authors of [94; 101] integrated SWIPT-based NOMA, using EH protocol with relay nodes that enable communication between the BS and many users, similar to C-SWIPT-NOMA. More then one antenna was utilised on the BS and also at the users in this approach. At the BS and many users, TAS and maximum-ratio combining (MRC) methods were also employed. The performance of these methods was evaluated using a closed-form definition of outage across Nakagami-in fading and Rayleigh channel distributions. _SWIPT-FD/CR-NOMA:_ The authors in [102] looked at the effects of two different power distribution strategies in C-SWIPT-NOMA. The first method used fixed power, whereas the second used CR. Users' power allocation coefficients were fixed under SWIPT-F-NOMA, and weak users were provided more power than strong users. The power allocation coefficients of SWIPT-CR-NOMA, on the other hand, were opportunistic. It offers a variety of reliability, complexity, and fairness trade-offs. _SWIPT-NOMA-HETNET:_ Having poor CSI, the authors in [103; 104] used femtocells in a SWIPT-NOMA system for better resource allocation. Subchannel allocation was developed using the many-to-many matching approach. The macro-users' preference lists were created based on the energy that they gathered from media broadcast satellite (MBS) RF transmissions.
## 3 Experiments and Results
### Statistical Analysis of the Best Fitted Methods  
We conducted a statistical analysis shuffling randomly over a hundred times, training and test subsets. Results in terms of error were registered as new variables for statistical representation. We considered RMSE as the main figure of merit for this exercise, and the mean and its STD were computed. This type of approach results in a better and more complete representation of estimators' behavior whether they are presented in consolidated terms, by sector, or by market. Table 3 depicts these results when the previously outperforming ML techniques were applied, specifically bagging trees, the GPR, and the SVR. The comparative analysis between the exclusive generalization capabilities of endogenous variables, versus the evaluation of both endogenous and exogenous characteristics, consolidated previous results of the incremental predictive power of exogenous information in terms of the mean. In terms of STD no significant difference was found among this two scenarios. Uneven performance was observed in the analysis of the models when they were consolidated by sectors of activity in different markets. In the case of consumer activities, both _cyclical_ and _non-cyclical_ highlighted that the incorporation of exogenous information does not add predictive power to the
## 3 Theoretical Framework
### Containers
The application should also consist of a variety of types of components, such as:  
* Presentation components are responsible for the control of the user interface and the consumption of remote services;
* Domain or business logic, which refers to the domain logic of the application;
* The access logic to databases is the data access components responsible for obtaining access to the databases (SQL or NoSQL); and. * Application integration logic, which includes a messaging channel, based mainly on message agents [59]. The application will require scalability, allowing its vertical subsystems to scale horizontally autonomously as some subsystems will require greater scalability than others [60]. Considering, for example, a mobile shopping application with the following microservices: (a) A Shopping cart service that maintains the number of items. (b) A Shipping service that supports for handling shipments and cargo. (c) An Inventory service that allows an organization to react to customer demand rapidly. (d) A recommendation service, which provides recommendations to users. (e) An Order service, which maintains an order history. (f) A review service designed to identify potential service delivery improvements. (g) A Catalogue service that keeps product catalogs [61]. Moreover, also considering, e-commerce Web applications [62], which need to be developed independently with the freedom in the use of technologies. This composition requires programming language on the server-side and the framework on the client-side. Likewise, the authentication service should be used, which subsequently would allow security in access to microservices. Finally, the orchestration of the containers is required, where the microservices would be deployed. This solution may work significantly better than a monolithic model concerning performance, availability, and response time indicators. Finally, considering rendering applications with mobile platforms such as ANDROID and IOS. On the server-side, there are non-functional components that support the microservices (i.e., Config, Eureka, Gateway, and Composition) and the functional elements. The last ones are authentication microservices, assists, notes, co-participation, surveys, notification, and reports developed with spring boot. Each one will have private access to its database. How could access security be added to services [63; 64]?
# Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
## 4 Experimental Results
### Performance Metrics  
In machine learning and pattern recognition tasks, performance evaluation metrics play a key role in quantifying and comparing the performance of models. In this section, we will take a closer look at the main performance metrics used to evaluate the performance of classification models: precision, recall, and F1 score [32].  
First, precision is a metric that indicates the percentage of samples that a model predicts as true that are actually true. More specifically, precision indicates how many results the model predicts as positive classes that are actually positive. This metric is represented by the following formula:  
\[\text{Precision}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Positives}} \tag{9}\]  
where true positives refers to the number of samples that the model predicts are true and are actually true, while false positives refers to the number of samples that the model predicts are false but are actually true. Precision is a measure of how reliable a model's positive predictions are, which is important for reducing unnecessary misdiagnoses.  
Recall is a metric that shows the percentage of samples that the model predicts as true out of those that are actually true. In other words, recall shows how many of the true positive samples in a positive class the model correctly detects. Recall is calculated with the following formula:  
\[\text{Recall}=\frac{\text{True Positives}}{\text{True Positives}+\text{False Positives}} \tag{10}\]  
Recall indicates how well the model detects true positives. It is particularly important for ensuring that positive cases are not missed.  
The F1 score is a metric calculated as the harmonic mean of the precision and recall, which balances the accuracy and precision of the model. It is a useful metric for evaluating a model's performance from different angles. The F1 score is calculated using the following formula:  
\[\text{F1 score}=2*\frac{\text{Precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{11}\]  
The F1 score represents a balance between precision and recall, as shown in Equation (11). It evaluates a model's performance by considering both the accuracy of its positive class predictions and the detection rate of actual positive samples. This provides a more comprehensive view of a model's performance than considering accuracy alone. This concludes our detailed discussion of performance evaluation metrics. These metrics can be used to evaluate the performance of a model. They can help us to select and refine the right model for a particular problem.  
Figure 6: Samples of EMNIST dataset.
## 3 Literature Review
### The Relationship Between Supply Chain Risk Management and Supply Chain Resilience  
The importance of supply chain risk management in detecting disruptions, minimizing their effects, and strengthening SCR has been widely explored. Studies by [PERSON] and [PERSON] (2008) [43] and [PERSON] and [PERSON] (2018) [58] identified supply chain risk management as a vital tool for addressing risks effectively. By mitigating risk impacts and enhancing knowledge reserves related to risks, supply chain risk management plays a direct role in improving SCR. [PERSON] and [PERSON] (2021) [6] underscored the significance of supply chain risk management practices in building resilience and robustness, particularly during the COVID-19 pandemic, further validating the Resource-Based View (RBV) and OIPT.  
From an information processing perspective, [PERSON] et al. (2024) [29] explored how information processing capabilities (e.g., disruption orientation and visibility) and digital supply chains enhance SCR through supply chain risk management. Their findings indicate that information processing capabilities significantly influence supply chain risk management and SCR, with a stronger effect on supply chain risk management. Similarly, [PERSON] and [PERSON] (2023) [59] demonstrated through empirical research that the Internet of Things (IoT), as a mediating variable, enables supply chain risk management to directly and indirectly enhance SCR.  
Other studies have examined the specific mechanisms through which different supply chain risk management practices contribute to SCR. [PERSON] and [PERSON] (2020) [60], using the dynamic capability framework, identified resilience capability as a mediator between supply chain risks and SCR with mitigation strategies serving as a moderator. [PERSON] et al. (2023) [10] showed that procurement digitalization enhances SCR by improving information sharing and risk management capabilities, thereby reducing uncertainty. [PERSON] et al. (2022) [50], using a contingency RBV approach, confirmed the significance of fourth industrial revolution technologies, such as big data analytics and additive manufacturing, in enhancing risk control capabilities and improving SCR.  
These studies collectively suggest a close interdependence between supply chain risk management and SCR. For instance, fostering a risk management-oriented culture is considered essential for building resilient supply chains. Thus, this study proposes the following hypothesis:  
**H1:**: _Supply chain risk management positively influences SCR._
## 3 Simulation Results
### Time-Domain Current Response
#### 3.1.1 Impulse Response  
Figure 2a shows that, when subjected to a positive impulsive voltage \(\delta(t)\), the phase conductor immediately responds with a current. Hence, the phase conductor current shows a discontinuity, due to the infinite power injected into the system by the impulsive voltage waveform. As can be observed in Figure 2b,c, the phase current impulse immediately induces a negative current in both the pipeline and the soil, due to [PERSON]'s law. Indeed, since the magnetoquasistatic approximation was adopted in the FEM model, the magnetic field instantaneously generated by the phase conductor propagates with infinite speed.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Conductor** & **x (m)** & **y (m)** & **Radius (m)** & **Conductivity (S m\({}^{-1}\))** \\ \hline Phase \(a\) & \(3.5\) & \(12\) & \(1.5\times 10^{-2}\) & \(3.5\times 10^{7}\) \\ Phase \(b\) & \(-3\) & \(14\) & \(1.5\times 10^{-2}\) & \(3.5\times 10^{7}\) \\ Phase \(c\) & \(2.9\) & \(16\) & \(1.5\times 10^{-2}\) & \(3.5\times 10^{7}\) \\ pipeline & \(2\) & \(-1.1\) & \(5\times 10^{-1}\) & \(5.5\times 10^{6}\) \\ soil & - & - & \(6\times 10^{2}\) & \(0.01\) \\ \hline \end{tabular}
\end{table}
Table 1: Geometrical and electrical data.  
Figure 1: Cross-section of the considered pipeline–power line corridor. The physical characteristics of the conductors are reported in Table 1.  
Figure 2: Current response of Phase Conductor \(a\) (**a**), the pipeline (**b**), and the soil (**c**), as a result of different voltage waveforms enforced in Phase Conductor \(a\) at \(t=0\) s.  
Due to the skin effect, an intense current density develops in the upper layers of the soil, screening the pipeline from the phase conductor magnetic field. This is the reason behind the observed delay in the pipeline current response with respect to the other conductors. The dynamics of the magnetic field diffusion in the soil are regulated by the product \(\mu_{s}\sigma_{s}\), where \(\mu_{s}\) and \(\sigma_{s}\) are the soil's magnetic permeability and electrical conductivity, respectively. Hence, defining \(d\) as the pipeline depth, \((\mu_{s}\sigma_{s})d^{2}\) yields a characteristic magnetic field penetration time \(\tau_{p}=1.5\times 10^{-8}\) s, compatible with the dynamics of the pipeline current in Figure 2b.  
Focusing on the pipeline and soil impulsive response in Figure 2b,c, the induced negative current becomes positive at 0.138 s and 2.7 ms, respectively. This effect was obtained because of the progressive decrease of the phase conductor current from its initial value caused by the impulsive voltage waveform.
## 6 Limitations  
The accuracy of the deep learning models was limited to select healthy control and Moyamoya cases when segmented by the Nested U-Net. Moyamoya patients have smaller arteries with generally lower blood flow, which may lead to relatively poorer segmentation accuracy. The arteries of interests in this study also are smaller 2D objects than the vessels typically used in cardiac studies previously used in DL studies [9]. This created a challenge for automatic segmentation, especially with the presence of other blood vessels of similar shape and even size. Other limitations included the requirements of having a reconstructed phase image based on several cardiac phases. The accuracy of our models was also limited by the small number of Moyamoya cases. Additional data augmentation may be considered to increase the number of training cases. Whilst we understand that there are various other implementations of the standard U-NET for image segmentation tasks and the importance of comparison between these techniques, we presented two such implementations and their results in this work. Since our institution treats several new Moyamoya patients each month, we will consider adopting different U-Net structures, such as UNet++ and MAnet, in future studies. Furthermore, the performance of the model may also be improved by including additional MR scans (such as MRA) obtained in the same session. Other challenges regarding data leaking may also need more systematic investigation to improve the performance of the segmentation algorithm.
## 3 Results and Discussions
### Low Frequency Mini-Brillouin-Zone Mode
#### 3.2.4 Indications of Stimulated Subharmonic Decay of MBZ-Center Mode  
Our first approach of providing an indication of the stimulated subharmonic decay of the mode \(\omega(0)\approx 32\) GHz into the mode \(\omega(0)/2\approx 15\) GHz consists in the evaluation of the dependencies of the observed mode amplitudes on pump laser power. The pump power is varied from 43 mW to 67 mW in steps of 4 mW. In the considered interval of pump powers, the amplitudes of the evaluated individual spectral lines are well above the noise level. A small time interval ranging from 500 ps to 800 ps is chosen to capture the decaying center mode at 32 GHz. The acoustic signals and their spectra are presented in Figure 7a,b. The dependencies of the 15 GHz, 32 GHz, 63 GHz, 80 GHz and 95 GHz modes amplitudes on the pump power are presented in Figure 7c. Those modes are normalized by their amplitudes at 43 mW power. This plot demonstrates the following tendency: the decrease of the 32-GHz mode amplitude is accompanied by an increase of the amplitudes of several other frequency components, which could be related to the detection of the zone-edge mode around 15 GHz frequency through a Raman-Nath-like probe light scattering process which accounts for the presence of frequencies at \(m\omega(0)/2\) (\(m=1\), 2, 3,... ). The experimental dependences in Figure 7c are importantly nonlinear and multiple nonlinear processes could contribute to the details of these dependencies, including the pump absorption saturation in the QWs. However, it is clear that the decay in the amplitude of the 32 GHz mode is overall consistent with the hypothesis that its energy loss via a nonlinear process of the stimulated subharmonic decay could provide a contribution to the total dynamics of the \(\sim\)15 GHz mode. In Figure 7d, the amplitude ratio between the 32 GHz and 15 GHz modes decreases from 1.5 to 0.5, which is clearly a sign that the mode \(\sim\)32 GHz could play a role in the excitation of the otherwise unexplainable mode at \(\sim\)15 GHz.
# Estimating the Memory Consumption of a Hardware IP Defragmentation Block
### Significance of the Research  
The evolution of fiber networks leads to a rapid increase in network data throughput. Leading examples of this trend are represented by an adoption of 100 Gb and 400 Gb Ethernet standards. Meanwhile, the general-purpose CPU computing power lags behind. In effect, the network processing tasks are being shifted from software to hardware. The hardware takes the form of a Smart NIC, such as Intel X/00 [11] or Mellanox Connect6 [10], or becomes a SOC with integrated network functions, such as Marvell Octeon family [12].  
This high-end networking hardware is used predominantly in:  
* 5G infrastructure, e.g., packet data gateways;
* Security appliances, such as firewall or Intrusion Detection System (IDS).  
In both cases IP defragmentation plays a significant role in the performance of a system. The effectiveness of IDS, for instance, depends on the ability to reconstruct the original data stream from fragmented packets. That is, the core functionality relies on Deep Packet Inspection (DPI). Some of the popular evasion techniques (means to bypass security measures) rely on a specially crafted fragmented packet stream.  
In spite of those challenges, there is a pressing need to equip the networking hardware with the IP defragmentation function. As always, the industry strives to optimize the use of resources such as SRAM, which is relatively expensive (w.r.t silicon area) and energy-consuming.  
This work attempts to address the resource optimization problem in a systematic manner. That is, by doing design space search and interpreting the results using modern statistical techniques.  
In summary, the significance of this work stems from the significance of network accelerator hardware, which becomes ubiquitous both in the cloud operators' data centers as well as in the telecom infrastructure. Moreover, this research originates from the industry itself, as one of the authors has been working for the networking silicon company. In this case it is not a purely theoretical endeavor but an effect of cooperation between academia and industry.
## 5 Ocean-Based Climate Change Solutions Enhanced by DL
### Mitigation Strategies Using DL
#### 5.1.1 Developing Ocean-Based Renewable Energy with DL
Focusing on the Mediterranean Sea and the Persian Gulf, regions with complex wind patterns shaped by coastal orography, the model demonstrated robust performance, obtaining biases of \(-1.1^{\circ}\) and \(-4.6^{\circ}\) compared to ERA5 data and in situ measurements, respectively. (TCNs) to extract features from X-band radar images. Their study demonstrated that TCNs outperform traditional methods like SVMs in wave height estimation by capturing temporal dependencies more effectively. [PERSON] et al. [280] introduced Transformers for wave height estimation from X-band radar images, treating the input images as sequences. After denoising, patching, and flattening, the images were processed using a Transformer encoder module with attention mechanisms to capture long-range dependencies. This approach achieved an impressive RMSE of 0.14 m, marking a significant advancement in radar-based wave height estimation. Transformers have also been applied to wave height retrieval in other contexts. For example, [PERSON] et al. [281] utilized a Transformer-based architecture to estimate wave height from GNSS-R data, addressing CNN limitations in capturing global features from DDMs. Their method achieved a notable RMSE of 0.44 m compared to ERA5 data, demonstrating the potential of Transformers in wave height retrieval tasks. In addition to the above-mentioned wind and wave energy, tidal energy, generated by the gravitational interactions between the Earth, Moon, and Sun, exhibits predictable tidal cycles and potential for sustainable power generation [282]. Ocean remote sensing technologies have been utilized to study tides, such as Ref. [283], which mapped tidal spatial patterns across the Great Bahama Bank using Landsat time series data. The integration of  
Figure 6: Different applications of AI for facilitating ocean-based climate change solutions. DL with ocean remote sensing in tidal energy research presents promising opportunities for further exploration.
# Reversible Data Hiding in Encrypted Image Based on Multi-MSB Embedding Strategy
## 2 Proposed Method
### Data Extraction and Image Recovery
The flow chart of steps is shown in Figure 6.  
Since most of pixels have been restored to their original values except for the edged pixels in \(I_{ED}\), the image reconstructed \(\overline{I}\) is approximated with the original image. In the last scenario, if the recipient has both data-hiding key \(K_{h}\) and encryption key \(K_{e}\), he can extract the embedded data and recover the original image without error. The recipient uses the data-hiding key to extract data, which includes secret bits and the bit planes of pixels in \(I_{ED}\) which are encrypted. By using the encryption key, these encrypted bit planes can be decrypted directly. As introduced in the second scenario, only the pixels in \(I_{ED}\) cannot be restored to original values, but we can obtain the bit planes of these edged pixels in this scenario. All the pixels in \(I_{ED}\) can be restored to original values by replacing these bit planes. Therefore, all the pixels in the reconstructed image \(\overline{I}\) can be restored losslessly in this scenario.
## 4 Analysis and Results
### Informal Security Analysis
#### 4.1.5 Mutual Authentication  
In mutual authentication, any two entities must authenticate each other's identities prior to communicating and exchanging messages to avoid adversary breaches. In the proposed scheme, the mutual authentication between B and C is achieved based on correctly computing the received Hash5 and Hash6, respectively. Before the mutual authentication begins, the AM plays a significant role in creating the authentication parameters and verifying the authenticity of C and B to ensure that the messages are not manipulated and to prevent any adversary from becoming a part of the network.