esa-sceva/satcom-chunk-collection huggingface dataset:
# FANET Routing Protocol Analysis for Multi-UAV-Based Reconnaissance Mobility Models
## 4 Experimental Setup
### Experiment Environment  
To simulate the reconnaissance scenario based on a mobility model and to conduct a performance analysis of routing protocols based on the simulation, we linked the self-developed mobility model simulator with the network simulator NS-3. First, we used our mobility model simulator so that nodes with a FANET protocol move according to the mobility model for reconnaissance. In detail, we generated a trace file for each of the 5 mobility models (RDPZ, RWP, DPR, EGM, MP) described in Section 3. We used the mobility model simulator developed in a previous study [33].  
Next, we input the trace file of the mobility model into NS-3, a network simulator, and we simulated three routing protocols: AODV, DSDV, and OLSR. We linked the trace file generated by our mobility model simulator using the NS-2 MobilityHelper in NS-3. We implemented the three routing protocols by modifying the protocols provided by NS-3, and we analyzed their performance based on a reconnaissance mobility model.  
We conducted this protocol simulation using the trace files of these reconnaissance mobility models to match the same experimental conditions in the evaluation of each routing protocol. Additionally, since the movement of the mobility model is random, a single trace file can produce different results depending on a specific movement path. Therefore, we created 10 trace files for each mobility model in the experiment. We derived the result of the experiment by averaging the resulting values of the 10 experiments.  
Based on the scenario presented above, we analyzed the performance of three ad hoc routing protocols (AODV, DSDV, and OLSR) by setting up several configuration parameters for a network simulator in NS-3. We fixed the simulation area at 4000 m \(\times\) 4000 m. We set the simulation time to 3600 s, the transmission range to 1000 m, and the node speed to 10 m/s.  
We ran the simulation by changing the number of nodes. Table 2 shows the parameters of the simulator in detail (As shown in Table 2, we used Friis transmission formula as the propagation model. Friis transmission formula includes the Free Space Path Loss (FSPL). FSPL is a mathematical model used in telecommunications to predict the decrease in power density (attenuation) of an electromagnetic wave as it propagates through free space. The FSPL model assumes an ideal free-space environment with no obstructions or reflections. Based on the assumptions, The FSPL model estimates the maximum distance a radio signal can travel. The FSPL model is expressed as an algebraic equation and is used to design and optimize wireless communication systems).
# Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
## 2 System Model
### DNN Network  
The deep learning method has found widespread applications in various fields such as natural language processing, image recognition, and the Internet of Things. Figure 1 illustrates the basic structure of a DNN network, which can be described as three parts. The input layers receive the input data matrix from different users and transmit it to the hidden layers. With interconnected neuron nodes in the hidden layers, the DNN network enhances accuracy in data identification. As the data enter the hidden layer, the function expression can be denoted as  
\[z_{j}=\sum_{i=1}^{n}w_{ij}x_{i}+b_{j}\,, \tag{10}\]  
in which \(x_{i}\) denotes the \(i\)-th node of the input layer, and \(b_{i}\) denotes the threshold value of the node in the hidden layer. Between two different layers, \(w_{ij}\) represents the weight vector. Since the neural network introduces the nonlinear activation function, the DNN structure can be used for nonlinear mapping. In this paper, we choose the Relu function as the activation function. Then, the output of the \(j\)-th node can be denoted as  
\[O_{j}=f(z_{j})\,. \tag{11}\]  
By introducing the Relu function in each hidden layer, the final output can be expressed as  
\[\hat{y}=f(a_{j})=f(\sum_{j=1}^{m}w_{ji}O_{j}+b_{i})\,, \tag{12}\]  
where \(a_{j}\) denotes the \(j\)-th node in the hidden layer which is directly connected with the output layer.  
By introducing a DNN algorithm, its nonlinear activation function and hidden layer structure can enable the receivers to have the ability of nonlinear detection, enhancing detection accuracy and reducing errors. Details about the selection of the activation function, the number of neuron nodes, and the hidden layers in the DNN network model used in this paper are provided in Sections 3.2.2 and 4.2.
## 4 Experiments
### Results and Discussion
According to this method, the CH role is moved to next sensor node if the number of frames, received by current CH node from cluster members, reaches a predetermined threshold. The threshold of frame number was calibrated independently for both the fixed (Figure 14) and the variable  
It should be noted here that the results shown in Figures 6-17 were obtained using the data suppression approach to reduce the number of transmissions for each considered method. Extensive experiments were conducted to compare the above-discussed methods for 10 different scenarios. The experiments involved dynamic scenarios, where the probability of event occurrence changes in time. Each dynamic scenario assumes different changes of the event occurrence probabilities during 15-min cycles, similarly to the second scenario, which is analyzed above in this section. Static scenarios were also included in the experiment. In this case, the event probability for a given node was constant, but different probabilities were assigned to particular nodes. The plot in Figure 18 show results of the extended experiments. The columns in this plot correspond to average values of the WSN lifetime for all considered scenarios, while the error bars show maximum and minimum lifetime observed during tests. The average lifetime values in Figure 18 were determined by taking into account the experimental results of the 10 scenarios that were mentioned earlier in this section. Similarly, the maximum and minimum lifetimes were taken from the set of the results obtained for 10 scenarios. The comparison presented in Figure 18 additionally includes the basic versions of RRCH, EDCR, and FDCR algorithms without suppression of unnecessary transmissions. It can be observed that the suppression approach used by smart sensor nodes greatly contributes to the lifetime extension. At the same time, the proposed method allows us to take full advantage of the smart sensor node capabilities to suppress unnecessary transmissions for prolonging the WSN lifetime. Figure 16: Energy consumption by sensor nodes for FDCR algorithm and fixed probability of event occurrence. Figure 17: Energy consumption by sensor nodes for FDCR algorithm with a variable number of detected events.
# A Test Method for Acoustic Emission Properties of Natural Cellulose Fiber-Reinforced Composites
## 3 Results and Discussion
### The Relation between Dynamic Elastic Modulus and Flexural Modulus  
For some specific circumstances, nondestructive tests of the materials are preferred. The acoustic vibration test proposed here could be a nondestructive testing method for the estimation of the modulus of a material. The flexural moduli of the composites were determined using regular flexural tests, and were compared with those using the current acoustic vibration test, as shown in Figure 6a. For comparison purposes, we also tested ailanthus, a natural wood with obvious anisotropy. The modulus of its vertical strips was 29.8 times as much as that of its horizontal strips. The dynamic elastic moduli of the horizontal strips and vertical strips were 1.36 GPa and 8.54 GPa, respectively, which were close to their corresponding flexural moduli of 0.43 GPa and 12.81 GPa, respectively. This is also close to the elastic modulus of similar wood reported in the literature [28], which  
Figure 5: Frequency spectra of composites with different number of layers: (**a**) hemp; (**b**) ramie. Acoustic vibration performance of composites with different layers: (**c**) hemp; (**d**) ramie.  
proved that our test method can generate results comparable to those produced using other methods. The modulus of the composite materials decreased as the number of layers increased.  
As shown in Figure 6b, the measured dynamic elastic moduli and flexural moduli were correlated and analyzed, and the Pearson correlation coefficient of hemp (R12) and ramie (R22) were 0.999 and 0.995, respectively, indicating that the two are highly correlated. The correlation between the flexural and dynamic elastic modulus can be expressed by the following linear regression equations:  
\[\text{E}_{\text{d1}}=0.801\text{E}_{1}-3.868,\text{R}_{1}{}^{2}=0.999 \tag{7}\]  
\[\text{E}_{\text{d2}}=0.756\text{E}_{2}-3.527,\text{R}_{2}{}^{2}=0.995 \tag{8}\]  
where E\({}_{\text{d1}}\) and E\({}_{\text{d2}}\) are the dynamic elastic moduli of hemp and ramie composites, respectively, and E\({}_{1}\) and E\({}_{2}\) are the corresponding flexural moduli. Therefore, the dynamic elastic moduli are proportional to the flexural moduli, and, thus, may be used to predict one another if necessary.
## 2 Edge Cloud Deployment Areas and Use Cases
For instance, to find cracks in train wheels, the railway industry places high-definition cameras in bungalows next to the track. The wheel could break due to cracks that derail the entire train. In this instance, the bandwidth requirement is very bursty in nature, which produces GBs of data in a brief amount of time. To prevent serious accidents, cracks also must be reliably detected and reported within minutes. Deployment of surveillance cameras is also increasingly used in road safety and traffic management. Therefore, video processing and analytics applications need to continuously process and analyze captured video locally or at the edge to provide decisions in milliseconds or microseconds. Edge computing has potential use cases in smart health as well. The healthcare sector generates enormous amounts of data using devices, sensors, and medical equipment. The requirement of latency, processing time, and storage is therefore very crucial. Edge computing can aid with these by utilizing automation and machine learning capabilities. It helps to better diagnose critical diseases to provide better patient care. Additionally, edge computing enables medical monitoring systems to react instantly in the present rather than having to wait for a cloud server to do so [22].
## 3 Experimental Results and Analysis
### Analysis of Detection Robustness  
Figure 10 shows the matching results of the circles with different illuminations by the method without any light processing and by the method based on image similarity, where \"General\" indicates that the image has not undergone any light processing. It is obvious that the method based on image similarity can improve the matching effect of the images with different illuminations and enhance the robustness of detection.  
Table 3 compares the matching average pixel errors of the circle models with different illuminations by the method without any light processing and by the method based on image similarity. It can be seen from the table that the matching results of the original image with different illuminations have large average errors, and the matching error can be greatly reduced by using similarity mapping to preprocess the original image.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **KAZE** & \multicolumn{2}{c}{**Haar**} & \multicolumn{2}{c}{**Improved Haar**} \\ \hline Model\_1 & 12.52 & 10.01 & 3.12 & 4.50 & 2.75 & 3.75 \\ Model\_2 & 14.78 & 16.09 & 8.12 & 13.25 & 4.87 & 6.80 \\ Model\_3 & 11.62 & 13.98 & 4.01 & 6.625 & 2.65 & 3.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of average pixel errors under different illuminations by three matching algorithms.  
Figure 10: Matching results of different illumination images by robust enhancement algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **KAZE** & \multicolumn{2}{c}{**Haar**} & \multicolumn{2}{c}{**Improved Haar**} \\ \hline Model\_1 & 12.52 & 10.01 & 3.12 & 4.50 & 2.75 & 3.75 \\ Model\_2 & 14.78 & 16.09 & 8.12 & 13.25 & 4.87 & 6.80 \\ Model\_3 & 11.62 & 13.98 & 4.01 & 6.625 & 2.65 & 3.25 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of average pixel errors under different illuminations by three matching algorithms.  
Table 4 compares the average pixel errors of circle radii of three circular models, which are obtained by the method without any light processing and by the method based on image similarity. It can be seen from the table that the average pixel error of image detection method based on similarity mapping is approximately 2 pixels, which means that its detection results are excellent.
# Data Augmentation for Voiceprint Recognition Using Generative Adversarial Networks
## 2 Literature Review
### Application of Generative Adversarial Networks in Speech Synthesis and Enhancement
Moreover, traditional methods like SVM and GMM, though augmented by GAN-generated data [16; 24; 25], may lag behind deep learning models in performance, particularly in more complex environments. Lastly, although GANs have proven effective in data augmentation and noise reduction, as seen in SEGAN [18], their adversarial training process is sensitive to hyperparameters and can lead to instability in training, as noted by [PERSON] et al. [23]. Therefore, while GANs and deep learning have driven significant progress, challenges such as model scalability, data dependency, and training stability remain.
###### Abstract  
A novel joint satellite-transmitter and ground-receiver (SGS) digital pre-distortion (DPD) (JSG-DPD) technique is proposed to improve the linearity and power efficiency of the space-borne active phased arrays (APAs) in low Earth orbit (LEO) satellite communications. Different from the conventional DPD technique that requires a complex RF feedback loop, the DPD coefficients based on a generalized memory polynomial (GMP) model are extracted at the ground-receiver and then transmitted to the digital baseband front-end of the LEO satellite-transmitter via a satellite-ground bi-directional transmission link. The issue of the additive white Gaussian noise (AWGN) of the satellite-ground channel affecting the extraction of DPD coefficients is tackled using a superimposing training sequences (STS) method. The proposed technique has been experimentally verified using a 28 GHz phased array. The performance improvements in terms of error vector amplitude (EVM) and adjacent channel power ratio (ACPR) are 7.5% and 3.6 dB, respectively. Requiring limited space-borne resources, this technique offers a promising solution to achieve APA DPD for LEO satellite communications.  
joint satellite-transmitter and ground-receiver digital pre-distortion (JSG-DPD); active phased array (APA); generalized memory polynomial (GMP); superimposing training sequences (STS); low Earth orbit (LEO) satellite communications 202214319[[https://doi.org/10.3390/rs14174319](https://doi.org/10.3390/rs14174319)]([https://doi.org/10.3390/rs14174319](https://doi.org/10.3390/rs14174319))
## 4 Investigation of Proposed Method by Solving Test Functions
### Two-Objective Test Function Results  
The solution space acquired by the proposed multi-fidelity/multi-objective EGO is compared with that acquired by the single-fidelity/multi-objective EGO as shown in Figure 4. Because the single-fidelity/multi-objective EGO finds local optimum points at the beginning of the optimization process, the solution for additional samples stalls earlier. On the other hand, the developed multi-fidelity/multi-objective EGO can find a solution close to the global optimum solution of this multi-objective optimization problem. The histories of the hypervolumes of the two methods are compared in Figure 5. These results also indicate that the proposed multi-fidelity/multi-objective EGO provides better solutions, which shows higher diversity because its hypervolume is larger than that of the single-fidelity/multi-objective EGO. These results also suggest that the proposed multi-fidelity/multi-objective EGO obtains better solutions than the single-fidelity/multi-objectiveEGO, because the non-dominated solutions of the former can dominate all the non-dominated solutions of the latter. In addition, comparisons of the hypervolumes show that the solution of additional samples by the single-fidelity/multi-objective EGO continues to stall earlier.  
To investigate the reason for the superiority of the proposed multi-fidelity/multi-objective EGO, the cross-validation [29; 30] of \(f_{1}\) and \(f_{2}\) was compared, as shown in Figure 6. It can be seen that the linear regression line nearly coincides with the predicted line in the case of the proposed multi-fidelity/multi-objective EGO. Thus, the multi-fidelity surrogate model achieves higher accuracy than the single-fidelity surrogate model. More specifically, the proposed multi-fidelity/multi-objective EGO achieves higher accuracy because the dataset obtained by low-fidelity evaluation enables the surrogate model to predict the solution in the uncertainty region.  
Figure 4: Initial sampling data and additional sampling data of two-objective test problem: (**a**) multi-fidelity approach; (**b**) single-fidelity approach.  
Figure 5: Hypervolume comparison of multi-fidelity approach and single-fidelity approach of two-objective test problem.
## 4 Methodological Conception and Execution  
### Constitution of Architectural Framework  
The privacy-preserving federated learning model that we propose uses the HFL framework, which is suitable for healthcare applications. In the conventional FL setup, updates from all the nodes are collected at a centralized server, which creates a bottleneck in large and geographically dispersed healthcare systems. In contrast, our HFL model introduces a two-tier aggregation system:  
1. Local Node Training: Every involved center (for instance, hospitals and clinics) conducts a local training of a model on the relevant dataset. The model is updated from the patient data collected at the local healthcare center and the raw data is not transferred out of the institution's network.
2. Regional Hubs: Updates from local hospitals in a similar geographic area are transmitted to the regional level for further aggregation. This step also helps to reduce communication overhead and thus scale the system by limiting the amount of data that is transferred to the central server.
3. Central Server: Finally, the updates from all the regional centers are gathered and processed to come up with the final global model in the central server. What is more, after the training and updating of the global model, the updated model is then sent back to these local nodes for further rounds of training and updating.  
This architecture extends the FedProx algorithm, an enhancement of the basic Federated Averaging (FedAvg) that aims to stabilize the model's convergence on different data distributions. The FedProx algorithm makes it possible for the local models to be partially decentralized while at the same time being synchronized with the global model. The optimization is defined as  
\[min_{w}=\left(\sum\
olimits_{i=0}^{n}\int i(w)+\mu/2\Big{|}\Big{|}w-w^{k}\Big{|} \Big{|}^{2}\right) \tag{2}\]  
A federated learning architecture with Differential Privacy in the healthcare domain is shown in Figure 1. It describes healthcare data sources that are exchanging information amongst themselves and a central federated server connected to regional hubs. Local and Global Differential Privacy mechanisms are incorporated into the system to ensure the secure exchange of data on the network while protecting privacy.  
Here, f\({}_{\text{i}}\)(w) is the local objective function for node i, w represents the global model parameters, and w\({}^{\text{k}}\) are the current parameters at iteration k. The proximal term \(\mu 2/\|\text{w}-\text{w}\|\) penalizes significant deviations from the global model, ensuring stability across institutions with heterogeneous data. The training process involves local model updates with differential privacy at client level, regional aggregation, and global model updates with secure multiparty computation and noise injection, as outlined in Algorithm 1.  
Figure 1: Proposed architecture with local and global differential privacy for healthcare data.
# ChatGPT as a Virtual Dietitian: Exploring Its Potential as a Tool for Improving Nutrition Knowledge
## 3 ChatGPT as a Nutrition Tool
### Can ChatGPT Effectively Replace Dietitians and Nutrition Professionals?
#### 3.3.2. Real-Time Monitoring and Feedback  
Due to its inherent reliance on text-based communication and web-based platform, a significant limitation of ChatGPT is its inability to perform real-time monitoring and feedback. Unlike other mobile nutrition applications designed for proactive engagement, ChatGPT relies on users to initiate interactions. These dependencies indicate delayed responses, a lack of contextual awareness, and the absence of mechanisms to track user engagement or provide immediate interventions. Consequently, it falls short in delivering immediate and dynamic support comparable to real-time human interactions or dedicated monitoring devices. On the other hand, nutrition applications are strategically designed to offer push notifications as a means of real-time engagement [60]. These apps can send timely reminders to users, encouraging them to record their meals, stay hydrated, or engage in physical activity. Other mobile applications use algorithms that analyze user data, such as dietary habits and exercise routines, to create personalized push notifications [61]. For example, if a user consistently logs high-calorie meals, the app may send a notification suggesting healthier alternatives. This proactive approach fosters a continuous connection between users and the application as well as provides immediate feedback to help individuals make better dietary choices and maintain their nutritional goals.
## 3 Description of Comprehensive Techno-Economic (CTE) Model
### Technical Level in the CTE Model
#### 3.3.2 \"IT and Technological Development\" Area  
Another area at the Technical Level in the CTE model is the \"Technological and IT Development area\". It consists of two segments: \"IT Development\" and \"Technological Development\". This area has ten items which are related to technological and IT issues. All items in this area are calculated as QoIT&T (Quality of IT&T item). The items may be changed or supplemented over the coming years and decades in accordance with changes and developments in the telecommunications and IT markets, as well as customer needs for various services.  
The result of the mathematical equation for one of the items from the area is presented here. The billing system is one of the most important systems for a telecom operator. Billing systems face several important issues, one of which is the implementation of new changes regarding existing tariff models, tariff options, tariff groups and groups of tariffs. This last issue is crucial for assessing the potential of the telecom--the speed of response and action in terms of creating new and/or supplementing existing tariffs, groups, options and more.  
The equation describing this item is as follows:  
\[QoS_{\text{BS}}=\left(\frac{RefT_{TM}\cdot F_{TM}}{MaxT_{TM}}+\frac{RefT_{GTM} \cdot F_{GTM}}{MaxT_{GTM}}+\frac{RefT_{ET}\cdot F_{ET}}{MaxT_{ET}}\right)\cdot 0.1\]  
The CTE Model gives definitions for calculating Ref values and factors F values. Factors F values define the importance of all parts from the equations.