esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments and Results
### Experimental Methodology
#### 4.1.1 Image Classification  
Over the years, machine learning and deep learning have been increasingly used for classification and detection problems that include training and inference tasks. Due to the intensive resource usage of the training task, it is generally performed on high-performance computers and GPU servers. However, training on edge devices is gaining importance attracting attention in various applications, such as edge AI, IoT, and autonomous systems, as it enables real-time processing, reduced latency, and enhanced privacy by keeping data and computation local. The training of machine learning models at the edge involves dealing with resource constraints and customized algorithms that cater to edge devices [39]. In this work, we deploy image classification training on Apache Spark because machine learning and deep learning training are some of the scenarios associated with batch processing. Apache Spark ships with machine learning libraries but not for complicated deep learning training. There are some frameworks available to provide deep learning on Apache Spark, such as BigDL [60] and TensorFlow on Spark [61]. However, the use of distributed deep learning libraries such as BigDL, Horovod, and TensorFlow on Spark is still not compatible with edge devices due to their limited computational resources, including processing power, memory, and storage. These libraries are designed for the training of very large and complex deep learning models. More edge-friendly solutions and optimizations are still being explored [39]. Thus, we utilize a multilayer perceptron for the training of images. Although a multilayer perceptron is of low computational complexity, it is beneficial for quick experimentation, model prototyping, and scenarios involving limited computational resources. The selection of the MNIST dataset for image classification in our experiments is justified by its widespread use and established reputation as a benchmark dataset in the field of machine learning [62; 63; 64]. The MNIST dataset consists of a large collection of hand-written digits (0-9) in a consistent format, making it suitable for training and evaluating image classification models. Its simplicity and well-defined nature facilitate experimentation in Apache Spark's big data platform in edge, fog, and cloud computing layers. The MLP model consists of four layers and is trained for 1000 iterations. As this model showed nearly 95% accuracy in all the experiments on edge-, fog-, and cloud-layer computation, we focused on execution time as the key performance metric for the training. In our research, the primary focus is the evaluation of the efficiency and computational performance of big data platforms in edge, fog, and cloud computing layers for the training machine learning models as a representative example of the batch processing paradigm. The choice of execution time as a key performance metric is rooted in its direct reflection of the computational efficiency of the batch-processing-based training process [65].
###### Abstract  
Geomorphological changes in recent decades in three estuaries along the Iberian coast were analysed using aerial orthophotographs. A hierarchical classification scheme, based on a literature review representing 26 estuarine eco-geomorphological features relevant to estuarine dynamics and functioning, is described. The estuaries selected were San Vicente de la Barquera (N Spain), Guadiana River (SW border between Spain and Portugal) and the Ebro River Delta mouth (NE Spain). For these systems, a 60-year time series of high-resolution maps was developed, analysing the changes in feature surfaces. The main subsystems analysed were beach, dunes, saltmarshes and the drainage network. The results of the cartographies showed general behaviour common to all transitional systems, relationships among main subsystems and processes inherent to each one. This work illustrates how beaches and dunes serve as a protective barrier for the tidal flats, acting as a sediment buffer for the entire system. The subsystems are connected by the drainage network responsible for the exchange of matter and energy between them. Furthermore, an accuracy assessment was performed in one of the study zones to identify the limitations of mapping with aerial photographs. The results explain the changes with time but also the processes and relationships between the estuarine features at a long-term scale. This work adds an important perspective towards a general understanding of their dependence on intrinsic and boundary conditions.  
Keywords: estuarine subsystems; estuarine connectivity; coastal conservation; cartography; aerial photographs +
Footnote †: journal: Applied Sciences
## 4 Experiment and Analysis
### Evaluate Metrics  
Different from the classification of balanced datasets, a single classification accuracy does not fully reflect the classification performance of the model. We evaluate and compare  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Service Category** & **Sample Size** & **Proportion (\%)** \\ \hline VoIP & 684,601 & 54.20\% \\ File transfer & 271,804 & 21.52\% \\ P2P & 228,300 & 18.07\% \\ Browsing & 39,323 & 3.11\% \\ Video-streaming & 16,923 & 1.34\% \\ Audio-streaming & 13,727 & 1.09\% \\ Mail & 5076 & 0.40\% \\ Chat & 3419 & 0.27\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sample size and proportion in the dataset.  
the performance of our methods by three typical metrics, including precision (PR), recall (RC), and F1, which are designed as:  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{9}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{10}\]  
\[\text{F1}=\frac{2*\text{Precision}*\text{Recall}}{\text{Precision}+\text{ Recall}} \tag{11}\]  
where PR denotes the ratio of correctly predicted samples to total positively predicted samples, RC denotes the ratio of correctly predicted samples to actual positive samples in the dataset, and F1 is the harmonic mean of precision and recall.
# Comparison of Current Five-Point Cylindicity Error Separation Techniques
## 3 Applicative and Comparative Analysis for In Situ Measurement of Cylindricity  
Three error separation models for in situ measurement of cylindricity have been investigated as above. Theoretically, they all can be used to measure and reconstruct cylindrical profile. However, their validity and accuracy need to be verified by numerical verifications through experiments so as to further reveal characteristic features as well as main benefits and drawbacks.
# Low Complexity DSP for High Speed Optical Access Networking +
## 4 Simulation Results at 20 km Transmission  
Due to the absence of a high-bandwidth APD in our laboratory, we performed realistic simulations using the VPIphotonics Design Suite platform (VPItransmissionMaker) to predict the impact of a realistic APD in the proposed setup. For our simulations, we used two cases, the first being identical to the experimental setup using the EDFA and a PIN with a transimpedance amplifier (TIA). While the second replaced the latter components with a single APD + TIA. For simplicity, we used an 8 tap FFE in the DSP unit for comparison with the corresponding experimental measurements with a simplified DSP. A total of 220 symbols were used for effective BER Monte-Carlo simulation. The simulation parameters for the PAM-4 with the APD set-up were identical to [8], while the following additional parameters were considered in the simulation: a single-polarization system was used using an standard SMF (SSMF) with attenuation, chromatic dispersion, chromatic dispersion slope and a Kerr nonlinear index of 0.2 dB/km, 16 ps/(km \(\times\) nm), 0.08 ps/km \(\times\) nm\({}^{2}\), and 2.6 \(\times\) 10\({}^{-20}\)m\({}^{2}\)/W, respectively. For both EDFAs, the noise figure was set 6 dB. For the PIN and APD, the thermal noise was set at 1 pA/\(\surd\)(Hz), the dark current at 10 nA and the responsivity at 0.9 A/W. The avalanche multiplication factor for the APD was 2.5. The bandwidth limitation of the modulator and PIN/APD was simulated using Bessel low-pass filters of the 3 rd order, while the TIA was modelled with a 4 th filter order. Gaussian OBPFs of the 2 nd order were also simulated. Identical PAM-4 parameters to the experiment were used, with a current noise spectral density of 10 nA/\(\surd\)(Hz). The laser linewidth was set to 10 MHz and its relative intensity noise (RIN) was set to \(-130\) dB/Hz at a measured power of 1 mW. The laser average power was set to 10 mW, while the side mode separation was set to 200 GHz and the side mode suppression ratio to 100 dB. The MZM was assumed to be ideal with an insertion loss of 6 dB and extinction ratio of 30 dB.  
In Figure 4a, together with the experimental traces from Section 3, the simulated results are depicted for the case of an 8 tap FFE at 20 km of transmission. Figure 4d shows the received histograms for the afore-mentioned simulated DSP case without using an equalizer. Our simulated results designate the following: (1) An implementation penalty of ~0.4 dB is observed at the KP4 FEC-limit when compared to the experimental curve, mainly induced from the electro-optical components; (2) A ~4.3 dB sensitivity penalty is observed when using an APD without receiver pre-amplification, confirming the prediction reported in [7; 8; 12].
# Side-Scan Sonar Image Augmentation Method Based on CC-WGAN
## 1 Introduction
However, the simulation is constrained in its capacity to model shipwcks as entities with consistent reflection coefficients across their entire structure, failing to reflect material differences in different parts of shipwcks. As a result, few GAN networks can meet the data augmentation needs for a low number of samples [17; 18]. In 2017, [PERSON] et al. [19] proposed the Wasserstein GAN with a gradient penalty (WGAN-GP). The WGAN-GP is an improved GAN algorithm with the following advantages over traditional GAN algorithms: It solves the instability problem of traditional GAN training, making it easier to train the generator and discriminator. By introducing a gradient penalty mechanism, the training process becomes smoother and more stable. Establishing a more effective loss function between the generator and discriminator enables the generator to learn better distributions of generated samples, resulting in higher-quality samples that are capable of generating more realistic images or data. It also improves the stability of the optimization process and addresses issues like gradient vanishing. Therefore, the WGAN-GP network is chosen as the principal approach for augmenting side-scan sonar data. However, using WGAN-GP to improve underwater target images captured by black-and-white SSS is impractical because the original network's dataset mainly includes daily life images and natural scenery images, resulting in unrealistic-looking targets (Figure 1). Figure 1: The unrealistic-looking targets. To improve the learning ability of the network for targets and considering the features of black-and-white waterfall images in SSS, this paper proposes a method for augmenting underwater target samples based on the CC-WGAN. First, the generator incorporates the CBAM to compensate for the convolution operation's limitation of only calculating correlations within local pixel regions, thereby improving the clarity of the generated images. It also provides better stability, avoiding the mode collapse problem, where the generated image modes concentrate on a few pixels, lacking diversity. Then, the discriminator incorporates the CBAM to help the model better understand the relevance and importance of input data, enhance target learning, reduce information diffusion, and improve the model's generalization ability. Finally, based on this model, existing few-sample SSS images are augmented, and we utilize the augmented images for discrimination and detection with YOLOv5. The experiments demonstrate that the CC-WGAN-based augmentation approach can effectively produce a substantial number of high-quality samples that align with the characteristics of SSS images, compensating to some extent for the limited number of underwater samples. This method provides a novel solution to the problem of low model accuracy in underwater target detection with side-scan sonar due to limited samples.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 3 Resource Management Techniques in C-RAN  
There are two types of resources in C-RAN, similar to other cellular network architectures: computational resources and radio resources. The computational resources, which are required in the base stations, include memory, processing power, data storage, time, and bandwidth. Computational resource management can be performed by managing the resources in the BBU pool, i.e., by managing the BBU resources. In this study, we focus on different RRH clustering techniques for BBU resource management (also called BBU-RRH association techniques) by switching on/off BBUs to allocate computational resources optimally according to the user demand. In cellular communication, radio resource implies radio frequency spectrum, which is highly limited. RRM focuses on effectively utilizing the limited radio frequency spectrum resources and radio infrastructure. It includes strategies and algorithms for transmit power control, dynamic channel allocation, spectrum management, cache management, and joint optimization. The resource management techniques reviewed in this paper are categorized, as shown in Figure 4, into RRM and CRM. The RRM techniques are classified into power control schemes, joint optimization schemes, and sum-rate optimization. For CRM,we considered the RRH clustering techniques. These are categorized into location-aware, load-aware, interference-aware, QoS-aware, and throughput-aware techniques.
# SASC: Secure and Authentication-Based Sensor Cloud Architecture for Intelligent Internet of Things
## 5 Numerical Results  
This section provides results of the proposed scheme in comparison with other two popular methods, i.e., secure and authentication protocol, SEER and SecLEACH, concerning network lifetime, packet drop ratio, energy consumption, end-to-end delay, and transmission overhead.
## 3 Materials and Methods
### The Final Model  
The final model was built considering the most frequently selected wavelengths. To this aim, a threshold number (S) was introduced; therefore, to each wavelength, the S value  
Figure 1: This figure depicts general Step1 and Step 2 layout, as previously described, of programmed script of MATLAB evaluating models.  
was assigned, measuring the number of replicates of the second step (from 1 to 10) in which the specific wavelength was selected.  
For example, S \(\geq\) 5 implies the identification of all the wavelengths that the algorithm selected in 5 to 10 of the considered 10 replicates; S \(\geq\) 10 implies the identification of the wavelengths that have been used in all 10 replicates.  
The measure of the robustness of the selected optimal model was carried out by varying S (from 1 to 10, selecting a decreasing number of wavelengths) and changing EXTCV from 20% to 70% with increments of 5%.  
The values of FNR and FPR were represented against EXTCV. An exponential fit (y = a \(\ast\) exp(p \(\ast\) x)) was considered on these data and a threshold of 0.01% and 0.1% was used to express a SCORE (with regard to FNR and FPR respectively) for each subset of wavelengths (arising from the variation of S). A higher SCORE measured a better statistical performance.  
For example, for each fixed S value, a subset of wavelengths was selected, so the SCORE for FNR represents the EXTCV value at which the FNR reaches 0.01% (using the exponential fit of the data), while the SCORE for FPR represents the EXTCV value at which the FPR reaches 0.1% (using the exponential fit of the data).  
In conclusion, while the second step identified the optimal model to use, the third step identified the optimal subset of wavelengths to consider.  
This allowed the final model coefficients and their statistical reliability to be determined, expressed through their percent coefficient of variation (CV%).
# Velocity-Aware Handover Self-Optimization Management for Next Generation Networks
## Abbreviations  
The following abbreviations are used in this manuscript:  
3 GPP 3 rd generation partnership project  
4G  Fourth generation  
5G  Fifth generation  
AWGN  Additive white Gaussian noise  
BS  Base station  
HCP  Handover control parameter  
HetNets  Heterogeneous networks  
HO  Handover  
HOF  Handover failure  
HOM  Handover margin  
HOP  Handover probability  
HOPP  Handover ping-pong  
Mmwave  Millimeter wave  
MR  Measurement report  
MRO  Mobility robustness optimization  
NSA  Non-standalone  
QoS  Quality of service  
RLF  Radio link failure  
RSRP  Reference signal received power  
SA  Standalone  
SINR  Signal-to-noise-ratio  
TTT  Time to trigger  
UDSC  Ultra-dense small cell  
UE  User equipment  
UHO  Unnecessary handover
# Formal Algebraic Model of an Edge Data Center with a Redundant Ring Topology
## 3 Basics of the System Proposed
In order to facilitate the handoff procedure, the signals coming from the antenna located on each corner covers their semiaxes acting as a boundary to separate a given quadrant to the other ones, hence there is an overlap of signals along each section of the coordinate axis. Hence, when a drone passes through a coordinate axis, which the drone knows because the power received from the antenna where its associated VM is currently allocated is lower than the power received from another antenna, it means that the drone has entered into a new quadrant. This very fact makes the drone send a control signal to its associated VM with the identification of the new quadrant, and upon receipt, the VM gets migrated from the current host, that being located within the current quadrant, to the new host, that being situated in the host within the new quadrant, thus getting the migration process completed. In order to clarify the sequence of events of the handoff process, the following list is given, whose scheme is exhibited in Figure 2:  
1. The drone sends a message indicating the number of quadrant it got into to the current host;
2. The current host asks for resources to the new host to perform the VM migration;
3. The new host informs positively about having enough resources (supposing it has enough room left);
4. The current host proceeds to migrate the associate VM of the drone to the new host;
5. The new host acknowledges the successful migration to the current host;
6. The current host acknowledge the successful migration to the drone;
7. The drone sends a hello message to the new host, where its VM is now located
8. The new host acknowledges that message. Regarding the application layer protocol, MQTT may be a convenient solution due to its lightweightness and the fact of allowing subjects to label the different messages exchanged so as to quickly distinguish among pictures, temperature or humidity, although CoAP may also be an interesting solution [71].
###### Abstract  
It has been proved that artificial neural networks (ANN) can be used to predict the compressive strength and elastic modulus of recycled aggregate concrete (RAC) made with recycled aggregates from different sources. This paper is a further study of the use of ANN to analyze the significance of each aggregate characteristic and determine the best combinations of factors that would affect the compressive strength and elastic modulus of RAC. The experiments were carried out with 46 mixes with several types of recycled aggregates. The experimental results were used to build ANN models for compressive strength and elastic modulus, respectively. Different combinations of factors were selected as input variables until the minimum error was reached. The results show that water absorption has the most important effect on aggregate characteristics, further affecting the compressive strength of RAC, and that combined factors including concrete mixes, curing age, specific gravity, water absorption and impurity content can reduce the prediction error of ANN to 5.43%. Moreover, for elastic modulus, water absorption and specific gravity are the most influential, and the network error with a combination of mixes, curing age, specific gravity and water absorption is only 3.89%.  
Keywords: recycled aggregate; recycled aggregate concrete; artificial neural networks; aggregate characteristic; input variable +
Footnote †: journal: Applied Sciences
# Fluorinated Polymers for Photonics--From Optical Waveguides to Polymer-Clad Glass Optical Fibers
## 1 Introduction  
Organic polymers containing C-F bonds found a wide application in photonics. Low refractive indices and apparently low propagation optical losses at the communication wavelength of 1550 nm are the most interesting properties of fluorinated polymers in view of applications in optics. Low absorption in the wavelength region ca. 1550 nm is the main challenge for the material, as this absorption contributes mainly to the optical propagation losses. The presence of -OH and -NH groups has to be avoided to reduce the propagation losses in this wavelength region (these chemical bonds absorb very strongly in the near infrared region). Therefore, polyurethanes, epoxy resins thermally cured by amine-containing hardeners, phenolic resins and polyamides are generally not considered for such applications. C-H bonds are normally present in almost all organic polymers, and they also absorb in this region. However, the absorption caused by C-H is a result of the overtones of the C-H stretching vibration and is consequently two orders of magnitude lower than the first overtone of -OH or -NH vibration lying exactly in the 1550 nm region. Nevertheless, the absorption of -CH groups in the infrared region also influences optical propagation losses in optical devices. The use of fluorinated and highly fluorinated polymers leads to a reduction in optical propagation losses, making fluorinated polymers versatile for integrated optical devices. In addition, the substitution of hydrogen for fluorine or another halogen also reduces the concentration of C-H bonds.  
Therefore, here we review our previous research in two application areas involving fluorinated UV-cured thermosetting polymers, namely, their application as core and cladding layers for the fabrication of channel waveguides used in optical microchips [1; 2; 3; 4; 5] and their application as cladding layers for optical glass fibers [4; 5; 6].  
While for the first application, tunability of the refractive index and low propagation losses are important features, for the second application, a low refractive index is the most important. Two classes of polymers (oxirane-containing fluorinated polymers and a monomeric mixture of fluorinated acrylates) are used for passive optical waveguides, with the emphasis in our own work being on nanocomposites [1; 2; 3].