esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Raman Spectroscopy and Hepatitis Diagnosis
### Strategies Based on the Sample Type
#### 4.1.2 Diagnosis Using Blood Serum
In 2020, three works were published by Chinese researchers introducing neural network approaches to hepatitis B diagnosis, using a sample size of around 1000 samples, including 435 healthy and 499-699 HBV-positive serum samples. [PERSON] et al. applied a multiscale convolutional independent circulation neural network (MCSIR) model to Raman data and obtained an accuracy of 96.15% [40]. [PERSON] et al. applied a multiscale fusion convolution neural network along with 10-fold cross-validation and claimed the best performance [80]. [PERSON] et al. applied long short-term memory (LSTM) neural network to the Raman spectral data and claimed a diagnostic accuracy of 97.32% [40; 56; 80]. They all claimed that these models provided a better diagnostic performance over that of conventional methods, including LDA, KNN, SVM, RF, and ANN classifiers. [PERSON] et al. proposed a Raman-spectroscopy-based analysis for the differentiation of mutated genomes of HCV, including HCV2, HCV3a, HCV3b, and HCV4 [31]. The sample size included 55 healthy, 55 HCV1-positive, and 55 HCV2-positive serum samples. An SVM model achieved 91.1% accuracy. In the second part, 17 serum samples of HCV3a were distinguished from 13 samples of HCV3b and HCV4 (considering one group). Applying the SVM algorithm to the normalized Raman data provided 90% accuracy in discriminating between these mutated classes of HCV. [PERSON] et al., from the same group, applied a multiscale convolutional neural network (MSCNN) to the discrimination of 499 healthy, 435 HBV-positive, and 374 HCV-positive serum samples using Raman spectroscopy in 2021 [81]. This model showed a greater than 94.92% accuracy and an even higher precision and sensitivity. A similar approach was applied by [PERSON] et al. to HBV serum samples using an MSCNN in 2022 and showed 97.86% accuracy in its discrimination [82]. Recently, in 2023, [PERSON] et al. extended the use of artificial intelligence in Raman-based diagnosis to serum samples from multiple diseases using a decision fusion strategy [41]. The samples included samples from HBV infection, thyroid dysfunction, and glioma tumor patient sera as the control group, while HCV infection, esophageal cancer, cervical cancer, and cervical inflammation patients, along with a control group, were used as the target domain. Three deep learning neural networks, namely GoogLeNet, ResNet, and CNN-LSTM, were fine-tuned by freezing the different layers. The decision fusion model was constructed by further combining the model layers with a logistic regression algorithm, and a higher accuracy and efficiency were claimed.
# A Full-Coverage Path-Planning Algorithm for a Glass-Curtain-Wall-Cleaning Robot Driven by Ropes
## 3 Wall Modeling
### Region Decomposition  
Because a glass curtain wall is usually larger than the cleaning body, when the wall curvature is too large or some raised obstacles are present, the wall and rope controlling the cleaning body inevitably collide, causing friction, which we call interference, as shown in Figure 6. This considerably reduces the safety of the robot. So, reasonably decomposing the working area is an important issue in the path planning of a full-coverage robot. To avoid interference, according to the curvature of the wall, the whole facade working area is divided into several subareas by changing the winch horizontal position, so that the robot can separately operate in each subarea. Furthermore, to obtain more detailed information about the obstacle shape, the subareas should be as small as possible. Therefore, we suggest that concave regions should also be decomposed.  
Depending on the height of the winch relative to a wall bulge, we can divide the area by setting an empirical value so that interference does not occur in the subarea.  
First, the overlooking shape of the glass curtain wall is abstracted into a curve to facilitate slope calculation. The upper left vertex of the curtain wall is selected as the origin of the coordinates to establish a coordinate system, and \(0\), \(\pm\sqrt{3}/2\) is taken as the ultimate slope (empirical value). From the beginning to the absolute value of the ultimate slope, the range can be viewed as a partition. Then, the end point of this partition is selected as the starting point of the next partition, and the starting point is used as the origin of the coordinates to establish the coordinate system. Then, the slope of the curve is calculated. The previous process is repeated until the end point is reached. The wall shown in the image can eventually be broken down into four subregions, as shown in Figure 7.  
Figure 5: Application scenario of the jump obstacle-crossing mode.  
Figure 6: Interference problem between rope and wall surface.
## 1 Introduction
RB1 and RB2 resource allocated to the Cellular User equipments (UE) and the D2D pair TX-D2D pair Rx, respectively. D2D candidate Tx-D2D candidate Rx has joined the network, it will contend for the resources, i.e., either reusing RB1 or RB2. by sharing the value function between devices and imposing the neighboring factor in our learning algorithm. A set of actions is considered based on the level of transmission power for a particular Resource block (RB). Further, a set of states is defined considering the appropriate number of system-defined variables. In addition, the reward function is composed of Signal-to-noise-plus-interference ratio (SINR) and the channel gains (between the base station and user, and also between users). Moreover, our proposed reinforcement learning algorithm is an on-policy learning algorithm which considers both exploitation and exploration. This action selection strategy helps to learn the best action to execute, which has a positive impact on selecting the proper level of power allocation to resource blocks. Consequently, this method shows better performance regarding overall system throughput. * We perform realistic throughput evaluation of the proposed algorithm while varying the transmission power and the number of D2D users. We compare our method with existing distributed reinforcement learning and random allocation of resources in terms of D2D and system throughput considering the system model where Resource block (RB)-power level combination is used for resource allocation. Moreover, we consider fairness among D2D pairs by computing a fairness index which shows that our proposed algorithm achieves balance among D2D users throughput. The rest of the paper is organized as follows. Section 2 describes the related works. This is followed by the system model in Section 3. The proposed cooperative reinforcement learning based resource allocation algorithm is described in Section 4. Section 5 presents the simulation results. Section 6 concludes the paper with future works.
Furthermore, the decoding process must be sufficiently robust to handle the increased interference and varying power levels inherent to irregular NOMA schemes. Advanced multi-user detection techniques and iterative decoding algorithms have been proposed to address these challenges, leveraging the unique structure of irregular constellations to improve decoding performance [3]. To facilitate the incorporation of advanced NOMA techniques into communication systems, achieving optimal performance in irregular NOMA setups requires a delicate balance between complexity and practicality. The synchronization process, in particular, must be finely tuned in order to align with the irregular structure of the constellations, which is required for minimizing the error rates and maximizing the throughput. Since many modulation and coding schemes (MCSs) have high implementation complexities, analytical derivations of bit error rate (BER), outage probability, and other performance indicators, are often interactable, and performance characterization mainly relies on simulation tests. These simulations require working out implementation details, carrying out numerical programming, and accounting for hardware imperfections, which, in turn requires additional research and development effort. This need for the development of comprehensive NOMA simulations is the main motivation for the study presented in this manuscript, which details a _symbolic, user-comprehensive framework_, building on conventional software-defined radio (SDR) signal processing steps employed in NOMA transmission and reception on the downlink path. The proposed symbolic framework facilitates a high-level transceiver design thereby promoting understanding of the effects that different modulation impairments have on the performance of NOMA communication links. Subsequently, ongoing research continues to refine these techniques, exploring new methods to enhance the efficiency and reliability of NOMA systems in real-world applications.
###### Abstract  
The integration of inverter-based resources (IBRs) is reshaping power grid operation by reducing system inertia, which impacts small-signal rotor angle stability and increases low-frequency oscillations (LFOs). While power-electronics-based flexible AC transmission systems (FACTSs) have been the primary solution, the shift of IBR control toward grid forming (GFM) is changing this approach. GFM control inherently provides inertia and affects small-signal stability, but implementing power oscillation damping (POD) algorithms in these inverters presents challenges, particularly regarding active-power-based ones (POD-P). Although various POD-P solutions are emerging for GFM inverters, few studies have evaluated their impact on the GFM device itself and their inherent capabilities, such as inertia and damping. This paper proposes that any design methodology should consider, besides the impact of POD controls on the grid, their effect on the properties of GFM devices. It introduces a theoretical framework using the network frequency perturbation (NFP) approach to assess this impact. Additionally, a simple POD-P control method is proposed for GFM controllers, with simplicity as its key advantage. The desired damping effect, along with the absence of impact on other frequency components, is verified through NFP analysis. The theoretical findings are experimentally validated with test bench results.  
grid forming; inverters; power distribution; power grids; power oscillation damping
# Assessment of Interventions in Fuel Management Zones Using Remote Sensing
## 2 Materials and Methods
### FMZs Vector Information Processing  
All FMZ types are present in a single vector file and to analyze each one of them individually it is necessary to previously separate them by their type. This separation was made in 4 types of FMZ: roads, powerlines, settlements, and primary FMZ, as presented in Figure 4. The used approach to detect if an FMZ has been treated consists of the comparison of the vegetation inside the FMZ with the vegetation outside the FMZ. To make this comparison possible, buffers that are external to the FMZ with a width of 20 m were created. The shape of the resulting polygons that represent the FMZ varies depending on its type and because of that the process to create the buffers needs to be adapted.  
For powerlines, roads, and primary FMZ the process of buffer creation is similar. The final result, along with the intermediate results, are presented in Figure 5. First, take the original FMZ, Figure 5a, and apply a buffer with 20 m for each side obtaining Figure 5b. Finally, cut the original FMZ from the buffer in order to obtain only the exterior zones as presented in Figure 5c.  
Figure 3: Temporal distribution of the images used from both satellites, Sentinel-2 and both orbits from Sentinel-1 throughout 2018.  
For FMZs surrounding houses and settlements, buffer creation is more complex, as only the buffer on the outer part of the FMZs is needed. These FMZs have a circular shape and many holes, where the actual buildings are. Applying a simple buffer creates many intersection zones inside the polygon. To solve this the processing chain presented in Figure 6 is applied. First, extract all the points belonging to the FMZ vector file and apply the Concave hull algorithm [31], as a result, the envelope of a set of points is obtained, present in Figure 6b. After applying Concave Hull, the resulting polygon is dissolved, and its holes are filled resulting in the polygon presented in Figure 6c. Finally, apply a 20 m buffer to the initial FMZ and use the dissolved polygon to cut into the buffer, resulting in only the outer buffer of the FMZ (Figure 6d).  
Figure 4: Vector information of Fuel Management Zones (FMZs) in the north of Macão.  
Figure 5: Example of the phases to create the outer buffers. (**a**) Polygon that represents a Fuel Management Zones (FMZ) along electrical lines, with a width of 15 m. (**b**) Polygon resulting from using the 20 m Buffer function. (**c**) Polygons that represent only external areas of the FMZ, after removing the middle of the FMZ and roads from OpenStreetMaps.  
Some faults on the final buffers can be observed in Figures 5c and 6d. Road information extracted from OpenStreetMap [32] was cut into the final FMZ buffers, due to roads not being part of these FMZs. This removes unwanted buffer zones where FMZs have areas in its interior that do not belong to the FMZs (e.g., roads).
# An Empirical Performance Analysis of the Speak Correct Computerized Interface
## 3 Speak Correct Computerized Interface  
### Speak Correct Phonetic Editor  
The Speak Correct system has four primary phases: the trainer phase, which is used to train speech characteristics; the decoder phase, which is used for speech decoding using pronunciation assumption; the evaluation phase, which is used to assess and create speaker feedback; as well as the phonetic editor, which is used to enhance the suggested language model using the adaptation of mapping and generation guidelines. The system employs a decoder that detects user input speech and calculates the confidence and mistakes in pronunciation. Furthermore, the system analyzes feedback messages and detects errors, provides help for correcting the faults, and performs an evaluation. To distinguish specific speech sounds, the phonetic editor comprises mapping and generation procedures as well as analytic methods (e.g., neural networks and Gaussian models). Figure 1 shows the data flow diagram (DFD) for the suggested Speak Correct system. Each word is presented to the users in graphs and a lattice form, with a visual indicating different phonetic levels and corresponding teachings (Saudi or Egyptian accent defects). As a result, the Speak Correct provides users with the option of choosing their own levels and examples [23; 24; 25; 26].  
At the phonetic phase, skilled annotators insert the utterances, which are then mapped and generated using automatic voice recognition and backed by trained occurrences in the format of a grammar structure (Lattice graph) for the intended word. Mistakes are recognized and evaluated, and the decoder and assessment stages are used to assess the results.  
The difficulty of determining the correct \"underlying\" sequences of symbols/patterns is addressed in the extraction phase. As a result, the Veterbi algorithm provides an efficient technique of addressing the decoding problem by examining all the potential strings and computing the probability of obtaining the observed sequence by employing additional rules (such as the Bays rule [27]).  
The reference speech techniques are commonly subjected to further preprocessing in order to adjust them to the speaker's speech qualities. In these kinds of circumstances, the speaker adaptability module of the Maximum Likelihood Linear Regression (MLLR) is employed to improve the adapted component.  
Figure 1: Speak Correct with phonetic analyzer overview.
# Time-Series Prediction of Long-Term Sustainability of Grounds Improved by Chemical Grouting
## 3 Results and Discussion  
### Uniaxial Compression Strength of the Improved Body  
Figure 4 shows the experimental results of the uniaxial compressive strength with respect to the time for the improved body in a 10-year period, with the GBFS being injected. The data were obtained from long-term curing experiment results for various grouting materials. Several samples were prepared for each batch of the GBFS injected curing test and the mean strength of the samples at a particular interval of measurement was adopted as the strength of that period. The data were used as the benchmark to determine the precision of each method by comparing the difference between the predicted results and the benchmark data. The data were recorded six times in the first year where the initial strength gain of the samples was rapid. After that, the data was recorded over 2 years except for the 10-year data. It can be seen in the figure that in the initial 28 days, approximately 85% of the total strength is gained, reaching the value of 3.1 N/mm\({}^{2}\). Then, the gain in strength becomes negligible and the maximum strength of 3.79 N/mm\({}^{2}\) is reached in a year. The data show a decrease in strength during the following 5-year period, after which the strength starts to increase again, reaching a final strength of 3.73 N/mm\({}^{2}\) at the end of 10 years.  
Figure 3: Working steps of the machine learning predictive (MLP) model.
# Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programability
## 3 Related Work
### Dynamic QoS Management in SDN Environment
From the aspect of large or carrier-grade networks based on SDN technology such as 5G and 6G networks, specific components of the OpenFlow architecture will be subject to continuous development. Changes within the OpenFlow standards, which relate to QoS, represent a considerable challenge because, within the OpenFlow protocol, there is still much free space for implementing new, more comprehensive, and feature-rich solutions. In order to perform QoS experiments, the authors in [46] introduce an architectural extension for the OpenFlow environment. Further, the authors identify current QoS limitations in OpenFlow switches, propose and implement enhancements related to queue configuration, and extend the OpenFlow protocol by defining queue settings based on specific queue types. Some authors have focused on management and control plane aspects to provide centralized end-to-end QoS [47; 48]. Without extending the OpenFlow specification, they have opted to implement a QoS policy management framework that provides an interface for specifying QoS-based SLAs and enforces the use of the OpenFlow API. Using GI/M/1/K and M/M/1 queues from the queuing theory, the authors in [49] model switches and a controller. They compare two different switch implementations (a shared buffer and two priority buffers) and conclude that priority buffering should be the preferred mechanism for mobile networks.
# Mobility Prediction and Resource-Aware Client Selection for Federated Learning in IoT
## 3 System Models and Problem Formulation
### Computational Latency with Mobility Awareness  
Computational latency refers to the time required for a client to complete a local training iteration before transmitting updates to the server. It is influenced by factors  
Figure 1: Federated Learning with mobile clients.  
such as the size of the dataset, model complexity, and the client's processing capacity. For instance, an IoT device with limited computational resources will take longer to process updates compared to a more powerful device. MACS addresses this by selecting clients with sufficient processing capability to ensure timely updates, reducing overall training delays. The computational latency for each client, \(t_{u}^{\text{comp}}\), depends on the client's computational resources and is estimated as follows:  
\[t_{u}^{\text{comp}}=\frac{C_{u}|\mathcal{D}_{u}|v\log_{2}(1/\eta)}{f_{u}(t+k)}, \tag{1}\]  
where \(f_{u}(t+k)\) is the predicted computational capacity of client \(u\) at future time \(t+k\), derived from the mobility prediction model that anticipates changes in client resources based on their historical movement patterns. \(C_{u}\) is the average number of CPU cycles required for processing each data sample. \(|\mathcal{D}_{u}|\) is the number of data samples available to client \(u\). \(v\log_{2}(1/\eta)\) indicates the number of iterations needed to achieve the desired accuracy \(\eta\), where \(v\) is defined as follows:  
\[v=\frac{2}{(2-L\delta)\delta\gamma}, \tag{2}\]  
with \(\delta\) being the learning rate, and \(L\) and \(\gamma\) depending on the eigenvalues of the Hessian matrix of the loss function.
# A Survey of Candidate Waveforms for beyond 5G Systems
## 4 Performance Results and Discussion  
In this section we perform a comparison between the candidate waveforms mentioned in previous section, regarding the PAPR and PSD/spectral efficiency of the transmitted signal, BER performance and implementation complexity. For the TIBWB-OFDM with and without WTO cases we consider that OFDM component sub-symbols with \(N=64\) sub-carriers and the number of packed OFDM blocks per TIBWB-OFDM block is \(N_{s}=42\). For the CP-OFDM and F-OFDM cases, in order to perform a fair comparison, the total number of carriers was chosen in a way that the overall signal's length is similar to the length of the TIBWB-OFDM block. This way, the total number of carriers for these cases is \(NN_{s}=64\times 64\). Also, for GFDM, the same procedure was applied and the number of time-slots employed in transmission was adjusted to \(N_{s}=64\). The carrier multiplication factor and the number of time-slots is \(N_{s}=64\) (and not 63) in order to provide the same conditions regarding channel coding. Quadrature phase shift keying (QPSK) modulation with a Gray coding rule are applied upon bits carried on each carrier. Furthermore, for all the transmission scenarios, the transmission channel is a severely time dispersive channel with 32 symbol-spaced multipath components with uncorrelated Rayleigh fading.
## 3 Experimental Implementation
### Parameter Identification  
The procedure for performing parameter identification is as follows:  
1. Choose the output frequency of the function generator.
2. Select the correct mode of \(C_{p}\).
3. Log the data of \(I_{pi}\), \(\varphi_{i}\).
4. Perform the GD Algorithm.  
Figure 8: The experimental setup.  
Figure 9: Circuits on signal processing board; (**a**) current detection and (**b**) phase detection.  
Figure 10 shows the waveform of input voltage \(u_{p}\), input current \(i_{p}\), ZCD signal of \(i_{p}\), and RMS output of the circuit during the parameter identification process. \(u_{p}\) is kept at 20 Vpp in all cases. It should be noted that the RMS output is 0.2 times the actual value, and it exhibits a certain degree of fluctuation, which means that digital filtering by DSP after ADC sampling is necessary. The ZCD signal has a time delay of 0.3 us, which needs to be compensated for during the phase-difference calculation.  
Figure 11 shows that the measured results are similar to the simulated results. When the load resistance is large, such as for Cases 4, 8, and 12, the identification errors of \(R_{L}\) and \(f_{s}\) are larger, and this trend is also observed in the simulation results. The maximum identification errors for mutual inductance, load resistance, and \(f_{s}\) are 2.03 uH, 9.47 \(\Omega\), and 2.62 kHz, respectively. Although the resistance-identification error in Case 4 is relatively large, the experimental results still prove the feasibility of the identification method.