esa-sceva/satcom-chunk-collection huggingface dataset:
# A Flexible FPGA-Based Stochastic Decoder for 5G LDPC Codes
## 4 Proposed Architecture
### Layered Decoding Schedule  
In partially parallel architectures, only a few SVNs and SCNs are instantiated simultaneously. Figure 5 shows the proposed architecture that connects the SCND to the SVND. The SCND consists of \(M\) D flip-flops, used as memory to store updated SCN messages received from SVND. Here, \(M\) is the total number of SCNs of the Tanner graph of the decoded LDPC code. The architecture adopted shuffled iterative decoding [26], in which the total number of block columns of the BGM is subdivided into vertical layers: each layer consists of one block column of BGM or \(\eta_{v}\) columns of the PCM, where \(\eta_{v}\) is the SVN parallelism factor; these columns are mapped as SVNs of the Tanner graph of the decoded LDPC code. Before the next vertical layer is processed, the SVNs of each layer are processed in parallel, and the results are used to update the connected SCNs. Using this scheduling, the 3808 columns of the \(1/3\)-rate codeword length \(N=3808\) LDPC code PCM are divided into \(N_{B}=68\) vertical layers: each layer consists of \(\eta_{v}=56\) columns. Accordingly, the proposed decoder has an SVND, which comprises \(\eta_{v}=56\) stochastic variable node processing units (SVNPUs), which function based on the SVN update. These units process a layer of \(\eta_{v}=56\) columns of the PCM during each clock cycle, with these updated values being written back to SCND immediately. Hence, each decoding cycle (DC) requires \(\tau_{D}=N_{B}=68\) clock cycles. Each DC creates one new bit in each stochastic bit sequence.
## 2 Internet of Things Environment
### MAC Layer  
The IEEE 802.15.4 MAC layer operates slightly differently compared to the IEEE 802.11 access mechanism. The 802.11 MAC protocol operates based on the distributed coordinated function (DCF) mechanism, which first senses the channel and then waits for random backoff if the channel is found busy. If the channel is idle, it waits for a period known as the DCF interframe space (DIFS). After the DIFS period, it randomly selects a contention window (CW) size from 0 to \(2^{BE}-1\). The value of BE increases by one each time there is a collision [45]. Similar to the 802.11 MAC, IEEE 802.15.4 has three variables, i.e., a BE, the number of backoffs (NB), and a CW. The initial value of BE is set to 3 and can increase up to a maximum of five backoff stages. Transmission starts if the channel is free. Otherwise, the node takes a deferred approach and increases the BE period by one prior to frame transmission [46]. Incrementing the BE during channel sensing is an energy-efficient approach; however, this approach performs poorly as the network size increases. If the BE is increased and no collision is detected after frame transmission, this may add an unnecessary access delay, resulting in network performance degradation.
# A Systematic Review on Hybrid AI Models Integrating Machine Learning and Federated Learning
## 2 Methodology
### Search and Study Selection Strategy
The study selection process strictly adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring transparency, methodological rigor, and replicability throughout the review process. This process was divided into four distinct stages: Identification, Screening, Eligibility, and Inclusion. In the identification phase, the initial search using the refined keyword combinations across the targeted databases, resulted in the identification of 488 articles. Following automated and manual duplicate removal, a total of 395 unique records progressed to the screening phase. The screening phase involved a rigorous review of titles and abstracts to evaluate their alignment with the study's objectives and the refined inclusion/exclusion criteria. Articles that did not explicitly address the combination of federated learning and machine learning/deep learning for cybersecurity applications were excluded, leading to the removal of 194 articles that lacked direct relevance or methodological focus. During the eligibility phase, the remaining 201 full-text articles were assessed for conformity with the predefined inclusion and exclusion criteria. Studies that failed to meet quality standards or did not provide sufficient evaluation metrics relevant to our five dimensions (accuracy, privacy, scalability, explainability, robustness) were excluded. A total of 97 articles were excluded during this phase for various reasons, including failure to meet quality assessment criteria 68 studies) and insufficient evaluation metrics (29 studies). Additionally, 19 articles could not be retrieved despite repeated attempts. Finally, in the inclusion phase, 85 studies were included in the systematic review for detailed analysis. These studies demonstrated strong methodological foundations and clear relevance to the application of FL+ML/DL in cybersecurity, particularly in enhancing intrusion detection systems (IDS), malware detection, anomaly detection, and network threat analysis. The entire study selection process is visually represented in the updated PRISMA flow diagram (see Figure 2), which outlines each step from identification to final inclusion, providing transparency and replicability in the selection methodology. Figure 1: Visualization of research trends in hybrid AI models for cybersecurity applications.
# An Identification Method of Feature Interpretation for Melanoma Using Machine Learning
## 2 Methodology
### Feature Extraction  
#### 2.2.1 Texture Feature  
Texture characterization [25; 26] captures changes in surface or structural patterns in an image. Gray scale covariance matrix (GLCM) [27; 28] is a fundamental method to analyze texture features, which accurately reveals the roughness and repetition direction of the texture. In total, six feature parameters were selected: Contrast, Similarity, Homogeneity, Energy, Correlation, and Angular Second Matrix (ASM). The covariance matrix can have features extracted from scanned images with different orientation angles, as well as from images with different gray levels. The formulas [25; 26; 27; 28] are shown in (1)-(6). To explore the effect of this, four direction angles (\(0^{\circ}\), \(45^{\circ}\), \(90^{\circ}\), and \(135^{\circ}\)) and three gray levels (8, 16, and 32) were selected.  
Contrast quantifies the change in intensity within an image and represents the difference between the intensity of one pixel compared to another. Mathematically, it is calculated as follows:  
\[\text{Contrast}=\sum_{i}\sum_{j}\text{P(i,j)}*|\text{i}-\text{j}|^{2} \tag{1}\]  
Dissimilarity assesses the level of distinction between pairs of pixels with varying gray levels in an image. It is calculated as follows:  
\[\text{Dissimilarity}=\sum_{i}\sum_{j}\text{P(i,j)}*|\text{i}-\text{j}| \tag{2}\]  
Angular second moment gauges the uniformity of the distribution of gray levels and the texture thickness within an image. This metric is calculated as follows:  
\[\text{ASM}=\sum_{i}\sum_{j}\text{P(i, j)}^{2} \tag{3}\]  
Homogeneity quantifies the extent of variation in the textual components of the image, particularly in its uniformity. It is calculated using the following formula:  
\[\text{Homogeneity}=\sum_{i}\sum_{j}\frac{\text{P(i, j)}}{1+\text{|i}-\text{j}|^{2}} \tag{4}\]  
Figure 3: Results of the thresholding stage: (**a1–a3**) the original images; (**b1–b3**) the images after k-means; (**c1–c3**) the masks; (**d1–d3**) the lesions; (**e1–e3**) the cropped images.  
Energy measures the stability of gray level variations within the texture of an image. The calculation for energy is as follows:  
\[\text{Energy}=\sum_{i}\sum_{j}\text{P(i, j)} \tag{5}\]  
Correlation evaluates the similarity of gray levels within an image, either along rows or columns. The calculation for correlation is as follows:  
\[\text{Correlation}=\sum_{i}\sum_{j}\frac{\text{(i-Mean)*(j-Mean)*P(i,j)^{2}}}{ \text{Variance}} \tag{6}\]  
In calculating the texture features, the variables \"i\" and \"j\" represent the coordinates of the pixels in the image are the i-axis and j-axis in the GLCM, and P(i,j) denotes the frequency of occurrence at pixels at a fixed location. The \"mean\" refers to the average value in the texture feature, while the \"variance\" represents the dispersion or spread of the values in the texture feature.
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## Abbreviations  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} GNSS & Global navigation satellite system \\ MLE & Maximum likelihood estimation \\ GLRT & Generalized likelihood ratio test \\ PVT & Position, velocity, and time \\ GPS & Global Positioning System \\ PMU & Phasor measurement units \\ INS & Inertial navigation system \\ SSSC & Spread spectrum security code \\ NMA & Navigation message authentication \\ SQM & Signal quality monitoring \\ RAIM & Receiver autonomous integrity monitoring \\ TEXBAT & Texas Spoofing Test Battery \\ AWGN & Additive white Gaussian noise \\ ECEF & Earth-centered Earth-fixed \\ ROC & Receiver operating characteristic \\ SCER & Security code estimation and replay \\ \end{tabular}
# Real-Time Guitar Amplifier Emulation with Deep Learning +
## 1 Introduction
To further validate the neural network models, emulations of two vacuum tube guitar amplifiers are presented: the Blackstar HT-5 Metal, and the Mesa Boogie 5:50 Plus. An existing guitar amplifier dataset [26], was used to train both the WaveNet-style network, as well as a variant of the Recurrent Neural Network (RNN) model mentioned previously [21]. Listening tests were conducted to evaluate the perceptual quality of the neural network emulations, and real-time C++ implementations of both the RNN and WaveNet-style neural networks were used to measure the processing speed of the models. This allows for the networks to be compared both in terms of perceptual quality and computational efficiency. The rest of this paper is structured as follows. Section 2 details the WaveNet-style deep neural network used for black-box modelling in this work, as well describing experiments which were carried out to select appropriate parameters for the model. Section 3 describes the RNN model. In Section 4, the neural network models of the two amplifiers are presented, along with the results of the listening tests. Section 5 concludes the paper.
## 3 Results and Discussion
### Determination of Characteristic Volatiles in WRTs with Different Cultivars
The first three odorants were reported to exist in the tea leaves, indicating that the characteristics might have originated from their own property from cultivar differences, while dihydroactinidiolide, sourced from the complex oxidation of \(\beta\)-carotene, was mainly generated during tea processing [4], and it was reported to be detected after roasting during RG manufacturing [6]. Compared with the other WRTs, the concentrations of key differential VFCs in the SX samples were generally unattractive, and thus, the number of characteristic key odorants of SX was fewer than that of other WRTs. Only naphthalene presented the highest average concentrations and AI values in SX, which has been identified as a common VFC among WRTs with SX, Huangmeigui, and Zimudan cultivars [14]. In addition, some potential odorants presented significantly high but equal concentrations and AI value levels in both types of WRTs, including _N_-ethylpyrrole and 2-acetylfuran in the DHP and SX samples and _trans_-linalo oxide (furanoid) in the RG and JF samples, and they were considered as the common key odorants to distinguish between the two cultivars. _N_-Ethylpyrrole and 2-acetylfuran with roasted and baked scents were similarly generated from theanine and _D_-glucose during the roasting steps of the WRTs, as mentioned above. _trans_-Linalool oxide (furanoid) naturally exists as free or glycolistically bound forms in fresh tea leaves, and it was reported to sharply increase after the roasting steps of RG, which might be due to the pyrolysis of its glycoside precursors at high temperatures [6]. Moreover, the odor characteristics of the above compounds were highly consistent with the preponderant aroma attributes of DHP and SX (roasted) and RG and JF (floral) shown in Figure 4, and thus the odorants might contribute much to the formation of the sensory differences.
## 2 Materials and Methods
### Comparison of RSM and ANN Models  
The coefficient of determination (\(r^{2}\)) (Equation (2)), the root mean square error (_RMSE_) (Equation (3)), and the mean absolute percentage error (_MAPE_) (Equation (4)) allow one to see how well RSM and ANN models work [29].  
\[r^{2}=\frac{\left(\sum_{i=1}^{n}\left(A_{i}-\overline{A_{i}}\right)\left(F_{i }-\overline{F_{i}}\right)\right)^{2}}{\sum_{i=1}^{n}\left(A_{i}-\overline{A_{i} }\right)^{2}\left(F_{i}-\overline{F_{i}}\right)^{2}} \tag{2}\]  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}\left(F_{i}-A_{i}\right)^{2}} \tag{3}\]  
\[MAPE=\frac{1}{n}\sum_{i=1}^{n}\frac{\left|A_{i}-F_{i}\right|}{A_{i}} \tag{4}\]  
where \(n\) is the number of values, \(A_{i}\) is the actual value, and \(F_{i}\) is the forecasted value with the RSM or ANN model.  
The lowest _RMSE_ and _MAPE_ and the highest \(r^{2}\) were used to identify the models with the best performance.
# Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios
###### Abstract  
Low-power wide-area network (LPWAN) technologies are becoming a widespread solution for wireless deployments in many applications, such as smart cities or Industry 4.0. However, there are still challenges to be addressed, such as energy consumption and robustness. To characterize and optimize these types of networks, the authors have developed an optimized use of the adaptative data rate (ADR) mechanism for uplink, proposed its use also for downlink based on the simulator ns-3, and then defined an industrial scenario to test and validate the proposed solution in terms of packet loss and energy.  
IoT; LPWAN; LoRaWAN; ns-3; adaptive data rate +
Footnote †: journal: Applied Sciences
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 5 BackCom: Wireless Communications
### BackCom Modulation Techniques  
Different amplitude modulation (AM) and frequency modulation (FM) techniques for backscattered signals have been proposed for efficient data transmission. AM techniques include OOK, differential modulation, and quadrature amplitude modulation (QAM). Frequency shift keying (FSK), Gaussian frequency shift keying (GFSK), and frequency shifting techniques are among FM techniques.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Mode** & **References** & **Techniques** & **Objective(s)** \\ \hline \multirow{6}{*}{Full-duplex} & [77] & \(\bullet\) Multi-access design based on a & time & \(\bullet\) Address the problem of interference. \\  & [78] & \(\bullet\) In-band full-duplex. & & \(\bullet\) Same frequency used for transmission and reception. \\  & [92] & \(\bullet\) Hybrid access point (H-AP). & \(\bullet\) Energy harvesting with data transfer. \\  & [93] & \(\bullet\) Relaying. & \(\bullet\) Extended coverage. \\  & [94] & \(\bullet\) Full-duplex feedback channel. & \(\bullet\) Facilitate network layer and link layer. \\  & [95] & \(\bullet\) Optimal policies for time allocation. & \(\bullet\) Extended coverage. \\ \hline Half-duplex & [95] & \(\bullet\) Time allocation policies for half-duplex. & \(\bullet\) Maximize throughput. \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary of communication modes in BackCom.
## Data, Methodology and Results
### Results  
In this section, the improvements of the positioning by the OTCP method are quantified and analyzed. The improvement of the positioning is the position difference between the OTCP method and the geoid correction positioning method, that is, the correction of the specular reflection point on geoid, as expressed in Equation (14). The arithmetic mean of the spatial distances and the coordinates difference modulo in the geodetic and the spatial coordinates systems between the specular points by the two methods of each track are calculated and then all averaged, see Table 1. In the spatial coordinates system, the OTCP method improves the positioning by about 0.31 m relative to the geoid static-state elevation correction positioning method. The error of the TPX08 model in the open sea is less than 1 cm [23]; it can be neglected relative to the positioning improvement. The positioning in the \(X\), \(Y\) and \(Z\) directions are improved by \(\sim\)0.14 m, \(\sim\)0.078 m and \(\sim\)0.27 m, respectively. In the geodetic coordinates system, latitude and longitude directions are improved by about \(6\times 10^{-7\circ}\) and about \(1\times 10^{-6\circ}\), respectively. In order to further study the relationship between the improvement of the positioning and the tidal height, the tidal height modulo corresponding to the specular reflection point is taken as comparison. The average value of the tidal height modulo is about 0.28 m, and the standard deviation is about 0.16 m, both of which are very close to those of the positioning correction. The tracks of the specular reflection points are globally distributed, the corresponding tidal amplitude and phase varies with the location of the track. The tidal positioning correction and the corresponding tidal height modulo are shown in Figures 3 and 4; they are all very close for all the tracks. Their correlation coefficient is 0.998, and the correlation does not decrease significantly with the increase of the tidal height modulo. The slope of the fitting line of the tidal height modulo and the tidal positioningcorrection is \(1.071\pm 0.019\), the SSE is \(9.382\times 10^{-3}\), and the RMSE is \(1.513\times 10^{-2}\). The insignificant fitting error indicates a good positive correlation of the positioning correction and the tidal height modulo. It can be considered that for a specular reflection point on the ocean tidal surface with specific time and position, the tidal elevation positioning correction \(C\) is about \(1.07\) times the tidal height modulo:  
\[C=1.07|H_{\mathrm{T}}|+\lambda \tag{16}\]  
where \(\lambda\), which is \((2.569\pm 7.1)\times 10^{-3}\) m, is the intercept of the fitted line.
## 5 Results and Discussion
### Qualitative Evaluation  
Figure 2 presents qualitative comparisons of enhanced outputs from several methods. Our model successfully brightens dark regions while preserving natural textures and minimizing color distortion. Unlike GAN-based models (e.g., EnlightenGAN), it avoids over-saturation or hallucination of details.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Method** & **PSNR**\(\uparrow\) & **SSIM**\(\uparrow\) & **LPIPS**\(\downarrow\) & **Params (M)**\(\downarrow\) & **FLOPs (G)**\(\downarrow\) \\ \hline SID [11] & **28.76** & **0.890** & 0.145 & 6.7 & 120.3 \\ Retinex-Net [8] & 16.77 & 0.560 & 0.420 & 2.3 & 40.2 \\ Zero-DCE [9] & 23.63 & 0.785 & 0.187 & 0.9 & 9.6 \\ RetinexDIP [10] & 22.59 & 0.765 & 0.205 & 2.8 & 55.8 \\ [PERSON] et al. [13] & 25.40 & 0.830 & 0.163 & 1.1 & 11.0 \\ Ours & 27.82 & 0.865 & **0.139** & **1.3** & 12.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative comparison on the LOL dataset. Best results are bolded. Best values for each column are highlighted in bold. (Arrows (\(\uparrow\) or \(\downarrow\)) indicate direction of better performance.)  
Figure 2: Visual comparison of enhancement results on LOL test samples. Our model restores detail with low noise and better structure retention.
# High Performance MRAM with Spin-Transfer-Torque and Voltage-Controlled Magnetic Anisotropy Effects
## 3 High Performance MRAM Writing  
In general, MRAM is implemented based on a hybrid magnetic/CMOS architecture. The main MRAM array architectures, e.g., 1T-1 MTJ and cross-point, have been reported in [14; 35; 36]. In the classic 1T-1 MTJ array (see Figure 3), MTJ locates in series with the access transistor, where gate is connected to the word-line (WL), drain to the bit-line (BL) crossing the MTJ and source to the source-line (SL). Once non-volatile data is stored the MRAM array, and a sense amplifier (e.g., pre-charge SA) is required for the read operation [35; 36; 37; 38]. Other assisted blocks (e.g., write control circuit, voltage pulse controller in VCMA-MTJ) are needed as well.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **STT-MTJ** & **VCMA-MTJ** \\ \hline TMR & Tunnel magnetoresistance ratio & 150\% & 100\% \\ \(\rm T_{ox}\) & MTJ oxide barrier thickness & 0.7 nm & 1.3 nm \\ \(\rm Rp\), Rap & MTJ resistance & 5 k\(\Omega\)/12.5 k\(\Omega\) & 100 k\(\Omega\)/200 k\(\Omega\) \\ \(\rm Tf\)L & Free layer thickness & 1.2 nm & 1.19 nm \\ \(\rm z\) & Thermal stability & 71.6 & 73.7 \\ Area & MTJ layout surface & 24 F\({}^{2}\) & 12 F\({}^{2}\) \\ \(\rm W/L\) & Access transistor width/length 1  & 300 nm/30 nm & 80 nm/30 nm \\ \hline \hline \end{tabular}
\end{table}
Table 2: Physical parameters in spin transfer torque (STT)-MTJ and voltage-controlled magnetic anisotropy (VCMA)-MTJ [25; 26; 34].  
Figure 3: (**a**) VCMA-MTJ based MRAM bit-cell with Fully Depleted Silicon-on-Insulator (FD-SOI) technology. Precessional switching is implemented with 0.5 ns pulse duration. STT-assisted precessional method is implemented with a shorter (0.18 ns) pulse duration at 1.2 V \(\rm V_{b}\) plus a lower \(\rm V_{b2}\) (e.g., 0.6 V). (**b**) Array structure of the MTJ based MRAMs.  
In this section, a PMA-STT-MTJ [34] compact model, a VCMA-MTJ compact model [25] and a 28 nm CMOS design-kit (ultra-thin body and buried oxide fully depleted silicon-on-insulator, UTBB-FD-SOI) are utilized to study MRAM writing performance. The design space of MRAM writing is explored with energy versus switching delay. Reliability issues including process variation, dielectric breakdown and thermal stability are discussed.