esa-sceva/satcom-chunk-collection huggingface dataset:
# WISCANet: A Rapid Development Platform for Beyond 5G and 6G Radio System Prototyping
## 4 Over-the-Air Demonstrations
### Distributed Mosaic Beamforming  
In a previous study [73], we demonstrated a novel, distributed, multi-stage beamforming technique for distributed mesh relay networks. A simple example system that executes this technique is depicted in Figure 10. In this particular configuration, two mesh relay networks \(A\) and \(B\) execute the multi-stage beamforming technique to relay communications between a simple transmitter and receiver. Given \(N_{a}\) and \(M_{b}\) nodes in meshes \(A\) and \(B\), this technique increases the received SNR realized at the receiver by as much as \(N_{a}^{2}M_{b}\)[73, 76]. To quickly validate this system concept and performance bound, we implemented this system on WISCANet using Ettus X310 products. Doing so allowed us to quickly (\(<\)2 weeks) validate the system concept over-the-air before committing to a multi-million dollar research program to transition the technology to an embedded solution [73].  
We implemented the application depicted in Figure 10 on WISCANet with \(N_{a}=4\) and \(M_{b}=4\) using Ettus X310s, which provide up to 4 RF channels per device. Distributed coherent applications require precisely aligned local oscillators, which is enabled intrinsicly by WISCANet's phase stability: this guarantees oscillator frequency stability if we elect to enable clock sharing.  
The proposed technique is an iterative approach that requires an initial acquisition iteration. For a given transmit event: (1) the transmitter transmits to mesh \(A\); (2) mesh \(A\) computes and applies a beamforming pre-distortion filter to the received signal; (3) mesh \(A\) re-transmits this signal to mesh \(B\); (4) mesh \(B\) computes and applies another beamforming pre-distortion filter to the received signal; and (5) mesh \(B\) re-transmits this signal to the final receiver. Computing the pre-distortion filters requires channel state information (CSI) measurements provided by a feedback link, so in the first iteration the beamforming gain is limited. After the CSI is measured and communicated on the feedback link (in this case implemented using WISCANet's genie channel feature), the system approaches  
Figure 9: System architecture of the WISCANet deployment maintained in the WISCA laboratory at Arizona State University.  
the theoretical SNR gain of \(N_{a}^{2}M_{b}\). Since these demonstrations are executed over-the-air, real-world effects such as time-varying channels and noise cause some fluctuation in the actual realized performance. The SNR gain results from three independent trials of this demonstration are depicted in Figure 11.
# Three-Dimensional Processing of Reflections for Passive-Source Seismology Based on Geometric Design
## 2 Extracting 3D Virtual Shot Records Based on Geometry
### Model Test
#### 2.3.3 Forward Model with a Wide Azimuth  
We also designed a geometry with a wide azimuth: The maximum offsets along the direction of the line and the direction vertical to it were both 1500 m, that is, each source received data from 51 \(\times\) 51 receivers at most, and the aspect ratio was one. Figure 13a shows the source point and the range of its corresponding receiver point, and Figure 13b shows folds of the geometry designed on the basis of this scenario. Figure 14a shows the shot records collected by using this geometry at the center of the model, and Figure 14b shows shot records depicted by the red box in Figure 14a. Figure 15 shows the results of migration imaging and stacking, performed by calculating shot records by extracting one line from every four with a time window of correlation of 40 s. The velocities required for the migration and nmo were obtained from the model of velocity.  
Figure 12: Migration profile of 3D virtual shot records in the case of a geometry with a narrow azimuth.  
Figure 13: (**a**) Coverage of folds of a source in the geometry with a wide azimuth (the red point is the source point, the green rectangle is the range of the receiver corresponding to the source point, east is the inline direction, and north is the crossline direction). (**b**) Folds of this geometry.  
A comparison of the complete model (Figure 9b), narrow azimuth model (Figure 12) and wide azimuth model (Figure 15) shows that the three methods generated similar migration profiles by using a model-compliant geometry and the processing workflow of conventional seismic exploration, and their results were consistent with the 3D model of velocity in Figure 6a. This shows that the proposed method of generating virtual shot records of a 3D passive source based on SPS files is accurate. Due to the geometric design, data based on narrow and wide azimuths from all receiver points are not needed to participate in the calculation of each source, which significantly reduces the computation time (narrow azimuth model and wide azimuth model took about 1/20 and 1/4, respectively, of the time taken to calculate the virtual shot records by using all of the receiver points) and ensures uniform folds.  
Figure 14: (**a**) Three-dimensional (3D) virtual shot records of the 5050 th source in the geometry with a wide azimuth (source at line 51; source point 5050); (**b**) 3D virtual shot records of the 5050 th source at receiver lines 50–52 in the geometry with a wide azimuth.  
Figure 15: Migration profile of 3D virtual shot records in the geometry with a wide azimuth.
## 2 Data
### VIIRS Sensor Data Record  
The VIIRS sensor data record (SDR) has 22 channels that span the visible and infrared wavelengths ranging from 0.41 \(\upmu\)m to 12.01 \(\upmu\)m. Among these channels, five are high-resolution bands (I-bands); sixteen serve as moderate-resolution bands (M-bands); and another is a unique panchromatic Day/Night band (DNB). In this study, we used seven moderate-resolution reflectance bands (i.e., M3, M4, M5, M7, M8, M10, and M11) and one thermal band (i.e., M16) at a 750 m resolution (Table 1) to get the spectral signatures of smoke, cloud, bright surface, dust, and other surface types. These SDR datasets were downloaded from the National Oceanic and Atmospheric Administr  
Figure 1: The fire-prone regions (gray box) for selecting smoke plume samples in (**a,b**) North America, (**c**) South America, (**d**) Africa, (**e**) Indonesia, (**f**) Siberia, and (**g**) Australia. The base map is \(1^{\circ}\times 1^{\circ}\) global fire detection density derived from 2018 VIIRS 750 m active fire observations in geographic projection.  
tion (NOAA) Comprehensive Large Array-data Stewardship System (CLASS) ([[https://www.avl.class.noaa.gov/saa/products/](https://www.avl.class.noaa.gov/saa/products/)]([https://www.avl.class.noaa.gov/saa/products/](https://www.avl.class.noaa.gov/saa/products/))).  
To enhance the contrast between smoke aerosols and non-smoke areas, we used the corrected reflectance, rather than top-of-atmosphere (TOA) reflectance, for the blue band (M3). The corrected reflectance is derived from the TOA reflectance by performing a simple atmosphere Rayleigh scattering correction [38] without any adjustments for aerosol scattering [39]. In addition, we calculated the spatial standard deviation of the blue band corrected reflectance for a better separation of smoke from clouds. However, for bands of longer wavelengths, we used TOA reflectance directly (without any corrections) due to the negligible impacts from Rayleigh scattering on fine particle-dominated smoke aerosols at longer wavelengths.
# A New Hybrid Prime Code for OCDMA Network Multimedia Applications
###### Abstract  
This paper presents a new family of spreading code sequences called hybrid prime code (HPC), to be used as source code for the optical code division multiple access (OCDMA) network for large network capacity. The network capacity directly depends on the number of available code sequences provided and their correlation properties. Therefore, the proposed HPC is designed based on combining two or more different code words belonging to two or more different prime numbers. This increases the number of code sequences generated. The code construction method utilized allows the generation of different code sets, each with different code length and weight, according to the number of prime numbers used. In addition, the incoherent pulse position modulation (PPM) OCDMA system is proposed based on the HPC code. Furthermore, the bit error rate (BER) performance analysis is introduced versus the received optical power and the number of active users. Moreover, the error vector magnitude (EVM) is calculated versus the optical signal-to-noise ratio. This work proves that using two prime numbers simultaneously generates far more codes than using prime numbers separately. It also achieved an OCDMA system capacity higher than the system that uses the optical orthogonal codes (OOCs), modified prime codes (MPCs) families, and two code families with separate simultaneously prime numbers, at a BER below \(10^{-9}\) which is the optimum level.  
Keywords:optical code division multiple access (OCDMA); 2D codes; hybrid prime codes (HPC); bit error rate (BER) performance analysis +
Footnote †: journal: Electronics
## 2 Materials and Methods
### Random Forest-Based Post-Processing  
The first post-processing step reevaluates the initial label received by each pixel of the test data records. The decision is made by a random forest classifier that relies on morphological features. The details of this post-processing step are listed in the following:  
1. The RF is trained to separate positive and negative pixels using six features extracted from the intermediary labels produced by the BDT ensemble. Let us consider \(K=5\) concentric cubic neighborhoods of the current pixel, denoted by \(\mathcal{N}_{k}\) (\(k=1\ldots K\)), each having the size \((2k+1)\times(2k+1)\times(2k+1)\). Inside the neighborhood \(\mathcal{N}_{k}\) of the current pixel, the count of brain pixels \(n_{k}\) and the count of positive intermediary labeled brain pixels \(n_{k}^{+}\) is extracted. The ratio \(\rho_{k}=n_{k}^{+}/n_{k}\) is called the rate of positives within neighborhood \(\mathcal{N}_{k}\). The feature vector has the form \((\rho_{1},\rho_{2},\ldots\rho_{K},\eta)\), where \(\eta\) is the normalized value of the number of complete neighborhoods of the current pixel, determined as: \[\eta=\frac{1}{K}\sum_{k=1}^{K}\delta\left(n_{k},(2k+1)^{3}\right)\,\] (13) where: \[\delta(u,v)=\left\{\begin{array}{ll}1&\text{if }u=v\\ 0&\text{otherwise}\end{array}\right.\.\] (14)
2. To assure that testing runs on data never seen by the training process, pixels from HGG tumor volumes are used to train the random forest applied in the segmentation of LGG tumor volumes, and vice versa. Each forest is trained using the feature vectors of \(10^{7}\) randomly selected voxels, whose feature values fulfil \(\sum_{k=1}^{K}\rho_{k}>0\).
3. Pixels whose features satisfy \(\sum_{k=1}^{K}\rho_{k}=0\) are declared negatives by default; they are not used for training the RF and are not tested with the RF either.
4. The number of trees in the RF is set to 100, while the maximum allowed depth of the trees is eight. The result of this post-processing step can be seen in Figure 1, represented by segmentation results \(S_{1}^{\prime}\) and \(S_{2}^{\prime}\).
## 1 Introduction
In the case of non-PCM, the heating process is faster and reaches the temperature \(T_{2}\) at time \(t_{2}\). The scheme shows the time gain, \(\Delta t=t_{4}-t_{2}\), i.e., the difference in the heating time of both materials needed to reach \(T_{2}\). From a practical point of view, temperature \(T_{2}\) may be a critical value that should not be exceeded. For example, \(T_{2}\) can be the maximum temperature:  
* Of the internal wall of a building, for which the temperature inside is within the range ensuring the thermal comfort of the residents;
* At which food or drugs can be stored, and the use of PCMs in their packaging can extend the transport time without the risk of deterioration of their properties;
* Of the inner layer of clothing containing PCMs for which the skin of the wearer will not burn. The \(\Delta t\) may take different values because it depends on the type and amount of PCM used in the tested system. Figure 1: Comparison of PCM and non-PCM heating processes. The aim of the research described in this current article was to investigate the potential use of inserts containing PCM macrocapsules in multilayer protective clothing. The results of the PCM efficiency evaluation were compared with analogous multilayer clothing assemblies with similar geometry with PP macrogranules (non-PCM). The research involved the analysis of heat transfer through textile assemblies under conditions of exposure to contact heat and radiant heat.
## 4 Implementation Procedure
### Implementation of Proposed Model
#### 4.2.3 Fragments Data Allocation Using GA  
* The GA evaluates the fitness of each solution by calculating its total allocation cost based on the given cost function.
* Solutions with lower total allocation costs are considered fit and have a higher probability of being selected for reproduction.
* Once the GA completes, the best solution represents the allocation of fragmented data across the nodes that minimizes the total allocation cost.
* The allocated fragments are evaluated based on various metrics.
* The GA algorithm is implemented by defining functions for initializing the population and updating the population. It is then applied to further optimize the data fragmentation, considering the total allocation cost as the fitness function.
* The GA algorithm efficiently and effectively allocates fragmented data by finding the best sites for data allocation, representing the optimized data allocation.
# HH-NIDS: Heterogeneous Hardware-Based Network Intrusion Detection Framework for IoT Security
## 4 Implementation
### SoC FPGA
#### 4.4.2 Verilog Implementation Approach  
This approach uses a hardware description language to implement the _Neuron Network Architecture_ block. Figure 5 shows the prototype implementation on an FPGA-based architecture. The data flow is similar to the _HLS implementation approach_; however, the number of neurons in the hidden and output layers is maximised for the designed neuron network architecture (11 inputs, 32 hidden neurons, 16 output neurons).  
By using Verilog, the processing performance is optimised manually with a pipeline strategy, in which buffers are placed between each calculation step. The pipelined NN architecture could be described as having four phases:  
* _Input buffering_ (P1): contains _the Pre-processor_ block to receive the eleven input features from DMA through the AXI4-Stream interface.  
Figure 4: The generated FPGA block architecture using the HLS implementation approach.  
Figure 5: Pipeline implementation of the _Neuron Network Architecture_ block on an FPGA-based architecture includes four phases: Input buffering (P1), Layer_0 calculation (P2), Layer_1 calculation (P3), and Output buffering (P4).  
* _Layer_0 calculation_ (P2): has 32 neurons running simultaneously. These neurons have the same architecture, which has six calculation stages: multiplication, four additions, and an activation. Figure 6 illustrates a single neuron architecture on FPGA. From the P1 phase, the 11-input features are fed into the corresponding eleven 2-input multipliers (_MULx 11_ block). Next, these results are accumulated by the next four addition stages, represented as _ADD x6_, _ADD x3_, _ADD x2_, and _ADD x1_ blocks. The final summed result is passed to the _ACT_ block, which is implemented as a hardware-efficient ReLU (Rectified Linear Unit) activation function.
* _Layer_1 calculation_ (P3): receives buffered results from the P2 phase; then, these data are distributed to sixteen neurons in this block. Each neuron shares the same calculation architecture with Layer_0 neurons; except that each neuron in Layer_1 has five calculation stages: a multiplication (_MUL x16_ block) and four additions (_ADD x8_, _ADD x4_, _ADD x2_, and _ADD x1_ blocks). The final results are buffered and will be read in the next clock cycle.
* _Output buffering_ (P4): includes the _Comparator_, the _FIFO buffer_, and the _Post-processor_ blocks. The _Comparator_ block returns the maximum index from sixteen inputs, corresponding to sixteen output neurons in the P3 phase. These indices are stored in a FIFO (First in, first out) buffer. The _Post-processor_ block reads data from the buffer and sends it to DMA; this block also sends a signal to the _Pre-processor_ block to indicate if the buffer can store all the results.
# An Efficient Grid-Based Geocasting Scheme for Wireless Sensor Networks
## 4 Simulation Results
### Total Energy Consumed versus Number of Rounds  
We observe the total energy consumed under various numbers of rounds. As shown in Figure 11, the proposed GB-FERMA has less total energy consumption compared to FERMA-QL, FERMA, and GEAR. The GB-FERMA uses the grid-based data aggregation tree to transmit data, which can effectively reduce energy consumption. It is proved that GB-FERMA can extend the lifetime of WSN more than the other three schemes.  
The comparisons of GB-FERMA, FERMA-QL, FERMA, and GEAR are shown in Table 1. GB-FERMA is a geocasting scheme that is grid based; while FERMA-QL, FERMA, and PEDAP are geocasting schemes without being grid based. The data transmission of GB-FERMA, FERMA-QL, and FERMA is the greedy routing with optimal relay nodes, while the data transmission of GEAR is the greedy routing. The routing path types of GB-FERMA, FERMA-QL, FERMA, and GEAR are a grid-based shared tree path, Q-learning based shared tree path, shared tree path, and chain path, respectively. The energy performance of GB-FERMA is the best among the four schemes.  
Figure 11: Total energy consumed versus number of rounds.  
Figure 10: Number of alive nodes versus number of rounds: (**a**) initial power 0.25 J; (**b**) initial power 0.5 J.
# Characterization of Surface Ozone Behavior at Different Regimes
## 3 Results and Discussion
### Linear Correlation Analysis  
Figure 5 shows the variation in linear correlation between O\({}_{3}\) and meteorological parameters on a monthly basis. Negative correlations were observed for NO (\(-0.547\) to \(-0.296\)) and NO\({}_{2}\) (\(-0.807\) to \(-0.276\)) concentrations. The effect of these air pollutants was more significant in winter periods than in summer periods. Regarding the effect of meteorological variables, temperature was usually positively correlated with O\({}_{3}\), which was in agreement with what was expected. The highest value (\(R\) = 0.661) was determined in September 2013 and an unusual negative correlation (\(R\) = \(-0.376\)) was determined in July 2013. \(RH\) was negatively correlated in almost all periods. The highest impact was also observed in September 2013 (\(R\) = \(-0.685\)) and an unusual positive correlation was determined in July 2013 (\(R\) = 0.419). [PERSON] et al. [42] demonstrated that \(RH\) favors O\({}_{3}\) decomposition, justifying the associated negative effect. Regarding \(WS\), this variable can have two different effects on O\({}_{3}\) concentrations. Low \(WS\) can promote the accumulation of O\({}_{3}\) produced in the region (increasing its concentration), while high values reduce the levels of other air pollutants (such as NO\({}_{x}\)) that influence the O\({}_{3}\) chemistry (in the case of NO\({}_{x}\), its concentration decrease leads to the increase in O\({}_{3}\) levels). Thus, the effect of \(WS\) on O\({}_{3}\) concentrations depends on the studied environment: urban or rural. In this study (urban environment), \(WS\) was positively correlated with O\({}_{3}\) concentrations, with the highest value (\(R\) = 0.593) in February 2013. Figure 4: Monthly average values of (**a**) NO concentrations, (**b**) temperature, (**c**) NO\({}_{2}\) concentrations, (**d**) relative humidity, (**e**) O\({}_{3}\) concentrations, and (**f**) wind speed. Figure 5: \(Cont.\)  
Regarding the combined effect NO\({}_{2}\)-\(WS\), similar conclusions were drawn: O\({}_{3}\) concentrations presented a decreasing tendency with NO\({}_{2}\) concentrations, and their variability with \(WS\) is almost insignificant. Figures S1 and S2 present the effect of the same combinations of variables on O\({}_{3}\) concentrations considering \(T\) (simulation II) and NO\({}_{2}\) (simulation III) as threshold variables, respectively. Similar analysis can be performed through these figures. The application of this statistical methodology allows for the determination of the influence of environmental and meteorological variables on O\({}_{3}\) concentration. Consequently, it is possible to develop more accurate predictive models for this secondary pollutant, which is important for the definition of policy measures for human health protection.
###### Abstract  
This study presents a clinical utility-driven machine learning framework for retinal Optical Coherence Tomography classification, addressing challenges posed by manual interpretation variability and dataset heterogeneity. The methodology integrates biomimetic data partitioning, deep biomarker extraction via pretrained VGG16 networks, and automated model selection optimized for clinical decision-making. Stratified data curation preserved pathological distributions across training, validation, and testing subsets, while SMOTE optimization mitigated class imbalance. Cross-pathology testing evaluated generalizability on anatomically distinct retinal conditions excluded from training, assessing the framework's robustness to unseen pathologies. Clinical utility metrics prioritized alignment with ophthalmological imperatives, emphasizing negative predictive value to minimize false negatives and enhance diagnostic reliability. The framework advances AI-driven Optical Coherence Tomography diagnostics by harmonizing computational performance with patient-centered outcomes, enabling standardized disease detection across diverse clinical datasets through robust feature generalization.  
Keywords:retinal OCT classification; machine learning; clinical utility; SMOTE optimization; logistic regression +
Footnote †: journal: Applied sciences