esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 System Model and Complexity Analysis
### A Linear Block Coding Framework for Multi-Way Relaying
With Rayleigh fading, \(f_{ij}\) are zero-mean circularly symmetric complex Gaussian (CSCG) with normalized power \(E\{[f_{ij}]^{2}\}=1\). Then, the received signal at node \(j\) from node \(i\) is as follows:  
\[y_{ij}[k]=\sqrt{P_{i}}f_{ij}b_{i}[k]+n_{ij}[k] \tag{3}\]  
where the noise samples \(n_{ij}[\cdot]\) are uncorrelated zero-mean CSCG with variance \(\sigma_{n}^{2}=N_{0}\), and \(P_{i}\) is the transmission power of node \(i\). All the nodes in the system are assumed to use the same transmission power. In this work, we assume the relays know whether a detected symbol is correct, as in [49]. Practical techniques that can be used to achieve such conditions are discussed in [50]. The relays employ conventional differential detection (CDD) to demodulate the received signals from the terminals. Using CDD, the symbols are detected based on the phase of \(y_{ij}[k]y_{ij}^{*}[k-1]\). The phase of \(y_{ij}[k]y_{ij}^{*}[k-1]\) is denoted by \(\varphi_{ij}[k]\), and the phase of \(a_{i}[k]\) is denoted by \(\theta_{i}[k]\), where \(\theta_{i}[k]=0\) or \(\pi\) for BPSK. Based on (2) and (3),  
\[ \begin{split} y_{ij}[k]y_{ij}^{*}[k-1]=(\sqrt{P_{i}}f_{ij}b_{i}[ k]+n_{ij}[k])(\sqrt{P_{i}}f_{ij}^{*}b_{i}^{*}[k-1]+n_{ij}^{*}[k-1])\\ = P_{i}|f_{ij}|^{2}a_{i}[k]+\sqrt{P_{i}}f_{ij}b_{i}[k]n_{ij}^{*}[ k-1]+\sqrt{P_{i}}f_{ij}^{*}b_{i}^{*}[k-1]n_{ij}[k]+n_{ij}[k]n_{ij}^{*}[k-1]\\ = P_{i}|f_{ij}|^{2}e^{\theta_{i}[k]}+\sqrt{P_{i}}f_{ij}b_{i}[k]n_{ ij}^{*}[k-1]+\sqrt{P_{i}}f_{ij}^{*}b_{i}^{*}[k-1]n_{ij}[k]+n_{ij}[k]n_{ij}^{*}[k-1] \\ = |y_{ij}[k]y_{ij}^{*}[k-1]|e^{\theta_{ij}[k]}\end{split}  \tag{4}\]  
where  
\[\varphi_{ij}[k]=\theta_{i}[k]+\theta_{n}[k] \tag{5}\]  
and \(\theta_{n}[k]\) is the phase noise due to the following term:  
\[\sqrt{P_{i}}f_{ij}b_{i}[k]n_{ij}^{*}[k-1]+\sqrt{P_{i}}f_{ij}^{*}b_{i}^{*}[k-1] n_{ij}[k]+n_{ij}[k]n_{ij}^{*}[k-1].\]  
Hence the detection decision rule for the received symbol from node \(i\) at node \(j\) is as follows:  
\[\hat{m}_{i}[k]= \begin{cases}0&\text{if }-\pi/2\leq\varphi_{ij}[k]<\pi/2\\ 1&\text{otherwise}\end{cases}  \tag{6}\]  
If a relay \(R_{l}\) correctly detects all the symbols from all the terminals in its relevant group, then it will perform a binary linear combination of the corresponding symbols using a parity check column from a systematic generator matrix. The combined signal will be transmitted to all the terminals at transmission phase \(K+l\). If \(R_{l}\) fails to detect all the symbols correctly, it will remain silent at phase \(K+l\), and the terminals will receive only noise. Figure 1: Relation between transmission time slots and phases.
## 2 Materials and Methods
### Surface Modification of Silica NPs with (3-Glycidoxypropyl)trimethoxysilane (NPs-Gly)  
Pristine silica NPs were functionalized with (3-glycidoxypropyl)trimethoxysilane to obtain silica NPs bearing glycidyl surface functionality (NPs-Gly), according to Scheme S1. For this purpose, 6.0 g of silica nanoparticles (40 mL of EtOH) were added in a 250 mL flask. Then, 100 mL EtOH was added, and the mixture was kept under nitrogen atmosphere. The reaction mixture was stirred at 1000 rpm. Subsequently, 10 mL of (3-glycidoxypropyl)trimethoxysilane was added drop by drop. At the end of the addition time, the reaction mixture was heated and maintained at 60 \({}^{\circ}\)C for 24 h. The functionalized NPs were washed three times with EtOH and another three times with water, and were finally redispersed in water. SEM analysis indicated that the obtained silica NPs size was approximately 560 \(\pm\) 3 nm, which had the tendency to pack and self-assemble (Figure S1). Zeta potential (\(\zeta\)) analysis was done before and after the surface modification, Table S1. The samples were prepared with the same concentration for a better comparation. The comparative Fourier transform infrared (FTIR) spectra of silica NPs and NP-Gly are shown in Figure S2 and demonstrate successful functionalization; see spectra analysis and discussion in the Supplementary Information (SI).
# Lab-to-Field Generalization Gap: Assessment of Transfer Learning for Bearing Fault Detection
## 3 Results and Discussions
### Sensitivity vs. Specificity  
The results in Figure 6 indicate that the TL model consistently achieved high specificity across all scenarios, which means that it is more effective at correctly identifying true negative cases (absence of unbalance). In contrast, the ML model exhibits stronger sensitivity and is better at detecting true positive cases, particularly under specific noise conditions.  
To further investigate this trade-off, Figure 8b compares the sensitivity and specificity of the ML and TL models across different scenarios. The color scheme represents data availability, with ML values shown as cross markers enclosed in blue ovals and TL values shown as circle markers in red ovals. Figure 8b illustrates that the ML model consistently  
Figure 8: Model performance results. (**a**) Classification accuracy differences between TL and ML models (TL accuracy minus ML accuracy) for each investigated scenario, grouped by noise type. (**b**) Sensitivity vs. specificity of ML and TL models per for each investigated data availability level.  
achieved high sensitivity, but its specificity varied widely across scenarios without a clear pattern. In contrast, the TL model maintains high specificity, with sensitivity improving as data availability increases, as indicated by the upward trend of TL markers in blue compared to green, with orange in between. Notably, only a few ML cases achieved high specificity at the cost of a lower sensitivity.
## 4 Conclusions  
In conclusion, a comprehensive simulation platform was established combining channel modeling with performance evaluation under solar irradiance based on the photon tracing algorithm. The user-friendly GUI facilitates both the system configuration and visualizing simulation results. We also analyzed the trade-off between different codependent factors such as FOV and apertures. Simulation results indicate that both diffusive and collimated links in harbor water exhibit serious spatial spread and the temporal dispersion becomes more severe with the increase of FOV, water turbidity, divergence angle, and link range, thus limiting the reliable maximum transmission data rate. Furthermore, a narrow FOV and wide aperture size can mitigate the SNR degradation resulting from ambient noises and statistical attenuation, and optimal BER performance is observed. This study offers valuable insights into the practical UOWC system design for different oceanic scenarios with strong ambient noises and is also beneficial for developers to select proper hardware devices and estimate reliable link performance. In further work, we will extend the capabilities of the simulation platform to take the turbulence effect into account.  
Conceptualization, X.W. and M.Z.; methodology, X.W. and M.Z.; software, X.W., M.Z. and H.Z.; validation, X.W.; formal analysis, X.W. and M.Z.; investigation, X.W. and M.Z.; resources, H.Z.; data curation, X.W. and M.Z.; writing--original draft preparation, X.W.; writing--review and editing, M.Z.; visualization, X.W. and H.Z.; supervision, M.Z. and X.R.; project administration, M.Z. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are available on request from the corresponding author.
## 3 Concepts and Methods
### Bitmaps and Seeds Synchronization and Task Scheduling
#### 3.3.3 Task Scheduling  
In ensemble fuzzing, unlike traditional parallel fuzzing, different fuzzers are used for each fuzzing instance, making the instances non-equivalent. Therefore, randomly assigning each subtask to a fuzzing instance after task division, as done in traditional parallel fuzzing approaches like AFLTeam, is not suitable. Otherwise, from the perspective of each subtask, it can only be fuzzed by one of the fuzzers, violating the basic principle of ensemble fuzzing to comprehensively utilize the advantages of different fuzzers on the same target. In other words, at each subtask, ensemble fuzzing degenerates into traditional parallel fuzzing, which leads to serious performance degradation.  
To this end, we designed a rotating subtask scheduling strategy based on the topology structure of a token ring. The periodic and sequentially rotating subtask allocation strategy designed for n heterogeneous fuzzers and n subsets of seed corpora is as follows: As shown in Figure 7a, when allocating subtasks for the first time, subtasks were assigned to each fuzzer in order according to their respective IDs. And, as shown in Figure 7b, in every test cycle, the subtasks were rotated sequentially among the fuzzers until each subtask was fuzzed by all fuzzers.
# Microbes as Medicines: Harnessing the Power of Bacteria in Advancing Cancer Treatment
## 2 Mechanism of Tumor Suppression
### Biofilm Based Anti-Cancer Therapy  
Bacteria form spatially structured, dense networks of colonies in extracellular polymeric matrix, adhering to biological or non-biological surfaces referred to as biofilms. The phenomenon of quorum sensing regulates the process of biofilm formation, which will further enable the bacteria to persist in the host cells and escape the host defense immune system [112]. In addition, the biofilm formation can result in the progression of colon and colorectal carcinogenesis [113]. Biofilm can be exploited as potential anti-cancer agents due to its ability to deliver therapeutics and to curb the spread of metastatic tumors.  
Anti-cancer agents induced and promoted biofilm formation in _Pseudomonas aeruginosa_ during cancer treatment with hydroxyurea and doxorubicin [114]. To escape the drug attack, the bacteria growing on the cancer cells are triggered by an SOS response, which is an inducible DNA damage repair system [115]. This leads to the development of various unique bacterial phenotypes that attack or penetrate the cancer cells. Also, DNA and many proteins are released by the bacteria which coat the cancer cells and halt the metastatic escape of the tumor [116; 117]. [PERSON] et al. fabricated naturally produced nanowires found in the form of biofilms of zetaproteo bacteria _Mariproofundus ferrooxydans_ as a potential drug carrier for cancer therapy. Biofilm derived nanowires were found to be magnetic nanomaterial capable of generating active and passive trigger response with alternating magnetic field. The hyperthermia induced by the alternating magnetic field contributed to decreased cell viability. The biofilm derived nanowires were tested for their carrier capabilities by loading doxorubicin and were found to cause cytotoxicity in human breast cancer cells (MDA-MB231-TXSA) [118]. In another study, biofilm synthesized by _Lactobacillus reuteri_ was used to formulate mesoporous silica nanoparticles for the targeted release of 5-FU in the colorectum and this biofilm when coated with zinc gallogermanate facilitated colorectal cancer imaging [119].
## 6 Results and Discussion
### Model Implementation and Training Protocols
#### 6.2.2 Bayesian Regularized Feed-Forward Neural Network  
A single-hidden-layer network (15 neurons) was trained using the Bayesian regularization function (trainbr), which balances squared error and applies automatic weight decay. Using the same 80/20 data split and preprocessing, the network was trained for up to 1000 epochs and achieved on the test set: R\({}^{2}\) = 0.967, MAE = 1.164 MPa, and coefficient of determination (COD) = 0.967, as illustrated in Figure 14.
# 5G Network Deployment Planning Using Metaheuristic Approaches
## 4 Experimental Setup  
### Link Budget Calculation and Simulation Setup  
The experimental setup used a Jupyter notebook for simulation and optimization, leveraging results from a separate link budget calculator. The calculator required inputs such as system parameters (bandwidth, frequency, distance, average building height, and street width), design parameters (transmit power, antenna gains, transmitter and receiver heights, cable losses, noise figure, and SINR target), and loss parameters (rain loss, penetration loss, vegetation loss, body loss, snow loss, slow fading, and interference margin). The results, including propagation losses and cell edge distances for urban and rural models, were used in the Jupyter notebook. The notebook utilized Python libraries: NumPy for numerical computations, Matplotlib for data visualization, Pandas for data manipulation, SciPy for advanced scientific computing, and Folium for creating interactive maps.
# Reviewing 6D Pose Estimation: Model Strengths, Limitations, and Application Fields
## 6 Analysis
### Model Capabilities  
In this section, we analyze the capabilities of the 84 evaluated 6D pose estimation models as presented in Tables 18 and 19. These tables indicate whether each model can handle symmetrical objects, multiple objects within the same scene, multiple instances \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
\begin{tabular}{l} 0.5 \\ 20 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Single Image Models and Input Types (Part III). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
\begin{tabular}{l} 0.5 \\ 20 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Non-Single Image Models and Input Types. \begin{table}
\begin{tabular}{l c c c c c c c} \hline \hline
\begin{tabular}{l} 0.5 \\ 20 \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 15: _Cont._of the same object, transparent objects, objects with varying shapes, and occlusions. A check mark (\(\checkmark\)) signifies that the authors have declared the model's ability to manage the respective capability, an X indicates the inability to handle it, and a dash (\(-\)) denotes the absence of related information in the article. The statistical analysis of these capabilities reveals that a majority of the models (66%) can effectively handle symmetrical objects, highlighting the importance of symmetry in pose estimation tasks. Furthermore, 47% of the models are capable of managing multiple objects within a single scene, which is crucial for complex environments. However, only 5% of the models can handle multiple instances of the same object in one scene, indicating a significant area for improvement. Transparent objects present an even greater challenge, with merely 2% of the models addressing this capability. Conversely, 37% of the models are designed to handle objects with different shapes, and a substantial 53% can manage occlusions, underscoring the necessity of robustness in real-world applications where objects may be partially obscured. These findings suggest that while there has been considerable progress in developing models that can manage symmetrical objects and occlusions, there remains a considerable gap in handling multiple instances of the same object and transparent objects. The ability to process objects with varying shapes is moderately addressed, reflecting ongoing efforts to enhance model versatility. The relatively low percentage of models capable of handling transparent objects underscores a critical challenge in the field, likely due to the inherent difficulty in accurately perceiving and estimating the poses of such objects.
# Parallel Implementation on FPGA of Support Vector Machines Using Stochastic Gradient Descent
###### Abstract  
Sequential Minimal Optimization (SMO) is the traditional training algorithm for Support Vector Machines (SVMs). However, SMO does not scale well with the size of the training set. For that reason, Stochastic Gradient Descent (SGD) algorithms, which have better scalability, are a better option for massive data mining applications. Furthermore, even with the use of SGD, training times can become extremely large depending on the data set. For this reason, accelerators such as Field-programmable Gate Arrays (FPGAs) are used. This work describes an implementation in hardware, using FPGA, of a fully parallel SVM using Stochastic Gradient Descent. The proposed FPGA implementation of an SVM with SGD presents speedups of more than 10,000\(\times\) relative to software implementations running on a quad-core processor and up to 319\(\times\) compared to state-of-the-art FPGA implementations while requiring fewer hardware resources. The results show that the proposed architecture is a viable solution for highly demanding problems such as those present in big data analysis.  
reconfigurable computing; machine learning; FPGA; SVM; SGD +
Footnote †: journal: Computer and Communications Communications
## 2 Methods
### The Photogrammetric Processing
In Ig/Ir and Ig/Ir scenarios, the calibration method was again set to \"alternative\". For camera optimization, since the GCPs were imported and measured on each of the two blocks, both external and internal parameters were optimized. Finally, Jg/Ir is a two-step scenario in which the two blocks were jointly processed, and so the obtained internal and external parameters were used to separately generate the dense point clouds for each block. For the first step (image orientation), the parameters were set as equal to Ig/Ir; for the second step (single-block dense point cloud generation), the calibration method was set to geolocation-based, since accurate positioning and orientation are available from the first step. Besides, in this case, neither interior nor external parameters were optimized because they were directly imported in the first step of the project. All dense point cloud generation was performed by adopting the default options: half image size resolution images, point density was set to optimal, and a cloud point was accepted only if it was positively matched in at least three images. The average density was between 11 to 14 points per m\({}^{3}\). In a preliminary test, the original image size resolution was also evaluated, but higher point density did not significantly improve the generation of orthophotos and reflectance maps; the requirements for precise agriculture are lower in comparison to other applications, such as 3D mapping, and the obtained resolution was considered satisfactory for the research aims. Pix4 Dmapper allows generation of orthophotos and reflectance maps during step 3 of the processing, together with the computation of the DSM. In this study, products were generated with GSD equal to 0.10 m and project settings were the same for all considered scenarios. Reflectance maps were generated by setting camera and sun irradiance correction in the radiometric processing and calibration panel. This allows one to apply corrections to the camera parameters stored in the image metadata (i.e., vignetting, dark current, ISO), as well as for the sun irradiance information acquired with the proper sensor (see Figure 1c). Images of the calibration target are required to perform corrections. Hence, during the survey, the prescribed radiometric calibration procedure of the Parrot Sequoia camera was performed and the suitable calibration target (see Figure 1b) was imaged several times, with different exposure times. Acquisitions were taken at the beginning of each flight, so that different calibration data were stored for each flight, ensuring similar sky and illumination conditions between calibration images and flight images. For the radiometric processing and calibration, calibration images with the highest value of exposure time were retained and the software automatically detected target on them, defining the proper reflectance values for each spectral band as equal to 0.172, 0.215, 0.266 and 0.369 for green, red, red-edge, and NIR, respectively.
# Electronic and Transport Properties of Epitaxial Graphene on SiC and 3C-SiC/Si: A Review
## 6 Conclusions  
Epitaxial graphene is a practical approach for electronics and photonic applications thanks to its wafer-scale integration compatibility [5]. The knowledge and the control of electronic and transport properties of epitaxial graphene are crucial for graphene-based electronic and photonics applications.  
This work has reviewed the impact of different interactions the epitaxial graphene makes with its surroundings, such as substrate, ambient, and metal contacts. The review concluded that the electronic and charge transport properties of EG on both SiC and Si substrates are always dominated by the interfaces between graphene and its environment. Within the diffusion regime, the grain sizes do not affect carrier transport in EG. The sheet carrier concentration and the mobility of graphene follow a power-law dependence, which is universal for the substrate-supported graphene. Lastly, different methods to tune the electronic and transport properties of EG are discussed, which points out that the control of the charge transfer/doping in EG is directly linked for engineering the graphene interfaces. Hence, it is critical for the integration of graphene into future micro-electronic or nano-electronic devices.  
The manuscript was prepared through the contributions from A.P., D.K.G., and F.I. All authors have read and agreed to the published version of the manuscript.  
A.P. and F.I. kindly acknowledge funding from the Air Force Office for Scientific Research through the Asian Office for Aerospace Research and Development (AOARD, grant 18 IOA052).  
Figure 21: Ambipolar field-effect transistor on Si (111) substrates. Reprinted with permission from Reference [80]. Copyright (2010) Institute of Electrical and Electronics Engineers.  
**Acknowledgments:** The authors gratefully acknowledge discussions with [PERSON], [PERSON], and [PERSON] from the Centre of Excellence on Future Low-Energy Electronics Technologies, Monash University, Melbourne, Australia.  
**Conflicts of Interest:** The authors declare no conflict of interest.