esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Proposed Schemes: DAE-EDMR and T-DAE-EDMR
### Computational Complexity and Memory Utilization of DAE-EDMR and T-DAE-EDMR  
Since the training process is performed offline and will not affect the online denoising overhead, we mainly consider the complexity of online denoising. Recalling the dimension of the latent space \(N^{\prime}\) and the depth of the NN model \(I\), FLOPs of \(\mathcal{O}(IN^{\prime}N)\) and \(\mathcal{O}(IN^{\prime}(k+2))\) are basically required in DAE-EDMR and T-DAE-EDMR, respectively, in terms of the online complexity for the fully connected NN model's operation. The proposed DAE-EDMR requires an additional online complexity of \(\mathcal{O}(N^{3})\) FLOPs [24] for EVD of the noisy EDM \(\hat{\mathbf{D}}\in\mathbb{R}^{N\times N}_{+}\) compared to conventional NN-based works requiring online complexity for matrix multiplications (As is generally known, the FLOPs required to extract the eigenvalues are \(4N^{3}/3\). Additionally, in the case of the extraction of the eigenvaluesand eigenvectors together, the required FLOPs are \(8N^{3}/3\). In our work, we only take the eigenvalues because these alone are sufficient for denoising and reducing the number of FLOPs required in offline training. Furthermore, since EDMs are inherently symmetric matrices, extracting the eigenvalues can be performed more efficiently. Investigating this aspect further could be an intriguing direction of future work, potentially leading to more computationally efficient approaches to EDM-based processing.). Additionally, T-DAE-EDMR can reduce the online complexity of DAE-EDMR to \(\mathcal{O}((k+2)N^{2})\) FLOPs regarding the truncated eigenproblem. Furthermore, in contrast to traditional EDM-based NNs, which require a memory storage proportional to \(\mathcal{O}(N^{2})\) due to their pairwise distance representations, our proposed model significantly reduces the memory usage by limiting the input and output dimensions to \(k+2\). This allows for a more scalable and efficeint implementation, making it particularly suitable for large-scale networks.
# Performance Analysis of Dual-Hop Hybrid RF-UOWC NOMA Systems
###### Abstract  
The hybrid combination between underwater optical wireless communication (UOWC) and radio frequency (RF) is a vital demand for enabling communication through the air-water boundary. On the other hand, non-orthogonal multiple access (NOMA) is a key technology for enhancing system performance in terms of spectral efficiency. In this paper, we propose a downlink NOMA-based dual-hop hybrid RF-UOWC with decode and forward (DF) relaying. The UOWC channels are characterized by exponential-generalized Gamma (EGG) fading, while the RF channel is characterized by Rayleigh fading. Exact closed-form expressions of outage probabilities and approximated closed-form expressions of ergodic capacities are derived, for each NOMA individual user and the overall system as well, under the practical assumption of imperfect successive interference cancellation (SIC). These expressions are then verified via Monte-Carlo simulation for various underwater scenarios. To gain more insight into the system performance, we analyzed the asymptotic outage probabilities and the diversity order. Moreover, we formulated and solved a power allocation optimization problem to obtain an outage-optimal performance. For the sake of comparison and to highlight the achievable gain, the system performance is compared against a benchmark orthogonal multiple access (OMA)-based system.  
hybrid RF-UOWC; exponential-generalized Gamma; non-orthogonal multiple access; outage probability; optimal power allocation +
Footnote †: journal: _Sensors_
# A General Model for Side Information in Neural Networks
## 4 A Formalism for Side Information
Now, we would like the probability, \(1-\delta\), for \(0\leq\delta\leq 1\) and a given margin, \(\epsilon\geq 0\), for the difference between \(\mathcal{L}(y_{t},\hat{y}_{t})\) and \(\mathcal{L}(y_{t},\hat{y}_{s_{t}})\) to be minimised; that is, we wish to evaluate  
\[\arg\;\min_{\delta}\;P(\mathcal{L}(y_{t},\hat{y}_{t})-\mathcal{L}(y_{t},\hat{y }_{s_{t}})\geq\epsilon)\geq 1-\delta. \tag{5}\]  
The probability in (5) can be estimated by parallel or repeated applications of the train and test cycle for the NN without and with side information, and the losses \(\mathcal{L}(y_{t},\hat{y}_{t})\) and \(\mathcal{L}(y_{t},\hat{y}_{s_{t}})\) are recorded for every individual run in order to minimise over \(\delta\) for a given \(\epsilon\). Each time the NN is trained its parameters are initialised randomly and the input is presented to the network in a random order, as was done in [22]; other methods of randomly initialising the network are also possible [23]. When (5) is satisfied, then we say that side information is _individually useful_ for \(t\), where \(t\in\{1,2,\ldots,n\}\). In the case when side information is individually useful for all \(t\in\{1,2,\ldots,n\}\) we say that side information is (strictly) _globally_ useful. We define side information to be _useful on average_ if the following modification of (5) is satisfied:  
\[\arg\;\min_{\delta}\;P\left(\left(\frac{1}{n}\sum_{t\in\{1,2,\ldots,n\}} \mathcal{L}(y_{t},\hat{y}_{t})-\mathcal{L}(y_{t},\hat{y}_{s_{t}})\right)\geq \epsilon\right)\geq 1-\delta, \tag{6}\]  
noting that we can estimate (6) in a similar way to estimating (5) with the difference that, in order to minimise over \(\delta\) for a given \(\epsilon\), the average loss is calculated for each run of the NN. In the case where the greater or equal operator (\(\geq\)) in the inequality \(\mathcal{L}(y_{t},\hat{y}_{t})-\mathcal{L}(y_{t},\hat{y}_{s_{t}})\geq\epsilon\), contained in (5), is substituted with equality to zero (\(=0\)) or is within a small margin thereof (i.e., \(\epsilon\) is close to 0), we may say that side information is _neutral_. Moreover, we note that there may be situations when side information is _malicious_, which can be captured by swapping \(\mathcal{L}(y_{t},\hat{y}_{t})\) and \(\mathcal{L}(y_{t},\hat{y}_{s_{t}})\) in (5). We will not deal with these cases herein and leave them for further investigation. (Similar modifications can be made to (6) for side information to be neutral or malicious on average.) We note that malicious side information may be adversarial, i.e., intentionally targeting the model, or simply noisy, which has a similar effect but is not necessarily deliberate. In both cases, the presence of side information will cause the loss function to increase. Here, we do not touch upon the methods for countering adversarial attacks, which is an ongoing endeavour [24].
# Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau
## 3 Results
### Main Results in the Downscaling Procedure Using Random Forest
Therefore, a combination of ntree = 1000 and \(m_{try}\) = 6 was applied to build the model and downscale the TMPA data, and ntree = 1000 and \(m_{try}\) = 8 was employed to build the model and downscale the IMERG data over the TP. Figure 8a,b demonstrated the variable importance in \(RF_{TMPA}\) and \(RF_{IMERG}\), respectively. These indicated that NDVI, DEM, LST-d and LST-n were the main factors contributed to establish the random forest models and that these factors influenced the spatial patterns and amount of precipitation at annual scale. The spatial distribution of the dominant factors which influenced the spatial distribution of precipitation over the TP was firstly explored in [25]. In central and northeast TP where were mainly covered by grassland and meadows (which were easily influenced by precipitation), the NDVI was the most important factor. The dominant factors were LST-d and LST-n in the northwest and west, where the temperatures were low with high elevations, and the precipitation in these regions could be influenced through thermal and dynamic mechanisms. In the southeast regions, topography played the most important roles in influencing the precipitation. Orographic rainfall typically occurred in the southeast where the topography varied greatly from valleys to huge mountains ranging in elevation from 100 to 4000 m.  
Figure 8: The order of variables’ importance in (**a**) \(RF_{TMPA}\) and (**b**) \(RF_{IMERG}\). Note: \(RF_{TMPA}\) and \(RF_{IMERG}\) denoted the random forest models based on TMPA data and IMERG data, respectively. Figure 7: Boxplots of Variation of the OOB error rate for (**a**) \(RF_{TMPA}\) and (**b**) \(RF_{IMERG}\) at different \(m_{try}\) values. Note: OOB error denoted the ‘out of bag’ error provided by the bagging model. \(RF_{TMPA}\) and \(RF_{IMERG}\) denoted the random forest models based on TMPA data and IMERG data, respectively.
# Analysis of Six Active Power Control Strategies of Interconnected Grids with VSC-HVDC
## 4 Analysis of Active Power Control Strategies  
Excluding the frequency control, two major active power control strategies can be applied for the VSC-HVDC. The first one is the step control, which releases active power step-by-step at certain times, as shown in Figure 4a. The second one is the ramp-rate control, which transfers active power with a specific slope, as illustrated in Figure 4b. In this section, each control strategy is introduced and then defined.
# Quantitative Reconstruction of Absorption Coefficients for Photoacoustic Tomography
## 1 Introduction
The quantitative reconstruction algorithm developed by [PERSON] et al. for the photoacoustic image is to model optical sources according to acoustic data in order to generate a quantitative image of the absorption coefficient of the deep tissue [25]. [PERSON] et al. [26], demonstrated the performance of the iterative photoacoustic inversion scheme, based on the finite element method, experimentally. [PERSON] et al. proposed an iterative optimization algorithm for fluence correction based on the finite element method [27]. [PERSON] et al. achieved recovery of maps of absorption coefficients with initial pressure distribution and boundary pressure measurement [28]. [PERSON] et al. demonstrated improvements in the reconstruction of large phantoms using hybrid diffuse optical tomography and PAT techniques [29]. [PERSON] et al. introduced a fluence-compensation and reconstruction method for 3-D planar PAT to update the absorption distribution, with fluence compensation being integrated into model-based reconstruction [30]. [PERSON] and [PERSON] et al. proposed a quantitative reconstruction method of diffusion equation based on a regularization Newton method. However, PAT images need to be segmented to obtain prior structural information [31; 32; 33; 34]. The quantitative reconstruction is highly underdetermined due to the uncertainty parameter values of the light fluence, absorption, scattering coefficient distribution, and even the Gruneisen coefficient [15; 35; 36; 37; 38; 39], which leads to considerable difficulty for reconstructing an accurate absorption distribution. In this study, the research focuses on developing an accurately quantitative estimate of the distribution of optical absorption coefficients under the assumption that the Gruneisen coefficient is constant in the tissue and the absorption and scattering distributions of the background are approximately known. In order to recover the absorption distribution, a novel method for quantitative reconstruction of the distribution of optical absorption coefficients in tissue is proposed. This method has the advantage of weakening the dependence on prior knowledge of the tissue model (including various optical properties and geometric information), while also providing a way to update the absorption coefficient based on the deviation of optical energy deposition that can accelerate the iteration. This method has potential to greatly facilitate clinical diagnosis. The rest of this paper is organized as follows. Section 2 describes the principle of the proposed method. The numerical simulation is presented in Sections 3 and 4 shows the phantom experiment. Finally, we discuss and conclude our study overall findings in Section 5.
# Green Synthesis, Characterization, and Antifungal Efficiency of Biogenic Iron Oxide Nanoparticles
###### Abstract  
The high incidence of fungal resistance to commercial fungicides and the negative effects of chemical fungicides on the environment and human health necessitate the development of novel biofungicides for the efficient management of fungal diseases. This study aims to greenly synthesize iron oxide nanoparticles (IONPs) using the aqueous extract of _Laurus noblils_ leaves and characterize these nanoparticles using various physicochemical techniques. The biogenic IONPs were tested against two pathogenic strains of _Alternaria allernta_ and compared to the metalaxyl-mancozeb fungicide. The food poisoning technique was used to assess the antifungal efficacy of the greenly synthesized IONPs and the commercial metalaxyl-mancozeb fungicide against the tested pathogenic _A. allernta_ strains. The biogenic IONPs showed a higher antifungal efficiency against the _A. allernta_ OR236467 and _A. allernta_ OR236468 strains at concentrations of 800 ppm compared to metalaxyl-mancozeb fungicide, with relative growth inhibition percentages of 75.89 and 60.63%, respectively. The commercial metalaxyl-mancozeb fungicide (800 ppm) showed growth inhibition percentages of 72.23 and 58.54% against the same strains. The biogenic IONPs also showed potential antioxidant activities against 1,1-diphenyl-1-2-picrylhydrazyl (DPPH) radicals, with DPPH inhibition percentages of 34.61% to 83.27%. In conclusion, the biogenic IONPs derived from _L. noblils_ leaves have the potential to be employed as biofungicides for the effective control of fungal phytopathogens, reducing reliance on harmful chemical fungicides.  
green synthesis; _Laurus noblils_; characterization; antifungal; metalaxyl + mancozeb; antioxidant
# Review of the Research Progress in Combat Simulation Software
## 2 Top-Level Specification of Simulation Software
### Smp  
SMP2 [8; 14] is a simulation model portability specification designed by the European Space Agency. It ensures the platform independence and reusability of the model by defining high-level common concepts and low-level common types. As shown in Figure 1, we took China's aircraft carrier as an example to illustrate the architecture of the SMP2, which includes three different levels of abstraction.  
* The _system_ layer does not contain any abstract content and only describes the system objects in the real world to be simulated. In terms of abstract system elements, the system engineer can define that an aircraft carrier is composed of power, command and control, communication, and other subsystems according to the existing knowledge system, while in terms of specific system elements, China's existing aircraft carriers include three warships named Liaoning, Shandong, and Fujian.
* The _simulation_ layer mainly describes the platform-independent model of the simulation system, focusing on the model specification (catalog file) and the integration, configuration, and scheduling of the model instances. Concretely speaking, Figure 1 defines the basic model specification of an aircraft carrier, and the Fujian aircraft carrier represents a derived specific model specification, which adds the interface of the ejection system model.  
Figure 1: SMP2 high-level view.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Specification** & **Promulgator** & **Year** & **Description** \\ \hline SMP2 & Europe & 2004 & Simulation Model Portability \\ DIS & USA & 1989 & Distributed Interactive Simulations \\ HLA & USA & 1995 & High Level Architecture \\ LVC & USA & 2007 & Live, Virtual, and Constructive \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simulation software specifications.  
* The _software_ layer describes the platform-related parts of the simulation system. SMP2 provides a convenient tool to generate a large number of C++ codes from the catalog file and finally presents various simulation models in the form of libraries.
## 2 Materials and Methods
### Aerosol Chemical Characterization  
PM chemical composition was determined at TS-MI and OB sites. A coupled ion chromatography system (Dionex ICS-90 and ICS-2000) was used to analyze the ionic inorganic fraction [6, 81]. For ion chromatography analysis, samples were extracted in 3 mL of ultrapure water (Milli-Q Water; resistivity of 18.2 \(\mathrm{M}\Omega\cdot\mathrm{cm}\) at 25 \({}^{\circ}\mathrm{C}\)) with a 20 min ultrasonic bath. Prior to analysis, the obtained solutions were filtered (0.45 \(\mathrm{\SIUnitSymbolMicro m}\) PTFE membrane, 15 mm Syringe Filters, Phenomenex), in order to remove any possible solid particle in suspension. The characterization of the ionic fraction was performed by means of the ICS2000 and ICS90 coupled ion chromatography systems (Dionex) equipped with an AS3000 Autosampler. Anions (F\({}^{-}\), Cl\({}^{-}\), NO3\({}^{-}\), SO4\({}^{2-}\)) were separated with Dionex AG14A-5\(\mathrm{\SIUnitSymbolMicro m}\) and AS14A-5\(\mathrm{\SIUnitSymbolMicro m}\) Guard and Analytical columns in an isocratic run of Na\({}_{2}\)CO\({}_{3}\)/NaHCO\({}_{3}\) (concentration 8.0 mM/1.0 mM, Dionex) at a flow rate of 0.5 mL min\({}^{-1}\). The electrical signal of the eluent was lowered by means of a chemical suppressor (Dionex AMMS III 2 mm MicroMembrane Suppressor, regenerant solution: H\({}_{2}\)SO\({}_{4}\), 0.05 M, Fluka 84720). Cations (Na\({}^{+}\), NH\({}_{4}\)\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\)) were separated with CG12A-5 \(\mathrm{\SIUnitSymbolMicro m}\) and CS12A-5 \(\mathrm{\SIUnitSymbolMicro m}\) Guard and Analytical columns by means of an isocratic run of MSA (methanesulfonic acid, CH\({}_{3}\)SO\({}_{3}\)H, 20 mM, Fluka 64280) at a flow rate of 0.5 mL/min. In this case too, the signal of the eluent was decreased with a chemical suppressor (Dionex CMMS III 4 mm MicroMembrane  
Figure 1: Location of the Milano Torre Sarca (MI-TS, black star) and Oasi Le Bine (OB, green star) sampling sites.  
Suppressor, regenerant solution: tetrabutylammonium hydroxide, TBA-OH, 0.1 M, Fluka). Elemental and organic carbon (EC and OC) were determined using a Thermal Optical Transmission method (TOT, Sunset Laboratory inc.; NIOSH 5040 procedure; [82; 83]). The chemical methods as well as the main results of the chemical analysis were yet validated and published [7].
# An Evaluation Survey of Knowledge-Based Approaches in Telecommunication Applications
## 2 Knowledge-Based Approaches
### Categories of Knowledge Representation and Reasoning Approaches
#### 2.1.7 Instance-Based Formalisms  
Instance-based formalisms use past instances or cases to make predictions or decisions about new situations. They rely on similarity measures and retrieve relevant past instances to infer likely outcomes or solutions for new cases. Representative approaches constitute tensor/space, regression, instance-based reasoning, case-based reasoning, k-nearest neighbors, and interpolation methods [19], which have wide use in telecommunication applications. These representations are effective in handling high-dimensional and complex data, allowing for automatic feature extraction and pattern recognition, but lack interpretable and explicit knowledge representation.
## 3 Results and Discussion
### DFT Investigations  
3.2.1. Molecular Geometry  
The structure of the SQ compound (**5**) was optimized using DFT at the B3 LYP/6-311g (d,p) computational level (Figure 3). This configuration represents the compound in its lowest energy, most stable state. The optimized geometry yields an electronic energy of \(-\)1873.70 Hartree. Additionally, the compound displays a notable dipole moment of 16.93 Debye, indicating a strong presence of dipole-dipole interactions within the SQ compound.
## 1 Introduction  
Going with the trend of increasing connectivity and services offered by computing devices, the amount of sensitive information processed by and stored on computing devices is growing rapidly. Recently, Physical Unclonable Function (PUF) has sprouted up as a promising primitive to enforce data privacy and access control to electronic devices. Among the PUF implementations [1; 2; 3; 4; 5], SRAM-based PUF (SPUF) has attracted tremendous attention. This is because SRAM, being an integral part of computer memory sub-system, plays a pivotal role in trusted computing platforms. The ability to use the storage cells of SRAM inseparably as PUF will replace or augment memory curtaining as a stronger fortification to the roots of trust for memory authentication. Unfortunately, existing SPUF cells cannot be doubled as regular storage elements. The exploitation of process-variation induced device mismatches in the cross-coupled inverter cell for random, unique and reliable response bit generation is detrimental to the regular memory operation, as it will result in increased parametric failures due principally to destructive read and unsuccessful write operations. Archived literatures reported wide varieties of design approaches that either improve the qualities of SPUF or harness the readability and writability of SRAM cells as data storage elements, albeit independently [5; 6]. However, no design strategy has been proposed to tackle the conflicting quality requirements to unify the two different modes of operation in an SRAM cell.  
In this paper, we expand the design space exploration of our preliminary proposal in [7], by taking into consideration circuit-level techniques that can be used to mitigate conflicting design criteria of both PUF response reliability and memory data stability. The proposed design procedure provides greater opportunity to meet tighter functional and performance specifications of both modes of operation at a small cost of area, delay and power overheads.  
The remainder of this paper is organized as follows. Section 2 analyzes the impacts of process variations (PV) and transistor sizing on the two different working modes of SRAM. Section 3 presents the proposed design methodology to expand the design space for PUF and memory stability optimization. Section 4 discusses the comparison results for seven different design configurations. The paper is concluded in Section 5.
## 4 Results
### Tests with Seven Classification Classes
#### 4.2.2 Analysis of the Current Signal  
The network training with current signals required more iterations to reach convergence with respect to the case with three classification classes. The steady validation accuracy was about 85%, which is lower than that obtained for the radial flux signal but higher than that achieved for the current signal in the first tests.  
Figure 11 shows the training and validation progress of the seven classes of current signal classification. The maximum number of epochs was set to 24 and the process lasted about 7 min. The confusion matrix is shown in Figure 12.  
Figure 10: Confusion matrix (test set) for the seven classes of radial flux classification.  
Figure 9: Training and validation accuracy progress for the seven classes of radial flux classification: training in light blue and in blue (smoothed), validation in black dashed.  
As can be seen from Figure 12, the most misclassified classes are those with the generalized roughness defect. Also, the baseline class was sometimes classified incorrectly as \"BF Step 2\" (Bearing Fault Step 2).