esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Related Work
### Evaluation Improvements  
The development of automated approaches for evaluating GAN output quality is well established in the image data domain. An overview of GAN evaluation methods in the FTS data domain is covered in Section 4. In principle, evaluation scores should reward both the generation of high intra-mode variability and also high inter-mode separation 'class label certainty'. The earliest image GAN evaluation metric, inception score [38], measured high intra-mode variability by the high _unconditional_ label entropy of \(p(\mathcal{C})=\int p(\mathcal{C}|\hat{x})d\hat{x}\) and high inter-mode separation by the low _class-conditional_ label entropy of \(p(\mathcal{C}|\hat{x})\) determined by feeding generated samples into a pretrained inception model [45]. [PERSON] et al. [39] argued that, as the inception score approach did not measure scores for synthetic data versus scores for real-world data, a proposed measure of synthetic data quality, the Frechet inception distance (FID), could examine the Wasserstein-2 [44] distance between high layer feature abstractions of real samples \(\mathbb{P}_{r}\) and synthetic samples \(\mathbb{P}_{g}\). FID scores were shown to correlate with various forms of induced image noise. This approach was extended to the conditional GAN case by [PERSON] et al. [40]. The authors observed that though mode class variability is implicitly desired for _unconditional_ GANs, it is explicitly desired for _conditional_ GANs. The proposed Frechet joint distance (FJD) measures intra-class conditional quality and consistency as well as inter-class conditional diversity. This is measured also using the Wasserstein-2 distance [44] between joint data and class distributions \((x^{(i)},\mathcal{C}^{(i)})\), \((x^{(i)},\mathcal{C}^{(i)})\).
## 3 Results
### Power Optimization of System under Prescribed Displacement  
The system under prescribed displacement naturally lacks resonance (and by extension, anti-resonance). There is thus no optimal value of gamma. An optimal load coefficient can be found from the zeros of \(\partial\overline{P}_{PD}/\partial\xi_{C}\) (assuming \(\omega_{N}\) and \(B\) are constants):  
\[\xi_{C}=\sqrt{\gamma^{2}+\xi_{E}^{2}} \tag{22}\]  
Applying Equation (22) in Equation (12), we find:  
\[\overline{P}_{PD\_OptLoad}=\frac{\alpha}{\left(2Q\gamma\right)^{2}} \tag{23}\]  
Figure 10: Dimensionless power at optimal load and anti-resonant excitation frequency. \(\eta=\{5,\,1\}\), from left to right. The color scale is linear and independent for each plot. Y axis is identical for both plots but only specified in the left plot.  
Figure 9: Power-optimum normalized load resistance at anti-resonant excitation frequency. \(\eta=\{5,\,1\}\), from left to right. The color scale is logarithmic and independent for each plot. Y axis is identical for both plots but only specified in the left plot.  
A comparative investigation with the case of inertial load can be made by setting the excitation frequency as equal for both cases. For a fair comparison, we assume the excitation frequency follows the power optimal value for the case of inertial load (see Figure 4). The corresponding power under prescribed displacement, with a power optimal load (Equation (23)), is shown in Figure 11. From Figure 4, we see that the value of \(\gamma\) is small when \(k\ll 1\), unless \(\xi_{E}\gg 1\). As \(\gamma^{2}\) is in the denominator of Equation (23), the value of \(\overline{P}_{PD\_OptLoad}\) naturally escalates in the region \(k\ll 1\), \(\xi_{E}<1\). This is a result of using the reference power as normalization, which decreases with decreasing \(Q\) (or equivalently \(k\)), while the (non-normalized) output power, \(P_{PD\_OptLoad}\), is independent of \(Q\). A similar argument can be made for the case of anti-resonant frequencies. At resonance, the dimensionless power under prescribed displacement is generally larger than that for the case of inertial load. For anti-resonant frequencies, the dimensionless power under inertial load can be larger than that for prescribed displacement if the resistive loss coefficient is small and the mechanical quality factor is sufficiently large.
## 3 Modeling the GNSS Observations with the Proposed HFM
### Constructing Tomography Constraint Equations  
Although side signals with an elevation angle greater than \(15^{\circ}\) are utilized, there are still some empty voxels in the three-dimensional tomographic model [10]. Usually, spatial constraints are introduced in the tomography system to relieve the ill-conditioned problem.  
The following horizontal constraint, assuming that the WVD of a voxel is equal to the weighted mean of the WVD of the surrounding voxels in the same layer [7], is applied for the tomography system:  
\[x_{i}=w_{1j}x_{1}+w_{2j}x_{2}+\ldots+w_{i-1}\;_{j}x_{i-1}+w_{i+1}\;_{j}x_{i+1 }+\ldots+w_{n_{h}}\;_{j}x_{n}, \tag{21}\]  
where \(n_{h}\) represents the number of voxels in a certain layer and \(x_{i}\) is the WVD of the \(i\)th voxel. \(w_{ij}\) denotes the horizontal weighted coefficient of the \(j\)th surrounding voxel in this layer corresponding to the \(i\)th voxel. The Gauss weighting function proposed in [PERSON] et al. [21] is introduced to calculate the coefficients:  
\[w_{ij}=\frac{e^{-\frac{d_{ij}^{2}}{2\sigma^{2}}}}{\sum_{i=1}^{n_{h}}e^{-\frac{ d_{ij}^{2}}{2\sigma^{2}}}}, \tag{22}\]  
where \(d_{ij}\) is the distance between the \(i\)th voxel and the \(j\)th surrounding voxel. \(\sigma\) represents the smoothing factor and is 1.5 times longer than the horizontal length of voxels [21].  
In addition, the WVD can be described by exponential decay with an increasing height [43]:  
\[x_{k+1}=e^{\frac{h_{k}^{-}h_{k+1}}{H_{scale}}}\cdot x_{k}, \tag{23}\]  
where \(x_{k}\) and \(h_{k}\) denote the WVD and height of the \(k\)th layer, and \(H_{scale}\) is the scale height mentioned in Equation (11).  
As a result, the final tomographic system equations can be written as follows:  
\[\left(\begin{array}{c}P_{integral}\cdot Y_{integral}\\ P_{inside}\cdot Y_{inside}\\ 0\\ 0\end{array}\right)=\left(\begin{array}{c}P_{integral}\cdot A_{integral}\\ P_{inside}\cdot A_{inside}\\ P_{hz}\cdot A_{lnz}\\ P_{vert}\cdot A_{pert}\end{array}\right)\cdot X, \tag{24}\]where \(Y_{integral}\) and \(Y_{inside}\) are the observation vector of the integral signals and side signals, respectively. Similarly, both \(A_{integral}\) and \(A_{inside}\) represent the observation matrix, including the distance traveled in each voxel. The weighting matrix was also imposed on the tomographic observation equations. \(P_{integral}\) and \(P_{inside}\), representing the weighting matrix of integral and inside rays, have a functional relationship with the elevation angle of the signal path [26]. However, for the weighting matrix of horizontal and vertical constraints, \(P_{ntz}\) and \(P_{vert}\), having a similar impact on voxels, are by default the identity matrix [26].  
Due to the morbidity of the coefficient matrix, the Algebraic Reconstruction Technique (ART), with the advantages of simple iteration and fast convergence [10,44], is introduced to solve the tomography observation equations in this work.
# The Need for Machine-Processable Agreements in Health Data Management +
## 3 The Need for New Policy Languages and Policy Management Systems
There is a need to develop theoretical results, algorithms and systems for expressing, supporting and managing detailed privacy preferences, personal consent and bilateral agreements of data access, usage and sharing. Systems need to achieve the following:  
(1) Create a bottom-up setting where individuals and organisations can create data sharing policies backed-up by a formal machine-processable technical language. This is in contrast to the classic service-client data sharing model where clients commit their data to a service provider after being presented with coarse-grained \"Terms and Conditions\" written in natural language, and on which the clients get only a few and simple opt-in/opt-out options. (2) Enable rich and more dynamic expressions of access and usage policies. Current access control systems are based on a predefined set of static rules in order to prohibit access to certain fields of a data repository; these access control rules cannot express sharing contracts such as a policy that prohibits a data item to be shared depending on its history of sharing, or on the amount of data already given to the data requester. (3) Develop algorithms that will allow data requesters to obtain the maximal result set of the query that still abides by the contracts of the data owners. (4) Support the goal of data auditing [24] which is to determine if private information was disclosed in answering queries, as well as the goal of accountability [25] which aims to understand the responsibilities of different parties in data processing. Data sharingagreements would be central in data auditing and accountability scenarios, since they inform exactly how to (or not to) use a particular dataset. Towards these general objectives as well as to implement our particular health data sharing requirements we make steps to present an ontology-based framework and a policy language to share health information.
# Strategies for Scaleable Communication and Coordination in Multi-Agent (UAV) Systems
## 5 A Proposed Control/Communication Architecture for Multi-Agent Systems
### Multi-Agent Coordination and Communication in the Literature  
Despite the absence of a universally accepted architecture, multi-agent systems have received sustained attention in the literature. Usually the scope of work falls within individual modules in Figure 8. There have been studies that explore ways of disseminating the minimally required local state information for global coordination [9; 58]. To achieve any coordination, global tasks must be converted into individual local tasks [59; 60]. The reverse approach is also of interest; inferring the global effect of local controllers in robot swarms [61]. The problem of designing multi-agent controllers is still unresolved. Some approaches rely on factoring \"Partially Observable MDPs\" or POMDPs [10]. Others use evolutionary methods to weed out \"unfit\" controllers with fitness functions [60; 62]. The promise of deep RL for multi-agent control appears in recent surveys [63]. The closest realization of the architecture in Figure 8 appears in [1]. The local controllers in [1] are largely independent but include some nearest-neighbor interaction.
# Biosynthesis of Zinc Oxide Nanoparticles Using Seaweed: Exploring Their Therapeutic Potentials
## 2 Materials and Methods
### Applications of Biosynthesisized Zinc Oxide Nanoparticles
#### 2.5.2 Antioxidant Activity of Zinc Oxide Nanoparticles  
DPPH Radical Scavenging Activity  
The capacity of the ZnO-NPs to reduce the stable 2,2-diphenyl-picrylhydrazyl (DPPH) free radical was assessed as described in ref. [24]. Three hundred microliters of ZnO-NPs at various concentrations (3.37, 6.75, 12.5, 25, and 50) mg/mL were mixed with 2.7 mL of DPPH reagent solution and allowed to stand for 30 min in the dark at room temperature. At 517 nm, the samples' absorbance was measured, and as a negative control, the absorbance of DPPH alone was also measured. Using ascorbic acid as a reference, the antioxidant capabilities of the samples were examined. Additionally, the ascorbic acid and DPPH IC\({}_{50}\) values were computed. The following formula was used to determine the percentage of DPPH scavenging activity:  
\[\begin{split}&\text{Reduction in DPPH activity (\%)}\\ &=(\text{Ac}-\text{As})/(\text{Ac})\times 100\end{split}\]  
where Ac is the absorbance of the control and As is the absorbance of the sample.  
Total Antioxidant Capacity Assay  
The total antioxidant activity of the ZnO-NPs was evaluated using the phosphor-molybdenum method [25]. A known weight of ZnO-NPs was dissolved in 10 mL of 1 mM dimethyl sulfoxide to create various quantities of ZnO-NPs. A total of 300 microliters of each concentration was added to 3 mL of the phosphomolybdate reagent solution (0.6 M sulfuric acid, 28 mM sodium phosphate, and 4 mM ammonium molybate). Following the reaction's light shielding, the vials were incubated in a water bath at 95 \({}^{\circ}\)C for 90 min. The absorbance was measured at 695 nm in relation to a blank after the mixture was cooled to room temperature. An ascorbic acid reference was used. The findings were expressed as mg of ascorbic acid (mg AAE/g DW) per gram of the dry weight of the NPs.
# An Interoperable UMLS Terminology Service Using FHIR
## 8 Conclusions  
In this research, we have identified key challenges with UMLS and proposed an interoperable solution that uses the HL7 FHIR standard. Leveraging FHIR resources, extensions, and integrated support to REST, this research has designed and implemented a light-weight terminology service, UMLS FHIR API, which expresses UMLS knowledge as a FHIR ConceptMap. However, out of the box, the FHIR ConceptMap cannot express the UMLS knowledge. FHIR ConceptMap is profiled to align it with the UMLS knowledge structure and other requirements, identified by the critical gap analysis. The HAPI library, an implementation of the FHIR standard, enabled us to implement the profiled ConceptMap to develop the UMLS FHIR terminology service. The terminology server is available at [[http://umls.it.ilstu.edu/umlsfhiir](http://umls.it.ilstu.edu/umlsfhiir)]([http://umls.it.ilstu.edu/umlsfhiir](http://umls.it.ilstu.edu/umlsfhiir)), whereas the UML FHIR API is available at [[http://umls.it.ilstu.edu/umlsfhiir/fhiir](http://umls.it.ilstu.edu/umlsfhiir/fhiir)]([http://umls.it.ilstu.edu/umlsfhiir/fhiir](http://umls.it.ilstu.edu/umlsfhiir/fhiir)).  
Conceptualization, R.S.; software, R.S.; validation, R.S., M.S. and M.H.; investigation, R.S. and M.S.; resources, M.S. and M.H.; data curation, M.S. and M.H.; writing--original draft preparation, R.S.; writing--review and editing, M.S. and M.H.; visualization, R.S.; supervision, R.S.; project administration, M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure A2: Profiled ConceptMap as FHIR structure.
# An Efficient Carrier Synchronization Scheme for Demodulation Systems
## 4 Carrier Synchronization Scheme Simulation and Hardware Implementation
### Carrier Synchronization Scheme Hardware Implementation  
The proposed scheme was verified in the simulation environment of vivado2017.4 and the hardware platform of xcku040-ffva1156-2-e FPGA. The resource consumption and the maximum operating frequency for different functional modules were analyzed, as shown in Table 2. Frame synchronization utilizes 1.67% DSP resources, the CFR algorithm utilizes 12.50% DSP resources, and the CPR algorithm utilizes 0.73% DSP resources. The proposed RLR architecture can be realized by controlling the valid signal.  
The operating frequency of the proposed scheme can be up to 200 MHz with less resource consumption and less complexity of implementation. Only 15.1% of the DSP resources are utilized.
## 3 Problem Statement and Assumptions for the Localization Scheme
### Path Loss Model  
On the other hand, a model of the channel that relates the transmission power to the received power is needed, as it will provide a relation between transmission power \(P_{TX}\) and \(SNR\) at the receiver for a certain distance between them. Many channel models have been proposed for outdoor and indoor environments [26] (e.g., Nakagami fading model, Rayleigh fading, Ricean fading, _etc._) but one of the most popular, due to its simplicity, is the log-normal shadowing path loss model [27], which establishes the following relation between the received power (\(P_{RX}\)) and the distance (\(d\)) between transmitter and receiver:  
\[P_{RX}(dBm)=P_{TX}(dBm)+A-10\eta\log\frac{d}{d_{0}}+\mathcal{V} \tag{2}\]  
where \(P_{TX}\) is the transmission power, \(A\) is a constant term, \(\eta\) is the path loss exponent, and \(\mathcal{V}\sim\mathcal{N}(0,\sigma^{2})\) is a zero-mean Gaussian random variable with standard deviation \(\sigma\). The constant term \(A\) depends on the transmitter's and receiver's antenna gains and on the power loss for a reference distance \(d_{0}\), and has to be experimentally determined. On the other hand, the path loss exponent \(\eta\) typically ranges between 2 and 4 depending on the environment, and it has to be experimentally determined too. The random variable \(\mathcal{V}\) models the effects of shadowing and considers the randomness across an ensemble of many deployment environments. As the experiments in [28] indicate, if the environment is static and the transmitter and receiver stay at fixed positions, the standard deviation of the measured received power is lower than \(\sigma\), whereas if a node moves or the environment changes (due to people movement or long lags of time) the standard deviation of the measured received power is \(\sigma\). For this reason, if a channel model like Equation (2) is used to estimate distances, averaging a number of received signal strength measurements in a static situation does not significantly reduce the estimation errors, whereas averaging measurements taken at slightly different positions or in a changing environment is more effective to reduce estimation errors [29].  
As previously said, this channel model provides a relation between the transmitted power (\(P_{TX}\)), the \(SNR\) at the receiver and the distance (\(d\)) between transmitter and receiver, _i.e._,:  
\[SNR(dB)=P_{TX}(dBm)+A-10\eta\log\frac{d}{d_{0}}-N_{RX}(dBm)+\mathcal{V} \tag{3}\]  
where \(SNR\), which is a random variable, is the signal to noise ratio at the receiver expressed in decibels and \(N_{RX}\) is the noise level at the receiver.
###### Abstract  
The International Roughness Index (IRI) is the one of the most important roughness indexes to quantify road surface roughness. In this paper, we propose a new hybrid approach between adaptive network based fuzzy inference system (ANFIS) and various meta-heuristic optimizations such as the genetic algorithm (GA), particle swarm optimization (PSO), and the firefly algorithm (FA) to develop several hybrid models namely GA based ANGIS (GANFIS), PSO based ANFIS (PSOANFIS), FA based ANFIS (FAANFIS), respectively, for the prediction of the IRI. A benchmark model named artificial neural networks (ANN) was also used to compare with those hybrid models. To do this, a total of 2811 samples in the case study of the north of Vietnam (Northwest region, Northeast region, and the Red River Delta Area) within the scope of management of the DRM-I Department were used to validate the models in terms of various criteria like coefficient of determination (R) and the root mean square error (RMSE). Experimental results affirmed the potentiality and effectiveness of the proposed prediction models whereas the PSOANFIS (RMSE = 0.145 and R = 0.888) is better than the other models named GANFIS (RMSE = 0.155 and R = 0.872), FAANFIS (RMSE = 0.170 and R = 0.849), and ANN (RMSE = 0.186 and R = 0.804). The results of this study are helpful for accurate prediction of the IRI for evaluation of quality of road surface roughness.  
International Roughness Index; ANFIS; machine learning; ANN; particle swarm optimization
## 3 Results
### Comparison of the Proteomic Profiles of Two Yeast Strains at Three Bars of Pressure
#### 3.2.2 One-Way Analysis of Variance (ANOVA) of Proteins  
Of the 767 proteins common to both strains, 574 proteins were found in 100% of all G1 and N62 replicates. In total, 127 proteins exceeded the statistical cut-off (\(q<0.05\)) and showed statistical differences in the quantification values according to the one-way ANOVA and HSD Tukey tests. As a result of the statistical cut-off, 64 proteins were found with significant differences upwards for the G1 strain and 63 proteins were found with significant differences upwards for the N62 strain, as shown in Figure 4.  
Figure 4: Heat map representing quantification of proteins with significant differences at \(q\)-value < 0.05. Wine with strain G1 (red) and wine with strain N62 (green). The code column on the right represents the accession number of the proteins (Uniprot database).  
Some of the proteins that presented significant differences for the G1 strain were as follows: EXG1 is a glucanase that is responsible for maintaining the cell wall, integrating mannoproteins, and binding beta-glucan; LYS9 is part of the lysine biosynthetic pathway; NCA3 is involved in the organization of mitochondria; ERG8 intervenes in the ergosterol biosynthesis pathway; GLY1 catalyzes the cleavage of L-allo-threonine and L-threonine into glycine and acetaldehyde; and KGD1 is involved in the tricarboxylic acid (TCA) cycle.  
Some of the 63 proteins with significant differences for strain N62 were as follows: ENO1 catalyzes the conversion of 2-phosphoglycerate to phosphoenolpyruvate; TEF2 delivers aminoacyl-tRNA to the A-site of ribosomes during protein biosynthesis; ILV5 is involved in the biosynthesis of branched-chain amino acids; MDH3 catalyzes the interconversion of malate and oxaloacetate and participates in the glyoxylate cycle; PDA1 catalyzes the direct oxidative decarboxylation of pyruvate to acetyl-CoA and CO\({}_{2}\); PDC6 decarboxylulates pyruvate to acetaldehyde and CO\({}_{2}\), and also participates in the catabolism of amino acids; ACO2 catalyzes the reversible dehydration of (R)-homocitrate to cis-homoaconitate, a step in the alpha-aminoadipate pathway for lysine biosynthesis; ICP55 cleaves preprotein intermediates carrying N-ter amino acid residues important for the stabilization of the mitochondrial proteome; and the MRPL3 component of the mitochondrial ribosome involved in the translation of protein synthesis is encoded by the mitochondrial genome.
## 1 Introduction
We evaluate HUE-Net extensively on different challenging datasets and demonstrate that it achieves state-of-the-art results on the IJB-C unconstrained face dataset [17] and N-SpectralFace dataset [14]. The model also outperforms strong baselines in calibration, modality robustness, and inference efficiency. Our contributions can be summarized as follows:  
* We propose a novel hybrid deep learning architecture that fuses RGB, NIR, and event-based data streams through adaptive spectral attention, enabling spectral rebalancing under dynamic visual conditions. * We introduce a perturbed multi-branch variational module (PMB-VM) that enables both uncertainty estimation and probabilistic ensemble learning in a compact, end-to-end-trainable form. * We design a spectral consistency training loss and a robustness-aware evaluation protocol that collectively improve generalization in the presence of occlusion, injury, and missing modalities. * We validate our model on multiple benchmark datasets and edge deployment platforms, achieving new state-of-the-art performance in both accuracy and reliability. The remainder of this paper is structured as follows: Section 2 reviews related work in multispectral vision, uncertainty-aware deep learning, and efficient face recognition. Section 3 introduces the architecture and methodological components of HUE-Net. Section 4 outlines the experimental setup, datasets, and metrics. Section 5 presents quantitative and qualitative results. Finally, Section 6 concludes with a summary of findings and future directions.