esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Keratin-Based Nanoparticles
### Wool Keratin-Based Nanoparticles
Notably, keratin scaffold has found pioneering application as an electrochemical biosensor for extremely stable (92.9% activity after 35 days), repeatable (2.16%), and reproducible (3.75%) dsDNA absorption. In detail, [PERSON] and collaborators extracted keratin from wool waste, through chemical reduction using a 2.0 M urea or 1.5 M thiourea solution under a nitrogen atmosphere. After the addition of NaHS, KerSH nanoparticles were prepared and subsequently mixed with Pd\({}^{2+}\) ions at ambient temperature for 120 min, providing an adsorbed Pd\({}^{2+}\) content of about 63.7 mg Pd/1 g KerSH [64]. Finally, the Ker-SH-Pd\({}^{2+}\) dispersion was drop-casted onto a glassy carbon electrode (GCE) surface, obtaining GCE/KerSH-PdNPs electrode as an efficient and stable platform for dsDNA immobilization via passive adsorption. In conclusion, keratin nanoparticle composites have demonstrated an impressive efficacy in the encapsulation and delivery of different therapeutic agents. For instance, the conjugation of Pluronic and wool-extracted keratin allowed for the obtainment of a thermosensitive copolymer, capable of encapsulating curcumin up to 7.4% [20]. These nanoparticles showed a dual redox and temperature-triggered release, a good biocompatibility (80%) even at high concentrations (\(\leq\)1 mg/ mL), and an excellent cellular internalization. Recently, [PERSON] et al. developed an innovative nanosystem, keratin-coated gold nanoparticles (Ker-AuNPs), as promising agents for plasmonic photothermal (PPT) cancer therapy. Ker-AuNPs were investigated in terms of physical-chemical properties, morphology, and photothermal features, revealing a mean hydrodynamic diameter of 32 nm and a \(\zeta\)-potential of \(-\)18 mV, a good photothermal conversion efficiency and high thermal stability. Preliminary in vitro experiments performed on a human glioblastoma cell line (U87-MG) further confirmed the efficient cellular internalization via an endocytosis-based uptake pathway, an excellent biocompatibility for concentrations up to 50 uM, and localized photothermal heating capabilities, leading to a reduction in the cell viability of approximately 30% [65].
## 4 Parametric Analysis  
The verified finite element model is employed to conduct a parameter analysis of factors that affect the flexural behaviors of EPCBs. The main discussed factors included the deviator spacing, the effective height of external tendons and the interfacial shear capacity. The structural details of the reference model are shown in Figure 17. The material properties were as follows: for the concrete slab, the compressive strength \(f_{\rm c}\) = 30 MPa and the elastic modulus \(E_{\rm c}\) = 30 GPa; for the steel beam, the yield strength \(f_{y}\) = 300 MPa and the elastic modulus \(E_{\rm s}\) = 200 GPa; for the external tendons, the yield strength \(f_{yy}\) = 1860 MPa, the elastic modulus \(E_{p}\) = 195 GPa, and the initial effective prestress \(\sigma_{pe}\) = 1000 MPa.  
In the reference beam, the deviator spacing \(S_{\rm d}\) = 6 m, the effective height of external tendon \(d_{\rm p}\) = 550 mm, and the average interfacial shear capacity at the unit length was set \(V_{\rm u}\) = 900 N/mm (shear connector degree \(\eta\) = 1). During the parameter analysis, the parameters' values were all the same as those of the reference beam except the discussed parameter. For each parameter analysis, two loading types including one-point loading at mid-span and uniform loading were applied and analyzed.
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 2 Materials and Methods
### Link Budget  
The link budget is used to obtain the signal strength in the receiver, considering all the losses in the path between the transmitter and the receiver. The received power, which represents all the gains and losses is defined by the equation: P\({}_{r}\)(dBm) = P\({}_{t}\) + G\({}_{t}\) + G\({}_{r}\) - L\({}_{path}\), where P\({}_{r}\) and P\({}_{t}\) are the power received and transmitted. G\({}_{t}\) and G\({}_{r}\) are the gains of the transmitter and receiver; Lpath is the total path loss [5].
# Anomaly Detection in Financial Time Series by Principal Component Analysis and Neural Networks +
## Appendix A Literature Review
### Scarcity of Anomalies and Data Augmentation  
The scarcity of anomalies within the data sets is another typical problem in anomaly detection. Anomalies are, by definition, rare events; therefore, they are under-represented in the data set used to fit the models. This under-representation does not help with the design of a reliable model able to identify anomalies. Classical methods used to overcome this issue consider data augmentation. These techniques aim to produce new synthetic samples that will ultimately enhance model performance, leveraging a better representation of the feature space. As reported in [24], time series can be augmented using a simple transformation of time domain [25], frequency domain [26], or more advanced generative approaches involving deep learning techniques such as recurrent generative adversarial networks [27]. In practice, the use of generative models for the data augmentation of time series with anomalies presents two limitations. First, training such models requires a large number of samples to guarantee satisfactory performance. Although restricted Boltzman machines do not exhibit this problem, [PERSON]. [28] showed that they fail at fitting multivariate complex distributions with nonlinear dependence structures. A more fundamental limitation affects the very idea behind the generative models. Such networks are trained to learn a given distribution. However, by definition, anomalies are different to each other and therefore there is not a distribution that characterizes them.
# A Framework for Assessing the Interference from NGSO Satellite Systems to a Radio Astronomy System
## 3 Framework of the Interference Analysis
### Aggregate EPFD  
In practice, there are multiple NGSO satellite systems around the globe and the interference to an RAS telescope is the sum of these NGSO satellite systems. Assumingthat there are total \(Z\) NGSO satellite systems in the globe, the expression for the aggregate EPFD for RAS telescope in cell \(c\) is given below  
\[\overline{EPFD}_{agg}^{c}(t)=\sum_{z=1}^{Z}\overline{EPFD}_{z}^{c}(t), \tag{37}\]  
where \(\overline{EPFD}_{z}^{c}(t)\) is the EPFD contributed by the \(z\)th NGSO satellite system. Based on Equation (36), we are able to obtain the cumulative distribution function (CDF) generated by each NGSO system at cell \(c\). Assuming that the interference of the NGSO satellite systems is independent of each other, the aggregate EPFD according to the ITU Rec. RA. 1323 is the convolution of all NGSO satellite systems. The power density function (PDF) of the aggregate EPFD is formulated as follows:  
\[p_{agg}^{c}(e)=p_{1}^{c}(e)\otimes p_{2}^{c}(e)\otimes\cdots\otimes p_{z}^{c}( e)\otimes\cdots\otimes p_{Z}^{c}(e) \tag{38}\]  
where \(\otimes\) represents the convolution operator, and \(p_{z}^{c}(e)\) and \(p_{agg}^{c}(e)\) are denoted as the PDF of the EPFD of the \(z\)th NGSO satellite system and the PDF of the aggregate EPFD, respectively.
# Mitigating Class Imbalance in Network Intrusion Detection with Feature-Regularized GANs
## 3 Proposed Methodology
### Comparison with Other Feature Alignment Losses  
While various feature alignment losses have been proposed to improve representation learning--such as Triplet Loss [24] and Center Loss [25]--we adopt Soft Nearest Neighbor Loss (SNNL) [23] due to its effectiveness and compatibility in the GAN-based data augmentation setting, especially under class imbalance.  
Triplet Loss encourages a margin between intra-class and inter-class distances but requires constructing informative triplets of anchor, positive, and negative samples. In imbalanced datasets, especially where minority-class samples are scarce, reliable triplet construction becomes difficult. Furthermore, it relies on complex mining strategies such as hard negative sampling, which significantly increases training complexity and may introduce instability [26].  
Center Loss minimizes the intra-class variance by pulling features toward their respective class centers. However, the effectiveness of Center Loss depends on stable center estimation, which is challenging when the number of minority-class samples is limited. In addition, Center Loss is typically used alongside a classification loss (e.g., softmax), making it less directly applicable to unsupervised GAN training [25].  
By contrast, SNNL is a non-parametric and fully differentiable loss that encourages intra-class cohesion and inter-class dispersion through pairwise distance-based relationships in the embedding space. It avoids the need for explicit triplet construction or center computation and integrates seamlessly into adversarial learning. Prior work [23] also demonstrates that SNNL improves both interpretability and generalization in representation learning, making it a natural fit for GAN-based augmentation where better feature alignment of synthetic samples is crucial.
# Analysis of All-Optical Generation of Graphene Surface Plasmons by a Frequency-Difference Process
## 1 Introduction
Even though the relation of the observed resonant peak in the differential reflectance to the generation of graphene surface plasmons looks doubtless, its quantitative explanation is lacking. Several authors [8; 22; 23] used the density-matrix perturbation theory to calculate the graphene's nonlinear response for DF generation, but there is a strong disagreement between the experimentally observed magnitude of the resonance and the published theoretical predictions. Theoretical values of the second-order conductivity calculated specifically for the scheme of Figure 1 in Refs. [8; 22; 23] differ by orders of magnitude from the experimental data and also between themselves [22]. In this work, we carefully recalculate the relevant NL conductivities and the differential reflectance of the probe beam (which actually involves two second-order processes) and compare our calculated results to the experimental data of [6] and to the previous calculations. The derived analytical formulae for the second-order conductivities are verified by numerical calculations. We will assume that the incident optical fields are monochromatic plane waves characterized by well-defined frequencies, \(\omega_{a}=2\pi c/\lambda_{a}\) and \(\omega_{b}=2\pi c/\lambda_{b}\), wavevectors and incidence angles, so that the in-plane wavevector projections are \(\textbf{q}_{j}=(2\pi/\lambda_{j})\sqrt{\epsilon_{1}}\sin{\theta_{j}\textbf{e}_ {x}}\) for \(j=a,b\), as shown in Figure 1. Throughout the article, we call the fields \(a\) and \(b\) the probe and pump, respectively. Electric current and the corresponding electromagnetic field oscillating with the difference frequency \(\omega=\omega_{b}-\omega_{a}\) and wavevector \(\textbf{q}=\textbf{q}_{b}-\textbf{q}_{a}=(q_{a}+q_{b})\textbf{e}_{x}\) represent the DF excitation, which can be resonant with intrinsic surface plasmons of the doped graphene. The article is organized as follows. In Section 2.1 we present our formalism and final expressions for the second-order conductivities and compare the one describing the SP generation with those obtained in the previous calculations [8; 22; 23]. In Section 2.2, explicit formulae for the differential transmittance and reflectance measured in experiments [6; 21] are presented based on the results of Section 2.1. In Section 3, we compare our calculated optical spectra representing the NL effects with the experimental data and also outline an alternative way of detecting the optically generated SPs (Section 3.2). The last section is devoted to a summary and concluding remarks. All detailed derivations are left for Supplementary Information (SI).
## 5 Design of the PVME Model  
We will discuss the design of the PVME model that prevents the virtual machine escape attack in this section.  
Figure 4 is the detailed architecture diagram of the PVME model combined with the design concepts of reference monitor. The dark module is the newly added one, including the QEMU_Compliance module and Hy_PVME model. The main task of the QEMU_Compliance module is to check the compliance of the request sent from the Guest OS. In particular, it filters the invalid or malicious requests such that only the compliant request can pass the QEMU and received by the Hypervisor.  
The Hy_PVME module includes four parts: PVME_Hook, Running_Model, Visit Matrix and Learning Matrix, which respectively stand for the hook function of the PVME model, compliance at the running mode, access rule matrix and learning matrix. The PVME model has two running modes: Learning mode and Enforce mode. The Learning mode is used in the pre-simulation environment to record all operations of the VMs in Learning Matrix. Then these records will be analyzed, deduplications combined and added to the Visit Matrix. The Enforce mode means when the PVME model is under this mode all compliant operations must comply with the rules of Visit Matrix. Adding a Learning mode can help us collect more noncompliant operations, which can make our PVME model more versatility. The PVME hook function is mainly used to receive messages from the  
Figure 4: PVME model architecture diagram.  
QEMU and send them to the PVME model to check compliance, i.e., matching them with the data of Visit Matrix. If the result satisfies the rules then the Sys_Call (system call) of the Hypervisor is returned. If not, the system will verify the running mode (through the Running_Model) of the PVME model. If the running mode is Enforce then return Error and reject the detailed request message. If the running mode is Learning then the system will return Sys_Call and record the message to the Learning Matrix.  
Figure 5 shows the detailed process of the PVME model. A request by Guest OS will go through the QEMU_Compliance module and the Hy_PVME module, which will enter Error state if the rules defined in the PVME model is not met; otherwise, it will be passed to the default Sys_Call in the Hypervisor.
## 5 Development of the Models
### Estimation of the Coefficients and Validation of the Models  
To estimate the coefficients, the GRETL software tool was used, which performed the required iterations based on the specified values of the parameters p, d, q, P, D, and Q (parameters obtained from the Autocorrelation and Partial Autocorrelation functions for the analyzed time series), to obtain the coefficients of each of the models [27], in addition to yielding the calculations of the AIC function (Akaike Information Criterion) [30] with which the effectiveness of each one was validated to represent the specified series. The prediction of known future values was made through the Analysis tool of the same software, which made the automatic prediction based on the previously generated ARIMA model and the number of specified values.
## 4 Discussion
### Balancing Data Sensitivity with Clinical Relevance: Lessons from HMMDs and NGS  
As molecular diagnostics grow more sophisticated, they bring heightened sensitivity that can detect a broad range of pathogens, often identifying low levels of DNA that may not correlate with active infection. The anticipated integration of NGS into clinical microbiology promises even deeper insights, capturing microbial profiles in high detail. However, interpreting such expansive data will require a framework that differentiates clinically relevant findings from incidental or low-utility results.  
For technologies like HMMDs, incorporating an \"indeterminate\" category could address this need by designating cases with ambiguous MFI values as requiring further clinical context. In the Luminex xTAG GPP, introducing an indeterminate category would allow clinicians to consider molecular findings alongside patient symptoms and history, particularly for MFI values that do not clearly indicate active infection. This approach aligns with future challenges anticipated with NGS, where data richness necessitates interpretive flexibility to avoid over-diagnosis and to ensure molecular findings are clinically actionable.
# PAM-UNet: Enhanced Retinal Vessel Segmentation Using a Novel Plenary Attention Mechanism
## 3 Experiments
### Datasets  
In this section, experiments undertaken on two datasets, DRIVE [44] and CHASE_DB1 [45], are reported to verify the effectiveness of PAM-UNet. The DRIVE public dataset consists of a total of 40 color fundus images with a resolution of \(584\times 565\), where binary mask and GT labels are provided for each image. Each fundus image corresponds to two images manually segmented by an expert. The annotation of the first expert is selected as the GT label for network training, and the first 20 images are used for training and the remaining 20 images are used for testing. The CHASE_DB1 public dataset contains 28 color fundus images with a resolution of \(999\times 960\). Similarly, each color fundus image has an expert segmentation result corresponding to it, and the annotation of the first expert is selected as the GT label. In this paper, the dataset is divided into 20 training images and 8 test images. DRIVE and CHASE_DB1 details are shown in Table 1.  
Since fundus images constitute small-sample data, data augmentation [46] is necessary during the training process, including random scaling, random horizontal flipping, vertical rotation, and random cropping. Simultaneously, the training images are converted tograyscale, followed by adaptive histogram equalization to enhance the contrast between vessels and background while suppressing noise. Additionally, local adaptive gamma correction [47] is applied to correct retinal images, mitigating uneven illumination and central line reflection phenomena. The preprocessed images are shown in Figure 6.  
The image size of each dataset varies and is cropped at the time of input to the network for training when the image in DRIVE is cropped to \(480\times 480\) and the image in CHASE_DB1 is cropped to \(800\times 800\).
## 5 Results and Analysis
### Sliding-Friction Work  
When the initial speed difference of the shift was 600 r/min, the expected friction coefficient of the synchronization process was 0.08. The simulation results of sliding-friction work before and after compensation were compared, as shown in Figure 11.  
The final value sliding-friction work after the synchronization phase of the synchronizer was less than that before the compensation, and the time to reach the final value after compensation was shorter than that before compensation. It can be seen from the trend of the sliding-friction work that after the compensation, the sliding-friction work growth in the early stage was slow, and the growth in the later stage was fast, while that before the compensation sliding-friction work had a fast growth in the early stage and a slow growth in the later stage. The shifting speed difference was 600 r/min, the final value sliding-friction work in the before compensation phase was 77.89 J, and the final value sliding-friction work in the after compensation was 73.25 J. After the compensation, the final sliding-friction work value was reduced by 4.64 J. It can be seen from the comparison of the sliding-friction work before and after compensation that the sliding-friction work can be reduced by compensating the friction coefficient.
###### Abstract  
This paper presents an investigation on the feedback controller design for active noise control headphones under the condition that the frequency responses of the primary and secondary paths corresponding to the feedback microphone do not match to the ones corresponding to the human ear. The influence of such mismatches on the performance are analyzed first, and then an optimization method is proposed to enhance the comprehensive performance at the human ear. In the proposed method, the feedback loop is constructed directly with the feedback microphone and any extra filters of the virtual sensing techniques are avoided. Cascade biquad filters are used as the controller, which is in accordance with current applications. A differential evolution algorithm was used to solve the proposed optimization problem, and the optimal parameters of the controller were found. It has been shown by the experimental results that, at the dummy head ear position, good noise reduction performance could be obtained at the low frequency band with limited noise enhancement for high frequencies, even if large frequency response mismatches exist.  
active noise control; headphones; feedback control; differential evolution Article  
Feedback Controller Optimization for Active Noise Control
## 2 Data and Methods  
### Theory of GDS  
GDS is a type of EM induction with long-period changing ring currents in the magnetosphere [25]. The period of the geomagnetic field ranges from about several days to more than a hundred days, and GDS can detect the electrical conductivity of the mantle from the depth 200 km to 1600 km. The ring currents in the magnetosphere are concentric with the magnetic equator of Earth, so numerical modeling is conducted in the geomagnetic spherical coordinate system. The C-response of GDS is widely used and can be obtained through the recorded vertical component (\(H_{r}\)), pointing downwards toward the center of the Earth, and the co-latitudinal (\(H_{\theta}\)) components, pointing toward magnetic north pole, of the magnetic field (H) on Earth's surface. A single spherical harmonic function \(P_{1}^{0}\) is assumed to describe the inducing currents [17; 25; 26; 27], and C-response can be determined by  
\[C(\omega)=-\frac{a_{0}\tan\theta}{2}\frac{H_{r}(\omega)}{H_{\theta}(\omega)} \tag{1}\]  
where \(a_{0}\) is the average radius of Earth (approximately 6371 km), \(\omega\) is the angular frequency, and \(\theta\) represents the geomagnetic colatitude (\(0^{\circ}\)-\(180^{\circ}\)).  
Equation (1) shows that C-responses are related to the components of \(\mathbf{H}\). If a positive time harmonic \(e^{\mathrm{i}\omega t}\) is considered, \(\mathbf{H}\) satisfies  
\[\
abla\times(\rho\
abla\times\mathbf{H})+\mathrm{i}\omega\mu_{0}\mathbf{H}=0. \tag{2}\]  
where \(\rho\) is the electrical resistivity, reciprocal with electrical conductivity \(\sigma\); \(\mu_{0}\) is the vacuum magnetic permeability; and \(\mathrm{i}\) is the imaginary unit. The staggered-grid finite difference method developed in a spherical coordinate system [28] is applied to solve Equation (2). Besides the conductive Earth, the resistive air is included into the model's calculation parameters. The outer boundary of the model's domain is \(2a_{0}\) from the Earth's surface, and the resistivity of air is set to \(10^{10}\)\(\Omega\cdot\)m. Considering the superconductive core of Earth, the core---mantle boundary is chosen to be the inner boundary of the model domain [25]. We specify the tangential components of the magnetic field at the boundaries in order to ensure that Equation (2) can be calculated throughout the whole model domain, while the resulting numerical modeling system can be well-conditioned. The location of the ring currents is \(10a_{0}\) from the Earth's surface, measured radially, in order to guarantee that the induced secondary magnetic field of the conductive Earth is negligible. Modification of the biconjugate gradient and an iterative method are utilized to seek the solution of Equation (2) [3]. A divergence correction [29] is applied in order to eliminate the accumulation of error in the calculation of \(\
abla\cdot\mathbf{H}\).