esa-sceva/satcom-chunk-collection huggingface dataset:
# ASDNet: An Efficient Self-Supervised Convolutional Network for Anomalous Sound Detection
These methods, characterized by more sophisticated decision boundaries and better modeling of anomalous pattern distributions, can more accurately capture anomaly characteristics [19]. Nevertheless, clustering-based and feature-space scoring methods have received limited attention in existing research. Therefore, further exploration and optimization of diverse anomaly score computation techniques remain critical for advancing the performance and robustness of ASD systems, highlighting the need for innovative solutions to address these challenges. Balancing model performance and complexity, especially for efficient deployment in resource-constrained edge computing environments, remains a critical challenge in ASD research. To address this issue, this paper proposes an efficient ASD method that integrates spectral features, lightweight network architectures, and diverse anomaly scoring mechanisms, achieving an optimal balance between detection performance and computational efficiency. The main contributions of this work are as follows:  
1. Spectral Feature Input: A single-feature input approach based on spectral features is proposed, addressing the limitations of Log-Mel features in capturing high-frequency and low-frequency information. This approach provides higher-quality and more discriminative input data for anomaly detection. 2. Lightweight Network Design: A dual-network architecture framework is proposed to meet the needs of different application scenarios: the lightweight ASDNet is specifically optimized for resource-constrained environments, significantly reducing computational overhead while maintaining good detection performance, making it suitable for embedded devices or real-time detection tasks. In contrast, the network combining SpecNet and MobileFaceNet is designed for high-precision application scenarios, demonstrating notable advantages, particularly in the pAUC metric, making it ideal for tasks requiring high sensitivity and robustness. 3. Diverse Anomaly Scoring Mechanisms: Multiple anomaly scoring methods are introduced, including cosine similarity, K-Means, GMM, and LOF. These methods construct sophisticated decision boundaries, improving the model's robustness and adaptability in diverse anomalous scenarios. The remainder of this paper is structured as follows: Section 2 provides a detailed description of the proposed method. Section 3 outlines the datasets, implementation details, evaluation metrics, experimental results, and comparisons. Section 4 presents relevant discussions. Finally, Section 5 presents the conclusions and summarizes the study.
## 1 Introduction
The sequences of pulses used in the simulations are similar to the ones used to probe single dots by ultrafast pump-probe transient absorption spectroscopy [66; 67; 68; 69; 70; 10] and in 2D electronic spectroscopy [25; 26; 28; 29; 30; 31; 32]. Ultrafast 10-30 fs intradot beatings of electronic coherences in single QDs with lifetimes in the range of 50-150 fs were reported using these experimental set-ups [25; 26; 27; 28; 29; 30; 31; 32]. The dephasing time is ascribed to the coupling to environment, the effect of size dispersion, and the onset of coupling to the phonon modes, which span a wide range of time scales from 100 fs to several ps. Our simulations show that, when the interdot distance is sufficiently short and the QD diameters are adequately chosen, interdot electronic coherences have beating frequencies in the 10-30 fs range, and thus could be observed experimentally by ultrafast fs pump-probe spectroscopy before extensive dephasing owing to the coupling to environment and to the phonon modes kicking in. Building ultrafast fs electronic coherences in QD ensembles with fs laser pulse sequences is an ideal way to tune and manipulate the spatial and temporal localization of the electronic density before the onset of dephasing, an ability that is crucial for the development of light harvesting [71] and quantum information devices [59; 72; 73]. In Section 2, we outline the essentials of the EMA modeling for a dimer including both local and CT excitons. We systematically model the level structure of dimers as a function of the QD size and QD separation to be able to account for size dispersion and linker length. The effect of the size dispersion on the electronic structure of the dimer is analyzed in Section 3.1. The results on the coherent excitonic quantum dynamics of a QD dimer excited by a sequence of fs laser pulses are reported in Section 3.2. In Section 4, we show that the coherences can be probed at the level of the ensemble by pump probe femtosecond (fs) transient absorption spectroscopy. Conclusions and perspectives are given in Section 5.
## 2 Setting the Stage: Fundamental Mathematics and Knowledge
### Unified Mathematical Formulation  
Table 1 presents a set of seven important instructions for the computation of the BEC. The first column shows the essential instructions, while the second column articulates  
the requisite mathematical formulations for each of them. The initial values, or the system's initial state, are stored in variables \(W_{1}\), \(Z_{1}\), \(W_{2}\), and \(Z_{2}\). On the other hand, \(W_{d}\), \(Z_{d}\), \(W_{a}\), and \(Z_{a}\) are used to hold onto the final projective points. Variables A, B, and C effectively store intermediate results. Consequently, the initial, intermediate, and final values are designated as (\(W_{1}\), \(W_{2}\), \(Z_{1}\), \(Z_{2}\)), (\(A\), \(B\), \(C\)), and (\(W_{d}\), \(Z_{d}\), \(W_{a}\), \(Z_{a}\)), respectively. For an effective management of these initial, intermediate, and final values, a total of \(11\times m\) storage units are required. The number 11 signifies the stipulated storage units, with \(m\) representing their individual width. Moreover, parameters \(e_{1}\), \(e_{2}\), and \(w\) outlined in Table 1 play instrumental roles in shaping the curve's operations. These parameters orchestrate vital functions such as point multiplication, doubling, and addition, contributing to both operational efficiency and security enhancement. Parameter \(e_{1}\) is computed as the fourth root of a specific constant, \(e\), denoted as \(e_{1}=\sqrt[4]{e}\). Similarly, \(e_{2}\) signifies the square root of \(e\), calculated as \(e_{2}=\sqrt{e}\). This constant \(e\) finds its composition from the combination of terms \(d_{1}^{4}\), \(d_{1}^{3}\), and \(d_{1}^{2}d_{2}\) formulated as \(e=d_{1}^{4}+d_{1}^{3}+d_{1}^{2}d_{2}\). Furthermore, parameter \(w\) consists of a rational function that simplifies computations through expression \(w(P)=\frac{x+y}{d_{1}(x+y+1)}\), where \(P=(x,y)\) belongs to the specific elliptic curve \(E_{B,d_{1},d_{2}}\).
## 3 Numerical Modeling
### Optimal Test Conditions
#### 3.2.2 Mortar  
The elastic modulus and the Poisson coefficient ratio of the mortar tested by [PERSON] et al. [8] were 27.45 GPa and 0.18, respectively (Table 3). Table 5 presents for each loading case the stresses at the center obtained numerically for all arch loading angles and the initial position of the crack for simulated mortar disk.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Loading Arch Angle (\({}^{\circ}\))**} & \multicolumn{2}{c}{**Stresses at the Center of the Disk**} & \multirow{2}{*}{\(\sigma_{c}^{G}/\sigma_{t}^{G}\)**} & \multirow{2}{*}{**Crack Position**} \\ \cline{2-2} \cline{5-5}  & \(\sigma_{t}^{G}\)(MPa) & & & \\ \hline
5 & – & – & – & Edge Contact \\
10 & – & – & – & (shear failure) \\
15 & – & – & – & Edge Contact \\
20 & 2.500 & \(-\)7.697 & 3.0788 & Center \\
25 & 2.498 & \(-\)7.818 & 3.1397 & Center \\
30 & 2.498 & \(-\)7.981 & 3.1950 & Center \\
35 & 2.496 & \(-\)8.173 & 3.2744 & Center \\
40 & 2.490 & \(-\)8.418 & 3.3807 & Center \\
45 & 2.493 & \(-\)8.724 & 3.4994 & Center \\ \hline \hline \end{tabular} \({}^{\text{-}}\) Insignificant.  
\end{table}
Table 4: Predicted stresses at the center of the disk and the initial position of the crack of Concrete tested by [8].  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Material**} & \multirow{2}{*}{**Elastic Modulus (GPa)**} & \multirow{2}{*}{**Poisson’s Ratio**} & \multicolumn{1}{c}{**Radius**} \\ \cline{3-4}  & & & **(mm)** \\ \hline Concrete & 30.32 & 0.18 & 70 \\ Mortar & 27.45 & 0.18 & 70 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Numerical input parameters.
## 1 Introduction
[PERSON] and [PERSON] [8] propose a fuzzy logic-DSS system to assess the ergonomic performance of work systems; the proposed decision support system considers physical, environmental, and sensory factors. [PERSON] et al. [30], introduces the use of fuzzy logic to develop a strain index for minimising road accidents and fatalities. [PERSON] et al. [31] proposed a fuzzy interface as DSS for evaluating manual material handling. [PERSON] et al. [32] modelled the electromyographic responses for 10 trunk muscles in manual material handling using fuzzy logic. [PERSON] [15] provides an informative description of some of the main concepts in the field of fuzzy logic like an adaptive neuro-fuzzy inference system and fuzzy c-means clustering. [PERSON] et al. [33] presented a model for assessing the exposure to risk factors associated with MSDs based on RULA. [PERSON] [34] proposes a fuzzy logic approach to posture-based ergonomic evaluation tools using RULA. However, the three most relevant studies for this investigation include those developed by [PERSON] and [PERSON] [35]. They designed a fuzzy ergonomic index to evaluate a company's ergonomics, which incorporatesbiomechanical aspects such as physical work, vertical reach, and clearance. However, unlike the model proposed in this work, their model does not consider anthropometric factors or productivity. [PERSON] et al. [36] studied elbow flexion angle and torque; they analysed only biomechanic aspects. Finally, [PERSON] et al. [37] found that the reliability of biomechanics in design needs improvement due to a lack of sufficient fundamental parameters regarding human structure. Analysing the information mentioned above, it becomes evident that previous investigations have not simultaneously addressed fuzzy logic methods in combination with anthropometric, biomechanical, and productivity factors. A comparison between fuzzy models is presented in Table 1.  
Therefore, this paper proposes new scenarios for analysing productivity from a biomechanic and anthropometric point of view. Thus, this article sets a new frame of reference for studying ergonomic risk levels. This article reports a proposed biomechanical fuzzy model for analysing the ergonomic risk level associated with upper limb movements inside workplaces by analysing three main groups of sets: biomechanics, anthropometrics, and productivity, which directly impact the quality of the workers' lives. The DSS was developed in Excel and applied to evaluate 123 female workers at the same time. This paper's contribution and research's novelty lie in the following:  
1. The design of a fuzzy logic model to define and quantify workers' anthropometric characteristics to determine the biomechanical risk level using only four general rules. 2. The fuzzification of ergonomic risk using standardised data relating to biomechanics, anthropometrics, and productivity. 3. A proposed DSS fuzzy model for implementing multiple assessments that establishes a record for measuring and monitoring the risk level at work using biomechanics and anthropometric characteristics.
# Thermal Effect of the Back Radiation from Disk to Head after Laser Heating in HAMR
## 2 Numerical Analysis  
### Theoretical Model  
The HAMR disk is consisted of the following several layers: lubricant film (\(\sim\)1 nm), carbon overcoat (\(\sim\)2 nm), magnetic recording layer (\(\sim\)15 nm), interlayer (\(\sim\)30 nm), heat sink (\(\sim\)15 \(\upmu\)m), and substrate. A schematic diagram of an HAMR recording system is shown in Figure 1. The thickness of the magnetic recording layer is around 15 nm, much smaller than the horizontal size of the disk so that it can be simplified as a nanoscale film while the head/disk interface system is simplified as a multilayered structure, shown in Figure 2. Note that to analyze the thermal effect of radiation individually, in order to make targeted corrections to the effects of radiation in the future, the conduction and convection heat transfer is ignored here. Meanwhile, due to the neglect of the conduction heat transfer, assume that the lower surface of the magnetic recording layer is thermal insulation material. In Figure 2, there are 4 layers, numbered from 0 to 3 from bottom to top. The half space 0 under the magnetic recording film is the dielectric substrate, which is in thermal equilibrium, at a temperature of \(T_{0}\), the permittivity is \(\varepsilon_{0}\). The film layer 1 represents for the magnetic recording film, which is assumed to be in thermal equilibrium and at a temperature of \(T_{1}\). The magnetic recording layer is made of magnetic material, the permittivity and permeability of which is represented as \(\varepsilon_{1}\) and \(\mu_{1}\), respectively. The thickness of film layer 1 is assumed to be \(d_{1}\). The film 2 between the head and disk is vacuum, in a thickness of \(d_{2}\). Similarly, the half space 3 represents for the head, which is in thermal equilibrium, and the temperature, which is \(T_{3}\), the permittivity and permeability is represented as \(\varepsilon_{3}\) and \(\mu_{3}\), respectively.
# Collaborative Indoor Positioning by Localization Comparison at an Encounter Position
## 4 Experiments
### Ablation Study on the Collaborative Localization Correction  
We evaluated the two modules in the collaborative localization correction, updating the number of particles and the offset using Equations (4) and (5), respectively. Specifically, \(o=0.5\) was fixed to determine the number of particles. Furthermore, the offset was updated by adding a normal distribution: \(\theta_{o}=\theta_{o}+N(0,\sigma)\).  
Table 3 shows the improvement rates from the baseline method. Specifically, the improvement rate was calculated by (Baseline \(-\) Ours)/Baseline. The full proposed method achieved the highest improvement rate of 13.0%. The method using \(o=0.5\) achieved the highest improvement rate in route 2 of AER2F and Agriculture. However, there was significant deterioration, for example, for route 2 of Mitsukoshi. The method with \(\theta_{o}+N(0,\sigma)\) obtained slightly higher improvement rates than the full proposed method on some routes. On the other hand, there was a significant decrease at route 1 in Mitsukoshi. Figure 10 shows examples of the results. The heading directions significantly deviated in both methods before the encounter. The method with \(\theta_{o}+N(0,\sigma)\) accumulated error after the encounter, whereas the full proposed method successfully estimated the direction.  
Figure 10: Ablation results for Mitsukoshi route 1. Red dots indicate the estimated positions. Blue cross mark depicts the detected encounter. The non-English term in the map indicates store names.  
Figure 9: Results from Agriculture route 1. Red dots indicate the estimated positions. Cyan dots are starting position. Blue dots represent turns. Red and blue cross marks depict the detected encounters of the two uses. The non-English term in the map indicates the location of the map board in the physical facility.
# Photodynamic Inactivation of Human Coronaviruses
###### Abstract  
Photodynamic inactivation (PDI) employs a photosensitizer, light, and oxygen to create a local burst of reactive oxygen species (ROS) that can inactivate microorganisms. The botanical extract PhytoQuinTM is a powerful photosensitizer with antimicrobial properties. We previously demonstrated that photoactivated PhytoQuin also has antiviral properties against herpes simplex viruses and adenoviruses in a dose-dependent manner across a broad range of sub-cytotoxic concentrations. Here, we report that human coronaviruses (HCoVs) are also susceptible to photodynamic inactivation. Photoactivated-PhytoQuin inhibited the replication of the alphacoronavirus HCoV-229E and the betacoronavirus HCoV-OC43 in cultured cells across a range of sub-cytotoxic doses. This antiviral effect was light-dependent, as we observed minimal antiviral effect of PhytoQuin in the absence of photoactivation. Using RNase protection assays, we observed that PDI disrupted HCoV particle integrity allowing for the digestion of viral RNA by exogenous ribonucleases. Using lentivirusises pseudotyped with the SARS-CoV-2 Spike (S) protein, we once again observed a strong, light-dependent antiviral effect of PhytoQuin, which prevented S-mediated entry into human cells. We also observed that PhytoQuin PDI altered S protein electrophoretic mobility. The PhytoQuin constituent emodin displayed equivalent light-dependent antiviral activity to PhytoQuin in matched-dose experiments, indicating that it plays a central role in PhytoQuin PDI against CoVs. Together, these findings demonstrate that HCoV lipid envelopes and proteins are damaged by PhytoQuin PDI and expands the list of susceptible viruses.  
Keywords: antiviral; coronavirus; emodin; HCoV-229E; HCoV-OC43; lentiivirus; natural product; PhytoQuin; photodynamic inactivation; photosensitizer; ROS; SARS-CoV-2; spike +
Footnote †: journal: viruses
## 2 Materials and Methods
### Bacterial Abundance and Composition  
To determine the abundance of bacteria in each water sample collected, a 10-mL subsample was preserved with 2% glutaraldehyde final concentration (Sigma-Aldrich, St. Louis, MO, USA) and stored in the dark at room temperature until further processing. Preserved samples were then filtered using a 0.2-\(\upmu\)m-pore GTTP Millipore filter membrane (Millipore Filter Corporation, Cork, Ireland) and stained using the DAPI method [36]. The bacterial cells were enumerated using an Olympus epifluorescence microscope (XC10, Olympus, Tokyo, Japan) under 1000\(\times\) magnification.  
Next-generation sequencing (NGS) was used to analyze the bacterial community. For DNA extraction, a 0.5-L seawater sample obtained from each experiment was filtered onto a 0.2-\(\upmu\)m-pore and 47-mm-diameter ISOPORE membrane filter (MILLIPORE, Cork, Ireland). Loaded filters were placed in a 2-mL micro-tube (Axygen Sciences, CA, USA) which contained 800 mL of extraction buffer (100 mM Tris-HCl, 100 mM Ethylenediaminetetraacetic Acid Disodium Salt (Na\({}_{2}\)-EDTA), 100 mM sodium phosphate, 1.5 M NaCl, and 1% Cetrimonium bromide (CTAB)) and stored at \(-80\)\({}^{\circ}\)C until further processing. DNA extraction was performed following the EX DNA extraction protocol [37], and an exploration of the bacteria community was targeted using primers 27F (5\({}^{\prime}\)-AGA GTT TGA TCM TGG CTC AG-3\({}^{\prime}\)) and 519R (5\({}^{\prime}\)-GWA TTA CCG CCGG CKG CTG-3\({}^{\prime}\)) [38; 39], for the V3-V4 region of 16S ribosomal RNA (rRNA) [40]. High-throughput sequencing was performed using the Illumina Miseq platform (Molecular Research LP; Shallowater, TX, USA) following the manufacturer's guidelines. Clustering analysis was performed on the sequence data using the Bray-Curtis distance and Ward's method [41] with the cluster package in R (version 1.14.3, R-studio). The relationship between the bacterial communities and the DOC was then assessed using redundancy analysis (RDA) in the CANOCO 5.0 software package [42] in BMs.  
Figure 2: Schematic diagram designed four mesocosms for this study (**A**), and installed mesocosms at pier using ropes (**B**). Bioreactor (20 L) of _Tetraselmis striata_ enclosed with semi-permeable membrane (**C**) and installed view in mesocosm using ropes (**D**). Sampling for _T. striata_ cells using a suction flask through tubes which connected to the faucet of each bioreactor (**E**).
## Materials and Methods
### Computational Procedure
In the first test, the average Nusselt numbers obtained for a single vertical plate suspended in free air at several Rayleigh numbers have been compared with the predictions of the Churchill-Chu correlation [PERSON] and [PERSON] (1975). In the second test, the average Nusselt numbers obtained for a pair of face-to-face vertical plates have been compared with the predictions of the correlating equation developed by [PERSON] and [PERSON] [PERSON] and [PERSON] (1975). In the third test, the optimal distance between pairs of face-to-face vertical plates calculated at different Rayleigh numbers have been compared with the numerical results obtained by [PERSON] (1975). It is apparent that a satisfactory degree of agreement between our numerical results and the literature data was achieved in any validation test carried out; this is shown in Figure 2 and in Tables 3 and 4. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(W/H\) & \(L/H\) & \(Ra_{\rm H}\) & **Mesh Size** & \(Nu_{\rm L}\) & **\%** & \(Nu_{\rm U}\) & **\%** \\ \hline \multirow{4}{*}{0.5} & \multirow{4}{*}{1} & \multirow{4}{*}{\(10^{4}\)} & \(80\times 80\) & 6.18 & – & 6.18 & – \\  & & & \(100\times 100\) & 6.32 & 2.27 & 6.32 & 2.27 \\  & & & \(120\times 120\) & 6.40 & 1.27 & 6.40 & 1.27 \\  & & & \(140\times 140\) & 6.45 & 0.78 & 6.45 & 0.78 \\ \hline \multirow{4}{*}{0.5} & \multirow{4}{*}{0} & \multirow{4}{*}{\(10^{4}\)} & \(100\times 100\) & 5.78 & – & 5.44 & – \\  & & & \(120\times 120\) & 5.91 & 2.25 & 5.56 & 2.21 \\  & & & \(140\times 140\) & 6.01 & 1.69 & 5.56 & 1.62 \\  & & & \(160\times 160\) & 6.06 & 0.83 & 5.70 & 0.88 \\ \hline \multirow{4}{*}{0.5} & \multirow{4}{*}{0} & \multirow{4}{*}{\(10^{5}\)} & \(120\times 120\) & 9.57 & – & 9.24 & – \\  & & & \(140\times 140\) & 9.81 & 2.51 & 9.41 & 1.81 \\ \cline{1-1}  & & & \(160\times 160\) & 9.93 & 1.22 & 9.52 & 1.16 \\ \cline{1-1}  & & & \(180\times 180\) & 10.03 & 0.71 & 9.59 & 0.73 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Grid sensitivity analysis for \(W/H=0.5\), \(L/H=0\) and \(1\), \(Ra_{\rm H}=10^{4}\) and \(10^{5}\). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mesh Size** & \(\Delta\tau\) & \(Nu_{\rm L}\) & **\%** & \(Nu_{\rm U}\) & **\%** \\ \hline \multirow{4}{*}{\(160\times 160\)} & \(10^{-2}\) & 10.24 & – & 9.84 & – \\  & \(10^{-3}\) & 10.07 & \(-1.66\) & 9.65 & \(-1.93\) \\ \cline{1-1}  & \(10^{-4}\) & 9.93 & \(-1.39\) & 9.52 & \(-1.35\) \\ \cline{1-1}  & \(10^{-5}\) & 9.89 & \(-0.40\) & 9.51 & \(-0.11\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Time-step sensitivity analysis for \(W/H=0.5\), \(L/H=0\) and \(Ra_{\rm H}=10^{5}\).