esa-sceva/satcom-chunk-collection huggingface dataset:
# Effect of Near Ultraviolet Radiation on Varroa Destructor Using Digital Holographic Interferometry
## 2 Materials and Methods  
### Digital Holographic Interferometry  
DHI is an advanced optical technique that combines the principles of holography and interferometry to capture the phase difference information between two holograms recorded before and after an object undergoes deformation [13]. This phase difference is related to the deformation by the following equation [14; 15; 16]:  
\[\Delta\varphi(x,y)=\frac{2\pi}{\lambda}\hat{e}\cdot\Delta\vec{U} \tag{1}\]  
In this last equation, \(\lambda\) represents the wavelength of the laser used, \(\hat{e}\) is the sensitivity vector, and \(\Delta\vec{U}\) is the displacement field. Thus, any external deformation experienced by the mite due to UVA irradiation is contained within the phase difference.  
The experimental setup consisted of a laser beam with a wavelength of 355 nm, a pco.ultraviolet CCD camera, two 5050 beam splitters, an optical fiber, and a set of UV lenses. The experimental setup is schematically shown in Figure 1. The laser beam was split into a reference beam (Rb) and an object beam (Ob) by a beam splitter (Bs1). The reference beam was directed into the optical fiber (Of) and transmitted to a second beam splitter (Bs2), which reflected 50% of the beam intensity toward the CCD camera. The object beam was collimated using special UV lenses (L1 and L2) and directed toward the specimen. The CCD camera collected the backscattered light at an angle (\(\alpha\)) of 14\({}^{\circ}\). A special UV lens (L3) and an aperture (A) in front of it were used to capture an image on the CCD camera.  
The laser used in this study was the Cobolt Zouk, a high-performance continuous-wave diode-pumped solid-state laser. This laser operates at a wavelength of 355 nm and delivers an output power of up to 20 mW. For the camera, the authors used a pco.ultraviolet, a high-sensitivity, high-speed scientific camera designed for ultraviolet (UV) imaging applications. This UV camera has a resolution of 1392 \(\times\) 1040 (h \(\times\) v) and a 14-bit dynamic range. Additionally, a single-mode optical fiber was used in the experimental setup.  
The lenses used in the experimental setup were the 1\({}^{\prime\prime}\) UVFS Plano-Convex Lens Set-UV Coated, with a range of 290-370 nm.  
Specimens were carefully mounted on adhesive slide sheets and continuously exposed during 1 h to UVA irradiation.  
Holograms were recorded with an exposure time of 10 ms, and the CCD camera (Sae Manufacturing Specialties Corp., Bayville, NY, USA) was set to record one hologram every 1.6 min, during which a UVA irradiation dose of \(0.428\pm 0.024\) W/cm\({}^{2}\) per second was measured. The holographic setup ensured the acquisition of high-resolution holograms, capturing the intricate details of the mite's surface. Digital reconstruction of the holograms was performed using image-processing algorithms through MATLAB R2023 software. This process enabled the generation of phase maps of the mite, allowing precise measurements of surface deformations.
## 3 _Apergillus_ Is a Microbial Chassis for Natural Product Synthesis
### A. niger Is a Chassis for Natural Product Synthesis  
One of the earliest demonstrations of heterologous expression for a complete fungal biosynthetic pathway is the reconstitution of the BGC from _Penicillium chrysogenum_ in _A.  
niger_[138]. Although it is not as widely utilized as _A. oryzae_ and _A. nidulans_ in the field of heterologous natural product biosynthesis, it is still a strong candidate host, especially for the production of non-ribosomal peptides (NRPs), due to its substantial industrial advantages [139]. For instance, [PERSON] et al. introduced _mica_, an NRPS-like gene from _A. nidulans_ into _A. niger_, and their findings confirmed that only _micA_ is necessary and sufficient for the synthesis of microperfuranone [140]. [PERSON] et al. successfully expressed the non-ribosomal peptide synthetase gene _esyn1_, derived from _Fusarium oxysporum_, in the Tet-on system of _A. niger_, yielding approximately 5 g/L of the cyclic depsipeptide emniatin [141]. In addition, the polyketide synthase gene _terA_ from _A. terreus_ was successfully expressed in _A. niger_, yielding a mixture of three distinct compounds with varying sizes and structures: osrellinic acid, 4-hydroxy-6-methylpyranone, and 6,7-dihydroxymellein [142]. [PERSON] et al. introduced the mushroom polyketide synthase genes from _Cortinarius odorifer CoPKS1_-6 individually in _A. niger_; the result indicated that _CoPKS1_ and _CoPKS4_ are a new class of atrochrysone carboxylic acid synthases that either exclusively generate an octaketide or synthesize both hepta- and octaketides simultaneously [143].
## 2 Materials and Methods
### A Case Study with Real Data: Prediction of Type 2 Diabetes Onset
#### 2.3.2 Data Pre-Processing  
Since the clinical examinations were performed only in even waves, we assigned to each subject a baseline wave among waves 2, 4, and 6 (not wave 8 because the follow-up would have been too short). Specifically, subjects that entered the study in wave 1 were assigned baseline wave 2, subjects that entered in waves 3/4 were assigned baseline wave 4, and subjects recruited in waves 5/6 were assigned baseline wave 6.  
We then selected the subjects who (i) were free of diabetes at the baseline wave; (ii) had the clinical examination at the baseline wave; and (iii) had information on diabetes diagnosis in the follow-up period. A subject was defined as having diabetes at baseline if they answered \"Yes\" to question Q1:\"Has a doctor ever told you that you have diabetes or high blood sugar?\" or they presented a value of fasting plasma glucose concentration \(\geq\) 126 mg/dL or a value of glycated haemoglobin \(\geq\) 6.5%. Diabetes onset during follow-up was defined as a positive answer to Q1. The time of diabetes onset was defined as the time passed between the baseline visit and the first follow-up wave at which the subject answered \"Yes\" to Q1. Note that we did not use fasting plasma glucose or glycated haemoglobin to define the outcome because these variables were not collected at all the ELSA waves.  
Potential predictive variables for diabetes development were selected from the set of variables collected at the ELSA baseline visits. In total, 16 variables were selected which are reported in Table 3. In particular, economic deprivation was measured by question Q2: \"How often you find you have too little money to spend on what you feel you and your household's needs are?\", whose possible answers (\"never\", \"rarely\", \"sometimes\", \"often\", \"most of the time\") were coded with integers between 1 and 5. Depression was measured by a reduced version of the Center for Epidemiologic Studies Depression (CESD) scale that includes 8 items. The derived score is an integer going from 1 to 8, with higher values representing a major presence of depression symptoms. Life expectation was coded as anumber between 0 and 100, representing the self-reported probability of living to 75 years if the respondent is under 65 years, to 80 years if the respondent is aged 66 to 69 years, and to 85 years if the respondent is aged 70 to 74 years, etc. Self-reported health status was measured on a 5-level scale ranging between \"excellent\" and \"poor\".  
Subjects with missing values in any of the selected variables were removed from the analysis. The final selected sample included 6201 subjects, 449 of whom developed diabetes during the 15-year observation period after the baseline.
# An Improvement on Estimated Drifter Tracking through Machine Learning and Evolutionary Search
## 1 Introduction  
The worldwide increase of large ships and maritime transportation volume causes a number of accidents that are beyond the capacity of individual nations. Pollutants released from accidents may extensively contaminate the marine environment due to ocean currents, weather, and weathering. Therefore, pollutants released during an accident should be removed as soon and as much as possible. An accurate prediction of the pollutant movement can help track and address them, as a variety of studies have proven [1; 2; 3; 4; 5]. The prediction model for oil spills generally calculates the movement and spread of the oil spill using the Lagrangian particle approach [6; 7; 5]. This forecasting method uses physics for critical parameters such as flow velocity, wind velocity, water level, and temperature in the current state. Parameter optimization using evolutionary computation [8] showed better results than MOHID [6], a numerical water modelling system. Machine learning and ensemble methods [9; 10] were also used to estimate the movement of drifters.  
Predicting the movement of a drifter on the ocean is an essential step in tracking the spread of an oil spill [11]. While spilled oil may sink or evaporate, most of it floats on the surface. Conventional numerical models can predict oil spills more accurately if the trajectories of drifting particles can be predicted accurately. Therefore, we aimed to predict more accurate trajectories by using various machine learning methods including deep learning, which has attracted much recent attention, rather than by using evolutionary computation as in our previous study [8]. We integrated artificial intelligence (AI) technology to predict the future ocean state, utilizing the continuity of parameter data over time. We expanded the area covered by our previous study [8], which predicted the trajectory of drifting objects in the ocean and systematically compared a wide range of regression functions andartificial neural networks for predicting the movement of drifters. In order to avoid the look-ahead bias, we used wind and flow forecasts made by the Korea Meteorological Administration and private technical agencies instead of actual future parameters.  
In sum, the contributions of this study are summarized as follows. We applied evolutionary computation and machine learning to the prediction of drifter trajectories. We extended and improved our previous study [8] that used evolutionary computation, and we also first predicted the trajectories using various machine learning techniques. This study was the first time that machine learning techniques have been applied to the prediction of drifter trajectories--before this study, only numerical models such as MOHID have been applied to drifter trajectories. Our methods could significantly improve on the results of the representative numerical model, MOHID.
## 2 Pattern-Coupled Bayesian Model for Sound Field Reconstruction
### Pattern-Coupled Hierarchical Bayesian Model
While it is theoretically possible to solve for the maximum a posteriori (MAP) estimates of \(\mathbf{W}\) directly using this posterior, its practical application is often computationally challenging. To address this, Stochastic Variational Inference (SVI) [35] is employed as an efficient alternative for approximating the posterior distribution. SVI works by introducing a family of parameterized candidate distributions \(q_{\theta}(\mathbf{W},\mathbf{\gamma})\), such that \(p\Big{(}\mathbf{W},\mathbf{\gamma}\Big{|}\widetilde{\mathbf{P}}\Big{)}\approx q_{\theta}( \mathbf{W},\mathbf{\gamma})\). The approximation is achieved by maximizing the Evidence Lower Bound (ELBO), defined as:  
\[\text{ELBO}=\mathbb{E}_{q_{\theta}}\Big{[}\log p\Big{(}\mathbf{W},\mathbf{\gamma} \Big{|}\widetilde{\mathbf{P}}\Big{)}-\log q_{\theta}(\mathbf{W},\mathbf{\gamma})\Big{]}. \tag{12}\]  
The ELBO provides a tractable optimization objective that facilitates efficient inference. The variational distribution \(q_{\theta}(\mathbf{W},\mathbf{\gamma})\) is factorized as \(q_{\theta}(\mathbf{W},\mathbf{\gamma})=q_{\theta}(\mathbf{W}|\mathbf{\gamma})q_{\theta}(\mathbf{ \gamma})\), with the two components defined as:  
\[q_{\theta}(\mathbf{W}|\mathbf{\gamma})=\prod_{l=1}^{L}\prod_{n=1}^{N}\mathcal{N}\Big{(} w_{l,n}\Big{|}u_{l,n},\widetilde{\gamma}_{l,n}^{-1}\Big{)}, \tag{13}\]  
\[q_{\theta}(\mathbf{\gamma})=\prod_{l=1}^{L}\prod_{n=1}^{N}\text{Gamma}\Big{(} \gamma_{l,n}\Big{|}a,b_{l,n}^{-1}\Big{)}, \tag{14}\]  
where \(\theta=\{a,\mathbf{B},\mathbf{\mu}\}\) are the variational parameters to optimize. Here, \(\mathbf{B}\in\mathbb{R}^{L\times N}\) contains elements \(b_{l,n}\), and \(\mathbf{\mu}\in\mathbb{R}^{L\times N}\) contains elements \(\mu_{l,n}\). The variational parameters \(\mathbf{\mu}\) serve as estimates for the coefficients \(\mathbf{W}\).
## 1 Introduction
This research attempts to tackle important network security issues by designing and deploying a sophisticated intrusion detection system (IDS) that integrates explainable artificial intelligence (XAI), federated learning (FL), and machine learning (ML). This study aims to achieve the following goals:  
1. To assess the efficacy of an intrusion detection system utilizing cutting-edge machine learning methodologies, federated learning for decentralized training, and explainable artificial intelligence for enhanced interpretability and transparency. 2. To minimize computational overhead by utilizing federated learning, which allows decentralized data processing and eliminates the need for central data aggregation. 3. To reduce the dangers of single points of failure and centralized data breaches by implementing a distributed, node-based architecture in federated learning. 4. To ensure the reliability and trustworthiness of IDS predictions by employing explainable AI, which provides insights into model decisions and fosters greater user trust in automated systems. This research work was structured in the following manner: Section 2 highlights relevant research on XAI, federated learning, and IDS. Section 3 details the proposed methodology, including model architecture and SHAP-based interpretability. Section 4 discusses the experimental setup, along with the results and analysis. Section 5 discusses key findings, and Section 6 finishes with contributions and future study directions.
# Structural Health Monitoring for Prefabricated Building Envelope under Stress Tests
## 3 Results and Test Analysis
### Force Washers  
This paragraph reports structure behavior during the stress test analysis of the forces. The results obtained are evident in the graphs below (Figure 25). The error bar is delineated by a light blue dashed line surrounding the force washer's signal curves. This band was computed considering the uncertainty related to the sensitivity deviation declared by the washer constructor, which is 1.5%.  
Figure 24: Power spectrum comparison between rain (static) test of day 1 and test of day 2.  
Figure 25: Strain gauge-based force washer data collected during the 3 days: day 1 (a); day 2 (b); day 3 (c).  
By observing the trend of the bolt's tightness during day 1, it is possible to notice that the tightness at the end of the test is equal to the tightness measured before starting the test; this means that the structure did not experience any modification. On the contrary, during the second day, a negative trend of the bolt's tightness is evident. Table 4 reports the value of the tightness measured by all the washers at the beginning and at the end of the tests performed during day 2 and their difference. It is possible to see that all the washers experience a decrease in tightness, particularly those connected to channels 2 and 7 (see Figure 4) for the force channels position. An important drop in the bolt tightness is visible immediately after the first test (wind resistance). As for day 1 and day 3, the tightness of the bolts remains almost constant.  
The force washers allow for the visualization of trends in the load curves of individual tests, enabling the identification of instances when the grab brackets experience stress in shear or compression, as well as any loosening of the anchor bolts. Continuous monitoring of the clamping forces will be highly beneficial for the health monitoring of the entire structure and for triggering alerts when the clamping forces fall below the specified limits.
## 5 Case Study: UAM Vehicle's Turning Performance Analysis and Validation  
### Selection of Holding Area Concept and UAM Vehicle for Turning Performance Analysis  
A performance analysis has been conducted to confirm if UAM vehicles could turn within the radius of the conceptually designed holding areas. Additionally, in order for UAM vehicles to turn, an analysis has been performed on how to establish turning procedures such as the turning speed and back angle. A performance analysis of the turning radius is not necessary because conventional aircraft fly at a sufficiently high altitude without obstacles. However, since the holding areas for UAM established at low altitudes with many obstacles have a fixed radius, the UAM vehicles should be able to turn within that radius in order to perform holding procedures [6]. In addition, UAM vehicles should not be pushed out by crosswinds such that they deviate from the turning radius and should perform precise navigational flight. That is, the flight performance for turning should be satisfied.  
In this study, the multicopter-type UAM vehicle, which is relatively easy to turn even in a narrow radius, has not been considered, and the performance has been analyzed mainly for fixed-wing-type UAM vehicles with high speeds and low turn rates compared to multicopter-type UAM vehicles. Additionally, among the three holding area concepts, the concept 1 holding area that requires the narrowest radius was targeted, and the turning radius was assumed to be 400 m (800 m in diameter) to enable flight operations within the width of the Han River [6]. The concept of the width of the Han River and the holding area applied to the Han River is shown in Figure 14. It is assumed in the performance analysis that the UAM vehicles are turning at a constant speed in fixed-wing mode.  
Figure 13: Concept of holding area location in Seoul and GyungIn area.  
The UAM vehicles selected for the performance analysis of this study were the vectored thrust-type (tilt prop) UAM vehicle designed by Konkuk Aerospace Design Air-worthiness Institute (KADA) and the Optionally Piloted Personal Air Vehicle (OPPAV) designed by Korea Aerospace Research Institute (KARI), the same type of vehicle. The performance specifications of KADA's UAM vehicle and KARI's OPPAV are shown in Figure 15 and Table 6.  
The turning performance of the OPPAV designed by KARI has also been analyzed. As a result of the calculation, the bank angle was 20\({}^{\circ}\) at the turn rate of 3\({}^{\circ}\)/s, and the turn rate was 3.9\({}^{\circ}\) at the bank angle of 25\({}^{\circ}\). When performing the turn procedure at the OPPAV cruise speed of 66.9 m/s (about 241 km/h), the turning radius of 3\({}^{\circ}\)/s was calculated as 1328 m, and the turning radius at the bank angle of 25\({}^{\circ}\) was calculated as 981 m. Like KADA's UAM vehicle, OPPAV cannot turn within a radius of 400 m at cruise speed. OPPAV also requires different methods or procedures when turning in the holding area of concept 1. The graph of the performance analysis calculation results of OPPAV is shown in Figure 17.
## 3 Results and Discussion
### FTIR Spectroscopic Analysis  
Figure 3A,B show the FTIR spectrum of the freeze-dried _T. ficus_ fruit powder and the ethanol extract. The spectrum is basically a plot of transmitted (or absorbed) frequencies vs. intensity of the transmission (or absorption). The x-axis displays frequencies in inverse centimeters (wavenumbers). Strong bands were observed at absorption peaks 3286 cm\({}^{-1}\)  
Figure 2: UV scan for the five different solvent extracts of _T. ficus_ fruit flesh.  
2928 cm\({}^{-1}\), 2852 cm\({}^{-1}\), 1022 cm\({}^{-1}\), and 1612 cm\({}^{-1}\) in the dry powder, while in the ethanol extract, strong bands appeared at 2924 cm\({}^{-1}\), 2856 cm\({}^{-1}\), and 3313 cm\({}^{-1}\).  
The remaining bands ranged from weak to medium in strength, indicating the presence of various organic compounds. Peaks 3286 cm\({}^{-1}\) and 1022 cm\({}^{-1}\) are characteristic peaks of OH groups. Peaks 2928 cm\({}^{-1}\), 2924 cm\({}^{-1}\), 2852 cm\({}^{-1}\), and 2856 cm\({}^{-1}\) corresponds to C-H stretching. Together, these peaks represent the polysaccharide characteristics peak. A ketone and an amine produced notable bands at 1612 cm\({}^{-1}\) and 3313 cm\({}^{-1}\), respectively [45].  
As shown in Supplementary Tables S2 and S3, the spectrum profile is indicative of various functional groups such as alcohols, carboxylic acids, alkanes, alkynes, esters, aldehydes, ketones, aromatics, halides, nitro compounds, and proteins. The functional groups present in the dried fruit powder and the ethanol fruit extract of _T. focus_ did not differ significantly. The presence of phenols and flavonoids is indicated by C-H out-of-plane bending (oop bend) vibration for substituted benzene rings. The presence of phenols, flavonoids, and terpenes was detected in the phytochemical screening, which was supported by the identification of benzenoid compounds. C=O may be related to carotenoids and chlorophylls [46].
## 4 Experiments
### Main Results  
#### 4.2.1 Multivariate Forecasting Results  
According to the experimental results on multivariate forecasting tasks, as shown in Table 2, DESTformer performs best in all prediction length settings over all benchmark tests. It verifies the effectiveness of explicit seasonal and trend decomposition via thehigh-frequency filtering (for seasonal data) and low-frequency filtering (for trend data), followed by multi-scale attention mechanisms in both trend and frequency views. Note that when the input length is set to 96 and the prediction length is set to 336, the MSE of the DESTformer decreases by 6.4%, 5.2%, 6.0%, 3.4%, and 8.9% on the EET dataset, Electricity dataset, Exchange dataset, Traffic dataset, and Weather dataset, respectively. Finally, on the relatively special ILI dataset, when the input length is set to 36 and the prediction length is set to 60, the DESTformer reduces the MSE by 4.4%. Overall, in the above experimental settings, the average MSE of the DESTformer decreases by 5.7%. According to the experimental results, it can clearly be seen that the DESTformer performs well on the Exchange dataset, and no obvious periodicity is observed. In addition, it can also be seen from the experimental results that as the prediction length increases, the performance change of the DESTformer remains relatively stable, indicating that it maintains better long-term stability. This is meaningful for real-world applications, such as weather warnings and long-term energy consumption planning.  
To better illustrate the predictive performance of DESTformer, we visualize the predicted sequences and their corresponding true sequences on six datasets. As shown in Figure 2, DESTformer can capture the long-term temporal patterns and accurately fit the fluctuations of future long sequences across different tasks.