esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation and Analysis
### Data Acquisition  
Original sound data was acquired from the National Coal Mining Equipment Research and Experiment Center (Zhangjiakou, China) at the China Coal Zhangjiakou Coal Mining Machinery Co., Ltd. (Zhangjiakou, China). The test platform (China Coal Zhangjiakou Coal Mining Machinery Co., Ltd., Zhangjiakou, Hebei, China) was 70 meters long and 2 meters high and equipped with a shearer and 43 hydraulic supports. The cutting wall was composed of three sections: a 30 m long pure coal seam with the Protodikonov hardness of f2 (S1), a 30 m long pure coal seam with the hardness of f3 (S2) and a 10 m long coal seam gripping rocks (S3). The traction speed of the shearer was 3 m/min and the cutting drum rotation speed was 25 r/min. The shearer was pulled towards the right direction. An industrial microphone was installed to collect the cutting sound of S1, S2, S3 and the condition with no-load (S4). The arrangement of the experimental site and the installation of microphone were shown in Figure 4. The sampling frequency of the sound signal was 44.1 kHz, and a 100 sound series with a duration time of 0.2 s were recorded for each situation. In addition, a series of 300 were treated as training samples, and the remaining 100 segments as test samples. Four kinds of typical cutting sound signals were shown in Figure 5.  
\begin{table}
\begin{tabular}{c c} \hline
**Operating System** & **Windows 10 (64 bits)** \\ \hline CPU & Intel Xeon E5-2690 (8 cores, 2.9 GHz) \\ Memory & 16 GB (DDR3) \\ Hard disk space & SSD (512 GB) \\ Matlab version ( MathWorks Inc., Na tick, MD, USA, 2012) & 8.0 \\ \hline \end{tabular}
\end{table}
Table 1: Configuration of the work station.  
Figure 4: The experimental site. (**a**) Site layout of the platform; and (**b**) installation of the microphone.
# Model-Robust Estimation of Multiple-Group Structural Equation Models
## 2 Model-Robust Estimation in Structural Equation Modeling
### Robust Moment Estimation Using Robust Loss Functions
#### 2.2.2 Standard Error Estimation
We obtain  
\[F_{\mathbf{\theta}}(\hat{\mathbf{\theta}},\mathbf{\zeta})~{}=~{}F_{\mathbf{\theta}}(\mathbf{ \theta}_{0},\mathbf{\zeta}_{0})+F_{\mathbf{\theta}\mathbf{\theta}}(\mathbf{\theta}_{0},\mathbf{ \zeta}_{0})(\hat{\mathbf{\theta}}-\mathbf{\theta}_{0})+F_{\mathbf{\theta}\zeta}(\mathbf{\theta }_{0},\mathbf{\zeta}_{0})(\mathbf{\zeta}-\mathbf{\zeta}_{0})~{}. \tag{19}\]  
As the parameter estimate \(\hat{\mathbf{\theta}}\) is a nonlinear function of \(\mathbf{\zeta}\), the Taylor expansion (19) provides the approximation  
\[\hat{\mathbf{\theta}}-\mathbf{\theta}_{0}~{}=~{}-F_{\mathbf{\theta}\mathbf{\theta}}(\mathbf{ \theta}_{0},\mathbf{\zeta}_{0})^{-1}F_{\mathbf{\theta}\mathbf{\theta}}(\mathbf{\theta}_{0},\bm {\zeta}_{0})(\mathbf{\zeta}-\mathbf{\zeta}_{0})~{}. \tag{20}\]  
By defining \(\mathbf{A}~{}=~{}-F_{\mathbf{\theta}\mathbf{\theta}}(\mathbf{\theta},\mathbf{\zeta}_{0})^{-1}F_{ \mathbf{\theta}\mathbf{\zeta}}(\mathbf{\theta},\mathbf{\zeta}_{0})\), we get, using the multivariate delta formula [22; 41]  
\[\text{Var}(\hat{\mathbf{\theta}})~{}=~{}\mathbf{A}\mathbf{V}_{\mathbf{\zeta}}\mathbf{A}^{\top} \tag{21}\]  
This approach is ordinarily used for differentiable discrepancy functions in the SEM literature [3; 7; 25; 37]. We also apply the Formula (21) for differentiable approximations \(\bar{\rho}_{\varepsilon}(x)~{}=~{}(x^{2}+\varepsilon)^{p/2}\) of the robust \(L_{p}\) loss function \(\rho(x)~{}=~{}|x|^{p}\) for \(p\leq 1\) and a sufficiently small \(\varepsilon>0\) in RME. As an alternative to the multivariate delta formula, standard errors can be obtained with resampling approaches, such as bootstrap or jackknife [42]. However, these approaches are computationally more demanding because SEM estimation has to be carried out in each replication sample.
## 2 Research Methodology  
### Governing Equation  
In addressing the heat transfer-flow-solidification process within the molten pool [28; 29; 30; 31], a \"heat-flow\" coupled model has been established for simulation and computation. This model assumes the molten metal liquid to be an incompressible Newtonian fluid while neglecting the impact of turbulence. The specific governing equations are as follows:  
Continuity equation:  
\[\frac{\partial\rho}{\partial t}+\frac{\partial\left(\rho v_{j}\right)}{ \partial x_{i}}=0 \tag{1}\]  
Conservation of momentum equation:  
\[\frac{\partial\left(\rho v_{i}\right)}{\partial t}+\frac{\partial\left(\rho v _{i}v_{j}\right)}{\partial x_{j}}=-\frac{\partial p}{\partial x_{i}}+\frac{ \partial}{\partial x_{i}}\left(\mu\frac{\partial v_{i}}{\partial x_{j}} \right)-\frac{\mu}{k_{P}}+b_{j} \tag{2}\]  
Conservation of energy equation:  
\[\frac{\partial\left(\rho h\right)}{\partial t}+\frac{\partial\left(\rho v_{i} h\right)}{\partial x_{i}}=\frac{\partial}{\partial x_{i}}\left(\frac{k}{c_{P}} \frac{\partial h}{\partial x_{i}}\right)-\frac{\partial\left(\rho\Delta H \right)}{\partial t}-\frac{\partial\left(v_{i}\Delta_{H}\right)}{\partial x_ {i}}+S \tag{3}\]  
In the equation, subscripts \(i\) and \(j\) denote Cartesian coordinate directions; \(\rho\) represents density; \(v_{i}\) stands for the velocity component in the \(i\) direction; \(t\) is time; \(p\) signifies pressure; \(\mu\) is the dynamic viscosity of the molten metal; \(k_{P}\) is the mushy zone coefficient; \(b_{j}\) represents the body force, including buoyancy, gravity, and other fluid-driving forces; \(h\) denotes the latent heat of fusion; \(k\) stands for the thermal conductivity coefficient; \(c_{P}\) represents the specific heat capacity; \(\Delta H\) represents the latent heat absorbed during material melting; and \(s\) signifies the volumetric heat source.  
The boundary conditions for the flow field primarily consider the influence of surface tension on molten pool flow while disregarding the effects of recoil pressure and the Marangoni effect on molten pool flow. The thermal boundary conditions encompass heat sources, thermal convection, and heat conduction, among which thermal convection is the primary cause of energy loss.
###### Abstract
In this case, the lateral constraint caused by the soil on the girder-abutment-pile system of an IAB increases because the abutment is constrained by the girder. As a result, the structural stiffness of an IAB increases, which could limit the horizontal deformation of the abutments and piles [8; 20; 21; 22; 28]. The stress at the girder-abutment connections and in the piles, as well as the pile displacement, significantly increase [23]. With an increase in the span length, the soil-structure interaction, horizontal deformation of abutments and piles, and seismic vulnerability of IABs increase under seismic loads [24]. The seismic performance of an IAB is significantly influenced by the relative stiffness between the girder and abutment [30]. With a decrease in the stiffness of the girder or an increase in the flexibility of the abutment, the restraint effect of the girder on the abutment and the stiffness of the IAB decrease, and the internal forces in the abutment increase [8; 25]. The large displacement produced by the girder during an earthquake can easily be transferred to the substructure due to the rigid connection between the girder and abutment in an IAB. The seismic response of the piles is the largest among the different bridge components [19; 31]. The maximum displacement and bending moment of the piles under seismic loads can be found at the abutment-pile connection [21; 26], producing shear failure [22; 32] and plastic rotation [27]. In summary, the bending moment (\(M_{\text{a}}\)) at the top of the abutment in IABs cannot be zero because of the constraint of the girder, as observed in analyses of whole IABs [21; 23], and the seismic performance of an IAB is significantly influenced by the girder. The seismic performance of IAP-soil systems [33; 34; 35; 36] have been experimentally analyzed using pseudo-static low-cycle tests without considering the girder. In these cases, the \(M_{\text{a}}\) is zero, which is different than the findings in the literature [21; 23]. To address this gap in the literature, the influence of girders on IAP-soil systems, so-called GIAP-soil systems, should be considered to accurately reflect the actual boundary conditions of girder-abutment connections in IABs. Therefore, the seismic performance of a GIAP-soil system was analyzed. The paper is organized as follows: Section 2 presents the design of the pseudo-static low-cycle test on the GIAP specimen-soil system based on an IAB; Section 3 discusses the main results of the GIAP specimen by comparing with the results of the IAP specimen without considering the girder; Section 4 summarizes the main conclusions obtained from this study. This paper aims to investigate the impact of the girder on the seismic performance of the girder-integral abutment-RC pile-soil system. The actual mechanical behavior of the system under seismic loading is obtained, and the failure modes, hysteretic characteristics, energy dissipation capacity, skeleton curve, displacement ductility, and stiffness degradation of the system are examined.
###### Abstract
In summary, the contributions of this study are as follows:  
* An NHPP-based SRM that simultaneously considers the defect detection rate of the Rayleigh distribution and the uncertainty of the operating environment is presented and discussed, as the risk of failure gradually increases during the life cycle. * This study presents results from an empirical study of the proposed models applied to three real-world case studies. The goodness-of-fit results indicate that our proposed model has a fairly accurate prediction capability. * Many existing studies have applied SRMs to cost models to find the minimized total expected cost under various environmental conditions. However, in this study, we investigated whether there was a change in the optimal release time depending on the operating period depending on the SRM applied to the cost model. * This study shows that the proposed model applied to the cost model has many changes in timing depending on the operating period. In this study, we propose a new SRM that considers the uncertainty of operating environments and establish a cost model that addresses the risk level and time of error removal. The optimal testing release policies for NHPP SRMs to minimize the expected total software cost are determined. We show the impact of the operating time, \(x\), by changing it and comparing the ORTimes and minimum expected total costs (ETCs). In Section 2, the new NHPP SRM is presented, and in Section 3, the optimal software release policies are discussed. In Section 4, we discuss the criteria for model comparison and provide numerical examples with results for the best model selection. Finally, Section 5 summarizes the conclusions.
_Applied_  
_sciences_  
_Eucalyptus globulus Essential Oil as a Natural Food_  
_Preservative: Antioxidant, Antibacterial and_  
_Antifungal Properties In Vitro and in a Real Food_  
_Matrix (Orangina Fruit Juice)_  
**[PERSON] 1,2,*(r)**, [PERSON] 2, [PERSON] 1,3(r)**, [PERSON] 1  
**and [PERSON] 1,*(r)**  
1 The Pharmaceutical Research Institute, Albany College of Pharmacy and Health Sciences,  
Rensselaer, NY 12144, USA; [EMAIL_ADDRESS] (H.G.N.); [EMAIL_ADDRESS] (M.R.)  
2 Departement de Biologie et Physologie Cellulaire, Faculte des Sciences de la Nature et de la Vie,  
Universite-Saad Dahlab-Blida 1, BP 270, Route de Soumaa, Blida 9000, Algeria; [EMAIL_ADDRESS]  
3 Drug Radiation Research Department, National Center for Radiation Research and Technology (NCRRT),  
Atomic Energy Authority (AEA), Cairo 11762, Egypt  
* Correspondence: [EMAIL_ADDRESS] (M.N.B.); [EMAIL_ADDRESS] (S.A.M.); Tel.: +213-664-983-174 (M.N.B.); +1-518-694-7397 (S.A.M.); Fax: +1-518-694-7567 (S.A.M.)  
check for updates  
**Abstract: The potential application of _Eucalyptus globulus_ essential oil (EGEO) as a natural beverage pressure is described in this research. The chemical composition of EGEO was determined using gas chromatography analyses and revealed that the major constituent is 1,8-cineole (94.03% \(\pm\) 0.23%). The _in vitro_ antioxidant property of EGEO was assessed using different tests. Percentage inhibitions of EGEO were dose-dependent. In addition, EGEO had a better metal ion chelating effect with an IC\({}_{50}\) value of 8.43 \(\pm\) 0.03 mg/mL, compared to ascorbic acid (140.99 \(\pm\) 3.13 mg/mL). The _in vitro_ antimicrobial effect of EGEO was assessed against 17 food spoilage microorganisms. The diameter of the inhibitory zone (DIZ) ranged from 15 to 85 mm for Gram-positive bacteria and from 10 to 49 mm for yeast strains. _Candida albicans_, _C. parapsilosis_ and _Saccharomyces cerevisiae_ were the most sensitive fungal species to the EGEO vapor with DIZ varying from 59 to 85 mm. The anti-yeast effectiveness of EGEO alone and in association with heat processing was estimated in a real juice matrix (Orangina fruit juices) in a time-dependent manner. The combination of EGEO-heat treatment (70 \({}^{\circ}\)C for 2 min) at different concentrations (0.8 to 4 mL/mL) was effective at reducing _S. cerevisiae_ growth in the fruit juice of Orangina, compared to juice preserved with synthetic preservatives. Current findings suggest EGEO as an effective and potent inhibitor of food spoilage fungi in a real Orangina juice, and might be a potential natural source of preservative for the food industry.  
**Keywords:** natural food preservative; _Eucalyptus globulus_ essential oil; eucalypttol; antioxidant effect; vapor phase; Orangina fruit juice
## 1 Space-Bore X-ray and Gamma-ray Observational Research
### 3U Cubesat VZlusat-2  
VZLUSAT-2 is a 3U Cubesat from Czech Republic launched as technology demonstrator mission on 13 January 2022 [24]. Deployed into LEO (sun synchronous, 500 km) the telemetry and communication with the satellite are performed via ground station of the Faculty of Electrical Engineering, University of West Bohemia [25]. The mission of VZLUSAT-2 is to verify technologies at the platform and payload levels for future missions of a Czech-based satellite constellation.  
The nanosatellite is equipped with an experimental camera and a system for precise attitude control and carries payloads for Earth observation and space radiation monitoring. For X-ray and Gamma-ray astronomy it carries a dedicated payload onboard (see below) which consists of an X-ray and Gamma-ray optics and collimator system coupled to the high-sensitivity semiconductor pixel detector Timepix. Part of the VZLUSAT-2 mission is to verify the new concept of this payload.
## 1 Introduction
The main reasons are that in engineering, the reliability of emergency extension is synthetically affected by start failure, continuous movement failure, movement precision failure, and static strength failure. In this case, it is urgent to develop an integrated reliability approach by considering many reliability models (i.e., starting reliability model, continuous movement reliability model, movement precision reliability model, and static strength reliability), for the comprehensive reliability sensitivity analyses of the emergency extension system. To address this issue, this paper will systematically analyze the emergency extension fault of the NLG under the actual loading conditions. Firstly, the working principle of detailed emergency extension and failure modes are introduced. Secondly, the safety boundary equation and variables distribution of the reliability model are determined to establish the reliability models comprising starting reliability model, continuous movement reliability model, movement precision reliability model, and static strength reliability. In respect of the starting reliability model, the calculation approach of failure probability is introduced in detail under different flight speeds. Next, the sensitivity analysis of different torques to the emergency extension of landing gear is conducted to find the torques with high influence level. Finally, a promising design plan is explored to solve the problem of emergency extension fault and improve the reliability of NLG emergence extension for aircrafts, in respect of experimental validation. The rest of the paper is organized as follows. In Section 2, the reliability models of NLG emergence extension are discussed including working principle, fault tree analysis, reliability approach, and mixture of models. The reliability sensitivity analyses of emergence extension are performed to find the failure probabilistic of emergence extension and the effect levels of bottom events in the fault tree model in Section 3. Section 4 proposes and validates efficient driving plans (driving mechanisms) by the experiment of NLG emergence extension. Conclusions and findings are summarized in Section 5.
# Pixel-Level Analysis for Enhancing Threat Detection in Large-Scale X-ray Security Images
## 6 Experiment Results and Discussion
### Ablation Study  
We examined each module of our threat detection pipeline to determine their individual contribution to the final algorithm performance. Table 8 shows the performance of four different cases represented by the different configurations of the threat detection pipeline. First, we only considered the detection model (Det) without the preprocessing and verification by the segmentation model. Then, we added the preprocessing algorithm to the detection model (Crop + Det) but still omitting the segmentation. Next, we combined detection and segmentation (Det + Segm) but removed the preprocessing algorithm. Finally, we merged all the modules into one threat detection pipeline (Crop + Det + Segm). Table 8 shows the results of the evaluation using SIXray100 subset. As demonstrated in our previous work [37], we can achieve a marginal yet valuable improvement by simply cropping out irrelevant areas in the image, such as the air gaps/spaces, which is very common in X-ray security images. Cropping the images to expose only relevant information and extract more features mostly enhances the detection model's ability to detect more objects with higher confidence. Due to being trained on a balanced training subset, the detection model is forced to fit the vast amount of unseen data from the negative samples to the more familiar targets in the positive samples. While this increases the detection of true positives, it also inadvertently increases the detection of false positives. This issue proves to be the main limiting factor and is thereby rectified by integrating the segmentation model to verify the initial predictions.  
Figure 11: Exemplar images of unsuppressed detections. Images are wrongly predicted to contain (**a**) knife, (**b**) knife, and (**c**) gun. Images on the right are the inputs, and images on the left are the outputs with overlayed masks.
# Hospital Length-of-Stay Prediction Using Machine Learning Algorithms--A Literature Review
## 3 Ethical Considerations
### Features to Consider in Hospital Admission and Hospital LoS Predictions  
In the realm of predicting hospital admissions and LoSs, identifying the most relevant features is crucial for developing effective predictive models. Features that can significantly impact hospital admission and hospital LOS predictions are [24]:  
1. Patient demographics, as also used in [25]. Demographic information provides a foundation for understanding patient backgrounds and potential hospital LoS variations: 1. Age--Older patients may have longer stays due to slower recovery times; 2. Gender--Gender differences can influence disease progression and treatment outcomes;
2. Admission Details [25]. Information about the admission process can indicate the severity and type of care required: 1. Admission type--Emergency vs. elective admissions often correlate with different hospital LoS outcomes;* Admission source--Whether the patient was referred from another facility or admitted directly;
* Clinical Parameters. Clinical parameters capture the medical status of patients, which directly affects the hospital LoS:
* Diagnosis--Primary and secondary diagnoses influence treatment complexity and duration;
* Comorbidities--Presence of additional health conditions affect hospital LOS through increased treatment needs;
* Severity of illness;
* Treatment and Procedures. Treatments and procedures performed during a patient's stay influence the length of their hospital admission:
* Procedures performed--Complexity and number of procedures can extend hospital LoS;
* Medication Regimens--Duration and type of medication impact recovery time;
* Patient Functional Status. A patient's functional status can influence their recovery trajectory and hospital LoS:
* Functional independence--Measures of a patient's ability to perform daily activities;
* Rehabilitation needs--Extent of rehabilitation services required post-discharge.  
Selecting the most important features for predicting hospital LOS involves a multi-faceted approach that incorporates patient demographics, clinical parameters, admission details, and other relevant factors. Each feature plays a distinct role in shaping hospital LoS outcomes, and effective feature selection is crucial for developing accurate and actionable predictive models in healthcare settings.
## 4 Results and Discussion
### The Output Power  
The power of ECDL can be described by the function [11]:  
\[P_{\text{out}}=\frac{\
u_{g}^{2}n\
u\omega(\
u)a_{m}S}{2R_{SP}} \tag{4}\]  
with the threshold loss of the laser \(a_{m}\)  
\[a_{m}=\frac{1}{l}\left(\ln\left(1/r_{1}r_{eff}\right)+a_{in}\right) \tag{5}\]  
where \(h\) is the Planck constant, \(\
u\) is the frequency. \(S\) is the number of photons in the gain medium. \(a_{in}\) is the intracavity loss-of-gain medium, \(r_{1}\) is the absorption coefficient of the diode cavity and \(r_{eff}\) is the effective reflection of the external cavity.  
The output power is dependent on the combined result of optical gain and intracavity loss. On the one hand, optical gain and loss are related to the structure of the gain chip. On the other hand, from Formulas (4) and (5), they are related to the efficiency of optical elements in the external cavity. In our experiment, a relatively high output power is obtained. The output power is measured as a function of wavelength under an injection current of 200 mA, as shown in Figure 7. Over the tunable range of wavelength, the optical gain is different for different output wavelengths. Therefore, the power varies with wavelength. As shown in Figure 7, the power in the middle part is higher and the power on both sides is lower. The maximum output power is 25.6 mW at a wavelength of 1550 nm. The gain loss of the wide bandpass filter is relatively small, and it has high output power at a low current condition. If the injection current is enhanced, it would obtain higher output power.
# Generalized Replay Spoofing Countermeasure Based on Combining Local Subclassification Models
## 3 Related Works
### Conventional Approaches  
Conventional approaches consist of two main aspects. While the first involves extracting an audio feature suitable for discriminating genuine from spoofed utterances, the second component trains a model able to categorize the extracted features. In particular, the work in [4] employed the Cepstral coefficient (CQCC) feature [4] and employed the Gaussian mixture model (GMM) [6] as a classifier. This system is considered to be a baseline approach to assess antispoofing systems [1; 2]. Similarly, the countermeasure proposed in [37] extracted a combination of cochlear filter Cepstral coefficients (CFCCs) [38] and the instantaneous frequency (IF) [39], and fed them into a GMM classifier. Alternatively, the authors in [5] proposed a countermeasure based on LFCC [40] features and a GMM classifier after comparing 19 different features coupled with SVM [17] and GMM [6; 41] classifiers. On the other hand, the study in [42] combined mel-frequency Cepstral coefficient (MFCC) [43], mel-frequency principal coefficient (MFCC) [44], and CosPhase principal coefficient (CosPhasePC) [45] features and conveyed them to an SVM classifier.