esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The primary aim of this study was to investigate volatile constituents for the differentiation of Chinese marinated pork hocks from four local brands, Dahongmen (DHM), Daoxiangcun (DXC), Henghuitong (HHT) and Tianfuhao (TFH). To this end the volatile constituents were evaluated by gas chromatography-mass spectrometry/olfactometry (GC-MS/O), electronic nose (E-nose) and chemometrics. A total of 62 volatile compounds were identified and quantified in all pork hocks, and 24 of them were considered as odour-active compounds because their odour activity values (OAVs) were greater than 1. Hexanal (OAV at 3.6-20.3), octanal (OAV at 30.3-47.5), nonanal (OAV at 68.6-166.3), 1,8-cineole (OAV at 36.4-133.3), anethole (OAV at 5.9-28.3) and 2-pentylfuran (OAV at 3.5-29.7) were the key odour-active compounds contributing to the integral flavour of the marinated pork hocks. According to principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) of GC-MS/O and E-nose data, the results showed that the marinated pork hocks were clearly separated into three groups: DHM, HHT, and DXC-TFH. Nine odour-active compounds, heptanal, nonanal, 3-carene, D-limonene, \(\beta\)-phellandrene, \(p\)-cymene, eugenol, 2-ethylfuran and 2-pentylfuran, were determined to represent potential flavour markers for the discrimination of marinated pork hocks. This study indicated the feasibility of using GC-MS/O coupled with the E-nose method for the differentiation of the volatile profile in different brands of marinated pork hocks.  
characterization; discrimination; Chinese marinated pork hocks; odour activity value; key odour-active compounds; potential flavour markers +
Footnote †: journal: molecules  
0000-0002-2400-0002-2400]D. Han
# Multiscale Modeling and Optimization of Aluminum Foam Material Properties Under Dynamic Load
## 1 Introduction
Recent approaches have combined nanoindentation experiments with numerical simulations to extract more reliable material properties for use in finite element models. Building on this, an improved representative model has been proposed, featuring a mix of spherical and cruciform-shaped cells with varying wall thicknesses, better capturing the heterogeneity observed in real foams. Unlike traditional models, which tend to overpredict foam strength, this new approach offers significantly better agreement with experimental results and can replicate various deformation patterns seen in metallic foams [28; 29; 30]. Modeling the mechanical behavior of closed-cell cellular materials remains a significant challenge due to the intricate microstructure and extremely thin cell walls characteristic of these systems. Traditional finite element approaches, particularly those relying on continuum elements, often fail to capture the complexity of low-density foams, especially when the wall thickness approaches the resolution limit of meshing techniques. Recent developments have leveraged X-ray tomography to generate detailed three-dimensional representations of the foam structure, offering a pathway to more accurate numerical modeling. By extracting direct thickness measurements from tomographic images and applying them to shell-based finite element models, it becomes possible to simulate larger volumes of material with improved fidelity. This modeling approach enables both global mechanical responses and local stress-strain distributions to be analyzed, offering insights into structural deformation patterns that correlate well with experimental compression data [31; 32; 33]. This study begins by analyzing CT scan data to accurately extract the volume air fraction of closed-cell foam samples. This volumetric information is then used to determine the corresponding material properties, which serve as input parameters for numerical simulations. A computational model is developed to investigate the dynamic response of the material under vibrational loading. The simulated behavior is then compared against results from experimental vibration tests carried out on the same foam specimens. This integrated approach enables us to assess the accuracy of the model and to explore how the foam's microstructure influences its macroscopic mechanical performance. The paper is organized as follows: The introduction reviews relevant theoretical background on the mechanical behavior of closed-cell foams. Section 2 outlines the method used to model the foam structure. Section 2 also details the computational model employed to simulate the material's dynamic response. In the third section, simulation results are compared with experimental data, and key mechanical characteristics such as stiffness and damping behavior are discussed in relation to the foam's relative density and internal structure.
## 5 Addressing the Research Questions
### RQ1: What Are the Primary Fields of Application Where VR, AI, and IoT Are Used in Combination?
#### 5.2.2 Industry 4.0  
The industrial sector similarly benefits from the convergence, particularly through the concept of DTs, which enable real-time monitoring of operations, prediction of equipment failures, and remote execution of dangerous tasks. IoT devices collect data from machinery (where inclusion of legacy devices and tools is also possible [70]), AI analyzes these data to proactively predict maintenance needs, and XR (especially VR and AR) provides operators with immersive interfaces for troubleshooting and remote assistance. For example, in manufacturing settings, the integration of XR devices such as HoloLens with IoT sensors and AI algorithms allows workers to visualize machine components, identify potential faults, and receive real-time guidance. This reduces downtime, enhances productivity, and minimizes human error, offering a competitive advantage in Industry 4.0.
# A Lightweight Fault Diagnosis with Domain Adaptation for Defected Bearings
## 2 Methodology
### Knowledge Distillation
Knowledge distillation is a highly effective technique for compressing deep learning models, making them more suitable for deployment in resource-constrained environments. By leveraging the soft targets produced by the teacher model, the student model can achieve high accuracy with fewer parameters, enabling real-time bearing fault diagnosis on edge devices. This approach not only enhances the efficiency of the diagnostic system but also ensures that the model remains robust in practical applications.
## 3 Results
### Characterization of the Multisource Harvesting Section Integrated into the Smart Jacket
Considering the operating modalities described in Section 3.2, the mean current absorbed by the garment in a measurement cycle is given by the weighted average of the current values just reported, taking into account the time duration of the active and sleep phases:  
\[\bar{\text{I}}\ =\ \alpha_{1}\text{I}_{\text{active}}\ +\ \alpha_{2}\text{I}_{\text{sleep}}\ =\ \frac{40\,\text{s}}{940\,\text{s}}\ \times\ \text{I}_{\text{active}}\ +\ \frac{900\,\text{s}}{940\,\text{s}}\ \times\ \text{I}_{\text{sleep}}\ =\ 0.56\ \text{ mA} \tag{13}\]  
Therefore, the wearable device consumes a 1.86 mW (3.3 V \(\times\) 0.56 mA) mean power; alternatively, the system requires a 0.56 mAh (Q\({}_{\text{hourly}}\)) charge for every operation hour; thus, the charge needed for daily operation of the system is given by:  
\[\text{Q}_{\text{daily}}\text{=Q}_{\text{hourly}}\ \times\ 24\ \text{h}=13.53\ \text{mAh} \tag{14}\]  
Assuming a 40% discharge margin for the LiPo battery, the following relationship provides the autonomy of the smart garment in the absence of any contribution from the harvesting unit. \[\text{Autonomy}=\frac{\text{Battery Capacity}\ \times\ (1\ -\ \text{Discharge Margin})}{\text{Q}_{\text{daily}}}=\frac{380\ \text{mAh}\ \times\ (1\ -\ 0.4)}{13.53\ \text{mAh}/\text{day}}=16.85\ \text{days} \tag{15}\]  
However, the mean current absorbed from the battery was monitored in each measurement cycle for four days. The results demonstrate that the mean absorbed current is 0.57 mA with a \(\pm\)3% deviation, in accordance with that reported in Equation (13) (i.e., 0.56 mA). Therefore, a reduction in the battery charge of 54.7 mAh (0.57 mAh \(\times\) 24 h \(\times\) 4 days) was measured over four days, corresponding to about 25% battery capacity curtailed at a 40% discharge margin (380 mAh \(\times\) 0.6 \(\times\) 0.25 = 57 mAh). Adopting suitable operational strategies, optimizing the hardware and firmware of the different portions of the wearable device, and exploiting low-power operating modes for the MCU and sensors enabled around 16 days of autonomy when no energy contributions are available from the harvesting section. Lastly, considering that the sensing and communication units require approximately 0.56 mAh per hour of operation (13.53 mAh/24 h), the energy harvesting section is capable of providing, in the worst-case scenario (scenario 5 of Figure 17), much more charge (0.850 mAh) than required (0.56 mAh), ensuring the energy autonomy of the designed smart electronic garment.
# Accurate Algorithms for Spatial Operations on the Spheroid in a Spatial Database Management System
## 2 Experiments
### ArcGIS  
ArcGIS has been chosen as the renowned software representing the GIS Desktop group. ArcGIS Desktop claims that it can create geodetic geometries that are spatially accurate and geodetically correct in any projection, which is especially important when using large distances as airplanes flight paths or effective weapon ranges [28].  
The intersection of the two geodesics can be by calculated through a spatial analysis operation after introducing their coordinates from the graphical interface.  
The ArcGIS official documentation [28] mentions that \"Geodetic features contain densified geometry, which is a shape created by a series of connected vertices\", which means that ArcGIS is not using true geodesics either as we will prove in the next section.  
For the test, we used a WGS84 dataset, an ArcGIS cluster tolerance and resolution of 0.0000017 (some 0.03 mm) and 0.000005\" (some 0.015 mm), respectively.  
Figure 2 shows a zoom of the intersection zone of geodesics AB-CD (Case 3 in Table 1) with ArcGIS. It can be seen how ArcGIS is splitting the original line, calculating each of the new vertices on the ellipsoid, but every segment is treated as a straight line (Euclidian geometry) and not a geodesic.  
This ArcGIS methodology has two major drawbacks. First, it greatly densifies all the geometries. Each geodesic is divided into thousands of segments (e.g., the geodesic  
Figure 2: Intersection by densification of the geodesics in ArcGIS.  
AB from the example 4, is divided into 947 segments), which seriously affects both the performance and the complexity of any spatial operation. Although the segment size used by ArcGIS to split the lines is user-defined, a noticeable improvement in accuracy requires an enormous densification, which is clearly inefficient.  
Apart from the computation-expensive densification, the most important drawback is the approximate nature of the process, which uses planar calculations between split segments (Cartesian coordinates instead of geographic coordinates).
# Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition
## 4 Experiments and Results
### Results
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Training Set** & **EER\%** \\ \hline GMM-UBM [16] & VoxCeleb1 & 15.0 \\ x-vector(cosine) [43] & VoxCeleb1 & 11.3 \\ i-vector-400/PLDA [16] & VoxCeleb1 & 8.8 \\ VGG-M [16] & VoxCeleb1 & 7.8 \\ x-vector(PLDA) [43] & VoxCeleb1 & 7.1 \\ Res-SA + CRL [9] & VoxCeleb1 & 5.5 \\ i-vector-2048/PLDA [43] & VoxCeleb1 & 5.4 \\ Res-DA + CRL (ours) & VoxCeleb1 & 5.2 \\ Res-DA + CRL + CEL (ours) & VoxCeleb1 & 5.1 \\ \hline VGG-M [16; 19] & VoxCeleb2 & 5.9 \\ ResNet-34 [19] & VoxCeleb2 & 5.0 \\ ResNet-50 [19] & VoxCeleb2 & 4.2 \\ Res-SA + CRL [9] & VoxCeleb2 & 4.0 \\ Res-DA + CRL + CEL (ours) & VoxCeleb2 & 3.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Speaker verification results on the Voxceleb1 dataset (lower is better). \begin{table}
\begin{tabular}{l l l l} \hline \hline \(\omega_{1}\) & \(\omega_{2}\) & **Top-1\%** & **Top-5\%** \\ \hline
1 & 1.0005 & 89.3 & 96.3 \\
1 & 0.9995 & 89.9 & 96.7 \\
1.0004 & 0.9995 & 89.4 & 96.5 \\
0.9995 & 1 & 89.2 & 96.3 \\
1.0004 & 1 & 92.0 & 97.6 \\
1 & 1 & 90.0 & 96.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Speaker identification results on the VoxCeleb1 dataset with different weight positions (higher is better).
## 2 Methods
### Variable Extraction  
Variable extraction was performed using PostgreSQL software (version 16.4), in combination with Navicat Premium (version 16.2). Structured Query Language (SQL) queries were applied to retrieve data from the database, targeting variables across multiple domains. Demographic information included age, race, weight change during hospitalization, height, weight at admission, body mass index (BMI), and gender. Laboratory measurements encompassed calculated total CO\({}_{2}\), glucose, lactate, pH, potassium, sodium, hemoglobin A1c, alanine aminotransferase (ALT), alkaline phosphatase, anion gap, aspartate aminotransferase (AST), bicarbonate, C-reactive protein (CRP), troponin T, calcium, chloride, creatine kinase (CK), creatinine, lactate dehydrogenase (LDH), magnesium, phosphate, hematocrit, creatine kinase-MB index, platelet count,thrombin time (PT), red cell distribution width (RDW), white blood cell differential counts, etc. Physiological parameters included heart rate, systolic blood pressure (SBP), hemoglobin, temperature, cardiac output, count of atrial fibrillation episodes (AF count), and so on. Data on medication use, including prescriptions for aspirin, antiplatelet drugs, statins, and angiotensin-converting enzyme inhibitors (ACEIs), were gathered. Furthermore, information on comorbidities was gathered, including congestive heart failure, cerebrovascular disease, diabetes, renal disease, and others. Additionally, key variables such as length of stay (LOS), Acute Physiology Score III (APSIII), and the Charlson Comorbidity Index (CCI) were recorded. All variables are systematically documented in Table S1.  
Figure 1: A flowchart of the study population selection process for patients with non-ST-segment elevation myocardial infarction.
###### Abstract  
Triangular silver nanoplates (TSNPs) exhibit unique optical and antimicrobial properties due to their shape, sharp edges, and vertices. In this study, TSNPs were incorporated into biopolymer blends (bacterial cellulose (BC) with polylactic acid (PLA), polycaprolactone (PCL), and polyhydroxy-butyrate (PHB)). Antimicrobial activity of materials was tested against _Escherichia coli_ ATCC 95922 and _Staphylococcus aureus_ ATCC 25923 (\(10^{6}\) CFU/mL). After incubation (24 h at 37 \({}^{\circ}\)C, 100 rpm), optical density was measured at 630 nm. In order to assess biosensing applications, specifically fibronectin (Fn) behavior, TSNPs were protected with gold (AuTSNP) and analyzed via sucrose sensitivity test and monitored by localized surface plasmon resonance (LSPR). Additionally, AuTSNPs were coated with polyethylene glycol (PEGAuTSNP). Fibronectin functionalization of PEGAuTSNPs and pH-conformation was monitored (FnPEGAuTSNP). Eventually, adequate Fn and anti-Fn antibody concentrations were determined. BC/PHB/TSNPs showed antimicrobial activity against _E. coli_ and _S. aureus_ with 80 and 95% of growth inhibition, respectively. The sucrose sensitivity test indicated that the LSPR\({}_{\rm max}\) of the spectra is directly proportional to the sucrose concentration. LSPR\({}_{\rm max}\) of Fn-PEGAuTSNPs at pH 7 and pH 4 were measured at 633 and 643 nm, respectively. A total of 5 ug of Fn was determined to be adequate concentration, while 0.212 mg/mL of anti-Fn antibody indicated system saturation.  
nanoparticles; silver; triangular silver nanoplates; biosensing; antimicrobial; biopolymers; fibronectin +
Footnote †: journal:  
Article
# The Impact of Inter-Modulation Components on Interferometric GNSS-Reflectometry
## 1 Introduction
However, the presence of the IM component in the modulation scheme is not presented in any official Open Service Signal-In-Space Interface Control Documents (OS SIS ICD), and its impact on iGNSS-R has been neglected in previous studies. This paper fixes the omission of the IM component from previous studies on iGNSS-R and investigates the impact of the GPS L1 IM components on iGNSS-R ocean altimetry performance. In Section 2, the GPS L1 signal is taken as an example to introduce the theoretical basis of the IM component and revise the ACF of the GPS L1 composite signal as used in [13] for modern GNSS signals. After verifying the existence of this component with an experimental dataset, the effects of the IM component to ocean altimetry are studied analytically, including the SNR, the altimetric sensitivity, as well as the final altimetric precision, in Section 3. Figure 1: A simplified block diagram of an interferometric Global Navigation Satellite System Reflectometry (GNSS-R) receiver.
# Polysaccharide Hydrogels Based on Cellulose and Chitosan for Drug Sustained-Release Applications
## 2 Experimental
### Physiochemical Characterization of CAA-CS Hydrogels
#### 2.3.3 Morphological Characterization of CAA-CS Polysaccharide Hydrogel  
The microstructure characteristics of the hydrogel were jointly characterized via scanning electron microscopy (SEM) and porosity measurement. The hydrogel samples were treated with liquid nitrogen, then fixed with conductive adhesive and sputtered with gold under vacuum conditions. Subsequently, the morphology of the hydrogel was observed by SEM. To determine the porosity of the hydrogel, the samples were immersed in anhydrous ethanol for 48 h to reach equilibrium, and the volume of adsorbed ethanol was measured to calculate the average porosity. The porosity was calculated using Equation (2a,b) [27].  
\[\text{Porosity }(\%)=\frac{v_{a}}{v_{e}}\times 100\% \tag{2a}\] \[\text{Va}=\frac{w_{48}-w_{0}}{\rho} \tag{2b}\]  
where \(w_{48}\) is the mass of the gel after soaking for 48 h, \(w_{0}\) is the initial mass of the gel sample, \(\rho\) is the density of ethanol, and \(v_{\text{e}}\) is the external surface volume of the gel (three samples per group were tested, and the results were averaged).
## 4 Conclusions  
ZIF-8 was synthesized from aqueous solution, and should be considered as a facile and environmentally-friendly method. Adsorption of toluene by the ZIF-8 and its activated form was examined in a continuous mode using a fixed bed reactor. Adsorption parameters were calculated from the obtained breakthrough curves. The results of thermal activation under dry air and N\({}_{2}\) atmospheres revealed that an oxidizing condition would result a ZIF-8 adsorbent with higher adsorption capacity for capturing toluene from polluted air streams. ZIF-8 activated under dry airflow at 300 \({}^{\circ}\)C for 3 h proved to be the most efficient adsorbent for toluene under the tested conditions.  
Experimental data of the dynamic adsorption of toluene showed that toluene removal efficiencies of activated ZIF-8 did not change significantly by increasing the RH from 40% to 60%. However, its adsorption capacity dropped dramatically at RH higher than 80%. It can be concluded that activated ZIF-8 should be considered an efficient adsorbent for toluene removal at low and medium RH (e.g., 40-60%).  
The breakthrough curve of toluene adsorption on activated ZIF-8 under different conditions were simulated by fitting the experimental data into the [PERSON] and [PERSON] mathematical models. The statistical parameters, such as root mean square error (RMSE), sum square error (SSE), squared correlation coefficient (R\({}^{2}\)), and adjusted squared correlation coefficient (R\({}^{2}\)\({}_{\text{adj}}\)), were calculated and evaluated. It was concluded that both models could be applicable for curve fitting of toluene dynamic adsorption on activated ZIF-8 at different RH.  
The authors would like to thank Hamadan University of Medical Sciences for the financial support under PhD thesis scheme (number: 9312126593).  
[PERSON], [PERSON], [PERSON], and [PERSON] conceived and designed the experiments; [PERSON] performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON] contributed reagents/materials/analysis tools; [PERSON], [PERSON], [PERSON], and [PERSON] wrote the paper.  
The authors state that they have no conflict of interest.
## 3 Results and Discussions
This can be explained by the AZO band gap widening effect, as described by the Burstein-Moss shift [48]. As shown in Figure 5c, the photovoltaic device performance characteristics were extracted for ITO/AZO/n-Si devices with different Al doping concentrations. The photovoltaic performance improved with moderately increasing Al doping concentrations from 0 w.t.% to \(\sim\)6.34 w.t.% in AZO inter-layers. The best performance device (i.e., Al doping in AZO of 6.34 w.t.%) achieved the overall conversion efficiency (Eff) of 5.03%, with J\({}_{\text{sc}}\) and V\({}_{\text{oc}}\) of 26.04 mA/cm\({}^{2}\) and 0.42 V, respectively. To the best of our knowledge, this is the highest V\({}_{\text{oc}}\) value reported for ZnO/Si heterojunction devices prepared by the sputtering method. One can also see in Figure 5c that when the Al doping concentration was too high (i.e., \(\sim\)6.89 w.t.%), the device performance rapidly decreased due to the deteriorated AZO thin film and junction properties. The overall performance enhancement of our best cell confirmed the validity of our proposed device architecture and the junction engineering approach. The performance could be further improved by optimizing light absorption via antireflection coating and nanoplasmonic structures [49; 50; 51].
## 1 Introduction
The acoustic target identification of Chinese sturgeon from the echogram was done based on the assumption that the body size of an adult sturgeon was much larger than those of other Yangtze fish species. Hence, the TS of the sturgeon should be distinctively larger than those of other fishes. However, the assumption ignored the complexity of acoustic backscattering characteristics of fishes [9], and therefore might lead to some inaccurate results. In general, the TS has been estimated using three techniques: (1) in situ measurements of free swimming in the natural habitat (i.e., in situ TS); (2) ex situ measurements of dead or live fish in a controlled environment such as a net cage (i.e., ex situ TS); and (3) theoretical or numerical backscattering models based on fish anatomy (i.e., TS model) [9; 25]. The TS from a theoretical or numerical backscattering model can be examined to determine how variables such as species, body length, frequency, and orientation influence the TS values of an individual fish. For Acipenseriformes species, only the TS of American paddlefish has been estimated by an ex situ TS experiment and a Kirchhoff-ray-mode (KRM) backscatter model [10]. For the Chinese sturgeon, no acoustic TS characteristics have been studied yet. Since 2009, fishing for the Chinese sturgeon has been banned, including for a scientific research purpose [18]. Hence, hydroacoustic methods are the only available means for studying the sturgeon in the Yangtze River. The accuracy of results such as the abundance and spatial distribution of the sturgeon using the hydroacoustic methods should be improved. In this way, the hydroacoustic methods will become more feasible and accurate to study the adult population of the fish in near future. In this study, the acoustic TS of Chinese sturgeon was estimated by the combination of the ex situ measurements and theoretical calculations. This study provides a good understanding on the TS of Chinese sturgeon to establish a better foundation for future hydroacoustic studies on the fish.