esa-sceva/satcom-chunk-collection huggingface dataset:
# Postbiotic Potential of Lactic Acid Bacteria Strains in Functional Minimally Processed Oranges
## 1 Introduction  
In accordance with consumption trends, the demand for ready-to-eat fresh-cut produce has grown significantly, especially in Europe [1]. The term ready-to-eat fresh produce refers to 'any food normally consumed in its raw state or any other food, including processed food, for which it is reasonably foreseeable that the food will be consumed without further processing that would significantly minimise biohazards' [2]. The growth of this global market segment, and specifically of ready-to-eat vegetables and fruits, has led to some concerns related to microbiological safety, as these products represent a favourable matrix for the growth of pathogens that can lead to product spoilage and health risks forconsumers [3]. Currently, chemical agents, including chlorine; organic acids; or physical treatments, such as heat treatments, ultraviolet light, and ozone, have been widely used to reduce the microbial density in fruits and vegetables [4]. Cheap green alternatives to chemical preservatives, provided by microorganisms or their metabolites, are known as 'postbiotics' and are recognised as biopreservatives. In detail, postbiotics represent a complex mixture of different substances, such as organic acids, bacterioxins, exopolysaccharide, peptides, enzymes, etc. [5], that are suitable as biopreservatives, protective cultures, or food additives in various forms (dipping, coating, spraying, edible films, and wrapping) due to their interesting traits [6]. When postbiotics are obtained from lactic acid bacteria (LAB), which are included in the Generally Recognised as Safe (GRAS) species, they can be considered as safe for human health [7]. In particular, when cells are grown in a liquid medium, the derived metabolites and residual nutrients constitute the cell-free supernatant (CFS), a complex matrix characterised by a high antimicrobial activity [8].  
Citrus is among the most significant tree fruit crops globally, both in terms of economic value and total production, with approximately 140 million tons/year produced. The Italian production is about 3 million tons, concentrated mainly in Sicily, where Citrus fruits, especially blood oranges, represent an important source of income. Oranges exhibit a high nutraceutical value thanks to the high content of vitamin C and flavonoids, the most abundant phenolic compounds in Citrus fruit [9]. Oranges suitable for processing can be valorised as juice or ready-to-eat orange slices, complying with market demands.  
Therefore, this study aimed to evaluate the functional and antibacterial effects of LAB strains isolated from minimally processed fruit. The mix of the CFS from the most promising strains was applied to minimally processed oranges to assess its impact on shelf-life extension. Orange fruits were chosen for their global economic relevance and, as an ideal matrix for harbouring a poor native microbiota, due to their intrinsic characteristics, which make them a suitable model for evaluating the efficacy of antimicrobial postbiotic compounds.
# Antibacterial and Antiproliferative Potential of Green, Roasted, and Spent Coffee Extracts
## 3 Materials and Methods
### Cell Proliferation Assay and Morphological Changes  
Lung carcinoma (A549) and cervical carcinoma (C33A) cell cultures were maintained in DMEM-F12 growth media containing 10% fetal bovine serum, streptomycin (0.1 mg\(\cdot\)mL\({}^{-1}\)), and penicillin (100 U\(\cdot\)mL\({}^{-1}\)) at 37 \({}^{\circ}\)C with a 5% CO\({}_{2}\) concentration. Inhibition of cell proliferation by coffee extracts was measured by 3-(4,5-DiMethylThiazol-2-yl)-2,5-diphenylTetrazolium Bromide (MTT) assay. A549 and C33A cells were plated in 96-well culture plates at a density of 4 \(\times\) 10\({}^{3}\). Then, the cells were exposed to different concentrations of coffee extracts obtained from GB, RC, or SCG (at 12.5, 25, 50, 75, 100, 125, 150, and 200 \(\upmu\)g\(\cdot\)mL\({}^{-1}\)) for 24 h, and with paclitaxel (120 nM) as a positive control. After that, 10 \(\upmu\)L of MTT in PBS (5 mg\(\cdot\)mL\({}^{-1}\)) was added. After incubation for 4 h at 37 \({}^{\circ}\)C, the purple formazan crystals were dissolved with 100 \(\upmu\)L DMSO and the absorbance was measured at 545 nm on a plate reader (Stat-fax-100; Awareness Technology, Inc. Palm City, FL, USA), according to [PERSON] et al. [60]. Cytotoxicity was expressed using the IC\({}_{50}\) value, defined as the concentration of aqueous and ethanolic coffee extracts inhibiting cell proliferation by 50%. In addition, morphological changes in A549 and C33A cells were visualized, employing an optical microscope (AE31E; Motic, Hong Kong) and documented using an MD500 digital camera (AmScope, Irvine, CA, USA).
## 2 Numerical Methods
### Blade Element Momentum Method  
BEM [16] divides the wind turbine blade into a limited number of blade elements along its axial direction. When neglecting three-dimensional wind effects, the wind loads exerted on the blade elements can be calculated according to their two-dimensional airfoil characteristics. Then, the total wind loads on the blade can be calculated by integrating the loads of each blade element along the blade axial direction. Assuming that wind turbine rotor is divided into \(N\) segments whose widths are labeled as \(dr\) (Figure 1a), blade element lift force (\(dL\)), drag force (\(dD\)), thrust force (\(dT\)), and torque (\(dM\)) (Figure 1b) can be respectively calculated as  
\[dL=\frac{1}{2}\rho V_{rel}^{2}cC_{l}dr, \tag{1}\]  
\[dD=\frac{1}{2}\rho V_{rel}^{2}cC_{d}dr, \tag{2}\]  
\[dT=\frac{1}{2}\rho BC_{n}V_{1}^{2}(1-a)^{2}c\frac{dr}{\sin^{2}\varphi}, \tag{3}\]  
\[dM=\frac{1}{2}\rho BC_{t}V_{1}(1-a)\omega r\big{(}1+a^{\prime})c\frac{rdr}{ \sin\varphi\cos\varphi}, \tag{4}\]in which, \(\rho\) is air density; \(V_{rel}\) means local relative wind velocity at specified blade element; \(c\) denotes blade element chord length; \(C_{l}\) and \(C_{d}\) are lift and drag coefficients, respectively; \(B\) represents the number of blades, which is 3 in this study; \(V_{1}\) means incoming wind velocity; \(\varphi\) indicates inflow wind angle or called attack angle; and \(C_{n}\) and \(C_{t}\) are normal and thrust coefficients, respectively, which can be computed as  
\[C_{n}=C_{l}\cos\varphi+C_{d}\sin\varphi, \tag{5}\]  
\[C_{t}=C_{l}\sin\varphi-C_{d}\cos\varphi, \tag{6}\]  
In Equations (3) and (4), \(a\) and \(a^{\prime}\) are axial and tangential induce factors, respectively, which can be calculated as  
\[a=\frac{Bc(C_{l}\cos\varphi+C_{d}\sin\varphi)}{8\pi v\sin^{2}\varphi+Bc(C_{l} \cos\varphi+C_{d}\sin\varphi)}, \tag{7}\]  
\[a^{\prime}=\frac{Bc(C_{l}\sin\varphi-C_{d}\cos\varphi)}{8\pi v\sin\varphi\cos \varphi-Bc(C_{l}\sin\varphi-C_{d}\cos\varphi)}, \tag{8}\]
# Multimodal Deep Learning for Group Activity Recognition in Smart Office Environments
## 2 Background  
### Activity Recognition in Smart Buildings  
The majority of related work in the area of activity recognition for office space and building environments has largely been focused so far on occupant presence detection. Inferring this information alone has been shown to have a great impact in terms of optimizing the energy consumption based on adapting the heating, cooling, and ventilation systems in response to occupancy data. For instance, in [8] the authors deployed a sensor network, consisting of plug meters for measuring appliance energy consumption, light sensors, and PIR sensors that were triggered by human motion. A Gaussian mixture model with 97% accuracy was used for the task of determining whether a user was present in the office or not. Based on this result the authors concluded that 35.5% potential savings could be achieved by simply turning off the appliances and lightning when the office was empty. Similarly, in [4] the authors proposed a mechanism based on detecting activity patterns in smart meeting rooms, coupled with a scheduling algorithm designed to coordinate the heat supply accordingly. Other solutions, instead of resorting to sensor deployments in the environment, proposed capturing data from the user's smartphone device in order to recognize different activities. A notable examples is reported in [9], wherein the authors collected accelerometer data for determining floor localization within a building without any prior knowledge about the premise. Alternatively, in this work we are concerned with processing audio-visual data streams from office environments for group activity recognition. Group activity recognition is a complex classification task, given that it extends beyond identifying the activities of individuals, by focusing on the combinations of activities and interactions between them. Thus, we hypothesized that the audio and visual modalities are a suitable choice for this task.  
Figure 1: Images showing different camera angles from the AMI Corpus dataset.
## 2 Materials and Methods
### Materials and Experimental Set-Ups
#### 2.2.2 Classification and Performance Calculation  
The classification process used two methods: k-nearest neighbor (\(k\)-\(NN\)) and a support vector machine (SVM). The distance metric calculated (\(d\)) in \(k\)-\(NN\) used the Euclidean distance (Equation (24)) [65]. The \(k\)-\(NN\) method calculates the \(k\)-nearest neighbor between two classes of data (testing and training) \(x\) and \(y\), respectively. The SVM classifier was tested against the linear kernel (\(SVM\)-1) and polynomial kernel (\(SVM\)-2):  
\[d(x,y)=\sqrt{\sum_{i=1}^{l}(x_{i}-y_{1})^{2}} \tag{24}\]  
Before evaluating the proposed idea against the datasets, we found the best accuracy result with the shortest running time for \(k\)-\(NN\) was when \(k\) = 1. This means we used the single nearest neighbor between the testing and training data. Due to the similar features of the eye images and eyebrow images from different classes (respondents), \(k\) = 1 in the \(k\)-\(NN\) yielded better performance than other values of \(k\). Table 1 displays these preliminary results. These results were evaluated against the eyebrow images using a combination of the scale-space method (\(O\) = 0 and \(L\) = 2) with the max principal curvature (\(SS\) + \(X\)).  
The images in each dataset were divided in two; the first random half was used for the training data, while the remaining random half was used for validation. We calculated the  
\begin{table}
\begin{tabular}{c c c} \hline \hline \(k\)-\(NN\) & _Acc (\%)_ & **Training Time (s)** \\ \hline \(k\) = 1 & 91.00 & 13.47 \\ \(k\) = 3 & 89.20 & 17.02 \\ \(k\) = 5 & 85.50 & 17.31 \\ \(k\) = 7 & 84.10 & 14.69 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The accuracy (%) results and training time (s) of the eyebrow images using \(SS\) + \(X\) with \(O\) = 0 and \(L\) = 2 with its respective value of \(k\) in the \(k\)-\(NN\) classifier.  
Figure 2: Example of face images from datasets and cropped results for eye and eyebrow images: (a) EYB, (b) ABD, (c) PES, and (d) RFFMDS v1.0.  
validation accuracy (_Acc_) of the recognition system (Equation (25)) for each combination of methods [66]. _TP_ and _TN_ are true positive and true negative, respectively, while _FP_ and _FN_ are false positive and false negative, respectively. The _Acc_ displays how accurately the system recognized individuals:  
\[Acc=\frac{TP+TN}{TP+FP+TN+FN}\times 100\% \tag{25}\]  
All simulations in this work were compiled using MATLAB Version: 9.10.0.1710957 (R2021a) Update 4 running on an Intel(R) CoreTM) i7-7500U CPU at 2.70 GHz with a 2.90 GHz processor and 16 GB RAM.
## 2 Materials and Methods
### Problem Definition and Methodology  
The objective is to estimate the DPT using just ambient temperature and relative humidity. To address this problem, mathematical models derived from empirical data are used. By leveraging upon known relationships between temperature, humidity, and dew point, it is possible to construct equations that approximate the DPT based on ambient conditions. These equations are formulated through non-linear regression fitting and parametric optimization and the subsequent validation processes, ensuring their reliability and accuracy across various scenarios.  
Following the development of these equations, their efficacy is assessed through established statistical measures, quantifying the correspondence between forecasted and observed DPT. This systematic approach's workflow is shown in Figure 1. The observed data constitutes a dataset utilized for dual purposes: as training and testing data. The training dataset serves as the foundation for deriving equations that model the data distribution. Should the obtained equations yield satisfactory results (R\({}^{2}\) > 0.95, STD < 2), the process proceeds to optimization; otherwise, it reverts to mathematical modeling. Subsequent to the optimization phase, where the coefficients are tuned, the results undergo statistical scrutiny to ascertain any enhancements compared to prior iterations. Specifically, an approximation target of R\({}^{2}\)\(\geq\) 0.99 and STD < 1 is sought. Subsequently, an iterative simplification procedure ensues, wherein the decimal precision of equation components is successively reduced. Acceptance of modifications is contingent upon the demonstration of improved results after each iteration; otherwise, iterations are performed on a term-by-term basis of the equation. Should any variation yield superior outcomes, the process concludes.
## 4 The Proposed Model
### Sequence Learning Module  
The data of AD patients exhibit temporal characteristics and are prone to varying degrees of missing values. To address this, recurrent neural networks (RNNs) are an effective method as they can capture correlations between different time points and handle missing data efficiently. Therefore, in this study, we employ RNNs for AD progression prediction. Among the diverse range of RNN models available, we choose MinimalRNN [26] as the backbone of our model. MinimalRNN has a simpler structure compared to other networks and utilizes stronger constraints on the update rules. Additionally, MinimalRNN utilizes fewer parameters, which helps prevent overfitting issues caused by excessive parameterization. It is crucial to emphasize that the calculation of loss in the model does not take into account any missing data. Figure 2 illustrates the structure of MinimalRNN, where \(\mathbf{x}_{t}\) represents the model input, \(\mathbf{h}_{t}\) represents the hidden state, \(\mathbf{O}_{t}\) represents the transformed input, and \(\mathbf{Z}_{t}\) represents the forget gate. The update equation for the hidden state is as follows:  
\[\mathbf{O}_{t}=tanh(\mathbf{W}_{x}\mathbf{x}_{t}) \tag{1}\]  
\[\mathbf{Z}_{t}=sigmoid(\mathbf{U}_{t}\mathbf{h}_{t-1}+\mathbf{W}_{O}\mathbf{O}_{t}) \tag{2}\]  
\[\mathbf{h}_{t}=\mathbf{Z}_{t}\odot\mathbf{h}_{t-1}+(1-\mathbf{Z}_{t})\odot\mathbf{O}_{t} \tag{3}\]  
Figure 1: Architecture of our proposed multi-task evidential sequence learning model.  
Figure 2: Illustration of the deep evidential sequence module.  
where \(W_{x}\), \(U_{h}\), and \(W_{O}\) represent weight matrices, and \(\odot\) denotes element-wise product.
# False Information Detection via Multimodal Feature Fusion and Multi-Classifier Hybrid Prediction
###### Abstract  
In the existing false information detection methods, the quality of the extracted single-modality features is low, the information between different modalities cannot be fully fused, and the original information will be lost when the information of different modalities is fused. This paper proposes a false information detection via multimodal feature fusion and multi-classifier hybrid prediction. In this method, first, bidirectional encoder representations for transformers are used to extract the text features, and S win-transformer is used to extract the picture features, and then, the trained deep autoencoder is used as an early fusion method of multimodal features to fuse text features and visual features, and the low-dimensional features are taken as the joint features of the multimodalities. The original features of each modality are concatenated into the joint features to reduce the loss of original information. Finally, the text features, image features and joint features are processed by three classifiers to obtain three probability distributions, and the three probability distributions are added proportionally to obtain the final prediction result. Compared with the attention-based multimodal factorized bilinear pooling, the model achieves 4.3% and 1.2% improvement in accuracy on Weibo dataset and Twitter dataset. The experimental results show that the proposed model can effectively integrate multimodal information and improve the accuracy of false information detection.  
Keywords:false information detection; hybrid fusion method; computer vision; deep autoencoder +
Footnote †: journal:
# Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
## 1 Introduction
The undamaged mode shapes were reconstructed with fitted polynomial functions in which the minimum number of polynomial function coefficients are determined by a fit value threshold. Taking a plate structure with various damaged cases as an example, the ODS data of the damaged plate at resonance frequencies of the first several modes are obtained using the CSLDV system and the corresponding ODSs of the undamaged state are also reconstructed. After that, modal rotational mode shapes and rotational damage indicators are derived and plotted to identify the damage locations of the plate structure. The rest of the paper is organized as follows. The theory of the scan strategy and mode shape demodulation method, modal rotational damage indicators are introduced in Section 2. Then, numerical investigation of a plate structure is presented and analyzed in Section 3. In Section 4, the effectiveness of the proposed damage detection method is experimentally demonstrated through three different cases and the results are discussed. Finally, conclusions are drawn in Section 5. Methodology of Damage Detection Using Continuously Scanning Laser Doppler Vibrometer (CSLDV) Measurement Data
# Designing and Implementing Weather Generators as Web Services
## 1 Introduction
Interoperability on data and web service is the key to addressing these issues. Here, we proposed a web service framework for making weather generations available on the web for scientists and application developers. Climatological data sources required for identifying statistical characteristics of the weather were seamlessly connected with the weather generators by Sensor Observation Service (SOS) API. SOS API is one of the well-known standards that are defined by Open-Geospatial Consortium (OGC). It defines data format and protocol for finding, describing and transmitting sensor and sensor data in the Internet, which brings in the interoperability of data access to climatological data. Users call a generation service endpoint, then the service obtains historical data sources via SOS, performs necessary format conversion, and then invokes a weather generator. When users need to translate probabilistic seasonal climate forecasts to weather realizations, we encode the weather realizations using International Consortium for Agricultural Systems Applications (ICASA) standard, for agricultural simulations. For the purpose of publishing and sharing weather realizations on a broader scale (i.e., other domains), we provide an SOS access to the weather realizations for higher levels of interoperability. SOS publishes data in Observation and Measurement (O & M) form, which is a standard of OGC, as well as ISO. By this way, it is convenient to convert initial weather data to other formats used in applications with other domains. This enables people to share the same set of weather realizations that has been produced through the stochastic models. With this scheme, the time for manual data preparation can be dramatically reduced. Consequently, connecting data sources and running weather generators can be automated on the web. This web service framework was implemented in order to provide weather generators as public services.
## 3 Results and Discussion
### Dynamic Property Analysis Based on FMR Spectroscopy
For all samples, \(\alpha\) increased with an increase in temperature, consistent with the typical behavior in ferromagnetic materials owing to electron scattering through  
Figure 5: Ferromagnetic resonance (FMR) derivate absorption line (black open symbols) fitted with Lorentzian function (red line) (**a**) at 18 GHz for W buffer and capping layers sample, (**b**) at 19 GHz for Ta buffer and capping layers sample, (**c**) FMR absorption spectra at 373 K for W buffer and capping layers sample with frequency of 5–20 GHz, (**d**) FMR absorption spectra at 403 K for W buffer and capping layers sample with frequency of 5–20 GHz, and (**e**) FMR absorption spectra of Ta buffer and capping layers sample at different temperatures. interband and/or intraband transitions [65; 66]. This increase is attributed to the reduction in electron lifetime at high temperatures [67]. These findings highlight that the thermal stability of \(\alpha\) was superior for the sample with the W buffer and capping layers compared to the sample with the Ta buffer and capping layers at higher temperatures. At temperatures of 373, 403, 423, and 443 K, \(\alpha\) values for the samples with Ta buffer and capping layers increased by approximately 19.7, 41.8, 63.6, and 69.4%, respectively, compared to those at room temperature. In contrast, for the sample with W buffer and capping layers, the increase in \(\alpha\) values were 1.4, 2.5, 8.0, and 13.0%, respectively. Consequently, the sample with W buffer and capping layers exhibited higher \(\alpha\) values at various temperatures, but showed less significant temperature dependence than the sample with Ta buffer and capping layers. This finding aligns with previous research and may be attributed to differences in interfacial morphologies and spin-mixing conductance [68; 69; 70]. Figure 6b shows the temperature dependence of \(H_{\text{k}}\) for the prepared samples, measured as a function of temperature and represented by solid symbols. As the ambient temperature increased from 306 to 443 K, \(H_{\text{k}}\) values for the samples with Ta and W buffer and capping layers decreased from 220.6 to 163.0 Oe and from 120.3 to 92.4 Oe, respectively. Unlike \(\alpha,H_{\text{k}}\) tends to decrease linearly for all samples with an increase in the temperature, as indicated by the solid lines in Figure 6b [43]. In Figure 6b, the data points denote experimental values and the lines represent linear fits to the experimental values. By comparing the slopes of the fitted lines for \(H_{\text{k}}\) of the prepared samples, the effects of the Ta and W buffer and capping layers on the temperature dependence of \(H_{\text{k}}\) were examined. The slopes of the fitted lines for the Ta and W buffer and capping layers samples were \(-0.412\) and \(-0.169\), respectively. Although \(H_{\text{k}}\) for the W buffer and capping layers samples was lower than that for the Ta buffer and capping layers sample, the thermal stability was higher for the W buffer and capping layers sample, consistent with \(\alpha\).