esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Phytochemistry
### Chemical Composition of Fruits
#### 3.2.2 Fixed Oils  
Research carried out using both Soxhlet extraction and supercritical CO\({}_{2}\) extraction methods revealed that bay laurel berries have a fixed oil content ranging from 15% to 35.87%. The yield and composition of the laurel vegetable oil may differ across various studies in the literature, which could be due to factors such as the plant's origin, the extraction method used, and other influential factors.  
The oil obtained from berries consisted of fatty acids (lauric acid, 54%; linoleic acid, 17%; oleic acid, 15%; and palmitic acid, 5%) [3], as well as volatile compounds such as \(\beta\)-ocimene (22%), bicyclogermacrene (4.5%), 1,8-cineole (9.5%), and \(\beta\)-elemene (2%) [4].  
A study showed that whole berries contained 34.8% saturated fatty acids (lauric and palmitic acids) and 62.4% unsaturated fatty acids (oleic and linoleic acids) [41]. Another study identified the primary fatty acids in laurel fruits to be 40.79% linoleic acid and 38.08% lauric acid [42]. Meanwhile, using the supercritical CO\({}_{2}\) extraction technique, [PERSON] et al. (2008) [41] found that lauric acid (27.7%) was the most abundant fatty acid present in whole berries.  
After the initial extraction, the spent matrix underwent another extraction process at a higher pressure (250 bar) and 40 \({}^{\circ}\)C (density \(\rho\)CO\({}_{2}\) = 0.886 g/cm\({}^{3}\)) to extract the fixed oil. The resulting oil was analyzed and found to have a composition of approximately 48% saturated fatty acids, 29% mono-unsaturated fatty acids (specifically oleic acid), and 23% polyunsaturated fatty acids (specifically linoleic acid) [43]. These findings were consistent with the fatty acid compositions previously reported in the literature for _L. nobilis_[42; 44].  
For most vegetable oils, the major compounds are triacylglycerglycerols (TAG), present in 95-98% [43; 45]. In laurel oil from Madeira, TAG accounted for 85%, with the other 15% being composed of hydrocarbons (mainly mono- and sesquiterpenes), lactones, sterols, and wax esters [45].
## 3 Results and Discussion
### Impact of Hydrothermal Synthesis Under Different Conditions
This isotherm corresponds to type I(b) in the IUPAC classification, which is typically observed for solids containing wider micropores and narrow mesopores [35]. The pore size distribution analysis of MIL-100(Fe), as depicted by the BJH pore size distribution curve, reveals distinct characteristics. Specifically, it exhibits two maxima at 1.678 nm and 2.116 nm. These two pores signify the openings within the MIL-100(Fe) framework, aligning closely with the literature [29; 36]. Similar results were obtained for the other batches. Figure 3: N\({}_{2}\) adsorption–desorption isotherms and BJH pore size distribution of MIL-100(Fe) sample MF-S1. Furthermore, accordingly, and following the Rouquerol method [37] for the determination of BET surface area, the selected points for BET fitting were in the range of 0.05 to 0.15. The results of the analysis are summarized in Table 2. In the table, PSD (pore size distribution) refers to the pore size at which the BjH pore size distribution reaches a maximum. The MF-S2 batch is characterized by a slight lower specific surface area (1067 m\({}^{2}\)/g) compared to the MF-S1 sample (1199 m\({}^{2}\)/g). This lower surface area in MF-S2 can be attributed to its lower purity, as reported in [38] for the decrease in surface area due to the impurities in, and less regular crystalline structure of, MIL-100(Fe). However, MF-S3 exhibited the lowest BET surface area (500 m\({}^{2}\)/g). This material was prepared under diluted synthesis conditions compared to MF-S1. These findings agree with [1], which reported that diluted synthesis conditions for MIL-100(Fe) resulted in a lower BET surface area. In summary, the MF-S1 batch has emerged as the most promising choice regarding synthesis procedure based on the previously discussed evaluation process. Compared to alternative batches, MF-S1 consistently demonstrated higher outcomes in terms of crystallinity, purity, and sorption performance. This indicates its potential to produce high-quality MOFs with optimal characteristics for the intended application. Furthermore, this batch exhibited consistent results across multiple syntheses, suggesting a well-defined and reliable procedure. This is crucial for ensuring reproducible outcomes and facilitating further research and development. Therefore, the MF-S1 batch was identified as a reference point for subsequent analyses designed to assess the impact of the washing procedure on the performances of the synthetized MOF. This approach lays a simplified strategy for a clear evaluation of the washing procedure's impact on the MOF's key properties in order to contribute to the development of an optimized effective synthesis protocol for this application field.
# A Mask-Based Adversarial Defense Scheme
## 4 Results
### The Main Result and Comparison  
The main experiment is conducted on three different models: LeNet with MNIST dataset, VGG16 with CIFAR-10, and ResNet18 with SVHN, with the results presented in Tables 4 and 5. Of all the attacks, CW [29] is the most successfully defended method by MAD. One possible explanation is that CW usually produces smaller and highly correlated perturbation, which can be more easily countered by masking, as discussed in the preliminary study and presented in Figure 3. For the attack methods with different perturbation degrees (\(\epsilon\)), MAD tends to achieve better defense on the attack methods with smaller \(\epsilon\), which is not surprising. We have noticed that some works [30] use the method of random ablation to process images, which is similar to using \(1\times 1\) grid to mask images. We proved in Section 4.2 and Figure 3 that this method will greatly reduce the accuracy of classification. We compare MAD with four state-of-the-art adversarial defense methods, including MagNet [8], Denoised smoothing [16], Parseval networks [21], and PCL [10] on the same backbone VGG16 model with default settings, and all are tested with adversarial samples that are generated from CIFAR-10. Note that the adversarial samples used for comparison are generated only from correctly classified benign samples. None of these defense methods uses adversarial samples in the training phase. The results are shown in Table 6.  
In order to make the comparison more comprehensive and more convincing, the selected defense methods have inherently different underlying defense approaches. MagNet and denoised smoothing apply additional denoising structures to reduce the adversarial perturbations. Parseval network restricts the layers' Lipschitz constant to be less than 1 during the training, in order to boost robustness of a model. PCL enforces hidden layer features of different classes to be apart as much as possible during the training, which is imposed by adding new branch structures and introducing a new loss function.
# Health Monitoring of Civil Infrastructures by Subspace System Identification Method:
## 7 Application of Subspace Identification in Civil Engineering Structures  
In general, an individual program or a combination of software packages implement the process of damage detection [182; 183]. A structural monitoring program is considered an algorithm for analyzing response signals, extracting damage features and deploying pattern recognition paradigms, ultimately leading to damage identification [184]. Subspace methods have been used as the central part of many of the structural monitoring programs used in industry [185; 186]. In this subsection, the industrial software packages used in modal identification and SHM, which have adapted SSI as their core identification process, are further investigated.  
Table 10 provides some of the commercially available software that use subspace identification algorithms as their standard technique for SHM and modal analysis. Most of the available algorithms are generally used for modal analysis. The SHM algorithms are composed of (i) identification, (ii) feature extraction, and (iii) pattern recognition steps and the implementation of a unified algorithm for huge diversity of each category is quite challenging and rewarding.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 4 Experiments
### Ablation Studies  
To thoroughly examine the impacts of various factors, we conducted ablation studies on both the modules of UPGCN and their modality features.
# NDVI or PPI: A (Quick) Comparison for Vegetation Dynamics Monitoring in Mountainous Area
## 2 Materials and Methods
### Methodology and Implementation
#### 2.2.3 Vegetation Indices  
For vegetation indices images pre-processing, we first mosaicked the four tiles and extracted the value for our region of interest for PPI, NDVI, and Quality flag (QFLAG2). Second, following the recommendation of [34], we applied a medium filter by masking the overlying PPI and NDVI pixel for the QFLAG2 values ranging from 4 to 2048, corresponding to snow, cloud, and shadow filtering with the addition of surrounding pixels.  
_Ndvi_  
As mentioned before, the NDVI is one of the most widely used remote sensing indices for vegetation monitoring. As noted by [40], the popularity of this index is due to its relative robustness against noise and sun-sensor geometry variations and the availability of long-term time-series at a global scale. The NDVI is calculated following:  
\[\text{NDVI}=\frac{\rho\text{ NIR}-\rho\text{ Red}}{\rho\text{ NIR}+\rho\text{ Red}} \tag{3}\]  
where \(\rho\) NIR is the reflectance in the near-infrared band and \(\rho\) Red is the reflectance of the red band. The result is used as a proxy of the land surface greenness [8; 41]: a value of \(-1\) indicates a water surface; a value between 0 and 0.2 corresponds to almost non-vegetated areas; a value close to 1 represents a dense and green vegetation cover. A healthy vegetation cover tends to have a high absorption of photosynthetically active radiation, captured in the red band, and a low absorption of low infrared radiation, which could induce damage to plants due to overheating [51]. This index, however, suffers from two major limitations [40]: (1) its sensitivity to soil background (e.g., snow) and (2) its saturation at high vegetation density. In particular, this index shows difficulties in adequately identifying small amplitude variations, like evergreen coniferous forests [39].  
_Ppi_  
Ref. [40] have developed a physically-based spectral index to characterize the phenological dynamics of vegetation: the Plant Phenology Index (PPI). This index has an almost linear relationship with the green Leaf Area Index (LAI) and is based on the Near-Infrared (NIR) and Red reflectance values. It follows [PERSON]'s law, modified for canopy reflectance. PPI has the same unit as LAI (m\({}^{2}\cdot\)m\({}^{-2}\)) and is formulated as [39; 40; 42; 52]:  
\[\text{PPI}=-K*\ln\left(\frac{\text{DVI}max-\text{DVI}}{\text{DVI}max-\text{ DVIs}}\right) \tag{4}\]where DVI (Difference Vegetation Index) is the difference between NIR and Red reflectance; DVI\({}_{max}\) is the maximum canopy DVI of a specific site; DVI\({}_{s}\) is the soil DVI. K is a gain factor given by:  
\[\text{K}=\frac{1}{4*(dc+0.5*(1-dc)/\cos(\theta s)}*\frac{1+\text{DVI}max}{1- \text{DVI}max} \tag{5}\]  
where dc is an instantaneous diffuse fraction of solar radiation when the sun is at zenith angle \(\theta\)s (obtained from the corresponding scene metadata), calculated as:  
\[\text{dc}=0.0336+0.0477/\cos(\theta s) \tag{6}\]  
For further information about the PPI formulation, we refer to the reference paper of [40].
# Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
## 2 Research Procedure
### Trainer-Based Assessment  
In parallel with the objective data collection, a subjective assessment was conducted by qualified trainers who supervised each test drive. Trainers used a structured evaluation form to rate participant performance on a 5-point Likert-type scale (where 1 represented \"very poor\" and 5 represented \"very good\") across several key competencies. These included system operation, situational awareness, response to takeover requests, and overall safety. The \"mean trainer rating\" presented in the results is the average of these scores. This direct, expert-driven assessment served as the primary benchmark for validating the output of the fuzzy logic model.  
The questionnaire used to evaluate the driver's skills consisted of the following issues:  
* Was the theoretical introduction helpful in understanding how the systems work and the purpose of their use?
* Were the systems found to be useful?
* Were the systems easy to use?
* Which actions were found to be difficult?
* Assessment of the level of difficulty of individual actions.
* Was the driver able to monitor the functioning of the system?
* Did the driver feel safer when using the systems during the drive?
* How much time did the driver need to adapt to operating the vehicle?
* Level of driving skills.  
These items were designed to guide the trainer's systematic observation of the driver during the experimental scenario. The trainer used these observations and questionnaire items to form a final cumulative assessment, reflecting the overall level of driver competence. This aggregated score, derived from expert judgment and supported by structured observations, was then used as the reference point for comparison with the fuzzy model's quantitative output.
## 3 Model Design  
### Node Data Collection and Transmission  
The base station (BS) collects information of all nodes in the area and trains the encounter matrix at period, T, in the area. As shown in Figure 1, the base station collects the information of all nodes in the region over a period of time and trains the final probability matrix according to the designed model through these features. If the social relationship and the cooperative relationship cannot be obtained in the initial stage of the model application, the final probability matrix is the historical encounter probability matrix. When a node has a transmission task, the probability table of known nodes and destination nodes is requested from the base station. Specifically, when a new node enters the area, its characteristic information is sent to the BS. If it carries a probability forecast table, the BS obtains the table and updates its own matrix based on the probability forecast table obtained from the BS. If it does not carry a probability forecast table, it is sent to the matrix trained by the BS in the area. After the T period, the BS trains the matrix, M, according to Algorithm 1 (refer to Section 3.5), and transmits the matrix information according to the node information collected in the past T periods. During the period, the matrix, M, is updated by Algorithm 2 (refer to Section 3.5) according to the obtained node's probability forecast table and communication between BS before the T period. The main update idea is to add a record without some node information. The exchange of information when the mobile devices meet is also the same as updating the records carried. The message carried by the mobile devices is sent to the encounter devices whose probability of encounter is greater than the mobile device's own and destination mobile device's probability of encounter. If there is no record of the destination node, it is sent to nodes in the encounter node that have a higher probability of encountering mobile devices in another area.
## 1 Introduction
With reference to filtering devices, CFD simulation was used to optimize the functioning of cyclone separators [40; 41; 42; 43], fabric filters [8; 44], as well as cyclone separators combined with cartridge or fabric filters [5; 45; 46]. Both Refs. [45] and [5] highlight how combining cyclones with filtering inserts can be beneficial; the separation efficiency is higher in the combined system compared to standard cyclones, while the average cleaning intervals are longer compared to cartridge filters. Furthermore, in addition to higher efficiency, cyclone-bag filters decrease the floor space demand, as they allow the combination of two otherwise separate operations. In this study, CFD simulation was used to characterize in detail the functioning of a cyclone bag separator for industrial grain milling plants. The analysis started from a validated model [46] and was extended to evaluate the device functioning at increasing clogging levels. The simulations allowed the characterization of the fluid flow inside the domain and define a location for a new sensor, that would enable the detection of clogging and failure conditions. Moreover, they would be able to know, for a given operating condition, the flow distribution inside the filter, allowing an accurate control of the sleeve cleaning system, and increasing the cleaning frequency on those sleeves most affected by the airflow. The simulation results were validated with experimental tests by reproducing in the real pilot plant, the simulated conditions. The data collected during both the simulation and the experimental campaigns were integrated to generate a digital model of the plant, that was connected to its physical counterpart by means of a LabView Data Acquisition module. The generated digital twin collects and analyses the data at different time scales, allowing it to detect abnormal values, identify variations and trends in the functioning, as well as estimate the remaining useful life (RUL) of the device. In this way, it is possible to enhance the system performance by continuously monitoring the plant and its conditions, thus being able to react to functioning variations in a timely manner and optimize the maintenance operations. The contribution of this article to the existing literature can be summarized in four points. (i) development of a simulative model capable of locally reproducing filter behavior as the degree of sleeve clogging changes; (ii) conceptualization of a digital twin-based predictive maintenance model developed based on a hybrid-approach model representation type, on the basis of simulative results and experimental data; (iii) development of a control model, based on a non-deep learning method, for routine sleeve cleaning operations, depending on specific operating conditions, to ensure better operating conditions and increase RUL; (iv) application of digital twin technology in a context, such as wheat and cereal milling, for which no specific study is yet present in the literature.
## 3 Results
### Combined Analysis of HS-SPME\(\times\) GC-O-MS and HS-GC-IMS  
#### 3.3.1 Results of GC-O-MS and GC-IMS Combined Analysis  
Through the analysis of GC-O-MS and GC-IMS data, it was found that 24 compounds were detected through the use of both methods (Figure S2A), including nine aldehydes (37.50%), four ketones (16.67%), three pyrazine heterocyclic compounds (12.50%), two esters (8.33%), two alcohols (8.33%), two hydrocarbons (8.33%), one furan heterocyclic compound (4.17%), and one acid (4.17%). As depicted in Figure S2B, the six groups of samples exhibited discernible discrepancies. Notably, the abundances of p-Xylene and Isoamyl acetate were higher in the 120 \({}^{\circ}\)C sample than those in other groups. However, the overall abundance was highest in the 160 \({}^{\circ}\)C samples. These results suggest that treatment at 160 \({}^{\circ}\)C is optimal for achieving maximum volatile compound abundance.  
The PLS-DA model was established using data from 24 compounds detected via GC-IMS and GC-O-MS, as illustrated in Figure 5B. The separation of six samples is clearly observed in the PLS-DA model. The model parameters were R\({}^{2}\)Y = 0.979 and Q\({}^{2}\) = 0.904, which meant the PLS-DA model was reliable. Meanwhile, as shown in Figure 5C, the results of 200 permutation tests indicated a good model fit with no evidence of overfitting (R\({}^{2}\) = 0.35, Q\({}^{2}\) = \(-\)0.693). Specifically, the 120 \({}^{\circ}\)C sample and 130 \({}^{\circ}\)C sample were distributed in the fourth quadrant, indicating their VOCs were relatively similar. The 140 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 160 \({}^{\circ}\)C samples were distributed in the third quadrant, suggesting that their VOCs were relatively similar. In contrast, the 170 \({}^{\circ}\)C sample was distributed in the third quadrant alone, which indicates that the content of the 170 \({}^{\circ}\)C sample was significantly different from other samples. The six samples could be divided into three categories: the 120 \({}^{\circ}\)C and 130 \({}^{\circ}\)C samples belong to one group, while the 140 \({}^{\circ}\)C, 150 \({}^{\circ}\)C, and 160 \({}^{\circ}\)C samples from another group. The remaining sample is categorized separately at a temperature of 170 \({}^{\circ}\)C. The results were consistent with the OPLS-DA model established via GC-IMS.
# Process Mining IPTV Customer Eye Gaze Movement Using Discrete-Time Markov Chains +
## 3 Methodology
### Data Manipulation  
#### 3.3.1 Data Collection  
Tobii Studio is utilised to capture gaze data from an eye tracker. As the entire experiment comprised numerous tasks, it was required to partition the entire data set and obtain the relevant information for the evaluated task. The target dataset was afterwards exported as '.csv' files. MongoDB was used to store the dataset in order to gain an overview of the data structure prior to beginning the study due to the dataset's raw data format and massive information on its numerous features. Also, the Tobii software (version 1803, Windows) allows the user to define certain AOIs on the screen concerning sections of the screen which may be of more interest than others. Thus, we had a list of pre-defined AOIs on BT Player during the eye-tracking experiment.