esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed System
### Target Articles Retrieval'  
The first module of our pipeline is concerned with retrieving news articles on the same topic or event as the reference article. As previously mentioned, we denote the retrieved articles as target articles. Given a reference article, already read by the user, the module collects articles from a pre-defined set of sources. In the current implementation, sources are widely popular newspaper websites. Each source homepage is scraped in order to retrieve target articles referring to the same topic or event reported in the reference article. In order to assess the degree of similarity of the article acquired by scraping with the reference article, we exploit a simple yet effective and efficient method based on named entity recognition (NER). Specifically, named entities are first extracted from the reference and target articles using the spaCy NLP pipeline spaCy.io (accessed on 3 November 2023) spaCy is an open-source text processing library in Python. The version utilized is 3.5.0, and the module exploited is the en_core_web_lg. As for spaCy, we chose it for its widespread use, both in industry and academia, and its ease of implementation in the pipeline. As for the specific en_core_web_lg model, we used it for two main reasons. First, the model has been trained on web-based data and, thus, is expected to be effective for our task. Second, our goal was to achieve a good balance between the accuracy of the model and its computational cost.  
Figure 1: Flowchart of the proposed system.  
Although a more accurate Transformer-based model exists for the English language, it is more expensive to run locally and requires GPU acceleration. The chosen model allows the module to easily run on a CPU-only system while achieving good accuracy.  
Articles are considered as target articles if they contain at least a third of the named entities extracted from the reference article. We chose this threshold after an empirical evaluation, guided starting with the following consideration. The use of named entities provides reasonable confidence in retrieving articles whose topics align with that of the reference article, as they refer to the same named entities. Simultaneously, selecting articles that do not share all named entities with the reference article, but only some, ensures that the target articles contain new or different information with respect to the reference article.  
This step allows us to efficiently retrieve possible target articles from the web without performing in-depth similarity calculations, which is a more expensive task. Thus, we are able to reduce the number of articles that have to be compared with the reference article, with negligible loss of information. The system will calculate the similarity between the target and reference articles in the subsequent steps, involving more comprehensive analysis and specific techniques.  
Note that the set of sources can be redefined provided an appropriate functionality for retrieving information from the new sources. In addition to this, the threshold could be considered as a parameter in future upgraded versions of the application.
# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 3 Experimental Setup and Results
### Dataset
#### 3.1.2 Data Selection and Augmentation  
We set a data-driven mask over all the objects (labeled and unlabeled) of the KOI data (8054 records) to filter out light curves without a transit behavior, obtaining 4317 objects to train in an unsupervised fashion. The process is described below:  
* Check for Kepler _flags_ (in the metadata) and remove objects with \"secondary event\" or \"not transit-like\" flags;
* Remove objects with a \"transit score\" (in the Kepler metadata) less than 0.55;
* Perform a Mandel-Agol fit and remove objects with (SMSE) residual greater than 1.  
The candidate objects that are not transits (\"not transit-like\" flags) could be because the detection was from instrumental artifacts errors, non-eclipsing variable stars or another object (non-planetary) in the background. On the other hand, the objects with a statistically different secondary event (\"secondary event\" flag) are most likely caused by an eclipsing binary system, i.e., two stars orbiting around their barycenter.  
Furthermore, as a data augmentation step, we doubled this dataset by mirroring each folded light curves [52]. This represents of the same object, with the same properties, orbits the star in the opposite direction.
# The Inactivation of Microscopic Fungi in Bakery Products Using Hurdle Technology--A Case Study
## 3 Results
### Mycological Testing of Bakery Products  
Microbiological analysis was also performed on the bread samples. The amount of microscopic fungi was checked both in the half-baked products and in their packaging process.  
Regardless of the type of bread, after the half-baking process, the amount of microscopic fungi was below the detection threshold (Table 5). However, after the packaging process, the content of the tested microorganisms in the crumb was at the level of 4.8-9.8 \(\times\) 10\({}^{1}\) cfu/g. Higher content was found on the crust, which may indicate that the microscopic fungi come from the external environment.
# Zero-Shot Detection of Visual Food Safety Hazards via Knowledge-Enhanced Feature Synthesis
## 4 Experimental Evaluation
### Ablation Studies  
To analyze the contribution of each component in our framework, we conducted comprehensive ablation studies. Table 6 presents the results of our ablation studies on the FSVH dataset. We evaluated the impact of different components by removing or replacing them with alternatives and measuring the resulting performance.  
Figure 3: Confusion matrix for unseen hazard classes in GZSD setting. Values show recall-based confusion rates (normalized by true class). Darker cells indicate higher confusion rates. Notable patterns include 23% mutual confusion between Aspergillus and Penicillin molds, while Glass Fragments achieved 91% correct classification.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Method**} & **Mold** & **Glass** & **Insect** & **Bacterial** & **Chemical** \\  & **Growth** & **Fragments** & **Parts** & **Colonies** & **Residue** \\ \hline ConSE [56] & 48.3 & 45.7 & 40.1 & 37.4 & 36.2 \\ SYNC [57] & 50.2 & 46.9 & 43.3 & 39.8 & 38.5 \\ DeViSE [23] & 51.6 & 49.5 & 44.8 & 41.2 & 37.9 \\ DSES [8] & 58.7 & 55.2 & 49.3 & 43.5 & 41.4 \\ SB [8] & 60.1 & 56.8 & 51.7 & 44.2 & 42.5 \\ ZSD-YOLO [58] & 61.3 & 58.5 & 52.9 & 46.8 & 43.1 \\ PL [10] & 62.5 & 60.3 & 54.1 & 47.9 & 44.7 \\ RRFS [29] & 65.2 & 62.8 & 56.5 & 48.3 & 45.9 \\ \hline ZSFDet (Ours) & **73.6** & **69.5** & **63.4** & **54.7** & **50.2** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Class-wise average precision (AP@50) for selected unseen hazard classes. Note: Values represent average precision at IoU = 0.5 for each unseen hazard class. Higher values indicate better detection performance for that specific hazard type.
## 4 The Digital-Twin-Based Detection and Protection Framework  
The combination of digital twins and runtime verification is still a very young idea [4]. Digital twins can make full use of the physical model, sensor update data, history data, and other information to map a real satellite network in a virtual digital space, thus reflecting the operation status of the satellite network. At the same time, the ground station staff can also find problems in the satellite network and make adjustments in time. In this section, we first define the digital-twin information in the proposed framework. Then we propose the detection and protection framework and describe the overall process and detection algorithm in detail.
## 5 Results and ANN Model Validation
### Effects of a Relief Well
#### 5.1.2 With a Relief Well  
In case of with a relief well, it was assumed that there was a relief well removing the pore pressure to maximize both the injectivity and capacity. As shown in the previous section, the pore pressure build-up in aquifer A was primarily focused on managing it so as to not reach the fault reactivation pressure. The location of the relief well was optimized to achieve the maximum mass of cumulative CO\({}_{2}\) injection. The relief well was operated with a BHP of the hydrostatic pressure, 7.5 MPa, to remove any increase in the pore pressure by the injection process. As a result, the optimal location of the relief well was 596.6 m north-east from the injection well (321.6 m and 502.5 m in the x- and y-direction, respectively) (Figure 12). In addition, it was found that the depth of the perforated interval of the relief well was optimized to be deeper (52.8 m) than that of the injection well to prevent CO\({}_{2}\) breakthrough. Figure 13a shows the pore pressure at the top layers of each aquifer at faults EF1 and EF2 during the injection process with a relief well. Although the initial pore pressures (t = 0) at aquifer A were lower than those of aquifer B, the pore pressures in aquifer A exceeded at time 0.17 years and 0.5 years in aquifer B at faults EF1 and EF2, respectively. However, it shows that the pore pressure of fault EF1 in A reached the fault reactivation pressure in 7.84 years. In other words, operation of the relief well extended the injection process for 1 year (365 days). When the process is finished, the pore pressure at EF1 of aquifer A was decreased and stabilized at 11.2 MPa. As shown in Figure 13b, which visualizes the pressure distribution at the end of injection in the top layers of aquifers A and B, the pore pressure at aquifer A was effectively managed by the relief well. Therefore, it was concluded that the installment and operation of the relief well effectively manage the pore pressure in aquifer A and that more CO\({}_{2}\) can be injected for a longer period of time.  
Figure 11: (**a**) Estimated pore pressures at fault EF1 and EF2 in each aquifer, A and B, at the top layer and (**b**) pressure distribution at the end of injection of 6.85 years without a relief well.
# Secured Real-Time Machine Communication Protocol
## 2 Existing Protocols
### Transport Layer Protocols  
PROFINET is an industrial Ethernet protocol developed by Siemens designed to address the diverse needs of industrial automation. It primarily operates at the transport and application layers of the TCP/IP model for non-real-time communication, where it employs Transport Layer Security (TLS) for encryption. While PROFINET supports user authentication and authorization, these features are typically provided through associated proprietary software suites rather than being natively integrated into the protocol itself [12]. For real-time communication, including real-time (RT) and isochronous real-time (IRT) operations, PROFINET bypasses the transport layer and functions directly at layer 2 (data link layer) of the OSI model. This design ensures low-latency, high-speed data transfer necessary for time-critical industrial applications, but it also means that no encryption is supported at layer 2, leaving real-time communication potentially exposed to security risks such as eavesdropping or tampering [12]. Although PROFINET is an open technology and does not require license fees, its implementation often incurs significant costs. These arise from the purchase of third-party software development kits (SDKs), specialized hardware components like managed switches, and industrial-grade network interfaces. Such requirements make PROFINET relatively expensive for deployment, particularly in cost-sensitive environments [13]. Furthermore, the protocol's inherent complexity, especially in configuring its real-time features and diagnostic capabilities, demands specialized expertise for installation and maintenance, contributing to increased operational costs [14].
# Potential of Halophytes-Associated Microbes for the Phytoremediation of Metal-Polluted Saline Soils
## 3 Selection of Microbes for the Phytoremediation of Metal Contaminated Saline Soils  
### Frequently Investigated PGP Properties  
#### 3.1.1 Production of Indole-3-Acetic Acid  
Diverse PGP effects that modulate plant growth and help plants to tolerate salt and/or metal stress are frequently investigated, namely the production of several phytohormones: indole-3-acetic acid (IAA), abscisic acid (ABA), cytokins (Cyt), gibberell acids (GAs), or jasmonic acid (JAs) [42; 56; 59; 92; 100]. IAA is the most frequently tested.  
Elevated metal concentrations induce ethylene production in plants, leading to root growth restriction, such as the decrease of root hair number, and roots branching and elongation [39; 101]. Ethylene acts synergistically with IAA and the underlying mechanisms are nowadays well described [102]. IAA is often produced by bacteria and fungi [103; 104; 105]. Though its role for microorganisms remains unclear, IAA is considered as one of the most common phytohormones involved in biochemical and biological pathways of plant development such as cell enlargement and division, tissue differentiation, and responses to light and gravity [106]. Many studies have demonstrated the interest of treating plants with IAA-producing bacterial strains and identified the implication of this phytohormone in the improvement of several growth parameters and metal-bioaccumulation capacities [100; 107]. Moreover, IAA-producing microbes can also enhance nutrition; increase the number of root hairs, root and root elongation, leaf area and number, and germination rate; and can also limit pathogen aggressions [107; 108; 109]. [PERSON] and [PERSON] [111] showed that, under 0.3 M NaCl in the growth solution, inoculating _Mediago truncatula_ with an IAA-overproducing _Sinorhizobium melidi_ strain can lead to higher internal proline contents, produced as an osmolyte, and enhance the activity of antioxidant enzymes compared with _M. truncatula_ plants inoculated with the wild-type strain. However, the inoculation on a halophyte species was not evaluated. The glycophyte _Arachis hypogaea_ inoculated with a _Bacillus licheniformis_ strain isolated from the halophyte _Sueda fritucosa_ improved biomass, total length, and root length in the presence of NaCl [112]. The in vitro PGP characterization revealed the ability of this strain to produce IAA, among other PGP features. Inoculating plants with IAA producers can improve growth but does not necessarily lead to an increased metal accumulation by plants. For instance, [PERSON] et al. [113] showed that an IAA-producing _Penicillium roqueforti_ strain isolated from the halophyte _Solanum surattense_ could improve wheat growth and limited the absorption of Ni, Cd, Cu, Zn, and Pb in a metal-polluted soil compared with the non-inoculated plant.
# A Novel Method for Cross-Modal Collaborative Analysis and Evaluation in the Intelligence Era
## 2 Literature Review
At the same time, multi-dimensional data channels are used to extract the frequency and spectral features from audio streams and motion-related behavioral features from video streams. On this basis, the Hidden Markov Model and the Echo State Network are used to identify the paralinguistic behavior in the communication process, and a classification model with a certain level of accuracy is obtained. Researchers from the Norwegian University of Science and Technology collected cross-modal data of learners in adaptive learning activities and used the fuzzy set qualitative comparative analysis (fsQCA) method to describe the relationship between learner participation patterns and learning performance [10]. [PERSON] et al. proposed a Wearable Internet of Things in Education (WiotTED) system based on IoT technology and real-time monitoring data from wearable devices and used machine learning techniques and cross-modal learning analysis methods to build a model that could \"explain\" student engagement. This study selects a decision tree and rule system based on a set of correlated variables, and the obtained rules could be easily interpreted by non-professionals [11]. Overall, the research on cross-modal learning analysis has a rapid speed of development in spite of its comparatively short history. In recent years, it has received extensive attention from academic groups across different disciplines. The evolutionary process covers knowledge model and framework design, cross-modal feature extraction and sentiment analysis, cross-modal representation learning, and in-depth learning. Its research ideas and research results have further enriched the fields of learning analysis and learning science. Major research teams in China are from normal universities such as Beijing Normal University, East China Normal University, and South China Normal University. By analyzing cross-modal learning from the perspective of learning science, the research mainly focuses on theoretical discussion (Such as [PERSON] [12], [PERSON] [13]) and framework construction (Such as [PERSON] [14], [PERSON] [15], [PERSON] [16], [PERSON] [17], etc.). Previous studies indicate that cross-modal learning analysis is an effective method for analyzing learning behavior and obtaining learners' status. However, current methods rarely apply theories to practice from the perspectives of computer science and data science. Meanwhile, the accuracy, comprehensiveness, and interpretability of analytical models need to be improved. Based on deep learning and cross-modal learning, we established a multi-modal sentiment analysis model for learning behavior, and the robustness of the analytical model was improved using deep learning and pre-training, implying an important research trend.
# Portable and Highly Versatile Impedance Meter for Very Low Frequency Measurements
## 3 Results and Discussion
### System Testing: An Example of Application
Here we are interested in the ability of the system we designed to provide data on the impedance of the device at a very low frequency. The real part of the impedance (R) and the opposite of the imaginary part (\(-\)X) of the DUT as obtained using the approach we propose are reported in Figure 12 (symbols). The explored frequency range was from 10 mHz up to 100 Hz. A standard LCR meter HP4284 was also used to measure the impedance of the DUT. The HP4284 minimum test frequency is 20 Hz and the values of R and \(-\)X obtained for the device in Figure 11 are also reported in Figure 12 (continuous lines). As it can be noticed, we obtain essentially the same result with the standard LCR meter and with the system in Figure 2 in the frequency range in which the explored frequency range for the two instruments overlap (from 20 to 100 Hz). This fact is a further indication of the soundness of the approach we propose. In the particular case of the results reported in Figure 12, it is interesting to notice that at frequencies below 500 mHz, the reactance (X) of the device shows the same frequency dependence that would be expected from an ideal capacitor (\(1\,\mathrm{X}\,\mathrm{l}\propto 1/f\)). While this fact, because of the complexity of systems such as the one tested in this work, does not allow, in itself, to draw a definite conclusion on the nature of the conduction mechanisms occurring in the device, it certainly demonstrates that exploring very low frequencies, as can be done with the system we propose, can provide information that would be otherwise hidden because of the limitation of standard instrumentation. When investigating the electrical properties of devices and materials by means of EIS, Nyquist plots may result in a useful representation in the process of the extraction of an equivalent circuit [56]. If we use the data in Figure 12 to create the Nyquist plot, we obtain Figure 13. It can be noticed that the plot exhibits an almost vertical line that approaches 90\({}^{\circ}\) in the low frequency region, which corresponds to an ideal capacitor and fast ion diffusion in electrode materials [57]. The results obtained are in agreement with what has been reported in the literature about characterizations performed on similar structures [55]. Figure 12: Real (R) and imaginary (X) part of the impedance of the device in Figure 11 as measured by the system proposed in this paper (symbols, from 10 mHz to 100 Hz) and as measured using an HP 4284 LCR meter (solid line, above 20 Hz).
# Multi-Relational Graph Convolution Network for Service Recommendation in Mashup Development
## 3 Multi-Relational GCN Based Service Recommendation Model
### Overall Framework  
We present an overall framework of our proposed approach, which is shown in Figure 1. Our approach consists of three main components: multi-relational graph construction, graph convolution with relation fusion, and model prediction. Firstly, we construct three graphs with three different relations between mashup and service based on mashup-service composition relation, textual descriptions, and annotated tags of mashup and service. Then, we use three strategies to fuse the three types of relations and adopt a graph convolutional neural network (GCN) architecture to propagate high-order relational information between mashups and services. Finally, the embeddings of mashups and services from different propagation layers are combined and the model outputs a preference score between a mashup-service pair.
## 4 Biosensor Application of Ramified or Dendritic Electrodeposited Structures
### Dendritic-Like Nanostructures
The electrodeposition of Au nanostructures in the presence of cysteine onto ITO-coated glass was found to trend toward more bumpy and dendritic structures at longer deposition times [83]. The number of particles deposited in 60 s increased as the potential became more negative, from 16 \(\upmu\)m\({}^{-2}\) at zero V (versus Ag/AgCl), to 160 \(\upmu\)m\({}^{-2}\) at \(-\)0.3 V, and to 400 \(\upmu\)m\({}^{-2}\) at \(-\)0.5 V. Deposition was carried out from 0.5 M of H\({}_{2}\)SO\({}_{4}\) with 2 mM of NaAuCl\({}_{4}\) and 0.125 mM of L-cysteine as a structure-directing agent. The size decreased with more negative potential from 83 \(\pm\) 38 nm at zero V, to 39 \(\pm\) 13 nm at \(-\)0.3 V, and to 22 \(\pm\) 9 nm at \(-\)0.5 V. The gold nuclei clearly form more rapidly at lower applied potential, as expected. Deposition at zero V and at longer times up to 1800 s resulted in a trend in morphology to bumpy, and then ultimately dendritic structures. The bumpy structures formed at 60 s had a plasmon absorbance peak refractive index sensitivity of 163 nm of RIU\({}^{-1}\).