esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characteristics of Magnetic Parameters
Among them, the relationship diagram of \(\chi_{\rm ARM}\) and \(\chi_{\rm Hf}\) (as shown in Figure 2a) was mainly used for analyzing the particle size distribution characteristics of the samples [26; 27]. The values of \(\chi_{\rm fd}\)% and \(\chi_{\rm ARM}/\)SIRM of the dustfall samples in 2019 and 2020 fall within the range of PSD and MD, and the largest part of the SP particle fraction is less than 50%. Both the Dearing plot and King plot prove that the magnetic particles in the study (before and after COVID-19 outbreak) are mainly PSD and MD magnetic particles with relatively coarse particle size, and the volume fraction of SP particles is mostly less than 50%. In summary, it can be concluded that the average values of the magnetic parameters (\(\chi_{\rm ARM}\), SIRM, HIRM) before the epidemic (in 2019) are generally higher as a whole, which is  
Figure 2: Dearing and King plot of dustfall [26; 32]. consistent with previous research conclusions [39]. Additionally, the average Air Quality Index (AQI) in December 2019 (AQI\({}_{\text{2019}}\) = 69) and in December 2020 (AQI\({}_{\text{2020}}\) = 68) in Shanghai [40] shows the same trend as the magnetic parameters in this study. There is a significant change trend between the magnetic parameter values and the concentration of magnetic particles after the epidemic. This further confirms that the interference of the external environment has a negative impact on the changes in magnetic parameters [39; 41; 42]. There may be two factors for this temporal variation. On the one hand, man-made activities were obviously restricted after the epidemic, and the flow of people and vehicles was reduced to a certain extent, leading to a decrease in the content of magnetic particles and the magnetic parameter values in 2020. On the other hand, it is most likely due to the \"Three-year plan on defending the blue sky\" issued by the State Council in 2018 [43], which has made a significant contribution to exploring how to achieve coordinated control of the types of air pollutants and the content of air pollutants at the city level, and to speed up the improvement of ambient air quality to win the \"Blue Sky Defense\". On the whole, the magnetic results demonstrate that ferrimagnetic minerals dominated the magnetism of dust produced by human activities. This is further evidence that the significant differences in air quality before and after the COVID-19 outbreak may be related to anthropogenic activities.
## 4 Performance Analysis
### Simulations Results Corresponding to Network Radius
#### 4.4.1 Analysis of \(D_{ba}\)  
For the analysis based on network radius, we consider the distance between the AUV and BS1 when BS1 is stationary, denoted as \(D_{BA1F}\). As shown in Figure 8, despite changes in network radii, the minimum value of \(D_{BA1F}\) remains approximately 50 m. This is because \(D_{BA1F}\) reaches its minimum value when the AUV is aligned perpendicularly with BS1, regardless of the network radius. However, as the network radius increases, both the range of \(D_{BA1F}\) and the timepoint when \(D_{BA1F}\) reaches its minimum value also increase. This is caused by the fact that the AUV starts from the network edge of BS1, and as the network radius increases, it takes longer to reach the point where the distance to BS1 is minimized. In addition, as the network radius increases, the distance that the AUV travels also increases, resulting in an expansion in the range of \(D_{BA1F}\).  
Furthermore, an increase in network radius also enlarges the overlapping region. This implies that the time taken to traverse this overlapping region increases as well. The characteristics derived from the analysis of \(D_{BA1F}\) based on network radius are not directly influencing factors for handover decision errors; thus, it is essential to analyze \(|\Delta D_{BA12}|\), which represents variability.
## 3 Results and Discussion
### Integration of Renewable Energy Sources and Hybrid Microgrid Configurations
#### 3.2.2 Hybrid AC/DC Microgrid Configurations  
Hybrid AC/DC microgrids have emerged as a viable alternative to overcome the limitations of conventional AC-only architectures, offering enhanced flexibility for integrating diverse energy sources and storage technologies [16; 17; 83]. These configurations support efficient power conversion and distribution, reducing energy losses and improving overall system resilience [36; 58].  
Studies highlight that adopting hybrid configurations facilitates the integration of DERs, including photovoltaics, wind turbines, and battery storage systems [22; 47; 60; 84]. Additionally, hybrid microgrids offer operational advantages in load balancing, fault tolerance, and the simultaneous support of AC and DC loads with improved efficiency [23; 61].  
Despite these benefits, challenges remain regarding control coordination and the development of robust protection schemes, which require further investigation and standardization efforts [34; 48; 62].
# Discretly Exploiting Inter-Session Information for Session-Based Recommendation
## 2 Related Work  
### Session-Based Recommendation  
In recent years, session-based recommendation (SBR) has gained considerable attention, with numerous approaches proposed in this field. SBR is concerned with predicting the user's next interaction based solely on the current anonymous session. Note that a session is defined as a sequence of user interactions arranged in chronological order.  
Early traditional models adopt Markov chain [23], but they lack global optimization, leading to restricted and less diverse recommendations due to their focus only on transition probability between current and previous states. With the vigorous development of a Neural Network, many models [9, 10, 14, 24] based on it have emerged to improve the recommendation accuracy of SBR [25, 26, 27, 28, 29, 30]. A Recurrent Neural Network (RNN) is first introduced into SBR due to its excellent sequential relationship modeling ability. GRU4 Rec is the first RNN-based SBR model using a Gated Recurrent Unit (GRU) to analyze the interactive state transition [9]. However, it has been recognized that the global relationship also presents in sessions, and therefore, attention mechanism has been proposed to model such a relationship. Then, a Neural Attentive Recommendation Machine (NARM) [10] is proposed, which uses RNN and attention mechanism together. A Short-Term Attention/Memory Priority (STAMP) [11] also adopts the attention mechanism, and it achieves better results by emphasizing more short-term interest and focusing more on users' recent clicks. GNN has achieved great success in other fields and exhibited great potential for representation learning, so it has been introduced in SBR too. SR-GNN [12] makes the first attempt to use GNN to model relations in a session, and it achieves excellent performance.  
The model builds a directed intra-session graph based on the sequential relationship of a session and uses Graph Gated Neural Network (GGNN) convolution to learn more accurate embeddings. TAGNN [13] further improves on SR-GNN by adding an embedding expression that is sensitive to the prediction target. This approach treats the user's interest as a dynamically changing interest, which is more in line with realistic scenarios.  
Disen-GNN [14] notices that previous models all ignore the fact that the user's interest is often driven by certain factors (e.g., preference for the brands). Therefore, it introduces Disentangled Represent Learning (DRL) to learn independent factor-level embeddings and explore the factor-level intra-session information. Disentangled ID and MOdality (DIMO) [31] disentangles the effects of ID and modality by analyzing the distinction between two factors on modeling user behaviors. However, these models all face a limit, which is that the intra-session information is insufficient.
## 6 Conclusions
### Public Service Innovation Implications  
This research is among the first to apply such an approach in this context. Also, it contributes to the literature by establishing a classification system that categorizes challenges into four groups: critical challenges, localized influence challenges, connector challenges, and low-connectivity challenges. For UAE government entities, efforts to address these challenges should prioritize critical challenges. These include the \"lack of information about technology to innovate in services,\" \"a culture of resistance to change among employees,\" and \"insufficient employee motivation and empowerment.\" Additional critical challenges encompass \"the service-oriented-thinking gap between managers and employees,\" \"overlapping functions between departments,\" \"unclear value propositions for new or improved services,\" \"poor internal communication and coordination between departments,\" and \"the absence of a clear and formalized innovation process.\" Strategies for overcoming these challenges were proposed. In addition to these strategies, we have demonstrated that adopting open innovation can collectively address these challenges.
## 4 Discussion  
In this study, we demonstrated the degree to which five smoothing methods (SG, LO, SP, AG and DL) improved MODIS NDVI time-series data for representing ground-measured NDVI, ground measured GPP, and for extracting phenological parameters correlated with topographical elevation. To cover a range of possible settings chosen by users we employed a total of 987 different settings for the five smoothing methods in each experiment. The three independent experiments together provide insights in the choice of methods across different sites.
# Security Analysis of the MQTT-SN Protocol for the Internet of Things
###### Abstract  
The expansion of the Internet of Things (IoT) paradigm has brought with it the challenge of promptly detecting and evaluating attacks against the systems coexisting in it. One of the most recurrent methods used by cybercriminals is to exploit the vulnerabilities found in communication protocols, which can lead to them accessing, altering, and making data inaccessible and even bringing down a device or whole infrastructure. In the case of the IoT, the Message Queuing Telemetry Transport (MQTT) protocol is one of the most-used ones due to its lightness, allowing resource-constrained devices to communicate with each other. Improving its effectiveness, a lighter version of this protocol, namely MQTT for Sensor Networks (MQTT-SN), was especially designed for embedded devices on non-TCP/IP networks. Taking into account the importance of these protocols, together with the significance that security has when it comes to protecting the high-sensitivity data exchanged in IoT networks, this paper presents an exhaustive assessment of the MQTT-SN protocol and describes its shortcomings. In order to do so, seven different highly heterogeneous attacks were designed and tested, evaluating the different security impacts that they can have on a real MQTT-SN network and its performance. Each one of them was compared with a non-attacked implemented reference scenario, which allowed the comparison of an attacked system with that of a system without attacks. Finally, using the knowledge extracted from this evaluation, a threat detector is proposed that can be deployed in an IoT environment and detect previously unmodeled attacks.  
Internet of Things; cybersecurity; protocols; MQTT-SN +
Footnote †: journal:  
_Applied_  
_sciences_
## 3 Results
### Comparison Experiments  
In order to verify the performance of the water body recognition method proposed in this paper, this study used our method and Deeplabv3+ [15], MF2 AM [28], and U-Net [13] to extract water bodies from test images. Deeplabv3+ is a classic network architecture in the field of semantic segmentation that successfully captures rich multi-scale contextual information by introducing atrous convolution and the Atrous Spatial Pyramid Pooling module. This network adopts an encoder-decoder structure and has demonstrated excellent performance in water extraction tasks. MF2 AM is an improvement based on DeepLabV3+ for water extraction from SAR imagery. The MF2 AM network emphasizes the enhancement of semantic and edge feature learning through its Semantic Embedding Branch and Feature Aggregation Module, thus demonstrating a superior performance in accurately detecting in SAR images. The U-Net structure is renowned for its symmetric encoder-decoder architecture and skip connections. This structure can efficiently capture contextual information while preserving image details, and has wide applications and recognition in the field of water extraction and analysis. The pixel size of each image is 2000 \(\times\) 2000. In the experimental outcomes, the blue region signifies the presence of water bodies, the green region denotes areas that were missed, and the red region indicates false detections. Scene I is a large-scale water area and some small water areas dominated by lakes with blurred water boundaries, Scene II is a large-scale water area and tributaries dominated by rivers with multi-scale features, and Scene III is a slender tributary and some small water areas with noise interference, with a complex background. The generalization ability of the model can be effectively verified through the inconsistency of the three scene types mentioned above.
# Multi-Perception Graph Convolution Transfer Network Bearing Fault Diagnosis Method
## 4 Experimental Verification and Analysis
### Experiment I
#### 4.1.3 Analysis of Experimental Results  
To demonstrate the effectiveness of the proposed model, six models are applied for validation comparison. They are Baseline [25], maximum mean discrepancy (MMD) [26], multi kernel maximum mean discrepancy (MKMMD) [27], correlation alignment (CORAL) [28], domain adversarial neural networks (DANNs) [29], adversarial discriminative domain adaptation (ADDA) [30], and the proposed MPGCTN. Each transfer learning method adopts the same feature extraction network and configuration of the training set and test set to verify the advantages of this method. A feature extraction network is employed to generate a Baseline for direct transfer. The diagnosis accuracy metric is the most important for this investigation. The average of ten experiments is utilized as the outcome to reduce the effect of chance on the results.  
The experimental results are listed in Table 3. Table 3 shows that, for each transfer task, the approach suggested in this study can produce the highest diagnostic accuracy. This method not only has higher precision, but also has smaller mean difference, which is better than other methods. In addition, on the most difficult task \(T_{0-2}\), the diagnosis result of the worst-performing method is 89.83%, while the diagnosis result of the proposed MPGCTN method is 99.64%, an improvement of 9.81%.  
The results show that the proposed method combines a graph convolutional network with an adversarial domain adaptation and can extract more data features so as to obtain  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Working Condition** & **Speed (r/min)** & **Load (HP)** \\ \hline
0 & 1797 & 0 \\
1 & 1772 & 1 \\
2 & 1750 & 2 \\
3 & 1730 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data corresponding to different working conditions.  
Figure 6: Bearing failure simulation test bench from CWRU.  
the best diagnostic results. It is confirmed that the proposed MPGCTN is superior in diagnosing bearing faults in varying operating situations.
## 4 Results and Discussion
### Discussion
This study provided new insights through the application of semi-supervised learning. This study used a small amount of labeled data and a large amount of unlabeled data for learning. Supervised learning, which is the mainstream approach in medical research using deep learning, requires a large amount of labeled data. However, labeling medical data requires specialized knowledge and is time-consuming, making labeled data limited and difficult to obtain. On the other hand, unlabeled data continue to increase and can be easily and abundantly prepared. Our research method can efficiently utilize limited labeled data and a large amount of unlabeled data. Furthermore, it can utilize features obtained from labeled data while also leveraging features from a large amount of unlabeled data, enabling learning from more features. For future consideration, as mentioned earlier, to improve accuracy, it is suggested to divide the mammography image into smaller patches, resize them, input them into the CNN, and use a co-training method. In addition, we believe that by devising a method for extracting patches that satisfied the classification probability of no calcification during transition learning, more features without calcification can be learned, and the number of false positives can be reduced, leading to improved accuracy. Although this study only classified the presence or absence of calcification, we believe that a classifier that learns the morphology and distribution of calcification in patches classified as having calcifications can be used to develop tools for classifying the distribution, distinguishing between benign and malignant calcifications, and determining the BI-RADS category.
## 1 Introduction  
Plants represent abundant sources of bioactive natural substances. Among these, secondary metabolites have the advantage of being widely variable in chemical structure, thus allowing potential applications in medical, pharmaceutical, cosmetic, and agro-food fields. Members of the _Opuntia_ genus are widely used as food but have also been investigated for their potential usefulness in the prevention and treatment of several human diseases, ranging from atherosclerotic cardiovascular diseases to cancer and microbial infections [1]. _Opuntia focus-indica_ L., commonly called \"prickly pear\" or \"prickly fig\", is native to Mexico, but it is also widely diffused in the Mediterranean basin thanks to its adaptation to the climate of this region [2]. Although this plant has long been marginalized, its culture is now growing and its socio-economic and environmental importance is increasing as a result [3].  
Indeed, modern medical research is rediscovering this plant, and there is an increasing interest in its beneficial properties. In Algeria, despite its abundance, especially in the area of Souk Ahras, prickly pear generates little interest: it is not valued, and its consumption remains seasonal. The seeds of this plant are considered waste, although their importance continues to grow in other countries such as Mexico, Argentina, Spain, and even in neighboring countries like Morocco and Tunisia [4; 5; 6]. The content of their oil in unsaponifiable materials and essential fatty acids explains why they are widely used in cosmetology and as table oil or cooking oil [7; 8]. The seeds also contain various phytochemicals such as secondary metabolites (polyphenols, flavonoids, and tannins), which could justify their industrial exploitation as a natural antioxidant [9; 10]. Nevertheless, to the best of our knowledge, few studies have been reported in the literature on the phenolic composition and antioxidant properties of prickly pear seed oil [11]. Moreover, very little information exists on seeds from _O. ficus-indica_ growing in Algeria. Hence, the present study aims to fill the gaps in the research by examining the composition and antioxidant activity of phenolic compounds from Algerian prickly pear seed oil; the larger goal is to valorize unexploited local natural sources. This is a comparative study carried out on three different cultivars (i.e., orange, green and yellow) collected from the Souk Ahras area located in the North-East of Algeria.
# Comparative Study on Physicochemical and Nutritional Qualities of Kiwifruit Varieties
###### Abstract  
In order to study the physicochemical and nutritional characteristics of kiwifruit varieties, 14 kiwifruits from different species with different flesh colors were selected for research. The pectin content was significantly higher in green-fleshed kiwifruits than those in red-fleshed and yellow-fleshed kiwifruits. Red-fleshed kiwifruits had the highest total flavonoid content, and green-fleshed kiwifruits in _A. eriantha_ had the highest chlorophyll a content, chlorophyll b content and total carotenoid content. The energy and carbohydrate contents of yellow-fleshed kiwifruits were significantly lower than those of red-fleshed kiwifruit. Moreover, the protein contents in _A. chinensis_ and _A. chinensis_ var. _deliciosa_ were higher than those in other species. The content of vitamin C in _A. eriantha_ was far higher than in other kiwifruits. Red-fleshed kiwifruits had a significantly higher vitamin E and vitamin B1 content than green-fleshed kiwifruits. In addition, 1-pentanol, trans-2-hexen-1-ol, n-hexane and styrene presented only in red-fleshed kiwifruits. Therefore, these could be used as a characteristic fragrance for red-fleshed kiwifruits. Moreover, the varieties were ranked comprehensively by principal component analysis (PCA), among which the top four highest-ranking kiwifruits among the 14 varieties were 'Huate', 'MHYX', 'Jinkui' and 'Xuxiang', respectively. This study provides a reference for consumers and markets on quality improvement and processing.  
nutritional quality; kiwifruit; species; flesh color; principal component analysis 1