esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Probing the Metal-Support Interactions at the Interface  
Theoretical simulations can help predict and understand metal-support interactions, and in many cases, they are aimed at supporting experimental evidence deriving from characterization and catalytic tests.  
Gaining a deeper insight on metal-support interactions and on the role of surface doping requires a huge amount of information to be collected. The combination of different complementary analytical techniques that are able to probe the metal-support interface then represents the most suitable approach. Considering the nature and the effects of SMSI, the characterization process should provide details on the electronic phenomena and structural rearrangements occurring at the metal-support interface.  
Electron microscopies are the ideal tools to give a direct illustration of the morphology and structure of the metal-support interface and describe the metal dispersion on the support.  
Among spectroscopic techniques, X-ray photoelectron spectroscopy (XPS) and X-ray absorption spectroscopy (XAS) are the most used ones. The former elucidates the electronic properties of the atoms exposed at the interface. XAS is usually divided in two categories: X-ray absorption near-edge structure (XANES or NEXAFS, including metal K-edge, metal L-edge, and ligand K-edge) and extended X-ray absorption fine structure (EXAFS). Precious evidence on the local structure, the individual environment of atoms, binding geometry, binding distances, and coordination number can be derived from the analysis of XANES and EXAFS spectra.  
Infrared spectroscopy as well as thermal techniques (thermal programmed desorption, TPD or thermogravimetric analysis, TGA) can be used to monitor the adsorption of probe molecules onto the metal surfaces, thus attaining indirect information on the electronic structure of metal phases and on eventual changes induced by SMSI. Similarly, electrochemical characterization techniques (e.g., voltammetry, chronoamperometry) allow investigating the electrical behavior of supported metal species, and then deducing the effect resulting from the interactions with the support.  
Far from claiming to give an exhaustive overview of the current scenario in the field of material characterization, herein we report selected examples. The latter point out the potential, the applicability, and the limitations of the main characterization techniques that are usually employed for the study of the metal-support interface.
## 3 Experimental Results and Analysis  
In order to verify the performance of the point cloud classification algorithm based on single point multilevel features, we use two urban scenes' ALS data for a qualitative and quantitative comparison and analysis. This section begins by briefly introducing the experimental data, before the classification performance of the proposed method is compared with the other methods on the datasets.
## 2 Study Area and Data  
### Study Areas  
The main study areas were in the Canadian Arctic, specifically the Beaufort Sea and Baffin Bay. Based on many years of C-CORE monitoring experience, these areas were selected as having high probability for EIF locations. Also, the Beaufort Sea is largely a closed sea and extreme ice features in the area tend to persist for several years. There is an interest in developing an inventory of this region, to assess the risk they pose for transportation and resource exploration. VHR, medium- and low-resolution electro-optical and infrared data including Landsat 8 and moderate-resolution imaging spectroradiometer (MODIS) were used for research. VHR and medium-resolution SAR and VHR electro-optical and infrared satellites were tasked over areas of the Lincoln Sea, Meighen Island, Baffin Bay, Axel Heiberg, Prince Patrick and Borden Islands. Figure 1 shows all sub-areas that were monitored.
# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 3 What We Have Investigated  
### The Simulation  
The nodes/sensors are located anywhere in that network. The communications network is a \"mesh\" where a node forwards information from other nodes.  
Data are transferred from node to node. The node can be a receiver or transmitter. Nodes that have information are called sources. Nodes that require information from other nodes are called sinks. The network has at least one sink and can have one or more sources. The sink acts as the forwarder to the external user network. Two or more sinks, or mobile sink models can be investigated in future research with our models. Information collection is a two-stage process initiated by the sink.  
1. A sink needs some specified information, called an interest. A diffusion is initiated into the network of requests for that interest.
2. A source that can satisfy a received interest, starts a process of transmitting that interest back to the sink.  
Each node can only communicate with its direct neighbors according to the squared energy consumption rule, where the energy required for transmission is proportional to the square of the distance between the nodes. Energy consumption is proportional to the length and repetition of the data packets. Energy consumption is proportional to the square of the number of interest packets propagated. We can enhance our network through nodes replenishing their energy-by-energy harvesting.  
In this model, a gradient is developed from the source to determine the distance of each node from the source. Similarly, a gradient is developed from the sink to determine the distance of each node from the sink. To search the data the distance from the source is used to come closer to the source without using a random search. The interest packets follow the distance from the source. When the source is detected an event pack is created and the event packet follows the distance from the sink to reach the sink. A significant energy saving is achieved as no random search is undertaken.
# Distance Protection for Coexistence of 5G Base Station and Satellite Earth Station
## 4 Numerical Results
### Restriction Zone  
#### 4.2.1 Restriction Zone and BS Tx Power Control (Omni Antenna Results)  
Figure 22 shows the sizes of the exclusion and restriction zones. For the UMa and UMi cases, all the BSs located outside the exclusion zone require Tx power control. As illustrated in Figure 22, in the RMa, most of the BSs only need to decrease their Tx power by 1 dB; however, most of the BSs in the UMa must decrease their Tx power by 5-10 dB, and most of the BSs in the UMi must decrease their Tx power by 10-15 dB. The UMi requires the highest mean Tx power reduction because it has the smallest ISD, which indicates a larger number of BSs in the area. Figure 23 also shows the BS Tx power reduction against the number of BSs, i.e., nearly 390 BSs need to only decrease their Tx power by 1 dB in the RMa scenario.  
Figure 19: RMa, UMa, and UMi scenarios with an ES antenna elevation angle of 48\({}^{\circ}\) for the conventional omnidirectional antenna.  
Figure 20: RMa, UMa, and UMi scenarios with an ES antenna elevation angle of 15\({}^{\circ}\) for a 4 \(\times\) 4 antenna array.  
* RMa, ISD = 1732 m, in 100 \(\times\) 100 km area: (1) About 3337 BSs can be deployed outside the exclusion zone in the RMa (2) The interference threshold for the restriction zone in the RMa is \(-\)47.43 dB (3) About 998 BSs within the restriction zone require Tx power control in the RMa
* UMa, ISD = 500 m, in 30 \(\times\) 30 km area: (1) About 3484 BSs can be deployed outside the exclusion zone in the UMa (2) The interference threshold for the restriction zone in the UMa is \(-\)47.62 dB (3) About 3484 BSs within the restriction zone require Tx power control in the UMa
* UMi, ISD = 200 m, in 30 \(\times\) 30 km area: (1) About 22,061 BSs can be deployed outside the exclusion zone in the UMi (2) The interference threshold for the restriction zone in the UMi is \(-\)55.64 dB (3) About 22,061 BSs within the restriction zone need Tx power control in the UMi
# Mechanistic Insights into Fish Spoilage and Integrated Preservation Technologies
## 3 Preservation Technologies and Optimization
### Freezing
#### 3.1.3 Pulsed Electric Field (PEF)  
Pulsed electric field (PEF) technology involves delivering short, high-power electrical pulses to a product placed in a treatment chamber between electrodes. PEF installations consist of treatment chambers with parallel stainless-steel electrodes (spacing 1-3 cm) surrounded by cooling jackets; pulse generators deliver 1-3 kV/cm square-wave pulses at controlled frequencies (100-500 Hz), and real-time voltage and current monitoring ensures uniform electroporation without overheating [71]. This process causes minimal thermal increases and primarily damages the cell membranes of biological cells, including those of microbes, plants, and animals [72]. The most accepted theory, introduced by [PERSON], posits that the cell membrane acts as a capacitor with a low dielectric constant. Exposure to an electric field increases the transmembrane potential until it exceeds a critical value, causing the membrane to collapse, forming pores, and increasing permeability. This can result in either reversible or irreversible damage, depending on the intensity of the PEF treatment [73].  
Low-intensity PEF treatments create small, reversible pores, while higher-intensity treatments result in irreversible cell membrane damage [72]. This process, known as electroporation, has been applied in various fields, including food processing, due to its ability to alter cell permeability without significant thermal effects [74]. For example, mild PEF treatment improved the texture and microstructure of salmon fillets [75]. However, the same benefits were not observed in the tenderness of mussels and mollusks [76].  
PEF technology is also effective in improving the extraction of high-value compounds from fish by-products, such as proteins from mussels and abalone viscera, and calcium and chondroi sulfate from fish bones [77; 78; 79; 80]. More recently, PEF has been explored as a preservation method due to its ability to inactivate micro-organisms while maintaining the nutritional and sensory quality of raw fish [81; 82; 83]. Combining PEF with other non-thermal technologies like UV irradiation, microwaves, high-intensity light pulses, and high hydrostatic pressure further enhances microbial inactivation [84; 85].  
Although primarily used for liquid foods, PEF has shown promising results in fish preservation. For instance, a study on Pacific white shrimp (_Litopenaeus vannanei_) treated with PEF every second day during 10 days of storage at 4 \({}^{\circ}\)C found significant reductions in microbial load [86]. The treatment conditions varied in intensity, with higher densities leading to greater microbial inactivation.
## 5 Results
### Application Domains and Tasks  
To gain an idea of the research areas that have been enhanced with XAI, the application domains and tasks were scrutinised. The number of articles on different domains and tasks are illustrated in Figure 2. Among the selected articles, approximately 50% of the publications were domain-agnostic. Half of the remaining articles were published in the domain of healthcare. Other domains of interest among XAI researchers were found to be industry, transportation, the judicial system, entertainment, academia, etc. Table 4 presents the application domains and corresponding tasks on which the selected articles substantially contributed. It is evident from the content of the table that most of the published articles were not specific to one domain, and safety-critical domains, such as healthcare, industry, and transportation, received more attention from XAI researchers than domains, such as telecommunication and security. Some domains can be clustered together in a miscellaneous domain because of the small number of articles (as can be seen in Figure 2a). In the case of application tasks, most of the selected articles were published on supervised and decision-support tasks. A good number of works have been published on recommendation systems and systems developed on image processing tasks, e.g., object detection and facial recognition. Other noteworthy applications in the selected articles were predictive maintenance and anomaly detection. It was also observed that several articles presented works on supervised tasks, i.e., classification or prediction without specifying the application. Moreover, very few articles have been published on modelling gene relationships, business prediction, natural language processing, etc. Figure 8 presents a chord diagram [45] illustrating the distribution of the articles published from different application domains for various tasks. Most of the studies not specific to one domain were for decision support and image processing tasks. Figure 7: Number of publications proposing new methods of XAI from different countries of the world and the top 10 countries based on the publication count shown in parentheses, which is approximately 72% of the 137 articles selected for this SLR. The countries were determined from the affiliations of the first authors of the articles.
# Remote Sensing of the Water Quality Parameters for a Shallow Dam Reservoir
## 3 Results
### Maps of Chlorophyll a Concentrations for Lake Dobczyce  
The map of chlorophyll a concentrations (Figure 16) was developed from satellite images using models (14) and (17). Long retention times observed in Lake Dobczyce (average 157 days), contribute to the algae growth and high chlorophyll a concentrations; higher concentrations are noted in the middle of the lake. In this region, towards the dam,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & **Value at** & **Value at** & **Units** \\  & **Known Turbidity** & **Turbidity Calculated from Model (17)** & \\ \hline
1 & 2 & 3 & 4 \\ \hline \(a_{0}\) & \(-32.25907512\) & \(-75.68466110\) & \(\ln(\text{mg}/\text{m}^{3})\) \\ \hline \(a_{3}\) & \(-43.55040094\) & \(-72.72855795\) & – \\ \hline \(a_{b,\ 665}\) & 0.015293030 & 0.006693771 & – \\ \hline \(k_{665}\) & 0.007665104 & 0.011140396 & (NTU\(\cdot\)m)\({}^{-1}\) \\ \hline \(a_{4}\) & 42.36776151 & 62.29811979 & – \\ \hline \(a_{b,\ 705}\) & 0.010635655 & 0.008960131 & – \\ \hline \(k_{705}\) & 0.003498578 & 0.000530822 & (NTU\(\cdot\)m)\({}^{-1}\) \\ \hline \(a_{5}\) & \(-23.86732103\) & \(-47.45269805\) & – \\ \hline \(a_{7}\) & 4.731529537 & 9.465054057 & – \\ \hline \(a_{11}\) & \(-5.149196218\) & \(-9.056892001\) & – \\ \hline \(a_{12}\) & 4.533167105 & 6.641324249 & – \\ \hline \(a_{13}\) & \(-2.345421643\) & \(-5.388690882\) & – \\ \hline \(a_{15}\) & 0.193746731 & 0.802524849 & – \\ \hline \hline \end{tabular}
\end{table}
Table 2: Model parameters (14).  
low flow velocities are also observed (Figure 17); therefore, concentrations slightly decrease due to sedimentation of the suspended solids and algae. There are stagnant zones at the northern and southern banks of the lake, where flow velocities are very low (Figure 17). Such conditions promote algae growth, and the observed concentration of chlorophyll a is high. Moreover, in the lake branches, where a water exchange is low and the retention times exceed the average one, high concentrations of chlorophyll a are observed (Figure 16).  
In October, water temperatures in the lake are low (around 13 \({}^{\circ}\)C), and the algae growth slows down. At that time, higher concentrations of algae could be found only in the northern branch of the lake (stagnant zone) and at the southern shores, where low flow velocities (Figure 17) are responsible for stagnant zones (Figure 18). The chlorophyll a model showed a good fit to the measurement data (Figure 18, table).  
Figure 14: Model fit (C\({}_{\rm chl}\) model) (14) to the measured concentrations of chlorophyll-a (C\({}_{\rm chl}\) data) at the known turbidity values.  
Figure 15: Model fit (C\({}_{\rm chl}\) model) (14) to the measured concentrations of chlorophyll-a (C\({}_{\rm chl}\) data) for turbidity model (17).
## 3 Method Descriptions
### Data Preprocessing  
The root mean square (RMS) [26] of a sensor-generated vibration signal was used to assess the vibration level in the data preprocessing. As shown in Figure 3, the raw signals underwent pre-emphasis filtering through a Butterworth filter, were converted into the frequency domain via a fast Fourier transform (FFT) [27], and their features were extracted through the mel scale. The final step generated the spectrogram of the signals.
## 7 Findings  
The findings are as follows:  
1. The first maintenance intervention in terms of general maintenance advances by 3 years when compared to the BF mixture.
2. Full depth reclamation is required for the second round at the 30 th year of the analysis period for the SBS-7% scenario with additional costs of 9.4 million euros.
3. BF mixture delays the first intervention in terms of general maintenance by 3 years when compared to SBS-7%, 2 years when compared to GF, and 1 year when compared to CF.
4. Glass fibers exhibit the worst performance among the fiber-reinforced scenarios with one extra general maintenance intervention required, adding additional costs of 1.2 million euros.
5. BF exhibits the highest salvage value at 58.3 million euros.
6. BF mixture can add up to 13 years of service life more than that of SBS-7% if the maintenance intervention schedule is kept the same for all the scenarios.
7. The SBS-7% scenario leads to the highest maintenance costs with the least salvage value for the analysis period of 40 years when compared to fiber-reinforced mixtures, with a 53% increase in costs when compared to the BF scenario.  
Significant cost savings can be achieved using the BF-modified asphalt mixtures over the SBS-7% mixtures with a 20% higher slave value for a 40 year analysis period with identical schedule of maintenance interventions.  
Conceptualization, M.F. and N.R.; methodology, M.F.; software, M.F.; validation, M.F.; formal analysis, M.F.; investigation, M.F.; resources, N.R.; data curation, N.R.; writing--original draft preparation, M.F.; writing--review and editing, M.F.; visualization, M.F.; supervision, M.F.; project administration, M.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
# Novel Feature Extraction Method for Detecting Malicious MQTT Traffic Using Seq2 Seq
## 5 Feature Extraction
### Source Port Index  
To detect malicious MQTT traffic, we first extracted the source port index. As discussed in Section 4.1, in a DoS attack, a client has many connections with a broker through various ports. Therefore, each client connects to a broker by using several ports. The original source port number is not used, and each source port is provided with an index number for each  
Figure 11: Brute-force attack.  
Figure 10: Malformed attack.  
client. For example, if a client has two source ports, a source port index of 1001 is assigned to the first source port, and a source port index of 1002 is assigned to the second source port. We do not use the destination port because it is a broker port, which is port 1883.
## 5 Conclusions  
In the present study, a GC-MS-based metabolomics analysis was conducted to investigate the metabolite profiles of the _Japonica_ and _India_ brown rice samples. Despite the pronounced variations in the concentrations of the phytochemicals during the germination process, the subspecies-dependent metabolite signature of the _Japonica_ BR, especially the higher levels of essential amino acids and the biofunctional metabolite GABA, were consistently expressed or even amplified in the GBR. By contrast, the typical flavor profile was not improved in response to the germination treatment, and the VOC fingerprints of the GBR were cultivar-dependent rather than subspecies-dependent. Nevertheless, the elaborated data in the present study demonstrated that germination could be utilized as a practical and economic strategy to improve the nutritional quality of brown rice, and that rice from the _Japonica_ subspecies seems to be more desirable in terms of the abundance of nutrients for consumers. Further studies are needed to compare the texture and the digestion properties of the GBR in the _Japonica_ and the _India_ subspecies.  
**Supplementary Materials:** The following are available online at [[https://www.mdpi.com/article/10.3390/foods10102448/s1](https://www.mdpi.com/article/10.3390/foods10102448/s1)]([https://www.mdpi.com/article/10.3390/foods10102448/s1](https://www.mdpi.com/article/10.3390/foods10102448/s1)), Table S1. Metabolites identified by GC-MS in BR and GBR samples. Table S2. VOCs identified by HS-SPME-GC-MS in BR and GBR samples. Figure S1. Exemplary GC chromatogram of the BR. Figure S2. The exemplary interface for the identification of metabolites via MS-DIAL. Figure S3. PCA score plot of the metabolomics data of real samples and the QC sample. Figure S4. The cross-validation parameters and the permutation test result of the OPLS-DA model. Figure S5. PCA score plot of the volatile profiling data.  
**Author Contributions:** Conceptualization, C.Z. and S.L.; methodology, C.Z.; software, Y.Z. and R.Z.; validation, Y.H. and B.L.; formal analysis, K.Z.; resources, J.L., J.W. and M.Z.; writing, C.Z. and S.L.; supervision, S.L.; project administration, S.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work was financially supported by the Natural Science Foundation of Jiangsu Province (BK20200890, BK20200908), China Postdoctoral Science Foundation (2021M691318), Open Project Program of China-Canada Joint Lab of Food Nutrition and Health (ZJ-2020-06), Open project Program of National Engineering Laboratory for Agri-Product Quality Traceability (AQT-2021-YB4), Jiangsu Planned Projects for Postdoctoral Research Funds (2020Z032), Senior Talent Program of Jiangsu University (20 JDG43, 20 JDG074), and Jiangsu \"Mass Innovation and Entrepreneurship\" (Shuang Chuang Ph. Ds) Talent Program.
## 2 Materials and Methods
### Characterization of ZnO Nanoparticles  
To assure the formation of ZnO nanoparticles loaded with LPME, we scanned (200-800 nm) the UV-visible spectra (UV-2450 double-beam spectrophotometer, Shimadzu, Tokyo, Japan) of the LPME, ZnO, and LP-ZnO NPs (50 and 100 mg). The X-ray powder diffraction spectra (Bruker D8 Advance diffractometer, Coventry, UK) were scanned to analyze the crystallinity nature of the fabricated nanoparticles. A Cu-K radiation source (1.54 nm; 40 mA; 40 kV) and monochromator were included in the X-ray powder diffractometer. The scanning of the scattered radiations was conducted at 20: 10-90\({}^{\circ}\) and a 0.02\({}^{\circ}\) scan rate. Then, the diffraction patterns of the LPME, ZnO, and LP-ZnO NPs' colloidal solutions were compared with the JCPDS card 36-1451. A Nicolet 6700 Fourier-transform infrared (FT-IR) spectrometer (Waltham, MA, USA) was used to detect the functional groups of the LPME, ZnO\({}_{2}\), and LP-ZnO NPs. The IR-ray scanning was performed at a wavenumber range of 500-4000 cm\({}^{-1}\). Microstructural images of nanoparticles were photographed by a transmission electron microscope (TEM) (JEM-1011, JEOL Ltd., Tokyo, Japan), which was worked at 160 kV. A thermal gravimetric analysis (TGA) of the samples was conducted by heating the samples from room temperature to 800 \({}^{\circ}\)C (at a heating rate of 10 \({}^{\circ}\)C/min) using a thermogravimetric device (Pyris 1 TGA, PerkinElmer, USA).  
Before beginning the microbiological assays, the nanoparticles were decontaminated by UV radiation for 10 min. The nanoparticle dilutions were prepared fresh for the experiments.