esa-sceva/satcom-chunk-collection huggingface dataset:
# Interference Signal Feature Extraction and Pattern Classification Algorithm Based on Deep Learning
## 3 Interference Monitoring Research
### Interference Monitoring Technology Based on Artificial Feature Extraction
#### 3.3.5 Experimental Evaluation
In Table 2, the root mean square error of the start and end frequencies of the four types of interference information or the prediction of a single frequency point is less than 6 KHz. Among them, the prediction error of single tone interference frequency point is the smallest, followed by small slope sweep interference. The root mean square error of starting and ending frequency prediction of frequency hopping interference and frequency sweeping interference is large. The results in Table 3 are consistent with the results in Table 2. The average absolute error of the end time of frequency sweeping interference is 2.2 ms and the average absolute error of the start and end time of other interference signals is less than 2 ms. In addition, the average absolute error of the prediction of the starting and ending frequency of the interference is the largest, followed by frequency hopping interference. The prediction error of single frequency interference and small slope sweep interference is the smallest. The average absolute error of the start and end frequency is less than 5 KHz.
# Survey on Security Threats in Agricultural IoT and Smart Farming
## 4 Mitigation Measures and Strategies for Security Threats in Agriculture  
In the previous section, the main vulnerabilities, risks and threats in agriculture concerning the adoption and utilization of state-of-the-art technology, techniques and tools were discussed. Thus, it is of high importance for agriculture stakeholders to invest and adopt measures and strategies which can mitigate the risks and avoid disastrous consequences. Nevertheless, despite the increasing concern related to cyber agroterrorism, a large number of companies are still not investing sufficiently in the improvement of the cybersecurity protection of IoT and ICT systems [61]. Although, many larger corporations and industries have invested in safe and efficient security systems and measures, smaller agricultural companies and farms often lack in terms of financial resources, time and plans to design and implement adequate measures and/or systems against possible cyber-attacks [83]. Agriculture stakeholders must focus on the possible attacks featured in detail previously such as data privacy and integration, risk management (also in relation to third parties), integrity protection, non-repudiation, trusted origin, access control, scalability and robustness in terms of points of failure [62; 79; 80; 112] in order to enhance cybersecurity. To address these challenges, some emerging architectures embedded blockchain technology in order to ensure data privacy and integrity, addressing
# Modern Technologies for Waste Management: A Review
## 2 Overview of Technologies
### Artificial Intelligence and Robots  
Artificial intelligence (AI) has been around for a couple of decades; it can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision making, computer vision, and language translation [32]. The goal of AI is to create machines and systems that can think like us, humans, making them capable of solving complex problems and learning from experience.  
Figure 1: Simplified Smart Waste Bin diagram.  
Computer vision allows the interaction of robots with waste, physically. Robots can efficiently detect kinds of waste based on their visual characteristics such as shape and color [33]. It may serve a purpose in sorting waste on a conveyor belt, where individual pieces are automatically transported to the right container for further recycling. It also has the potential in spotting illegal waste dumping [34], a problem that can have a serious impact on our health and environment. By analyzing satellite images, AI-powered software could be used to locate where illegal dumping is likely to occur [35]. This can prevent further pollution and damage to the environment [36].  
Effective waste recycling relies heavily on effective sorting which can be long, tedious, and requires an accurate process. In response to this, many automated systems have been developed which can do that faster and more effectively. AI systems with computer vision can distinguish types of waste [37; 38] and use air pressure, magnets, or some robotic arm to separate it from other types of waste. The Finnish company ZenRobotics pioneered the technology for automated waste sorting using AI and robotics, with examples such as the Heavy Picker and Fast Picker. Where the Light Picker just has a visible light sensor, the Heavy Picker has NIR sensors, a 3D laser sensing system, a high-resolution RGB camera, an image metal detector, and other sensors. The Heavy Picker is the strongest waste-sorting robot on the market and is designed to handle big and heavy waste such as building materials or metals [39]. Robots with computer vision are able to sort recyclable materials from non-recyclable waste. AI algorithms can distinguish different types of plastic so that it can be separated. It is challenging for human workers to do it manually [40]. Moreover, AI algorithms can be used to optimize waste collection and routing which can reduce the amount of time and resources required for waste collection.  
These technologies in waste management have the potential to make a significant impact. Many of the barriers could be excluded with the help of robotics, AI, and computer vision. It has the potential to revolutionize the design and operation of municipal waste-sorting plans [41].
## 2 Materials and Methods
### Experimental Setup of Interaction Experiments
#### 2.1.3 Bivoj Laser  
The diode-pumped solid-state laser (DPSSL) called \"Bivoj\", with a laser wavelength of 1030 nm, is capable of delivering 10 ns pulses with an energy of more than 100 J at a 10 Hz repetition rate, and is classified as the most powerful laser in its class. For the interaction experiments, the energy output was set to \(\sim\)5 J, the repetition rate to 1 Hz, and the laser beam was focused into a square spot with an edge length of 3.1 mm to reach an intensity of about 5 GW/cm\({}^{2}\) and, thus, to achieve the desired ablation.  
\begin{table}
\begin{tabular}{l c c c} \hline  & **PALS** & **Ti:Sapphire** & **Bivoj** \\ \hline Wavelength & 1315 nm & 810 nm & 1030 nm \\ Pulse duration & 350 ps & 50 fs & 10 ns \\ Repetition rate & 1 shot per 30 min & 1 kHz & 10 Hz \\ Energy & 120–650 J & 1 mJ & 5 J \\ Spot size & 10–1 mm (diameter) & 100 μm (diameter) & 3.1 \(\times\) 3.1 mm \\ Fluence & 600–83,000 J/cm\({}^{2}\) & 13 J/cm\({}^{2}\) & 50 J/cm\({}^{2}\) \\ Irradiance & 2–240 TW/cm\({}^{2}\) & 250 TW/cm\({}^{2}\) & 5 GW/cm\({}^{2}\) \\ Pressure & \(\sim\)10\({}^{-2}\) mbar & \(6\times 10^{-2}\)–\(6\times 10^{-3}\) mbar & atm \\ Excitation temperature & 4500–6000 K & 8000–12,000 K & 7000 K \\ Ionization temperature & 8000–13,000 K & 5000–12,000 K & 13,000 K \\ Thermodynamic temperature & 13,000–37,000 K & 17,000–30,000 K & \(>\)10,000 K \\ Electron density & \(10^{15}\)–\(10^{16}\) cm\({}^{-3}\) & \(10^{16}\)–\(10^{17}\) cm\({}^{-3}\) & \(10^{16}\) cm\({}^{-3}\) \\ \hline \end{tabular} \({}^{1}\) The uncertainty of spectroscopy-based data was estimated to be \(<\)3\(\%\).  
\end{table}
Table 2: Set experimental parameters and mean values of plasma temperatures and electron densities for the three lasers used in this study \({}^{1}\).  
First of all, the meteorite samples were ablated in air by 10 laser pulses, each with an energy of 5 ], accumulated in one spot. Second, the parameters were kept and the surface of the sample was exposed to a continuously flowing water stream. The plasma formed under water increased the pressure to 1-5 GPa. Considering the meteorite fragility, only one shot was focused on the meteorite specimen's surface. In this particular case, emission spectra were not recorded and the typical laser plasma temperature estimated from previous experiments was adopted for comparison with the other lasers used in this study, as summarized in Table 2.
## 2 Results and Discussion
### Multivariate PCA and PLS-DA Analyses  
The unsupervised principal components analysis (PCA) score plot of all volatile compounds (Figure 4) showed that replicates of the individual cultivar showed good repeatability, which again confirmed the feasibility of the optimized HS-SPME GC-MS method. Furthermore, according to the results of PCA, the samples of four cultivars were distributed into separate groups based on the first three principal components, indicating that the cultivars had significant impact on volatiles. The PCA score plot showed 36.35%, 27.02%, and 20.3% variances by PC1, PC2, and PC3, respectively. Anna and Ayamurasaki were closely located, indicating that they probably have a similar flavor, which is consistent with the sensory analysis results as both of them were evaluated as having higher overall taste and presented sweetness and a caramel taste. In contrast, Jieshu95-16 and Shuangzai were far from each other, which means they have significantly different flavors.  
The PLS-DA model was applied to distinguish between the four cultivars for the GC-MS results. The variable importance projection (VIP) method was used to explore the characteristic volatile metabolites in the PLS-DA model. In the VIP score plot (Figure 5), 17 compounds appeared to exhibit a VIP value greater than 1, indicating that these volatile compounds might play as differential volatiles for four cultivars in the PLS-DA model. Among them, camphene, \(p\)-cymen-7-ol, perillyl alcohol, 1-octen-3-ol, and (+)-cyclosativene could be considered as differential volatiles for Anna. The same situation was shown in other cultivars such as benzeneacetaldehyde and (\(-\))-myrtenol for Ayamurasaki; (E,E)-2,4-decadien, hexanal, and (E)-2-hexen  
Figure 4: Principal components analysis results of the four different cultivars of sweet potato.  
octenal, trans-\(\beta\)-ionone, nonanal, nerol oxide, geranylacetone, and 4-(1-methylethyl)-1,5-cyclohexadiene-1-methanol for Shuangzai. Furthermore, the GC-MS technology could easily distinguish the four cultivars and identify different compounds, which could further confirm the practicability of the optimized method.
###### Abstract  
To study the generation and dissipation process of agglomerate fog in mountainous expressways and deeply understand the hazard mechanisms of agglomerate fog sections in mountainous expressways, based on the analysis of the geographical location characteristics of mountainous expressways and the spatial and temporal distribution characteristics of agglomerate fog, the airspace constitutive model of agglomerate fog in mountainous expressways was constructed based on [PERSON] constitutive theory. Firstly, the properties of the Newtonian fluid and cluster fog were compared and analyzed, and the influence mechanism of environmental factors such as the altitude difference, topography, water system, valley effect, and vegetation on the generation and dissipation of agglomerate fog in mountainous expressways was analyzed. Based on [PERSON]'s constitutive theory, the constitutive model of temperature, humidity, wind speed, and agglomerate fog points in the foggy airspace of the mountainous expressway was established. Then, the time and spatial distribution of fog in Chongqing and Guizhou from 2021 to 2023 were analyzed. Finally, the model was verified by using the meteorological data and fog warning data of Liupanshui City, Guizhou Province in 2023. The results show that the foggy airspace of mountainous expressways can be defined as \"the space occupied by the agglomerate fog that occurs above the mountain expressway\"; The temporal and spatial distribution of foggy airspace on expressways in mountainous areas is closely related to the topography, water system, vegetation distribution, and local microclimate formed by thermal radiation. The horizontal and vertical movements of the atmosphere have little influence on the foggy airspace on expressways in mountainous areas. The specific manifestation of time distribution is that the occurrence of agglomerate fog is concentrated from November to April of the following year, and the daily occurrence time is mainly concentrated between 4:00-8:00 and 18:00-22:00. The calculation results of the foggy airspace constitutive model of the expressway in the mountainous area show that when there is low surface radiation or no surface radiation, the fogging value range is [90, 100], and the fogging value range is [50, 70] when there is high surface radiation (>200), and there is generally no fog in other intervals. The research results can provide a theoretical basis for traffic safety management and control of mountainous expressway fog sections.  
traffic engineering; agglomerate fog airspace in mountainous highway; constitutive equation; temporal and spatial characteristics of agglomerate fog
# The SSP-Tree: A Method for Distributed Processing of Range Monitoring Queries in Road Networks
## 4 Performance Evaluation
### Simulation Setup  
Each set of simulations was conducted on a real road network of the city of Oldenburg in Germany (normalized to [0, 10,000]2), which consists of 6105 vertices and 7035 edges, and is obtained from [[https://iapg.jade-hs.de/personen/brinkhoff/generator/](https://iapg.jade-hs.de/personen/brinkhoff/generator/)]([https://iapg.jade-hs.de/personen/brinkhoff/generator/](https://iapg.jade-hs.de/personen/brinkhoff/generator/))[15]. The number of queries whose query points are uniformly placed on the road network is varied from 1000 to 10,000, and the query distance of each query is varied from 50 to 500. On the other hand, the number of moving objects is varied from 10,000 to 100,000, and the minimum computational capability of each moving object is varied from 10 to 100. The movement of each moving object follows the _random waypoint model_[16], which is one of the most widely used mobility models: each moving object \(o\) chooses a random point of destination on the road network and moves along the shortest path to the destination at a constant speed distributed uniformly from 0 to the maximum speed, which we set to 50 per simulation time step. Upon reaching the destination, \(o\) remains stationary for a certain period of time. When this period expires, \(o\) chooses a new destination and repeats the same process during the entire simulation time steps. We list the set of used parameters and their default values (stated in boldface) in the simulations in Table 2. In each simulation, we evaluated the effect of one parameter while the others were fixed at their default values. We ran each simulation for 1000 simulation time steps and measured the average of (i) the total number of messages transmitted and (ii) CPU-time (in ms) consumed at each simulation time step. We set each object in CM sends the location-update message to the server at each simulation time step. We set 5% of queries to be updated (i.e., reinserted after they are deleted) at each simulation time step. Please note that this update rate is sufficient to study the performance of the proposed method because we focus on the queries with the static query points.
## 3 Results
Figure 6: Seasonal patterns of Sentinel-1 for a typicaliferous forest (D02.BLAN) between 2017 and 2019. (**a**): VH/VV processed by FT (cannot detect any phenological metrics); (**b**): VV-VH processed by FT; and (**c**): Gcc. Red lines: FT function; and dashed line: phenological metrics. An evaluation of the performance of the satellite-derived phenological metrics was performed by comparison to those obtained from the in-situ PhenoCam dataset (Table 2). In deciduous forest sites, the SOS extracted from VV-VH was strongly correlated with that from Gcc (R\({}^{2}\) values of 0.81 for FT and 0.75 for DL), which exceeded the correlation between the NDVI and Gcc (R\({}^{2}\) values of 0.63 for FT and 0.77 for DL), and the EOS extracted from VV-VH through the DL method was also slightly higher than that from the NDVI. Figures 7 and 8 show the relationships between the in-situ and satellite remote sensing for the extracted phenological metrics. Generally, for deciduous forests, VV-VH had a higher relationship with Gcc, and VH/VV behaved the worst among the calculated indices. Foriferous forest, it is remarkable that the SOS derived from VV-VH using FT had a negative correlation with Gcc phenology, but this negative correlation did not occur when using DL in estimating SOS based on the same data. Additionally, VH/VV cannot effectively extract phenological phases regardless of whether FT or DL was applied as the smoothing algorithm. Due to the lack of PhenoCam data at coniferous forest sites, the correlation between S1 and S2 phenological metrics was also extracted (Table 3). The SOS of DB derived from S1 backscatter is closely related to that of S2 NDVI, with the highest R\({}^{2}\) value of 0.72 for VH/VV processed by DL. The EOS of DB and EN both had low correlations with those from the NDVI (R\({}^{2}\) value close to 0), and the SOS extracted from VV-VH still had a negative correlation with that from the NDVI. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Forest Type**} & \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**SOS**} & \multicolumn{2}{c}{**EOS**} \\ \cline{3-6}  & & **FT** & **DL** & **FT** & **DL** \\ \hline \multirow{3}{*}{DB} & VH/VV & 0.42 & 0.66 & 0.01 & 0.02 \\  & VV-VH & 0.81 & 0.75 & 0.00 & 0.26 \\  & NDVI & 0.63 & 0.77 & 0.17 & 0.15 \\ \hline \multirow{3}{*}{EN} & VH/VV & 0.14 & 0.07 & 0.07 & 0.20 \\  & VV-VH & 0.30 \({}^{\star}\) & 0.20 & 0.20 & 0.01 \\ \cline{1-1}  & NDVI & 0.25 & 0.03 & 0.35 & 0.00 \\ \hline \hline \end{tabular} \({}^{\star}\)Negative correlation.  
\end{table} 
Table 2: Correlation of Sentinel-1 and Sentinel-2 extracted phenological metrics to PhenoCam extracted metrics (shown by R\({}^{2}\) values). Figure 7: Correlations of phenological metrics in day of year between PhenoCam (x-axis) and satellite remote sensing (y-axis) in deciduous forests. (**a,e**): SOS extracted by FT; (**b,f**): EOS extracted by FT; (**c,g**): SOS extracted by DL; and (**d,h**): EOS extracted by DL. Black lines: VH/VV; red lines: VV-VH; and green lines: NDVI.
# Wastewater Treatment Using Alkali-Activated-Based Sorbents Produced from Blast Furnace Slag
## 5 Conclusions  
Alkali-activated materials based on slags from the metallurgical industry can be formed to obtain monolithic foams to be applied in metal capture for wastewater treatment.  
Monoliths have been proven able to effectively capture Cu(II), Fe(III), Ni(II), and Mn(II), in solutions simulating the metal concentrations of a real mine effluent.  
Promising capture efficiency values were attained up to 80-100% and 98-100% for single ion and multi-ion solutions, respectively.  
Considering filtration results and solution equilibria evaluation, metal capture appears to be related to pH values reached during filtration, i.e., pH = 12-12.5, which results in the presence of insoluble oxides and hydroxies in solution.  
Metals are thus captured in neutral form, via precipitation induced by the monolith surface, and trapping into monolith porosity.  
Such a mechanism is very efficient in terms of metal removal, but could be not so effective in terms of strengthening metal-sorbent interaction; thus, this point deserves further validation, being fundamental in the case of transfer of the exhaust monoliths to landfill or reuse for building applications.  
The proposed use of waste-based geopolymers as sorbent solids is, in the authors' opinion, a very good opportunity to contribute to cleaner and sustainable processes. Their production requires quite low energy input, keeping the alkaline activation, either solid or liquid, at a low dosage, and curing the fresh mixes at room temperature, thus mitigating the release of greenhouse gases. Moreover, the possibility of reuse of the exhausted sorbent material in other applications makes them, compared to other sorbents, appealing in the present and future circular economy scenario.  
M.B. and C.C.: conceptualization, methodology, supervision, formal analysis, funding acquisition, writing--original draft; M.I.A. and B.M.: investigation, methodology, validation; S.M. and R.S.: investigation, validation, formal analysis, writing--review and editing; S.L.: data curation, investigation, validation, writing--review and editing; R.B.: data curation, validation, writing--review and editing. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
Thanks are due to Opigeo (S.r.l.) Italy for providing alkali-activated materials. [PERSON] and [PERSON] are acknowledged for experimental support in filtration tests and XRD.  
The authors declare no conflict of interest.
## 3 Results and Discussion
### Characterization Results
#### 3.1.2 Scanning Electron Microscope (SEM)-EDX Characterization  
Saudi low-grade pyrophyllite SEM images are shown in Figure 3. Pyrophylite has a fine schistose texture, which agrees well with the results of the microscopic study. It can be  
Figure 1: Photomicrographs of Saudi low-grade pyrophyllite ore.  
Figure 2: Photomicrographs of impurities in Saudi low-grade pyrophyllite ore: pyrite is gray in color and hematite is brown in color.  
seen that the pyrophyllite in the particles is highly foliated. Moreover, the particles were fine and had anhedral flakes arranged with sizes of about 2-4 \(\upmu\)m in width and 0.1 \(\upmu\)m in thickness.  
The EDX pattern of pyrophyllite ore reveals peaks corresponding to aluminum and silicon, indicating the presence of alumina and silicates, which are the primary components of pyrophyllite, as shown in Figure 4a,b. The association of potassium (K) with aluminum (Al) and silicon (Si) suggests the presence of feldspar, muscovite, and sericite. The low occurrence of iron (Fe) indicates that Fe exists as an impurity.
# Direct Temperature Measurements of Cardiac Stent during MRI Examinations
## 4 Discussion
The temperature rise found in the postmortem test was comparable to that found in our study. In [24], the authors investigated the heating of a coronary stent during an MRI examination (4.7 T and 200 MHz of proton resonance frequency). The experimental data were used for theoretical computation and the results, in the normal situation of a fully open stent, showed an increase in stent temperature of less of 1 \({}^{\circ}\)C. They also concluded that there is a risk of hazardous heating when the stent in the vessel partially reoccluded. The experimental results presented were valid for the particular geometrical parameters(length and diameter) and material of the stent, the exposure system and the average RF irradiation power. Despite the different test conditions with respect to our study, we found a very similar temperature trend. In [33], the authors showed the cooling effect of the flow on RF-induced heating of a vascular stent. The temperature rise was less than 4 \({}^{\circ}\)C. Our results show a 1D temperature rise trend on the stent length very similar to that it is possible to obtain from the simulated 2D map of temperature rise on stent. In [32], a numerical model of an RF birdcage body coil and an anatomically realistic virtual patient were used to simulate the in vivo electric field at 64 MHz (1.5 T MRI system). The results showed that peak SAR values in blood vessels were up to ten times higher than those found in the ASTM standard gel phantom. Peaks were found in the anatomical locations, where stents are usually implanted. The above results indicate that the blood flow has a significant cooling effect that reduces the overall temperature rise of a vascular stent due to RF emission during the MR exam. On the other hand, the scenario changes in the event of partial reocclusion of the stent. In particular, the increase in temperature due to RF may enable protein coagulation and the formation of blood dots in stent surrounding (near the stent tips).