esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Early-Warning Systems
### Infrasonic System (IS)  
The IS consists of two devices (Figure 1a,b): one infrasonic array, ETN, was deployed in September 2007 on the southern edge of the Valle del Bove, at an elevation of 2100 m a.s.l. and 5500 m from the summit craters; the second array, MVT, was installed in 2012 in the forested area of Monte Vetore, on the south flank of Etna volcano at an elevation of 1800 m a.s.l, about 6500 m from the summit craters. Both arrays have a triangular geometry (Figure 1c for MVT and Figure 1d for ETN) and are equipped with four differential pressure transducers (all sensors--1 MBAR-D-4V), with a sensitivity of 25 mV/Pa, able to detect atmospheric pressure fluctuations in the range of + / - 100 Pa. Sensors have a flat response between 0.01 Hz and 20 Hz and are installed within waterproof cases buried \(\sim\)1 m deep. For both arrays, the minimum detectable pressure is approximately \(10^{-2}\) Pa. The limited power requirement of the array (<1 W) and the use of fiber optic has allowed, for the last 10 years, an operative efficiency of \(>\)95% [32]. Both arrays are displaced in the Etna summit area, as highlighted in Figure 1e, about 30 km from Catania city.  
For all detections having values of back-azimuth and apparent velocity consistent with the position of the summit craters, we estimated an infrasonic parameter (IP), obtained as the product between the mean infrasonic amplitude (\(A_{P}\)) and the number of detections (\(N_{D}\)) per minute (IP = \(A_{P}\times N_{D}\)), as described in [6]. The IP is strongly related to the persistence of the infrasound signal, and here, it is used to derive the EW.  
The efficiency of the IS in the evaluation of EW has already been tested for several explosive activities of Etna [6]. When the explosive activity increases, the IP increases with respect to the value of \(-1\), which occurs when in one minute there is no infrasound data detection, and this allows to identify the onset of the Strombolian activity. The  
Figure 1: (**a**) The DEM of Etna located in southern Italy, showing the summit craters (Southeast crater, SE; Bocca Nuova, BN; Voragine, VOR; Northeast crater, NE) and the locations of the two infrasound arrays (ETN, MVT), from [6]. (**b**) Etna location in Sicily, Italy. Infrasonic arrays at Etna with a triangular geometry and a maximum aperture of 150 m for the MVT array and 250 m for the ETN array, panels are shown in (**c**,**d**), where m1, m2, m3, and m4 identify respective differential pressure transducers. (**e**) A map of the area, which includes all the sensors used in this work.  
infrasonic system-early warning (IS-EW) operates on two IP thresholds that correspond to the beginning of the precursory violent Strombolian phase (EW1), when IP > 60 for more than 5 min (orange in Figure 2), and to the onset of the lava fountain (EW2), when IP > 120 for more than 5 min (red in Figure 2). These two thresholds were used to define two alert levels in the EW procedure and to deliver automatic notifications. The latency of the automatic notification for lava fountains at Etna was below 2 min [6].
# The Relationship between Radon and Geology: Sources, Transport and Indoor Accumulation
## 4 Risk Assessment and Mitigation
### Techniques Used to Measure Radon Concentration in Rocks and Soils
#### 4.1.4 Gamma Spectroscopy  
Gamma spectroscopy is an indirect technique for measuring the concentration of radon in rocks and soils, and is based on the detection of gamma radiation emitted by the radioactive decay products of radon, such as lead-214 and bismuth-214 [175]. Gamma spectroscopy can be performed in situ or in the laboratory using collected samples. While this technique can provide useful information about the presence of uranium, thorium, and other radioactive elements, it does not directly measure the concentration of radon and may be less precise than other techniques [176]. However, gamma spectroscopy can be used as a complementary technique to measure radon indirectly by detecting the decay products of radon, which are also radioactive and emit gamma radiation [177]. This technique can be particularly useful in areas with complex geology, where the concentration of radon may vary widely depending on local geological factors [178].
## 7 Iridium  
### Transfer Dehydrogenation  
[PERSON] _et al._ have investigated three different types of supported iridium pincer complexes for the transfer dehydrogenation of alkanes, comparing covalent attachment of the complex to Merrifield resin or silica, to adsorption onto \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) (Scheme 26) [77]. Cyclooctane was used as the alkane partner in the reaction, together with _tert_-butylethylene as the hydrogen acceptor, forming cyclooctene and 2,2-dimethylbutane. Application of polystyrene-supported complex **37** in the reaction afforded 85% of 2,2-dimethylbutane after 2 days. However, a second reaction using recovered catalyst afforded only 20% product, indicating decomposition of the iridium pincer complex. Overall, the catalyst adsorbed onto \(\gamma\)-Al\({}_{2}\)O\({}_{3}\) was found to be more robust and efficient than the covalently bound catalysts in this case.  
**Scheme 26.** Transfer hydrogenation employing a polymer-supported iridium pincer complex.
# Innovative Process for Dried Caper (_Capparis spinosa_ L.) Powder Production
## 3 Results and Discussion
### Chemical Data
#### 3.2.2. Polyphenols, Flavononoids, and Antioxidant Capacity  
The polyphenol and flavonoid content and antioxidant capacity in the dried powder and undried caper samples are also reported in Table 1. In our samples, the total amounts of flavonoids and polyphenols are similar to those reported by [PERSON] (1999) and [PERSON] et al. (2016) for dry-salted capers. Regarding the total polyphenol content, no statistically significant differences were observed among the samples, except for the DrS samples (40d), which showed the lowest amount at both drying temperatures. The flavonoid content increased in the BrS (Rt) and BrS (60 \({}^{\circ}\)C) powders compared with the undried capers; for both trials, a higher temperature determined a greater flavonoid content, which was probably due to the thermal destruction of cell walls and subcellular compartments during the heat process that favored their release ([PERSON] et al., 2016). This behavior was opposite to that observed by [PERSON] et al. (2016), who found a lower content in primed and dry salted samples; however, the same authors affirmed that the polyphenol and flavonoid amounts decreased following the fermentation process. In fact, as reported in literature, their amount could be influenced by several factors such as the extraction and analysis methods, harvest regions, and processing since a portion of the polyphenols and flavonoids could be released into the brine or salt ([PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016; [PERSON] et al., 2016).  
Regarding the antioxidant capacity (AC), the DrS (10d) samples showed the highest values, even higher than the undried capers. Generally, a high drying temperature negatively influences antioxidant activity due to the degradation of thermolabile non-phenolic antioxidants ([PERSON] et al., 2016). In our case, the high drying temperature did not negatively influence the AC, except for the BrS (60 \({}^{\circ}\)C) samples.
## 2 The Overall Structure of the Control System
### Control Methods
#### 2.1.2 A Maximum Power Point Tracking (\(Mppt\)) Method  
Maximum power point tracking (\(MPPT\)) methods are widely used by renewable technologies to improve their energy efficiency. Examples include photovoltaic systems [30], thermoelectric generators [31], and wind turbines [32]. The most commonly used \(MPPT\) method is the Perturb & Observe (\(P\&O\)) method [33]. The principle of operation is based on disturbing, accelerating, or decelerating the rotational speed of an \(AST\) and analysing the change in power output, measured at the converter. If the power increases, the perturbation is maintained and the procedure continues. Otherwise, the disturbance is reversed [34]. There are always variations in the \(MPP\) with this process. There are two main advantages to this method: simplicity and ease of application. Due to the principle of operation, the disadvantages are the production of steady-state oscillation and slow dynamic performance during maximum power point tracking.  
The \(MPPT\) method acts on the product of the two parameters of Equation (2): the head (\(h\)) and the flow rate (\(q\)). Therefore, this control system ensures that the mechanical power of Equation (2) is maximised. For this purpose, the speed of the electrical generator shall be increased or decreased.  
An algorithm is used to find the drive speed that leads to maximum power generation. This algorithm is implemented by Codesys V3.5 software in the actual power converter driving the generator. Figure 4 shows how an \(MPTT\) algorithm can be implemented.  
Figure 3: A diagram of a water level control system.
# A Deep Learning Method for Lightweight and Cross-Device IoT Botnet Detection +
## 5 Results
### Separate Autoencoding  
We process the test set of a given device after the AE is retrained for that specific device beforehand. Table 6 provides the evaluation metrics for all the IoT devices. The results show that the use of individual AEs leads to high detection figures. The recall values range from 0.9995 (Samsung SHH 1011 N webcam) to 1.0 (Dammini doorbell). The minimum false positive rate (0.0002) is obtained for the Provision PT-737E security camera. Overall, the results are outstanding for all the IoT devices, with recall and precision close to 1.0 with a reasonable false positive rate. Moreover, the F1 score--the harmonic mean of precision and recall--is computed as a key metric, since precision and recall are both relevant indicators of the model performance. From the analysis of the values in Table 6, it is possible to note that the F1 score is within 0.9997-0.9999, i.e., close to 1.0. This result indicates a good trade-off between precision and recall.  
It is worth pointing out that the rightmost column of Table 6 reports the network configuration used for each IoT device. In particular, as highlighted in Section 4, _Configuration A_ refers to a \(N\)-48-6-48-\(N\) network design, while _Configuration B_ refers to a \(N\)-64-6-48-\(N\) network design. We emphasize that the use of multiple AE designs is almost mandatory, since it was not trivial to find a _\"one fits all\"_ configuration for all the IoT devices. As previously explained, the selection of two \"best\" configurations--\(A\) and \(B\)--is guided by experimental tests carried out by analyzing the RE figures obtained by multiple AE models.  
**Finding:** Different from similar proposals in the area, which rely on complex cascades and ensembles of autoencoders--possibly complemented by the use of feature selection methods--if not other schemes, such as CNNs and LSTMs, a \"minimal\" and simple autoencoder with three hidden layers is more than enough to obtain remarkable results when train-test is performed separately for each device.  
Overall, the results obtained are notable. However, the hypothesis of training an AE for each device remains unrealistic, especially if the intrusion detection system is intended to be deployed in time-dependent and rapidly evolving IoT environments.
## 4 Experiments
### Baselines  
The following is a description of the models compared in this paper:  
1. Static Average Item Rating (Avg): Avg is a simple recommendation method that calculates the average rating of each item based on historical user ratings.
2. Time-Aware Average Item Rating (T-Avg): T-Avg extends the traditional average item rating by incorporating temporal dynamics. It calculates item ratings based on recent user interactions, giving higher weight to more recent data.
3. Static Matrix Factorization (MF): This refers to a conventional matrix factorization model that presumes the selection bias remains constant over time.
4. Time-Aware Matrix Factorization (TMF) [17]: TMF is an advanced recommendation method that incorporates temporal dynamics into matrix factorization. It models how user preferences and item characteristics evolve over time by assigning time-dependent weights to user-item interactions.
5. MF-StaticIPS [5]: MF-StaticIPS is a recommendation system approach that combines matrix factorization (MF) with static inverse propensity scoring (Static IPS).
6. TMF-StaticIPS [32]: TMF-StaticIPS is a recommendation system approach that integrates temporal matrix factorization (TMF) with static inverse propensity scoring (Static IPS).
7. MF-DANCER [8]: MF-DANCER is a recommendation system method that combines matrix factorization removal and enhanced negative sampling. It corrects selection bias and addresses data sparsity by modeling bias and improving negative sampling.
8. TMF-DANCER [8]: TMF-DANCER extends MF-DANCER by incorporating temporal dynamics into matrix factorization.
9. Causal Intervention for Sentiment Debiasing (CISD) [33]: CISD aims to eliminate sentiment bias through causal inference. The model comprises two components: during the training phase, causal intervention is employed to block the influence of sentiment polarity on user and item representations, thereby reducing confounding effects; during the inference phase, adjusted sentiment information is introduced to enhance the personalization and accuracy of recommendations.  
\begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline CPU & 12 vCPU Intel(R) Xeon(R) Platinum 8352V CPU \\  & @ 2.10 GHz \\ Memory & 90 GB \\ GPU & NVIDIA RTX 3090 (24 GB) \\ CUDA & 11.7 \\ Python & 3.10 \\ PyTorch & 1.12.1 + cu116 \\ Transformers & 4.21.3 \\ NLTK & 3.7 \\ Numpy & 1.22.4 \\ \hline \end{tabular}
\end{table}
Table 2: Experimental environment.
## 5 Conclusions, Limitations and Future Research  
This study proposed a new approach to using social behaviors, reflected in consumption data, as a means to assess the academic performance of college students. The findings indicate significant differences in social behaviors among student groups with varying academic performance, and that the richness and quality of social behaviors have a substantial impact on academic performance. Additionally, social data have proven effective in predicting academic performance. Campus card usage data are easy to collect and provide rich behavioral information. Combined with the generalizability of machine learning techniques, the predictive model developed in this study has significant practical value for educational management, especially in residential universities. We expect that this study will stimulate further research interest in the rational application of campus big data in educational management.  
There are several limitations to this study. First, despite the large volume of consumption data, these data still only capture some of the students' social behaviors, while others, such as playing games together, going to the library, and exercising, cannot be captured. Second, although this study compared changes in the number of students' social relationships across semesters, it did not analyze them from a community perspective. Despite these limitations, this study demonstrates the potential of behavioral big data to analyze college student peer relationships and provide insights into instructional management practices.  
Future research could incorporate more behavioral characteristics and explore additional directions to provide educators with more comprehensive references for student management. With the gradual improvement of campus informatization, a wider variety of data will become available, such as demographic data, internet usage, movement trajectories, book borrowing records, and reward and punishment histories. All of these can be used to analyze student characteristics and behaviors according to actual situations. In terms of application direction, the research should not be limited to predicting academic performance, but can also explore predictions regarding mental health status, emergencies, and other aspects.  
Conceptualization, J.T.; Data curation, J.T.; Investigation, J.L.; Methodology, J.T.; Project administration, D.L.; Resources, J.L.; Software, J.T.; Supervision, D.L.; Writing--original draft, J.T.; Writing--review and editing, J.T. and J.L. 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. The data are not publicly available due to privacy restrictions.  
We thank the reviewers for their insightful comments on the restructuring of the article, which greatly optimised the article structure.  
The authors declare no conflicts of interest.
## 3 Results and Discussion  
In this section, comprehensive experimental evaluations were conducted using our custom-developed RS dataset, ACCF, alongside the LoveDA, LandCoverNet, and PASCAL-VOC datasets. Furthermore, the proposed methodology of this paper was juxtaposed against a spectrum of contemporary RS semantic segmentation models. This comparative analysis was undertaken to substantiate the efficacy of the RRMSA-Net framework.
# Satellite-Aided Consensus Protocol for Scalable Blockchains
###### Abstract  
In this work, we propose a satellite-aided permissionless consensus protocol for scalable space-terrestrial blockchains. We design its working principle and workflow by taking full advantage of satellites for extensive coverage and ubiquitous connectivity. Based on the proposed protocol, we demonstrate how such a space-terrestrial blockchain grows and evolves through several typical cases in the presence of adversarial nodes, user misbehavior, and transmission outage. Taking proof of work (PoW) as a benchmark, we assess the system security by considering both adversarial miners and possible colluding satellites. Then, we analyze the maximum blockchain throughput under network capacity limits and evaluate the impact of information propagation delay via a Markov model. Simulation results support that the proposed satellite-aided consensus protocol achieves higher throughput and exhibits greater scalability than PoW.  
Keywords:blockchain; consensus protocol; performance analysis; satellite; space-terrestrial network +
Footnote †: journal: Sensors
## 4 Blasting Experiment
### Experimental Results
#### 4.3.1 Residual Gas Pressure Behavior Analysis  
Table 4 shows the outcomes of analyzing how the residual gas pressure behaves in the shock chamber. A PE sensor was utilized to evaluate how well the stemming materials controlled the pressure from blasting gas. This evaluation was based on when the peak pressure occurred in the shock chamber or when the gas pressure returned to its level.  
The performance of the restraint was then assessed. The study showed that the configuration using \"Sand\" reached a voltage of 2.531 V, while the \"STF + Sand\" setup achieved a peak voltage of 3.172 V. There was no difference in peak voltage (3.162 V) between the spike plug case and the STF and sand stemming scenarios. However, the configuration with \"STF + Twist Plug + Sand\" displayed peak voltage at 3.529 V. The waveforms captured by the measuring tools for all test scenarios  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Peak Voltage (V)** & \multicolumn{2}{c}{**Time of Return to**} \\  & & **Atmospheric Pressure (ms)** \\ \hline Only Sand & 2.531 & 4.425 \\ STF + Sand & 3.172 & 4.921 \\ STF + Spike plug + Sand & 3.162 & 4.781 \\ STF + Twist Plug + Sand & 3.529 & 5.044 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the residual gas pressure behavior analysis using voltage data.  
Figure 9: Shock chamber measurement system schematic. (**a**) Experimental measurement schematic and (**b**) shock chamber measurement system image.  
After the NRC explosion, the highest voltage recorded and how long the gas lingers in the blasting hole demonstrate how well the stemming material resists being pushed out by pressure. A higher peak voltage indicates an impact that creates cracks in the rock when the shockwave from detonation spreads, showing that the stemming material can contain the impact within the blasting hole. Additionally, the time it takes for gas pressure to settle to zero in the blasting hole indicates how long the gas pressure is sustained. The longer the gas pressure remains in the blasting hole, the better the blasting efficiency is believed to be. Hence, if it takes time for the pressure to drop back to zero, it means the gas stays longer in the blasting hole, which signifies blasting efficiency.  
In an experiment testing stemming materials, it was found that the \"STF + Twist Plug + Sand\" combination prolonged both the gas duration and high gas-pressure levels compared to stems used in this study.
# On the Use of Bright Scatterers for Monitoring Doppler, Dual-Polarization Weather Radars
## 3 Results  
The present section is aimed at providing a concise description of the experimental results and their interpretation. It is divided into six subheadings that correspond to the six radar observables introduced in Section 2.2, that is, the 2 nd and the 1 st Doppler moments of the horizontal polarization, backscattered power of both orthogonal polarizations, followed by three polarimetric quantities (differential reflectivity, copolar correlation coefficient and differential phase shift). As stated in the introduction, when the 5-min power returns are analyzed, a random scattering pattern can be expected that is characterized by rapid short-term fluctuations; the phase (2 nd and the 1 st Doppler moments) is expected to be quite stable because there are rarely moving objects inside the 314 \(\times\) 314 \(\times\) 83.33 m radar cells during both calm and windy days. As stated at the end of the introductory section, the results are based on the analysis of five clear sky days; since the sampling is performed every 5 min, there are 1440 samples per day. In turn, each \(1^{\circ}\) sample has been derived by the radar signal processor using 33 pulses.