esa-sceva/satcom-chunk-collection huggingface dataset:
# Analysis of Lightweight Feature Vectors for Attack Detection in Network Traffic
## 2 Feature Sets
### Unsw-Nb15 Argus/Bro Feature Vector  
The UNSW-NB15 Argus/Bro vector is the analysis format chosen by the authors who published the dataset used in the experiments. The dataset itself is introduced later in Section 3. [PERSON] and [PERSON] [5; 6] extracted features from raw _pcaps_ with Argus [[https://qosient.com/argus/](https://qosient.com/argus/)]([https://qosient.com/argus/](https://qosient.com/argus/)) and Bro [[https://www.bro.org/](https://www.bro.org/)]([https://www.bro.org/](https://www.bro.org/)), which are popular tools for network security and have been widely used in research for many years [7; 8]. The meaning and description of features are provided in the referred works [5; 6], and we show them here in Table 1. We keep this feature vector for our analysis since: (1) It is the one proposed by dataset authors, (2) features are extracted with such popular network security tools, and (3) this vector is being used by other security experts that also use the UNSW-NB15 dataset for their research, e.g., the authors of [9]. The UNSW-NB15 Argus/Bro vector consists of 45 predictors (i.e., features used to guess the sample class), and mixes nominal, numerical, and binary features.
# Sesame Plant Disease Classification Using Deep Convolution Neural Networks
## 4 Model Evaluation and Discussion
### Discussion
#### 4.4.3 Sample Classification of Sesame Plant Diseases  
The Figure 11 shows the graphical user interface of the developed model, which shows an example of the classification of sesame plant diseases. The classification result is displayed at the bottom of the interface.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Trained with the Original Images**} \\ \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Testing Accuracy** & **Training Accuracy** & **Validation Accuracy** \\ \hline Proposed model & 0.90 & 0.90 & 0.90 & 90.0\% & 94.0\% & 92.2\% \\ \hline InceptionV3 & 0.73 & 0.73 & 0.73 & 73.3\% & 82.0\% & 75.6\% \\ \hline Xception & 0.89 & 0.89 & 0.89 & 88.9\% & 92.0\% & 86.7\% \\ \hline \multicolumn{6}{c}{**Trained with Preprocessed and Segmented Images**} \\ \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Testing Accuracy** & **Training Accuracy** & **Validation Accuracy** \\ \hline Proposed model & 0.94 & 0.94 & 0.94 & 94.4\% & 96.0\% & 95.6\% \\ \hline InceptionV3 & 0.82 & 0.82 & 0.82 & 82.2\% & 92.2\% & 86.7\% \\ \hline Xception & 0.90 & 0.90 & 0.90 & 90.0\% & 92.0\% & 90.0\% \\ \hline \multicolumn{6}{c}{**Trained with Augmented Images**} \\ \hline
**Model** & **Precision** & **Recall** & **F1-Score** & **Testing Accuracy** & **Training Accuracy** & **Validation Accuracy** \\ \hline Proposed model & 0.97 & 0.97 & 0.97 & 96.7\% & 98.0\% & 97.7\% \\ \hline InceptionV3 & 0.89 & 0.89 & 0.89 & 88.9\% & 92.0\% & 88.9\% \\ \hline Xception & 0.93 & 0.93 & 0.93 & 93.3\% & 94.0\% & 90.0\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Summary performance result of proposed model, Xception, and Inception-V3.  
Figure 11 illustrates the categorization of sesame leaf disease through a system based on image processing and deep learning techniques. The process starts by uploading an image that displays a sesame leaf exhibiting clear disease symptoms. The image subsequently goes through preprocessing, which generally involves converting the image to grayscale, reducing noise, and enhancing contrast to emphasize features related to the disease. Subsequently, segmentation is carried out to distinguish the affected areas from the healthy sections of the leaf, facilitating the extraction of significant features for classification. Once these steps are completed, the system identifies the condition as \"Bacteria Blight Infected\", signifying that the sesame leaf is impacted by bacterial blight. The interface features buttons for uploading images, preprocessing, segmenting, classifying, and resetting the process, maintaining an organized workflow. The findings indicate that the system effectively recognizes and categorizes diseases, supporting early identification and possible management approaches.
# A Review of Requirements for Gamma Radiation Detection in Space Using CubeSats
## 5 The Geant4 Simulation for the Detection of Short Burst of Gamma-Rays
Figure 15: Extra-galactic photons flux as modeled by Integral [30] (up to 890 keV) and FERMI-LAT [31] at higher energies. To prove the discrimination capability, a 175 \(\upmu\)s duration event consisting of eight gamma-rays generated in the energy range between 30 keV and 30 MeV has been used to contaminate the pure background spectrum. In order to reject background events, a selection of data reduction rules has been applied to the 1,000,000 event sample. Table 4 shows the data reduction protocol and its effect. The expected event signature consists of multiple gamma-ray interactions characterized by no energy release in the VETO and at least one detection unit fired with deposited energy less than 30 MeV (for higher energy depositions, the electronics is expected to saturate), and detected within 1 millisecond time window span. By using the set of data constraints presented in Table 4, the event is localized in time, within the 1 ms time window. Figure 17 shows the signal output for each detector channel in correspondence of the event. The number of gamma-rays can be inferred by counting number of peaks in each quadrant, while the energy released in the interactions is proportional to the peak amplitude. The two highlighted peaks (red star) in CH3 and CH4 correspond to two gamma-rays simultaneously detected: in this case, either the arrival time delay between two gammas is less than 100 ns (the minimal time granularity corresponding to the 10 MHz ADC sampling rate considered in the simulation) or a gamma-ray interacted via the Compton process in one channel and eventually released energy in the other. This latter option has been confirmed by looking at time structure of the event used to contaminate the background sample. Note that the number of gamma-rays ultimately detected (6) is less than the ones emitted at the source (8): the details of the simulation confirm that the first missing gamma-ray has been absorbed by the passive material shielding the target, while the second passed unseen through the detector. \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Reduction Rule** & **Surviving Events** \\ \hline CUT1: Selection of all the Events with VETO OFF & 911516 \\ \hline CUT2: CUT1 and at least one detection channel ON & 365142 \\ \hline CUT3: CUT2 and deposited energy less than 30 MeV & 352795 \\ \hline CUT4: CUT3 and at least 2 events within 1 ms & 6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Data reduction protocol used in order to discriminate the event from the particle background composed of 1,000,000 of events. Figure 16: The expected response of a detection channel for the first 2 s of mission. The horizontal red line represents the ADC limit: each peak that crosses such limit corresponds to a saturated event (mostly protons). By randomly changing the event features, the discrimination efficiency provided by the reduction data protocol presented in Table 4 has been assessed and it is larger than 90%.
# Lossless WDM PON Photonic Integrated Receivers Including SOAs
## 3 SOA Design, Modelling and Analysis
\begin{table}
\begin{tabular}{c c c c} \hline
**Symbol** & **Description** & **Value** & **Unit** \\ \hline \(\lambda_{p}\) & Peak wavelength & 1500 & nm \\ \(\lambda_{gap}\) & bandgap wavelength & 1655 & nm \\ \(R_{1}=R_{2}\) & Facet reflectivities & \(4\times 10^{-4}\) & – \\ \(\overline{n}\) & Average refractive index & 3.45 & – \\ \(a\) & differential gain coefficient & \(1.67\times 10^{-16}\) & cm\({}^{2}\) \\ \(n_{tr}\) & Transparency carrier density & \(0.47\times 10^{18}\) & cm\({}^{-3}\) \\ \(\alpha\) & Internal loss coefficient & 6 & cm\({}^{-1}\) \\ \(\tau_{c}\) & Free carrier life time & 200 & ps \\ \(L\) & Length of active region & 1600 & \(\mu\)m \\ \(w\) & Width of active region & 0.45 & \(\mu\)m \\ \(d\) & Thickness of active region & 0.39 & \(\mu\)m \\ \(E_{g0}\) & Band gap energy & 0.74 & ev \\ \(I\) & Inject current dc bias & 500 & mA \\ \(G_{gp}\) & Single pass peak Gain & 16.5 & dB \\ \hline \end{tabular}
\end{table} 
Table 1: SOA geometrical and material parameters used in the simulation. Figure 4: SOA gain as a function of input power. Simulation (solid line) and measured (dots) for wavelength = 1550 nm. where \(n_{sp}\) is the spontaneous emission factor and \(BW_{o}\) is the optical filter bandwidth. The factor of two accounts for the unpolarized nature of ASE [40]. The effects of ASE on the carrier dynamics in the semiconductor optical amplifier can be accounted for by using the models presented in [41; 42].
###### Abstract  
The optimization of energy consumption in response to global warming scenarios presents fundamental challenges in the built environment, particularly in Mediterranean climates, where comfort and energy efficiency require priority-based adaptation. This study examines the effectiveness of passive energy retrofit strategies applied to an educational building in Granada, Spain, accommodating both teaching and residential uses. The research uses advanced climatic data based on Shared Socioeconomic Pathways (SSPs), incorporating precise projections of climate evolution. Using simulations conducted in DesignBuilder, it evaluates three intervention packages for the building envelope--window replacement, facade insulation, and roof insulation--across three temporal scenarios: 2024, 2050, and 2080. The results indicate that passive measures could reduce heating demand by up to 90% in future scenarios, while cooling demand is projected to increase by more than 80% by the end of the century. Additionally, climate projections under the SSP scenarios show up to an 83% increase in energy demand, emphasizing the need for integrated passive and active strategies. The research includes a sensitivity analysis of the interaction between passive strategies and advanced climate scenarios. It offers decision-making models for energy retrofitting and provides replicable key insights to support energy retrofitting policies and climate resilience in the Mediterranean region.  
energy retrofitting; passive strategies; SSP climate scenarios; energy efficiency; built environment; schools; climate change Article
# Experimental Study and Modelling on the Structural Response of Fiber Reinforced Concrete Beams
## 2 Materials and Methods
### Testing Technological Properties  
Slump Cone Test Slump cone test is one of the tests done to check the workability of a concrete [53]. The workability of the FRC mix was evaluated by means of [PERSON]'s cone slump test in accordance with EN 12350-2. Additionally, time to final cone spread was determined as an additional flowability parameter. Measurements were done for all three FRC groups.  
Sieve Segregation Test  
The test aims to investigate the resistance of high performance concrete (HPC) to segregation [54] by measuring a portion of a prepared high performance concrete specimen passing through a 5 mm sieve. If the HPC has a weak resistance to segregation, the paste passes easily through the sieve. Thus, the sieved portion indicates whether the HPC is stable or not. This test was also performed for all concrete groups.  
The equipment required for this test includes: (1) a sieve with a perforated plate with 5 mm square holes, (2) a frame with a \(\O\) = 300 \(\pm\) 1 mm, a height is 40 \(\pm\) 0.5 mm or \(\O\) = 315 \(\pm\) 1 mm, a height is 75 \(\pm\) 0.5 mm, (3) a tray of a shape, with volume suitable to hold the materials passing through the sieve and easily removed by the operator without forcing the passage of excess materials, (4) a digital type scale with a capacity \(\approx\)10 kg, which can be zeroed, (5) a hard plastic or metal bucket with a maximum inside diameter of 300 \(\pm\) 10 mm and a capacity of 10 \(\div\) 12 L and a top suitable to cover the bucket to protect fresh concrete from intensive drying.  
The scale was placed in a stable and aligned position. 10 \(\pm\) 0.5 L of representative fresh FRC was poured into the beaker and covered with a top. It was left to stand for 15 \(\pm\) 0.5 min. During the waiting period, only the pot was weighed; the weight was noted as Wp. The sieve was placed on the pot without removing it from the scale. The bucket was filled after 15 \(\pm\) 0.5 min. The surface of the concrete in the bucket was inspected for clear water leaking out, and the result was recorded. The scale was zeroed, and concrete in the amount of 4.8 \(\pm\) 0.2 kg was poured onto the central part of the sieve from a height of 50 \(\pm\) 5 cm. The weight of the concrete poured (on the sieve) was noted as Wc. Two minutes later the concrete was poured, the sieve was carefully removed from the pan without any shaking that could force excess material through the sieve. The pan with the sifted materials was weighed and the weight was noted as Wps.  
The sifted part, the mass fraction of the sample that passed through the sieve, was calculated according to the equation \(\Pi\) = ((Wps - Wp)/Wc) \(\times\) 100, expressing the result in percent.
# A Survey of Machine Learning Models in Renewable Energy Predictions
## 2 Machine-Learning Models in Renewable-Energy Predictions
### Data Preprocessing Techniques
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Pre-Processing Techniques** & **Solar** & **Wind** & **Biomass** & **Wave** & **Tidal** & **Total** \\ \hline Data splitting & 1 & 1 & 1 & & & 3 \\ kWMD & 1 & & & & & 1 \\ Decomposition & 6 & 13 & & 1 & 21 \\ Discretization & 3 & & & & 1 & 4 \\ Encoding categorical features & 1 & & & & 1 \\ Feature selection & 1 & 1 & & & 2 \\ Imputation of missing values & 2 & & & & 2 \\ Normalization & 2 & 6 & & & 8 \\ Standardization & 1 & 1 & & & 2 \\ Transformation & 1 & & & & 1 \\ Total & 19 & 22 & 1 & 1 & 2 & 45 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Pre-processing techniques used by machine-learning models in renewable-energy predictions with various renewable energy sources. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Pre-Processing Techniques** & **Number of Papers Used** & **References** \\ \hline Data splitting & 3 & [57, 95, 129] \\ kWMD & 1 & [71] \\ Decomposition & 21 & [25, 27, 23, 49, 50, 51, 54, 56
## 3 Numerical Calculation and Simulation Analysis
### Multiple Model Comparative Analysis
#### 3.2.1 With or Without Subsidy Comparison  
To verify whether subsidies have a positive effect, the operating subsidy was chosen as representative to compare with the subsidy-free scenario for convenient appraisal and analysis, as shown in Figure 7.  
In the aspect of ecological level, the supply chain with the government subsidy is more ecological than without the subsidy, and the local optimal solution can be obtained through the optimization of the government subsidy coefficient and the cost-sharing contract coefficient, as shown in Figure 7a. In addition, the government subsidy can increase the total revenue of the supply chain in most instances, as shown in Figure 7b, and is regarded as a positive incentive to improve the eco-product stakeholders' enthusiasm.
## 3 Results
### Changes in the Composition of the Microbial Community during the Degradation of 170 mM Butyrate  
Samples of enrichment culture for DNA extraction were taken twice on days 20 and 120, and it can be seen that at the first stage of butyrate degradation, the microbial community was more diverse than at the end of the experiment (Figures 2-4, Table S1).  
Among methanogenic archaea, _Methanosarcina thermophila_ TM-1 (100% 16S rRNA similarity) dominated (86% of cloned archaeal sequences) on day 20 of the experiment with 170 mM butyrate (Table S1, Figure 4). Acetoclastic methanogens related to _Methanodtrix thermophila_ PT (95.8% 16S rRNA similarity) were a minor part of the community responsible for 4.3% of the archaeal sequences. The bacterial community was mainly dominated by _Syntroplacicus_ (30.4%) and _Anarobaculum_ (38.0%) with the lower content of _Anarolineae_ (15.3%).  
The composition of the archaeal part of the community changed on day 120. The share of methanogens related to _Methanosarcina thermophila_ decreased to 25%, while the abundance of _Methanothermobacter thermautotrophic_ and _Methanomassilicoccus luminysensis_ became more numerous (53.1 and 21.9%, respectively) (Table S1). Similarly, the composition of the bacterial part of the community changed by the end of the experiment (day 120). Syntrophic acetate-oxidizing bacteria (SAOB) related to _Syntroplacicus schinki_ and _Thermacetogenium__phatum_ became more abundant (43 and 7% of cloned bacterial sequences, respectively). Over 26% of the sequences belonged to unclassified _Firmicutes_, in which syntrophic bacteria are common. Oppositely, the abundance of bacteria related to _Anarobachum hydrogeniformans_ and _Anarolineae_ decreased to 14.3 and 7.1%, respectively (Figures 2 and 3).  
Figure 3: Phylogenetic tree of bacterial 16S rRNA gene sequence of representative clones of each OTU and related type strains of the most represented bacterial groups of the enrichment culture. The scale bar represents 5% sequence divergence.  
Figure 2: Relative abundance of bacterial (**A**) and archaeal (**B**) community at the genus level in the enrichment culture during the degradation of 170 mM butyrate.  
The results obtained by cloning indicate that high acetate accumulation causes a shift of the major methanogenic pathway from acetoclastic to hydrogenotrophic coupled with syntrophic acetate oxidation. An increase in acetate concentration was the factor driving the changes in the composition of the microbial community.
## 4 Discussion
The clinical implications of this study are significant, as it demonstrates that aligner retention is influenced not only by the design and material of the aligner but also by the type of malocclusion and the characteristics of teeth. Therefore, when planning the setup, various factors must be considered for the optimal placement of grip points. In Noxi aligners with high margins with both horizontal and vertical gradients, in some situations such as dental crowding, grip points can be reduced or even eliminated, thanks to its enhanced retention. The enhanced retention provides improved fitting and minimizes force dissipation. This characteristic allows these aligners to be worn for fewer hours daily, potentially lowering the compliance demands on the patient. This study has several limitations that should be considered when interpreting the results. First, it is an in vitro analysis, which, while enabling rigorous control of experimental variables, does not fully replicate clinical conditions. Validation through clinical studies is, therefore, essential. Second, the selection of occlusal models may have introduced bias, potentially limiting the generalizability of the findings to a broader patient population. Finally, while the configurations tested were comprehensive, they do not encompass all possible clinical scenarios due to additional variables influencing aligner retention. Future research should focus on longitudinal clinical studies to assess the durability and long-term effectiveness of Noxi aligners. Aspects related to patient compliance should also be investigated, leveraging the support that artificial intelligence provides in remote monitoring. Another potential direction for future research could involve using artificial intelligence to customize the design of these aligners and their differentiated thicknesses based on the characteristics of each patient.
# Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors
###### Abstract  
Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology--an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy--to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/- 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.  
Keywords: phenology; scaling; RGB imagery; remote sensing; NDVI +
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