esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of Network Intrusion Detection Systems Using Artificial Intelligence/Machine Learning
###### Abstract  
The rapid growth of the Internet and communications has resulted in a huge increase in transmitted data. These data are coveted by attackers and they continuously create novel attacks to steal or corrupt these data. The growth of these attacks is an issue for the security of our systems and represents one of the biggest challenges for intrusion detection. An intrusion detection system (IDS) is a tool that helps to detect intrusions by inspecting the network traffic. Although many researchers have studied and created new IDS solutions, IDS still needs improving in order to have good detection accuracy while reducing false alarm rates. In addition, many IDS struggle to detect zero-day attacks. Recently, machine learning algorithms have become popular with researchers to detect network intrusion in an efficient manner and with high accuracy. This paper presents the concept of IDS and provides a taxonomy of machine learning methods. The main metrics used to assess an IDS are presented and a review of recent IDS using machine learning is provided where the strengths and weaknesses of each solution is outlined. Then, details of the different datasets used in the studies are provided and the accuracy of the results from the reviewed work is discussed. Finally, observations, research challenges and future trends are discussed.  
Intrusion Detection Systems (IDS); machine learning; network security; Intrusion Prevention Systems (IPS); deep learning algorithms +
Footnote †: journal:  
Fortunately, thanks to, on the one hand, the improvement made to the network and the processing power of the network's components, and on the other hand, the advent of machine learning, IDS is now a key component of the security of many systems. Most machine learning techniques have been evaluated in the development of IDS. However, the use of deep learning algorithms has not been sufficiently explored. With deep learning, many more possibilities can be achieved to tackle the issue of false alarm rates and lack of high accuracy.  
The first goal of this paper is to present the key concepts of IDS and machine learning. The second goal of the paper is to provide the recent trends and observations on recent machine learning intrusion detection systems. We review related published work, their advantages, disadvantages, datasets, techniques, and evaluation metrics. We discuss what has been done and what could be done to improve the IDS using new machine learning techniques. Finally, future trends and research challenges are outlined.  
The paper is organized as follows: Section 2 describes the concept of IDS. Section 3 explains the different metrics used to assess IDSs. Section 4 provides the basic understanding of machine learning, Section 5 provides datasets description and Section 6 reviews the relevant papers used in the study. Evaluation metrics are discussed in Section 7. Observations, research challenges and future trends are provided in Section 8. Finally, Section 9 concludes this paper.
## 4 Discussion
The effect of increasing the energy per pump pulse is a reduced dispersion on the collapse time data, as shown in Figure 8. Therefore, it would be suggested to use higher-energy pump pulses to get accurate measurements of the collapse time, i.e., the IOP. However, this could cause photo damage to the tissue. Thus, it is required to determine a balanced energy range for the IOP measurement to induce the cavitation above the plasma formation threshold in aqueous humor. Operating at the balanced energy will guarantee that cavitation is induced in every patient with the least collateral damage; such a study is not included in this work. Figure 9 clearly shows how the collapse time measured using STM traces without averaging for increasing pressure produces the expected linear trend and a minimal data dispersion for the M-type pig eyes. However, it was found that the dispersion in the collapse time increases for an S-type pig eye, perhaps because of the disturbed growth of the bubble due to the proximity to the walls in this smaller volume of the AC. The corneal damage assessment experiments clearly show that the corneal structure is not compromised by the delivery of up to 20 pulses at a 1 Hz repetition rate; that number is higher than the estimated number of pulses needed for the IOP estimation. From the resulting images, it is possible to remark that there was no convincing evidence of cumulative damage, even when up to 2000 cavitation bubbles were induced close (about 1 millimeter) to the cornea. Therefore, the IOP measurement technique based on laser-induced cavitation seems to be viable and safe. However, a more detailed study of collateral damage within the eye tissue is still needed, including of the retina and other structures.
# Effect of Pole Head Profile on Torque Performance of a Disc-Type Multipole Magnetorheological Brake
## 5 Results
### Maximum Magnetic Flux Flow  
Poles are the main source of magnetic flux generated in the MRF effective area, which is between the core and rotor disk surface. On the pole head surface area, the produced magnetic flux penetrates orthogonally across the circular disc surface and the stator, which results in creating a yield resistance to produce field torque for the brake. Magnetic flow presents in the form of a loop from one pole to the adjacent pole through the MR gap and stator, and then reverts through the MR gap to the adjacent pole. To increase this flux, more poles are required, but the pole radius has to be reduced due to compactness.  
As shown in Figure 8, observations of this study for all pole head profiles explain that at different pole head radii, the density of the flux is distributed equally, indicated by color contours. The red color arrows on the pole head area indicate flux concentration. Higher density flux occurs at the middle distance between the two effective areas. The gaps between those magnetic coils were optimized to prevent magnetic flux leakages as well as to avoid magnetic saturation. It can be seen that the density of the flux is distributed equally, and the white color occurs on the sides of a pole, indicating the effective area that indicates the flux concentration transferring to the adjacent pole. The 90\({}^{\circ}\) corner angle of the square reduces the flux flow due to its shape configuration; the distance between the center of the pole head area and the vertex is high, so the concentration of flux varies. The major drawback of using the circle pole head profile is that the shank radius and pole head radius cannot be of the same dimension. The ellipse pole head shape is similar to a circle shape, but it is placed in the radial direction. It is also feasible for manufacturing and also able to flow at a comparatively higher maximum flux than other profiles. At the 120\({}^{\circ}\) higher corner angle of the hexagonal shape, the flux flow can travel in more directions from the six vertices. The trapezoid pole head shape assembled in the MRB obtained inferior braking due to its configuration. The distance between the center of the shank and the edges of the pole head was greater, so in this area the flow of flux was less, which simultaneously affected the torque.  
Figure 7: Stress induced in the MR fluid at (**a**) inner MR fluid layer, (**b**) outer MR fluid layer of a circle pole head.
## 4 Discussion  
### Comparison of the New Construction Method with Conventional Variants  
Compared with conventional variants, presented in Section 1.1, the use of deck slab elements (Figure 6) enables a significant acceleration of the construction process on-site. Figure 11 shows a simplified construction process of building the deck slab using (a) a form-work carriage and (b) the new modular construction method presented. Since there are different general conditions for each bridge project, the individual work steps are not provided with a time component.  
Instead of casting one slab section (15-25 m) in a week by using a formwork carriage (according to the benchmark values in [23]), it is estimated that up to two slab sections can be produced in one day using the new construction method. For the transportation carriage specially designed for the transport of the precast elements, there is no need to wait until the concrete of the construction section reaches a sufficient compressive strength. After a construction section has been installed, the transportation carriage can be moved back to the assembly area to pick up the next precast elements. The elements are able to carry their own weight and an additional concrete layer without any temporary supporting structure. Therefore, the placing of the additional reinforcement at the joints between the sections and casting the top layer of concrete is independent of the installation of the deck slab elements as can be seen in Figure 11. Work steps can thus run in parallel.  
Since the deck slab elements serve as formwork for the top layer of CIP concrete, no time-consuming application of formworks needs to be carried out for the deck slab. The thin concrete plates, which are stiffened by cross-beams serve as a formwork and can be fully taken into account in the structural analysis.  
As the majority of the reinforcement required for the deck slab is already placed in the precast element, there is no need to carry out laborious reinforcement placing work on the construction site. Only the splice and the top longitudinal reinforcement have to placed on-site. In case of ordinary construction methods, all reinforcement works have to be carried out on-site.  
Compared with the production time for conventional methods (approximately one construction section per week), the new construction method presented here is significantly faster and meets the requirements of all parties involved in the project.
## 3 Results
### Sensor Response of FCS with Different Optical Fibre Configurations  
In Figure 6, the sensor response of the FCSs with different optical fiber configurations are illustrated (for all three different fiber configurations the Corning CC was applied). Before the measurements were performed, the FCS samples were strained to a force of 40 N and the maximum applied strain was 2.4 m\(\,\)during the tests. In case of configuration one (straight arrangement, Figure 6a) and two (offset arrangement, Figure 6b), a linear response to an applied force of \(6.4\times 10^{-4}\) mm/N (R\({}^{2}\) = 0.96) and \(6.7\times 10^{-4}\) mm/N (R\({}^{2}\) = 0.99), respectively, with a relatively low hysteresis of \(4.4\times 10^{-5}\) mm/N and \(1.9\times 10^{-5}\) mm/N, respectively, were obtained. In case of the third configuration (meander arrangement, Figure 6c), no correlation between the applied force and the length change of the optical fiber of the FCS could be observed (R\({}^{2}\) = 0.25). The obtained result for configuration three suggests that due to the meander shape and thus due to the periodic spatial variation of the optical fiber inside the FCS, the bond between the optical fiber and the carbon filament is less strong (less bonding length) and thus the FCS is less sensitive to applied force. Therefore, the obtained results (from the three different fiber configurations) suggest that for sufficient sensor sensitivity, the bonding length between the optical fiber and the carbon filament to be at least \(\geq\)150 mm long (compare configuration two).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **400 Tex Filament** & **800 Tex Filament** & **1600 Tex Filament** \\ \hline \multirow{2}{*}{**Before exposure**} & 0.0093 mm/N & 0.0065 mm/N & 0.0018 mm/N \\  & (\(\sigma=3.5\times 10^{-4}\)) & (\(\sigma=2.1\times 10^{-4}\)) & (\(\sigma=1.1\times 10^{-4}\)) \\ \hline \multirow{2}{*}{**After exposure**} & 0.0174 mm/N & 0.0077 mm/N & 0.0023 mm/N \\  & (\(\sigma=7.3\times 10^{-4}\)) & (\(\sigma=4.1\times 10^{-4}\)) & (\(\sigma=1.7\times 10^{-4}\)) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Change of the sensor response from 1D-FCS due to exposure times to highly alkaline 5% NaOH solution. \(\sigma\) specifies the standard deviation of the measurement.  
Figure 6: Sensor response for the 2D-FCSs with straight (bonding length 300 mm) (**a**), offset (bonding length 150 mm) (**b**), and meander (bonding length 60 mm) (**c**) optical fiber configurations.
## 5 O&M Service Platform Implementation
### Maintenance Service Activity Management System  
As shown in Figure 7, the maintenance service management system of the operation and maintenance service platform generates the optimal operation and maintenance service scheme dependeing on the situation of ongoing maintenance tasks, in consideration of constraints including the time window, operation and maintenance cost, and maintenance distance according to the location of the agricultural machinery failure, thus realizing the allocation of maintenance service vehicles and performing maintenance route planning. Meanwhile, upon the request of the operation and maintenance personnel performing the maintenance task, new maintenance work orders can be inserted through dynamic optimization scheduling, so as to maximize the satisfaction of the maintenance tasks.
# On-Orbit Absolute Radiometric Calibration and Validation of ZY3-02 Satellite Multispectral Sensor
## 3 Methodology
### Radiative Transfer Calculations  
The Radiative Transfer Model (MODTRAN 6.0) is commonly used for radiometric calibration. The reflectance measurements and the atmospheric parameters are inputs for a Radiative Transfer Model that computes a band-specific TOA spectral radiance (also known as at-sensor radiance). The apparent reflectance of the ZY-3 02 satellite sensor \(\rho^{*}(\mu_{s},\phi_{s};\mu_{v},\phi_{v})\) can be expressed as:  
\[\rho^{*}(\mu_{s},\mu_{v},\phi_{s}-\phi_{v})=\rho_{a}(\lambda)+\frac{\rho}{1-S( \lambda)\rho}T_{\theta_{s}}(\lambda)T_{\theta_{v}}(\lambda) \tag{2}\]  
where \(\mu_{s}=\cos\theta_{s}\) and \(\mu_{v}=\cos\theta_{v}\) are the cosine values of the solar and viewing zenith angles, \(\theta_{s}\) is the solar zenith angle, \(\phi_{s}\) is the solar azimuth angle, \(\theta_{v}\) is the viewing zenith angle, and \(\phi_{v}\) is the viewing azimuth angle. \(\rho_{a}(\lambda)\) is the atmospheric path reflectance, \(S(\lambda)\) is the atmospheric spherical albedo, \(T_{\theta_{s}}(\lambda)\) is total transmittance from the solar to the earth, and \(T_{\theta_{v}}(\lambda)\) is the total transmittance from the earth to the satellite sensor [24]. The band-specific TOA radiance \(L(\theta_{s},\phi_{s};\theta_{v},\phi_{v})\) can be determined as follows:  
\[L(\theta_{s},\phi_{s};\theta_{v},\phi_{v})=\frac{\mu_{s}E_{s}\rho^{*}}{\pi d^{ 2}} \tag{3}\]  
where \(\rho^{*}\) is the apparent reflectance of the satellite sensor, \(\mu_{s}=\cos\theta_{s}\) is the cosine values of the solar zenith angles, \(\theta_{s}\) is the solar zenith angles, \(E_{s}\) is the solar irradiance at the top of atmosphere, and \(d\) is the Solar-Earth distance factor.  
We acquired one calibratable ZY3-02 MUX image, with a viewing zenith angle of less than 2 degrees under clear-sky conditions. Table 4 shows the geometric conditions of ZY3-02 for radiometric calibration.
## 3 Results
### Repeatability Measurements  
All the sensorial elements were tested according to the methodology presented in Section 2.2.2 for repeatability. The research team conducted three repeatability tests--one repeatability test for each of the test weights--1 kg, 5 kg and 10 kg, and in the first stage, eight different sensorial elements were tested. The results are presented in Table 1.  
The maximum load of the sensors used in the sensorial element is, according to their datasheet, 50 kg. The datasets were recorded as digital values, where 0 represents the absence of the weight load on the sensor, and the maximum digital value is 1024. Value 1 to Value 10 represents the mean value recorded during the stable measurement phase, presented in Section 2.2.2, Figure 10. Therefore, we considered it more beneficial to indicate the standard deviation as a percentage related to its average value computed over the ten measurements, under the SD (%) column.  
We analyzed the data from Table 1 following the criteria enounced in Section 2.2.2, especially the data obtained for measurements conducted for the 10 kg test weights. We noted that the FSR_1, FSR_5 and FSR_6 exhibit a standard deviation (SD%) higher than 10% of the mean value. Therefore, we concluded that the sensors used on the sensorial elements cannot provide reliable, repeatable data. Based on this analysis, the research team decided to replace them with similar sensors, which passed a similar repeatability test. Another conclusion regards the readings for the 1 kg load. Since the maximum load for a sensor is 50 kg, and 1 kg represents 2% of its full load, one can observe that the standard deviation exhibits high percentage values; thus, the repeatability is low. However, since such low loads appear only in short, transitory moments between gait phases, we can consider them acceptable.  
Also, one can note that the output response of the sensors is powerful nonlinear. Thus, in future work, we will consider using more test weight values to determine the nonlinear characteristic.
# Attention to Fires: Multi-Channel Deep Learning Models for Wildfire Severity Prediction
## 4 Experimental Results
### Hardware and Software Setting  
The entire framework was developed in Python 3, the neural network models exploit the PyTorch [74] framework. Further software packages that were used in this work are Pandas [75], NumPy [76], and MatPlotLib [77]. To allow total reproducibility of the experiments, the full source code, software versions, and the specific dataset information are provided in the GitHub repository at [[https://github.com/dbdmg/rescue](https://github.com/dbdmg/rescue)]([https://github.com/dbdmg/rescue](https://github.com/dbdmg/rescue)) (accessed on 19 November 2021).  
Experimental results are obtained using the computing cluster of HPC@PoliTO [78], using a single NVIDIA Tesla V100 SXM2 GPU. On this hardware configuration, the training time distribution has an average of 50 min, independently of the architecture. Models with higher-time outliers converge within 2 h. The full dataset has a size of 24 GB and is reachable through the GitHub repository.  
Figure 4: Distribution of the severity levels for each fold and globally. Unburnt pixels percentages are not shown and correspond to the complementary of the shown bars.
## 3 Proposed Method
### Feature Representation and Classifier  
Having obtained the spatial and channel recalibrated features, we multiple them in an element-wise fashion instead of vector concatenation as follows:  
\[\mathbf{f}=\mathbf{f}_{sp}\odot\mathbf{f}_{ch}\in R^{c\times 1} \tag{10}\]  
It is obvious that one feature representation \(\mathbf{f}\in R^{c\times 1}\) can be deduced for each feature map \(\mathbf{F}\in R^{h\times w\times c}\). For the feature maps \(\mathbf{F}_{p16}^{(L)}\), \(\mathbf{F}_{p19}^{(L)}\) and \(\mathbf{F}_{res}^{(L)}\) at different layers, the corresponding recalibrated features are computed firstly and then concatenated to form the final representation of 11008 dimensionality for the leaf image \(\mathbf{x}\), which is followed by the \(L_{2}\) normalization. Afterwards, in order to remove redundant features, white principle component analysis is used to reduce the feature vector's dimensionality. In the recognition phase, the linear support vector machine (SVM) classifier is employed, in which the parameters are set to \(s\)=1 and \(c\)=10 without fine-tuning for the sake of simplicity.  
In summary, the procedures of our method can be summarized as follows: (1) Split a plant leaf dataset into training set and testing set randomly. (2) Download the pretrained CNN models from the vlfeat website. (3) Extract CNN features from multiple CNNs and layers for each leaf image using MATCONVNET toolbox, where network fine-tuning is not required. (4) Call the Liblinear library to train an SVM classifier with parameter setting [\(s\) 1\(\prec\) 10 -q]. (5) Predict the label for each test leaf image. It is worth noting that the parameters of linear SVM are not fine-tuned; we did not even try the other \(c\) and \(s\) values. We believe that the performance of our method may be enhanced if we select the optimal parameters.
## 4 Results and Discussion
### Compressive Performance of Fiber Reinforced Geopolymer Concrete
#### 4.1.2 Stress-Strain Relationship  
By conducting a compressive test and using strain gauges, geopolymer concrete embedded by fibers exhibited compressive performance, including compressive stress, strain at peak stress, Poisson's ratio, and elastic modulus. All these values are shown in Table 6.  
As shown in Figure 12, geopolymer concrete specimens using polypropylene fiber and hooked end steel fiber had a similar compressive performance. The stress-strain curves of series P1, P2, H1 and H2 had the same effect. However, hooked end steel fiber specimens had better results than polypropylene fiber specimens with a higher compressive stress and smaller strain. Among four series, the H2-10 series specimen had the highest compressive stress of 59.30 MPa, and the P2-15 series specimen had the smallest compressive stress of 27.13 MPa. Compared with the research of [PERSON] [6], the non-fiber geopolymer concrete had strain ranging from 0.0024 to 0.0026. From Figure 12, it can be seen the strains at peak stress of all specimens ranged from 0.0022 to 0.0030. However, most values were within the range from 0.0024-0.0026.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Specimen** & **Compressive Stress (MPa)** & **Strain** & **Poisson’s Ratio** & **Elastic Modulus E (GPa)** \\ \hline GC & 32.61 & 0.0021 & 0.128 & 25.40 \\ P1-05 & 43.33 & 0.0024 & 0.152 & 26.10 \\ P1-10 & 36.18 & 0.0026 & 0.139 & 25.37 \\ P1-15 & 32.45