esa-sceva/satcom-chunk-collection huggingface dataset:
# Resampling Imbalanced Network Intrusion Datasets to Identify Rare Attacks
## 2 Background  
To adequately address resampling, the resampling techniques used in this paper are briefly explained next. Though oversampling and undersampling are aimed at changing the ratios between the minority and majority classes, respectively, this is particularly important when the minority classes are minimal, that is, in the case of rare attacks.  
A minority class refers to very low-occurring data categories. In this work, since a binary classification was performed, the dataset for each experimentation contained only two data types--one attack category and benign data. For example, for the analysis of worms, the dataset contained only worms and benign data. Out of these two categories of data, worms would be considered the minority class. Table 1, which presents the UNSW-NB15 dataset, shows that worms account for 0.006% of the data and 0.007% of the benign data. The raw data count for worms is 174 compared to the benign data count of 2,218,761.  
Oversampling is the process of generating samples to increase the size of the minority class. Random oversampling involves randomly selecting samples from the minority class, with replacement, and adding them to the dataset [6]. Random oversampling can also lead to overfitting the data [3].  
Various oversampling techniques are available, including various types of Synthetic Minority Oversampling Techniques (SMOTE) and ADASYN. SMOTE generates synthetic samples rather than resampling with replacement. These samples are generated along the line segment adjoining k-minority class neighbors [7]. In Borderline SMOTE (BSMOTE), a variant of the original SMOTE, borderline minority samples are identified and then synthetic samples are generated [8].  
ADASYN [9] is a pseudo-probabilistic oversampling technique that uses a weighted distribution for different minority data points. This weighted distribution is based on the level of difficulty in learning, and more synthetic data are generated for minority class examples that are harder to learn than minority examples that are easier to learn [10].  
Random undersampling samples the majority class by randomly picking samples with or without replacement from the majority class [11]. After random undersampling, the number of cases of the majority class decreases, significantly reducing the model's training time. However, the removed data points may include significant information leading to a decrease in classification results [3].
## 2 Experimental Design and Data Collection
### The Experiment Design  
The fault arc signal is obtained by drawing--arc fault simulation. The main body of the fault occurrence device is composed of a 6 mm carbon rod and 6 mm copper rod, in which the carbon rod is used as the static electrode and the copper rod is used as the moving electrode.  
Figure 1: Experimental platform. (**a**) Physical and simulation diagrams of the experimental platform; (**b**) Working principle diagram of inverter.  
The electrodes are moved under the control of a stepper motor to adjust the gap between the electrodes to produce an arc. The fault occurrence device is shown in Figure 2:  
In order to ensure the generality of the simulation system, this paper uses the frequency converter to convert single-phase electricity into three-phase electricity, and it uses the triangle connection method to connect a 220 V three-phase motor. In this paper, when the maximum speed of the motor is working normally, one of the branches simulates the fault. The collection point is fixed in phase A, and arc faults occur in three phases of A, B and C respectively. The voltage between the collection point of phase A and the neutral line of the inverter is collected as experimental data. An experimental circuit diagram is shown in Figure 1. Four groups of experiments are carried out: namely normal, A-phase fault, B-phase fault and C-phase fault. Here, 1, 2, and 3 are the locations where faults occur. The switch is on to simulate the occurrence of faults, and the switch is closed to simulate the normal situation of branches. In total, 2000 groups of experimental data were collected for each group.
## 3 Results and Discussions
### FTIR Analyses  
Comparison of the FTIR spectra of pullulan and oxidized pullulan in Figure 5 proves the success of oxidation, even for the case where the solid catalysts were reused three times. The infrared spectra of both pullulan and its oxidized derivative display a strong adsorption band at 3424 cm\({}^{-1}\) due to the OH stretching vibration bond and an adsorption band at 2924 cm\({}^{-1}\) due to the sp\({}^{3}\) C-H bond. Another characteristic band of pullulan at 1638 cm\({}^{-1}\) is attributed to the stretching vibration of O-C-O. The modified pullulan showed a band at 1614 cm\({}^{-1}\) attributed to the C=O stretching of the free carboxylate groups, and the band at 1420 cm\({}^{-1}\) is assigned to the C-O symmetric stretching of dissociated carboxyl groups. Other changes that occurred after oxidation are visible in the 1000-1300 cm\({}^{-1}\) FTIR region by increasing the absorption intensity of some bands as follows: the stretching vibration of C-O at 1200-1030 cm\({}^{-1}\), the stretching vibration of C-O-C at \(\alpha\)-1, and 4-glycosidic linkage at 930 cm\({}^{-1}\) and 1025 cm\({}^{-1}\) due to the C-CO. These results indicated that the primary hydroxyl groups in pullulan were converted to carboxyl groups in the presence of the new proposed catalytic systems. Further, after oxidation, the spectra of the samples do not essentially differ. This can suggest that the catalysts are still efficient after three reutilizations, being able to perform the oxidation reaction of the primary OH groups.  
The zeta potentials of all oxidized samples were measured in deionized water at a concentration of 1 mg/mL. The resulting values are strongly dependent on the number of negatively charged groups introduced after oxidation, ranging from \(\zeta\) of \(-16.2\pm 0.4\) mV (DO = 15) in the sample oxidized with AMPS, recycle 2, to \(\zeta\) of \(-27.3\pm 0.6\) mV (DO = 29) in the sample oxidized with BMPS, recycle 3.  
Figure 4: Selected \({}^{13}\)C-NMR spectra of the pullulan obtained after oxidation in the presence of NHPI-supported catalysts catalysts/NaCIO/NaBr.  
_Materials_**2019**, _12_, 3585  
Figure 5: FTIR spectra of pullulan oxidized in the presence of NHPI-supported catalysts catalysts/NaClO/NaBr.
# A Self-Scrutinized Backoff Mechanism for IEEE 802.11 ax in 5G Unlicensed Networks
## 4 Analytical Model
### Normalized Throughput  
Let \(\theta\) be the normalized throughput of the network and be defined as the fraction of the communication channel used for successful transmission of the data payload. To compute \(\theta\), let \(\gamma_{tr}\) be the probability that there is at least one transmission in the considered slot time. Since there are \(n\) number of WEs in the system contending for the medium and each transmits with probability \(\gamma\), the transmission probability \(\gamma_{tr}\) can be defined as,  
\[\gamma_{tr}=1-(1-\gamma)^{n}. \tag{21}\]  
If the probability \(\gamma_{s}\) that a transmission is successful is given by the probability that only one WE transmits in the considered slot time, \(\gamma_{s}\) can be obtained as,  
\[\gamma_{s}=\frac{n\gamma(1-\gamma)^{n-1}}{\gamma_{tr}}=\frac{n\gamma(1-\gamma )^{n-1}}{1-\left(1-\gamma\right)^{n}}. \tag{22}\]  
Thus, \(\theta\) can be expressed as the ratio,  
\[\theta=\frac{E[\textit{mean~{}payload~{}transmitted~{}in~{}a~{}slot~{}time}]}{E[ \textit{total~{}length~{}of~{}a~{}slot~{}time}]} \tag{23}\]  
Assume \(E[P]\) is the average data frame payload size (assuming that all the data frames have the same fixed size), then the slot time for transmitting average payload data successfully can be obtained as \(\gamma_{tr}\gamma_{s}E[P]\), since \(\gamma_{tr}\gamma_{s}\) is the probability for the successful transmission of a data frame in a given slot time. The average length of a given slot time is the sum of three cases; no transmission in a slot time that is \((1-\gamma_{tr})\sigma\), a successfully transmitted data frame that is \(\gamma_{tr}\gamma_{s}\), and a collision that is \(\gamma_{tr}(1-\gamma_{s})\). Finally, the relation (23) can be written as follows:  
\[\theta=\frac{\gamma_{tr}\gamma_{s}E[P]}{(1-\gamma_{tr})\cdot\sigma+\gamma_{tr} \gamma_{s}\cdot T_{s}+\gamma_{tr}(1-\gamma_{s})\cdot T_{c}} \tag{24}\]  
where \(T_{s}\) and \(T_{c}\) are the average time the communication channel has been busy due to successful transmission and collision, respectively. For analytical evaluation, the values of \(E[P]\), \(T_{s}\), \(T_{c}\), and idle slot time \(\sigma\) must be expressed with the same time unit. Let \(P_{hdr}=PHY_{hdr}+MAC_{hdr}\) be the time to transmit a data frame header, and \(\delta\) be the channel propagation delay. If ACK is the time to receive an acknowledgement, \(T_{s}\) and \(T_{c}\) can be obtained as,  
\[T_{s}=P_{hdr}+E[P]+SIFS+\delta+ACK+DIFS+\delta, \tag{25}\]  
\[T_{c}=P_{hdr}+E[P]+DIFS+\delta. \tag{26}\]  
The corresponding values for \(T_{s}\) and \(T_{c}\) depend upon the 802.11 standard. The PHY and MAC layer parameters to compute \(T_{s}\) and \(T_{c}\) are shown in Table 1.
## 4 R-SpaceFibre Use Case Scenario: CubeSats
The Sphinx CDH has a mass of less than 200 g and may be housed in a 10 cm \(\times\) 10 cm area, with a maximum power consumption of 7 W. Considering the communication interfaces, the Sphinx mounts four SpaceWire ports that have a maximum data-rate of 400 Mbps and seems not to be the ideal solution for increasing CubeSat data-rate capability: four separate interfaces require separate connectors and cables, which could represent an issue in volume contained systems such as CubeSats. Moreover, the overall data-rate is still below the one easily obtainable with one R-SpaceFibre interface. The Sphinx CDH interface and R-Spacefibre may be compared considering hardware resources needed to be implemented on a FPGA device. Table 4 shows the results obtained for R-SpaceFibre and four SpaceWire interfaces realised, respectively, by IngeniArs [28] and STAR-Dundee [29] on a RTAX2000. The two communication interfaces need similar hardware resources, but R-SpaceFibre increases the achievable data-rate from 1.6 Gbps of the Sphinx SpaceWire solution to 1.8 Gbps and can be further improved to 3.125 Gbps implementing the design on higher performance FPGAs such as RTG4. Furthermore, R-SpaceFibre reduces cable volume and mass: considering state-of-the-art cables from Axon, the Sphinx requires four SpaceWire cables (one per port), whose weight is 42 g/m [30] for a total of 168 g/m. On the other hand, R-SpaceFibre requires only one SpaceFibre cable, weighting 15 g/m [31], achieving around 90% mass reduction, as shown in Table 5. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Resources** & **R-SpaceFibre** & **4 SpW (IngeniArs)** & **4 SpW (STAR-Dundee)** \\ \hline LUT & 16.18\% & 11.88\% & 7.16\% \\ DFF & 14.23\% & 14.12\% & 17.22\% \\ Block RAM & 4 & 8 & 8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: R-SpaceFibre and four SpaceWire interfaces resources utilisation on RTAX2000. Figure 6: CubeSat on-board electronics architecture. Currently, CubeSat missions already plan to mount SAR and Interferometric SAR (InSAR) as payloads. Those instruments require data-rate in the order of Gbps, which is not easily targetable with state-of-the-art technology. For example, SRI International has prototyped the CubeSat Imaging Radar for Earth Science (CIRES), designed for a 6U CubeSat constellation [32] and capable of a moderate resolution (25 m). Moreover, other present and future missions, such as Capella 1 [33] and Iceye [34], will mount SAR as payloads, while Intuition-1 [35], HyperCube [36] and Waypoint 1 [37] will mount hyper-spectral imagers. On-board data-handling bandwidth of these missions is expected to be in the order of Gbps, falling in the operational range of R-SpaceFibre. This brief analysis suggests that R-SpaceFibre may be a valid solution for on-board data handling of CubeSats mounting high resolution payloads in the near future: even if R-SpaceFibre performances are reduced compared to full SpaceFibre implementations, it allows significantly reducing the required hardware resources, combining high data-rate with other advantages proper for the SpaceFibre protocol.
## 4 Evaluation
### Application Evaluation
#### 4.4.2 Application Success Criteria
Any specifications regarding the form of input and output data (file format and formatting) can be adapted in the software prototype. Validation result: SC-11 is fulfilled. Success Criterion SC-12 Acceptable Application Effort  
The answers to questions 18 to 22 (cf. Appendix C) in the survey of pilot users (questionnaire and interviews) are used as the basis for evaluation. The questions capture all activities required for application of the method and are rated based on a Likert Scale [87] from strongly disagree (\(-2\)) to strongly agree (2). SC-12 is considered fulfilled if the average of the users' ratings for each question is \(\geq 0\) (neutral). In this case, it is assumed that the majority of users accept the application effort. Evaluation result: Pilot users' feedback varied, but overall the results indicate improvement potentials regarding application effort (cf. Figure 17). As part of the interviews, issues were discussed. The finding was that dependency analysis is the main issue. It is the only method requiring one-time preparation effort (creation of dependency model), which is perceived as too high in case it needs to be created from scratch. Application effort for subsequent steps also do not reach scores higher than 0. For recurring preparation effort, a fully automated interface to requirements engineering software tools is missing. For application effort, the correction of requirement dependencies is criticized. Additionally, lead time of dependency analysis is an issue. Lastly, manual capturing of changes as a follow-up activity should be automated based on an interface to requirements engineering software tools. Overall, dependency analysis stands out as the source of too many application issues for different steps. Still, all pilot users agreed that it is already a major improvement in comparison to manual dependency analysis as currently implemented. Although application effort should be further reduced, using manual analysis as a benchmark, it clearly reduces application effort. As a result the application effort is partly acceptable.
# A Systematic Literature Review on Military Software Defined Networks
## 5 Additional Contributions
### SDN Control Plane Resilience and Security
[78] suggested a procedure for dynamically setting up a full network on a new site after a disaster, where the use of SDN can accelerate the provisioning time. The procedure includes a recovery provisioning, an upgrade procedure, and a prioritization policy for network resources. Making a resilient SDN network also includes operational management tools to ensure attack protection of the SDN controller. [PERSON] et al. [66] shows that SDN networks are vulnerable, and that there is a need for additional visual analytics tools for protection against the new threats arising from SDN, such as Denial of Service attacks towards network controllers. [PERSON] et al. [68] points out a set of physical infrastructure attacks such as wiretapping, electromagnetic pulses (EMP), and critical hub nodes \"take-out\". The author suggest that centralization of network controllers in OpenFlow introduces new vulnerabilities and makes security even more complex. Correspondingly, [PERSON] et al. [77] pinpoint that the network controllers must be both secured from passive and active attacks, as well as physical and network attacks.
## 2 System Structure and Coupling Mechanisms  
### The Parallel-Series DC Wind Farm  
The system should satisfy the following requirements:  
1. The wind turbines can track the maximum power point at the steady state without overcurrent and overvoltage.
2. The different strings should not be disturbed from each other during a fault or power fluctuations.
3. The system can achieve fault ride-through of onshore and offshore faults.
4. Galvanic separation should be provided for wind generators and DC/DC converters.
5. The system can be started up.  
Figure 1 shows the proposed parallel-series DR system. The MV side DR (MVDR) is connected in parallel at the common MV DC link. The HV side DR (HVDR) is connected in series.  
The whole system includes the wind turbine, MVDR, MMC-HVDR, offshore cable, and onshore converter.  
1. The wind turbines in offshore wind farms are normally composed of a permanent magnet synchronous generator (PMSG), as shown in Figure 1a. The transformer in a wind turbine achieves galvanic separation between the generators and MVDC.
2. MVDR is installed in the wind turbine as a distributed system. Several MVDRs are connected in parallel. The output current is \(I_{dc}(i,j)\), and the output voltage is \(V_{dd}\). The start-up problem caused by the DR can be solved by installing a small energy storage system in the wind turbines. S2 is a bypass breaker and will be open if one unit of MVDR is broken, and the other units will not be disturbed.
3. The MMC-HVDR is composed of the MMC converter, HVDR converter, and transformer, as shown in Figure 1b. It collects the power from the parallel-connected MVDR and boosts the MVDC voltage \(V_{ddj}\) to a higher DC voltage \(V_{outj}\). Then, HVDR is connected in series to match the transmission voltage of \(V_{dc}\). The transformer achieves galvanic separation and makes it easier to gain a high transmission ratio. A bypass breaker, S1 and S2, can be used for the unit outage. S1 is closed and S2 is open if one of the MMC-HVDR units is broken, while another unit can keep operating without being disturbed.
4. The offshore cable transmits the power from HVDR to the onshore converter. The current in the HVDC link is \(I_{dc}\).
5. MMC with zero DC side voltage operation capability, such as the full bridge submodule (FB SM) or hybrid topology, is used as the onshore converter station to achieve DC fault ride-through.  
Figure 1: The topology of a parallel–series DR system. (**a**) The topology of wind turbine and MVDR; (**b**) The topology of MMC-HVDR; (**c**) The whole system topology.
## 4 Optimization of Balanced Partition Routing Algorithm  
This section mainly explores the two optimization directions of the balanced partition routing algorithm. As shown in Figure 5, in order to further reduce the calculation pressure of a single node, the balanced partition routing algorithm can further optimize the grouping on the basis of the original grouping. One way is to divide the larger group into smaller groups, each of which belongs to the larger group. This article calls this partitioning method 2. Another method is to divide the original large group into several smaller groups. After the separation, the original large group is cleared. We call it partitioning method 3. This section will explore the calculation pressure of a single node, average path length and variance of average path length under conditions of these two optimization directions.
# Enhanced Bit Repair IP Fast Reroute Mechanism for Rapid Network Recovery
## 2 Related Works
### Full Repair Coverage with Multiple Failures Support  
Repair Coverage is the term that describes the efficiency of IP Fast Reroute mechanisms. If a particular IP Fast Reroute mechanism can repair all possible network failures, this means that the mechanism provides 100% repair coverage. The repair coverage is usually used to refer to the testing scenarios with only one failure.  
If the FRR mechanism uses only metrics to calculate an alternative FRR next-hop router, it cannot provide 100% repair coverage. Provider topologies are diverse, and there may be situations where line metrics do not meet the requirements of a specific FRR mechanism to calculate a new next-hop router [60, 61].  
Scientific articles present various results of measurements of repair coverage of specific FRR mechanisms [60, 62, 63]. In the current situation, there is no precise methodology for measuring repair coverage. Therefore, different topologies and tests are used for repair coverage measurements.  
Nowadays, connection reliability is more important than before. Therefore, when addressing the development of FRR mechanisms, it is appropriate, or even highly desirable, to consider solutions that support recovery scenarios for multiple network failures [32, 35, 64, 65, 66].
# Enhanced Feature Selection via Hierarchical Concept Modeling
###### Abstract  
The objectives of feature selection include simplifying modeling and making the results more understandable, improving data mining efficiency, and providing clean and understandable data preparation. With big data, it also allows us to reduce computational time, improve prediction performance, and better understand the data in machine learning or pattern recognition applications. In this study, we present a new feature selection approach based on hierarchical concept models using formal concept analysis (FCA) and a decision tree (DT) for selecting a subset of attributes. The presented methods are evaluated based on all learned attributes with 10 datasets from the UCI Machine Learning Repository by using three classification algorithms, namely decision trees, support vector machines (SVM), and artificial neural networks (ANN). The hierarchical concept model is built from a dataset, and it is selected by top-down considering features (attributes) node for each level of structure. Moreover, this study is considered to provide a mathematical feature selection approach with optimization based on a paired-samples \(t\)-test. To compare the identified models in order to evaluate feature selection effects, the indicators used were information gain (IG) and chi-squared (CS), while both forward selection (FS) and backward elimination (BS) were tested with the datasets to assess whether the presented model was effective in reducing the number of features used. The results show clearly that the proposed models when using DT or using FCA, needed fewer features than the other methods for similar classification performance.  
formal concept analysis; feature selection methods; hierarchical concept model; the paired-samples \(t\)-test; classification +
Footnote †: journal: applied sciences  
Article Enhanced Feature Selection via Hierarchical Concept Modeling