esa-sceva/satcom-chunk-collection huggingface dataset:
# Interleaved Boost Converter with ZVT-ZCT for the Main Switches and ZCS for the Auxiliary Switch
## 1 Introduction
By doing so, the required filter design will be easier and the corresponding size will be smaller. In general, the total loss created from multiple phases will be smaller than that created from a single phase. Accordingly, the multiphase converter with soft switching is presented. The literatures [33; 34; 35; 36] adopt multiple phases with the corresponding number of auxiliary resonance circuits, leading to increasing the number of components to increase conduction loss as well as cost. In the literature [35], the two-phase converter uses the same auxiliary resonant circuit. However, only the ZVS is used, such that the efficiency improvement is limited. In the literature [36], the two-phase converter uses one snubber circuit so as to make the main switches achieve soft switching. However, the resonant inductor locates in the power path, thereby making conduction loss increased. On the other hand, there are differences in component features and line impedance between the two phases. Consequently, the current-sharing control will be needed. As for current sharing, there are two types of current-sharing control methods. One is passive; the other is active. The former employs capacitors or differential-mode transformers or both to do current sharing [37; 38; 39; 40], whereas the latter contains current regulators and current sensors to balance currents [41; 42; 43; 44; 45]. Based on what has been discussed above, a two-phase converter with one resonant inductor, two resonant capacitors, two resonant diodes, one auxiliary diode, one auxiliary switch, and two main switches are proposed. This converter can achieve ZVT and ZCT for the main switches and ZCS for the auxiliary switch, so as to further increase the overall efficiency. In addition, the proposed current-sharing control is adopted herein so that the output voltage is regulated by the first phase and the current sharing between the two phases is controlled by the second phase.
## 3 DetNet Environment Setting and Notation Description  
The DetNet is a centralized network architecture for managing network node behavior and topology information. Figure 1 shows the system model of this study. This study applies an SDN controller as a supervisor to manage the DetNet environment and control all switches that store all flow tables. When Hybrid DDoS attackers launch attacks, the behavior of flows is recorded in the flow table. The controller can obtain the links (\(L\)) and switches of all flow tables in the network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Attack Type** & **Flow/Packet-Based** \\ \hline Calculate SYN response rate [8] & SYN & Packet-based \\ Use flow to calculate IP repetition rate [9] & DNS & Flow-based \\ Calculate entropy to detect [10] & SYN & Flow-based \\ Use the dynamic resource allocation mechanism [11] & SYN & Flow-based \\ Use ML to train the packet feature [12] & DNS & Packet-based \\ Use ML to train the packet feature [13] & DNS & Packet-based \\ Analyze server logs to train an abnormal packet [14] & HTTP & Packet-based \\ Use correlation IP and traffic to detect [15] & HTTP & Flow-based \\ Analyze user trace logs to train model [16] & HTTP & Packet-based \\ Probe to measure link utilization [19] & Crossfire & Flow-based \\ Analyze node-to-node traffic variation topology [20] & Crossfire & Packet-based \\ Use flow to measure link utilization [21] & Crossfire & Flow-based \\ Analyze traceroute packet and reroute [22] & Crossfire & Packet-based \\ Analyze traceroute packet and reroute [23] & Crossfire & Packet-based \\ Use correlation to cluster traffic to measure link utilization [24] & Hybrid & Flow-based \\ \hline \hline \end{tabular}
\end{table}
Table 1: DDoS attack detection comparison table.  
Table 2 shows the notations used in this study. \(T\) represents the size of the topology, \(SW\) represents the switches, \(TA\) represents the servers in the target area, \(F\) represents all flows (\(f_{m}\)) in the flow table in the SDN switches, \(\alpha\) represents how to set the links as target links, \(L\) represents links in SDN, \(TL\) represents target links in SDN, and SP represents the system that determines the attack standard.
## 4 Experiments and Results
### Simulation Results
#### 4.2.2 Efficiency of the Leading Autonomous Vehicles Regarding the Flattening Velocity  
To evaluate the efficiency of leading autonomous vehicles in terms of the flattening velocity, the spatial-temporal diagram for the urban network over the AV penetration rates is shown in Figure 8. The points are color-coded regarding velocity. If the points are near the bottom, the traffic flow is more congested, whereas those near the top express a better traffic flow. Concerning the behavior of HVs, the congestion occurs with a lower AV penetration rate. As seen in Figure 8, almost all points approach the top as the AV penetration rate becomes higher. This means that the leading AVs can help to mitigate stop-and-go waves in an urban network. In contrast, almost all the points approach the bottom with lower AV penetration rates due to human-driven behaviors. Full automation traffic obtained a superior smoothing velocity in entire cases. Therefore, the traffic flow was less congested and smoother with a higher AV penetration rate.
## 6 Baseline Statement and AAMO Approach Results
### Aamo Assessment  
This section aims to study the impact of using different _HO-Threshold_ values on the performance of the proposed _AAMO_ approach when the fleet is asked to explore an environment under non-ideal communication conditions. Moreover, these results are compared with the one achieved by other approaches like _Yamauchi_ and _MinPos_--when they are subject to non-ideal communication conditions too--and also with an _event-based-connectivity_ strategy that does make all efforts in favour of connectivity (regardless the total exploration time).  
This last comparison is namely important because the performance of this kind of strategy may serve as an upper bound on the connectivity level over time and the total exploration time as well. To do so, typically two strategies (based on different connection requirements, see Section 1.1.1) can be considered: the ones which force the robots to be connected only on task-arrival time (kind of event-based connectivity) or the ones which force the robots to keep always connected--even during the path traversal periods (continuous connectivity). In the former, the robots are forced to select only between tasks which location would not cause isolation on arrival--regarding the current task assignment of the fleet. Nevertheless, it does not take into account the connectivity level along the path between the current robot location and the location of the task under consideration. Conversely, the latter imposes stronger restrictions on the fleet mobility in order to guarantee connectivity at all times. Consequently, depending on the application field the latter strategy would be recommended but is more complex to implement than the former. On the contrary, the former allows a simpler implementation but could lead to a lower level of connectivity along the exploration. Concerning this document, a connectivity-at-task-arrival-time based strategy is used for comparison purposes.  
Besides, it is also important to highlight that, despite _Yamauchi_ and _MinPos_ assume ideal communication conditions, neither approach needs to be modified or adapted in order to properly run under non-ideal communication condition. Nevertheless, in the _MinPos_ case, some severe degradation is expected because of the following working hypothesis are not guaranteed anymore: All robots share the same map and know the position of the other fleet members, at all times. This could lead to incoordinations that, in turn, would harm the dispersion strategy on which the approach is strongly based. Conversely, in the _Yamauchi_ case, the level of expected degradation is fewer due to the coordination level between robots is fewer as well. Robots only try to avoid going to the same task simultaneously.  
Figure 16: Over-sensing ratio (OSR) under ideal communication conditions. This shows how as fleet size increases the trend of OSR is upward as well. This is expected since the more robots sensing the environment the higher the probability of simultaneously sensing the same cells.
###### Abstract  
Detecting malicious activities in Vehicular Ad hoc Networks (VANETs) is an important research field as it can prevent serious damage within the network and enhance security and privacy. In this regard, a number of approaches based on machine learning (ML) algorithms have been proposed. However, they encounter several challenges due to data being constantly generated over time; this can impact the performance of models trained on fixed datasets as well as cause the need for real-time data analysis to obtain timely responses to potential threats in the network. Therefore, it is crucial for machine learning models to learn and improve their predictions or decisions in real time as new data become available. In this paper, we propose a new approach for attack detection in VANETs based on incremental online machine learning. This approach uses data collected from the monitoring of the VANET nodes' behavior in real time and trains an online model using incremental online learning algorithms. More specifically, this research addresses the detection of black hole attacks that pose a significant threat to the Ad hoc On Demand Distance Vector (AODV) routing protocol. The data used for attack detection are gathered from simulating realistic VANET scenarios using the well-known simulators Simulation of Urban Mobility (SUMO) and Network Simulator (NS-3). Further, key features which are relevant in capturing the behavior of VANET nodes under black hole attack are monitored over time. The performance of two online incremental classifiers, Adaptive Random Forest (ARF) and K-Nearest Neighbors (KNN), are assessed in terms of Accuracy, Recall, Precision, and F1-score metrics, as well as training and testing time. The results show that ARF can be successfully applied to classify and detect black hole nodes in VANETs. ARF outperformed KNN in all performance measures but required more time to train and test compared to KNN. Our findings indicate that incremental online learning, which enables continuous and real-time learning, can be a potential method for identifying attacks in VANETs.  
Keywords:VANETs; routing attacks; detection; incremental learning; online learning
## 3 Results and Discussions
### Comparing the Different Proposed Data Segmentation Methods  
The purpose of this experiment is to evaluate the impact of sliding-window segmentation on deep-learning-based fall-detection approaches. It has not been evaluated how segmentation impacts fall datasets. An inappropriate segmentation method, such as the conventional sliding window, can decrease the classifier's overall performance. We found that using multi-window segmentation increased the F1 score for our dataset. There is a difference in the length of the fall data between the different datasets, and the highest F score can be obtained when the window size is the same as the fall data. When the length of the fall data is known and uniform, selecting the window size is trivial. The real world, however, includes fall data of varying lengths. As this dataset has a varied length of fall data, the results from our dataset, shown in Table 1, can more accurately reflect the classifier's performance in real time. Multi-window segmentation has a better result than the other two segmentation methods.  
Although conventional sliding windows use overlapping windows, the window signal for a fall event does not always align perfectly with the fall patterns observed in the input signals. As a result, our classifiers would ordinarily produce false negatives and false positives. To solve this problem, we propose more robust data-segmentation methods. When windowing from peak signals, the window is aligned with the fall patterns of the input signals. Nevertheless, capturing the entire fall in one observation window remains difficult. Using multi-windows, we can segment the data according to the class of labels. Multi-windowing segmentation not only provides better results but also helps detect falls on a real-time basis (0.2 s). Because the data are segmented based on the label, we can make the input sequence (observation windows) small.
# Effect of Pseudomonas Fluorescens on Isofetamid Dissipation and Soil Microbial Activity
## 2 Materials and Methods  
### Pot Experiment  
This study was conducted as a pot experiment in a phytotron (Pol-Eko KK 1450 TCP+). KENJA 400 S.C. was applied at a dose of 1 L/ha (IS) containing the active substance isofetamide (400 g/L) to pots with an area of 0.08 m\({}^{2}\) containing agricultural soil (black sandy loam) free from isofetamide and 5-week lettuce seedlings at the 3 leaf stage (BBCH-13, from German: Biologische Bundesanstalt, Bundesortenamt und CHemische Industrie). Then, 24 h after isofetamid application, _Pseudomonas fluorescens_ (ATCC 13525) inoculum (P) was added as a spray at a concentration of 10\({}^{6}\) cfu/mL suspended in King B broth medium. The strains were obtained from ATCC (American Type Culture Collection). The control was a variant with soil without fungicide application. Soil samples for biological tests were collected from a depth of 0-5 cm after 24 h (D1) and 21 days (D21) after isofetamid application, while pesticide dissipation was determined after 24 h (D1), 4 days (D4), 9 days (D9), 14 days (D14), 21 days (D21) and 31 days (D31). The experiment was performed in three replicates. The following research variants were obtained: C (control), IS (variant with isofetamid) and IS+P (variant with isofetamid and _P. fluorescens_ innoculum). The properties of the soil used in the experiment were as follows: pH 7.2; total nitrogen content N 0.75%; total phosphorus content P 0.12%; total organic carbon content (TOC) 3.21%.
# Mitigating Timing Side-Channel Attacks in Software-Defined Networks: Detection and Response +
## 3 Proposed Methodology  
In contrast to other forms of attacks, timing side-channel attacks can be performed gradually. Consequently, they stand out and are challenging to detect and avoid. Timing attacks allow an attacker to infiltrate a network using legitimate probing packets to ascertain RTT. Consequently, typical methods based on the traffic volume, packet size, or packet type cannot be used to mitigate such threats. Machine learning is an artificial intelligence (AI) component that enables software programs to become increasingly adept at forecasting without explicitly designing them to do so. Machine learning algorithms use historical data as inputs to anticipate new output values. These features make this technology an excellent option for identifying timed side-channel attempts. The attributes of the timing probe activity are fed into the machine learning model and the prediction model determines whether the incoming packets are attack probes or regular traffic. We used machine learning to develop the proposed solution as it can predict the results accurately and effectively. To determine the most suitable classification technique, we compared the results of our model with those of four classification methods. The response mechanism is distinct because of its strategic use of historical data, which enables it to expedite response techniques against the roots of malicious activities. Compared with some of the more common response mechanisms, which generally value the speed of the reaction above thorough investigation, this approach stands out. Our proposed technique generates a customised response, as opposed to the potentially ineffective strategy of blocking all traffic from a specific IP address. This tailored response is formulated using various parameters such as the IP of the destination and the type of traffic. The resulting customised flow entries ensured the preservation of legitimate traffic.
# Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis
## 4 Results  
### IBI Distributions Estimation  
The normalized IBI histograms providing the probability density function (pdf) were extracted for each experimental task of the three datasets under investigation. The IBI histograms were grouped according to their experimental phase and induced affective state (neutral, stress). For each aggregated histogram, the best fitted distribution to the data was extracted among the candidate distributions (beta, exponential, gamma, logistic, log-logistic, lognormal, normal, Rayleigh, Weibull distributions). The aggregated IBI histograms for each experimental phase along with their best fitted distributions, are shown in Figure 3.  
Inspecting the behaviour of the distribution for each task in Figure 3, it can be deduced that the stressful states (e.g., interview, emotional recall) distributions have offset to the right in relation to the stressful state for the first 2 phases. This can be partially attributed to the fact that during stress conditions eye blink rate increases. The best fitted distribution on each phase is lognormal in all cases except phase 3 neutral state which is log-logistic. All best-fitted distributions are presented in Table 3. The differentiation of distribution of IBI that each task follows for the dataset was checked using 2 samples Kolmogorov-Smirnov test [25].  
It can be observed that in most cases the best-fitted distribution among the distributions described is the lognormal distribution. During the 3 out of 4 experimental phases (social exposure, emotional recall and mental tasks), the 2-sample Kolmogorov-Smirnov \(\chi^{2}\) test revealed that the data of neutral and stress states come from different distributions. Then, it was investigated whether the IBI distributions present significant differences between the two emotional states (neutral, stress) for each experimental phase utilizing all the 3 available datasets. The IBI comparison was performed using the Z-test of two log-normal samples [26] and the results are shown in Table 4.  
Figure 3: Normalized histograms of IBI for each experimental phase (**phase 1–4**) and induced affective state (neutral blue, stress red) and the corresponding best fitted distribution.  
It can be observed, that during the social exposure, emotional recall and mental tasks a statistically significant difference between the neutral and stress state is present. Especially, during 2 of these phases (social exposure, emotional recall) significantly reduced IBI exists during stress compared to neutral states. These results indicate that during stress conditions there are increased blinks, as supported also in relevant literature [7,8].
## 3 Results and Discussion
### Responses of Vegetation Dynamics to Climatic Drivers
#### 3.3.5. Temporal Variation in NDVI in Typical Regions
Figure 12: (**a**) Mean NDVI; (**b**) mean temperature and (**c**) accumulate precipitation in the growing season in Loess Plateau from 1982 to 2010.  
after 2001 was much higher than any other regions in China that it can hardly be only explained by temperature rise. The study by [PERSON] _et al._[61] showed that from 1999 to 2010, the total afforestation area was \(5165\times 103\) h\(\mathrm{m}^{2}\) in Shanxi province, mainly located in Loess Plateau, ranked the second in all provinces of China, which can partly explain the extremely high increasing rate of NDVI in this area. Therefore in Loess Plateau, NDVI increased rapidly after 2001 due to temperature rise and afforestation. (3) Central China  
Central China (\(27.5^{\circ}\)N\(-35^{\circ}\)N, \(105^{\circ}\)E\(-115^{\circ}\)E), mainly covered with deciduous broadleaf forest, belongs to the humid or semi-humid zone. The vegetation growth was dominated by temperature in this area (Figure 9a). As Figure 13 shows, in response to increasing temperature, the growing season NDVI in this area strongly increased by \(1.41\times 10^{-3}/\)year on average. Although both NDVI and climatic drivers varied stably without an abrupt change point, the interannual fluctuation of the NDVI series was quite large. [PERSON] _et al._[32] and [PERSON] _et al._[62] suggested that extreme climate such as drought and floods could have great impact on vegetation growth. The influence of extreme climate can be universal while here we only take drought in central China as an example. We analyzed the relationship between NDVI and drought in central China using the 3-month Standard Precipitation Index (SPI) [63], a good indicator of drought in terms of soil water available for vegetation [64]. Negative lower SPI  
Figure 13: (**a**) Mean NDVI; (**b**) mean temperature; (**c**) accumulate precipitation in the growing season and (**d**) mean SPI in winter and spring in Central China from 1982 to 2010.  
values mean more severe drought. NDVI series corresponded well with winter-spring mean SPI (mean SPI values from December of last year to May) (Figure 13d). For example, in 1984, 1999, 2000 and 2005 when NDVI fluctuated to minima (Figure 13a), the corresponding SPI values were low as well (Figure 13d), suggesting that drought before or during the early growing season could play important role in impeding vegetation growth in this area, which is consistent with previous study by [PERSON] _et al._[62] suggesting that spring drought has significant influence on vegetation growth in Inner Asia. Hence the growing season NDVI in central China was promoted by rising temperature, meanwhile the interannual fluctuation was influenced by drought.
# Vegetation Growth Response and Trends after Water Deficit Exposure in the Loess Plateau, China
## 2 Materials and Methods
### Data Sources
#### 2.2.4 Solar-Induced Chlorophyll Fluorescence (SIF)  
Solar-induced chlorophyll fluorescence was selected for comparison with NDVI and EVI data, as it provided a signal corresponding to vegetation photosynthesis and was a more accurate assessment of the actual photosynthetic state of the vegetation than were traditional indices [46; 47]. Integrating SIF with traditional vegetation indices provided a comprehensive analysis of vegetation status, enabling the dynamic monitoring of vegetation growth changes. A global, OCO-2-based SIF dataset (GOSIF) was generated by a machine learning approach based on discrete OCO-2 SIF soundings, remote sensing data from the MODIS, and meteorological reanalysis data through a data-driven approach [48].