esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Proposed Taxonomy of the Attacks, DDoS Attacks, and DDoS Detection Approaches on CC
### Taxonomy of DDoS Attack Detection Approaches on CC
#### 5.3.4 Entropy-Based Detection  
Entropy is the ratio of arbitrariness in the data. Entropy-based detection approach analyzes random data, the entropy, or the Shannon-Wiener index to evaluate uncertainty associated with the data. Maximum randomness in the data implies a maximum entropy value [137; 138]. For example, if the data only has one class, its entropy value will be lower. On the contrary, the data with numerous classes will have a higher entropy value. This way, the tested headers are broken down for port and IP, and their entropy is computed. Entropy is usually used to calculate the randomness of IP source addresses or port numbers. A high entropy value indicates the traffic originates from various sources, which is the clue to detecting DDoS attacks [139]. A threshold can be put in place to distinguish DDoS attack traffic from normal traffic. The administrator should be alerted of DDoS attacks if the entropy value exceeds the threshold. If the detection of DDoS attacks involves multiple levels, the procedure can be partitioned into three stages:  
* First stage: The client is permitted to go through the switch, and the detection calculation confirms that it is genuine. * Second stage: The entropy is calculated based on the data packet size and the client's authentication. * Third stage: The entropy value is compared with the threshold to determine if it is a DDoS attack or not. Once the location of any abnormality is discovered, an information message is sent to CSP owners to take necessary action. The authors in [140] proposed an approach to detect HTTP flooding DDoS attacks in a CC using information-theoretic entropy (ITE) and ML \begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Refs** & **Detection** & **Feature** & **Features Used** & **Parallel Processing** & **Algorithm Used** \\ \hline
[133] & L3,7 & Unknown & Block Device Write Request (Block Device Write Reql), Block Device Read Reql (Block Device Read Reql), Packets Transmitted (Packs TX), Packets Received (Packs RX), and CPU Utilization (CPU Util) & Unknown & ✗ & NA \\
[111] & L3,4,7 & Unknown & IP MAC address, port, data content, Size, Protocol, Time, TTL & Unknown & ✗ & E-CARGO, Advanced Research Projects Agency (DARPA), Common Intrusion \\
[134] & L3,4,7 & Unknown & protocol, port numbers, traffic, HTTP, telen, flag, source bytes, destination bytes & KDD99 & ✗ & IOS decision tree, Random Forest, OneR \\
[129] & L3,7 & Unknown & Failed login attempts number, flag, src byte, DST byte, land, root access number, protocol, service, age error, TCP/IP connection. Duration, protocol type, service used, urgent, run failed logins, logged in, root shell, num root, count, syn error rate, rejection error rate & KDD99 & ✗ & Decision Tree, SVM \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Hybrid-based DDoS detection approaches on CC.
###### Abstract  
The magnitude of ecosystem services provided by winter cover crops is linked to their performance (i.e., biomass and associated nitrogen content, forage quality, and fractional ground cover), although few studies quantify these characteristics across the landscape. Remote sensing can produce landscape-level assessments of cover crop performance. However, commonly employed optical vegetation indices (VI) saturate, limiting their ability to measure high-biomass cover crops. Contemporary VIs that employ red-edge bands have been shown to be more robust to saturation issues. Additionally, synthetic aperture radar (SAR) data have been effective at estimating crop biophysical characteristics, although this has not been demonstrated on winter cover crops. We assessed the integration of optical (Sentinel-2) and SAR (Sentinel-1) imagery to estimate winter cover crops biomass across 27 fields over three winter-spring seasons (2018-2021) in Maryland. We used log-linear models to predict cover crop biomass as a function of 27 VIs and eight SAR metrics. Our results suggest that the integration of the normalized difference red-edge vegetation index (NDVI_RE1; employing Sentinel-2 bands 5 and 8A), combined with SAR interferometric (InSAR) coherence, best estimated the biomass of cereal grass cover crops. However, these results were season- and species-specific (R\({}^{2}\) = 0.74, 0.81, and 0.34; RMSE = 1227, 793, and 776 kg ha\({}^{-1}\), for wheat (_Triticum aestivum_ L), tritcate (_Triticale hexapolide_ L.), and cereal rye (_Scale cereale_), respectively, in spring (March-May)). Compared to the optical-only model, InSAR coherence improved biomass estimations by 4% in wheat, 5% in tritcate, and by 11% in cereal rye. Both optical-only and optical-SAR biomass prediction models exhibited saturation occurring at \(\sim\)1900 kg ha\({}^{-1}\); thus, more work is needed to enable accurate biomass estimations past the point of saturation. To address this continued concern, future work could consider the use of weather and climate variables, machine learning models, the integration of proximal sensing and satellite observations, and/or the integration of process-based crop-soil simulation models and remote sensing observations.  
winter cover crops; biomass; optical synthetic aperture radar integration; remote sensing; winter cover crop performance 20221420077[[https://doi.org/10.3390/rs14092077](https://doi.org/10.3390/rs14092077)]([https://doi.org/10.3390/rs14092077](https://doi.org/10.3390/rs14092077))
# Sewer Cleaning Robot: A Visually Assisted Cleaning Robot for Sewers
## 2 Hardware System Design of SCR
### Control System Hardware Parameters  
The hardware control system of SCR is shown in Figure 3. The main controller collects video data from both the front and rear cameras and sends control commands to various subsystems. To address the slippery working conditions within sewers, an integrated servo hub motor with an IP65 waterproof rating is selected as the primary driving component. This motor is controlled by two 24 V dual closed-loop brushless DC servo drivers, and the drives receive speed control commands via the RS485 communication protocol. The pressing mechanism consists of two linear actuators, which are controlled by a single dual-synchronous driver. This driver also receives position control commands through the RS485 protocol. The rolling DoF for the arm is achieved using a 48 V hollow integrated motor, which provides a rolling range of \(\pm\)180\({}^{\circ}\). The motor receives angle control commands via the CAN communication protocol. The pitch DoF of arm employs an actuator drive controller identical to that used in the pressing mechanism.  
The overall system uses five linear actuators. The end-effector uses a 48 V waterproof high-speed motor, delivering a continuous power of 1400 W and reaching a maximum speed of 3000 rpm. This motor is regulated by a DC chopper PWM three-phase driver with a peak current of 30 A. Finally, the camera gimbal is controlled by a 24 V DC servo motor, which receives position control commands through the RS485 protocol, enabling the precise directional adjustments of the camera.
## 5 Two-Way Measurement Method in CCFD  
Introducing CCFD into the BDS-3 ISL system enables simultaneous transmission and reception of signals on the same frequency, thereby enhancing the overall performance of the constellation. This section provides a detailed analysis of the fundamental principle, system model, and key errors of inter-satellite two-way measurement in CCFD. It has been proved in theory that this approach can achieve higher ranging precision.
## 3 Experiments and Results Analysis
### Comparative Analysis of Experimental Results
#### 3.3.2 GEFCom2012 Dataset  
To further validate the generalizations of the proposed model, all of the contrast experiments mentioned above were performed using the GEFCom2012 dataset. Tables 5-7 show the maximum, minimum and average values of the MAPE, RMSE and R\({}^{2}\), respectively. One can see that compared with VCLA, the VCTA model significantly reduces the MAPE by 45.7% and the RMSE by 30%. This again demonstrates that the VCT-based models effectively extract the in-depth features from the load series. Furthermore, the proposed model that is stacked with LSTM based on the VCTA outperforms the latter by 12.7% and 14.5% in terms of the MAPE and RMSE, respectively, which further proves that LSTM can enhance the capture of temporal dependencies. Additionally, the influence of SAM on the extraction of important features from the load data can also demonstrated. One can find that the proposed model further decreases the MAPE by 15.2% and the RMSE by 12.2% compared with the VCTL. At the same time, the R\({}^{2}\) results of all of the models are over 99%, which demonstrates the effectiveness of VDM decomposition; moreover, the R\({}^{2}\) value of the proposed model is the highest among these models. Therefore, these above analyses show that the proposed model has good generalization capabilities in power load forecasting.  
Figure 11: Maximum, minimum and average values of RMSE for each model based on ISO-NE dataset.  
Figure 10: Load forecasting profiles over 24 h based on ISO-NE dataset when the MAPE is a maximum (**a**) and a minimum (**b**).  
Figure 12 shows the predicted load over 24 h and the actual load data when the MAPE is a maximum and a minimum, respectively. As shown in Figure 12, all of the models are in accord with the trends in the actual load during the rising or falling stages. However, the proposed model is better able to capture trends in the actual load compared with the deviations of the other models, especially during the turning area. Furthermore, in comparing Figures 10 and 12, one can see that the changing trends of the GEFCom2012 dataset have more peaks and valleys, which proves that the GEFCom2012 dataset has more complexity and volatility compared to the ISO-NE dataset. Thus, the evaluation indices of the load forecasting for all of the models in the GEFCom2012 dataset are higher than those of the corresponding models with the ISO-NE dataset. In addition, Figure 13 shows the maximum, minimum and average values of the RMSE for all of the models with the GEFCom2012 dataset. It can be seen that although the GEFCom2012 dataset has obviously nonlinear and volatile characteristics, the proposed model still achieves the best reliability and the highest prediction accuracy and stability.
# Reliability Analysis of an Air Traffic Network: From Network Structure to Transport Function
###### Abstract  
To scientifically evaluate the reliability of air traffic networks, a definition of air traffic network reliability is proposed in this paper. Calculation models of the connectivity reliability, travel-time reliability, and capacity reliability of the air traffic network are constructed based on collected historical data, considering the current status and the predicted future evolution trends. Considering the randomness and fuzziness of factors affecting reliability, a comprehensive evaluation model of air traffic networks based on the uncertainty transformation model is established. Finally, the reliability of the US air traffic network is analyzed based on data published by the Transportation Statistics Bureau of the US Department of Transportation. The results show that the connectivity reliability is 0.4073, the capacity reliability is 0.8300, the travel-time reliability is 0.9180, and the overall reliability evaluated is \"relatively reliable\". This indicates that although the US structural reliability is relatively low, the US air traffic management is very efficient, and the overall reliability is strong. The reliability in nonpeak hours is much higher than that in peak hours. The method can identify air traffic network reliability efficiently. The main factors affecting reliability can be found in the calculation process, and are beneficial for air traffic planning and management. The empirical analysis also reflects that the evaluation model based on the uncertainty transformation model can transform the quantitative data of network structure and traffic function into the qualitative language of reliability.  
air traffic network; reliability; uncertainty transformation model; capacity; travel time
# Research on Rolling Bearing Fault Diagnosis Method Based on ECA-MRANet
###### Abstract  
Most fault diagnosis models use a single input and have weak generalization performance. In order to obtain more fault information, a fault diagnosis method based on a Multi-channel Residual Attention Network with Efficient Channel Attention (ECA-MRANet) is proposed in this paper. In this method, the original time domain signal is first processed by a multi-domain transform, the result of which is input to the MRANet for feature extraction. Finally, the extracted features are fused by ECA to realize fault identification. The experimental results show that the proposed method can enhance the ability of the network to discriminate key features, and shows good generalization performance under different working conditions and with small-sample transfer between data sets.  
fault diagnosis; attention mechanism; feature fusion; ECA-MRANet +
Footnote †: journal: Applied Sciences
# Ultrafast Optical Signal Processing with Bragg Structures
## 1 Introduction  
In the next generation of high-speed information networks, the direct processing of optical signals is required. On the other hand, the basic signal-processing capabilities, such as switching, logic operations, and buffering, are still lacking in practically usable forms. Useful for achieving these objectives should be the deceleration of optical signals and the creation of standing ones. These effects have been demonstrated with the help of various techniques, such as electromagnetically-induced transparency [1; 2], but those interference-based techniques are often not suitable for broadband signal processing, when the carrier waves are represented by picosecond or even femtosecond pulses [2]. The light can also be retarded in optical fibers by a stimulated Brillouin scattering effect [3], and in photonic-crystal waveguides by manipulating the dispersion [4; 5]. Light-matter interactions can be enhanced by the retardation of light. Since the light-matter interaction time \(t\) is inversely proportional to the group velocity \(v_{g}\), the use of slow light with a small \(v_{g}\) implies longer interaction times, and consequently, a more efficient energy conversion [6]. Slow light also offers the possibility to compress optical signals in space, thus reducing the device size [7; 8].  
In this article, we review both theoretical and experimental results concerning the processing of ultrafast optical signals in one-dimensional (1D) Bragg gratings, which exhibit a 1-D photonic bandgap that makes it possible to significantly reduce the speed of light launched at a carrier frequency close to the bandgap. We discuss both artificially engineered Bragg structures, made of optoelectronicmaterials, and those produced by natural self-assembly, such as cholesteric liquid crystals. We provide a critical review of the performance of the Bragg gratings and the limitations in their use. In particular, we describe how some of the inevitable deleterious effects that accompany a strong dispersion experienced by light at the band-edge, can be balanced by the material nonlinearity, which provides the laser-induced self-phase modulation of the optical field. Theoretical modeling of these processes in some Bragg structures have shown that ultrafast laser pulses can be decelerated, stopped, and buffered; as a result, stationary nonlinear optical frequency conversion can be very efficient, even with a very thin resonant absorption Bragg reflector sample [9; 10; 11; 12; 13]. In a tailored optical structure, such as one into which defects and spatial chirp are integrated, various optical logic operations can be efficiently realized [14; 15; 16]. Recently, BG structures consisting of cholesteric liquid crystals (CLCs), which possess extraordinarily large ultrafast optical nonlinearities due to photonic crystal band-edge enhancement, have also been shown to be highly effective for direct-action compression, or for the stretch and recompression of pico- and femto-second pulses, opening up new possibilities for efficient broadband optical-signal processing [17; 18].
## 2 System Model
### Wi-Fi Channel Model  
The Wi-Fi channel gain for user \(j\) is expressed as  
\[G_{\rm WiFi}^{j}=|H_{\rm WiFi}|^{2}10^{\frac{-L(d_{j})+X_{\sigma}}{10}} \tag{5}\]  
where \(H_{\rm WiFi}\) is the channel transfer function, which follows the standard Rayleigh distribution; \(X_{\sigma}\) represents shadow fading in the Wi-Fi channel, typically modeled using a zero-mean lognormal distribution to simulate the random variation in signal strength due to obstacles, with a standard deviation of 10 dB [22]; \(L(d_{j})\) denotes the free-space path loss, and \(d_{j}\) is the length of the communication link between Wi-Fi AP and user \(j\). The signal-to-noise ratio (SNR) between the Wi-Fi router and user \(j\) is formulated by  
\[\xi_{\rm WiFi}^{j}=\frac{G_{\rm WiFi}^{j}P_{\rm WiFi}}{N_{\rm WiFi}B_{\rm WiFi}} \tag{6}\]  
where \(N_{\rm WiFi}\) is the power spectral density of the noise at the receiver, while \(B_{\rm WiFi}\) and \(P_{\rm WiFi}\) represent the system bandwidth and transmit power of the Wi-Fi AP, respectively.
## 4 Discussion
### Analysis of Physical and Chemical Properties of Biosurfactant  
Physical properties viz., SFT, CMC, IFT, CA, emulsification clearly proved the potency of CFBS. We observed the maximum reduction in SFT of PBS from 72 to 27 mN/m at a CMC value of 625 ug/mL (Figure 2A,B). In addition, noticeable reduction in CA on various surfaces and IFT values against various hydrocarbons entirely proved the potency of CFBS isolated in the present studies (Figures 3 and 4). It is important to note that the CA measurements of CFBS isolated from _Lactobacillus_ sp. have hardly documented in literature. The emulsification with n-decane, and xylene was observed to be comparatively better. Our results are comparable with the [PERSON] et al. [13] for emulsions of octane/water stabilized by BS isolated from _L. pentosus_ and SDS. Along with these results; the stability of BS at a wide physiological condition would contribute towards broadening its scope as antib Biofilm agent. Our studies revealed the anionic nature of the CFBS. Similar results were reported by [PERSON] et al. [35] for the BS isolated from _Enterococcus facecium_. Structural examination of CFBS confirmed the glycolipid type. Closer observation in literature, we can say that our results are comparable with that of the work reported by [PERSON] et al. [5; 6] who suggested the Xylolipidic type BS from _Lactobacillus_ spp. In the literature, there are some instances, where researchers have claimed the production of glycolipid type BS from _Lactobacillus_ sp. [5; 6].
# High-Performance Elastic Management for Cloud Containers Based on Predictive Message Scheduling
## 1 Introduction  
Container technology, represented typically by Docker (dotCloud, Inc., San Francisco, CA, USA), has been widely used by a wide range of sandbox environments to deploy applications based on operating systems with a time-sharing multiplexing mechanism. Containers can be started quickly, with little resource consumption. The combination of container and micro-service architectures provides an application-oriented lightweight infrastructure that can be extended by creating replicas of bottleneck components [1]. In container-based micro-service architectures, there are a great deal of log data, such as user behavior and system logs, which default to real-time consumption, and are not suitable for asynchronous processing; additionally, there exists the problem of message coupling and response delay [2]. Therefore, dynamic adjustment of message queues is studied in this paper. In the micro-service architecture, a user's events are achieved by calling a number of micro-services; therefore, event-based micro-services can be decoupled by optimizing the messaging mechanism. In the container-based cloud environment [3], existing research mostly focuses on job scheduling, ignoring the impact of message scheduling on container performance. In order to enhance the adaptive of optimization micro-services framework in containers, highly elastic management for cloud containers is researched in this paper. This paper proposes an elastically scalable strategy for containers in cloud system, based on container resource prediction and message queue mapping. By establishing a multi-channel message queue-based processing model to control the scale of the container cluster, we can achieve a decentralized control to address the dynamic expansion of container resources in cloud containers.  
The paper is organized as follows: Section 2 describes the background of our work and the related work; Section 3 describes the framework of the elastically scalable model; Section 4 describes the policy of elastic scalable strategy for containers; Section 5 shows the implementation of the prototype; Section 6 gives the experimental results; and Section 7 draws the conclusions.
# FPGA-Based Frequency Tracking Strategy with High-Accuracy for Wireless Power Transmission Systems
## 4 Experimental Verification
### Frequency-Tracking Experiment
Figure 17 shows the comparison results of transmission efficiency and output power for the two operating modes. In Figure 17, the output power of the WPT system in the fixed-frequency mode gradually increases as the coil distance gradually becomes farther away, and the transmission efficiency first increases and then decreases. The output power of the frequency-tracking mode is significantly higher than that of the fixed-frequency mode at any distance, which  
Figure 16: Different load and transmission characteristics diagram: (**a**) output power versus load resistance diagram; (**b**) transmission efficiency versus load resistance diagram. Figure 17: Different coil distance versus output power and transmission efficiency diagram: (**a**) output power versus coil distance diagram; (**b**) transmission efficiency versus coil distance diagram. is especially obvious within 25 cm, indicating that frequency splitting occurs in this range. After 25 cm, the transmission efficiency of the frequency-tracking mode is higher than that of the fixed-frequency mode. Under the above conditions of varying the distance and loads, Figure 18 shows the relationship between the voltage and current phase difference at the transmitter side for comparing the frequency-tracking mode and the fixed-frequency mode. The figure shows that as the distance and the load resistance increase, the phase difference in the fixed frequency mode gradually changes from less than 0\({}^{\circ}\) to more than 0\({}^{\circ}\), and the system changes from capacitive to inductive. When operating in frequency-tracking mode, whether the system is frequency split or not, the phase difference between the current and voltage at the transmitter side is fixed at about 11.5\({}^{\circ}\). At this time, the system is weakly inductive, and ZVS is maintained to prevent excessive voltage spikes and polarity reversals that may damage the components. This also avoids the excessive equivalent impedance of the system caused by its large phase difference, which significantly increases the system loss. The output power in the frequency-tracking mode is always higher than that in the fixed-frequency mode.