esa-sceva/satcom-chunk-collection huggingface dataset:
# QiOi: Performance Isolation for Hyperledger Fabric
## 3 Motivation
### Motivating Example  
This subsection demonstrates that the performance of Fabric can be affected by co-located tasks. In the same experimental setting in Section 3.1, we run the sysbench CPU benchmark [34] on containers (i.e., sysbench containers). The sysbench CPU benchmark is one of the most commonly used benchmarks in the related works and performs computation-intensive operation similar to the map phase in Spark [22; 35]. We therefore believe believe that sysbench represents a realistic interference case in multi-tenant environments. In addition, we measured the performance and CPU utilization of Fabric and sysbench containers, respectively. Furthermore, we increased the number of sysbench containers from 0 to 16 and compared the results. Note that when Fabric components run with 16 sysbench containers concurrently, the maximum CPU utilization of the entire system is 2286% including Caliper and other system daemons. This means that Fabric components and sysbench containers have sufficient computing resources on the experiment server with 24 CPU cores.  
Figure 2(a) shows Fabric performance in terms of throughput (left y-axis as bars) and latency (right y-axis as a solid line) depending on the number of sysbench containers (x-axis). We found that the Fabric throughput decreased, while latency increased as the number of sysbench containers increased. Note that the value of zero indicates that there are only Fabric components without co-located tasks on the host server, which results in the maximum baseline Fabric performance. When Fabric components run with eight sysbench containers concurrently, throughput decreases by 32% and latency increases by 5.7 times compared to the maximum performance. When there are 16 sysbench containers, the Fabric throughput decreases by 53% and latency increases by a factor of 11.5.  
Figure 2(b) depicts the average CPU utilization of the Fabric and sysbench containers (left y-axis as bars) and the average throughput of sysbench containers (right y-axis as a solid line) depending on the number of sysbench containers (\(x\)-axis). As the number of sysbench containers increased, the CPU utilization of sysbench containers increased while that of Fabric components decreased. For example, when eight sysbench containers ran concurrently with Fabric components, the sysbench containers utilized 33% of CPU, while Fabric components consumed 18% of CPU, which is a 40% reduction compared to the case without sysbench containers. Note that throughput of sysbench containers does not decrease even though the sysbench containers run with Fabric components.  
In summary, we show that the CPU utilization of Fabric components decreases when we increase the number of sysbench containers. This results in performance degradation of Fabric up to 53% compared to the maximum performance. Therefore, it is necessary  
Figure 2: Impact of co-located tasks on Fabric.  
to develop the performance isolation technique for Fabric to prevent the performance interference by co-located tasks.
# A Multi-Model Proposal for Classification and Detection of DDoS Attacks on SCADA Systems
## 4 Materials and Method
### Physical Testbed  
The test environment aimed to simulate the industrial control systems of a plant as approximately as possible without completely copying them [28]. In addition, it aimed to contribute to the performance of national and international industrial control system stan  
Figure 2: Organizational chart of the Materials and Method section.  
dards and directives. The preparation and use of a testbed provides a suitable environment for performing real cyberattacks and even observing the results of the attack.  
In order to contribute to cybersecurity research, a testbed environment including a SCADA system was prepared in the study. In this environment there are storage tanks, specific processes are operated, and Modbus TCP/IP communication is used. A SCADA system is usually realized by integrating Modbus communication protocol [29]. A simplified version of a real water plant was shown in this testbed. The SCADA system controls and monitors the water circulation processes and the status of the storage tanks. This section explained the configuration and architectural structure of the prepared SCADA system test environment. The equipment used in the test environment was selected from the components frequently used in real SCADA systems. The architectural structure of the test environment is shown in Figure 3.  
As shown in Figure 3, there are two water circulation and storage tanks in RTUs. There were sensors and actuators connected to RTUs. The sensors monitor the water levels in the tanks, and the water pumps operate according to the levels. In order to prevent problems such as the overflowing of the tanks and running out of water in the tanks, the water level is continuously controlled. In addition, an alarm was generated according to the state of the water level and, thus, attracting the attention of the operator who is interested in the system. LEDs and buzzers are designed for alarm events. Modbus TCP/IP wired and wireless communication protocols were used for communication in the environment. The  
Figure 3: The architectural structure of the testbed.  
data received from the RTUs were transmitted to the MTU and the processes were followed through the HMI simulators on the MTU. Incoming data were checked and stored, and new commands were sent to RTUs.  
Attackers scanned the network and attacked the appropriate RTU. Whether there is an attack or not is checked on MTU. When 5 different DDoS attacks were applied to the RTU, network traffic packets were listened to and recorded separately for each attack. In addition, the same listening and recording operations were performed for baseline operation without attack. Google Colab, an environment offered by Google Research, enables Python coding for machine learning, data analysis, and training. The data were preprocessed in this environment to make the packets suitable for analysis. Pandas' libraries were added to this environment and different models were generated for attack detection using deep learning and machine learning algorithms.
# Advancing the Solar Radiation Pressure Model for BeiDou-3 IGSO Satellites
## 2 Materials and Methods  
### ECOM-Type Models  
To improve GPS orbit accuracy, the ECOM model was developed in the 1990s [8]. The ECOM models the SRP force in a Sun-oriented \(DYB\) frame with axes \(D\) pointing from the satellite to the Sun, \(Y\) along the solar panel axis, and \(B\) completing a right-handed system. The modelled force in each direction is described by a constant and optional periodic terms depending on the satellite's argument of latitude \(u\), which is expressed as follows [8]:  
\[\begin{array}{l}a_{D}=D_{0}+D_{c}\cos u+D_{s}\sin u\\ a_{Y}=Y_{0}+Y_{c}\cos u+Y_{s}\sin u\\ a_{B}=B_{0}+B_{c}\cos u+B_{s}\sin u\end{array} \tag{1}\]  
In general, only five parameters \(D_{0}\), \(Y_{0}\), \(B_{0}\), \(B_{c}\), \(B_{s}\) are estimated for the proper modeling of SRP forces acting on GPS satellites, which is called the five-parameter ECOM model or ECOM1 model. It is worth mentioning that some studies show that the \(D_{s}\) term should be considered during eclipse seasons and kept also active during Earth's shadow transitions because it helps to improve the orbit quality during eclipse seasons [22; 23]. In this study, the ECOM1 model with the \(D_{s}\) term active also in Earth's shadows is named the ECOM1D model.  
However, as mentioned earlier, the ECOM1 model has been found to cause systematic once-per-revolution radial orbit errors for navigation satellites with a notably stretched body, such as Galileo and BeiDou-3 MEOs [9; 13]. In order to cope with this systematic effect, the extended ECOM model (ECOM2) was developed [11]:  
\[\begin{array}{l}a_{D}=D_{0}+\sum\limits_{i=1}^{n_{D}}\{D_{2|c}\cos(2i\Delta u )+D_{2|s}\sin(2i\Delta u)\}\\ a_{Y}=Y_{0}\\ a_{B}=B_{0}+\sum\limits_{i=1}^{n_{B}}\{B_{2i-1,c}\cos((2i-1)\Delta u)+B_{2i-1,s}\sin((2i-1)\Delta u)\}\end{array} \tag{2}\]  
where \(\Delta u\) is the difference between the satellite's argument of the latitude and the Sun's argument of the latitude in the orbital plane, and the upper limit values \(n_{D}\) and \(n_{B}\) are defined by users. At the beginning of 2015, the nine-parameter ECOM2 model (\(n_{D}=2,n_{B}=1\)) was recommended by CODE. Since 28 June 2015, the estimated parameter number of the ECOM2 model in CODE products was reduced from 9 to 7 by excluding 4-times-per-revolution parameters as they deteriorated the GLONASS orbit solutions [24]. Following the CODE data processing strategy, the seven-parameter ECOM2 model (\(n_{D}=1,n_{B}=1\)) is used in this study.
## 2 Materials and Methods
### Analysis and Characterizations
#### 2.3.2 Nano Yarn Tensile Strength Testing  
The tensile strength test was fulfilled to find the strength of dry warp yarn in the extraction from nanocoated p-aramid fabric (120 mm \(\times\) 300 mm, width and length, respectively). The TS EN ISO 2062 test standard was generally followed [57]. The warp yarn linear density and length were 336 tex and 150 mm, respectively. The testing instrument was the Instron 4411 and the testing speed was 150 mm/min. Therefore, the tensile strength of nano-processed yarn was obtained in order to identify the possible effect of the nanocoating on the filamentary yarn structure. The crimp of the nanocoated warp yarn was disregarded, and the nanocoated filaments exhibited no entanglements, with nanoparticles evenly distributed across the filament surfaces. Yarn tensile strength was calculated by using Equation (1) as follows:  
\[\sigma_{y}=\frac{F_{max}}{W_{y}} \tag{1}\]  
where \(\sigma_{y}\) is the yarn tensile strength (N/tex, tenacity), \(F_{max}\) is the max. tensile force, and \(W_{y}\) is the yarn linear density (g/1000 m, tex).
# Technology and Data Fusion Methods to Enhance Site-Specific Crop Monitoring
## 2 Sensor Types
### Multispectral Sensors  
The launch of satellite missions by government and commercial companies have resulted in further progress and revisiting period of multispectral systems [99]. The development and use of lightweight and compact multispectral sensors was rapidly followed by their wide adoption for the collection of spatial resolution data [100]. A multispectral camera array (Parrot SA France, MicaSense, and Tetracam, among others) contains a standalone camera for the efficient acquisition of reflected radiation from the target source [101]. The data collected are then processed to deliver a single compound image with different spectral bands. This system is exclusively developed for crop parameter monitoring and offers benefits, such as low cost, simple solutions, and reliable software choices [102]. The applications of multispectral sensors, such as RapidEye, Sentinel-2 and WorldView-2 series, are considered as a trade-off between support delivered by remotely sensed data. [PERSON] et al. [103] applied this system and tested whether field spectrometry estimations resampled to various multispectral sensor resolutions can be used in maize production.
## 4 Results and Discussion
### Cognitive Load and Task Performance  
The NASA TLX experiment results showed that the CL of participants in immersive and non-immersive environments increases compared with paper-based drawings. Because both VEs can be very distractive and over stimulative as a realistic three-dimensional 360\({}^{\circ}\) experience, learners will obtain a lot more information from these VEs than they will obtain from the traditional medium [71]; that is why in our experiment, participants' mental demand was slightly high in the VEs. Temporal demand was also less for the paper-based and monitor-based medium than for the Oculus Quest 2 because the immersive environment has a higher level of immersion and temporal dissociation [72].  
Participants obtained higher CL in the Oculus Quest 2 at 2.15 compared with monitor-based and paper-based at 2.045 and 2.005, respectively. Still, their performance was better in the VRE considering the time required to complete the task and identifying the number of errors in the design review process because participants had to focus on a single source of information or display system at a time. Nevertheless, the simulated VEs reduced the participants' design review effort because, in the simulated environment, one should not shift the gaze between the pages. That is why the effort was less for these environments than a traditional paper-based review. Virtual display systems have reduced the effort to change page shifts of the drawings, which positively impacts the participants' performance. It provides the idea of dimensions or depth information of drawing such as the distance between the slab and door and windows, slab and the floor height, size of beam and column, and whether their size is optimum or not because participants can adjust their height in the VE. This led to an increase in participants' performance in the VEs compared with the traditional environment when it came to finding errors in the design of the building. Moreover, the participants of the latter two environments completed the task earlier than the first group of participants.
## 2 TBM Selection Decision-Making Method
### Analysis of Influencing Factors  
When the TBM method is adopted for construction, appropriate TBMs shall be selected for construction according to different construction conditions. The TBMs mainly include the following three types: open TBM, single-shield TBM and double-shield TBM. In this study, the influencing factors of TBM selection are determined according to the engineering practice, TBM tunnelling experience and the comprehensive consideration of the advantages and disadvantages of the different types of TBM. This paper has summarized three main influencing factors as follows: 1. Geological conditions; 2. adverse geology; 3. tunnel design.  
1. Geological conditions. One of the geological conditions affecting the TBM selection is the uniaxial compressive strength (_UCS_) of rock mass, and the other is the integrity of rock mass (_K_v). Open TBM is mainly applicable to hard rock stratum with relatively complete rock mass. Single-shield TBM is mainly applicable to soft rock with a certain self-stabilizing ability. Double-shield TBM is mainly applicable to a soft rock-hard rock stratum with relatively complete rock mass and a certain self-stabilizing ability. Based on this, two factors affecting the TBM selection are determined, namely _UCS_ and _K_v.  
Figure 1: Flow chart of TBM selection decision-making model.  
2. Adverse geology. Adverse geology will lead to a significant reduction in TBM tunnelling efficiency, and even cause serious problems, such as TBM jamming and shutdown. Different types of TBM have different adaptability levels to adverse geology. For example, when the open TBM meets a rock burst disaster, the protection of personnel and equipment is poor. Shield TBM can resist rock bursts to a certain extent. After comprehensive consideration, four factors affecting adverse geology are determined in this paper, including rock burst, water inrush, fracture zone and large deformation of the surrounding rock.
3. Tunnel design. The design scheme of the tunnel will also limit the selection of TBM, and the turning radius of different types of TBM is different. Therefore, the size of the radius of the tunnel's flat surface will limit the selection of TBM. In the aspect of tunnel design, one influencing factor is determined as the radius of the tunnel's flat surface.
## 3 Recent Advances on IEEE 802.15.4-Based WMSN Approaches
### WMSN Solutions Developed on Top of the IEEE 802.15.4 PHY and MAC (Non-beacon Mode) Layers
#### 3.2.2 IETF: 6 LoWPAN and ROLL
The information in the _preamble_ reveals the destination/-s node/s that must remain in active mode to receive data, while the rest of nodes switch to the sleep state to save energy. The main drawback of this approach is the extra-energy wasted by senders to transmit the _preambles_ and by receivers which must activate their radio transceiver a time interval defined by the user to receive it. To conclude, we refer hereafter to the IETF solution composed of 6 LoWPAN and RPL as _6 LoWPAN/RPL_.
# Exploring Multi-Stage GAN with Self-Attention for Speech Enhancement
## 4 Experiments
### Experimental Settings  
This work aims at improving the performance of the existing multi-generator SEGAN networks (i.e., ISEGAN and DSEGAN) with a self-attention mechanism. We conduct severalexperiments on the ISEGAN and DSEGAN networks with and without the self-attention block illustrated in Figure 4 using the training setup presented in Figure 3. We set three experimental objectives:  
* To quantitatively show the effects of a self-attention mechanism in the generators of a multi-generator SEGAN.
* To qualitatively show the effects of a self-attention mechanism in the generation of a multi-generator SEGAN.
* To investigate the overall performance of the model in terms of training the parameters and the generation of the enhanced speech.  
We set up the experiments with the number of generators \(N=\{2,3,4\}\) and with only one discriminator in the ISEGAN network with and without self-attention, and in the DSEGAN network with and without self-attention. The TensorFlow deep learning framework [53] was used to implement the networks, and all the experiments were performed on an 8 GB GPU GeForce RTX 2080 machine. Following previous works [22; 23], each network was trained for 100 epochs using the RMSprop optimization technique [54] with a mini-batch size of 64, and a learning rate set to \(2\times 10^{-4}\). We sampled raw speech segments sampled at 16 kHz after every epoch to monitor performance during training of the different network setups for \(N=\{2,3,4\}\) generators to investigate the effects of self-attention in the multi-generator SEGANs for speech denoising and for generating synthetic speech signals. To further understand the impact of the self-attention mechanism in a specific generator of the multi-generator SEGAN setup, we performed an ablation study by placing the self-attention block in one generator at a time and at different convolutional layers of the generator.
# Multistage Smoothing Compressor for Multistep Pulse Compressors
## 4 Sample Design for a 100 PW Laser
In the asymmetric four-grating compressor, the strongest laser fluence on the last grating G4 is decreased to approximately 296 \(\times\) 0.60 = 178 mJ/cm\({}^{2}\) if the total compression efficiency is 60%. It is 1.28-fold lower than the damage threshold of a compressed femtosecond laser pulse, which is approximately 229 mJ/cm\({}^{2}\)[22,30]. Then, the 2500 \(\times\) 0.60 = 1500 J compressed pulse can support 100 PW peak-power with a 15 fs pulse duration. Since the LSIM of the output laser beam can be smoothed to less than 1.1 after 3 stages of beam smoothing processes, it is theoretically possible to achieve 100 PW using this MS-MPC. However, in practice, achieving approximately 75 PW would be safer and more feasible for a real 10 s to 100 s PW laser system. This is because the laser fluence on the last grating would be almost 1.70 times lower than its damage threshold when the beam LSIM is smoothed to less than 1.2. Note that we only demonstrate the capabilities of this MS-MPC method for 10 s to 100 s of the PW system; the proposed parameters are not the optimized values, where enough margins have already been saved on many parameters, such as the beam size and light or spectral cutting. After the main compressor with the asymmetric four-grating compressor setup, the output laser beam with smoothed spatial intensity and diffused wavefront aberration is focused by PM. At the focal point, the spatial chirp is automatically compensated owing to spatio- temporal focusing. Note that the damage threshold of the optics after the asymmetric four-grating compressor can be several times higher than that of the gratings. This is because the grating with a broad diffraction spectral bandwidth can only be coated with gold, which is the weakest film, whereas the reflective optics after asymmetric four-grating compressor can be coated with other kinds of coating film, of which the damage  
Figure 6: (**a**) One\(-\)dimensional beam profile (before G3) without light cutting (red dotted line) and with about 160 mm light cutting on both sides (blue solid line). The insets on the right are the enlarged diffraction-induced modulations. (**b**,**d**) The simulation results and (**c**,**e**) the experimental results with light cutting on G2. fluence threshold is several times higher. In the future, using a negative chirped pulse, this smoothed laser beam can be used to induce further pulse self-compression in a glass plate after the PM [22; 27].
## 5 Simulation Results
### Trajectory Tracking in the Presence of Disturbance
#### 5.3.1 Hierarchical Controllers  
The circular trajectory in the presence of disturbances is presented in Figures 15 and 16. The flight following a circular trajectory in the presence of disturbances is presented in Figure 15 separately for each axis, and in a 3D view in Figure 16.  
Figure 14: Disturbance signal.  
Figure 13: Quadcopter tracking of a circular trajectory in 3D space (solid red line) achieved using the TTRM and TTGA approaches.  
The results show that the RMHC and GAHC controllers can stabilize the quadcopter, and achieve the desired path in a disturbed environment. However, it can be clearly seen that for both strategies, PID controllers yield better performance in terms of eroor margin between the system response and the desired reference.
# Jointly Optimize the Residual Energy of Multiple Mobile Devices in the MEC-WPT System
## 4 Formalization of the Problem  
The goal of this article was to maximize the residual energy of multiple MDs in multiple time blocks, which requires to maximize the harvested energy and minimize the consumed energy at the same time. The amount of harvest energy is directly proportional to the energy harvest time \(t_{i}^{har}\), larger \(t_{i}^{har}\) will surely harvest more energy but also minimize the maximum available time for task offloading, which brings high energy consumption and the risk of violating time constraints. With each given \(t_{i}^{har}\), we should coordinate the task-offloading size \(R_{i,j}^{off}\), the task-offloading time \(t_{i,j}^{off}\) and the CPU frequency of local execution to minimize the energy consumption. In short, in order to maximize the residual energy of MDs, we need to jointly optimize the energy harvesting time, task-offloading size, task-offloading time and CPU frequency to find the optimal combination. The following is the formulation to describe the residual energy maximization problem under multiple MDs and multiple time slots.