esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Hybrid Process m-IME/H\({}_{2}\)O\({}_{2}\)
Depending on the H\({}_{2}\)O\({}_{2}\) dose and pH correction, an efficiency improvement by 20.6 up to 33.1% was achieved compared to the 120 min m-IME process. By extending the process time of m-IME/H\({}_{2}\)O\({}_{2}\) to 240 min, further efficiency improvement up to 11% was achieved. However, in the case of lower doses of H\({}_{2}\)O\({}_{2}\) (in the ratio COD/H\({}_{2}\)O\({}_{2}\) = 1/1) the correction of the pH value resulted in an improvement in the efficiency of the COD removal max by 8% in relation to the efficiency of m-IME/H\({}_{2}\)O\({}_{2}\), conducted without a correction of pH. In the case of higher doses of H\({}_{2}\)O\({}_{2}\) (in the ratio COD/H\({}_{2}\)O\({}_{2}\) = 1/2), the positive effect was almost insignificant. Figure 6: Total COD removal after the m-IME and m-IME/H\({}_{2}\)O\({}_{2}\) processes carried out for 15–240 min, depending on the COD/H\({}_{2}\)O\({}_{2}\) ratios: 1/1, 1/2 and 1/3. H\({}_{2}\)O\({}_{2}\) addition from the beginning of the z-IME/H\({}_{2}\)O\({}_{2}\) process (sample 1.3). The presented results are innovative and suggest that the mechanism of the process is complex and requires much more extensive research in order to describe it accurately. The observed trends may be related to the change in the stability of the H\({}_{2}\)O\({}_{2}\) molecule depending on pH--as the pH increases, the stability of the molecule decreases. Similarly, the Fenton reaction (8), which results in the formation of hydroxyl radicals, is more effective at pH < 4. On the other hand, acidic impurities present in LL will be more easily oxidized due to the dominance of dissociated forms in solutions at a higher pH. The resultant of all these phenomena determines the efficiency of the process at a given pH value. The efficiency of COD removal in the m-IME/H\({}_{2}\)O\({}_{2}\) process conducted with the addition of hydrogen peroxide after 120 min of the m-IME process was higher than the process carried out with the addition of hydrogen peroxide from the beginning of the m-IME process. For the dose of COD/H\({}_{2}\)O\({}_{2}\) 1/2 hydrogen peroxide after a total time of 150 min of the process (120 min m-IME + 30 min m-IME/H\({}_{2}\)O\({}_{2}\)), the COD removal was obtained after 240 min of the m-IME/H\({}_{2}\)O\({}_{2}\) process (77.7%) with the addition of H\({}_{2}\)O\({}_{2}\) from the beginning, regardless of the pH correction. In the case of dose 1/3, that effect was even more noticeable--the efficiency of the m-IME/H\({}_{2}\)O\({}_{2}\) process after 135 min (120 min m-IME + 15 min m-IME/H\({}_{2}\)O\({}_{2}\)) reached the efficiency (77.7%) of the m-IME/H\({}_{2}\)O\({}_{2}\) process after 180 min with the addition of H\({}_{2}\)O\({}_{2}\) from the beginning, and, after 150 min, it even slightly exceeded it. Those results seem to be valuable, because they show that the simple change of the moment of the addition of H\({}_{2}\)O\({}_{2}\) results in a significant improvement in process efficiency.
# A Novel Proprietary Internet Video Traffic Dataset Generation Algorithm
###### Abstract  
Considering the exponential growth of network traffic, particularly driven by over-the-top (OTT) streaming applications, video category network traffic constitutes a significant portion of overall network traffic. However, most research has focused on the categorization and diversity of network traffic using benchmark datasets, with limited attention paid to video category network traffic. Additionally, there is a lack of proprietary Internet video traffic datasets, and the few proprietary datasets available often lack transparency and interpretability. This paper introduces a novel framework for generating proprietary Internet video traffic datasets, addressing existing gaps in dataset quality and consistency. We propose the nYFTQC algorithm, which enables the creation of fifteen detailed datasets specifically designed for Internet video traffic analysis. The proposed datasets demonstrate superior performance metrics, including completeness, consistency, and transparency. This comprehensive approach enhances the accuracy and interpretability of traffic sample analysis, providing valuable resources for future research in video category network traffic.  
Keywords:network traffic classification; proprietary dataset; algorithm; interpretability +
Footnote †: journal: Applied sciences
## 4 Results
### Scenarios  
A total of 100 different scenarios were randomly generated. A safety margin corresponding to half the diameter of the robot tool was added to the obstacles to avoid unfeasible scenarios. Table 3 presents the following parameters used in generating the scenarios.  
One of the inputs for TPP is a map \(M\) of the obstacles inside the robot workspace. This map can be generated using object detection from digital images acquired by a static camera or an image generated by light detection and ranging (LIDAR) sensors. Figure 6 shows an example of an SCARA workspace (Figure 6a), a map \(M\) (Figure 6b), and a free space \(F\) (Figure 6c).  
The conditions set for the experiments are shown in Table 3. Random points are used only for the PRM algorithm. Fewer random points lead to faster PRM solutions, at the price of more susceptibility to collisions. Neighbor is used in the Dijkstra algorithm to find the shortest path. Closer neighbors lead to fewer collisions and help the Dijkstra algorithm achieve faster results on both PRM and TPP. Figure 7 shows the PRM, TPP*, and TPP roadmap construction and the final shortest path.  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Parameter** & **Description Points** \\ \hline Scenarios & 100 random scenarios with 6 obstacles of different shapes \\ Grid & Obstacles and workspace are simulated in a 60 cm \(\times\) 60 cm \\  & grid with a 1 cm resolution \\ Start point & Fixed at location \(x,y=15\) cm, 15 cm \\ Goal point & Random location inside the SCARA workspace \\ Safety margin & Half the robot tool diameter is added to obstacle boundary (4 cm in this paper) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Scenario parameters.  
Figure 6: Examples of (**a**) an SCARA workspace, (**b**) a map \(M\), and (**c**) a free space \(F\) on a 60 cm \(\times\) 60 cm grid.  
Figure 8 presents the paths collision avoidance PRM, TPP*, and TPP results for three scenarios, different on each line.
## 3 Material and Methods
### Numerical Calculations  
Numerical calculations, in general, provide more detailed and reliable results in comparison to the analytical solution, but at the same time are more complex and time consuming. As a result, it is challenging to implement such solutions to the regular evaluation of rockburst risk and its characteristics in terms of the continuously progressing exploitation front. Still, knowing the strengths of FEM analyses, the authors proposed to use them for the validation of results obtained with simplified analytical solutions.  
Calculations were performed for 2-dimensional cases relating to the points in tunnels and galleries located far enough from the intersections and other underground workings, with the use of RS2 software by Roccience Inc., Toronto, Canada. The geometry of the numerical model and geologic profile were obtained based on 12 boreholes that were drilled from the surface to the level of the excavations. The strength parameters of modeled rocks have been determined in the laboratory. The dimensions of workings are consistent with the geometries of workings in the analyzed area. The models have been solved using the following input data:  
* the galleries' rectangular cross-section size: \(B\times H\) = 6 m \(\times\) 3 m;
* overburden vertical pressure \(p_{z}\) = 20 MPa;
* strength parameters of the rock mass: cohesion \(c\) = 8 MPa, angle of internal friction \(\phi\) = 35\({}^{\circ}\);
* deformation parameters of the rock mass: modulus of deformation: \(E\) = 40 GPa, [PERSON]'s ratio \(\
u\) = 0.2 (\(p_{x}\) = 5 MPa).  
For 2D FEM analysis, the areas prone to rockburst occurrence were identified on the basis of the maximum shear strain distribution.
## 3 System Model
### Flow Specification  
The delay-critical applications are modeled as periodic tasks that send recurrent flows grouped in classes. Each flow is transmitted within its period, and the hyper cycle is defined as the least common multiple of the periods of all flows. We consider a set of \(n\)_aperiodic_ unicast time-triggered flows \(F\triangleq\{f_{1},f_{2},\ldots,f_{n}\}\). For simplicity, the multicast case can be split into multiple unicast flows with the same source and different destinations. Each flow \(f_{k}\triangleq\left(f_{k}.class,f_{k}.src,f_{k}.dest,f_{k}.size,f_{k}.prd,f_{k}. ddl,f_{k}.td\right)\in F\) is characterized by a 7-tuple, where \(f_{k}.src,f_{k}.dest\in V\) are the flow's source and destination node, respectively. \(f_{k}.class\) is the flow category which affects the selection of routes. \(f_{k}.size\) defines the frame length in bytes and \(f_{k}.prd\) is the period of the flow. Moreover, \(f_{k}.ddl\) denotes the maximum allowed end-to-end delay. We assume that all flows are transmitted simultaneously over the network. When all flows are released, we can safely restrict our attention to the first cycle, called the hyper cycle. \(f_{k}.td\) denotes the transmission duration of a flow related to the link transmission rate and flow size. Suppose that the size of the flow is 1000 bytes, then the transmission duration of flow sent to 1000 Mb/s link on the port is 8 \(\upmu\)s. We assume that the unit for \(f_{k}.prd\) and \(f_{k}.td\) is microsecond.
# Simulation of the Rollover Angle of a Self-Propelled Radish Harvester for Different Load Conditions
## 1 Introduction
There are many studies that have been conducted on the rollover angle analysis due to the stability of the machinery, especially agricultural machinery. This is because the agricultural field is highly uneven and has a sloppy field due to variable soil hardness levels due to stone, crop straw, vibration of the machine, and so on [24]. [PERSON] et al. [25] developed a 3D simulation model of a tractor-mounted radish collector and analyzed the overturning angles. They also determined the CoG and transverse overturning angles mathematically and evaluated the simulation results by indoor test. They found that the tractor-mounted radish collector could safely be performed at 30\({}^{\circ}\) sloppy field. [PERSON] et al. [26] recently published their research on the automatic pepper translpanter. They developed the 3D simulation model pepper translpanter and theoretically analyzed both static and dynamic overturning. They stated that the translpanter overturned at 29.3\({}^{\circ}\) and 49\({}^{\circ}\) for uphill and downhill, respectively. The rollover index was developed for buses and estimated the lateral load transfer ratio (LTR) [27]. Moreover, they stated that this approach decreased the changes of rollover considering the road bank angle. Another study was conducted on the lateral stability of a narrow-track tractor [28]. In this study, a kinematic model was developed to calculate the lateral stability angle for the tractor. They reported that 39.3\({}^{\circ}\) was the lowest stability angle. Therefore, the rollover angle was studied in this study for the stability of the radish harvester, especially for upland sloppy fields. This study was mainly focused on a stability analysis of the self-propelled radish harvester to identify the exact rollover slop angle on the basis of the CoG and operational safety, awareness, and comfort. The purpose of this study was to simulate a rollover of a self-propelled radish harvester prototype to derive an improvement plan. Finally, this study will recommend for the users the best conditions to perform the radish harvester with high stability, safety, and comfort. The specific objectives of the study were (a) to determine the lateral overturning angles according to the deflection and tilt angles using a 3D simulation model, (b) to validate the simulation results by indoor test, and (c) to improve the rollover angle by attaching and simulating three additional loads (50, 100, and 150 kg) at three positions (front, center, and rear).
## 3 Results
### Comparative Analysis
#### 3.2.5 Comparison with Deep Learning-Based Methods  
Table 4 incorporates two deep learning-based matching methods (SuperGlue and Red-Feat) for comparative analysis. However, these methods exhibited lower post-adjustment accuracy compared to the proposed approach. To investigate the underlying causes, additional SAR image datasets were selected and an independent matching accuracy evaluation framework was established. More than 20 spatially distributed corresponding feature points were manually selected in each pair of images. Using these reference points to compute affine transformation matrices as ground truth, the matching quantity and positional accuracy of each algorithm were systematically evaluated. Compared with the accuracy assessment in Table 4, the RMSE metric derived from this independent evaluation framework more accurately reflects the inherent matching accuracy of the algorithms. Figure 11 shows the matching results of different algorithms.  
Figure 12 displays the matching results, including NCM and RMSE, of the SuperGlue, RedFeat, and STSU-SAR-SIFT algorithms on an additional dataset. While STSU-SAR-SIFT does not always surpass the other algorithms in terms of the number of matched points, its RMSE consistently remains lower than that of the two deep learning-based methods. This is primarily due to the multiplicative noise in SAR images, which causes biases in corner detection. Our proposed feature detection method more accurately extracts reliable corners. Furthermore, since we match co-origin images, the STSU-SAR-SIFT algorithm achieves  
Figure 10: Matching results of Sentinel-1.  
better results than the others. When the images are from different sources, the performance of the deep learning-based methods becomes more pronounced.
# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Benefits
#### 8.1.7 Enhanced User Experience  
KDN can improve network traffic routing through efficient resource allocation and knowledge-based routing optimization, which can help improve network speed and reduce latency so that users can access network resources quickly and easily even during periods of high network traffic. Compared to SDN, KDN can have a higher level of consistent network performance as it optimizes network resources based on usage patterns, allowing users to use the network with minimal performance issues. The network reliability is much higher in KDN due to detecting and proactively correcting network issues even before they affect the users with the help of machine learning, which will help reduce network downtime and make sure users obtain continuous access to network resources. Furthermore, users feel more secure in a KDN scenario than in an SDN scenario, as the knowledge plane can detect diverse threats in real time and take corrective actions to have a minimum impact on the end user experience. Furthermore, user application performance can be improved by prioritizing network traffic based on the criticality of the application or using the QoS requirements of the specific application such that critical applications or applications with strict QoS requirements are prioritized over others. In the knowledge composition plane, user intent is considered when orchestrating rules from the composed knowledge. Thus, application plane policies are updated such that they are aligned with the high-level intents of the users and, at the same time, based on network performance. Thus, in the policy update process, high-level user intentions will not be violated, which will cause the KDN to evolve over time without violating the principle of adhering to cater the requirements of the end user.
## 4 Conclusions  
In summary, the modulation bandwidth of the IMC laser for the O-band was enhanced to 38.7 GHz by exploiting the PPR effect under MIL and mitigating the roll-off effect in the CPR frequency. The MIL behavior and modulation response of the IMC laser were analyzed both numerically and experimentally. The characteristics of the modulation response with the tuning current of each section and the dependence of the PPR frequency on the SOA length were investigated to optimize the IMC laser. By adopting phase-shifted grating and AR coatings, a relatively wide locking range was achieved. As a result, 40 Gb/s NRZ at 1306 nm for both BTB and 25-km SMF transmissions were demonstrated using the IMC laser. Enhanced bandwidth and large-signal modulation were achieved for the IMC laser, demonstrating that an effective combination of a proper PPR peak and higher CPR frequency further enhanced the modulation bandwidth beyond 40 GHz. Whereas the dynamic ER of the eye diagram was 1.1 dB, it can be further improved by modifying the bias condition of the DFB1 section, adopting complex-coupled gratings, more reliable electrical isolation, and detuned gratings. Thus, IMC lasers could play a promising role in high-speed applications in the future due to their simple conventional manufacturing process and highly improved modulation performance.  
Conceptualization, Q.K., Y.M., and W.Z.; methodology, Y.M. and W.Z.; formal analysis, W.Z.; investigation, W.Z.; writing--original draft preparation, W.Z.; writing--review and editing, Q.K. and Y.M.; visualization, W.Z.; supervision, Q.K., Y.H., D.L., L.Z., and W.W.; project administration, Q.K.; funding acquisition, Q.K. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Key R&D Program of China (Grant No. 2018 YFA0209000) and the National Natural Science Foundation of China (NSFC) under Grant No. 61574137.  
The authors declare no conflict of interest.
# Blended Filter-Based Detection for Thruster Valve Failure and Control Recovery Evaluation for RLV
## 4 Results
### Running Time and Stability Testing of the Algorithm  
This section describes the 1000 cases of random thruster valve failures and AoA command settings that were used to evaluate the running time and stability of the algorithm on the computer, as shown in Figure 14a,b.  
The computer configuration is: Intel(R) Core(TM) i5-4200H CPU @ 2.80 GHz, RAM 4.00 GB, Windows 8 operating system.  
Figure 14c shows that the algorithm has good stability in evaluating whether the remaining fault-free thruster can track the attitude control response performance under the failure of a thruster  
Figure 14: Running time and stability tests of thruster fault detection and control recovery evaluation: (**a**) random fault assignment statistics; (**b**) random AoA command given statistics; (**c**) AoA response output based on the thruster fault detection and control recovery; (**d**) running time statistics for each sample.  
valve. At the same time, Figure 14d shows that the algorithm can complete a fault detection and performance recovery evaluation between 3.2 s and 4.8 s.
# Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle
## 4 Simulation Study and Results  
In order to verify the feasibility of the proposed CAS, simulation experiments are carried out in two different environments. The first simulation experiment is carried out in a complex environment and tested in a static and a dynamic environment, respectively. The second experiment is based on the establishment of the real environment model with multiple dynamic obstacles. Table 1 is the parameter information of the USV and CAS. The dynamic obstacles are obtained by environmental awareness equipment, such as radar, etc., and the detection area is set to a circle with the USV as the center and a radius of 250 m.  
All of the simulation results are presented by time-dependent motion sequences. The algorithm has been coded in MATLAB 2010(b), and simulations were run on a computer with a Core i7 3.6 GHz processor and 4 GB of RAM.
## 4 Results and Discussion
### Model Evaluation and Comparison
The results of each model in the test set are shown in Table 5. The accuracy of the proposed model in impact damage location prediction and impact damage energy prediction was the highest. This also indicates that the model can extract more effective characteristics for impact damage signals, and it can provide better decision-making references for non-professionals and reduce learning difficulties. In general, it may not be possible to collect many samples in real engineering applications. In this paper, comparative experiments with small samples were conducted to discuss the stability of multi-task learning. Experiments were conducted in this paper using 50% and 25% samples. The confusion matrices for the location prediction task and the energy prediction task obtained from the experiments using 50% of the samples are shown in Figure 13. The confusion matrices for the location prediction task and the energy prediction task obtained from the experiments using 25% of  
Figure 14: _Cont_. Figure 13: Confusion matrix obtained from 50% of the samples. (**a**) Damage location confusion matrix for single-task learning; (**b**) damage energy confusion matrix for single-task learning; (**c**) damage location confusion matrix for single-task learning; (**d**) damage energy confusion matrix for single-task learning. samples decreased to 25%, compared to single-task learning, multi-task learning took less time to train and computed faster, reducing the total training time by 30.19%. The experimental results show that multi-task learning has higher accuracy than single-task learning for both multiple samples and fewer samples, as well as better stability and shorter training time.
# Enhanced Collaborative Filtering for Personalized E-Government Recommendation
## 4 Experiments
### Experimental Settings
#### 4.1.3 Parameter Settings  
We empirically select the optimal parameter settings for the proposed method. Specifically, we conduct a grid search to find out the optimal hyperparameters, which include the max number of epochs, learning rate, batch size, scale factors \(\gamma_{1}\) and \(\gamma_{2}\), and threshold \(\varepsilon\). The size of the embedding layers is fixed to 64 for all the models, that is the dimension of latent features.  
* Batch size A batch is a set of samples that are randomly sampled from the training set. Its size is usually set to a larger value. We conduct experiments to search the optimal value of batch size in [128,256,521,1024].
* Max number of the epoch Epoch means one training over the entire sample dataset. Similarly, epoch is searched in [20,30,50,100].
* Learning rate The learning rate is the hyperparameter for updating weights in the process of gradient descent. Likewise, the learning rate is tested in [0.001,0.003,0.005,0.01].
* Negative items selection parameters For scale parameter \(\gamma_{1}\) in the constructed metric function, we apply a grid search in [1,1.5,2,2.5,3], for scale parameter \(\gamma_{2}\), we apply a grid search in [0.02,0.04,0.06,0.08,0.10]. For distance constraint \(\varepsilon\), we apply a grid search in [0.5,0.8,1.0,1.3,1.5].  
Consequently, the best values of batch size, max number of the epoch, learning rate, scale parameters \(\gamma_{1}\) and \(\gamma_{2}\), threshold \(\varepsilon\) are determined: batch size = 1024, epoch = 30, learning rate = 0.003, \(\gamma_{1}\) = 2 and \(\gamma_{2}\) = 0.06, \(\varepsilon=1\), respectively.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**User**} & **Recommendation** & \multirow{2}{*}{**Test Item**} & **Recall@3** & **Precision@3** & **NDCG@3** \\  & **topK = 3** & & & & \\ \hline
**User1** & [item1, item2, item3] & Item1 & \(1/(1+0)\) & \(1/3\) & \((2-1)/log_{2}(2)\) \\
**User2** & [item2, item4, item5] & Item4 & \(1/(1+0)\) & \(1/3\) & \((2-1)/log_{2}(3)\) \\
**User3** & [item1, item4, item3] & Item5 & \(0/(0+1)\) & \(0/3\) & \(0\) \\
**User4** & [item3, item4, item5] & Item5 & \(1/(1+0)\) & \(1/3\) & \((2-1)/log_{2}(4)\) \\
**mean** & & & 0.75 & 0.25 & 0.53 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Example evaluation metric for four users.