esa-sceva/satcom-chunk-collection huggingface dataset:
# Hierarchical Model Predictive Control for Hydraulic Hybrid Powertrain of a Construction Vehicle
## 3 MPC-Based Hierarchical Control Scheme
### MPC-Based Lower-Layer Engine Speed Tracking Controller Design
This result, together with the comparison between the RB+MPC and the RB+PID, means that the designed MPC tracking controller of the lower layer has an indelible contribution to fuel economy. In order to verify the performance of the controller under different driving conditions and prediction biases, another driving cycle, shown in Figure 11, is also considered in simulation, which does not belong to the database used to predict vehicle and torque demand by Markov chain offline calculations. The corresponding prediction information is based on the Markov model. Moreover, according to simulation results, the root mean square error of the prediction speed based on the Markov model is 0.5743 when the predicted time domain length is 1 s, which is larger than that of the first driving cycle. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**EMS+Engine Control** & **Final SOC Value** & **SOC Range** & **Fuel Economy (mpg)** \\ \hline +PID (without SOC) & - & - & 31.74 \\ RB+PID (practice) & 0.587 & 0.19–0.76 & 34.63 \\ RB+MPC & 0.587 & 0.19–0.76 & 35.43 \\ SDP+MPC & 0.603 & 0.29–0.80 & 37.48 \\ SMPC+PID & 0.593 & 0.39–0.80 & 37.39 \\ SMPC+MPC (proposed) & 0.593 & 0.39–0.80 & 38.21 \\ PMPC+MPC (benchmark) & 0.603 & 0.40–0.79 & 39.24 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation comparison of different algorithms in the first driving conditions. Similar to the first driving conditions, Figure 12 shows the simulation results for different upper-layer EMSs for the second driving conditions. Figure 13 shows the simulation results of the comparisons among the RB+PID, RB+MPC, SMPC+MPC, SMPC+PID, and SDP+MPC. Table 3 lists the simulation results in the second driving conditions. Figure 11: Second driving conditions. Figure 12: Simulation results of different upper-layer EMS in the second driving conditions. From Table 3, it can be seen that the final value of SOC has basically returned to the initial value, and the working range of SOC meets the actual use environment, and there is no case of bladder contraction or expansion. In the hybrid hydraulic system, fuel economy is greatly improved by implementing different optimization strategies under the constraint conditions, in which the rule-based control strategy is improved by 11.3%. Accordingly, the PMPC method that is used as a performance benchmark, further improved by 9.76%. In addition, fuel economy is improved by 6.8% and 4.9% as compared with RB strategy, respectively, using the upper-layer EMS of SMPC and SDP based on the deviations predicted by the same Markov model. This shows that even if the predicted deviation of demand torque is large, SMPC still has better adaptability to driving condition to achieve a significant increase in fuel economy. In addition, the strategy of adopting MPC in the lower-layer controller still has a certain effect on the improvement of fuel economy as compared with the PID control method.
## 3 Numerical Simulation
### Parameter and Boundary Conditions  
Due to the fact that this project spans across a lake, a fluid-structure coupling model has been established. The boundary conditions were set as follows:  
1. The normal displacement of nodes on the four lateral sides of the model are constrained to be fixed.
2. The displacement in all three directions at the bottom nodes are set to be fixed.
3. The top boundary is an unconstrained free boundary.
4. The normal displacement of nodes on the sides along the longitudinal direction of the multi-arch culvert bridge is also set to be fixed.
5. Non-permeable boundaries are set at the four lateral side boundaries and bottom boundary of the model.
6. The boundary of the top surface is set as permeable.  
Additionally, for the structural boundaries, constraints are applied to the nodes at the ends of the corrugated steel pipes, restricting the normal displacement. Since the water depth in the lake is 2 m, a vertically uniform load with a value of 20 kPa is applied to simulate the hydrostatic pressure of the water pressure. All geological layers are modeled using the Mohr-Coulomb constitutive model. The tunnel segments, corrugated steel pipes,  
Figure 4: The position of the tunnel and multi-arch culvert bridge in the s-s’ profile.  
Figure 3: 3D finite element model.  
and shield shells are modeled using linear elastic models. The computational parameters are listed in Tables 2-4.
# A Real-Time Hybrid Approach to Combat In-Browser Cryptojacking Malware
## 5 Experimental Evaluation
### Performance Evaluation  
In addition to the dynamic analysis of calculating the CPU usage, the proposed approach has integrated two static analysis techniques: the blacklisting technique and the signature-based detection technique. As we have seen in the previous sections, high accuracy and low false negative rates were achieved. On the other hand, integrating multiple techniques increases the execution time. This section will calculate and compare the performance evaluation of the static analysis with the dynamic analysis.  
For experimental purposes, the execution time (in seconds) required for the static and dynamic techniques on five sample websites has been presented. If the in-browser cryptojacking malware is identified at the first defense layer (blacklisting), the second and third defenses will not be run. If the second technique (signature-based detection) identifies the malware, the third defense technique (dynamic analysis) will not be executed. Out of five websites, two contained WASM code. We have chosen the samples so that all three defense techniques will be executed to calculate the performance overhead for the worst case. It can be seen that the blacklisting technique has the lowest execution time. The signature-based detection takes relatively more time than the blacklisting technique, while the dynamic analysis takes the most time. It is because a dynamic analysis requires the malware to be executed for some time. In contrast, a static analysis is performed without executing the
# An FPGA Based Energy Efficient DS-SLAM Accelerator for Mobile Robots in Dynamic Environment
## 3 Algorithm Implementation  
### DS-SLAM Overview  
The DS-SLAM algorithm introduces semantic information through deep learning, which endows the robot with learning ability to understand the surrounding environment and reduces the impact of dynamic objects. As can be seen from the algorithm in Figure 2, the DS-SLAM algorithm is a five-thread parallel architecture, including tracking, semantic segmentation, local mapping, loop closure and dense mapping creation. The core idea is to extract ORB feature points from the tracking thread and then check these feature points to get potential dynamic points through a moving mobile consistency check. At the same time, the segmentation results are obtained in parallel with a semantic segmentation thread. Finally, dynamic objects are removed by combining the results of two threads. Table 1 shows the processing time of some major modules in the corresponding thread. The results show that the semantic segmentation thread, whether running on CPU or GPU, is the bottleneck that affects the system's real-time performance. Figure 2 shows the overall framework of the DS-SLAM algorithm based on the HERO platform. Firstly, the images and model parameters are read from CPU. The computationally complex semantic segmentation thread can be accelerated on FPGA with the help of Intel OpenCL SDK (Software Development Kit) and BSP (Board Support Package). Finally, the output is transmitted back to CPU to complete the other threads, enough for real-time processing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Module** & **ORB Feature Extraction** & **Moving Consistency Check** & **Semantic Segmentation** \\ \hline Thread & Tracking & Tracking & Semantic segmentation \\ \hline \multirow{3}{*}{Platform} & Intel Core & Intel Core & Intel Core & NVIDIA Quadro P4000 \\  & i7-6800K & i7-6800K & i7-8750H & GPU \\ \cline{1-1}  & CPU & CPU & CPU & GPU \\ \hline Time (ms) & 9.37 & 29.5 & 2582 & 37.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Execution time of different modules in the Semantic Simultaneous Localization and Mapping towards Dynamic Environment (DS-SLAM) algorithm [9].  
The extraction of semantic information is realized by the semantic segmentation network. In order to reduce the complexity of the whole system and ensure the accuracy and real-time, the SegNet algorithm is selected to achieve semantic segmentation. It is a typical convolutional encoder-decoder architecture.
# Weakly Coupled Distributed Calculation of Lyapunov Exponents for Non-Linear Dynamical Systems
## 5 Performance Experiments  
We started the experiments over a node to verify the performance without the latency of the network. We ought to recall the importance of controlling the affinity so that the MPI processes do not commute within the node and so that the final result remains as reliable as possible. Thus, the following environment variable was defined: export I_MPI_PIN_PROCESSOR_LIST=allcores:grain=core.  
In Figure 3 we show the speed-up obtained using just one node with four cores executing from one to four MPI processes. The behaviour is practically linear, so the serial fraction is very low.  
From Figure 3 we can conclude that execution time is considerably reduced. In the next experiment we launch from 1 to 17 MPI processes to measure, taking one node as a unit of computation, the performance. In Figure 4 we show computing times per computation unit, while in Figure 5 we feature the corresponding speed-up along the cluster. From the results we can observe a low introduction of the overhead latency as expected in the design.  
An important metric indicator which must be calculated is the efficiency \(E\), defined as  
\[E=\tfrac{S(n)}{n}\times 100\% \tag{18}\]  
where \(S(n)\) is the obtained speed-up with n processes, and indicates how busy are the nodes during execution. Figure 6 shows that the efficiency obtained is high, since on average every node is kept busy 99.3% of the time. The efficiency also indicates that the partitioning of tasks  
Figure 3: Speed-up obtained in one node. As can be observed the speed-up is almost linear. The computing times are 154,158 s, 77,395 s, 53,214 s and 41,082 s.  
Figure 2: Lyapunov exponents for varying \(x\) and fixed \(\theta\). (**a**) shows two regions. The first half describes chaos, while the second half shows stability. (**b**) has a large region of pure stability.  
herein implemented is scalable, which means that we can increase the number of computing units to improve time reduction while not losing efficiency in the use of many nodes. The scalability must be contemplated as a good design of the parallel program, since it allows scaling of the algorithm, a fact that we could expect when increasing the number of processing units in the cluster. Similar implementations where the problem consists of processing recursively the first, second, third, and fourth Runge-Kutta coefficients in a parallel fashion, achieve a maximum performance which decreases when adding more processing units [19].  
Figure 4: Computing times obtained from 1 to 17 processes in the cluster; the processes follow an ordered affinity.  
Figure 5: Speed-up obtained in the cluster. The perfect speedup is 17 and the obtained speedup is 16.85, giving us a very good performance.  
Figure 6: Efficiency on the cluster.
# Design of Time-Synchronized Switch Based on Zynq
## 6 Conclusions  
In this paper, a time synchronization switch based on the Zynq platform is proposed. It mainly discusses the design of the hardware schematic, the time synchronization module, and the AXI-WB Bridge. Additionally, AXI communication testing, time synchronization functionality, clock jitter, and synchronization performance tests were completed in the experimental environment. The test results indicate that the PS side can cyclically read from and write to the various modules on the PL side, and the instantaneous precision of time synchronization can reach the sub-nanosecond level. This makes it suitable for distributed systems with high time synchronization requirements. The use of the GTH interface this time will provide faster transmission speeds compared to the previously used GTX interface, while also reducing the physical size. Additionally, by utilizing the development tools provided by Xilinx, the development efficiency has been further improved. Next, two aspects need to be expanded. Firstly, there are plans to enhance the functionality of the existing switch by adding an ID allocation feature, which will assign a unique identifier to each slave device and incorporate it into the time synchronization protocol to achieve time synchronization across the entire network. At the same time, data aggregation capability will be increased, allowing the switch to collect data from the ADC board. Secondly, research is needed on how to modify the configuration of the GTH module and the time synchronization algorithm to further improve time synchronization accuracy.  
Conceptualization, Y.M.; methodology, Y.M. and R.H.; software, R.H. and Z.W.; investigation, J.L.; data curation, J.L.; writing--original draft preparation, R.H.; writing--review and editing, Y.M., R.H., Z.W. and J.L.; supervision, Y.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.  
Figure 24: PPS deviation test.
# Converting MST to TSP Path by Branch Elimination
## 4 Experimental Results
### Results
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Swap** & **1** & **2** & **3** & **4** & **5** \\ \hline Max & 1074 & 1192 & 497 & 1126 & 681 \\ Average & 108 & 155 & 145 & 227 & 381 \\ Min & 1 & 1 & 1 & 13 & 134 \\ Count & 130 & 84 & 47 & 16 & 3 \\ \% & 31\% & 25\% & 21\% & 9\% & 2\% \\ \hline \end{tabular}
\end{table} 
Table 3: Cost of the link swaps (meters) in MST-to-TSP during the algorithm with O-Mopsi data. Figure 15: Results for the O-Mopsi and Dots as a function of the problem size. Statistics for the Dots games are similar; see Table 4. Branch eliminations also increase the cost (5.65 on average) but it does not vary much during the iterations; only the 9 th and 10 th swap are somewhat more costly. In some cases, the swaps can also decrease the path length of the Dots instances. This happens rarely though; only 14 times with Dots instances but never with the O-Mopsi instances. Figure 16 shows more demanding O-Mopsi game instances. Only thr3 ee games require 5 swaps, while 13 games require 4 swaps. Kruskal-TSP suffers from the sub-optimality of the last selected links. In case of Odense, the last link goes all the way from the northmost to the southmost node. Christofides works reasonably well with this example but contains too many random-like selections in the tour. MST-to-TSP is the only algorithm that does not produce any crosses, and it also manages to find at least one starting point correctly in all three cases. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Swap** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** \\ \hline Max & 30.62 & 25.75 & 29.18 & 42.99 & 35.55 & 29.42 & 27.46 & 29.68 & 26.49 & 15.41 \\ Average & 5.65 & 5.98 & 5.95 & 5.94 & 6.74 & 6.83 & 6.71 & 6.60 & 10.76 & 11.01 \\ Min & 0.00 & \(-4.12\) & \(-5.84\) & \(-6.43\) & 0.00 & 0.00 & 0.04 & 0.00 & 1.90 & 3.26 \\ Count & 4525 & 2374 & 1169 & 622 & 341 & 171 & 87 & 43 & 17 & 6 \\ \% & 33\% & 19\% & 8\% & 4\% & 3\% & 2\% & 1\% & 0.4\% & 0.2\% & 0.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cost of the link swaps in MST-to-TSP during the algorithm with **Dots** data. For O-Mopsi and Dots datasets, all the algorithms work in real-time (<1 s). Theoretical time complexities are as follows. Christofides requires O(\(N^{3}\)) time due to the perfect matching by [PERSON]. MST-based variants require O(\(N^{2}\)) to O(\(N^{2}\)log\(N\)) depending on the data structure. The proposed algorithm takes O(\(N^{3}\)) due to the search of the leaf nodes but there are probably more efficient ways to implement it. Random mix local search is fast, O(\(IR\)), where \(I\) and \(R\) are the number iterations and repeats, respectively. The operations themselves are O(1) due to random choices.
## 4 Results
### Total Knee Arthroplasty  
Four studies reported on the application of AR in TKA [26; 27; 28; 29], and among those, two were preclinical [26; 27] and two were clinical [28; 29]. [PERSON] et al. [27; 29] documented the results of the imageless navigation AR-KNEE system, firstly in a pilot study using 10 femoral sawbones specimens (Pacific Research Laboratories) to verify its accuracy and secondly in a clinical study of 74 patients to compare it with a conventional intramedullary guide [29]. No preoperative CT scan was required, and real-time information was provided on a smartphone screen, enabling the surgeon to visualize the aimed varus/valgus and posterior slope angles superimposed on the surgical field. The authors reported that the absolute values of the differences between the angles measured using CT images and the angles displayed on the smartphone screen were <1\({}^{\circ}\) in both the coronal and sagittal planes for tibial and femoral cuts [27; 29]. Moreover, increased accuracy was detected in the AR-KNEE group compared with the conventional group when considering the absolute difference between the measured lateral distal femoral angle (LDFA) on a standing long-leg X-ray and the target (90\({}^{\circ}\)): 1.1\({}^{\circ}\)\(\pm\) 1.0\({}^{\circ}\) vs. 2.2\({}^{\circ}\)\(\pm\) 1.6\({}^{\circ}\), respectively (\(p\) < 0.001) [29]. Similarly, [PERSON] et al. [26] developed a camera-augmented mobile C-arm (Cam-C) technology to intraoperatively assess lower limb alignment requiring only 3 X-ray images, and evaluated the accuracy, efficiency, and reliability by intraoperatively controlling the alignment of the mechanical axis deviations. The authors compared the results on 25 cadaveric specimens with the data obtained from a CT scan and reported no statistical difference between the augmented reality system and ground-truth CT (\(p\) > 0.05). Finally, [PERSON] et al. [28] reported in a clinical pilot study (5 TKAs) the results of the Knee+ AR navigation system (Pixee medical company, Besancon, France) that allows the surgeon to view the tibial and femur axis superimposed on the surgical field through smart glasses without needing additional preoperative images. The authors reported a coronal error \(\leq\)1\({}^{\circ}\) for both tibial and femoral cuts, and a sagittal error \(\leq\)2\({}^{\circ}\) for tibial cuts. In addition, discrepancies between the reported angles measured on standard X-rays and the angles reported by the AR system were \(\leq\)1\({}^{\circ}\) (Table 2). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
# AV-Teller: Browser Fingerprinting for Client-Side Security Software Identification
## 2 Background and Related Work
### Antivirus Testing
#### 2.4.1 EICAR Test File  
The EICAR test file [27], developed by the European Institute for Computer Antivirus Research (EICAR) [28] is a benign code string universally acknowledged as a standard text signature by antivirus software. Upon scanning or execution, the file activates antivirus detection mechanisms, enabling the evaluation of the signature-based detection capabilities of the software in a controlled environment. Though the EICAR file does not emulate real-world malware, it serves as a safe benchmark for verifying antivirus efficacy without risking exposure to actual threats.
## 2 Materials and Methods
### Experimental Measurements  
Two sets of acquisitions were performed in order to apply our correction model. First, point source images were acquired to estimate the point spread function (PSF) of the system [19, 20] under different conditions, then, a series of acquisitions of the IEC phantom under the same conditions were performed to test the recovery method.
## 3 Results and Discussion  
### Viscosity Measurement Results  
Table 5 shows the viscosity measurements of SPRG samples screw mixed and stored at respective times for predetermined periods (outlined in Table 5). The maximum viscosity of the SPRG evaluated in this study was obtained from the data sheet of the product specification, which was found to be 3,512,000 cps (to be labelled \(o\) for future calculation in the upcoming sections below).  
For this SPRG sample, it was shown that after 10 min of screw mixing, an average viscosity change throughout the different storage periods of up to 95% was possible, whereas with samples that were screw mixed for longer than 20 min, the viscosity percentage reached around 65%. For a clearer illustration, refer to the below graph in Figure 12.  
Figure 12: SPRG viscosity change over different storage periods based on respective screw-mixing time.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Original Viscosity (a. cps) (\(o\))**} & \multirow{2}{*}{\begin{tabular}{c} **Screw** \\ **Mixing** \\ **Time (min)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Viscosity Measurement at Different Storage Period** \\ **Intervals (cps)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Average Viscosity** \\ **over 7 Day Period** \\ \end{tabular} } \\ \cline{5-10}  & & & & & & & & & **Viscosity** & **cps/\(o\) cps** \\ \hline \multirow{5}{*}{3,512,000 cps} & NSM 1 & 3,431,000 & 3,421,000 & 3,452,000 & 3,443,000 & 3,461,000 & 3,441,000 & 98 \\ \cline{2-10}  & 10 & 3,221,000 & 3,241,000 & 3,236,000 & 3,451,000 & 3,479,000 & 3,326,000 & 95 \\ \cline{2-10}  & 20 & 2,531,000 & 2,024,000 & 2,248,000 & 2,498,000 & 2,038,000 & 2,268,000 & 65 \\ \cline{2-10}  & 30 & 1,784,000 & 2,131,000 & 2,152,000 & 2,064,000 & 2,021,000 & 2,030,000 & 58 \\ \cline{2-10}  & 60 & 1,540,000 & 1,993,000 & 1,787,000 & 1,538,000 & 1,553,000 & 1,682,000 & 48 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Viscosity measurement in accordance with predetermined screw-mixing and storage times.