esa-sceva/satcom-chunk-collection huggingface dataset:
# EMG Based Gesture Recognition Using the Unbiased Difference Power
## 3 Proposed Method
### Unbiased Difference Power  
To extract the unbiased difference power, we subtract the noise-biased part from the noisy feature. By deriving the noisy features, we demonstrate the degree of noise bias. The recorded signal \(x[n]\) can be expressed using the noiseless EMG signal \(s[n]\) and noise signal \(v[n]\) as  
\[x[n]=s[n]+v[n]. \tag{2}\]  
Let \(\Delta x[n]\) be  
\[\Delta x[n]=(x[n]-x[n-1])^{2}. \tag{3}\]  
Taking the expectation of defined \(\Delta x[n]\), we can obtain difference power as  
\[E[\Delta x[n]] \tag{4}\] \[= E[s^{2}[n]+s^{2}[n-1]+v^{2}[n]+v^{2}[n-1]\] \[-2s[n]s[n-1]-2v[n]v[n-1]].\]  
Assuming that both the noiseless EMG, \(s[n]\), and the noise, \(v[n]\), are uncorrelated, Equation (4) is reduced to  
\[E[\Delta x[n]] \tag{5}\] \[= E[s^{2}[n]+s^{2}[n-1]+v^{2}[n]+v^{2}[n-1]]\] \[= 2\sigma_{s}^{2}+2\sigma_{v}^{2},\]  
where \(\sigma_{s}^{2}=E[s^{2}[n]]\) and \(\sigma_{v}^{2}=E[v^{2}[n]]\).  
As can be seen in Equation (5), the difference power feature is biased by \(2\sigma_{v}^{2}\). Thus, the feature becomes more contaminated as the noise power, \(\sigma_{v}^{2}\), increases more.  
Intuitively, the simplest method to remove the noise effect on the difference power is to subtract the noise power as  
\[\overline{\Delta}x[n]=\Delta x[n]-2\sigma_{v}^{2}. \tag{6}\]  
Subsequently, we obtain  
\[E[\overline{\Delta}x[n]=E[\Delta x[n]-2\sigma_{v}^{2}] \tag{7}\] \[= E[\Delta x[n]]-2\sigma_{v}^{2}.\]  
In addition, Equation (7) expresses the unbiased difference power.  
Figure 2 shows the difference between the difference power and unbiased difference power. The value of the difference power increases as the SNR decreases. As mentioned above, the increase in the difference power as the SNR decreased is due to noise bias. However, using the proposed unbiased difference power, the feature value stabilizes, although the SNR decreases. It is observed that the noise bias is removed in the unbiased difference power.
# An Extensible Automated Failure Localization Framework Using NetKAT, Felix, and SDN Traceroute
## 7 Materials and Methods  
In order to test our method of localizing failures in a network, we created a tool that is able to transform networks described with the GML (Graph Modeling Language) [26] format into NetKAT expressions by applying the shortest path routing between hosts as a network policy. The tool also assigns IP and MAC addresses to hosts and names each network node. This way, we were able to test our method on different topologies supplied by the The Internet Topology Zoo [27].  
In order to emulate the network, we developed a Python script that instructs Mininet [28] to set it up based on the given NetKAT expressions that are written into files by our GML (Graph Modeling Language) transforming tool. The script reads host and switch data from the topology descriptions, while the policy file is parsed and translated into OpenFlow rules, then these are installed on their respective switches. We opted to have our measurement point--where our framework runs--on a node in the root namespace of the host where Mininet is executed. This node is always connected to the same switch as host \(h1\) is, via a dedicated switch, as Figure 15 shows. When the hosts are started by Mininet, the SSH daemon is initiated on each of them in order for our framework to be able to access them remotely.  
To handle the connection with our measurement point, we installed additional rules on each switch. Traffic destined to the measurement point was forwarded by similar rules as when forwarding to host \(h1\) except at \(h1\)'s switch. There, the traffic was forwarded to switch \(s0\).  
**Author Contributions:** Conceptualization, I.P. and A.G.; Formal analysis, I.P. and A.G.; Funding acquisition, A.G.; Investigation, I.P.; Methodology, I.P.; Software, I.P.; Supervision, A.G.; Validation, I.P. and A.G.; Visualization, I.P.; Writing--original draft, I.P. and A.G. Writing--review and editing, I.P. and A.G.  
**Funding:** Project nos. 123957 and 129589 has been implemented with the support provided from the National Research, Development and Innovation Fund of Hungary, financed under the FK_17 and KH_18 funding schemes respectively.  
**Acknowledgments:** The research leading to these results has been conducted in the High Speed Networks Laboratory.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
Figure 15: Connection of the measurement point to the network under test.
# Design and Evaluation of Novel Submerged Floating Tunnel Models Based on Dynamic Similarity
## 3 Model Design
### Material Property Testing  
The elastic module reflects the ability of the material to resist deformation under load, which influences the bending stiffness and dynamic characteristics of the structure. Within the similarity relation between the scale model and the prototype, the elastic modulus is one of the reference physical quantities, which is used to derive the scales of other physical values. The elastic modulus of the PE pipe was measured using the uniaxial tensile test, and then the elastic modulus similarity ratio was calculated. The specimen was cut from the PE pipe used in this study. The length of the specimen was 160 mm, the width was 20 mm, and the thickness was 10 mm, as shown in Figure 1a. The uniaxial tensile test was carried out using an INSTRON universal material testing machine as Figure 1b shows. The initial clip distance was 60 mm and the loading procedure was controlled by displacement to obtain the complete stress-strain curve of the material. The initial loading speed was 0.60 mm/min, which increased to 5.00 mm/min after a displacement of 0.18 mm until the specimen fractured.  
Figure 1c illustrates the stress-strain curve of the specimen, which can be divided into four stages. The first is a linear viscoelastic stage, and the stress increases linearly with the strain. The second stage is the yielding and necking stage, and the stress decreases with the increase in strain. The third is the viscoelastic flow stage: the necking develops continuously, and the stress remains nearly unchanged with the increase in strain. The final  
Figure 1: Material property testing for PE pipe. (**a**) Specimens of PE pipe; (**b**) testing devices for material properties; (**c**) the stress–strain curve.  
stage is the fracture stage in which the cross-section of the specimen decreases sharply and finally breaks. The PE pipe material adopted in this experiment demonstrated a tensile yield stress of 16 MPa, a yield strain of 17%, a tensile fracture stress of 10.5 MPa, and an elongation at break of 300%. The elastic modulus of the material was calculated based on the gradient of the stress-strain curve between 0.05% and 0.25% strain. Four specimens show good consistency in this range and the elastic modulus for the PE pipe material can be determined as 600 Mpa. The elastic modulus of concrete is 36 Gpa, so the elastic modulus similarity ratio \(\lambda_{E}\) can be calculated to be 1:60.
# A Hyper Heuristic Algorithm to Solve the Low-Carbon Location Routing Problem
## 5 Simulation Experiment and Analysis
### Experiments and Analyses
#### 5.3.1 Impact of Customer Distribution on Cost and Carbon Emissions  
The SESMAM-HH was used to solve LCLIENT and RLRPTWSPDHF problems, which was the model of RLCLRPTWSPDHF without considering carbon emission. The results were used to analyze the impact of customer distribution on logistics cost, carbon emission, and customer satisfaction. Meanwhile, the calculation results of LLEENT and RLRPTWSPDHF were compared to verify the validity of the RLCLRPTWSPDHF. The results are shown in Table 4, where TC is the logistics cost (yuan), FC is the fuel consumption and carbon emission cost (yuan), PF is the sum of the vehicle and customer waiting costs (yuan), CO\({}_{2}\) is the carbon emission (kg), D is the vehicle travel distance (km), CWT is the customer waiting time (minutes), and MT is the algorithm's average running time (seconds). \(\Delta\)TC, \(\Delta\)CO\({}_{2}\), \(\Delta\)D, and \(\Delta\)CWT are the increments of TC, CO2, D, and CWT values in RLRPTWSPDHF model and corresponding values in the RLRPTWSPDHF model.  
It can be seen from the results that CC\({}_{1}\) helps to reduce TC and can reduce TC by 3.39%, 8.89%, and 8.73% on average compared with the other three categories. At N \(\leq\) 75, CO\({}_{2}\) and FC were the lowest in the CC1 case, which were 9.12%, 24.98%, and 16.89% lower than CC2, CC3, and CR, respectively; however, when N = 100, the advantage of CC1 was not obvious.  
The impact of customer location on PF and CWT values was similar to its impact on TC, which means that CC1 helped to reduce customer waiting times and the corresponding penalty costs. The impact of customer position on D was similar to the influence of customer distribution on CO\({}_{2}\) and FC, which means CC1 helps to reduce driving distance, but when N = 100, CC2 is in first place  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Instance**} & \multicolumn{8}{c}{**RLCLRPTWSPDHF**} & \multicolumn{8}{c}{**RLRPTWSPDHF**} \\ \cline{2-11}  & **TC** & **FC** & **PF** & **CO2** & **D** & **CWT** & **MT** & \(\Delta\)**TC** & \(\Delta\)**CO\({}_{2}\)** & \(\Delta\)**D** & \(\Delta\)**CWT** \\ \hline  
*Av.** & 1608.79 & 332.94 & 71.88 & 100.45 & 137.52 & 118.22 & 94.53 & 1.68 & 9.31 & 8.48 & 12.76 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of two models on LCLIENT.  
with 159.63 km, which is lower than the 172.78 km of CC1. At the same time, compared with the RLRPTWSPDHF model, the RLCLRPTWSPDHF model can reduce TC by 1.68%, CO\({}_{2}\) by 9.31%, D by 8.48% and C-WT by 12.76%.
## 4 Simulation Experiments and Results
### Simulation Experiments  
#### 4.1.1 The Simulation Model and Environment  
FlexSim software (Flexsim-3D 7.7.2, FlexSim Software Products Inc., Orem City, UT, USA, 2016) was used in this research to build the simulation model. Figure 13 shows the simulation model that includes one Inter-bay and five Intra-bays for 450 nm wafer fabrication. The simulation environment is detailed as follows.  
* The total simulation horizon is set to 2 weeks (14 days), and the fab runs 24 h a day; and 7 days a week. The first 7 days is the \"warm-up\" time. The time unit used for simulation is second.
* This simulation model includes 1 Inter-bay and 5 Intra-bays. A total number of 69 sets of machines are distributed in five Intra-bays. The Inter-bay is 145 feet long and each Intra-bay is 100 feet long.
* Each wafer lot in the fab contains 12 pieces of 450 mm wafers.
* The inter-arrival time of transport is probabilistic and assumed to be of exponential distribution.
* The AMHS is equipped with ARB conveyors that move with one direction.
* Both hot lot and normal lot are considered in this system.
* The loading time and unloading time of a lot are assumed to be fixed (5 s).
* The normal speed of ARB is 1 ft/s and the normal travel speed of a lot from a line to another is assumed to be 0.5 ft/s.
* No failures and activities on the conveyor and device during the simulation process.
* Each machine can load/unload a lot at a time.
# Effect of Aggregate Type on the Shear Behavior of Reinforced Lightweight Concrete Beams
## 5 Test Results and Analysis
### Effect of Coarse Aggregate Type on Shear Resistance of RC Beams  
In this study, ALWAC mix proportions were developed and applied in the preparation of RC beams. The mix proportions exhibited good repeatability in ensuring the designed concrete properties, even though only a limited number of LWAs were considered. Furthermore, through beam testing, we observed that ALWAC beams displayed earlier and wider crack regions compared to NWC beams. Thus, it was initially expected that the use of LWAs might negatively influence the shear behavior of RC beams because of the lower cracking load. However, the effect of the coarse aggregate was not significant. Studies have shown that although LWAs tend to decrease the initial cracking load, their overall impact on the ultimate shear resistance of RC beams is minimal. This is because the primary factors influencing shear strength are concrete strength and reinforcement detailing rather than the type of coarse aggregate. Consequently, while LWAs might slightly alter the initial cracking  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{**ID**} & \multirow{3}{*}{
\begin{tabular}{c} **Experimental,** \\ \(V_{e}\) \\ \end{tabular} } & \multicolumn{4}{c}{**Estimated Values and Percentage Differences (Estimated/Experimental)**} \\ \cline{3-8}  & & **ACI 318** & **BS 8110** & **CSA** & **EC2** & **JSCE** \\  & & [23; 24; 32] & [26] & [34] & [25] & [35] \\ \cline{2-8}  & **kN** & **kN (\%)** & **kN (\%)** & **kN (\%)** & **kN (\%)** & **kN (\%)** \\ \hline LNS & 16.49 & 11.77 (71.39) & 14.54 (88.14) & 14.54 (88.20) & 14.90 (90.37) & 8.52 (51.68) \\ LNC & 16.11 & 11.48 (71.28) & 14.36 (89.13) & 14.28 (88.66) & 14.96 (92.88) & 8.51 (52.81) \\ NNN & 16.69 & 12.71 (76.14) & 19.43 (116.43) & 21.05 (126.11) & 20.40 (122.26) & 13.50 (80.89) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Measured and estimated shear resistances of beams.  
Figure 9: Results of (**a**) experimental and estimated shear resistance; (**b**) percentage differences (estimated/experimental).  
behavior, they do not substantially affect the ultimate shear capacity, making them a viable option for structural applications where weight reduction is a priority.
# Study on Vibration Effects and Optimal Delay Time for Tunnel Cut-Blasting Beneath Existing Railways
## 2 Numerical Simulation of Blasting Vibration of Tunnel Crossing Under the Existing Railway  
### Engineering Background  
Zhongliangshan Tunnel is a part of the interval of Chongqing Metro Line 15, with a tunnel length of 4905 m and a maximum depth of 313 m. The tunnel passes through sandstones, mudstones, and greywacke, encountering complex stratigraphic conditions. Through the coal seam mining area and faults, the surrounding rock classes III-V. In addition, the tunnel runs beneath the Chongqing-Guiyang Railway, the Caiyuanba-Geleshan Connecting Line, and other existing railway lines. The relative position of the new tunnel (Zhongliangshan Tunnel) and the Chongqing-Guiyang Railway are shown in Figure 1. The smallest depth under the Chongqing-Guiyang Railway section is 42.31 m. Zhongliangshan tunnel crosses several existing railways, and the geological conditions are complex. This is of great significance for blasting in tunnels crossing under existing railways. To assess the vibration impact of drilling and blasting operations during the construction of a tunnel beneath an existing railway and to develop an effective vibration mitigation strategy, a numerical simulation of the blasting process was conducted.
## 3 Results and Discussions
### Advantages and Disadvantages of the Proposed Approach to the Temperature Effect Compensation  
The advantage of this approach concerning the implementation of the driver for LED light source in terms of the output voltage stabilization at the environment temperature changes are the ability to reduce the drop of the LED device luminous flux when the environment temperature increases.  
The disadvantage is that, at the same time there is no complete stabilization of the luminous flux when the environment temperature changes.
## 4 Numerical Results
### Algorithm Performance and Robustness Analysis  
#### 4.2.1 Convergence Performance Comparison  
Cumulative Reward Convergence: Figure 5 shows the cumulative reward convergence curves of different algorithms during the training process. As shown in Figure 5, the proposed MAPPO algorithm demonstrates the fastest convergence speed and achieves the highest final cumulative reward. As shown in Figure 5, the cumulative reward of MAPPO stabilizes after approximately 750 training episodes, significantly outperforming other baseline algorithms. MADDPG and MAAC also learn and achieve higher cumulative rewards than the Greedy and Random policies, but their convergence is slower, and their final performance is lower than MAPPO. The cumulative rewards of the Greedy and Random policies remain at a low level with significant fluctuations, indicating that these simple strategies cannot effectively address the complex joint optimization problem. This validates the advantage of MAPPO in learning multi-agent cooperation and optimizing complex objectives.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameter** & **Symbol** & **Value** \\ \hline Reward Function Weights & \(\lambda_{E}\) & 0.5 \\ Weight for Remaining Cache & \(\lambda_{Q}\) & 0.1 \\ Weight for Remaining Cores & \(\lambda_{C}\) & 0.1 \\ Weight for Fairness & \(\lambda_{F}\) & 0.8 \\ Energy Budget & \(E_{max}\) & 1000.0 \\ \hline Training Configuration & & \\ Max Steps per Episode & \(|T|\) & 200 \\ Warmup Steps & - & 10000 \\ UAV Maximum Velocity & \(v_{max}\) & 30.0 \\ \hline Algorithm Hyperparameters & & \\ Actor Learning Rate & \(\psi\) & 0.001 \\ Critic Learning Rate & \(\phi\) & 0.001 \\ Discount Factor & \(\gamma\) & 0.99 \\ Clip Parameter & \(\epsilon\) & 0.2 \\ Entropy Coefficient & \(\beta\) & 0.01 \\ Batch Size & - & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Reinforcement learning parameter settings.  
Figure 5: Algorithm comparison: cumulative reward vs. training episodes.
## 5 Results and Discussion  
### Audio Tagging  
Table 3 presents the clip-level tagging results on the DCASE 2017 task 4 evaluation set and parameter sizes of each model. The results show that the DSNet had an absolute improvement of 0.0347 over the baseline CNN in terms of F1 score. The performance of the DSNet indicates that DenseNet and SENet are suitable not only for image processing but also for audio processing. The DSNet-RNN showed almost the same performance as the DSNet in clip-level metrics, which means the structured prediction has little effect on the clip-level performance.  
The class-wise F1 score results for the CNN, DSNet and DSNet-RNN models are presented in Table 4. While there was some variation across classes, the DSNet and DSNet-RNN showed better performance than CNN on most classes. The performance of the DSNet was considerably better compared to the baseline CNN for the \"air horn, truck horn\", \"police car\", \"skateboard\" and \"motorcycle\" classes and the DSNet-RNN showed better performance than the baseline CNN in the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Output Size** & **CNN** \\ \hline Convolution & 800 \(\times\) 128 \(\times\) 32 & [3 \(\times\) 3, 32 conv] \(\times\) 2 \\ \hline Max-pooling & 800 \(\times\) 64 \(\times\) 32 & 1 \(\times\) 2 max pool \\ \hline Convolution & 800 \(\times\) 64 \(\times\) 32 & [3 \(\times\) 3, 32 conv] \(\times\) 2 \\ \hline Max-pooling & 400 \(\times\) 32 \(\times\) 32 & 2 \(\times\) 2 max pool \\ \hline Convolution & 400 \(\times\) 32 \(\times\) 64 & [3 \(\times\) 3, 64 conv] \(\times\) 2 \\ \hline Max-pooling & 200 \(\times\) 16 \(\times\) 64 & 2 \(\times\) 2 max pool \\ \hline Convolution & 200 \(\times\) 16 \(\times\) 64 & [3 \(\times\) 3, 64 conv] \(\times\) 2 \\ \hline Max-pooling & 100 \(\times\) 8 \(\times\) 64 & 2 \(\times\) 2 max pool \\ \hline Reshape & 100 \(\times\) 512 & 100 \(\times\) 8 \(\times\) 64 to 100 \(\times\) 512 \\ \hline Segment-level prediction & 100 \(\times\) 17 &
\begin{tabular}{c} 256 dense(ReLU) \\ 17 dense(sigmoid) \\ \end{tabular} \\ \hline Clip-level prediction & 17 & global LSE pooling \\ \hline Parameters & - & 0.29 M \\ \hline \hline \end{tabular}
\end{table}
Table 2: Baseline CNN architecture.  
\"air horn, truck horn\", \"police car\", \"screaming\" and \"motorcycle\" classes. The best performing class for all models was \"civil defense siren\", which consists of long and high volume sounds, and the worst performing class was \"car passing by\", which consists of short and low volume sounds.
# Impact of Mesh Resolution and Temperature Effects in Jet Ejector CFD Calculations
## 2 Materials and Methods
### Governing Equations  
In STAR-CCM+, the governing equations used for simulations are based on the Navier-Stokes equations, which describe the motion of fluid substances. These equations consist of the continuity equation (mass conservation), the momentum equation ([PERSON]'s second law for fluids), and the energy equation (first law of thermodynamics). The solver also incorporates turbulence and viscosity models (k-\(\omega\)). Additionally, transport equations are solved for scalar quantities, like spacious concentration and temperature, enabling simulations of multiphase flows. The software uses the finite volume method to discretize these governing equations and transform them into algebra that can be solved numerically [31]. For this case, the coupled implicit solver and the _GammaReTheta_ transitions were also applied.
## 6 Improving Projection Stability
### Optimal Simplification Rate Selection  
In a 3D object, the number of meshes for its shape representation differs based on the existence of curvilinear subparts. For rectilinear subparts, a relatively small number of triangles are needed for its representation. For curvilinear parts, a larger number of triangles are densely included to express the individual curvatures. Since automatic simplification uniformly reduces the number of triangle meshes in a 3D object, there is the risk of losing geometric details for these curvilinear components. Such losses in detail may make the model unusable for performing safety or quality inspections. Thus, determining an optimal simplification rate, which reduces mesh sizes while minimizing the loss of detail, needs to be investigated.  
Table 5 shows the changes in the shape of the model according to the simplification rate. The level of simplification was adjusted from 10% (=90% of triangle meshes of the existing model) to 50% (=50% of triangle meshes of the existing model) with 10% increments. It was confirmed that rectilinear objects in the AR model do not get lost, even if the simplification rate was increased to 40%. In addition, there was no change in the shapes of the overall model, and details were observable up to a simplification rate of 20% (row 3 in Table 5).  
Figure 7: Simplification using edge reduction algorithm.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**No.**} & \multirow{2}{*}{**Simplification Rate**} & \multicolumn{2}{c}{**Changes in Model Shape According to Simplification Rate**} & \multirow{2}{*}{**Description**} \\ \cline{3-3} \cline{5-5}  & & **Rectilinear Elements** & **Curvilineal Elements** & **Curvilineal Elements** \\ \hline
1 & 0\% & & \\ (original  
However, for curvilinear components, changes in the shape of the model were observed from a simplification rate of 30% (row 4, column (b) in Table 5). At 50%, the degradation of the geometric details was observed even in the rectilinear components, revealing that simplification rates should not exceed this threshold.  
These experiments revealed that the optimal simplification rates needed to differ for rectilinear and curvilinear components. Specifically, the optimal simplification rates for rectilinear and curvilinear components were observed to be 40% (=60% of triangle meshes of the existing model) and 20% (=80% of triangle meshes of the existing model), respectively.