esa-sceva/satcom-chunk-collection huggingface dataset:
# Energy Reduction Multipath Routing Protocol for MANET Using Recoil Technique
## 5 Results and Discussion  
In this section, we experimentally demonstrate the proposed multipath routing protocol named AOMDV-ER. This protocol is an enhancement of the well-known AOMDV and AOMR-LR routing protocols. The enhancement is observed in terms of network lifetime, routing overheads, PDR and energy consumption over the existing protocols. In this section, we have demonstrated the formally proved Lemmas through experiment. The performance evaluation has been carried out by considering various parameters. The NS2.34 (Snapshot depicted in Figure 8)has been used for the simulation and used random way point model with 150 nodes in an area of 1500 \(\times\) 1500 m. Table 5 shows the setting parameters in simulation:  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Number of nodes & 150 & \\ Traffic type & CBR & \\ Area Size & \(1500\times 1500\) & m\({}^{2}\) \\ Mobility Model & RWP & \\ Transmission range & 500 & m \\ Packet size & 64, 128, 256, 512, 1024 & Bytes \\ Routing Protocols & AOMDV-ER, AOMDV, AOMR-LM, & \\ SRMP & Protocol \\ Node speed & 0.25, 5, 7.5, 10 & m/s \\ Simulation Time & 10, 20, 30, 40, 50 & s \\ Initial energy & 75 & Joules \\ Transmission energy consumption & 0.02 & Joules \\ Receive energy consumption & 0.01 & Joules \\ Queue size & 50 & Packets \\ Number of runs & 5 & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation Parameter.  
Figure 8: Simulation tool snap shot.
## 3 Model Development
### Numerical Models  
To verify the developed analytical solution, a two-dimensional numerical model was built via a commercial finite-element code (COMSOL Multiphysics 5.6). The transient heat Equation (1) subjected to the initial and boundary conditions in Equation (2) was solved by the finite-element method. All the parameters involved in the numerical simulation  
\begin{table}
\begin{tabular}{l l l} \hline
**Parameter** & **Value** & **Unit** \\ \hline Inner radius, \(a\) & 0.05 & m \\ Outer radius, \(b\) & 50 & m \\ Distance between neighboring boreholes, \(d\) & 10 & m \\ Specific heat, \(c_{p}\) & 1000 & J/(kg\textless{}K) \\ Thermal conductivity, \(k\) & 1.5 & W/(m\textless{}K) \\ Mass density, \(\rho\) & 2500 & kg/m\({}^{3}\) \\ Thermal diffusivity, \(\alpha\) & \(5\times 10^{-7}\) & m\({}^{2}\)/s \\ Undisturbed temperature, \(T_{0}\) & 283.15 & K \\ \hline \end{tabular}
\end{table}
Table 1: Prescribed geometrical and thermophysical parameters of the ground. The listed values of parameters are consistent with the work [39].  
Figure 4: Heat transfer rate \(q\) (W) over a year period \(t\) (d) for seasonal energy storage in: solar-assisted geothermal borehole systems (S-GBS) and waste-heat-assisted geothermal borehole systems (W-GBS).  
remained the same as the ones in the analytical model, as listed in Table 1; two kinds of heat flux boundaries were expressed in Equation (15) for S-GBS models and Equation (16) for W-GBS models. Further, unstructured meshes are used to discretize the computational domain and a mesh independency study is conducted for the three borehole arrangements. The details can be found in Appendix A.  
All the above-mentioned three scenarios shown in Figure 2 were studied over a one-year time simulation for demonstrating seasonal changes, i.e., \(t=1\) (yr), with a uniform time step of 1 (h). For each borehole scenario, temperature distributions over the line \(y=0\) were recorded at four seasons. That is, spring at \(t=3\) (mon), summer at \(t=6\) (mon), fall at \(t=9\) (mon), and winter at \(t=12\) (mon).
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 5 Result and Analysis
### Comparison of Models Using Different Loss  
The speech enhancement performance of FullSubNet-pMix was compared using different loss functions during training. Three loss functions, namely MAE loss, MSE loss, and Huber loss, were compared. The training dataset consisted of reverberant noisy speech. The final results are presented in Table 5. The value of the hyperparameter \(\delta\) in Huber loss is set to 1.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Look Ahead** & **WB-PESQ** & **NB-PESQ** & **STOI** & **SI-SDR** \\ \hline Noisy & - & 1.582 & 2.454 & 91.52 & 9.07 \\ DCCRN-E [4] & 37.5 & - & 3.266 & - & - \\ PoCoNet [18] & - & 2.748 & - & - & - \\ TRU-NET [19] & 0 & 2.860 & 3.360 & 96.32 & 17.55 \\ FullSubNet [11] & 32 & 2.742 & 3.245 & 96.04 & 17.27 \\ FullSubNet-pMix & 2 & 2.830 & 3.317 & 96.37 & 17.95 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of speech enhancement results among different models (without reverberation).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Look Ahead** & **WB-PESQ** & **NB-PESQ** & **STOI** & **SI-SDR** \\ \hline Noisy & - & 1.822 & 2.753 & 86.62 & 9.033 \\ DCCRN-E [4] & 37.5 & - & 3.077 & - & - \\ PoCoNet [18] & - & 2.832 & - & - & - \\ TRU-NET [19] & 0 & 2.740 & 3.350 & 91.29 & 14.87 \\ FullSubNet [11] & 32 & 2.782 & 3.268 & 95.83 & 17.12 \\ FullSubNet-pMix & 2 & 2.866 & 3.342 & 96.24 & 17.66 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of speech enhancement results among different models (with reverberation).  
The experimental results were compared with other models on the Interspeech 2021 test dataset. The model used is FullSubNet-pMix with a Magphase set to 4, a lookahead set to 2, and a sub-band width set to 10. From the experimental results, it can be observed that using MSE loss as the loss function yields the best training performance with this model.
# MSEANet: Multi-Scale Selective Edge Aware Network for Polyp Segmentation
## 2 Related Work
### Edge Feature Extractor  
The edges of polyps are often blurred and challenging to distinguish. Inspired by Edge-Prioritized Polyp Segmentation (EPPS) [23], this paper improves the EFE to accurately capture high-frequency features of polyp edges. This module extracts and fuses edge information through multi-level convolution and upsampling operations, as illustrated in Figure 1b. The design of EFE focuses on efficiently extracting edge-related high-frequency information from the feature maps generated at the initial stage of the encoder. Specifically, the input feature map undergoes successive 3 \(\times\) 3 convolutions to extract base features. Atrous Separable Convolution is employed for downsampling, which helps to preserve spatial information typically lost with traditional max-pooling operations. After two downsampling operations, the resolution of the feature map is progressively reduced while its semantic information becomes more enriched. The downsampled feature maps are then upsampled using bilinear interpolation, spliced with the preceding layer's feature maps processed by \(1\times 1\) convolution, and further fused through a 3 \(\times\) 3 convolution.  
This process is conducted at two scales to ensure the fine-grained edge information is fully integrated with the deep semantic features. The upsampled feature maps are passed through a convolution layer and a sigmoid function to generate edge prediction maps \(Edge_{pred}\). Simultaneously, the EFE module outputs fused edge-enhanced features \(f^{e}\) for further processing during the subsequent decoding stage.  
Manual annotation is not required for ground truth edge labeling. Instead, the Canny operator is used to extract edge ground truth from polyp masks. This approach provides greater accuracy and eliminates the variability and uncertainty often associated with manual labeling.
# Event Detection of Muscle Activation Using an Electromyogram
## 3 Proposed Algorithm
### CFAR-Based EMG Binarization  
First, we detect the activation of the EMG signal by using the modified CFAR threshold. The CFAR is an algorithm, which sets a threshold to maintain a false alarm rate constantly. We use the median CFAR detector to calculate the decision threshold. Let \(X_{c}=\{\bar{x}_{c}[n-N-L_{\text{G}}],\bar{x}_{c}[n-N-L_{\text{G}}\ +\ 1],...,\bar{x}_{c}[n-L_{G}-1]\}\) be the training cell, and let \(\bar{x}_{c}[n]\) be the test cell for the channel \(c\), where \(L_{\text{G}}\) and \(N\) represent the guard and the training cell length, respectively.  
Subsequently, the CFAR threshold is calculated as  
\[CFAR_{th_{c}}[n]=\alpha\cdot median(X_{c}), \tag{2}\]  
where \(\alpha=N(P_{fa}^{-1/N}-1)\). Here, \(P_{fa}\) denotes the false alarm rate and is set to 0.05. The CFAR threshold \(CFAR_{th_{c}}\) is modified by setting the upper limit to  
\[th_{c}[n]=\min(CFAR_{th_{c}}[n],M_{c}) \tag{3}\]  
where \(M_{c}\) represents the upper limit. The EMG signal is binarized using this threshold. When the EMG signal is activated, the state is mapped to 1, and when the EMG signal is inactivated, the state is mapped to 0, that is,  
Figure 3: Mean absolute value (MAV) of EMG (Black solid line represents MAV (\(\bar{x}_{1}[n]\)) of EMG).  
Figure 2: Pre-processed data and activation state related to muscle state.  
\[x_{Bc}[n]=\begin{cases}1,&if\ \bar{x}_{c}[n]\geq th_{c}[n]\\ 0,&otherwise\end{cases}. \tag{4}\]  
Figure 4 illustrates the binarized EMG using the modified CFAR threshold. There is a notable existence of abnormal peaks and holes that disturb the detection of activation. Morphological hole filling is adopted to refine the binarized EMG, thereby removing these abnormal peaks and holes.
# Knowledge Graph Alignment Network with Node-Level Strong Fusion
## 5 Experiments
### Experimental Results
#### 5.4.1 DBP15K Results
Table 3 shows the comparison results of different methods on DBP15K for relation alignment. -R for models that compute approximately relation embedding though entity embedding. It can be seen from the experimental results SFEA achieves best results on relation alignment. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**DBP15K\({}_{\text{ZH-EN}}\)**} & \multicolumn{3}{c}{**DBP15K\({}_{\text{JA-EN}}\)**} & \multicolumn{3}{c}{**DBP15K\({}_{\text{FR-EN}}\)**} \\  & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** & **Hits@1** & **Hits@10** & **MRR** \\ \hline JAPE & 0.412 & 0.745 & 0.490 & 0.363 & 0.685 & 0.476 & 0.324 & 0.667 & 0.430 \\ BootEA & 0.629 & 0.848 & 0.703 & 0.623 & 0.854 & 0.701 & 0.653 & 0.874 & 0.731 \\ \hline GCN-Align & 0.413 & 0.744 & 0.549 & 0.399 & 0.745 & 0.546 & 0.373 & 0.745 & 0.532 \\ AliNet & 0.539 & 0.826 & 0.628 & 0.549 & 0.831 & 0.645 & 0.552 & 0.852 & 0.657 \\ RDGCN & 0.708 & 0.846 & 0.746 & 0.767 & 0.895 & 0.812 & 0.886 & 0.957 & 0.911 \\ HGCN & 0.720 & 0.857 & 0.768 & 0.766 & 0.897 & 0.813 & 0.892 & 0.961 & 0.917 \\ NMN & 0.733 & 0.869 & 0.781 & 0.785 & 0.912 & 0.827 & 0.902 & 0.967 & 0.924 \\ MRAEA & 0.757 & 0.929 & 0.827 & 0.757 & 0.933 & 0.826 & 0.780 & 0.948 & 0.849 \\ HMAN & 0.562 & 0.851 & 0.670 & 0.557 & 0.860 & 0.674 & 0.550 & 0.876 & 0.666 \\ AttrGNN & 0.796 & 0.929 & 0.845 & 0.783 & 0.921 & 0.834 & 0.918 & 0.977 & 0.910 \\ MHGCN & 0.732 & 0.867 & 0.793 & 0.767 & 0.891 & 0.821 & 0.864 & 0.923 & 0.898 \\ EVA & 0.761 & 0.907 & 0.814 & 0.762 & 0.913 & 0.817 & 0.793 & 0.942 & 0.847 \\ SSP & 0.739 & 0.925 & 0.808 & 0.721 & 0.935 & 0.800 & 0.739 & 0.947 & 0.818 \\
**SFEA** & **0.855** & **0.931** & **0.883** & **0.876** & **0.953** & **0.904** & **0.942** & **0.983** & **0.959** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance on entity alignment. The relation embedding representations required for relation alignment are calculated from the embedding representation of the head entity and the embedding representation of the tail entity. Therefore, the high performance of SFEA in the relation alignment stage is affected by the entity embedding representation in the SFEA model. Compared with the best comparison model HGCN, SFEA has a significant improvement, because HGCN uses a simple addition merging method, which will cause insufficient use of information. The precise entity embeddings obtained by SFEA demonstrate that the model improvement is effective. The relation alignment between HGCN and RDGCN are better in the comparison models because HGCN combines structure and relation, while RDGCN has the best performance in the comparison models because RDGCN uses dual relation graphs to improve relation learning. However, compared to all models, SFEA employs strong fusion and sub-alignment tasks to obtain approximate relation embeddings. The accurate entity embedding results obtained by SFEA show that SFEA has a significant improvement and the model improvement is effective.
## 2 Background Information
### Numerical Simulation of Hydrodynamic Noise
#### 2.2.2 Large Eddy Simulation (LES)  
Large eddy simulation (LES) has been shown to achieve an optimal balance between computational efficiency and simulation accuracy, rendering it particularly well-suited for the capture of large-scale turbulent structures. This approach reduces computational requirements by directly simulating large-scale turbulence, while modeling small-scale turbulence. Consequently, LES is well-suited for predicting noise in complex engineering scenarios, such as surface turbulence and the wake flow around warships and submarines, the Equation (8) is as follows:  
\[\frac{\partial_{u}}{\partial_{t}}+u\
abla u\ =\ -\frac{1}{\rho}\
abla p+vv^{2}u+f- \
abla\tau \tag{8}\]  
where  
\(u\): Filtered velocity field,  
\(\tau\): Subgrid stress tensor representing the effect of small-scale vortices, the Equation (9) is as follows:  
\[\tau\ =\ -v_{t}\Big{(}\
abla u+\
abla u^{\tau}\Big{)} \tag{9}\]  
LES has become a staple in a multitude of engineering applications concerning hydrodynamic noise, being frequently utilized to predict flow noise in large geometries. Its performance lies between that of direct numerical simulation (DNS) and Reynolds-averaged Navier-Stokes (RANS) methods. In practical applications, LES has proven to be particularly effective in the study of flow noise on surfaces with complex shapes. Notably, its capacity to simulate wake noise and vortex shedding noise is particularly noteworthy. For instance, [PERSON], [PERSON] [63] employed LES in conjunction with the Ffowcs Williams-Hawkings equation to examine the noise produced by a 10-blade rotor ingesting a turbulent wake from a circular cylinder in a low-Mach-number flow. Similarly, [PERSON] [64] investigated hypersonic flow in an axisymmetric coaxial nozzle/jet configuration. In this study, the flow field predictions for a compressible flow obtained using an axisymmetric RANS solver and the corresponding predicted acoustic characteristics based on the acoustic analogy of RANS and Lighthill were compared with predictions derived from LES combined with [PERSON]'s surface integral. The outcomes of this study indicated the efficacy of the LES-based method in the context of realistic flows and complex geometries, yielding favorable outcomes.  
Despite its superior computational efficiency compared to DNS, LES imposes significant demands on computational resources. To obtain accurate results within a reasonable time frame, LES typically relies on high-performance computing systems. The accuracy of the results is contingent, to a certain extent, on the configuration and performance of the small-scale model.
# On the Energy Efficiency of On-Off Keying Transmitters with Two Distinct Types of Batteries
## 5 Simulation Results
### Impact of \(\gamma\) on BECPB  
In this part, we use equation Equations (19) and (22) to compute BECPB when \(\gamma\) increases from 0.02 to 0.22 with \(d=50\) m and \(B=10\) kHz for the two types of batteries. The results are shown in Figure 3. From the results, we can observe the impact of \(\gamma\) on the BECPB, and compare the BECPB for different batteries. Moreover, we can justify if the theoretical analysis agrees with the evaluated results.  
Specifically, the following points can be observed:  
* When \(\gamma\) is fixed, the battery with IDC-based BUF corresponds to a higher BECPB than that with MDC-based BUF. As \(\gamma\) increases, both BECPBs and their gap increase. As shown in Figure 3, the BECPB for the battery with IDC-based BUF is about 18% higher than that with MDC-based BUF when \(\gamma=0.07\).
* Each realistic battery corresponds to a higher BECPB than the ideal one, and with the increase of \(\gamma\), the BECPB gap between the two batteries and the ideal one gradually expands. In particular, when \(\gamma=0.07\), the BECPB for battery with IDC-based BUF is about 35% higher than that for the ideal one, meanwhile the BECPB for battery with MDC-based BUF is about 15% higher. The above observations corroborate the theoretical analysis made in Section 4.1.
## 3 Results and Discussion  
### Design of Cellulose-Active Protein Modules for Artificial Complex Formation  
We constructed cellulose-acting protein complexes composed of EG, CBH, and CBM using a _Saccharolobus solfatarcus_ proliferating cell nuclear antigen (PCNA). The _S. solfatarcus_ PCNA is a DNA sliding clamp that acts as a scaffold for DNA-processing enzymes and is composed of three distinct subunits, PCNA1, PCNA2, and PCNA3 [31]. The three subunits self-assemble in a characteristic manner; PCNA1 and PCNA2 almost irreversibly form a heterodimer, followed by the integration of PCNA3. This property allows the PCNA subunits to be used as interlocks to assemble multiple proteins. We have demonstrated that artificial multienzyme complexes constructed from proteins fused to the PCNA subunits can exhibit synergistically enhanced multienzymatic reactions [32].  
Distinctive cellulose-active protein modules derived from the thermophilic organism _A. thermocellus_, which effectively degrades cellulosic biomass [33], were selected as functional components of the artificial cellulose-digesting enzyme complexes (Figure 1A). A non-cellulosomal processive EG from _A. thermocellus_, Cel9I (EC 3.2.1.91), is composed of three domains, a family-9 GH catalytic domain and two family-3 CBMs, CBM3c and CBM3b [34]. A truncated form of Cel9I, Cel9*, lacking CBM3b is unable to bind to crystalline cellulose, but retains the GH activity against soluble substrates [35]. A further truncated form, Cel9**,lacking the CBM3c, shows significantly reduced activity against soluble substrates as well as crystalline substrates because CBM3c has a function to feed the cellulose chain into the catalytic site of Cel9I [35; 36]. Cbh9A (formerly CbhA), a component of the _A. thermocellus_ cellulose, is a processive 1_A_-\(\beta\)-D-glucan CBH that digests cellulose strands from the non-reducing end to release cellobiose [37]. This enzyme also contains CBMs as well as a family-9 GH catalytic domain [38]. A truncated form of Cbh9A, Cbh9A*, which lacks the C-terminal CBM3b and the N-terminal family-4 CBM (CBM4) [39], is expected to retain the catalytic activity against the soluble substrate but show significantly reduced activity for amorphous cellulose as well as crystalline cellulose. CipA, a scaffoldin protein of the _A. thermocellus_ cellulosome, contains a family-3 CBM, CBM3a, which has a high affinity for crystalline cellulose [40; 41; 42]. Fusion proteins were constructed by genetically linking the above functional protein moieties, Cel9*, Cel9**, Cbh9A*, and CBM3a, to the C-termini of the PCNA subunits (Figure 1B).
## 2 TH-2 Spaceborne GNSS Data Assessment
### Quality of GNSS Observations
#### 2.3.3 Noise Level of Carrier Phase  
In the POD processing, the weight of carrier phase is greater than that of the pseudorange. Therefore, its quality is important for the POD accuracy of LEOs. The geometry-free combination [29] is adopted to assess the noise level of the carrier phase observations: \(\phi_{\mathrm{GF}}=\phi_{1}-\phi_{2}\), where \(\phi_{1}\) and \(\phi_{2}\) are the carrier phase measurements from two frequencies. This combination only contains ionospheric delays, initial ambiguities, and noises from the two frequencies. Furthermore, the ionospheric delays and ambiguities are eliminated by a three-order sliding polynomial, and the remaining residuals are considered as the noises of the carrier phase observations. The average RMS of the noises for the GPS is 3.1 mm, while those for the BDS2 MEOs, IGSOs, and GEOs are 2.5, 2.9, and 2.5 mm, respectively. Figure 7 shows the noise levels for the GPS, and BDS2 decrease as the elevation angle increases. The RMS of the noises are smaller than 5 mm, which indicates the good quality of the carrier phase observations.
# European Geostationary Navigation Overlay Service Performance Prediction +
## 3 EGNOS Performance Prediction  
Predicted EGNOS performance relies on predicting the UDREI and GIVEI values as computed by the EGNOS ground segment's processing facilities. These are complicated data processors of which only some characteristics are public [5; 6], while many implementation details are proprietary. Prediction models thus need to mimic the internal workings of the ground segment's processing, as well as consider the expected status of the GPS space segment and the expected signal reception conditions to the extent possible. An overview of a prediction system's architecture is shown in Figure 1.
# Numerical Analysis for Shear Behavior of Binary Interfaces under Different Bonded Conditions
## 3 Shear Mechanical Behavior under Different Bonded Conditions
### Influence of Bonded Conditions on Stress Distribution  
The state of stress distribution at the same displacement inside the interface model under different bonded conditions are plotted in Figures 10-12, in which the stress is represented by blue. According to Figure 10, the stress distribution during shearing of the unbonded binary interface is relatively uniform, and whatever the normal stress is, there is no obvious stress concentration inside the model, with only a slight tendency on the right, which corresponds to a larger number of cracks. This stress concentration is mainly caused by the lower rock mass being subjected to the shear and pressure of the upper rock mass, as well as the absolute restriction by the right wall.  
Figure 11: Stress distribution in 50% bonded condition.  
Figure 10: Stress distribution in unbonded condition.  
At a bonded degree of 50%, a significant stress concentration occurs during shear at the binary interface, which is located in the middle of the interface, i.e., the boundary between bonded and unbonded parts. This may be explained by the varying degrees of coordination deformation of the blocks at the boundary, which in turn leads to the concentration of compressive stresses. As the normal stress increases, the area of stress concentration will slowly move to the left, and thus, cracks start to expand to the unbonded part.  
At any normal stress, the left of the binary interface model in the fully bonded state is a clear stress concentration region. This is caused by the fact that the left model is directly extruded by the wall; the upper block stays in line with the wall displacement, while the lower block remains essentially immobile, resulting in extremely non-coordinated deformation in this region. Under high stress, multiple stress concentration regions appear on the failure surface, which are caused by the interlocking of asperities on both sides of the failure surface and further shear degradation.