esa-sceva/satcom-chunk-collection huggingface dataset:
# Round Window Reinforcement-Induced Changes in Intracochlear Sound Pressure
## 4 Discussion
### Comparison with Numerical Calculations and RWM Impedance Estimation
#### 4.3.1 LEM of the Cochlea Proposed by [PERSON] et al. (2009)  
[PERSON] et al. proposed a simple LEM of the cochlea (cf. Figure 8) that describes the cochlea input impedance \(Z_{C}\) as a serial circuit containing two lumped parameters, the impedance of the cochlear partition (\(Z_{DIFF}\)) and \(Z_{RWM}\) (Equation (1)):  
\[Z_{C}=Z_{DIFF}+Z_{RWM} \tag{1}\]  
\(Z_{DIFF}\) was experimentally determined in several studies [8; 11; 19; 23]. The magnitude of \(Z_{DIFF}\) represents a resistive behavior independent of the frequency, and the phase is approximately close to zero cycles for most frequencies. Hence, \(Z_{DIFF}\) can be approximated with one lumped parameter describing the resistive behavior (\(R_{DIFF}\)). The \(Z_{DIFF}\) value from [PERSON] et al. (2001) was used in both models. \(Z_{RWM}\) was calculated using the proposed iterative Foster form network model with predetermined parameter values (Table 1). An adapted figure of the RWM LEM from [PERSON] et al. (2009) is added for clarification in Table 1 [11].  
Figure 8: LEM circuit of the cochlea proposed by [PERSON] et al. (2009). Ground connection represents the sound pressure at the surgically opened middle ear cavity that is assumed to be zero.  
The \(U_{STAP}\) is considered as the input to the cochlea. According to [PERSON] et al. (2004)'s findings, the magnitude of \(U_{STAP}\) approximately equals the magnitude of \(U_{RWM}\)[20].  
\[U_{STAP}=U_{RWM} \tag{2}\]  
\[Z_{DIFF}=(P_{SV}-P_{ST})/U_{STAP} \tag{3}\]  
As we have confirmed by experiments (cf. Appendix A), and as is also reported in the literature [5; 13], the input to the cochlea can be assumed to not be affected by the RWM reinforcement--i.e., \(U_{STAP}=U^{\prime}_{STAP}\), where the apostrophe denotes the conditions after RWM reinforcement. The model further assumes equal RWM motion before and after reinforcement (\(U_{RWM}=U^{\prime}_{RWM}\)). Considering these assumptions and using the equations for \(P_{ST}\) (\(P^{\prime}_{ST}\)) ((4) and (5)) and \(P_{SV}\) (\(P^{\prime}_{SV}\)) ((7) and (8)), respectively, the relative pressure gain (\(P_{ST}\) and \(P_{SV}\)) caused by the RWM reinforcement can be derived as the ratio between the RWM impedance after and before reinforcement for ST (6) and the ratio between the cochlea input impedance after \(Zc^{\prime}\) and before reinforcement \(Zc\) for SV (9).  
Before reinforcement for ST:  
\[P_{ST}=U_{RWM}\;*\;Z_{RWM} \tag{4}\]  
After reinforcement for ST:  
\[P_{ST}=U_{RWM}\;*\;Z_{RWM} \tag{5}\]  
From Equations (4) and (5) and the assumption \(U_{RWM}=U^{\prime}_{RWM}\):  
\[\frac{P_{ST}}{P_{ST}}=\frac{Z_{RWM}}{Z_{RWM}} \tag{6}\]  
Similarly, before reinforcement for SV:  
\[P_{SV}=U_{STAP}\;*\;(Z_{DIFF}+Z_{RWM}) \tag{7}\]  
After reinforcement (for SV):  
\[P_{SV}=U_{STAP}\;*\;(Z_{DIFF}+Z^{\prime}_{RWM}) \tag{8}\]  
Since \(U_{STAP}=U^{\prime}_{STAP}\):  
\[\frac{P_{tSV}}{P_{SV}}=\frac{Z_{DIFF}+Z_{tRWM}}{Z_{DIFF}+Z_{RWM}} \tag{9}\]
## 2 Related Concepts
### ResNet Variants and Feature Reuse Networks  
Deep convolutional neural networks have shown a significant increase in the accuracy for various segmentation and classification tasks. However, a common obstacle in training deep neural network architectures is the appearance of vanishing or exploding gradients. As the depth of CNN increases, information about the gradient passes through many layers, and it can vanish or accumulate large errors by the time it reaches the end of the network. This problem has been largely addressed using activation functions with a small derivate such as rectified linear unit (ReLU), implementation of gradient clipping, intermediate normalization layers, or careful weight initialization. Nevertheless, with the increasing network depth, accuracy gets saturated and then degrades rapidly. The introduction of skip connections in ResNets [6] allows for copying of the activations from layer to layer, thus preserving information and significantly increasing the performance. Nevertheless, when the depth of the network goes very deep, ResNets become challenging to converge. These difficulties were addressed in Pre-ResNets [24] by introducing forward and backward signals that directly propagate from one block to any other using identity mappings after-addition activation and as the skip-connections. This ultimately constructs a new residual block with the BN-ReLU-Conv order. [PERSON] [25] introduces level-wise shortcut connections to alleviate the learning capability and significantly boost network performance. Moreover, the deep network initialization problem and incompatibility between ReLU and element-wise summation were addressed in weighted residual networks (WNR) [26]. Although deeper residual networks showed performance improvement, diminishing feature reuse slows down network training. This was addressed by increasing and decreasing the width and depth, respectively, in improved WNRs [27].  
Furthermore, another efficient way to alleviate network performance is by reusing features. DenseNet [28] introduces connections between all successive layers in a feed-forward manner where features from each preceding layer are used as inputs to every other layer. This means that each layer is receiving cumulative knowledge from all prior layers, i.e., it reuses features. A variety of compelling benefits are obtained with the introduction of direct connections between layers. First, it allows more depth of the network while simultaneously alleviating the vanishing and exploding gradient problems. Second, the use of features from all layers leads to improvements in the performance. Finally, it efficiently utilizes parameters. This allows for less propensity to overfitting and leads to a reduction of computational costs. CondenseNet [29] combines dense connectivity with a group convolution to further facilitate feature reuse through the network. Here, the group convolutions aim at removing direct connections between layers allowing distinctly smooth feature reuse.
# NOx Formation Mechanism and Emission Prediction in Turbulent Combustion: A Review
## 2 NOx Formation Mechanism
### N\({}_{2}\)O Route  
The mechanism of the N\({}_{2}\)O pathway is similar to the thermal NOx pathway, namely the radical [O\(\cdot\)] impacts the nitrogen. However, the N\({}_{2}\)O pathway is a three-molecular mechanism that needs a M to exist. The main elementary reactions are as follows:  
\[\text{N}_{2}+\text{O}\cdot+\text{M}\rightarrow\text{N}_{2}\text{O}+\text{M} \tag{6}\]  
\[\text{N}_{2}\text{O}+\text{O}\cdot\rightarrow\text{2 NO} \tag{7}\]  
The N\({}_{2}\)O route would be dominant in the lean-fuel situation [30]. This pathway is often ignored because it is attributed to a small amount of NOx generation. However, when the situation is in lean-fuel conditions, the radical [CH\(\cdot\)] would be a small amount, the prompt pathway would be restrained, the low temperature would occur in lean-fuel conditions, and the thermal pathway would also be suppressed. At that time, the N\({}_{2}\)O pathway would be mainly the route to product NO, especially when pressure is high, and the three-body reaction is still active, because of the existence of the third molecule, M.  
The low reactor temperature and oxygen environment would prompt the N\({}_{2}\)O route. In the moderate or intense low-oxygen dilution (MILD) combustion of the CH\({}_{4}\)/H\({}_{2}\) fuel-blend system, with the reactor temperature increasing, the N\({}_{2}\)O route decreased, and with the initial mass fraction of oxygen increased, the N\({}_{2}\)O route increased [31]. Analogically, in methane oxy-fuel combustion, the N\({}_{2}\)O route dominates the NO production at the area of mass fraction of oxygen from around 3% to 10% [32].
###### Abstract  
In this paper, remote and in situ techniques to estimate the dynamic response of a building to ambient vibration are reported: data acquired through a real-aperture radar (RAR) interferometer and conventional accelerometers are analyzed. A five-story reinforced concrete housing building, which was damaged during the May 11 th 2011 Lorca (Spain) earthquake, is used as a case study. The building was monitored using both types of instruments. The dynamic properties of the building are estimated first taking acceleration measurements using a set of 10 high-precision accelerometers installed on the roof of the building. Further, the displacement-time histories, recorded with the RAR device pointing to a corner of the building, are analyzed. Then, the ability and shortcomings of RAR measurements to deal with the fundamental frequencies of vibration of the structure are investigated. The advantages and limitations of from-inside (accelerometric) and from-outside (RAR) measurements are highlighted and discussed. A relevant conclusion is that, after strong earthquakes, RAR may be an interesting and useful tool, as it allows surveying the structural response of mid-rise buildings remotely, without the need to enter the structures, which may be dangerous for inspectors or technicians in cases of severely damaged buildings. Given that the instrumented building suffered significant damage, the ability of these kinds of measurements to detect damage is also discussed.  
Keywords:real-aperture radar; modal identification; accelerometers; cross power spectrum; magnitude-squared coherence; buildings; remote sensing +
Footnote †: journal:  
_Article_
## 2 Materials and Methods
### Comparative Analysis  
For the junction tensile tests under the aforementioned four different stress states, Figures 19 and 20 show the geocell junction test results under different clamping distances in terms of strength and peak elongation. The possible failure modes of geocells in practical applications are analyzed, and the differences between different clamping distances are compared more clearly in order to provide more data support for test and practical engineering applications.  
Figure 19: Junction test results under various clamping distances: (**a**) axial stress versus clamping distance; (**b**) pre-peak elongation versus clamping distance.  
Figure 18: Different failure modes of the junction: (**a**) Directional change of the junction, (**b**) failure at the connection of the junction and the strip.  
The tensile strength and shear strength of the junctions decrease slightly as the clamping distance increases, whereas the peeling strength and splitting strength stay almost unchanged, as seen in Figures 19a and 20. Overall, the yield strength of HDPE welded junctions under different stress states from high to low is: weld tensile strength, splitting strength, shear strength, and peeling strength. The failure modes of HDPE welded junctions in practical applications are mainly controlled by peel strength, which is consistent with the research [11; 20; 21]. Therefore, more attention should be paid to the control of the peeling force when the geocell of HDPE welded connections is employed in practical engineering.  
The elongation required for four types of tests to attain peak strength increases with the clamping distance, as shown in Figure 19b, and the weld tensile and shear strength tests are more obviously affected by the strip. Comparing the four types of testing, the elongation required for the peel strength test to reach the peak is the largest at the 25 mm and 50 mm clamping distances; the peak elongation of the shear strength test is the largest at the 100 mm clamping distance. In addition, under the 100 mm clamping distance, there is minimal difference in the peak elongation of the tests (highlighted area in Figure 20). It is further confirmed that the peeling force is easily capable of causing geocell destruction.  
There are anchor nails at the junctions of the geocells laid vertically along the slope, more filling pressure is dispersed to the depth of the slope through the anchor nails, and the junctions are subjected to the splitting force and weaken the peeling force. The junctions of the geocells laid along the horizontal direction of the subgrade are mainly subject to a peeling force, and rives aggravate the peeling force. Therefore, the comprehensive analysis shows that the construction method of vertically laying down the slope is more reliable in the construction of geocell slope protection, which helps to improve the service life of geocells in slope protection.
# Research on the Reliability of Bridge Structure Construction Process System Based on Copula Theory
## 2 Basic Theory of the Copula Function
### Identification of Optimal Copula Functions  
From the analysis of the current status of domestic and international research in the first section of this paper, it can be seen that the correlation between different failure modes is different, and different Copula functions are needed to describe the correlation between them. Therefore, how to accurately select the optimal Copula function to describe the correlation between failure modes is the primary problem that needs to be solved at present. There are many methods to identify the optimal Copula function, and there are two main ones used at present: (1) AIC (Akaike Information Criterion) [32]; and (2) BIC (Bayesian Information Criterion) [33].  
AIC and BIC are based on the principle that the Copula function with the smallest AIC or BIC value is considered to be the optimal Copula function for fitting the correlation between the two data sets. AIC is based on the concept of entropy, which is a standard to measure the goodness of statistical model fitting. The specific method is to find a model that contains the least free parameters and can best interpret the data. The AIC value is defined as the sum of \(-2\) times the logarithmic sum of the Copula density function values at the original observed data points of the variable and 2 times the number of parameters associated with the Copula function [32]. When there is a large difference between the two models, the difference is mainly reflected in the likelihood function term. When the likelihood function difference is not significant, the model complexity plays a role, so the model with less parameters is a better choice. Generally, when the complexity of the model increases, the likelihood function will also increase so that the AIC becomes smaller. However, when the complexity of the model is too large, the growth rate of the likelihood function slows down, resulting in the increase of AIC. If the model is too complex, it is easy to cause over fitting. The model  
\[\left\{\begin{array}{l}u_{i}=\frac{rank(x)}{N+1}\\ v_{i}=\frac{rank(y_{i})}{N+1}\end{array},i=1,2,\cdots,N\right. \tag{13}\]  
where \(rank(\cdot)\) denotes the rank of samples \(x_{i}\) or \(y_{i}\), i.e., the number of samples arranged from smallest to largest is less than or equal to \(x_{i}\) or \(y_{i}\).  
In the reliability analysis process, the observed data of the failure modes cannot be known, but a method to establish a random vector of two failure modes using the Latin hypercube sampling method is proposed in Section 2.2.2 of this paper. This two-dimensional random vector can be brought into Equations (11)-(13) when calculating the correlation between the failure modes to expand the calculation. We found the AIC and BIC values of the alternative Copula functions, which could be compared to identify the optimal Copula function suitable for the correlation of these two failure modes.
## 4 Experiments
### Datasets' Settings  
The CUB-200-2001 dataset, a few sample images of which are illustrated in Figure 6, has been extensively used in the research of fine-grained image classification [4]. The CUB-200-2001 dataset contains 11,788 images of birds, with 200 types of birds in general. Each has a different posture, which results in large intra-class variance and small inter-class variance. Differences between classes are normally small and regional, such as the beak, the color of the wings, or another regional area. The dataset not only provides classification labels for all bird image data but also provides essential part annotations. However, our method only uses a weakly supervised method, so only image label data was used in the model for training and testing. We used 70% of the data as a training set and 30% for testing.  
The Stanford Dogs dataset provides images of data for 120 different types of dogs [33]. There are 20,580 images in total, including different perspectives and poses. Only target frame information is provided, and key point information is excluded. The sample image data are presented in Figure 7. In the figure, two distinct dog breeds are shown. From the analysis of the pictures, the backgrounds of such a dataset are complicated, as some backgrounds are set on sofa, grass, etc. Hence, when we used the Stanford Dogs dataset, in the data pre-processing stage, we cropped images according to the provided label box to reduce the impact of the responsible background. Moreover, dogs of different breeds in this type of dataset have large intra-class differences. We used 70% of the data as a training set and 30% for testing.  
The FGVC-Aircraft provides image data of 102 categories of aircraft [34]. Each category has more than 100 diverse images. There are 10,200 images in total, and only label box information provided. Sample images are presented in Figure 8. We also applied image cropping to such a kind of dataset during our data pre-processing stage. We cropped the image according to the label box to reduce the impact of the background. We used 80% of the data as a training set and 20% for testing.  
Figure 8: Sample images from the FGVC-Aircraft dataset.  
Figure 6: Sample images from the CUB-200-2001 dataset.  
Figure 7: Sample images from the Stanford Dogs dataset.  
We used a subset of the CompCars dataset, which was proposed by [PERSON] et al. (2019), and contains 300,000 images of 500 categories of vehicles. We used 15 categories of vehicle type, 55 categories of vehicle brands, and 250 types of vehicle models. Each type of vehicle has approximately 300 images, covering rainy days, nights, foggy days, and different angles of view. We used 70% of the data as a training set and 30% for testing. The visualization of the CompCars dataset is shown in Figure 9.
## 7 Heuristic Security Analysis
### Mutual Authentication  
The improved protocol establishes robust mutual authentication through a bidirectional verification framework. Throughout the authentication phase, both \(RSU_{j}\) and \(RSU_{k}\) can deduce the vehicle's pseudonymous RID from the transmitted vehicle data. In the initial authentication, the vehicle sends \(R_{i}\) and \(\beta\), enabling the RSUs to verify the legitimacy of its pseudonymous RID using the TA's public key, \(Y\). The RSU cryptographically validates vehicle registration credentials using the TA's public key through elliptic curve operations, while the vehicle authenticates the RSU by verifying timestamped session key hashes. This step ensures the authentication of \(V_{i}\) by the RSUs. Also, in both the initial and handover authentication phases, the vehicle receives \(M_{6}\) and \(M_{11}\), respectively. By verifying the source of these messages as being from \(RSU_{j}\) and \(RSU_{k}\), in handover scenarios, infrastructure nodes authenticate vehicles through blockchain-stored identity records, while vehicles confirm RSU legitimacy via temporary identity decryption and session key verification. The protocol achieves mutual authentication.
## 4 Experimental Results and Discussion
### Experimental Study of the Output Performance of the Energy Harvester
#### 4.2.1 Output Performance of VEH-30  
The VEH-30 consists of a cantilevered beam and a solid cylindrical bluff body. The cylindrical bluff body is fixed to the free end of the cantilevered beam. The upper surface of the cantilever is covered with piezoelectric sheet and placed 10 mm away from the fixed end of the cantilever. The size of the piezoelectric sheet is 60 mm \(\times\) 20 mm \(\times\) 0.2 mm. Due to the fluid-solid coupling effect, the cylindrical body deflects the beam when subjected to a vibrational force, which causes a strain inside the piezoelectric element and generates an electrical charge based on the piezoelectric effect [23]. When the output performance test of veh-30 is carried out in the wind tunnel, it is subject to stable and controllable air flow, and the maximum wind velocity is 14.5 m/s. A photo of a prototype VEH-30 energy harvester is shown in Figure 5. Table 4 shows the material parameters and dimensional parameters of VEH-30.  
We extracted the experimental data, and drew the curve of the output voltage of VEH-30 changing with the wind velocity, as well as the maximum output voltage waveform of VEH-30 at different wind velocities, as shown in Figure 6.  
From Figure 6a, it can be seen that in the wind velocity range of 2.0 m/s-15.0 m/s, when the wind velocity is less than 7.1 m/s, the output voltage of the VEH-30 harvester is less than 1 V; when the wind velocity is 7.1 m/s, the output voltage of the energy harvester is 1.10 V, which is considered as the starting wind velocity of the VEH-30; with the increase in wind velocity, the output voltage of the VEH-30 increases rapidly, when the wind velocity was 12.5 m/s, the output voltage reached a maximum of 3.59 V; thereafter, the output voltage decreased as the wind velocity increased. The maximum output voltage is reached at a wind velocity of 12.5 m/s because the intrinsic frequency of the VEH-30 resonates with the excitation frequency caused by the ambient wind velocity at this time, and the cantilever beam deformation amplitude is at its maximum, so that its output performance reaches its highest. As can be seen from Figure 6a, the VEH-30 has a small range of wind velocity bands to achieve relatively large voltage output. Figure 6b shows that the output voltage of the VEH-30 approximates the periodic and constant-amplitude, and the amplitude of the voltage output first increases and then gradually decreases with the increase in the airflow velocity.