esa-sceva/satcom-chunk-collection huggingface dataset:
# Finite-Time Tracking Control of a Flexible Link Manipulator Based on an Extended State Observer
## 3 Control Design
### Controller Design Based on an Extended State Observer
#### 3.2.1 Extended State Observer Design and Stability Analysis
If the condition \(\|\varepsilon\|\geq\frac{2l_{up}\|E^{T}P_{\varepsilon}\|}{(1-q)\lambda_{\min} \{Q_{\varepsilon}\}}\) is satisfied, then  
\[\dot{V}_{\varepsilon}\leq-\frac{q\lambda_{\min}\{Q_{\varepsilon}\}}{\lambda_{ \max}\{P_{\varepsilon}\}}V_{\varepsilon}-\frac{\big{(}(1-q)\lambda_{\min}\{Q _{\varepsilon}\}\|\varepsilon\|-2l_{up}\|E^{T}P_{\varepsilon}\|\big{)}}{ \sqrt{\lambda_{\max}\{P_{\varepsilon}\}}}\sqrt{V_{\varepsilon}}. \tag{30}\]  
According to Lemma 1 in [29; 30], the observer error \(\varepsilon\) converges to the set \(\left\{\varepsilon\|\|\varepsilon\|\leq\frac{2l_{up}\|E^{T}P_{\varepsilon}\| }{(1-q)\lambda_{\min}\{Q_{\varepsilon}\}}\right\}\) in finite-time \(t_{1}\). We define that \(\phi=\frac{q\lambda_{\min}\{Q_{\varepsilon}\}}{\lambda_{\max}\{P_{\varepsilon}\}}\), \(\varphi=\frac{\big{(}(1-q)\lambda_{\min}\{Q_{\varepsilon}\}\|\varepsilon\|-2l _{up}\|E^{T}P_{\varepsilon}\|\big{)}}{\sqrt{\lambda_{\max}\{P_{\varepsilon}\}}}\), and then \(t_{1}\) satisfies  
\[t_{1}\leq\frac{2}{\phi}\ln\frac{\phi\sqrt{V(0)}+\varphi}{\varphi}. \tag{31}\]  
When \(Q_{\varepsilon}\) is selected as the three-dimensional unit matrix, \(\big{\|}E^{T}P_{\varepsilon}\big{\|}\) is a function of adjustable parameters, including \(\eta_{1},\eta_{2},\eta_{3}\). Therefore, we can choose the appropriate\(\eta_{1},\eta_{2},\eta_{3}\), satisfying the condition (26), so that the observer error can be reduced to a neighborhood of zero in finite-time. \(\square\)
## 3 Realization of the Objective of the Actuated Links
### Tracking Controller Design  
The sliding mode surface is designed as follows:  
\[S_{1r}=\mu_{1r}e_{1r}+\dot{e}_{1r} \tag{10}\]  
and  
\[e_{1r}=q_{r}-T_{1}^{d},e_{1r}=q_{r}-\hat{T}_{1}^{d} \tag{11}\]  
where \(\mu_{1r}\) is the constant greater than zero.  
The derivative of \(S_{1r}\) is  
\[\dot{S}_{1r}=\mu_{1r}\dot{e}_{1r}+\dot{e}_{1r}=\mu_{1r}\dot{e}_{1r}+f_{1r}+g_{ rr}\tau_{1r}-\hat{T}_{1}^{d} \tag{12}\]  
Let  
\[\dot{S}_{1r}=-\varphi_{1r}S_{1r}-\varepsilon_{1r}\text{sgn}(S_{1r}) \tag{13}\]  
where \(\varphi_{1r}\) and \(\varepsilon_{1r}\) are the positive coefficients.  
The tracking controller is designed as follows:  
\[\tau_{1r}=\Big{(}-\varphi_{1r}S_{1r}-\varepsilon_{1r}\text{sgn}(S_{1r})-\mu_{ 1r}\dot{e}_{1r}-f_{r}+\hat{T}_{1}^{d}\Big{)}\Big{/}g_{rr} \tag{14}\]  
The Lyapunov function \(V_{1}\) is constructed as follows:  
\[V_{1}=\dfrac{1}{2}S_{1r}^{2} \tag{15}\]  
The derivative of (15) is the following:  
\[\dot{V}_{1}=S_{1r}\dot{S}_{1r}=-\varphi_{1r}S_{1r}^{2}-\varepsilon_{1r}|S_{1r}| \leq 0 \tag{16}\]  
When \(\dot{V}_{1}\equiv 0\) and \(S_{1r}\equiv 0\), depend on [PERSON]'s invariance theorem [57], we can acquire \(e_{1r}\to 0\) and \(\dot{e}_{1r}\to 0\). Thus, the actuated link tracks \(T_{1}^{d}\) to reach its target states. However, the underactuated link rotates freely.
# Influence of Soil Particle Size on the Engineering Properties and Microstructure of a Red Clay
## 3 Results and Analysis
### Results of UCS Test
The residual strength of the soil sample with a particle size of 5 mm is smaller than the residual strength of the soil sample with a particle size of 1.25-2 mm. Figure 8: Stress-strain curve of red clay: (**a**) 1.3 g/cm\({}^{3}\); (**b**) 1.4 g/cm\({}^{3}\); (**c**) 1.5 g/cm\({}^{3}\). Figure 7: Failure pattern of soil sample: (**a**) 0.25 mm; (**b**) 0.5 mm; (**c**) 2 mm; (**d**) 5 mm. The failure patterns displayed in Figure 7 show that after the shear failure, the friction force generated by the relative movement of the soil sample along the shear zone is smaller than the resistance of the soil mass after split failure when the soil particle is large. In addition, except for the one corresponding to 0.25 mm, the other curves at the dry density of 1.3 g/cm\({}^{3}\) follow the same rule, whereby the smaller the particle size, the greater the residual strength, which shows that the interaction force between the small particles is intensive. By defining the axial stress corresponding to the peak point of the stress-strain curve as the UCS of red clay, the curve of the relationship between the dry density and UCS of red clay with different particle sizes can be derived (Figure 9). The relationship between the particle size and the UCS of red clay at various dry densities is shown in Figure 10. According to these two figures, the greater the dry density, the greater the UCS of the red clay under different particle size conditions. Furthermore, as soil's particle size increases, the red clay's UCS first decreases and then increases under different dry density conditions. In Figure 9, at the same dry density, the UCS is most prominent at a particle size of 0.25 mm, smallest at a particle size of 2.0 mm, and closest at particle sizes of 0.5 mm, and 5 mm. The average UCS is 87.46 kPa for different particle sizes at a dry density of 1.3 g/cm\({}^{3}\), increasing by 64.17% and 140.59% for dry densities of 1.4 g/cm\({}^{3}\) and 1.5 g/cm\({}^{3}\), respectively. In Figure 10, the average UCS for different dry densities is 107.22 kPa at a particle size of 2.0 mm, increasing by 68.53%, 40.03%, and 40.42% at particle sizes of 0.25 mm, 0.5 mm, and 5.0 mm, respectively. The UCS is mainly related to the failure mode of the soil mass. Videlicet, when the soil particle size is smaller than 2.0 mm, the soil mass undergoes split failure. The larger the particle size, the smaller the UCS of the red clay. However, when the particle size of the soil is relatively large, the soil undergoes shear failure, and the UCS is mainly related to the friction between the particles, enhancing the strength of the soil. Hence, with the growth in particle size, the failure pattern of the soil mass changes from split failure to shear failure. Figure 9: The fitting curve of dry density and UCS at different particle sizes.
## 4 Results and Discussion
### Degradability Study  
To evaluate the effect of chemicals and enzymes in the degradation of Ch and Alg, degradability studies of HFs were performed using TCA 20% and trypsin. HFs were weighed and soaked in 2 mL of 20% TCA or trypsin solutions, then incubated for 24 h. Figure 5A,B described that not only in TCA, but also in trypsin solutions, ChAlg HF and ChAlg/\(\alpha\)-M HF significantly improved the degradability of HFs compared to Ch HF and Alg HF, respectively. In another study, combination of Alg and Ch (2:1) as a microparticle loaded mangosteen rind extract increased 2-fold in releasing \(\alpha\)-M from HFs in the colon and intestine compared to a low alginate concentration (0.25:1) [30]. Based on these results, Alg increased the degradability of Ch, and closely related to the release time of \(\alpha\)-M from ChAlg HF.  
Figure 4: Mechanical properties of ChAlg/\(\alpha\)-M HF. (**A**) Tensile strength; (**B**) elongation at break. Each value represents the mean \(\pm\) SEM of three experiments. * \(p<0.05\), compared to the Ch HF. \(\dagger\)\(p<0.
# Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
## 5 Results  
### Simulation Setup  
We consider a two-tier HetNet with four SBSs deployed within a macro cell coverage region radius of 250 m to assess the proposed matching game-based UE-slice association and UE-slice pairing algorithms. SBSs and UEs are uniformly distributed in the coverage area of the MBS. The simulations are dependent on 1000 runs, and the results are averaged. The essential simulation parameters are given in Table 3[15,54].
## 2 Materials and Methods
### Cascade Network Registration Method  
The input of the deformable image registration model is usually an image pair that has been affine aligned, where the image to be registered is called the fixed image and the image to be registered is called the moving image. For a pair of fixed image, \(F\), and moving image, \(M\), CNN can generate a displacement vector field (DVF) \(\varphi\) for floating image warping by performing feature extraction and deformation prediction on them. In the registration task, the training process of CNN is the process of continuously updating the network parameters to obtain the optimal DVF. After obtaining the DVF, the STN can be used to perform trilinear interpolation on the floating image according to the deformation information in the DVF, thereby obtaining the deformed image. For the MCANet proposed in this paper, the floating image needs to go through two stages of deformation in total, as shown in Figure 1.  
In the first stage, \(M\) and \(F\) are passed into the registration subnet together as the initial input. After the deformation field \(\varphi_{1}\) is generated by the registration subnet \(U_{1}\), the first warping deformation is performed on \(M\) to obtain a rough deformed image \(I_{1}\). This stage can be expressed as Equation (1):  
\[I_{1}=U_{1}(M,\varphi_{1})=M\circ\varphi_{1}, \tag{1}\]  
After obtaining the deformed image \(I_{1}\), it enters the second stage of registration. At this time, \(I_{1}\) is sent to the registration subnet \(U_{2}\) together with \(F\) again as the moving image. After the prediction of \(U_{2}\), the deformation field \(\varphi_{2}\) between \(F\) and \(I_{1}\) can be generated. According to the voxel displacement in \(\varphi_{2}\), rough deformed image \(I_{1}\) is warped again, and then we can obtain the fine deformed image \(I_{2}\). This process can be expressed by Equation (2):  
\[I_{2}=U_{2}(M\circ\varphi_{1},\varphi_{2})=U_{2}(I_{1},\varphi_{2})=I_{1}\circ \varphi_{2}, \tag{2}\]  
Since \(I_{1}\) is the moving image in the second stage, after the spatial transformation in the first stage, \(I_{1}\) may lose part of the spatial information in the original moving image \(M\). Therefore, in this study, the deformation field \(\varphi_{1}\) is input to the end of \(U_{2}\) as auxiliary information to assist in the generation of \(\varphi_{2}\). Through the above process, the network architecture completes the coarse-to-fine registration of the original input images.  
Figure 1: The network architecture of the multi-constraint cascaded attention network.
###### Abstract
The optimization of iterative update mainly has the following three optimization objectives: (1) In order to increase the population diversity, the predator strategy was introduced in the studies of [10; 11; 12] to increase the diversity of pigeons and overcome the local optimal solution; [PERSON] introduced the concept of dynamics to improve population diversity [13]. (2) In order to solve the problem of the pigeons being easily trapped in the local optimal solution, [PERSON] added the global update operator at the beginning of the iteration, and on this basis, added the population mutation operator further, which can expand the search scope and make the population out of the local optimal solution [14]. [PERSON] [15] was able to effectively reduce the probability of optimization results falling into local optimum by implementing compass and landmark operators with Cauchy variation. (3) To solve the convergence speed, efficiency, and robustness of the algorithm, References [16; 17] introduced the parallel competition and hierarchical search strategy and the pigeon-group hierarchy network mechanism, respectively, to improve the global search ability and local convergence ability of the algorithm. [PERSON] et al. added interference factors in the map and compass stages, which could improve efficiency, and selected classification learning strategy in the landmark stage [18]. [PERSON] et al. proposed an optimization algorithm based on evolutionary game theory to improve the adaptability of the original PIO algorithm. Meanwhile, the robustness and efficiency of the algorithm are enhanced [19]. In Reference [20], mutation mechanism was added in the iterative update stage to enhance the exploration ability of the pigeons and improve the efficiency of the algorithm. It can be seen from the above that the optimization of PIO algorithm is mainly focused on the iterative update stage only, while there are not many studies on the simultaneous optimization of initialization and iterative update stage. Therefore, a target search algorithm based on chaotic disturbance pigeon-inspired optimization is proposed in this paper. The chaos mapping is used to initialize the population, which can enrich the diversity of the population and enhance the global search ability. In addition, this paper also adds disturbance factors in the traditional iteration update stage to avoid the algorithm easily falling into local optimum and premature convergence, so that the algorithm has a better global search ability. The remainder of this paper is organized as follows. The second section describes the basic idea and process of PIO algorithm and points out the problems to be optimized at each stage. The third section illustrates CDPIO algorithm. Simulation validation, together with comparison with the traditional approach, is presented in Section 4. In the fifth section, the performance of CDPIO algorithm will be verified by combining the practical problem of UAV cooperative target search. Section 6 provides conclusions and some possible paths for future work.
# MAToC: A Novel Match-Action Table Architecture on Corundum for 8 \(\times\) 25G Networking
## 4 Implementation
### Resource Utilization Analysis  
As mentioned earlier, the only resource that matters in MAToC is BRAM, as the primitive Corundum allocates several buffers in the data path, together with consumption of DMA RAM for packet data, descriptor and completion in both receive and transmit direction of each independent channel. This has already slightly exceeded the BRAM capacity of this part (984 for zu19 eg-ffvc1760-2e) in the 8 \(\times\) 25G mode, leading to failure in placement. Even if resources are reduced below capacity, the heavy use of distributed resources in a centralized design will lead to the increase of propagation time in all paths, resulting in an unacceptable drop of maximal clock frequency. In this section, we collect some architecture parameters that should be carefully analyzed and set in order to achieve optimal resource utilization and meet the timing constraints.  
In order to reduce the use of BRAM, we disabled functions such as PTP timestamp and statistics gathering. We also managed to set parameters for buffers and DMA RAM. The instantiations of block RAM can be configured as 36 \(\times\) 1024 or 64 \(\times\) 512 (Width \(\times\) Depth). In some cases, improper parameters can lead to waste in space utilization. For example, let the size parameter of a buffer with data width of 16 bytes equal 8192 bytes; its depth is resolved as 8192 \(\div\) 16 = 512 and it would be synthesized as two BRAM configured as 64 \(\times\) 512. If the SIZE is larger than 8192, then the synthesis could result in four BRAM as 32 \(\times\) 1024, which is double that of the former one. If it is 4096 bytes, the synthesizer would result in the same numbers of instantiated BRAM as the original setting, wasting half of its address space. By carefully setting the buffer size, BRAMs would be configured to the width of 64 as much as possible, and making full use of the memory space, i.e., letting the buffer depth equal 512 can benefit to the utilization result. As for the packet buffer for each channel, their depths are preset according to the delay cycles in the MAT pipeline and the maximum frame length. Specifically, it should be the larger one of the maximum frame length and the internal data size multiplies the delay cycles. Based on this result, trade-off between resources and performance is then to be considered by the designer.
## Appendix A Frequency-Dependent Constitutive Equation for the Dissipative Structure
### Linear Viscoelastic Constitutive Equation in the Frequency Domain  
The general theory of linear viscoelasticity is used (see [112; 113; 114]). With respect to the presentation detailed in [41], we present here a summary of those results with additional developments. In this section, \(\mathbf{x}\) is fixed in \(\Omega_{S}\) and will be omitted in all the quantities. The Latin indices, such as \(i\), \(j\), \(k\), and \(h\), take the values 1, 2, and 3. The convention for summations over repeated Latin indices is used. The general constitutive equation in the frequency range is written as  
\[\sigma_{ij}(\omega)=(a_{ijkh}(\omega)+i\omega\,b_{ijkh}(\omega))\ \varepsilon_{klh}(\omega)\,. \tag{1}\]  
It can be proven that  
\[\lim_{|\omega|\rightarrow+\infty}a_{ijkh}(\omega)=a_{ijkh}(+\infty)\,, \tag{2}\]  
\[\lim_{|\omega|\rightarrow+\infty}\omega\ b_{ijkh}(\omega)=0\,, \tag{3}\]  
in which \(a_{ijkh}(+\infty)\) is called the initial elasticity tensor. It can be deduced that  
\[\sigma_{ij}(\infty)=a_{ijkh}(+\infty)\,\varepsilon_{klh}(\infty)\,. \tag{4}\]  
Equation (4) shows that viscoelastic materials behave elastically at high frequencies with elasticity coefficients defined by the initial elasticity tensor \(a_{ijkh}(+\infty)\) that differs from the equilibrium modulus tensor \(a_{ijkh}(0)\) that is such that \[\sigma_{ijkh}(0)=a_{ijkh}(0)\,\epsilon_{ijkh}(0)\,, \tag{100}\]  
in which \(\sigma_{ijkh}(0)=\{\sigma_{ijkh}(\omega)\}_{\omega=0}\) and \(\epsilon_{ijkh}(0)=\{\epsilon_{ijkh}(\omega)\}_{\omega=0}\). The reader should be aware of the fact that the constitutive equation of an elastic material in a static deformation process is defined by equilibrium modulus tensor \(a_{ijkh}(0)\), and not by the initial elasticity tensor \(a_{ijkh}(+\infty)\). Referring to [112; 115], it has been proven that \(a_{ijkh}(0)-a_{ijkh}(+\infty)\) is a negative-definite tensor. The tensors \(a_{ijkh}(\omega)\) and \(b_{ijkh}(\omega)\) are even functions:  
\[a_{ijkh}(-\omega)=a_{ijkh}(\omega)\quad,\quad b_{ijkh}(-\omega)=b_{ijkh}( \omega)\,. \tag{101}\]  
Due to the symmetry properties of tensors \(\mathcal{G}_{ijkh}(t)\), it can directly be deduced that tensors \(a_{ijkh}(\omega)\) and \(b_{ijkh}(\omega)\) must satisfy the symmetry properties  
\[a_{ijkh}(\omega)=a_{ijkh}(\omega)=a_{ijhk}(\omega)=a_{khij}( \omega)\,, \tag{102}\] \[b_{ijkh}(\omega)=b_{ijkh}(\omega)=b_{ijhk}(\omega)=b_{khij}( \omega)\,. \tag{103}\]  
In addition, the following positive-definiteness properties can be shown. For all second-order real symmetric tensors \(X_{ij}\),  
\[a_{ijkh}(\omega)\,X_{kh}\,X_{ij} \geq c_{a}(\omega)\,X_{ij}\,X_{ij}\,, \tag{104}\] \[b_{ijkh}(\omega)\,X_{kh}\,X_{ij} \geq c_{b}(\omega)\,X_{ij}\,X_{ij}\,, \tag{105}\]  
in which the positive constants \(c_{a}(\omega)\) and \(c_{b}(\omega)\) are such that \(c_{a}(\omega)\geq c_{0}>0\) and \(c_{b}(\omega)\geq c_{0}>0\), where \(c_{0}\) is a positive real constant independent of \(\omega\).
## 1 Introduction  
Fault detection is always one of the core problems in the complex electrical engineering field. Generally speaking, fault detection can be divided into three categories based on analytical models, signal processing, and knowledge [1; 2; 3; 4; 5]. Among these fault detection technologies, the analytical models based fault detection technology was the earliest development and the most systematical research. It can construct a system model based mainly on the connections between components in a composition system and can be roughly divided into state estimation method, parameter estimation method and equivalence space method [6; 7; 8]. The fault detection based on signal processing can avoid establishing an object's mathematical model, and it can directly analyze measurable information by signal model, such as correlation function, frequency spectrum, high order statistics, autoregressive moving average process and so on. The fault can be detected by extracting amplitude, variance, frequency and other characteristic values [9; 10]. The fault detection based on signal processing mainly includes principle component analysis method, absolute value testing, tendency testing, Kullback detection, self-adaptable filter detection, _etc_. Similar to signal processing based fault detection, the fault detection based on knowledge is mainly applied to a nonlinear system. At the knowledge level, on the basis of knowledge processing technologies, the dialectical logic and mathematical logic will be integrated, and the symbol processing and numerical processing will be unified. It will also mainly include an expert system, fuzzy reasoning, neural network and rough set, _etc_. [11; 12; 13; 14].  
In fact, whichever fault detection technology we will adopt, the effective extraction of the fault feature is always the key problem. According to complex electrical engineering, we have carried out large numbers of basic research [15; 16; 17; 18]. In this paper, based on the fundamental of feature extraction about the fault coefficient, we will discuss the fault coefficient in complex electrical engineering.  
The paper is organized as follows. In Section 2, the fundamental of feature extraction about fault coefficient are introduced. In Section 3, the Phasor Measurement Unit and Wide Area Measurement System are described, and they have provided a real-time data platform for improving the security and stabilization of power system. In Section 4, for general fault types in complex electrical engineering, the feature extraction process of fault coefficient is clarified in detail. Finally, the paper is concluded in Section 5.
## 3 Performance Implication of NVM Based Storage
### Effectiveness of Buffer Cache
Thus, for adopting the buffer cache more efficiently, read requests may be cached but write requests may be set to bypass the buffer cache layer as storage access time approaches the memory access time. To investigate the efficiency of the buffer cache in more realistic situations, we capture system call traces while executing a couple of representative I/O benchmarks, and perform replay experiments as the storage device changes. Specifically, file request traces were collected while executing two popular Filebench workloads: proxy and web server. Then, we evaluate the total storage access time with and without buffer cache for the given workloads. In this experiment, the cache replacement policy is set to LRU (Least Recently Used), which is most commonly used in buffer cache systems. Note that LRU removes the cached item whose access time is the oldest among all items in the buffer cache when free cache space is needed. Figure 5 shows the normalized storage access time when the underlying storage is HDD and the buffer cache size is varied from 5% to 50% of the maximum cache usage of the workload. The cache size of 100% implies the configuration that complete storage accesses within the workload can be cached simultaneously, which is equivalent to the infinite cache capacity. This is an unrealistic condition and in practical aspects, the cache size less than 50% indicates most of the real system situations. As shown in the figure, the effect of the buffer cache is very large in HDD storage. In particular, buffer cache improves the storage access time by 70% on average. This significant improvement happens as HDD is excessively slower than buffer cache, and thus reducing the number of storage accesses by caching  
Figure 7 compares the storage access time of two configurations that adopt the same RAMDisk but differentiate the management policy of the buffer cache. Specifically, the LRU-cache inserts all requested file data in the cache and replaces the oldest data when free space is needed. Note that this is the most common configuration of buffer cache, which is also shown in Figure 4. The other configuration, AC-cache, is an admission-controlled buffer cache that allows the admission of file data into the buffer cache only after its second access happens. The rationale of this process is to filter out single-access data, thereby allowing only multiple accessing data to be cached. Although this is a very simple approach, the results in Figure 7 show that AC-cache improves the storage access time by 20-40%. This implies that buffer cache is still effective under fast storage like STT-MRAM, but more judicious management will be necessary to get the maximized performances.