esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion  
The presence of cracks leads to stress concentration at the tip, which makes the initiation point of secondary cracks in the specimen appear at the prefabricated crack tip. The analysis is carried out for different types of crack extension damage of the specimen according to the final failure pattern of the specimen. When the lateral pressures of the specimens containing penetration cracks are \(\sigma_{2}\) = 2 MPa and 4 MPa, the secondary crack classification diagram is shown in Figure 30a. Due to the limitation of the lateral pressure, the expansion direction of secondary cracks is not shifted to the axial stress direction, and the crack expansion mode is mainly compound cracks. When the lateral pressures of the specimens containing penetration cracks are \(\sigma_{2}\) = 6 MPa, the crack extension pattern is shown in Figure 30b. The high lateral pressure increases the peak strength and leads to severe misalignment of the specimen holes along the prefabricated crack. The secondary cracks are not only compound cracks, but also coplanar shear cracks that crack along the direction of the prefabricated cracks.  
At the lateral pressure \(\sigma_{2}\) = 4 MPa, specimens with different hole depths exhibit different cracking patterns. When the hole depth is H = 0 mm and 20 mm, it is a specimen with single crack or shallow hole, and the cracking pattern is shown in Figure 31a. The specimen exhibits a wing crack pattern of vertical prefabricated cracks, which is determined to be a composite crack. However, the anti-wing cracks that appear in the later stage are tensile cracks perpendicular to axial stress, and the final fracture mode is tensile composite fracture. At the hole depth \(H\) = 60 mm, the expansion pattern of the specimen is similar to that of the specimen with a penetration hole at a lateral pressure of \(\sigma_{2}\) = 6 MPa, and its main propagation mode is compound-shear fracture, as shown in Figure 31c. At the specimen hole depth \(H\) = 80 mm, the hole is close to penetration, which can be regarded as the hole that divides the prefabricated crack into upper and lower parts, resulting in wing cracks above and below the hole, respectively, and the fracture mode is compound fracture, as shown in Figure 31d.  
Figure 30: Types of fracture propagation in specimens with penetrating hole-crack under different lateral stress (M—compound crack; 5—shear crack).
## 2 Digital Twin
### Architecture
#### 2.2.3 Architecture for IoT Systems  
As massive numbers of IoT devices have been implemented across a variety of real-world applications and physical locations, and the collection, aggregation, storage, and analysis of their generated data is a prodigious challenge. Figure 5 illustrates a generic architecture for IoT systems [47], which consists of four layers (i.e., object layer, communication layer, application layer, and end-user layer).  
Figure 5: DT architecture for IoT.  
The object layer represents all IoT sensors and is used to provide data and information for different IoT-driven applications. It contains all the components that make up the physical system. The communication layer provides communication network infrastructure to collect IoT devices and collect data for DT. For example, a number of edge gateways can be deployed to collect and aggregate information from sensors and send the information to the application layer. IoT sensors and gateways use different communication protocols to collect and transmit information. After the gateway in the object layer obtains the information, it transmits the data to the application layer. The application layer includes the digital systems of DT. At this level, the DT system first maps all sensor and gateway information to the digital system. At the same time, we can use the IoT naming service to name the mapped IoT sensors and local gateways, so that the digital system can locate different resources [48]. The digital system can then use ML/DL models to perform prediction and analysis. By doing so, the digital system can control the actuators in the target layer based on the prediction results. Furthermore, the prediction and analysis results can provide different services in the end-user layer. The end-user layer provides services for users, who can send requests to the application layer. After that, the digital system will analyze the information obtained and respond with the results accordingly.  
Based on the architecture of the DT system, data representation and communication protocols are essential for data sharing within the DT, as well the provisioning of services to end users.
# Making Sense of Machine Learning: A Review of Interpretation Techniques and Their Applications
## 3 Techniques for Explainable AI
### Model-Based Interpretation Techniques  
The realm of model-based interpretation techniques serves the fundamental purpose of delving into the internal processes of a model and offering insights into how it operates and arrives at specific predictions. The central premise of these techniques revolves around leveraging the model's structural architecture and parameter configuration to glean insights into its behavior and predictive outputs. This holistic comprehension not only affords users the ability to scrutinize the robustness of the resultant solutions but also bolsters the likelihood of fostering user trust in the yielded outcomes [24].  
Rule-based and Bayesian models, decision trees, and random forests, as well as linear models, stand as prominent exemplars of model-based interpretation techniques. Their deployment has effectively contributed to elevating the level of explainability within AI models, paving the way for a more comprehensive understanding of complex phenomena.
## 2 Materials and Methods
### Chlorination Reaction  
#### 2.2.1 Apparatus and Equipment  
Column chromatography (CC) was accomplished on Kieselgel 60 (40-63 \(\upmu\)m, Merck, Darmstadt, Germany). HPLC was performed on Shimadzu LC-8A system (Shimadzu, Milan, Italy). GC was achieved on a Shimadzu 2010 series GC FID (Shimadzu, Milano, Italy). The \({}^{1}\)H- and \({}^{13}\)C-NMR spectra were recorded with a 400 MHz NMR spectrometer (Bruker DRX, Bruker Avance) referenced in ppm to the residual solvent signals (CDCl\({}_{3}\), at \(\delta_{\rm H}\) 7.27 and \(\delta_{\rm C}\) 77.0). Analysis of heteronuclear single-quantum coherence (HSQC), optimized for \({}^{1}\)J\({}_{\rm HC}\) = 155 Hz, and heteronuclear multiple bond coherence (HMBC), optimized for \({}^{n}\)J\({}_{\rm HC}\) = 8 Hz, were used to measure the proton-detected heteronuclear correlations. The MALDI TOF mass spectrometric analyses were performed on a Voyager-De Pro MALDI mass-spectrometer (PerSeptive Biosystems, Framingham, MA, USA). The UV-Vis spectra were run on a Perkin Elmer Lambda 7 spectrophotometer. The IR spectra were run on a Jasco FT/IR-430 instrument equipped with a single reflection ATR accessory. pH was recorded on a WTW pH 7110 pH-meter (Xylem Analytics, Weilheim, Germany) equipped with a WTW SenTix41 electrode with temperature sensor.
# A Review on Microfluidic Platforms Applied to Nerve Regeneration
## 4 Microfluidic Platforms Applied to Axonal Guidance
### Chemical Gradients Guidance  
Chemical guidance method uses the gradient of solution concentration to direct the orientation of axon growth. [PERSON] [84] designed a microfluidics-based turning-assay  
Figure 6: Axon guidance methods. (**A**) Topographical guidance microfluidic device using aligned PLGA nanofibers by [PERSON] (**B**) Mechanical guidance microfluidic device using steer force of adjustable flow to guide the orientation of growth cone designed by [PERSON] (**C**) Chemical gradients guidance microfluidic device using net transmission structure to generate solution concentration gradients by Yin, B. S.  
chip to against shear stress generated by chemical gradients affecting the migration of axon cone growth. This platform generates a smooth gradient of surface-bound laminin to fine-tune the polarity of growth cone responses to gradients of BDNFs, thereby realizing the guidance of axonal growth cone. The platform consists of a custom microfabricated PDMS chip and a glass coverslip. The PDMS chip consists of two layers. The bottom fluidic layer contains a gradient generation network, a symmetric cell seeding network and a chemotaxis observation chamber. The top layer contains pneumatic control valves which can control the flow in the specified fluidic channels on the bottom layer. The glass coverslip is wet etched with an array of micro-vias. The mechanism of gradient generator is using a serpentine mixing network to split solution of different concentrations introduced from three inlets into nine gradients and pass them into micro-wells connected to micro-channels, where the axons are cultured. The key feature of this platform is using flow network to generate different gradients of solutions to enable the axon guidance. [PERSON] [85] designed a pyramid-shaped microfluidic platform capable of analyzing the effectiveness of tacrolimus to neural regeneration. As shown in Figure 6C, the platform consists of an upstream concentration gradient generator and a downstream cell culture chamber. As two kinds of solutions are injected into the device through two inlets, they pass through a serpentine channel where diffusive mixing is diluted and different gradients are formed. In the study of [PERSON], [PERSON] [54], the special design of asymmetric side channel makes the flow rate stable and adjustable so that molecule gradients can be generated within the 3D collagen. This allows quantitative investigation of growth promoting and regeneration of injured dopaminergic neurons by 3D chemical gradients formed by three natural molecules.
## 2 Materials and Methods
### Cupula-Endolymph Interaction and FSI Modeling  
This study is based on the hypothesis that ultrasound-induced endolymph flow adjacent to the cupula can generate copula deformation. To confirm this, it is essential to analyze the interaction between the cupula and the endolymph. For this analysis, the FSI method was applied. Due to the low Young's modulus \(E\) of the cupula [25; 26], nonlinear structural deformation caused inaccuracies in the calculations. To address this, the cupula was treated as a nearly incompressible solid, as in other studies [27; 28]. The material properties of the cupula, taken from previous studies, were \(\rho_{c}\) = 1000 kg/m\({}^{3}\), \(E\) = 5.4 Pa, and \(\
u\) = 0.3 [28; 46]. The strain tensor of the cupula was defined using two Lame coefficients, \(\lambda_{1}\) and \(\lambda_{2}\)[46], as follows:  
\[\lambda_{1}=\frac{E}{2(1+\
u)} \tag{12}\]  
\[\lambda_{2}=\frac{\
u E}{(1+\
u)(1-2\
u)} \tag{13}\]  
For the fluid boundary conditions, no-slip and fixed wall conditions (\(u_{e}\) = 0) were applied where the fluid interfaces with the temporal bone. At the boundary adjoining the cupula, the no-slip condition was maintained, along with an automatic-from-frame condition. The coupling conditions between the cupula and the endolymph in the two-way FSI model were defined as follows [47]:  
\[u_{e}=u_{c} \tag{14}\]  
\[u_{c}=\frac{\partial d_{c}}{\partial t} \tag{15}\]  
\[\sigma_{e}\cdot n_{e}=\sigma_{c}\cdot n_{c} \tag{16}\]  
where \(u\) is the velocity vector, \(d\) is the displacement vector, \(\sigma\) is the stress tensor, and \(n\) is the normal vector to the FSI boundary. The subscripts \(e\) and \(c\) denote endolymph and cupula, respectively.
## 1 Introduction
Our main purpose was to propose an improved and unified baseline estimation model from local deviations and global deviations. We investigated local deviations from different kinds of users' rating distributions and found a standard deviation proportion (SDP) pattern for baseline estimation. To our knowledge, this study is the first to improve the baseline estimation from the perspective of a normal distribution. This work has the following main contributions:  
1. [leftmargin=*,noitemsep,topsep=0 pt]
2. Observed that four kinds of personalized users exist in recommender systems with personalized rating criteria;
3. Proposed an improved and unified baseline estimation model based on users' personalized rating distributions and system ratings' global distribution; and
4. Proposed application instances of SDP to existing latent factor-based CF recommendations with excellent improved predictive accuracies. Since the standard deviations can be calculated from historical data, the proposed SDP has no additional cost compared to classical baseline estimation in real systems. Experiments on real datasetswith full ratings and cross validation prove that the proposed SDP is more efficient than classical baseline estimation and can effectively improve the predictive accuracies of existing latent factor-based CF recommendations. The rest of this article is organized as follows. We introduce the related work in Section 2 and problem statements in Section 3. We describe SDP in Sections 4 and 5, and experiments and evaluation in Section 6, followed by conclusions in Section 7.
# An Optimal Fuzzy PID Controller Design Based on Conventional PID Control and Nonlinear Factors
## 2 The Proposed Optimal Fuzzy PID Controller Design
### The Nonlinear Factor for Tuning MFs  
In the previous subsection, the fuzzy sets representing input/output the fuzzy variables \(e(t)\), \(\int e(t)\), \(\dot{e}(t)\), and \(u(t)\) are equally-spaced, which is one of the reasons the results show a linear FLC. To exhibit the nonlinearity of a FLC, we proposed a nonlinear factor \(\gamma\) to tune MFs. Without losing its generality, let us take fuzzy variables \(e(t)\) and \(u(t)\) as an example. To illustrate the concept of nonlinear factor \(\gamma\), \(e(t)\) is supposed to have \(m=7\) fuzzy sets, and \(u(t)\) has \(3m-2=19\) fuzzy singletons, as shown in Figure 3. It is noted that the variable \(m\) should be an odd number. In this paper, we use \(p_{k}\), \(k\geq 0\), to represent the center of fuzzy sets \(E_{k+\frac{m+1}{2}}\) or \(U_{k+\frac{3m-1}{2}}\). The nonlinear factor \(\gamma\) is defined as:  
\[\gamma=\frac{P_{k}-P_{k-1}}{P_{k-1}-P_{k-2}},\ \gamma>0. \tag{5}\]  
It can be observed that the nonlinear factor \(\gamma\) represents the center distance ratio of the MFs before and after.  
In Figure 3a, it is the linear case with nonlinear factor \(\gamma=1\). Figure 3b shows the case of \(\gamma<1\), where the closer the MF is to the zero, the wider the MF will be. On the contrary, if \(\gamma>1\), the closer MF to the zero will have a narrower shape, as shown in Figure 3c. With the definition of nonlinear factors, each MF itself can exhibit an asymmetrical shape and influence the system response.  
From Equation (4) in Section 2.1, we get four parameters for tuning a linear FLC. Adding the nonlinear factor for each fuzzy variable in this subsection, there will be a total of eight parameters, \([a_{e},\ a_{i},\ a_{d},\ a_{u},\ \gamma_{e},\ \gamma_{i},\ \gamma_{d},\ \gamma_{u}]\), which need to be adjusted for an optimal fuzzy PID controller design.  
Figure 2: Sliced cube fuzzy associative memory (FAM) representation of the knowledge base.
## 5 Issues Relating to the Fundamental Design Aspects
### Flight Time  
An optimal design of UAV flight time and visiting sequence can also help reduce the energy consumed in AoI-sensitive applications. The trajectory of a UAV can be optimized when several parameters relating to the UAV's flight and sensor node deployment are well planned. Particularly, the flight time of UAVs plays a significant role in determining which sensor nodes or IoT devices are visited. If the flight time is not optimized, some IoT devices would have to wait longer, which might conflict with the purpose of AoI minimization in UAV-based data gathering. The trade-off between UAV flight time and speed is a prime design consideration in optimizing energy consumption. Similarly, UAV acceleration, altitude, and coverage radius should all be factored in. The choice of hovering spots and the impact of physical characteristics, such as interference and path loss, must also be considered.
# A Hierarchical Mapping System for Flat Identifier to Locator Resolution Based on Active Degree
## 5 Performance Analysis and Simulation
### Average Delay of One-Hop  
This section evaluates the average delay of one-hop. Average delay of one-hop is the ratio of average delay of mapping to hop count. It can accurately reflect physical network structure and delay characteristics. To some extent, it can also reflect the matching degree between physical topology and logical topology.  
Figure 7 gives the comparison of the average hop delay performance of several mapping resolution systems under different network sizes. Figure 7a gives a comparison of the average delay of one-hop under a small-scale network. It can be seen from the figure that the performance of the proposed system and Lchord is better than that of LISP-DHT and DHT-MAP, which is because the proposed system and Lchord are mapping systems based on location aware DHT. Figure 7b gives a comparison of the average delay of one-hop under a large-scale network. It can be seen from the figure that the performance gap between the proposed system and the Lchord increases with the scale of the number of increasing of mapping servers, which shows that the advantage of the proposed mapping system in a large-scale network is more obvious. This result can be explained by two points. For the first point, the structure of HMAA is in accordance with the current Internet. This can guarantee that the mapping requests are met in the shortest path. For the second point, the difference between HMAA and Lchord is that the construction of the overlay in HMAA not only depends on the physical topology, but also depends on the logical topology. This can avoid bottleneck nodes which may lead to high one-hop delay efficiently.  
Figure 6: Average mapping delay comparisons: (**a**) Average mapping delay in small-scale networks; and (**b**) average mapping delay in large-scale networks.  
Figure 7: Average delay of one-hop: (**a**) small scale networks; and (**b**) large-scale networks.
## 1 Introduction
[17] used a median filtering algorithm to remove the noise of CT image and made it better reflect the complex pore structure in the rocks. More accurate and intuitive data can be obtained by improving the spatial and temporal resolution of CT images, improving image processing software and developing new image reconstruction algorithms [18]. [PERSON] [19], using Avizo, an advanced three-dimensional visualization image processing software application, reconstructed the core by scanning method and made it digitalized and visualized. The reconstructed core mode can show the pore size, pore throat size and connection mode more intuitively, and at the same time, it can get the quantitative distribution state of porosity ratio, matrix skeleton structure ratio and other data. [PERSON] et al. [20] quantified the three-dimensional pore network in soil column by using the improved method through Avizo version 5 software, including the continuous change of pore network along depth, macropore size distribution, network density, surface area, length density, length distribution, average hydraulic radius, curvature, inclination (angle), and connectivity (path number and node density). On this basis, [PERSON] et al. [21] found that large porosity, path number, hydraulic radius, and large pore angle are the best predictors of hydraulic parameters of silty sand. When the construction process of AGF is applied in the clay layer, the boundary conditions (such as temperature boundary and water replenishment condition) with seepage are more complex than the case where there is no seepage. In order to explore the response characteristics of soft clay in AGF construction under the presence of seepage, a model test was designed to obtains the micro characteristics of frozen and thawed soil by CT, and puts forward a settlement calculation method based on CT in this paper.
## 4 Modeling of Multi-Parameter Degradation
### Reliability Modeling of Multiple Degradation Processes  
Assume that the product has \(n\) multiple performance degradation indicators when any one of the degradation performances reaches the failure threshold specified, the product is considered to fail, and its failure time is \(T\). At a time \(t\), the performance degradation trajectories can be expressed as \(X(t)=(X_{1}(t),X_{2}(t),\cdots,X_{n}(t))\), and the corresponding failure threshold is \(l=(l_{1},l_{2},\cdots,l_{n})\), then the product's life can be described as follows:  
\[\begin{array}{l}R(t)=P(T>t)\\ =P(\min(T_{1},T_{2},\cdots,T_{n})>t)\\ =P(X_{1}(t)<l_{1},X_{2}(t)<l_{2},\cdots,X_{n}(t)<l_{n})\end{array} \tag{19}\]  
When there are two performance degradation parameters, and taking the Frank function as an example, the reliability of the product can be shown as in Equation (20):  
\[\begin{array}{l}R(t)=P(T>t)\\ =P(\min(T_{1},T_{2},\cdots,T_{n})>t)\\ =1-P(T_{1}\leq t)-P(T_{2}\leq t)+P(T_{1}\leq t,T_{2}\leq t)\\ =R_{1}(t)+R_{2}(t)-1+C(F_{1}(t),F_{2}(t);\theta)\\ =R_{1}(t)+R_{2}(t)-1-\frac{1}{\theta}ln\left(1+\frac{\left(\mathrm{e}^{-\theta T _{1}}(t)-1\right)\left(\mathrm{e}^{-\theta T_{2}(t)}-1\right)}{\mathrm{e}^{- \theta}-1}\right)\end{array} \tag{20}\]
## 7 Conclusions  
In this paper, we investigated the task offloading and resource allocation problem for multiple-HAP drones and multiple-satellite collaborative networks. The problem was formulated to minimize the total delay of all IoTs' tasks. We transformed and decomposed it into two subproblems: the task splitting optimization subproblem and the resource allocation optimization subproblem. We designed a DDPG-based algorithm to obtain an optimal task splitting strategy, and we optimized resource allocation strategies through convex optimization. Simulation results show that the proposed algorithm can achieve lower total delay compared with baseline algorithms. As the number of IoT devices, HAP drones, and satellites increases, the scale of the neural network in the proposed algorithm will also expand. This can lead to difficulties in training the algorithm and challenges in achieving convergence. Therefore, we believe that future research should focus on designing optimization algorithms that combine multi-agent reinforcement learning with convex optimization techniques.  
Conceptualization, B.H. and C.G.; methodology, C.G., B.H. and X.B.; validation, C.G. and H.W.; formal analysis, C.G. and B.H.; investigation, C.G. and X.B.; writing--original draft preparation, C.G.; writing--review and editing, C.G., X.B. and B.H.; supervision, S.C. and H.W.; project administration, B.H. and S.C.; funding acquisition, B.H. and S.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (NSFC) under Grant 61931005.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflicts of interest.
## 4 Experimental Results  
In this subsection, AUC, EER, and \(BPCER_{10}\) are selected as metrics to evaluate the performance of the anti-spoofing method that incorporates the contribution weights. A total of 10,000 B-scans from blue-collar and white-collar workers are randomly selected for the training set. In total, 1000 bona fide samples and 1000 spoof samples are selected for the validation set and 50,000 bona fide samples and 50,000 spoof samples are selected for the test set. The algorithm model proposed in this paper is trained and tested using the Pytorch framework. During the entire training process, the training epoch is set to 100, the batchsize of images is 15, the learning rate is 0.001, and the optimizer is Adam. All experiments are run on the Linux operating system, and the GPU used is the NVIDIA RTX3090.