esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 3D QoS-Aware Routing (3 DQR)
### Concept Principles
These objectives are achieved by the federation of DQRs and a centralised M-QR, which provide optimal paths on SDNCs/M-SDNC requests (domain-level and E2E). Unlike traditional IP networking, where a router is a DP- and CP-integrating device that makes local packet routing decisions, in 3 DQR, the router is an Application Plane (AP) function interacting with the SDNC's NorthBound Interfaces (NBIs). The selection of the most profitable paths is made by the intelligent DGA implementing the DDQN algorithm [47] and embedded in each DQR/M-QR. The data flow within DQR and DGA is shown in Figure 2.  
Upon the arrival of a path request (cf. Section 4.2), DQR collects the most recent network state information (e.g., OF-based statistics--cf. Section 5.2) and extends its node/edge feature vectors by additional node/link state information such as node centrality or nodes/links expiry information (probability of nodes' reachability or existence of ISLs and gateway links in the considered time-frame). The latter is provided by a dedicated Geographic Information System-based Mobility Management Function (GIMMF) entity, which estimates NTN topology (active ISLs, FLs, etc.) and calculates the aggregated persistent graph within a time frame (i.e., containing only the set of nodes and links that is not expected to change), using the orbital/trajectory parameters of nodes. DQR computes then \(k\) shortest paths using delay as the metric. For each candidate path, DQR/M-QR performs virtual path allocation using flow's QoS class requirements and feeds the outcome state to the DGA to obtain the value of the allocation. The path is selected based on the highest Q-value across \(k\) virtual path allocations. Additionally, to tackle the issue of variable size of network states in NTN, DGAs exploit the GNN architecture to extract the information about the relationship between nodes and links to enable effective routing regardless of topology shape and size (cf. Section 5.3). Figure 2: M-QR/QR and DGA architecture and data flow. It must be noted that SDNCs and M-SDNC are solely responsible for verifying the feasibility of a path and its enforcement while the reasoning is left to DQRs. Moreover, as each satellite/aerial node can act as an access point capable of serving the User Equipment (UE), they are considered BNs. Finally, to enable straightforward integration of the concept with 3 rd Generation Partnership Project (3 GPP) networks, QoS classes are aligned with the 3 GPP definition, i.e., 5G QoS Identifier (5 QI) [48].
## 3 An Analytical Framework for Updates of ETP
The political stream consists mainly of administrative and legislative turnover, public opinion or the \"national mood\" on an issue, and changes in government, such as a new administration or key personnel adjustments. Moreover, the advent of a new president or new secretary of state will signify potential ideological changes on public problems, and thus certain issues may receive more attention than others. Figure 1: An analytical framework combined with the “phase” model and the “stream” model. The policy stream includes a wide variety of solutions floating around in the \"policy primeval soap\". These solutions may be generated by the experts from bureaucratic institutions, academic institutions and think tanks who share a common concern and constitute a policy community. For a solution to be considered seriously by policy-makers, it needs to be designed sufficiently feasible, cost effective, and acceptable to the general public. By doing so, the solution can survive the initial process of policy choice. Otherwise, it may either be combined with other solutions to form into new proposals, or just disappear. By combining the phase model and the stream model, we can integrate the concept of \"a crucial decision\" and the concept of \"coupling/decoupling of streams\". An important feature of MSA is coupling. At critical moments, the streams are joined together and create fleeting opportunities for certain issues listed onto the government agenda and for advocates of proposals to push their pet solutions. Kingdom labels these moments policy windows [6]. In line with the MSA, we can understand the critical decision in the phase model as the policy solution generated from the policy window. Two conditions for policy windows can be distinguished: political attention paid to the problem; and a good fit between the recognized problem and the proposed policy solution. As the \"combined\" model shows in Figure 1, a crucial decision is made in phase 1 when the problem, politics, and policy streams converge and create a policy window. This crucial decision heralds a new phase, during which actors see opportunities and participate with varying interests and strategies. The new blood will decouple the three streams. That being said, problems change, or the solution for the old problem arouses new problems, political attention shifts, and policy solutions also evolve. The new streams in the next phase will converge again when the conditions for a policy window are present. In this article, we adopt the combined model to depict the developmental trajectory of the ETP in China across 1972-2016. We use the concepts in the MSA and in the phase model as metaphors to describe the change of environmental problems, the shift of political attention, and the evolution of environmental policies in China. We do not examine the applicability of the MSA in the Chinese context. In the next section, we present our data sources and the way that we operationalize the combined model.
# An Improved Deviation Coupling Control Method for Speed Synchronization of Multi-Motor Systems
## 2 Research Methods  
### Deviation Coupling Control Strategy  
The structure of the deviation coupling control structure is illustrated in Figure 1. The deviation coupling synchronization control strategy enables synchronous control among multiple motors through a speed compensator. By calculating the deviation between the states of each motor and using the speed deviation as an input to the compensator, each axis can be controlled based on the deviation coupling synchronous control strategy for motion control. This approach facilitates synchronous motion across multiple axes, where \(\omega_{ref}\) represents the reference input speed of the \(i\)th motor after the speed compensator, \(\omega_{i}\) is the actual speed of the \(i\)th motor, \(e_{i}\) denotes the compensated speed error of the \(i\)th motor after the speed compensator, \(T_{Li}\) stands for the load torque of the \(i\)th motor (\(i=1,2\),..., \(n\)), \(G_{i}\)(s) signifies the transfer function of the \(i\)th motor, and \(K_{ij}\) is the synchronization coefficient of the system. The value of \(K_{ij}\) equals the ratio between the torques of each motor as follows:  
\[K_{ij}=J_{i}/J_{j} \tag{1}\]  
where \(J_{i}\) is the rotational inertia of the \(i\)th motor (kg\(\cdot\)m\({}^{2}\)), and \(J_{j}\) is the rotational inertia of the \(j\)th motor with the difference in speed of the \(i\)th motor (kg\(\cdot\)m\({}^{2}\)).  
The output deviation \(e_{i}\) of the speed compensator is obtained by summing the weighted speed differences between the \(i\)th motor and the other motors. This can be expressed using the following equation:  
\[e_{i}=\sum_{j=1,j\
eq i}^{n}K_{ij}(\omega_{i}-\omega_{j}) \tag{2}\]  
Further, the controller input is  
\[E_{i}=\omega_{ref}-\omega_{i}-e_{i} \tag{3}\]  
The controller dynamically adjusts parameters based on real-time speed deviations among each motor to achieve system precision control. To attain synchronous control across multiple motors, it is imperative for the deviations among them to converge, satisfying the subsequent control conditions as follows:  
\[\left\{\begin{aligned} &\lim_{t\rightarrow\infty}e_{i1}(t)=\lim_{t \rightarrow\infty}[\omega_{i}(t)-\omega_{1}(t)]=0\\ &\lim_{t\rightarrow\infty}e_{i2}(t)=\lim_{t\rightarrow\infty}[ \omega_{i}(t)-\omega_{2}(t)]=0\\ &\qquad\qquad\vdots\\ &\lim_{t\rightarrow\infty}e_{in}(t)=\lim_{t\rightarrow\infty}[ \omega_{i}(t)-\omega_{n}(t)]=0\end{aligned}\right. \tag{4}\]  
where \(e_{i\bar{\jmath}}\) (\(j=1,\,2,\ldots,\,n\)) represents the speed deviation between the \(i\)th motor and the \(j\)th motor, and \(n\) denotes the total number of motors in the system.
## 5 Conclusions  
In this study, the TSK fuzzy system and recurrent cerebellar model with a self-organizing architecture are introduced into a cerebellar model controller in combination with an integrated error function and an improved compensation controller to design an adaptive TSK fuzzy self-organizing recurrent cerebellar model controller, ATFSORC. By adopting self-organizing and recurrent artificial neural network architecture, CMAC could possess dynamic state and parameter correction capabilities, in which the number of association memory layers can be modulated in accordance with the layer decision-making mechanism, thereby achieving better control response and the robustness resisting external disturbance. Simulation results show that when the proposed ATFSORC is applied to the synchronization and stability control of chaotic system, it possesses superior control performance when the parameters of the chaotic system or the cut-in control time point change. Furthermore, the simulation results also demonstrate that the proposed ATFSORC has better control performance in comparison with the FCMAC and CMAC.  
For future research studies, it is expected that the proposed ATFSORC may have better dynamic response characteristics when combining with other advanced control theories such as function links, wavelet analysis, Petri net, etc. Moreover, the synchronization and stability control of a chaotic system proposed in this study may also be applied in conjunction with other advanced control theories for daily life applications such as the chaotic synchronous control of the number of vehicles on the highway and its gateways, cardiac pacemaker control to ensure the stabilization of heart beat frequency within a certain range under various physical conditions, etc.  
Figure 19: Comparisons on simulation results of chaotic system stability control: (**a**) Phase diagram of uncontrolled chaotic system; (**b**) Phase diagram of uncontrolled chaotic system; (**c**) Phase diagram of uncontrolled chaotic system; (**d**) Stable phase diagram for CMAC; (**e**) Stable phase diagram for FCMAC; (**f**) Stable phase diagram for ATFSORC; (**g**) \(x\) response for CMAC; (**h**) \(x\) response for FCMAC; (**i**) \(x\) response for ATFSORC; (**j**) Error \(e_{x}\) for CMAC; (**k**) Error \(e_{x}\) for FCMAC; (**l**) Error \(e_{x}\) for ATFSORC; (**m**) \(y\) response for CMAC; (**n**) \(y\) response for FCMAC; (**o**) \(y\) response for ATFSORC; (**p**) Error \(e_{y}\) for CMAC; (**q**) Error \(e_{y}\) for FCMAC; (**r**) Error \(e_{y}\) for ATFSORC.  
**Author Contributions:** Contribution of each author is the same. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 2 Materials and Methods
### Boundary Conditions  
In general, in structural analysis, boundary conditions are put in displacements and/or forces in those regions of the structure where these entities are known. Zero displacement restrictions should be placed on some model boundaries to ensure equilibrium of the solution. In addition, restrictions must be set in nodes that are distant from the region of interest, which in our case, is the area surrounding the implant. It proceeds in this way to prevent the overlapping stress or strain field associated with the reaction forces, with the bone-implant interface.  
In the FEM models considered in this paper, the lateral faces and bottom of the bone tissue are considered without displacement (nodes movements are fixed on those faces in all directions), (Figure 5a), considering that the influence of the loading no longer exists at that certain distance.  
\begin{table}
\begin{tabular}{c c c c c} \hline  & \begin{tabular}{c} **Magnesium Alloy** \\ **(Intermediate Part)** \\ \end{tabular} & \
\begin{tabular}{c} **Titan Alloy** \\ **(Implant and Screw)** \\ \end{tabular} &
\begin{tabular}{c} **Ceramic** \\ **(Crown)** \\ \end{tabular} \\ \hline Constant name & Value & Unit & Value & Unit & Value & Unit \\ \hline Elastic modulus & \(4.2\times 10^{10}\) & N/m\({}^{2}\) & \(1.048\times 10^{11}\) & N/m\({}^{2}\) & \(2.2059\times 10^{11}\) & N/m\({}^{2}\) \\ \hline Poisson Coefficient & 0.33 & 0.31 & 0.22 & \\ \hline \end{tabular}
\end{table}
Table 1: Material constants of the implant, its components and crown [4].  
\begin{table}
\begin{tabular}{c c c c} \hline  & \
\begin{tabular}{c} **Trabecular Bone** \\ \end{tabular} &
\begin{tabular}{c} **Cortical Bone** \\ \end{tabular} \\ \hline Constant name & Value & Unit & Value & Unit \\ \hline Elastic modulus & \(1.8\times 10^{8}\) & N/m\({}^{2}\) & \(0.2\times 10^{9}\)–\(1.8\times 10^{10}\) & N/m\({}^{2}\) \\ \hline Poisson coefficient & 0.3 & 0.25 & \\ \hline \end{tabular}
\end{table}
Table 2: Material constants for the two types of bone [4].
## 1 Introduction
The cell membrane continuity is broken due to the formation of numerous pores within it [4; 19]. Its consequence is an increase of permeability of the cell membrane, which improves the efficiency of many processes, such as osmotic dehydration [20; 21], freezing [22], extraction [23], or drying [24; 25]. The majority of the studies in the literature concern the impact of pulsed electric field pretreatment on the subsequent convective drying [24; 26; 27; 28; 29], less frequently vacuum drying [30; 31; 32], and freeze-drying [25; 33]. Only a few articles present results on the effect of this nonthermal pretreatment on the subsequent microwave-convective drying. So far, the impact of the pulsed electric field on microwave-convective drying has been mainly investigated in the case of vegetables, i.e., carrots [34; 35] and potatoes [36; 37; 38; 39]. The literature also provides information on the impact of PEF pretreatment on the chemical properties (total phenolic compounds and antioxidant activity) of apples dried with the use of microwave and hot air. The use of this nonthermal pretreatment led to a reduction of microwave-convective drying time of carrots and apples, by 38% and 27%, respectively [34], as well as potatoes by 23% [36]. Moreover, the application of PEF before microwave-convective drying of carrots increased the carotenoid retention by 20%, however it did not significantly affect the antioxidant activity and total phenolic compounds content in the obtained dried apples (in reference to the samples untreated with PEF before drying). The electroporation of the tissues of these plant materials caused an increase in the mobility of water molecules, which, by accelerating the drying process, reduced the oxidative and thermal degradation of nutrients contained in these materials [34]. As revealed, the microwave-convective drying process of fruits with PEF pretreatment has not been analysed in great detail so far. Apart from the partially studied impact of PEFpretreatment on the chemical properties of some microwave-convective dried plant materials, there is no information about the impact of the electric field on the physical properties of materials dried with this method (i.e., colour, hygroscopicity, or rehydration). These properties are particularly important for dried products and determine their quality [5]. Therefore, conducting further research in this area seems justified. This study aimed to determine the effect of pulsed electric field pretreatment on the microwave-convective drying of apples and the selected physicochemical properties of the obtained dried materials. Furthermore, the most optimal parameters of PEF pretreatment (specific energy intake) and microwave-convective drying (power of microwave) were determined, which are essential for minimizing the drying time of apples and obtaining dried material with the desired properties.
# Image Semantic Segmentation Fusion of Edge Detection and AFF Attention Mechanism
By introducing full-scale skip connections, Unet3+ network [20] integrates low-level details and high-level semantics in full-scale feature mapping; its long join mode helps to recover the information loss caused by downsampling. The DeeplabV3+ [21] network adopts the cross-layer fusion method, which combines shallow detail features with deep abstract features to improve the segmentation accuracy of high-resolution images. PSP-Net [22] has introduced the pyramid pooling module (PPM); PPM aggregates contextual information from different regions and obtains global information. Based on the deep Res-Net [23] network, an effective optimization strategy was developed to achieve high-precision segmentation. All the above methods improve the accuracy of semantic segmentation, but this \"improvement\" depends too much on the complexity of the scene. The above method is suitable for object segmentation in simple scenes, but for complex scenes, there will still be discontinuous edge segmentation, edge splitting and small objects disappearing. Aimed at the problem of complex scene image segmentation, a model is proposed in this paper. Based on the improved PSP-Net semantic segmentation framework, the model introduces improved edge detection network holistically-nested edge detection (HED) and an attentional feature fusion (AFF) [24] attention mechanism. In this model, the improved HED network extracts the edge information of objects, and the feature fusion module in AFF fuses the edge information with improved PSP-Net semantic information. At the same time, AFF has the semantic association structure between its channels, so the response recognition ability of specific semantic events is enhanced. Thus, small objects are adequately identified. There are several important works in this paper which make a contribution to image semantic segmentation:  
1. We use the MobileNetV3 [25] network instead of the original PSP-Net network encoder, and obtain an improved PSP-Net network for image semantic segmentation. MobileNetV3 can extract deep semantic information, shallow contour information and middle-layer hybrid features. The features are incorporated into the decoder by means of hops. 2. Based on the original HED model, we proposed an improved HED network. We use the output of five edges, respectively, during the convolution layer. The last full-link layer in HED is removed to obtain a complete convolutional network, and expanded convolution is introduced to increase the receptive field for feature extraction. 3. To better fuse the semantic features and edge features, we designed a fusion module by introducing AFF into the improved PSP-Net and the improved HED network. AFF fuses the edge information with the deep semantic information; the model can enhance the attention correspondence between the edge feature information and the semantic information, and the accuracy of image segmentation in complex scenes is improved.
# Deep Learning Based Lithology Classification Using Dual-Frequency Pol-SAR Data
## 3 Results
### Comparison with Other Classifiers
#### 3.3.2 Computational Burden  
The computational burden of the proposed method is also an important issue, and the execution times of all methods are given in Table 6. Please note that the time shown in Table 6 is for the training step, which is the most time consuming of all the steps. The computational burden of the SVM is low when the number and dimension of samples are small. However, in this experiment, the number of samples is too large. Therefore, the computational burden of SVM is heavier than other methods. For the methods using SSAE, the time consumed is almost related to the input dimension because they have the same number of samples and the same architecture of the SSAE.  
From Table 6, we can see that the proposed method has an acceptable computational burden, and the times consumed for the methods using SSAE are significantly less than the method with SVM.
# Low-Latency Short-Packet Transmission over a Large Spatial Scale
## 2 System Model
As a result, the overall error probability is given by:  
\[\epsilon =1-\prod_{t=1}^{L}(1-\epsilon_{t}) \tag{6}\] \[\approx\sum_{i=1}^{L}\epsilon_{t_{i}}.\]  
The first-order approximation in Equation (6) gives us a reliable result when \(\epsilon_{t}\approx 0\). Then, we focus on the overall latency from the source to the destination. Since the data packet is transmitted serially, the overall latency \(T\) is the sum of the latency from \(R_{t-1}\) to \(R_{t}\), named \(t_{t}\). \[T=\sum_{i=1}^{L}t_{i}. \tag{7}\]  
In the URLLC communication without retransmission, the physical layer latency can be divided into the following components [11]:  
\[t_{t}=t_{t}^{c}+t_{t}^{p}+t_{t}^{d}, \tag{8}\]  
where \(t_{t}^{c}\) is the time consumed by channel coding, \(t_{t}^{p}\) is the time consumed by electronic wave propagation, and \(t_{t}^{d}\) is the time consumed by decoding messages. Thus, we have:  
\[y_{ij}=h_{t}x_{ij}+z_{t}, \tag{1}\]0  
The propagation delay \(t_{i}^{p}\) is caused by the signal travel time with the speed of light \(c\), which is presented by:  
\[y_{ij}=h_{t}x_{ij}+z_{t}, \tag{1}\]1  
We assume that each relay is equipped with a strong calculation force so that the time consumed by encoding messages is negligible for \(t_{t}^{d}\) and \(t_{t}^{p}\). Thus, the time to transfer for the data packet from \(R_{t-1}\) to \(R_{t}\) is:  
\[y_{ij}=h_{t}x_{ij}+z_{t}, \tag{1}\]2  
To meet the latency constraint, the overall delay \(\sum_{i=1}^{L}t_{i}\) is no greater than a constant \(T^{th}\), which gives:  
\[y_{ij}=h_{t}x_{ij}+z_{t}, \tag{1}\]3  
\(T^{th}\) represents the maximal tolerable latency from the source to the destination. Since \(d_{1},d_{2},\cdots,d_{L}\) are constants when the positions of the relays are fixed, Equation (12) can be simplified as:  
\[y_{ij}=h_{t}x_{ij}+z_{t}, \tag{1}\]4  
where \(N^{th}(T^{th})\) is a linear function of \(T^{th}\), i.e., \(N^{th}(T^{th})=\frac{T^{th}-\sum_{i=1}^{L}d_{t}}{T}\). By this means, we rewrite the latency constraint in Equation (12) as Equation (13), which is given as a constraint on the overall blocklength. We consider an overall energy constraint assumption. The energy to transmit the packet from \(R_{i-1}\) to \(R_{i}\) equals \(n_{t}\tau\gamma_{t}N_{0}\), for \(1\leq t\leq L\). \(N_{0}\) denotes the single-side noise density. Since \(\tau\) and \(N_{0}\) are constants, we have the normalized energy constraint:  
\[y_{ij}=h_{t}x_{ij}+z_{t}, \tag{1}\]5  
where \(E^{th}\) is the overall normalized energy of all source nodes and relay nodes. The error probability \(\epsilon_{i}\) is a function of blocklength \(n_{t}\) and signal-to-noise ratio \(\gamma_{t}\), as shown in Equation (5). We next minimize the error probability \(\epsilon\) subject to the energy and latency constraints.